pax_global_header00006660000000000000000000000064143575064540014527gustar00rootroot0000000000000052 comment=121b9ff3cfa753c086812ca0beb23b3474e0ba38 slamd202303071458299/000077500000000000000000000000001435750645400136025ustar00rootroot00000000000000slamd202303071458299/.gitignore000066400000000000000000000024331435750645400155740ustar00rootroot00000000000000legacy/* .DS_Store debug/* *.DS_Store *.json *.mat src/.vscode/* preds/* *.h5 *.pth *.checkpoint # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # Experiments exp/ # ipython-notebook scripts scripts/ slamd202303071458299/LICENSE000066400000000000000000000021021435750645400146020ustar00rootroot00000000000000MIT License Copyright (c) 2019 Xingyi Zhou All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. slamd202303071458299/NOTICE000066400000000000000000000172661435750645400145220ustar00rootroot00000000000000Portions of this software are derived from tf-faster-rcnn. ============================================================================== tf-faster-rcnn licence ============================================================================== MIT License Copyright (c) 2017 Xinlei Chen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Portions of this software are derived from human-pose-estimation.pytorch. ============================================================================== human-pose-estimation.pytorch licence ============================================================================== MIT License Copyright (c) Microsoft Corporation. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE Portions of this software are derived from CornerNet. ============================================================================== CornerNet licence ============================================================================== BSD 3-Clause License Copyright (c) 2018, University of Michigan All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.slamd202303071458299/README.md000066400000000000000000000063271435750645400150710ustar00rootroot00000000000000#

An Official PyTorch Implementation of “EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography (MICCAI-2021)”

by Jiancong Chen#, Yingying Zhang#, Jingyi Wang, Xiaoxue Zhou, Yihua He*, Tong Zhang*. This repo contains the **official PyTorch implemetation** for EllipseNet. Please refer to https://git.openi.org.cn/OpenMedIA/EllipseFit.Mindspore for a MindSpore version. Please be noted that the MindSpore version is not an Ellipse Detection Framework but using a 2D Unet to train a segmentation network and then using ellipses to fit the segmentation results. ## Framework ![](readme/EllipseNet.png) ## Installation Please refer to [INSTALL.md](readme/INSTALL.md) for installation instructions. ## Experiments | Methods | Setting | DiceT | DiceC | Diceall | Pavg | |----------|----------|----------|------------|---------------|-------------| |EllipseNet (exp6) | only IoU loss | 0.8813 | 0.8520 | 0.8666 | 0.8855 | |EllipseNet (exp1) | w/o IoU loss | 0.9338 | 0.9108 | 0.9224 | 0.8841 | |EllipseNet (exp3) | w/ IoU loss | **0.9430** | **0.9224** | **0.9336** | **0.8949** | ## Training and Evaluation Prepare the elliptical dataset in coco-format. An example script is given in [scripts](scripts)/prepare_label.ipynb. We provide scripts for all the experiments in the [experiments](experiments) folder. Usage: ~~~ chmod +x experiments/miccai21/*.sh ./experiments/miccai21/exp3_base_theta5_iou.sh ~~~ ## Reproduction If you need the docker for reproduction, please contact via email. We will provide the docker image. ## License EllipseNet itself is released under the MIT License (refer to the LICENSE file for details). Portions of the code are borrowed from [CenterNet](https://github.com/xingyizhou/CenterNet), [Rotated_IoU](https://github.com/lilanxiao/Rotated_IoU), [human-pose-estimation.pytorch](https://github.com/Microsoft/human-pose-estimation.pytorch) (image transform, resnet), [CornerNet](https://github.com/princeton-vl/CornerNet) (hourglassnet, loss functions), [dla](https://github.com/ucbdrive/dla) (DLA network), [DCNv2](https://github.com/CharlesShang/DCNv2) (deformable convolutions), [tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn) (Pascal VOC evaluation) and [kitti_eval](https://github.com/prclibo/kitti_eval) (KITTI dataset evaluation). Please refer to the original License of these projects (See [NOTICE](NOTICE)). ## Citation If you find this project useful for your research, please cit our work use the following BibTeX entry. @inproceedings{chen2021ellipsenet, title={Ellipsenet: Anchor-free ellipse detection for automatic cardiac biometrics in fetal echocardiography}, author={Chen, Jiancong and Zhang, Yingying and Wang, Jingyi and Zhou, Xiaoxue and He, Yihua and Zhang, Tong}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, pages={218--227}, year={2021}, organization={Springer} } ## Contact If you have any questions about this paper, welcome to email to [zhangt02@pcl.ac.cn](mailto:zhangt02@pcl.ac.cn) slamd202303071458299/data/000077500000000000000000000000001435750645400145135ustar00rootroot00000000000000slamd202303071458299/data/.gitignore000066400000000000000000000000161435750645400165000ustar00rootroot00000000000000* !.gitignore slamd202303071458299/experiments/000077500000000000000000000000001435750645400161455ustar00rootroot00000000000000slamd202303071458299/experiments/miccai 2021/000077500000000000000000000000001435750645400177375ustar00rootroot00000000000000slamd202303071458299/experiments/miccai 2021/exp1_base_theta5.sh000077500000000000000000000003521435750645400234170ustar00rootroot00000000000000cd src # train python main.py eldet --exp_id exp1_base_theta5 --dataset coco_fhd --batch_size 24 --lr 5e-4 --gpus 5 --num_workers 16 --num_epochs 500 --input_h 608 --input_w 896 --iou_weight 0 --theta_weight 5 --not_rand_crop cd .. slamd202303071458299/experiments/miccai 2021/exp3_base_theta5_iou.sh000077500000000000000000000003561435750645400243010ustar00rootroot00000000000000cd src # train python main.py eldet --exp_id exp3_base_theta5_iou --dataset coco_fhd --batch_size 24 --lr 5e-4 --gpus 5 --num_workers 16 --num_epochs 500 --input_h 608 --input_w 896 --iou_weight 1 --theta_weight 5 --not_rand_crop cd .. slamd202303071458299/experiments/miccai 2021/exp6_base_onlyiou.sh000077500000000000000000000003551435750645400237330ustar00rootroot00000000000000cd src # train python main.py eldet --exp_id exp6_base_onlyiou --dataset coco_fhd --batch_size 24 --lr 5e-4 --gpus 5 --num_workers 16 --num_epochs 500 --input_h 608 --input_w 896 --iou_weight 1 --ellipse_weight 0 --not_rand_crop cd .. slamd202303071458299/images/000077500000000000000000000000001435750645400150475ustar00rootroot00000000000000slamd202303071458299/images/16004479832_a748d55f21_k.jpg000066400000000000000000004127421435750645400206420ustar00rootroot00000000000000JFIFHHcmp3.10.3.1Lq4 0xdac642c7C       C X S!1AQa"q2#BRb$3r 4CS%t&6DFdsEc:!1A"Q2aq#BR3$CSrb ?},ی*,~v+{e,CKzUe> V&nktf`{YХ9#I.*tfBHmA q} >aڝؙ6鏴s~U 8z+D&l8 0HJb40Gb |@2|ǝ,da `TAƀC^Ɵ@ |y3ZJj?-%7 Ky%^WǏAE}Ҿ1t"X"Xo?qN0rsW`e3<`q+3GulJ= <&\nS'׬R2%`*7U60PɍFuZjY8ciYC3K&Sq2{b/ 1+ mZXm.$X+{ք>`GU# y}hEʚ;z AYS _>:}$&~ -ͳnsR*>(}Zhؖ;%X8֨͆,g=R@:Nӕ#րn' M0]@]pm'И{stRFU}_+L@{8ZKtH: H^LѳBYE)SfA;D;lSFrgwa|*=whhҿ&w\܉~Y%`(G7}R `*RFDcG4JNu70E#ד~rUg@_ qW˾zQ/BGG ^cc[1S+`6z;Oirm*=ig9g(`yV.Ms@`n`>P2< y _JxΕ3˽ohf`,[qX5hdݞ5G0qro<,.:L)t*Fa}>/˵Q@ 唓qhV(T"O5Sk zΟ]j(ٷ6V)AV;h7bW_C źLi9ƭ6 [Z=k @]eɂhH8í)bT6~AC?,zb!W>= %.}8Xc) 'thJ~bb$6mᢇ~GtWj 2]0gȓ\١9/zFY`3ag$^.$cnΧmo f8T!_);=jKHܛd ҥpS)D^g9uٯ*{V% N̿zZn-ۈb Ұ t>Ĉ-aUK`{֯BzdE$wevAj3 S4BUE{myp;ԓ Ņ=u%ֱl$8 U#Wm{"dྙw}O;G!H|IG=mY/-/`;09p}X28"„[ ><˲PRzI1i>4ѴHNG>t;OKҠŝJhsC%WKN7G:TgO%onI=8w7S<ܖcUUѵP&4}WZcg${rkϜ>.4R:T8>S^_/b_ٹ+_Kڳ˒F n &: Z+hM/Z RY,'FOR XO7v I޺!ќbxE1J+&5?-UP H0Ql,QY& fL֥jAѩd@7>$ZgL:@Dw^''NV_ķodHwrkG݌60|* ;Jo=M)F \@5\`6O|s@aʢ\@> Sh'TC[NuwB$E|<#|}H!8٠4iCK`:$?#O[[پ\r=z{& ry ѹ-F)X 5c Gc F0gf#-BVHIPn0 s2 jK9ŨfFCވ}iU4kۗO< +ʇVpؿ%IyWDń+b;i'~OzTKu$}B ,Y1ڀ ņ;B~ʚ#\ɪjvtDo^9 qp#= tq:]ڤ;g|2ƪ,\c7P5t-PUVv…+x6gGmwE*VR^HZ h&aN8I:>t=)Ѕ UBbT nmؖl:Oj6&{?E4nLc gl`WƯ*: П ~ByW'!cVrH[{S,{SJ[c2 3FܾT>)=cVʿ*U͑lP v1HyT&#WLI#5j2_oR@JKFOޑgvH8S27fV^7 ;[[tV(._(G*Gc__"6H8#QNDgx>wypt",My)Q0)()t֑пٚͺ`=ZbivySG!NX{{:E+y~b;w'ecnNk[MEQ .+$h+[,մd] $I_Ik!v5ٍQw*) Ê#avc\r-NM?+俼L5`Y] X6CjT\OGZtgGznٙvpW,*asrcqB6z9OMc+8-QTyʶGhRn’*#SV?71Iz`Te1enmd׳βD04rY_h\C45WЎۡ{)t{n:~IsrBPjh)WWWяm-cSoIIS4 Ab(\xu=e`0xw#ў殡^vm__/'W[f8SLmNʊ gJ74oeƟɍ-^^,klj-X>%0 \sJ_j/4<l ''^1D}o Z1yEc.VJ"CtEŜ ڱk"]oh3s!s^vyQa;: d|4#h4wUXT.>ROmʝ"Qc'<ӤKmҡˣ1?0`O'wjk|Z㳍i$11|U_ h]:?T>+ZG5''+죃F]X%2O7]Ih?P]ԷRf>W늖hf:6TrG'ԁ$0e\<Y@#@zkӭ,zZICھ/'=~K,-"cּi+Z;SW9-PR@TԨcZ+umwvof 9m"XOތqJٴtG`NjR9; 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NVIDIA GPUs are needed for both training and testing. After install Anaconda: 0. [Optional but recommended] create a new conda environment. ~~~ conda create --name EllipseNet python=3.6 ~~~ And activate the environment. ~~~ conda activate EllipseNet ~~~ 1. Install pytorch: ``` pip install torch==1.7.0 torchvision==0.8.0 ``` ~~~ # PYTORCH=/path/to/pytorch # usually ~/anaconda3/envs/EllipseNet/lib/python3.6/site-packages/ # for pytorch v0.4.0 sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py # for pytorch v0.4.1 sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py ~~~ 2. Install [COCOAPI](https://github.com/cocodataset/cocoapi): ~~~ # COCOAPI=/path/to/clone/cocoapi git clone https://github.com/cocodataset/cocoapi.git $COCOAPI cd $COCOAPI/PythonAPI make python setup.py install --user ~~~ 3. Clone this repo: ~~~ EllipseNet_ROOT=/path/to/clone/EllipseNet git clone https://git.openi.org.cn/capepoint/EllipseNet $EllipseNet_ROOT ~~~ 4. Install the requirements ~~~ pip install -r requirements.txt ~~~ 5. Compile deformable convolutional (from [DCNv2](https://github.com/CharlesShang/DCNv2/tree/pytorch_0.4)). ~~~ cd $EllipseNet_ROOT/src/lib/models/networks/DCNv2 ./make.sh ~~~ 6. 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IEmwBfKaa>qaaaaf_1 3x$ֶ'ǵΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐΐ>@יz\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\z\,faaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa(0݄,[IENDB`slamd202303071458299/readme/INSTALL.md000066400000000000000000000021501435750645400164650ustar00rootroot00000000000000# Installation ## Requirements * Ubuntu 16.04 * Python 3.6+ * [PyTorch]((http://pytorch.org/)) 1.7.0+ * GPU 2. Install [COCOAPI](https://github.com/cocodataset/cocoapi) ~~~ COCOAPI=/path/to/clone/cocoapi # Need to replace with your own path git clone https://github.com/cocodataset/cocoapi.git $COCOAPI cd $COCOAPI/PythonAPI make python setup.py install --user ~~~ 3. Clone this repo ~~~ EllipseNet_ROOT=/path/to/clone/EllipseNet git clone https://git.openi.org.cn/capepoint/EllipseNet $EllipseNet_ROOT ~~~ 4. Install the requirements ~~~bash cd $EllipseNet_ROOT pip install -r requirements.txt ~~~ 5. Compile **Deformable convolutional** (from [DCNv2](https://github.com/CharlesShang/DCNv2/tree/pytorch_0.4)) ~~~bash cd $EllipseNet_ROOT/src/lib/models/networks/DCNv2 export TORCH_CUDA_ARCH_LIST=7.5 # May need for hardware related issue rm -rf build bash ./make.sh ~~~ 6. Compile **Rotated_IOU related operation** ```bash cd $EllipseNet_ROOT/src/lib/models/Rotated_IoU/cuda_op python setup.py install ``` slamd202303071458299/requirements.txt000066400000000000000000000001771435750645400170730ustar00rootroot00000000000000opencv-python Cython numba progress matplotlib easydict scipy pycocotools tensorboard progress torch==1.7.0 torchvision==0.8.0 slamd202303071458299/scripts/000077500000000000000000000000001435750645400152715ustar00rootroot00000000000000slamd202303071458299/scripts/Inference_ellipse.ipynb000066400000000000000000031516171435750645400217650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['CUDA_VISIBLE_DEVICES'] = '1'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using tensorboardX\n" ] } ], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import os\n", "import sys\n", "\n", "lib_path = os.path.join('../src/lib')\n", "if not lib_path in sys.path:\n", " sys.path.append(lib_path)\n", " \n", "lib_path = os.path.join('../src/')\n", "if not lib_path in sys.path:\n", " sys.path.append(lib_path)\n", " \n", "lib_path = '../../Rotated_IoU/'\n", "if not lib_path in sys.path:\n", " sys.path.append(lib_path)\n", " \n", "import json\n", "import cv2\n", "from glob import glob\n", "import math\n", "import numpy as np\n", "import time\n", "from progress.bar import Bar\n", "import torch\n", "\n", "import matplotlib.pyplot as plt\n", "from PIL import Image\n", "\n", "from external.nms import soft_nms\n", "from fhd_opts import opts\n", "from logger import Logger\n", "from utils.utils import AverageMeter\n", "from datasets.dataset_factory import dataset_factory\n", "from detectors.detector_factory import detector_factory\n", "from models.losses import _transpose_and_gather_feat\n", "from models.Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_iou" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class PrefetchDataset(torch.utils.data.Dataset):\n", " def __init__(self, opt, dataset, pre_process_func):\n", " self.images = dataset.images\n", " self.load_image_func = dataset.coco.loadImgs\n", " self.img_dir = dataset.img_dir\n", " self.pre_process_func = pre_process_func\n", " self.opt = opt\n", " \n", " def __getitem__(self, index):\n", " img_id = self.images[index]\n", " img_info = self.load_image_func(ids=[img_id])[0]\n", " img_path = os.path.join(self.img_dir, img_info['file_name'])\n", " image = cv2.imread(img_path)\n", " images, meta = {}, {}\n", " for scale in opt.test_scales:\n", " if opt.task == 'ddd':\n", " images[scale], meta[scale] = self.pre_process_func(\n", " image, scale, img_info['calib'])\n", " else:\n", " images[scale], meta[scale] = self.pre_process_func(image, scale)\n", " return img_id, {'images': images, 'image': image, 'meta': meta}\n", "\n", " def __len__(self):\n", " return len(self.images)\n", "\n", "def prefetch_test(opt):\n", " os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str\n", "\n", " Dataset = dataset_factory[opt.dataset]\n", " opt = opts().update_dataset_info_and_set_heads(opt, Dataset)\n", " print(opt)\n", " Logger(opt)\n", " Detector = detector_factory[opt.task]\n", " \n", " split = 'val' if not opt.trainval else 'test'\n", " dataset = Dataset(opt, split)\n", " detector = Detector(opt)\n", " \n", " data_loader = torch.utils.data.DataLoader(\n", " PrefetchDataset(opt, dataset, detector.pre_process), \n", " batch_size=1, shuffle=False, num_workers=1, pin_memory=True)\n", "\n", " results = {}\n", " num_iters = len(dataset)\n", " bar = Bar('{}'.format(opt.exp_id), max=num_iters)\n", " time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']\n", " avg_time_stats = {t: AverageMeter() for t in time_stats}\n", " for ind, (img_id, pre_processed_images) in enumerate(data_loader):\n", " ret = detector.run(pre_processed_images)\n", " results[img_id.numpy().astype(np.int32)[0]] = ret['results']\n", " Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(\n", " ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)\n", " for t in avg_time_stats:\n", " avg_time_stats[t].update(ret[t])\n", " Bar.suffix = Bar.suffix + '|{} {tm.val:.3f}s ({tm.avg:.3f}s) '.format(\n", " t, tm = avg_time_stats[t])\n", " bar.next()\n", " bar.finish()\n", " dataset.run_eval(results, opt.save_dir)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fix size testing.\n", "training chunk_sizes: [32]\n", "The output will be saved to /data/cc/workspace/Repository/CenterNet/scripts/../../exp/eldet/default\n", "heads {'hm': 2, 'reg': 2, 'l': 1, 'ratio_al': 1, 'ratio_bl': 1, 'theta': 1, 'sincos': 2}\n", "Namespace(K=2, aggr_weight=0.0, agnostic_ex=False, angle_bins_weight=0, arch='dla_34', aug_ddd=0.5, aug_rot=0, batch_size=32, cat_spec_wh=False, center_thresh=0.1, chunk_sizes=[32], data_dir='/data/cc/Data/CHD/detection/', dataset='coco_fhd', debug=0, debug_dir='/data/cc/workspace/Repository/CenterNet/scripts/../../exp/eldet/default/debug', debugger_theme='white', demo='', dense_hp=False, dense_wh=False, dep_weight=1, dim_weight=1, down_ratio=4, ellipse_reg_weight=0, ellipse_weight=1, eval_oracle_dep=False, eval_oracle_hm=False, eval_oracle_hmhp=False, eval_oracle_hp_offset=False, eval_oracle_kps=False, eval_oracle_offset=False, eval_oracle_wh=False, exp_dir='/data/cc/workspace/Repository/CenterNet/scripts/../../exp/eldet', exp_id='default', fix_res=True, flip=0.5, flip_test=False, gaussian_noise=False, gpus=[0], gpus_str='5', head_conv=256, heads={'hm': 2, 'reg': 2, 'l': 1, 'ratio_al': 1, 'ratio_bl': 1, 'theta': 1, 'sincos': 2}, hide_data_time=False, hm_hp=True, hm_hp_weight=1, hm_weight=1, hp_weight=1, input_h=512, input_res=512, input_w=512, iou_weight=1, keep_res=False, kitti_split='3dop', load_model='/data/cc/workspace/Repository/CenterNet/exp/eldet/exp3_base_theta5_iou/model_last.pth', lr=0.000125, lr_step=[80, 120, 200, 400], master_batch_size=32, mean=array([[[0.216, 0.216, 0.216]]], dtype=float32), metric='loss', mse_loss=False, nms=False, no_color_aug=False, norm_wh=False, not_cuda_benchmark=False, not_hm_hp=False, not_prefetch_test=False, not_rand_crop=False, not_reg_bbox=False, not_reg_hp_offset=False, not_reg_offset=False, num_angle_bins=0, num_classes=2, num_epochs=140, num_iters=-1, num_stacks=1, num_workers=4, off_weight=1, output_h=128, output_res=128, output_w=128, pad=31, peak_thresh=0.2, print_iter=0, rect_mask=False, reg_bbox=True, reg_hp_offset=True, reg_loss='l1', reg_offset=True, resume=True, root_dir='/data/cc/workspace/Repository/CenterNet/scripts/../..', rot_weight=1, rotate=0, save_all=False, save_dir='/data/cc/workspace/Repository/CenterNet/scripts/../../exp/eldet/default', save_image=False, scale=0.2, scores_thresh=0.1, seed=317, shift=0.1, sincos_weight=0, std=array([[[0.222, 0.222, 0.222]]], dtype=float32), task='eldet', test=False, test_scales=[1.0], theta_weight=1, trainval=False, val_intervals=5, vis_thresh=0.3, wh_weight=0.1)\n" ] } ], "source": [ "args = ['eldet']\n", "opt = opts().parse(args)\n", "# opt.data_dir = '/data/cc/Data/CHD/fake_data/'\n", "# opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/fake_0214_eldet_fhd_dla_iouw5_nocrop_896x608/model_last.pth'\n", "# opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/fake_0209_eldet_fhd_dla_rl_nocrop_896x608/model_best.pth'\n", "opt.data_dir = '/data/cc/Data/CHD/detection/'\n", "opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/exp3_base_theta5_iou/model_last.pth'\n", "opt.num_classes = 2\n", "opt.dataset = 'coco_fhd'\n", "opt.resume = True\n", "opt.gpus_str = '5'\n", "\n", "torch.manual_seed(opt.seed)\n", "torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test\n", "Dataset = dataset_factory[opt.dataset]\n", "opt = opts().update_dataset_info_and_set_heads(opt, Dataset)\n", "\n", "exp_name = opt.load_model.split('/')[-2]\n", "save_pred_folder = os.path.join('../exp/prediction/', exp_name)\n", "if not os.path.exists(save_pred_folder):\n", " os.makedirs(save_pred_folder)\n", " \n", "print(opt)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[0.216 0.216 0.216]]] [[[0.222 0.222 0.222]]]\n", "==> initializing Fetal Heart Disease val data.\n", "loading annotations into memory...\n", "Done (t=0.01s)\n", "creating index...\n", "index created!\n", "Loaded val 417 samples\n", "Creating model...\n", "loaded /data/cc/workspace/Repository/CenterNet/exp/eldet/exp3_base_theta5_iou/model_last.pth, epoch 344\n" ] } ], "source": [ "Detector = detector_factory[opt.task]\n", "split = 'val'\n", "# split = 'train'\n", "# opt.mean = [0, 0, 0]\n", "# opt.std = [1, 1, 1]\n", "print(opt.mean, opt.std)\n", "dataset = Dataset(opt, split)\n", "detector = Detector(opt)\n", "\n", "model = detector.model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/cc/Data/CHD/detection/images/normal_0003.png\n" ] } ], "source": [ "ind = 0\n", "img_id = dataset.images[ind]\n", "img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", "img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", "print(img_path)\n", "\n", "image = cv2.imread(img_path)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/cc/miniconda3/lib/python3.7/site-packages/torch/nn/functional.py:1625: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" ] }, { "data": { "text/plain": [ "{'results': {1: array([[ 6.0708961e+01, -6.7859993e+01, 9.9511987e+02, 8.6655090e+02,\n", " 5.2791443e+02, 3.9934546e+02, 9.3441089e+02, 7.2774559e-01,\n", " 6.8294001e-01, -4.7140598e-01, -1.5526435e-01, 1.1694402e-01,\n", " 8.5105705e-01]], dtype=float32),\n", " 2: array([[ 2.9612418e+02, 3.3623119e+01, 7.4247394e+02, 4.7997293e+02,\n", " 5.1929907e+02, 2.5679800e+02, 4.4634979e+02, 7.8611207e-01,\n", " 6.1427921e-01, 1.7749281e-01, -8.0613390e-02, -3.7832953e-02,\n", " 7.7176607e-01]], dtype=float32)},\n", " 'tot': 1.068162202835083,\n", " 'load': 0.02400350570678711,\n", " 'pre': 0.020003080368041992,\n", " 'net': 1.0161545276641846,\n", " 'dec': 0.004000663757324219,\n", " 'post': 0.004000425338745117,\n", " 'merge': 0.0}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret = detector.run(img_path)\n", "ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# With Ground Truth" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'anns' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mann\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mcx_gt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcy_gt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_gt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb_gt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta_gt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mann\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ellipse'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0ml_gt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_gt\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb_gt\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpt1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcx_gt\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ml_gt\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcy_gt\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ml_gt\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcx_gt\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ml_gt\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcy_gt\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ml_gt\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'anns' is not defined" ] } ], "source": [ "ann = anns[class_idx]\n", "cx_gt, cy_gt, a_gt, b_gt, theta_gt = ann['ellipse']\n", "l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", "pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", "pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", "ratio_al, ratio_ba = a_gt / l_gt, b_gt / a_gt\n", "angle_gt = theta_gt * 180\n", "print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l, ratio_al, ratio_ba, theta_gt, a_gt, b_gt, int(angle_gt))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.34587318389231014, 0.4469070802020421)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(thetas), max(thetas)" ] }, { "cell_type": "raw", "metadata": { "scrolled": true }, "source": [ "ind += 1\n", "\n", "\n", "\n", "# Load Image\n", "img_id = dataset.images[ind]\n", "img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", "img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", "image = cv2.imread(img_path)\n", "shape = image.shape[:2]\n", "print(img_path)\n", "\n", "# Load annotations\n", "ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", "anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", "# Inference\n", "color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", "ret = detector.run(img_path)\n", "\n", "for class_idx in range(opt.num_classes):\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6]) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_ba = ret['results'][class_id][0, 8]\n", " a = ratio_al * l / 2\n", " b = ratio_ba * a\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(\"Predict: \", pt1, pt2, (cx, cy), l, ratio_al, ratio_ba, \"({})'\".format(theta), int(a), int(b), int(angle))\n", " \n", " # Annotations\n", " ann = anns[class_idx]\n", " cx_gt, cy_gt, a_gt, b_gt, theta_gt = ann['ellipse']\n", " theta_gt = theta_gt / math.pi\n", " theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt\n", " l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", " pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", " pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", " ratio_al, ratio_ba = 2 * a_gt / l_gt, b_gt / a_gt\n", " angle_gt = theta_gt * 180\n", " print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_ba, \"({})'\".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)\n", " img = cv2.ellipse(img, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2)\n", "# img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2)\n", "# img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2)\n", " \n", " \n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", " \n", " pred_ell = [cx, cy, a, b, angle]\n", " gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", " error = compute_single_class_metric(pred_ell, gt_ell, shape)\n", " print(\"dice: {} angle_error: {}\".format(error[0], error[1]))\n", " \n", "img = Image.fromarray(image)\n", "# img.save(os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", "img\n" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(360, 480, 3)" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image.shape" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([271, 257, 78.13416111469269, 54.25057339668274, 37.65756279230118],\n", " [271, 260, 41.6, 50.313, -29.025620983028098])" ] }, "execution_count": 229, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ell1 = [cx, cy, a, b, angle]\n", "ell2 = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", "ell1, ell2" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [], "source": [ "iouloss, bboxiou = compute_rotated_bbox_iou(ell1, ell2)" ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.488799124956131" ] }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bboxiou.item()" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.6571257002316224, 0.48934267365528195, 66.68318377532927)" ] }, "execution_count": 223, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compute_single_class_metric(ell1, ell2, shape)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Calculate Metrics: Dice and difference of angles\n", "# input 2 ellipse ell1, ell2 in [cx, cy, a, b, angle], image shape [h, w], num_classes\n", "\n", "def compute_dice(pred, label):\n", " \n", " ints = np.sum((pred == 1) * (label == 1))\n", " sums = np.sum(pred == 1) + np.sum(label == 1) + 1e-5\n", " \n", " return (2.0 * ints) / sums\n", "\n", "def compute_iou(pred, label):\n", " \n", " inter = np.sum((pred == 1) * (label == 1))\n", " union = np.sum((pred == 1) + (label == 1)) + 1e-5\n", " \n", " return inter / union\n", "\n", "def compute_rotated_bbox_iou(ell1, ell2):\n", " cx1, cy1, a1, b1, angle1 = ell1\n", " cx2, cy2, a2, b2, angle2 = ell2\n", " theta1 = angle1 / 180.0 * math.pi\n", " theta2 = angle2 / 180.0 * math.pi\n", " bbox1 = torch.tensor([[[cx1, cy1, 2 * a1, 2 * b1, theta1]]], dtype=torch.float).cuda()\n", " bbox2 = torch.tensor([[[cx2, cy2, 2 * a2, 2 * b2, theta2]]], dtype=torch.float).cuda()\n", " return cal_diou(bbox1, bbox2) # iou_loss, iou\n", "\n", "def compute_single_class_metric(ell1, ell2, shape):\n", " cx1, cy1, a1, b1, angle1 = ell1\n", " cx2, cy2, a2, b2, angle2 = ell2\n", " h, w = shape\n", " pred = np.zeros([h, w], np.uint8)\n", " pred = cv2.ellipse(pred, (cx1, cy1), (int(a1), int(b1)), int(angle1), 0.0, 360.0, (1), thickness=-1)\n", " label = np.zeros([h, w], np.uint8)\n", " label = cv2.ellipse(label, (cx2, cy2), (int(a2), int(b2)), int(angle2), 0.0, 360.0, (1), thickness=-1)\n", " angle_error = abs(angle1 - angle2)\n", " angle_error = angle_error if angle_error <= 90 else (180 - angle_error)\n", " bbox_iou_loss, bbox_iou = compute_rotated_bbox_iou(ell1, ell2)\n", " return compute_dice(pred, label), compute_iou(pred, label), bbox_iou.item(), angle_error" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Ratio_ba" ] }, { "cell_type": "raw", "metadata": { "scrolled": true }, "source": [ "angle_errors = []\n", "dices = []\n", "\n", "for ind in range(len(dataset)):\n", " cls_dice = []\n", " cls_angle_error = []\n", " \n", " # Load Image\n", " img_id = dataset.images[ind]\n", " img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", " img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", " image = cv2.imread(img_path)\n", "\n", " # Load annotations\n", " ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", " anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", " # Inference\n", " color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", " ret = detector.run(img_path)\n", "\n", " for class_idx in range(opt.num_classes):\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6]) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_ba = ret['results'][class_id][0, 8]\n", " a = ratio_al * l / 2\n", " b = ratio_ba * a\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(\"Predict: \", pt1, pt2, (cx, cy), l, ratio_al, ratio_ba, \"({})'\".format(theta), int(a), int(b), int(angle))\n", "\n", " # Annotations\n", " ann = anns[class_idx]\n", " cx_gt, cy_gt, a_gt, b_gt, theta_gt = ann['ellipse']\n", " theta_gt = theta_gt / math.pi\n", " theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt\n", " l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", " pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", " pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", " ratio_al, ratio_ba = 2 * a_gt / l_gt, b_gt / a_gt\n", " angle_gt = theta_gt * 180\n", " print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_ba, \"({})'\".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)\n", " img = cv2.ellipse(img, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2)\n", " # img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2)\n", " # img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2)\n", "\n", "\n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", " \n", " pred_ell = [cx, cy, a, b, angle]\n", " gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", " error = compute_single_class_metric(pred_ell, gt_ell, shape)\n", " print(\"dice: {} iou:{} angle_error: {}\".format(error[0], error[1], error[2]))\n", " cls_dice.append(error[0])\n", " cls_angle_error.append(error[1])\n", "\n", "# img = Image.fromarray(image)\n", "# img.save(os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", "# print(\"Saving\", img_path, \"to\", os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", " dices.append(cls_dice)\n", " angle_errors.append(cls_angle_error)\n", "# img\n" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "232 232\n", "[0.75267374 0.79902569] 20.781101087723894\n" ] } ], "source": [ "print(len(dices), len(angle_errors))\n", "print(np.mean(dices, axis=0), np.mean(angle_errors))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Ratio_bl" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predict: (60, -67) (995, 866) (527, 399) 934 0.7277456 0.68294 (-0.4714059829711914)' 339 318 -84\n", "Ground Truth: (33, -66) (976, 876) (505, 405) 943.309756133159 0.7123852961668505 0.7017885648863686 (0.000954929658551372)' 336 331 0\n", "dice: 0.954505224717805 iou:0.9129698658323465 bbox_iou: 0.8849083185195923 angle_error: 85.0249642733537\n", "Predict: (296, 33) (742, 479) (519, 256) 446 0.78611207 0.6142792 (0.17749281227588654)' 175 136 31\n", "Ground Truth: (306, 66) (723, 483) (515, 275) 416.739677443845 0.7576288438303181 0.6526856325953067 (0.3043042511917039)' 157 136 54\n", "dice: 0.8804807415256337 iou:0.7864811032599078 bbox_iou: 0.7250504493713379 angle_error: 22.826059004847124\n", "Predict: (38, -77) (504, 388) (271, 155) 466 0.7880692 0.615622 (-0.034031517803668976)' 183 143 -6\n", "Ground Truth: (33, -79) (502, 389) (268, 155) 468.13601835791275 0.7747278264812237 0.632294863869444 (-0.03883380611442246)' 181 148 -6\n", "dice: 0.9795564276709431 iou:0.959931988630402 bbox_iou: 0.9527820944786072 angle_error: 0.8644118959356275\n", "Predict: (94, 59) (322, 288) (208, 174) 228 0.8042879 0.59700924 (-0.20297521352767944)' 91 68 -36\n", "Ground Truth: (90, 55) (331, 296) (211, 176) 241.14216375408097 0.840466871677766 0.5418629324951003 (-0.20690142601946396)' 101 65 -37\n", "dice: 0.9380399898943371 iou:0.8833100879203579 bbox_iou: 0.8629269599914551 angle_error: 0.7067182485212129\n", "Predict: (-118, -61) (712, 769) (297, 354) 831 0.78167427 0.6220581 (-0.13070206344127655)' 324 258 -23\n", "Ground Truth: (-128, -47) (696, 777) (284, 365) 824.5891044368703 0.7667576452296007 0.6419366896213036 (-0.17284226819779835)' 316 264 -31\n", "dice: 0.9644282118776389 iou:0.9313001985369691 bbox_iou: 0.8721253871917725 angle_error: 7.585236856173925\n", "Predict: (154, 60) (564, 471) (359, 266) 410 0.7751775 0.6355405 (-0.35191428661346436)' 158 130 -63\n", "Ground Truth: (194, 113) (529, 448) (362, 281) 334.5198034795548 0.7669441310540538 0.6417138769278272 (-0.36344505882715383)' 128 107 -65\n", "dice: 0.8003991461965242 iou:0.6672212208408859 bbox_iou: 0.665023148059845 angle_error: 2.0755389984641113\n", "Predict: (232, 48) (779, 595) (505, 322) 546 0.7637967 0.6407311 (-0.2222595065832138)' 208 174 -40\n", "Ground Truth: (243, 58) (758, 573) (501, 316) 515.7906087745297 0.75536466421069 0.6553046803295943 (-0.20709007351617736)' 194 169 -37\n", "dice: 0.9480384586666282 iou:0.9012101882213106 bbox_iou: 0.8880983591079712 angle_error: 2.730497952066557\n", "Predict: (395, 119) (687, 411) (541, 265) 291 0.77424854 0.6623567 (-0.42173463106155396)' 112 96 -75\n", "Ground Truth: (402, 129) (681, 408) (542, 269) 279.39259386032404 0.752518157675678 0.6585715013333052 (-0.45352675661716657)' 105 92 -81\n", "dice: 0.9470248865563817 iou:0.8993801225360967 bbox_iou: 0.8704103827476501 angle_error: 5.72258260001027\n", "Predict: (204, -14) (752, 533) (478, 259) 548 0.80823284 0.60021377 (0.03604985773563385)' 221 164 6\n", "Ground Truth: (209, -21) (774, 543) (492, 261) 564.547465614717 0.840790241584296 0.541361034483108 (-0.014005634992086789)' 237 152 -2\n", "dice: 0.9359528969518198 iou:0.8796160379278244 bbox_iou: 0.8259525299072266 angle_error: 9.009988690989715\n", "Predict: (256, 126) (523, 393) (390, 259) 267 0.8222692 0.5864348 (-0.09051601588726044)' 109 78 -16\n", "Ground Truth: (242, 126) (531, 415) (387, 271) 289.8851374251533 0.8338820063253961 0.5519427502257204 (-0.11300000959524568)' 120 80 -20\n", "dice: 0.8964183004973563 iou:0.8122808677430491 bbox_iou: 0.8032035231590271 angle_error: 4.047118867437344\n", "Predict: (208, -72) (1096, 814) (652, 371) 887 0.7855943 0.623052 (0.1391344666481018)' 348 276 25\n", "Ground Truth: (199, -85) (1100, 815) (650, 365) 900.848696943055 0.7953921701074445 0.606095121026205 (0.17061409899451183)' 358 273 30\n", "dice: 0.9783150070090004 iou:0.9575505304672891 bbox_iou: 0.9045459032058716 angle_error: 5.666333822353803\n", "Predict: (333, 51) (797, 515) (565, 283) 464 0.77743304 0.63086206 (0.3695344030857086)' 180 146 66\n", "Ground Truth: (371, 91) (792, 512) (582, 302) 420.4088206543721 0.7932231285750306 0.6089310866540156 (0.3621718192824186)' 166 128 65\n", "dice: 0.8722574450907514 iou:0.773454403483908 bbox_iou: 0.7634546756744385 angle_error: 1.3252650845922034\n", "Predict: (115, 23) (683, 591) (399, 307) 568 0.7650147 0.63094735 (-0.20236323773860931)' 217 179 -36\n", "Ground Truth: (134, 24) (693, 583) (414, 304) 558.2153757968334 0.7612187310201469 0.6484952147426203 (-0.18385345182476065)' 212 181 -33\n", "dice: 0.9531132551701472 iou:0.9104263282319557 bbox_iou: 0.8997740745544434 angle_error: 3.3317614644927573\n", "Predict: (280, 92) (587, 399) (433, 246) 306 0.776664 0.6427637 (0.5446528792381287)' 118 98 98\n", "Ground Truth: (274, 87) (593, 406) (434, 247) 319.1682010225956 0.7742375312085229 0.6328951297554212 (-0.4350647832185067)' 123 101 -78\n", "dice: 0.9642339802254162 iou:0.9309380321583293 bbox_iou: 0.9207866191864014 angle_error: 3.650820757805633\n", "Predict: (33, -10) (465, 421) (249, 205) 431 0.7843806 0.61935014 (0.10364292562007904)' 169 133 18\n", "Ground Truth: (16, -33) (493, 443) (255, 205) 476.8667587198756 0.7727609300955054 0.6346972072712541 (0.05672399093545)' 184 151 10\n", "dice: 0.8949590953791735 iou:0.8098877531472568 bbox_iou: 0.8016344308853149 angle_error: 8.445408243233228\n", "Predict: (177, 94) (415, 332) (296, 213) 237 0.81251657 0.5782223 (-0.10640274733304977)' 96 68 -19\n", "Ground Truth: (175, 94) (412, 331) (294, 213) 237.32631222011602 0.8155606438635512 0.5786716134223883 (-0.10714076925446703)' 96 68 -19\n", "dice: 0.9861955784858104 iou:0.9727670915193454 bbox_iou: 0.971339225769043 angle_error: 0.1328439458551074\n", "Predict: (64, -66) (991, 860) (528, 396) 927 0.7697263 0.6370155 (-0.20762313902378082)' 356 295 -37\n", "Ground Truth: (-9, -98) (983, 894) (487, 398) 992.617373210846 0.7592452241312082 0.6508046478275576 (-0.276994432174633)' 376 323 -49\n", "dice: 0.8973325231659279 iou:0.813783431558449 bbox_iou: 0.7833346128463745 angle_error: 12.486832767153388\n", "Predict: (236, 237) (714, 715) (475, 476) 477 0.7662563 0.65957403 (-0.4523948132991791)' 182 157 -81\n", "Ground Truth: (230, 210) (773, 753) (502, 482) 542.5166839830827 0.760935123633678 0.6488279722858743 (-0.4800761395598916)' 206 176 -86\n", "dice: 0.8654569280823905 iou:0.76282421487938 bbox_iou: 0.7515372037887573 angle_error: 4.982638726928258\n", "Predict: (124, -7) (827, 696) (476, 344) 703 0.7646929 0.6396199 (0.24532920122146606)' 268 224 44\n", "Ground Truth: (111, -43) (872, 717) (492, 337) 761.2729181679853 0.7494605237935204 0.6620490338903472 (0.22320006140957238)' 285 252 40\n", "dice: 0.910649039076488 iou:0.8359556026870101 bbox_iou: 0.8368285894393921 angle_error: 3.983245166140861\n", "Predict: (334, 240) (679, 585) (506, 413) 344 0.76342875 0.63426435 (0.40616747736930847)' 131 109 73\n", "Ground Truth: (312, 232) (701, 621) (507, 427) 389.28139385282725 0.7708048849451086 0.6370712906298305 (0.43583106538034455)' 150 124 78\n", "dice: 0.8688141428656386 iou:0.76805605143138 bbox_iou: 0.7670827507972717 angle_error: 5.339445841986489\n", "Predict: (49, 5) (754, 709) (402, 357) 704 0.7826337 0.621928 (-0.11966819316148758)' 275 218 -21\n", "Ground Truth: (23, -8) (754, 722) (389, 357) 730.4265077364047 0.7677733407265409 0.640721544252733 (-0.12223099629457562)' 280 234 -22\n", "dice: 0.9481795268655628 iou:0.9014651749836899 bbox_iou: 0.8965932726860046 angle_error: 0.4613045639558493\n", "Predict: (120, 210) (513, 603) (316, 407) 393 0.7873769 0.6126348 (-0.27486079931259155)' 154 120 -49\n", "Ground Truth: (126, 222) (505, 601) (316, 412) 378.92474833401945 0.828845308681579 0.5594778407377169 (-0.24127889372731334)' 157 106 -43\n", "dice: 0.9365312389389872 iou:0.8806382220429482 bbox_iou: 0.8498600721359253 angle_error: 6.044743005350078\n", "Predict: (84, -48) (897, 764) (491, 358) 813 0.779249 0.6273192 (-0.15130679309368134)' 316 255 -27\n", "Ground Truth: (77, -49) (906, 779) (492, 365) 829.81646163474 0.7929464290256893 0.6092913594458792 (-0.16889288717412243)' 329 252 -30\n", "dice: 0.9725303658089322 iou:0.9465295454445332 bbox_iou: 0.9266954660415649 angle_error: 3.165496934479396\n", "Predict: (376, 85) (801, 510) (589, 297) 424 0.7833597 0.61896914 (-0.31520652770996094)' 166 131 -56\n", "Ground Truth: (375, 69) (824, 518) (600, 294) 449.02542566763407 0.8121232766670146 0.5834858897142515 (-0.33377740821732615)' 182 131 -60\n", "dice: 0.9293968941210798 iou:0.8681059199413436 bbox_iou: 0.8594244122505188 angle_error: 3.342758491325739\n", "Predict: (470, 29) (1483, 1041) (977, 535) 1012 0.789782 0.6115417 (-0.07547759264707565)' 399 309 -13\n", "Ground Truth: (481, -5) (1548, 1061) (1015, 528) 1067.0875267437063 0.752558698207777 0.6585251747289665 (-0.03660563691113593)' 401 351 -6\n", "dice: 0.9034280438120369 iou:0.8238657196310613 bbox_iou: 0.8083378672599792 angle_error: 6.99695203246915\n", "Predict: (651, 391) (1091, 831) (871, 611) 440 0.777834 0.639683 (-0.21346771717071533)' 171 140 -38\n", "Ground Truth: (628, 351) (1117, 840) (873, 596) 488.20951275041745 0.8022703158597253 0.5969609202371094 (-0.22472677964575621)' 195 145 -40\n", "dice: 0.9069195899760961 iou:0.829691559430896 bbox_iou: 0.816191554069519 angle_error: 2.0266312455073603\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (230, -6) (694, 456) (462, 225) 463 0.79658115 0.60977423 (0.05329335480928421)' 184 141 9\n", "Ground Truth: (231, -15) (702, 455) (467, 220) 470.1115898550045 0.7699150750817146 0.638146360298261 (0.05780273689597959)' 180 150 10\n", "dice: 0.9635768155425873 iou:0.9297136825890652 bbox_iou: 0.9206072092056274 angle_error: 0.8116887756051678\n", "Predict: (283, 100) (531, 349) (407, 224) 248 0.79879737 0.59948784 (-0.2020818293094635)' 99 74 -36\n", "Ground Truth: (286, 94) (539, 347) (413, 221) 253.76078887802979 0.7967976490535958 0.6042462299945799 (-0.21294931385695598)' 101 76 -38\n", "dice: 0.9524249614668602 iou:0.9091711108356374 bbox_iou: 0.9043974876403809 angle_error: 1.9561472185486437\n", "Predict: (8, -54) (830, 767) (419, 356) 822 0.786973 0.61763996 (0.05171983689069748)' 323 253 9\n", "Ground Truth: (2, -60) (845, 782) (424, 361) 843.0632812215225 0.7757567131288124 0.6310321085615069 (0.05570423008216337)' 327 266 10\n", "dice: 0.9680600876127056 iou:0.9380973407387557 bbox_iou: 0.9374802112579346 angle_error: 0.7171907744638606\n", "Predict: (91, 134) (511, 553) (301, 344) 419 0.8193429 0.5790659 (-0.09638165682554245)' 171 121 -17\n", "Ground Truth: (101, 144) (502, 545) (302, 345) 400.4554405224132 0.7967478218894178 0.6043119296476519 (-0.1161831084570836)' 159 121 -20\n", "dice: 0.9615591908438305 iou:0.9259643711664101 bbox_iou: 0.8942270874977112 angle_error: 3.564261293677408\n", "Predict: (52, -59) (899, 787) (476, 363) 846 0.77545524 0.63002205 (-0.154030442237854)' 328 266 -27\n", "Ground Truth: (50, -65) (893, 777) (472, 356) 842.5574436820317 0.7941867999833685 0.6076737008725793 (-0.1944873404582961)' 334 256 -35\n", "dice: 0.9684775293736237 iou:0.9388816598299894 bbox_iou: 0.8824392557144165 angle_error: 7.282241679679579\n", "Predict: (183, 221) (618, 656) (401, 438) 435 0.7703723 0.63365614 (-0.3801511526107788)' 167 137 -68\n", "Ground Truth: (179, 214) (634, 669) (407, 442) 454.43971340982074 0.7587761144656778 0.6513515242297115 (-0.3889746809165922)' 172 148 -70\n", "dice: 0.9469587776837078 iou:0.8992608813553914 bbox_iou: 0.9042037129402161 angle_error: 1.5882350950464144\n", "Predict: (103, -50) (759, 605) (431, 277) 655 0.78658324 0.61242676 (0.06454890221357346)' 257 200 11\n", "Ground Truth: (96, -63) (765, 605) (431, 271) 669.1665837562423 0.7926217669488523 0.6097136496412714 (0.056847807237428216)' 265 204 10\n", "dice: 0.9718877435566357 iou:0.9453128658525765 bbox_iou: 0.9411709308624268 angle_error: 1.3861970957061427\n", "Predict: (355, 99) (684, 429) (520, 264) 329 0.8131706 0.5795196 (-0.12687872350215912)' 133 95 -22\n", "Ground Truth: (363, 105) (680, 422) (522, 264) 317.64191320416137 0.8060397238428602 0.5918614395171609 (-0.1609351400195277)' 128 94 -28\n", "dice: 0.9711277346709115 iou:0.9438758992694714 bbox_iou: 0.8892555832862854 angle_error: 6.130154973126341\n", "Predict: (162, -59) (801, 579) (481, 260) 638 0.7888444 0.61837023 (0.0369969978928566)' 251 197 6\n", "Ground Truth: (162, -60) (795, 572) (479, 256) 632.974693509938 0.7750199258041868 0.6319367963700745 (0.052202821334141676)' 245 200 9\n", "dice: 0.9807974937802703 iou:0.9623185655401247 bbox_iou: 0.9409683346748352 angle_error: 2.7370482194313137\n", "Predict: (236, 45) (583, 391) (409, 218) 346 0.7786688 0.62771314 (0.29830870032310486)' 134 108 53\n", "Ground Truth: (237, 40) (596, 399) (417, 220) 358.9146575385296 0.7534214452386108 0.6575379273126101 (0.26483382530491384)' 135 118 47\n", "dice: 0.9402904338887893 iou:0.8873095647700429 bbox_iou: 0.8642461895942688 angle_error: 6.025477503274381\n", "Predict: (133, 54) (921, 842) (527, 448) 788 0.76874495 0.6303767 (0.18309730291366577)' 302 248 32\n", "Ground Truth: (149, 64) (926, 841) (538, 453) 776.4735980057532 0.7472501337974675 0.6645428786313695 (0.1858281423365986)' 290 258 33\n", "dice: 0.9666586059961338 iou:0.9354687730553812 bbox_iou: 0.9229757189750671 angle_error: 0.4915510961279139\n", "Predict: (258, 160) (673, 575) (465, 368) 414 0.7830936 0.6277171 (0.4210171103477478)' 162 129 75\n", "Ground Truth: (286, 184) (655, 553) (471, 369) 369.6803542305163 0.7697136099977022 0.6383893471732632 (0.45734647525137206)' 142 118 82\n", "dice: 0.8905175746028242 iou:0.80264234404978 bbox_iou: 0.7893385887145996 angle_error: 6.539285682652363\n", "Predict: (140, -25) (923, 757) (531, 366) 782 0.75505966 0.6497324 (0.2561908960342407)' 295 254 46\n", "Ground Truth: (144, -19) (899, 735) (522, 358) 754.954569358978 0.742145319387538 0.670239005811486 (0.2402591328740268)' 280 253 43\n", "dice: 0.9691599820273639 iou:0.9401652682570677 bbox_iou: 0.9189411401748657 angle_error: 2.8677173688385054\n", "Predict: (286, 52) (672, 438) (479, 245) 386 0.7712739 0.6459154 (-0.40481501817703247)' 148 124 -72\n", "Ground Truth: (290, 48) (685, 443) (488, 246) 394.06617966529427 0.7558781122830605 0.6547123638449155 (-0.3561239314449266)' 148 129 -64\n", "dice: 0.9546065363752329 iou:0.913155256457466 bbox_iou: 0.8594515323638916 angle_error: 8.764395611779051\n", "Predict: (211, 52) (652, 493) (432, 273) 441 0.8060025 0.6050707 (0.03778284043073654)' 177 133 6\n", "Ground Truth: (209, 48) (656, 495) (433, 272) 447.51174862343004 0.7824599042977326 0.6227009701023302 (0.044881693951914485)' 175 139 8\n", "dice: 0.9777792486528031 iou:0.9565245543726009 bbox_iou: 0.9436964988708496 angle_error: 1.2777936338120295\n", "Predict: (274, 153) (465, 344) (370, 248) 190 0.8105117 0.6204835 (-0.004179779440164566)' 76 58 0\n", "Ground Truth: (264, 133) (483, 352) (374, 243) 218.1822117863874 0.800835221943155 0.598884752932697 (0.26897185382530314)' 87 65 48\n", "dice: 0.8323966526687908 iou:0.7129104713269278 bbox_iou: 0.6373679041862488 angle_error: 49.167293987784184\n", "Predict: (99, -8) (889, 782) (494, 387) 790 0.7899348 0.62029785 (0.0940815657377243)' 312 245 16\n", "Ground Truth: (98, 6) (875, 783) (487, 395) 776.5606041411063 0.7563504984258436 0.6541665869875791 (0.10440564266828335)' 293 254 18\n", "dice: 0.9596217326664052 iou:0.9223777185636892 bbox_iou: 0.9007527232170105 angle_error: 1.858333847500628\n", "Predict: (181, 170) (614, 603) (397, 386) 432 0.8200633 0.5798524 (-0.12395548820495605)' 177 125 -22\n", "Ground Truth: (190, 170) (609, 589) (400, 380) 418.6421348741667 0.8079836495714203 0.5892049066540844 (-0.1263690248149649)' 169 123 -22\n", "dice: 0.9612796599456027 iou:0.9254460732859634 bbox_iou: 0.92194002866745 angle_error: 0.4344365898015923\n", "Predict: (79, -5) (752, 667) (416, 330) 673 0.7872739 0.6201569 (0.1444588601589203)' 264 208 26\n", "Ground Truth: (109, 25) (736, 652) (423, 339) 627.527144671846 0.7687508087833692 0.6395484297493941 (0.14476616701888956)' 241 200 26\n", "dice: 0.934881345055177 iou:0.8777250691413411 bbox_iou: 0.8755210638046265 angle_error: 0.055315234794470314\n", "Predict: (159, 80) (515, 436) (337, 258) 356 0.78036726 0.6254786 (0.3299791216850281)' 138 111 59\n", "Ground Truth: (192, 115) (503, 426) (348, 271) 311.4463198947774 0.7873716410675499 0.6164786280501484 (0.30646758920025524)' 122 96 55\n", "dice: 0.864902148651488 iou:0.7619626339913974 bbox_iou: 0.7510221004486084 angle_error: 4.232075847259111\n", "Predict: (336, 91) (779, 534) (557, 313) 443 0.7796096 0.62830967 (-0.11914069950580597)' 172 139 -21\n", "Ground Truth: (325, 44) (836, 555) (581, 300) 510.4079203539067 0.8109239365114251 0.5851515779631925 (-0.24796340133717296)' 206 149 -44\n", "dice: 0.8516141759201376 iou:0.7415749637997215 bbox_iou: 0.6741318106651306 angle_error: 23.188086329646055\n", "Predict: (446, 238) (667, 459) (557, 348) 220 0.74962217 0.6518983 (-0.31603917479515076)' 82 71 -56\n", "Ground Truth: (440, 251) (667, 478) (554, 365) 227.56234859923555 0.779074399131929 0.6269314800017811 (-0.4128479223803765)' 88 71 -74\n", "dice: 0.8687416836158673 iou:0.7679428040738271 bbox_iou: 0.7232417464256287 angle_error: 17.425574565340625\n", "Predict: (25, 9) (577, 561) (301, 285) 552 0.7683668 0.6442127 (-0.24221955239772797)' 212 177 -43\n", "Ground Truth: (53, 10) (584, 541) (319, 276) 530.7954762015215 0.7394561890557327 0.6732046824459649 (-0.2752083891595085)' 196 178 -49\n", "dice: 0.9363105588019714 iou:0.8802480522392034 bbox_iou: 0.851115882396698 angle_error: 5.937990617120498\n", "Predict: (225, 101) (512, 389) (368, 245) 287 0.8185468 0.56988347 (-0.04674339294433594)' 117 81 -8\n", "Ground Truth: (224, 95) (515, 386) (370, 241) 290.201724488329 0.7994439054731748 0.6007407444162561 (-0.0340591578216656)' 116 87 -6\n", "dice: 0.9562609829660855 iou:0.9161878279529849 bbox_iou: 0.9125757813453674 angle_error: 2.28316232208066\n", "Predict: (110, -33) (841, 697) (475, 331) 731 0.78747934 0.61450493 (-0.09257889539003372)' 287 224 -16\n", "Ground Truth: (163, 0) (824, 660) (494, 330) 661.8100095284144 0.7661322625828777 0.642682936003159 (-0.11204507993669431)' 253 212 -20\n", "dice: 0.910085322829648 iou:0.8350060256023172 bbox_iou: 0.830605685710907 angle_error: 3.503913218398907\n", "Predict: (227, 218) (559, 550) (393, 384) 332 0.767816 0.636689 (-0.332733154296875)' 127 105 -59\n", "Ground Truth: (249, 235) (554, 540) (402, 388) 304.30300560461114 0.7686154776397509 0.639711065663724 (-0.34097471929757495)' 116 97 -61\n", "dice: 0.9114314740529801 iou:0.8372752402151843 bbox_iou: 0.832085907459259 angle_error: 1.4834817001259921\n", "Predict: (156, 51) (707, 602) (431, 326) 550 0.7632159 0.64509857 (-0.24380716681480408)' 209 177 -43\n", "Ground Truth: (137, 36) (728, 627) (433, 332) 590.8198007683899 0.7580450069844069 0.6522022442356441 (-0.34734091702125075)' 223 192 -62\n", "dice: 0.9272441144777678 iou:0.8643570517689331 bbox_iou: 0.7667369842529297 angle_error: 18.636075037160403\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (243, 250) (535, 541) (389, 396) 291 0.75718385 0.6332008 (0.4738999903202057)' 110 92 85\n", "Ground Truth: (235, 249) (560, 574) (398, 412) 324.74449651995644 0.7393258471594936 0.6733478237299777 (0.4912169855763242)' 120 109 88\n", "dice: 0.8528546523135685 iou:0.7434582322402388 bbox_iou: 0.7354970574378967 angle_error: 3.117059146101326\n", "Predict: (74, 32) (892, 849) (483, 441) 817 0.7829899 0.61620396 (-0.0808897614479065)' 319 251 -14\n", "Ground Truth: (58, -5) (917, 853) (488, 424) 859.4904197813958 0.7725507867427805 0.6349529761353284 (-0.08135766847358006)' 332 272 -14\n", "dice: 0.9395216848807099 iou:0.885941439335349 bbox_iou: 0.8887385129928589 angle_error: 0.08422326462124197\n", "Predict: (388, 230) (791, 633) (590, 432) 402 0.8154736 0.57244444 (0.10220272839069366)' 163 115 18\n", "Ground Truth: (393, 229) (794, 630) (594, 430) 401.2975123820231 0.8297135908558292 0.5581893559976987 (0.08899710574199104)' 166 112 16\n", "dice: 0.9757060661981556 iou:0.9525645266458364 bbox_iou: 0.9370424151420593 angle_error: 2.377012076766473\n", "Predict: (125, 5) (820, 699) (472, 352) 694 0.75035715 0.66123456 (0.26031285524368286)' 260 229 46\n", "Ground Truth: (117, 5) (824, 712) (471, 359) 706.0297885443645 0.7262668067549667 0.6874129220533636 (0.4824929562598915)' 256 242 86\n", "dice: 0.9552204479273653 iou:0.9142794248149269 bbox_iou: 0.7050106525421143 angle_error: 39.99241818291756\n", "Predict: (258, 93) (611, 445) (435, 269) 352 0.7826127 0.6280635 (0.34592369198799133)' 137 110 62\n", "Ground Truth: (248, 97) (605, 454) (427, 276) 357.4245256777996 0.7542851165256382 0.6565469998316223 (0.29570988426474154)' 134 117 53\n", "dice: 0.9401280176857876 iou:0.8870203509229803 bbox_iou: 0.8424589037895203 angle_error: 9.03848539018496\n", "Predict: (168, -59) (915, 687) (542, 314) 746 0.7781004 0.6303697 (-0.13722971081733704)' 290 235 -24\n", "Ground Truth: (163, -68) (934, 702) (549, 317) 770.9973140938948 0.7747549687666674 0.6322616059601913 (-0.15902528070242505)' 298 243 -28\n", "dice: 0.9664488962164319 iou:0.9350760622077691 bbox_iou: 0.9161251187324524 angle_error: 3.9232025793158414\n", "Predict: (256, 139) (653, 536) (454, 338) 396 0.8204654 0.5710772 (0.034583866596221924)' 162 113 6\n", "Ground Truth: (250, 139) (643, 532) (447, 336) 393.7124437962305 0.8223768516891868 0.5689431551620788 (0.04870141258611997)' 161 112 8\n", "dice: 0.9693333796871226 iou:0.9404916784758821 bbox_iou: 0.9272596836090088 angle_error: 2.5411582781816477\n", "Predict: (212, 268) (664, 720) (438, 494) 452 0.75864637 0.6490029 (0.27685999870300293)' 171 146 49\n", "Ground Truth: (187, 258) (680, 751) (434, 505) 493.38668149839634 0.7446127221843019 0.6674967370416756 (0.42532683913627944)' 183 164 76\n", "dice: 0.9248145016832903 iou:0.860144136180373 bbox_iou: 0.7156739830970764 angle_error: 26.724031277989766\n", "Predict: (309, 318) (552, 561) (430, 440) 242 0.7611869 0.64646167 (-0.30169105529785156)' 92 78 -54\n", "Ground Truth: (297, 303) (562, 568) (430, 436) 265.7709136305175 0.7908164107536343 0.6120534327023575 (-0.3016281097127632)' 105 81 -54\n", "dice: 0.9152303761005509 iou:0.8437094438638031 bbox_iou: 0.842917263507843 angle_error: 0.011330205315900344\n", "Predict: (208, 75) (681, 548) (445, 312) 473 0.78225887 0.6289479 (-0.10006773471832275)' 185 148 -18\n", "Ground Truth: (197, 69) (690, 562) (444, 316) 492.0647710616967 0.7947287084914434 0.606964809440813 (-0.054619638034141604)' 195 149 -9\n", "dice: 0.9587396764436769 iou:0.92074926390088 bbox_iou: 0.8652881383895874 angle_error: 8.180657403152606\n", "Predict: (374, 157) (629, 412) (501, 284) 254 0.7867138 0.641489 (-0.2543398141860962)' 99 81 -45\n", "Ground Truth: (379, 161) (630, 412) (505, 287) 250.10442110446587 0.8231921654595802 0.5677628543027161 (-0.22141401839444796)' 102 71 -39\n", "dice: 0.9313387191496706 iou:0.8715003863605953 bbox_iou: 0.8386031985282898 angle_error: 5.926643242496681\n", "Predict: (-112, -92) (603, 622) (245, 265) 715 0.7893439 0.6106709 (-0.01333147007972002)' 282 218 -2\n", "Ground Truth: (-81, -71) (591, 601) (255, 265) 672.7375204580163 0.7935843977254686 0.6084601907164555 (-0.01432394487827058)' 266 204 -2\n", "dice: 0.9494143016688171 iou:0.9037000057938417 bbox_iou: 0.8868109583854675 angle_error: 0.17864546373910084\n", "Predict: (146, 110) (505, 469) (326, 289) 359 0.7950048 0.6072008 (0.20998306572437286)' 142 108 37\n", "Ground Truth: (141, 109) (512, 480) (327, 295) 371.4541781323774 0.7932768490626266 0.6088611013533963 (0.20995486249183148)' 147 113 37\n", "dice: 0.9581389820495618 iou:0.9196418385385267 bbox_iou: 0.9332254528999329 angle_error: 0.005076581857451856\n", "Predict: (51, -88) (871, 731) (461, 321) 819 0.78880054 0.6110519 (-0.027504539117217064)' 323 250 -4\n", "Ground Truth: (74, -68) (847, 704) (461, 318) 773.7149425492569 0.7696400408630985 0.6384780399514522 (0.022470339529578776)' 297 247 4\n", "dice: 0.9506717585059391 iou:0.9059812944254202 bbox_iou: 0.8460567593574524 angle_error: 8.995478156423252\n", "Predict: (375, 122) (801, 548) (588, 335) 426 0.77970314 0.6239419 (0.2772618532180786)' 166 132 49\n", "Ground Truth: (395, 144) (780, 529) (588, 337) 384.29173899525864 0.7945369858803215 0.6072157590743293 (0.31092392760682835)' 152 116 55\n", "dice: 0.8922801004906179 iou:0.8055105815881938 bbox_iou: 0.8069973587989807 angle_error: 6.059173389974951\n", "Predict: (-14, -57) (830, 787) (408, 365) 844 0.78967065 0.6115939 (0.057743631303310394)' 333 258 10\n", "Ground Truth: (8, -46) (821, 766) (415, 360) 813.2595489854392 0.7924850077739932 0.6098913939821203 (0.08804217608343967)' 322 248 15\n", "dice: 0.9635011038019811 iou:0.9295727253894809 bbox_iou: 0.8957400918006897 angle_error: 5.4537380604232695\n", "Predict: (288, 117) (738, 567) (513, 342) 449 0.82916695 0.5662876 (-0.08005263656377792)' 186 127 -14\n", "Ground Truth: (279, 119) (726, 566) (503, 343) 446.29608288668635 0.8290460395861065 0.5591803503759695 (-0.15087888605111677)' 185 124 -27\n", "dice: 0.9365075048308336 iou:0.8805962515813203 bbox_iou: 0.8126092553138733 angle_error: 12.748724907720991\n", "Predict: (287, 25) (981, 718) (634, 372) 693 0.78878635 0.61622036 (-0.08695188909769058)' 273 213 -15\n", "Ground Truth: (309, 32) (1006, 729) (658, 381) 696.2377171081728 0.7819426994866684 0.6233503146060823 (-0.06238873769202297)' 272 217 -11\n", "dice: 0.9347682370155637 iou:0.8775256892421648 bbox_iou: 0.865204393863678 angle_error: 4.42136725302017\n", "Predict: (384, 253) (724, 593) (554, 423) 339 0.761215 0.6444226 (-0.31314727663993835)' 129 109 -56\n", "Ground Truth: (392, 245) (749, 602) (571, 424) 356.5447343602202 0.7593437061574132 0.6506897385998369 (-0.31862819606997445)' 135 116 -57\n", "dice: 0.9015624500471646 iou:0.8207680537558789 bbox_iou: 0.8246007561683655 angle_error: 0.9865654974064952\n", "Predict: (-69, -112) (797, 754) (363, 320) 867 0.7867097 0.61649954 (-0.05955415964126587)' 341 267 -10\n", "Ground Truth: (0, -62) (765, 702) (383, 320) 764.8108133545185 0.7611921665262117 0.6485263954683201 (-0.09218020460382903)' 291 248 -16\n", "dice: 0.8844406376966685 iou:0.7928225673894532 bbox_iou: 0.7872663736343384 angle_error: 5.87268809326137\n", "Predict: (300, 115) (696, 511) (498, 313) 395 0.8292432 0.55195147 (0.020620005205273628)' 163 109 3\n", "Ground Truth: (295, 116) (688, 509) (492, 313) 392.49894876801903 0.8382901432800203 0.5452243902097218 (0.025335128505233007)' 164 107 4\n", "dice: 0.9741329103410896 iou:0.9495702904993064 bbox_iou: 0.946263313293457 angle_error: 0.8487221939926881\n", "Predict: (337, 25) (912, 601) (625, 313) 575 0.79680294 0.6105722 (0.02384903095662594)' 229 175 4\n", "Ground Truth: (312, 5) (903, 596) (608, 301) 591.2057818932424 0.7799517767288443 0.6258396168168957 (0.02196338214668156)' 230 185 3\n", "dice: 0.9316748248034677 iou:0.8720891788701639 bbox_iou: 0.873637855052948 angle_error: 0.33941678578998813\n", "Predict: (405, 177) (678, 449) (542, 313) 272 0.8226775 0.6152725 (-0.08745519816875458)' 111 83 -15\n", "Ground Truth: (396, 161) (685, 450) (541, 306) 289.2480470184717 0.8039881423474191 0.5946453287168293 (-0.1476957871892789)' 116 86 -26\n", "dice: 0.9348919797219933 iou:0.8777438174561626 bbox_iou: 0.8287532329559326 angle_error: 10.843306023694378\n", "Predict: (75, -14) (907, 817) (491, 401) 831 0.7938815 0.6116964 (-0.007512589916586876)' 329 254 -1\n", "Ground Truth: (75, 10) (872, 807) (474, 409) 797.0593883419227 0.776745132239021 0.6298150518548963 (-0.03201963611509251)' 309 251 -5\n", "dice: 0.9492692908790141 iou:0.903437277162241 bbox_iou: 0.8849648833274841 angle_error: 4.411268315731014\n", "Predict: (352, 149) (794, 592) (573, 371) 442 0.8023092 0.5990844 (-0.20673105120658875)' 177 132 -37\n", "Ground Truth: (354, 149) (793, 588) (574, 369) 438.15486771231923 0.7988043173579453 0.6015909428925166 (-0.22950142793851308)' 175 131 -41\n", "dice: 0.9826383898264771 iou:0.9658693428179156 bbox_iou: 0.9296678304672241 angle_error: 4.098667811746381\n", "Predict: (-73, -8) (743, 807) (334, 399) 816 0.7612913 0.651335 (0.27255722880363464)' 310 265 49\n", "Ground Truth: (-107, -24) (711, 794) (302, 385) 818.4588678730288 0.7199873116793178 0.6939872268426767 (0.4103662744856411)' 294 284 73\n", "dice: 0.9154912833552784 iou:0.8441529969327003 bbox_iou: 0.7329553365707397 angle_error: 24.805628222761158\n", "Predict: (187, 244) (615, 672) (401, 458) 428 0.8080489 0.58773994 (0.14255666732788086)' 172 125 25\n", "Ground Truth: (159, 230) (602, 673) (381, 452) 443.37312288410084 0.8074463279849788 0.5899410372431928 (0.1300590810597)' 179 130 23\n", "dice: 0.9205035716160618 iou:0.852715717637068 bbox_iou: 0.852434515953064 angle_error: 2.249565528272555\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (255, 10) (976, 731) (616, 371) 721 0.79108095 0.6126991 (-0.007916359230875969)' 285 220 -1\n", "Ground Truth: (252, 2) (987, 737) (620, 370) 735.3257196453828 0.7591139342565019 0.6509577826692106 (0.035839354749298)' 279 239 6\n", "dice: 0.9570793962214484 iou:0.9176915220141437 bbox_iou: 0.866144597530365 angle_error: 7.876028516431315\n", "Predict: (305, 146) (717, 558) (511, 352) 412 0.78142655 0.62122303 (0.28269243240356445)' 160 127 50\n", "Ground Truth: (323, 147) (736, 560) (530, 354) 413.99993797584074 0.766275477119788 0.6425121735538101 (0.26699716331346524)' 158 133 48\n", "dice: 0.9152799368282794 iou:0.8437936827240029 bbox_iou: 0.832359254360199 angle_error: 2.8251484362178587\n", "Predict: (237, 18) (977, 758) (607, 388) 739 0.79004616 0.6145055 (0.09073084592819214)' 291 227 16\n", "Ground Truth: (265, 38) (950, 723) (608, 381) 685.807898120166 0.7621405373614065 0.647411616601422 (0.12312109475839185)' 261 222 22\n", "dice: 0.9325387072752176 iou:0.8736042361731321 bbox_iou: 0.843460202217102 angle_error: 5.830244789435948\n", "Predict: (325, 136) (706, 517) (515, 327) 381 0.7807665 0.6247947 (0.3231911361217499)' 148 119 58\n", "Ground Truth: (336, 141) (707, 512) (522, 327) 370.34400663707254 0.7554165721227978 0.655244841690678 (0.34370984588375875)' 139 121 61\n", "dice: 0.9524789590804073 iou:0.909269524786108 bbox_iou: 0.891940712928772 angle_error: 3.693367757161596\n", "Predict: (196, 65) (705, 575) (451, 320) 509 0.77135175 0.63608044 (-0.19160190224647522)' 196 161 -34\n", "Ground Truth: (160, 46) (707, 593) (434, 320) 546.8684799035323 0.7812957149685603 0.6241610415363698 (-0.13515203923864072)' 213 170 -24\n", "dice: 0.9315969444359498 iou:0.8719527144595488 bbox_iou: 0.8093187808990479 angle_error: 10.16097534141021\n", "Predict: (354, 145) (605, 395) (479, 270) 250 0.7778636 0.64257705 (-0.16284503042697906)' 97 80 -29\n", "Ground Truth: (346, 135) (601, 390) (474, 263) 255.13005283580372 0.7703835664020895 0.6375807091008928 (-0.4057802736895979)' 98 81 -73\n", "dice: 0.9031480400781188 iou:0.8234001242450639 bbox_iou: 0.6857780814170837 angle_error: 43.728343787271385\n", "Predict: (39, 49) (879, 889) (459, 469) 840 0.78187436 0.6240227 (-0.12781505286693573)' 328 262 -23\n", "Ground Truth: (47, 24) (916, 893) (482, 459) 868.2436201527772 0.7756532663971638 0.6311592590839576 (-0.11509851640906199)' 336 274 -20\n", "dice: 0.9433927245562899 iou:0.8928508694586856 bbox_iou: 0.8884373307228088 angle_error: 2.2889765624172718\n", "Predict: (337, 195) (724, 582) (531, 388) 387 0.7776023 0.6422882 (-0.3677099049091339)' 150 124 -66\n", "Ground Truth: (346, 172) (737, 563) (542, 368) 391.632480777578 0.82730625242499 0.5617511590540056 (-0.2950084332976387)' 162 110 -53\n", "dice: 0.8838045914318655 iou:0.7918009558609617 bbox_iou: 0.7519139051437378 angle_error: 13.086264890069138\n", "Predict: (267, 121) (751, 605) (509, 363) 483 0.7666317 0.6246832 (0.16206447780132294)' 185 150 29\n", "Ground Truth: (270, 92) (805, 627) (538, 360) 534.3376567265309 0.7805663605203494 0.6250729212052112 (0.1714393662635928)' 208 167 30\n", "dice: 0.8711495249392015 iou:0.7717138311805931 bbox_iou: 0.7881454825401306 angle_error: 1.6874799232085742\n", "Predict: (408, 278) (670, 539) (539, 408) 261 0.77356833 0.63843304 (0.4010295867919922)' 100 83 72\n", "Ground Truth: (413, 271) (686, 544) (550, 408) 273.45182848172726 0.7714338615735247 0.6363095136942085 (0.3794902155258135)' 105 87 68\n", "dice: 0.9150329159887446 iou:0.8433738953681034 bbox_iou: 0.8429131507873535 angle_error: 3.8770868279121657\n", "Predict: (148, -40) (839, 650) (493, 305) 691 0.7886566 0.6176832 (-0.052419260144233704)' 272 213 -9\n", "Ground Truth: (178, -30) (853, 644) (516, 307) 674.7117469645834 0.7648362464735446 0.644224740350958 (-0.004138028520389279)' 258 217 0\n", "dice: 0.9436912143539694 iou:0.8933857477828465 bbox_iou: 0.8384074568748474 angle_error: 8.690621692291996\n", "Predict: (224, 133) (596, 505) (410, 319) 372 0.7988594 0.59819525 (-0.18114003539085388)' 148 111 -32\n", "Ground Truth: (241, 141) (588, 488) (415, 315) 346.2921130433092 0.7785334688413258 0.6276030894553438 (-0.20435494692999362)' 134 108 -36\n", "dice: 0.9369017025240443 iou:0.8812935781653196 bbox_iou: 0.8671567440032959 angle_error: 4.178684077045155\n", "Predict: (70, 33) (836, 799) (453, 416) 766 0.78651434 0.62031174 (-0.0886225551366806)' 301 237 -15\n", "Ground Truth: (89, 52) (828, 791) (459, 422) 738.6182821024673 0.7646493644759912 0.6444465450341578 (-0.10211264227026162)' 282 238 -18\n", "dice: 0.9595052450500534 iou:0.9221625005656188 bbox_iou: 0.9071320295333862 angle_error: 2.4282156840445843\n", "Predict: (338, 165) (748, 576) (543, 371) 410 0.79414946 0.6048043 (-0.25648489594459534)' 162 123 -46\n", "Ground Truth: (344, 163) (755, 574) (550, 369) 410.61521717539887 0.7988013748158075 0.6015948500381099 (-0.25241973974374604)' 164 123 -45\n", "dice: 0.9692062439060241 iou:0.9402523426011741 bbox_iou: 0.9349921941757202 angle_error: 0.7317281161528726\n", "Predict: (202, 64) (776, 638) (489, 351) 573 0.7524491 0.64855576 (-0.25392138957977295)' 215 185 -45\n", "Ground Truth: (183, 52) (780, 649) (482, 351) 596.0678151787764 0.7849039791884715 0.6196174170035116 (-0.2723436001838545)' 233 184 -49\n", "dice: 0.9520811418465737 iou:0.9085447164528258 bbox_iou: 0.8884838223457336 angle_error: 3.31599790873468\n", "Predict: (400, 148) (699, 447) (549, 297) 298 0.80259246 0.5953697 (-0.13049979507923126)' 119 88 -23\n", "Ground Truth: (380, 131) (713, 464) (547, 298) 332.35512347487594 0.8076000068652175 0.5897306409805246 (-0.12496612288075937)' 134 98 -22\n", "dice: 0.8876399982755088 iou:0.7979790687362013 bbox_iou: 0.806602418422699 angle_error: 0.9960609957249424\n", "Predict: (-138, -91) (731, 778) (296, 343) 870 0.78933746 0.6202805 (-0.07034849375486374)' 343 269 -12\n", "Ground Truth: (-137, -73) (717, 781) (290, 354) 855.3179782349953 0.7854903288557014 0.6188739316485729 (-0.052521131220325465)' 335 264 -9\n", "dice: 0.9711044717674205 iou:0.9438319490372052 bbox_iou: 0.9245144128799438 angle_error: 3.20892525621689\n", "Predict: (153, 58) (603, 508) (378, 283) 449 0.79659694 0.60710293 (-0.25560832023620605)' 178 136 -46\n", "Ground Truth: (157, 74) (596, 513) (377, 294) 439.8705319068328 0.8088243566981111 0.5880503039807791 (-0.22300556782536696)' 177 129 -40\n", "dice: 0.9538689373663716 iou:0.9118063418985114 bbox_iou: 0.8754388689994812 angle_error: 5.86849543395104\n", "Predict: (257, 63) (968, 774) (612, 418) 710 0.7754838 0.6310041 (0.16889534890651703)' 275 224 30\n", "Ground Truth: (283, 81) (950, 748) (617, 415) 666.5001439099619 0.7632016356604375 0.6461604006167762 (0.15176898451493295)' 254 215 27\n", "dice: 0.9399746525396606 iou:0.8867473356101324 bbox_iou: 0.8812583088874817 angle_error: 3.082745590485132\n", "Predict: (334, 193) (723, 582) (529, 388) 388 0.81095976 0.5721908 (0.04758146405220032)' 157 111 8\n", "Ground Truth: (354, 202) (725, 573) (540, 388) 371.0646182055088 0.808484520703748 0.5885174422074768 (0.031194368846011487)' 150 109 5\n", "dice: 0.9499784631205893 iou:0.9047228363942493 bbox_iou: 0.8976200222969055 angle_error: 2.94967713711399\n", "Predict: (104, 70) (616, 582) (360, 326) 511 0.7924494 0.6260599 (-0.08795064687728882)' 202 159 -15\n", "Ground Truth: (96, 47) (649, 600) (373, 324) 552.2903953863403 0.7821971984463096 0.623030932412464 (-0.0975914726689534)' 216 172 -17\n", "dice: 0.9272892045602252 iou:0.8644354177307111 bbox_iou: 0.8678067922592163 angle_error: 1.735348642499627\n", "Predict: (260, 141) (533, 414) (396, 277) 273 0.7707511 0.64739364 (-0.4978642165660858)' 105 88 -89\n", "Ground Truth: (257, 132) (532, 407) (395, 270) 274.94703562686396 0.7971717152733864 0.6037526450195371 (-0.3589887204205807)' 109 83 -64\n", "dice: 0.9263513744995772 iou:0.8628068368902433 bbox_iou: 0.7345432639122009 angle_error: 24.997589306190918\n", "Predict: (45, -42) (813, 725) (429, 341) 768 0.7486135 0.6597324 (-0.311959832906723)' 287 253 -56\n", "Ground Truth: (86, 1) (795, 710) (441, 356) 708.0920084000384 0.7369070598311412 0.675994071846065 (-0.3147436462410339)' 260 239 -56\n", "dice: 0.9182891480872325 iou:0.8489229320972793 bbox_iou: 0.8431843519210815 angle_error: 0.5010864001759572\n", "Predict: (275, 59) (658, 442) (466, 251) 382 0.78663987 0.62682766 (0.37926238775253296)' 150 119 68\n", "Ground Truth: (285, 69) (656, 440) (471, 255) 371.7927860408268 0.7782130553986275 0.6280003506425236 (0.38095210256726225)' 144 116 68\n", "dice: 0.9632004102207975 iou:0.9290131089132869 bbox_iou: 0.935675323009491 angle_error: 0.3041486666512725\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (79, 84) (628, 633) (354, 358) 548 0.7757365 0.6342968 (-0.17186100780963898)' 212 173 -30\n", "Ground Truth: (64, 85) (633, 654) (349, 370) 568.1519650269636 0.7648622670509932 0.6441938469448578 (-0.22084222981681567)' 217 183 -39\n", "dice: 0.9519784097591041 iou:0.9083576317738687 bbox_iou: 0.8521653413772583 angle_error: 8.816619961291806\n", "Predict: (248, 160) (520, 433) (384, 297) 272 0.7797087 0.6355618 (0.48475682735443115)' 106 86 87\n", "Ground Truth: (251, 163) (520, 432) (386, 298) 268.9265327854431 0.7808601026643174 0.624705930872336 (-0.432771782820485)' 104 84 -77\n", "dice: 0.958758878863703 iou:0.9207846860844472 bbox_iou: 0.810272753238678 angle_error: 14.844850168515109\n", "Predict: (172, 11) (671, 509) (421, 260) 498 0.764557 0.62268484 (0.14802537858486176)' 190 155 26\n", "Ground Truth: (146, -17) (697, 533) (422, 258) 550.5415929646007 0.7977090298211826 0.6030425389155789 (0.1455324491807275)' 219 166 26\n", "dice: 0.895441274203093 iou:0.8106778329572819 bbox_iou: 0.8097745180130005 angle_error: 0.44872729274416656\n", "Predict: (334, 172) (600, 439) (467, 305) 266 0.7654788 0.6408115 (0.3863399028778076)' 101 85 69\n", "Ground Truth: (338, 177) (619, 458) (479, 318) 280.34935526232266 0.7896790052998314 0.6135202267151988 (0.373124017802138)' 110 86 67\n", "dice: 0.8899682027866158 iou:0.8017501886169258 bbox_iou: 0.8004623055458069 angle_error: 2.378859313620538\n", "Predict: (97, -29) (946, 820) (522, 395) 849 0.7871152 0.6151632 (-0.03300292789936066)' 334 261 -5\n", "Ground Truth: (84, -7) (897, 805) (491, 399) 812.2845886141138 0.7599183939377213 0.6500184878563948 (-0.0512478916755903)' 308 264 -9\n", "dice: 0.9376388286361769 iou:0.8825989257800788 bbox_iou: 0.8762550354003906 angle_error: 3.284093479721337\n", "Predict: (197, 210) (650, 663) (423, 436) 452 0.80114573 0.6012229 (-0.22048701345920563)' 181 135 -39\n", "Ground Truth: (175, 220) (640, 685) (408, 453) 464.6756130033079 0.780204490734527 0.6255245419946986 (-0.19607888988921507)' 181 145 -35\n", "dice: 0.9073450492039369 iou:0.8304040068119242 bbox_iou: 0.8252592086791992 angle_error: 4.393462242598304\n", "Predict: (114, 47) (778, 711) (446, 379) 663 0.7627548 0.63155985 (-0.19937282800674438)' 252 209 -35\n", "Ground Truth: (143, 62) (774, 693) (459, 378) 630.6664795278087 0.7757190462481656 0.6310784113625156 (-0.20206194653197196)' 244 199 -36\n", "dice: 0.9541512859858884 iou:0.9123224737961615 bbox_iou: 0.9092321991920471 angle_error: 0.48404133454096154\n", "Predict: (331, 123) (696, 487) (513, 305) 364 0.77295494 0.6341802 (-0.35741281509399414)' 140 115 -64\n", "Ground Truth: (335, 129) (692, 486) (514, 308) 357.5024475440693 0.7608339519590858 0.6489466060827497 (-0.3440281557699425)' 136 116 -61\n", "dice: 0.9803158593839334 iou:0.9613916901675573 bbox_iou: 0.9389280676841736 angle_error: 2.409238678329295\n", "Predict: (-28, -129) (831, 730) (401, 300) 860 0.7928263 0.61324644 (-0.01218799315392971)' 340 263 -2\n", "Ground Truth: (-5, -102) (837, 740) (416, 319) 843.3004731031521 0.7891090082651973 0.6142531832027545 (-0.021197099984843537)' 332 259 -3\n", "dice: 0.9440163145970516 iou:0.8939686546737019 bbox_iou: 0.8909454941749573 angle_error: 1.6216392295644888\n", "Predict: (264, 20) (733, 489) (498, 255) 469 0.82080215 0.5780283 (-0.12522214651107788)' 192 135 -22\n", "Ground Truth: (293, 52) (716, 475) (505, 264) 422.05403078278965 0.8353669774130184 0.5496926532598356 (-0.10936893845775364)' 176 116 -19\n", "dice: 0.8814038827581949 iou:0.7879554283913746 bbox_iou: 0.783789336681366 angle_error: 2.8535774495983617\n", "Predict: (36, -30) (529, 462) (283, 215) 492 0.7883292 0.61813915 (0.005779004655778408)' 193 152 1\n", "Ground Truth: (52, -20) (545, 472) (299, 226) 492.2566114700746 0.7633335769281853 0.6460045281064386 (0.04507034144862798)' 187 159 8\n", "dice: 0.947767109962798 iou:0.900719906150519 bbox_iou: 0.8408506512641907 angle_error: 7.072440622712923\n", "Predict: (91, 99) (354, 362) (223, 230) 262 0.81822294 0.58429265 (-0.14887240529060364)' 107 76 -26\n", "Ground Truth: (112, 117) (339, 344) (226, 231) 227.6074912299681 0.8018540998529752 0.5975198762794213 (-0.14069296969323547)' 91 68 -25\n", "dice: 0.8648149368026181 iou:0.7618272692619523 bbox_iou: 0.756338357925415 angle_error: 1.4722984075262708\n", "Predict: (51, 59) (680, 688) (366, 373) 628 0.7405859 0.6529442 (0.30866730213165283)' 232 205 55\n", "Ground Truth: (65, 57) (690, 682) (378, 370) 625.0093045099409 0.7257203960437771 0.6879897577479357 (0.35930703030676453)' 226 215 64\n", "dice: 0.9611235591376259 iou:0.925156757178741 bbox_iou: 0.8567918539047241 angle_error: 9.115151071520103\n", "Predict: (205, 129) (542, 466) (374, 298) 337 0.7711079 0.6539371 (0.5057384967803955)' 129 110 91\n", "Ground Truth: (214, 117) (559, 462) (387, 290) 344.89935914118485 0.7712394730519423 0.6365451085402843 (-0.3717859470626675)' 133 109 -66\n", "dice: 0.9121894793817472 iou:0.8385554856219886 bbox_iou: 0.7442440986633301 angle_error: 22.045600108248664\n", "Predict: (25, -137) (851, 688) (438, 275) 826 0.7885185 0.6093944 (0.025680817663669586)' 325 251 4\n", "Ground Truth: (13, -144) (854, 696) (434, 276) 841.3252242147504 0.7746944715800343 0.6323357302124649 (0.03424780531837912)' 325 266 6\n", "dice: 0.9683662878554683 iou:0.9386725893655171 bbox_iou: 0.933835506439209 angle_error: 1.5420577778477167\n", "Predict: (345, 92) (768, 514) (556, 303) 422 0.7758026 0.6307928 (0.30640408396720886)' 163 133 55\n", "Ground Truth: (357, 93) (762, 498) (560, 296) 404.46723473230804 0.7483078331433101 0.6633516313813994 (0.30067318005421184)' 151 134 54\n", "dice: 0.9444125467677678 iou:0.8946795870640138 bbox_iou: 0.8775790929794312 angle_error: 1.0315627043394642\n", "Predict: (248, 56) (881, 690) (564, 373) 633 0.7716712 0.63422513 (-0.17971177399158478)' 244 200 -32\n", "Ground Truth: (249, 54) (870, 675) (560, 365) 620.999251249146 0.7667255621359411 0.6419750091454691 (-0.16711269024649011)' 238 199 -30\n", "dice: 0.9676780741144901 iou:0.937380142618235 bbox_iou: 0.932154655456543 angle_error: 2.2678350741170377\n", "Predict: (387, 291) (671, 575) (529, 433) 283 0.7417918 0.657521 (-0.40651729702949524)' 104 93 -73\n", "Ground Truth: (390, 275) (689, 574) (540, 425) 299.54586023512326 0.7891012074560602 0.6142632044908664 (-0.3670112987699107)' 118 92 -66\n", "dice: 0.9091691776475663 iou:0.8334648774288349 bbox_iou: 0.8079380393028259 angle_error: 7.111079686725219\n", "Predict: (154, -21) (827, 651) (491, 314) 672 0.79774684 0.60591644 (0.012939775362610817)' 268 203 2\n", "Ground Truth: (152, -29) (831, 649) (492, 310) 678.704308991773 0.7663690846057454 0.6424005184933709 (0.007191464992756802)' 260 218 1\n", "dice: 0.9642119338995359 iou:0.9308969329310792 bbox_iou: 0.908175528049469 angle_error: 1.0346958665737227\n", "Predict: (401, 157) (746, 502) (573, 329) 344 0.7891955 0.6124535 (0.20656581223011017)' 135 105 37\n", "Ground Truth: (405, 158) (760, 513) (583, 336) 354.30780607262943 0.8254461092512546 0.5644809303439453 (0.17239429592214417)' 146 100 31\n", "dice: 0.9376531205368255 iou:0.8826242526458408 bbox_iou: 0.8455350399017334 angle_error: 6.150872935433881\n", "Predict: (180, -39) (926, 707) (553, 333) 746 0.7898105 0.6150546 (0.09147606790065765)' 294 229 16\n", "Ground Truth: (193, -24) (902, 684) (548, 330) 708.0091798077198 0.7712555367549009 0.6365256452217064 (0.09867606471697511)' 273 225 17\n", "dice: 0.9544610421564118 iou:0.9128890272295319 bbox_iou: 0.908327579498291 angle_error: 1.2959994269371435\n", "Predict: (267, 78) (643, 454) (455, 266) 376 0.7900165 0.60502267 (0.25301384925842285)' 148 113 45\n", "Ground Truth: (278, 92) (633, 447) (456, 270) 355.4658835725308 0.7718659164769492 0.6357853466235276 (0.2466901617924378)' 137 113 44\n", "dice: 0.9576084821365002 iou:0.9186648833244806 bbox_iou: 0.9059975743293762 angle_error: 1.1382637438773102\n", "Predict: (90, -23) (763, 649) (427, 313) 673 0.8078219 0.6000112 (-0.020759819075465202)' 271 201 -3\n", "Ground Truth: (91, -18) (762, 652) (427, 317) 670.4497877246289 0.7965518220424098 0.6045702562985689 (-0.035839354749298)' 267 202 -6\n", "dice: 0.9829135137220872 iou:0.9664011143429078 bbox_iou: 0.9466785788536072 angle_error: 2.7143164212899045\n", "Predict: (346, 103) (694, 451) (520, 277) 347 0.80589145 0.5934252 (-0.1673385202884674)' 139 102 -30\n", "Ground Truth: (341, 103) (690, 452) (516, 278) 349.8178131599361 0.8164306940808164 0.5774434359854795 (-0.20581683397144224)' 142 101 -37\n", "dice: 0.968900715009823 iou:0.9396774191527575 bbox_iou: 0.8850437998771667 angle_error: 6.926096462935469\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (23, -36) (889, 829) (456, 396) 866 0.7637332 0.6339497 (-0.19690315425395966)' 330 274 -35\n", "Ground Truth: (1, -22) (872, 848) (437, 413) 870.5351376343175 0.7696415354591286 0.6384762383175389 (-0.18550983245041486)' 335 277 -33\n", "dice: 0.9503481404883446 iou:0.9053936615998455 bbox_iou: 0.9057719111442566 angle_error: 2.0507979246380614\n", "Predict: (283, 44) (772, 534) (528, 289) 489 0.7596757 0.6353447 (0.5073083639144897)' 185 155 91\n", "Ground Truth: (289, 60) (766, 537) (528, 299) 476.9324126372625 0.7219723190880852 0.6919219395788603 (0.46307605320268025)' 172 165 83\n", "dice: 0.949100382680404 iou:0.9031313429356466 bbox_iou: 0.8475655913352966 angle_error: 7.961815928125716\n", "Predict: (121, 157) (882, 917) (502, 537) 760 0.76399 0.6394705 (-0.21107541024684906)' 290 242 -37\n", "Ground Truth: (111, 137) (884, 910) (498, 524) 772.3863073902851 0.7555312599620275 0.6551125973603249 (-0.1593435905886088)' 291 253 -28\n", "dice: 0.9589956495603428 iou:0.9212215579649798 bbox_iou: 0.857793390750885 angle_error: 9.311727538483247\n", "Predict: (394, 297) (804, 707) (599, 502) 409 0.81989545 0.57028836 (-0.00016375724226236343)' 167 116 0\n", "Ground Truth: (396, 324) (767, 695) (582, 510) 371.4730967862949 0.8461070336429165 0.5330130276268906 (-0.03870414372495212)' 157 99 -6\n", "dice: 0.8682014622354922 iou:0.7670989432009128 bbox_iou: 0.7605835199356079 angle_error: 6.937269566884156\n", "Predict: (51, -82) (979, 846) (515, 382) 928 0.7686538 0.6326406 (-0.1769484430551529)' 356 293 -31\n", "Ground Truth: (76, -59) (971, 835) (524, 388) 895.7787820126127 0.7601787558196618 0.6497139826111725 (-0.21161124311748558)' 340 291 -38\n", "dice: 0.9642584671418616 iou:0.9309836832370535 bbox_iou: 0.8891984224319458 angle_error: 6.239304011219886\n", "Predict: (344, 43) (813, 512) (579, 278) 468 0.76367635 0.65112495 (-0.46960923075675964)' 178 152 -84\n", "Ground Truth: (341, 50) (800, 509) (571, 280) 459.6519309216486 0.7793723378507136 0.626561057673642 (-0.46434929274741543)' 179 144 -83\n", "dice: 0.9641835271239341 iou:0.9308439789982923 bbox_iou: 0.9283446073532104 angle_error: 0.9467888416819648\n", "Predict: (-4, -102) (745, 647) (370, 272) 750 0.7749644 0.62740576 (-0.14570090174674988)' 290 235 -26\n", "Ground Truth: (-18, -111) (746, 653) (364, 271) 764.9465557305294 0.8009474588912742 0.5987346391312354 (-0.14763095599454368)' 306 229 -26\n", "dice: 0.9710302425755712 iou:0.9436917222971808 bbox_iou: 0.9246432185173035 angle_error: 0.3474097646028831\n", "Predict: (233, 0) (635, 402) (434, 201) 401 0.7742066 0.63259107 (-0.3733637034893036)' 155 126 -67\n", "Ground Truth: (221, -3) (636, 411) (429, 204) 415.16698443397445 0.8125453435559704 0.5828979882153567 (-0.36178867820149974)' 168 121 -65\n", "dice: 0.9555839679045461 iou:0.9149457098885388 bbox_iou: 0.8792523145675659 angle_error: 2.083504551804694\n", "Predict: (16, -63) (796, 716) (406, 326) 780 0.79085225 0.61118233 (0.047543637454509735)' 308 238 8\n", "Ground Truth: (26, -50) (787, 710) (407, 330) 760.0493037586443 0.7644398818954801 0.6446950185689543 (0.049656342244671345)' 290 245 8\n", "dice: 0.9685576106211344 iou:0.9390321947155961 bbox_iou: 0.9178411960601807 angle_error: 0.3802868622290898\n", "Predict: (87, 130) (466, 509) (276, 320) 378 0.8241868 0.57091093 (-0.060031525790691376)' 155 107 -10\n", "Ground Truth: (100, 129) (479, 508) (290, 319) 378.72945578077235 0.825058614349964 0.5650471510298211 (-0.09740282517223994)' 156 107 -17\n", "dice: 0.9388476895855821 iou:0.884743575801035 bbox_iou: 0.8499575257301331 angle_error: 6.72683388867874\n", "Predict: (270, 68) (850, 648) (560, 358) 580 0.7867848 0.61162055 (0.07073601335287094)' 228 177 12\n", "Ground Truth: (273, 73) (846, 646) (560, 360) 572.6322894458538 0.7719683436429743 0.6356609760030283 (0.06079718826110402)' 221 182 10\n", "dice: 0.9797148906642955 iou:0.9602363904949823 bbox_iou: 0.9421939849853516 angle_error: 1.788988516518046\n", "Predict: (332, 206) (605, 479) (468, 343) 273 0.8059855 0.61486554 (-0.08963729441165924)' 110 83 -16\n", "Ground Truth: (328, 192) (631, 495) (480, 344) 303.66459012535523 0.7705870477140679 0.6373347643862814 (-0.10026761414789406)' 117 96 -18\n", "dice: 0.8860500448224783 iou:0.7954127927419103 bbox_iou: 0.7886569499969482 angle_error: 1.9134575525222672\n", "Predict: (44, -3) (670, 622) (357, 309) 626 0.77344066 0.6304278 (-0.17637252807617188)' 242 197 -31\n", "Ground Truth: (34, -22) (721, 664) (378, 321) 687.8287860245455 0.7758064373609357 0.6309709753620467 (-0.18723104427080403)' 266 217 -33\n", "dice: 0.8995623190492243 iou:0.8174586663299322 bbox_iou: 0.8133445382118225 angle_error: 1.9545329150337878\n", "Predict: (144, 208) (470, 534) (307, 371) 325 0.7605158 0.6334137 (-0.3858060836791992)' 123 102 -69\n", "Ground Truth: (126, 207) (473, 554) (300, 381) 347.33819037934774 0.7921271188161267 0.6103561481922353 (-0.3938141604040842)' 137 106 -70\n", "dice: 0.9231721270201559 iou:0.8573070498867331 bbox_iou: 0.8651501536369324 angle_error: 1.4414538104792882\n", "Predict: (232, 74) (812, 654) (522, 364) 579 0.7898873 0.61772674 (-0.055772989988327026)' 228 178 -10\n", "Ground Truth: (210, 47) (843, 680) (527, 364) 632.8409186802004 0.7617490996084073 0.6478721395813997 (0.005411268065124442)' 241 205 0\n", "dice: 0.9019916160717675 iou:0.8214797166163744 bbox_iou: 0.8015251755714417 angle_error: 11.013166449621265\n", "Predict: (306, 245) (603, 543) (455, 394) 297 0.7712327 0.64045954 (-0.28615689277648926)' 114 95 -51\n", "Ground Truth: (287, 234) (622, 569) (455, 402) 334.01235914858 0.7964016681241146 0.6047680406644581 (-0.23968734429639438)' 133 101 -43\n", "dice: 0.8905615167086985 iou:0.8027137422407826 bbox_iou: 0.7810848355293274 angle_error: 8.364518726417081\n", "Predict: (157, 9) (927, 779) (542, 394) 769 0.78143036 0.62350655 (-0.13647428154945374)' 300 239 -24\n", "Ground Truth: (235, 53) (918, 736) (577, 395) 683.195844908325 0.7600309689671427 0.649886856468775 (-0.19155772028790685)' 259 222 -34\n", "dice: 0.8895365839653798 iou:0.8010498780246602 bbox_iou: 0.7602113485336304 angle_error: 9.915018972921558\n", "Predict: (422, 117) (827, 522) (625, 320) 404 0.7718184 0.6277801 (-0.3496275246143341)' 155 126 -62\n", "Ground Truth: (432, 123) (825, 516) (629, 320) 393.4308322640715 0.7763092644325313 0.6303522237259278 (-0.3691746367784621)' 152 124 -66\n", "dice: 0.9772478001552618 iou:0.9555078936066975 bbox_iou: 0.9287007451057434 angle_error: 3.518480189543027\n", "Predict: (-1, -72) (724, 653) (361, 290) 726 0.7273666 0.6858891 (-0.48255854845046997)' 264 248 -86\n", "Ground Truth: (5, -57) (712, 649) (359, 296) 706.2288759007239 0.7335214088199095 0.6796663466752308 (-0.49624511256052967)' 259 240 -89\n", "dice: 0.9730631996198867 iou:0.9475395168034826 bbox_iou: 0.933843195438385 angle_error: 2.463581539810747\n", "Predict: (197, 26) (515, 343) (356, 184) 317 0.78776413 0.62731934 (0.2832930088043213)' 124 99 50\n", "Ground Truth: (201, 26) (514, 339) (358, 183) 313.1329797003184 0.7919957847855775 0.6105265570651918 (0.26661402223254627)' 124 95 47\n", "dice: 0.9753143186180072 iou:0.9518180417068007 bbox_iou: 0.934502363204956 angle_error: 3.0022175829195064\n", "Predict: (-61, -160) (915, 816) (427, 328) 976 0.79303586 0.6126495 (0.01685035601258278)' 387 298 3\n", "Ground Truth: (-77, -157) (911, 831) (417, 337) 988.5443073954754 0.7853183657952378 0.6190921291251382 (0.024698508732865276)' 388 306 4\n", "dice: 0.9717719593545037 iou:0.9450938127931708 bbox_iou: 0.9423196911811829 angle_error: 1.4126674896508495\n", "Predict: (285, 37) (806, 557) (546, 297) 520 0.8100405 0.5898348 (-0.18667100369930267)' 210 153 -33\n", "Ground Truth: (260, 28) (811, 579) (536, 304) 551.7025990187104 0.809388972961601 0.5872729267113926 (-0.18926471988988502)' 223 162 -34\n", "dice: 0.9415852576082434 iou:0.8896184264029547 bbox_iou: 0.8924720883369446 angle_error: 0.46686891430482547\n", "Predict: (167, -42) (843, 633) (505, 295) 675 0.7943499 0.6101699 (0.056541815400123596)' 268 205 10\n", "Ground Truth: (160, -40) (825, 624) (493, 292) 664.0788899641367 0.762054038530467 0.6475134302540801 (0.08530704949725591)' 253 215 15\n", "dice: 0.9579481165343132 iou:0.9192902308744372 bbox_iou: 0.8824081420898438 angle_error: 5.177742137483817\n", "Predict: (247, 67) (598, 418) (423, 243) 351 0.77968854 0.6251799 (0.31377026438713074)' 136 109 56\n", "Ground Truth: (257, 71) (602, 416) (430, 244) 345.415213336066 0.7527172804257388 0.6583439038591302 (0.2816394180779196)' 130 113 50\n", "dice: 0.9561184597058727 iou:0.915926206949185 bbox_iou: 0.8791067004203796 angle_error: 5.783552335658008\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (137, -18) (883, 727) (510, 354) 745 0.78094786 0.6316587 (-0.1265486478805542)' 290 235 -22\n", "Ground Truth: (132, -23) (881, 725) (507, 351) 749.58463369789 0.752115204414958 0.659031652720752 (-0.13750987083139757)' 281 247 -24\n", "dice: 0.972193172587658 iou:0.9458909462931866 bbox_iou: 0.9250220656394958 angle_error: 1.9730201311518059\n", "Predict: (264, 223) (632, 591) (448, 407) 368 0.7628641 0.6488545 (-0.3574448823928833)' 140 119 -64\n", "Ground Truth: (280, 209) (661, 590) (471, 400) 380.5832120364743 0.7967298357105143 0.6043356425767863 (-0.3495042550298022)' 151 115 -62\n", "dice: 0.8795900144276582 iou:0.7850608489340936 bbox_iou: 0.7771334052085876 angle_error: 1.4293129253545942\n", "Predict: (66, 37) (498, 469) (282, 253) 432 0.75215334 0.6415607 (-0.24830003082752228)' 162 138 -44\n", "Ground Truth: (45, 21) (516, 492) (281, 257) 470.21475563406136 0.7904813610087296 0.6124860960852797 (-0.2838675872812061)' 185 144 -51\n", "dice: 0.9131359630100063 iou:0.8401565715053769 bbox_iou: 0.8193408846855164 angle_error: 6.402160161663083\n", "Predict: (214, 100) (450, 336) (332, 218) 235 0.81227094 0.5825508 (-0.07011539489030838)' 95 68 -12\n", "Ground Truth: (210, 94) (465, 349) (338, 222) 254.70173371219914 0.8062764120484828 0.5915389652205721 (-0.08135766847358006)' 102 75 -14\n", "dice: 0.9128124997962471 iou:0.8396090827250815 bbox_iou: 0.8417490124702454 angle_error: 2.0236092449889025\n", "Predict: (50, -77) (917, 789) (483, 355) 867 0.782011 0.6216904 (-0.12378522753715515)' 339 269 -22\n", "Ground Truth: (63, -89) (934, 781) (499, 346) 870.7921586256964 0.7881122874178215 0.6155314958806326 (-0.13878311037613275)' 343 268 -24\n", "dice: 0.9654185323744261 iou:0.9331488747716689 bbox_iou: 0.9217692017555237 angle_error: 2.6996189110159676\n", "Predict: (215, 260) (601, 646) (408, 453) 386 0.77071357 0.6270996 (-0.33689743280410767)' 148 121 -60\n", "Ground Truth: (207, 242) (624, 659) (416, 451) 416.55689616665813 0.825817560975802 0.5639373688486848 (-0.2616507264430759)' 172 117 -47\n", "dice: 0.9151766770461035 iou:0.8436181797365333 bbox_iou: 0.768185019493103 angle_error: 13.544407144985719\n", "Predict: (63, -21) (803, 718) (433, 348) 739 0.77444357 0.62755245 (-0.15636245906352997)' 286 231 -28\n", "Ground Truth: (70, -5) (777, 701) (424, 348) 706.7247541412428 0.7825479392921771 0.6225903329715031 (-0.1851915225642311)' 276 220 -33\n", "dice: 0.9579616047915617 iou:0.9193150743739547 bbox_iou: 0.891981840133667 angle_error: 5.189231430126206\n", "Predict: (292, 79) (650, 437) (471, 258) 357 0.7730373 0.65127647 (-0.39508190751075745)' 137 116 -71\n", "Ground Truth: (305, 107) (620, 422) (463, 265) 315.63223790354493 0.7521475042500203 0.6589947889402964 (-0.3704478763231972)' 118 104 -66\n", "dice: 0.8724178191943127 iou:0.7737066388996561 bbox_iou: 0.7689082622528076 angle_error: 4.434125613760841\n", "Predict: (203, 36) (753, 586) (478, 311) 549 0.74001884 0.6744976 (-0.40717998147010803)' 203 185 -73\n", "Ground Truth: (197, 43) (750, 596) (474, 320) 553.9018281573009 0.7409146876537361 0.6715991554632618 (-0.3809521025672622)' 205 186 -68\n", "dice: 0.9691631432319757 iou:0.9401712180437418 bbox_iou: 0.9113665819168091 angle_error: 4.721018202512255\n", "Predict: (337, 101) (605, 369) (471, 235) 268 0.778168 0.6382454 (0.3809855580329895)' 104 85 68\n", "Ground Truth: (327, 93) (618, 384) (473, 239) 291.33500837352176 0.7757392469299279 0.6310535799538646 (0.37579431319358647)' 113 91 67\n", "dice: 0.9251332484736005 iou:0.8606957533664847 bbox_iou: 0.8585455417633057 angle_error: 0.9344240710925504\n", "Predict: (104, 28) (724, 647) (414, 338) 619 0.7989541 0.60440034 (0.06750418245792389)' 247 187 12\n", "Ground Truth: (104, 24) (727, 647) (416, 336) 623.5443437928052 0.7802588618487503 0.6254567199307182 (0.07785625972555843)' 243 195 14\n", "dice: 0.977907990051715 iou:0.9567709956965563 bbox_iou: 0.9422444701194763 angle_error: 1.8633739081742178\n", "Predict: (339, 184) (661, 506) (500, 345) 322 0.81089985 0.5845749 (-0.13292425870895386)' 130 94 -23\n", "Ground Truth: (312, 169) (681, 538) (497, 354) 369.71924423270156 0.8060170106061306 0.591892370801953 (-0.1709324088806956)' 149 109 -30\n", "dice: 0.8591676014551791 iou:0.7531058922862667 bbox_iou: 0.7498917579650879 angle_error: 6.841467030913513\n", "Predict: (38, -32) (1141, 1070) (590, 519) 1103 0.7576528 0.6547831 (0.2869643270969391)' 417 361 51\n", "Ground Truth: (30, -27) (1127, 1069) (579, 521) 1097.6504221526998 0.744076605371523 0.6680943087160605 (0.2816394180779196)' 408 366 50\n", "dice: 0.9802951652491746 iou:0.9613518852136451 bbox_iou: 0.9508470892906189 angle_error: 0.9584836234235112\n", "Predict: (239, 80) (833, 674) (536, 377) 594 0.75671345 0.6494115 (0.5052368640899658)' 224 192 90\n", "Ground Truth: (258, 111) (819, 672) (539, 392) 560.3934652367032 0.7967294904329615 0.6043360977754295 (-0.47708168819476704)' 223 169 -85\n", "dice: 0.9170754716620647 iou:0.8468507709116031 bbox_iou: 0.825289249420166 angle_error: 3.1826605887480923\n", "Predict: (55, -37) (734, 641) (394, 302) 678 0.73007387 0.6831565 (-0.45822253823280334)' 247 231 -82\n", "Ground Truth: (71, -24) (712, 616) (392, 296) 640.2774811751542 0.7493157484149504 0.6622128881087583 (-0.41819435925076576)' 239 212 -75\n", "dice: 0.9407556475881577 iou:0.8881384596916735 bbox_iou: 0.8605413436889648 angle_error: 7.205072216766766\n", "Predict: (208, 29) (545, 366) (376, 198) 336 0.7679868 0.6351233 (0.39802804589271545)' 129 106 71\n", "Ground Truth: (215, 36) (556, 377) (386, 207) 341.48464240138236 0.7613812386162743 0.6483044111242404 (0.3729943554126675)' 130 110 67\n", "dice: 0.9301694914203198 iou:0.8694550061534737 bbox_iou: 0.8548159003257751 angle_error: 4.50606428640863\n", "Predict: (117, 72) (894, 849) (506, 461) 776 0.73469436 0.6806847 (-0.4622977375984192)' 285 264 -83\n", "Ground Truth: (103, 36) (878, 811) (491, 424) 775.2418977325723 0.7249350191775468 0.6888172602149644 (0.4999351688052649)' 281 267 89\n", "dice: 0.9168265418165906 iou:0.846426336326779 bbox_iou: 0.8229058384895325 angle_error: 6.7980768473368585\n", "Predict: (298, 149) (728, 579) (513, 364) 429 0.7900634 0.6158738 (-0.2643056809902191)' 169 132 -47\n", "Ground Truth: (281, 108) (766, 593) (524, 351) 484.30116415305054 0.7805060734492538 0.6251481978769737 (-0.2590394161588704)' 189 151 -46\n", "dice: 0.8774546450809003 iou:0.7816652050946636 bbox_iou: 0.7824881076812744 angle_error: 0.9479276696427732\n", "Predict: (0, -64) (814, 750) (407, 342) 814 0.77333 0.62953764 (0.16386300325393677)' 314 256 29\n", "Ground Truth: (-3, -61) (821, 763) (409, 351) 824.3500126354096 0.7625110576397895 0.6469751826593116 (0.15297739286493295)' 314 266 27\n", "dice: 0.972151015762836 iou:0.9458111363356893 bbox_iou: 0.9380518794059753 angle_error: 1.9594098700206857\n", "Predict: (262, 131) (714, 583) (488, 357) 451 0.8181451 0.5704033 (-0.005888327024877071)' 184 128 -1\n", "Ground Truth: (290, 127) (739, 576) (515, 352) 449.48845897976065 0.8125547891240642 0.5828848211023749 (-0.02629005816378427)' 182 131 -4\n", "dice: 0.9032068451280664 iou:0.8234978866489449 bbox_iou: 0.8251563906669617 angle_error: 3.672311605003296\n", "Predict: (341, -12) (1209, 856) (775, 421) 868 0.786125 0.6180544 (0.12020996958017349)' 341 268 21\n", "Ground Truth: (363, 2) (1204, 843) (784, 423) 840.628753828942 0.7583109631885315 0.6518929997385166 (0.08269573921305043)' 318 274 14\n", "dice: 0.9618790350537438 iou:0.9265577591947971 bbox_iou: 0.8732050061225891 angle_error: 6.752561466082151\n", "Predict: (471, 125) (903, 557) (687, 341) 431 0.77779764 0.63064784 (0.3570288121700287)' 167 135 64\n", "Ground Truth: (476, 135) (889, 548) (683, 342) 413.26988003482666 0.7936702282110643 0.6083482299237807 (0.3443464656561264)' 164 125 61\n", "dice: 0.9532046331345319 iou:0.9105930951804025 bbox_iou: 0.9037846326828003 angle_error: 2.282822372502409\n", "Predict: (78, -11) (843, 754) (461, 371) 765 0.7918338 0.61398095 (0.08062797039747238)' 302 234 14\n", "Ground Truth: (68, -14) (843, 760) (456, 373) 774.2377848129088 0.7818967400903665 0.623407962602386 (0.07925916165976388)' 302 241 14\n", "dice: 0.9806567581589904 iou:0.9620476380339283 bbox_iou: 0.9594379663467407 angle_error: 0.24638557278753126\n", "Predict: (171, 119) (570, 518) (370, 318) 399 0.78237045 0.61239976 (0.2815454304218292)' 156 122 50\n", "Ground Truth: (167, 114) (558, 505) (363, 310) 391.06542291539915 0.7787392649794099 0.6273477163259052 (0.29348171506145504)' 152 122 52\n", "dice: 0.9561212202059531 iou:0.9159312735474822 bbox_iou: 0.913414716720581 angle_error: 2.148531235132644\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (-88, -14) (594, 667) (252, 326) 682 0.7820201 0.62600523 (-0.09360411018133163)' 266 213 -16\n", "Ground Truth: (-83, -13) (605, 675) (261, 331) 689.7709806131307 0.7795862903974471 0.6262948313888175 (-0.10981691073340778)' 268 216 -19\n", "dice: 0.9742539039797105 iou:0.949800255403984 bbox_iou: 0.9325466752052307 angle_error: 2.9183040993737066\n", "Predict: (148, 102) (502, 456) (325, 279) 353 0.7814846 0.6212966 (-0.3305775821208954)' 137 109 -59\n", "Ground Truth: (144, 88) (519, 463) (332, 276) 375.98959306874434 0.7910750868724657 0.6117190588249813 (-0.3341605492982451)' 148 115 -60\n", "dice: 0.9352590626487102 iou:0.878391193425242 bbox_iou: 0.8836485743522644 angle_error: 0.6449340919229556\n", "Predict: (81, -1) (691, 608) (386, 303) 610 0.7715067 0.636068 (-0.20095239579677582)' 235 194 -36\n", "Ground Truth: (88, -37) (729, 603) (409, 283) 641.1601023925303 0.7819170252940565 0.6233825194495702 (-0.13801682821429473)' 250 199 -24\n", "dice: 0.9102370800537121 iou:0.8352615632201688 bbox_iou: 0.7934021949768066 angle_error: 11.328402164846597\n", "Predict: (185, 201) (496, 512) (341, 356) 310 0.76210535 0.6574727 (-0.4047565460205078)' 118 101 -72\n", "Ground Truth: (196, 202) (499, 505) (348, 354) 302.23204658672444 0.7255087687634761 0.688212922319325 (-0.41768740186786857)' 109 104 -75\n", "dice: 0.9418127461690701 iou:0.8900246556169034 bbox_iou: 0.8777491450309753 angle_error: 2.327554052524931\n", "Predict: (172, 113) (825, 766) (498, 439) 653 0.7666828 0.6404663 (0.24908114969730377)' 250 209 44\n", "Ground Truth: (219, 115) (798, 694) (509, 405) 578.6325143854258 0.7673263927651542 0.6412567402889551 (0.23293800549179963)' 222 185 41\n", "dice: 0.863443971100334 iou:0.759702072880877 bbox_iou: 0.7559893727302551 angle_error: 2.9057659569907486\n", "Predict: (375, 323) (698, 646) (536, 484) 322 0.76324034 0.6372445 (0.3942636251449585)' 122 102 70\n", "Ground Truth: (390, 329) (695, 634) (543, 482) 305.81655531380244 0.7586247244265167 0.6515278409160975 (0.40259717482776003)' 116 99 72\n", "dice: 0.9435442334636049 iou:0.8931223278349152 bbox_iou: 0.8797701597213745 angle_error: 1.5000389429042826\n", "Predict: (142, 32) (722, 611) (432, 322) 579 0.80430627 0.60478634 (0.006211156956851482)' 232 175 1\n", "Ground Truth: (148, 54) (691, 597) (420, 326) 543.3530598570326 0.8009341110814899 0.5987524945301718 (0.015597184423005743)' 217 162 2\n", "dice: 0.9283756125959656 iou:0.8663255740613712 bbox_iou: 0.8678517937660217 angle_error: 1.6894849439077668\n", "Predict: (197, 174) (504, 481) (351, 328) 307 0.81859666 0.5795023 (-0.11421847343444824)' 125 88 -20\n", "Ground Truth: (194, 172) (511, 489) (353, 331) 316.51230578920627 0.822546217755551 0.568698267674553 (-0.12032113697747288)' 130 90 -21\n", "dice: 0.963376283504943 iou:0.9293403847272838 bbox_iou: 0.9285822510719299 angle_error: 1.0984794377444338\n", "Predict: (116, 41) (597, 523) (357, 282) 481 0.7661787 0.6354746 (-0.20540985465049744)' 184 152 -36\n", "Ground Truth: (113, 50) (582, 519) (348, 285) 468.2696592392038 0.7569527365404942 0.6534696279429191 (-0.18869293131225284)' 177 153 -33\n", "dice: 0.9655966338596815 iou:0.9334817204458871 bbox_iou: 0.9166693687438965 angle_error: 3.009046200884029\n", "Predict: (269, 114) (505, 350) (387, 232) 235 0.77366656 0.62549615 (0.49279871582984924)' 90 73 88\n", "Ground Truth: (258, 106) (501, 349) (380, 228) 242.60270733031814 0.7588456123424029 0.651270555628522 (-0.4894957737559349)' 92 79 -88\n", "dice: 0.9269503059648636 iou:0.8638465777657743 bbox_iou: 0.8688628077507019 angle_error: 3.1869918745588564\n", "Predict: (305, 130) (691, 516) (498, 323) 386 0.78067493 0.6272873 (-0.1316729336977005)' 150 121 -23\n", "Ground Truth: (269, 92) (682, 505) (476, 299) 413.21110936178854 0.7732851144624824 0.6340584608305023 (-0.18334649444186343)' 159 131 -33\n", "dice: 0.8312829382095152 iou:0.711278174493305 bbox_iou: 0.7278386354446411 angle_error: 9.30124093394933\n", "Predict: (326, 209) (551, 435) (438, 322) 225 0.7804346 0.6431198 (-0.16744135320186615)' 87 72 -30\n", "Ground Truth: (330, 219) (549, 438) (440, 329) 219.3075948434071 0.7594812214275086 0.6505292263219077 (-0.39343101932316527)' 83 71 -70\n", "dice: 0.9055469152655399 iou:0.8273967407979363 bbox_iou: 0.6892377138137817 angle_error: 40.678139901833845\n", "Predict: (124, -32) (989, 832) (556, 400) 865 0.78929454 0.61305267 (0.06148742139339447)' 341 265 11\n", "Ground Truth: (103, -50) (982, 828) (543, 389) 879.1844593712971 0.7671200199356238 0.6415036048331828 (0.0705351323433312)' 337 282 12\n", "dice: 0.9546628372877102 iou:0.9132582972662033 bbox_iou: 0.9010962247848511 angle_error: 1.6285879709886117\n", "Predict: (444, 235) (862, 653) (653, 444) 417 0.78293484 0.6123666 (0.2209908813238144)' 163 127 39\n", "Ground Truth: (449, 239) (860, 650) (655, 445) 410.2973261233858 0.7750477026125443 0.631902728808016 (0.2309633149799617)' 159 129 41\n", "dice: 0.9842437457972385 iou:0.968976308759963 bbox_iou: 0.9520402550697327 angle_error: 1.7950380581065133\n", "Predict: (222, 97) (752, 627) (487, 362) 530 0.801997 0.60576683 (0.03227852284908295)' 212 160 5\n", "Ground Truth: (227, 92) (766, 631) (497, 362) 538.1307418239548 0.7627588764186976 0.646682995326525 (0.08467042972488832)' 205 174 15\n", "dice: 0.9435685251925903 iou:0.8931658585471484 bbox_iou: 0.8426772356033325 angle_error: 9.430543237644967\n", "Predict: (287, 249) (538, 499) (413, 374) 250 0.8043892 0.6158015 (-0.15532441437244415)' 100 76 -27\n", "Ground Truth: (291, 234) (562, 505) (427, 370) 270.95402775378705 0.8340898338863733 0.5516286332374366 (-0.1362366312866624)' 113 74 -24\n", "dice: 0.9106783074091023 iou:0.8360049318793048 bbox_iou: 0.8337907195091248 angle_error: 3.435800955440712\n", "Predict: (52, 14) (401, 364) (226, 189) 349 0.79135716 0.6073447 (-0.04516908526420593)' 138 105 -8\n", "Ground Truth: (58, 16) (399, 357) (229, 187) 340.8904408222677 0.7663400574385816 0.6424351456489843 (-0.05029296201703893)' 130 109 -9\n", "dice: 0.9656351700628125 iou:0.9335537540670746 bbox_iou: 0.9172917604446411 angle_error: 0.9222978155099391\n", "Predict: (91, 113) (279, 302) (185, 208) 188 0.78922063 0.6098995 (-0.24965091049671173)' 74 57 -44\n", "Ground Truth: (98, 118) (281, 301) (190, 210) 182.53697954113295 0.7980300778844358 0.602617619051884 (-0.24675499298717296)' 72 55 -44\n", "dice: 0.9380136719648154 iou:0.8832634161122064 bbox_iou: 0.89021897315979 angle_error: 0.5212651517169817\n", "Predict: (143, 54) (680, 592) (412, 323) 537 0.7643078 0.6394225 (0.23953275382518768)' 205 171 43\n", "Ground Truth: (149, 51) (670, 572) (410, 312) 520.3548331667537 0.7687062260298357 0.6396020153681248 (0.18659442449843655)' 200 166 33\n", "dice: 0.961634048517825 iou:0.9261032174111428 bbox_iou: 0.8512808680534363 angle_error: 9.528899278815203\n", "Predict: (296, 230) (573, 508) (434, 369) 277 0.7646347 0.63877946 (0.42493197321891785)' 105 88 76\n", "Ground Truth: (305, 237) (570, 502) (438, 370) 265.8486493477069 0.7948056178522863 0.6068640950249448 (0.4332845862908745)' 105 80 77\n", "dice: 0.9486390784812333 iou:0.902296308589115 bbox_iou: 0.8961637616157532 angle_error: 1.5034703529522062\n", "Predict: (-62, -47) (741, 755) (339, 353) 803 0.7838941 0.61540747 (0.09238331019878387)' 314 247 16\n", "Ground Truth: (-61, -40) (717, 738) (328, 349) 778.5648954326157 0.7621972214278445 0.6473448815335405 (0.08581400688015317)' 296 252 15\n", "dice: 0.9677411273807386 iou:0.9374984832294878 bbox_iou: 0.9251564741134644 angle_error: 1.1824745973535258\n", "Predict: (219, 217) (590, 588) (404, 403) 370 0.7675806 0.63026553 (0.303851842880249)' 142 116 54\n", "Ground Truth: (218, 210) (597, 589) (408, 400) 379.48428417524747 0.7852762615655049 0.619145534605318 (0.32932106981075293)' 149 117 59\n", "dice: 0.9589230366183461 iou:0.9210875567773077 bbox_iou: 0.9014378786087036 angle_error: 4.584460847490703\n", "Predict: (51, -37) (825, 736) (438, 349) 773 0.77025807 0.63800365 (-0.18010953068733215)' 297 246 -32\n", "Ground Truth: (56, -31) (803, 715) (430, 342) 747.9133925288409 0.7842913442379364 0.6203926880238441 (-0.18194359250765801)' 293 232 -32\n", "dice: 0.9629216037894999 iou:0.928494516237181 bbox_iou: 0.9268903732299805 angle_error: 0.33013112765865316\n", "Predict: (316, 59) (728, 471) (522, 265) 411 0.7734093 0.6413383 (-0.40764179825782776)' 158 131 -73\n", "Ground Truth: (321, 57) (718, 454) (520, 256) 397.9142466461838 0.7820227665200746 0.623249863733871 (-0.40005069573828966)' 155 124 -72\n", "dice: 0.9505330624959915 iou:0.9057294027353394 bbox_iou: 0.8948314189910889 angle_error: 1.366398453516851\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (256, 83) (806, 633) (531, 358) 550 0.7944835 0.61280924 (0.06725002080202103)' 218 168 12\n", "Ground Truth: (205, 38) (828, 661) (517, 350) 622.4895181703865 0.8305778409243522 0.5569025499721126 (0.08804217608343967)' 258 173 15\n", "dice: 0.9015938447493845 iou:0.8208200950436901 bbox_iou: 0.8065861463546753 angle_error: 3.7425879506553557\n", "Predict: (435, 232) (748, 544) (591, 388) 312 0.76838803 0.6375823 (0.2990443706512451)' 119 99 53\n", "Ground Truth: (446, 229) (767, 550) (607, 390) 321.24400491215397 0.8176526129159283 0.5757119111081124 (0.2370112028174538)' 131 92 42\n", "dice: 0.9024374079308622 iou:0.8222195385044754 bbox_iou: 0.7768962383270264 angle_error: 11.165970210082435\n", "Predict: (71, 49) (850, 828) (461, 439) 779 0.73209894 0.68196213 (-0.5127482414245605)' 285 265 -92\n", "Ground Truth: (60, 62) (795, 797) (428, 430) 735.7112460632908 0.7233435710648618 0.690488289690081 (0.4264704162915443)' 266 254 76\n", "dice: 0.9053037873842325 iou:0.8269908829053282 bbox_iou: 0.7988036274909973 angle_error: 10.940641611101114\n", "Predict: (250, 130) (686, 566) (468, 348) 435 0.7846991 0.6144355 (-0.288892537355423)' 170 133 -52\n", "Ground Truth: (252, 144) (665, 557) (459, 351) 412.7321178730824 0.7882061654819826 0.6154112776803731 (-0.25547317621611365)' 162 127 -45\n", "dice: 0.9393930503733376 iou:0.885712705073267 bbox_iou: 0.8669300079345703 angle_error: 6.015485005075675\n", "Predict: (142, 2) (929, 789) (536, 396) 787 0.7317348 0.68174034 (-0.5316200256347656)' 287 268 -95\n", "Ground Truth: (144, -10) (921, 766) (533, 378) 777.0329499937567 0.7284118389117832 0.6851395426722606 (0.48790422432501596)' 283 266 87\n", "dice: 0.964185328424268 iou:0.9308473367706813 bbox_iou: 0.9103922843933105 angle_error: -3.5143649927607044\n", "Predict: (338, 70) (763, 495) (551, 283) 425 0.7645416 0.63563585 (-0.39147183299064636)' 162 135 -70\n", "Ground Truth: (340, 66) (767, 493) (554, 280) 426.37510922191507 0.7961487259879484 0.6051009883546438 (-0.3809521025672622)' 169 129 -68\n", "dice: 0.9688748974729352 iou:0.9396288530833281 bbox_iou: 0.9162607192993164 angle_error: 1.8935514762091543\n", "Predict: (153, 79) (903, 828) (528, 453) 749 0.7806535 0.62159365 (0.11746415495872498)' 292 232 21\n", "Ground Truth: (162, 77) (913, 828) (538, 453) 750.0421099751667 0.7728152756905763 0.6346310342705425 (0.1432394487827058)' 289 238 25\n", "dice: 0.9714259138458881 iou:0.9444394204778156 bbox_iou: 0.9152971506118774 angle_error: 4.639552888316551\n", "Predict: (238, 185) (654, 601) (446, 393) 416 0.78023005 0.628419 (0.3576183617115021)' 162 130 64\n", "Ground Truth: (232, 181) (649, 598) (441, 390) 416.7972821408508 0.7698706631478539 0.638199938909652 (0.40959999232380345)' 160 133 73\n", "dice: 0.9668483909566443 iou:0.9358243093207735 bbox_iou: 0.8618948459625244 angle_error: 9.356693510214242\n", "Predict: (76, -82) (818, 660) (447, 289) 742 0.7692153 0.63402385 (-0.19897453486919403)' 285 235 -35\n", "Ground Truth: (91, -72) (788, 624) (440, 276) 697.8074145321185 0.7830584608596894 0.6219481062565063 (-0.20785635567801533)' 273 217 -37\n", "dice: 0.9388669679198355 iou:0.884777817234991 bbox_iou: 0.8812354207038879 angle_error: 1.598727745587837\n", "Predict: (202, 164) (572, 534) (387, 349) 370 0.7692274 0.6549164 (-0.4288627803325653)' 142 121 -77\n", "Ground Truth: (193, 155) (582, 544) (388, 350) 388.0515159666304 0.7469678330671076 0.6648601780547769 (-0.43576623418560945)' 144 129 -78\n", "dice: 0.9613407306273313 iou:0.9255592849115606 bbox_iou: 0.9218839406967163 angle_error: 1.242621693547946\n", "Predict: (104, 82) (781, 759) (443, 421) 676 0.75971305 0.6406884 (-0.2298763394355774)' 256 216 -41\n", "Ground Truth: (105, 65) (798, 758) (452, 412) 692.2117239226739 0.776704556451661 0.6298650903068279 (-0.2088112853365667)' 268 218 -37\n", "dice: 0.9647859601440393 iou:0.9319676153911893 bbox_iou: 0.9078835844993591 angle_error: 3.7917097378219253\n", "Predict: (226, 323) (554, 651) (390, 487) 328 0.763297 0.654738 (-0.4208243787288666)' 125 107 -75\n", "Ground Truth: (237, 334) (554, 651) (396, 493) 316.15791325854866 0.7418634491308531 0.670550984522187 (-0.4784197589342374)' 117 106 -86\n", "dice: 0.9347767450978622 iou:0.8775406852939389 bbox_iou: 0.8321287631988525 angle_error: 10.36716843696675\n", "Predict: (177, -32) (850, 641) (513, 304) 673 0.7811902 0.61976284 (0.1356145143508911)' 262 208 24\n", "Ground Truth: (161, -35) (834, 637) (498, 301) 672.7262551736776 0.7950930945335412 0.6064874038470027 (0.14546761798599234)' 267 204 26\n", "dice: 0.9616326863248059 iou:0.9261006906324953 bbox_iou: 0.9189764261245728 angle_error: 1.7735586543182187\n", "Predict: (238, 59) (609, 430) (423, 244) 371 0.82386285 0.5647737 (0.0010568643920123577)' 152 104 0\n", "Ground Truth: (251, 70) (594, 413) (423, 242) 342.06987955679466 0.8315552376857901 0.5554420641951139 (-0.017825353626292277)' 142 95 -3\n", "dice: 0.9207763763072733 iou:0.8531840446159783 bbox_iou: 0.8438225388526917 angle_error: 3.3987992432948344\n", "Predict: (323, 113) (737, 527) (530, 320) 413 0.7873704 0.63151354 (-0.01169145479798317)' 162 130 -2\n", "Ground Truth: (314, 88) (761, 535) (538, 312) 446.14593150672124 0.776118250883979 0.630587393344327 (-0.02342526918813015)' 173 140 -4\n", "dice: 0.9308304541179429 iou:0.8706107068828025 bbox_iou: 0.8663091063499451 angle_error: 2.112086590226456\n", "Predict: (475, 227) (690, 442) (582, 334) 215 0.771092 0.62661827 (-0.03880816698074341)' 82 67 -6\n", "Ground Truth: (485, 205) (714, 434) (600, 320) 229.40704618646743 0.8095740871404656 0.5870177147503146 (0.16507316853991694)' 92 67 29\n", "dice: 0.7693719465684132 iou:0.6251864195871627 bbox_iou: 0.6195672154426575 angle_error: 36.69864039371886\n", "Predict: (122, 23) (780, 681) (451, 352) 658 0.77902657 0.62274075 (-0.14242485165596008)' 256 204 -25\n", "Ground Truth: (139, 29) (788, 678) (464, 354) 648.940597346167 0.7726100078348895 0.6348809146551594 (-0.13330701111627308)' 250 206 -23\n", "dice: 0.9635747201408916 iou:0.9297097811738827 bbox_iou: 0.9249408841133118 angle_error: 1.6412112971436628\n", "Predict: (344, 116) (695, 467) (520, 291) 351 0.7765009 0.63669163 (-0.3487900495529175)' 136 111 -62\n", "Ground Truth: (370, 124) (689, 443) (530, 284) 318.1630498282288 0.7925936092709218 0.6097502525976461 (-0.3783407922830567)' 126 97 -68\n", "dice: 0.8946009470174255 iou:0.8093013510402636 bbox_iou: 0.7893415689468384 angle_error: 5.3191336914250655\n", "Predict: (84, -10) (786, 691) (435, 340) 701 0.73402816 0.66909933 (0.27923423051834106)' 257 234 50\n", "Ground Truth: (94, 0) (799, 705) (447, 353) 704.3120371255911 0.7282595965466043 0.6853013643921753 (0.41800571175405254)' 256 241 75\n", "dice: 0.9513039705583773 iou:0.9071303255194916 bbox_iou: 0.745708703994751 angle_error: 24.97886662242807\n", "Predict: (265, 78) (561, 374) (413, 226) 295 0.7881789 0.63519233 (-0.3987692892551422)' 116 93 -71\n", "Ground Truth: (269, 66) (580, 377) (425, 222) 311.9095050555529 0.7880875574991517 0.6155631581852681 (-0.3417351553719209)' 122 96 -61\n", "dice: 0.9227228714869254 iou:0.8565324994512094 bbox_iou: 0.8110201954841614 angle_error: 10.266144098979836\n", "Predict: (24, -108) (987, 853) (506, 372) 962 0.7783339 0.63182235 (0.1836167424917221)' 374 303 33\n", "Ground Truth: (20, -101) (959, 837) (490, 368) 938.6251338548313 0.767237072634557 0.6413636054338876 (0.16450137996228462)' 360 301 29\n", "dice: 0.9706321987976272 iou:0.9429401207846811 bbox_iou: 0.9151102900505066 angle_error: 3.44076525529875\n", "Predict: (165, 16) (667, 518) (416, 267) 502 0.7782305 0.63252145 (0.3699529767036438)' 195 158 66\n", "Ground Truth: (169, 13) (674, 518) (422, 266) 504.8992575791729 0.7636137193953835 0.645673359796692 (0.3472112546317805)' 192 163 62\n", "dice: 0.9739967438016304 iou:0.9493115522952255 bbox_iou: 0.9218805432319641 angle_error: 4.093509972935394\n", "Predict: (0, -14) (791, 776) (395, 380) 790 0.7812685 0.624553 (-0.13915704190731049)' 308 246 -25\n", "Ground Truth: (2, -4) (777, 770) (390, 383) 775.242260522477 0.7749190551030108 0.632060486059884 (-0.1890112411984366)' 300 245 -34\n", "dice: 0.9736936170005597 iou:0.9487358094323484 bbox_iou: 0.8670233488082886 angle_error: 8.973755872402698\n", "Predict: (267, 99) (692, 524) (480, 312) 424 0.7782831 0.627306 (-0.3459542989730835)' 164 132 -62\n", "Ground Truth: (287, 106) (694, 513) (491, 310) 407.18008674786637 0.7776362604786914 0.6287144394625567 (-0.34975773372125085)' 158 128 -62\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9442121103700127 iou:0.8943198909971606 bbox_iou: 0.8948715925216675 angle_error: 0.6846182546701272\n", "Predict: (192, -52) (969, 724) (581, 335) 776 0.7301427 0.6738847 (-0.4152572751045227)' 283 261 -74\n", "Ground Truth: (218, -49) (975, 707) (597, 329) 756.2269598896881 0.73349937177711 0.6796901291048627 (-0.40100562539684115)' 277 257 -72\n", "dice: 0.9568368479500767 iou:0.9172456351336737 bbox_iou: 0.9119393229484558 angle_error: 2.565296947382677\n", "Predict: (382, 16) (780, 414) (581, 215) 397 0.779009 0.6328207 (0.36665716767311096)' 154 125 65\n", "Ground Truth: (373, 12) (794, 433) (584, 223) 421.4545700025093 0.7355478432661302 0.6774727819378018 (0.3643999884857052)' 155 142 65\n", "dice: 0.921150171442077 iou:0.8538261276579707 bbox_iou: 0.8353298306465149 angle_error: 0.40629225373304223\n", "Predict: (242, 15) (969, 742) (606, 379) 727 0.79402065 0.61201686 (0.07499577850103378)' 288 222 13\n", "Ground Truth: (237, 19) (934, 716) (586, 368) 697.3498841327788 0.7614972226752235 0.6481681725122894 (0.13209860276627314)' 265 226 23\n", "dice: 0.9386919932682041 iou:0.8844670795981491 bbox_iou: 0.8182693719863892 angle_error: 10.278508367743083\n", "Predict: (308, 174) (689, 555) (499, 364) 381 0.82850367 0.56250197 (-0.012226710096001625)' 157 107 -2\n", "Ground Truth: (320, 181) (687, 548) (504, 365) 366.2998002947859 0.8305655633859504 0.5569208605514597 (0.012414085561167836)' 152 102 2\n", "dice: 0.9551163449967092 iou:0.9140887030084714 bbox_iou: 0.8943905234336853 angle_error: 4.4353432182905035\n", "Predict: (228, 78) (969, 819) (599, 448) 741 0.78542995 0.6250567 (-0.0936962366104126)' 291 231 -16\n", "Ground Truth: (215, 71) (986, 842) (601, 457) 771.4137836466238 0.790963310398285 0.6118635808771316 (-0.08976338790382897)' 305 236 -16\n", "dice: 0.9609596321099736 iou:0.9248530295905529 bbox_iou: 0.9175301790237427 angle_error: 0.7079127671850536\n", "Predict: (299, 238) (702, 641) (501, 439) 402 0.8142255 0.5750526 (0.07893618196249008)' 163 115 14\n", "Ground Truth: (317, 252) (688, 623) (503, 438) 370.58829848768835 0.847301442817768 0.5311122903858738 (0.029284509528908743)' 157 98 5\n", "dice: 0.9016838573121592 iou:0.8209693204595211 bbox_iou: 0.8033491373062134 angle_error: 8.937301038044641\n", "Predict: (330, 84) (835, 589) (583, 336) 504 0.8063155 0.609691 (0.04639553278684616)' 203 153 8\n", "Ground Truth: (292, 77) (839, 624) (566, 351) 547.570010840623 0.75771863284303 0.6525813921975476 (0.09981964187224002)' 207 178 17\n", "dice: 0.888100428994818 iou:0.7987236007222804 bbox_iou: 0.7822447419166565 angle_error: 9.616339635370895\n", "Predict: (478, 178) (811, 511) (645, 345) 333 0.84028435 0.60641694 (-0.04551765322685242)' 139 100 -8\n", "Ground Truth: (552, 228) (779, 455) (666, 342) 227.80632553991998 0.7819537037780121 0.6233365103600532 (-0.16029852024716018)' 89 71 -28\n", "dice: 0.6264958599239968 iou:0.45612957518229946 bbox_iou: 0.44766104221343994 angle_error: 20.6605560636554\n", "Predict: (50, -1) (724, 673) (387, 335) 674 0.73249215 0.67224216 (-0.3978163003921509)' 246 226 -71\n", "Ground Truth: (109, 37) (720, 648) (415, 343) 611.3735437390139 0.7266784841276235 0.6869777148539676 (-0.34816618429033186)' 222 210 -62\n", "dice: 0.8985104094434768 iou:0.8157230146764332 bbox_iou: 0.7812765836715698 angle_error: 8.937020898327425\n", "Predict: (217, 84) (569, 435) (393, 260) 351 0.76544875 0.63795376 (0.4507004916667938)' 134 111 81\n", "Ground Truth: (228, 99) (563, 434) (396, 267) 335.5294323900662 0.7391303893474545 0.6735623709375996 (0.445250699576388)' 124 113 80\n", "dice: 0.9557838026197074 iou:0.9153121786633434 bbox_iou: 0.9072933197021484 angle_error: 0.9809625762730434\n", "Predict: (82, 7) (403, 328) (242, 168) 320 0.76111406 0.645881 (0.24784980714321136)' 121 103 44\n", "Ground Truth: (87, 12) (402, 327) (245, 170) 314.4178959855816 0.7431065565387359 0.6691731058770536 (0.2539464579799298)' 116 105 45\n", "dice: 0.9754615383364792 iou:0.952098505655555 bbox_iou: 0.9418893456459045 angle_error: 1.097397150609325\n", "Predict: (137, 39) (313, 214) (225, 127) 175 0.76867795 0.63489056 (0.5418248176574707)' 67 55 97\n", "Ground Truth: (138, 43) (309, 214) (224, 129) 170.19083519390813 0.7689720768506135 0.6392823672087745 (-0.4432760090645501)' 65 54 -79\n", "dice: 0.9690182574539116 iou:0.9398985621809818 bbox_iou: 0.9312132596969604 angle_error: 2.681851190036241\n", "Predict: (268, 91) (964, 788) (616, 439) 696 0.7957934 0.61196053 (0.017218120396137238)' 276 212 3\n", "Ground Truth: (256, 78) (973, 795) (615, 437) 716.1592649795156 0.7911703830518854 0.6115958019652629 (0.0818056407492342)' 283 219 14\n", "dice: 0.9625851594395259 iou:0.9278690855429439 bbox_iou: 0.8373389840126038 angle_error: 11.625753663557454\n", "Predict: (337, 266) (683, 612) (510, 439) 346 0.80934566 0.62091446 (-0.13525523245334625)' 140 107 -24\n", "Ground Truth: (321, 245) (692, 616) (507, 431) 370.02118082077413 0.7959436250290053 0.605370750677373 (-0.1193662073189215)' 147 112 -21\n", "dice: 0.939408267261611 iou:0.885739760422336 bbox_iou: 0.8785223364830017 angle_error: 2.8600245241964544\n", "Predict: (132, -6) (805, 665) (468, 329) 672 0.7993861 0.603488 (0.032776542007923126)' 268 202 5\n", "Ground Truth: (140, -6) (803, 656) (472, 325) 663.3633866079737 0.7657100320192657 0.6431859349092262 (0.022470339529578776)' 253 213 4\n", "dice: 0.9629629629346683 iou:0.9285714285188094 bbox_iou: 0.9014782905578613 angle_error: 1.8551164461019827\n", "Predict: (369, 176) (719, 526) (544, 351) 350 0.76513124 0.63407815 (0.2985275387763977)' 133 110 53\n", "Ground Truth: (379, 182) (734, 537) (557, 360) 355.39807612872636 0.798389240287351 0.6021416951128584 (0.22459711725628595)' 141 107 40\n", "dice: 0.9207149695796665 iou:0.853078606326161 bbox_iou: 0.7875114679336548 angle_error: 13.307475873620113\n", "Predict: (181, 25) (874, 718) (527, 371) 692 0.78632045 0.6245734 (-0.10934575647115707)' 272 216 -19\n", "Ground Truth: (196, 56) (835, 695) (516, 376) 638.8459860748911 0.7746499325143847 0.6323902925057113 (-0.09122527494527766)' 247 202 -16\n", "dice: 0.9186932976323412 iou:0.849613986134318 bbox_iou: 0.8501054644584656 angle_error: 3.2616866746582964\n", "Predict: (426, 154) (771, 499) (599, 327) 345 0.7752822 0.64493215 (-0.327090859413147)' 133 111 -58\n", "Ground Truth: (456, 166) (755, 465) (606, 316) 298.9338715435238 0.7983906232092906 0.6021398614703071 (-0.295073264492374)' 119 90 -53\n", "dice: 0.8411310032330701 iou:0.7258206109402349 bbox_iou: 0.7146574854850769 angle_error: 5.763167085739141\n", "Predict: (179, 94) (695, 610) (437, 352) 515 0.76293015 0.64774245 (0.2719358801841736)' 196 166 48\n", "Ground Truth: (221, 126) (696, 601) (459, 364) 475.80594313648504 0.7476115108086454 0.6641363029577697 (0.30328449033841737)' 177 158 54\n", "dice: 0.9068376336514432 iou:0.8295543842041634 bbox_iou: 0.8110103607177734 angle_error: 5.642749827763879\n", "Predict: (292, 154) (566, 427) (429, 291) 273 0.7805604 0.6267953 (0.4607471525669098)' 106 85 82\n", "Ground Truth: (309, 176) (550, 417) (430, 297) 240.00060308257562 0.7828396995125738 0.6222234364495306 (-0.4637126729750478)' 93 74 -83\n", "dice: 0.8668676608880379 iou:0.7650189046474517 bbox_iou: 0.744724690914154 angle_error: 13.597231402447619\n", "Predict: (43, 41) (790, 788) (416, 414) 746 0.76642305 0.6319751 (-0.20055493712425232)' 285 235 -36\n", "Ground Truth: (46, 66) (763, 783) (405, 425) 716.1542208100152 0.7903325618325877 0.612678089788707 (-0.18589297353133374)' 283 219 -33\n", "dice: 0.9464284834793314 iou:0.8983049262804979 bbox_iou: 0.8838871121406555 angle_error: 2.639153446725345\n", "Predict: (271, 129) (685, 543) (478, 336) 413 0.77382386 0.63898295 (-0.37357378005981445)' 159 131 -67\n", "Ground Truth: (287, 147) (664, 524) (476, 336) 376.11682951444755 0.7829003567326119 0.6221471139754237 (-0.36401684740478624)' 147 117 -65\n", "dice: 0.9052577258770291 iou:0.8269140118867309 bbox_iou: 0.8169842958450317 angle_error: 1.7202478779050807\n", "Predict: (81, -73) (870, 715) (476, 321) 789 0.78337324 0.6228342 (-0.09653975069522858)' 309 245 -17\n", "Ground Truth: (105, -65) (876, 705) (491, 320) 771.8166270533436 0.7670215686595778 0.6416213160509868 (-0.10185916357881303)' 296 247 -18\n", "dice: 0.9676887914620456 iou:0.937400256297292 bbox_iou: 0.9366816878318787 angle_error: 0.9574943190451997\n", "Predict: (339, 41) (762, 465) (551, 253) 423 0.78775024 0.6149497 (-0.2965027391910553)' 166 130 -53\n", "Ground Truth: (336, 38) (769, 471) (553, 255) 433.1552427894646 0.7963288122260024 0.6048639705080178 (-0.2774365583627951)' 172 131 -49\n", "dice: 0.9725464856793348 iou:0.9465600848349484 bbox_iou: 0.9276568293571472 angle_error: 3.4319125490868316\n", "Predict: (115, -19) (711, 576) (413, 278) 595 0.7687214 0.62893933 (-0.1873866617679596)' 228 187 -33\n", "Ground Truth: (159, 0) (708, 548) (434, 274) 548.6744580750958 0.7578045485454183 0.6524816213533333 (-0.2520365986628271)' 207 179 -45\n", "dice: 0.9279824822960892 iou:0.8656411550845534 bbox_iou: 0.7927733659744263 angle_error: 11.636988641076158\n", "Predict: (318, 74) (611, 367) (464, 220) 292 0.7785653 0.6372162 (0.5455027222633362)' 113 93 98\n", "Ground Truth: (315, 57) (610, 352) (463, 205) 295.03363692975756 0.7547139448815219 0.6560540079912408 (-0.4449323896902042)' 111 96 -80\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9116294348178787 iou:0.8376094172165821 bbox_iou: 0.8416703343391418 angle_error: 1.7216798483627258\n", "Predict: (104, -89) (932, 739) (518, 325) 828 0.7894811 0.61156017 (-0.04476524144411087)' 326 253 -8\n", "Ground Truth: (120, -79) (925, 725) (523, 323) 805.8061569012737 0.797648410222744 0.6031227185814915 (-0.03883380611442246)' 321 243 -6\n", "dice: 0.9720420319202888 iou:0.945604841933492 bbox_iou: 0.9408864378929138 angle_error: 1.0676583593439135\n", "Predict: (194, 155) (636, 596) (415, 375) 441 0.80094737 0.59783876 (-0.21587713062763214)' 176 131 -38\n", "Ground Truth: (209, 159) (636, 586) (423, 373) 427.2606240879213 0.8176180539588267 0.575760990203905 (-0.2460535420200702)' 174 123 -44\n", "dice: 0.9533004616546985 iou:0.9107680157781906 bbox_iou: 0.8802244067192078 angle_error: 5.431754050638851\n", "Predict: (104, -27) (833, 701) (468, 337) 729 0.7698587 0.62840277 (-0.1639886200428009)' 280 229 -29\n", "Ground Truth: (124, -23) (843, 695) (484, 336) 718.3900998092889 0.7749939763198296 0.6319686200025911 (-0.15998021036097643)' 278 227 -28\n", "dice: 0.9611886218264732 iou:0.9252773333273145 bbox_iou: 0.9218930006027222 angle_error: 0.7215137427284048\n", "Predict: (344, 60) (745, 460) (544, 260) 400 0.7635835 0.65100247 (0.14771509170532227)' 152 130 26\n", "Ground Truth: (349, 83) (714, 448) (532, 266) 364.8395761482024 0.7729424613886956 0.6344761235715533 (-0.44461407980402046)' 141 115 -80\n", "dice: 0.8608844015284524 iou:0.7557480581282333 bbox_iou: 0.6975587010383606 angle_error: 73.38074912831831\n", "Predict: (108, 25) (435, 352) (271, 189) 327 0.7743829 0.6276404 (-0.15357282757759094)' 126 102 -27\n", "Ground Truth: (122, 15) (449, 342) (286, 179) 327.5298720788686 0.7674048122837354 0.6411628920046486 (-0.14419437844125718)' 125 105 -25\n", "dice: 0.9068879101594988 iou:0.8296385325788731 bbox_iou: 0.8438307046890259 angle_error: 1.6881208445400766\n", "Predict: (172, 138) (333, 299) (252, 218) 160 0.762102 0.65395063 (-0.3972451686859131)' 60 52 -71\n", "Ground Truth: (180, 135) (335, 290) (258, 213) 155.5798987658753 0.7869268521908414 0.617046294293235 (-0.38840289233895975)' 61 48 -69\n", "dice: 0.9060058967947825 iou:0.8281634189255122 bbox_iou: 0.8225380778312683 angle_error: 1.5916097424515954\n", "Predict: (251, -48) (837, 537) (544, 244) 585 0.79223543 0.613446 (-0.06690407544374466)' 231 179 -12\n", "Ground Truth: (244, -58) (843, 540) (544, 241) 599.1176853006427 0.7749362293770732 0.6320394294653177 (-0.05506761030979578)' 232 189 -9\n", "dice: 0.9698510985724288 iou:0.9414669056370568 bbox_iou: 0.9345805644989014 angle_error: 2.1305637241107984\n", "Predict: (306, 118) (617, 429) (462, 274) 311 0.7816598 0.62522227 (-0.24592934548854828)' 121 97 -44\n", "Ground Truth: (307, 117) (626, 436) (467, 277) 318.94809085492267 0.7921414400781654 0.6103375614452138 (-0.1951239602306637)' 126 97 -35\n", "dice: 0.9552286029432013 iou:0.9142943668195299 bbox_iou: 0.8552335500717163 angle_error: 9.144969346419224\n", "Predict: (27, 40) (316, 329) (171, 184) 289 0.7898001 0.6167441 (-0.08925934135913849)' 114 89 -16\n", "Ground Truth: (46, 51) (313, 318) (180, 185) 266.7092779038629 0.7557217416060527 0.6548928532698419 (-0.09854640232750478)' 100 87 -17\n", "dice: 0.9180404691429077 iou:0.8484979733166792 bbox_iou: 0.8490858674049377 angle_error: 1.6716709743059326\n", "Predict: (131, 90) (286, 244) (209, 167) 154 0.7822149 0.63349533 (-0.3104757070541382)' 60 48 -55\n", "Ground Truth: (139, 95) (284, 240) (212, 168) 145.88614875991485 0.7760846452004597 0.6306287525034648 (-0.33536895764824504)' 56 46 -60\n", "dice: 0.9388573715323721 iou:0.8847607723460841 bbox_iou: 0.8648365139961243 angle_error: 4.480785106939237\n", "Predict: (130, -99) (1056, 826) (593, 363) 925 0.75223595 0.6534519 (0.1943136602640152)' 347 302 34\n", "Ground Truth: (195, -71) (1102, 835) (649, 382) 906.6992198871685 0.7612094340236538 0.6485061276143724 (0.2460535420200702)' 345 294 44\n", "dice: 0.8994991820988415 iou:0.8173543967139785 bbox_iou: 0.7825517654418945 angle_error: 9.3131787160899\n", "Predict: (340, 29) (830, 519) (585, 274) 490 0.7626579 0.642951 (-0.4266146421432495)' 186 157 -76\n", "Ground Truth: (370, 42) (825, 497) (598, 270) 454.70459777310367 0.7809597741899168 0.624581324646546 (-0.4458873193487556)' 177 142 -80\n", "dice: 0.9254403096349006 iou:0.8612274570996302 bbox_iou: 0.8463463187217712 angle_error: 3.4690818969910993\n", "Predict: (236, 89) (855, 707) (545, 398) 618 0.7938836 0.61037576 (0.019638391211628914)' 245 188 3\n", "Ground Truth: (229, 78) (848, 697) (539, 388) 619.9552907266781 0.7472313034977142 0.6645640518965099 (0.07639437268410976)' 231 206 13\n", "dice: 0.9420434216626672 iou:0.8904367541654378 bbox_iou: 0.8300684094429016 angle_error: 10.216076665046552\n", "Predict: (311, 260) (602, 551) (457, 406) 290 0.7981648 0.6320675 (-0.1428016722202301)' 115 91 -25\n", "Ground Truth: (322, 263) (593, 534) (458, 399) 271.8670526856831 0.8189885379653633 0.5738098767722256 (-0.10790705141630505)' 111 78 -19\n", "dice: 0.9060116864900446 iou:0.8281730940828737 bbox_iou: 0.8049371242523193 angle_error: 6.28103174470651\n", "Predict: (36, 62) (523, 549) (279, 306) 486 0.7535202 0.6434803 (-0.24645984172821045)' 183 156 -44\n", "Ground Truth: (57, 77) (518, 538) (288, 308) 461.55289962906744 0.754973049199873 0.6557558196324651 (-0.23198307583324818)' 174 151 -41\n", "dice: 0.9498896068068127 iou:0.9045616660533924 bbox_iou: 0.8994515538215637 angle_error: 2.6058178610932075\n", "Predict: (169, 111) (438, 380) (304, 245) 269 0.7688611 0.65269846 (-0.47681841254234314)' 103 87 -85\n", "Ground Truth: (178, 124) (429, 375) (304, 250) 250.48828677604868 0.7604507278629916 0.6493956347964207 (-0.42583379651917674)' 95 81 -76\n", "dice: 0.9239710724841976 iou:0.8586860899895542 bbox_iou: 0.8226580023765564 angle_error: 9.177230884169944\n", "Predict: (222, -20) (963, 721) (593, 350) 741 0.7911107 0.6134454 (-0.07682137936353683)' 293 227 -13\n", "Ground Truth: (235, 9) (940, 714) (588, 362) 704.1636244510221 0.78958921008376 0.6136357871891955 (-0.08339719018015317)' 278 216 -15\n", "dice: 0.9462396332311082 iou:0.8979647205109159 bbox_iou: 0.8974141478538513 angle_error: 1.1836459469909393\n", "Predict: (318, 211) (700, 594) (509, 403) 382 0.7816361 0.6075663 (-0.2577323615550995)' 149 116 -46\n", "Ground Truth: (323, 219) (708, 604) (516, 412) 385.54768824621425 0.8012860909769273 0.5982813722713716 (-0.22950142793851308)' 154 115 -41\n", "dice: 0.9334056594400681 iou:0.87512714435561 bbox_iou: 0.8735036849975586 angle_error: 5.0815680509855525\n", "Predict: (129, -42) (1032, 859) (581, 408) 902 0.73528826 0.67814165 (-0.4156399369239807)' 331 305 -74\n", "Ground Truth: (163, -9) (984, 811) (574, 401) 820.7 0.7241379310344827 0.689655172413793 (-0.4166028098198469)' 297 283 -74\n", "dice: 0.9086376256031566 iou:0.8325718816404383 bbox_iou: 0.8291458487510681 angle_error: 0.1733171212559057\n", "Predict: (320, 49) (824, 552) (572, 301) 503 0.7763291 0.6311664 (0.3012637794017792)' 195 158 54\n", "Ground Truth: (340, 67) (821, 548) (581, 308) 480.1045603157712 0.7600552674608974 0.6498584387425802 (0.2784563192160817)' 182 156 50\n", "dice: 0.950957091418778 iou:0.9064997090585167 bbox_iou: 0.8868283033370972 angle_error: 4.105342833425546\n", "Predict: (220, 41) (839, 660) (529, 351) 618 0.77983016 0.62928814 (-0.11896995455026627)' 240 194 -21\n", "Ground Truth: (230, 35) (853, 658) (542, 347) 623.16012094485 0.7640989594745587 0.6450990467594079 (-0.12477747538404595)' 238 201 -22\n", "dice: 0.9580168066904867 iou:0.9194167553198868 bbox_iou: 0.915357232093811 angle_error: 1.0453537500803414\n", "Predict: (299, 189) (622, 512) (461, 350) 322 0.8051789 0.59089595 (0.09921520948410034)' 129 95 17\n", "Ground Truth: (289, 191) (620, 522) (455, 357) 331.96342189464184 0.8402311266948116 0.5422284147231383 (0.08085071109068283)' 139 90 14\n", "dice: 0.9296385602467896 iou:0.8685277007560485 bbox_iou: 0.8501007556915283 angle_error: 3.305609710815153\n", "Predict: (179, -14) (743, 549) (461, 267) 563 0.79577214 0.61183774 (-0.028260428458452225)' 224 172 -5\n", "Ground Truth: (173, -19) (760, 567) (467, 274) 586.7945543339679 0.810140442662389 0.5862358426118182 (-0.06321400496110396)' 237 172 -11\n", "dice: 0.9500936194348387 iou:0.9049317510799452 bbox_iou: 0.8739265203475952 angle_error: 6.291643770477313\n", "Predict: (374, 90) (701, 416) (537, 253) 326 0.7962031 0.6318114 (-0.2650320529937744)' 129 102 -47\n", "Ground Truth: (394, 93) (699, 398) (547, 246) 305.4826686016737 0.7777986263103445 0.6285135614366177 (-0.24630702071151878)' 118 96 -44\n", "dice: 0.9185194737015063 iou:0.8493167018413706 bbox_iou: 0.8395693898200989 angle_error: 3.3705058108060157\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (173, -6) (838, 658) (506, 325) 665 0.7649647 0.6459559 (0.2468361109495163)' 254 214 44\n", "Ground Truth: (174, -2) (847, 670) (511, 334) 673.1828297305271 0.8108317323222536 0.5852793366071397 (0.31060561772064454)' 272 197 55\n", "dice: 0.9406564319469249 iou:0.8879616210590864 bbox_iou: 0.8176295757293701 angle_error: 11.478511218803085\n", "Predict: (280, 53) (641, 415) (461, 234) 361 0.7709116 0.6330015 (0.44398945569992065)' 139 114 79\n", "Ground Truth: (295, 65) (630, 400) (463, 233) 334.14987585812446 0.7661232832799081 0.6426936399377341 (0.47326196956056155)' 128 107 85\n", "dice: 0.9276286915225025 iou:0.8650256531382832 bbox_iou: 0.8567820191383362 angle_error: 5.269052494915357\n", "Predict: (156, 39) (647, 530) (401, 285) 490 0.7548341 0.64913243 (-0.2534966468811035)' 184 159 -45\n", "Ground Truth: (141, 45) (646, 550) (394, 298) 505.4329607613655 0.7740531986886304 0.6331205616625474 (-0.20078870698723672)' 195 160 -36\n", "dice: 0.9404873648555846 iou:0.8876603578468819 bbox_iou: 0.8372734785079956 angle_error: 9.487429180896022\n", "Predict: (280, 93) (561, 373) (421, 233) 280 0.7676147 0.6401578 (0.5182991027832031)' 107 89 93\n", "Ground Truth: (293, 100) (554, 361) (424, 231) 261.22787293855146 0.7657682074648111 0.6431166709362541 (-0.4360197128770581)' 100 84 -78\n", "dice: 0.9371621738934869 iou:0.8817546297976495 bbox_iou: 0.843036413192749 angle_error: 8.22261318115298\n", "Predict: (-16, 61) (638, 717) (310, 389) 655 0.77655864 0.63276905 (-0.15479767322540283)' 254 207 -27\n", "Ground Truth: (-8, 66) (640, 715) (316, 391) 648.5711722733289 0.7697659429574534 0.6383262434389035 (-0.13967320883994905)' 249 207 -25\n", "dice: 0.9785403537997646 iou:0.9579823906307726 bbox_iou: 0.9428790211677551 angle_error: 2.722403589381681\n", "Predict: (238, 177) (551, 489) (394, 333) 312 0.77921075 0.6261464 (-0.3404017388820648)' 121 97 -61\n", "Ground Truth: (240, 189) (535, 484) (388, 337) 295.13376000044457 0.7889846285279232 0.6144129360183221 (-0.33607040861534776)' 116 90 -60\n", "dice: 0.9369128279711734 iou:0.8813132663271079 bbox_iou: 0.8800641894340515 angle_error: 0.7796394480090711\n", "Predict: (128, 54) (747, 673) (438, 364) 619 0.79001796 0.61628383 (0.03853171318769455)' 244 190 6\n", "Ground Truth: (128, 51) (741, 664) (435, 358) 612.6956411302434 0.764901802035795 0.6441469034641036 (0.05079991939993611)' 234 197 9\n", "dice: 0.9694952703784186 iou:0.9407965267024381 bbox_iou: 0.9201771020889282 angle_error: 2.2082771182034815\n", "Predict: (371, 176) (673, 478) (522, 327) 301 0.8017257 0.6278973 (-0.19310027360916138)' 120 94 -34\n", "Ground Truth: (377, 185) (668, 476) (523, 331) 291.13754550727396 0.8068282625338379 0.5907860482244213 (-0.17907880353200378)' 117 86 -32\n", "dice: 0.9430143794095783 iou:0.8921733286048109 bbox_iou: 0.8840233087539673 angle_error: 2.523864613888364\n", "Predict: (117, 106) (380, 369) (249, 238) 263 0.7742511 0.6221346 (0.1432655155658722)' 101 81 25\n", "Ground Truth: (108, 93) (385, 370) (247, 232) 276.25819778605666 0.7707934161103379 0.6370851667406451 (0.10542540352156982)' 106 88 18\n", "dice: 0.927800500742752 iou:0.8653245048010875 bbox_iou: 0.8525357246398926 angle_error: 6.811220167974426\n", "Predict: (204, 189) (338, 323) (271, 256) 134 0.76486754 0.636387 (0.45017650723457336)' 51 42 81\n", "Ground Truth: (206, 194) (339, 327) (273, 261) 132.66558693195458 0.7839259781313339 0.620854299180437 (0.33861688770481807)' 52 41 60\n", "dice: 0.9256984050565318 iou:0.861674607404119 bbox_iou: 0.7559577822685242 angle_error: 20.08073151535595\n", "Predict: (114, 0) (676, 561) (395, 280) 562 0.7900504 0.6165966 (-0.11405301094055176)' 222 173 -20\n", "Ground Truth: (102, 3) (681, 582) (392, 293) 578.3872903479122 0.7826831736356015 0.6224203159503252 (-0.1403746598070517)' 226 180 -25\n", "dice: 0.9549519920686729 iou:0.9137876775240217 bbox_iou: 0.8900048136711121 angle_error: 4.737896795969988\n", "Predict: (314, 74) (601, 361) (458, 218) 286 0.77722263 0.63600564 (-0.35247617959976196)' 111 90 -63\n", "Ground Truth: (326, 105) (567, 346) (447, 226) 240.55099537520104 0.7467190053395133 0.665139629750602 (-0.33982529605481815)' 89 80 -61\n", "dice: 0.8335853090391265 iou:0.7146560442859585 bbox_iou: 0.7107971906661987 angle_error: 2.2771590380898843\n", "Predict: (217, 12) (681, 476) (449, 244) 464 0.7873098 0.6094683 (-0.052471525967121124)' 182 141 -9\n", "Ground Truth: (210, 2) (699, 491) (455, 247) 488.8335632216757 0.7787435819487122 0.6273423575478458 (-0.01909859317102744)' 190 153 -3\n", "dice: 0.9381445684971269 iou:0.8834955688547406 bbox_iou: 0.8659911155700684 angle_error: 6.007127903296864\n", "Predict: (284, 160) (510, 386) (397, 273) 226 0.7729694 0.6427294 (-0.20467011630535126)' 87 72 -36\n", "Ground Truth: (276, 163) (499, 386) (388, 275) 222.16708545596939 0.8041965335833209 0.5943634707589041 (-0.2408309214516593)' 89 66 -43\n", "dice: 0.9223021206816275 iou:0.8558076789247928 bbox_iou: 0.820942759513855 angle_error: 6.508944926335445\n", "Predict: (293, -25) (772, 453) (533, 213) 479 0.7825417 0.62028426 (-0.07222301512956619)' 187 148 -13\n", "Ground Truth: (305, -60) (814, 448) (560, 194) 508.11881481795183 0.7592791070691317 0.6507651170493866 (0.05538592019597958)' 192 165 9\n", "dice: 0.8538789373722842 iou:0.7450163557893201 bbox_iou: 0.7057637572288513 angle_error: 22.969608358598236\n", "Predict: (317, 51) (672, 406) (494, 228) 355 0.7741443 0.6190027 (-0.31174492835998535)' 137 109 -56\n", "Ground Truth: (374, 108) (567, 301) (471, 205) 192.2455805785922 0.7398661627066755 0.6727540867818638 (0.3309774504364071)' 71 64 59\n", "dice: 0.4690949763379054 iou:0.3064167724891112 bbox_iou: 0.29909029603004456 angle_error: 64.30997181664935\n", "Predict: (167, 99) (794, 726) (481, 413) 626 0.7727712 0.6252294 (0.15434612333774567)' 241 195 27\n", "Ground Truth: (126, 61) (815, 750) (471, 406) 688.0573842870956 0.7655669600083953 0.6433562230549295 (0.17939711341818765)' 263 221 32\n", "dice: 0.8944278249259842 iou:0.8090180316505381 bbox_iou: 0.8105906248092651 angle_error: 4.5091782144795545\n", "Predict: (369, 267) (714, 612) (542, 439) 345 0.79205126 0.60075605 (0.03369404375553131)' 136 103 6\n", "Ground Truth: (371, 261) (726, 616) (549, 439) 354.4712620227485 0.8256240529344191 0.564220633454807 (0.07753794983937456)' 146 100 13\n", "dice: 0.9514348540889273 iou:0.9073683764896159 bbox_iou: 0.8488174080848694 angle_error: 7.891903095091786\n", "Predict: (73, -40) (694, 579) (384, 269) 620 0.7809114 0.6281814 (-0.16064943373203278)' 242 194 -28\n", "Ground Truth: (90, -41) (689, 557) (390, 258) 598.3381499119039 0.7705976964161263 0.6373218890624734 (-0.16583945070175496)' 230 190 -29\n", "dice: 0.9548960593212397 iou:0.9136852538332852 bbox_iou: 0.9182868003845215 angle_error: 0.9342030545499931\n", "Predict: (159, 155) (492, 488) (325, 322) 332 0.78222555 0.62349594 (-0.32176777720451355)' 129 103 -57\n", "Ground Truth: (162, 151) (495, 484) (329, 318) 332.8183586342556 0.7708769463704492 0.6369840920733983 (-0.3278591827693044)' 128 106 -59\n", "dice: 0.9661723162704599 iou:0.9345583712606602 bbox_iou: 0.9351224899291992 angle_error: 1.0964530016623542\n", "Predict: (133, 97) (698, 662) (415, 380) 565 0.76916194 0.6388067 (-0.22513528168201447)' 217 180 -40\n", "Ground Truth: (113, 85) (730, 702) (422, 394) 616.2520923420869 0.7872524994701214 0.6166307664056719 (-0.1990085100596044)' 242 190 -35\n", "dice: 0.9319752380736762 iou:0.8726157588263551 bbox_iou: 0.8185396194458008 angle_error: 4.702818892033811\n", "Predict: (303, 167) (627, 491) (465, 329) 324 0.772251 0.6230226 (-0.3494528532028198)' 125 100 -62\n", "Ground Truth: (304, 149) (641, 486) (473, 318) 336.5290001054887 0.7798258097154694 0.6259965706787957 (-0.34530139531467785)' 131 105 -62\n", "dice: 0.9247821690505966 iou:0.8600882002059304 bbox_iou: 0.8636125922203064 angle_error: 0.7472624198655566\n", "Predict: (188, 13) (956, 780) (572, 397) 767 0.78544825 0.6213422 (0.12637078762054443)' 301 238 22\n", "Ground Truth: (186, -1) (983, 795) (585, 397) 797.7489843829323 0.7910733982170416 0.6117212425879469 (0.1263690248149649)' 315 244 22\n", "dice: 0.9630767059040256 iou:0.928782978825516 bbox_iou: 0.9322581887245178 angle_error: 0.00031730500431592645\n", "Predict: (273, 179) (664, 570) (469, 375) 390 0.8238462 0.5739175 (-0.028532452881336212)' 160 111 -5\n", "Ground Truth: (278, 185) (647, 554) (463, 370) 369.4536732474046 0.8337337596146471 0.5521666578840357 (0.004138028520389279)' 154 102 0\n", "dice: 0.9295732052196751 iou:0.8684136175892757 bbox_iou: 0.8497328758239746 angle_error: 5.8806866523105885\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (84, 91) (823, 831) (453, 461) 739 0.7899776 0.6137784 (-0.08979012072086334)' 291 226 -16\n", "Ground Truth: (73, 96) (812, 835) (443, 466) 738.612382414484 0.8169156303981396 0.5767571870477299 (-0.09981964187224002)' 301 213 -17\n", "dice: 0.9612634306503866 iou:0.9254159899773864 bbox_iou: 0.904161810874939 angle_error: 1.8053138072478028\n", "Predict: (362, 204) (755, 597) (558, 400) 393 0.797647 0.6018797 (-0.23902538418769836)' 156 118 -43\n", "Ground Truth: (371, 219) (734, 582) (553, 401) 362.6098073687473 0.8112852824767665 0.5846504857062835 (-0.2459238796305998)' 147 106 -44\n", "dice: 0.9170552808804505 iou:0.8468163376114253 bbox_iou: 0.8410919904708862 angle_error: 1.241729179722256\n", "Predict: (294, 220) (811, 737) (552, 479) 517 0.8027455 0.61049014 (-0.032881949096918106)' 207 157 -5\n", "Ground Truth: (312, 208) (837, 733) (575, 471) 525.0321773377323 0.7767562020063855 0.6298013993669873 (0.0238732414637843)' 203 165 4\n", "dice: 0.9129434369682746 iou:0.839830665685085 bbox_iou: 0.809158444404602 angle_error: 10.215934300926433\n", "Predict: (353, 369) (615, 631) (484, 500) 262 0.8058844 0.621073 (-0.1195329800248146)' 105 81 -21\n", "Ground Truth: (378, 370) (623, 615) (501, 493) 245.7163662111256 0.8218093206965913 0.5697626176015826 (-0.1674310001326739)' 100 70 -30\n", "dice: 0.8536223336545368 iou:0.7446257535492636 bbox_iou: 0.7300575971603394 angle_error: 8.621643619414673\n", "Predict: (123, 41) (634, 552) (378, 296) 510 0.7774301 0.62413263 (0.12122200429439545)' 198 159 21\n", "Ground Truth: (99, 21) (640, 562) (370, 292) 540.4604087775532 0.7773298342985833 0.6290932591510876 (0.16029852024716018)' 210 170 28\n", "dice: 0.9375106193746574 iou:0.8823717549279152 bbox_iou: 0.8553260564804077 angle_error: 7.0337728714976535\n", "Predict: (324, 190) (567, 433) (446, 312) 243 0.7970869 0.5991435 (0.04847514629364014)' 96 72 8\n", "Ground Truth: (316, 179) (573, 436) (445, 308) 256.8262854538063 0.8117159800510223 0.584052367283798 (0.04920836996901723)' 104 75 8\n", "dice: 0.9372374172731635 iou:0.8818878576515176 bbox_iou: 0.8832138776779175 angle_error: 0.13198026156787712\n", "Predict: (314, 67) (876, 629) (595, 348) 561 0.7965052 0.6123038 (-0.06444725394248962)' 223 171 -11\n", "Ground Truth: (318, 69) (875, 626) (597, 348) 556.1617659961893 0.747983815922453 0.6637169661219201 (-0.04156893270060624)' 208 184 -7\n", "dice: 0.9537679974037293 iou:0.9116218917380773 bbox_iou: 0.873630702495575 angle_error: 4.118097823539008\n", "Predict: (404, 263) (664, 523) (534, 393) 259 0.76587945 0.66825473 (-0.37256377935409546)' 99 86 -67\n", "Ground Truth: (395, 255) (670, 530) (533, 393) 274.3688874489963 0.7906144243134137 0.6123143245650632 (-0.2581493176950543)' 108 84 -46\n", "dice: 0.9404270588658842 iou:0.8875529209525647 bbox_iou: 0.760453462600708 angle_error: 20.59460309862741\n", "Predict: (194, -5) (831, 631) (513, 313) 636 0.7996364 0.6102316 (-0.044109538197517395)' 254 194 -7\n", "Ground Truth: (190, -1) (823, 631) (507, 315) 632.2112714718079 0.7820550222854541 0.6232093886633111 (-0.042523862359157616)' 247 197 -7\n", "dice: 0.9815464675505715 iou:0.9637616604754724 bbox_iou: 0.9580567479133606 angle_error: 0.2854216509047607\n", "Predict: (435, 108) (770, 443) (602, 275) 334 0.7862105 0.6310704 (-0.29544326663017273)' 131 105 -53\n", "Ground Truth: (432, 116) (751, 435) (592, 276) 318.17725732679264 0.7708973358459634 0.6369594159643106 (-0.33918867628245053)' 122 101 -61\n", "dice: 0.9332428533944181 iou:0.8748409667223643 bbox_iou: 0.8363058567047119 angle_error: 7.874173737410004\n", "Predict: (42, -25) (525, 457) (284, 216) 482 0.7751838 0.62712675 (-0.13206060230731964)' 186 151 -23\n", "Ground Truth: (48, -16) (511, 446) (280, 215) 463.52529890395414 0.7593976010205583 0.6506268389516534 (-0.1371915609452138)' 176 150 -24\n", "dice: 0.9669976974575554 iou:0.9361041065228629 bbox_iou: 0.9267051815986633 angle_error: 0.9235725548209466\n", "Predict: (198, 48) (456, 306) (327, 177) 257 0.77275914 0.64064705 (-0.3911598324775696)' 99 82 -70\n", "Ground Truth: (213, 57) (446, 290) (330, 174) 233.22242623727246 0.7932770573777377 0.6088608299423748 (-0.3710196649008296)' 92 71 -66\n", "dice: 0.8925246703303323 iou:0.805909302373851 bbox_iou: 0.8026551008224487 angle_error: 3.6252301638131996\n", "Predict: (61, -28) (860, 770) (461, 371) 798 0.7354875 0.67114395 (-0.38027292490005493)' 293 267 -68\n", "Ground Truth: (84, -21) (861, 755) (473, 367) 776.5821534287277 0.7211497167780305 0.6927792476618844 (-0.3589887204205807)' 280 269 -64\n", "dice: 0.9634223793355698 iou:0.9294261810495495 bbox_iou: 0.9093435406684875 angle_error: 3.8311568063053585\n", "Predict: (250, 48) (671, 469) (461, 258) 420 0.77961534 0.6277051 (0.3882085382938385)' 163 131 69\n", "Ground Truth: (239, 53) (654, 468) (447, 261) 415.935696655144 0.7537559351630675 0.6571544644955628 (0.38509013108765155)' 156 136 69\n", "dice: 0.9274493468710059 iou:0.8647137961879202 bbox_iou: 0.8639349937438965 angle_error: 0.5613132971136565\n", "Predict: (55, -89) (896, 751) (475, 330) 841 0.79153854 0.6138026 (-0.03347212076187134)' 332 258 -6\n", "Ground Truth: (74, -85) (917, 757) (496, 336) 842.7429413267132 0.7736238039248636 0.6336451767360217 (-0.043160482131525235)' 325 267 -7\n", "dice: 0.9527285187233987 iou:0.9097244943231368 bbox_iou: 0.9038085341453552 angle_error: 1.7439050465377015\n", "Predict: (370, 80) (798, 508) (584, 294) 428 0.79165465 0.606917 (-0.25727346539497375)' 169 129 -46\n", "Ground Truth: (379, 105) (770, 496) (575, 301) 391.12183280916446 0.7772514201433679 0.6291901381022416 (-0.25369297928848117)' 152 123 -45\n", "dice: 0.9231538953921141 iou:0.8572756046030032 bbox_iou: 0.849992036819458 angle_error: 0.6444874991686689\n", "Predict: (202, -62) (996, 731) (599, 334) 793 0.7676592 0.6323016 (-0.17788958549499512)' 304 250 -32\n", "Ground Truth: (204, -48) (989, 736) (597, 344) 784.147540239718 0.7552048174747368 0.655488889046145 (-0.1421548567346842)' 296 257 -25\n", "dice: 0.9714813251978703 iou:0.9445441769783789 bbox_iou: 0.8922935128211975 angle_error: 6.432251176855964\n", "Predict: (460, 38) (873, 450) (667, 244) 412 0.77210164 0.6301868 (-0.3662693500518799)' 159 129 -65\n", "Ground Truth: (464, 58) (853, 447) (659, 253) 389.26065932739726 0.7873130578608918 0.6165534436865404 (-0.3720394257541161)' 153 120 -66\n", "dice: 0.9328796228451159 iou:0.8742028011238285 bbox_iou: 0.8600802421569824 angle_error: 1.0386136264025225\n", "Predict: (54, 2) (753, 700) (403, 351) 698 0.7995131 0.6022853 (-0.032301537692546844)' 279 210 -5\n", "Ground Truth: (52, 0) (755, 702) (404, 351) 703.7639830539781 0.7849762325186063 0.6195258787015236 (-0.012732395447351628)' 276 218 -2\n", "dice: 0.9803223383033979 iou:0.9614041526341546 bbox_iou: 0.933188796043396 angle_error: 3.522445604135139\n", "Predict: (121, 212) (474, 564) (298, 388) 352 0.80834967 0.5916152 (-0.13512705266475677)' 142 104 -24\n", "Ground Truth: (117, 199) (484, 566) (301, 383) 367.58049581010147 0.7970227020732717 0.6039493458724999 (-0.16615776058793874)' 146 111 -29\n", "dice: 0.9523070481675014 iou:0.9089562419045012 bbox_iou: 0.8801895976066589 angle_error: 5.585527426172753\n", "Predict: (166, -73) (965, 725) (566, 326) 798 0.7303916 0.6725773 (-0.41640961170196533)' 291 268 -74\n", "Ground Truth: (203, -55) (948, 689) (576, 317) 744.8731884126317 0.7288435245695217 0.6846803025449738 (-0.4073718231205169)' 271 255 -73\n", "dice: 0.9397407745468577 iou:0.8863311461828814 bbox_iou: 0.8848209381103516 angle_error: 1.6268019446607127\n", "Predict: (345, 1) (786, 441) (565, 221) 440 0.7710699 0.6354703 (0.41914695501327515)' 169 139 75\n", "Ground Truth: (360, 31) (749, 420) (555, 226) 389.4658062320747 0.7993821152414177 0.600822963802281 (0.38731830029093806)' 155 117 69\n", "dice: 0.8716255789763496 iou:0.7724613060491208 bbox_iou: 0.7670779228210449 angle_error: 5.72915785002067\n", "Predict: (126, 36) (826, 736) (476, 386) 700 0.7983332 0.60609615 (-0.02863166667521)' 279 212 -5\n", "Ground Truth: (146, 36) (831, 721) (489, 379) 684.7741715018171 0.7829354878034813 0.6221029030135806 (-0.0070028174960433945)' 268 213 -1\n", "dice: 0.9632723071239413 iou:0.9291468856702964 bbox_iou: 0.9031197428703308 angle_error: 3.893192852249989\n", "Predict: (216, 246) (555, 585) (385, 416) 339 0.81565475 0.58394545 (-0.11177285760641098)' 138 98 -20\n", "Ground Truth: (219, 253) (548, 582) (384, 418) 328.5366963308665 0.8114588202089773 0.5844096021670543 (-0.1540619849129547)' 133 96 -27\n", "dice: 0.9631731450154288 iou:0.928962382084289 bbox_iou: 0.8726731538772583 angle_error: 7.61204291517787\n", "Predict: (-2, 77) (845, 925) (421, 501) 847 0.76704913 0.6374798 (-0.2244311273097992)' 324 269 -40\n", "Ground Truth: (1, 91) (842, 932) (422, 512) 840.4282320246031 0.7818299944744891 0.6234916677390487 (-0.2663605435410976)' 328 262 -47\n", "dice: 0.9695232397277055 iou:0.9408492040825037 bbox_iou: 0.8806976079940796 angle_error: 7.547294921633714\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (329, 240) (726, 638) (527, 439) 397 0.80825496 0.59925437 (-0.12867967784404755)' 160 118 -23\n", "Ground Truth: (337, 259) (716, 638) (527, 449) 378.80853004651306 0.818355383818669 0.5747125070633133 (-0.12923381379061902)' 155 108 -23\n", "dice: 0.9360239454177562 iou:0.8797415518766292 bbox_iou: 0.8840798735618591 angle_error: 0.09974447038286627\n", "Predict: (164, 4) (777, 617) (470, 310) 613 0.75218767 0.6364401 (-0.23392248153686523)' 230 195 -42\n", "Ground Truth: (162, 0) (775, 613) (469, 307) 613.9244581575164 0.7696093448011383 0.6385150400693491 (-0.25776617661413526)' 236 196 -46\n", "dice: 0.9815337792556565 iou:0.9637371954646715 bbox_iou: 0.9256932735443115 angle_error: 4.291865113908607\n", "Predict: (354, 59) (693, 397) (524, 228) 338 0.7700313 0.6546502 (-0.43443533778190613)' 130 110 -78\n", "Ground Truth: (368, 71) (681, 384) (525, 228) 312.56403530156825 0.7890927046742108 0.6142741272672476 (-0.45352675661716657)' 123 96 -81\n", "dice: 0.9052562798381907 iou:0.8269115987295993 bbox_iou: 0.8222753405570984 angle_error: 3.436455390346879\n", "Predict: (222, 164) (743, 685) (483, 425) 521 0.7549333 0.6596199 (0.1748957335948944)' 196 171 31\n", "Ground Truth: (203, 170) (740, 707) (472, 439) 537.8258293574231 0.7327316363195803 0.6805177067030882 (0.4936338022763242)' 197 183 88\n", "dice: 0.9250985664702889 iou:0.860635717483875 bbox_iou: 0.7061521410942078 angle_error: 57.372852362657355\n", "Predict: (363, 221) (633, 491) (498, 356) 269 0.7778074 0.64960825 (-0.4396896958351135)' 104 87 -79\n", "Ground Truth: (358, 222) (635, 499) (497, 361) 276.5939263613718 0.75927191447299 0.650773508904996 (-0.39177463869751117)' 105 90 -70\n", "dice: 0.9593723348184594 iou:0.9219169996322307 bbox_iou: 0.8657737374305725 angle_error: 8.624710284768426\n", "Predict: (-17, -23) (895, 888) (439, 432) 912 0.7688475 0.6352288 (-0.19976356625556946)' 350 289 -35\n", "Ground Truth: (-12, -3) (858, 867) (423, 432) 871.4913837875852 0.750442301744437 0.6609359664540213 (-0.20588166516617745)' 327 288 -37\n", "dice: 0.955446212888157 iou:0.9146931681995374 bbox_iou: 0.9038116931915283 angle_error: 1.1012578039094407\n", "Predict: (271, 87) (748, 564) (510, 326) 477 0.77387613 0.64828426 (-0.42411014437675476)' 184 154 -76\n", "Ground Truth: (277, 102) (732, 557) (505, 330) 454.12300194991224 0.7872977111153554 0.6165730403387115 (-0.42647041629154436)' 178 140 -76\n", "dice: 0.9359520353467707 iou:0.8796145159224991 bbox_iou: 0.8768967986106873 angle_error: 0.42484894466213063\n", "Predict: (-15, -65) (820, 769) (402, 352) 835 0.7460327 0.66476095 (0.3137907385826111)' 311 277 56\n", "Ground Truth: (13, -69) (858, 775) (436, 353) 844.1414824447381 0.7802341357428992 0.6254875645618632 (0.2773127420608168)' 329 264 49\n", "dice: 0.9382009105026221 iou:0.8835955123551075 bbox_iou: 0.8335031270980835 angle_error: 6.566039373922969\n", "Predict: (142, 22) (595, 475) (368, 249) 453 0.76493406 0.6468834 (-0.456336110830307)' 173 146 -82\n", "Ground Truth: (142, 27) (587, 472) (365, 250) 445.5332811990592 0.7705013620444319 0.6374383508133751 (-0.44397746003165284)' 171 142 -79\n", "dice: 0.978068773195433 iou:0.9570788596544937 bbox_iou: 0.94627445936203 angle_error: 2.2245571437577496\n", "Predict: (114, 22) (802, 711) (458, 366) 688 0.7997904 0.60501194 (0.03802504390478134)' 275 208 6\n", "Ground Truth: (97, 19) (780, 702) (439, 361) 683.2979949655934 0.769968599170984 0.6380817786856732 (0.04857175019664972)' 263 218 8\n", "dice: 0.9467798910898976 iou:0.8989382970190802 bbox_iou: 0.8903603553771973 angle_error: 1.8984071325363079\n", "Predict: (369, 163) (730, 525) (549, 344) 361 0.79475796 0.6125921 (-0.23525504767894745)' 143 110 -42\n", "Ground Truth: (374, 172) (713, 511) (544, 342) 339.9407763066973 0.7954738555872165 0.6059879083589113 (-0.201678805451053)' 135 103 -36\n", "dice: 0.9369321331594791 iou:0.8813474307563053 bbox_iou: 0.8585531711578369 angle_error: 6.043723601021\n", "Predict: (128, -86) (821, 606) (474, 260) 692 0.786876 0.6164135 (-0.05340632051229477)' 272 213 -9\n", "Ground Truth: (117, -87) (818, 613) (468, 263) 701.9096342122681 0.7954898647696049 0.6059668927002825 (-0.05729577951308232)' 279 212 -10\n", "dice: 0.983590021665304 iou:0.9677099227979198 bbox_iou: 0.9637157320976257 angle_error: 0.7001026201417595\n", "Predict: (193, 101) (575, 483) (384, 292) 381 0.795626 0.60225356 (-0.22913333773612976)' 151 114 -41\n", "Ground Truth: (185, 110) (562, 487) (374, 299) 377.6292821061418 0.7891337195515468 0.6142214361830274 (-0.254011289174665)' 149 115 -45\n", "dice: 0.9471357523533674 iou:0.8995801258048319 bbox_iou: 0.8814153671264648 angle_error: 4.4780312589363405\n", "Predict: (215, 124) (814, 722) (514, 423) 598 0.76581514 0.6402213 (-0.2183692455291748)' 228 191 -39\n", "Ground Truth: (158, 122) (807, 771) (483, 447) 648.9561337286211 0.7700797850983613 0.6379475876456165 (-0.17271260580832792)' 249 207 -31\n", "dice: 0.8827521130423969 iou:0.7901130298364084 bbox_iou: 0.7759367823600769 angle_error: 8.21819514975244\n", "Predict: (385, 195) (718, 529) (552, 362) 333 0.7729506 0.6412207 (-0.37299883365631104)' 128 106 -67\n", "Ground Truth: (374, 211) (709, 546) (542, 379) 335.17082450595245 0.802456479905289 0.5967106483531299 (-0.4088985413567007)' 134 100 -73\n", "dice: 0.8990434210706364 iou:0.8166020697609349 bbox_iou: 0.7998177409172058 angle_error: 6.461947386070136\n", "Predict: (246, 107) (826, 688) (536, 397) 580 0.79488087 0.604519 (0.027731453999876976)' 230 175 4\n", "Ground Truth: (237, 88) (854, 705) (546, 397) 617.9098140052479 0.7676492414408739 0.6408702225218853 (0.042971834634811745)' 237 198 7\n", "dice: 0.9214354077097511 iou:0.8543163889268366 bbox_iou: 0.8472427725791931 angle_error: 2.7432685142882587\n", "Predict: (294, 200) (602, 508) (448, 354) 307 0.8126163 0.59803796 (0.19591505825519562)' 124 91 35\n", "Ground Truth: (310, 233) (577, 500) (444, 367) 266.9760949298645 0.7948576821297344 0.6067959007436899 (0.24159720361349712)' 106 81 43\n", "dice: 0.8494914175382244 iou:0.7383616519579053 bbox_iou: 0.7147006988525391 angle_error: 8.222786164494273\n", "Predict: (201, 82) (781, 662) (491, 372) 579 0.79096943 0.6141004 (0.045553259551525116)' 228 177 8\n", "Ground Truth: (200, 75) (765, 640) (483, 358) 564.3196060248129 0.7488451499617382 0.6627450047935344 (0.07130141450516911)' 211 187 12\n", "dice: 0.9357001219581361 iou:0.8791696224560976 bbox_iou: 0.8527383804321289 angle_error: 4.634667891655919\n", "Predict: (264, 212) (553, 501) (408, 356) 289 0.81100786 0.606731 (-0.12272460013628006)' 117 87 -22\n", "Ground Truth: (263, 207) (558, 502) (411, 355) 295.8249813656717 0.8277191428175112 0.5611426027432282 (-0.11140846016432673)' 122 83 -20\n", "dice: 0.9675825198968759 iou:0.9372008306176954 bbox_iou: 0.9087984561920166 angle_error: 2.036905194951597\n", "Predict: (145, 40) (762, 658) (454, 349) 617 0.7722257 0.6287948 (-0.15433374047279358)' 238 193 -27\n", "Ground Truth: (127, 41) (750, 664) (439, 353) 622.7005353586907 0.7582200001290084 0.6519987970881286 (-0.1808000153523931)' 236 203 -32\n", "dice: 0.9473182407988707 iou:0.8999094289595958 bbox_iou: 0.8874015808105469 angle_error: 4.76392947832791\n", "Predict: (224, 224) (564, 564) (394, 394) 340 0.7620803 0.6466657 (-0.3852619528770447)' 129 109 -69\n", "Ground Truth: (213, 217) (574, 578) (394, 398) 360.0058048476441 0.7646598924050696 0.6444340532180678 (-0.3864930330218571)' 137 116 -69\n", "dice: 0.9388901796604538 iou:0.8848190466845525 bbox_iou: 0.8920159935951233 angle_error: 0.22159442606623259\n", "Predict: (165, 148) (715, 699) (440, 424) 550 0.77811694 0.6359666 (-0.0802633985877037)' 213 174 -14\n", "Ground Truth: (186, 187) (679, 680) (433, 434) 492.2005759972249 0.7204705099776221 0.693485576095556 (-0.43245347293430125)' 177 170 -77\n", "dice: 0.8837013808449808 iou:0.7916352897703102 bbox_iou: 0.6932676434516907 angle_error: 63.39421338238756\n", "Predict: (268, 215) (551, 498) (409, 356) 282 0.78908247 0.63071007 (0.4023321866989136)' 111 88 72\n", "Ground Truth: (283, 237) (542, 496) (413, 367) 259.6065875589447 0.7490256770000064 0.662540968691508 (0.38865637103040845)' 97 86 69\n", "dice: 0.918863355067536 iou:0.8499049212460422 bbox_iou: 0.8424615263938904 angle_error: 2.4616468203309267\n", "Predict: (87, 10) (553, 476) (320, 243) 466 0.7914973 0.62281 (-0.08972295373678207)' 184 145 -16\n", "Ground Truth: (100, 28) (543, 471) (322, 250) 442.54790937931233 0.7706826600499667 0.6372191440142922 (-0.11255203731959162)' 170 141 -20\n", "dice: 0.9405502773315143 iou:0.887772451308502 bbox_iou: 0.8708606958389282 angle_error: 4.10923504490572\n", "Predict: (246, 89) (493, 336) (370, 212) 247 0.77650976 0.65436965 (-0.3731846511363983)' 95 80 -67\n", "Ground Truth: (248, 103) (477, 332) (363, 218) 229.18048028573463 0.7849185051699062 0.6195990156882432 (-0.34141684548573714)' 89 71 -61\n", "dice: 0.8961785744597983 iou:0.8118872797030693 bbox_iou: 0.798244297504425 angle_error: 5.718205017119011\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (216, 1) (903, 688) (560, 345) 687 0.778546 0.6262298 (-0.1464180201292038)' 267 215 -26\n", "Ground Truth: (207, -1) (896, 687) (552, 343) 688.0586599702092 0.7622925638676058 0.6472326066200251 (-0.1801633955800255)' 262 222 -32\n", "dice: 0.9713844750899003 iou:0.9443610868840414 bbox_iou: 0.896507203578949 angle_error: 6.074167581147904\n", "Predict: (315, 229) (667, 580) (491, 405) 351 0.7642914 0.6449881 (-0.3743964433670044)' 134 113 -67\n", "Ground Truth: (321, 232) (670, 581) (496, 407) 349.05025087514264 0.766921093248994 0.6417414095488679 (-0.37305918660740267)' 133 112 -67\n", "dice: 0.9692382194625386 iou:0.9403125317249897 bbox_iou: 0.9510224461555481 angle_error: 0.24070621672831294\n", "Predict: (87, 31) (786, 730) (437, 381) 699 0.7330681 0.6715334 (-0.4128452241420746)' 256 234 -74\n", "Ground Truth: (107, 38) (794, 725) (451, 382) 687.8630454501827 0.7355592124721049 0.6774604379204862 (-0.3975042166488194)' 252 233 -71\n", "dice: 0.9604775550464504 iou:0.9239603817206359 bbox_iou: 0.9214037656784058 angle_error: 2.7613813487859318\n", "Predict: (268, 95) (605, 432) (437, 263) 337 0.7708221 0.63874143 (0.4344348907470703)' 129 107 78\n", "Ground Truth: (282, 120) (583, 421) (433, 271) 300.7363105047343 0.7733352836897917 0.6339972704991952 (0.35644224133111035)' 116 95 64\n", "dice: 0.8883514300222315 iou:0.7991297375932371 bbox_iou: 0.7547678351402283 angle_error: 14.038676894872793\n", "Predict: (-5, -31) (855, 830) (425, 399) 861 0.7832123 0.6246791 (-0.10857301950454712)' 337 268 -19\n", "Ground Truth: (6, -22) (851, 822) (429, 400) 845.7830227664776 0.7696536611373096 0.6384616213195085 (-0.0953633034656669)' 325 270 -17\n", "dice: 0.9804980637022732 iou:0.9617422280362756 bbox_iou: 0.9414732456207275 angle_error: 2.3777488869984396\n", "Predict: (288, 101) (744, 557) (516, 329) 455 0.7908396 0.61670214 (-0.2964930236339569)' 179 140 -53\n", "Ground Truth: (303, 117) (738, 552) (521, 335) 435.60036885200174 0.7660415919284329 0.6427910075878103 (-0.32168163254234217)' 166 140 -57\n", "dice: 0.943511350154605 iou:0.8930634042236771 bbox_iou: 0.875238299369812 angle_error: 4.533949603509349\n", "Predict: (10, -43) (513, 459) (262, 208) 503 0.73392373 0.6819073 (-0.46747085452079773)' 184 171 -84\n", "Ground Truth: (31, -23) (480, 425) (256, 201) 449.7871113538048 0.7342851577181063 0.6788411501631997 (-0.46275774331649644)' 165 152 -83\n", "dice: 0.8874794087156965 iou:0.7977195349626585 bbox_iou: 0.7964062690734863 angle_error: 0.8483600167742367\n", "Predict: (114, 10) (389, 285) (252, 148) 274 0.77710176 0.63093823 (0.3182680010795593)' 106 86 57\n", "Ground Truth: (117, 21) (386, 290) (252, 156) 269.65253619426613 0.7701540765423588 0.6378578982698156 (0.36510143945280793)' 103 86 65\n", "dice: 0.9438202245534023 iou:0.8936170209795157 bbox_iou: 0.8542110919952393 angle_error: 8.430018907184746\n", "Predict: (42, -90) (992, 859) (517, 384) 949 0.7867001 0.61489606 (-0.07324115931987762)' 373 291 -13\n", "Ground Truth: (64, -82) (991, 844) (528, 381) 927.3062829507843 0.7750190128261463 0.6319379160629512 (-0.08689859892817486)' 359 293 -15\n", "dice: 0.9775258347943322 iou:0.9560396419382443 bbox_iou: 0.9340439438819885 angle_error: 2.4583391294935026\n", "Predict: (170, 237) (621, 688) (396, 463) 451 0.7908016 0.61134607 (-0.2686097025871277)' 178 137 -48\n", "Ground Truth: (187, 238) (626, 677) (407, 458) 438.1458247843975 0.8180472795247404 0.5751509788413571 (-0.26897185382530314)' 179 126 -48\n", "dice: 0.9367069822028975 iou:0.8809490578086723 bbox_iou: 0.8615963459014893 angle_error: 0.06518722287157885\n", "Predict: (306, 32) (971, 696) (638, 364) 664 0.7997541 0.60793 (0.0356321781873703)' 265 201 6\n", "Ground Truth: (350, 65) (967, 682) (659, 374) 617.3160151008558 0.7803944628283986 0.625287519775323 (0.039788735772973836)' 240 193 7\n", "dice: 0.9235132533819138 iou:0.8578956092894251 bbox_iou: 0.8662986755371094 angle_error: 0.7481803654086363\n", "Predict: (372, 165) (709, 502) (541, 334) 336 0.7805297 0.6186708 (0.2594200074672699)' 131 103 46\n", "Ground Truth: (411, 201) (702, 492) (557, 347) 290.7856535732119 0.7686692835543355 0.6396464121059888 (0.24191551349968093)' 111 93 43\n", "dice: 0.8633818885001748 iou:0.7596059571502857 bbox_iou: 0.7520363330841064 angle_error: 3.1508089141660136\n", "Predict: (141, 22) (757, 637) (449, 330) 615 0.80114985 0.6070476 (-0.04800906777381897)' 246 186 -8\n", "Ground Truth: (132, 27) (743, 638) (438, 333) 610.446283333759 0.7895862636229182 0.6136395784970196 (-0.037430904180216995)' 241 187 -6\n", "dice: 0.9695781890508877 iou:0.9409527037843057 bbox_iou: 0.9349095821380615 angle_error: 1.9040694468483554\n", "Predict: (343, 125) (696, 478) (520, 301) 353 0.7908763 0.6170941 (-0.27184751629829407)' 139 108 -48\n", "Ground Truth: (347, 130) (698, 481) (523, 306) 351.39172955549196 0.770479146855517 0.637465202392095 (-0.271706980411487)' 135 112 -48\n", "dice: 0.9633222060251388 iou:0.9292397422072097 bbox_iou: 0.9233317375183105 angle_error: 0.025296459625273826\n", "Predict: (9, -79) (916, 827) (463, 373) 906 0.72807807 0.68139553 (-0.4454342722892761)' 329 308 -80\n", "Ground Truth: (0, -87) (911, 823) (456, 368) 911.4048499519848 0.7184008292631909 0.6956293902028289 (-0.47084515286056156)' 327 317 -84\n", "dice: 0.983759486542995 iou:0.9680380515400656 bbox_iou: 0.920519232749939 angle_error: 4.573958502831374\n", "Predict: (269, 21) (732, 484) (500, 252) 463 0.8027376 0.59815043 (-0.21536026895046234)' 185 138 -38\n", "Ground Truth: (260, 5) (751, 496) (506, 251) 490.7703646961581 0.7818157484662881 0.623509531162193 (-0.2347830336141672)' 191 153 -42\n", "dice: 0.9321364909060736 iou:0.8728985333500007 bbox_iou: 0.8699013590812683 angle_error: 3.496097639466875\n", "Predict: (122, 93) (684, 655) (403, 374) 561 0.77989036 0.6213718 (0.09535830467939377)' 218 174 17\n", "Ground Truth: (129, 105) (668, 644) (399, 375) 539.0874070130001 0.7509357383119836 0.6603752849144463 (0.09740282517223994)' 202 178 17\n", "dice: 0.962542058600454 iou:0.9277889928742273 bbox_iou: 0.9074510931968689 angle_error: 0.36801368871230977\n", "Predict: (208, 179) (492, 463) (350, 321) 283 0.7821343 0.63231194 (0.3968769311904907)' 110 89 71\n", "Ground Truth: (222, 191) (489, 458) (356, 325) 267.2899728759012 0.7856635912694709 0.6186539592967606 (0.32142815385089346)' 105 82 57\n", "dice: 0.917319796100547 iou:0.8472675428964779 bbox_iou: 0.7951846718788147 angle_error: 13.580779921127508\n", "Predict: (139, 50) (780, 692) (459, 371) 641 0.7691476 0.64007854 (0.23779623210430145)' 246 205 42\n", "Ground Truth: (142, 82) (747, 687) (445, 385) 605.7519060341452 0.7595650883087184 0.6504313004634457 (0.21161124311748564)' 230 197 38\n", "dice: 0.9232140785482885 iou:0.8573794104807957 bbox_iou: 0.8464366793632507 angle_error: 4.7132980176268475\n", "Predict: (241, 115) (573, 446) (407, 281) 331 0.7791504 0.6318199 (0.48017415404319763)' 128 104 86\n", "Ground Truth: (260, 145) (555, 440) (408, 293) 294.8158272277796 0.7470019573604785 0.6648218375622253 (0.48567605512172946)' 110 98 87\n", "dice: 0.8951391892667296 iou:0.8101827674317154 bbox_iou: 0.8003092408180237 angle_error: 0.9903421941357351\n", "Predict: (119, -8) (849, 722) (484, 356) 730 0.7840895 0.62110424 (-0.1351809948682785)' 286 226 -24\n", "Ground Truth: (161, 26) (846, 711) (504, 369) 684.3204421789546 0.7715683581200461 0.6361464208403095 (-0.1520872944011168)' 264 217 -27\n", "dice: 0.9209366498517204 iou:0.8534592984973217 bbox_iou: 0.8443683981895447 angle_error: 3.043133915910893\n", "Predict: (373, 108) (747, 483) (560, 295) 374 0.77718157 0.634116 (-0.3625619113445282)' 145 118 -65\n", "Ground Truth: (402, 126) (737, 461) (570, 294) 334.21184852724775 0.8011804523985021 0.5984228293560763 (-0.35739717098966184)' 133 100 -64\n", "dice: 0.8750832084295345 iou:0.7779092773678441 bbox_iou: 0.776868999004364 angle_error: 0.9296532638759487\n", "Predict: (140, 108) (596, 564) (368, 336) 455 0.7966327 0.61675346 (-0.028548825532197952)' 181 140 -5\n", "Ground Truth: (150, 121) (581, 552) (366, 337) 430.82448648144407 0.7780105600252057 0.628251198557763 (0.01769569123682202)' 167 135 3\n", "dice: 0.94178424245816 iou:0.8899737466070792 bbox_iou: 0.8486019968986511 angle_error: 8.324013018423596\n", "Predict: (313, 219) (536, 441) (425, 330) 222 0.79468197 0.63470876 (-0.13574077188968658)' 88 70 -24\n", "Ground Truth: (306, 206) (535, 435) (421, 321) 229.49204354835484 0.8011781025482879 0.5984259753696568 (-0.10841400879920227)' 91 68 -19\n", "dice: 0.9077471965066902 iou:0.8310779277503136 bbox_iou: 0.8353040814399719 angle_error: 4.918817356287178\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (167, 86) (727, 646) (447, 366) 560 0.7637912 0.6447778 (0.2432241290807724)' 213 180 43\n", "Ground Truth: (159, 93) (700, 634) (430, 364) 541.021051143114 0.7442741814744727 0.6678741968293906 (0.24344223173586468)' 201 180 43\n", "dice: 0.9410352562201342 iou:0.888637003023543 bbox_iou: 0.8858965635299683 angle_error: 0.03925847791661141\n", "Predict: (273, 147) (561, 435) (417, 291) 288 0.7862864 0.6264572 (0.4548523426055908)' 113 90 81\n", "Ground Truth: (290, 159) (545, 414) (418, 287) 255.53668162516317 0.7838796321767493 0.6209128137334936 (0.43379154367377154)' 100 79 78\n", "dice: 0.8751156658213392 iou:0.7779605771292999 bbox_iou: 0.7779103517532349 angle_error: 3.790943807727473\n", "Predict: (47, -60) (755, 647) (401, 293) 708 0.7824102 0.61606187 (-0.13079006969928741)' 276 218 -23\n", "Ground Truth: (82, -23) (731, 625) (407, 301) 648.0786150151847 0.7802726216913541 0.6254395541048718 (-0.14406471605178683)' 252 202 -25\n", "dice: 0.9165757064629375 iou:0.8459988500632442 bbox_iou: 0.8463178873062134 angle_error: 2.389436343449894\n", "Predict: (310, 52) (656, 398) (483, 225) 345 0.77355456 0.6503634 (-0.3803287744522095)' 133 112 -68\n", "Ground Truth: (319, 63) (648, 392) (484, 228) 328.48673710821265 0.7462401744373849 0.6656767999980631 (-0.37579431319358647)' 122 109 -67\n", "dice: 0.9434985820348245 iou:0.8930405260146317 bbox_iou: 0.8942204117774963 angle_error: 0.8162030265521452\n", "Predict: (119, 17) (774, 671) (447, 344) 654 0.74008995 0.6656596 (-0.34258362650871277)' 242 217 -61\n", "Ground Truth: (156, 52) (755, 651) (456, 352) 599.904316267186 0.739241222265994 0.6734407288712789 (-0.2934168838667198)' 221 202 -52\n", "dice: 0.9191857805358692 iou:0.8504567796967021 bbox_iou: 0.8235766887664795 angle_error: 8.850013675558728\n", "Predict: (263, 84) (617, 439) (440, 262) 354 0.7691057 0.6376897 (0.47576776146888733)' 136 112 85\n", "Ground Truth: (290, 114) (603, 427) (447, 271) 312.6624327481637 0.7389311148425998 0.6737809788926017 (0.4614845037717613)' 115 105 83\n", "dice: 0.8842322066019194 iou:0.7924876590217597 bbox_iou: 0.7871366739273071 angle_error: 2.5709863854826835\n", "Predict: (37, 18) (761, 742) (399, 380) 724 0.79861265 0.6091248 (-0.0016363502945750952)' 289 220 0\n", "Ground Truth: (55, 27) (762, 734) (409, 381) 707.5533969955908 0.7693553621697787 0.638821044347013 (0.0336111855460115)' 272 226 6\n", "dice: 0.9635026489972938 iou:0.9295756019686906 bbox_iou: 0.8775076866149902 angle_error: 6.344556451305587\n", "Predict: (320, 179) (670, 530) (495, 355) 350 0.8147818 0.6087693 (-0.12179436534643173)' 142 106 -21\n", "Ground Truth: (331, 191) (648, 508) (490, 350) 316.9969982507721 0.814297931603115 0.5804471367720652 (-0.18449007159712827)' 129 92 -33\n", "dice: 0.8823177103704722 iou:0.7894172776620889 bbox_iou: 0.7614893913269043 angle_error: 11.285227125125374\n", "Predict: (160, 78) (725, 643) (442, 361) 565 0.7958939 0.6121252 (0.04895541071891785)' 224 172 8\n", "Ground Truth: (136, 51) (741, 656) (439, 354) 605.6885585777562 0.7697124097815529 0.6383907942853458 (0.03934076349731974)' 233 193 7\n", "dice: 0.9231710235534714 iou:0.8573051466351517 bbox_iou: 0.862653911113739 angle_error: 1.7306364998876589\n", "Predict: (352, 246) (654, 549) (503, 398) 302 0.7676821 0.6387001 (0.32289665937423706)' 115 96 58\n", "Ground Truth: (360, 241) (665, 546) (513, 394) 305.7789536577035 0.7953850227123791 0.6061045005977339 (0.298321194548947)' 121 92 53\n", "dice: 0.9231229603825865 iou:0.8572222514007245 bbox_iou: 0.8542229533195496 angle_error: 4.423583668552212\n", "Predict: (142, 3) (774, 635) (458, 319) 631 0.78819484 0.6231655 (-0.11043381690979004)' 248 196 -19\n", "Ground Truth: (150, 17) (763, 630) (457, 324) 612.8135458326619 0.7659197535561126 0.6429361796574707 (-0.09631823312421828)' 234 197 -17\n", "dice: 0.9662015046538684 iou:0.9346129918000796 bbox_iou: 0.9165305495262146 angle_error: 2.5408050814029153\n", "Predict: (385, 125) (683, 423) (534, 274) 297 0.78071195 0.6589738 (-0.4216397702693939)' 115 97 -75\n", "Ground Truth: (397, 127) (688, 418) (543, 273) 290.9721059895604 0.7746721948944729 0.6323630211021039 (-0.35580562155874285)' 112 92 -64\n", "dice: 0.9289918190617102 iou:0.8673993678067318 bbox_iou: 0.815845251083374 angle_error: 11.850146767917195\n", "Predict: (88, -1) (721, 632) (404, 315) 633 0.77073175 0.63587356 (0.20312581956386566)' 243 201 36\n", "Ground Truth: (104, -14) (765, 646) (435, 316) 660.2297037637735 0.7924878220674644 0.6098877371080409 (0.1757718883681878)' 261 201 31\n", "dice: 0.9211098357522198 iou:0.853756819995141 bbox_iou: 0.8333461880683899 angle_error: 4.923707615222014\n", "Predict: (187, 80) (493, 387) (340, 233) 306 0.7650569 0.63495284 (0.5128253698348999)' 117 97 92\n", "Ground Truth: (191, 87) (496, 392) (344, 240) 305.9987718929604 0.7712449253964776 0.6365385024098555 (-0.48917746386975114)' 118 97 -88\n", "dice: 0.9537131934082556 iou:0.9115217619105364 bbox_iou: 0.9041122198104858 angle_error: -0.36051006683720743\n", "Predict: (117, 38) (684, 604) (401, 321) 566 0.79689777 0.6095517 (0.026774367317557335)' 225 172 4\n", "Ground Truth: (128, 43) (693, 608) (411, 326) 564.2475411519309 0.808574192576149 0.5883942344209608 (0.010185916357881302)' 228 166 1\n", "dice: 0.9642676652210688 iou:0.931000831819047 bbox_iou: 0.9113239645957947 angle_error: 2.985921172741686\n", "Predict: (170, 147) (470, 447) (320, 297) 299 0.79505104 0.59671825 (0.19872437417507172)' 118 89 35\n", "Ground Truth: (191, 164) (468, 441) (330, 303) 277.51806780820596 0.7995803723790508 0.6005590962646211 (0.2323662169141672)' 110 83 41\n", "dice: 0.9182908182315457 iou:0.8489257868092227 bbox_iou: 0.8306843638420105 angle_error: 6.055531693037189\n", "Predict: (259, 166) (653, 559) (456, 363) 393 0.748079 0.6580651 (-0.30281510949134827)' 146 129 -54\n", "Ground Truth: (257, 177) (646, 566) (452, 372) 388.00731449291004 0.7491869074166944 0.6623586473772419 (-0.24293527435296747)' 145 128 -43\n", "dice: 0.9562626945703907 iou:0.9161909702717645 bbox_iou: 0.8414260745048523 angle_error: 10.778370324908543\n", "Predict: (381, 218) (582, 419) (481, 318) 201 0.7837029 0.6302648 (-0.006967634428292513)' 78 63 -1\n", "Ground Truth: (383, 226) (576, 419) (480, 323) 192.1695152098792 0.7726303510618787 0.6348561574230799 (-0.44174929082836634)' 74 61 -79\n", "dice: 0.8661133883724583 iou:0.7638447967290742 bbox_iou: 0.6960104703903198 angle_error: 78.26069815201329\n", "Predict: (106, 4) (833, 730) (469, 367) 726 0.7949396 0.61146235 (0.04089977592229843)' 288 221 7\n", "Ground Truth: (115, 7) (852, 744) (484, 376) 737.2432874377358 0.7621012080729885 0.6474579126504606 (0.07091827342425013)' 280 238 12\n", "dice: 0.9458120395255095 iou:0.8971948788902876 bbox_iou: 0.87077397108078 angle_error: 5.403329550351305\n", "Predict: (185, 136) (557, 508) (371, 322) 371 0.78256357 0.61061037 (0.25220441818237305)' 145 113 45\n", "Ground Truth: (205, 161) (538, 494) (372, 328) 332.37721660787764 0.7634638817598959 0.6458505254686348 (0.23930420321547544)' 126 107 43\n", "dice: 0.9026992695138457 iou:0.8226543958591085 bbox_iou: 0.8258799910545349 angle_error: 2.3220386940415665\n", "Predict: (159, 39) (852, 732) (506, 386) 693 0.7880826 0.61354685 (-0.027064897119998932)' 273 212 -4\n", "Ground Truth: (154, 45) (835, 726) (495, 386) 681.4178112435864 0.7717467775611356 0.6359299578758679 (-0.014642254764454372)' 262 216 -2\n", "dice: 0.9716112999847014 iou:0.944789941750865 bbox_iou: 0.929594099521637 angle_error: 2.236075623998021\n", "Predict: (218, 203) (611, 597) (415, 400) 393 0.7902375 0.6164286 (-0.25210845470428467)' 155 121 -45\n", "Ground Truth: (227, 207) (598, 578) (413, 393) 371.14952609965707 0.8053842965698352 0.5927530133527046 (-0.21390424351550735)' 149 110 -38\n", "dice: 0.9328253349786689 iou:0.8741074592133644 bbox_iou: 0.8476342558860779 angle_error: 6.8767580139799165\n", "Predict: (99, 56) (732, 689) (415, 372) 632 0.76444423 0.64033604 (-0.21970145404338837)' 241 202 -39\n", "Ground Truth: (79, 24) (744, 689) (412, 357) 665.1249862815259 0.7654561330590343 0.6434880794251825 (-0.2517831199713784)' 254 214 -45\n", "dice: 0.9440009362659076 iou:0.8939410731367975 bbox_iou: 0.8628239631652832 angle_error: 5.774699867038208\n", "Predict: (195, 257) (530, 592) (363, 425) 334 0.76464206 0.6560547 (-0.40748539566993713)' 127 109 -73\n", "Ground Truth: (191, 262) (542, 613) (367, 438) 351.11259788278744 0.7594885561156128 0.6505206631071927 (-0.44779717866585833)' 133 114 -80\n", "dice: 0.9281678772191864 iou:0.8659638552454485 bbox_iou: 0.8377322554588318 angle_error: 7.256120939265813\n", "Predict: (143, -86) (730, 499) (437, 206) 586 0.77049375 0.62735945 (-0.16935569047927856)' 225 183 -30\n", "Ground Truth: (163, -91) (738, 483) (451, 196) 574.8522814393276 0.7739831855341769 0.6332061500888696 (-0.13400846208337588)' 222 182 -24\n", "dice: 0.9507304307558324 iou:0.9060878716235743 bbox_iou: 0.8725647926330566 angle_error: 6.362501111262482\n", "Predict: (242, 101) (541, 400) (392, 250) 298 0.76488745 0.6512693 (-0.41636255383491516)' 113 97 -74\n", "Ground Truth: (239, 104) (532, 397) (386, 251) 293.1072174136966 0.7728571203358382 0.6345800749678449 (-0.4234169798191767)' 113 93 -76\n", "dice: 0.9607225865779042 iou:0.9244139958882306 bbox_iou: 0.9223426580429077 angle_error: 1.2697966771670792\n", "Predict: (161, -7) (749, 580) (455, 286) 587 0.79021615 0.6136776 (0.043939657509326935)' 231 180 7\n", "Ground Truth: (156, -2) (745, 586) (451, 292) 588.7314367994969 0.7609140127378073 0.6488527299929067 (0.06741686467622847)' 223 191 12\n", "dice: 0.9629950634599617 iou:0.9286311275170879 bbox_iou: 0.8997253179550171 angle_error: 4.225897290042276\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (234, 113) (518, 397) (376, 255) 284 0.7892164 0.61475825 (0.2686375677585602)' 112 87 48\n", "Ground Truth: (232, 109) (519, 396) (376, 253) 287.8058872087227 0.7690391678453891 0.6392016570063562 (0.23860275224837268)' 110 91 42\n", "dice: 0.9716330179894529 iou:0.9448310136230023 bbox_iou: 0.9025864005088806 angle_error: 5.406266791833751\n", "Predict: (223, 28) (845, 650) (534, 339) 622 0.74157375 0.6651663 (-0.3479160964488983)' 230 206 -62\n", "Ground Truth: (211, 18) (856, 663) (534, 341) 645.538226985823 0.7374466454493248 0.6754053931643853 (-0.3621069880876835)' 238 218 -65\n", "dice: 0.9547272477020032 iou:0.9133761935371107 bbox_iou: 0.9182392954826355 angle_error: 2.5543604949813243\n", "Predict: (413, 83) (761, 430) (587, 256) 347 0.78978914 0.61299133 (-0.2518826127052307)' 137 106 -45\n", "Ground Truth: (422, 94) (743, 415) (583, 255) 320.9348078909485 0.7976697874634623 0.6030944454793086 (-0.23300283668653476)' 128 96 -41\n", "dice: 0.9174632395244797 iou:0.8475123183082212 bbox_iou: 0.8494808077812195 angle_error: 3.398359683365271\n", "Predict: (-90, -120) (753, 724) (331, 301) 844 0.7816536 0.6187187 (0.1118449717760086)' 329 261 20\n", "Ground Truth: (-80, -111) (758, 727) (339, 308) 838.7724818471336 0.782748616828915 0.6223380133435691 (0.10663381187156988)' 328 261 19\n", "dice: 0.9785517126215049 iou:0.9580041640021906 bbox_iou: 0.9556943774223328 angle_error: 0.9380087827989705\n", "Predict: (2, 3) (443, 444) (222, 224) 440 0.78482103 0.6265371 (0.3639635443687439)' 172 137 65\n", "Ground Truth: (19, 20) (426, 427) (223, 224) 407.94570072008355 0.7705829463213263 0.6373397232549874 (0.36885632689227826)' 157 130 66\n", "dice: 0.9288259557472069 iou:0.8671102149372163 bbox_iou: 0.8585633635520935 angle_error: 0.8807008542361814\n", "Predict: (84, 19) (838, 773) (461, 396) 754 0.7345538 0.6757598 (-0.43087467551231384)' 276 254 -77\n", "Ground Truth: (116, 26) (841, 751) (479, 389) 724.1298557689774 0.7153413105028222 0.6987752210032241 (-0.39374932920934913)' 259 253 -70\n", "dice: 0.9428287440762808 iou:0.8918410700189442 bbox_iou: 0.8615431189537048 angle_error: 6.6825623345336425\n", "Predict: (251, 103) (624, 476) (438, 289) 373 0.7794422 0.63041085 (0.36980754137039185)' 145 117 66\n", "Ground Truth: (260, 101) (637, 478) (449, 290) 376.0908875311924 0.7423152468081148 0.6700507998325259 (0.3431380573061264)' 139 126 61\n", "dice: 0.940990825601744 iou:0.888557765456969 bbox_iou: 0.8628976941108704 angle_error: 4.800507131567784\n", "Predict: (-24, 26) (620, 671) (298, 349) 644 0.78692687 0.6183542 (-0.11778497695922852)' 253 199 -21\n", "Ground Truth: (13, 70) (590, 647) (302, 359) 577.1175847468174 0.7703248207122866 0.6376516843815152 (-0.11859992515708351)' 222 184 -21\n", "dice: 0.8960039521804656 iou:0.8116006882000466 bbox_iou: 0.8106672167778015 angle_error: 0.14669067561389681\n", "Predict: (218, 141) (527, 450) (373, 296) 309 0.779916 0.65382236 (-0.36556923389434814)' 120 101 -65\n", "Ground Truth: (214, 147) (515, 448) (365, 298) 300.543987462734 0.7693382987030148 0.6388415939407441 (-0.3220647736232609)' 115 96 -57\n", "dice: 0.9338623255301699 iou:0.8759303304749659 bbox_iou: 0.851115345954895 angle_error: 7.830802848795699\n", "Predict: (188, 50) (739, 601) (464, 325) 550 0.7292281 0.6698235 (-0.3619708716869354)' 200 184 -65\n", "Ground Truth: (192, 55) (761, 624) (477, 340) 569.4798926213286 0.725381186153101 0.6883473939626126 (-0.4767633783085833)' 206 196 -85\n", "dice: 0.933626518117851 iou:0.8755155055712753 bbox_iou: 0.7567762136459351 angle_error: 20.662651191896614\n", "Predict: (295, 107) (593, 406) (444, 257) 298 0.7757684 0.63158476 (0.39822983741760254)' 115 94 71\n", "Ground Truth: (306, 115) (599, 408) (453, 262) 292.4523995729903 0.7881487733954216 0.6154847772246628 (0.32906759111930445)' 115 90 59\n", "dice: 0.9253864978025746 iou:0.8611342551627132 bbox_iou: 0.8111444115638733 angle_error: 12.449204333693658\n", "Predict: (31, 0) (299, 267) (165, 133) 268 0.73002225 0.6685026 (-0.4028512239456177)' 97 89 -72\n", "Ground Truth: (31, -8) (312, 272) (172, 132) 281.7185500530627 0.7633079183443789 0.6460348456490339 (-0.3803154827948947)' 107 91 -68\n", "dice: 0.9262351403617406 iou:0.8626051896257623 bbox_iou: 0.8518990278244019 angle_error: 4.056433407130143\n", "Predict: (95, 21) (241, 168) (168, 95) 146 0.77753377 0.63211155 (0.3975972533226013)' 56 46 71\n", "Ground Truth: (96, 17) (245, 166) (171, 92) 148.40757048075412 0.7626026060084109 0.6468672702411062 (0.34498308542849393)' 56 48 62\n", "dice: 0.9416582580914842 iou:0.8897487652649745 bbox_iou: 0.8451205492019653 angle_error: 9.47055022093933\n", "Predict: (178, 255) (668, 745) (423, 500) 490 0.772074 0.64312553 (-0.1584250032901764)' 189 157 -28\n", "Ground Truth: (166, 255) (655, 744) (411, 500) 489.31345747281466 0.7600281462127201 0.6498901576146973 (-0.10459429016499677)' 185 159 -18\n", "dice: 0.955337743176984 iou:0.9144943611558416 bbox_iou: 0.8508024215698242 angle_error: 9.689528362532332\n", "Predict: (322, 316) (615, 609) (469, 463) 293 0.77312577 0.64612705 (-0.33114391565322876)' 113 94 -59\n", "Ground Truth: (351, 341) (602, 592) (477, 467) 251.28490769642335 0.7644470245386494 0.6446865491647902 (-0.2850759956312061)' 96 81 -51\n", "dice: 0.8460509159325377 iou:0.7331787230597393 bbox_iou: 0.7160544395446777 angle_error: 8.292225603964077\n", "Predict: (121, 18) (718, 614) (419, 316) 596 0.78422856 0.62106866 (-0.12712723016738892)' 233 185 -22\n", "Ground Truth: (135, 43) (708, 616) (422, 330) 573.7044599478028 0.7729450108167986 0.6344730177504943 (-0.12050978447418637)' 221 182 -21\n", "dice: 0.9409597466300408 iou:0.8885023431695106 bbox_iou: 0.8801947832107544 angle_error: 1.1911402247764578\n", "Predict: (322, 120) (623, 421) (473, 270) 300 0.7719709 0.6647118 (-0.4092244505882263)' 115 99 -73\n", "Ground Truth: (333, 119) (622, 408) (478, 264) 289.0875661456231 0.7712887931253416 0.6364853475134002 (-0.36790139723372683)' 111 92 -66\n", "dice: 0.9324379680283769 iou:0.8734274356931814 bbox_iou: 0.8445890545845032 angle_error: 7.438149603809904\n", "Predict: (264, -99) (1077, 712) (671, 306) 812 0.78913605 0.6145889 (0.09784874320030212)' 320 249 17\n", "Ground Truth: (291, -78) (1054, 684) (673, 303) 762.726339938513 0.7709187020437782 0.6369335560630608 (0.10809569891301851)' 294 242 19\n", "dice: 0.9505793963888411 iou:0.9058135442717674 bbox_iou: 0.8897784948348999 angle_error: 1.8444520282889485\n", "Predict: (368, 78) (764, 474) (566, 276) 396 0.8176963 0.580509 (-0.11519837379455566)' 161 114 -20\n", "Ground Truth: (362, 55) (799, 492) (581, 274) 436.44478604286246 0.8321969046603068 0.5544802177479412 (-0.0923098669932993)' 181 121 -16\n", "dice: 0.9124407293398306 iou:0.8389802321173369 bbox_iou: 0.8357376456260681 angle_error: 4.119931224226146\n", "Predict: (165, -69) (967, 732) (566, 331) 801 0.7516839 0.65945035 (0.3051919639110565)' 301 264 54\n", "Ground Truth: (180, -64) (989, 744) (585, 340) 809.1474824603979 0.7432464575818959 0.6690177152302942 (0.36312674894097)' 300 270 65\n", "dice: 0.9491783774821555 iou:0.9032725984528712 bbox_iou: 0.8431680798530579 angle_error: 10.428261305384424\n", "Predict: (297, 40) (699, 442) (498, 241) 401 0.802396 0.596767 (0.08057567477226257)' 160 119 14\n", "Ground Truth: (301, 47) (692, 438) (497, 243) 390.0813877026178 0.7936138707443449 0.608421748593998 (0.09453803619658582)' 154 118 17\n", "dice: 0.9735524402168213 iou:0.9484677818538234 bbox_iou: 0.9329231381416321 angle_error: 2.513225056378186\n", "Predict: (117, -67) (848, 663) (483, 298) 730 0.77883375 0.6196845 (-0.12660814821720123)' 284 226 -22\n", "Ground Truth: (139, -67) (866, 659) (503, 296) 727.7755367281866 0.7852036392553668 0.619237632012241 (-0.15629015411624123)' 285 225 -28\n", "dice: 0.9525195236869742 iou:0.9093434629346986 bbox_iou: 0.88534015417099 angle_error: 5.342761061827197\n", "Predict: (232, 211) (554, 533) (393, 372) 322 0.76418877 0.64572966 (-0.36813077330589294)' 123 103 -66\n", "Ground Truth: (225, 212) (558, 545) (392, 379) 333.1182278771308 0.7762649364698803 0.6304068118345586 (-0.34345636719231015)' 129 105 -61\n", "dice: 0.9539103281669126 iou:0.9118819866516348 bbox_iou: 0.8946115970611572 angle_error: 4.441393100444905\n", "Predict: (253, 24) (952, 723) (602, 374) 698 0.7777974 0.6243408 (0.1463015228509903)' 271 217 26\n", "Ground Truth: (275, 37) (956, 718) (616, 378) 680.6068933091994 0.7759368957200389 0.6308105372141063 (0.11166193885577537)' 264 214 20\n", "dice: 0.9605870367046482 iou:0.924163033005867 bbox_iou: 0.8843631744384766 angle_error: 6.2351251191386865\n", "Predict: (343, 129) (697, 483) (520, 306) 353 0.7768573 0.626789 (0.3199031949043274)' 137 110 57\n", "Ground Truth: (356, 138) (693, 475) (525, 307) 337.45767721597326 0.7507133993513089 0.6606280284959172 (0.2959633629561902)' 126 111 53\n", "dice: 0.9493344361538432 iou:0.9035552975379034 bbox_iou: 0.883165717124939 angle_error: 4.309169750664694\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (127, -4) (870, 738) (499, 367) 743 0.74617094 0.66286194 (0.3650118410587311)' 277 246 65\n", "Ground Truth: (163, 17) (852, 706) (508, 362) 688.822692785306 0.7310502474356665 0.6823236297566213 (0.39050139915277604)' 251 235 70\n", "dice: 0.9281331398471075 iou:0.8659033825476399 bbox_iou: 0.8523949980735779 angle_error: 4.588120456928095\n", "Predict: (287, 79) (668, 460) (477, 270) 381 0.76297235 0.64677894 (-0.40423065423965454)' 145 123 -72\n", "Ground Truth: (298, 81) (667, 450) (483, 266) 369.25895174524885 0.786607876741169 0.6174528712774358 (-0.37394928507121894)' 145 114 -67\n", "dice: 0.9459593819902082 iou:0.8974600843906194 bbox_iou: 0.87750244140625 angle_error: 5.450646450318402\n", "Predict: (165, -18) (899, 715) (532, 348) 733 0.7789399 0.62411904 (-0.18370437622070312)' 285 228 -33\n", "Ground Truth: (248, 49) (883, 684) (566, 367) 634.6838392081524 0.7515773532143731 0.6596449667323155 (-0.2303915264023293)' 238 209 -41\n", "dice: 0.8456890541248716 iou:0.732635393562626 bbox_iou: 0.7130205631256104 angle_error: 8.403687032692709\n", "Predict: (452, 104) (798, 450) (625, 277) 345 0.77137065 0.6616685 (-0.42686301469802856)' 133 114 -76\n", "Ground Truth: (466, 123) (779, 436) (623, 280) 312.95551526055584 0.7546631661160152 0.6561124184983482 (-0.4640309828612317)' 118 102 -83\n", "dice: 0.8851476174509222 iou:0.793959479574379 bbox_iou: 0.7972267270088196 angle_error: 6.69023426937656\n", "Predict: (238, 12) (1065, 838) (652, 425) 826 0.7661736 0.64298546 (0.22878488898277283)' 316 265 41\n", "Ground Truth: (264, 52) (1061, 849) (663, 451) 797.9498893765196 0.775279260309656 0.6316186100280079 (0.18041687427147415)' 309 252 32\n", "dice: 0.938252532500398 iou:0.8836870924778067 bbox_iou: 0.8372691869735718 angle_error: 8.706242648033758\n", "Predict: (368, 124) (793, 549) (581, 336) 424 0.7710762 0.630646 (0.3692212402820587)' 163 133 66\n", "Ground Truth: (399, 154) (782, 537) (591, 346) 383.04637329702 0.743868157652693 0.6683263903441103 (0.37153246837121867)' 142 128 66\n", "dice: 0.9098087815160983 iou:0.8345405522357296 bbox_iou: 0.8340241312980652 angle_error: 0.41602105604879114\n", "Predict: (263, -58) (1049, 727) (656, 334) 786 0.7879169 0.61906826 (0.11947757005691528)' 309 243 21\n", "Ground Truth: (298, -28) (1041, 714) (670, 343) 742.7187947345885 0.7750389569792753 0.6319134554386995 (0.08053240120449905)' 287 234 14\n", "dice: 0.9442679318795586 iou:0.8944200525808348 bbox_iou: 0.8537256717681885 angle_error: 7.010130393434922\n", "Predict: (366, 120) (735, 489) (551, 305) 369 0.8307754 0.5723253 (-0.042516134679317474)' 153 105 -7\n", "Ground Truth: (382, 109) (769, 496) (576, 303) 386.68425253170057 0.8385653097507947 0.5448010841422344 (-0.06589014644004466)' 162 105 -11\n", "dice: 0.8983980730139931 iou:0.8155378553775942 bbox_iou: 0.8137454390525818 angle_error: 4.207322116930895\n", "Predict: (80, 5) (710, 635) (395, 320) 629 0.7403125 0.6689003 (-0.363822877407074)' 232 210 -65\n", "Ground Truth: (74, -1) (713, 637) (394, 318) 638.6931455182528 0.7287944191452067 0.6847325716093844 (-0.37840562347779194)' 232 218 -68\n", "dice: 0.9803072969126371 iou:0.9613752201467394 bbox_iou: 0.9416864514350891 angle_error: 2.6248942927292376\n", "Predict: (259, 69) (607, 417) (433, 243) 348 0.7945155 0.6049462 (-0.2276276797056198)' 138 105 -40\n", "Ground Truth: (252, 66) (609, 423) (431, 245) 356.4880271145161 0.7824273994772886 0.6227418121077206 (-0.2144760320931397)' 139 111 -38\n", "dice: 0.9655128678364411 iou:0.933325159702216 bbox_iou: 0.9272001385688782 angle_error: 2.367296570246417\n", "Predict: (152, -76) (889, 659) (521, 291) 736 0.77996504 0.62444776 (0.13684602081775665)' 287 229 24\n", "Ground Truth: (155, -79) (886, 651) (521, 286) 731.4826348970972 0.7608656356938605 0.6489094577983722 (0.1024957833511806)' 278 237 18\n", "dice: 0.9736445255996843 iou:0.9486425998444354 bbox_iou: 0.8965187668800354 angle_error: 6.1830427439836875\n", "Predict: (273, 21) (648, 396) (461, 208) 374 0.7893945 0.617796 (0.328580766916275)' 147 115 59\n", "Ground Truth: (269, 22) (644, 397) (457, 210) 375.4787495931028 0.7734498964714104 0.6338574426859438 (0.2981915321594767)' 145 119 53\n", "dice: 0.9718699924074108 iou:0.9452792791089586 bbox_iou: 0.9044561982154846 angle_error: 5.470062256223699\n", "Predict: (181, 30) (708, 557) (445, 293) 527 0.7974835 0.60929936 (0.05905254930257797)' 210 160 10\n", "Ground Truth: (184, 38) (717, 571) (451, 305) 533.6122440311879 0.7727858657904005 0.6346668461756775 (0.10058592403407786)' 206 169 18\n", "dice: 0.9496424880350681 iou:0.904113577727022 bbox_iou: 0.8622041344642639 angle_error: 7.476007451669979\n", "Predict: (228, 145) (498, 415) (363, 280) 270 0.8227731 0.59039253 (-0.0682484358549118)' 111 79 -12\n", "Ground Truth: (238, 148) (495, 405) (367, 277) 257.24739588963774 0.8355536477120008 0.5494088657777279 (-0.06366197723675814)' 107 70 -11\n", "dice: 0.9212607581373069 iou:0.8540161721998145 bbox_iou: 0.8509659767150879 angle_error: 0.8255625512676605\n", "Predict: (38, 61) (618, 641) (328, 351) 580 0.79828364 0.61399347 (-0.009996142238378525)' 231 178 -1\n", "Ground Truth: (34, 54) (617, 637) (326, 346) 582.438592570925 0.7751100386518863 0.6318262640798269 (0.018968930781557147)' 225 184 3\n", "dice: 0.9746201086456646 iou:0.9504966080019118 bbox_iou: 0.9067240953445435 angle_error: 5.213713143588421\n", "Predict: (251, 184) (542, 474) (396, 329) 290 0.78997105 0.6531898 (-0.29935377836227417)' 114 94 -53\n", "Ground Truth: (251, 169) (546, 464) (399, 317) 294.5177081603074 0.7734950859932775 0.6338022972065123 (-0.28705068614304396)' 113 93 -51\n", "dice: 0.9282612573719199 iou:0.8661264359032783 bbox_iou: 0.8617726564407349 angle_error: 2.2145565994614387\n", "Predict: (112, 2) (705, 595) (409, 298) 592 0.745368 0.6570698 (-0.29753294587135315)' 220 194 -53\n", "Ground Truth: (123, 11) (670, 558) (397, 285) 547.5249623533159 0.7659559450905465 0.6428930627876207 (-0.28577744659830884)' 209 176 -51\n", "dice: 0.9261809255452641 iou:0.862511150694134 bbox_iou: 0.8538580536842346 angle_error: 2.115989869147974\n", "Predict: (272, 58) (596, 383) (434, 221) 324 0.76700675 0.6352952 (0.49962830543518066)' 124 102 89\n", "Ground Truth: (282, 68) (599, 385) (441, 227) 317.3702459651818 0.7815225376458611 0.6238770096353716 (-0.4949070418210594)' 124 99 -89\n", "dice: 0.9459747103684496 iou:0.8974876785917808 bbox_iou: 0.8902167081832886 angle_error: 0.9836374938768131\n", "Predict: (112, 172) (585, 645) (348, 409) 473 0.7561594 0.64752233 (-0.21819566190242767)' 178 153 -39\n", "Ground Truth: (87, 148) (614, 675) (351, 412) 527.5486792477069 0.761400825745212 0.6482814069171733 (-0.2545830777522974)' 200 171 -45\n", "dice: 0.8870036380067083 iou:0.7969510667745151 bbox_iou: 0.7954955101013184 angle_error: 6.549734852976549\n", "Predict: (262, 224) (510, 472) (386, 348) 247 0.7804609 0.6357453 (0.3956066071987152)' 96 78 71\n", "Ground Truth: (249, 208) (520, 479) (385, 344) 270.73149131935133 0.7513200588847085 0.6599380039954345 (-0.42201407788497125)' 101 89 -75\n", "dice: 0.899261300398197 iou:0.8169616465047597 bbox_iou: 0.6966863870620728 angle_error: 32.82827668493644\n", "Predict: (168, 91) (810, 733) (489, 412) 642 0.74634326 0.6534167 (-0.2906860411167145)' 239 209 -52\n", "Ground Truth: (208, 109) (787, 688) (498, 399) 578.4235819397408 0.708843852155935 0.7053654324254449 (-0.3389351975910019)' 205 204 -61\n", "dice: 0.9116343700643753 iou:0.8376177499452067 bbox_iou: 0.7980684041976929 angle_error: 8.684848165371733\n", "Predict: (342, 158) (682, 498) (512, 328) 340 0.7758142 0.6326879 (0.44976019859313965)' 131 107 80\n", "Ground Truth: (345, 165) (678, 498) (512, 332) 333.7658303002271 0.7370436925155566 0.6758450971361958 (0.4433408402592853)' 123 112 79\n", "dice: 0.9627110159527333 iou:0.9281029980637152 bbox_iou: 0.8924919366836548 angle_error: 1.1554845000937917\n", "Predict: (-47, -104) (854, 797) (403, 346) 902 0.7756816 0.6301179 (-0.17399446666240692)' 349 284 -31\n", "Ground Truth: (-27, -90) (859, 796) (416, 353) 887.3154810685994 0.7829368638574344 0.6221011712037562 (-0.19544227011684748)' 347 276 -35\n", "dice: 0.9678008409547119 iou:0.937610569117165 bbox_iou: 0.9166070818901062 angle_error: 3.860604621799297\n", "Predict: (241, 14) (729, 501) (485, 258) 487 0.77221286 0.63922495 (-0.41469013690948486)' 188 155 -74\n", "Ground Truth: (233, 6) (742, 515) (488, 261) 508.77058218415107 0.7509003338202461 0.6604155424190461 (-0.40164224516920866)' 191 168 -72\n", "dice: 0.9519664214724243 iou:0.9083358023794236 bbox_iou: 0.9068482518196106 angle_error: 2.3486205132497133\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (178, 20) (840, 682) (509, 351) 661 0.79497874 0.6072001 (0.03959087282419205)' 262 200 7\n", "Ground Truth: (168, 22) (809, 663) (489, 343) 641.9967314340471 0.7472062964066359 0.6645921686344781 (0.04615493349664965)' 239 213 8\n", "dice: 0.9350496935149526 iou:0.8780219018868195 bbox_iou: 0.8625152707099915 angle_error: 1.1815309210423681\n", "Predict: (234, 173) (575, 514) (404, 344) 341 0.80130523 0.5948855 (-0.17345723509788513)' 136 101 -31\n", "Ground Truth: (237, 186) (562, 511) (400, 349) 325.2816641374057 0.8116043082234139 0.584207537501181 (-0.16870423967740908)' 132 95 -30\n", "dice: 0.9511458645472305 iou:0.9068428415326215 bbox_iou: 0.8961449265480042 angle_error: 0.8555391756856885\n", "Predict: (164, -63) (823, 595) (493, 266) 658 0.78820884 0.6177283 (-0.09053081274032593)' 259 203 -16\n", "Ground Truth: (175, -70) (838, 592) (507, 261) 663.7697728971997 0.7949804609161137 0.6066350358836877 (-0.06321400496110396)' 263 201 -11\n", "dice: 0.9635081318510371 iou:0.9295858090731914 bbox_iou: 0.8973594307899475 angle_error: 4.917025400259954\n", "Predict: (414, 77) (774, 438) (594, 257) 360 0.8201752 0.566449 (0.03686623275279999)' 147 101 6\n", "Ground Truth: (417, 82) (770, 435) (594, 259) 353.6431764589838 0.8397843356506689 0.5429201318755503 (0.04507034144862798)' 148 96 8\n", "dice: 0.9733904831298937 iou:0.9481603931527296 bbox_iou: 0.9335062503814697 angle_error: 1.476739565249039\n", "Predict: (41, 24) (726, 708) (384, 366) 684 0.762109 0.62975717 (-0.1937105357646942)' 260 215 -34\n", "Ground Truth: (50, 31) (717, 698) (384, 365) 667.1836665416803 0.7753667032669819 0.6315112631338352 (-0.16666471797083604)' 258 210 -29\n", "dice: 0.9834956662865394 iou:0.967527273291266 bbox_iou: 0.9204710125923157 angle_error: 4.868247202894473\n", "Predict: (256, 101) (619, 464) (438, 283) 362 0.7649379 0.6464552 (-0.4405870735645294)' 138 117 -79\n", "Ground Truth: (264, 124) (603, 463) (434, 294) 338.9598022951984 0.764090603800983 0.6451089436545189 (-0.40705351323433314)' 129 109 -73\n", "dice: 0.922933551033179 iou:0.8568956464278555 bbox_iou: 0.8409599661827087 angle_error: 6.036040859435332\n", "Predict: (85, 26) (806, 748) (445, 387) 721 0.7582195 0.64261127 (-0.2275724709033966)' 273 231 -40\n", "Ground Truth: (44, -11) (813, 757) (429, 373) 768.8236712302763 0.7459942005716617 0.6659524402789192 (-0.2603126557036056)' 286 256 -46\n", "dice: 0.9246641890810567 iou:0.8598841214921246 bbox_iou: 0.8403346538543701 angle_error: 5.893233264037626\n", "Predict: (294, 98) (689, 493) (491, 295) 394 0.76478386 0.63319623 (0.5226420760154724)' 150 124 94\n", "Ground Truth: (272, 82) (691, 501) (482, 292) 419.2436093204046 0.7378383191133946 0.6749774921046778 (-0.4850394353493619)' 154 141 -87\n", "dice: 0.9230793383423413 iou:0.8571470222527666 bbox_iou: 0.8577513694763184 angle_error: -1.3826720456701764\n", "Predict: (69, 58) (683, 672) (376, 365) 613 0.78414005 0.62076634 (-0.10585812479257584)' 240 190 -19\n", "Ground Truth: (116, 73) (677, 634) (397, 354) 560.1140684182108 0.7497080035605606 0.6617687733621451 (-0.15584218184058707)' 209 185 -28\n", "dice: 0.9113296041936877 iou:0.8371033213580876 bbox_iou: 0.7963765263557434 angle_error: 8.997130268642021\n", "Predict: (280, 157) (609, 486) (444, 321) 329 0.78658146 0.61916196 (-0.27835842967033386)' 129 101 -50\n", "Ground Truth: (298, 156) (613, 471) (456, 314) 314.70360004296106 0.7922820074697675 0.6101550791722341 (-0.3484844941765156)' 124 96 -62\n", "dice: 0.9261193650263526 iou:0.8624043817021426 bbox_iou: 0.791488766670227 angle_error: 12.622691611112714\n", "Predict: (248, 29) (963, 744) (605, 386) 714 0.75275254 0.6559691 (0.2293442040681839)' 268 234 41\n", "Ground Truth: (284, 88) (911, 715) (598, 402) 626.8588264194739 0.74426965105505 0.6678792454616282 (0.36312674894097)' 233 209 65\n", "dice: 0.8741995031542936 iou:0.7765136945699046 bbox_iou: 0.7050318717956543 angle_error: 24.080858077101496\n", "Predict: (359, 147) (730, 519) (545, 333) 371 0.7975006 0.58820003 (0.1702369749546051)' 147 109 30\n", "Ground Truth: (367, 156) (744, 533) (556, 345) 376.5034517238853 0.793081706509776 0.6091152656103288 (0.12095775674984047)' 149 114 21\n", "dice: 0.9140196049372831 iou:0.8416538724757523 bbox_iou: 0.8196510672569275 angle_error: 8.870259276857634\n", "Predict: (57, 15) (590, 548) (324, 282) 532 0.74012774 0.67099404 (-0.39032840728759766)' 196 178 -70\n", "Ground Truth: (75, 43) (566, 534) (321, 289) 491.02887384755695 0.7440173469583385 0.6681603006951815 (-0.39177463869751117)' 182 164 -70\n", "dice: 0.9224069946402289 iou:0.8559882906183759 bbox_iou: 0.8527647852897644 angle_error: 0.2603216537844304\n", "Predict: (217, 72) (511, 365) (364, 219) 293 0.7998823 0.596612 (-0.19915854930877686)' 117 87 -35\n", "Ground Truth: (211, 93) (484, 366) (348, 230) 272.8617890288048 0.817021689967978 0.5766069355478386 (-0.2039718058490746)' 111 78 -36\n", "dice: 0.8723842711356937 iou:0.7736538687823445 bbox_iou: 0.7634676098823547 angle_error: 0.8663861772535952\n", "Predict: (348, 35) (948, 634) (648, 334) 599 0.79451674 0.6098003 (0.05367088317871094)' 237 182 9\n", "Ground Truth: (363, 45) (938, 620) (651, 333) 574.3808480268123 0.7478174132643596 0.6639044482593877 (0.0716197243913529)' 214 190 12\n", "dice: 0.9491137879243061 iou:0.903155619531473 bbox_iou: 0.8667422533035278 angle_error: 3.2307914182755546\n", "Predict: (422, 138) (722, 438) (572, 288) 299 0.7848081 0.62628055 (0.2995551526546478)' 117 93 53\n", "Ground Truth: (439, 151) (724, 436) (582, 294) 284.79065451661154 0.7632624053937171 0.64608861660967 (0.27209012149240586)' 108 92 48\n", "dice: 0.9257433998626559 iou:0.8617525828971394 bbox_iou: 0.8505498170852661 angle_error: 4.943705609203555\n", "Predict: (32, -133) (914, 747) (473, 306) 881 0.7861283 0.6177076 (0.049577102065086365)' 346 272 8\n", "Ground Truth: (47, -137) (934, 749) (491, 306) 887.4992454599609 0.7854158790171585 0.6189684135621982 (0.0512478916755903)' 348 274 9\n", "dice: 0.966238392387224 iou:0.934682024628985 bbox_iou: 0.9394572377204895 angle_error: 0.30074212989070936\n", "Predict: (121, 13) (605, 497) (363, 255) 484 0.807824 0.5869828 (0.1755020022392273)' 195 142 31\n", "Ground Truth: (137, 34) (586, 483) (362, 259) 449.2756930794276 0.8003860559098337 0.5994849134924919 (0.19639719977539885)' 179 134 35\n", "dice: 0.9281360753879465 iou:0.8659084927444243 bbox_iou: 0.8635936379432678 angle_error: 3.7611355565108795\n", "Predict: (54, -22) (517, 440) (286, 208) 462 0.73987746 0.6628686 (-0.35469502210617065)' 170 153 -63\n", "Ground Truth: (93, -7) (510, 409) (302, 201) 416.5648051624141 0.7270657440249045 0.6865678435999694 (-0.37840562347779194)' 151 143 -68\n", "dice: 0.9018167298261918 iou:0.8211896450430011 bbox_iou: 0.8086912035942078 angle_error: 4.267908246891835\n", "Predict: (189, 12) (442, 265) (316, 139) 252 0.77754784 0.63434154 (0.3814641535282135)' 97 79 68\n", "Ground Truth: (213, 41) (434, 262) (324, 152) 220.12006746319156 0.7827091913317276 0.6223875977273591 (0.4481154885520421)' 86 68 80\n", "dice: 0.8552450647084985 iou:0.747098822937783 bbox_iou: 0.7123991847038269 angle_error: 11.99724030428915\n", "Predict: (118, 100) (830, 812) (474, 456) 711 0.74867725 0.6444257 (-0.2568390965461731)' 266 229 -46\n", "Ground Truth: (104, 99) (821, 816) (463, 458) 717.6933193391171 0.7663440430328425 0.6424303913328484 (-0.24598871082533502)' 275 230 -44\n", "dice: 0.9705652926470631 iou:0.9428138430874854 bbox_iou: 0.9331664443016052 angle_error: 1.9530694297508546\n", "Predict: (311, 168) (709, 566) (510, 367) 397 0.7701358 0.6344194 (0.523970901966095)' 152 125 94\n", "Ground Truth: (332, 183) (695, 546) (514, 365) 363.5927480079876 0.7744983956440561 0.6325758730340442 (-0.4659408421783344)' 140 115 -83\n", "dice: 0.9173443443879853 iou:0.8473094281019753 bbox_iou: 0.8410828709602356 angle_error: 1.8158860540027035\n", "Predict: (75, 89) (664, 678) (369, 384) 589 0.7781471 0.6216301 (0.13259543478488922)' 229 183 23\n", "Ground Truth: (100, 108) (665, 673) (383, 391) 564.9629268474171 0.7505519032304954 0.6608115015320793 (0.16552114081557115)' 212 186 29\n", "dice: 0.9516872421193787 iou:0.9078275876785179 bbox_iou: 0.8634911775588989 angle_error: 5.926627085522746\n", "Predict: (146, 172) (469, 495) (307, 333) 322 0.77137524 0.6342135 (0.4063737690448761)' 124 102 73\n", "Ground Truth: (165, 190) (450, 475) (308, 333) 285.30660869317416 0.7582789651839421 0.6519302193943536 (0.4226506976573389)' 108 93 76\n", "dice: 0.8861083660334718 iou:0.7955067970822903 bbox_iou: 0.7933064103126526 angle_error: 2.9298471502433046\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (66, 118) (598, 649) (332, 383) 531 0.74811006 0.65024716 (-0.2751891016960144)' 198 172 -49\n", "Ground Truth: (62, 109) (599, 646) (331, 378) 536.8578938080356 0.7646120225379015 0.6444908494234032 (-0.31060561772064466)' 205 173 -55\n", "dice: 0.9749228165897529 iou:0.9510725946960991 bbox_iou: 0.8956248760223389 angle_error: 6.374972884433447\n", "Predict: (217, 169) (509, 460) (363, 315) 291 0.7654706 0.6282317 (0.49475035071372986)' 111 91 89\n", "Ground Truth: (234, 185) (495, 446) (365, 316) 260.83711392361323 0.7897649107570159 0.613409639422925 (0.5)' 103 80 90\n", "dice: 0.8991056070004978 iou:0.8167046836806847 bbox_iou: 0.8093807101249695 angle_error: 0.9449368715286255\n", "Predict: (202, 38) (919, 755) (561, 397) 717 0.76022977 0.64471346 (0.2446695864200592)' 272 231 44\n", "Ground Truth: (205, 34) (930, 759) (568, 397) 725.9452845772882 0.7566162514848905 0.6538591958433809 (0.23586762566218888)' 274 237 42\n", "dice: 0.979462435562846 iou:0.9597514777450042 bbox_iou: 0.9497773051261902 angle_error: 1.584352936416657\n", "Predict: (319, 108) (709, 498) (514, 303) 390 0.7751344 0.63200516 (0.45185160636901855)' 151 123 81\n", "Ground Truth: (347, 136) (688, 477) (518, 307) 341.1749470051985 0.762072368684323 0.6474918570050632 (0.47103380035727505)' 130 110 84\n", "dice: 0.8708528980764709 iou:0.7712484020841502 bbox_iou: 0.7708291411399841 angle_error: 3.452794917886166\n", "Predict: (150, -79) (936, 706) (543, 313) 785 0.7944376 0.6104596 (0.085422083735466)' 311 239 15\n", "Ground Truth: (180, -67) (925, 677) (553, 305) 745.5471713285485 0.7651749237857453 0.6438224413683317 (0.08116902097686662)' 285 240 14\n", "dice: 0.9440119986794305 iou:0.8939609138540335 bbox_iou: 0.8777704238891602 angle_error: 0.7655512965478888\n", "Predict: (244, 59) (617, 433) (431, 246) 373 0.7959139 0.59418863 (0.24866341054439545)' 148 110 44\n", "Ground Truth: (264, 86) (605, 427) (435, 257) 340.85804276267265 0.7638194419290122 0.6454299808122121 (0.2603774868983408)' 130 110 46\n", "dice: 0.9191452407324436 iou:0.8503873742993041 bbox_iou: 0.8404366374015808 angle_error: 2.1085337437101614\n", "Predict: (249, -15) (921, 657) (585, 321) 672 0.7957943 0.6076946 (0.05563439428806305)' 267 204 10\n", "Ground Truth: (243, -12) (916, 660) (580, 324) 673.6480802110252 0.7738521274145065 0.6333663117786126 (0.07575775291174218)' 260 213 13\n", "dice: 0.9730457675476172 iou:0.9475064582225527 bbox_iou: 0.9198808073997498 angle_error: 3.622204552262243\n", "Predict: (316, 96) (673, 453) (494, 275) 357 0.7888158 0.6020935 (0.2425403743982315)' 140 107 43\n", "Ground Truth: (328, 105) (667, 444) (498, 275) 338.3836733886551 0.764824134120523 0.6442391200996662 (0.24828171122335674)' 129 109 44\n", "dice: 0.9564213688877299 iou:0.916482323778855 bbox_iou: 0.8964333534240723 angle_error: 1.0334406285225413\n", "Predict: (29, 15) (388, 374) (208, 194) 359 0.7875604 0.60828114 (0.06601690500974655)' 141 109 11\n", "Ground Truth: (42, 26) (391, 375) (217, 201) 349.85819804029177 0.8184916106125417 0.5745184795608295 (0.06716338598477983)' 143 100 12\n", "dice: 0.9297879142140266 iou:0.8687884640465278 bbox_iou: 0.8592464923858643 angle_error: 0.20636657550599047\n", "Predict: (151, 124) (347, 319) (249, 221) 195 0.7736039 0.6316838 (0.32025471329689026)' 75 61 57\n", "Ground Truth: (151, 121) (344, 314) (248, 218) 193.6285668593351 0.7643500254149854 0.6448015498183227 (0.30423941999696874)' 74 62 54\n", "dice: 0.9705428479919952 iou:0.9427714850481203 bbox_iou: 0.92903071641922 angle_error: 2.8827527939858726\n", "Predict: (85, -93) (987, 808) (536, 357) 902 0.78916025 0.6142158 (-0.05773910880088806)' 355 277 -10\n", "Ground Truth: (93, -80) (974, 800) (534, 360) 881.6251489176111 0.7760645222518932 0.6306535160466014 (-0.06939155518806638)' 342 278 -12\n", "dice: 0.9793964168663595 iou:0.95962470938936 bbox_iou: 0.9398482441902161 angle_error: 2.0974403496920964\n", "Predict: (195, 204) (636, 645) (416, 424) 440 0.79834336 0.6025852 (-0.2316369116306305)' 175 132 -41\n", "Ground Truth: (211, 224) (614, 627) (413, 426) 403.9013027956211 0.7568185541473178 0.6536250271358672 (-0.2616507264430759)' 152 132 -47\n", "dice: 0.9292419619126056 iou:0.8678356163194745 bbox_iou: 0.8297114968299866 angle_error: 5.402486666240172\n", "Predict: (66, -73) (842, 702) (454, 314) 775 0.7973426 0.6055402 (0.04494442045688629)' 308 234 8\n", "Ground Truth: (70, -59) (829, 699) (450, 320) 759.0658461451154 0.783162624189985 0.6218169377492513 (0.05366470837559034)' 297 236 9\n", "dice: 0.9731358620164157 iou:0.947677327525847 bbox_iou: 0.9356902241706848 angle_error: 1.56965182536673\n", "Predict: (339, 138) (764, 564) (551, 351) 425 0.79279166 0.61093324 (0.21899466216564178)' 168 129 39\n", "Ground Truth: (340, 135) (767, 562) (554, 349) 426.61942771045955 0.7969632368236945 0.6040278132267584 (0.21313796135366947)' 170 128 38\n", "dice: 0.9816710410482888 iou:0.9640018899775188 bbox_iou: 0.9629238247871399 angle_error: 1.0542061461550176\n", "Predict: (119, 16) (772, 669) (445, 342) 653 0.74911886 0.65031034 (-0.27444973587989807)' 244 212 -49\n", "Ground Truth: (118, 36) (759, 677) (439, 357) 641.1894244948212 0.7609638920423911 0.6487942316387346 (-0.25267321843519464)' 243 208 -45\n", "dice: 0.959922968306708 iou:0.9229344933652757 bbox_iou: 0.90081387758255 angle_error: 3.9197731400466154\n", "Predict: (303, 77) (649, 423) (476, 250) 345 0.7681514 0.6383216 (-0.48967570066452026)' 132 110 -88\n", "Ground Truth: (312, 98) (641, 427) (477, 263) 328.45672694587944 0.737004238734581 0.6758881209839841 (-0.49204225284540526)' 121 111 -88\n", "dice: 0.934216475163417 iou:0.8765536841139112 bbox_iou: 0.891785740852356 angle_error: 0.42597939255929873\n", "Predict: (-68, -54) (826, 841) (378, 393) 895 0.7815771 0.6249229 (-0.11505039781332016)' 349 279 -20\n", "Ground Truth: (-103, -43) (817, 877) (357, 417) 921.1313399727534 0.7562634879197636 0.6542671754256311 (-0.09772113505842374)' 348 301 -17\n", "dice: 0.9468445515144877 iou:0.8990548858443403 bbox_iou: 0.855492889881134 angle_error: 3.1192672958813574\n", "Predict: (255, 90) (711, 546) (483, 318) 456 0.7722404 0.6302111 (-0.37594661116600037)' 176 143 -67\n", "Ground Truth: (258, 107) (685, 534) (472, 321) 426.60823456656345 0.7755077684708394 0.6313380243905629 (-0.38445351131528394)' 165 134 -69\n", "dice: 0.934199064518809 iou:0.8765230292249968 bbox_iou: 0.8738489747047424 angle_error: 1.5312420268710412\n", "Predict: (5, -29) (780, 745) (392, 357) 775 0.73304546 0.6809684 (-0.5584519505500793)' 284 263 -100\n", "Ground Truth: (-2, -29) (770, 743) (384, 357) 772.1831930675518 0.723157412662243 0.690683253388737 (0.48026478705660497)' 279 266 86\n", "dice: 0.97829633592635 iou:0.957514757288596 bbox_iou: 0.8929212689399719 angle_error: -6.969012769203175\n", "Predict: (147, 59) (545, 457) (346, 258) 397 0.7823708 0.6118479 (0.23982954025268555)' 155 121 43\n", "Ground Truth: (158, 66) (549, 457) (354, 262) 391.4190097785237 0.7564681137166531 0.6540305749198341 (0.24382537281678368)' 148 128 43\n", "dice: 0.9525935872962402 iou:0.9094784753486748 bbox_iou: 0.8992336392402649 angle_error: 0.7192498615376621\n", "Predict: (107, -90) (872, 673) (489, 291) 764 0.77956283 0.6229199 (-0.09369087219238281)' 297 237 -16\n", "Ground Truth: (93, -95) (878, 689) (486, 297) 785.6559616015142 0.7857128694621875 0.6185913730092714 (-0.11713803811563497)' 308 243 -21\n", "dice: 0.9694109826364979 iou:0.9406377967392741 bbox_iou: 0.9150086045265198 angle_error: 4.220489866185389\n", "Predict: (255, 181) (591, 517) (423, 349) 335 0.77181506 0.6471447 (-0.36736777424812317)' 129 108 -66\n", "Ground Truth: (272, 184) (621, 533) (447, 359) 348.44781598397196 0.7659913127774558 0.6428509226480663 (-0.4004986680139439)' 133 112 -72\n", "dice: 0.8494343096770923 iou:0.7382753690827488 bbox_iou: 0.7604355812072754 angle_error: 5.963560877847726\n", "Predict: (40, -34) (836, 761) (438, 363) 795 0.79367834 0.6099635 (0.01960078440606594)' 315 242 3\n", "Ground Truth: (50, -34) (831, 746) (441, 356) 780.8738694565211 0.7786148618638766 0.6275021090679269 (0.0)' 304 245 0\n", "dice: 0.9716919108703632 iou:0.9449423972661797 bbox_iou: 0.9226313829421997 angle_error: 3.5281411930918694\n", "Predict: (107, 137) (544, 574) (326, 355) 436 0.8105545 0.59054536 (-0.14002996683120728)' 176 128 -25\n", "Ground Truth: (130, 145) (539, 554) (335, 350) 409.49294609309203 0.7882187057909005 0.6153952159720759 (-0.12541409515641352)' 161 126 -22\n", "dice: 0.9471543211458783 iou:0.899613628254357 bbox_iou: 0.8843107223510742 angle_error: 2.6308569014628738\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (3, -36) (608, 569) (306, 266) 605 0.76675683 0.6350433 (-0.21312187612056732)' 231 192 -38\n", "Ground Truth: (2, -48) (617, 566) (310, 259) 615.7204883256687 0.8088085575229329 0.5880720340890838 (-0.22147884958918318)' 249 181 -39\n", "dice: 0.9543172066902662 iou:0.912625906054854 bbox_iou: 0.881934642791748 angle_error: 1.5042552243508567\n", "Predict: (188, 33) (509, 355) (348, 194) 321 0.7708913 0.6341141 (-0.3872835636138916)' 123 101 -69\n", "Ground Truth: (180, 22) (517, 359) (349, 191) 336.9100400759823 0.7772045022491649 0.6292480923162406 (-0.37522252461595407)' 130 106 -67\n", "dice: 0.9487225858201948 iou:0.9024474158996152 bbox_iou: 0.9071900248527527 angle_error: 2.170987019628754\n", "Predict: (161, -3) (776, 611) (468, 303) 614 0.795826 0.6097347 (0.06322173774242401)' 244 187 11\n", "Ground Truth: (127, -41) (796, 627) (462, 293) 668.4184321216762 0.7779558058407062 0.6283189987249611 (0.09867606471697511)' 260 209 17\n", "dice: 0.9131966484412467 iou:0.8402593230242527 bbox_iou: 0.8273614645004272 angle_error: 6.381778855419199\n", "Predict: (364, 176) (695, 507) (529, 341) 331 0.76407206 0.6370858 (0.3039180338382721)' 126 105 54\n", "Ground Truth: (358, 160) (705, 507) (532, 334) 347.1257724859968 0.7720544576125106 0.6355563818267047 (0.25006190815098917)' 134 110 45\n", "dice: 0.9329308959956443 iou:0.8742928574116378 bbox_iou: 0.836314857006073 angle_error: 9.694102623710926\n", "Predict: (144, 25) (761, 642) (453, 334) 616 0.74736106 0.653577 (0.3172265887260437)' 230 201 57\n", "Ground Truth: (140, 28) (787, 675) (464, 352) 646.1267136282171 0.7235546683634029 0.6902670801143033 (0.32683942191601784)' 233 223 58\n", "dice: 0.9153450610384525 iou:0.8439043866935296 bbox_iou: 0.827418863773346 angle_error: 1.7303099741953432\n", "Predict: (268, 87) (593, 412) (430, 249) 325 0.7539216 0.6404146 (-0.3497295379638672)' 122 104 -62\n", "Ground Truth: (288, 94) (599, 405) (444, 250) 310.522893300961 0.7703393378090094 0.6376341463754719 (-0.46785070149543717)' 119 99 -84\n", "dice: 0.9135674885466823 iou:0.8408874724529422 bbox_iou: 0.7453278303146362 angle_error: 21.261809435682594\n", "Predict: (84, 18) (689, 622) (387, 320) 604 0.8006177 0.6091683 (-0.04035753756761551)' 241 183 -7\n", "Ground Truth: (79, 7) (698, 626) (389, 317) 619.5903652704745 0.766403137648384 0.6423598918073201 (-0.03488442509074663)' 237 199 -6\n", "dice: 0.9597028834986402 iou:0.9225276781754742 bbox_iou: 0.9089096784591675 angle_error: 0.9851602458363988\n", "Predict: (318, 176) (620, 478) (469, 327) 301 0.80726415 0.58371097 (0.12382136285305023)' 121 87 22\n", "Ground Truth: (319, 177) (614, 472) (467, 325) 294.24490632804503 0.8209827759284325 0.5709529592084144 (0.07976611904266118)' 120 84 14\n", "dice: 0.9634123918362119 iou:0.9294075910697033 bbox_iou: 0.8714200854301453 angle_error: 7.9299438858700295\n", "Predict: (226, 96) (750, 620) (488, 358) 523 0.78976136 0.6167948 (0.05301038920879364)' 206 161 9\n", "Ground Truth: (195, 78) (748, 631) (472, 355) 553.8015328111687 0.7808067951798912 0.6247725574966522 (0.08804217608343967)' 216 173 15\n", "dice: 0.9292036591252522 iou:0.867768803137834 bbox_iou: 0.8490713238716125 angle_error: 6.305721637436285\n", "Predict: (405, 260) (687, 542) (546, 401) 281 0.7672087 0.63889575 (0.355411559343338)' 107 89 63\n", "Ground Truth: (419, 264) (690, 535) (555, 400) 270.92416374328815 0.7786311751796485 0.6274818667008308 (0.32537753487456933)' 105 85 58\n", "dice: 0.9320248921336208 iou:0.872702823566401 bbox_iou: 0.8622872829437256 angle_error: 5.40612440437836\n", "Predict: (254, -21) (979, 703) (616, 340) 725 0.7957965 0.6126102 (-0.06197497993707657)' 288 222 -11\n", "Ground Truth: (244, -28) (987, 714) (616, 343) 742.5041644839441 0.7719471855061821 0.6356866702936944 (-0.09453803619658582)' 286 236 -17\n", "dice: 0.9675671887866614 iou:0.9371720641554915 bbox_iou: 0.8956436514854431 angle_error: 5.861350126711667\n", "Predict: (325, 191) (712, 577) (519, 384) 386 0.7815277 0.6268504 (-0.2699965834617615)' 150 120 -48\n", "Ground Truth: (324, 197) (705, 578) (515, 388) 380.5004028171324 0.8044984912857418 0.5939546931534132 (-0.22950142793851308)' 153 113 -41\n", "dice: 0.9578882220705282 iou:0.9191799213455931 bbox_iou: 0.8708956837654114 angle_error: 7.28912799418471\n", "Predict: (122, 123) (681, 682) (402, 403) 559 0.7659174 0.635398 (-0.19956755638122559)' 214 177 -35\n", "Ground Truth: (120, 124) (683, 687) (402, 406) 563.9782136075826 0.7484899058418603 0.6631461836223165 (-0.18869293131225284)' 211 187 -33\n", "dice: 0.9726129643052924 iou:0.9466860399378334 bbox_iou: 0.933479368686676 angle_error: 1.957432512415096\n", "Predict: (293, 188) (601, 496) (447, 342) 308 0.77643037 0.6445529 (-0.35369226336479187)' 119 99 -63\n", "Ground Truth: (302, 199) (589, 486) (446, 343) 287.6454457070371 0.756855369167675 0.6535823973777614 (-0.4411126710559987)' 108 94 -79\n", "dice: 0.9261114949138862 iou:0.8623907328625541 bbox_iou: 0.7834776043891907 angle_error: 15.735673384417225\n", "Predict: (-56, -99) (722, 679) (332, 290) 779 0.7757868 0.6276383 (-0.1676848977804184)' 302 244 -30\n", "Ground Truth: (-10, -61) (706, 655) (348, 297) 717.4535835271854 0.7697306316110616 0.6383688234552468 (-0.16927602825504162)' 276 229 -30\n", "dice: 0.9287332675825544 iou:0.8669486687846202 bbox_iou: 0.8559948801994324 angle_error: 0.2864034854321815\n", "Predict: (251, 84) (630, 462) (440, 273) 378 0.8303341 0.55571425 (0.06290210783481598)' 156 105 11\n", "Ground Truth: (237, 75) (636, 474) (437, 275) 398.10032918851 0.8239129082575669 0.5667164366828954 (0.07098310461898533)' 164 112 12\n", "dice: 0.942823060096085 iou:0.8918308984130666 bbox_iou: 0.8909555673599243 angle_error: 1.4545794211504823\n", "Predict: (112, -14) (802, 675) (457, 330) 689 0.76295286 0.6340747 (-0.21580491960048676)' 262 218 -38\n", "Ground Truth: (97, -14) (784, 672) (441, 329) 686.9929839525292 0.7505171261481582 0.6608509993624201 (-0.20524504539380972)' 257 227 -36\n", "dice: 0.9572012251525369 iou:0.9179155636163391 bbox_iou: 0.9068491458892822 angle_error: 1.9007773572018678\n", "Predict: (354, 77) (695, 418) (524, 247) 341 0.7788024 0.628562 (0.5202339291572571)' 132 107 93\n", "Ground Truth: (373, 83) (690, 400) (532, 242) 316.05104033367775 0.7844808855501193 0.6201529974179764 (-0.44334084025928533)' 123 98 -79\n", "dice: 0.9126115140682772 iou:0.8392690614949 bbox_iou: 0.8262650370597839 angle_error: 6.5565415050223805\n", "Predict: (99, 35) (958, 893) (528, 464) 858 0.7684695 0.6382369 (-0.20514653623104095)' 329 273 -36\n", "Ground Truth: (89, 29) (942, 882) (516, 456) 852.6432812120202 0.7437037433739184 0.6685093433091424 (-0.21090979215038286)' 317 285 -37\n", "dice: 0.9626017407084135 iou:0.9278998996641369 bbox_iou: 0.916157603263855 angle_error: 1.0373860654815417\n", "Predict: (386, 164) (863, 642) (625, 403) 477 0.81951994 0.57087034 (-0.0145833445712924)' 195 136 -2\n", "Ground Truth: (395, 181) (842, 628) (619, 405) 447.50808533030994 0.8106982016475038 0.5854642822969675 (0.021197099984843537)' 181 131 3\n", "dice: 0.9443102936779726 iou:0.894496070221149 bbox_iou: 0.8577283024787903 angle_error: 6.440480020104468\n", "Predict: (108, -120) (935, 706) (521, 292) 827 0.7860185 0.6176465 (0.08380137383937836)' 325 255 15\n", "Ground Truth: (104, -119) (921, 697) (513, 289) 817.065514935002 0.7652213789119932 0.6437672259878005 (0.060478878374920234)' 312 263 10\n", "dice: 0.971628446267944 iou:0.9448223676956193 bbox_iou: 0.9121484160423279 angle_error: 4.198049183602462\n", "Predict: (191, 72) (631, 512) (411, 292) 439 0.8170068 0.57648796 (-0.12322302907705307)' 179 126 -22\n", "Ground Truth: (185, 56) (642, 513) (414, 285) 457.72943610390627 0.8083377882562236 0.5887189652771827 (-0.13369015219719207)' 185 134 -24\n", "dice: 0.9531527069884068 iou:0.9104983251629338 bbox_iou: 0.9034886360168457 angle_error: 1.8840821616250203\n", "Predict: (34, -55) (891, 800) (463, 372) 856 0.7748158 0.6324888 (-0.1677471548318863)' 331 270 -30\n", "Ground Truth: (42, -59) (899, 797) (471, 369) 857.6929129729358 0.7862277859596569 0.6179367836477903 (-0.17029578910832802)' 337 265 -30\n", "dice: 0.9806429361120775 iou:0.962021033504996 bbox_iou: 0.95501708984375 angle_error: 0.4587541697595121\n", "Predict: (144, 215) (598, 669) (371, 442) 454 0.77567464 0.6320358 (-0.37004244327545166)' 176 143 -66\n", "Ground Truth: (141, 212) (604, 675) (373, 444) 463.0371043447814 0.7653814276967978 0.6435769341242826 (-0.38610989194093814)' 177 149 -69\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9766701492854362 iou:0.9544040453852228 bbox_iou: 0.9375817179679871 angle_error: 2.8921407597875657\n", "Predict: (298, 26) (841, 568) (569, 297) 542 0.8019967 0.6071141 (0.00710490345954895)' 217 164 1\n", "Ground Truth: (301, 13) (872, 584) (587, 299) 571.2495724777393 0.7965870294243985 0.6045238659910894 (0.013050705333535419)' 227 172 2\n", "dice: 0.9393926516059379 iou:0.8857119960825617 bbox_iou: 0.8793348670005798 angle_error: 1.0702443373175643\n", "Predict: (344, 116) (644, 417) (494, 266) 300 0.7769712 0.619236 (0.24960164725780487)' 116 92 44\n", "Ground Truth: (363, 126) (644, 407) (504, 267) 280.43399697611557 0.7707767329594121 0.637105350729701 (0.23459438611745373)' 108 89 42\n", "dice: 0.9259011495315886 iou:0.8620260129017043 bbox_iou: 0.8588590025901794 angle_error: 2.701307005263203\n", "Predict: (148, 33) (811, 695) (479, 364) 662 0.7801962 0.6241241 (0.15851059556007385)' 258 206 28\n", "Ground Truth: (119, 34) (742, 657) (431, 346) 622.9675892179304 0.7639305932391233 0.6452984183409418 (0.15297739286493295)' 237 201 27\n", "dice: 0.8670178432278748 iou:0.765252866557065 bbox_iou: 0.7875639200210571 angle_error: 0.995976485125361\n", "Predict: (362, 276) (656, 571) (509, 423) 294 0.77113575 0.6342334 (0.39100775122642517)' 113 93 70\n", "Ground Truth: (361, 279) (656, 574) (509, 427) 295.47315465876085 0.7422603256572946 0.6701116391730014 (0.44442543230730713)' 109 99 79\n", "dice: 0.9604305346161761 iou:0.9238733596908518 bbox_iou: 0.8520157933235168 angle_error: 9.615182594558746\n", "Predict: (23, -68) (752, 660) (387, 295) 728 0.7846071 0.6165676 (0.04067191481590271)' 285 224 7\n", "Ground Truth: (37, -66) (750, 646) (394, 290) 712.0562962125958 0.781786500535058 0.623546203244914 (0.04138028520389279)' 278 222 7\n", "dice: 0.9746718310467357 iou:0.9505950002736758 bbox_iou: 0.9495512843132019 angle_error: 0.12750666983821368\n", "Predict: (99, 118) (477, 495) (288, 306) 377 0.82010907 0.58087164 (-0.146791473031044)' 154 109 -26\n", "Ground Truth: (118, 117) (491, 490) (305, 304) 372.5913830511919 0.8402824494655163 0.5421488772654905 (-0.14292113889652203)' 156 101 -25\n", "dice: 0.9148803854757486 iou:0.8431147803708804 bbox_iou: 0.8352087736129761 angle_error: 0.6966601442139577\n", "Predict: (12, -105) (891, 774) (451, 334) 879 0.7752786 0.63071626 (-0.1459917575120926)' 340 277 -26\n", "Ground Truth: (23, -95) (862, 743) (443, 324) 838.8207225289561 0.7652266840341942 0.6437609199400283 (-0.16934085944977664)' 320 270 -30\n", "dice: 0.9513357939124647 iou:0.9071882003695029 bbox_iou: 0.8899767398834229 angle_error: 4.2028383487831285\n", "Predict: (133, 195) (563, 625) (348, 410) 429 0.7762334 0.6267568 (-0.345747172832489)' 166 134 -62\n", "Ground Truth: (152, 200) (553, 601) (353, 401) 400.71926115424003 0.7637566487781035 0.6455042846079649 (-0.37465073603832166)' 153 129 -67\n", "dice: 0.9404424190502444 iou:0.887580284411971 bbox_iou: 0.8613153696060181 angle_error: 5.202641377049872\n", "Predict: (358, 173) (683, 498) (521, 336) 324 0.7627682 0.6314067 (0.15787622332572937)' 123 102 28\n", "Ground Truth: (364, 166) (691, 493) (528, 330) 327.4941636670797 0.7462545202742705 0.6656607176108701 (0.38286196188436505)' 122 109 68\n", "dice: 0.9221401671489533 iou:0.8555288350525139 bbox_iou: 0.6946682333946228 angle_error: 40.49743294055442\n", "Predict: (426, 181) (631, 387) (529, 284) 205 0.799972 0.62559825 (-0.05211067199707031)' 81 64 -9\n", "Ground Truth: (446, 198) (617, 369) (532, 284) 171.37987114010792 0.7700670978571776 0.637962902368017 (-0.3350506477620613)' 65 54 -60\n", "dice: 0.8088548622986332 iou:0.6790565118366139 bbox_iou: 0.6112858057022095 angle_error: 50.92919563769838\n", "Predict: (51, -86) (894, 756) (473, 334) 842 0.74507827 0.6600289 (-0.31268802285194397)' 313 277 -56\n", "Ground Truth: (59, -66) (876, 750) (468, 342) 816.203622055183 0.7604916018836704 0.6493477677365257 (-0.3141070264686662)' 310 265 -56\n", "dice: 0.9672666803179226 iou:0.9366083788365533 bbox_iou: 0.9257055521011353 angle_error: 0.2554206510099988\n", "Predict: (265, 0) (725, 460) (495, 229) 460 0.75984496 0.6541666 (0.5108499526977539)' 174 150 91\n", "Ground Truth: (274, 2) (715, 443) (495, 223) 441.7050481984556 0.7582027902237839 0.6520188102323957 (-0.4579830950237396)' 167 144 -82\n", "dice: 0.9583586011118886 iou:0.9200465746991987 bbox_iou: 0.8881336450576782 angle_error: 5.610051410131177\n", "Predict: (230, 110) (677, 557) (453, 333) 447 0.7653337 0.6405133 (-0.1986992359161377)' 171 143 -35\n", "Ground Truth: (228, 108) (683, 563) (456, 336) 455.1369746043492 0.7545772353444786 0.6562112433507089 (-0.26215768382597315)' 171 149 -47\n", "dice: 0.9686653427545401 iou:0.9392347439789327 bbox_iou: 0.841482937335968 angle_error: 11.422520623770382\n", "Predict: (381, 184) (576, 379) (479, 281) 195 0.77641255 0.6189429 (0.4432339668273926)' 75 60 79\n", "Ground Truth: (373, 167) (588, 382) (481, 275) 214.2308490297324 0.7726524949589645 0.6348292069790799 (0.484721125463178)' 82 68 87\n", "dice: 0.8944114889496062 iou:0.8089913019264705 bbox_iou: 0.7955971956253052 angle_error: 7.467688554441381\n", "Predict: (154, 12) (947, 805) (551, 408) 793 0.7649733 0.64303845 (0.24666284024715424)' 303 254 44\n", "Ground Truth: (158, 4) (981, 827) (570, 416) 822.2772884155321 0.7389696986169638 0.6737386618904643 (0.20842814425564773)' 303 277 37\n", "dice: 0.9356653103425576 iou:0.8791081597121512 bbox_iou: 0.8560349941253662 angle_error: 6.882245278471167\n", "Predict: (265, 93) (701, 528) (483, 311) 435 0.77512074 0.6344911 (0.4073895812034607)' 168 138 73\n", "Ground Truth: (284, 119) (681, 516) (483, 318) 396.6502156812725 0.7804357284120231 0.6252360144921234 (0.42965351515338224)' 154 124 77\n", "dice: 0.9037457587087397 iou:0.8243943098858454 bbox_iou: 0.8247442245483398 angle_error: 4.0075081109858814\n", "Predict: (181, 47) (790, 655) (486, 351) 608 0.78485966 0.6177646 (0.10854674875736237)' 238 187 19\n", "Ground Truth: (171, 34) (798, 661) (485, 348) 626.5868247705181 0.7926690769182758 0.6096521422069545 (0.16934085944977664)' 248 191 30\n", "dice: 0.961842818769387 iou:0.926490550909869 bbox_iou: 0.841993510723114 angle_error: 10.942939924634569\n", "Predict: (234, 151) (557, 475) (395, 313) 323 0.81422573 0.5678633 (0.05152473598718643)' 131 91 9\n", "Ground Truth: (235, 143) (552, 460) (394, 302) 317.4011955175973 0.8599400501781268 0.5103950529733227 (0.046791553269017225)' 136 81 8\n", "dice: 0.9170890258763374 iou:0.8468738869494647 bbox_iou: 0.8498887419700623 angle_error: 0.8519728892704581\n", "Predict: (227, -22) (878, 628) (552, 302) 651 0.7466178 0.65732455 (0.21949636936187744)' 243 213 39\n", "Ground Truth: (203, 6) (882, 685) (543, 346) 679.1383450490775 0.7489696373472218 0.6626043180752531 (-0.44843379843822595)' 254 225 -80\n", "dice: 0.875223637026859 iou:0.7781312497653892 bbox_iou: 0.6821638345718384 angle_error: 59.77256979598138\n", "Predict: (368, 65) (675, 372) (522, 218) 307 0.78306675 0.6277086 (0.3037581145763397)' 120 96 54\n", "Ground Truth: (367, 74) (672, 379) (520, 227) 305.7377641312895 0.7728976519188734 0.6345307082074846 (0.3112422374930121)' 118 97 56\n", "dice: 0.9436005991912465 iou:0.893223338132196 bbox_iou: 0.8861779570579529 angle_error: 1.347142125001028\n", "Predict: (275, 161) (902, 787) (589, 474) 626 0.77883506 0.62590104 (-0.12637123465538025)' 243 195 -22\n", "Ground Truth: (261, 141) (924, 804) (593, 473) 662.5916920728783 0.7582316621391343 0.6519852349016239 (-0.13178029288008933)' 251 216 -23\n", "dice: 0.932864332253521 iou:0.8741759463663087 bbox_iou: 0.8801736235618591 angle_error: 0.9736304804476354\n", "Predict: (387, 378) (684, 674) (535, 526) 296 0.7573241 0.65504044 (-0.3432712256908417)' 112 96 -61\n", "Ground Truth: (384, 376) (693, 685) (539, 531) 308.22076503701044 0.8117558204423915 0.5839969931240226 (-0.2823408690450223)' 125 90 -50\n", "dice: 0.92638468259923 iou:0.8628646290572806 bbox_iou: 0.8033172488212585 angle_error: 10.967464196247484\n", "Predict: (230, 80) (895, 745) (563, 412) 665 0.74558383 0.6580435 (-0.2979554235935211)' 247 218 -53\n", "Ground Truth: (230, 70) (907, 747) (569, 409) 677.288963843351 0.7339732766042475 0.6791783486175225 (-0.3064675892002552)' 248 230 -55\n", "dice: 0.9657466407615046 iou:0.933762150381188 bbox_iou: 0.9297085404396057 angle_error: 1.5321898092121344\n", "Predict: (404, 147) (757, 499) (580, 323) 352 0.7731929 0.6272571 (0.4936179518699646)' 136 110 88\n", "Ground Truth: (410, 152) (751, 493) (581, 323) 341.9818160370519 0.7800122330805421 0.6257642657140994 (0.4761267585362157)' 133 107 85\n", "dice: 0.975061452892345 iou:0.951336502704419 bbox_iou: 0.9358493685722351 angle_error: 3.1484148000748036\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (202, 28) (924, 750) (563, 389) 722 0.7976516 0.60743666 (0.0657038539648056)' 287 219 11\n", "Ground Truth: (167, 1) (940, 774) (554, 388) 772.3360371263275 0.7643538196100528 0.6447970521392936 (0.059712596213082225)' 295 249 10\n", "dice: 0.9220194921930484 iou:0.8553211171404287 bbox_iou: 0.8533220887184143 angle_error: 1.078426395310208\n", "Predict: (464, 254) (839, 630) (651, 442) 375 0.7713519 0.62632114 (0.28649476170539856)' 144 117 51\n", "Ground Truth: (471, 257) (838, 624) (655, 441) 366.9220381988523 0.7564168163962525 0.6540899019805747 (0.2662957123463624)' 138 120 47\n", "dice: 0.9686786697698797 iou:0.9392598032988777 bbox_iou: 0.9167720675468445 angle_error: 3.6358288846265054\n", "Predict: (307, -54) (1072, 710) (690, 328) 765 0.7953894 0.6102065 (0.03268234431743622)' 304 233 5\n", "Ground Truth: (328, -33) (1063, 701) (696, 334) 735.6247774076129 0.7715292054192389 0.6361939053348106 (0.02342526918813015)' 283 234 4\n", "dice: 0.9633416162440971 iou:0.9292758649708955 bbox_iou: 0.9107886552810669 angle_error: 1.666273523275093\n", "Predict: (416, 92) (810, 486) (613, 289) 394 0.7927754 0.60130084 (0.231533944606781)' 156 118 41\n", "Ground Truth: (411, 90) (816, 495) (614, 293) 405.25506153038975 0.8233432020317377 0.5675438059464002 (0.2215436807839183)' 166 115 39\n", "dice: 0.9650386254409187 iou:0.9324392669746236 bbox_iou: 0.9054191708564758 angle_error: 1.7982474881152868\n", "Predict: (91, -12) (739, 635) (415, 311) 648 0.79151684 0.62196565 (-0.12867552042007446)' 256 201 -23\n", "Ground Truth: (62, -9) (737, 665) (400, 328) 674.3446963119084 0.7703211767528018 0.6376560864966153 (-0.13114367310772176)' 259 215 -23\n", "dice: 0.9310293751427572 iou:0.870958802321713 bbox_iou: 0.8672816157341003 angle_error: 0.4442674837765139\n", "Predict: (314, 96) (653, 435) (483, 265) 338 0.777394 0.63027066 (-0.3231460452079773)' 131 106 -58\n", "Ground Truth: (318, 98) (647, 427) (483, 263) 328.9079238996835 0.8075269116380676 0.589830727395822 (-0.29405350363908733)' 132 97 -52\n", "dice: 0.954867277479237 iou:0.9136325529796695 bbox_iou: 0.8822883367538452 angle_error: 5.23665748240019\n", "Predict: (255, 32) (773, 549) (514, 290) 517 0.79222214 0.6160506 (0.08274758607149124)' 204 159 14\n", "Ground Truth: (253, 36) (762, 545) (508, 291) 509.810949368489 0.7847301053372151 0.619837609198928 (0.1361069688971921)' 200 158 24\n", "dice: 0.9684696282026268 iou:0.9388668086574442 bbox_iou: 0.857850193977356 angle_error: 9.604688908626152\n", "Predict: (455, 170) (726, 441) (590, 305) 271 0.7986791 0.59632343 (0.02169455774128437)' 108 80 3\n", "Ground Truth: (458, 184) (711, 437) (585, 311) 252.39847791934088 0.8249257353546237 0.5652411265554139 (0.01419428248880028)' 104 71 2\n", "dice: 0.9160521501354782 iou:0.8451072164126457 bbox_iou: 0.8442288041114807 angle_error: 1.3500495454471362\n", "Predict: (224, -19) (1041, 796) (633, 388) 816 0.8036685 0.6051866 (-0.058031752705574036)' 327 246 -10\n", "Ground Truth: (261, 15) (1014, 768) (638, 392) 752.0029052098137 0.77198903884291 0.6356358422134485 (-0.06639710382294195)' 290 239 -11\n", "dice: 0.925693902460773 iou:0.861666804815815 bbox_iou: 0.8546870946884155 angle_error: 1.505763201126225\n", "Predict: (550, 146) (946, 541) (748, 344) 395 0.7925865 0.6164956 (-0.24953733384609222)' 156 121 -44\n", "Ground Truth: (566, 159) (939, 532) (753, 346) 372.02758526216843 0.7943335701618761 0.6074818345546539 (-0.21950415907734522)' 147 113 -39\n", "dice: 0.9363462875264097 iou:0.8803112108252605 bbox_iou: 0.8625528216362 angle_error: 5.405971458374459\n", "Predict: (219, 15) (784, 580) (501, 297) 564 0.7933707 0.6209493 (-0.05684550851583481)' 223 175 -10\n", "Ground Truth: (183, -8) (796, 604) (490, 298) 612.900887520976 0.7845999406935795 0.6200023653693842 (-0.06143380803347161)' 240 190 -11\n", "dice: 0.9221577086650139 iou:0.8555590331521087 bbox_iou: 0.8575674295425415 angle_error: 0.8258939131746246\n", "Predict: (298, 191) (584, 477) (441, 334) 285 0.7663486 0.6381334 (-0.2710709869861603)' 109 90 -48\n", "Ground Truth: (288, 179) (599, 490) (444, 335) 311.28756773118965 0.8377077244060851 0.5461188226662569 (-0.22918311805232927)' 130 85 -41\n", "dice: 0.9112904445453555 iou:0.8370372428345235 bbox_iou: 0.7768459916114807 angle_error: 7.539816408089578\n", "Predict: (180, -79) (819, 559) (499, 239) 639 0.7913591 0.61067814 (-0.07408784329891205)' 252 195 -13\n", "Ground Truth: (174, -89) (839, 575) (507, 243) 664.7286098973024 0.7964092294474723 0.6047580832455929 (-0.09899437460315891)' 264 201 -17\n", "dice: 0.956250116408962 iou:0.9161678783799894 bbox_iou: 0.8979136943817139 angle_error: 4.483175634764436\n", "Predict: (274, 141) (588, 455) (431, 298) 314 0.76109326 0.6341232 (-0.31311798095703125)' 119 99 -56\n", "Ground Truth: (287, 144) (592, 449) (440, 297) 305.2927544767481 0.8123350337123325 0.5831910433156392 (-0.28807044699633055)' 124 89 -51\n", "dice: 0.9285279298941859 iou:0.8665908853811638 bbox_iou: 0.8443056344985962 angle_error: 4.508556112926122\n", "Predict: (60, 86) (654, 681) (357, 384) 594 0.7652722 0.63059455 (-0.1902572214603424)' 227 187 -34\n", "Ground Truth: (12, 80) (633, 701) (323, 391) 620.882489377821 0.7702004939440353 0.6378018494237564 (-0.21409289101222084)' 239 198 -38\n", "dice: 0.8942552415427111 iou:0.808735682175294 bbox_iou: 0.8045456409454346 angle_error: 4.29042051933812\n", "Predict: (254, 161) (561, 469) (407, 315) 307 0.7706652 0.6558955 (-0.4189140796661377)' 118 100 -75\n", "Ground Truth: (270, 175) (549, 454) (410, 315) 278.48896941171654 0.8082115441608243 0.5888922650919912 (-0.4201042185678686)' 112 82 -75\n", "dice: 0.8754606855189824 iou:0.7785060728828438 bbox_iou: 0.7748180031776428 angle_error: 0.21422500231156505\n", "Predict: (226, 9) (878, 661) (552, 335) 652 0.7994214 0.6059103 (-0.02991000935435295)' 260 197 -5\n", "Ground Truth: (242, 28) (853, 639) (548, 334) 611.5583232660642 0.7419831972470512 0.670418477522095 (0.028011269984173578)' 226 205 5\n", "dice: 0.930806396481892 iou:0.8705686167772962 bbox_iou: 0.8028537631034851 angle_error: 10.425830280934775\n", "Predict: (312, 208) (624, 520) (468, 364) 311 0.8009264 0.61946386 (-0.1877249777317047)' 124 96 -33\n", "Ground Truth: (311, 202) (632, 523) (472, 363) 321.03641045214795 0.7787278696765261 0.6273618612802817 (-0.1801633955800255)' 125 100 -32\n", "dice: 0.9684412270019241 iou:0.938813427166433 bbox_iou: 0.9278094172477722 angle_error: 1.3610847873022607\n", "Predict: (130, 161) (605, 635) (368, 398) 474 0.7882852 0.6166795 (0.06810084730386734)' 186 146 12\n", "Ground Truth: (119, 154) (628, 663) (374, 409) 509.4702310793046 0.7781298608167172 0.6281034307384066 (0.08613231676633692)' 198 160 15\n", "dice: 0.9232551248054708 iou:0.8574502150648003 bbox_iou: 0.8534186482429504 angle_error: 3.245664503244525\n", "Predict: (303, 260) (554, 512) (429, 386) 251 0.8077839 0.5904291 (-0.11198719590902328)' 101 74 -20\n", "Ground Truth: (300, 259) (567, 526) (434, 393) 267.5520800218155 0.7923690968229963 0.6100419775719613 (-0.09485634608276962)' 106 81 -17\n", "dice: 0.9239715529991018 iou:0.8586869200107037 bbox_iou: 0.8486256003379822 angle_error: 3.083552968725659\n", "Predict: (119, 55) (743, 678) (431, 367) 623 0.78158325 0.62266123 (0.11895687133073807)' 243 193 21\n", "Ground Truth: (122, 47) (749, 674) (436, 361) 626.9191379308818 0.7804004861212896 0.6252800022882987 (0.12827888413206764)' 244 196 23\n", "dice: 0.9750516027791178 iou:0.951317749676903 bbox_iou: 0.9493069052696228 angle_error: 1.6779623042393226\n", "Predict: (211, 166) (502, 457) (356, 311) 291 0.7816727 0.6284443 (0.35254281759262085)' 113 91 63\n", "Ground Truth: (204, 163) (509, 468) (357, 316) 305.17556683981104 0.7825462650008127 0.6225924374205634 (0.3535774523554563)' 119 95 63\n", "dice: 0.9533648427759602 iou:0.9108855518522255 bbox_iou: 0.9167784452438354 angle_error: 0.18623425731038168\n", "Predict: (290, 99) (871, 681) (581, 390) 581 0.7752732 0.6317263 (-0.16933946311473846)' 225 183 -30\n", "Ground Truth: (303, 87) (882, 666) (593, 377) 579.4121558717939 0.7871840370931428 0.6167181623284187 (-0.12509578527022974)' 228 178 -22\n", "dice: 0.9480054934881554 iou:0.9011506121182218 bbox_iou: 0.8562291264533997 angle_error: 7.963862012011571\n", "Predict: (399, 298) (687, 586) (543, 442) 288 0.75590426 0.64977133 (-0.36528104543685913)' 108 93 -65\n", "Ground Truth: (404, 292) (691, 579) (548, 436) 287.16868303490196 0.7791727065614789 0.6268092958385826 (-0.3176732664114231)' 111 90 -57\n", "dice: 0.9484269590586301 iou:0.9019125844169575 bbox_iou: 0.8533671498298645 angle_error: 8.56940022457848\n", "Predict: (299, -29) (850, 521) (575, 246) 551 0.76843643 0.64241904 (-0.21927179396152496)' 211 176 -39\n", "Ground Truth: (303, -34) (834, 496) (569, 231) 530.9985744010995 0.7617798229613523 0.6478360142265717 (-0.2119943841984046)' 202 172 -38\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9359112318108248 iou:0.879542440245396 bbox_iou: 0.8759573698043823 angle_error: 1.3099337573616623\n", "Predict: (404, 150) (669, 416) (536, 283) 265 0.76119757 0.656744 (-0.3546459376811981)' 100 87 -63\n", "Ground Truth: (409, 152) (680, 423) (545, 288) 271.63146563680726 0.7553690420915541 0.6552996339459437 (-0.4657521946816209)' 102 89 -83\n", "dice: 0.9266292771321473 iou:0.8632891296460568 bbox_iou: 0.7614749073982239 angle_error: 19.999126260076096\n", "Predict: (-61, -49) (600, 613) (269, 281) 662 0.76674575 0.6374722 (-0.18830177187919617)' 253 211 -33\n", "Ground Truth: (-56, -50) (582, 588) (263, 269) 638.601501786521 0.7640476864445398 0.6451597731095347 (-0.21861406061352906)' 243 206 -39\n", "dice: 0.9494150287590564 iou:0.9037013233090648 bbox_iou: 0.8870226740837097 angle_error: 5.456211972179922\n", "Predict: (142, 34) (466, 357) (304, 195) 323 0.7540259 0.65218776 (0.13241861760616302)' 121 105 23\n", "Ground Truth: (141, 25) (468, 352) (305, 189) 327.2100356223812 0.775294069197699 0.6316004324467119 (-0.48312957603225914)' 126 103 -86\n", "dice: 0.8946021747657085 iou:0.8093033606033154 bbox_iou: 0.7254706025123596 angle_error: 69.20132514508401\n", "Predict: (191, -37) (829, 600) (510, 281) 637 0.76670194 0.6369214 (0.2162867784500122)' 244 202 38\n", "Ground Truth: (205, -19) (790, 565) (498, 273) 585.2660670293469 0.7775882212169669 0.6287738530064949 (0.2163858914102425)' 227 184 38\n", "dice: 0.917470891106817 iou:0.8475253769802761 bbox_iou: 0.84520024061203 angle_error: 0.01784033284145181\n", "Predict: (281, 34) (639, 392) (460, 213) 357 0.7657941 0.6368934 (0.4501107335090637)' 136 113 81\n", "Ground Truth: (291, 43) (628, 380) (460, 212) 336.700816179587 0.7603189172652811 0.6495499550061955 (0.49427042204869176)' 128 109 88\n", "dice: 0.9522946858876493 iou:0.9089337173921502 bbox_iou: 0.8614079356193542 angle_error: 7.948743937133045\n", "Predict: (115, 107) (692, 683) (404, 395) 576 0.7501555 0.64559126 (-0.26273491978645325)' 216 185 -47\n", "Ground Truth: (109, 117) (692, 700) (401, 409) 583.1671640138871 0.7614832031067942 0.6481846429731399 (-0.24789857014243788)' 222 189 -44\n", "dice: 0.9520048014190624 iou:0.9084056899384145 bbox_iou: 0.9057164192199707 angle_error: 2.670542935922768\n", "Predict: (287, 164) (611, 488) (449, 326) 323 0.7743648 0.6467924 (0.47406572103500366)' 125 104 85\n", "Ground Truth: (299, 179) (592, 472) (446, 326) 293.0126572283184 0.7782462442307128 0.6279592210811062 (-0.41532957027511164)' 114 92 -74\n", "dice: 0.8935083094587333 iou:0.8075147035416529 bbox_iou: 0.7379438281059265 angle_error: 19.90884756417924\n", "Predict: (359, 76) (929, 646) (644, 361) 569 0.78561646 0.6276763 (-0.09623726457357407)' 223 178 -17\n", "Ground Truth: (341, 60) (926, 645) (634, 353) 585.0843981649143 0.7657014977755822 0.6431960947520049 (-0.10523090993736439)' 224 188 -18\n", "dice: 0.9540873720507814 iou:0.9122056150345136 bbox_iou: 0.9104553461074829 angle_error: 1.61885616548226\n", "Predict: (414, 207) (722, 516) (568, 362) 308 0.814766 0.570289 (0.05486816167831421)' 125 87 9\n", "Ground Truth: (409, 191) (736, 518) (573, 355) 327.4935856715365 0.8427645977677786 0.5382822983800546 (0.01814366351247607)' 138 88 3\n", "dice: 0.9192851557488876 iou:0.8506269351615241 bbox_iou: 0.8280168771743774 angle_error: 6.610409669850865\n", "Predict: (179, 122) (707, 650) (443, 386) 527 0.7582625 0.647219 (-0.24318745732307434)' 199 170 -43\n", "Ground Truth: (185, 119) (714, 648) (450, 384) 528.015809039843 0.7714049333876383 0.6363445833392578 (-0.2122478628898532)' 203 168 -38\n", "dice: 0.9740430079969548 iou:0.9493994539627479 bbox_iou: 0.9040914177894592 angle_error: 5.569126997979808\n", "Predict: (340, 170) (636, 466) (488, 318) 295 0.77587086 0.62428296 (0.5162923336029053)' 114 92 92\n", "Ground Truth: (346, 182) (617, 453) (482, 318) 270.35817914758934 0.754828282404563 0.6559224527961954 (-0.44779717866585833)' 102 88 -80\n", "dice: 0.9163435800281704 iou:0.8456034247939871 bbox_iou: 0.822871208190918 angle_error: 6.46388779162254\n", "Predict: (227, 39) (824, 635) (526, 337) 596 0.80157155 0.6081029 (0.03570634126663208)' 238 181 6\n", "Ground Truth: (237, 65) (792, 620) (515, 343) 555.9062843645501 0.7589156875286146 0.6511888967288757 (0.09803944494460753)' 210 181 17\n", "dice: 0.926875430108284 iou:0.8637165303202504 bbox_iou: 0.7982616424560547 angle_error: 11.21995866203558\n", "Predict: (297, 145) (603, 452) (450, 299) 306 0.7839561 0.62080115 (0.2607836127281189)' 119 94 46\n", "Ground Truth: (324, 157) (603, 436) (464, 297) 278.46683393179876 0.7568585350872293 0.6535787311912875 (0.24350706293059987)' 105 91 43\n", "dice: 0.8840407727241684 iou:0.7921801721037789 bbox_iou: 0.7800739407539368 angle_error: 3.109778963553424\n", "Predict: (282, 49) (944, 712) (613, 381) 662 0.7657625 0.6409394 (0.17557218670845032)' 253 212 31\n", "Ground Truth: (306, 87) (915, 696) (611, 392) 609.7670649059361 0.77412839618161 0.6330286140651843 (0.20874645414183152)' 236 193 37\n", "dice: 0.9184968526799653 iou:0.849278021017323 bbox_iou: 0.8368914127349854 angle_error: 5.971368138008614\n", "Predict: (383, 135) (723, 474) (553, 305) 339 0.77872926 0.6334713 (0.48307499289512634)' 131 107 86\n", "Ground Truth: (409, 151) (726, 468) (568, 310) 317.46823924922 0.7626715685720201 0.6467859603391948 (0.4576647851375558)' 121 102 82\n", "dice: 0.892468478727112 iou:0.8058176779487018 bbox_iou: 0.8008710741996765 angle_error: 4.573837396362691\n", "Predict: (46, -50) (839, 742) (443, 345) 793 0.74647665 0.65358126 (-0.30021828413009644)' 295 259 -54\n", "Ground Truth: (69, -33) (836, 733) (453, 350) 766.4538161298435 0.7457096409096284 0.6662710645483811 (-0.3214281538508934)' 285 255 -57\n", "dice: 0.9649268581400938 iou:0.9322306019903409 bbox_iou: 0.9158361554145813 angle_error: 3.8177765497434564\n", "Predict: (275, 23) (703, 451) (489, 237) 427 0.76586354 0.6371408 (0.44991251826286316)' 163 136 80\n", "Ground Truth: (282, 35) (691, 444) (487, 240) 409.47352958646786 0.7671460480418344 0.6414724787345094 (0.39973238585210596)' 157 131 71\n", "dice: 0.962664888027074 iou:0.9280172597225254 bbox_iou: 0.8601784706115723 angle_error: 9.032423833936292\n", "Predict: (64, -26) (928, 836) (496, 405) 863 0.7417721 0.66871333 (0.3207581639289856)' 320 288 57\n", "Ground Truth: (100, 37) (881, 818) (491, 428) 780.720085264879 0.7246592097192593 0.6891074152619885 (0.42965351515338224)' 282 269 77\n", "dice: 0.9008717088613174 iou:0.8196238019931467 bbox_iou: 0.7423009276390076 angle_error: 19.6011632203914\n", "Predict: (214, 84) (666, 535) (440, 309) 451 0.790861 0.6013293 (0.23910550773143768)' 178 135 43\n", "Ground Truth: (247, 122) (660, 535) (454, 329) 412.15997639751487 0.7679348265847494 0.640527986990616 (0.22281692032865347)' 158 132 40\n", "dice: 0.9050421234702848 iou:0.8265542838402729 bbox_iou: 0.8277521133422852 angle_error: 2.9319457325011555\n", "Predict: (144, 7) (855, 719) (500, 363) 711 0.77906215 0.63344395 (0.20947638154029846)' 276 225 37\n", "Ground Truth: (153, 29) (822, 698) (488, 364) 669.082351358934 0.7610871800246005 0.648649600633656 (0.2081746655641991)' 254 217 37\n", "dice: 0.9400223653423438 iou:0.8868322638203074 bbox_iou: 0.8850106000900269 angle_error: 0.2343088756978844\n", "Predict: (229, 107) (605, 483) (417, 295) 375 0.8213764 0.5646601 (0.01400080882012844)' 154 105 2\n", "Ground Truth: (243, 115) (602, 474) (423, 295) 358.5023728847551 0.8104772017601215 0.5857701814082749 (0.019735212943395024)' 145 105 3\n", "dice: 0.9676379641058753 iou:0.9373048702510722 bbox_iou: 0.9305238127708435 angle_error: 1.032192742187985\n", "Predict: (277, 32) (942, 697) (609, 364) 664 0.77613497 0.6320141 (-0.1615563929080963)' 257 209 -29\n", "Ground Truth: (310, 33) (941, 664) (626, 349) 631.4777072518078 0.743915413965162 0.6682737888508287 (-0.13846480048994894)' 234 211 -24\n", "dice: 0.9399214304776999 iou:0.8866526100053641 bbox_iou: 0.8649665713310242 angle_error: 4.156486635266528\n", "Predict: (401, 251) (731, 581) (566, 416) 329 0.75887096 0.650129 (-0.36434319615364075)' 124 106 -65\n", "Ground Truth: (403, 240) (734, 571) (569, 406) 330.32168806180437 0.8004379050961057 0.59941568221507 (-0.29602819415092535)' 132 99 -53\n", "dice: 0.9351404144387468 iou:0.878181900335587 bbox_iou: 0.8053750395774841 angle_error: 12.29670036048877\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (174, 8) (761, 595) (468, 301) 587 0.7755258 0.62485814 (0.15232791006565094)' 227 183 27\n", "Ground Truth: (174, 13) (751, 590) (463, 302) 576.4750017129971 0.7489310008536074 0.6626479879697921 (0.22084222981681556)' 215 191 39\n", "dice: 0.9608306964086806 iou:0.9246142020247285 bbox_iou: 0.828388512134552 angle_error: 12.332577555209632\n", "Predict: (265, 86) (554, 374) (410, 230) 288 0.7867868 0.626408 (0.4025672674179077)' 113 90 72\n", "Ground Truth: (276, 97) (551, 372) (414, 235) 275.0203810338426 0.7972936375686916 0.6035916297402442 (0.417876049364582)' 109 83 75\n", "dice: 0.9382987809346685 iou:0.8837691471812567 bbox_iou: 0.8721926212310791 angle_error: 2.7555807504013785\n", "Predict: (191, 18) (794, 621) (493, 319) 603 0.79846257 0.6141739 (-0.021552983671426773)' 240 185 -3\n", "Ground Truth: (210, 46) (763, 599) (487, 323) 552.5004457409966 0.7554527841877341 0.6552030913106264 (0.026101410667070838)' 208 181 4\n", "dice: 0.9180596576057285 iou:0.848530756856811 bbox_iou: 0.8038511276245117 angle_error: 8.57779098092957\n", "Predict: (395, 146) (711, 462) (553, 304) 315 0.80138147 0.62372637 (-0.1888248771429062)' 126 98 -33\n", "Ground Truth: (404, 150) (709, 455) (557, 303) 304.9126941339111 0.8023214667886548 0.5968921711080665 (-0.19085626932080413)' 122 91 -34\n", "dice: 0.9473329830798511 iou:0.8999360366125275 bbox_iou: 0.8967956304550171 angle_error: 0.3656505920216304\n", "Predict: (261, 99) (760, 598) (510, 348) 499 0.77533126 0.6452406 (-0.18233579397201538)' 193 160 -32\n", "Ground Truth: (268, 102) (757, 591) (513, 347) 489.9841243183293 0.777735402203407 0.6285917945372059 (-0.1526590829787492)' 190 154 -27\n", "dice: 0.9710446580966384 iou:0.943718953147568 bbox_iou: 0.9052497744560242 angle_error: 5.341807978787912\n", "Predict: (420, 160) (706, 446) (563, 303) 285 0.78653306 0.6430021 (-0.25981640815734863)' 112 91 -46\n", "Ground Truth: (422, 165) (709, 452) (566, 309) 286.8386607415395 0.7949974365745468 0.606612789050725 (-0.28959716523251433)' 114 87 -52\n", "dice: 0.9524961511589279 iou:0.9093008605292879 bbox_iou: 0.88303542137146 angle_error: 5.360536273529824\n", "Predict: (85, 82) (668, 665) (377, 373) 583 0.79454285 0.61352813 (-0.07391145080327988)' 231 178 -13\n", "Ground Truth: (98, 92) (667, 661) (383, 377) 569.5697678564059 0.7824677943797709 0.6226910556274728 (-0.08294921790449916)' 222 177 -14\n", "dice: 0.96929340104107 iou:0.9404164114405682 bbox_iou: 0.9331173896789551 angle_error: 1.6267980782194709\n", "Predict: (319, 182) (628, 491) (474, 336) 308 0.7797788 0.6467669 (-0.3490660786628723)' 120 99 -62\n", "Ground Truth: (330, 200) (607, 477) (469, 339) 276.32651784437917 0.7994455317690876 0.6007385801947804 (-0.32397463294036377)' 110 83 -58\n", "dice: 0.8699114909958507 iou:0.7697728842163254 bbox_iou: 0.7659382820129395 angle_error: 4.5164602300515355\n", "Predict: (152, -12) (878, 712) (515, 349) 725 0.7693266 0.632548 (0.18923304975032806)' 278 229 34\n", "Ground Truth: (176, 13) (865, 702) (521, 358) 689.9882817120882 0.7678826061876258 0.640590589311535 (0.23134645606088067)' 264 221 41\n", "dice: 0.9527488345089229 iou:0.9097615413606724 bbox_iou: 0.8656968474388123 angle_error: 7.5804131358994695\n", "Predict: (261, 57) (664, 460) (463, 259) 403 0.7695943 0.635512 (0.47885945439338684)' 155 128 86\n", "Ground Truth: (294, 86) (647, 439) (471, 263) 352.7885614982436 0.7771850618840573 0.6292721029763467 (0.4885408440973835)' 137 111 87\n", "dice: 0.8682906576109198 iou:0.767238216641321 bbox_iou: 0.766295850276947 angle_error: 1.7426501467193987\n", "Predict: (213, 111) (614, 512) (414, 312) 401 0.7514937 0.6584062 (-0.28974810242652893)' 150 132 -52\n", "Ground Truth: (196, 101) (619, 524) (408, 313) 423.1162993976951 0.7659927080600798 0.6428492600904083 (-0.2625408249068921)' 162 136 -47\n", "dice: 0.9467660749387454 iou:0.8989133870556654 bbox_iou: 0.8792281150817871 angle_error: 4.897309953534624\n", "Predict: (329, 149) (534, 354) (432, 252) 204 0.77012485 0.6351855 (0.4755655527114868)' 78 64 85\n", "Ground Truth: (320, 143) (535, 358) (428, 251) 215.95766001695796 0.7666224943676443 0.6420980852872332 (-0.4563915455928207)' 82 69 -82\n", "dice: 0.930496118723107 iou:0.8700259391412186 bbox_iou: 0.8142915964126587 angle_error: 12.247722305224642\n", "Predict: (83, -48) (776, 644) (430, 297) 692 0.7896948 0.6096239 (0.05609465390443802)' 273 210 10\n", "Ground Truth: (91, -30) (760, 638) (426, 304) 669.016878115941 0.7685874853382835 0.639744697032632 (0.0749914707499042)' 257 214 13\n", "dice: 0.9609814258884147 iou:0.9248934040569041 bbox_iou: 0.9017488360404968 angle_error: 3.4014270321839124\n", "Predict: (342, 106) (685, 450) (514, 278) 343 0.80770427 0.5858135 (-0.13958197832107544)' 138 100 -25\n", "Ground Truth: (342, 110) (679, 447) (511, 279) 336.7728094071729 0.8001417943281876 0.5998108943402651 (-0.06416893461965545)' 134 101 -11\n", "dice: 0.9492976649446581 iou:0.9034886792125167 bbox_iou: 0.8160281181335449 angle_error: 13.574347866255598\n", "Predict: (38, 13) (732, 708) (385, 361) 694 0.76798534 0.6398699 (-0.21548917889595032)' 266 222 -38\n", "Ground Truth: (26, 10) (737, 721) (382, 366) 710.7663898215784 0.7647097665049141 0.6443748699414028 (-0.23675772412600515)' 271 229 -42\n", "dice: 0.9746045977298391 iou:0.950467103297056 bbox_iou: 0.9251174330711365 angle_error: 3.8283381414098727\n", "Predict: (280, 103) (632, 455) (456, 279) 351 0.7681991 0.6521134 (-0.4414507746696472)' 134 114 -79\n", "Ground Truth: (288, 111) (629, 452) (459, 282) 340.0938526701122 0.7819370973986746 0.6233573419089053 (-0.4614845037717613)' 132 106 -83\n", "dice: 0.9533069725207637 iou:0.9107799015501465 bbox_iou: 0.8979859948158264 angle_error: 3.6060712383805367\n", "Predict: (203, 0) (1024, 821) (613, 411) 820 0.7759447 0.63531905 (0.17088307440280914)' 318 260 30\n", "Ground Truth: (225, 28) (1030, 833) (628, 431) 805.0563337705008 0.7612982772640571 0.6484018299131943 (0.21033800357275048)' 306 261 37\n", "dice: 0.9560793200484614 iou:0.9158543732392055 bbox_iou: 0.8612544536590576 angle_error: 7.101887250589442\n", "Predict: (355, 129) (765, 540) (560, 334) 410 0.77502024 0.63255167 (0.45799165964126587)' 158 129 82\n", "Ground Truth: (368, 141) (759, 532) (564, 337) 390.6566177143298 0.7726529804256994 0.6348286161156282 (0.4490704182105935)' 150 124 80\n", "dice: 0.9548326321681609 iou:0.9135691196989107 bbox_iou: 0.9065702557563782 angle_error: 1.6058234575210264\n", "Predict: (181, -39) (795, 574) (488, 267) 614 0.7797464 0.6233452 (-0.11781366914510727)' 239 191 -21\n", "Ground Truth: (179, -43) (800, 577) (490, 267) 620.0716056843758 0.7826128394710136 0.6225087497337829 (-0.11109015027814294)' 242 193 -19\n", "dice: 0.9885398631039746 iou:0.9773394195619132 bbox_iou: 0.9706085324287415 angle_error: 1.210233396053578\n", "Predict: (248, 114) (579, 445) (414, 279) 331 0.81557304 0.5781606 (0.03197113424539566)' 134 95 5\n", "Ground Truth: (246, 105) (587, 446) (417, 276) 340.50578100232013 0.8218245199134909 0.5697406940608686 (0.03660563691113593)' 139 97 6\n", "dice: 0.9651189427211422 iou:0.932589243887457 bbox_iou: 0.9323978424072266 angle_error: 0.8342104798332484\n", "Predict: (21, -47) (616, 548) (319, 250) 595 0.78822833 0.61097413 (-0.029038485139608383)' 234 181 -5\n", "Ground Truth: (10, -48) (613, 554) (312, 253) 602.9642445153776 0.7921564244392248 0.6103181131341774 (-0.05443099053742821)' 238 184 -9\n", "dice: 0.9767609704724485 iou:0.9545775154056012 bbox_iou: 0.9152892231941223 angle_error: 4.570650971607568\n", "Predict: (81, 119) (383, 422) (232, 271) 302 0.79987663 0.5974737 (-0.197601318359375)' 120 90 -35\n", "Ground Truth: (69, 118) (384, 433) (227, 276) 315.3711401507754 0.8075120630196125 0.5898510558419041 (-0.18334649444186343)' 127 93 -33\n", "dice: 0.9514883154374868 iou:0.9074656290878541 bbox_iou: 0.9041580557823181 angle_error: 2.5658683051520796\n", "Predict: (242, -25) (813, 546) (528, 260) 571 0.7875897 0.61413366 (-0.0827438086271286)' 224 175 -14\n", "Ground Truth: (236, -38) (823, 548) (530, 255) 586.6597531244154 0.761865796348931 0.6477349059249541 (-0.05061127190322272)' 223 190 -9\n", "dice: 0.955149695438152 iou:0.9141497985576876 bbox_iou: 0.8842518329620361 angle_error: 5.78385661030306\n", "Predict: (323, 175) (583, 435) (453, 305) 260 0.76897913 0.6369144 (-0.256497323513031)' 99 82 -46\n", "Ground Truth: (323, 172) (590, 439) (457, 306) 266.8328689573307 0.7809382735127811 0.6246082075692542 (-0.24573523213388643)' 104 83 -44\n", "dice: 0.9591414044513007 iou:0.921490593009591 bbox_iou: 0.9204052686691284 angle_error: 1.9371764482460208\n", "Predict: (157, -47) (985, 781) (571, 366) 828 0.78885514 0.61477816 (0.07261509448289871)' 326 254 13\n", "Ground Truth: (224, -7) (979, 747) (602, 370) 754.0611333201042 0.7532174447172998 0.6577716024377622 (0.06550700535912572)' 283 248 11\n", "dice: 0.9156456398279225 iou:0.8444155097809702 bbox_iou: 0.8462621569633484 angle_error: 1.2794560422791381\n", "Predict: (256, 110) (665, 519) (460, 315) 409 0.7888341 0.61265266 (0.2954283356666565)' 161 125 53\n", "Ground Truth: (291, 152) (648, 509) (470, 331) 356.3399796879379 0.7866588538436984 0.6173879231644548 (0.28004786864700065)' 140 110 50\n", "dice: 0.8671518022581831 iou:0.7654616072892517 bbox_iou: 0.761067271232605 angle_error: 2.768484063538054\n", "Predict: (62, -101) (920, 756) (491, 327) 858 0.793571 0.6119147 (-0.047692395746707916)' 340 262 -8\n", "Ground Truth: (89, -69) (896, 737) (493, 334) 807.9180038221701 0.773556223581275 0.6337276772862902 (-0.06480555439202296)' 312 256 -11\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9449182497603726 iou:0.8955877111378007 bbox_iou: 0.8803743124008179 angle_error: 3.0803685561567065\n", "Predict: (378, 64) (813, 499) (596, 282) 434 0.79697454 0.6064016 (-0.25461938977241516)' 172 131 -45\n", "Ground Truth: (386, 73) (811, 498) (599, 286) 424.61450119372984 0.7832233686438943 0.6217404239794211 (-0.2815745868831844)' 166 132 -50\n", "dice: 0.9658763113534893 iou:0.9340046282255214 bbox_iou: 0.9057832360267639 angle_error: 4.8519354799384615\n", "Predict: (222, -1) (940, 716) (581, 357) 717 0.75292283 0.6643218 (0.3879132568836212)' 269 238 69\n", "Ground Truth: (227, 16) (900, 689) (564, 353) 672.9698469203505 0.7127986524138786 0.701368719802187 (-0.08498873961107212)' 239 236 -15\n", "dice: 0.9178134193551344 iou:0.848110146411368 bbox_iou: 0.8261405229568481 angle_error: 85.1223593690448\n", "Predict: (394, 72) (768, 446) (581, 259) 373 0.76616675 0.62935084 (-0.35543709993362427)' 142 117 -63\n", "Ground Truth: (418, 85) (757, 424) (588, 255) 339.62614836905595 0.7516617940812812 0.6595487452179024 (-0.3754760033074027)' 127 112 -67\n", "dice: 0.9231858333497639 iou:0.8573306907928406 bbox_iou: 0.8417680263519287 angle_error: 3.6070026072801227\n", "Predict: (-83, 38) (647, 769) (281, 404) 731 0.7953765 0.61659354 (-0.08820793777704239)' 290 225 -15\n", "Ground Truth: (-69, 75) (619, 764) (275, 420) 689.5823398666761 0.7723747683314744 0.635167078212419 (-0.07957747154594767)' 266 219 -14\n", "dice: 0.9330314995837001 iou:0.8744695829536286 bbox_iou: 0.8625343441963196 angle_error: 1.5534839215970493\n", "Predict: (186, 166) (556, 536) (371, 351) 370 0.78535914 0.6123551 (-0.2931714951992035)' 145 113 -52\n", "Ground Truth: (182, 183) (539, 540) (361, 362) 356.0957808286978 0.7773695025416915 0.6290442405094282 (-0.2777548682489789)' 138 112 -49\n", "dice: 0.9329094430547007 iou:0.8742551763603725 bbox_iou: 0.8752411007881165 angle_error: 2.7749928510404303\n", "Predict: (84, 10) (767, 694) (425, 352) 683 0.7650182 0.6313474 (-0.1991443634033203)' 261 215 -35\n", "Ground Truth: (123, 39) (750, 666) (437, 353) 627.7449270014056 0.7544847909204044 0.6563175300643688 (-0.20810983436946395)' 236 206 -37\n", "dice: 0.928543497408901 iou:0.8666180056431669 bbox_iou: 0.8626512289047241 angle_error: 1.6137847739058557\n", "Predict: (291, 97) (651, 456) (471, 276) 359 0.76996166 0.65090567 (-0.48746660351753235)' 138 116 -87\n", "Ground Truth: (305, 110) (642, 447) (474, 279) 336.1425703061128 0.7824180072178655 0.6227536125798263 (-0.4134197109580089)' 131 104 -74\n", "dice: 0.9199244790963184 iou:0.8517223669013743 bbox_iou: 0.797103762626648 angle_error: 13.328440660714222\n", "Predict: (221, 168) (834, 781) (527, 475) 613 0.7591909 0.6550972 (0.24852235615253448)' 232 200 44\n", "Ground Truth: (217, 206) (774, 763) (496, 485) 556.492073621898 0.7080064904351155 0.7062059257056333 (0.022600001919049137)' 197 196 4\n", "dice: 0.8622667347964929 iou:0.7578812724986631 bbox_iou: 0.6671873927116394 angle_error: 40.666023762027365\n", "Predict: (311, 240) (655, 584) (483, 412) 344 0.7818557 0.6332004 (0.34334316849708557)' 134 108 61\n", "Ground Truth: (335, 267) (636, 568) (486, 418) 300.59317624324075 0.7465305859718296 0.6653510984499512 (0.29628167284237394)' 112 100 53\n", "dice: 0.8735068836202365 iou:0.7754214126291739 bbox_iou: 0.7572985887527466 angle_error: 8.47106921784809\n", "Predict: (216, 14) (780, 579) (498, 297) 564 0.73814344 0.6708735 (-0.4087245464324951)' 208 189 -73\n", "Ground Truth: (193, -7) (780, 579) (487, 286) 587.3228780968778 0.73223096875355 0.6810563914965028 (-0.46880563115398843)' 215 200 -84\n", "dice: 0.947573698555605 iou:0.9003705981645596 bbox_iou: 0.8301183581352234 angle_error: 10.814595249868802\n", "Predict: (369, 40) (702, 373) (536, 206) 333 0.777513 0.62872916 (-0.29369932413101196)' 129 104 -52\n", "Ground Truth: (388, 64) (675, 351) (532, 208) 287.90982835603234 0.7859821989826792 0.6182491268755276 (-0.25388162678519466)' 113 89 -45\n", "dice: 0.8574194327891718 iou:0.750423608973354 bbox_iou: 0.7430696487426758 angle_error: 7.167185522247117\n", "Predict: (132, -91) (1019, 795) (576, 352) 886 0.7948292 0.6102731 (0.0553564727306366)' 352 270 9\n", "Ground Truth: (162, -80) (1027, 784) (595, 352) 864.2292692937448 0.7788997942063477 0.6271483959840042 (0.06780000575714741)' 336 271 12\n", "dice: 0.9642904090808876 iou:0.9310432359954821 bbox_iou: 0.9211021065711975 angle_error: 2.239835944771947\n", "Predict: (250, 44) (725, 519) (488, 281) 474 0.79265213 0.6048575 (0.27968496084213257)' 187 143 50\n", "Ground Truth: (283, 80) (702, 499) (493, 290) 419.18985713397217 0.8026959485626607 0.5963884758788439 (0.27820284052463307)' 168 125 50\n", "dice: 0.880246831605053 iou:0.7861079177536937 bbox_iou: 0.7809252738952637 angle_error: 0.2667816571499131\n", "Predict: (120, 0) (843, 723) (482, 361) 723 0.8007285 0.61059964 (0.027722442522644997)' 289 220 4\n", "Ground Truth: (117, 25) (806, 714) (462, 370) 689.0309462890618 0.7622473313116815 0.6472858764937016 (0.03526756617166561)' 262 223 6\n", "dice: 0.9359235933759453 iou:0.8795642752246583 bbox_iou: 0.8669894337654114 angle_error: 1.3581222568237106\n", "Predict: (204, 140) (576, 512) (390, 326) 371 0.79466397 0.60387397 (0.22036893665790558)' 147 112 39\n", "Ground Truth: (213, 148) (576, 511) (395, 330) 362.9553107973487 0.7995309377413298 0.6006249075708315 (0.2361859355483727)' 145 109 42\n", "dice: 0.9663720628499116 iou:0.9349322208863527 bbox_iou: 0.9226287007331848 angle_error: 2.847059800284079\n", "Predict: (268, 215) (894, 841) (581, 528) 625 0.7862751 0.6311573 (-0.13632071018218994)' 245 197 -24\n", "Ground Truth: (215, 135) (934, 854) (575, 495) 719.719200869339 0.7525018081299218 0.6585901827093981 (-0.3654197493389917)' 270 237 -65\n", "dice: 0.8584767737879745 iou:0.7520449466776964 bbox_iou: 0.6460707783699036 angle_error: 41.23782704822432\n", "Predict: (343, 396) (676, 729) (509, 563) 333 0.7943974 0.6037262 (-0.15515656769275665)' 132 100 -27\n", "Ground Truth: (328, 387) (665, 724) (497, 556) 336.76664780824126 0.8174027974312472 0.5760665471554232 (-0.19735212943395022)' 137 97 -35\n", "dice: 0.908780557569139 iou:0.8328119186959215 bbox_iou: 0.8277611136436462 angle_error: 7.595201113414845\n", "Predict: (215, 66) (783, 634) (499, 350) 568 0.7802834 0.6287327 (-0.13123254477977753)' 221 178 -23\n", "Ground Truth: (231, 59) (810, 638) (521, 349) 578.7611803464362 0.7660741857886948 0.6427521620875253 (-0.09931268448934269)' 221 186 -17\n", "dice: 0.932671120797309 iou:0.8738366767458094 bbox_iou: 0.8535687327384949 angle_error: 5.745574852278271\n", "Predict: (306, 248) (600, 541) (453, 395) 293 0.7635247 0.67065233 (-0.4260256886482239)' 111 98 -76\n", "Ground Truth: (311, 247) (598, 534) (455, 391) 287.63129520968334 0.774352456458645 0.6327545125690232 (-0.3485493253712508)' 111 91 -62\n", "dice: 0.9474080090039101 iou:0.900071458939525 bbox_iou: 0.8102469444274902 angle_error: 13.945745389855162\n", "Predict: (66, -30) (915, 818) (491, 394) 848 0.77291834 0.63590103 (0.20857617259025574)' 327 269 37\n", "Ground Truth: (79, -1) (888, 807) (484, 403) 808.0841540334769 0.7548718743651162 0.6558722842844452 (0.2373943438983727)' 305 265 42\n", "dice: 0.9511500078232061 iou:0.9068503741409005 bbox_iou: 0.8788182735443115 angle_error: 5.1872708354610495\n", "Predict: (189, 85) (627, 523) (408, 304) 437 0.7690331 0.63695496 (0.45712098479270935)' 168 139 82\n", "Ground Truth: (213, 103) (602, 492) (408, 298) 388.5669183036559 0.7772149037247533 0.6292352449029874 (0.4264704162915443)' 151 122 76\n", "dice: 0.8825785133319254 iou:0.7898349225086018 bbox_iou: 0.7893492579460144 angle_error: 5.51710233020971\n", "Predict: (167, -19) (595, 408) (381, 194) 427 0.7962713 0.61299556 (0.0578472837805748)' 170 130 10\n", "Ground Truth: (173, 2) (558, 387) (366, 195) 384.97376858689995 0.7648573072415294 0.6441997357646436 (0.05538592019597958)' 147 124 9\n", "dice: 0.9038538533899737 iou:0.8245742195830899 bbox_iou: 0.8205201029777527 angle_error: 0.4430454452271402\n", "Predict: (219, 96) (415, 292) (317, 194) 195 0.8154377 0.59941536 (-0.11634159833192825)' 79 58 -20\n", "Ground Truth: (208, 80) (431, 303) (320, 192) 222.55161760814053 0.8222811497250883 0.569081462364385 (-0.08645062665252068)' 91 63 -15\n", "dice: 0.8888550081143908 iou:0.7999451148189105 bbox_iou: 0.7964246273040771 angle_error: 5.380374902293363\n", "Predict: (76, 33) (576, 534) (326, 284) 500 0.7839675 0.62676185 (-0.1301320642232895)' 195 156 -23\n", "Ground Truth: (61, 36) (564, 539) (313, 288) 502.24996133001343 0.7729418215821798 0.6344769030068956 (-0.1436225898636247)' 194 159 -25\n", "dice: 0.9535574553918916 iou:0.9112372774837798 bbox_iou: 0.9079916477203369 angle_error: 2.428294615260338\n", "Predict: (137, 158) (402, 422) (270, 290) 264 0.8153937 0.58418584 (-0.015500425361096859)' 107 77 -2\n", "Ground Truth: (123, 149) (406, 432) (265, 291) 282.7865708975587 0.8179949962468214 0.5752253350776223 (-0.0238732414637843)' 115 81 -4\n", "dice: 0.9399590383133989 iou:0.8867195441371027 bbox_iou: 0.8823077082633972 angle_error: 1.5071068984837392\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (161, -96) (955, 697) (558, 300) 794 0.786502 0.61523175 (-0.06211692839860916)' 312 244 -11\n", "Ground Truth: (178, -82) (947, 686) (563, 302) 768.0360224078034 0.7577769570956087 0.6525136652169977 (-0.08339719018015317)' 291 250 -15\n", "dice: 0.9644484647607044 iou:0.9313379700971035 bbox_iou: 0.896058976650238 angle_error: 3.8304471206779205\n", "Predict: (279, 148) (672, 541) (475, 344) 392 0.7909982 0.61044425 (-0.26341870427131653)' 155 119 -47\n", "Ground Truth: (284, 152) (673, 541) (479, 347) 389.4445988276125 0.790869871933533 0.6119843508357359 (-0.22472677964575621)' 154 119 -40\n", "dice: 0.9694320136859352 iou:0.9406773998028128 bbox_iou: 0.8887653946876526 angle_error: 6.964546432600855\n", "Predict: (158, 166) (838, 846) (498, 506) 679 0.77532244 0.63323075 (-0.17207776010036469)' 263 214 -30\n", "Ground Truth: (159, 181) (812, 834) (486, 508) 652.7851830702042 0.7550630939290045 0.6556521365681341 (-0.19856053778395022)' 246 214 -35\n", "dice: 0.9611035189831638 iou:0.9251196211988983 bbox_iou: 0.8879213929176331 angle_error: 4.766899983045398\n", "Predict: (401, 284) (719, 602) (560, 443) 318 0.78869575 0.61596346 (0.5210941433906555)' 125 97 93\n", "Ground Truth: (390, 275) (727, 612) (559, 444) 336.82044177870205 0.7722215392463949 0.6353533617790407 (-0.44334084025928533)' 130 107 -79\n", "dice: 0.9316255917285874 iou:0.8720029088266168 bbox_iou: 0.8672454953193665 angle_error: 6.401702943010662\n", "Predict: (132, 53) (958, 879) (545, 466) 825 0.7976863 0.6096211 (-0.06797318905591965)' 329 251 -12\n", "Ground Truth: (157, 83) (938, 864) (548, 474) 780.9270214943263 0.782749198292969 0.6223372820036692 (-0.04920836996901723)' 305 243 -8\n", "dice: 0.9448780912770794 iou:0.8955155640931806 bbox_iou: 0.8826230764389038 angle_error: 3.3776674356424348\n", "Predict: (415, 185) (870, 640) (643, 413) 454 0.7923672 0.6073587 (-0.24878162145614624)' 179 137 -44\n", "Ground Truth: (421, 213) (844, 636) (633, 425) 423.0842049710672 0.8169437571502735 0.5767173464126037 (-0.2315999347523292)' 172 122 -41\n", "dice: 0.9091276054627048 iou:0.8333950056993792 bbox_iou: 0.8133562207221985 angle_error: 3.092703606687067\n", "Predict: (238, -47) (931, 646) (584, 299) 693 0.79844016 0.60624593 (-0.03562783822417259)' 276 210 -6\n", "Ground Truth: (258, -35) (931, 637) (595, 301) 673.9073184466837 0.7867313286076824 0.6172955666349718 (-0.00846470453749193)' 265 208 -1\n", "dice: 0.9633393503054818 iou:0.9292716479489769 bbox_iou: 0.8980286121368408 angle_error: 4.889364063602519\n", "Predict: (493, 87) (871, 465) (682, 276) 377 0.78556246 0.62037486 (-0.2962213456630707)' 148 116 -53\n", "Ground Truth: (509, 89) (864, 444) (687, 267) 354.8401954457809 0.8019046422926417 0.5974520438240298 (-0.28475768574502236)' 142 106 -51\n", "dice: 0.9273962381780936 iou:0.8646214671136656 bbox_iou: 0.846549391746521 angle_error: 2.0634587852486987\n", "Predict: (143, 83) (824, 764) (483, 423) 681 0.7794156 0.61976194 (0.11903258413076401)' 265 211 21\n", "Ground Truth: (133, 80) (814, 761) (474, 421) 681.5752189186458 0.7484691136649014 0.6631696509111955 (0.15877180201097638)' 255 226 28\n", "dice: 0.9574177820748371 iou:0.9183139378496105 bbox_iou: 0.8699585199356079 angle_error: 7.1530592184382265\n", "Predict: (227, 189) (589, 551) (408, 370) 361 0.7760649 0.63097614 (0.341709166765213)' 140 113 61\n", "Ground Truth: (234, 191) (587, 544) (411, 368) 353.6894359519379 0.7746909354602077 0.6323400623998042 (0.3459380150870453)' 137 111 62\n", "dice: 0.9747143773402995 iou:0.9506759441449948 bbox_iou: 0.947498619556427 angle_error: 0.7611926979298147\n", "Predict: (76, 39) (742, 705) (409, 372) 665 0.74424833 0.66656595 (-0.3481641709804535)' 247 221 -62\n", "Ground Truth: (92, 69) (725, 702) (409, 386) 633.6174573478859 0.7315391876040244 0.6817993964500438 (-0.38184220103107847)' 231 216 -68\n", "dice: 0.9484587438808895 iou:0.9019700733201242 bbox_iou: 0.8706137537956238 angle_error: 6.0620454091125\n", "Predict: (247, 112) (597, 462) (422, 287) 349 0.77037734 0.6393475 (0.4159816801548004)' 134 111 74\n", "Ground Truth: (270, 136) (573, 439) (422, 288) 302.7996038636775 0.7678015328734336 0.6406877602367675 (0.3793605531363433)' 116 97 68\n", "dice: 0.8616512783477956 iou:0.756930861306886 bbox_iou: 0.7518214583396912 angle_error: 6.591802863322272\n", "Predict: (192, 103) (804, 715) (498, 409) 611 0.7945718 0.615441 (-0.06150485575199127)' 242 188 -11\n", "Ground Truth: (226, 119) (799, 692) (513, 406) 572.0207082999706 0.753612576861358 0.6573188602165495 (-0.04901972247230377)' 215 188 -8\n", "dice: 0.9381839932306567 iou:0.8835655021675118 bbox_iou: 0.8698959350585938 angle_error: 2.2473239903437516\n", "Predict: (291, 299) (585, 593) (438, 446) 293 0.77447164 0.6344623 (-0.2669067978858948)' 113 92 -48\n", "Ground Truth: (314, 315) (583, 584) (449, 450) 269.4368005748287 0.8100155566514292 0.5864083883972628 (-0.2705634032562221)' 109 79 -48\n", "dice: 0.8987200981260113 iou:0.8160687365643445 bbox_iou: 0.8157793879508972 angle_error: 0.658188966658912\n", "Predict: (110, 15) (826, 731) (468, 373) 715 0.7804151 0.6237149 (-0.12476254254579544)' 278 222 -22\n", "Ground Truth: (136, 28) (799, 691) (468, 360) 663.9700445321309 0.7568022144042422 0.6536439460997368 (-0.12216616509984035)' 251 217 -21\n", "dice: 0.929860847349328 iou:0.8689158274847876 bbox_iou: 0.8538188934326172 angle_error: 0.4673479402719174\n", "Predict: (342, 117) (716, 491) (529, 304) 374 0.77549654 0.63860285 (-0.3755948841571808)' 145 119 -67\n", "Ground Truth: (356, 130) (705, 479) (531, 305) 348.6776218572107 0.7571463823626525 0.6532452492557048 (-0.3885915398356732)' 132 113 -69\n", "dice: 0.9277377357763854 iou:0.8652153178664046 bbox_iou: 0.8667526245117188 angle_error: 2.339398022128634\n", "Predict: (129, -51) (972, 791) (551, 369) 843 0.7538753 0.6721573 (0.3469182550907135)' 317 283 62\n", "Ground Truth: (136, -37) (917, 743) (527, 353) 780.508062318385 0.7364587603267096 0.6764824420027812 (0.2972366025009253)' 287 264 53\n", "dice: 0.9116954375832859 iou:0.8377208633204237 bbox_iou: 0.8033334612846375 angle_error: 8.942697466161874\n", "Predict: (290, 48) (737, 494) (514, 271) 446 0.769068 0.630861 (-0.3557722568511963)' 171 140 -64\n", "Ground Truth: (301, 53) (736, 488) (519, 271) 434.07351805425776 0.7562635967093639 0.654267049676366 (-0.34880280406269937)' 164 142 -62\n", "dice: 0.9687311359968389 iou:0.9393584639377509 bbox_iou: 0.928043007850647 angle_error: 1.2545015019294468\n", "Predict: (76, 5) (559, 489) (317, 247) 483 0.78527504 0.6195844 (-0.08298031240701675)' 189 149 -14\n", "Ground Truth: (75, 5) (554, 484) (315, 245) 479.9705921033079 0.7704180341124103 0.6375390597558468 (-0.08721690881435866)' 184 153 -15\n", "dice: 0.9807025834356883 iou:0.9621358472007975 bbox_iou: 0.9526196718215942 angle_error: 0.7625873533215426\n", "Predict: (124, 119) (391, 385) (257, 252) 266 0.811507 0.5788563 (0.14242909848690033)' 107 76 25\n", "Ground Truth: (132, 121) (389, 378) (261, 250) 256.74223599556035 0.8059133675363721 0.592033482183385 (0.14674085753072752)' 103 76 26\n", "dice: 0.9617125573794325 iou:0.9262488573993874 bbox_iou: 0.9159579873085022 angle_error: 0.7761166278888929\n", "Predict: (265, -58) (855, 530) (560, 236) 589 0.7858733 0.62040675 (-0.09321651607751846)' 231 182 -16\n", "Ground Truth: (276, -60) (853, 516) (565, 228) 576.4652794262635 0.7725911965474552 0.6349038061134705 (-0.08371550006633695)' 222 183 -15\n", "dice: 0.9662187569175491 iou:0.9346452775991089 bbox_iou: 0.9283590316772461 angle_error: 1.7101828820126723\n", "Predict: (355, 138) (627, 410) (491, 274) 272 0.7611566 0.64272416 (-0.33179837465286255)' 103 87 -59\n", "Ground Truth: (350, 125) (641, 416) (496, 271) 291.4788246030919 0.7913645195807929 0.6113445813521706 (-0.24828171122335674)' 115 89 -44\n", "dice: 0.9317434208993842 iou:0.8722093915712981 bbox_iou: 0.7804275155067444 angle_error: 15.032999417311046\n", "Predict: (287, 117) (709, 539) (498, 328) 422 0.7815991 0.63310975 (-0.17836786806583405)' 164 133 -32\n", "Ground Truth: (280, 87) (723, 530) (502, 309) 443.9594646406359 0.749307147374984 0.662222620342105 (-0.2826591789312061)' 166 147 -50\n", "dice: 0.9157702037753757 iou:0.8446274092117383 bbox_iou: 0.7531986236572266 angle_error: 18.772435955766973\n", "Predict: (349, 236) (617, 505) (483, 371) 268 0.7820195 0.6447238 (-0.0075226034969091415)' 104 86 -1\n", "Ground Truth: (366, 243) (607, 484) (487, 364) 241.4663519085009 0.7489573539775386 0.6626182022273188 (-0.45040848895006386)' 90 80 -81\n", "dice: 0.8592734298593957 iou:0.7532685328382269 bbox_iou: 0.7098304629325867 angle_error: 79.71945938156784\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (241, 69) (848, 676) (544, 373) 606 0.7974889 0.6115068 (-0.06474035233259201)' 241 185 -11\n", "Ground Truth: (264, 101) (823, 660) (544, 381) 559.6642323715175 0.7540235298078992 0.6568474073146945 (-0.05557456769269298)' 211 183 -10\n", "dice: 0.9217919687365418 iou:0.8549296072821626 bbox_iou: 0.8390082716941833 angle_error: 1.6498412351818263\n", "Predict: (449, 180) (790, 520) (619, 350) 340 0.78370553 0.63559365 (-0.31975075602531433)' 133 108 -57\n", "Ground Truth: (479, 206) (770, 497) (625, 352) 291.8834078463522 0.7591250274720579 0.6509448461010714 (-0.328112661460753)' 110 95 -59\n", "dice: 0.8428664178886498 iou:0.7284089157197594 bbox_iou: 0.7311152815818787 angle_error: 1.505142978378963\n", "Predict: (81, 55) (856, 830) (468, 442) 775 0.7418338 0.66022 (-0.333395779132843)' 287 255 -60\n", "Ground Truth: (72, 51) (861, 840) (467, 446) 788.4310192401108 0.7538896688423962 0.6570010404959054 (-0.27508457285753035)' 297 259 -49\n", "dice: 0.976065922645132 iou:0.9532507455622595 bbox_iou: 0.8525465130805969 angle_error: 10.496017129556279\n", "Predict: (345, 169) (741, 565) (543, 367) 395 0.7697675 0.63690287 (-0.3856571912765503)' 152 125 -69\n", "Ground Truth: (344, 152) (743, 551) (544, 352) 398.12096666716764 0.7616026921121338 0.6480442418288663 (-0.45352675661716657)' 151 129 -81\n", "dice: 0.9353386922161789 iou:0.8785316845628235 bbox_iou: 0.8136426210403442 angle_error: 12.21652176131093\n", "Predict: (218, 38) (809, 628) (514, 333) 590 0.796284 0.6139401 (0.0384795144200325)' 234 181 6\n", "Ground Truth: (205, 25) (810, 630) (508, 328) 604.7615455003731 0.7698141580989671 0.6382680957014683 (0.026101410667070838)' 232 193 4\n", "dice: 0.9600771947361877 iou:0.923219675418726 bbox_iou: 0.9135375022888184 angle_error: 2.2280586755330996\n", "Predict: (261, 171) (570, 480) (415, 325) 308 0.8299933 0.5904825 (-0.0808865875005722)' 127 90 -14\n", "Ground Truth: (273, 169) (594, 490) (434, 330) 321.41411987652316 0.8080852207108382 0.5890655957265847 (-0.10154085369262923)' 129 94 -18\n", "dice: 0.8860311371560711 iou:0.7953823187607418 bbox_iou: 0.7921628355979919 angle_error: 3.7177679145702633\n", "Predict: (108, -26) (888, 754) (498, 363) 780 0.7967971 0.60805035 (-0.016071341931819916)' 310 237 -2\n", "Ground Truth: (117, -16) (872, 738) (495, 361) 754.2671331988422 0.7691810692314156 0.639030893412843 (-0.007828084765124421)' 290 241 -1\n", "dice: 0.9675942950858083 iou:0.9372229255225105 bbox_iou: 0.9155251383781433 angle_error: 1.483786290005189\n", "Predict: (398, 136) (793, 531) (596, 333) 395 0.79480404 0.6045888 (-0.2468344122171402)' 156 119 -44\n", "Ground Truth: (403, 142) (788, 527) (596, 335) 384.6561823187039 0.7856002682146576 0.6187343683528969 (-0.2752083891595085)' 151 119 -49\n", "dice: 0.9765069577699789 iou:0.9540924241578942 bbox_iou: 0.9078989028930664 angle_error: 5.107315849626296\n", "Predict: (161, -57) (849, 630) (505, 286) 687 0.77225184 0.62917084 (0.17190764844417572)' 265 216 30\n", "Ground Truth: (169, -44) (870, 656) (520, 306) 700.413515646293 0.7663044587378397 0.6424776077954053 (0.15304222405966814)' 268 225 27\n", "dice: 0.9326236250500921 iou:0.8737532954051567 bbox_iou: 0.8673236966133118 angle_error: 3.395776389211367\n", "Predict: (283, 41) (621, 379) (452, 210) 337 0.7841333 0.62304693 (0.36883142590522766)' 132 104 66\n", "Ground Truth: (291, 47) (626, 382) (459, 215) 334.4015244522668 0.7984413361671506 0.6020726141418619 (0.31092392760682835)' 133 100 55\n", "dice: 0.9416318748971884 iou:0.8897016572619443 bbox_iou: 0.8373655080795288 angle_error: 10.423349693711877\n", "Predict: (131, 45) (714, 629) (422, 337) 583 0.7843986 0.62432015 (-0.1258198618888855)' 228 181 -22\n", "Ground Truth: (144, 62) (715, 633) (430, 348) 570.593927079495 0.7562435902685001 0.654290174294104 (-0.1112787977748565)' 215 186 -20\n", "dice: 0.9427237450932331 iou:0.8916531897109187 bbox_iou: 0.8812069892883301 angle_error: 2.617391540525219\n", "Predict: (333, 139) (634, 440) (483, 289) 301 0.7764618 0.6363069 (-0.34459829330444336)' 116 95 -62\n", "Ground Truth: (336, 140) (629, 433) (483, 287) 293.3973410445296 0.7932041891432102 0.6089557572809886 (-0.3402084371357371)' 116 89 -61\n", "dice: 0.9644600951433048 iou:0.9313596613901355 bbox_iou: 0.9192367196083069 angle_error: 0.7901741103671256\n", "Predict: (53, -57) (806, 695) (429, 319) 752 0.77937394 0.62263465 (-0.09100908786058426)' 293 234 -16\n", "Ground Truth: (70, -38) (795, 686) (433, 324) 724.5908086637588 0.7491124556230501 0.6624428494824319 (-0.07944780915647742)' 271 240 -14\n", "dice: 0.9586351451320692 iou:0.920556460735989 bbox_iou: 0.896786630153656 angle_error: 2.0810301667392306\n", "Predict: (328, 118) (729, 520) (528, 319) 401 0.8171934 0.5765516 (0.10653059184551239)' 163 115 19\n", "Ground Truth: (333, 119) (728, 514) (531, 317) 394.41541552530623 0.8049938909642558 0.5932830989588597 (0.09981964187224002)' 158 117 17\n", "dice: 0.9754812390281323 iou:0.952136042977663 bbox_iou: 0.9439345598220825 angle_error: 1.2079709951890258\n", "Predict: (44, 34) (689, 679) (366, 356) 645 0.7973006 0.6093391 (-0.057177767157554626)' 257 196 -10\n", "Ground Truth: (73, 66) (680, 673) (377, 370) 606.2349945986292 0.7571750296333651 0.6532120440559197 (-0.06989851257096358)' 229 198 -12\n", "dice: 0.9173123787431471 iou:0.8472548874977183 bbox_iou: 0.8284158706665039 angle_error: 2.2897341744136117\n", "Predict: (287, 154) (615, 482) (451, 318) 328 0.7855882 0.6297685 (-0.28324800729751587)' 128 103 -50\n", "Ground Truth: (298, 169) (605, 476) (452, 323) 307.58912450215144 0.784702646397733 0.619872371328481 (-0.289850643923963)' 120 95 -52\n", "dice: 0.928008637301833 iou:0.8656866739728815 bbox_iou: 0.8646209836006165 angle_error: 1.1884745927604783\n", "Predict: (165, 197) (665, 697) (415, 447) 500 0.77970177 0.62255836 (0.11878994107246399)' 194 155 21\n", "Ground Truth: (141, 149) (730, 738) (436, 444) 588.7747701252152 0.7964947273475504 0.6046454740652171 (0.15838866093005743)' 234 178 28\n", "dice: 0.8390847794478132 iou:0.7227786875330177 bbox_iou: 0.7195061445236206 angle_error: 7.12776957436682\n", "Predict: (309, 348) (597, 636) (453, 492) 288 0.7698394 0.64574736 (0.3499232828617096)' 110 92 62\n", "Ground Truth: (320, 348) (631, 659) (476, 504) 311.85910664914053 0.8026701631054963 0.5964231796804982 (0.25165345758190816)' 125 93 45\n", "dice: 0.8526807743544542 iou:0.7431940085155363 bbox_iou: 0.6992758512496948 angle_error: 17.68856855036426\n", "Predict: (199, -84) (993, 709) (596, 312) 793 0.79562515 0.61630094 (0.08671893179416656)' 315 244 15\n", "Ground Truth: (241, -39) (956, 675) (599, 318) 715.8693148026391 0.7754571798527848 0.6314001601320399 (0.06939155518806638)' 277 226 12\n", "dice: 0.8981516906951849 iou:0.8151318862229342 bbox_iou: 0.8136313557624817 angle_error: 3.1189277890980343\n", "Predict: (299, 119) (667, 488) (483, 304) 368 0.82548773 0.5736033 (-0.08082904666662216)' 151 105 -14\n", "Ground Truth: (314, 118) (667, 471) (491, 295) 353.59287260916335 0.8522060916456123 0.5232062474417808 (-0.08339719018015317)' 150 92 -15\n", "dice: 0.9099726090666894 iou:0.8348162776786248 bbox_iou: 0.8232060074806213 angle_error: 0.4622658324355804\n", "Predict: (164, -25) (816, 626) (490, 300) 651 0.77693725 0.62929714 (0.1255645751953125)' 252 204 22\n", "Ground Truth: (168, -3) (791, 619) (480, 308) 622.2951988373364 0.7522731990776093 0.6588512988144893 (0.22504508953194)' 234 205 40\n", "dice: 0.9408500175323035 iou:0.8883066922592325 bbox_iou: 0.7764652371406555 angle_error: 17.906492580592946\n", "Predict: (233, 92) (566, 425) (400, 259) 332 0.7963832 0.5962763 (0.08516915887594223)' 132 98 15\n", "Ground Truth: (239, 96) (570, 427) (405, 262) 331.7529974906029 0.8023680328842844 0.5968295735010155 (-0.012095775674984046)' 133 99 -2\n", "dice: 0.9394100836756659 iou:0.8857429900251751 bbox_iou: 0.782273530960083 angle_error: 17.50768821916673\n", "Predict: (165, -19) (818, 633) (491, 307) 652 0.7630743 0.6337224 (-0.2049848437309265)' 248 206 -36\n", "Ground Truth: (178, -15) (825, 631) (502, 308) 647.4659888550132 0.7530619498056508 0.6579496179457143 (-0.18671824080041477)' 243 213 -33\n", "dice: 0.9687877113467058 iou:0.9394648628672654 bbox_iou: 0.9176345467567444 angle_error: 3.287988527492111\n", "Predict: (397, 98) (738, 439) (568, 269) 340 0.8128597 0.5846522 (-0.028347214683890343)' 138 99 -5\n", "Ground Truth: (406, 96) (757, 447) (582, 272) 351.2945388075368 0.7973764720363774 0.6034821967902784 (-0.00910132430985966)' 140 106 -1\n", "dice: 0.9237404646838144 iou:0.8582878333453615 bbox_iou: 0.845839262008667 angle_error: 3.464260267325523\n", "Predict: (132, 95) (373, 336) (253, 216) 240 0.78837174 0.62177074 (-0.15174123644828796)' 94 74 -27\n", "Ground Truth: (121, 78) (372, 329) (247, 204) 251.46614882325613 0.7714676544251376 0.6362685425005516 (-0.16233804195373325)' 96 80 -29\n", "dice: 0.8889941234068075 iou:0.8001704960668925 bbox_iou: 0.8208683729171753 angle_error: 1.9074249909801502\n", "Predict: (162, 166) (298, 302) (230, 234) 135 0.78381133 0.65492934 (-0.2849062383174896)' 52 44 -51\n", "Ground Truth: (170, 180) (289, 299) (230, 240) 119.70757077144286 0.7438961414564395 0.6682952421843364 (-0.43169303685995536)' 44 40 -77\n", "dice: 0.8599398744623913 iou:0.7542934404945322 bbox_iou: 0.676358699798584 angle_error: 26.421623737643827\n", "Predict: (97, 12) (763, 679) (430, 345) 666 0.78729516 0.6167634 (-0.10024384409189224)' 262 205 -18\n", "Ground Truth: (102, 25) (745, 668) (424, 347) 642.9960678231245 0.7754417561028641 0.6314191024130533 (-0.11223372743340776)' 249 203 -20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dice: 0.9697095132286606 iou:0.941200103931345 bbox_iou: 0.9285868406295776 angle_error: 2.158179001472792\n", "Predict: (320, 114) (692, 485) (506, 300) 371 0.78000677 0.6269462 (-0.3234656751155853)' 144 116 -58\n", "Ground Truth: (345, 131) (680, 466) (513, 299) 335.4813191878201 0.7845980832471829 0.6200047159214569 (-0.33823374662389916)' 131 104 -60\n", "dice: 0.8989167039339683 iou:0.8163930078184207 bbox_iou: 0.8133968114852905 angle_error: 2.6582528714964866\n", "Predict: (-24, 0) (663, 687) (319, 343) 687 0.7944768 0.6216127 (-0.14113211631774902)' 272 213 -25\n", "Ground Truth: (4, 58) (627, 681) (316, 370) 623.9050500997728 0.7898188994001533 0.6133401227299016 (-0.17182250734451188)' 246 191 -30\n", "dice: 0.8879477044424461 iou:0.7984765716411317 bbox_iou: 0.7854551076889038 angle_error: 5.524270384817314\n", "Predict: (219, 127) (573, 481) (396, 304) 354 0.7748625 0.62030333 (-0.3216063380241394)' 137 109 -57\n", "Ground Truth: (224, 140) (559, 475) (392, 308) 334.92681422065925 0.8001748102002786 0.5997668489679536 (-0.30392111011078493)' 134 100 -54\n", "dice: 0.9414428986332464 iou:0.8893643029910284 bbox_iou: 0.876972496509552 angle_error: 3.183341024403802\n", "Predict: (252, 50) (810, 608) (531, 329) 557 0.77204937 0.62781566 (0.12660273909568787)' 215 174 22\n", "Ground Truth: (255, 70) (802, 617) (529, 344) 546.8567564728445 0.7590836084341464 0.6509931454374891 (0.11261686851432672)' 207 178 20\n", "dice: 0.9419985307031078 iou:0.8903565430104027 bbox_iou: 0.8889182806015015 angle_error: 2.5174567046450065\n", "Predict: (342, 140) (616, 414) (479, 277) 274 0.77761924 0.62913114 (0.42755791544914246)' 106 86 76\n", "Ground Truth: (362, 154) (605, 397) (484, 276) 243.73859257819635 0.7903303205387922 0.6126809809656655 (0.3465746348594129)' 96 74 62\n", "dice: 0.8768390568330633 iou:0.7806887001970276 bbox_iou: 0.7514343857765198 angle_error: 14.576990506151319\n", "Predict: (-8, 15) (767, 791) (379, 403) 776 0.75761306 0.6497007 (-0.2633828818798065)' 293 252 -47\n", "Ground Truth: (-42, 8) (770, 821) (364, 415) 812.0885469848715 0.7597841421244601 0.6501754050840421 (-0.23834927355692404)' 308 264 -42\n", "dice: 0.9572814481852162 iou:0.9180631211740984 bbox_iou: 0.8813391923904419 angle_error: 4.506049498118848\n", "Predict: (186, 96) (603, 514) (395, 305) 417 0.7689406 0.64085996 (0.47808873653411865)' 160 133 86\n", "Ground Truth: (188, 98) (611, 521) (400, 310) 423.31043126292076 0.7415456289689971 0.6709024371374535 (0.42838027560864705)' 156 142 77\n", "dice: 0.958981612377041 iou:0.9211956520448795 bbox_iou: 0.8573465347290039 angle_error: 8.947522966584884\n", "Predict: (146, 44) (685, 583) (416, 313) 538 0.79463756 0.6169688 (-0.0015010908246040344)' 213 165 0\n", "Ground Truth: (152, 39) (687, 574) (420, 307) 535.2871248068648 0.753375116476981 0.6575910080538626 (0.025146481008519465)' 201 176 4\n", "dice: 0.9560667292592047 iou:0.9158312662846363 bbox_iou: 0.8819344639778137 angle_error: 4.79656292996223\n", "Predict: (209, 182) (472, 446) (340, 314) 263 0.7904248 0.63340676 (-0.1930355578660965)' 103 83 -34\n", "Ground Truth: (212, 176) (485, 449) (349, 313) 273.7442600969014 0.8166162093074344 0.5771810519207611 (-0.1871662130760689)' 111 79 -33\n", "dice: 0.9393172506255225 iou:0.885577946072444 bbox_iou: 0.8774948120117188 angle_error: 1.0564820622049709\n", "Predict: (166, 92) (845, 771) (506, 431) 679 0.7980823 0.61000687 (0.039310939610004425)' 270 207 7\n", "Ground Truth: (182, 99) (863, 780) (523, 440) 680.9783315935978 0.7730201910655757 0.6343814185527037 (0.08435211983870454)' 263 216 15\n", "dice: 0.9496504205389987 iou:0.9041279580711812 bbox_iou: 0.8536209464073181 angle_error: 8.107412441166021\n", "Predict: (232, 247) (585, 600) (408, 424) 353 0.8102963 0.60038906 (-0.11960392445325851)' 143 105 -21\n", "Ground Truth: (240, 255) (583, 598) (412, 427) 343.93572618150614 0.7964046161794999 0.6047641584353223 (-0.10918029096104022)' 136 104 -19\n", "dice: 0.9582660524564328 iou:0.9198759958970775 bbox_iou: 0.9180085062980652 angle_error: 1.8762540285992948\n", "Predict: (42, 6) (672, 636) (357, 321) 630 0.7955309 0.6071104 (0.06857066601514816)' 250 191 12\n", "Ground Truth: (47, 10) (650, 613) (349, 312) 603.8339739001111 0.7634449532915146 0.6458729002626731 (0.05557456769269298)' 230 195 10\n", "dice: 0.9498038812342701 iou:0.9044062001967326 bbox_iou: 0.8852052688598633 angle_error: 2.3392976980419338\n", "Predict: (280, 219) (571, 511) (425, 365) 291 0.77702713 0.6194015 (0.25938066840171814)' 113 90 46\n", "Ground Truth: (282, 223) (561, 502) (422, 363) 278.3381741766659 0.7627484107345202 0.6466953393383653 (0.26285913479307593)' 106 90 47\n", "dice: 0.9662341821293874 iou:0.9346741451750367 bbox_iou: 0.9301679134368896 angle_error: 0.626123950444402\n", "Predict: (144, 40) (749, 645) (447, 343) 605 0.79620916 0.6114089 (0.06341401487588882)' 240 184 11\n", "Ground Truth: (148, 53) (743, 648) (446, 351) 594.8643946446955 0.7665679844099008 0.6421631609472331 (0.10363936050644551)' 228 191 18\n", "dice: 0.959519675891221 iou:0.9221891598124122 bbox_iou: 0.8717593550682068 angle_error: 7.240562213500205\n", "Predict: (399, 205) (675, 481) (537, 343) 275 0.81433654 0.6169966 (-0.08357880264520645)' 111 84 -15\n", "Ground Truth: (389, 198) (670, 479) (530, 339) 280.8412226223209 0.811782536307333 0.5839598562798931 (-0.04188724258679011)' 113 82 -7\n", "dice: 0.9406170930155687 iou:0.8878915138371136 bbox_iou: 0.8586107492446899 angle_error: 7.504480810514941\n", "Predict: (370, -46) (1009, 592) (690, 273) 639 0.7479343 0.66489196 (0.3400993347167969)' 238 212 61\n", "Ground Truth: (367, -31) (1016, 617) (692, 293) 649.4609962114738 0.7439399791803297 0.6682464420984003 (0.425515486632993)' 241 217 76\n", "dice: 0.9457968873779696 iou:0.8971676103531597 bbox_iou: 0.7984853982925415 angle_error: 15.3749073449153\n", "Predict: (523, 16) (856, 348) (690, 182) 332 0.76805794 0.641829 (-0.3739814758300781)' 127 106 -67\n", "Ground Truth: (525, 24) (844, 343) (685, 184) 319.6869649453978 0.7695027541770949 0.6386434931273202 (-0.31888167476142315)' 123 102 -57\n", "dice: 0.9513300683017984 iou:0.907177787353193 bbox_iou: 0.8469035029411316 angle_error: 9.917964192357893\n", "Predict: (136, -8) (900, 755) (518, 373) 764 0.8018947 0.60616654 (0.061614781618118286)' 306 231 11\n", "Ground Truth: (151, -3) (892, 737) (522, 367) 740.3349002606861 0.7792257258125569 0.6267433830778701 (0.10790705141630505)' 288 232 19\n", "dice: 0.9594791228569832 iou:0.9221142448303857 bbox_iou: 0.861228346824646 angle_error: 8.332608563673617\n", "Predict: (202, 165) (601, 565) (402, 365) 399 0.83139795 0.56334656 (-0.02219506911933422)' 165 112 -3\n", "Ground Truth: (218, 176) (579, 537) (399, 357) 360.4948794310399 0.8167550131771663 0.576984617169268 (-0.017188733853924696)' 147 104 -3\n", "dice: 0.906073724170642 iou:0.8282767716532854 bbox_iou: 0.8179089426994324 angle_error: 0.9011403477737145\n", "Predict: (83, 34) (905, 857) (494, 445) 822 0.77643585 0.6222254 (-0.17249883711338043)' 319 255 -31\n", "Ground Truth: (129, 75) (882, 828) (506, 452) 753.1190506155053 0.7749372420243812 0.6320381878681427 (-0.20740838340236112)' 291 238 -37\n", "dice: 0.9197895469160657 iou:0.8514910629591884 bbox_iou: 0.8345727324485779 angle_error: 6.283718332016527\n", "Predict: (403, 153) (763, 514) (583, 333) 360 0.7724089 0.6391841 (-0.4073997139930725)' 139 115 -73\n", "Ground Truth: (394, 167) (755, 528) (575, 348) 361.8721995898552 0.7532880401118328 0.6576907545529843 (-0.3952760474455329)' 136 119 -71\n", "dice: 0.918864715449536 iou:0.8499072489633893 bbox_iou: 0.8526052832603455 angle_error: 2.182259978557127\n", "Predict: (171, -21) (709, 516) (440, 247) 538 0.7936233 0.60894686 (0.07408424466848373)' 213 163 13\n", "Ground Truth: (166, -34) (713, 512) (440, 239) 547.2270324097668 0.7911926391746589 0.6115670099963193 (0.0893154156281748)' 216 167 16\n", "dice: 0.968807388717591 iou:0.9395018720244371 bbox_iou: 0.9300750494003296 angle_error: 2.741610772744391\n", "Predict: (379, 111) (652, 384) (515, 247) 272 0.8130152 0.5883552 (-0.1058657094836235)' 110 80 -19\n", "Ground Truth: (372, 104) (663, 395) (518, 250) 291.345525244511 0.8283429093254857 0.5602214067403977 (-0.09485634608276962)' 120 81 -17\n", "dice: 0.9414889992266383 iou:0.8894465891604098 bbox_iou: 0.8742209076881409 angle_error: 1.9816854121536984\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (3, -17) (737, 716) (370, 349) 733 0.7975327 0.610753 (-0.021163128316402435)' 292 223 -3\n", "Ground Truth: (2, -4) (725, 718) (364, 357) 723.2844915356612 0.7600937202908271 0.6498134627525425 (-0.02565343839141676)' 274 235 -4\n", "dice: 0.9581684733611286 iou:0.9196961781837663 bbox_iou: 0.898452877998352 angle_error: 0.8082558135025781\n", "Predict: (269, 142) (637, 510) (453, 326) 367 0.7934281 0.61053276 (-0.2418401539325714)' 145 112 -43\n", "Ground Truth: (286, 168) (609, 491) (448, 330) 322.9117958700177 0.7883453105642226 0.6152330219611097 (-0.25229007735427567)' 127 99 -45\n", "dice: 0.8737037320581492 iou:0.7757317119186773 bbox_iou: 0.7751308679580688 angle_error: 1.8809862159067663\n", "Predict: (218, 29) (800, 611) (509, 320) 582 0.7900951 0.6128251 (0.04539622366428375)' 229 178 8\n", "Ground Truth: (213, 12) (830, 629) (522, 321) 616.1327753528456 0.7550190780446551 0.6557028227700404 (0.07607606279792598)' 232 202 13\n", "dice: 0.9196050673275571 iou:0.8511749171692622 bbox_iou: 0.8373966813087463 angle_error: 5.5223710440556\n", "Predict: (280, 128) (587, 435) (433, 282) 307 0.78635466 0.6162495 (0.28581950068473816)' 120 94 51\n", "Ground Truth: (301, 155) (580, 434) (441, 295) 278.4388412632117 0.7919800233313771 0.6105470028130769 (0.28482251693975746)' 110 85 51\n", "dice: 0.8920667450419731 iou:0.8051628932067468 bbox_iou: 0.7894662618637085 angle_error: 0.17945707409652556\n", "Predict: (173, 48) (726, 601) (450, 324) 553 0.7740795 0.6325917 (-0.17534489929676056)' 214 174 -31\n", "Ground Truth: (185, 45) (738, 598) (462, 322) 552.6876295376983 0.7679527771501343 0.6405064652814959 (-0.1804817054662093)' 212 177 -32\n", "dice: 0.961427201968026 iou:0.9257196065503218 bbox_iou: 0.9244831204414368 angle_error: 0.9246251105007772\n", "Predict: (285, 245) (528, 488) (406, 367) 242 0.7651961 0.6613876 (-0.38672545552253723)' 92 80 -69\n", "Ground Truth: (289, 248) (530, 489) (410, 369) 240.78197969117207 0.7617347454126134 0.6478890164458578 (-0.360326791160051)' 91 78 -64\n", "dice: 0.963482962139248 iou:0.9295389529995209 bbox_iou: 0.9063096642494202 angle_error: 4.751759585247527\n", "Predict: (114, 18) (779, 683) (447, 351) 664 0.7802309 0.62024087 (0.1269417107105255)' 259 205 22\n", "Ground Truth: (106, 21) (771, 686) (439, 354) 664.74049121142 0.7442242597535045 0.6679298250522643 (0.1540619849129547)' 247 222 27\n", "dice: 0.9539248845341629 iou:0.9119085909135333 bbox_iou: 0.8806520104408264 angle_error: 4.881649356437254\n", "Predict: (240, 115) (550, 425) (395, 270) 309 0.7917448 0.62444425 (0.34325674176216125)' 122 96 61\n", "Ground Truth: (237, 116) (558, 437) (398, 277) 321.75175772635646 0.7783640461507417 0.6278131980612116 (0.35236904400545627)' 125 101 63\n", "dice: 0.9496410348384798 iou:0.9041109433406395 bbox_iou: 0.8993756771087646 angle_error: 1.6402144037931023\n", "Predict: (158, 54) (695, 591) (426, 323) 536 0.80083704 0.6036595 (0.01621740125119686)' 214 161 2\n", "Ground Truth: (152, 56) (689, 593) (421, 325) 537.4793419992995 0.774493022283529 0.632582451886017 (0.05538592019597958)' 208 170 9\n", "dice: 0.9635829408415305 iou:0.9297250873349406 bbox_iou: 0.8803412914276123 angle_error: 7.0503334100608885\n", "Predict: (221, 200) (491, 470) (356, 335) 270 0.81084836 0.5974948 (-0.11782263219356537)' 109 80 -21\n", "Ground Truth: (205, 188) (498, 481) (352, 335) 292.73945882986123 0.818932988939261 0.5738891527350974 (-0.14292113889652203)' 119 84 -25\n", "dice: 0.9321596598822685 iou:0.8729391697072393 bbox_iou: 0.860248863697052 angle_error: 4.517731206532197\n", "Predict: (76, 9) (948, 881) (512, 445) 872 0.7926429 0.6108885 (-0.07967223227024078)' 345 266 -14\n", "Ground Truth: (150, 36) (941, 827) (546, 432) 791.7320944966169 0.7854727682795197 0.6188962193221962 (-0.08072104870121255)' 310 245 -14\n", "dice: 0.9047256501574827 iou:0.8260265113371527 bbox_iou: 0.8239618539810181 angle_error: 0.18878695757491748\n", "Predict: (400, 155) (872, 627) (636, 391) 471 0.7933588 0.6031279 (-0.24271591007709503)' 186 142 -43\n", "Ground Truth: (408, 153) (869, 614) (639, 384) 460.9085233362473 0.8041118383259308 0.5944780496066209 (-0.24274078076876193)' 185 137 -43\n", "dice: 0.9663328517319402 iou:0.9348588211892578 bbox_iou: 0.9293783903121948 angle_error: 0.004476724500044327\n", "Predict: (35, 48) (565, 577) (300, 312) 529 0.741388 0.6622647 (0.37280964851379395)' 196 175 67\n", "Ground Truth: (38, 71) (529, 562) (284, 317) 491.6229837304192 0.7170616746293255 0.6970097235891242 (0.4022788649415763)' 176 171 72\n", "dice: 0.9105480537316911 iou:0.8357854211473796 bbox_iou: 0.8210609555244446 angle_error: 5.3044589570008185\n", "Predict: (158, 118) (410, 370) (284, 244) 252 0.7683109 0.64699894 (-0.3428601622581482)' 96 81 -61\n", "Ground Truth: (159, 117) (412, 370) (286, 244) 252.87694374932641 0.7520733095719667 0.6590794618477113 (-0.40259717482776003)' 95 83 -72\n", "dice: 0.9753416934770542 iou:0.9518701866447155 bbox_iou: 0.850837230682373 angle_error: 10.752662262530137\n", "Predict: (30, -2) (712, 680) (371, 339) 682 0.766063 0.63553506 (-0.20605145394802094)' 261 216 -37\n", "Ground Truth: (7, -29) (742, 705) (375, 338) 735.3730530526665 0.7415746303678383 0.6708703806211779 (-0.18372963552278243)' 272 246 -33\n", "dice: 0.9146929558952646 iou:0.8427964794513892 bbox_iou: 0.8393154740333557 angle_error: 4.017927316542931\n", "Predict: (139, 226) (512, 600) (326, 413) 373 0.7624967 0.64324546 (-0.4098809063434601)' 142 119 -73\n", "Ground Truth: (123, 228) (522, 627) (323, 428) 399.0967654742393 0.769948610419982 0.6381058981958551 (-0.3753521870054244)' 153 127 -67\n", "dice: 0.9164488130540931 iou:0.8457826673026791 bbox_iou: 0.8315664529800415 angle_error: 6.215169480846427\n", "Predict: (-2, 6) (519, 528) (258, 267) 521 0.77023786 0.63231784 (-0.15640142560005188)' 200 164 -28\n", "Ground Truth: (8, 10) (513, 515) (261, 263) 504.9314521595976 0.7511276993696202 0.6601569353113709 (-0.14342809627941933)' 189 166 -25\n", "dice: 0.970625571066626 iou:0.9429276109689035 bbox_iou: 0.9222139120101929 angle_error: 2.3351992777138584\n", "Predict: (174, 80) (446, 351) (310, 215) 271 0.7791574 0.62261486 (-0.3261861503124237)' 105 84 -58\n", "Ground Truth: (185, 92) (442, 349) (314, 221) 257.72328548270525 0.8018910655004376 0.597470266264835 (-0.3389351975910019)' 103 76 -61\n", "dice: 0.9297610699869714 iou:0.8687415902313075 bbox_iou: 0.8767744302749634 angle_error: 2.2948285101440717\n", "Predict: (212, 90) (809, 687) (510, 389) 596 0.7967696 0.61033434 (0.010986465960741043)' 237 181 1\n", "Ground Truth: (234, 93) (837, 696) (536, 395) 602.0616848562945 0.7751132678562463 0.6318223025449566 (0.014512592374984035)' 233 190 2\n", "dice: 0.9221352488956526 iou:0.8555203683494251 bbox_iou: 0.8576035499572754 angle_error: 0.6347027545637385\n", "Predict: (291, 191) (609, 510) (450, 351) 318 0.7858286 0.6104802 (0.2140926867723465)' 124 97 38\n", "Ground Truth: (346, 244) (593, 491) (470, 368) 246.7714278517673 0.7877816394399395 0.6159546156668697 (0.20340001727144225)' 97 76 36\n", "dice: 0.7613052246793374 iou:0.614602757553617 bbox_iou: 0.6086212396621704 angle_error: 1.9246805101627658\n", "Predict: (35, -67) (781, 678) (408, 305) 746 0.79267836 0.61346865 (-0.04612275958061218)' 295 228 -8\n", "Ground Truth: (29, -79) (782, 673) (406, 297) 752.7779702249528 0.7942979519197678 0.6075284055713464 (-0.04475203156244423)' 298 228 -8\n", "dice: 0.9762762021965526 iou:0.9536519560171401 bbox_iou: 0.9543389081954956 angle_error: 0.24673104327023232\n", "Predict: (317, 63) (709, 455) (513, 259) 392 0.7925193 0.603291 (-0.24026723206043243)' 155 118 -43\n", "Ground Truth: (311, 51) (716, 456) (514, 254) 404.57106186676276 0.7976596213059794 0.603107891291435 (-0.26317744467925985)' 161 122 -47\n", "dice: 0.9646674063319455 iou:0.9317463897415312 bbox_iou: 0.9066280126571655 angle_error: 4.123838271388934\n", "Predict: (54, 10) (376, 331) (215, 170) 321 0.7659028 0.6373679 (0.23798014223575592)' 122 102 42\n", "Ground Truth: (78, 13) (371, 306) (225, 160) 292.1462174254529 0.7476872434801861 0.6640510416654738 (0.19932681994578805)' 109 97 35\n", "dice: 0.8845884628423177 iou:0.793060169609189 bbox_iou: 0.7755506038665771 angle_error: 6.957598012194218\n", "Predict: (164, 122) (307, 266) (236, 194) 143 0.7481165 0.6394057 (0.3112250566482544)' 53 45 56\n", "Ground Truth: (159, 121) (310, 272) (235, 197) 150.17619413209272 0.7339512140189834 0.6792021903969835 (-0.4796929984789725)' 55 51 -86\n", "dice: 0.9141824746075384 iou:0.8419301155386244 bbox_iou: 0.6956413984298706 angle_error: 37.63475007709917\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (56, -48) (940, 835) (498, 393) 884 0.78538436 0.6184974 (0.07600598782300949)' 347 273 13\n", "Ground Truth: (79, -27) (936, 829) (508, 401) 857.6359259639256 0.7718893063580051 0.6357569494155433 (0.09740282517223994)' 331 272 17\n", "dice: 0.9644089624803439 iou:0.9312642998438838 bbox_iou: 0.9096325039863586 angle_error: 3.8514307228614797\n", "Predict: (362, 152) (844, 634) (603, 393) 481 0.81339043 0.5801144 (-0.13997359573841095)' 195 139 -25\n", "Ground Truth: (364, 147) (843, 626) (604, 387) 478.1173882008476 0.8044677091694156 0.5939963845880821 (-0.15138584343401407)' 192 142 -27\n", "dice: 0.9727414692701881 iou:0.946929560739795 bbox_iou: 0.9390023350715637 angle_error: 2.0542045852085593\n", "Predict: (43, -36) (787, 706) (415, 335) 743 0.782064 0.62458855 (-0.13158883154392242)' 290 232 -23\n", "Ground Truth: (60, -26) (775, 688) (418, 331) 714.5048838825387 0.7820016526183114 0.6232763554814426 (-0.1504957449701979)' 279 222 -27\n", "dice: 0.9587048472197568 iou:0.9206850187096604 bbox_iou: 0.9143534898757935 angle_error: 3.4032444167295886\n", "Predict: (308, 67) (705, 465) (506, 266) 397 0.7816945 0.61879855 (-0.31932246685028076)' 155 122 -57\n", "Ground Truth: (327, 90) (696, 459) (512, 275) 368.5737753883203 0.8102801119948151 0.5860427800985778 (-0.32492956259891514)' 149 108 -58\n", "dice: 0.9180603517584787 iou:0.8485319428404384 bbox_iou: 0.8460375070571899 angle_error: 1.0092772347541867\n", "Predict: (199, -120) (962, 642) (581, 260) 763 0.79924965 0.60768306 (-0.04964052885770798)' 304 231 -8\n", "Ground Truth: (218, -144) (1015, 652) (617, 254) 797.4009342858835 0.8132671685176385 0.5818904644443859 (-0.03151267873219528)' 324 232 -5\n", "dice: 0.9258345091909413 iou:0.86191049434441 bbox_iou: 0.8611134886741638 angle_error: 3.2630130225922853\n", "Predict: (303, 117) (678, 492) (491, 305) 375 0.79073817 0.6010732 (-0.21825219690799713)' 148 112 -39\n", "Ground Truth: (314, 115) (691, 492) (503, 304) 377.08786953706164 0.8147013595986434 0.5798807590083687 (-0.20785635567801533)' 153 109 -37\n", "dice: 0.9425766309965773 iou:0.8913900133348825 bbox_iou: 0.8836254477500916 angle_error: 1.871251421396721\n", "Predict: (257, 145) (624, 512) (441, 328) 366 0.76700395 0.63798684 (-0.005401059053838253)' 140 116 0\n", "Ground Truth: (239, 110) (654, 525) (447, 318) 415.0796771946321 0.7581484165326648 0.6520820335732407 (-0.004138028520389279)' 157 135 0\n", "dice: 0.8683560994667673 iou:0.767340414292514 bbox_iou: 0.7695774435997009 angle_error: 0.22734549602081533\n", "Predict: (286, 236) (496, 446) (391, 341) 209 0.7574649 0.6557453 (-0.2930434048175812)' 79 68 -52\n", "Ground Truth: (283, 226) (494, 437) (389, 332) 210.9914691166446 0.7750171164958258 0.6319402417463977 (-0.177935226376739)' 81 66 -32\n", "dice: 0.9085890468628427 iou:0.8324903138008554 bbox_iou: 0.7512694597244263 angle_error: 20.719472119351593\n", "Predict: (107, 0) (736, 628) (421, 313) 629 0.75887394 0.64542204 (-0.24912545084953308)' 238 202 -44\n", "Ground Truth: (84, -26) (775, 664) (430, 319) 691.2195387545119 0.7736094974449383 0.6336626432597946 (-0.2744479530851627)' 267 219 -49\n", "dice: 0.9025463493714552 iou:0.8224004256166443 bbox_iou: 0.819030225276947 angle_error: 4.558050402413329\n", "Predict: (211, 188) (560, 538) (385, 363) 349 0.7652985 0.64877415 (-0.4009084403514862)' 133 113 -72\n", "Ground Truth: (219, 188) (562, 531) (391, 360) 342.6835469000518 0.7991454578934691 0.6011377023014257 (-0.39827049881065735)' 136 103 -71\n", "dice: 0.9461618843399049 iou:0.8978246945900747 bbox_iou: 0.8811266422271729 angle_error: 0.47482947734918923\n", "Predict: (184, 59) (614, 488) (399, 273) 429 0.74956995 0.6681899 (-0.29291918873786926)' 160 143 -52\n", "Ground Truth: (182, 72) (593, 483) (388, 278) 410.75725542466074 0.7130196633951323 0.7011440362806292 (0.4344281634461391)' 146 144 78\n", "dice: 0.9390519186681678 iou:0.8851063828583907 bbox_iou: 0.6965030431747437 angle_error: 49.07747660687849\n", "Predict: (292, 109) (544, 361) (418, 235) 252 0.7674511 0.6512465 (-0.3923642039299011)' 96 82 -70\n", "Ground Truth: (317, 134) (522, 339) (420, 237) 204.17501136035233 0.794455692296821 0.6073221163247546 (-0.3478478744041481)' 81 62 -62\n", "dice: 0.7807308968192468 iou:0.6403269752202568 bbox_iou: 0.6337185502052307 angle_error: 8.012939314635545\n", "Predict: (169, 92) (701, 623) (435, 358) 531 0.77401465 0.6252964 (0.14143751561641693)' 205 166 25\n", "Ground Truth: (179, 89) (706, 616) (443, 353) 527.7494527102799 0.7382177243372273 0.6745625185810175 (0.10128737500118068)' 194 178 18\n", "dice: 0.9521986051319812 iou:0.9087586729658058 bbox_iou: 0.8609464764595032 angle_error: 7.227025310742526\n", "Predict: (358, 258) (633, 533) (495, 395) 274 0.76298535 0.6417183 (0.3412393629550934)' 104 87 61\n", "Ground Truth: (372, 273) (627, 528) (500, 401) 255.2645139771684 0.7913360022226492 0.611381494311265 (0.3287492812331206)' 101 78 59\n", "dice: 0.9183034547876889 iou:0.8489473862629819 bbox_iou: 0.8261588215827942 angle_error: 2.248214709955107\n", "Predict: (196, 85) (809, 698) (503, 391) 612 0.7752419 0.6330757 (0.18091697990894318)' 237 193 32\n", "Ground Truth: (196, 119) (765, 688) (481, 404) 568.1827213458713 0.761913348886363 0.6476789704697592 (0.14635771644980855)' 216 184 26\n", "dice: 0.8964117255967872 iou:0.8122700706307701 bbox_iou: 0.8053147792816162 angle_error: 6.220667422644233\n", "Predict: (286, 174) (586, 474) (436, 324) 299 0.7807146 0.63063025 (0.34968045353889465)' 116 94 62\n", "Ground Truth: (297, 195) (582, 480) (440, 338) 285.5738287448624 0.7875091390147071 0.6163029741679937 (0.28609575648449265)' 112 88 51\n", "dice: 0.912197422533784 iou:0.8385689107839187 bbox_iou: 0.7884162068367004 angle_error: 11.445245469792361\n", "Predict: (58, -58) (609, 491) (333, 216) 550 0.79138184 0.6153508 (0.016847066581249237)' 217 169 3\n", "Ground Truth: (100, -23) (589, 465) (345, 221) 488.5405371102791 0.7626388635092871 0.6468245232404711 (0.08294921790449916)' 186 158 14\n", "dice: 0.8900509995590885 iou:0.8018845903780519 bbox_iou: 0.7618810534477234 angle_error: 11.898387238184986\n", "Predict: (275, 82) (527, 334) (401, 208) 252 0.79358006 0.63825446 (-0.15051095187664032)' 99 80 -27\n", "Ground Truth: (295, 87) (536, 328) (416, 208) 240.96424402803 0.7934289204242276 0.6086629183993754 (-0.06926189279859607)' 95 73 -12\n", "dice: 0.8725105514703364 iou:0.773852520401109 bbox_iou: 0.7474557161331177 angle_error: 14.624830634047965\n", "Predict: (111, -22) (848, 713) (479, 345) 736 0.7919881 0.6150144 (0.05027191340923309)' 291 226 9\n", "Ground Truth: (110, -23) (849, 715) (480, 346) 739.4041019334421 0.7496847779861214 0.6617950843394803 (0.0291548471394385)' 277 244 5\n", "dice: 0.9583040719660456 iou:0.9199460669628436 bbox_iou: 0.886192262172699 angle_error: 3.801071928563027\n", "Predict: (376, 210) (727, 561) (551, 385) 351 0.7645578 0.63170385 (0.2733072340488434)' 134 110 49\n", "Ground Truth: (389, 215) (744, 570) (567, 393) 354.9641606754124 0.7664379397692948 0.642318366919551 (0.23000838532141032)' 136 114 41\n", "dice: 0.9132871812150161 iou:0.8404126328758419 bbox_iou: 0.8184674978256226 angle_error: 7.793792770937948\n", "Predict: (8, -146) (914, 759) (461, 306) 906 0.77989084 0.6219814 (-0.02767610177397728)' 353 281 -4\n", "Ground Truth: (9, -148) (926, 768) (468, 310) 916.2140721927382 0.7557425944603257 0.6548687890855509 (-0.029921129301276325)' 346 300 -5\n", "dice: 0.9656062998876824 iou:0.9334997881201652 bbox_iou: 0.9208049774169922 angle_error: 0.40410495491382825\n", "Predict: (120, 48) (577, 505) (348, 276) 456 0.81966186 0.5732856 (0.11823412030935287)' 186 130 21\n", "Ground Truth: (137, 73) (554, 490) (346, 282) 417.06175566695157 0.8200751935478945 0.5722557792870102 (0.09581127574132099)' 171 119 17\n", "dice: 0.9143438048074998 iou:0.8422038292199633 bbox_iou: 0.8294761776924133 angle_error: 4.036112022245739\n", "Predict: (73, -51) (729, 604) (401, 276) 655 0.7734798 0.6238348 (0.1660740077495575)' 253 204 29\n", "Ground Truth: (84, -51) (713, 577) (399, 263) 629.3947555961997 0.7596250139502859 0.6503613135642586 (0.12102258794457565)' 239 204 21\n", "dice: 0.9524866419756398 iou:0.9092835281566783 bbox_iou: 0.8586632013320923 angle_error: 8.109255564896731\n", "Predict: (297, 178) (627, 508) (462, 343) 329 0.7699497 0.63469994 (0.33394113183021545)' 126 104 60\n", "Ground Truth: (308, 186) (607, 485) (458, 336) 299.1455191708544 0.7666302361327293 0.6420888420203824 (0.343391535997575)' 114 96 61\n", "dice: 0.9106077724320614 iou:0.8358860559020028 bbox_iou: 0.8327669501304626 angle_error: 1.701072750124716\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (183, 104) (655, 576) (419, 340) 472 0.7824476 0.61720526 (0.11761289089918137)' 184 145 21\n", "Ground Truth: (187, 105) (654, 572) (421, 339) 467.22660931928954 0.7783589220867302 0.6278195508328674 (0.16488452104320359)' 181 146 29\n", "dice: 0.9775561691620956 iou:0.9560976746869089 bbox_iou: 0.8746652007102966 angle_error: 8.508893425924\n", "Predict: (231, 189) (471, 429) (351, 309) 239 0.8135983 0.59555256 (-0.018428567796945572)' 97 71 -3\n", "Ground Truth: (239, 175) (492, 428) (366, 302) 252.3808697821608 0.8355387640196862 0.5494315005716864 (-0.014005634992086789)' 105 69 -2\n", "dice: 0.8881958519774128 iou:0.7988779800445289 bbox_iou: 0.7911970019340515 angle_error: 0.7961279048745808\n", "Predict: (333, -10) (1095, 750) (714, 370) 761 0.78640693 0.6179808 (-0.0889987200498581)' 299 235 -16\n", "Ground Truth: (326, -7) (1085, 751) (706, 372) 758.9578405049915 0.7746042910747605 0.6324461971176423 (-0.052202821334141676)' 293 240 -9\n", "dice: 0.9747091952582349 iou:0.9506660849296605 bbox_iou: 0.893059253692627 angle_error: 6.6232617688289555\n", "Predict: (453, 242) (814, 602) (634, 422) 360 0.7929956 0.618164 (-0.19957953691482544)' 142 111 -35\n", "Ground Truth: (429, 226) (820, 617) (625, 422) 390.73182619285063 0.8333081110195923 0.5528088205781079 (-0.20562818647472877)' 162 108 -37\n", "dice: 0.9233973748408411 iou:0.8576956374264191 bbox_iou: 0.8384242057800293 angle_error: 1.0887569207826004\n", "Predict: (149, 76) (919, 846) (534, 461) 770 0.74500257 0.64520746 (-0.2861281633377075)' 286 248 -51\n", "Ground Truth: (187, 91) (938, 842) (563, 467) 750.962131039908 0.7437339073630586 0.6684757849305221 (-0.2816394180779196)' 279 251 -50\n", "dice: 0.9253016499300437 iou:0.8609873178551101 bbox_iou: 0.8597332239151001 angle_error: 0.8079741467618291\n", "Predict: (402, 146) (812, 556) (607, 351) 409 0.7736211 0.6283377 (0.5025585293769836)' 158 128 90\n", "Ground Truth: (415, 174) (778, 537) (597, 356) 363.03746936645535 0.7822883971057164 0.6229164179516934 (0.48885915398356733)' 142 113 87\n", "dice: 0.8851135084392153 iou:0.79390459489746 bbox_iou: 0.7897520065307617 angle_error: 2.4658875708149424\n", "Predict: (162, -25) (734, 546) (448, 260) 572 0.74399596 0.6654705 (0.3538959324359894)' 212 190 63\n", "Ground Truth: (175, -26) (738, 536) (457, 255) 562.4345549697316 0.7273166209035906 0.686302071217465 (0.46084788399939375)' 204 193 82\n", "dice: 0.9569505297157274 iou:0.9174545953749633 bbox_iou: 0.7787399888038635 angle_error: 19.25135128141278\n", "Predict: (269, 44) (554, 329) (412, 187) 285 0.785044 0.6080656 (0.2812363803386688)' 111 86 50\n", "Ground Truth: (264, 43) (565, 344) (415, 194) 300.46152985032876 0.7836011489300563 0.6212642267147658 (0.2743182906956924)' 117 93 49\n", "dice: 0.9314513634080372 iou:0.8716976761851621 bbox_iou: 0.8717518448829651 angle_error: 1.245256135735751\n", "Predict: (242, 27) (840, 625) (541, 326) 598 0.78039867 0.61961675 (0.0659031942486763)' 233 185 11\n", "Ground Truth: (265, 49) (826, 610) (546, 330) 561.1494741367936 0.7787907146705556 0.6272838454343657 (0.05697746962689853)' 218 176 10\n", "dice: 0.9418710156920659 iou:0.8901287363450248 bbox_iou: 0.8896103501319885 angle_error: 1.6066304319199993\n", "Predict: (323, 145) (623, 445) (473, 295) 299 0.7881255 0.5999872 (0.23837772011756897)' 117 89 42\n", "Ground Truth: (336, 150) (627, 441) (482, 296) 291.1629033788474 0.8017917024829339 0.597603602590813 (0.24032396406876197)' 116 87 43\n", "dice: 0.944254519550301 iou:0.8943959856196516 bbox_iou: 0.8855104446411133 angle_error: 0.35032391121473694\n", "Predict: (0, 10) (610, 620) (305, 315) 610 0.77671015 0.6227979 (0.12953101098537445)' 236 189 23\n", "Ground Truth: (6, 14) (629, 637) (318, 326) 623.9390015826868 0.7853331796170194 0.6190733373297723 (0.17716894421490115)' 245 193 31\n", "dice: 0.9540928362950373 iou:0.912215605174089 bbox_iou: 0.8514918088912964 angle_error: 8.574827981314805\n", "Predict: (213, 165) (537, 488) (375, 327) 323 0.82166755 0.5699769 (-0.0038482595700770617)' 132 92 0\n", "Ground Truth: (213, 158) (558, 503) (386, 331) 344.05838509764584 0.7965682915189478 0.6045485563183364 (0.006873155106573048)' 137 104 1\n", "dice: 0.9082334916147131 iou:0.8318935272689235 bbox_iou: 0.8179060220718384 angle_error: 1.9298546417970197\n", "Predict: (-118, -138) (996, 976) (439, 419) 1115 0.7715344 0.63196015 (-0.15829814970493317)' 430 352 -28\n", "Ground Truth: (-92, -110) (968, 950) (438, 420) 1060.6013805780192 0.7561955082152585 0.654345744507494 (-0.12814922174259735)' 401 347 -23\n", "dice: 0.9602920720069827 iou:0.9236171487704881 bbox_iou: 0.8861759901046753 angle_error: 5.426807033220445\n", "Predict: (253, 11) (837, 596) (545, 303) 584 0.76947826 0.64058423 (-0.40796926617622375)' 224 187 -73\n", "Ground Truth: (276, 43) (813, 580) (545, 312) 536.6600685312817 0.7612528376073628 0.6484551775063085 (-0.3802506516001595)' 204 174 -68\n", "dice: 0.91746856605148 iou:0.8475214088749596 bbox_iou: 0.8423569202423096 angle_error: 4.989350623691564\n", "Predict: (37, -78) (831, 715) (434, 318) 793 0.7826142 0.6194303 (0.12389695644378662)' 310 245 22\n", "Ground Truth: (31, -75) (812, 705) (422, 315) 781.3062914913714 0.7619521389795113 0.6476333360046777 (0.11007038942485639)' 297 253 19\n", "dice: 0.9682519996012685 iou:0.938457839731287 bbox_iou: 0.9213699102401733 angle_error: 2.4887820634074416\n", "Predict: (155, 39) (544, 428) (350, 233) 389 0.7792235 0.62390697 (0.3430447280406952)' 151 121 61\n", "Ground Truth: (166, 50) (535, 419) (351, 235) 369.43718968723226 0.7903914607167972 0.6126021048167949 (0.3573971709896618)' 146 113 64\n", "dice: 0.949587784506016 iou:0.9040144150070144 bbox_iou: 0.8975396156311035 angle_error: 2.5834397308139927\n", "Predict: (270, 95) (795, 620) (532, 357) 525 0.7860633 0.6344384 (0.12298762798309326)' 206 166 22\n", "Ground Truth: (291, 141) (630, 480) (461, 311) 339.6854470182672 0.8462122899970516 0.5328459066727883 (0.07257465404990428)' 143 90 13\n", "dice: 0.5276433652203297 iou:0.3583665484003395 bbox_iou: 0.3449949622154236 angle_error: 9.074335307974017\n", "Predict: (391, 231) (583, 422) (487, 326) 191 0.77300763 0.6204299 (-0.10068923234939575)' 73 59 -18\n", "Ground Truth: (426, 222) (597, 393) (512, 308) 170.01764614298128 0.8293256800027802 0.5587655290798873 (0.0001296623894702087)' 70 47 0\n", "dice: 0.6999504456970803 iou:0.5384028965514244 bbox_iou: 0.5257269740104675 angle_error: 18.147401052995875\n", "Predict: (303, 72) (1083, 852) (693, 462) 779 0.73293245 0.67319167 (0.2984013855457306)' 285 262 53\n", "Ground Truth: (344, 85) (1075, 816) (710, 451) 730.3178954099373 0.7263056311967547 0.6873719008599956 (0.3847069900067326)' 265 251 69\n", "dice: 0.9462501997049777 iou:0.897983752348093 bbox_iou: 0.7882814407348633 angle_error: 15.535008802980357\n", "Predict: (472, 181) (899, 608) (686, 394) 426 0.77958506 0.6118499 (0.2583109438419342)' 166 130 46\n", "Ground Truth: (450, 155) (911, 616) (681, 386) 461.973324013411 0.7784215696176444 0.6277418736662409 (0.20715490471091255)' 179 145 37\n", "dice: 0.9079564013927566 iou:0.8314287108598358 bbox_iou: 0.8069450259208679 angle_error: 9.208087043583895\n", "Predict: (4, -65) (797, 726) (401, 330) 792 0.7351457 0.6836369 (-0.4720868468284607)' 291 270 -84\n", "Ground Truth: (28, -74) (779, 676) (404, 301) 751.8165899313475 0.7297843747370639 0.6836773847288156 (-0.46874079995925333)' 274 257 -84\n", "dice: 0.926513213961498 iou:0.8630876746798329 bbox_iou: 0.8571820259094238 angle_error: 0.6022884364573287\n", "Predict: (252, 24) (638, 410) (445, 217) 386 0.78353363 0.6150994 (-0.2907367944717407)' 151 118 -52\n", "Ground Truth: (248, 5) (645, 402) (447, 204) 397.0993292162554 0.7889512193796354 0.6144558352228306 (-0.28736899602922783)' 156 122 -51\n", "dice: 0.936767988928665 iou:0.8810569839641657 bbox_iou: 0.8810651302337646 angle_error: 0.6062037196523207\n", "Predict: (182, -27) (831, 622) (506, 297) 649 0.7639514 0.626178 (-0.18189659714698792)' 247 203 -32\n", "Ground Truth: (196, -36) (837, 604) (517, 284) 640.0744242851764 0.7435572832511037 0.6686722414787666 (-0.18723104427080403)' 237 214 -33\n", "dice: 0.9472611079872232 iou:0.8998063196621472 bbox_iou: 0.8900958895683289 angle_error: 0.9602004822869006\n", "Predict: (308, 211) (615, 518) (461, 365) 307 0.7603035 0.65852827 (-0.4072708785533905)' 116 101 -73\n", "Ground Truth: (308, 207) (625, 524) (467, 366) 317.4268150046558 0.8001844456533217 0.5997539936794806 (-0.3943211177869814)' 127 95 -70\n", "dice: 0.9400884919227636 iou:0.8869499809735614 bbox_iou: 0.8599083423614502 angle_error: 2.33095693795363\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Predict: (-7, -125) (721, 603) (357, 238) 728 0.7862478 0.61344236 (0.008870233781635761)' 286 223 1\n", "Ground Truth: (2, -113) (703, 587) (353, 237) 701.7469187363776 0.7696991402151208 0.6384067931594273 (-0.007191464992756802)' 270 224 -1\n", "dice: 0.9716831020380994 iou:0.9449257363794682 bbox_iou: 0.9171559810638428 angle_error: 2.8911057793906614\n", "Predict: (274, 50) (637, 413) (456, 231) 362 0.81898165 0.5731693 (-0.10098136216402054)' 148 103 -18\n", "Ground Truth: (278, 42) (641, 405) (460, 224) 362.0058130141006 0.8184509456715805 0.574576408782414 (-0.13515203923864072)' 148 104 -24\n", "dice: 0.9543329513860767 iou:0.9126547046223614 bbox_iou: 0.8839201331138611 angle_error: 6.150721873431632\n", "Predict: (92, 57) (442, 407) (267, 232) 350 0.7930373 0.61776507 (-0.08148852735757828)' 138 108 -14\n", "Ground Truth: (97, 55) (446, 404) (272, 230) 348.03808874891837 0.7588479782468085 0.6512677989204836 (-0.06480555439202296)' 132 113 -11\n", "dice: 0.9646754402616546 iou:0.9317613797411067 bbox_iou: 0.9042940735816956 angle_error: 3.0029351337999586\n", "Predict: (224, 134) (409, 320) (317, 227) 185 0.8035088 0.5942029 (0.10506179183721542)' 74 54 18\n", "Ground Truth: (225, 137) (406, 318) (316, 228) 180.2811205312414 0.8115547516504695 0.5842763773023387 (0.10332105062026153)' 73 52 18\n", "dice: 0.9747655648670803 iou:0.9507733367741436 bbox_iou: 0.943122148513794 angle_error: 0.31333341905170187\n", "Predict: (121, -17) (824, 685) (473, 333) 702 0.77794963 0.618198 (0.1147070825099945)' 273 216 20\n", "Ground Truth: (129, -10) (810, 670) (470, 330) 681.3397820559137 0.7618577597710295 0.647744358429055 (0.09790978255513716)' 259 220 17\n", "dice: 0.9729990198120445 iou:0.9474178102867752 bbox_iou: 0.9211357831954956 angle_error: 3.0235139918743243\n", "Predict: (367, 144) (746, 523) (556, 333) 379 0.8206532 0.5655227 (-0.03188779950141907)' 155 107 -5\n", "Ground Truth: (388, 164) (737, 513) (563, 339) 348.96933349507947 0.8400222365216322 0.5425519718415878 (-0.059712596213082225)' 146 94 -10\n", "dice: 0.9058926632891774 iou:0.8279742150459674 bbox_iou: 0.8215824365615845 angle_error: 5.008463408099368\n", "Predict: (12, -52) (460, 395) (236, 171) 447 0.73200184 0.6708874 (-0.4067237079143524)' 163 149 -73\n", "Ground Truth: (30, -40) (445, 374) (238, 167) 414.23837690392713 0.7164232397251519 0.6976659240508437 (-0.37458590484358645)' 148 144 -67\n", "dice: 0.9348742559236248 iou:0.8777125716121918 bbox_iou: 0.8585512042045593 angle_error: 5.7848045527378815\n", "Predict: (119, -9) (354, 225) (236, 108) 235 0.7793255 0.62831706 (0.3547805845737457)' 91 73 63\n", "Ground Truth: (126, -3) (359, 229) (243, 113) 232.76566843072027 0.7733099181401604 0.6340282095506937 (0.3523042128107211)' 90 73 63\n", "dice: 0.9363952544341332 iou:0.8803977777815813 bbox_iou: 0.87244713306427 angle_error: 0.4457469173444295\n", "Predict: (128, 56) (839, 767) (483, 412) 710 0.7778833 0.6281353 (-0.14488555490970612)' 276 222 -26\n", "Ground Truth: (131, 48) (840, 757) (486, 403) 709.9727757287599 0.7664716431435363 0.6422781486683534 (-0.09256334568474789)' 272 228 -16\n", "dice: 0.9668249618606343 iou:0.9357804110344937 bbox_iou: 0.8612217903137207 angle_error: 9.417997660492482\n", "Predict: (355, 152) (753, 549) (554, 351) 397 0.7827637 0.6370776 (-0.31885749101638794)' 155 126 -57\n", "Ground Truth: (380, 149) (755, 524) (568, 337) 375.37681842649795 0.780506375508558 0.6251478207516153 (-0.3214281538508934)' 146 117 -57\n", "dice: 0.9028688984343481 iou:0.822936198915441 bbox_iou: 0.8180567026138306 angle_error: 0.46271931021098567\n", "Predict: (101, 34) (880, 813) (491, 424) 779 0.7914217 0.6138332 (-0.04181738942861557)' 308 239 -7\n", "Ground Truth: (111, 67) (848, 804) (480, 436) 737.1293613905228 0.7941184148407279 0.6077630650268653 (-0.021645072260497767)' 292 224 -3\n", "dice: 0.941167603103075 iou:0.8888730698657454 bbox_iou: 0.874902069568634 angle_error: 3.6310170902612047\n", "Predict: (204, 271) (598, 665) (401, 468) 394 0.79379207 0.6025452 (-0.22339750826358795)' 156 118 -40\n", "Ground Truth: (200, 254) (615, 669) (408, 462) 414.1259946634599 0.8315923280301788 0.5553865320309338 (-0.1435577586688896)' 172 115 -25\n", "dice: 0.923495139117389 iou:0.8578643466228556 bbox_iou: 0.7814453840255737 angle_error: 14.371154927045705\n", "Predict: (127, -57) (899, 714) (513, 328) 771 0.76039517 0.6491481 (0.2662169337272644)' 293 250 47\n", "Ground Truth: (77, -73) (906, 755) (492, 341) 829.2861588643573 0.7099817037900229 0.7042201220381423 (-0.13846480048994894)' 294 292 -24\n", "dice: 0.9154885992977416 iou:0.844148432837987 bbox_iou: 0.7552666664123535 angle_error: 72.8427121590984\n", "Predict: (347, 213) (739, 605) (543, 409) 391 0.7622041 0.6399741 (0.3534396290779114)' 149 125 63\n", "Ground Truth: (330, 194) (769, 633) (550, 414) 438.39245073792046 0.7842470814022642 0.6204486403498926 (0.21154641192275048)' 171 136 38\n", "dice: 0.890054809987522 iou:0.8018907762260906 bbox_iou: 0.7020960450172424 angle_error: 25.54077908792896\n", "Predict: (151, 18) (637, 503) (394, 260) 485 0.7900081 0.61409146 (-0.07505498826503754)' 191 148 -13\n", "Ground Truth: (155, 6) (652, 503) (404, 255) 496.9752794576406 0.7805337931965748 0.625113587820779 (-0.1024957833511806)' 193 155 -18\n", "dice: 0.9534246574823158 iou:0.9109947643030826 bbox_iou: 0.8903343081474304 angle_error: 4.9393431155057534\n", "Predict: (211, 162) (472, 422) (341, 292) 260 0.7647674 0.6585931 (-0.3668486177921295)' 99 85 -66\n", "Ground Truth: (226, 169) (465, 408) (346, 289) 238.32775289504156 0.7776433828989284 0.6287056298726064 (-0.3372788169653479)' 92 74 -60\n", "dice: 0.8952132395254223 iou:0.8103040980875533 bbox_iou: 0.8093582987785339 angle_error: 5.322564148820689\n", "Predict: (187, -73) (956, 696) (571, 311) 769 0.7760465 0.62196267 (-0.15209801495075226)' 298 239 -27\n", "Ground Truth: (217, -56) (936, 662) (577, 303) 718.8466192144191 0.7590409211304188 0.6510429172101371 (-0.15119719593730058)' 272 234 -27\n", "dice: 0.9437931886719061 iou:0.8935685497853998 bbox_iou: 0.8946279883384705 angle_error: 0.16214742242130242\n", "Predict: (342, 210) (701, 568) (521, 389) 358 0.76928544 0.62138927 (-0.30921876430511475)' 137 111 -55\n", "Ground Truth: (355, 198) (730, 573) (543, 386) 375.445945243786 0.7848426750451821 0.6196950664866736 (-0.30589580062262284)' 147 116 -55\n", "dice: 0.880261312164138 iou:0.7861310158582036 bbox_iou: 0.7836204171180725 angle_error: 0.5981334628485442\n", "Predict: (36, -110) (946, 799) (491, 344) 910 0.7913132 0.61402875 (0.028576930984854698)' 360 279 5\n", "Ground Truth: (58, -90) (915, 766) (487, 338) 856.022735737784 0.7759256527544616 0.6308243665217464 (0.0359041859440332)' 332 270 6\n", "dice: 0.9432011352595037 iou:0.8925077105292998 bbox_iou: 0.8899348378181458 angle_error: 1.31890589265213\n", "Predict: (123, 80) (604, 561) (364, 321) 480 0.7927196 0.6139241 (0.2864431142807007)' 190 147 51\n", "Ground Truth: (135, 87) (618, 570) (377, 329) 483.50723448569005 0.8073053972298992 0.590133879389647 (0.27240843137858967)' 195 142 49\n", "dice: 0.9438863023230806 iou:0.8937354987434591 bbox_iou: 0.88798987865448 angle_error: 2.5262429223799856\n", "Predict: (252, 31) (945, 723) (598, 377) 692 0.7916649 0.61163926 (0.06589045375585556)' 273 211 11\n", "Ground Truth: (257, 24) (950, 717) (604, 371) 693.479045258038 0.7616899221568476 0.6479417122586688 (0.06480555439202296)' 264 224 11\n", "dice: 0.9650697652654217 iou:0.9324974117824731 bbox_iou: 0.9101323485374451 angle_error: 0.19528188548986947\n", "Predict: (483, 226) (856, 599) (669, 412) 372 0.76496106 0.62929237 (0.2763734757900238)' 142 117 49\n", "Ground Truth: (494, 228) (855, 589) (675, 409) 361.90927668685146 0.7996092353565075 0.6005206663659306 (0.22523373702865346)' 144 108 40\n", "dice: 0.9470682516743789 iou:0.8994583487300226 bbox_iou: 0.8483350276947021 angle_error: 9.205152977046666\n", "Predict: (109, -168) (1024, 746) (566, 289) 915 0.7850846 0.6189664 (-0.0996101126074791)' 359 283 -17\n", "Ground Truth: (126, -170) (1031, 734) (579, 282) 905.9492934176835 0.7772642521123415 0.6291742861785138 (-0.11077184039195916)' 352 285 -19\n", "dice: 0.9752244935139708 iou:0.9516469581304009 bbox_iou: 0.9388419389724731 angle_error: 2.00911100120641\n", "Predict: (215, 95) (693, 573) (454, 334) 477 0.79023623 0.6154445 (-0.2871224880218506)' 188 146 -51\n", "Ground Truth: (211, 102) (692, 583) (452, 343) 481.6707196581499 0.7972251256471049 0.6036821175394859 (-0.3039859413055201)' 192 145 -54\n", "dice: 0.9665222573140718 iou:0.93521342298302 bbox_iou: 0.9209724068641663 angle_error: 3.035421591060512\n", "Predict: (171, 68) (790, 688) (481, 378) 619 0.74598575 0.65906644 (0.29359927773475647)' 230 203 52\n", "Ground Truth: (195, 103) (742, 650) (469, 377) 546.3350674778253 0.7254998326024308 0.6882223426290701 (0.4525718269586152)' 198 188 81\n", "dice: 0.8871079322380283 iou:0.7971194673190585 bbox_iou: 0.697352945804596 angle_error: 28.615058860294567\n", "Predict: (295, 164) (623, 491) (459, 328) 327 0.77481246 0.6489148 (-0.36341696977615356)' 126 106 -65\n", "Ground Truth: (290, 148) (631, 489) (461, 319) 340.92172401300564 0.750905506949255 0.6604096604633237 (-0.37012956643701345)' 128 112 -66\n", "dice: 0.9431150364653594 iou:0.8923535379964254 bbox_iou: 0.8794167041778564 angle_error: 1.2082673989547743\n", "Predict: (173, -17) (630, 439) (402, 211) 457 0.78872705 0.6155642 (0.10929469019174576)' 180 140 19\n", "Ground Truth: (191, 0) (608, 417) (400, 209) 417.69054478644836 0.7788110218446922 0.6272586326653674 (0.08403380995252074)' 162 131 15\n", "dice: 0.9146673396787889 iou:0.8427529854377431 bbox_iou: 0.8383667469024658 angle_error: 4.546958443060502\n", "Predict: (218, 67) (456, 305) (337, 186) 238 0.8107237 0.5895316 (-0.08783654868602753)' 96 70 -15\n", "Ground Truth: (227, 67) (466, 306) (347, 187) 239.55153850476518 0.8140210712782421 0.5808353428597671 (-0.1024957833511806)' 97 69 -18\n", "dice: 0.9297693425729859 iou:0.868756035085262 bbox_iou: 0.8615880012512207 angle_error: 2.638662239727555\n", "Predict: (97, -76) (880, 706) (489, 314) 782 0.7931902 0.61355746 (-0.0009878096170723438)' 310 239 0\n", "Ground Truth: (128, -45) (857, 683) (493, 319) 729.241061907515 0.7715775062476657 0.6361353251098647 (-0.04125062281442249)' 281 231 -7\n", "dice: 0.9340525030227961 iou:0.8762650183724495 bbox_iou: 0.8430436253547668 angle_error: 7.247306375523026\n", "Predict: (174, 82) (592, 500) (383, 291) 417 0.788486 0.6159596 (0.22834788262844086)' 164 128 41\n", "Ground Truth: (203, 97) (594, 488) (399, 293) 390.37465329603566 0.8042427892005465 0.5943008800421931 (0.17188733853924698)' 156 116 30\n", "dice: 0.8963004347417186 iou:0.8120873315121507 bbox_iou: 0.7952585220336914 angle_error: 10.162897936054897\n", "Predict: (94, -104) (947, 748) (521, 321) 852 0.7826349 0.6163466 (0.10071396082639694)' 333 262 18\n", "Ground Truth: (80, -103) (929, 745) (505, 321) 849.6894788874346 0.7694858136364161 0.6386639042660095 (0.13801682821429473)' 326 271 24\n", "dice: 0.9605676663736806 iou:0.9241271752847057 bbox_iou: 0.882641613483429 angle_error: 6.7145161298216\n", "Predict: (374, 77) (848, 552) (611, 314) 474 0.8199725 0.57115114 (-0.03499472513794899)' 194 135 -6\n", "Ground Truth: (385, 89) (846, 550) (616, 320) 461.4741376111992 0.7948917828826512 0.6067512286807833 (-0.00910132430985966)' 183 140 -1\n", "dice: 0.9529795129241853 iou:0.910182297946936 bbox_iou: 0.884357750415802 angle_error: 4.660812149056079\n", "Predict: (70, -56) (823, 695) (447, 319) 752 0.7714666 0.6445936 (0.25542518496513367)' 290 242 45\n", "Ground Truth: (85, -52) (796, 658) (441, 303) 711.613100256031 0.721366708700708 0.6925532987274755 (0.3229548720870773)' 256 246 58\n", "dice: 0.9315367685933829 iou:0.8718472860942792 bbox_iou: 0.7997233867645264 angle_error: 12.155343681949851\n", "Predict: (164, 45) (566, 446) (365, 245) 401 0.82221746 0.55545616 (0.09577365219593048)' 164 111 17\n", "Ground Truth: (192, 66) (563, 437) (378, 252) 371.48671256991145 0.8285518420583474 0.5599123547678861 (0.1024957833511806)' 153 104 18\n", "dice: 0.9232737520467187 iou:0.857482348741608 bbox_iou: 0.8533934354782104 angle_error: 1.2099836079450235\n", "Predict: (84, 17) (458, 392) (271, 204) 374 0.7411312 0.6609282 (-0.3231688141822815)' 138 123 -58\n", "Ground Truth: (88, 25) (437, 374) (263, 200) 349.76582986907107 0.7774115616204866 0.6289922605714608 (-0.3545323820140077)' 135 110 -63\n", "dice: 0.9326986145243884 iou:0.8738849468547805 bbox_iou: 0.8481372594833374 angle_error: 5.645442209710723\n", "Predict: (178, 58) (364, 244) (271, 151) 186 0.77856874 0.62838507 (0.45358142256736755)' 72 58 81\n", "Ground Truth: (180, 63) (361, 244) (271, 154) 181.99171762473148 0.7924646345576398 0.6099178657617979 (0.4824929562598915)' 72 55 86\n", "dice: 0.9583108156572567 iou:0.9199584963166394 bbox_iou: 0.8960923552513123 angle_error: 5.204076064654316\n", "Predict: (26, 64) (731, 768) (378, 416) 704 0.78796834 0.61689776 (-0.0678383931517601)' 277 217 -12\n", "Ground Truth: (36, 62) (729, 755) (383, 409) 692.2284085011247 0.76960724734419 0.6385175681493024 (-0.03233794600127626)' 266 221 -5\n", "dice: 0.9692885614784291 iou:0.9404073004843672 bbox_iou: 0.8888793587684631 angle_error: 6.390080487087091\n", "Predict: (304, 245) (662, 602) (483, 423) 357 0.8245066 0.5674952 (0.09582418948411942)' 147 101 17\n", "Ground Truth: (314, 252) (657, 595) (486, 424) 342.887527501366 0.8362917195897644 0.548284743309163 (0.1157351361814295)' 143 94 20\n", "dice: 0.9502045011565826 iou:0.905132954183404 bbox_iou: 0.896622359752655 angle_error: 3.5839704055158137\n", "Predict: (105, 10) (878, 783) (491, 396) 772 0.79490346 0.61057544 (-0.05982208251953125)' 306 235 -10\n", "Ground Truth: (149, 38) (866, 755) (508, 397) 716.3373592826218 0.7712176292930676 0.6365715735622983 (-0.040295693155871004)' 276 228 -7\n", "dice: 0.9330421919240525 iou:0.8744883676390299 bbox_iou: 0.8609024882316589 angle_error: 3.5147500854588447\n", "Predict: (363, 126) (784, 547) (574, 337) 420 0.7914156 0.6086524 (-0.2680758833885193)' 166 127 -48\n", "Ground Truth: (375, 145) (776, 546) (576, 346) 401.2432232399695 0.7900096042504917 0.6130944667765168 (-0.23860275224837268)' 158 123 -42\n", "dice: 0.9489133237248717 iou:0.9027926479742456 bbox_iou: 0.8750969171524048 angle_error: 5.30516360522639\n", "Predict: (144, -1) (797, 651) (471, 324) 652 0.7954502 0.6085456 (0.04246685653924942)' 259 198 7\n", "Ground Truth: (130, -14) (801, 656) (466, 321) 671.7206034684361 0.7827927225768175 0.6222825350921987 (0.0783042320012125)' 262 209 14\n", "dice: 0.9634062773700814 iou:0.9293962102392876 bbox_iou: 0.8883088827133179 angle_error: 6.4507275831533555\n", "Predict: (215, 163) (551, 500) (383, 332) 336 0.80149466 0.6053751 (-0.16734062135219574)' 134 101 -30\n", "Ground Truth: (222, 172) (545, 495) (384, 334) 322.3417217798527 0.8123056443150318 0.5832319780447066 (-0.1913042415964582)' 130 94 -34\n", "dice: 0.949035592708439 iou:0.9030140184806041 bbox_iou: 0.8907002210617065 angle_error: 4.313451643967241\n", "Predict: (150, 19) (923, 792) (536, 406) 772 0.77927387 0.62292075 (-0.1323060840368271)' 300 240 -23\n", "Ground Truth: (185, 46) (890, 751) (538, 399) 704.1641948977525 0.7526653639027445 0.6584032578755585 (-0.10618583959591577)' 265 231 -19\n", "dice: 0.9190566662712893 iou:0.8502357501951617 bbox_iou: 0.8351543545722961 angle_error: 4.7016439993640375\n", "Predict: (416, 144) (834, 562) (625, 353) 418 0.7850816 0.6274693 (-0.3008538484573364)' 164 131 -54\n", "Ground Truth: (413, 147) (820, 554) (617, 351) 406.8613375389704 0.7772377732246695 0.6292069960456234 (-0.30735768766407146)' 158 128 -55\n", "dice: 0.9547597536456579 iou:0.9134356976548997 bbox_iou: 0.9090606570243835 angle_error: 1.1706910572123022\n" ] } ], "source": [ "# Using ratio_bl\n", "\n", "angle_errors = []\n", "dices = []\n", "ious = []\n", "bbox_ious = []\n", "\n", "\n", "for ind in range(len(dataset)):\n", " # Load Image\n", " \n", " cls_dice = []\n", " cls_angle_error = []\n", " cls_iou = []\n", " cls_bbox_iou = []\n", " \n", " img_id = dataset.images[ind]\n", " img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", " img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", " image = cv2.imread(img_path)\n", " shape = image.shape[:2]\n", "\n", " # Load annotations\n", " ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", " anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", " # Inference\n", " color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", " ret = detector.run(img_path)\n", "\n", " for class_idx in range(opt.num_classes):\n", " try:\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6]) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_bl = ret['results'][class_id][0, 8]\n", " a = ratio_al * l / 2\n", " b = ratio_bl * l / 2\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(\"Predict: \", pt1, pt2, (cx, cy), l, ratio_al, ratio_bl, \"({})'\".format(theta), int(a), int(b), int(angle))\n", "\n", " # Annotations\n", " ann = anns[class_idx]\n", " ellipse = ann['ellipse']\n", " cx_gt = ellipse[0]\n", " cy_gt = ellipse[1]\n", " if ellipse[2] >= ellipse[3]:\n", " a_gt = ellipse[2]\n", " b_gt = ellipse[3]\n", " theta_gt = ellipse[4]\n", " else:\n", " a_gt = ellipse[3]\n", " b_gt = ellipse[2]\n", " theta_gt = ellipse[4] + 0.5 * math.pi\n", " theta_gt = theta_gt / math.pi\n", " theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt\n", " l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", " pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", " pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", " ratio_al, ratio_bl = 2 * a_gt / l_gt, 2 * b_gt / l_gt\n", " angle_gt = theta_gt * 180\n", " print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_bl, \"({})'\".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)\n", " img = cv2.ellipse(img, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2)\n", " # img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2)\n", " # img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2)\n", "\n", "\n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", "\n", " pred_ell = [cx, cy, a, b, angle]\n", " gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", " error = compute_single_class_metric(pred_ell, gt_ell, shape)\n", " print(\"dice: {} iou:{} bbox_iou: {} angle_error: {}\".format(error[0], error[1], error[2], error[3]))\n", " cls_dice.append(error[0])\n", " cls_iou.append(error[1])\n", " cls_bbox_iou.append(error[2])\n", " cls_angle_error.append(error[3])\n", " \n", " except Exception as e:\n", " print(e)\n", " cls_dice.append(0)\n", " cls_iou.append(0)\n", " cls_bbox_iou.append(0)\n", " cls_angle_error.append(90)\n", " pass\n", "# img = Image.fromarray(image)\n", "# img.save(os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", "# print(\"Saving\", img_path, \"to\", os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", " dices.append(cls_dice)\n", " ious.append(cls_iou)\n", " bbox_ious.append(cls_bbox_iou)\n", " angle_errors.append(cls_angle_error)\n", "# img\n", "# break\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluation Done!\n", "Mean Dice: [0.9429411 0.92298839]\n", "Mean IoU: [0.89388239 0.85998948]\n", "Mean BBox IoU: [0.86629103 0.83840977]\n", "Mean Angle_err: [6.11726085 6.36656895]\n" ] } ], "source": [ "print(\"Evaluation Done!\")\n", "print(\"Mean Dice: \", np.mean(dices, axis=0))\n", "print(\"Mean IoU: \", np.mean(ious, axis=0))\n", "print(\"Mean BBox IoU: \", np.mean(bbox_ious, axis=0))\n", "print(\"Mean Angle_err: \", np.mean(angle_errors, axis=0))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([168., 141., 52., 25., 7., 9., 3., 0., 1., 2., 1.,\n", " 1., 2., 0., 0., 0., 1., 1., 1., 2.]),\n", " array([-1.38267205, 2.67243453, 6.7275411 , 10.78264767, 14.83775424,\n", " 18.89286081, 22.94796738, 27.00307395, 31.05818053, 35.1132871 ,\n", " 39.16839367, 43.22350024, 47.27860681, 51.33371338, 55.38881995,\n", " 59.44392652, 63.4990331 , 67.55413967, 71.60924624, 75.66435281,\n", " 79.71945938]),\n", " )" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "err_lung = np.array(angle_errors)[:, 0]\n", "err_heart = np.array(angle_errors)[:, 1]\n", "plt.figure()\n", "plt.subplot(211)\n", "plt.hist(err_lung, bins=20, color='green')\n", "plt.subplot(212)\n", "plt.hist(err_heart, bins=20, color='cyan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# For paper visualization" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predict: (90, -63) (760, 606) (425, 271) 670 0.80187404 0.6107166 (0.037225645035505295)' 268 204 6\n", "Ground Truth: (96, -63) (765, 605) (431, 271) 669.1665837562423 0.7926217669488523 0.6097136496412714 (0.056847807237428216)' 265 204 10\n", "name 'compute_single_class_metric' is not defined\n", "Predict: (348, 93) (691, 435) (519, 264) 342 0.79769695 0.5889566 (-0.12156718969345093)' 136 100 -21\n", "Ground Truth: (363, 105) (680, 422) (522, 264) 317.64191320416137 0.8060397238428602 0.5918614395171609 (-0.1609351400195277)' 128 94 -28\n", "name 'compute_single_class_metric' is not defined\n", "Saving /data/cc/Data/CHD/detection/images/normal_0032.png to ./ablation/normal_0032.png\n", "predicted ratio: 1.0, truth ratio: 1.0, error: 0.0, precision: 1.0\n", "Predict: (146, -75) (816, 594) (481, 259) 670 0.79927397 0.6150806 (0.05309740826487541)' 267 206 9\n", "Ground Truth: (162, -60) (795, 572) (479, 256) 632.974693509938 0.7750199258041868 0.6319367963700745 (0.052202821334141676)' 245 200 9\n", "name 'compute_single_class_metric' is not defined\n", "Predict: (231, 45) (576, 390) (403, 217) 344 0.6202789 0.77873975 (-0.2043541967868805)' 106 133 -36\n", "Ground Truth: (237, 40) (596, 399) (417, 220) 358.9146575385296 0.7534214452386108 0.6575379273126101 (0.26483382530491384)' 135 118 47\n", "name 'compute_single_class_metric' is not defined\n", "Saving /data/cc/Data/CHD/detection/images/normal_0033.png to ./ablation/normal_0033.png\n", "predicted ratio: 1.0, truth ratio: 1.0, error: 0.0, precision: 1.0\n", "Predict: (85, -10) (788, 692) (437, 341) 702 0.6614285 0.74001634 (0.19998927414417267)' 232 259 35\n", "Ground Truth: (86, 1) (795, 710) (441, 356) 708.0920084000384 0.675994071846065 0.7369070598311412 (0.18525635375896615)' 239 260 33\n", "name 'compute_single_class_metric' is not defined\n", "Predict: (263, 48) (655, 439) (459, 244) 391 0.63556224 0.76670146 (-0.10201770812273026)' 124 149 -18\n", "Ground Truth: (285, 69) (656, 440) (471, 255) 371.7927860408268 0.6280003506425236 0.7782130553986275 (-0.11904789743273772)' 116 144 -21\n", "name 'compute_single_class_metric' is not defined\n", "Saving /data/cc/Data/CHD/detection/images/normal_0100.png to ./ablation/normal_0100.png\n", "predicted ratio: 1.0, truth ratio: 1.0, error: 0.0, precision: 1.0\n", "Predict: (-19, -111) (735, 643) (357, 266) 754 0.7912546 0.6197181 (-0.1385948807001114)' 298 233 -24\n", "Ground Truth: (-18, -111) (746, 653) (364, 271) 764.9465557305294 0.5987346391312354 0.8009474588912742 (0.35236904400545627)' 229 306 63\n", "name 'compute_single_class_metric' is not defined\n", "Predict: (233, 12) (623, 402) (428, 207) 390 0.63565856 0.7758695 (0.1377996802330017)' 123 151 24\n", "Ground Truth: (221, -3) (636, 411) (429, 204) 415.16698443397445 0.8125453435559704 0.5828979882153567 (-0.36178867820149974)' 168 121 -65\n", "name 'compute_single_class_metric' is not defined\n", "Saving /data/cc/Data/CHD/detection/images/normal_0124.png to ./ablation/normal_0124.png\n", "predicted ratio: 1.0, truth ratio: 1.0, error: 0.0, precision: 1.0\n", "Predict: (178, -5) (803, 619) (490, 306) 625 0.7299554 0.699894 (0.21266192197799683)' 228 218 38\n", "Ground Truth: (168, -3) (791, 619) (480, 308) 622.2951988373364 0.7522731990776093 0.6588512988144893 (0.22504508953194)' 234 205 40\n", "name 'compute_single_class_metric' is not defined\n", "Predict: (234, 93) (566, 424) (400, 259) 331 0.7585532 0.6454848 (-0.002188762417063117)' 125 106 0\n", "Ground Truth: (239, 96) (570, 427) (405, 262) 331.7529974906029 0.8023680328842844 0.5968295735010155 (-0.012095775674984046)' 133 99 -2\n", "name 'compute_single_class_metric' is not defined\n", "Saving /data/cc/Data/CHD/detection/images/normal_0661.png to ./ablation/normal_0661.png\n", "predicted ratio: 1.0, truth ratio: 1.0, error: 0.0, precision: 1.0\n" ] } ], "source": [ "filelist = [\n", " 'normal_0032.png',\n", " 'normal_0033.png',\n", " 'normal_0100.png',\n", " 'normal_0124.png',\n", " 'normal_0661.png'\n", "]\n", "\n", "save_pred_folder = './ablation/'\n", "\n", "\n", "save_image = True\n", "with_gt = False\n", "with_box = False\n", "angle_errors = []\n", "dices = []\n", "ious = []\n", "bbox_ious = []\n", "ratios_pred = []\n", "ratios_gt = []\n", "ctr_precisions = []\n", "\n", "for ind in range(len(dataset)):\n", " # Load Image\n", "\n", " cls_dice = []\n", " cls_angle_error = []\n", " cls_iou = []\n", " cls_bbox_iou = []\n", " diameter_gt = [1, 1]\n", " diameter_pred = [1, 1]\n", "\n", " img_id = dataset.images[ind]\n", " img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", " img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", " image = cv2.imread(img_path)\n", " shape = image.shape[:2]\n", " \n", " if img_info['file_name'] not in filelist:\n", " continue\n", "\n", " # Load annotations\n", " ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", " anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", " # Inference\n", " color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", " ret = detector.run(img_path)\n", "\n", " for class_idx in range(opt.num_classes):\n", " try:\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6]) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_bl = ret['results'][class_id][0, 8]\n", " a = ratio_al * l / 2\n", " b = ratio_bl * l / 2\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(\"Predict: \", pt1, pt2, (cx, cy), l, ratio_al, ratio_bl, \"({})'\".format(theta), int(a), int(b), int(angle))\n", "\n", " # Annotations\n", " ann = anns[class_idx]\n", " cx_gt, cy_gt, a_gt, b_gt, theta_gt = ann['ellipse']\n", " theta_gt = theta_gt / math.pi\n", " theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt\n", " l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", " pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", " pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", " ratio_al, ratio_bl = 2 * a_gt / l_gt, 2 * b_gt / l_gt\n", " angle_gt = theta_gt * 180\n", " print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_bl, \"({})'\".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt))\n", " if with_gt and with_box:\n", " img = cv2.rectangle(image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)\n", " if with_gt:\n", " img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2)\n", " # img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2)\n", " # img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2)\n", "\n", "\n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " if with_box:\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(image, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", "\n", " pred_ell = [cx, cy, a, b, angle]\n", " gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", " error = compute_single_class_metric(pred_ell, gt_ell, shape)\n", " print(\"dice: {} iou:{} bbox_iou: {} angle_error: {}\".format(error[0], error[1], error[2], error[3]))\n", " diameter_pred[class_idx] = b\n", " diameter_gt[class_idx] = b_gt\n", " cls_dice.append(error[0])\n", " cls_iou.append(error[1])\n", " cls_bbox_iou.append(error[2])\n", " cls_angle_error.append(error[3])\n", "\n", " except Exception as e:\n", " print(e)\n", " cls_dice.append(0)\n", " cls_iou.append(0)\n", " cls_bbox_iou.append(0)\n", " cls_angle_error.append(90)\n", " pass\n", "\n", " if save_image:\n", " img = Image.fromarray(image)\n", " img.save(os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", " print(\"Saving\", img_path, \"to\", os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", " dices.append(cls_dice)\n", " ious.append(cls_iou)\n", " bbox_ious.append(cls_bbox_iou)\n", " angle_errors.append(cls_angle_error)\n", " try:\n", " ratio_p, ratio_g = float(diameter_pred[1]) / diameter_pred[0], float(diameter_gt[1]) / diameter_gt[0]\n", " ctr_precision = 1 - abs(ratio_p - ratio_g) / ratio_g\n", " except Exception as e:\n", " ctr_precision = 0\n", " ratio_p = 0\n", " print(\"predicted ratio: {}, truth ratio: {}, error: {}, precision: {}\".format(ratio_p, ratio_g, abs(ratio_p - ratio_g), ctr_precision))\n", " ratios_pred.append(ratio_p)\n", " ratios_gt.append(ratio_g)\n", " ctr_precisions.append(ctr_precision)\n", " # break" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0.9299634954160939, 0.9256375281124264],\n", " [0.9502288272981095, 0.8538003680097701],\n", " [0.9471889848351555, 0.6420789677409052],\n", " [0.961675231863847, 0.9315834357690742],\n", " [0.9475476682824306, 0.769730984948765],\n", " [0.9648238350849353, 0.9410642464316117],\n", " [0.8518621754097466, 0.8946814111849581],\n", " 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"metadata": {}, "source": [ "# With Ground Truth" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/cc/Data/CHD/detection/images/abnormal_0058.png\n", "(-704, -678) (1132, 1158) (213, 239) 459 0.39113346 0.7888333 0.5965246 179.53025695681572 141.61944866098284 107\n", "(-215, -254) (689, 651) (236, 198) 226 0.34028825 0.90244985 0.72942054 76.90514487028122 69.4030361653805 131\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ind += 1\n", "\n", "\n", "\n", "# Load Image\n", "img_id = dataset.images[ind]\n", "img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", "img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", "image = cv2.imread(img_path)\n", "print(img_path)\n", "\n", "# Load annotations\n", "# ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", "# anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", "# Inference\n", "color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", "ret = detector.run(img_path)\n", "\n", "for class_idx in range(opt.num_classes):\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6] / 4) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_ba = ret['results'][class_id][0, 8]\n", " a = ratio_al * l\n", " b = ratio_ba * a\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(pt1, pt2, (cx, cy), l, ratio_al, ratio_ba, theta, a, b, int(angle))\n", " \n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", " \n", "img = Image.fromarray(image)\n", "img\n", "img.save(os.path.join('../exp/pred', img_path.split('/')[-1]))" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataset.images)" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/cc/Data/CHD/detection/images/abnormal_0000.png\n", "(-936, -1078) (1752, 1610) (408, 265) 672 0.38649797 0.8034471 0.59329367 259.7266387939453 208.67662183324683 106\n", "(-185, -443) (1156, 898) (485, 227) 335 0.37068468 0.845624 0.7307339 124.17936891317368 105.00905833806092 131\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/cc/miniconda3/lib/python3.7/site-packages/torch/nn/functional.py:1625: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/data/cc/Data/CHD/detection/images/abnormal_0001.png\n", "(-1316, -1272) (1915, 1958) (299, 342) 807 0.38554302 0.7937233 0.63559026 311.1332159936428 246.95367833275895 114\n", "(-571, -638) (1256, 1189) (342, 275) 456 0.33497074 0.92716104 0.7286147 152.7466585636139 141.62075049294316 131\n", "/data/cc/Data/CHD/detection/images/abnormal_0002.png\n", "(-685, -837) (1439, 1287) (377, 224) 531 0.37524202 0.83682317 0.6011875 199.25351509451866 166.73995722180283 108\n", "(-284, -431) (938, 790) (327, 179) 305 0.35961708 0.8853992 0.6915283 109.68321070075035 97.11342946192383 124\n", "/data/cc/Data/CHD/detection/images/abnormal_0003.png\n", "(-692, -752) (1530, 1470) (418, 358) 555 0.39406824 0.78408897 0.555698 218.70787382125854 171.48643134713808 100\n", "(2, -134) (973, 836) (487, 350) 242 0.40083143 0.7550271 0.67525923 97.00120633840561 73.23854100667164 121\n", 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= [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", " red = (255, 0, 0)\n", " green = (0, 255, 0)\n", " ret = detector.run(img_path)\n", "\n", " for class_idx in range(opt.num_classes):\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6] / 4) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_ba = ret['results'][class_id][0, 8]\n", " a = ratio_al * l\n", " b = ratio_ba * a\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(pt1, pt2, (cx, cy), l, ratio_al, ratio_ba, theta, a, b, int(angle))\n", "\n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", " img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", " img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", "\n", " img = Image.fromarray(image)\n", " img.save(os.path.join('../exp/pred', img_path.split('/')[-1]))" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "img = Image.fromarray(image)\n", "img.save(os.path.join('../exp/pred', img_path.split('/')[-1]))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# image = np.array(Image.open(img_path).convert('RGB'))\n", "lung_pt1 = (int(cx - l / 2), int(cy - l / 2))\n", "lung_pt2 = (int(cx + l / 2), int(cy + l / 2))\n", "img = cv2.rectangle(img=image, pt1=lung_pt1, pt2=lung_pt2, color=(255, 0, 0), thickness=5)\n", "img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, (0, 255, 0), thickness=5)\n", "# img = cv2.rectangle(img=img, pt1=heart_pt1, pt2=heart_pt2, color=(0, 0, 255), thickness=5)\n", " \n", "plt.imshow(image)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "torch.Size([1, 3, 512, 512])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocessed_image, meta = detector.pre_process(image, scale=1.0)\n", "preprocessed_image = preprocessed_image.to(opt.device)\n", "preprocessed_image.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "output = model(preprocessed_image)[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(output['hm'].shape, output['reg'].shape, output['l'].shape, output['ratio_al'].shape, output['ratio_ba'].shape, output['theta'].shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from models.decode import _nms, _left_aggregate, _right_aggregate, _top_aggregate, \\\n", " _bottom_aggregate, _h_aggregate, _v_aggregate, _topk_channel, \\\n", " _topk\n", "from models.utils import _gather_feat, _transpose_and_gather_feat" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def test_eldet_decode(heat, l, ratio_al, ratio_ba, theta, reg=None, cat_spec_wh=False, K=2): # Decode ellipse information\n", " batch, cat, height, width = heat.size()\n", "\n", " # heat = torch.sigmoid(heat)\n", " # perform nms on heatmaps\n", " heat = _nms(heat)\n", " \n", " scores, inds, clses, ys, xs = _topk(heat, K=K)\n", " if reg is not None:\n", " reg = _transpose_and_gather_feat(reg, inds)\n", " reg = reg.view(batch, K, 2)\n", " xs = xs.view(batch, K, 1) + reg[:, :, 0:1]\n", " ys = ys.view(batch, K, 1) + reg[:, :, 1:2]\n", " else:\n", " xs = xs.view(batch, K, 1) + 0.5\n", " ys = ys.view(batch, K, 1) + 0.5\n", " l = _transpose_and_gather_feat(l, inds)\n", " ratio_al = _transpose_and_gather_feat(ratio_al, inds)\n", " ratio_ba = _transpose_and_gather_feat(ratio_ba, inds)\n", " theta = _transpose_and_gather_feat(theta, inds)\n", " if cat_spec_wh:\n", " l = l.view(batch, K, cat, 1)\n", " clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long()\n", " l = l.gather(1, clses_ind).view(batch, K, 1)\n", " else:\n", " l = l.view(batch, K, 1)\n", " clses = clses.view(batch, K, 1).float()\n", " scores = scores.view(batch, K, 1)\n", " bboxes = torch.cat([xs - l / 2, \n", " ys - l / 2,\n", " xs + l / 2, \n", " ys + l / 2], dim=2)\n", "# print(bboxes.shape, scores.shape, clses.shape, xs.shape, ys.shape, l.shape, ratio_al.shape, ratio_ba.shape, theta.shape)\n", "# print('bboxes:', bboxes, 'scores:', scores, 'clses:', clses, 'xs:', xs, 'ys:', ys, 'l:', l, 'ratio_al:', ratio_al, 'ratio_ba:', ratio_ba, 'theta:', theta)\n", " detections = torch.cat([bboxes, scores, clses, xs, ys, l, ratio_al, ratio_ba, theta], dim=2) # batch x max_dets x dim\n", " \n", " return detections" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def transform_preds(coords, center, scale, output_size):\n", " target_coords = np.zeros(coords.shape)\n", " trans = get_affine_transform(center, scale, 0, output_size, inv=1)\n", " for p in range(coords.shape[0]):\n", " target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)\n", " return target_coords\n", "\n", "def affine_transform(pt, t):\n", " new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T\n", " new_pt = np.dot(t, new_pt)\n", " return new_pt[:2]\n", "\n", "def get_affine_transform(center,\n", " scale,\n", " rot,\n", " output_size,\n", " shift=np.array([0, 0], dtype=np.float32),\n", " inv=0):\n", " if not isinstance(scale, np.ndarray) and not isinstance(scale, list):\n", " scale = np.array([scale, scale], dtype=np.float32)\n", "\n", " scale_tmp = scale\n", " src_w = scale_tmp[0]\n", " dst_w = output_size[0]\n", " dst_h = output_size[1]\n", "\n", " rot_rad = np.pi * rot / 180\n", " src_dir = get_dir([0, src_w * -0.5], rot_rad)\n", " dst_dir = np.array([0, dst_w * -0.5], np.float32)\n", "\n", " src = np.zeros((3, 2), dtype=np.float32)\n", " dst = np.zeros((3, 2), dtype=np.float32)\n", " src[0, :] = center + scale_tmp * shift\n", " src[1, :] = center + src_dir + scale_tmp * shift\n", " dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\n", " dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir\n", "\n", " src[2:, :] = get_3rd_point(src[0, :], src[1, :])\n", " dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])\n", "\n", " if inv:\n", " trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\n", " else:\n", " trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\n", "\n", " return trans\n", "\n", "def get_dir(src_point, rot_rad):\n", " sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n", "\n", " src_result = [0, 0]\n", " src_result[0] = src_point[0] * cs - src_point[1] * sn\n", " src_result[1] = src_point[0] * sn + src_point[1] * cs\n", "\n", " return src_result\n", "\n", "def get_3rd_point(a, b):\n", " direct = a - b\n", " return b + np.array([-direct[1], direct[0]], dtype=np.float32)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# output -> detection\n", "hm = output['hm'].sigmoid_()\n", "l = output['l']\n", "ratio_al = output[\"ratio_al\"]\n", "ratio_ba = output[\"ratio_ba\"]\n", "theta = output[\"theta\"]\n", "reg = output['reg'] if opt.reg_offset else None\n", "\n", "dets = test_eldet_decode(hm, l, ratio_al, ratio_ba, theta, reg=reg)\n", "dets" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[meta['c']], [meta['s']], meta['out_height'], meta['out_width'], opt.num_classes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ret = []\n", "i = 0\n", "num_classes = 2\n", "top_preds = {}\n", "c, s, h, w, num_classes = [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], opt.num_classes\n", "\n", "print(\"Origin:\", dets)\n", "dets[i, :, :2] = transform_preds(\n", " dets[i, :, 0:2], c[i], s[i], (w, h))\n", "dets[i, :, 2:4] = transform_preds(\n", " dets[i, :, 2:4], c[i], s[i], (w, h))\n", "print(\"Transformed:\", dets)\n", "classes = dets[i, :, -1]\n", "for j in range(num_classes):\n", " inds = (classes == j)\n", " top_preds[j + 1] = np.concatenate([\n", " dets[i, inds, :4].astype(np.float32),\n", " dets[i, inds, 4:5].astype(np.float32)], axis=1).tolist()\n", "ret.append(top_preds)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "classes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# eldet post processing\n", "def eldet_post_process(dets, c, s, h, w, num_classes):\n", " # dets: batch x max_dets x dim\n", " # return 1-based class det dict\n", " ret = []\n", " for i in range(dets.shape[0]):\n", " top_preds = {}\n", " dets[i, :, :2] = transform_preds(\n", " dets[i, :, 0:2], c[i], s[i], (w, h))\n", " dets[i, :, 2:4] = transform_preds(\n", " dets[i, :, 2:4], c[i], s[i], (w, h))\n", " classes = dets[i, :, -1]\n", " for j in range(num_classes):\n", " inds = (classes == j)\n", " top_preds[j + 1] = np.concatenate([\n", " dets[i, inds, :4].astype(np.float32),\n", " dets[i, inds, 4:5].astype(np.float32)], axis=1).tolist()\n", " ret.append(top_preds)\n", " return ret" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dets = dets.detach().cpu().numpy()\n", "dets = dets.reshape(1, -1, dets.shape[2])\n", "dets = eldet_post_process(\n", " dets.copy(), [meta['c']], [meta['s']],\n", " meta['out_height'], meta['out_width'], self.opt.num_classes)\n", "for j in range(1, self.num_classes + 1):\n", " dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)\n", " dets[0][j][:, :4] /= scale" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def post_process(self, dets, meta, scale=1):\n", " dets = dets.detach().cpu().numpy()\n", " dets = dets.reshape(1, -1, dets.shape[2])\n", " dets = eldet_post_process(\n", " dets.copy(), [meta['c']], [meta['s']],\n", " meta['out_height'], meta['out_width'], self.opt.num_classes)\n", " for j in range(1, self.num_classes + 1):\n", " dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)\n", " dets[0][j][:, :4] /= scale\n", " return dets[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch, cat, height, width = hm.size()\n", "heat = _nms(hm)\n", "scores, inds, clses, ys, xs = _topk(heat, K=2)\n", "scores, inds, clses, ys, xs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reg.shape, inds" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "K = 2\n", "cat_spec_wh = False\n", "if reg is not None:\n", " reg = _transpose_and_gather_feat(reg, inds)\n", " reg = reg.view(batch, K, 2)\n", " xs = xs.view(batch, K, 1) + reg[:, :, 0:1]\n", " ys = ys.view(batch, K, 1) + reg[:, :, 1:2]\n", "else:\n", " xs = xs.view(batch, K, 1) + 0.5\n", " ys = ys.view(batch, K, 1) + 0.5\n", "l = _transpose_and_gather_feat(l, inds)\n", "if cat_spec_wh:\n", " l = l.view(batch, K, cat, 1)\n", " clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long()\n", " l = l.gather(1, clses_ind).view(batch, K, 1)\n", "else:\n", " l = l.view(batch, K, 1)\n", "clses = clses.view(batch, K, 1).float()\n", "scores = scores.view(batch, K, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "results = {}\n", "num_iters = len(dataset)\n", "bar = Bar('{}'.format(opt.exp_id), max=num_iters)\n", "time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']\n", "avg_time_stats = {t: AverageMeter() for t in time_stats}\n", "for ind in range(num_iters):\n", " img_id = dataset.images[ind]\n", " img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", " img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", "\n", " if opt.task == 'ddd':\n", " ret = detector.run(img_path, img_info['calib'])\n", " else:\n", " ret = detector.run(img_path)\n", "\n", " results[img_id] = ret['results']\n", "\n", " Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)\n", " for t in avg_time_stats:\n", " avg_time_stats[t].update(ret[t])\n", " Bar.suffix = Bar.suffix + '|{} {:.3f} '.format(t, avg_time_stats[t].avg)\n", " bar.next()\n", "bar.finish()\n", "dataset.run_eval(results, opt.save_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(img_id)\n", "img_info = dataset.coco.loadImgs(ids=[img_id])[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Inference" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_image_folder = '/data/cc/Data/CHD/detection/images/'\n", "image_lists = sorted(glob(os.path.join(test_image_folder, \"*.png\")))\n", "save_folder = '/data/cc/Data/CHD/detection/predictions/'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "idx = 0\n", "for img_path in image_lists:\n", " \n", " idx += 1\n", " \n", " img_name = img_path.split('/')[-1]\n", " ret = detector.run(img_path)\n", " \n", " lung_pt1 = (int(ret['results'][1][0][0]), int(ret['results'][1][0][1]))\n", " lung_pt2 = (int(ret['results'][1][0][2]), int(ret['results'][1][0][3]))\n", " heart_pt1 = (int(ret['results'][2][0][0]), int(ret['results'][2][0][1]))\n", " heart_pt2 = (int(ret['results'][2][0][2]), int(ret['results'][2][0][3]))\n", " \n", " image = np.array(Image.open(img_path).convert('RGB'))\n", " img = cv2.rectangle(img=image, pt1=lung_pt1, pt2=lung_pt2, color=(255, 0, 0), thickness=5)\n", " img = cv2.rectangle(img=img, pt1=heart_pt1, pt2=heart_pt2, color=(0, 0, 255), thickness=5)\n", " \n", " result = Image.fromarray(img)\n", " result.save(os.path.join(save_folder, img_name))\n", " \n", " print(\"Processing img: {}, shape:{}, Lung pt1:{} pt2:{}, Heart pt1:{}, pt2:{}\".format(img_name, image.shape, lung_pt1, lung_pt2, heart_pt1, heart_pt2))\n", " \n", "# if idx > 1:\n", "# break\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "ret" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lung_pt1 = (int(ret['results'][1][0][0]), int(ret['results'][1][0][1]))\n", "lung_pt2 = (int(ret['results'][1][0][2]), int(ret['results'][1][0][3]))\n", "heart_pt1 = (int(ret['results'][2][0][0]), int(ret['results'][2][0][1]))\n", "heart_pt2 = (int(ret['results'][2][0][2]), int(ret['results'][2][0][3]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = np.array(Image.open(img_path).convert('RGB'))\n", "plt.imshow(img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_path" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "l = img[:]\n", "l = cv2.rectangle(img=l, pt1=lung_pt1, pt2=lung_pt2, color=(255, 0, 0), thickness=5)\n", "l = cv2.rectangle(img=l, pt1=heart_pt1, pt2=heart_pt2, color=(0, 0, 255), thickness=5)\n", "plt.imshow(l)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "result = Image.fromarray(l)\n", "result.save(os.path.join(save_folder, img_name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Rotated IoU" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import math\n", "from torch import nn, optim\n", "import argparse\n", "from utiles import box2corners\n", "from oriented_iou_loss import cal_diou, cal_giou" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'hm': tensor([[[[-4.3433, -5.5296, -5.5170, ..., -5.3087, -5.2807, -4.2913],\n", " [-5.4693, -7.2061, -7.2195, ..., -6.9011, -6.8778, -5.3490],\n", " [-5.4691, -7.1824, -7.1806, ..., -6.9330, -6.8808, -5.4019],\n", " ...,\n", " [-5.2143, -6.8130, -6.8661, ..., -7.1894, -7.0903, -5.5019],\n", " [-5.3262, -6.8891, -6.8383, ..., -7.1273, -7.0633, -5.4696],\n", " [-4.3603, -5.3907, -5.3109, ..., -5.4597, -5.4542, -4.3752]],\n", " \n", " [[-4.4292, -5.5015, -5.5068, ..., -5.3757, -5.3183, -4.2863],\n", " [-5.4769, -7.0954, -7.1240, ..., -6.9488, -6.8777, -5.3009],\n", " [-5.4566, -7.0729, -7.0850, ..., -6.9706, -6.9265, -5.3418],\n", " ...,\n", " [-5.1941, -6.6772, -6.7317, ..., -7.1623, -7.0690, -5.4433],\n", " [-5.2653, -6.7094, -6.6905, ..., -7.0680, -7.0011, -5.3971],\n", " [-4.2260, -5.1407, -5.1102, ..., -5.3456, -5.3033, -4.3035]]]],\n", " device='cuda:0', grad_fn=),\n", " 'reg': tensor([[[[0.5947, 0.8965, 0.9064, ..., 0.8972, 0.8681, 0.6239],\n", " [0.9423, 1.3869, 1.4444, ..., 1.4389, 1.3814, 0.9374],\n", " [0.9579, 1.4363, 1.4845, ..., 1.4816, 1.4424, 0.9737],\n", " ...,\n", " [0.9031, 1.3865, 1.4391, ..., 1.5909, 1.5352, 1.0329],\n", " [0.8918, 1.3612, 1.3998, ..., 1.5470, 1.4922, 1.0219],\n", " [0.6119, 0.9293, 0.9368, ..., 1.0443, 1.0021, 0.6804]],\n", " \n", " [[0.5585, 0.7890, 0.7637, ..., 0.7662, 0.7623, 0.5244],\n", " [0.8169, 1.1117, 1.0977, ..., 1.0912, 1.1173, 0.7487],\n", " [0.7769, 1.0917, 1.0763, ..., 1.0656, 1.0882, 0.7149],\n", " ...,\n", " [0.7009, 0.9803, 0.9778, ..., 1.1180, 1.1121, 0.7332],\n", " [0.7114, 0.9955, 0.9563, ..., 1.0811, 1.0819, 0.7115],\n", " [0.5229, 0.8187, 0.7654, ..., 0.8205, 0.8387, 0.5763]]]],\n", " device='cuda:0', grad_fn=),\n", " 'l': tensor([[[[12.8818, 17.9698, 17.0928, ..., 16.6068, 17.5050, 12.0376],\n", " [18.4828, 26.1562, 24.7120, ..., 23.9352, 25.1975, 17.1785],\n", " [17.1545, 24.3852, 23.3535, ..., 23.0167, 23.8729, 16.0442],\n", " ...,\n", " [16.8594, 23.6443, 22.4741, ..., 24.8678, 25.0808, 16.4195],\n", " [18.9540, 25.9951, 23.7003, ..., 25.6341, 26.6465, 17.7435],\n", " [13.6302, 18.5435, 16.4166, ..., 17.3674, 18.4046, 12.5582]]]],\n", " device='cuda:0', grad_fn=),\n", " 'ratio_al': tensor([[[[0.3939, 0.3641, 0.3669, ..., 0.3725, 0.3717, 0.3873],\n", " [0.3751, 0.3590, 0.3590, ..., 0.3565, 0.3556, 0.3774],\n", " [0.3765, 0.3623, 0.3607, ..., 0.3588, 0.3549, 0.3767],\n", " ...,\n", " [0.3866, 0.3717, 0.3698, ..., 0.3557, 0.3536, 0.3660],\n", " [0.3787, 0.3623, 0.3629, ..., 0.3515, 0.3452, 0.3624],\n", " [0.3994, 0.3830, 0.3852, ..., 0.3738, 0.3693, 0.3962]]]],\n", " device='cuda:0', grad_fn=),\n", " 'ratio_ba': tensor([[[[0.8064, 0.9032, 0.8995, ..., 0.8974, 0.9003, 0.8220],\n", " [0.8693, 0.9541, 0.9517, ..., 0.9500, 0.9522, 0.8879],\n", " [0.8655, 0.9511, 0.9483, ..., 0.9492, 0.9508, 0.8894],\n", " ...,\n", " [0.8428, 0.9372, 0.9355, ..., 0.9547, 0.9542, 0.8921],\n", " [0.8558, 0.9445, 0.9394, ..., 0.9555, 0.9569, 0.8971],\n", " [0.7564, 0.8530, 0.8405, ..., 0.8584, 0.8643, 0.7898]]]],\n", " device='cuda:0', grad_fn=),\n", " 'theta': tensor([[[[-0.0761, -0.1547, -0.1716, ..., -0.1909, -0.1875, -0.1333],\n", " [-0.0604, -0.1148, -0.1594, ..., -0.1636, -0.1516, -0.1360],\n", " [-0.0895, -0.1235, -0.1763, ..., -0.1882, -0.1892, -0.1701],\n", " ...,\n", " [-0.0823, -0.1339, -0.1927, ..., -0.2219, -0.2217, -0.1918],\n", " [-0.0764, -0.0815, -0.1561, ..., -0.2025, -0.2074, -0.1466],\n", " [ 0.0027, -0.0151, -0.0279, ..., -0.0821, -0.0618, -0.0642]]]],\n", " device='cuda:0', grad_fn=)}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 1, 5]) torch.Size([1, 1, 5])\n", "tensor([[[125.0000, 125.0000, 100.0000, 50.0000, 3.1416]]], device='cuda:0') tensor([[[125., 125., 100., 50., 0.]]], device='cuda:0')\n" ] }, { "data": { "text/plain": [ "(tensor([[0.]], device='cuda:0'), tensor([[1.]], device='cuda:0'))" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bbox1 = torch.tensor([[[125, 125, 100, 50, math.pi]]]).cuda()\n", "bbox2 = torch.tensor([[[125, 125, 100, 50, 0.0 * math.pi]]]).cuda()\n", "print(bbox1.shape, bbox2.shape)\n", "print(bbox1, bbox2)\n", "iou_loss, iou = cal_diou(bbox1, bbox2)\n", "iou_loss, iou" ] }, { "cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predict: (-37, -44) (794, 788) (378, 372) 832 0.49633774 0.36814708 (0.0029389741830527782)' 206 153 0\n", "Ground Truth: (8, -46) (821, 766) (415, 360) 813.2595489854392 0.7924850077739932 0.6098913939821203 (0.08804217608343967)' 322 248 15\n", "dice: 0.5669879778891793 iou:0.39566170355919866 bbox_iou: 0.3956793546676636 angle_error: 15.31857634206964\n", "Predict: (196, 35) (811, 651) (504, 343) 615 1.0 0.98237014 (0.0009649893036112189)' 307 302 0\n", "Ground Truth: (279, 119) (726, 566) (503, 343) 446.29608288668635 0.8290460395861065 0.5591803503759695 (-0.15087888605111677)' 185 124 -27\n", "dice: 0.39796982738597514 iou:0.2484159375953636 bbox_iou: 0.24851427972316742 angle_error: 27.331897563851037\n" ] } ], "source": [ "# Using ratio_bl\n", "\n", "angle_errors = []\n", "dices = []\n", "ious = []\n", "bbox_ious = []\n", "\n", "\n", "for ind in range(len(dataset)):\n", " ind = 34\n", " # Load Image\n", " \n", " cls_dice = []\n", " cls_angle_error = []\n", " cls_iou = []\n", " cls_bbox_iou = []\n", " \n", " img_id = dataset.images[ind]\n", " img_info = dataset.coco.loadImgs(ids=[img_id])[0]\n", " img_path = os.path.join(dataset.img_dir, img_info['file_name'])\n", " image = cv2.imread(img_path)\n", " shape = image.shape[:2]\n", "\n", " # Load annotations\n", " ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])\n", " anns = dataset.coco.loadAnns(ids=ann_ids)\n", "\n", " # Inference\n", " color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]\n", " ret = detector.run(img_path)\n", "\n", " for class_idx in range(opt.num_classes):\n", " try:\n", " class_id = class_idx + 1\n", " pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))\n", " pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))\n", " cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5])\n", " l = int(ret['results'][class_id][0, 6]) # !attention here!\n", " ratio_al = ret['results'][class_id][0, 7]\n", " ratio_bl = ret['results'][class_id][0, 8]\n", " a = ratio_al * l / 2\n", " b = ratio_bl * l / 2\n", " theta = ret['results'][class_id][0, 9]\n", " angle = theta * 180\n", " print(\"Predict: \", pt1, pt2, (cx, cy), l, ratio_al, ratio_bl, \"({})'\".format(theta), int(a), int(b), int(angle))\n", "\n", " # Annotations\n", " ann = anns[class_idx]\n", " cx_gt, cy_gt, a_gt, b_gt, theta_gt = ann['ellipse']\n", " theta_gt = theta_gt / math.pi\n", " theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt\n", " l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)\n", " pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2))\n", " pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2))\n", " ratio_al, ratio_bl = 2 * a_gt / l_gt, 2 * b_gt / l_gt\n", " angle_gt = theta_gt * 180\n", " print(\"Ground Truth:\", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_bl, \"({})'\".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt))\n", "# img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)\n", " img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color=color_maps[class_idx * 2 + 1], thickness=2)\n", " # img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2)\n", " # img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2)\n", "\n", "\n", " pt1 = (int(cx - l / 2), int(cy - l / 2))\n", " pt2 = (int(cx + l / 2), int(cy + l / 2))\n", "# img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)\n", "# img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)\n", "\n", " pred_ell = [cx, cy, a, b, angle]\n", " gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]\n", " error = compute_single_class_metric(pred_ell, gt_ell, shape)\n", " print(\"dice: {} iou:{} bbox_iou: {} angle_error: {}\".format(error[0], error[1], error[2], error[3]))\n", " cls_dice.append(error[0])\n", " cls_iou.append(error[1])\n", " cls_bbox_iou.append(error[2])\n", " cls_angle_error.append(error[3])\n", " \n", " except Exception as e:\n", " print(e)\n", " cls_dice.append(0)\n", " cls_iou.append(0)\n", " cls_bbox_iou.append(0)\n", " cls_angle_error.append(90)\n", " pass\n", " img = Image.fromarray(img)\n", " img.save('label.png')\n", "# print(\"Saving\", img_path, \"to\", os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", "# img.save(os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", "# print(\"Saving\", img_path, \"to\", os.path.join(save_pred_folder, img_path.split('/')[-1]))\n", " dices.append(cls_dice)\n", " ious.append(cls_iou)\n", " bbox_ious.append(cls_bbox_iou)\n", " angle_errors.append(cls_angle_error)\n", "# img\n", " break\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 } slamd202303071458299/scripts/fhd_opts.py000066400000000000000000000510611435750645400174540ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='eldet', help='ctdet | ddd | multi_pose | exdet | eldet | eldet_bbox') self.parser.add_argument('--dataset', default='coco_fhd', help='coco | kitti | coco_hp | pascal | coco_fhd') self.parser.add_argument('--data_dir', default='/data/cc/Data/CHD/detection/') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') self.parser.add_argument('--save_image', action='store_true') self.parser.add_argument('--debug', type=int, default=0, help='level of visualization.' '1: only show the final detection results' '2: show the network output features' '3: use matplot to display' # useful when lunching training with ipython notebook '4: save all visualizations to disk') self.parser.add_argument('--demo', default='', help='path to image/ image folders/ video. ' 'or "webcam"') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=4, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.3, help='visualization threshold.') self.parser.add_argument('--debugger_theme', default='white', choices=['white', 'black']) # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'res_18 | res_101 | resdcn_18 | resdcn_101 |' 'dlav0_34 | dla_34 | hourglass') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '64 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1.25e-4, help='learning rate for batch size 32.') self.parser.add_argument('--lr_step', type=str, default='80,120,200,400', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=140, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=32, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=5, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') # test self.parser.add_argument('--flip_test', action='store_true', help='flip data augmentation.') self.parser.add_argument('--test_scales', type=str, default='1', help='multi scale test augmentation.') self.parser.add_argument('--nms', action='store_true', help='run nms in testing.') self.parser.add_argument('--K', type=int, default=2, help='max number of output objects.') self.parser.add_argument('--not_prefetch_test', action='store_true', help='not use parallal data pre-processing.') self.parser.add_argument('--fix_res', action='store_true', help='fix testing resolution or keep ' 'the original resolution') self.parser.add_argument('--keep_res', action='store_true', help='keep the original resolution' ' during validation.') # dataset self.parser.add_argument('--not_rand_crop', action='store_true', help='not use the random crop data augmentation' 'from CornerNet.') self.parser.add_argument('--gaussian_noise', action='store_true', help='use Gaussian noise for augmentation') self.parser.add_argument('--shift', type=float, default=0.1, help='when not using random crop' 'apply shift augmentation.') self.parser.add_argument('--scale', type=float, default=0.2, help='when not using random crop' 'apply scale augmentation.') self.parser.add_argument('--rotate', type=float, default=0, help='when not using random crop' 'apply rotation augmentation.') self.parser.add_argument('--flip', type = float, default=0.5, help='probability of applying flip augmentation.') self.parser.add_argument('--no_color_aug', action='store_true', help='not use the color augmenation ' 'from CornerNet') # multi_pose self.parser.add_argument('--aug_rot', type=float, default=0, help='probability of applying ' 'rotation augmentation.') # ddd self.parser.add_argument('--aug_ddd', type=float, default=0.5, help='probability of applying crop augmentation.') self.parser.add_argument('--rect_mask', action='store_true', help='for ignored object, apply mask on the ' 'rectangular region or just center point.') self.parser.add_argument('--kitti_split', default='3dop', help='different validation split for kitti: ' '3dop | subcnn') # loss self.parser.add_argument('--mse_loss', action='store_true', help='use mse loss or focal loss to train ' 'keypoint heatmaps.') # ctdet self.parser.add_argument('--reg_loss', default='l1', help='regression loss: sl1 | l1 | l2') self.parser.add_argument('--hm_weight', type=float, default=1, help='loss weight for keypoint heatmaps.') self.parser.add_argument('--off_weight', type=float, default=1, help='loss weight for keypoint local offsets.') self.parser.add_argument('--wh_weight', type=float, default=0.1, help='loss weight for bounding box size.') # multi_pose self.parser.add_argument('--hp_weight', type=float, default=1, help='loss weight for human pose offset.') self.parser.add_argument('--hm_hp_weight', type=float, default=1, help='loss weight for human keypoint heatmap.') # ddd self.parser.add_argument('--dep_weight', type=float, default=1, help='loss weight for depth.') self.parser.add_argument('--dim_weight', type=float, default=1, help='loss weight for 3d bounding box size.') self.parser.add_argument('--rot_weight', type=float, default=1, help='loss weight for orientation.') self.parser.add_argument('--peak_thresh', type=float, default=0.2) # ellipse loss self.parser.add_argument('--iou_weight', type=float, default=1, help='loss weight for iou loss.') self.parser.add_argument('--ellipse_weight', type=float, default=1, help='loss weight for ellipse regression.') self.parser.add_argument('--ellipse_reg_weight', type=float, default=0, help='loss weight for ellipse l, a, b constrain.') self.parser.add_argument('--theta_weight', type=float, default=1, help='loss weight for angle regression.') self.parser.add_argument('--sincos_weight', type=float, default=0, help='loss weight for sin/cos regression.') self.parser.add_argument('--num_angle_bins', type=int, default=0, help='number of angle bins.') self.parser.add_argument('--angle_bins_weight', type=float, default=0, help='weight of angle bins classification.') # task # ctdet self.parser.add_argument('--norm_wh', action='store_true', help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') self.parser.add_argument('--dense_wh', action='store_true', help='apply weighted regression near center or ' 'just apply regression on center point.') self.parser.add_argument('--cat_spec_wh', action='store_true', help='category specific bounding box size.') self.parser.add_argument('--not_reg_offset', action='store_true', help='not regress local offset.') # exdet self.parser.add_argument('--agnostic_ex', action='store_true', help='use category agnostic extreme points.') self.parser.add_argument('--scores_thresh', type=float, default=0.1, help='threshold for extreme point heatmap.') self.parser.add_argument('--center_thresh', type=float, default=0.1, help='threshold for centermap.') self.parser.add_argument('--aggr_weight', type=float, default=0.0, help='edge aggregation weight.') # multi_pose self.parser.add_argument('--dense_hp', action='store_true', help='apply weighted pose regression near center ' 'or just apply regression on center point.') self.parser.add_argument('--not_hm_hp', action='store_true', help='not estimate human joint heatmap, ' 'directly use the joint offset from center.') self.parser.add_argument('--not_reg_hp_offset', action='store_true', help='not regress local offset for ' 'human joint heatmaps.') self.parser.add_argument('--not_reg_bbox', action='store_true', help='not regression bounding box size.') # ground truth validation self.parser.add_argument('--eval_oracle_hm', action='store_true', help='use ground center heatmap.') self.parser.add_argument('--eval_oracle_wh', action='store_true', help='use ground truth bounding box size.') self.parser.add_argument('--eval_oracle_offset', action='store_true', help='use ground truth local heatmap offset.') self.parser.add_argument('--eval_oracle_kps', action='store_true', help='use ground truth human pose offset.') self.parser.add_argument('--eval_oracle_hmhp', action='store_true', help='use ground truth human joint heatmaps.') self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', help='use ground truth human joint local offset.') self.parser.add_argument('--eval_oracle_dep', action='store_true', help='use ground truth depth.') def parse(self, args=''): if args == '': opt = self.parser.parse_args([]) else: opt = self.parser.parse_args(args) opt.gpus_str = opt.gpus opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] opt.lr_step = [int(i) for i in opt.lr_step.split(',')] opt.test_scales = [float(i) for i in opt.test_scales.split(',')] opt.fix_res = not opt.keep_res print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') opt.reg_offset = not opt.not_reg_offset opt.reg_bbox = not opt.not_reg_bbox opt.hm_hp = not opt.not_hm_hp opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp if opt.head_conv == -1: # init default head_conv opt.head_conv = 256 if 'dla' in opt.arch else 64 opt.pad = 127 if 'hourglass' in opt.arch else 31 opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 if opt.trainval: opt.val_intervals = 100000000 if opt.debug > 0: opt.num_workers = 0 opt.batch_size = 1 opt.gpus = [opt.gpus[0]] opt.master_batch_size = -1 if opt.master_batch_size == -1: opt.master_batch_size = opt.batch_size // len(opt.gpus) rest_batch_size = (opt.batch_size - opt.master_batch_size) opt.chunk_sizes = [opt.master_batch_size] for i in range(len(opt.gpus) - 1): slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) if i < rest_batch_size % (len(opt.gpus) - 1): slave_chunk_size += 1 opt.chunk_sizes.append(slave_chunk_size) print('training chunk_sizes:', opt.chunk_sizes) opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') # opt.data_dir = os.path.join(opt.root_dir, 'data') opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) opt.debug_dir = os.path.join(opt.save_dir, 'debug') print('The output will be saved to ', opt.save_dir) if opt.resume and opt.load_model == '': model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ else opt.save_dir opt.load_model = os.path.join(model_path, 'model_last.pth') return opt def update_dataset_info_and_set_heads(self, opt, dataset): input_h, input_w = dataset.default_resolution opt.mean, opt.std = dataset.mean, dataset.std opt.num_classes = dataset.num_classes # input_h(w): opt.input_h overrides opt.input_res overrides dataset default input_h = opt.input_res if opt.input_res > 0 else input_h input_w = opt.input_res if opt.input_res > 0 else input_w opt.input_h = opt.input_h if opt.input_h > 0 else input_h opt.input_w = opt.input_w if opt.input_w > 0 else input_w opt.output_h = opt.input_h // opt.down_ratio opt.output_w = opt.input_w // opt.down_ratio opt.input_res = max(opt.input_h, opt.input_w) opt.output_res = max(opt.output_h, opt.output_w) if opt.task == 'exdet': # assert opt.dataset in ['coco'] num_hm = 1 if opt.agnostic_ex else opt.num_classes opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, 'hm_b': num_hm, 'hm_r': num_hm, 'hm_c': opt.num_classes} if opt.reg_offset: opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2}) elif opt.task == 'ddd': # assert opt.dataset in ['gta', 'kitti', 'viper'] opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3} if opt.reg_bbox: opt.heads.update( {'wh': 2}) if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'ctdet': # assert opt.dataset in ['pascal', 'coco'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'eldet_bbox': # assert opt.dataset in ['coco_fhd'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'eldet': # assert opt.dataset in ['coco_fhd'] opt.heads = {'hm': opt.num_classes, 'reg': 2, 'l': 1, 'ratio_al': 1, 'ratio_bl': 1, 'theta': 1} # if opt.sincos_weight > 0: opt.heads.update({'sincos': 2}) if opt.num_angle_bins > 0: opt.heads.update({'angle_bins': opt.num_angle_bins}) elif opt.task == 'multi_pose': # assert opt.dataset in ['coco_hp'] opt.flip_idx = dataset.flip_idx opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34} if opt.reg_offset: opt.heads.update({'reg': 2}) if opt.hm_hp: opt.heads.update({'hm_hp': 17}) if opt.reg_hp_offset: opt.heads.update({'hp_offset': 2}) else: assert 0, 'task not defined!' print('heads', opt.heads) return opt def init(self, args=''): default_dataset_info = { 'ctdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'exdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'multi_pose': { 'default_resolution': [512, 512], 'num_classes': 1, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco_hp', 'num_joints': 17, 'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]}, 'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'dataset': 'kitti'}, 'eldet': {'default_resolution': [512, 512], 'num_classes': 2, 'mean': [0.216, 0.216, 0.216], 'std': [0.222, 0.222, 0.222], 'dataset': 'coco_fhd'}, } class Struct: def __init__(self, entries): for k, v in entries.items(): self.__setattr__(k, v) opt = self.parse(args) dataset = Struct(default_dataset_info[opt.task]) opt.dataset = dataset.dataset opt = self.update_dataset_info_and_set_heads(opt, dataset) return opt slamd202303071458299/scripts/jupyter_opts.py000066400000000000000000000454171435750645400204250ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='eldet', help='ctdet | ddd | multi_pose | exdet') self.parser.add_argument('--dataset', default='coco', help='coco | kitti | coco_hp | pascal | coco_fhd') self.parser.add_argument('--data_dir', default='/data/cc/Data/CHD/detection/') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') self.parser.add_argument('--debug', type=int, default=0, help='level of visualization.' '1: only show the final detection results' '2: show the network output features' '3: use matplot to display' # useful when lunching training with ipython notebook '4: save all visualizations to disk') self.parser.add_argument('--demo', default='', help='path to image/ image folders/ video. ' 'or "webcam"') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=4, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.3, help='visualization threshold.') self.parser.add_argument('--debugger_theme', default='white', choices=['white', 'black']) # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'res_18 | res_101 | resdcn_18 | resdcn_101 |' 'dlav0_34 | dla_34 | hourglass') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '64 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1.25e-4, help='learning rate for batch size 32.') self.parser.add_argument('--lr_step', type=str, default='90,120', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=140, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=32, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=5, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') # test self.parser.add_argument('--flip_test', action='store_true', help='flip data augmentation.') self.parser.add_argument('--test_scales', type=str, default='1', help='multi scale test augmentation.') self.parser.add_argument('--nms', action='store_true', help='run nms in testing.') self.parser.add_argument('--K', type=int, default=2, help='max number of output objects.') self.parser.add_argument('--not_prefetch_test', action='store_true', help='not use parallal data pre-processing.') self.parser.add_argument('--fix_res', action='store_true', help='fix testing resolution or keep ' 'the original resolution') self.parser.add_argument('--keep_res', action='store_true', help='keep the original resolution' ' during validation.') # dataset self.parser.add_argument('--not_rand_crop', action='store_true', help='not use the random crop data augmentation' 'from CornerNet.') self.parser.add_argument('--shift', type=float, default=0.1, help='when not using random crop' 'apply shift augmentation.') self.parser.add_argument('--scale', type=float, default=0.4, help='when not using random crop' 'apply scale augmentation.') self.parser.add_argument('--rotate', type=float, default=0, help='when not using random crop' 'apply rotation augmentation.') self.parser.add_argument('--flip', type = float, default=0.5, help='probability of applying flip augmentation.') self.parser.add_argument('--no_color_aug', action='store_true', help='not use the color augmenation ' 'from CornerNet') # multi_pose self.parser.add_argument('--aug_rot', type=float, default=0, help='probability of applying ' 'rotation augmentation.') # ddd self.parser.add_argument('--aug_ddd', type=float, default=0.5, help='probability of applying crop augmentation.') self.parser.add_argument('--rect_mask', action='store_true', help='for ignored object, apply mask on the ' 'rectangular region or just center point.') self.parser.add_argument('--kitti_split', default='3dop', help='different validation split for kitti: ' '3dop | subcnn') # loss self.parser.add_argument('--mse_loss', action='store_true', help='use mse loss or focal loss to train ' 'keypoint heatmaps.') # ctdet self.parser.add_argument('--reg_loss', default='l1', help='regression loss: sl1 | l1 | l2') self.parser.add_argument('--hm_weight', type=float, default=1, help='loss weight for keypoint heatmaps.') self.parser.add_argument('--off_weight', type=float, default=1, help='loss weight for keypoint local offsets.') self.parser.add_argument('--wh_weight', type=float, default=0.1, help='loss weight for bounding box size.') # multi_pose self.parser.add_argument('--hp_weight', type=float, default=1, help='loss weight for human pose offset.') self.parser.add_argument('--hm_hp_weight', type=float, default=1, help='loss weight for human keypoint heatmap.') # ddd self.parser.add_argument('--dep_weight', type=float, default=1, help='loss weight for depth.') self.parser.add_argument('--dim_weight', type=float, default=1, help='loss weight for 3d bounding box size.') self.parser.add_argument('--rot_weight', type=float, default=1, help='loss weight for orientation.') self.parser.add_argument('--peak_thresh', type=float, default=0.2) # task # ctdet self.parser.add_argument('--norm_wh', action='store_true', help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') self.parser.add_argument('--dense_wh', action='store_true', help='apply weighted regression near center or ' 'just apply regression on center point.') self.parser.add_argument('--cat_spec_wh', action='store_true', help='category specific bounding box size.') self.parser.add_argument('--not_reg_offset', action='store_true', help='not regress local offset.') # exdet self.parser.add_argument('--agnostic_ex', action='store_true', help='use category agnostic extreme points.') self.parser.add_argument('--scores_thresh', type=float, default=0.1, help='threshold for extreme point heatmap.') self.parser.add_argument('--center_thresh', type=float, default=0.1, help='threshold for centermap.') self.parser.add_argument('--aggr_weight', type=float, default=0.0, help='edge aggregation weight.') # multi_pose self.parser.add_argument('--dense_hp', action='store_true', help='apply weighted pose regression near center ' 'or just apply regression on center point.') self.parser.add_argument('--not_hm_hp', action='store_true', help='not estimate human joint heatmap, ' 'directly use the joint offset from center.') self.parser.add_argument('--not_reg_hp_offset', action='store_true', help='not regress local offset for ' 'human joint heatmaps.') self.parser.add_argument('--not_reg_bbox', action='store_true', help='not regression bounding box size.') # ground truth validation self.parser.add_argument('--eval_oracle_hm', action='store_true', help='use ground center heatmap.') self.parser.add_argument('--eval_oracle_wh', action='store_true', help='use ground truth bounding box size.') self.parser.add_argument('--eval_oracle_offset', action='store_true', help='use ground truth local heatmap offset.') self.parser.add_argument('--eval_oracle_kps', action='store_true', help='use ground truth human pose offset.') self.parser.add_argument('--eval_oracle_hmhp', action='store_true', help='use ground truth human joint heatmaps.') self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', help='use ground truth human joint local offset.') self.parser.add_argument('--eval_oracle_dep', action='store_true', help='use ground truth depth.') def parse(self, args=''): if args == '': opt = self.parser.parse_args([]) else: opt = self.parser.parse_args(args) opt.gpus_str = opt.gpus opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] opt.lr_step = [int(i) for i in opt.lr_step.split(',')] opt.test_scales = [float(i) for i in opt.test_scales.split(',')] opt.fix_res = not opt.keep_res print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') opt.reg_offset = not opt.not_reg_offset opt.reg_bbox = not opt.not_reg_bbox opt.hm_hp = not opt.not_hm_hp opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp if opt.head_conv == -1: # init default head_conv opt.head_conv = 256 if 'dla' in opt.arch else 64 opt.pad = 127 if 'hourglass' in opt.arch else 31 opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 if opt.trainval: opt.val_intervals = 100000000 if opt.debug > 0: opt.num_workers = 0 opt.batch_size = 1 opt.gpus = [opt.gpus[0]] opt.master_batch_size = -1 if opt.master_batch_size == -1: opt.master_batch_size = opt.batch_size // len(opt.gpus) rest_batch_size = (opt.batch_size - opt.master_batch_size) opt.chunk_sizes = [opt.master_batch_size] for i in range(len(opt.gpus) - 1): slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) if i < rest_batch_size % (len(opt.gpus) - 1): slave_chunk_size += 1 opt.chunk_sizes.append(slave_chunk_size) print('training chunk_sizes:', opt.chunk_sizes) opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') # opt.data_dir = os.path.join(opt.root_dir, 'data') opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) opt.debug_dir = os.path.join(opt.save_dir, 'debug') print('The output will be saved to ', opt.save_dir) if opt.resume and opt.load_model == '': model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ else opt.save_dir opt.load_model = os.path.join(model_path, 'model_last.pth') return opt def update_dataset_info_and_set_heads(self, opt, dataset): input_h, input_w = dataset.default_resolution opt.mean, opt.std = dataset.mean, dataset.std opt.num_classes = dataset.num_classes # input_h(w): opt.input_h overrides opt.input_res overrides dataset default input_h = opt.input_res if opt.input_res > 0 else input_h input_w = opt.input_res if opt.input_res > 0 else input_w opt.input_h = opt.input_h if opt.input_h > 0 else input_h opt.input_w = opt.input_w if opt.input_w > 0 else input_w opt.output_h = opt.input_h // opt.down_ratio opt.output_w = opt.input_w // opt.down_ratio opt.input_res = max(opt.input_h, opt.input_w) opt.output_res = max(opt.output_h, opt.output_w) if opt.task == 'exdet': # assert opt.dataset in ['coco'] num_hm = 1 if opt.agnostic_ex else opt.num_classes opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, 'hm_b': num_hm, 'hm_r': num_hm, 'hm_c': opt.num_classes} if opt.reg_offset: opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2}) elif opt.task == 'ddd': # assert opt.dataset in ['gta', 'kitti', 'viper'] opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3} if opt.reg_bbox: opt.heads.update( {'wh': 2}) if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'ctdet': # assert opt.dataset in ['pascal', 'coco'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'eldet': # assert opt.dataset in ['coco_fhd'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'multi_pose': # assert opt.dataset in ['coco_hp'] opt.flip_idx = dataset.flip_idx opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34} if opt.reg_offset: opt.heads.update({'reg': 2}) if opt.hm_hp: opt.heads.update({'hm_hp': 17}) if opt.reg_hp_offset: opt.heads.update({'hp_offset': 2}) else: assert 0, 'task not defined!' print('heads', opt.heads) return opt def init(self, args=''): default_dataset_info = { 'ctdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'exdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'multi_pose': { 'default_resolution': [512, 512], 'num_classes': 1, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco_hp', 'num_joints': 17, 'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]}, 'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'dataset': 'kitti'}, 'eldet': {'default_resolution': [512, 512], 'num_classes': 2, 'mean': [0], 'std': [1], 'dataset': 'coco_fhd'}, } class Struct: def __init__(self, entries): for k, v in entries.items(): self.__setattr__(k, v) opt = self.parse(args) dataset = Struct(default_dataset_info[opt.task]) opt.dataset = dataset.dataset opt = self.update_dataset_info_and_set_heads(opt, dataset) return opt slamd202303071458299/scripts/prepare_label.ipynb000066400000000000000000001653721435750645400211470ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "import math\n", "import os\n", "\n", "import cv2\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Ellipse\n", "import numpy as np\n", "from PIL import Image\n", "import shutil\n", "from glob import glob" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "new_data_folder = '/data/cc/Data/CHD/detection/added/'\n", "save_folder = '/data/cc/Data/CHD/detection/images/'\n", "image_lists = glob(os.path.join(new_data_folder, '*.jpg'))\n", "len(image_lists)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for img_path in image_lists:\n", " img_name = img_path.split('/')[-1].split('.')[0]\n", " save_path = os.path.join(save_folder, img_name + '.png')\n", " img = Image.open(img_path).convert('L')\n", " img.save(save_path)\n", " print(save_path, \"finished!\")\n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key in json_data['_via_img_metadata'].keys():\n", " file_name = json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if regions:\n", " break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "region = regions[0]\n", "ellipse = region['shape_attributes']\n", "label = int(region['region_attributes']['type'])\n", "regions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "region, ellipse, label" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ellipse" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "region['theta']/math.pi*360" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load and show image\n", "file_path = os.path.join(data_folder, file_name)\n", "image = plt.imread(file_path)\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.imshow(image, cmap='gray')\n", "\n", "# display label\n", "for region in regions:\n", " label = int(region['region_attributes']['type'])\n", " \n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 180\n", " else:\n", " continue\n", " \n", " if label == 1:\n", " color = 'yellow'\n", " dot = 'y.'\n", " elif label == 2:\n", " color = 'blue'\n", " dot = 'b.'\n", " \n", " ell = Ellipse(xy = (cx, cy), width = 2 * rx, height = 2 * ry, angle = angle, facecolor= color, alpha=0.2)\n", " ax.add_patch(ell)\n", " ax.plot(cx, cy, dot)\n", " \n", "plt.axis('scaled')\n", "plt.axis('equal') #changes limits of x or y axis so that equal increments of x and y have the same length\n", "\n", "plt.show()\n", "# plt.imshow(image, cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Normal File" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/train/normal/\"\n", "save_folder = \"/data/cc/Data/CHD/processed/train/normal_seg/\"\n", "if not os.path.exists(save_path):\n", " os.makedirs(save_path)\n", "json_path = \"train_normal_28Dec2020_10h54m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Display label file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "idx = 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "key = list(json_data['_via_img_metadata'].keys())[idx]\n", "# for key in json_data['_via_img_metadata'].keys():\n", "file_name = json_data['_via_img_metadata'][key]['filename']\n", "regions = json_data['_via_img_metadata'][key]['regions']\n", " \n", "idx += 1\n", " \n", "# load and show image\n", "file_path = os.path.join(data_folder, file_name)\n", "image = plt.imread(file_path)\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.imshow(image, cmap='gray')\n", "\n", "# display label\n", "for region in regions:\n", " label = int(region['region_attributes']['type'])\n", " \n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", " \n", " if label == 1:\n", " color = 'yellow'\n", " dot = 'y.'\n", " elif label == 2:\n", " color = 'blue'\n", " dot = 'b.'\n", " \n", " ell = Ellipse(xy = (cx, cy), width = 2 * rx, height = 2 * ry, angle = angle, facecolor= color, alpha=0.2)\n", " ax.add_patch(ell)\n", " ax.plot(cx, cy, dot)\n", " \n", "plt.axis('scaled')\n", "plt.axis('equal') #changes limits of x or y axis so that equal increments of x and y have the same length\n", "\n", "plt.show()\n", "# plt.imshow(image, cmap='gray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Display Label\n", "# load and show image\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.imshow(image, cmap='gray')\n", "\n", "# display label\n", "for region in regions:\n", " label = int(region['region_attributes']['type'])\n", " \n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", " \n", " if label == 1:\n", " color = 'yellow'\n", " dot = 'y.'\n", " elif label == 2:\n", " color = 'blue'\n", " dot = 'b.'\n", " \n", " ell = Ellipse(xy = (cx, cy), width = 2 * rx, height = 2 * ry, angle = angle, facecolor= color, alpha=0.2)\n", " ax.add_patch(ell)\n", " ax.plot(cx, cy, dot)\n", " \n", "plt.axis('scaled')\n", "plt.axis('equal') #changes limits of x or y axis so that equal increments of x and y have the same length\n", "\n", "plt.show()\n", "# plt.imshow(image, cmap='gray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(regions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Process Normal Annotations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "regions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/detection/images/\"\n", "json_path = \"./label_file/train_normal_refined.json\"\n", "save_img_folder = \"/data/cc/Data/CHD/segmentation/images/\"\n", "save_folder = \"/data/cc/Data/CHD/segmentation/labels/\"\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = 'normal_' + json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " \n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", " \n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " shutil.copy(file_path, os.path.join(save_img_folder, file_name))\n", "\n", " image = plt.imread(file_path)\n", " label_map = np.zeros_like(image, dtype=np.uint8)\n", " label_map_lung = np.zeros_like(image, dtype=np.uint8)\n", " label_map_heart = np.zeros_like(image, dtype=np.uint8)\n", " \n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 180\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " label_map_lung = cv2.ellipse(label_map_lung, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)), label_map_lung, cmap='gray')\n", " label_map_lung = Image.fromarray(label_map_lung)\n", " label_map_lung.save(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)))\n", " elif label == 2:\n", " label_map_heart = cv2.ellipse(label_map_heart, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (2, 2, 2), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)), label_map_heart, cmap='gray')\n", " label_map_heart = Image.fromarray(label_map_heart)\n", " label_map_heart.save(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)))\n", "\n", " plt.imsave(os.path.join(save_folder, \"{}_seg_vis.png\".format(file_idx)), label_map)\n", " label_map = Image.fromarray(label_map)\n", " label_map.save(os.path.join(save_folder, \"{}_seg.png\".format(file_idx)))\n", " \n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "regions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Process Segmentation annotations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/detection/images/\"\n", "train_img_folder = \"/data/cc/Data/CHD/segmentation/train/images/\"\n", "train_mask_folder = \"/data/cc/Data/CHD/segmentation/train/masks/\"\n", "val_img_folder = \"/data/cc/Data/CHD/segmentation/val/images/\"\n", "val_mask_folder = \"/data/cc/Data/CHD/segmentation/val/masks/\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Number of validation set: \", num_val)\n", "for idx, json_path in enumerate(json_files):\n", " print(json_path.split('/')[-1])\n", " with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", " if idx == 0 or idx == 1:\n", " prefix = 'normal_'\n", " elif idx == 2 or idx == 3:\n", " prefix = 'abnormal_'\n", " else:\n", " prefix = ''\n", " \n", " if idx == 0:\n", " prepare_segmentation(json_data, num_val=num_val, prefix=prefix)\n", " else:\n", " prepare_segmentation(json_data, prefix=prefix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Multiple json files\n", "def prepare_segmentation(json_data, num_val=0, prefix=''):\n", " num_processed = 0\n", " if num_val:\n", " print(\"Processing val set\")\n", " save_folder = val_mask_folder\n", " img_folder = val_img_folder\n", "\n", " \n", " for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = \"{}{}\".format(prefix, json_data['_via_img_metadata'][key]['filename'])\n", " if file_name.endswith('.jpg'):\n", " file_name = file_name.replace('.jpg', '.png')\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", " \n", " if num_processed == num_val:\n", " save_folder = train_mask_folder\n", " img_folder = train_img_folder\n", " print(\"Processing train set\")\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = Image.open(file_path)\n", " image.save(os.path.join(img_folder, file_name))\n", " image = np.array(image)\n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", "\n", " label_map_vis = np.stack([image, image, image], axis=-1)\n", " label_map = np.zeros_like(image, dtype=np.uint8)\n", " label_map_lung = np.zeros_like(image, dtype=np.uint8)\n", " label_map_heart = np.zeros_like(image, dtype=np.uint8)\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 180\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " label_map_lung = cv2.ellipse(label_map_lung, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)), label_map_lung, cmap='gray')\n", " label_map_lung = Image.fromarray(label_map_lung)\n", " label_map_lung.save(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)))\n", " elif label == 2:\n", " label_map_heart = cv2.ellipse(label_map_heart, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (2, 2, 2), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)), label_map_heart, cmap='gray')\n", " label_map_heart = Image.fromarray(label_map_heart)\n", " label_map_heart.save(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)))\n", "\n", "# plt.imsave(os.path.join(save_folder, \"{}_seg_vis.png\".format(file_idx)), label_map)\n", " label_map_vis[..., 0] = (1 - label_map) * label_map_vis[..., 0]\n", " label_map = Image.fromarray(label_map)\n", " label_map.save(os.path.join(save_folder, \"{}_seg.png\".format(file_idx)))\n", " label_map_vis = Image.fromarray(label_map_vis)\n", " label_map_vis.save(os.path.join(save_folder, \"{}_seg_vis.png\".format(file_idx)))\n", " num_processed += 1\n", "# break\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", "\n", " label_map = np.zeros_like(image, dtype=np.uint8)\n", " label_map_lung = np.zeros_like(image, dtype=np.uint8)\n", " label_map_heart = np.zeros_like(image, dtype=np.uint8)\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " label_map_lung = cv2.ellipse(label_map_lung, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)), label_map_lung, cmap='gray')\n", " label_map_lung = Image.fromarray(label_map_lung)\n", " label_map_lung.save(os.path.join(save_folder, \"{}_lung.png\".format(file_idx)))\n", " elif label == 2:\n", " label_map_heart = cv2.ellipse(label_map_heart, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (2, 2, 2), thickness=-1)\n", " # plt.imsave(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)), label_map_heart, cmap='gray')\n", " label_map_heart = Image.fromarray(label_map_heart)\n", " label_map_heart.save(os.path.join(save_folder, \"{}_heart.png\".format(file_idx)))\n", "\n", " plt.imsave(os.path.join(save_folder, \"{}_seg_vis.png\".format(file_idx)), label_map)\n", " label_map = Image.fromarray(label_map)\n", " label_map.save(os.path.join(save_folder, \"{}_seg.png\".format(file_idx)))\n", " \n", "# break" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "file_idx = file_path.split('/')[-1].split('.')[0]\n", "\n", "image = plt.imread(file_path)\n", "label_map = np.zeros_like(image, dtype=np.uint8)\n", "label_map_lung = np.zeros_like(image, dtype=np.uint8)\n", "label_map_heart = np.zeros_like(image, dtype=np.uint8)\n", "\n", "for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " label_map_lung = cv2.ellipse(label_map_lung, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", "# cv2.imwrite(os.path.join(save_folder, \"{}_lung.png\".format(file_name)), label_map_lung)\n", "# plt.imsave(os.path.join(save_folder, \"{}_lung.png\".format(file_name)), label_map_lung, cmap='gray')\n", " label_map_lung = Image.fromarray(label_map_lung)\n", " label_map_lung.save(os.path.join(save_folder, \"{}_lung.png\".format(file_name)))\n", " elif label == 2:\n", " label_map_heart = cv2.ellipse(label_map_heart, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", " label_map = cv2.ellipse(label_map, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (2, 2, 2), thickness=-1)\n", "# plt.imsave(os.path.join(save_folder, \"{}_heart.png\".format(file_name)), label_map_heart, cmap='gray')\n", " label_map_heart = Image.fromarray(label_map_heart)\n", " label_map_heart.save(os.path.join(save_folder, \"{}_heart.png\".format(file_name)))\n", " \n", "# plt.imsave(os.path.join(save_folder, \"{}_seg.png\".format(file_name)), label_map)\n", "label_map = Image.fromarray(label_map)\n", "label_map.save(os.path.join(save_folder, \"{}_seg.png\".format(file_name)))\n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "file_name = '0001'\n", "read_label = np.array(Image.open(os.path.join(save_folder, \"{}_seg.png\".format(file_name))))\n", "print(read_label.shape, np.unique(read_label))\n", "plt.imshow(read_label, cmap='gray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "read_label = plt.imread(os.path.join(save_folder, \"{}_seg.png\".format(file_name)))\n", "print(read_label.shape, np.unique(read_label))\n", "plt.imshow(read_label, cmap='gray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Functions to get ellipse's envelope\n", "def get_ellipse_param(major_radius, minor_radius, angle):\n", " a, b = major_radius, minor_radius\n", " sin_theta = np.sin(-angle)\n", " cos_theta = np.cos(-angle)\n", " A = a**2 * sin_theta**2 + b**2 * cos_theta**2\n", " B = 2 * (a**2 - b**2) * sin_theta * cos_theta\n", " C = a**2 * cos_theta**2 + b**2 * sin_theta**2\n", " F = -a**2 * b**2\n", " return A, B, C, F\n", "\n", "def calculate_rectangle(A, B, C, F):\n", " # y coordinates of upper and lower boundaries\n", " y = np.sqrt(4*A*F / (B**2 - 4*A*C))\n", " y1, y2 = -np.abs(y), np.abs(y)\n", " \n", " # x coordinates of left and right boundaries\n", " x = np.sqrt(4*C*F / (B**2 - 4*C*A))\n", " x1, x2 = -np.abs(x), np.abs(x)\n", " \n", " return (x1, y1), (x2, y2)\n", "\n", "def get_envelop(center_x, center_y, major_radius, minor_radius, angle):\n", " # Return the cordinates of upper left and bottom right corners\n", " A, B, C, F = get_ellipse_param(major_radius, minor_radius, angle)\n", " p1, p2 = calculate_rectangle(A, B, C, F)\n", " return (int(center_x+p1[0]), int(center_y+p1[1])), (int(center_x+p2[0]), int(center_y+p2[1])) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pt1, pt2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "l = np.zeros_like(image)\n", "l = cv2.ellipse(l, (cx, cy), (int(rx), int(ry)), angle, 0.0, 360.0, (1, 1, 1), thickness=-1)\n", "pt1, pt2 = get_envelop(cx, cy, rx, ry, angle)\n", "l = cv2.rectangle(l, pt1, pt2, (2, 2, 2), 5)\n", "points = cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15)\n", "for pt in points:\n", " l = cv2.circle(l, tuple(pt), 1, (3, 3, 3), 3)\n", "plt.imshow(l)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "points = cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 359, 15)\n", "len(points), points" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pt1, pt2 = get_envelop(cx, cy, rx, ry, angle)\n", "pt1, pt2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare COCO format json" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Functions to get ellipse's envelope\n", "def get_ellipse_param(major_radius, minor_radius, angle):\n", " a, b = major_radius, minor_radius\n", " sin_theta = np.sin(-angle)\n", " cos_theta = np.cos(-angle)\n", " A = a**2 * sin_theta**2 + b**2 * cos_theta**2\n", " B = 2 * (a**2 - b**2) * sin_theta * cos_theta\n", " C = a**2 * cos_theta**2 + b**2 * sin_theta**2\n", " F = -a**2 * b**2\n", " return A, B, C, F\n", "\n", "def calculate_rectangle(A, B, C, F):\n", " # y coordinates of upper and lower boundaries\n", " y = np.sqrt(4*A*F / (B**2 - 4*A*C))\n", " y1, y2 = -np.abs(y), np.abs(y)\n", " \n", " # x coordinates of left and right boundaries\n", " x = np.sqrt(4*C*F / (B**2 - 4*C*A))\n", " x1, x2 = -np.abs(x), np.abs(x)\n", " \n", " return (x1, y1), (x2, y2)\n", "\n", "def get_envelop(center_x, center_y, major_radius, minor_radius, angle):\n", " # Return the cordinates of upper left and bottom right +corners\n", " A, B, C, F = get_ellipse_param(major_radius, minor_radius, angle)\n", " p1, p2 = calculate_rectangle(A, B, C, F)\n", " return (int(center_x+p1[0]), int(center_y+p1[1])), (int(center_x+p2[0]), int(center_y+p2[1])) \n", "\n", "# Process json file\n", "def addCatItem(coco, name):\n", " global category_item_id\n", " category_item = dict()\n", " category_item['supercategory'] = 'none'\n", " category_item_id += 1\n", " category_item['id'] = category_item_id\n", " category_item['name'] = name\n", " coco['categories'].append(category_item)\n", " category_set[name] = category_item_id\n", " return category_item_id\n", " \n", "def addImgItem(coco, file_name, size):\n", " global image_id\n", " if file_name is None:\n", " raise Exception('Could not find filename tag in xml file.')\n", " if size['width'] is None:\n", " raise Exception('Could not find width tag in xml file.')\n", " if size['height'] is None:\n", " raise Exception('Could not find height tag in xml file.')\n", " image_id += 1\n", " image_item = dict()\n", " image_item['id'] = image_id\n", " image_item['file_name'] = file_name\n", " image_item['width'] = size['width']\n", " image_item['height'] = size['height']\n", " coco['images'].append(image_item)\n", " image_set.add(file_name)\n", " return image_id\n", "\n", "\n", "def addAnnoItem(coco, object_name, image_id, category_id, bbox, seg=None, ellipse=None):\n", " global annotation_id\n", " annotation_item = dict()\n", " annotation_item['segmentation'] = []\n", " annotation_item['segmentation'].append(seg)\n", " \n", " annotation_item['area'] = bbox[2] * bbox[3]\n", " annotation_item['iscrowd'] = 0\n", " annotation_item['ignore'] = 0\n", " annotation_item['image_id'] = image_id\n", " annotation_item['bbox'] = bbox\n", " annotation_item['ellipse'] = ellipse\n", " annotation_item['category_id'] = category_id\n", " annotation_id += 1\n", " annotation_item['id'] = annotation_id\n", " coco['annotations'].append(annotation_item)\n", " \n", "class NumpyEncoder(json.JSONEncoder):\n", " \"\"\" Special json encoder for numpy types \"\"\"\n", " def default(self, obj):\n", " if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n", " np.int16, np.int32, np.int64, np.uint8,\n", " np.uint16, np.uint32, np.uint64)):\n", " return int(obj)\n", " elif isinstance(obj, (np.float_, np.float16, np.float32,\n", " np.float64)):\n", " return float(obj)\n", " elif isinstance(obj, (np.ndarray,)):\n", " return obj.tolist()\n", " return json.JSONEncoder.default(self, obj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split into train and val set" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "json_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val_ratio = 0.2\n", "\n", "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/detection/images/\"\n", "json_folder = \"/data/cc/Data/CHD/detection/annotations/json/\"\n", "json_files = sorted(glob(os.path.join(json_folder, \"*.json\")))\n", "\n", "total_instances = 0\n", "\n", "for json_path in json_files:\n", " cur_instances = 0\n", "\n", " with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", "\n", " for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " if len(np.unique([regions[0]['region_attributes']['type'], regions[1]['region_attributes']['type']]))!=2:\n", " print(\"Error: \", json_path, file_name)\n", " cur_instances += 1\n", " \n", " print(json_path, cur_instances)\n", " total_instances += cur_instances\n", "\n", "num_val = int(val_ratio * total_instances)\n", " \n", "print(\"Done! Totally {} instances. Train:{} / Val:{}\".format(total_instances, (total_instances - num_val), num_val))\n", "\n", "# /data/cc/Data/CHD/detection/annotations/json/0 train_normal lastest (681).json 681\n", "# /data/cc/Data/CHD/detection/annotations/json/1 val_normal_new.json 204\n", "# /data/cc/Data/CHD/detection/annotations/json/2 train_abnormal_new.json 194\n", "# /data/cc/Data/CHD/detection/annotations/json/3 val_abnormal_new.json 36\n", "# /data/cc/Data/CHD/detection/annotations/json/4 new_data (994).json 971\n", "# Done! Totally 2086 instances. Train:1669 / Val:417\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initialize COCO json train and val\n", "coco_train = dict()\n", "coco_val = dict()\n", "for coco in [coco_train, coco_val]:\n", " coco[\"info\"] = 'Fetal Heart Disease Detection'\n", " coco[\"license\"] = ['none']\n", " coco['images'] = []\n", " coco['type'] = 'instances'\n", " coco['annotations'] = []\n", " coco['categories'] = []\n", "\n", " category_set = dict()\n", " image_set = set()\n", "\n", " category_item_id = 0\n", "\n", " addCatItem(coco, \"Lung\")\n", " addCatItem(coco, \"Heart\")\n", " \n", "image_id = 2021000000\n", "annotation_id = 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco_train, coco_val" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Multiple json files\n", "def prepare_coco(json_data, num_val=0, prefix=''):\n", " num_processed = 0\n", " coco = coco_val\n", " if num_val:\n", " print(\"Processing val json\")\n", "\n", " for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = \"{}{}\".format(prefix, json_data['_via_img_metadata'][key]['filename'])\n", " if file_name.endswith('.jpg'):\n", " file_name = file_name.replace('.jpg', '.png')\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " # print(\"Processing {}\".format(file_name))\n", "\n", " if num_processed == num_val:\n", " coco = coco_train\n", " print(\"Processing train json\")\n", "\n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", "\n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", "\n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(coco, file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 180\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " object_name = \"Lung\"\n", " elif label == 2:\n", " object_name = \"Heart\"\n", "\n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(coco, object_name, cur_image_id, label, bbox, seg[:-2], [cx, cy, rx, ry, ellipse['theta']]) # *Add Annotation Item\n", "\n", " num_processed += 1\n", "\n", " # break\n", " print(\"Done!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Number of validation set: \", num_val)\n", "for idx, json_path in enumerate(json_files):\n", " print(json_path.split('/')[-1])\n", " with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", " if idx == 0 or idx == 1:\n", " prefix = 'normal_'\n", " elif idx == 2 or idx == 3:\n", " prefix = 'abnormal_'\n", " else:\n", " prefix = ''\n", " \n", " if idx == 0:\n", " prepare_coco(json_data, num_val=num_val, prefix=prefix)\n", " else:\n", " prepare_coco(json_data, prefix=prefix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Normal Images\n", "num_processed = 0\n", "coco = coco_val\n", "print(\"Processing val json\")\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = \"normal_{}\".format(json_data['_via_img_metadata'][key]['filename'])\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", "# print(\"Processing {}\".format(file_name))\n", " \n", " if num_processed == num_val:\n", " coco = coco_train\n", " print(\"Processing train json\")\n", " \n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", " \n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " \n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(coco, file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 180\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " object_name = \"Lung\"\n", " elif label == 2:\n", " object_name = \"Heart\"\n", " \n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(coco, object_name, cur_image_id, label, bbox, seg[:-2], [cx, cy, rx, ry, ellipse['theta']]) # *Add Annotation Item\n", " \n", " num_processed += 1\n", " \n", "# break\n", "print(\"Done!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Save for train.json\n", "train_json_file = \"ellipse_annotations_train.json\"\n", "json.dump(coco_train, open(train_json_file, 'w'), cls=NumpyEncoder)\n", "\n", "val_json_file = \"ellipse_annotations_val.json\"\n", "json.dump(coco_val, open(val_json_file, 'w'), cls=NumpyEncoder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abnormal" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/train/abnormal/\"\n", "# save_folder = \"/data/cc/Data/CHD/processed/train/abnormal_seg/\"\n", "# if not os.path.exists(save_folder):\n", "# os.makedirs(save_folder)\n", "json_path = \"train_abnormal_24Dec2020_15h32m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def addCatItem(name):\n", " global category_item_id\n", " category_item = dict()\n", " category_item['supercategory'] = 'none'\n", " category_item_id += 1\n", " category_item['id'] = category_item_id\n", " category_item['name'] = name\n", " coco['categories'].append(category_item)\n", " category_set[name] = category_item_id\n", " return category_item_id\n", " \n", "def addImgItem(file_name, size):\n", " global image_id\n", " if file_name is None:\n", " raise Exception('Could not find filename tag in xml file.')\n", " if size['width'] is None:\n", " raise Exception('Could not find width tag in xml file.')\n", " if size['height'] is None:\n", " raise Exception('Could not find height tag in xml file.')\n", " image_id += 1\n", " image_item = dict()\n", " image_item['id'] = image_id\n", " image_item['file_name'] = file_name\n", " image_item['width'] = size['width']\n", " image_item['height'] = size['height']\n", " coco['images'].append(image_item)\n", " image_set.add(file_name)\n", " return image_id\n", "\n", "\n", "def addAnnoItem(object_name, image_id, category_id, bbox, seg=None, ellipse=None):\n", " global annotation_id\n", " annotation_item = dict()\n", " annotation_item['segmentation'] = []\n", "# seg = []\n", "# # bbox[] is x,y,w,h\n", "# # left_top\n", "# seg.append(bbox[0])\n", "# seg.append(bbox[1])\n", "# # left_bottom\n", "# seg.append(bbox[0])\n", "# seg.append(bbox[1] + bbox[3])\n", "# # right_bottom\n", "# seg.append(bbox[0] + bbox[2])\n", "# seg.append(bbox[1] + bbox[3])\n", "# # right_top\n", "# seg.append(bbox[0] + bbox[2])\n", "# seg.append(bbox[1])\n", " \n", " annotation_item['segmentation'].append(seg)\n", " \n", " annotation_item['area'] = bbox[2] * bbox[3]\n", " annotation_item['iscrowd'] = 0\n", " annotation_item['ignore'] = 0\n", " annotation_item['image_id'] = image_id\n", " annotation_item['bbox'] = bbox\n", " annotation_item['ellipse'] = ellipse\n", " annotation_item['category_id'] = category_id\n", " annotation_id += 1\n", " annotation_item['id'] = annotation_id\n", " coco['annotations'].append(annotation_item)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Prepare COCO json trainval\n", "coco = dict()\n", "coco[\"info\"] = 'Fetal Heart Disease Detection'\n", "coco[\"license\"] = ['none']\n", "coco['images'] = []\n", "coco['type'] = 'instances'\n", "coco['annotations'] = []\n", "coco['categories'] = []\n", "\n", "category_set = dict()\n", "image_set = set()\n", "\n", "category_item_id = 0\n", "image_id = 2020000000\n", "annotation_id = 0\n", "\n", "addCatItem(\"Lung\")\n", "addCatItem(\"Heart\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Abnormal Images\n", "\n", "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/abnormal/\"\n", "json_path = \"train_abnormal_24Dec2020_15h32m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", " \n", " \n", "num_processed = 0\n", "num_val_instance = 100\n", "\n", "# addCatItem(\"Lung\")\n", "# addCatItem(\"Heart\")\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", " \n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " file_name = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = \"abnormal_{}\".format(json_data['_via_img_metadata'][key]['filename'])\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", " \n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " \n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " object_name = \"Lung\"\n", " elif label == 2:\n", " object_name = \"Heart\"\n", " \n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(object_name, cur_image_id, label, bbox, seg[:-2], [cx, cy, rx, ry, ellipse['theta']]) # *Add Annotation Item\n", " \n", " num_processed += 1\n", " \n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Normal Images\n", "\n", "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/normal/\"\n", "json_path = \"train_normal_28Dec2020_10h54m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", " \n", " \n", "num_processed = 0\n", "num_val_instance = 100\n", "\n", "# addCatItem(\"Lung\")\n", "# addCatItem(\"Heart\")\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", " \n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " file_name = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = \"normal_{}\".format(json_data['_via_img_metadata'][key]['filename'])\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", " image = plt.imread(file_path)\n", " \n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " \n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 1:\n", " object_name = \"Lung\"\n", " elif label == 2:\n", " object_name = \"Heart\"\n", " \n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(object_name, cur_image_id, label, bbox, seg[:-2], [cx, cy, rx, ry, ellipse['theta']]) # *Add Annotation Item\n", " \n", " num_processed += 1\n", " \n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class NumpyEncoder(json.JSONEncoder):\n", " \"\"\" Special json encoder for numpy types \"\"\"\n", " def default(self, obj):\n", " if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n", " np.int16, np.int32, np.int64, np.uint8,\n", " np.uint16, np.uint32, np.uint64)):\n", " return int(obj)\n", " elif isinstance(obj, (np.float_, np.float16, np.float32,\n", " np.float64)):\n", " return float(obj)\n", " elif isinstance(obj, (np.ndarray,)):\n", " return obj.tolist()\n", " return json.JSONEncoder.default(self, obj)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "json_file = \"ellipse_annotations_trainval.json\"\n", "json.dump(coco, open(json_file, 'w'), cls=NumpyEncoder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "seg = cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15)\n", "seg" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "np.concatenate(seg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Crop Heart" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/train/abnormal/\"\n", "save_folder = \"/data/cc/Data/CHD/cropped/abnormal/\"\n", "if not os.path.exists(save_folder):\n", " os.makedirs(save_folder)\n", "json_path = \"train_abnormal_24Dec2020_15h32m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "num_processed = 0\n", "num_val_instance = 100\n", "\n", "addCatItem(\"Lung\")\n", "addCatItem(\"Heart\")\n", "\n", "broder_size = 10\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", " \n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " file_name = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", "# image = plt.imread(file_path)\n", " image = np.array(Image.open(file_path))\n", " \n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " \n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 2:\n", " object_name = \"Heart\" \n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(object_name, cur_image_id, label, bbox, seg) # *Add Annotation Item\n", " u, d = max(0, pt1[1] - broder_size), min(size['height'] - 1, pt2[1] + broder_size)\n", " l, r = max(0, pt1[0] - broder_size), min(size['width'] - 1, pt2[0] + broder_size)\n", " crop_image = image[u:d, l:r]\n", " Image.fromarray(crop_image).save(os.path.join(save_folder, \"abnormal_{}_heart.png\".format(file_idx)))\n", " \n", " num_processed += 1\n", " \n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Normal\n", "# Read json file into python dictionary\n", "data_folder = \"/data/cc/Data/CHD/processed/train/normal/\"\n", "save_folder = \"/data/cc/Data/CHD/cropped/normal/\"\n", "if not os.path.exists(save_folder):\n", " os.makedirs(save_folder)\n", "json_path = \"train_normal_28Dec2020_10h54m.json\"\n", "\n", "with open(json_path, 'r', encoding='utf-8') as f:\n", " json_data = json.loads(f.read())\n", " \n", "num_processed = 0\n", "num_val_instance = 100\n", "\n", "addCatItem(\"Lung\")\n", "addCatItem(\"Heart\")\n", "\n", "broder_size = 10\n", "\n", "for idx in range(len(json_data['_via_img_metadata'].keys())):\n", " \n", " bbox = None\n", " size = dict()\n", " current_image_id = None\n", " current_category_id = None\n", " file_name = None\n", " size['width'] = None\n", " size['height'] = None\n", "\n", " key = list(json_data['_via_img_metadata'].keys())[idx]\n", " file_name = json_data['_via_img_metadata'][key]['filename']\n", " regions = json_data['_via_img_metadata'][key]['regions']\n", " if len(regions) != 2:\n", " continue\n", " print(\"Processing {}\".format(file_name))\n", "\n", " # load and make labels\n", " file_path = os.path.join(data_folder, file_name)\n", "# image = plt.imread(file_path)\n", " image = np.array(Image.open(file_path))\n", " \n", " file_idx = file_path.split('/')[-1].split('.')[0]\n", " \n", " size['height'], size['width'] = image.shape\n", " cur_image_id = addImgItem(file_name, size) # *Add Image Item\n", "\n", " for region in regions:\n", " label = int(region['region_attributes']['type'])\n", "\n", " if region['shape_attributes']['name']=='ellipse':\n", " ellipse = region['shape_attributes']\n", " cx, cy, rx, ry, angle = ellipse['cx'], ellipse['cy'], ellipse['rx'], ellipse['ry'], ellipse['theta'] / math.pi * 360\n", " else:\n", " print(\"{} label error!\".format(file_name))\n", " continue\n", "\n", " if label == 2:\n", " object_name = \"Heart\" \n", " seg = np.concatenate(cv2.ellipse2Poly((cx, cy), (int(rx), int(ry)), int(angle), 0, 360, 15))\n", " pt1, pt2 = get_envelop(cx, cy, rx, ry, angle) \n", " bbox = [pt1[0], pt1[1], pt2[0] - pt1[0], pt2[1] - pt1[1]] # x, y, w, h\n", " addAnnoItem(object_name, cur_image_id, label, bbox, seg) # *Add Annotation Item\n", " u, d = max(0, pt1[1] - broder_size), min(size['height'] - 1, pt2[1] + broder_size)\n", " l, r = max(0, pt1[0] - broder_size), min(size['width'] - 1, pt2[0] + broder_size)\n", " crop_image = image[u:d, l:r]\n", " Image.fromarray(crop_image).save(os.path.join(save_folder, \"normal_{}_heart.png\".format(file_idx)))\n", " \n", " num_processed += 1\n", " \n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "image = np.array(Image.open(file_path))\n", "crop_image = image[pt1[1]-10:pt2[1]+10, pt1[0]-10:pt2[0]+10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Image.fromarray(crop_image).s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Image.fromarray(image)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco_val" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 } slamd202303071458299/src/000077500000000000000000000000001435750645400143715ustar00rootroot00000000000000slamd202303071458299/src/_init_paths.py000066400000000000000000000003471435750645400172500ustar00rootroot00000000000000import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) # Add lib to PYTHONPATH lib_path = osp.join(this_dir, 'lib') add_path(lib_path) slamd202303071458299/src/bash_eval.sh000066400000000000000000000016421435750645400166540ustar00rootroot00000000000000#/bin/bash MODEL_FILE=model_120 echo "Begin!" python evaluate.py eldet --load_model ../exp/eldet/exp1_base_theta5/${MODEL_FILE}.pth > ../exp/logs/exp1_${MODEL_FILE}.txt echo "Exp1 Done!" python evaluate.py eldet --load_model ../exp/eldet/exp2_base_theta5_noise/${MODEL_FILE}.pth > ../exp/logs/exp2_${MODEL_FILE}.txt echo "Exp2 Done!" python evaluate.py eldet --load_model ../exp/eldet/exp3_base_theta5_iou/${MODEL_FILE}.pth > ../exp/logs/exp3_${MODEL_FILE}.txt echo "Exp3 Done!" python evaluate.py eldet --load_model ../exp/eldet/exp4_base_theta5_iou01/${MODEL_FILE}.pth > ../exp/logs/exp4_${MODEL_FILE}.txt echo "Exp4 Done!" # python evaluate.py eldet --load_model ../exp/eldet/exp5_base_theta5_iou_step/${MODEL_FILE}.pth > ../exp/logs/exp5_${MODEL_FILE}.txt # echo "Exp5 Done!" # python evaluate.py eldet --load_model ../exp/eldet/exp6_base_onlyiou/${MODEL_FILE}.pth > ../exp/logs/exp6_${MODEL_FILE}.txt # echo "All Done!"slamd202303071458299/src/demo.py000066400000000000000000000032121435750645400156650ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths import os import cv2 from opts import opts from detectors.detector_factory import detector_factory image_ext = ['jpg', 'jpeg', 'png', 'webp'] video_ext = ['mp4', 'mov', 'avi', 'mkv'] time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge'] def demo(opt): os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str opt.debug = max(opt.debug, 1) Detector = detector_factory[opt.task] detector = Detector(opt) if opt.demo == 'webcam' or \ opt.demo[opt.demo.rfind('.') + 1:].lower() in video_ext: cam = cv2.VideoCapture(0 if opt.demo == 'webcam' else opt.demo) detector.pause = False while True: _, img = cam.read() cv2.imshow('input', img) ret = detector.run(img) time_str = '' for stat in time_stats: time_str = time_str + '{} {:.3f}s |'.format(stat, ret[stat]) print(time_str) if cv2.waitKey(1) == 27: return # esc to quit else: if os.path.isdir(opt.demo): image_names = [] ls = os.listdir(opt.demo) for file_name in sorted(ls): ext = file_name[file_name.rfind('.') + 1:].lower() if ext in image_ext: image_names.append(os.path.join(opt.demo, file_name)) else: image_names = [opt.demo] for (image_name) in image_names: ret = detector.run(image_name) time_str = '' for stat in time_stats: time_str = time_str + '{} {:.3f}s |'.format(stat, ret[stat]) print(time_str) if __name__ == '__main__': opt = opts().init() demo(opt) slamd202303071458299/src/evaluate.py000066400000000000000000000230521435750645400165530ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os os.environ['CUDA_VISIBLE_DEVICES'] = '7' import sys lib_path = os.path.join('../src/lib') if not lib_path in sys.path: sys.path.append(lib_path) lib_path = os.path.join('../src/') if not lib_path in sys.path: sys.path.append(lib_path) lib_path = '../../Rotated_IoU/' if not lib_path in sys.path: sys.path.append(lib_path) import json import cv2 from glob import glob import math import numpy as np import time from progress.bar import Bar import torch import matplotlib.pyplot as plt from PIL import Image from external.nms import soft_nms from fhd_opts import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory from models.losses import _transpose_and_gather_feat from models.Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_iou # Calculate Metrics: Dice and difference of angles # input 2 ellipse ell1, ell2 in [cx, cy, a, b, angle], image shape [h, w], num_classes def compute_dice(pred, label): ints = np.sum((pred == 1) * (label == 1)) sums = np.sum(pred == 1) + np.sum(label == 1) + 1e-5 return (2.0 * ints) / sums def compute_iou(pred, label): inter = np.sum((pred == 1) * (label == 1)) union = np.sum((pred == 1) + (label == 1)) + 1e-5 return inter / union def compute_rotated_bbox_iou(ell1, ell2): cx1, cy1, a1, b1, angle1 = ell1 cx2, cy2, a2, b2, angle2 = ell2 theta1 = angle1 / 180.0 * math.pi theta2 = angle2 / 180.0 * math.pi bbox1 = torch.tensor([[[cx1, cy1, 2 * a1, 2 * b1, theta1]]], dtype=torch.float).cuda() bbox2 = torch.tensor([[[cx2, cy2, 2 * a2, 2 * b2, theta2]]], dtype=torch.float).cuda() return cal_diou(bbox1, bbox2) # iou_loss, iou def compute_single_class_metric(ell1, ell2, shape): cx1, cy1, a1, b1, angle1 = ell1 cx2, cy2, a2, b2, angle2 = ell2 h, w = shape pred = np.zeros([h, w], np.uint8) pred = cv2.ellipse(pred, (cx1, cy1), (int(a1), int(b1)), int(angle1), 0.0, 360.0, (1), thickness=-1) label = np.zeros([h, w], np.uint8) label = cv2.ellipse(label, (cx2, cy2), (int(a2), int(b2)), int(angle2), 0.0, 360.0, (1), thickness=-1) angle_error = abs(angle1 - angle2) angle_error = angle_error if angle_error <= 90 else (180 - angle_error) bbox_iou_loss, bbox_iou = compute_rotated_bbox_iou(ell1, ell2) return compute_dice(pred, label), compute_iou(pred, label), bbox_iou.item(), angle_error if __name__ == '__main__': opt = opts().parse() # opt.data_dir = '/data/cc/Data/CHD/fake_data/' # opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/fake_0214_eldet_fhd_dla_iouw5_nocrop_896x608/model_last.pth' # opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/fake_0209_eldet_fhd_dla_rl_nocrop_896x608/model_best.pth' # opt.data_dir = '/data/cc/Data/CHD/detection/' # opt.load_model = '/data/cc/workspace/Repository/CenterNet/exp/eldet/0218_exp4_iou5_cond_896x608/model_last.pth' # opt.num_classes = 2 # opt.dataset = 'coco_fhd' # opt.resume = True # opt.gpus_str = '5' torch.manual_seed(opt.seed) torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) exp_name = opt.load_model.split('/')[-2] save_pred_folder = os.path.join('../exp/prediction/', exp_name) if not os.path.exists(save_pred_folder): os.makedirs(save_pred_folder) print(opt) Detector = detector_factory[opt.task] split = 'val' print(opt.mean, opt.std) dataset = Dataset(opt, split) detector = Detector(opt) angle_errors = [] dices = [] ious = [] bbox_ious = [] ratios_pred = [] ratios_gt = [] ctr_precisions = [] for ind in range(len(dataset)): # Load Image cls_dice = [] cls_angle_error = [] cls_iou = [] cls_bbox_iou = [] diameter_gt = [1, 1] diameter_pred = [1, 1] img_id = dataset.images[ind] img_info = dataset.coco.loadImgs(ids=[img_id])[0] img_path = os.path.join(dataset.img_dir, img_info['file_name']) image = cv2.imread(img_path) shape = image.shape[:2] # Load annotations ann_ids = dataset.coco.getAnnIds(imgIds=[img_id]) anns = dataset.coco.loadAnns(ids=ann_ids) # Inference color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)] ret = detector.run(img_path) for class_idx in range(opt.num_classes): try: class_id = class_idx + 1 pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1])) pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3])) cx, cy = int(ret['results'][class_id][0, 4]), int(ret['results'][class_id][0, 5]) l = int(ret['results'][class_id][0, 6]) # !attention here! ratio_al = ret['results'][class_id][0, 7] ratio_bl = ret['results'][class_id][0, 8] a = ratio_al * l / 2 b = ratio_bl * l / 2 theta = ret['results'][class_id][0, 9] angle = theta * 180 print("Predict: ", pt1, pt2, (cx, cy), l, ratio_al, ratio_bl, "({})'".format(theta), int(a), int(b), int(angle)) # Annotations ann = anns[class_idx] ellipse = ann['ellipse'] cx_gt = ellipse[0] cy_gt = ellipse[1] if ellipse[2] >= ellipse[3]: a_gt = ellipse[2] b_gt = ellipse[3] theta_gt = ellipse[4] else: a_gt = ellipse[3] b_gt = ellipse[2] theta_gt = ellipse[4] + 0.5 * math.pi theta_gt = theta_gt / math.pi theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2) pt1 = (int(cx_gt - l_gt / 2), int(cy_gt - l_gt / 2)) pt2 = (int(cx_gt + l_gt / 2), int(cy_gt + l_gt / 2)) ratio_al, ratio_bl = 2 * a_gt / l_gt, 2 * b_gt / l_gt angle_gt = theta_gt * 180 print("Ground Truth:", pt1, pt2, (cx_gt, cy_gt), l_gt, ratio_al, ratio_bl, "({})'".format(theta_gt), int(a_gt), int(b_gt), int(angle_gt)) img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1) img = cv2.ellipse(img, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2) # img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2 + 1], thickness=2) # img = cv2.ellipse(image, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=2) pt1 = (int(cx - l / 2), int(cy - l / 2)) pt2 = (int(cx + l / 2), int(cy + l / 2)) img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2) img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3) pred_ell = [cx, cy, a, b, angle] gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt] error = compute_single_class_metric(pred_ell, gt_ell, shape) print("dice: {} iou:{} bbox_iou: {} angle_error: {}".format(error[0], error[1], error[2], error[3])) diameter_pred[class_idx] = b diameter_gt[class_idx] = b_gt cls_dice.append(error[0]) cls_iou.append(error[1]) cls_bbox_iou.append(error[2]) cls_angle_error.append(error[3]) except Exception as e: print(e) cls_dice.append(0) cls_iou.append(0) cls_bbox_iou.append(0) cls_angle_error.append(90) pass if opt.save_image: img = Image.fromarray(image) img.save(os.path.join(save_pred_folder, img_path.split('/')[-1])) print("Saving", img_path, "to", os.path.join(save_pred_folder, img_path.split('/')[-1])) dices.append(cls_dice) ious.append(cls_iou) bbox_ious.append(cls_bbox_iou) angle_errors.append(cls_angle_error) try: ratio_p, ratio_g = float(diameter_pred[1]) / diameter_pred[0], float(diameter_gt[1]) / diameter_gt[0] ctr_precision = 1 - abs(ratio_p - ratio_g) / ratio_g except Exception as e: ctr_precision = 0 ratio_p = 0 print("predicted ratio: {}, truth ratio: {}, error: {}, precision: {}".format(ratio_p, ratio_g, abs(ratio_p - ratio_g), ctr_precision)) ratios_pred.append(ratio_p) ratios_gt.append(ratio_g) ctr_precisions.append(ctr_precision) # break print("Evaluation Done!") print("Mean Dice: ", np.mean(dices, axis=0)) print("Mean IoU: ", np.mean(ious, axis=0)) print("Mean BBox IoU: ", np.mean(bbox_ious, axis=0)) print("Mean Angle_err: ", np.mean(angle_errors, axis=0)) print("Mean Ratio_err: ", np.mean(np.abs(np.array(ratios_pred) - np.array(ratios_gt)))) print("Mean CTR precision: ", np.mean(ctr_precisions)) slamd202303071458299/src/lib/000077500000000000000000000000001435750645400151375ustar00rootroot00000000000000slamd202303071458299/src/lib/datasets/000077500000000000000000000000001435750645400167475ustar00rootroot00000000000000slamd202303071458299/src/lib/datasets/dataset/000077500000000000000000000000001435750645400203745ustar00rootroot00000000000000slamd202303071458299/src/lib/datasets/dataset/coco_fhd.py000066400000000000000000000074611435750645400225220ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import pycocotools.coco as coco from pycocotools.cocoeval import COCOeval import numpy as np import json import os import torch.utils.data as data class COCOFHD(data.Dataset): num_classes = 2 default_resolution = [512, 512] mean = np.array([0.216, 0.216, 0.216], dtype=np.float32).reshape(1, 1, 3) std = np.array([0.222, 0.222, 0.222], dtype=np.float32).reshape(1, 1, 3) def __init__(self, opt, split): super(COCOFHD, self).__init__() self.data_dir = opt.data_dir self.img_dir = os.path.join(self.data_dir, 'images') if split == 'test': self.annot_path = os.path.join( self.data_dir, 'annotations', 'detection_annotations_{}.json').format(split) else: if opt.task == 'eldet': self.annot_path = os.path.join( self.data_dir, 'annotations', 'ellipse_annotations_{}.json').format(split) else: self.annot_path = os.path.join( self.data_dir, 'annotations', 'ellipse_annotations_{}.json').format(split) self.max_objs = 2 self.class_name = [ '__background__', 'lung', 'heart'] self._valid_ids = [1, 2] self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)} self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \ for v in range(1, self.num_classes + 1)] self._data_rng = np.random.RandomState(123) self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32) self._eig_vec = np.array([ [-0.58752847, -0.69563484, 0.41340352], [-0.5832747, 0.00994535, -0.81221408], [-0.56089297, 0.71832671, 0.41158938] ], dtype=np.float32) # self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3) # self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3) self.split = split self.opt = opt print('==> initializing Fetal Heart Disease {} data.'.format(split)) self.coco = coco.COCO(self.annot_path) self.images = self.coco.getImgIds() self.num_samples = len(self.images) print('Loaded {} {} samples'.format(split, self.num_samples)) def _to_float(self, x): return float("{:.2f}".format(x)) def convert_eval_format(self, all_bboxes): # import pdb; pdb.set_trace() detections = [] for image_id in all_bboxes: for cls_ind in all_bboxes[image_id]: category_id = self._valid_ids[cls_ind - 1] for bbox in all_bboxes[image_id][cls_ind]: bbox[2] -= bbox[0] bbox[3] -= bbox[1] score = bbox[4] bbox_out = list(map(self._to_float, bbox[0:4])) detection = { "image_id": int(image_id), "category_id": int(category_id), "bbox": bbox_out, "score": float("{:.2f}".format(score)) } if len(bbox) > 5: extreme_points = list(map(self._to_float, bbox[5:13])) detection["extreme_points"] = extreme_points detections.append(detection) return detections def __len__(self): return self.num_samples def save_results(self, results, save_dir): json.dump(self.convert_eval_format(results), open('{}/results.json'.format(save_dir), 'w')) def run_eval(self, results, save_dir): # result_json = os.path.join(save_dir, "results.json") # detections = self.convert_eval_format(results) # json.dump(detections, open(result_json, "w")) self.save_results(results, save_dir) coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir)) coco_eval = COCOeval(self.coco, coco_dets, "bbox") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() slamd202303071458299/src/lib/datasets/dataset_factory.py000066400000000000000000000016341435750645400225010ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from .sample.ddd import DddDataset # from .sample.exdet import EXDetDataset # from .sample.ctdet import CTDetDataset # from .sample.multi_pose import MultiPoseDataset from .sample.eldet import ELDetDataset # from .dataset.coco import COCO # from .dataset.pascal import PascalVOC # from .dataset.kitti import KITTI # from .dataset.coco_hp import COCOHP from .dataset.coco_fhd import COCOFHD dataset_factory = { # 'coco': COCO, # 'pascal': PascalVOC, # 'kitti': KITTI, # 'coco_hp': COCOHP, 'coco_fhd': COCOFHD } _sample_factory = { # 'exdet': EXDetDataset, # 'ctdet': CTDetDataset, # 'ddd': DddDataset, # 'multi_pose': MultiPoseDataset, 'eldet': ELDetDataset } def get_dataset(dataset, task): class Dataset(dataset_factory[dataset], _sample_factory[task]): pass return Dataset slamd202303071458299/src/lib/datasets/sample/000077500000000000000000000000001435750645400202305ustar00rootroot00000000000000slamd202303071458299/src/lib/datasets/sample/eldet.py000066400000000000000000000214201435750645400216760ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch.utils.data as data import random import numpy as np import torch import json import cv2 import os from utils.image import flip, color_aug from utils.image import get_affine_transform, affine_transform from utils.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian from utils.image import draw_dense_reg import math minimum, maximum = 0.6, 1.1 class ELDetDataset(data.Dataset): def _coco_box_to_bbox(self, box): # coco box: upper-left corner + width and height # bounding box: upper-left corner and lower-right corner bbox = np.array([box[0], box[1], box[0] + box[2], box[1] + box[3]], dtype=np.float32) return bbox def _get_border(self, border, size): i = 1 while size - border // i <= border // i: i *= 2 return border // i def __getitem__(self, index): img_id = self.images[index] file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name'] img_path = os.path.join(self.img_dir, file_name) ann_ids = self.coco.getAnnIds(imgIds=[img_id]) anns = self.coco.loadAnns(ids=ann_ids) num_objs = min(len(anns), self.max_objs) img = cv2.imread(img_path) height, width = img.shape[0], img.shape[1] c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32) if self.opt.keep_res: input_h = (height | self.opt.pad) + 1 input_w = (width | self.opt.pad) + 1 s = np.array([input_w, input_h], dtype=np.float32) else: s = max(img.shape[0], img.shape[1]) * 1.0 input_h, input_w = self.opt.input_h, self.opt.input_w # When image is too large or too small minimum = 1.0 if min(img.shape[0], img.shape[1]) < 384 else 0.6 maximum = 1.0 if min(img.shape[0], img.shape[1]) > 1024 else 1.1 flipped = False if self.split == 'train': if not self.opt.not_rand_crop: io_scale = np.random.choice(np.arange(minimum, maximum, 0.1)) s = s * io_scale w_border = self._get_border(384, img.shape[1]) h_border = self._get_border(384, img.shape[0]) c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border) c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border) else: sf = self.opt.scale cf = self.opt.shift c[0] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf) c[1] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf) s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf) if np.random.random() < self.opt.flip: flipped = True img = img[:, ::-1, :] c[0] = width - c[0] - 1 trans_input = get_affine_transform( c, s, 0, [input_w, input_h]) inp = cv2.warpAffine(img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR) inp = (inp.astype(np.float32) / 255.) if self.split == 'train' and not self.opt.no_color_aug: color_aug(self._data_rng, inp, self._eig_val, self._eig_vec) inp = (inp - self.mean) / self.std inp = inp.transpose(2, 0, 1) if self.opt.gaussian_noise: do_transform = np.random.random() < 0.1 if do_transform: noise_std = np.random.uniform(0, 0.1) noise = np.random.normal(0, noise_std, size=inp.shape) inp += noise output_h = input_h // self.opt.down_ratio output_w = input_w // self.opt.down_ratio num_classes = self.num_classes trans_output = get_affine_transform(c, s, 0, [output_w, output_h]) hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32) # heat map of center point wh = np.zeros((self.max_objs, 2), dtype=np.float32) # width and height dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32) # dense map of w and h reg = np.zeros((self.max_objs, 2), dtype=np.float32) # regression of center point due to the float->int error ind = np.zeros((self.max_objs), dtype=np.int64) # index of the center point reg_mask = np.zeros((self.max_objs), dtype=np.uint8) cat_spec_wh = np.zeros((self.max_objs, num_classes * 2), dtype=np.float32) # category specific w,h and mask cat_spec_mask = np.zeros((self.max_objs, num_classes * 2), dtype=np.uint8) draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \ draw_umich_gaussian # parameters for ellipse regression l = np.zeros((self.max_objs, 1), dtype=np.float32) # length of the extended square a = np.zeros((self.max_objs, 1), dtype=np.float32) # length of the long axis b = np.zeros((self.max_objs, 1), dtype=np.float32) # length of the short axis ratio_al = np.zeros((self.max_objs, 1), dtype=np.float32) # ratio of the long axis to the length of square ratio_bl = np.zeros((self.max_objs, 1), dtype=np.float32) # ratio of the short axis to the length of square # ratio_ba = np.zeros((self.max_objs, 1), dtype=np.float32) # ratio of the short axis to the long axis theta = np.zeros((self.max_objs, 1), dtype=np.float32) # angle of the ellipse divided by pi (0, 1] sincos = np.zeros((self.max_objs, 2), dtype=np.float32) # encoded angle of the ellipse, sin and cos # Generate ground truth label gt_det = [] for k in range(num_objs): ann = anns[k] bbox = self._coco_box_to_bbox(ann['bbox']) ellipse = ann['ellipse'] ct = np.array(ellipse[:2], dtype=np.float32) cls_id = int(self.cat_ids[ann['category_id']]) if flipped: bbox[[0, 2]] = width - bbox[[2, 0]] - 1 ct[0] = width - ct[0] - 1 bbox[:2] = affine_transform(bbox[:2], trans_output) bbox[2:] = affine_transform(bbox[2:], trans_output) bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1) bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1) h, w = bbox[3] - bbox[1], bbox[2] - bbox[0] ct = affine_transform(ct, trans_output) ct[0] = np.clip(ct[0], 0, output_w - 1) ct[1] = np.clip(ct[1], 0, output_h - 1) if h > 0 and w > 0: radius = gaussian_radius((math.ceil(h), math.ceil(w))) radius = max(0, int(radius)) radius = self.opt.hm_gauss if self.opt.mse_loss else radius # ct = np.array( # [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32) ct_int = ct.astype(np.int32) draw_gaussian(hm[cls_id], ct_int, radius) wh[k] = 1. * w, 1. * h ind[k] = ct_int[1] * output_w + ct_int[0] reg[k] = ct - ct_int reg_mask[k] = 1 cat_spec_wh[k, cls_id * 2: cls_id * 2 + 2] = wh[k] cat_spec_mask[k, cls_id * 2: cls_id * 2 + 2] = 1 if self.opt.dense_wh: draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k], radius) gt_det.append([ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2, 1, cls_id]) # parameters for ellipse regression if ellipse[2] >= ellipse[3]: ell_a = ellipse[2] ell_b = ellipse[3] ell_alpha = ellipse[4] else: ell_a = ellipse[3] ell_b = ellipse[2] ell_alpha = ellipse[4] + 0.5 * math.pi a[k] = trans_output[0][0] * ell_a # length of the long axis b[k] = trans_output[0][0] * ell_b # length of the short axis l[k] = 2 * math.sqrt(a[k] * a[k] + b[k] * b[k]) # length of the extended square ratio_al[k] = 2 * a[k] / l[k] # ratio of the long axis to the length of square ratio_bl[k] = 2 * b[k] / l[k] # ratio of the short axis to the long axis # ratio_ba[k] = b[k] / a[k] # ratio of the short axis to the long axis angle = ell_alpha / math.pi # angle is in [-0.5, 0.5) angle = angle - 1 if angle > 0.5 else angle + 1 if angle < -0.5 else angle # angle = angle if angle >= 0 else 1 + angle theta[k] = -angle if flipped else angle sincos[k] = [np.sin(theta[k] * math.pi), np.cos(theta[k] * math.pi)] # sin in [-1, 1], cos in [0, 1] # ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh} ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh, 'a': a, 'b': b, 'l': l, 'ratio_al': ratio_al, 'ratio_bl': ratio_bl, 'theta': theta, 'sincos': sincos} if self.opt.dense_wh: hm_a = hm.max(axis=0, keepdims=True) dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0) ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask}) del ret['wh'] elif self.opt.cat_spec_wh: ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask}) del ret['wh'] if self.opt.reg_offset: ret.update({'reg': reg}) if self.opt.debug > 0 or not self.split == 'train': gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \ np.zeros((1, 6), dtype=np.float32) meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id} ret['meta'] = meta return retslamd202303071458299/src/lib/detectors/000077500000000000000000000000001435750645400171335ustar00rootroot00000000000000slamd202303071458299/src/lib/detectors/base_detector.py000066400000000000000000000117051435750645400223140ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np from progress.bar import Bar import time import torch from models.model import create_model, load_model from utils.image import get_affine_transform from utils.debugger import Debugger class BaseDetector(object): def __init__(self, opt): if opt.gpus[0] >= 0: opt.device = torch.device('cuda') else: opt.device = torch.device('cpu') print('Creating model...') self.model = create_model(opt.arch, opt.heads, opt.head_conv) self.model = load_model(self.model, opt.load_model) self.model = self.model.to(opt.device) self.model.eval() self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3) self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3) self.max_per_image = 100 self.num_classes = opt.num_classes self.scales = opt.test_scales self.opt = opt self.pause = True def pre_process(self, image, scale, meta=None): height, width = image.shape[0:2] new_height = int(height * scale) new_width = int(width * scale) if self.opt.fix_res: inp_height, inp_width = self.opt.input_h, self.opt.input_w c = np.array([new_width / 2., new_height / 2.], dtype=np.float32) s = max(height, width) * 1.0 else: inp_height = (new_height | self.opt.pad) + 1 inp_width = (new_width | self.opt.pad) + 1 c = np.array([new_width // 2, new_height // 2], dtype=np.float32) s = np.array([inp_width, inp_height], dtype=np.float32) trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height]) resized_image = cv2.resize(image, (new_width, new_height)) inp_image = cv2.warpAffine( resized_image, trans_input, (inp_width, inp_height), flags=cv2.INTER_LINEAR) inp_image = ((inp_image / 255. - self.mean) / self.std).astype(np.float32) images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height, inp_width) if self.opt.flip_test: images = np.concatenate((images, images[:, :, :, ::-1]), axis=0) images = torch.from_numpy(images) meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} return images, meta def process(self, images, return_time=False): raise NotImplementedError def post_process(self, dets, meta, scale=1): raise NotImplementedError def merge_outputs(self, detections): raise NotImplementedError def debug(self, debugger, images, dets, output, scale=1): raise NotImplementedError def show_results(self, debugger, image, results): raise NotImplementedError def run(self, image_or_path_or_tensor, meta=None): load_time, pre_time, net_time, dec_time, post_time = 0, 0, 0, 0, 0 merge_time, tot_time = 0, 0 debugger = Debugger(dataset=self.opt.dataset, ipynb=(self.opt.debug==3), theme=self.opt.debugger_theme) start_time = time.time() pre_processed = False if isinstance(image_or_path_or_tensor, np.ndarray): image = image_or_path_or_tensor elif type(image_or_path_or_tensor) == type (''): image = cv2.imread(image_or_path_or_tensor) else: image = image_or_path_or_tensor['image'][0].numpy() pre_processed_images = image_or_path_or_tensor pre_processed = True loaded_time = time.time() load_time += (loaded_time - start_time) detections = [] for scale in self.scales: scale_start_time = time.time() if not pre_processed: images, meta = self.pre_process(image, scale, meta) else: # import pdb; pdb.set_trace() images = pre_processed_images['images'][scale][0] meta = pre_processed_images['meta'][scale] meta = {k: v.numpy()[0] for k, v in meta.items()} images = images.to(self.opt.device) torch.cuda.synchronize() pre_process_time = time.time() pre_time += pre_process_time - scale_start_time output, dets, forward_time = self.process(images, return_time=True) torch.cuda.synchronize() net_time += forward_time - pre_process_time decode_time = time.time() dec_time += decode_time - forward_time if self.opt.debug >= 2: self.debug(debugger, images, dets, output, scale) dets = self.post_process(dets, meta, scale) torch.cuda.synchronize() post_process_time = time.time() post_time += post_process_time - decode_time detections.append(dets) results = self.merge_outputs(detections) torch.cuda.synchronize() end_time = time.time() merge_time += end_time - post_process_time tot_time += end_time - start_time if self.opt.debug >= 1: self.show_results(debugger, image, results) return {'results': results, 'tot': tot_time, 'load': load_time, 'pre': pre_time, 'net': net_time, 'dec': dec_time, 'post': post_time, 'merge': merge_time}slamd202303071458299/src/lib/detectors/detector_factory.py000066400000000000000000000010301435750645400230370ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from .exdet import ExdetDetector # from .ddd import DddDetector # from .ctdet import CtdetDetector # from .multi_pose import MultiPoseDetector from .eldet_bbox import EldetBBOXDetector from .eldet import EldetDetector detector_factory = { # 'exdet': ExdetDetector, # 'ddd': DddDetector, # 'ctdet': CtdetDetector, # 'multi_pose': MultiPoseDetector, 'eldet_bbox': EldetBBOXDetector, 'eldet': EldetDetector, } slamd202303071458299/src/lib/detectors/eldet.py000066400000000000000000000105611435750645400206050ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np from progress.bar import Bar import time import torch try: from external.nms import soft_nms except: print('NMS not imported! If you need it,' ' do \n cd $CenterNet_ROOT/src/lib/external \n make') from models.decode import eldet_decode, eldet_decode_sincos from models.utils import flip_tensor from utils.image import get_affine_transform from utils.post_process import eldet_post_process, eldet_post_process_sincos from utils.debugger import Debugger from .base_detector import BaseDetector class EldetDetector(BaseDetector): def __init__(self, opt): super(EldetDetector, self).__init__(opt) def process(self, images, return_time=False): with torch.no_grad(): output = self.model(images)[-1] hm = output['hm'].sigmoid_() l = output['l'] ratio_al = output["ratio_al"] ratio_bl = output["ratio_bl"] theta = output["theta"] sincos = output["sincos"] reg = output['reg'] if self.opt.reg_offset else None # if self.opt.flip_test: # hm = (hm[0:1] + flip_tensor(hm[1:2])) / 2 # l = (l[0:1] + flip_tensor(l[1:2])) / 2 # ratio_al = (ratio_al[0:1] + flip_tensor(ratio_al[1:2])) / 2 # ratio_bl = (ratio_bl[0:1] + flip_tensor(ratio_bl[1:2])) / 2 # theta = (theta[0:1] + flip_tensor(theta[1:2])) / 2 # reg = reg[0:1] if reg is not None else None torch.cuda.synchronize() forward_time = time.time() dets = eldet_decode_sincos(hm, l, ratio_al=ratio_al, ratio_bl=ratio_bl, theta=theta, sincos=sincos, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) # dets = eldet_decode(hm, l, ratio_al=ratio_al, ratio_bl=ratio_bl, theta=theta, # reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) if return_time: return output, dets, forward_time else: return output, dets def post_process(self, dets, meta, scale=1): # dets = [bboxes{4}, cx, cy, l, ratio_al, ratio_bl, thetas, sincos{2}, scores, clses] #[14] dets = dets.detach().cpu().numpy() dets = dets.reshape(1, -1, dets.shape[2]) # dets = eldet_post_process( dets = eldet_post_process_sincos( dets.copy(), [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], self.opt.num_classes) for j in range(1, self.num_classes + 1): dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 13) dets[0][j][:, :7] /= scale return dets[0] def merge_outputs(self, detections): results = {} for j in range(1, self.num_classes + 1): results[j] = np.concatenate( [detection[j] for detection in detections], axis=0).astype(np.float32) if len(self.scales) > 1 or self.opt.nms: soft_nms(results[j], Nt=0.5, method=2) scores = np.hstack( [results[j][:, 4] for j in range(1, self.num_classes + 1)]) if len(scores) > self.max_per_image: kth = len(scores) - self.max_per_image thresh = np.partition(scores, kth)[kth] for j in range(1, self.num_classes + 1): keep_inds = (results[j][:, 4] >= thresh) results[j] = results[j][keep_inds] return results def debug(self, debugger, images, dets, output, scale=1): detection = dets.detach().cpu().numpy().copy() detection[:, :, :4] *= self.opt.down_ratio for i in range(1): img = images[i].detach().cpu().numpy().transpose(1, 2, 0) img = ((img * self.std + self.mean) * 255).astype(np.uint8) pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) debugger.add_blend_img(img, pred, 'pred_hm_{:.1f}'.format(scale)) debugger.add_img(img, img_id='out_pred_{:.1f}'.format(scale)) for k in range(len(dets[i])): if detection[i, k, 4] > self.opt.center_thresh: debugger.add_coco_bbox(detection[i, k, :4], detection[i, k, -1], detection[i, k, 4], img_id='out_pred_{:.1f}'.format(scale)) def show_results(self, debugger, image, results): debugger.add_img(image, img_id='ctdet') for j in range(1, self.num_classes + 1): for bbox in results[j]: if bbox[4] > self.opt.vis_thresh: debugger.add_coco_bbox(bbox[:4], j - 1, bbox[4], img_id='ctdet') debugger.show_all_imgs(pause=self.pause) slamd202303071458299/src/lib/detectors/eldet_bbox.py000066400000000000000000000071421435750645400216200ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np from progress.bar import Bar import time import torch try: from external.nms import soft_nms except: print('NMS not imported! If you need it,' ' do \n cd $CenterNet_ROOT/src/lib/external \n make') from models.decode import ctdet_decode from models.utils import flip_tensor from utils.image import get_affine_transform from utils.post_process import ctdet_post_process from utils.debugger import Debugger from .base_detector import BaseDetector class EldetBBOXDetector(BaseDetector): def __init__(self, opt): super(EldetBBOXDetector, self).__init__(opt) def process(self, images, return_time=False): with torch.no_grad(): output = self.model(images)[-1] hm = output['hm'].sigmoid_() wh = output['wh'] reg = output['reg'] if self.opt.reg_offset else None if self.opt.flip_test: hm = (hm[0:1] + flip_tensor(hm[1:2])) / 2 wh = (wh[0:1] + flip_tensor(wh[1:2])) / 2 reg = reg[0:1] if reg is not None else None torch.cuda.synchronize() forward_time = time.time() dets = ctdet_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) if return_time: return output, dets, forward_time else: return output, dets def post_process(self, dets, meta, scale=1): dets = dets.detach().cpu().numpy() dets = dets.reshape(1, -1, dets.shape[2]) dets = ctdet_post_process( dets.copy(), [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], self.opt.num_classes) for j in range(1, self.num_classes + 1): dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5) dets[0][j][:, :4] /= scale return dets[0] def merge_outputs(self, detections): results = {} for j in range(1, self.num_classes + 1): results[j] = np.concatenate( [detection[j] for detection in detections], axis=0).astype(np.float32) if len(self.scales) > 1 or self.opt.nms: soft_nms(results[j], Nt=0.5, method=2) scores = np.hstack( [results[j][:, 4] for j in range(1, self.num_classes + 1)]) if len(scores) > self.max_per_image: kth = len(scores) - self.max_per_image thresh = np.partition(scores, kth)[kth] for j in range(1, self.num_classes + 1): keep_inds = (results[j][:, 4] >= thresh) results[j] = results[j][keep_inds] return results def debug(self, debugger, images, dets, output, scale=1): detection = dets.detach().cpu().numpy().copy() detection[:, :, :4] *= self.opt.down_ratio for i in range(1): img = images[i].detach().cpu().numpy().transpose(1, 2, 0) img = ((img * self.std + self.mean) * 255).astype(np.uint8) pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) debugger.add_blend_img(img, pred, 'pred_hm_{:.1f}'.format(scale)) debugger.add_img(img, img_id='out_pred_{:.1f}'.format(scale)) for k in range(len(dets[i])): if detection[i, k, 4] > self.opt.center_thresh: debugger.add_coco_bbox(detection[i, k, :4], detection[i, k, -1], detection[i, k, 4], img_id='out_pred_{:.1f}'.format(scale)) def show_results(self, debugger, image, results): debugger.add_img(image, img_id='ctdet') for j in range(1, self.num_classes + 1): for bbox in results[j]: if bbox[4] > self.opt.vis_thresh: debugger.add_coco_bbox(bbox[:4], j - 1, bbox[4], img_id='ctdet') debugger.show_all_imgs(pause=self.pause) slamd202303071458299/src/lib/external/000077500000000000000000000000001435750645400167615ustar00rootroot00000000000000slamd202303071458299/src/lib/external/.gitignore000066400000000000000000000002401435750645400207450ustar00rootroot00000000000000bbox.c bbox.cpython-35m-x86_64-linux-gnu.so bbox.cpython-36m-x86_64-linux-gnu.so nms.c nms.cpython-35m-x86_64-linux-gnu.so nms.cpython-36m-x86_64-linux-gnu.so slamd202303071458299/src/lib/external/Makefile000066400000000000000000000000701435750645400204160ustar00rootroot00000000000000all: python setup.py build_ext --inplace rm -rf build slamd202303071458299/src/lib/external/__init__.py000066400000000000000000000000001435750645400210600ustar00rootroot00000000000000slamd202303071458299/src/lib/external/nms.pyx000066400000000000000000000315401435750645400203230ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- # ---------------------------------------------------------- # Soft-NMS: Improving Object Detection With One Line of Code # Copyright (c) University of Maryland, College Park # Licensed under The MIT License [see LICENSE for details] # Written by Navaneeth Bodla and Bharat Singh # ---------------------------------------------------------- import numpy as np cimport numpy as np cdef inline np.float32_t max(np.float32_t a, np.float32_t b): return a if a >= b else b cdef inline np.float32_t min(np.float32_t a, np.float32_t b): return a if a <= b else b def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] cdef int ndets = dets.shape[0] cdef np.ndarray[np.int_t, ndim=1] suppressed = \ np.zeros((ndets), dtype=np.int) # nominal indices cdef int _i, _j # sorted indices cdef int i, j # temp variables for box i's (the box currently under consideration) cdef np.float32_t ix1, iy1, ix2, iy2, iarea # variables for computing overlap with box j (lower scoring box) cdef np.float32_t xx1, yy1, xx2, yy2 cdef np.float32_t w, h cdef np.float32_t inter, ovr keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) if ovr >= thresh: suppressed[j] = 1 return keep def soft_nms(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0): cdef unsigned int N = boxes.shape[0] cdef float iw, ih, box_area cdef float ua cdef int pos = 0 cdef float maxscore = 0 cdef int maxpos = 0 cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov for i in range(N): maxscore = boxes[i, 4] maxpos = i tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # get max box while pos < N: if maxscore < boxes[pos, 4]: maxscore = boxes[pos, 4] maxpos = pos pos = pos + 1 # add max box as a detection boxes[i,0] = boxes[maxpos,0] boxes[i,1] = boxes[maxpos,1] boxes[i,2] = boxes[maxpos,2] boxes[i,3] = boxes[maxpos,3] boxes[i,4] = boxes[maxpos,4] # swap ith box with position of max box boxes[maxpos,0] = tx1 boxes[maxpos,1] = ty1 boxes[maxpos,2] = tx2 boxes[maxpos,3] = ty2 boxes[maxpos,4] = ts tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # NMS iterations, note that N changes if detection boxes fall below threshold while pos < N: x1 = boxes[pos, 0] y1 = boxes[pos, 1] x2 = boxes[pos, 2] y2 = boxes[pos, 3] s = boxes[pos, 4] area = (x2 - x1 + 1) * (y2 - y1 + 1) iw = (min(tx2, x2) - max(tx1, x1) + 1) if iw > 0: ih = (min(ty2, y2) - max(ty1, y1) + 1) if ih > 0: ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) ov = iw * ih / ua #iou between max box and detection box if method == 1: # linear if ov > Nt: weight = 1 - ov else: weight = 1 elif method == 2: # gaussian weight = np.exp(-(ov * ov)/sigma) else: # original NMS if ov > Nt: weight = 0 else: weight = 1 boxes[pos, 4] = weight*boxes[pos, 4] # if box score falls below threshold, discard the box by swapping with last box # update N if boxes[pos, 4] < threshold: boxes[pos,0] = boxes[N-1, 0] boxes[pos,1] = boxes[N-1, 1] boxes[pos,2] = boxes[N-1, 2] boxes[pos,3] = boxes[N-1, 3] boxes[pos,4] = boxes[N-1, 4] N = N - 1 pos = pos - 1 pos = pos + 1 keep = [i for i in range(N)] return keep def soft_nms_39(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0): cdef unsigned int N = boxes.shape[0] cdef float iw, ih, box_area cdef float ua cdef int pos = 0 cdef float maxscore = 0 cdef int maxpos = 0 cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov cdef float tmp for i in range(N): maxscore = boxes[i, 4] maxpos = i tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # get max box while pos < N: if maxscore < boxes[pos, 4]: maxscore = boxes[pos, 4] maxpos = pos pos = pos + 1 # add max box as a detection boxes[i,0] = boxes[maxpos,0] boxes[i,1] = boxes[maxpos,1] boxes[i,2] = boxes[maxpos,2] boxes[i,3] = boxes[maxpos,3] boxes[i,4] = boxes[maxpos,4] # swap ith box with position of max box boxes[maxpos,0] = tx1 boxes[maxpos,1] = ty1 boxes[maxpos,2] = tx2 boxes[maxpos,3] = ty2 boxes[maxpos,4] = ts for j in range(5, 39): tmp = boxes[i, j] boxes[i, j] = boxes[maxpos, j] boxes[maxpos, j] = tmp tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # NMS iterations, note that N changes if detection boxes fall below threshold while pos < N: x1 = boxes[pos, 0] y1 = boxes[pos, 1] x2 = boxes[pos, 2] y2 = boxes[pos, 3] s = boxes[pos, 4] area = (x2 - x1 + 1) * (y2 - y1 + 1) iw = (min(tx2, x2) - max(tx1, x1) + 1) if iw > 0: ih = (min(ty2, y2) - max(ty1, y1) + 1) if ih > 0: ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) ov = iw * ih / ua #iou between max box and detection box if method == 1: # linear if ov > Nt: weight = 1 - ov else: weight = 1 elif method == 2: # gaussian weight = np.exp(-(ov * ov)/sigma) else: # original NMS if ov > Nt: weight = 0 else: weight = 1 boxes[pos, 4] = weight*boxes[pos, 4] # if box score falls below threshold, discard the box by swapping with last box # update N if boxes[pos, 4] < threshold: boxes[pos,0] = boxes[N-1, 0] boxes[pos,1] = boxes[N-1, 1] boxes[pos,2] = boxes[N-1, 2] boxes[pos,3] = boxes[N-1, 3] boxes[pos,4] = boxes[N-1, 4] for j in range(5, 39): tmp = boxes[pos, j] boxes[pos, j] = boxes[N - 1, j] boxes[N - 1, j] = tmp N = N - 1 pos = pos - 1 pos = pos + 1 keep = [i for i in range(N)] return keep def soft_nms_merge(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0, float weight_exp=6): cdef unsigned int N = boxes.shape[0] cdef float iw, ih, box_area cdef float ua cdef int pos = 0 cdef float maxscore = 0 cdef int maxpos = 0 cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov cdef float mx1,mx2,my1,my2,mts,mbs,mw for i in range(N): maxscore = boxes[i, 4] maxpos = i tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # get max box while pos < N: if maxscore < boxes[pos, 4]: maxscore = boxes[pos, 4] maxpos = pos pos = pos + 1 # add max box as a detection boxes[i,0] = boxes[maxpos,0] boxes[i,1] = boxes[maxpos,1] boxes[i,2] = boxes[maxpos,2] boxes[i,3] = boxes[maxpos,3] boxes[i,4] = boxes[maxpos,4] mx1 = boxes[i, 0] * boxes[i, 5] my1 = boxes[i, 1] * boxes[i, 5] mx2 = boxes[i, 2] * boxes[i, 6] my2 = boxes[i, 3] * boxes[i, 6] mts = boxes[i, 5] mbs = boxes[i, 6] # swap ith box with position of max box boxes[maxpos,0] = tx1 boxes[maxpos,1] = ty1 boxes[maxpos,2] = tx2 boxes[maxpos,3] = ty2 boxes[maxpos,4] = ts tx1 = boxes[i,0] ty1 = boxes[i,1] tx2 = boxes[i,2] ty2 = boxes[i,3] ts = boxes[i,4] pos = i + 1 # NMS iterations, note that N changes if detection boxes fall below threshold while pos < N: x1 = boxes[pos, 0] y1 = boxes[pos, 1] x2 = boxes[pos, 2] y2 = boxes[pos, 3] s = boxes[pos, 4] area = (x2 - x1 + 1) * (y2 - y1 + 1) iw = (min(tx2, x2) - max(tx1, x1) + 1) if iw > 0: ih = (min(ty2, y2) - max(ty1, y1) + 1) if ih > 0: ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) ov = iw * ih / ua #iou between max box and detection box if method == 1: # linear if ov > Nt: weight = 1 - ov else: weight = 1 elif method == 2: # gaussian weight = np.exp(-(ov * ov)/sigma) else: # original NMS if ov > Nt: weight = 0 else: weight = 1 mw = (1 - weight) ** weight_exp mx1 = mx1 + boxes[pos, 0] * boxes[pos, 5] * mw my1 = my1 + boxes[pos, 1] * boxes[pos, 5] * mw mx2 = mx2 + boxes[pos, 2] * boxes[pos, 6] * mw my2 = my2 + boxes[pos, 3] * boxes[pos, 6] * mw mts = mts + boxes[pos, 5] * mw mbs = mbs + boxes[pos, 6] * mw boxes[pos, 4] = weight*boxes[pos, 4] # if box score falls below threshold, discard the box by swapping with last box # update N if boxes[pos, 4] < threshold: boxes[pos,0] = boxes[N-1, 0] boxes[pos,1] = boxes[N-1, 1] boxes[pos,2] = boxes[N-1, 2] boxes[pos,3] = boxes[N-1, 3] boxes[pos,4] = boxes[N-1, 4] N = N - 1 pos = pos - 1 pos = pos + 1 boxes[i, 0] = mx1 / mts boxes[i, 1] = my1 / mts boxes[i, 2] = mx2 / mbs boxes[i, 3] = my2 / mbs keep = [i for i in range(N)] return keep slamd202303071458299/src/lib/external/setup.py000066400000000000000000000005601435750645400204740ustar00rootroot00000000000000import numpy from distutils.core import setup from distutils.extension import Extension from Cython.Build import cythonize extensions = [ Extension( "nms", ["nms.pyx"], extra_compile_args=["-Wno-cpp", "-Wno-unused-function"] ) ] setup( name="coco", ext_modules=cythonize(extensions), include_dirs=[numpy.get_include()] ) slamd202303071458299/src/lib/fhd_opts.py000066400000000000000000000510671435750645400173300ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='eldet', help='ctdet | ddd | multi_pose | exdet | eldet | eldet_bbox') self.parser.add_argument('--dataset', default='coco_fhd', help='coco | kitti | coco_hp | pascal | coco_fhd') self.parser.add_argument('--data_dir', default='/userhome/workspace/Data/CHD/detection') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') self.parser.add_argument('--save_image', action='store_true') self.parser.add_argument('--debug', type=int, default=0, help='level of visualization.' '1: only show the final detection results' '2: show the network output features' '3: use matplot to display' # useful when lunching training with ipython notebook '4: save all visualizations to disk') self.parser.add_argument('--demo', default='', help='path to image/ image folders/ video. ' 'or "webcam"') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=4, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.3, help='visualization threshold.') self.parser.add_argument('--debugger_theme', default='white', choices=['white', 'black']) # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'res_18 | res_101 | resdcn_18 | resdcn_101 |' 'dlav0_34 | dla_34 | hourglass') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '64 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1.25e-4, help='learning rate for batch size 32.') self.parser.add_argument('--lr_step', type=str, default='80,120,200,400', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=140, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=16, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=5, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') # test self.parser.add_argument('--flip_test', action='store_true', help='flip data augmentation.') self.parser.add_argument('--test_scales', type=str, default='1', help='multi scale test augmentation.') self.parser.add_argument('--nms', action='store_true', help='run nms in testing.') self.parser.add_argument('--K', type=int, default=2, help='max number of output objects.') self.parser.add_argument('--not_prefetch_test', action='store_true', help='not use parallal data pre-processing.') self.parser.add_argument('--fix_res', action='store_true', help='fix testing resolution or keep ' 'the original resolution') self.parser.add_argument('--keep_res', action='store_true', help='keep the original resolution' ' during validation.') # dataset self.parser.add_argument('--not_rand_crop', action='store_true', help='not use the random crop data augmentation' 'from CornerNet.') self.parser.add_argument('--gaussian_noise', action='store_true', help='use Gaussian noise for augmentation') self.parser.add_argument('--shift', type=float, default=0.1, help='when not using random crop' 'apply shift augmentation.') self.parser.add_argument('--scale', type=float, default=0, help='when not using random crop' 'apply scale augmentation.') self.parser.add_argument('--rotate', type=float, default=0, help='when not using random crop' 'apply rotation augmentation.') self.parser.add_argument('--flip', type = float, default=0.5, help='probability of applying flip augmentation.') self.parser.add_argument('--no_color_aug', action='store_true', help='not use the color augmenation ' 'from CornerNet') # multi_pose self.parser.add_argument('--aug_rot', type=float, default=0, help='probability of applying ' 'rotation augmentation.') # ddd self.parser.add_argument('--aug_ddd', type=float, default=0.5, help='probability of applying crop augmentation.') self.parser.add_argument('--rect_mask', action='store_true', help='for ignored object, apply mask on the ' 'rectangular region or just center point.') self.parser.add_argument('--kitti_split', default='3dop', help='different validation split for kitti: ' '3dop | subcnn') # loss self.parser.add_argument('--mse_loss', action='store_true', help='use mse loss or focal loss to train ' 'keypoint heatmaps.') # ctdet self.parser.add_argument('--reg_loss', default='l1', help='regression loss: sl1 | l1 | l2') self.parser.add_argument('--hm_weight', type=float, default=1, help='loss weight for keypoint heatmaps.') self.parser.add_argument('--off_weight', type=float, default=1, help='loss weight for keypoint local offsets.') self.parser.add_argument('--wh_weight', type=float, default=0.1, help='loss weight for bounding box size.') # multi_pose self.parser.add_argument('--hp_weight', type=float, default=1, help='loss weight for human pose offset.') self.parser.add_argument('--hm_hp_weight', type=float, default=1, help='loss weight for human keypoint heatmap.') # ddd self.parser.add_argument('--dep_weight', type=float, default=1, help='loss weight for depth.') self.parser.add_argument('--dim_weight', type=float, default=1, help='loss weight for 3d bounding box size.') self.parser.add_argument('--rot_weight', type=float, default=1, help='loss weight for orientation.') self.parser.add_argument('--peak_thresh', type=float, default=0.2) # ellipse loss self.parser.add_argument('--iou_weight', type=float, default=1, help='loss weight for iou loss.') self.parser.add_argument('--ellipse_weight', type=float, default=1, help='loss weight for ellipse regression.') self.parser.add_argument('--ellipse_reg_weight', type=float, default=0, help='loss weight for ellipse l, a, b constrain.') self.parser.add_argument('--theta_weight', type=float, default=1, help='loss weight for angle regression.') self.parser.add_argument('--sincos_weight', type=float, default=0, help='loss weight for sin/cos regression.') self.parser.add_argument('--num_angle_bins', type=int, default=0, help='number of angle bins.') self.parser.add_argument('--angle_bins_weight', type=float, default=0, help='weight of angle bins classification.') # task # ctdet self.parser.add_argument('--norm_wh', action='store_true', help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') self.parser.add_argument('--dense_wh', action='store_true', help='apply weighted regression near center or ' 'just apply regression on center point.') self.parser.add_argument('--cat_spec_wh', action='store_true', help='category specific bounding box size.') self.parser.add_argument('--not_reg_offset', action='store_true', help='not regress local offset.') # exdet self.parser.add_argument('--agnostic_ex', action='store_true', help='use category agnostic extreme points.') self.parser.add_argument('--scores_thresh', type=float, default=0.1, help='threshold for extreme point heatmap.') self.parser.add_argument('--center_thresh', type=float, default=0.1, help='threshold for centermap.') self.parser.add_argument('--aggr_weight', type=float, default=0.0, help='edge aggregation weight.') # multi_pose self.parser.add_argument('--dense_hp', action='store_true', help='apply weighted pose regression near center ' 'or just apply regression on center point.') self.parser.add_argument('--not_hm_hp', action='store_true', help='not estimate human joint heatmap, ' 'directly use the joint offset from center.') self.parser.add_argument('--not_reg_hp_offset', action='store_true', help='not regress local offset for ' 'human joint heatmaps.') self.parser.add_argument('--not_reg_bbox', action='store_true', help='not regression bounding box size.') # ground truth validation self.parser.add_argument('--eval_oracle_hm', action='store_true', help='use ground center heatmap.') self.parser.add_argument('--eval_oracle_wh', action='store_true', help='use ground truth bounding box size.') self.parser.add_argument('--eval_oracle_offset', action='store_true', help='use ground truth local heatmap offset.') self.parser.add_argument('--eval_oracle_kps', action='store_true', help='use ground truth human pose offset.') self.parser.add_argument('--eval_oracle_hmhp', action='store_true', help='use ground truth human joint heatmaps.') self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', help='use ground truth human joint local offset.') self.parser.add_argument('--eval_oracle_dep', action='store_true', help='use ground truth depth.') def parse(self, args=''): if args == '': opt = self.parser.parse_args() else: opt = self.parser.parse_args(args) opt.gpus_str = opt.gpus opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] opt.lr_step = [int(i) for i in opt.lr_step.split(',')] opt.test_scales = [float(i) for i in opt.test_scales.split(',')] opt.fix_res = not opt.keep_res print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') opt.reg_offset = not opt.not_reg_offset opt.reg_bbox = not opt.not_reg_bbox opt.hm_hp = not opt.not_hm_hp opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp if opt.head_conv == -1: # init default head_conv opt.head_conv = 256 if 'dla' in opt.arch else 64 opt.pad = 127 if 'hourglass' in opt.arch else 31 opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 if opt.trainval: opt.val_intervals = 100000000 if opt.debug > 0: opt.num_workers = 0 opt.batch_size = 1 opt.gpus = [opt.gpus[0]] opt.master_batch_size = -1 if opt.master_batch_size == -1: opt.master_batch_size = opt.batch_size // len(opt.gpus) rest_batch_size = (opt.batch_size - opt.master_batch_size) opt.chunk_sizes = [opt.master_batch_size] for i in range(len(opt.gpus) - 1): slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) if i < rest_batch_size % (len(opt.gpus) - 1): slave_chunk_size += 1 opt.chunk_sizes.append(slave_chunk_size) print('training chunk_sizes:', opt.chunk_sizes) opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') # opt.data_dir = os.path.join(opt.root_dir, 'data') opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) opt.debug_dir = os.path.join(opt.save_dir, 'debug') print('The output will be saved to ', opt.save_dir) if opt.resume and opt.load_model == '': model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ else opt.save_dir opt.load_model = os.path.join(model_path, 'model_last.pth') return opt def update_dataset_info_and_set_heads(self, opt, dataset): input_h, input_w = dataset.default_resolution opt.mean, opt.std = dataset.mean, dataset.std opt.num_classes = dataset.num_classes # input_h(w): opt.input_h overrides opt.input_res overrides dataset default input_h = opt.input_res if opt.input_res > 0 else input_h input_w = opt.input_res if opt.input_res > 0 else input_w opt.input_h = opt.input_h if opt.input_h > 0 else input_h opt.input_w = opt.input_w if opt.input_w > 0 else input_w opt.output_h = opt.input_h // opt.down_ratio opt.output_w = opt.input_w // opt.down_ratio opt.input_res = max(opt.input_h, opt.input_w) opt.output_res = max(opt.output_h, opt.output_w) if opt.task == 'exdet': # assert opt.dataset in ['coco'] num_hm = 1 if opt.agnostic_ex else opt.num_classes opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, 'hm_b': num_hm, 'hm_r': num_hm, 'hm_c': opt.num_classes} if opt.reg_offset: opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2}) elif opt.task == 'ddd': # assert opt.dataset in ['gta', 'kitti', 'viper'] opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3} if opt.reg_bbox: opt.heads.update( {'wh': 2}) if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'ctdet': # assert opt.dataset in ['pascal', 'coco'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'eldet_bbox': # assert opt.dataset in ['coco_fhd'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'eldet': # assert opt.dataset in ['coco_fhd'] opt.heads = {'hm': opt.num_classes, 'reg': 2, 'l': 1, 'ratio_al': 1, 'ratio_bl': 1, 'theta': 1} # if opt.sincos_weight > 0: opt.heads.update({'sincos': 2}) if opt.num_angle_bins > 0: opt.heads.update({'angle_bins': opt.num_angle_bins}) elif opt.task == 'multi_pose': # assert opt.dataset in ['coco_hp'] opt.flip_idx = dataset.flip_idx opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34} if opt.reg_offset: opt.heads.update({'reg': 2}) if opt.hm_hp: opt.heads.update({'hm_hp': 17}) if opt.reg_hp_offset: opt.heads.update({'hp_offset': 2}) else: assert 0, 'task not defined!' print('heads', opt.heads) return opt def init(self, args=''): default_dataset_info = { 'ctdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'exdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'multi_pose': { 'default_resolution': [512, 512], 'num_classes': 1, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco_hp', 'num_joints': 17, 'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]}, 'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'dataset': 'kitti'}, 'eldet': {'default_resolution': [512, 512], 'num_classes': 2, 'mean': [0.216, 0.216, 0.216], 'std': [0.222, 0.222, 0.222], 'dataset': 'coco_fhd'}, } class Struct: def __init__(self, entries): for k, v in entries.items(): self.__setattr__(k, v) opt = self.parse(args) dataset = Struct(default_dataset_info[opt.task]) opt.dataset = dataset.dataset opt = self.update_dataset_info_and_set_heads(opt, dataset) return opt slamd202303071458299/src/lib/logger.py000066400000000000000000000042641435750645400167760ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 import os import time import sys import torch USE_TENSORBOARD = True try: import tensorboardX print('Using tensorboardX') except: USE_TENSORBOARD = False class Logger(object): def __init__(self, opt): """Create a summary writer logging to log_dir.""" if not os.path.exists(opt.save_dir): os.makedirs(opt.save_dir) if not os.path.exists(opt.debug_dir): os.makedirs(opt.debug_dir) time_str = time.strftime('%Y-%m-%d-%H-%M') args = dict((name, getattr(opt, name)) for name in dir(opt) if not name.startswith('_')) file_name = os.path.join(opt.save_dir, 'opt.txt') with open(file_name, 'wt') as opt_file: opt_file.write('==> torch version: {}\n'.format(torch.__version__)) opt_file.write('==> cudnn version: {}\n'.format( torch.backends.cudnn.version())) opt_file.write('==> Cmd:\n') opt_file.write(str(sys.argv)) opt_file.write('\n==> Opt:\n') for k, v in sorted(args.items()): opt_file.write(' %s: %s\n' % (str(k), str(v))) log_dir = opt.save_dir + '/logs_{}'.format(time_str) if USE_TENSORBOARD: self.writer = tensorboardX.SummaryWriter(log_dir=log_dir) else: if not os.path.exists(os.path.dirname(log_dir)): os.mkdir(os.path.dirname(log_dir)) if not os.path.exists(log_dir): os.mkdir(log_dir) self.log = open(log_dir + '/log.txt', 'w') try: os.system('cp {}/opt.txt {}/'.format(opt.save_dir, log_dir)) except: pass self.start_line = True def write(self, txt): if self.start_line: time_str = time.strftime('%Y-%m-%d-%H-%M') self.log.write('{}: {}'.format(time_str, txt)) else: self.log.write(txt) self.start_line = False if '\n' in txt: self.start_line = True self.log.flush() def close(self): self.log.close() def scalar_summary(self, tag, value, step): """Log a scalar variable.""" if USE_TENSORBOARD: self.writer.add_scalar(tag, value, step) slamd202303071458299/src/lib/models/000077500000000000000000000000001435750645400164225ustar00rootroot00000000000000slamd202303071458299/src/lib/models/Rotated_IoU/000077500000000000000000000000001435750645400206005ustar00rootroot00000000000000slamd202303071458299/src/lib/models/Rotated_IoU/.gitignore000066400000000000000000000000731435750645400225700ustar00rootroot00000000000000__pycache__ .vscode build dist *.egg-info *.so test.py dataslamd202303071458299/src/lib/models/Rotated_IoU/LICENSE000066400000000000000000000020531435750645400216050ustar00rootroot00000000000000MIT License Copyright (c) 2020 Lanxiao Li Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. slamd202303071458299/src/lib/models/Rotated_IoU/box_intersection_2d.py000066400000000000000000000157031435750645400251230ustar00rootroot00000000000000''' torch implementation of 2d oriented box intersection author: lanxiao li 2020.8 ''' import torch from .cuda_op.cuda_ext import sort_v EPSILON = 1e-8 def box_intersection_th(corners1:torch.Tensor, corners2:torch.Tensor): """find intersection points of rectangles Convention: if two edges are collinear, there is no intersection point Args: corners1 (torch.Tensor): B, N, 4, 2 corners2 (torch.Tensor): B, N, 4, 2 Returns: intersectons (torch.Tensor): B, N, 4, 4, 2 mask (torch.Tensor) : B, N, 4, 4; bool """ # build edges from corners line1 = torch.cat([corners1, corners1[:, :, [1, 2, 3, 0], :]], dim=3) # B, N, 4, 4: Batch, Box, edge, point line2 = torch.cat([corners2, corners2[:, :, [1, 2, 3, 0], :]], dim=3) # duplicate data to pair each edges from the boxes # (B, N, 4, 4) -> (B, N, 4, 4, 4) : Batch, Box, edge1, edge2, point line1_ext = line1.unsqueeze(3).repeat([1,1,1,4,1]) line2_ext = line2.unsqueeze(2).repeat([1,1,4,1,1]) x1 = line1_ext[..., 0] y1 = line1_ext[..., 1] x2 = line1_ext[..., 2] y2 = line1_ext[..., 3] x3 = line2_ext[..., 0] y3 = line2_ext[..., 1] x4 = line2_ext[..., 2] y4 = line2_ext[..., 3] # math: https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection num = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4) den_t = (x1-x3)*(y3-y4) - (y1-y3)*(x3-x4) t = den_t / num t[num == .0] = -1. mask_t = (t > 0) * (t < 1) # intersection on line segment 1 den_u = (x1-x2)*(y1-y3) - (y1-y2)*(x1-x3) u = -den_u / num u[num == .0] = -1. mask_u = (u > 0) * (u < 1) # intersection on line segment 2 mask = mask_t * mask_u t = den_t / (num + EPSILON) # overwrite with EPSILON. otherwise numerically unstable intersections = torch.stack([x1 + t*(x2-x1), y1 + t*(y2-y1)], dim=-1) intersections = intersections * mask.float().unsqueeze(-1) return intersections, mask def box1_in_box2(corners1:torch.Tensor, corners2:torch.Tensor): """check if corners of box1 lie in box2 Convention: if a corner is exactly on the edge of the other box, it's also a valid point Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) Returns: c1_in_2: (B, N, 4) Bool """ a = corners2[:, :, 0:1, :] # (B, N, 1, 2) b = corners2[:, :, 1:2, :] # (B, N, 1, 2) d = corners2[:, :, 3:4, :] # (B, N, 1, 2) ab = b - a # (B, N, 1, 2) am = corners1 - a # (B, N, 4, 2) ad = d - a # (B, N, 1, 2) p_ab = torch.sum(ab * am, dim=-1) # (B, N, 4) norm_ab = torch.sum(ab * ab, dim=-1) # (B, N, 1) p_ad = torch.sum(ad * am, dim=-1) # (B, N, 4) norm_ad = torch.sum(ad * ad, dim=-1) # (B, N, 1) # NOTE: the expression looks ugly but is stable if the two boxes are exactly the same # also stable with different scale of bboxes cond1 = (p_ab / norm_ab > - 1e-6) * (p_ab / norm_ab < 1 + 1e-6) # (B, N, 4) cond2 = (p_ad / norm_ad > - 1e-6) * (p_ad / norm_ad < 1 + 1e-6) # (B, N, 4) return cond1*cond2 def box_in_box_th(corners1:torch.Tensor, corners2:torch.Tensor): """check if corners of two boxes lie in each other Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) Returns: c1_in_2: (B, N, 4) Bool. i-th corner of box1 in box2 c2_in_1: (B, N, 4) Bool. i-th corner of box2 in box1 """ c1_in_2 = box1_in_box2(corners1, corners2) c2_in_1 = box1_in_box2(corners2, corners1) return c1_in_2, c2_in_1 def build_vertices(corners1:torch.Tensor, corners2:torch.Tensor, c1_in_2:torch.Tensor, c2_in_1:torch.Tensor, inters:torch.Tensor, mask_inter:torch.Tensor): """find vertices of intersection area Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) c1_in_2 (torch.Tensor): Bool, (B, N, 4) c2_in_1 (torch.Tensor): Bool, (B, N, 4) inters (torch.Tensor): (B, N, 4, 4, 2) mask_inter (torch.Tensor): (B, N, 4, 4) Returns: vertices (torch.Tensor): (B, N, 24, 2) vertices of intersection area. only some elements are valid mask (torch.Tensor): (B, N, 24) indicates valid elements in vertices """ # NOTE: inter has elements equals zero and has zeros gradient (masked by multiplying with 0). # can be used as trick B = corners1.size()[0] N = corners1.size()[1] vertices = torch.cat([corners1, corners2, inters.view([B, N, -1, 2])], dim=2) # (B, N, 4+4+16, 2) mask = torch.cat([c1_in_2, c2_in_1, mask_inter.view([B, N, -1])], dim=2) # Bool (B, N, 4+4+16) return vertices, mask def sort_indices(vertices:torch.Tensor, mask:torch.Tensor): """[summary] Args: vertices (torch.Tensor): float (B, N, 24, 2) mask (torch.Tensor): bool (B, N, 24) Returns: sorted_index: bool (B, N, 9) Note: why 9? the polygon has maximal 8 vertices. +1 to duplicate the first element. the index should have following structure: (A, B, C, ... , A, X, X, X) and X indicates the index of arbitary elements in the last 16 (intersections not corners) with value 0 and mask False. (cause they have zero value and zero gradient) """ num_valid = torch.sum(mask.int(), dim=2).int() # (B, N) mean = torch.sum(vertices * mask.float().unsqueeze(-1), dim=2, keepdim=True) / num_valid.unsqueeze(-1).unsqueeze(-1) vertices_normalized = vertices - mean # normalization makes sorting easier return sort_v(vertices_normalized, mask, num_valid).long() def calculate_area(idx_sorted:torch.Tensor, vertices:torch.Tensor): """calculate area of intersection Args: idx_sorted (torch.Tensor): (B, N, 9) vertices (torch.Tensor): (B, N, 24, 2) return: area: (B, N), area of intersection selected: (B, N, 9, 2), vertices of polygon with zero padding """ idx_ext = idx_sorted.unsqueeze(-1).repeat([1,1,1,2]) selected = torch.gather(vertices, 2, idx_ext) total = selected[:, :, 0:-1, 0]*selected[:, :, 1:, 1] - selected[:, :, 0:-1, 1]*selected[:, :, 1:, 0] total = torch.sum(total, dim=2) area = torch.abs(total) / 2 return area, selected def oriented_box_intersection_2d(corners1:torch.Tensor, corners2:torch.Tensor): """calculate intersection area of 2d rectangles Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) Returns: area: (B, N), area of intersection selected: (B, N, 9, 2), vertices of polygon with zero padding """ inters, mask_inter = box_intersection_th(corners1, corners2) c12, c21 = box_in_box_th(corners1, corners2) vertices, mask = build_vertices(corners1, corners2, c12, c21, inters, mask_inter) sorted_indices = sort_indices(vertices, mask) return calculate_area(sorted_indices, vertices) slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/000077500000000000000000000000001435750645400222125ustar00rootroot00000000000000slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py000066400000000000000000000015511435750645400243620ustar00rootroot00000000000000import torch from torch import nn from torch.autograd import Function import sort_vertices class SortVertices(Function): @staticmethod def forward(ctx, vertices, mask, num_valid): idx = sort_vertices.sort_vertices_forward(vertices, mask, num_valid) ctx.mark_non_differentiable(idx) return idx @staticmethod def backward(ctx, gradout): return () sort_v = SortVertices.apply if __name__ == "__main__": import time v = torch.rand([8, 1024, 24, 2]).float().cuda() mean = torch.mean(v, dim=2, keepdim=True) v = v - mean m = (torch.rand([8, 1024, 24]) > 0.8).cuda() nv = torch.sum(m.int(), dim=-1).int().cuda() start = time.time() result = sort_v(v, m, nv) torch.cuda.synchronize() print("time: %.2f ms"%((time.time() - start)*1000)) print(result.size()) print(result[0,0,:])slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h000066400000000000000000000026011435750645400245160ustar00rootroot00000000000000#ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #include #include #include #include #include #define TOTAL_THREADS 512 inline int opt_n_thread(int work_size){ const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return max(min(1<(), mask.data_ptr(), num_valid.data_ptr(), idx.data_ptr()); return idx; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){ m.def("sort_vertices_forward", &sort_vertices, "sort vertices of a convex polygon. forward only"); }slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/sort_vert.h000066400000000000000000000002341435750645400244110ustar00rootroot00000000000000#pragma once #include #define MAX_NUM_VERT_IDX 9 at::Tensor sort_vertices(at::Tensor vertices, at::Tensor mask, at::Tensor num_valid);slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/sort_vert_kernel.cu000066400000000000000000000122251435750645400261340ustar00rootroot00000000000000#include #include #include #include "cuda_utils.h" #define MAX_NUM_VERT_IDX 9 #define INTERSECTION_OFFSET 8 #define EPSILON 1e-8 /* compare normalized vertices (vertices around (0,0)) if vertex1 < vertex2 return ture. order: minimum at x-aixs, become larger in anti-clockwise direction */ __device__ bool compare_vertices(float x1, float y1, float x2, float y2){ if (fabs(x1-x2) 0 && y2 < 0) return true; if (y1 < 0 && y2 > 0) return false; float n1 = x1*x1 + y1*y1 + EPSILON; float n2 = x2*x2 + y2*y2 + EPSILON; if (y1 > 0 && y2 > 0){ if (fabs(x1)*x1/n1 - fabs(x2)*x2/n2 > EPSILON) return true; else return false; } if (y1 < 0 && y2 < 0) { if (fabs(x1)*x1/n1 - fabs(x2)*x2/n2 < EPSILON) return true; else return false; } } __global__ void sort_vertices_kernel(int b, int n, int m, const float *__restrict__ vertices, const bool *__restrict__ mask, const int *__restrict__ num_valid, int *__restrict__ idx){ int batch_idx = blockIdx.x; vertices += batch_idx * n * m *2; mask += batch_idx * n * m; num_valid += batch_idx * n; idx += batch_idx * n * MAX_NUM_VERT_IDX; int index = threadIdx.x; // index of polygon int stride = blockDim.x; for (int i = index; i>>(b, n, m, vertices, mask, num_valid, idx); CUDA_CHECK_ERRORS(); }slamd202303071458299/src/lib/models/Rotated_IoU/cuda_op/utils.h000066400000000000000000000030021435750645400235160ustar00rootroot00000000000000#pragma once #include #include #define CHECK_CUDA(x) \ do { \ TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor"); \ } while (0) #define CHECK_CONTIGUOUS(x) \ do { \ TORCH_CHECK(x.is_contiguous(), #x " must ne a contiguous tensor"); \ } while (0) #define CHECK_IS_INT(x) \ do { \ TORCH_CHECK(x.scalar_type()==at::ScalarType::Int, \ #x " must be a int tensor"); \ } while (0) #define CHECK_IS_FLOAT(x) \ do { \ TORCH_CHECK(x.scalar_type()==at::ScalarType::Float, \ #x " must be a float tensor"); \ } while (0) #define CHECK_IS_BOOL(x) \ do { \ TORCH_CHECK(x.scalar_type()==at::ScalarType::Bool, \ #x " must be a bool tensor"); \ } while (0) slamd202303071458299/src/lib/models/Rotated_IoU/demo.py000066400000000000000000000171101435750645400220760ustar00rootroot00000000000000''' This demo is used to validate the back-propagation of the torch implementation of oriented 2d box intersection. This demo trains a network which takes N set of box corners and predicts the x, y, w, h and angle of each rotated boxes. In order to do the back-prop, the prediected box parameters and GT are converted to coordinates of box corners. The area of intersection area is calculated using the pytorch function with CUDA extension. Then, the GIoU loss or DIoU loss can be calculated. This demo first generates data and then do the training. The network is simply a shared MLP (implemented as Conv-layers with 1x1 kernel). Lanxiao Li 2020.08 ''' import torch import numpy as np import os from torch.utils.data import DataLoader, Dataset from torch import nn, optim import argparse from utiles import box2corners from oriented_iou_loss import cal_diou, cal_giou DATA = "./data" X_MAX = 3 Y_MAX = 3 SCALE = 0.5 BATCH_SIZE = 32 N_DATA = 128 NUM_TRAIN = 200 * BATCH_SIZE * N_DATA NUM_TEST = 20 * BATCH_SIZE * N_DATA NUM_EPOCH = 20 if not os.path.exists(DATA): os.mkdir(DATA) def create_data(num): print("... generating %d boxes, please wait..."%num) x = (np.random.rand(num) - 0.5) * 2 * X_MAX y = (np.random.rand(num) - 0.5) * 2 * Y_MAX w = (np.random.rand(num) - 0.5) * 2 * SCALE + 1 h = (np.random.rand(num) - 0.5) * 2 * SCALE + 1 alpha = np.random.rand(num) * np.pi corners = np.zeros((num, 4, 2)).astype(np.float) for i in range(num): corners[i, ...] = box2corners(x[i], y[i], w[i], h[i], alpha[i]) label = np.stack([x, y , w, h, alpha], axis=1) return corners, label def save_dataset(): train_data, train_label = create_data(NUM_TRAIN) np.save(os.path.join(DATA, "train_data.npy"), train_data) np.save(os.path.join(DATA, "train_label.npy"), train_label) test_data, test_label = create_data(NUM_TEST) np.save(os.path.join(DATA, "test_data.npy"), test_data) np.save(os.path.join(DATA, "test_label.npy"), test_label) print("data saved in: ", DATA) class BoxDataSet(Dataset): def __init__(self, split="train"): super(BoxDataSet, self).__init__() assert split in ["train", "test"], "split must be train or test" self.split = split try: self.data = np.load(os.path.join(DATA, split+"_data.npy")) self.label = np.load(os.path.join(DATA, split+"_label.npy")) except: save_dataset() self.data = np.load(os.path.join(DATA, split+"_data.npy")) self.label = np.load(os.path.join(DATA, split+"_label.npy")) def __len__(self) -> int: return self.data.shape[0] def __getitem__(self, index: int) : d = self.data[index, ...] l = self.label[index, ...] return torch.FloatTensor(d), torch.FloatTensor(l) def create_network(): return nn.Sequential(nn.Conv1d(8, 128, 1, bias=False), nn.BatchNorm1d(128), nn.ReLU(True), nn.Conv1d(128, 512, 1, bias=False), nn.BatchNorm1d(512), nn.ReLU(True), nn.Conv1d(512, 128, 1, bias=False), nn.BatchNorm1d(128), nn.ReLU(True), nn.Conv1d(128, 5, 1), nn.Sigmoid()) def parse_pred(pred:torch.Tensor): p0 = (pred[..., 0] - 0.5) * 2 * X_MAX p1 = (pred[..., 1] - 0.5) * 2 * Y_MAX p2 = (pred[..., 2] - 0.5) * 2 * SCALE + 1 p3 = (pred[..., 3] - 0.5) * 2 * SCALE + 1 p4 = pred[..., 4] * np.pi return torch.stack([p0,p1,p2,p3,p4], dim=-1) def main(loss_type:str="giou", enclosing_type:str="aligned"): ds_train = BoxDataSet("train") ds_test = BoxDataSet("test") ld_train = DataLoader(ds_train, BATCH_SIZE * N_DATA, shuffle=True, num_workers=4) ld_test = DataLoader(ds_test, BATCH_SIZE * N_DATA, shuffle=False, num_workers=4) net = create_network() net.to("cuda:0") optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9) lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) num_batch = len(ds_train)//(BATCH_SIZE*N_DATA) for epoch in range(1, NUM_EPOCH+1): # train net.train() for i, data in enumerate(ld_train, 1): box, label = data box = box.cuda() # (B*N, 4, 2) box = box.view([BATCH_SIZE, -1, 4*2]) # (B, N, 4*2) box = box.transpose(1, 2) # (B, 8, N) label = label.cuda() # (B*N, 5) label = label.view([BATCH_SIZE, -1, 5]) # (B, N, 5) optimizer.zero_grad() pred = net(box) # (B, 5, N) pred = pred.transpose(1,2) # (B, N, 5) pred = parse_pred(pred) iou_loss, iou = None, None if loss_type == "giou": iou_loss, iou = cal_giou(pred, label, enclosing_type) elif loss_type == "diou": iou_loss, iou = cal_diou(pred, label, enclosing_type) else: ValueError("unknown loss type") iou_loss = torch.mean(iou_loss) iou_loss.backward() optimizer.step() if i%10 == 0: iou_mask = (iou > 0).float() mean_iou = torch.sum(iou) / (torch.sum(iou_mask) + 1e-8) print("[Epoch %d: %d/%d] train loss: %.4f mean_iou: %.4f" %(epoch, i, num_batch, iou_loss.detach().cpu().item(), mean_iou.detach().cpu().item())) lr_scheduler.step() # validate net.eval() aver_loss = 0 aver_mean_iou = 0 with torch.no_grad(): for i, data in enumerate(ld_test, 1): box, label = data box = box.cuda() # (B*N, 4, 2) box = box.view([BATCH_SIZE, -1, 4*2]) # (B, N, 4*2) box = box.transpose(1, 2) # (B, 8, N) label = label.cuda() # (B*N, 5) label = label.view([BATCH_SIZE, -1, 5]) # (B, N, 5) pred = net(box) # (B, 5, N) pred = pred.transpose(1,2) # (B, N, 5) pred = parse_pred(pred) iou_loss, iou = None, None if loss_type == "giou": iou_loss, iou = cal_giou(pred, label, enclosing_type) elif loss_type == "diou": iou_loss, iou = cal_diou(pred, label, enclosing_type) else: ValueError("unknown loss type") iou_loss = torch.mean(iou_loss) aver_loss += iou_loss.cpu().item() iou_mask = (iou > 0).float() mean_iou = torch.sum(iou) / (torch.sum(iou_mask) + 1e-8) aver_mean_iou += mean_iou.cpu().item() print("... validate epoch %d ..."%epoch) n_iter = len(ds_test)/BATCH_SIZE/N_DATA print("average loss: %.4f"%(aver_loss/n_iter)) print("average iou: %.4f"%(aver_mean_iou/n_iter)) print("..............................") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--loss", type=str, default="diou", help="type of loss function. support: diou or giou. [default: diou]") parser.add_argument("--enclosing", type=str, default="smallest", help="type of enclosing box. support: aligned (axis-aligned) or pca (rotated) or smallest (rotated). [default: smallest]") flags = parser.parse_args() main(flags.loss, flags.enclosing)slamd202303071458299/src/lib/models/Rotated_IoU/image/000077500000000000000000000000001435750645400216625ustar00rootroot00000000000000slamd202303071458299/src/lib/models/Rotated_IoU/image/iou.png000066400000000000000000000351311435750645400231670ustar00rootroot00000000000000PNG  IHDR( pHYs   OiCCPPhotoshop ICC profilexڝSgTS=BKKoR RB&*! 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EO LF_<%EG[:EC®=5Wt͹K)EqrsZ_R^|mGa"/ m>|/FɗBj i){m֏nOJVOqoMy'~55cV=6PU"ςj .a|Fw,.Fq񳒢0zZQe{NP4N 7υQz=:x>Ȅ[= H-< Nh఼EP=ZFy٦{h$hȁN$ʒKx0U 6 ~eB Q+PS p9&#ڎӝq ;BO  [kcyV( Q)8jgO V ^>c($0ŽP4@w 'x SsXxʽj.6O+rV $"P 'Rty'%դIaGE @v9 wgj7qMD@gCkX7_şݑ <@.=/vU96n D2{e?,9vȦdM%zZ%Cis \H$ԙ~rh?Q-Jx/+͙oޢb  u6ȑ "*^&х?~5< ]@vD.mz6O&+h(h0P.Lj܆?KƠǷGkGE;3idʵa\MF66IBb,U'R6L.\3/cseo;..'u!ܸ\݊?Hς(U~ a1AGo  8J/OE6x=ޞʽna&"<&P4hQ\fݏ3J% c69݄_1YEOk. !P4pmXF0GOV]B!a;^>bLu%BѠe6昶ŌN7N71_BEC6t5cVe,U]B! *Qc_gR_‰f2jD!P4lOOECuδ!v19P3G!APzZ#zՌ 'BEGuf-E8ȮBeg|Ro1t:̢$"$4lO!X+Vtv]w != ;22*j®BDq!Br.h-GBӐҀ|/ δv?BӐzu]ͨ!"B Rޖ\V8J88B(dF{< |&EV DŽBE,Zp?-tv&!yXe;nAk( !P4(Ru/'V醻'BѠ4zZ$ quirB! @!ψMӊrBEiE4hi@e1!UP]Jx{CmGJBhP=-j~YB(TJC *X;:D#4 2: for _ in range(dim-2): idx_lines = torch.unsqueeze(idx_lines, 0) idx_points = torch.unsqueeze(idx_points, 0) idx_points = idx_points.repeat(*corners.size()[:-2], 1, 1, 1) # (..., 24, 2, 2) idx_lines = idx_lines.repeat(*corners.size()[:-2], 1, 1, 1) # (..., 24, 6, 2) corners_ext = corners.unsqueeze(-3).repeat( *([1]*(dim-2)), 24, 1, 1) # (..., 24, 8, 2) lines = torch.gather(corners_ext, dim=-2, index=idx_lines) # (..., 24, 2, 2) points = torch.gather(corners_ext, dim=-2, index=idx_points) # (..., 24, 6, 2) return lines, points, idx_lines, idx_points def point_line_distance_range(lines:torch.Tensor, points:torch.Tensor): """calculate the maximal distance between the points in the direction perpendicular to the line methode: point-line-distance Args: lines (torch.Tensor): (..., 24, 2, 2) points (torch.Tensor): (..., 24, 6, 2) Return: torch.Tensor: (..., 24) """ x1 = lines[..., 0:1, 0] # (..., 24, 1) y1 = lines[..., 0:1, 1] # (..., 24, 1) x2 = lines[..., 1:2, 0] # (..., 24, 1) y2 = lines[..., 1:2, 1] # (..., 24, 1) x = points[..., 0] # (..., 24, 6) y = points[..., 1] # (..., 24, 6) den = (y2-y1)*x - (x2-x1)*y + x2*y1 - y2*x1 num = torch.sqrt( (y2-y1).square() + (x2-x1).square() ) + 1e-8 d = den/num # (..., 24, 6) d_max = d.max(dim=-1)[0] # (..., 24) d_min = d.min(dim=-1)[0] # (..., 24) d1 = d_max - d_min # suppose points on different side d2 = torch.max(d.abs(), dim=-1)[0] # or, all points are on the same side # NOTE: if x1 = x2 and y1 = y2, this will return 0 return torch.max(d1, d2) def point_line_projection_range(lines:torch.Tensor, points:torch.Tensor): """calculate the maximal distance between the points in the direction parallel to the line methode: point-line projection Args: lines (torch.Tensor): (..., 24, 2, 2) points (torch.Tensor): (..., 24, 6, 2) Return: torch.Tensor: (..., 24) """ x1 = lines[..., 0:1, 0] # (..., 24, 1) y1 = lines[..., 0:1, 1] # (..., 24, 1) x2 = lines[..., 1:2, 0] # (..., 24, 1) y2 = lines[..., 1:2, 1] # (..., 24, 1) k = (y2 - y1)/(x2 - x1 + 1e-8) # (..., 24, 1) vec = torch.cat([torch.ones_like(k, dtype=k.dtype, device=k.device), k], dim=-1) # (..., 24, 2) vec = vec.unsqueeze(-2) # (..., 24, 1, 2) points_ext = torch.cat([lines, points], dim=-2) # (..., 24, 8), consider all 8 points den = torch.sum(points_ext * vec, dim=-1) # (..., 24, 8) proj = den / torch.norm(vec, dim=-1, keepdim=False) # (..., 24, 8) proj_max = proj.max(dim=-1)[0] # (..., 24) proj_min = proj.min(dim=-1)[0] # (..., 24) return proj_max - proj_min def smallest_bounding_box(corners:torch.Tensor, verbose=False): """return width and length of the smallest bouding box which encloses two boxes. Args: lines (torch.Tensor): (..., 24, 2, 2) verbose (bool, optional): If True, return area and index. Defaults to False. Returns: (torch.Tensor): width (..., 24) (torch.Tensor): height (..., 24) (torch.Tensor): area (..., ) (torch.Tensor): index of candiatae (..., ) """ lines, points, _, _ = gather_lines_points(corners) proj = point_line_projection_range(lines, points) # (..., 24) dist = point_line_distance_range(lines, points) # (..., 24) area = proj * dist # remove area with 0 when the two points of the line have the same coordinates zero_mask = (area == 0).type(corners.dtype) fake = torch.ones_like(zero_mask, dtype=corners.dtype, device=corners.device)* 1e8 * zero_mask area += fake # add large value to zero_mask area_min, idx = torch.min(area, dim=-1, keepdim=True) # (..., 1) w = torch.gather(proj, dim=-1, index=idx) h = torch.gather(dist, dim=-1, index=idx) # (..., 1) w = w.squeeze(-1).float() h = h.squeeze(-1).float() area_min = area_min.squeeze(-1).float() if verbose: return w, h, area_min, idx.squeeze(-1) else: return w, h if __name__ == "__main__": """ print(LINES.shape) print(POINTS.shape) print(LINES) print(POINTS) """ from utiles import box2corners import matplotlib.pyplot as plt box1 = [0, 0, 2, 3, np.pi/6] box2 = [1, 5, 4, 4, -np.pi/4] corners1 = box2corners(*box1) # 4, 2 corners2 = box2corners(*box2) # 4, 2 tensor1 = torch.FloatTensor(np.concatenate([corners1, corners2], axis=0)) w, h, a, i = smallest_bounding_box(tensor1, True) print("width:", w.item(), ". length:", h.item()) print("area: ", a.item()) print("index in 26 candidates: ", i.item()) print("colliniear with points: ", LINES[i.item()]) plt.scatter(corners1[:, 0], corners1[:, 1]) plt.scatter(corners2[:, 0], corners2[:, 1]) for i in range(corners1.shape[0]): plt.text(corners1[i, 0], corners1[i, 1], str(i)) for i in range(corners2.shape[0]): plt.text(corners2[i, 0], corners2[i, 1], str(i+4)) plt.axis("equal") plt.show()slamd202303071458299/src/lib/models/Rotated_IoU/oriented_iou_loss.py000066400000000000000000000326471435750645400247130ustar00rootroot00000000000000import torch import numpy as np from .box_intersection_2d import oriented_box_intersection_2d from .min_enclosing_box import smallest_bounding_box def box2corners_th(box:torch.Tensor)-> torch.Tensor: """convert box coordinate to corners Args: box (torch.Tensor): (B, N, 5) with x, y, w, h, alpha Returns: torch.Tensor: (B, N, 4, 2) corners """ B = box.size()[0] x = box[..., 0:1] y = box[..., 1:2] w = box[..., 2:3] h = box[..., 3:4] alpha = box[..., 4:5] # (B, N, 1) x4 = torch.FloatTensor([0.5, -0.5, -0.5, 0.5]).unsqueeze(0).unsqueeze(0).to(box.device) # (1,1,4) x4 = x4 * w # (B, N, 4) y4 = torch.FloatTensor([0.5, 0.5, -0.5, -0.5]).unsqueeze(0).unsqueeze(0).to(box.device) y4 = y4 * h # (B, N, 4) corners = torch.stack([x4, y4], dim=-1) # (B, N, 4, 2) sin = torch.sin(alpha) cos = torch.cos(alpha) row1 = torch.cat([cos, sin], dim=-1) row2 = torch.cat([-sin, cos], dim=-1) # (B, N, 2) rot_T = torch.stack([row1, row2], dim=-2) # (B, N, 2, 2) rotated = torch.bmm(corners.view([-1,4,2]), rot_T.view([-1,2,2])) rotated = rotated.view([B,-1,4,2]) # (B*N, 4, 2) -> (B, N, 4, 2) rotated[..., 0] += x rotated[..., 1] += y return rotated def params2corners_th(box)-> torch.Tensor: """convert box coordinate to corners Args: box (torch.Tensor): (B, N, 5) with x, y, w, h, alpha Returns: torch.Tensor: (B, N, 4, 2) corners """ B = box.size()[0] x = box[..., 0:1] y = box[..., 1:2] w = box[..., 2:3] h = box[..., 3:4] alpha = box[..., 4:5] # (B, N, 1) x4 = torch.FloatTensor([0.5, -0.5, -0.5, 0.5]).unsqueeze(0).unsqueeze(0).to(box.device) # (1,1,4) x4 = x4 * w # (B, N, 4) y4 = torch.FloatTensor([0.5, 0.5, -0.5, -0.5]).unsqueeze(0).unsqueeze(0).to(box.device) y4 = y4 * h # (B, N, 4) corners = torch.stack([x4, y4], dim=-1) # (B, N, 4, 2) sin = torch.sin(alpha) cos = torch.cos(alpha) row1 = torch.cat([cos, sin], dim=-1) row2 = torch.cat([-sin, cos], dim=-1) # (B, N, 2) rot_T = torch.stack([row1, row2], dim=-2) # (B, N, 2, 2) rotated = torch.bmm(corners.view([-1,4,2]), rot_T.view([-1,2,2])) rotated = rotated.view([B,-1,4,2]) # (B*N, 4, 2) -> (B, N, 4, 2) rotated[..., 0] += x rotated[..., 1] += y return rotated def params2corners_sincos(box)-> torch.Tensor: """convert box coordinate to corners Args: box (torch.Tensor): (B, N, 6) with x, y, w, h, sin, cos Returns: torch.Tensor: (B, N, 4, 2) corners """ B = box.size()[0] x = box[..., 0:1] y = box[..., 1:2] w = box[..., 2:3] h = box[..., 3:4] sin = box[..., 4:5] cos = box[..., 5:6] # alpha = box[..., 4:5] # (B, N, 1) x4 = torch.FloatTensor([0.5, -0.5, -0.5, 0.5]).unsqueeze(0).unsqueeze(0).to(box.device) # (1,1,4) x4 = x4 * w # (B, N, 4) y4 = torch.FloatTensor([0.5, 0.5, -0.5, -0.5]).unsqueeze(0).unsqueeze(0).to(box.device) y4 = y4 * h # (B, N, 4) corners = torch.stack([x4, y4], dim=-1) # (B, N, 4, 2) row1 = torch.cat([cos, sin], dim=-1) row2 = torch.cat([-sin, cos], dim=-1) # (B, N, 2) rot_T = torch.stack([row1, row2], dim=-2) # (B, N, 2, 2) rotated = torch.bmm(corners.view([-1,4,2]), rot_T.view([-1,2,2])) rotated = rotated.view([B,-1,4,2]) # (B*N, 4, 2) -> (B, N, 4, 2) rotated[..., 0] += x rotated[..., 1] += y return rotated def cal_iou_sincos(box1:torch.Tensor, box2:torch.Tensor): """calculate iou Args: box1 (torch.Tensor): (B, N, 6) # sincos box2 (torch.Tensor): (B, N, 5) # alpha Returns: iou (torch.Tensor): (B, N) corners1 (torch.Tensor): (B, N, 4, 2) corners1 (torch.Tensor): (B, N, 4, 2) U (torch.Tensor): (B, N) area1 + area2 - inter_area """ corners1 = params2corners_sincos(box1) corners2 = box2corners_th(box2) inter_area, _ = oriented_box_intersection_2d(corners1, corners2) #(B, N) area1 = box1[:, :, 2] * box1[:, :, 3] area2 = box2[:, :, 2] * box2[:, :, 3] u = area1 + area2 - inter_area iou = inter_area / u return iou, corners1, corners2, u def cal_iou(box1:torch.Tensor, box2:torch.Tensor): """calculate iou Args: box1 (torch.Tensor): (B, N, 5) box2 (torch.Tensor): (B, N, 5) Returns: iou (torch.Tensor): (B, N) corners1 (torch.Tensor): (B, N, 4, 2) corners1 (torch.Tensor): (B, N, 4, 2) U (torch.Tensor): (B, N) area1 + area2 - inter_area """ corners1 = box2corners_th(box1) corners2 = box2corners_th(box2) inter_area, _ = oriented_box_intersection_2d(corners1, corners2) #(B, N) area1 = box1[:, :, 2] * box1[:, :, 3] area2 = box2[:, :, 2] * box2[:, :, 3] u = area1 + area2 - inter_area iou = inter_area / u return iou, corners1, corners2, u def cal_diou(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"): """calculate diou loss Args: box1 (torch.Tensor): [description] box2 (torch.Tensor): [description] """ iou, corners1, corners2, u = cal_iou(box1, box2) w, h = enclosing_box(corners1, corners2, enclosing_type) c2 = w*w + h*h # (B, N) x_offset = box1[...,0] - box2[..., 0] y_offset = box1[...,1] - box2[..., 1] d2 = x_offset*x_offset + y_offset*y_offset diou_loss = 1. - iou + d2/c2 return diou_loss, iou def cal_diou_sincos(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"): """calculate diou loss Args: box1 (torch.Tensor): [description] box2 (torch.Tensor): [description] """ iou, corners1, corners2, u = cal_iou_sincos(box1, box2) w, h = enclosing_box(corners1, corners2, enclosing_type) c2 = w*w + h*h # (B, N) x_offset = box1[...,0] - box2[..., 0] y_offset = box1[...,1] - box2[..., 1] d2 = x_offset*x_offset + y_offset*y_offset diou_loss = 1. - iou + d2/c2 return diou_loss, iou def cal_giou(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"): iou, corners1, corners2, u = cal_iou(box1, box2) w, h = enclosing_box(corners1, corners2, enclosing_type) area_c = w*h giou_loss = 1. - iou + ( area_c - u )/area_c return giou_loss, iou def cal_iou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, verbose=False): """calculated 3d iou. assume the 3d bounding boxes are only rotated around z axis Args: box3d1 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) box3d2 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) """ box1 = box3d1[..., [0,1,3,4,6]] # 2d box box2 = box3d2[..., [0,1,3,4,6]] zmax1 = box3d1[..., 2] + box3d1[..., 5] * 0.5 zmin1 = box3d1[..., 2] - box3d1[..., 5] * 0.5 zmax2 = box3d2[..., 2] + box3d2[..., 5] * 0.5 zmin2 = box3d2[..., 2] - box3d2[..., 5] * 0.5 z_overlap = (torch.min(zmax1, zmax2) - torch.max(zmin1, zmin2)).clamp_min(0.) iou_2d, corners1, corners2, u = cal_iou(box1, box2) # (B, N) intersection_3d = iou_2d * u * z_overlap v1 = box3d1[..., 3] * box3d1[..., 4] * box3d1[..., 5] v2 = box3d2[..., 3] * box3d2[..., 4] * box3d2[..., 5] u3d = v1 + v2 - intersection_3d if verbose: z_range = (torch.max(zmax1, zmax2) - torch.min(zmin1, zmin2)).clamp_min(0.) return intersection_3d / u3d, corners1, corners2, z_range, u3d else: return intersection_3d / u3d def cal_giou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, enclosing_type:str="smallest"): """calculated 3d GIoU loss. assume the 3d bounding boxes are only rotated around z axis Args: box3d1 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) box3d2 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) enclosing_type (str, optional): type of enclosing box. Defaults to "smallest". Returns: (torch.Tensor): (B, N) 3d GIoU loss (torch.Tensor): (B, N) 3d IoU """ iou3d, corners1, corners2, z_range, u3d = cal_iou_3d(box3d1, box3d2, verbose=True) w, h = enclosing_box(corners1, corners2, enclosing_type) v_c = z_range * w * h giou_loss = 1. - iou3d + (v_c - u3d)/v_c return giou_loss, iou3d def cal_diou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, enclosing_type:str="smallest"): """calculated 3d DIoU loss. assume the 3d bounding boxes are only rotated around z axis Args: box3d1 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) box3d2 (torch.Tensor): (B, N, 3+3+1), (x,y,z,w,h,l,alpha) enclosing_type (str, optional): type of enclosing box. Defaults to "smallest". Returns: (torch.Tensor): (B, N) 3d DIoU loss (torch.Tensor): (B, N) 3d IoU """ iou3d, corners1, corners2, z_range, u3d = cal_iou_3d(box3d1, box3d2, verbose=True) w, h = enclosing_box(corners1, corners2, enclosing_type) x_offset = box3d1[...,0] - box3d2[..., 0] y_offset = box3d1[...,1] - box3d2[..., 1] z_offset = box3d1[...,2] - box3d2[..., 2] d2 = x_offset*x_offset + y_offset*y_offset + z_offset*z_offset c2 = w*w + h*h + z_range*z_range diou = 1. - iou3d + d2/c2 return diou, iou3d def enclosing_box(corners1:torch.Tensor, corners2:torch.Tensor, enclosing_type:str="smallest"): if enclosing_type == "aligned": return enclosing_box_aligned(corners1, corners2) elif enclosing_type == "pca": return enclosing_box_pca(corners1, corners2) elif enclosing_type == "smallest": return smallest_bounding_box(torch.cat([corners1, corners2], dim=-2)) else: ValueError("Unknow type enclosing. Supported: aligned, pca, smallest") def enclosing_box_aligned(corners1:torch.Tensor, corners2:torch.Tensor): """calculate the smallest enclosing box (axis-aligned) Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) Returns: w (torch.Tensor): (B, N) h (torch.Tensor): (B, N) """ x1_max = torch.max(corners1[..., 0], dim=2)[0] # (B, N) x1_min = torch.min(corners1[..., 0], dim=2)[0] # (B, N) y1_max = torch.max(corners1[..., 1], dim=2)[0] y1_min = torch.min(corners1[..., 1], dim=2)[0] x2_max = torch.max(corners2[..., 0], dim=2)[0] # (B, N) x2_min = torch.min(corners2[..., 0], dim=2)[0] # (B, N) y2_max = torch.max(corners2[..., 1], dim=2)[0] y2_min = torch.min(corners2[..., 1], dim=2)[0] x_max = torch.max(x1_max, x2_max) x_min = torch.min(x1_min, x2_min) y_max = torch.max(y1_max, y2_max) y_min = torch.min(y1_min, y2_min) w = x_max - x_min # (B, N) h = y_max - y_min return w, h def enclosing_box_pca(corners1:torch.Tensor, corners2:torch.Tensor): """calculate the rotated smallest enclosing box using PCA Args: corners1 (torch.Tensor): (B, N, 4, 2) corners2 (torch.Tensor): (B, N, 4, 2) Returns: w (torch.Tensor): (B, N) h (torch.Tensor): (B, N) """ B = corners1.size()[0] c = torch.cat([corners1, corners2], dim=2) # (B, N, 8, 2) c = c - torch.mean(c, dim=2, keepdim=True) # normalization c = c.view([-1, 8, 2]) # (B*N, 8, 2) ct = c.transpose(1, 2) # (B*N, 2, 8) ctc = torch.bmm(ct, c) # (B*N, 2, 2) # NOTE: the build in symeig is slow! # _, v = ctc.symeig(eigenvectors=True) # v1 = v[:, 0, :].unsqueeze(1) # v2 = v[:, 1, :].unsqueeze(1) v1, v2 = eigenvector_22(ctc) v1 = v1.unsqueeze(1) # (B*N, 1, 2), eigen value v2 = v2.unsqueeze(1) p1 = torch.sum(c * v1, dim=-1) # (B*N, 8), first principle component p2 = torch.sum(c * v2, dim=-1) # (B*N, 8), second principle component w = p1.max(dim=-1)[0] - p1.min(dim=-1)[0] # (B*N, ), width of rotated enclosing box h = p2.max(dim=-1)[0] - p2.min(dim=-1)[0] # (B*N, ), height of rotated enclosing box return w.view([B, -1]), h.view([B, -1]) def eigenvector_22(x:torch.Tensor): """return eigenvector of 2x2 symmetric matrix using closed form https://math.stackexchange.com/questions/8672/eigenvalues-and-eigenvectors-of-2-times-2-matrix The calculation is done by using double precision Args: x (torch.Tensor): (..., 2, 2), symmetric, semi-definite Return: v1 (torch.Tensor): (..., 2) v2 (torch.Tensor): (..., 2) """ # NOTE: must use doule precision here! with float the back-prop is very unstable a = x[..., 0, 0].double() c = x[..., 0, 1].double() b = x[..., 1, 1].double() # (..., ) delta = torch.sqrt(a*a + 4*c*c - 2*a*b + b*b) v1 = (a - b - delta) / 2. /c v1 = torch.stack([v1, torch.ones_like(v1, dtype=torch.double, device=v1.device)], dim=-1) # (..., 2) v2 = (a - b + delta) / 2. /c v2 = torch.stack([v2, torch.ones_like(v2, dtype=torch.double, device=v2.device)], dim=-1) # (..., 2) n1 = torch.sum(v1*v1, keepdim=True, dim=-1).sqrt() n2 = torch.sum(v2*v2, keepdim=True, dim=-1).sqrt() v1 = v1 / n1 v2 = v2 / n2 return v1.float(), v2.float() if __name__ == "__main__": box3d1 = np.array([0,0,0,3,3,3,0]) box3d2 = np.array([1,1,1,2,2,2,np.pi/3]) tensor1 = torch.FloatTensor(box3d1).unsqueeze(0).unsqueeze(0).cuda() tensor2 = torch.FloatTensor(box3d2).unsqueeze(0).unsqueeze(0).cuda() giou_loss, iou = cal_giou_3d(tensor1, tensor1) print(giou_loss) print(iou) slamd202303071458299/src/lib/models/Rotated_IoU/readme.md000066400000000000000000000112701435750645400223600ustar00rootroot00000000000000# Differentiable IoU of Oriented Boxes ![image](image/iou.png "possible shape of IoU") (image source: [here](https://stackoverflow.com/questions/11670028/area-of-intersection-of-two-rotated-rectangles)) ## Introduction This repo is an unofficial implementation of [IoU Loss for 2D/3D Object Detection](https://arxiv.org/pdf/1908.03851.pdf). It contains the Pytorch function which calculates the intersection area of oriented rectangles using GPU. ## Note The CUDA extension is modified recently to cover some corner cases. Please consider to update the code and re-compile the extension. ## Check List - [x] Pytorch function to find intersection points of oriented rectangles - [x] Pytorch function to check if corners of one rectangle lie in another - [x] CUDA extension to anti-clockwise sort vertices of the intersection polygon of two rectangles - [x] Pytorch function to calculate the intersection of area of rectangles using functions above - [x] Test cases - [x] Rotated 2d/3d GIoU and DIoU loss - [x] Demo to validate the back-propagation - [ ] Validate 2d/3d IoU loss in Object detection ## Requirements Code is tested on Ubuntu 18.04. Following dependencies are needed cudatoolkit=10.2 pytorch=1.5 # newer version should work as well numpy matplotlib argparse ## Usage First, compile the CUDA extension. cd cuda_op python setup.py install Then, run a demo which validate the Pytorch functions and CUDA extension. cd .. python demo.py This demo trains a network which takes N set of box corners and predicts the `x, y, w, h` and `angle` of each rotated boxes. In order to do the back-prop, the predicted box parameters and the GT are converted to coordinates of box corners. The area of intersection is calculated using the Pytorch function with CUDA extension. Then, the GIoU loss or DIoU loss can be calculated. This demo first generates data and then do the training. You are expected to see some information like followings: ... generating 819200 boxes, please wait... ... generating 81920 boxes, please wait... data saved in: ./data [Epoch 1: 10/200] train loss: 0.6721 mean_iou: 0.3703 [Epoch 1: 20/200] train loss: 0.4931 mean_iou: 0.5211 [Epoch 1: 30/200] train loss: 0.4532 mean_iou: 0.5546 [Epoch 1: 40/200] train loss: 0.4249 mean_iou: 0.5805 [Epoch 1: 50/200] train loss: 0.4361 mean_iou: 0.5713 [Epoch 1: 60/200] train loss: 0.4148 mean_iou: 0.5910 [Epoch 1: 70/200] train loss: 0.4272 mean_iou: 0.5803 [Epoch 1: 80/200] train loss: 0.4283 mean_iou: 0.5801 [Epoch 1: 90/200] train loss: 0.4203 mean_iou: 0.5879 Note the `train loss` drops and the `mean_iou` increases, which shows the functions are differentiable. ## GIoU and DIoU This repo implements both [GIoU-loss](https://giou.stanford.edu/GIoU.pdf) and [DIoU-loss](https://arxiv.org/abs/1911.08287) for rotated bounding boxes. In the demo, they can be chosen with python demo.py --loss giou python demo.py --loss diou # [default] Both losses need the smallest enclosing box of two boxes. Note there are different choices to determin the enclosing box. 1. axis-aligned box: the enclosing box is axis-aligned. This version is simple and fast but theortically non-optimal. 2. rotated box (approximated): the enclosing box is rotated as well. The size of rotated enclosing box can be estimated using [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis). The calculation if relatively simple but the result is not accurate. In the demo, this methode seems work well. 3. rotated box (accurate): real [smallest enclosing bounding box](https://en.wikipedia.org/wiki/Minimum_bounding_box). Since the brutal force search is used to get the minimum bounding box, the computational cost is high. The three types of enclosing box can be chosen with: python demo.py --enclosing aligned # simple and naive. bad performance. python demo.py --enclosing pca # approximated smallest box. slightly worse performance. python demo.py --enclosing smallest # [default]. smallest box. best performance. ## Acknowledgement The idea of calculating intersection area is inspired by this paper: @INPROCEEDINGS{8886046, author={D. {Zhou} and J. {Fang} and X. {Song} and C. {Guan} and J. {Yin} and Y. {Dai} and R. {Yang}}, booktitle={2019 International Conference on 3D Vision (3DV)}, title={IoU Loss for 2D/3D Object Detection}, year={2019}, pages={85-94},} Some code for CUDA extension is modified from: @article{pytorchpointnet++, Author = {Erik Wijmans}, Title = {Pointnet++ Pytorch}, Journal = {https://github.com/erikwijmans/Pointnet2_PyTorch}, Year = {2018} } slamd202303071458299/src/lib/models/Rotated_IoU/scripts/000077500000000000000000000000001435750645400222675ustar00rootroot00000000000000slamd202303071458299/src/lib/models/Rotated_IoU/scripts/Test rotaed iou.ipynb000066400000000000000000001161151435750645400262720ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "if '..' not in sys.path:\n", " sys.path.append('..')\n", "\n", "import torch\n", "import numpy as np\n", "from torch.utils.data import DataLoader, Dataset\n", "from torch import nn, optim\n", "import argparse\n", "from utiles import box2corners\n", "from oriented_iou_loss import cal_diou, cal_giou" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "DATA = \"./data\"\n", "X_MAX = 3\n", "Y_MAX = 3\n", "SCALE = 0.5\n", "BATCH_SIZE = 32\n", "N_DATA = 128\n", "NUM_TRAIN = 200 * BATCH_SIZE * N_DATA\n", "NUM_TEST = 20 * BATCH_SIZE * N_DATA\n", "\n", "NUM_EPOCH = 20\n", "\n", "if not os.path.exists(DATA):\n", " os.mkdir(DATA)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def create_data(num):\n", " print(\"... generating %d boxes, please wait...\"%num)\n", " x = (np.random.rand(num) - 0.5) * 2 * X_MAX\n", " y = (np.random.rand(num) - 0.5) * 2 * Y_MAX\n", " w = (np.random.rand(num) - 0.5) * 2 * SCALE + 1\n", " h = (np.random.rand(num) - 0.5) * 2 * SCALE + 1\n", " alpha = np.random.rand(num) * np.pi\n", " corners = np.zeros((num, 4, 2)).astype(np.float)\n", " for i in range(num):\n", " corners[i, ...] = box2corners(x[i], y[i], w[i], h[i], alpha[i])\n", " label = np.stack([x, y , w, h, alpha], axis=1)\n", " return corners, label\n", "\n", "def save_dataset():\n", " train_data, train_label = create_data(NUM_TRAIN)\n", " np.save(os.path.join(DATA, \"train_data.npy\"), train_data)\n", " np.save(os.path.join(DATA, \"train_label.npy\"), train_label)\n", " test_data, test_label = create_data(NUM_TEST)\n", " np.save(os.path.join(DATA, \"test_data.npy\"), test_data)\n", " np.save(os.path.join(DATA, \"test_label.npy\"), test_label)\n", " print(\"data saved in: \", DATA)\n", "\n", "class BoxDataSet(Dataset):\n", " def __init__(self, split=\"train\"):\n", " super(BoxDataSet, self).__init__()\n", " assert split in [\"train\", \"test\"], \"split must be train or test\"\n", " self.split = split\n", " try:\n", " self.data = np.load(os.path.join(DATA, split+\"_data.npy\"))\n", " self.label = np.load(os.path.join(DATA, split+\"_label.npy\"))\n", " except:\n", " save_dataset()\n", " self.data = np.load(os.path.join(DATA, split+\"_data.npy\"))\n", " self.label = np.load(os.path.join(DATA, split+\"_label.npy\"))\n", " def __len__(self) -> int:\n", " return self.data.shape[0]\n", " def __getitem__(self, index: int) :\n", " d = self.data[index, ...]\n", " l = self.label[index, ...]\n", " return torch.FloatTensor(d), torch.FloatTensor(l)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def create_network():\n", " return nn.Sequential(nn.Conv1d(8, 128, 1, bias=False),\n", " nn.BatchNorm1d(128),\n", " nn.ReLU(True),\n", " nn.Conv1d(128, 512, 1, bias=False),\n", " nn.BatchNorm1d(512),\n", " nn.ReLU(True),\n", " nn.Conv1d(512, 128, 1, bias=False),\n", " nn.BatchNorm1d(128),\n", " nn.ReLU(True),\n", " nn.Conv1d(128, 5, 1),\n", " nn.Sigmoid())\n", "\n", "def parse_pred(pred:torch.Tensor):\n", " p0 = (pred[..., 0] - 0.5) * 2 * X_MAX\n", " p1 = (pred[..., 1] - 0.5) * 2 * Y_MAX\n", " p2 = (pred[..., 2] - 0.5) * 2 * SCALE + 1\n", " p3 = (pred[..., 3] - 0.5) * 2 * SCALE + 1\n", " p4 = pred[..., 4] * np.pi\n", " return torch.stack([p0,p1,p2,p3,p4], dim=-1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def main(loss_type:str=\"giou\", enclosing_type:str=\"aligned\"):\n", " ds_train = BoxDataSet(\"train\")\n", " ds_test = BoxDataSet(\"test\")\n", " ld_train = DataLoader(ds_train, BATCH_SIZE * N_DATA, shuffle=True, num_workers=4)\n", " ld_test = DataLoader(ds_test, BATCH_SIZE * N_DATA, shuffle=False, num_workers=4)\n", " \n", " net = create_network()\n", " net.to(\"cuda:0\")\n", " optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n", " lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n", " num_batch = len(ds_train)//(BATCH_SIZE*N_DATA)\n", " \n", " for epoch in range(1, NUM_EPOCH+1):\n", " # train\n", " net.train()\n", " for i, data in enumerate(ld_train, 1):\n", " box, label = data\n", " box = box.cuda() # (B*N, 4, 2)\n", " box = box.view([BATCH_SIZE, -1, 4*2]) # (B, N, 4*2)\n", " box = box.transpose(1, 2) # (B, 8, N)\n", " label = label.cuda() # (B*N, 5)\n", " label = label.view([BATCH_SIZE, -1, 5]) # (B, N, 5)\n", " \n", " optimizer.zero_grad()\n", " pred = net(box) # (B, 5, N)\n", " pred = pred.transpose(1,2) # (B, N, 5)\n", " pred = parse_pred(pred)\n", "\n", " iou_loss, iou = None, None\n", " if loss_type == \"giou\":\n", " iou_loss, iou = cal_giou(pred, label, enclosing_type)\n", " elif loss_type == \"diou\":\n", " iou_loss, iou = cal_diou(pred, label, enclosing_type)\n", " else:\n", " ValueError(\"unknown loss type\")\n", " iou_loss = torch.mean(iou_loss)\n", " iou_loss.backward()\n", " optimizer.step()\n", "\n", " if i%10 == 0:\n", " iou_mask = (iou > 0).float()\n", " mean_iou = torch.sum(iou) / (torch.sum(iou_mask) + 1e-8)\n", " print(\"[Epoch %d: %d/%d] train loss: %.4f mean_iou: %.4f\"\n", " %(epoch, i, num_batch, iou_loss.detach().cpu().item(), mean_iou.detach().cpu().item()))\n", " lr_scheduler.step()\n", "\n", " # validate\n", " net.eval()\n", " aver_loss = 0\n", " aver_mean_iou = 0\n", " with torch.no_grad():\n", " for i, data in enumerate(ld_test, 1):\n", " box, label = data\n", " box = box.cuda() # (B*N, 4, 2)\n", " box = box.view([BATCH_SIZE, -1, 4*2]) # (B, N, 4*2)\n", " box = box.transpose(1, 2) # (B, 8, N)\n", " label = label.cuda() # (B*N, 5)\n", " label = label.view([BATCH_SIZE, -1, 5]) # (B, N, 5)\n", " \n", " pred = net(box) # (B, 5, N)\n", " pred = pred.transpose(1,2) # (B, N, 5)\n", " pred = parse_pred(pred)\n", "\n", " iou_loss, iou = None, None\n", " if loss_type == \"giou\":\n", " iou_loss, iou = cal_giou(pred, label, enclosing_type)\n", " elif loss_type == \"diou\":\n", " iou_loss, iou = cal_diou(pred, label, enclosing_type)\n", " else:\n", " ValueError(\"unknown loss type\")\n", " iou_loss = torch.mean(iou_loss)\n", " aver_loss += iou_loss.cpu().item()\n", " iou_mask = (iou > 0).float()\n", " mean_iou = torch.sum(iou) / (torch.sum(iou_mask) + 1e-8)\n", " aver_mean_iou += mean_iou.cpu().item()\n", " print(\"... validate epoch %d ...\"%epoch)\n", " n_iter = len(ds_test)/BATCH_SIZE/N_DATA\n", " print(\"average loss: %.4f\"%(aver_loss/n_iter))\n", " print(\"average iou: %.4f\"%(aver_mean_iou/n_iter))\n", " print(\"..............................\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "... generating 819200 boxes, please wait...\n", "... generating 81920 boxes, please wait...\n", "data saved in: ./data\n", "[Epoch 1: 10/200] train loss: 0.6570 mean_iou: 0.3814\n", "[Epoch 1: 20/200] train loss: 0.5029 mean_iou: 0.5106\n", "[Epoch 1: 30/200] train loss: 0.4490 mean_iou: 0.5581\n", "[Epoch 1: 40/200] train loss: 0.4389 mean_iou: 0.5680\n", "[Epoch 1: 50/200] train loss: 0.4135 mean_iou: 0.5910\n", "[Epoch 1: 60/200] train loss: 0.4179 mean_iou: 0.5879\n", "[Epoch 1: 70/200] train loss: 0.4249 mean_iou: 0.5827\n", "[Epoch 1: 80/200] train loss: 0.4280 mean_iou: 0.5803\n", "[Epoch 1: 90/200] train loss: 0.3796 mean_iou: 0.6244\n", "[Epoch 1: 100/200] train loss: 0.3888 mean_iou: 0.6174\n", "[Epoch 1: 110/200] train loss: 0.3506 mean_iou: 0.6527\n", "[Epoch 1: 120/200] train loss: 0.3598 mean_iou: 0.6450\n", "[Epoch 1: 130/200] train loss: 0.4119 mean_iou: 0.5988\n", "[Epoch 1: 140/200] train loss: 0.3848 mean_iou: 0.6230\n", "[Epoch 1: 150/200] train loss: 0.4175 mean_iou: 0.5945\n", "[Epoch 1: 160/200] train loss: 0.3684 mean_iou: 0.6382\n", "[Epoch 1: 170/200] train loss: 0.3532 mean_iou: 0.6518\n", "[Epoch 1: 180/200] train loss: 0.3843 mean_iou: 0.6241\n", "[Epoch 1: 190/200] train loss: 0.3561 mean_iou: 0.6501\n", "[Epoch 1: 200/200] train loss: 0.3459 mean_iou: 0.6598\n", "... validate epoch 1 ...\n", "average loss: 0.3515\n", "average iou: 0.6548\n", "..............................\n", "[Epoch 2: 10/200] train loss: 0.3817 mean_iou: 0.6274\n", "[Epoch 2: 20/200] train loss: 0.3371 mean_iou: 0.6689\n", "[Epoch 2: 30/200] train loss: 0.3172 mean_iou: 0.6871\n", "[Epoch 2: 40/200] train loss: 0.3007 mean_iou: 0.7024\n", "[Epoch 2: 50/200] train loss: 0.4187 mean_iou: 0.5943\n", "[Epoch 2: 60/200] train loss: 0.3543 mean_iou: 0.6541\n", "[Epoch 2: 70/200] train loss: 0.3077 mean_iou: 0.6974\n", "[Epoch 2: 80/200] train loss: 0.3135 mean_iou: 0.6920\n", "[Epoch 2: 90/200] train loss: 0.2802 mean_iou: 0.7231\n", "[Epoch 2: 100/200] train loss: 0.3184 mean_iou: 0.6879\n", "[Epoch 2: 110/200] train loss: 0.3856 mean_iou: 0.6263\n", "[Epoch 2: 120/200] train loss: 0.3221 mean_iou: 0.6849\n", "[Epoch 2: 130/200] train loss: 0.3558 mean_iou: 0.6535\n", "[Epoch 2: 140/200] train loss: 0.3690 mean_iou: 0.6415\n", "[Epoch 2: 150/200] train loss: 0.3319 mean_iou: 0.6760\n", "[Epoch 2: 160/200] train loss: 0.2850 mean_iou: 0.7190\n", "[Epoch 2: 170/200] train loss: 0.3546 mean_iou: 0.6551\n", "[Epoch 2: 180/200] train loss: 0.3736 mean_iou: 0.6374\n", "[Epoch 2: 190/200] train loss: 0.3568 mean_iou: 0.6527\n", "[Epoch 2: 200/200] train loss: 0.3866 mean_iou: 0.6263\n", "... validate epoch 2 ...\n", "average loss: 0.3000\n", "average iou: 0.7064\n", "..............................\n", "[Epoch 3: 10/200] train loss: 0.3242 mean_iou: 0.6832\n", "[Epoch 3: 20/200] train loss: 0.4593 mean_iou: 0.5597\n", "[Epoch 3: 30/200] train loss: 0.3375 mean_iou: 0.6698\n", "[Epoch 3: 40/200] train loss: 0.3929 mean_iou: 0.6206\n", "[Epoch 3: 50/200] train loss: 0.3437 mean_iou: 0.6647\n", "[Epoch 3: 60/200] train loss: 0.2854 mean_iou: 0.7197\n", "[Epoch 3: 70/200] train loss: 0.3194 mean_iou: 0.6880\n", "[Epoch 3: 80/200] train loss: 0.3113 mean_iou: 0.6955\n", "[Epoch 3: 90/200] train loss: 0.3275 mean_iou: 0.6812\n", "[Epoch 3: 100/200] train loss: 0.4332 mean_iou: 0.5836\n", "[Epoch 3: 110/200] train loss: 0.3225 mean_iou: 0.6852\n", "[Epoch 3: 120/200] train loss: 0.2877 mean_iou: 0.7176\n", "[Epoch 3: 130/200] train loss: 0.3813 mean_iou: 0.6313\n", "[Epoch 3: 140/200] train loss: 0.3497 mean_iou: 0.6604\n", "[Epoch 3: 150/200] train loss: 0.3427 mean_iou: 0.6663\n", "[Epoch 3: 160/200] train loss: 0.2867 mean_iou: 0.7189\n", "[Epoch 3: 170/200] train loss: 0.2933 mean_iou: 0.7128\n", "[Epoch 3: 180/200] train loss: 0.3301 mean_iou: 0.6782\n", "[Epoch 3: 190/200] train loss: 0.2909 mean_iou: 0.7148\n", "[Epoch 3: 200/200] train loss: 0.3287 mean_iou: 0.6802\n", "... validate epoch 3 ...\n", "average loss: 0.2968\n", "average iou: 0.7099\n", "..............................\n", "[Epoch 4: 10/200] train loss: 0.4414 mean_iou: 0.5756\n", "[Epoch 4: 20/200] train loss: 0.3645 mean_iou: 0.6468\n", "[Epoch 4: 30/200] train loss: 0.2850 mean_iou: 0.7200\n", "[Epoch 4: 40/200] train loss: 0.2774 mean_iou: 0.7282\n", "[Epoch 4: 50/200] train loss: 0.2550 mean_iou: 0.7489\n", "[Epoch 4: 60/200] train loss: 0.2708 mean_iou: 0.7341\n", "[Epoch 4: 70/200] train loss: 0.2699 mean_iou: 0.7349\n", "[Epoch 4: 80/200] train loss: 0.2856 mean_iou: 0.7204\n", "[Epoch 4: 90/200] train loss: 0.3431 mean_iou: 0.6667\n", "[Epoch 4: 100/200] train loss: 0.2744 mean_iou: 0.7311\n", "[Epoch 4: 110/200] train loss: 0.2648 mean_iou: 0.7404\n", "[Epoch 4: 120/200] train loss: 0.2645 mean_iou: 0.7406\n", "[Epoch 4: 130/200] train loss: 0.2253 mean_iou: 0.7776\n", "[Epoch 4: 140/200] train loss: 0.2825 mean_iou: 0.7231\n", "[Epoch 4: 150/200] train loss: 0.3988 mean_iou: 0.6161\n", "[Epoch 4: 160/200] train loss: 0.3016 mean_iou: 0.7057\n", "[Epoch 4: 170/200] train loss: 0.3240 mean_iou: 0.6837\n", "[Epoch 4: 180/200] train loss: 0.4013 mean_iou: 0.6138\n", "[Epoch 4: 190/200] train loss: 0.3149 mean_iou: 0.6927\n", "[Epoch 4: 200/200] train loss: 0.2344 mean_iou: 0.7688\n", "... validate epoch 4 ...\n", "average loss: 0.2809\n", "average iou: 0.7258\n", "..............................\n", "[Epoch 5: 10/200] train loss: 0.2442 mean_iou: 0.7598\n", "[Epoch 5: 20/200] train loss: 0.3113 mean_iou: 0.6972\n", "[Epoch 5: 30/200] train loss: 0.2774 mean_iou: 0.7281\n", "[Epoch 5: 40/200] train loss: 0.4667 mean_iou: 0.5560\n", "[Epoch 5: 50/200] train loss: 0.3947 mean_iou: 0.6208\n", "[Epoch 5: 60/200] train loss: 0.3382 mean_iou: 0.6710\n", "[Epoch 5: 70/200] train loss: 0.2938 mean_iou: 0.7139\n", "[Epoch 5: 80/200] train loss: 0.3134 mean_iou: 0.6950\n", "[Epoch 5: 90/200] train loss: 0.4168 mean_iou: 0.6012\n", "[Epoch 5: 100/200] train loss: 0.3273 mean_iou: 0.6808\n", "[Epoch 5: 110/200] train loss: 0.3183 mean_iou: 0.6897\n", "[Epoch 5: 120/200] train loss: 0.2916 mean_iou: 0.7149\n", "[Epoch 5: 130/200] train loss: 0.2401 mean_iou: 0.7635\n", "[Epoch 5: 140/200] train loss: 0.2881 mean_iou: 0.7186\n", "[Epoch 5: 150/200] train loss: 0.2365 mean_iou: 0.7673\n", "[Epoch 5: 160/200] train loss: 0.2462 mean_iou: 0.7584\n", "[Epoch 5: 170/200] train loss: 0.2883 mean_iou: 0.7184\n", "[Epoch 5: 180/200] train loss: 0.3567 mean_iou: 0.6539\n", "[Epoch 5: 190/200] train loss: 0.2558 mean_iou: 0.7487\n", "[Epoch 5: 200/200] train loss: 0.3290 mean_iou: 0.6811\n", "... validate epoch 5 ...\n", "average loss: 0.2815\n", "average iou: 0.7266\n", "..............................\n", "[Epoch 6: 10/200] train loss: 0.2657 mean_iou: 0.7406\n", "[Epoch 6: 20/200] train loss: 0.2865 mean_iou: 0.7205\n", "[Epoch 6: 30/200] train loss: 0.2597 mean_iou: 0.7464\n", "[Epoch 6: 40/200] train loss: 0.2820 mean_iou: 0.7248\n", "[Epoch 6: 50/200] train 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0.7580\n", "..............................\n", "[Epoch 7: 10/200] train loss: 0.3256 mean_iou: 0.6838\n", "[Epoch 7: 20/200] train loss: 0.3512 mean_iou: 0.6605\n", "[Epoch 7: 30/200] train loss: 0.2241 mean_iou: 0.7794\n", "[Epoch 7: 40/200] train loss: 0.2291 mean_iou: 0.7741\n", "[Epoch 7: 50/200] train loss: 0.2530 mean_iou: 0.7521\n", "[Epoch 7: 60/200] train loss: 0.4363 mean_iou: 0.5842\n", "[Epoch 7: 70/200] train loss: 0.2447 mean_iou: 0.7609\n", "[Epoch 7: 80/200] train loss: 0.3204 mean_iou: 0.6889\n", "[Epoch 7: 90/200] train loss: 0.2436 mean_iou: 0.7605\n", "[Epoch 7: 100/200] train loss: 0.3611 mean_iou: 0.6517\n", "[Epoch 7: 110/200] train loss: 0.2902 mean_iou: 0.7169\n", "[Epoch 7: 120/200] train loss: 0.2081 mean_iou: 0.7950\n", "[Epoch 7: 130/200] train loss: 0.2992 mean_iou: 0.7083\n", "[Epoch 7: 140/200] train loss: 0.2386 mean_iou: 0.7663\n", "[Epoch 7: 150/200] train loss: 0.2872 mean_iou: 0.7203\n", "[Epoch 7: 160/200] train loss: 0.3627 mean_iou: 0.6496\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[Epoch 7: 170/200] train loss: 0.3723 mean_iou: 0.6414\n", "[Epoch 7: 180/200] train loss: 0.2888 mean_iou: 0.7181\n", "[Epoch 7: 190/200] train loss: 0.2551 mean_iou: 0.7504\n", "[Epoch 7: 200/200] train loss: 0.3012 mean_iou: 0.7067\n", "... validate epoch 7 ...\n", "average loss: 0.3280\n", "average iou: 0.6830\n", "..............................\n", "[Epoch 8: 10/200] train loss: 0.5161 mean_iou: 0.5107\n", "[Epoch 8: 20/200] train loss: 0.3308 mean_iou: 0.6788\n", "[Epoch 8: 30/200] train loss: 0.4051 mean_iou: 0.6104\n", "[Epoch 8: 40/200] train loss: 0.2241 mean_iou: 0.7799\n", "[Epoch 8: 50/200] train loss: 0.2678 mean_iou: 0.7384\n", "[Epoch 8: 60/200] train loss: 0.2064 mean_iou: 0.7969\n", "[Epoch 8: 70/200] train loss: 0.2512 mean_iou: 0.7544\n", "[Epoch 8: 80/200] train loss: 0.3026 mean_iou: 0.7062\n", "[Epoch 8: 90/200] train loss: 0.2884 mean_iou: 0.7186\n", "[Epoch 8: 100/200] train loss: 0.3721 mean_iou: 0.6408\n", "[Epoch 8: 110/200] train loss: 0.3391 mean_iou: 0.6710\n", "[Epoch 8: 120/200] train loss: 0.2536 mean_iou: 0.7518\n", "[Epoch 8: 130/200] train loss: 0.2490 mean_iou: 0.7562\n", "[Epoch 8: 140/200] train loss: 0.3302 mean_iou: 0.6796\n", "[Epoch 8: 150/200] train loss: 0.3362 mean_iou: 0.6753\n", "[Epoch 8: 160/200] train loss: 0.3780 mean_iou: 0.6371\n", "[Epoch 8: 170/200] train loss: 0.2507 mean_iou: 0.7552\n", "[Epoch 8: 180/200] train loss: 0.3149 mean_iou: 0.6935\n", "[Epoch 8: 190/200] train loss: 0.3304 mean_iou: 0.6794\n", "[Epoch 8: 200/200] train loss: 0.2501 mean_iou: 0.7558\n", "... validate epoch 8 ...\n", "average loss: 0.3262\n", "average iou: 0.6845\n", "..............................\n", "[Epoch 9: 10/200] train loss: 0.3694 mean_iou: 0.6441\n", "[Epoch 9: 20/200] train loss: 0.3690 mean_iou: 0.6442\n", "[Epoch 9: 30/200] train loss: 0.3613 mean_iou: 0.6518\n", "[Epoch 9: 40/200] train loss: 0.3613 mean_iou: 0.6519\n", "[Epoch 9: 50/200] train loss: 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0.7720\n", "..............................\n", "[Epoch 10: 10/200] train loss: 0.2276 mean_iou: 0.7767\n", "[Epoch 10: 20/200] train loss: 0.1918 mean_iou: 0.8110\n", "[Epoch 10: 30/200] train loss: 0.3921 mean_iou: 0.6230\n", "[Epoch 10: 40/200] train loss: 0.1873 mean_iou: 0.8152\n", "[Epoch 10: 50/200] train loss: 0.3112 mean_iou: 0.6970\n", "[Epoch 10: 60/200] train loss: 0.2639 mean_iou: 0.7423\n", "[Epoch 10: 70/200] train loss: 0.3020 mean_iou: 0.7055\n", "[Epoch 10: 80/200] train loss: 0.2271 mean_iou: 0.7771\n", "[Epoch 10: 90/200] train loss: 0.2193 mean_iou: 0.7847\n", "[Epoch 10: 100/200] train loss: 0.3400 mean_iou: 0.6702\n", "[Epoch 10: 110/200] train loss: 0.2747 mean_iou: 0.7316\n", "[Epoch 10: 120/200] train loss: 0.2030 mean_iou: 0.8001\n", "[Epoch 10: 130/200] train loss: 0.2870 mean_iou: 0.7206\n", "[Epoch 10: 140/200] train loss: 0.2081 mean_iou: 0.7952\n", "[Epoch 10: 150/200] train loss: 0.2227 mean_iou: 0.7809\n", "[Epoch 10: 160/200] train loss: 0.2707 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iou: 0.8850\n", "..............................\n", "[Epoch 13: 10/200] train loss: 0.1252 mean_iou: 0.8757\n", "[Epoch 13: 20/200] train loss: 0.2185 mean_iou: 0.7853\n", "[Epoch 13: 30/200] train loss: 0.1559 mean_iou: 0.8459\n", "[Epoch 13: 40/200] train loss: 0.1475 mean_iou: 0.8539\n", "[Epoch 13: 50/200] train loss: 0.1514 mean_iou: 0.8501\n", "[Epoch 13: 60/200] train loss: 0.1626 mean_iou: 0.8392\n", "[Epoch 13: 70/200] train loss: 0.1483 mean_iou: 0.8532\n", "[Epoch 13: 80/200] train loss: 0.1282 mean_iou: 0.8728\n", "[Epoch 13: 90/200] train loss: 0.1598 mean_iou: 0.8419\n", "[Epoch 13: 100/200] train loss: 0.2023 mean_iou: 0.8007\n", "[Epoch 13: 110/200] train loss: 0.2211 mean_iou: 0.7826\n", "[Epoch 13: 120/200] train loss: 0.1825 mean_iou: 0.8199\n", "[Epoch 13: 130/200] train loss: 0.1472 mean_iou: 0.8543\n", "[Epoch 13: 140/200] train loss: 0.1839 mean_iou: 0.8186\n", "[Epoch 13: 150/200] train loss: 0.1557 mean_iou: 0.8462\n", "[Epoch 13: 160/200] train loss: 0.1372 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"stdout", "output_type": "stream", "text": [ "[Epoch 14: 110/200] train loss: 0.1465 mean_iou: 0.8549\n", "[Epoch 14: 120/200] train loss: 0.1665 mean_iou: 0.8356\n", "[Epoch 14: 130/200] train loss: 0.1784 mean_iou: 0.8240\n", "[Epoch 14: 140/200] train loss: 0.1548 mean_iou: 0.8468\n", "[Epoch 14: 150/200] train loss: 0.1288 mean_iou: 0.8722\n", "[Epoch 14: 160/200] train loss: 0.1558 mean_iou: 0.8459\n", "[Epoch 14: 170/200] train loss: 0.1769 mean_iou: 0.8254\n", "[Epoch 14: 180/200] train loss: 0.1891 mean_iou: 0.8136\n", "[Epoch 14: 190/200] train loss: 0.1311 mean_iou: 0.8699\n", "[Epoch 14: 200/200] train loss: 0.1438 mean_iou: 0.8576\n", "... validate epoch 14 ...\n", "average loss: 0.1228\n", "average iou: 0.8781\n", "..............................\n", "[Epoch 15: 10/200] train loss: 0.1907 mean_iou: 0.8121\n", "[Epoch 15: 20/200] train loss: 0.1352 mean_iou: 0.8660\n", "[Epoch 15: 30/200] train loss: 0.1419 mean_iou: 0.8594\n", "[Epoch 15: 40/200] train loss: 0.1268 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[default: diou]\")\n", "parser.add_argument(\"--enclosing\", type=str, default=\"smallest\", \n", " help=\"type of enclosing box. support: aligned (axis-aligned) or pca (rotated) or smallest (rotated). [default: smallest]\")\n", "flags = parser.parse_args([])\n", "main(flags.loss, flags.enclosing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 } slamd202303071458299/src/lib/models/Rotated_IoU/test_box_intersection_2d.py000066400000000000000000000053501435750645400261570ustar00rootroot00000000000000import numpy as np import torch from box_intersection_2d import box_intersection_th, box_in_box_th, oriented_box_intersection_2d from utiles import box2corners, box_intersection, box_in_box, box_intersection_area def test_box_intersection_th(): box1 = [0, 0, 2, 3, np.pi/6] box2 = [1, 1, 4, 4, -np.pi/4] corners1 = box2corners(*box1) # 4, 2 corners2 = box2corners(*box2) # 4, 2 tensor1 = torch.FloatTensor(np.stack([corners1, corners2], axis=0)) # 2, 4, 2 tensor1 = torch.unsqueeze(tensor1, 0) # 1, 2, 4, 2 tensor2 = torch.FloatTensor(np.stack([corners2, corners1], axis=0)) tensor2 = torch.unsqueeze(tensor2, 0) inters, mask = box_intersection_th(tensor1, tensor2) inters1 = inters[0,0,...] mask1 = mask[0,0,...] inters2, mask2 = box_intersection(corners1, corners2) error = inters1.numpy() - inters2 max_error = np.max(error) print("error of box_intersection_th:") print(error) print("max error:", max_error) def test_box_in_box_th(): box1 = [0, 0, 2, 3, np.pi/6] box2 = [1, 1, 4, 4, -np.pi/4] corners1 = box2corners(*box1) # 4, 2 corners2 = box2corners(*box2) # 4, 2 tensor1 = torch.FloatTensor(np.stack([corners1, corners2], axis=0)) # 2, 4, 2 tensor1 = torch.unsqueeze(tensor1, 0) # 1, 2, 4, 2 tensor2 = torch.FloatTensor(np.stack([corners2, corners1], axis=0)) tensor2 = torch.unsqueeze(tensor2, 0) c12_th, c21_th = box_in_box_th(tensor1, tensor2) c12_np, c21_np = box_in_box(corners1, corners2) error1 = c12_th[0,0, ...].numpy() == c12_np error2 = c21_th[0,0, ...].numpy() == c21_np print() print("Does box_in_box_th give same result as np implementation?") print(error1) print(error2) def test_area(): box1 = [0, 0, 2, 3, np.pi/6] box2 = [1, 1, 4, 4, -np.pi/4] corners1 = box2corners(*box1) # 4, 2 corners2 = box2corners(*box2) # 4, 2 tensor1 = torch.FloatTensor(np.stack([corners1, corners1], axis=0)) # 2, 4, 2 # test same box tensor1 = torch.unsqueeze(tensor1, 0).repeat([2,1,1,1]).cuda() # 2, 2, 4, 2 tensor2 = torch.FloatTensor(np.stack([corners2, corners1], axis=0)) tensor2 = torch.unsqueeze(tensor2, 0).repeat([2,1,1,1]).cuda() area, vertices = oriented_box_intersection_2d(tensor1, tensor2) area_cpu = area.detach().cpu().numpy() vertices = vertices.detach().cpu().numpy() print("CUDA: ") print(area_cpu) print(vertices[0,0,...]) # NOTE: coordinate of vertices might be different because of the normalization in numpy implementation print() print("Numpy: ") area_np, vertices_np = box_intersection_area(box1, box2) print(area_np) print(vertices_np) if __name__ == "__main__": # test_box_intersection_th() # test_box_in_box_th() test_area()slamd202303071458299/src/lib/models/Rotated_IoU/test_corner_cases.py000066400000000000000000000025361435750645400246650ustar00rootroot00000000000000''' test cases for issue #8 check the calculation if: 1. edges are collinear 2. boxes are exactly the same 3. input in different scale level ''' import torch from oriented_iou_loss import cal_iou device = torch.device('cuda:0') box_0 = torch.tensor([.0, .0, 2., 2., .0], device=device) def test_same_box(): expect = 1. box_1 = torch.tensor([.0, .0, 2., 2., .0], device=device) result = cal_iou(box_0[None, None, ...], box_1[None, None, ...])[0].cpu().numpy() print("expect:", expect, "get:", result[0,0]) def test_same_edge(): expect = 0. box_1 = torch.tensor([.0, 2, 2., 2., .0], device=device) result = cal_iou(box_0[None, None, ...], box_1[None, None, ...])[0].cpu().numpy() print("expect:", expect, "get:", result[0,0]) def test_same_edge_offset(): expect = 0.3333 box_1 = torch.tensor([.0, 1., 2., 2., .0], device=device) result = cal_iou(box_0[None, None, ...], box_1[None, None, ...])[0].cpu().numpy() print("expect:", expect, "get:", result[0,0]) def test_same_box2(): expect = 1 box_1 = torch.tensor([38, 120, 1.3, 20, 50], device=device) result = cal_iou(box_1[None, None, ...], box_1[None, None, ...])[0].cpu().numpy() print("expect:", expect, "get:", result[0,0]) if __name__ == "__main__": test_same_box() test_same_edge() test_same_edge_offset() test_same_box2()slamd202303071458299/src/lib/models/Rotated_IoU/utiles.py000066400000000000000000000224311435750645400224610ustar00rootroot00000000000000''' numpy implementation of 2d box intersection with test cases. to demonstrate the idea and validate the result of torch implementation author: lanxiao li 2020.8 ''' import numpy as np import matplotlib.pyplot as plt EPSILON = 1e-8 def line_seg_intersection(line1:np.array, line2:np.array): """find intersection of 2 lines defined by their end points Args: line1 (np.array): (2, 2), end points of line line2 (np.array): (2, 2), end points of line Returns: intersection: coordinte of intersection point. None is not exists. """ # https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection assert line1.shape == (2,2) assert line2.shape == (2,2) x1, y1 = line1[0,:] x2, y2 = line1[1,:] x3, y3 = line2[0,:] x4, y4 = line2[1,:] num = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4) if np.abs(num) < EPSILON: return None den_t = (x1-x3)*(y3-y4) - (y1-y3)*(x3-x4) t = den_t / num if t < 0 or t > 1: return None den_u = (x1-x2)*(y1-y3) - (y1-y2)*(x1-x3) u = - den_u / num if u < 0 or u > 1: return None return [x1+t*(x2-x1), y1+t*(y2-y1)] def box2corners(x, y, w, h, alpha): """ box parameters to four box corners """ x4 = np.array([0.5, -0.5, -0.5, 0.5]) * w y4 = np.array([0.5, 0.5, -0.5, -0.5]) * h corners = np.stack([x4, y4], axis=1) sin = np.sin(alpha) cos = np.cos(alpha) R = np.array([[cos, -sin],[sin, cos]]) rotated = corners @ R.T rotated[:, 0] += x rotated[:, 1] += y return rotated def box_intersection(corners1, corners2): """find intersection points pf two boxes Args: corners1 (np.array): 4x2 coordinates of corners corners2 (np.array): 4x2 coordinates of corners Returns: inters (4, 4, 2): (i, j, :) means intersection of i-th edge of box1 with j-th of box2 mask (4, 4) bool: (i, j) indicates if intersection exists """ assert corners1.shape == (4,2) assert corners2.shape == (4,2) inters = np.zeros([4,4,2]) # edges of box1, edges of box2, coordinates mask = np.zeros([4,4]).astype(np.bool) for i in range(4): line1 = np.stack([corners1[i, :], corners1[(i+1)%4, :]], axis=0) for j in range(4): line2 = np.stack([corners2[j, :], corners2[(j+1)%4, :]], axis=0) it = line_seg_intersection(line1, line2) if it is not None: inters[i, j, :] = it mask[i, j] = True return inters, mask def point_in_box(point, corners): """check if a point lies in a rectangle defined by corners. idea: check projection Args: point (2,): coordinate of point corners (4, 2): coordinate of corners Returns: True if point in box """ assert corners.shape == (4,2) a = corners[0, :] b = corners[1, :] d = corners[3, :] ab = b - a am = point - a ad = d - a # consider projection of AM on the edge AB and AD p_ab = np.dot(ab, am) norm_ab = np.dot(ab, ab) p_ad = np.dot(ad, am) norm_ad = np.dot(ad, ad) cond1 = p_ab > 0 and p_ab < norm_ab cond2 = p_ad > 0 and p_ad < norm_ad return cond1 and cond2 def box_in_box(corners1, corners2): """check if corners of 2 boxes lie in each other Args: corners1 (np.array): 4x2 coordinates of corners corners2 (np.array): 4x2 coordinates of corners Returns: c1_in_2 (4, ): i-th corner of box1 in box2 c2_in_1 (4, ): i-th corner of box2 in box1 """ assert corners1.shape == (4,2) assert corners2.shape == (4,2) c1_in_2 = np.zeros((4,)).astype(np.bool) c2_in_1 = np.zeros((4,)).astype(np.bool) for i in range(4): if point_in_box(corners1[i, :], corners2): c1_in_2[i] = True if point_in_box(corners2[i, :], corners1): c2_in_1[i] = True return c1_in_2, c2_in_1 def intersection_poly(corners1, corners2): """find all vertices of the polygon for intersection of 2 boxes vertices include intersection points of edges and box corner in the other box Args: corners1 (np.array): 4x2 coordinates of corners corners2 (np.array): 4x2 coordinates of corners Returns: poly_vertices (N, 2): vertices of polygon """ # corner1 = box2corners(*box1) # corner2 = box2corners(*box2) c1_in_2, c2_in_1 = box_in_box(corners1, corners2) corners_eff = np.concatenate([corners1[c1_in_2,:], corners2[c2_in_1,:]], axis=0) inters, mask = box_intersection(corners1, corners2) inters_lin = np.reshape(inters, (-1, 2)) mask_lin = np.reshape(mask, (-1, )) inter_points = inters_lin[mask_lin, :] poly_vertices = np.concatenate([corners_eff, inter_points], axis=0) return poly_vertices def compare_vertices(v1, v2): """compare two points according to the its angle around the origin point of coordinate system. Useful for sorting vertices in anti-clockwise order Args: v1 (2, ): x1, y1 v2 (2, ): x2, y2 Returns: int : 1 if angle1 > angle2. else -1 """ x1, y1 = v1 x2, y2 = v2 n1 = np.sqrt(x1*x1 + y1*y1) + EPSILON n2 = np.sqrt(x2*x2 + y2*y2) + EPSILON if y1 > 0 and y2 < 0: return -1 elif y1 < 0 and y2 > 0: return 1 elif y1 > 0 and y2 > 0: if x1/n1 < x2/n2: return 1 else: return -1 else: if x1/n1 > x2/n2: return 1 else: return -1 import functools def vertices2area(vertices): """sort vertices in anti-clockwise order and calculate the area of polygon Args: vertices (N, 2) with N>2: vertices of a convex polygon Returns: area: area of polygon ls: sorted vertices (normalized to centroid) """ mean = np.mean(vertices, axis=0, keepdims=True) vertices_normalized = vertices - mean # sort vertices clockwise ls = np.array(list(sorted(vertices_normalized, key=functools.cmp_to_key(compare_vertices)))) ls_ext = np.concatenate([ls, ls[0:1, :]], axis=0) total = ls_ext[0:-1, 0]*ls_ext[1:, 1] - ls_ext[1:, 0] * ls_ext[0:-1, 1] total = np.sum(total) area = np.abs(total) / 2 return area, ls def box_intersection_area(box1, box2): corners1 = box2corners(*box1) corners2 = box2corners(*box2) v = intersection_poly(corners1, corners2) if v.shape[0] < 3: return 0 else: return vertices2area(v) # -------------------------------------------------------- # tests # -------------------------------------------------------- def test_line_seg_intersection(): line1 = np.array([[0, 0], [0, 1]]) line2 = np.array([[1, 0], [1, 1]]) line3 = np.array([[0, 0], [1, 1]]) line4 = np.array([[1, 0], [0, 1]]) line5 = np.array([[0, 0], [1, 0]]) line6 = np.array([[0, 1], [1, 0.5]]) print(line_seg_intersection(line1, line2)) print(line_seg_intersection(line3, line4)) print(line_seg_intersection(line5, line6)) def test_box2corners(): corners = box2corners(1, 1, 2, 3, np.pi/6) plt.figure() plt.scatter(corners[:, 0], corners[:, 1]) for i in range(corners.shape[0]): plt.text(corners[i, 0], corners[i, 1], str(i)) plt.axis("equal") plt.show() corners = box2corners(3, 1, 4, 2, np.pi/4) plt.figure() plt.scatter(corners[:, 0], corners[:, 1]) for i in range(corners.shape[0]): plt.text(corners[i, 0], corners[i, 1], str(i)) plt.axis("equal") plt.show() def test_box_intersection(box1, box2): corners1 = box2corners(*box1) corners2 = box2corners(*box2) inters, mask = box_intersection(corners1, corners2) num_inters = np.sum(mask.astype(np.int)) inters_lin = np.reshape(inters, (-1, 2)) mask_lin = np.reshape(mask, (-1, )) inter_points = inters_lin[mask_lin, :] print("find %d intersections"%num_inters) corners1 = box2corners(*box1) corners2 = box2corners(*box2) plt.figure() plt.scatter(corners1[:, 0], corners1[:, 1]) plt.scatter(corners2[:, 0], corners2[:, 1]) plt.scatter(inter_points[:, 0], inter_points[:, 1], marker='x') for i in range(corners1.shape[0]): plt.text(corners1[i, 0], corners1[i, 1], str(i)) for i in range(corners2.shape[0]): plt.text(corners2[i, 0], corners2[i, 1], str(i)) plt.axis("equal") plt.show() def test_point_in_box(): p = np.random.rand(5000, 2) p[:, 0] *= 12 p[:, 0] -= 5 p[:, 1] *= 12 p[:, 1] -= 5 corners = box2corners(3, 1, 4, 2, np.pi/4) plt.figure() plt.scatter(corners[:, 0], corners[:, 1]) for i in range(corners.shape[0]): plt.text(corners[i, 0], corners[i, 1], str(i)) mask = [point_in_box(x, corners) for x in p] plt.scatter(p[mask, 0], p[mask, 1], marker="x") plt.axis("equal") plt.show() def test_intersection_area(box1, box2): area, corners = box_intersection_area(box1, box2) print(area) print(corners) plt.figure() plt.scatter(corners[:, 0], corners[:, 1]) for i in range(corners.shape[0]): plt.text(corners[i, 0], corners[i, 1], str(i)) plt.axis("equal") plt.show() if __name__ == "__main__": # test_line_seg_intersection() # test_box2corners() # test_point_in_box() box1 = np.array([0, 0, 2, 3, np.pi/6]) box2 = np.array([1, 1, 4, 4, -np.pi/4]) test_box_intersection(box1, box2) test_intersection_area(box1, box2) slamd202303071458299/src/lib/models/data_parallel.py000066400000000000000000000120701435750645400215610ustar00rootroot00000000000000import torch from torch.nn.modules import Module from torch.nn.parallel.scatter_gather import gather from torch.nn.parallel.replicate import replicate from torch.nn.parallel.parallel_apply import parallel_apply from .scatter_gather import scatter_kwargs class _DataParallel(Module): r"""Implements data parallelism at the module level. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension. In the forward pass, the module is replicated on each device, and each replica handles a portion of the input. During the backwards pass, gradients from each replica are summed into the original module. The batch size should be larger than the number of GPUs used. It should also be an integer multiple of the number of GPUs so that each chunk is the same size (so that each GPU processes the same number of samples). See also: :ref:`cuda-nn-dataparallel-instead` Arbitrary positional and keyword inputs are allowed to be passed into DataParallel EXCEPT Tensors. All variables will be scattered on dim specified (default 0). Primitive types will be broadcasted, but all other types will be a shallow copy and can be corrupted if written to in the model's forward pass. Args: module: module to be parallelized device_ids: CUDA devices (default: all devices) output_device: device location of output (default: device_ids[0]) Example:: >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2]) >>> output = net(input_var) """ # TODO: update notes/cuda.rst when this class handles 8+ GPUs well def __init__(self, module, device_ids=None, output_device=None, dim=0, chunk_sizes=None): super(_DataParallel, self).__init__() if not torch.cuda.is_available(): self.module = module self.device_ids = [] return if device_ids is None: device_ids = list(range(torch.cuda.device_count())) if output_device is None: output_device = device_ids[0] self.dim = dim self.module = module self.device_ids = device_ids self.chunk_sizes = chunk_sizes self.output_device = output_device if len(self.device_ids) == 1: self.module.cuda(device_ids[0]) def forward(self, *inputs, **kwargs): if not self.device_ids: return self.module(*inputs, **kwargs) inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, self.chunk_sizes) if len(self.device_ids) == 1: return self.module(*inputs[0], **kwargs[0]) replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) outputs = self.parallel_apply(replicas, inputs, kwargs) return self.gather(outputs, self.output_device) def replicate(self, module, device_ids): return replicate(module, device_ids) def scatter(self, inputs, kwargs, device_ids, chunk_sizes): return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim, chunk_sizes=self.chunk_sizes) def parallel_apply(self, replicas, inputs, kwargs): return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) def gather(self, outputs, output_device): return gather(outputs, output_device, dim=self.dim) def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None): r"""Evaluates module(input) in parallel across the GPUs given in device_ids. This is the functional version of the DataParallel module. Args: module: the module to evaluate in parallel inputs: inputs to the module device_ids: GPU ids on which to replicate module output_device: GPU location of the output Use -1 to indicate the CPU. (default: device_ids[0]) Returns: a Variable containing the result of module(input) located on output_device """ if not isinstance(inputs, tuple): inputs = (inputs,) if device_ids is None: device_ids = list(range(torch.cuda.device_count())) if output_device is None: output_device = device_ids[0] inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim) if len(device_ids) == 1: return module(*inputs[0], **module_kwargs[0]) used_device_ids = device_ids[:len(inputs)] replicas = replicate(module, used_device_ids) outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids) return gather(outputs, output_device, dim) def DataParallel(module, device_ids=None, output_device=None, dim=0, chunk_sizes=None): if chunk_sizes is None: return torch.nn.DataParallel(module, device_ids, output_device, dim) standard_size = True for i in range(1, len(chunk_sizes)): if chunk_sizes[i] != chunk_sizes[0]: standard_size = False if standard_size: return torch.nn.DataParallel(module, device_ids, output_device, dim) return _DataParallel(module, device_ids, output_device, dim, chunk_sizes)slamd202303071458299/src/lib/models/decode.py000066400000000000000000000747011435750645400202300ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import torch import torch.nn as nn import torch.nn.functional as F from .utils import _gather_feat, _transpose_and_gather_feat import pdb def _nms(heat, kernel=3): pad = (kernel - 1) // 2 hmax = nn.functional.max_pool2d( heat, (kernel, kernel), stride=1, padding=pad) keep = (hmax == heat).float() return heat * keep def _left_aggregate(heat): ''' heat: batchsize x channels x h x w ''' shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(1, heat.shape[0]): inds = (heat[i] >= heat[i - 1]) ret[i] += ret[i - 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape) def _right_aggregate(heat): ''' heat: batchsize x channels x h x w ''' shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(heat.shape[0] - 2, -1, -1): inds = (heat[i] >= heat[i +1]) ret[i] += ret[i + 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape) def _top_aggregate(heat): ''' heat: batchsize x channels x h x w ''' heat = heat.transpose(3, 2) shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(1, heat.shape[0]): inds = (heat[i] >= heat[i - 1]) ret[i] += ret[i - 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape).transpose(3, 2) def _bottom_aggregate(heat): ''' heat: batchsize x channels x h x w ''' heat = heat.transpose(3, 2) shape = heat.shape heat = heat.reshape(-1, heat.shape[3]) heat = heat.transpose(1, 0).contiguous() ret = heat.clone() for i in range(heat.shape[0] - 2, -1, -1): inds = (heat[i] >= heat[i + 1]) ret[i] += ret[i + 1] * inds.float() return (ret - heat).transpose(1, 0).reshape(shape).transpose(3, 2) def _h_aggregate(heat, aggr_weight=0.1): return aggr_weight * _left_aggregate(heat) + \ aggr_weight * _right_aggregate(heat) + heat def _v_aggregate(heat, aggr_weight=0.1): return aggr_weight * _top_aggregate(heat) + \ aggr_weight * _bottom_aggregate(heat) + heat ''' # Slow for large number of categories def _topk(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, -1), K) topk_clses = (topk_inds / (height * width)).int() topk_inds = topk_inds % (height * width) topk_ys = (topk_inds / width).int().float() topk_xs = (topk_inds % width).int().float() return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs ''' def _topk_channel(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) topk_inds = topk_inds % (height * width) topk_ys = (topk_inds // width).int().float() topk_xs = (topk_inds % width).int().float() return topk_scores, topk_inds, topk_ys, topk_xs def _topk(scores, K=40): batch, cat, height, width = scores.size() topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K) topk_inds = topk_inds % (height * width) topk_ys = (topk_inds // width).int().float() topk_xs = (topk_inds % width).int().float() topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K) topk_clses = (topk_ind // K).int() topk_inds = _gather_feat( topk_inds.view(batch, -1, 1), topk_ind).view(batch, K) topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K) topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K) return topk_score, topk_inds, topk_clses, topk_ys, topk_xs def agnex_ct_decode( t_heat, l_heat, b_heat, r_heat, ct_heat, t_regr=None, l_regr=None, b_regr=None, r_regr=None, K=40, scores_thresh=0.1, center_thresh=0.1, aggr_weight=0.0, num_dets=1000 ): batch, cat, height, width = t_heat.size() ''' t_heat = torch.sigmoid(t_heat) l_heat = torch.sigmoid(l_heat) b_heat = torch.sigmoid(b_heat) r_heat = torch.sigmoid(r_heat) ct_heat = torch.sigmoid(ct_heat) ''' if aggr_weight > 0: t_heat = _h_aggregate(t_heat, aggr_weight=aggr_weight) l_heat = _v_aggregate(l_heat, aggr_weight=aggr_weight) b_heat = _h_aggregate(b_heat, aggr_weight=aggr_weight) r_heat = _v_aggregate(r_heat, aggr_weight=aggr_weight) # perform nms on heatmaps t_heat = _nms(t_heat) l_heat = _nms(l_heat) b_heat = _nms(b_heat) r_heat = _nms(r_heat) t_heat[t_heat > 1] = 1 l_heat[l_heat > 1] = 1 b_heat[b_heat > 1] = 1 r_heat[r_heat > 1] = 1 t_scores, t_inds, _, t_ys, t_xs = _topk(t_heat, K=K) l_scores, l_inds, _, l_ys, l_xs = _topk(l_heat, K=K) b_scores, b_inds, _, b_ys, b_xs = _topk(b_heat, K=K) r_scores, r_inds, _, r_ys, r_xs = _topk(r_heat, K=K) ct_heat_agn, ct_clses = torch.max(ct_heat, dim=1, keepdim=True) # import pdb; pdb.set_trace() t_ys = t_ys.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) t_xs = t_xs.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) l_ys = l_ys.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) l_xs = l_xs.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) b_ys = b_ys.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) b_xs = b_xs.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) r_ys = r_ys.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) r_xs = r_xs.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) box_ct_xs = ((l_xs + r_xs + 0.5) / 2).long() box_ct_ys = ((t_ys + b_ys + 0.5) / 2).long() ct_inds = box_ct_ys * width + box_ct_xs ct_inds = ct_inds.view(batch, -1) ct_heat_agn = ct_heat_agn.view(batch, -1, 1) ct_clses = ct_clses.view(batch, -1, 1) ct_scores = _gather_feat(ct_heat_agn, ct_inds) clses = _gather_feat(ct_clses, ct_inds) t_scores = t_scores.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) l_scores = l_scores.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) b_scores = b_scores.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) r_scores = r_scores.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) ct_scores = ct_scores.view(batch, K, K, K, K) scores = (t_scores + l_scores + b_scores + r_scores + 2 * ct_scores) / 6 # reject boxes based on classes top_inds = (t_ys > l_ys) + (t_ys > b_ys) + (t_ys > r_ys) top_inds = (top_inds > 0) left_inds = (l_xs > t_xs) + (l_xs > b_xs) + (l_xs > r_xs) left_inds = (left_inds > 0) bottom_inds = (b_ys < t_ys) + (b_ys < l_ys) + (b_ys < r_ys) bottom_inds = (bottom_inds > 0) right_inds = (r_xs < t_xs) + (r_xs < l_xs) + (r_xs < b_xs) right_inds = (right_inds > 0) sc_inds = (t_scores < scores_thresh) + (l_scores < scores_thresh) + \ (b_scores < scores_thresh) + (r_scores < scores_thresh) + \ (ct_scores < center_thresh) sc_inds = (sc_inds > 0) scores = scores - sc_inds.float() scores = scores - top_inds.float() scores = scores - left_inds.float() scores = scores - bottom_inds.float() scores = scores - right_inds.float() scores = scores.view(batch, -1) scores, inds = torch.topk(scores, num_dets) scores = scores.unsqueeze(2) if t_regr is not None and l_regr is not None \ and b_regr is not None and r_regr is not None: t_regr = _transpose_and_gather_feat(t_regr, t_inds) t_regr = t_regr.view(batch, K, 1, 1, 1, 2) l_regr = _transpose_and_gather_feat(l_regr, l_inds) l_regr = l_regr.view(batch, 1, K, 1, 1, 2) b_regr = _transpose_and_gather_feat(b_regr, b_inds) b_regr = b_regr.view(batch, 1, 1, K, 1, 2) r_regr = _transpose_and_gather_feat(r_regr, r_inds) r_regr = r_regr.view(batch, 1, 1, 1, K, 2) t_xs = t_xs + t_regr[..., 0] t_ys = t_ys + t_regr[..., 1] l_xs = l_xs + l_regr[..., 0] l_ys = l_ys + l_regr[..., 1] b_xs = b_xs + b_regr[..., 0] b_ys = b_ys + b_regr[..., 1] r_xs = r_xs + r_regr[..., 0] r_ys = r_ys + r_regr[..., 1] else: t_xs = t_xs + 0.5 t_ys = t_ys + 0.5 l_xs = l_xs + 0.5 l_ys = l_ys + 0.5 b_xs = b_xs + 0.5 b_ys = b_ys + 0.5 r_xs = r_xs + 0.5 r_ys = r_ys + 0.5 bboxes = torch.stack((l_xs, t_ys, r_xs, b_ys), dim=5) bboxes = bboxes.view(batch, -1, 4) bboxes = _gather_feat(bboxes, inds) clses = clses.contiguous().view(batch, -1, 1) clses = _gather_feat(clses, inds).float() t_xs = t_xs.contiguous().view(batch, -1, 1) t_xs = _gather_feat(t_xs, inds).float() t_ys = t_ys.contiguous().view(batch, -1, 1) t_ys = _gather_feat(t_ys, inds).float() l_xs = l_xs.contiguous().view(batch, -1, 1) l_xs = _gather_feat(l_xs, inds).float() l_ys = l_ys.contiguous().view(batch, -1, 1) l_ys = _gather_feat(l_ys, inds).float() b_xs = b_xs.contiguous().view(batch, -1, 1) b_xs = _gather_feat(b_xs, inds).float() b_ys = b_ys.contiguous().view(batch, -1, 1) b_ys = _gather_feat(b_ys, inds).float() r_xs = r_xs.contiguous().view(batch, -1, 1) r_xs = _gather_feat(r_xs, inds).float() r_ys = r_ys.contiguous().view(batch, -1, 1) r_ys = _gather_feat(r_ys, inds).float() detections = torch.cat([bboxes, scores, t_xs, t_ys, l_xs, l_ys, b_xs, b_ys, r_xs, r_ys, clses], dim=2) return detections def exct_decode( t_heat, l_heat, b_heat, r_heat, ct_heat, t_regr=None, l_regr=None, b_regr=None, r_regr=None, K=40, scores_thresh=0.1, center_thresh=0.1, aggr_weight=0.0, num_dets=1000 ): batch, cat, height, width = t_heat.size() ''' t_heat = torch.sigmoid(t_heat) l_heat = torch.sigmoid(l_heat) b_heat = torch.sigmoid(b_heat) r_heat = torch.sigmoid(r_heat) ct_heat = torch.sigmoid(ct_heat) ''' if aggr_weight > 0: t_heat = _h_aggregate(t_heat, aggr_weight=aggr_weight) l_heat = _v_aggregate(l_heat, aggr_weight=aggr_weight) b_heat = _h_aggregate(b_heat, aggr_weight=aggr_weight) r_heat = _v_aggregate(r_heat, aggr_weight=aggr_weight) # perform nms on heatmaps t_heat = _nms(t_heat) l_heat = _nms(l_heat) b_heat = _nms(b_heat) r_heat = _nms(r_heat) t_heat[t_heat > 1] = 1 l_heat[l_heat > 1] = 1 b_heat[b_heat > 1] = 1 r_heat[r_heat > 1] = 1 t_scores, t_inds, t_clses, t_ys, t_xs = _topk(t_heat, K=K) l_scores, l_inds, l_clses, l_ys, l_xs = _topk(l_heat, K=K) b_scores, b_inds, b_clses, b_ys, b_xs = _topk(b_heat, K=K) r_scores, r_inds, r_clses, r_ys, r_xs = _topk(r_heat, K=K) t_ys = t_ys.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) t_xs = t_xs.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) l_ys = l_ys.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) l_xs = l_xs.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) b_ys = b_ys.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) b_xs = b_xs.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) r_ys = r_ys.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) r_xs = r_xs.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) t_clses = t_clses.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) l_clses = l_clses.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) b_clses = b_clses.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) r_clses = r_clses.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) box_ct_xs = ((l_xs + r_xs + 0.5) / 2).long() box_ct_ys = ((t_ys + b_ys + 0.5) / 2).long() ct_inds = t_clses.long() * (height * width) + box_ct_ys * width + box_ct_xs ct_inds = ct_inds.view(batch, -1) ct_heat = ct_heat.view(batch, -1, 1) ct_scores = _gather_feat(ct_heat, ct_inds) t_scores = t_scores.view(batch, K, 1, 1, 1).expand(batch, K, K, K, K) l_scores = l_scores.view(batch, 1, K, 1, 1).expand(batch, K, K, K, K) b_scores = b_scores.view(batch, 1, 1, K, 1).expand(batch, K, K, K, K) r_scores = r_scores.view(batch, 1, 1, 1, K).expand(batch, K, K, K, K) ct_scores = ct_scores.view(batch, K, K, K, K) scores = (t_scores + l_scores + b_scores + r_scores + 2 * ct_scores) / 6 # reject boxes based on classes cls_inds = (t_clses != l_clses) + (t_clses != b_clses) + \ (t_clses != r_clses) cls_inds = (cls_inds > 0) top_inds = (t_ys > l_ys) + (t_ys > b_ys) + (t_ys > r_ys) top_inds = (top_inds > 0) left_inds = (l_xs > t_xs) + (l_xs > b_xs) + (l_xs > r_xs) left_inds = (left_inds > 0) bottom_inds = (b_ys < t_ys) + (b_ys < l_ys) + (b_ys < r_ys) bottom_inds = (bottom_inds > 0) right_inds = (r_xs < t_xs) + (r_xs < l_xs) + (r_xs < b_xs) right_inds = (right_inds > 0) sc_inds = (t_scores < scores_thresh) + (l_scores < scores_thresh) + \ (b_scores < scores_thresh) + (r_scores < scores_thresh) + \ (ct_scores < center_thresh) sc_inds = (sc_inds > 0) scores = scores - sc_inds.float() scores = scores - cls_inds.float() scores = scores - top_inds.float() scores = scores - left_inds.float() scores = scores - bottom_inds.float() scores = scores - right_inds.float() scores = scores.view(batch, -1) scores, inds = torch.topk(scores, num_dets) scores = scores.unsqueeze(2) if t_regr is not None and l_regr is not None \ and b_regr is not None and r_regr is not None: t_regr = _transpose_and_gather_feat(t_regr, t_inds) t_regr = t_regr.view(batch, K, 1, 1, 1, 2) l_regr = _transpose_and_gather_feat(l_regr, l_inds) l_regr = l_regr.view(batch, 1, K, 1, 1, 2) b_regr = _transpose_and_gather_feat(b_regr, b_inds) b_regr = b_regr.view(batch, 1, 1, K, 1, 2) r_regr = _transpose_and_gather_feat(r_regr, r_inds) r_regr = r_regr.view(batch, 1, 1, 1, K, 2) t_xs = t_xs + t_regr[..., 0] t_ys = t_ys + t_regr[..., 1] l_xs = l_xs + l_regr[..., 0] l_ys = l_ys + l_regr[..., 1] b_xs = b_xs + b_regr[..., 0] b_ys = b_ys + b_regr[..., 1] r_xs = r_xs + r_regr[..., 0] r_ys = r_ys + r_regr[..., 1] else: t_xs = t_xs + 0.5 t_ys = t_ys + 0.5 l_xs = l_xs + 0.5 l_ys = l_ys + 0.5 b_xs = b_xs + 0.5 b_ys = b_ys + 0.5 r_xs = r_xs + 0.5 r_ys = r_ys + 0.5 bboxes = torch.stack((l_xs, t_ys, r_xs, b_ys), dim=5) bboxes = bboxes.view(batch, -1, 4) bboxes = _gather_feat(bboxes, inds) clses = t_clses.contiguous().view(batch, -1, 1) clses = _gather_feat(clses, inds).float() t_xs = t_xs.contiguous().view(batch, -1, 1) t_xs = _gather_feat(t_xs, inds).float() t_ys = t_ys.contiguous().view(batch, -1, 1) t_ys = _gather_feat(t_ys, inds).float() l_xs = l_xs.contiguous().view(batch, -1, 1) l_xs = _gather_feat(l_xs, inds).float() l_ys = l_ys.contiguous().view(batch, -1, 1) l_ys = _gather_feat(l_ys, inds).float() b_xs = b_xs.contiguous().view(batch, -1, 1) b_xs = _gather_feat(b_xs, inds).float() b_ys = b_ys.contiguous().view(batch, -1, 1) b_ys = _gather_feat(b_ys, inds).float() r_xs = r_xs.contiguous().view(batch, -1, 1) r_xs = _gather_feat(r_xs, inds).float() r_ys = r_ys.contiguous().view(batch, -1, 1) r_ys = _gather_feat(r_ys, inds).float() detections = torch.cat([bboxes, scores, t_xs, t_ys, l_xs, l_ys, b_xs, b_ys, r_xs, r_ys, clses], dim=2) return detections def ddd_decode(heat, rot, depth, dim, wh=None, reg=None, K=40): batch, cat, height, width = heat.size() # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 rot = _transpose_and_gather_feat(rot, inds) rot = rot.view(batch, K, 8) depth = _transpose_and_gather_feat(depth, inds) depth = depth.view(batch, K, 1) dim = _transpose_and_gather_feat(dim, inds) dim = dim.view(batch, K, 3) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) xs = xs.view(batch, K, 1) ys = ys.view(batch, K, 1) if wh is not None: wh = _transpose_and_gather_feat(wh, inds) wh = wh.view(batch, K, 2) detections = torch.cat( [xs, ys, scores, rot, depth, dim, wh, clses], dim=2) else: detections = torch.cat( [xs, ys, scores, rot, depth, dim, clses], dim=2) return detections def ctdet_decode(heat, wh, reg=None, cat_spec_wh=False, K=100): batch, cat, height, width = heat.size() # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 wh = _transpose_and_gather_feat(wh, inds) if cat_spec_wh: wh = wh.view(batch, K, cat, 2) clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long() wh = wh.gather(2, clses_ind).view(batch, K, 2) else: wh = wh.view(batch, K, 2) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) bboxes = torch.cat([xs - wh[..., 0:1] / 2, ys - wh[..., 1:2] / 2, xs + wh[..., 0:1] / 2, ys + wh[..., 1:2] / 2], dim=2) detections = torch.cat([bboxes, scores, clses], dim=2) return detections def eldet_decode(heat, l, ratio_al, ratio_bl, theta, reg=None, cat_spec_wh=False, K=2): # Decode ellipse information batch, cat, height, width = heat.size() # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 l = _transpose_and_gather_feat(l, inds) ratio_al = _transpose_and_gather_feat(ratio_al, inds) ratio_bl = _transpose_and_gather_feat(ratio_bl, inds) # ratio_ba = _transpose_and_gather_feat(ratio_ba, inds) theta = _transpose_and_gather_feat(theta, inds) if cat_spec_wh: l = l.view(batch, K, cat, 1) clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long() l = l.gather(1, clses_ind).view(batch, K, 1) else: l = l.view(batch, K, 1) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) bboxes = torch.cat([xs - l / 2, ys - l / 2, xs + l / 2, ys + l / 2], dim=2) # print(bboxes.shape, xs.shape, ys.shape, l.shape, ratio_al.shape, ratio_ba.shape, theta.shape, scores.shape, clses.shape) # print('bboxes:', bboxes, 'xs:', xs, 'ys:', ys, 'l:', l, 'ratio_al:', ratio_al, 'ratio_ba:', ratio_ba, 'theta:', theta, 'scores:', scores, 'clses:', clses) detections = torch.cat([bboxes, xs, ys, l, ratio_al, ratio_bl, theta, scores, clses], dim=2) return detections def eldet_decode_sincos(heat, l, ratio_al, ratio_bl, theta, sincos, reg=None, cat_spec_wh=False, K=2): # Decode ellipse information with sin and cos batch, cat, height, width = heat.size() # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 l = _transpose_and_gather_feat(l, inds) ratio_al = _transpose_and_gather_feat(ratio_al, inds) ratio_bl = _transpose_and_gather_feat(ratio_bl, inds) theta = _transpose_and_gather_feat(theta, inds) sincos = _transpose_and_gather_feat(sincos, inds) if cat_spec_wh: l = l.view(batch, K, cat, 1) clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long() l = l.gather(1, clses_ind).view(batch, K, 1) else: l = l.view(batch, K, 1) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) bboxes = torch.cat([xs - l / 2, ys - l / 2, xs + l / 2, ys + l / 2], dim=2) detections = torch.cat([bboxes, xs, ys, l, ratio_al, ratio_bl, theta, sincos, scores, clses], dim=2) return detections def decode_label_4iou(label, K=2): # Decode label for iou loss heat = label['hm'] reg = label['reg'] l = label['l'] * 4 ratio_al = label['ratio_al'] ratio_bl = label['ratio_bl'] theta = label['theta'] batch, cat, height, width = heat.size() ind = label['ind'] reg = label['reg'] xs = ind % width ys = ind // width # perform nms on heatmaps # heat = _nms(heat) # scores, inds, clses, ys, xs = _topk(heat, K=K) # (B, K) if reg is not None: reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) * 4 + reg[:, :, 0:1] ys = ys.view(batch, K, 1) * 4 + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) * 4 + 0.5 ys = ys.view(batch, K, 1) * 4 + 0.5 a = l * ratio_al / 2 b = l * ratio_bl / 2 angle = theta * math.pi bboxes = torch.cat([xs, ys, 2*a, 2*b, angle], dim=2) # temp_boxes = [] # for batch_idx in range(batch): # temp_box = [] # temp_box.append(bboxes[batch_idx, clses[batch_idx, 0], :]) # temp_box.append(bboxes[batch_idx, clses[batch_idx, 1], :]) # temp_box = torch.stack(temp_box, dim=0) # temp_boxes.append(temp_box) # bboxes = torch.stack(temp_boxes, dim=0) return bboxes def decode_output_4iou(output, K=2): # Decode output for iou loss heat = F.sigmoid(output['hm']) # (B, 2, w, h) l = output['l'] * 4 # (B, 1, w, h) ratio_al = output["ratio_al"] # (B, 1, w, h) ratio_bl = output["ratio_bl"] # (B, 1, w, h) theta = output["theta"] # (B, 1, w, h) reg = output['reg'] # (B, 2, w, h) batch, cat, height, width = heat.size() heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) # (B, K, 1) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) * 4 + reg[:, :, 0:1] ys = ys.view(batch, K, 1) * 4 + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) * 4 + 0.5 ys = ys.view(batch, K, 1) * 4 + 0.5 l = _transpose_and_gather_feat(l, inds) # (B, K, 1) ratio_al = _transpose_and_gather_feat(ratio_al, inds) # (B, K, 1) ratio_bl = _transpose_and_gather_feat(ratio_bl, inds) # (B, K, 1) theta = _transpose_and_gather_feat(theta, inds) # (B, K, 1) a = l * ratio_al / 2 b = l * ratio_bl / 2 angle = theta * math.pi bboxes = torch.cat([xs, ys, 2*a, 2*b, angle], dim=2) temp_boxes = [] for batch_idx in range(batch): temp_box = [] temp_box.append(bboxes[batch_idx, clses[batch_idx, 0], :]) temp_box.append(bboxes[batch_idx, clses[batch_idx, 1], :]) temp_box = torch.stack(temp_box, dim=0) temp_boxes.append(temp_box) bboxes = torch.stack(temp_boxes, dim=0) return bboxes def decode_output_4iou_sincos(output, K=2): # Decode output for iou loss heat = F.sigmoid(output['hm']) # (B, 2, w, h) l = output['l'] * 4 # (B, 1, w, h) ratio_al = output["ratio_al"] # (B, 1, w, h) ratio_bl = output["ratio_bl"] # (B, 1, w, h) # theta = output["theta"] # (B, 1, w, h) sincos = output["sincos"] # (B, 2, w, h) reg = output['reg'] # (B, 2, w, h) batch, cat, height, width = heat.size() heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) # (B, K, 1) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) * 4 + reg[:, :, 0:1] ys = ys.view(batch, K, 1) * 4 + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) * 4 + 0.5 ys = ys.view(batch, K, 1) * 4 + 0.5 l = _transpose_and_gather_feat(l, inds) # (B, K, 1) ratio_al = _transpose_and_gather_feat(ratio_al, inds) # (B, K, 1) ratio_bl = _transpose_and_gather_feat(ratio_bl, inds) # (B, K, 1) sincos = _transpose_and_gather_feat(sincos, inds) # (B, K, 1) # theta = _transpose_and_gather_feat(theta, inds) # (B, K, 1) a = l * ratio_al / 2 b = l * ratio_bl / 2 # angle = theta * math.pi bboxes = torch.cat([xs, ys, 2*a, 2*b, sincos], dim=2) temp_boxes = [] for batch_idx in range(batch): temp_box = [] temp_box.append(bboxes[batch_idx, clses[batch_idx, 0], :]) temp_box.append(bboxes[batch_idx, clses[batch_idx, 1], :]) temp_box = torch.stack(temp_box, dim=0) temp_boxes.append(temp_box) bboxes = torch.stack(temp_boxes, dim=0) return bboxes # def decode_output_4iou_backup(output, K=2): # Decode output for iou loss # heat = F.sigmoid(output['hm']) # (B, 2, w, h) # l = output['l'] * 4 # (B, 1, w, h) # ratio_al = output["ratio_al"] # (B, 1, w, h) # ratio_bl = output["ratio_bl"] # (B, 1, w, h) # theta = output["theta"] # (B, 2, w, h) # reg = output['reg'] # (B, 2, w, h) # batch, cat, height, width = heat.size() # heat = _nms(heat) # scores, inds, clses, ys, xs = _topk(heat, K=K) # if reg is not None: # reg = _transpose_and_gather_feat(reg, inds) # (B, K, 1) # reg = reg.view(batch, K, 2) # xs = xs.view(batch, K, 1) * 4 + reg[:, :, 0:1] # ys = ys.view(batch, K, 1) * 4 + reg[:, :, 1:2] # else: # xs = xs.view(batch, K, 1) * 4 + 0.5 # ys = ys.view(batch, K, 1) * 4 + 0.5 # l = _transpose_and_gather_feat(l, inds) # (B, K, 1) # ratio_al = _transpose_and_gather_feat(ratio_al, inds) # (B, K, 1) # ratio_bl = _transpose_and_gather_feat(ratio_bl, inds) # (B, K, 1) # theta = _transpose_and_gather_feat(theta, inds) # (B, K, 1) # a = l * ratio_al / 2 # b = l * ratio_bl / 2 # angle = theta * math.pi # bboxes = torch.cat([xs, ys, 2*a, 2*b, angle], dim=2) # temp_boxes = [] # for batch_idx in range(batch): # temp_box = [] # temp_box.append(bboxes[batch_idx, clses[batch_idx, 0], :]) # temp_box.append(bboxes[batch_idx, clses[batch_idx, 1], :]) # temp_box = torch.cat(temp_box, dim=0) # temp_boxes.append(temp_box) # bboxes = torch.cat(temp_boxes, dim=0) # return bboxes, clses.detach().cpu() def multi_pose_decode( heat, wh, kps, reg=None, hm_hp=None, hp_offset=None, K=100): batch, cat, height, width = heat.size() num_joints = kps.shape[1] // 2 # heat = torch.sigmoid(heat) # perform nms on heatmaps heat = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) kps = _transpose_and_gather_feat(kps, inds) kps = kps.view(batch, K, num_joints * 2) kps[..., ::2] += xs.view(batch, K, 1).expand(batch, K, num_joints) kps[..., 1::2] += ys.view(batch, K, 1).expand(batch, K, num_joints) if reg is not None: reg = _transpose_and_gather_feat(reg, inds) reg = reg.view(batch, K, 2) xs = xs.view(batch, K, 1) + reg[:, :, 0:1] ys = ys.view(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.view(batch, K, 1) + 0.5 ys = ys.view(batch, K, 1) + 0.5 wh = _transpose_and_gather_feat(wh, inds) wh = wh.view(batch, K, 2) clses = clses.view(batch, K, 1).float() scores = scores.view(batch, K, 1) bboxes = torch.cat([xs - wh[..., 0:1] / 2, ys - wh[..., 1:2] / 2, xs + wh[..., 0:1] / 2, ys + wh[..., 1:2] / 2], dim=2) if hm_hp is not None: hm_hp = _nms(hm_hp) thresh = 0.1 kps = kps.view(batch, K, num_joints, 2).permute( 0, 2, 1, 3).contiguous() # b x J x K x 2 reg_kps = kps.unsqueeze(3).expand(batch, num_joints, K, K, 2) hm_score, hm_inds, hm_ys, hm_xs = _topk_channel(hm_hp, K=K) # b x J x K if hp_offset is not None: hp_offset = _transpose_and_gather_feat( hp_offset, hm_inds.view(batch, -1)) hp_offset = hp_offset.view(batch, num_joints, K, 2) hm_xs = hm_xs + hp_offset[:, :, :, 0] hm_ys = hm_ys + hp_offset[:, :, :, 1] else: hm_xs = hm_xs + 0.5 hm_ys = hm_ys + 0.5 mask = (hm_score > thresh).float() hm_score = (1 - mask) * -1 + mask * hm_score hm_ys = (1 - mask) * (-10000) + mask * hm_ys hm_xs = (1 - mask) * (-10000) + mask * hm_xs hm_kps = torch.stack([hm_xs, hm_ys], dim=-1).unsqueeze( 2).expand(batch, num_joints, K, K, 2) dist = (((reg_kps - hm_kps) ** 2).sum(dim=4) ** 0.5) min_dist, min_ind = dist.min(dim=3) # b x J x K hm_score = hm_score.gather(2, min_ind).unsqueeze(-1) # b x J x K x 1 min_dist = min_dist.unsqueeze(-1) min_ind = min_ind.view(batch, num_joints, K, 1, 1).expand( batch, num_joints, K, 1, 2) hm_kps = hm_kps.gather(3, min_ind) hm_kps = hm_kps.view(batch, num_joints, K, 2) l = bboxes[:, :, 0].view(batch, 1, K, 1).expand(batch, num_joints, K, 1) t = bboxes[:, :, 1].view(batch, 1, K, 1).expand(batch, num_joints, K, 1) r = bboxes[:, :, 2].view(batch, 1, K, 1).expand(batch, num_joints, K, 1) b = bboxes[:, :, 3].view(batch, 1, K, 1).expand(batch, num_joints, K, 1) mask = (hm_kps[..., 0:1] < l) + (hm_kps[..., 0:1] > r) + \ (hm_kps[..., 1:2] < t) + (hm_kps[..., 1:2] > b) + \ (hm_score < thresh) + (min_dist > (torch.max(b - t, r - l) * 0.3)) mask = (mask > 0).float().expand(batch, num_joints, K, 2) kps = (1 - mask) * hm_kps + mask * kps kps = kps.permute(0, 2, 1, 3).contiguous().view( batch, K, num_joints * 2) detections = torch.cat([bboxes, scores, kps, clses], dim=2) return detectionsslamd202303071458299/src/lib/models/losses.py000066400000000000000000000277051435750645400203170ustar00rootroot00000000000000# ------------------------------------------------------------------------------ # Portions of this code are from # CornerNet (https://github.com/princeton-vl/CornerNet) # Copyright (c) 2018, University of Michigan # Licensed under the BSD 3-Clause License # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import torch import torch.nn as nn from .utils import _transpose_and_gather_feat import torch.nn.functional as F from .decode import decode_output_4iou, decode_label_4iou, decode_output_4iou_sincos from .Rotated_IoU.utiles import box2corners from .Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_diou_sincos import pdb def _slow_neg_loss(pred, gt): '''focal loss from CornerNet''' pos_inds = gt.eq(1) neg_inds = gt.lt(1) neg_weights = torch.pow(1 - gt[neg_inds], 4) loss = 0 pos_pred = pred[pos_inds] neg_pred = pred[neg_inds] pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, 2) neg_loss = torch.log(1 - neg_pred) * torch.pow(neg_pred, 2) * neg_weights num_pos = pos_inds.float().sum() pos_loss = pos_loss.sum() neg_loss = neg_loss.sum() if pos_pred.nelement() == 0: loss = loss - neg_loss else: loss = loss - (pos_loss + neg_loss) / num_pos return loss def _neg_loss(pred, gt): ''' Modified focal loss. Exactly the same as CornerNet. Runs faster and costs a little bit more memory Arguments: pred (batch x c x h x w) gt_regr (batch x c x h x w) ''' pos_inds = gt.eq(1).float() neg_inds = gt.lt(1).float() neg_weights = torch.pow(1 - gt, 4) loss = 0 pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * \ neg_weights * neg_inds num_pos = pos_inds.float().sum() pos_loss = pos_loss.sum() neg_loss = neg_loss.sum() if num_pos == 0: loss = loss - neg_loss else: loss = loss - (pos_loss + neg_loss) / num_pos return loss def _not_faster_neg_loss(pred, gt): pos_inds = gt.eq(1).float() neg_inds = gt.lt(1).float() num_pos = pos_inds.float().sum() neg_weights = torch.pow(1 - gt, 4) loss = 0 trans_pred = pred * neg_inds + (1 - pred) * pos_inds weight = neg_weights * neg_inds + pos_inds all_loss = torch.log(1 - trans_pred) * torch.pow(trans_pred, 2) * weight all_loss = all_loss.sum() if num_pos > 0: all_loss /= num_pos loss -= all_loss return loss def _slow_reg_loss(regr, gt_regr, mask): num = mask.float().sum() mask = mask.unsqueeze(2).expand_as(gt_regr) regr = regr[mask] gt_regr = gt_regr[mask] regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False) regr_loss = regr_loss / (num + 1e-4) return regr_loss def _reg_loss(regr, gt_regr, mask): ''' L1 regression loss Arguments: regr (batch x max_objects x dim) gt_regr (batch x max_objects x dim) mask (batch x max_objects) ''' num = mask.float().sum() mask = mask.unsqueeze(2).expand_as(gt_regr).float() regr = regr * mask gt_regr = gt_regr * mask regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False) regr_loss = regr_loss / (num + 1e-4) return regr_loss # Designed for angle regression def angle_loss(pred_angle, true_angle, size_average=False, use_smooth_l1=False): diff_angle = (pred_angle - true_angle) * math.pi sin, cos = torch.sin(diff_angle), torch.cos(diff_angle) mask = torch.ones_like(cos) mask[cos < 0] = -1 # if cos >=0 else torch.atan2(-sin, -cos) sin, cos = sin * mask, cos * mask if use_smooth_l1: loss = F.smooth_l1_loss(torch.atan2(sin, cos), torch.zeros_like(sin), size_average=False) else: loss = F.l1_loss(torch.atan2(sin, cos), torch.zeros_like(sin), size_average=False) return loss class FocalLoss(nn.Module): '''nn.Module warpper for focal loss''' def __init__(self): super(FocalLoss, self).__init__() self.neg_loss = _neg_loss def forward(self, out, target): return self.neg_loss(out, target) class RegLoss(nn.Module): '''Regression loss for an output tensor Arguments: output (batch x dim x h x w) mask (batch x max_objects) ind (batch x max_objects) target (batch x max_objects x dim) ''' def __init__(self): super(RegLoss, self).__init__() def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) loss = _reg_loss(pred, target, mask) return loss class RegL1Loss(nn.Module): def __init__(self): super(RegL1Loss, self).__init__() def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') loss = F.l1_loss(pred * mask, target * mask, size_average=False) loss = loss / (mask.sum() + 1e-4) return loss # Designed for angle regression class RegL1Loss4Angle(nn.Module): def __init__(self, use_smooth_l1=False): super(RegL1Loss4Angle, self).__init__() self.use_smooth_l1 = use_smooth_l1 def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') loss = angle_loss(pred * mask, target * mask, size_average=False, use_smooth_l1=self.use_smooth_l1) loss = loss / (mask.sum() + 1e-4) return loss # Designed for calculating Rotated IoU Loss class IoULoss(nn.Module): def __init__(self): super(IoULoss, self).__init__() def forward(self, output, target): bbox_pred = decode_output_4iou(output) bbox_label = decode_label_4iou(target) iou_loss, iou = cal_diou(bbox_pred, bbox_label) return iou_loss, iou class IoULossSincos(nn.Module): def __init__(self): super(IoULossSincos, self).__init__() def forward(self, output, target): bbox_pred = decode_output_4iou_sincos(output) bbox_label = decode_label_4iou(target) iou_loss, iou = cal_diou_sincos(bbox_pred, bbox_label) return iou_loss, iou class IoULoss4Angle(nn.Module): def __init__(self): super(IoULoss4Angle, self).__init__() def forward(self, output, target): bbox_pred = decode_output_4iou(output) bbox_label = decode_label_4iou(target) bbox_pred[:, :, 0:4] = bbox_label[:, :, 0:4] iou_loss, iou = cal_diou(bbox_pred, bbox_label) return iou_loss, iou class NormRegL1Loss(nn.Module): def __init__(self): super(NormRegL1Loss, self).__init__() def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') pred = pred / (target + 1e-4) target = target * 0 + 1 loss = F.l1_loss(pred * mask, target * mask, size_average=False) loss = loss / (mask.sum() + 1e-4) return loss class RegWeightedL1Loss(nn.Module): def __init__(self): super(RegWeightedL1Loss, self).__init__() def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) mask = mask.float() # loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') loss = F.l1_loss(pred * mask, target * mask, size_average=False) loss = loss / (mask.sum() + 1e-4) return loss class L1Loss(nn.Module): def __init__(self): super(L1Loss, self).__init__() def forward(self, output, mask, ind, target): pred = _transpose_and_gather_feat(output, ind) mask = mask.unsqueeze(2).expand_as(pred).float() loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean') return loss class BinRotLoss(nn.Module): def __init__(self): super(BinRotLoss, self).__init__() def forward(self, output, mask, ind, rotbin, rotres): pred = _transpose_and_gather_feat(output, ind) loss = compute_rot_loss(pred, rotbin, rotres, mask) return loss def compute_res_loss(output, target): return F.smooth_l1_loss(output, target, reduction='elementwise_mean') # TODO: weight def compute_bin_loss(output, target, mask): mask = mask.expand_as(output) output = output * mask.float() return F.cross_entropy(output, target, reduction='elementwise_mean') def compute_rot_loss(output, target_bin, target_res, mask): # output: (B, 128, 8) [bin1_cls[0], bin1_cls[1], bin1_sin, bin1_cos, # bin2_cls[0], bin2_cls[1], bin2_sin, bin2_cos] # target_bin: (B, 128, 2) [bin1_cls, bin2_cls] # target_res: (B, 128, 2) [bin1_res, bin2_res] # mask: (B, 128, 1) # import pdb; pdb.set_trace() output = output.view(-1, 8) target_bin = target_bin.view(-1, 2) target_res = target_res.view(-1, 2) mask = mask.view(-1, 1) loss_bin1 = compute_bin_loss(output[:, 0:2], target_bin[:, 0], mask) loss_bin2 = compute_bin_loss(output[:, 4:6], target_bin[:, 1], mask) loss_res = torch.zeros_like(loss_bin1) if target_bin[:, 0].nonzero().shape[0] > 0: idx1 = target_bin[:, 0].nonzero()[:, 0] valid_output1 = torch.index_select(output, 0, idx1.long()) valid_target_res1 = torch.index_select(target_res, 0, idx1.long()) loss_sin1 = compute_res_loss( valid_output1[:, 2], torch.sin(valid_target_res1[:, 0])) loss_cos1 = compute_res_loss( valid_output1[:, 3], torch.cos(valid_target_res1[:, 0])) loss_res += loss_sin1 + loss_cos1 if target_bin[:, 1].nonzero().shape[0] > 0: idx2 = target_bin[:, 1].nonzero()[:, 0] valid_output2 = torch.index_select(output, 0, idx2.long()) valid_target_res2 = torch.index_select(target_res, 0, idx2.long()) loss_sin2 = compute_res_loss( valid_output2[:, 6], torch.sin(valid_target_res2[:, 1])) loss_cos2 = compute_res_loss( valid_output2[:, 7], torch.cos(valid_target_res2[:, 1])) loss_res += loss_sin2 + loss_cos2 return loss_bin1 + loss_bin2 + loss_res class L1LossRegression(nn.Module): def __init__(self): super(L1LossRegression, self).__init__() def forward(self, output, target): loss = F.l1_loss(output, target, reduction='elementwise_mean') return loss class AdaptiveWingLoss(nn.Module): def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1): super(AdaptiveWingLoss, self).__init__() self.omega = omega self.theta = theta self.epsilon = epsilon self.alpha = alpha def forward(self, pred, target): ''' :param pred: BxNxHxH :param target: BxNxHxH :return: ''' y = target y_hat = pred delta_y = (y - y_hat).abs() delta_y1 = delta_y[delta_y < self.theta] delta_y2 = delta_y[delta_y >= self.theta] y1 = y[delta_y < self.theta] y2 = y[delta_y >= self.theta] loss1 = self.omega * \ torch.log(1 + torch.pow(delta_y1 / self.epsilon, self.alpha - y1)) A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))) * (self.alpha - y2) * ( torch.pow(self.theta / self.epsilon, self.alpha - y2 - 1)) * (1 / self.epsilon) C = self.theta * A - self.omega * \ torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - y2)) loss2 = A * delta_y2 - C return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2)) slamd202303071458299/src/lib/models/model.py000066400000000000000000000065271435750645400201060ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torchvision.models as models import torch import torch.nn as nn import os from .networks.msra_resnet import get_pose_net from .networks.dlav0 import get_pose_net as get_dlav0 from .networks.pose_dla_dcn import get_pose_net as get_dla_dcn from .networks.resnet_dcn import get_pose_net as get_pose_net_dcn from .networks.large_hourglass import get_large_hourglass_net _model_factory = { 'res': get_pose_net, # default Resnet with deconv 'dlav0': get_dlav0, # default DLAup 'dla': get_dla_dcn, 'resdcn': get_pose_net_dcn, 'hourglass': get_large_hourglass_net, } def create_model(arch, heads, head_conv): num_layers = int(arch[arch.find('_') + 1:]) if '_' in arch else 0 arch = arch[:arch.find('_')] if '_' in arch else arch get_model = _model_factory[arch] model = get_model(num_layers=num_layers, heads=heads, head_conv=head_conv) return model def load_model(model, model_path, optimizer=None, resume=False, lr=None, lr_step=None): start_epoch = 0 checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch'])) state_dict_ = checkpoint['state_dict'] state_dict = {} # convert data_parallal to model for k in state_dict_: if k.startswith('module') and not k.startswith('module_list'): state_dict[k[7:]] = state_dict_[k] else: state_dict[k] = state_dict_[k] model_state_dict = model.state_dict() # check loaded parameters and created model parameters msg = 'If you see this, your model does not fully load the ' + \ 'pre-trained weight. Please make sure ' + \ 'you have correctly specified --arch xxx ' + \ 'or set the correct --num_classes for your own dataset.' for k in state_dict: if k in model_state_dict: if state_dict[k].shape != model_state_dict[k].shape: print('Skip loading parameter {}, required shape{}, '\ 'loaded shape{}. {}'.format( k, model_state_dict[k].shape, state_dict[k].shape, msg)) state_dict[k] = model_state_dict[k] else: print('Drop parameter {}.'.format(k) + msg) for k in model_state_dict: if not (k in state_dict): print('No param {}.'.format(k) + msg) state_dict[k] = model_state_dict[k] model.load_state_dict(state_dict, strict=False) # resume optimizer parameters if optimizer is not None and resume: if 'optimizer' in checkpoint: optimizer.load_state_dict(checkpoint['optimizer']) start_epoch = checkpoint['epoch'] start_lr = lr for step in lr_step: if start_epoch >= step: start_lr *= 0.1 for param_group in optimizer.param_groups: param_group['lr'] = start_lr print('Resumed optimizer with start lr', start_lr) else: print('No optimizer parameters in checkpoint.') if optimizer is not None: return model, optimizer, start_epoch else: return model def save_model(path, epoch, model, optimizer=None): if isinstance(model, torch.nn.DataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() data = {'epoch': epoch, 'state_dict': state_dict} if not (optimizer is None): data['optimizer'] = optimizer.state_dict() torch.save(data, path) slamd202303071458299/src/lib/models/networks/000077500000000000000000000000001435750645400202765ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/000077500000000000000000000000001435750645400211525ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/.gitignore000066400000000000000000000000721435750645400231410ustar00rootroot00000000000000.vscode .idea *.so *.o *pyc _ext build DCNv2.egg-info distslamd202303071458299/src/lib/models/networks/DCNv2/LICENSE000066400000000000000000000027601435750645400221640ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2019, Charles Shang All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.slamd202303071458299/src/lib/models/networks/DCNv2/README.md000066400000000000000000000042621435750645400224350ustar00rootroot00000000000000## Deformable Convolutional Networks V2 with Pytorch 1.0 ### Build ```bash ./make.sh # build python test.py # run examples and gradient check ``` ### An Example - deformable conv ```python from dcn_v2 import DCN input = torch.randn(2, 64, 128, 128).cuda() # wrap all things (offset and mask) in DCN dcn = DCN(64, 64, kernel_size=(3,3), stride=1, padding=1, deformable_groups=2).cuda() output = dcn(input) print(output.shape) ``` - deformable roi pooling ```python from dcn_v2 import DCNPooling input = torch.randn(2, 32, 64, 64).cuda() batch_inds = torch.randint(2, (20, 1)).cuda().float() x = torch.randint(256, (20, 1)).cuda().float() y = torch.randint(256, (20, 1)).cuda().float() w = torch.randint(64, (20, 1)).cuda().float() h = torch.randint(64, (20, 1)).cuda().float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) # mdformable pooling (V2) # wrap all things (offset and mask) in DCNPooling dpooling = DCNPooling(spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1).cuda() dout = dpooling(input, rois) ``` ### Note Now the master branch is for pytorch 1.0 (new ATen API), you can switch back to pytorch 0.4 with, ```bash git checkout pytorch_0.4 ``` ### Known Issues: - [x] Gradient check w.r.t offset (solved) - [ ] Backward is not reentrant (minor) This is an adaption of the official [Deformable-ConvNets](https://github.com/msracver/Deformable-ConvNets/tree/master/DCNv2_op). I have ran the gradient check for many times with DOUBLE type. Every tensor **except offset** passes. However, when I set the offset to 0.5, it passes. I'm still wondering what cause this problem. Is it because some non-differential points? Update: all gradient check passes with double precision. Another issue is that it raises `RuntimeError: Backward is not reentrant`. However, the error is very small (`<1e-7` for float `<1e-15` for double), so it may not be a serious problem (?) Please post an issue or PR if you have any comments. slamd202303071458299/src/lib/models/networks/DCNv2/__init__.py000066400000000000000000000000001435750645400232510ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/dcn_v2.py000066400000000000000000000245241435750645400227060ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import absolute_import, division, print_function import math import torch from torch import nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.nn.modules.utils import _pair import _ext as _backend class _DCNv2(Function): @staticmethod def forward(ctx, input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups): ctx.stride = _pair(stride) ctx.padding = _pair(padding) ctx.dilation = _pair(dilation) ctx.kernel_size = _pair(weight.shape[2:4]) ctx.deformable_groups = deformable_groups output = _backend.dcn_v2_forward( input, weight, bias, offset, mask, ctx.kernel_size[0], ctx.kernel_size[1], ctx.stride[0], ctx.stride[1], ctx.padding[0], ctx.padding[1], ctx.dilation[0], ctx.dilation[1], ctx.deformable_groups, ) ctx.save_for_backward(input, offset, mask, weight, bias) return output @staticmethod @once_differentiable def backward(ctx, grad_output): input, offset, mask, weight, bias = ctx.saved_tensors grad_input, grad_offset, grad_mask, grad_weight, grad_bias = _backend.dcn_v2_backward( input, weight, bias, offset, mask, grad_output, ctx.kernel_size[0], ctx.kernel_size[1], ctx.stride[0], ctx.stride[1], ctx.padding[0], ctx.padding[1], ctx.dilation[0], ctx.dilation[1], ctx.deformable_groups, ) return ( grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, ) dcn_v2_conv = _DCNv2.apply class DCNv2(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1, ): super(DCNv2, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = _pair(stride) self.padding = _pair(padding) self.dilation = _pair(dilation) self.deformable_groups = deformable_groups self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size)) self.bias = nn.Parameter(torch.Tensor(out_channels)) self.reset_parameters() def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1.0 / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.zero_() def forward(self, input, offset, mask): assert 2 * self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == offset.shape[1] assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[1] == mask.shape[1] return dcn_v2_conv( input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups, ) class DCN(DCNv2): def __init__( self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1, ): super(DCN, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, deformable_groups) channels_ = self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1] self.conv_offset_mask = nn.Conv2d( self.in_channels, channels_, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, bias=True, ) self.init_offset() def init_offset(self): self.conv_offset_mask.weight.data.zero_() self.conv_offset_mask.bias.data.zero_() def forward(self, input): out = self.conv_offset_mask(input) o1, o2, mask = torch.chunk(out, 3, dim=1) offset = torch.cat((o1, o2), dim=1) mask = torch.sigmoid(mask) return dcn_v2_conv( input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups, ) class _DCNv2Pooling(Function): @staticmethod def forward( ctx, input, rois, offset, spatial_scale, pooled_size, output_dim, no_trans, group_size=1, part_size=None, sample_per_part=4, trans_std=0.0, ): ctx.spatial_scale = spatial_scale ctx.no_trans = int(no_trans) ctx.output_dim = output_dim ctx.group_size = group_size ctx.pooled_size = pooled_size ctx.part_size = pooled_size if part_size is None else part_size ctx.sample_per_part = sample_per_part ctx.trans_std = trans_std output, output_count = _backend.dcn_v2_psroi_pooling_forward( input, rois, offset, ctx.no_trans, ctx.spatial_scale, ctx.output_dim, ctx.group_size, ctx.pooled_size, ctx.part_size, ctx.sample_per_part, ctx.trans_std, ) ctx.save_for_backward(input, rois, offset, output_count) return output @staticmethod @once_differentiable def backward(ctx, grad_output): input, rois, offset, output_count = ctx.saved_tensors grad_input, grad_offset = _backend.dcn_v2_psroi_pooling_backward( grad_output, input, rois, offset, output_count, ctx.no_trans, ctx.spatial_scale, ctx.output_dim, ctx.group_size, ctx.pooled_size, ctx.part_size, ctx.sample_per_part, ctx.trans_std, ) return grad_input, None, grad_offset, None, None, None, None, None, None, None, None dcn_v2_pooling = _DCNv2Pooling.apply class DCNv2Pooling(nn.Module): def __init__( self, spatial_scale, pooled_size, output_dim, no_trans, group_size=1, part_size=None, sample_per_part=4, trans_std=0.0, ): super(DCNv2Pooling, self).__init__() self.spatial_scale = spatial_scale self.pooled_size = pooled_size self.output_dim = output_dim self.no_trans = no_trans self.group_size = group_size self.part_size = pooled_size if part_size is None else part_size self.sample_per_part = sample_per_part self.trans_std = trans_std def forward(self, input, rois, offset): assert input.shape[1] == self.output_dim if self.no_trans: offset = input.new() return dcn_v2_pooling( input, rois, offset, self.spatial_scale, self.pooled_size, self.output_dim, self.no_trans, self.group_size, self.part_size, self.sample_per_part, self.trans_std, ) class DCNPooling(DCNv2Pooling): def __init__( self, spatial_scale, pooled_size, output_dim, no_trans, group_size=1, part_size=None, sample_per_part=4, trans_std=0.0, deform_fc_dim=1024, ): super(DCNPooling, self).__init__( spatial_scale, pooled_size, output_dim, no_trans, group_size, part_size, sample_per_part, trans_std, ) self.deform_fc_dim = deform_fc_dim if not no_trans: self.offset_mask_fc = nn.Sequential( nn.Linear(self.pooled_size * self.pooled_size * self.output_dim, self.deform_fc_dim), nn.ReLU(inplace=True), nn.Linear(self.deform_fc_dim, self.deform_fc_dim), nn.ReLU(inplace=True), nn.Linear(self.deform_fc_dim, self.pooled_size * self.pooled_size * 3), ) self.offset_mask_fc[4].weight.data.zero_() self.offset_mask_fc[4].bias.data.zero_() def forward(self, input, rois): offset = input.new() if not self.no_trans: # do roi_align first n = rois.shape[0] roi = dcn_v2_pooling( input, rois, offset, self.spatial_scale, self.pooled_size, self.output_dim, True, # no trans self.group_size, self.part_size, self.sample_per_part, self.trans_std, ) # build mask and offset offset_mask = self.offset_mask_fc(roi.view(n, -1)) offset_mask = offset_mask.view(n, 3, self.pooled_size, self.pooled_size) o1, o2, mask = torch.chunk(offset_mask, 3, dim=1) offset = torch.cat((o1, o2), dim=1) mask = torch.sigmoid(mask) # do pooling with offset and mask return ( dcn_v2_pooling( input, rois, offset, self.spatial_scale, self.pooled_size, self.output_dim, self.no_trans, self.group_size, self.part_size, self.sample_per_part, self.trans_std, ) * mask ) # only roi_align return dcn_v2_pooling( input, rois, offset, self.spatial_scale, self.pooled_size, self.output_dim, self.no_trans, self.group_size, self.part_size, self.sample_per_part, self.trans_std, ) slamd202303071458299/src/lib/models/networks/DCNv2/make.sh000066400000000000000000000000621435750645400224210ustar00rootroot00000000000000#!/usr/bin/env bash python setup.py build develop slamd202303071458299/src/lib/models/networks/DCNv2/setup.py000066400000000000000000000037671435750645400227010ustar00rootroot00000000000000#!/usr/bin/env python import glob import os import sys import torch from setuptools import find_packages, setup from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension requirements = ["torch", "torchvision"] def get_extensions(): this_dir = os.path.dirname(os.path.abspath(__file__)) extensions_dir = os.path.join(this_dir, "src") main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) os.environ["CC"] = "g++" sources = main_file + source_cpu extension = CppExtension extra_compile_args = {"cxx": []} define_macros = [] if torch.cuda.is_available() and CUDA_HOME is not None: extension = CUDAExtension sources += source_cuda define_macros += [("WITH_CUDA", None)] extra_compile_args["nvcc"] = [ "-DCUDA_HAS_FP16=1", "-D__CUDA_NO_HALF_OPERATORS__", "-D__CUDA_NO_HALF_CONVERSIONS__", "-D__CUDA_NO_HALF2_OPERATORS__", ] else: # raise NotImplementedError('Cuda is not available') pass extra_compile_args['cxx'].append('-fopenmp') sources = [os.path.join(extensions_dir, s) for s in sources] include_dirs = [extensions_dir] ext_modules = [ extension( "_ext", sources, include_dirs=include_dirs, define_macros=define_macros, extra_compile_args=extra_compile_args, ) ] return ext_modules setup( name="DCNv2", version="0.1", author="charlesshang", url="https://github.com/charlesshang/DCNv2", description="deformable convolutional networks", packages=find_packages( exclude=( "configs", "tests", ) ), # install_requires=requirements, ext_modules=get_extensions(), cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, ) slamd202303071458299/src/lib/models/networks/DCNv2/src/000077500000000000000000000000001435750645400217415ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/000077500000000000000000000000001435750645400225305ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.cpp000066400000000000000000000250771435750645400252710ustar00rootroot00000000000000#include #include "cpu/dcn_v2_im2col_cpu.h" #include #include //#include #include //#include //#include //extern THCState *state; // author: Charles Shang // https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu // modified from the CUDA version for CPU use by Daniel K. Suhendro // edit by: James Bockman and Matthew Howe // modified for torch implementation to remove use of deprecated torch access to Blas at::Tensor dcn_v2_cpu_forward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int deformable_group) { // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); /*AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor");*/ const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_out = weight.size(0); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); // printf("Channels: %d %d\n", channels, channels_kernel); // printf("Channels: %d %d\n", channels_out, channels_kernel); AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); AT_ASSERTM(channels == channels_kernel, "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; // auto ones = at::ones({height_out, width_out}, input.options()); auto ones = at::ones({bias.sizes()[0], height_out, width_out}, input.options()); auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); auto output = at::zeros({batch, channels_out, height_out, width_out}, input.options()); using scalar_t = float; for (int b = 0; b < batch; b++) { auto input_n = input.select(0, b); auto offset_n = offset.select(0, b); auto mask_n = mask.select(0, b); auto output_n = output.select(0, b); // std::cout << "output_n: " << output_n << "output.select(0,b): " << output.select(0,b) << "\n"; // Do Bias first: // M,N,K are dims of matrix A and B // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm) // (N x 1) (1 x M) // torch implementation auto ones_T = at::transpose(ones.contiguous(), 2, 0); ones_T = at::mul(ones_T, bias.contiguous()); ones_T = at::transpose(ones_T, 2, 0); output_n = at::add(output_n, ones_T); modulated_deformable_im2col_cpu(input_n.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns.data_ptr()); //(k * m) x (m * n) // Y = WC // torch implementation auto weight_flat = weight.view({channels_out, channels * kernel_h * kernel_w}); auto product = at::matmul(weight_flat, columns); output.select(0, b) = at::add(output_n, product.view({channels_out, height_out, width_out})); } return output; } std::vector dcn_v2_cpu_backward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const at::Tensor &grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int deformable_group) { THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); /*AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor");*/ const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_out = weight.size(0); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); AT_ASSERTM(channels == channels_kernel, "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; auto ones = at::ones({height_out, width_out}, input.options()); auto columns = at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); auto grad_input = at::zeros_like(input); auto grad_weight = at::zeros_like(weight); auto grad_bias = at::zeros_like(bias); auto grad_offset = at::zeros_like(offset); auto grad_mask = at::zeros_like(mask); using scalar_t = float; for (int b = 0; b < batch; b++) { auto input_n = input.select(0, b); auto offset_n = offset.select(0, b); auto mask_n = mask.select(0, b); auto grad_output_n = grad_output.select(0, b); auto grad_input_n = grad_input.select(0, b); auto grad_offset_n = grad_offset.select(0, b); auto grad_mask_n = grad_mask.select(0, b); // Torch implementation auto weight_flat = weight.view({channels_out, channels*kernel_h*kernel_w}); weight_flat = at::transpose(weight_flat, 1, 0); auto grad_output_n_flat = grad_output_n.view({channels_out, height_out*width_out}); columns = at::matmul(weight_flat, grad_output_n_flat); // gradient w.r.t. input coordinate data modulated_deformable_col2im_coord_cpu(columns.data_ptr(), input_n.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_offset_n.data_ptr(), grad_mask_n.data_ptr()); // gradient w.r.t. input data modulated_deformable_col2im_cpu(columns.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_input_n.data_ptr()); // gradient w.r.t. weight, dWeight should accumulate across the batch and group modulated_deformable_im2col_cpu(input_n.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns.data_ptr()); // Torch implementation auto product = at::matmul(grad_output_n_flat, at::transpose(columns, 1, 0)); grad_weight = at::add(grad_weight, product.view({channels_out, channels, kernel_h, kernel_w})); // Torch implementation auto ones_flat = ones.view({height_out*width_out}); product = at::matmul(grad_output_n_flat, ones_flat); grad_bias = at::add(grad_bias, product); } return { grad_input, grad_offset, grad_mask, grad_weight, grad_bias }; } slamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp000066400000000000000000000467541435750645400265430ustar00rootroot00000000000000#include "dcn_v2_im2col_cpu.h" #include #include #include #include //#include #include //#include //#include // modified from the CUDA version for CPU use by Daniel K. Suhendro /*#define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) const int CUDA_NUM_THREADS = 1024; inline int GET_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; }*/ float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_width, const int height, const int width, float h, float w) { int h_low = floor(h); int w_low = floor(w); int h_high = h_low + 1; int w_high = w_low + 1; float lh = h - h_low; float lw = w - w_low; float hh = 1 - lh, hw = 1 - lw; float v1 = 0; if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; float v2 = 0; if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; float v3 = 0; if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; float v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); return val; } float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w, const int h, const int w, const int height, const int width) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; float weight = 0; if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); return weight; } float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w, const int height, const int width, const float *im_data, const int data_width, const int bp_dir) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; float weight = 0; if (bp_dir == 0) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } else if (bp_dir == 1) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } return weight; } void modulated_deformable_im2col_cpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int num_channels, const int deformable_group, const int height_col, const int width_col, float *data_col) { // launch channels * batch_size * height_col * width_col cores for(int index=0; index(0); const float h_im = h_in + i * dilation_h + offset_h; const float w_im = w_in + j * dilation_w + offset_w; //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { //const float map_h = i * dilation_h + offset_h; //const float map_w = j * dilation_w + offset_w; //const int cur_height = height - h_in; //const int cur_width = width - w_in; //val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, cur_height, cur_width, map_h, map_w); val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width, h_im, w_im); } *data_col_ptr = val * mask; // data_col_ptr += batch_size * height_col * width_col; data_col_ptr += height_col * width_col; } } } } void modulated_deformable_col2im_cpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int deformable_group, const int height_col, const int width_col, float *grad_im) { for(int index = 0; index < n; index++) { const int j = (index / width_col / height_col / batch_size) % kernel_w; const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; // compute the start and end of the output const int deformable_group_index = c / channel_per_deformable_group; int w_out = index % width_col; int h_out = (index / width_col) % height_col; int b = (index / width_col / height_col) % batch_size; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; const float offset_h = data_offset_ptr[data_offset_h_ptr]; const float offset_w = data_offset_ptr[data_offset_w_ptr]; const float mask = data_mask_ptr[data_mask_hw_ptr]; const float cur_inv_h_data = h_in + i * dilation_h + offset_h; const float cur_inv_w_data = w_in + j * dilation_w + offset_w; const float cur_top_grad = data_col[index] * mask; const int cur_h = (int)cur_inv_h_data; const int cur_w = (int)cur_inv_w_data; for (int dy = -2; dy <= 2; dy++) { for (int dx = -2; dx <= 2; dx++) { if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && abs(cur_inv_w_data - (cur_w + dx)) < 1) { int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; float weight = dmcn_get_gradient_weight_cpu(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); //atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); *(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad; } } } } } void modulated_deformable_col2im_coord_cpu_kernel(const int n, const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int offset_channels, const int deformable_group, const int height_col, const int width_col, float *grad_offset, float *grad_mask) { for(int index = 0; index < n; index++) { float val = 0, mval = 0; int w = index % width_col; int h = (index / width_col) % height_col; int c = (index / width_col / height_col) % offset_channels; int b = (index / width_col / height_col) / offset_channels; // compute the start and end of the output const int deformable_group_index = c / (2 * kernel_h * kernel_w); const int col_step = kernel_h * kernel_w; int cnt = 0; const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) { const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; const int bp_dir = offset_c % 2; int j = (col_pos / width_col / height_col / batch_size) % kernel_w; int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; int w_out = col_pos % width_col; int h_out = (col_pos / width_col) % height_col; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); const float offset_h = data_offset_ptr[data_offset_h_ptr]; const float offset_w = data_offset_ptr[data_offset_w_ptr]; const float mask = data_mask_ptr[data_mask_hw_ptr]; float inv_h = h_in + i * dilation_h + offset_h; float inv_w = w_in + j * dilation_w + offset_w; if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { inv_h = inv_w = -2; } else { mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cpu(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); } const float weight = dmcn_get_coordinate_weight_cpu( inv_h, inv_w, height, width, data_im_ptr + cnt * height * width, width, bp_dir); val += weight * data_col_ptr[col_pos] * mask; cnt += 1; } // KERNEL_ASSIGN(grad_offset[index], offset_req, val); grad_offset[index] = val; if (offset_c % 2 == 0) // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; } } void modulated_deformable_im2col_cpu(const float* data_im, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* data_col) { // num_axes should be smaller than block size const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * batch_size * height_col * width_col; modulated_deformable_im2col_cpu_kernel( num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, channels, deformable_group, height_col, width_col, data_col); /*cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); }*/ } void modulated_deformable_col2im_cpu(const float* data_col, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* grad_im){ const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; modulated_deformable_col2im_cpu_kernel( num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, deformable_group, height_col, width_col, grad_im); /*cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); }*/ } void modulated_deformable_col2im_coord_cpu(const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* grad_offset, float* grad_mask) { const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; modulated_deformable_col2im_coord_cpu_kernel( num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, grad_offset, grad_mask); /*cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); }*/ }slamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.h000066400000000000000000000117611435750645400261760ustar00rootroot00000000000000 /*! ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** * * COPYRIGHT * * All contributions by the University of California: * Copyright (c) 2014-2017 The Regents of the University of California (Regents) * All rights reserved. * * All other contributions: * Copyright (c) 2014-2017, the respective contributors * All rights reserved. * * Caffe uses a shared copyright model: each contributor holds copyright over * their contributions to Caffe. The project versioning records all such * contribution and copyright details. If a contributor wants to further mark * their specific copyright on a particular contribution, they should indicate * their copyright solely in the commit message of the change when it is * committed. * * LICENSE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * * CONTRIBUTION AGREEMENT * * By contributing to the BVLC/caffe repository through pull-request, comment, * or otherwise, the contributor releases their content to the * license and copyright terms herein. * ***************** END Caffe Copyright Notice and Disclaimer ******************** * * Copyright (c) 2018 Microsoft * Licensed under The MIT License [see LICENSE for details] * \file modulated_deformable_im2col.h * \brief Function definitions of converting an image to * column matrix based on kernel, padding, dilation, and offset. * These functions are mainly used in deformable convolution operators. * \ref: https://arxiv.org/abs/1811.11168 * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu */ /***************** Adapted by Charles Shang *********************/ // modified from the CUDA version for CPU use by Daniel K. Suhendro #ifndef DCN_V2_IM2COL_CPU #define DCN_V2_IM2COL_CPU #ifdef __cplusplus extern "C" { #endif void modulated_deformable_im2col_cpu(const float *data_im, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *data_col); void modulated_deformable_col2im_cpu(const float *data_col, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *grad_im); void modulated_deformable_col2im_coord_cpu(const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *grad_offset, float *grad_mask); #ifdef __cplusplus } #endif #endifslamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp000066400000000000000000000411701435750645400302240ustar00rootroot00000000000000/*! * Copyright (c) 2017 Microsoft * Licensed under The MIT License [see LICENSE for details] * \file deformable_psroi_pooling.cu * \brief * \author Yi Li, Guodong Zhang, Jifeng Dai */ /***************** Adapted by Charles Shang *********************/ // modified from the CUDA version for CPU use by Daniel K. Suhendro #include #include #include #include //#include #include //#include //#include /*#define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) const int CUDA_NUM_THREADS = 1024; inline int GET_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; }*/ template T bilinear_interp_cpu( const T *data, const T x, const T y, const int width, const int height) { int x1 = floor(x); int x2 = ceil(x); int y1 = floor(y); int y2 = ceil(y); T dist_x = static_cast(x - x1); T dist_y = static_cast(y - y1); T value11 = data[y1 * width + x1]; T value12 = data[y2 * width + x1]; T value21 = data[y1 * width + x2]; T value22 = data[y2 * width + x2]; T value = (1 - dist_x) * (1 - dist_y) * value11 + (1 - dist_x) * dist_y * value12 + dist_x * (1 - dist_y) * value21 + dist_x * dist_y * value22; return value; } template void DeformablePSROIPoolForwardKernelCpu( const int count, const T *bottom_data, const T spatial_scale, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const T *bottom_rois, const T *bottom_trans, const int no_trans, const T trans_std, const int sample_per_part, const int output_dim, const int group_size, const int part_size, const int num_classes, const int channels_each_class, T *top_data, T *top_count) { for(int index = 0; index < count; index++) { // The output is in order (n, ctop, ph, pw) int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int ctop = (index / pooled_width / pooled_height) % output_dim; int n = index / pooled_width / pooled_height / output_dim; // [start, end) interval for spatial sampling const T *offset_bottom_rois = bottom_rois + n * 5; int roi_batch_ind = offset_bottom_rois[0]; T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; // Force too small ROIs to be 1x1 T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); // Compute w and h at bottom T bin_size_h = roi_height / static_cast(pooled_height); T bin_size_w = roi_width / static_cast(pooled_width); T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); int part_h = floor(static_cast(ph) / pooled_height * part_size); int part_w = floor(static_cast(pw) / pooled_width * part_size); int class_id = ctop / channels_each_class; T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; T wstart = static_cast(pw) * bin_size_w + roi_start_w; wstart += trans_x * roi_width; T hstart = static_cast(ph) * bin_size_h + roi_start_h; hstart += trans_y * roi_height; T sum = 0; int count = 0; int gw = floor(static_cast(pw) * group_size / pooled_width); int gh = floor(static_cast(ph) * group_size / pooled_height); gw = std::min(std::max(gw, 0), group_size - 1); gh = std::min(std::max(gh, 0), group_size - 1); const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; for (int ih = 0; ih < sample_per_part; ih++) { for (int iw = 0; iw < sample_per_part; iw++) { T w = wstart + iw * sub_bin_size_w; T h = hstart + ih * sub_bin_size_h; // bilinear interpolation if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) { continue; } w = std::min(std::max(w, T(0.)), width - T(1.)); h = std::min(std::max(h, T(0.)), height - T(1.)); int c = (ctop * group_size + gh) * group_size + gw; T val = bilinear_interp_cpu(offset_bottom_data + c * height * width, w, h, width, height); sum += val; count++; } } top_data[index] = count == 0 ? static_cast(0) : sum / count; top_count[index] = count; } } template void DeformablePSROIPoolBackwardAccKernelCpu( const int count, const T *top_diff, const T *top_count, const int num_rois, const T spatial_scale, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int output_dim, T *bottom_data_diff, T *bottom_trans_diff, const T *bottom_data, const T *bottom_rois, const T *bottom_trans, const int no_trans, const T trans_std, const int sample_per_part, const int group_size, const int part_size, const int num_classes, const int channels_each_class) { for(int index = 0; index < count; index++) { // The output is in order (n, ctop, ph, pw) int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int ctop = (index / pooled_width / pooled_height) % output_dim; int n = index / pooled_width / pooled_height / output_dim; // [start, end) interval for spatial sampling const T *offset_bottom_rois = bottom_rois + n * 5; int roi_batch_ind = offset_bottom_rois[0]; T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; // Force too small ROIs to be 1x1 T roi_width = std::max(roi_end_w - roi_start_w, T(0.1)); //avoid 0 T roi_height = std::max(roi_end_h - roi_start_h, T(0.1)); // Compute w and h at bottom T bin_size_h = roi_height / static_cast(pooled_height); T bin_size_w = roi_width / static_cast(pooled_width); T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); int part_h = floor(static_cast(ph) / pooled_height * part_size); int part_w = floor(static_cast(pw) / pooled_width * part_size); int class_id = ctop / channels_each_class; T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; T wstart = static_cast(pw) * bin_size_w + roi_start_w; wstart += trans_x * roi_width; T hstart = static_cast(ph) * bin_size_h + roi_start_h; hstart += trans_y * roi_height; if (top_count[index] <= 0) { continue; } T diff_val = top_diff[index] / top_count[index]; const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; int gw = floor(static_cast(pw) * group_size / pooled_width); int gh = floor(static_cast(ph) * group_size / pooled_height); gw = std::min(std::max(gw, 0), group_size - 1); gh = std::min(std::max(gh, 0), group_size - 1); for (int ih = 0; ih < sample_per_part; ih++) { for (int iw = 0; iw < sample_per_part; iw++) { T w = wstart + iw * sub_bin_size_w; T h = hstart + ih * sub_bin_size_h; // bilinear interpolation if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) { continue; } w = std::min(std::max(w, T(0.)), width - T(1.)); h = std::min(std::max(h, T(0.)), height - T(1.)); int c = (ctop * group_size + gh) * group_size + gw; // backward on feature int x0 = floor(w); int x1 = ceil(w); int y0 = floor(h); int y1 = ceil(h); T dist_x = w - x0, dist_y = h - y0; T q00 = (1 - dist_x) * (1 - dist_y); T q01 = (1 - dist_x) * dist_y; T q10 = dist_x * (1 - dist_y); T q11 = dist_x * dist_y; int bottom_index_base = c * height * width; /*atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val);*/ *(offset_bottom_data_diff + bottom_index_base + y0 * width + x0) += q00 * diff_val; *(offset_bottom_data_diff + bottom_index_base + y1 * width + x0) += q01 * diff_val; *(offset_bottom_data_diff + bottom_index_base + y0 * width + x1) += q10 * diff_val; *(offset_bottom_data_diff + bottom_index_base + y1 * width + x1) += q11 * diff_val; if (no_trans) { continue; } T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; diff_x *= roi_width; T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; diff_y *= roi_height; /*atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y);*/ *(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w) += diff_x; *(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w) += diff_y; } } } } std::tuple dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { /*AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(bbox.is_cuda(), "rois must be a CUDA tensor"); AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor");*/ // const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_trans = no_trans ? 2 : trans.size(1); const int num_bbox = bbox.size(0); AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); auto pooled_height = pooled_size; auto pooled_width = pooled_size; auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); long out_size = num_bbox * output_dim * pooled_height * pooled_width; auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); const int num_classes = no_trans ? 1 : channels_trans / 2; const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; //cudaStream_t stream = at::cuda::getCurrentCUDAStream(); if (out.numel() == 0) { //THCudaCheck(cudaGetLastError()); return std::make_tuple(out, top_count); } /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); dim3 block(512);*/ AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "dcn_v2_psroi_pooling_cpu_forward", [&] { DeformablePSROIPoolForwardKernelCpu( out_size, input.contiguous().data_ptr(), spatial_scale, channels, height, width, pooled_height, pooled_width, bbox.contiguous().data_ptr(), trans.contiguous().data_ptr(), no_trans, trans_std, sample_per_part, output_dim, group_size, part_size, num_classes, channels_each_class, out.data_ptr(), top_count.data_ptr()); }); //THCudaCheck(cudaGetLastError()); return std::make_tuple(out, top_count); } std::tuple dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const at::Tensor &top_count, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { /*AT_ASSERTM(out_grad.is_cuda(), "out_grad must be a CUDA tensor"); AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(bbox.is_cuda(), "bbox must be a CUDA tensor"); AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor"); AT_ASSERTM(top_count.is_cuda(), "top_count must be a CUDA tensor");*/ const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_trans = no_trans ? 2 : trans.size(1); const int num_bbox = bbox.size(0); AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); auto pooled_height = pooled_size; auto pooled_width = pooled_size; long out_size = num_bbox * output_dim * pooled_height * pooled_width; const int num_classes = no_trans ? 1 : channels_trans / 2; const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); auto trans_grad = at::zeros_like(trans); if (input_grad.numel() == 0) { //THCudaCheck(cudaGetLastError()); return std::make_tuple(input_grad, trans_grad); } /*dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); dim3 block(512); cudaStream_t stream = at::cuda::getCurrentCUDAStream();*/ AT_DISPATCH_FLOATING_TYPES(out_grad.scalar_type(), "dcn_v2_psroi_pooling_cpu_backward", [&] { DeformablePSROIPoolBackwardAccKernelCpu( out_size, out_grad.contiguous().data_ptr(), top_count.contiguous().data_ptr(), num_bbox, spatial_scale, channels, height, width, pooled_height, pooled_width, output_dim, input_grad.contiguous().data_ptr(), trans_grad.contiguous().data_ptr(), input.contiguous().data_ptr(), bbox.contiguous().data_ptr(), trans.contiguous().data_ptr(), no_trans, trans_std, sample_per_part, group_size, part_size, num_classes, channels_each_class); }); //THCudaCheck(cudaGetLastError()); return std::make_tuple(input_grad, trans_grad); }slamd202303071458299/src/lib/models/networks/DCNv2/src/cpu/vision.h000066400000000000000000000051511435750645400242120ustar00rootroot00000000000000#pragma once #include at::Tensor dcn_v2_cpu_forward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int deformable_group); std::vector dcn_v2_cpu_backward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const at::Tensor &grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int deformable_group); std::tuple dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std); std::tuple dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad, const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const at::Tensor &top_count, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std);slamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/000077500000000000000000000000001435750645400226555ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_cuda.cu000066400000000000000000000353721435750645400253670ustar00rootroot00000000000000#include #include "cuda/dcn_v2_im2col_cuda.h" #include #include #include #include #include THCState *state = at::globalContext().lazyInitCUDA(); // author: Charles Shang // https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu // [batch gemm] // https://github.com/pytorch/pytorch/blob/master/aten/src/THC/generic/THCTensorMathBlas.cu __global__ void createBatchGemmBuffer(const float **input_b, float **output_b, float **columns_b, const float **ones_b, const float **weight_b, const float **bias_b, float *input, float *output, float *columns, float *ones, float *weight, float *bias, const int input_stride, const int output_stride, const int columns_stride, const int ones_stride, const int num_batches) { const int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < num_batches) { input_b[idx] = input + idx * input_stride; output_b[idx] = output + idx * output_stride; columns_b[idx] = columns + idx * columns_stride; ones_b[idx] = ones + idx * ones_stride; // share weights and bias within a Mini-Batch weight_b[idx] = weight; bias_b[idx] = bias; } } at::Tensor dcn_v2_cuda_forward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int deformable_group) { using scalar_t = float; // THCAssertSameGPU(THCudaTensor_checkGPU(state, 5, input, weight, bias, offset, mask)); AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor"); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_out = weight.size(0); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); // printf("Kernels: %d %d %d %d\n", kernel_h_, kernel_w_, kernel_w, kernel_h); // printf("Channels: %d %d\n", channels, channels_kernel); // printf("Channels: %d %d\n", channels_out, channels_kernel); AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); AT_ASSERTM(channels == channels_kernel, "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; auto ones = at::ones({batch, height_out, width_out}, input.options()); auto columns = at::empty({batch, channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); // prepare for batch-wise computing, which is significantly faster than instance-wise computing // when batch size is large. // launch batch threads int matrices_size = batch * sizeof(float *); auto input_b = static_cast(THCudaMalloc(state, matrices_size)); auto output_b = static_cast(THCudaMalloc(state, matrices_size)); auto columns_b = static_cast(THCudaMalloc(state, matrices_size)); auto ones_b = static_cast(THCudaMalloc(state, matrices_size)); auto weight_b = static_cast(THCudaMalloc(state, matrices_size)); auto bias_b = static_cast(THCudaMalloc(state, matrices_size)); const int block = 128; const int grid = (batch + block - 1) / block; createBatchGemmBuffer<<>>( input_b, output_b, columns_b, ones_b, weight_b, bias_b, input.data_ptr(), output.data_ptr(), columns.data_ptr(), ones.data_ptr(), weight.data_ptr(), bias.data_ptr(), channels * width * height, channels_out * width_out * height_out, channels * kernel_h * kernel_w * height_out * width_out, height_out * width_out, batch); long m_ = channels_out; long n_ = height_out * width_out; long k_ = 1; THCudaBlas_SgemmBatched(state, 't', 'n', n_, m_, k_, 1.0f, ones_b, k_, bias_b, k_, 0.0f, output_b, n_, batch); modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), input.data_ptr(), offset.data_ptr(), mask.data_ptr(), batch, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns.data_ptr()); long m = channels_out; long n = height_out * width_out; long k = channels * kernel_h * kernel_w; THCudaBlas_SgemmBatched(state, 'n', 'n', n, m, k, 1.0f, (const float **)columns_b, n, weight_b, k, 1.0f, output_b, n, batch); THCudaFree(state, input_b); THCudaFree(state, output_b); THCudaFree(state, columns_b); THCudaFree(state, ones_b); THCudaFree(state, weight_b); THCudaFree(state, bias_b); return output; } __global__ void createBatchGemmBufferBackward( float **grad_output_b, float **columns_b, float **ones_b, float **weight_b, float **grad_weight_b, float **grad_bias_b, float *grad_output, float *columns, float *ones, float *weight, float *grad_weight, float *grad_bias, const int grad_output_stride, const int columns_stride, const int ones_stride, const int num_batches) { const int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < num_batches) { grad_output_b[idx] = grad_output + idx * grad_output_stride; columns_b[idx] = columns + idx * columns_stride; ones_b[idx] = ones + idx * ones_stride; // share weights and bias within a Mini-Batch weight_b[idx] = weight; grad_weight_b[idx] = grad_weight; grad_bias_b[idx] = grad_bias; } } std::vector dcn_v2_cuda_backward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const at::Tensor &grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int deformable_group) { THArgCheck(input.is_contiguous(), 1, "input tensor has to be contiguous"); THArgCheck(weight.is_contiguous(), 2, "weight tensor has to be contiguous"); AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(weight.is_cuda(), "weight must be a CUDA tensor"); AT_ASSERTM(bias.is_cuda(), "bias must be a CUDA tensor"); AT_ASSERTM(offset.is_cuda(), "offset must be a CUDA tensor"); AT_ASSERTM(mask.is_cuda(), "mask must be a CUDA tensor"); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_out = weight.size(0); const int channels_kernel = weight.size(1); const int kernel_h_ = weight.size(2); const int kernel_w_ = weight.size(3); AT_ASSERTM(kernel_h_ == kernel_h && kernel_w_ == kernel_w, "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", kernel_h_, kernel_w, kernel_h_, kernel_w_); AT_ASSERTM(channels == channels_kernel, "Input shape and kernel channels wont match: (%d vs %d).", channels, channels_kernel); const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; auto ones = at::ones({height_out, width_out}, input.options()); auto columns = at::empty({channels * kernel_h * kernel_w, 1 * height_out * width_out}, input.options()); auto output = at::empty({batch, channels_out, height_out, width_out}, input.options()); auto grad_input = at::zeros_like(input); auto grad_weight = at::zeros_like(weight); auto grad_bias = at::zeros_like(bias); auto grad_offset = at::zeros_like(offset); auto grad_mask = at::zeros_like(mask); using scalar_t = float; for (int b = 0; b < batch; b++) { auto input_n = input.select(0, b); auto offset_n = offset.select(0, b); auto mask_n = mask.select(0, b); auto grad_output_n = grad_output.select(0, b); auto grad_input_n = grad_input.select(0, b); auto grad_offset_n = grad_offset.select(0, b); auto grad_mask_n = grad_mask.select(0, b); long m = channels * kernel_h * kernel_w; long n = height_out * width_out; long k = channels_out; THCudaBlas_Sgemm(state, 'n', 't', n, m, k, 1.0f, grad_output_n.data_ptr(), n, weight.data_ptr(), m, 0.0f, columns.data_ptr(), n); // gradient w.r.t. input coordinate data modulated_deformable_col2im_coord_cuda(c10::cuda::getCurrentCUDAStream(), columns.data_ptr(), input_n.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_offset_n.data_ptr(), grad_mask_n.data_ptr()); // gradient w.r.t. input data modulated_deformable_col2im_cuda(c10::cuda::getCurrentCUDAStream(), columns.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, grad_input_n.data_ptr()); // gradient w.r.t. weight, dWeight should accumulate across the batch and group modulated_deformable_im2col_cuda(c10::cuda::getCurrentCUDAStream(), input_n.data_ptr(), offset_n.data_ptr(), mask_n.data_ptr(), 1, channels, height, width, height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, deformable_group, columns.data_ptr()); long m_ = channels_out; long n_ = channels * kernel_h * kernel_w; long k_ = height_out * width_out; THCudaBlas_Sgemm(state, 't', 'n', n_, m_, k_, 1.0f, columns.data_ptr(), k_, grad_output_n.data_ptr(), k_, 1.0f, grad_weight.data_ptr(), n_); // gradient w.r.t. bias // long m_ = channels_out; // long k__ = height_out * width_out; // THCudaBlas_Sgemm(state, // 't', 'n', // k_, m_, 1, 1.0f, // grad_output_n.data_ptr(), k_, // ones.data_ptr(), 1, 1.0f, // grad_bias.data_ptr(), 1); THCudaBlas_Sgemm(state, 'N', 'N', 1, m_, k_, 1.0f, ones.data_ptr(), 1, grad_output_n.data_ptr(), k_, 1.0f, grad_bias.data_ptr(), 1); } return { grad_input, grad_offset, grad_mask, grad_weight, grad_bias }; } slamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.cu000066400000000000000000000475571435750645400266440ustar00rootroot00000000000000#include "dcn_v2_im2col_cuda.h" #include #include #include #include #include #include #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) const int CUDA_NUM_THREADS = 1024; inline int GET_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } __device__ float dmcn_im2col_bilinear_cuda(const float *bottom_data, const int data_width, const int height, const int width, float h, float w) { int h_low = floor(h); int w_low = floor(w); int h_high = h_low + 1; int w_high = w_low + 1; float lh = h - h_low; float lw = w - w_low; float hh = 1 - lh, hw = 1 - lw; float v1 = 0; if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; float v2 = 0; if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; float v3 = 0; if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; float v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); return val; } __device__ float dmcn_get_gradient_weight_cuda(float argmax_h, float argmax_w, const int h, const int w, const int height, const int width) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; float weight = 0; if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); return weight; } __device__ float dmcn_get_coordinate_weight_cuda(float argmax_h, float argmax_w, const int height, const int width, const float *im_data, const int data_width, const int bp_dir) { if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { //empty return 0; } int argmax_h_low = floor(argmax_h); int argmax_w_low = floor(argmax_w); int argmax_h_high = argmax_h_low + 1; int argmax_w_high = argmax_w_low + 1; float weight = 0; if (bp_dir == 0) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } else if (bp_dir == 1) { if (argmax_h_low >= 0 && argmax_w_low >= 0) weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; if (argmax_h_low >= 0 && argmax_w_high <= width - 1) weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; if (argmax_h_high <= height - 1 && argmax_w_low >= 0) weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; } return weight; } __global__ void modulated_deformable_im2col_gpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int num_channels, const int deformable_group, const int height_col, const int width_col, float *data_col) { // launch channels * batch_size * height_col * width_col cores CUDA_KERNEL_LOOP(index, n) { // NOTE(CharlesShang): different from Dai Jifeng's MXNet implementation, col_buffer is of shape (c*kw*kh, N, oh, ow) // here columns is of shape (N, c*kw*kh, oh * ow), need to adapt axis // index index of output matrix const int w_col = index % width_col; const int h_col = (index / width_col) % height_col; // const int b_col = (index / width_col / height_col) % batch_size; const int b_col = (index / width_col / height_col / num_channels) % batch_size; // const int c_im = (index / width_col / height_col) / batch_size; const int c_im = (index / width_col / height_col) % num_channels; // const int c_col = c_im * kernel_h * kernel_w; const int c_col = c_im * kernel_h * kernel_w; // compute deformable group index const int deformable_group_index = c_im / channel_per_deformable_group; const int h_in = h_col * stride_h - pad_h; const int w_in = w_col * stride_w - pad_w; // float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; float *data_col_ptr = data_col + ((b_col * num_channels * kernel_w * kernel_h + c_col) * height_col + h_col) * width_col + w_col; //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; const float *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const float *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; for (int i = 0; i < kernel_h; ++i) { for (int j = 0; j < kernel_w; ++j) { const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; const float offset_h = data_offset_ptr[data_offset_h_ptr]; const float offset_w = data_offset_ptr[data_offset_w_ptr]; const float mask = data_mask_ptr[data_mask_hw_ptr]; float val = static_cast(0); const float h_im = h_in + i * dilation_h + offset_h; const float w_im = w_in + j * dilation_w + offset_w; //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { //const float map_h = i * dilation_h + offset_h; //const float map_w = j * dilation_w + offset_w; //const int cur_height = height - h_in; //const int cur_width = width - w_in; //val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, cur_height, cur_width, map_h, map_w); val = dmcn_im2col_bilinear_cuda(data_im_ptr, width, height, width, h_im, w_im); } *data_col_ptr = val * mask; // data_col_ptr += batch_size * height_col * width_col; data_col_ptr += height_col * width_col; } } } } __global__ void modulated_deformable_col2im_gpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int deformable_group, const int height_col, const int width_col, float *grad_im) { CUDA_KERNEL_LOOP(index, n) { const int j = (index / width_col / height_col / batch_size) % kernel_w; const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; // compute the start and end of the output const int deformable_group_index = c / channel_per_deformable_group; int w_out = index % width_col; int h_out = (index / width_col) % height_col; int b = (index / width_col / height_col) % batch_size; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; const float offset_h = data_offset_ptr[data_offset_h_ptr]; const float offset_w = data_offset_ptr[data_offset_w_ptr]; const float mask = data_mask_ptr[data_mask_hw_ptr]; const float cur_inv_h_data = h_in + i * dilation_h + offset_h; const float cur_inv_w_data = w_in + j * dilation_w + offset_w; const float cur_top_grad = data_col[index] * mask; const int cur_h = (int)cur_inv_h_data; const int cur_w = (int)cur_inv_w_data; for (int dy = -2; dy <= 2; dy++) { for (int dx = -2; dx <= 2; dx++) { if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && abs(cur_inv_w_data - (cur_w + dx)) < 1) { int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; float weight = dmcn_get_gradient_weight_cuda(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); } } } } } __global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int channel_per_deformable_group, const int batch_size, const int offset_channels, const int deformable_group, const int height_col, const int width_col, float *grad_offset, float *grad_mask) { CUDA_KERNEL_LOOP(index, n) { float val = 0, mval = 0; int w = index % width_col; int h = (index / width_col) % height_col; int c = (index / width_col / height_col) % offset_channels; int b = (index / width_col / height_col) / offset_channels; // compute the start and end of the output const int deformable_group_index = c / (2 * kernel_h * kernel_w); const int col_step = kernel_h * kernel_w; int cnt = 0; const float *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; const float *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; const float *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; const float *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) { const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; const int bp_dir = offset_c % 2; int j = (col_pos / width_col / height_col / batch_size) % kernel_w; int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; int w_out = col_pos % width_col; int h_out = (col_pos / width_col) % height_col; int w_in = w_out * stride_w - pad_w; int h_in = h_out * stride_h - pad_h; const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); const float offset_h = data_offset_ptr[data_offset_h_ptr]; const float offset_w = data_offset_ptr[data_offset_w_ptr]; const float mask = data_mask_ptr[data_mask_hw_ptr]; float inv_h = h_in + i * dilation_h + offset_h; float inv_w = w_in + j * dilation_w + offset_w; if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { inv_h = inv_w = -2; } else { mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear_cuda(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); } const float weight = dmcn_get_coordinate_weight_cuda( inv_h, inv_w, height, width, data_im_ptr + cnt * height * width, width, bp_dir); val += weight * data_col_ptr[col_pos] * mask; cnt += 1; } // KERNEL_ASSIGN(grad_offset[index], offset_req, val); grad_offset[index] = val; if (offset_c % 2 == 0) // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; } } void modulated_deformable_im2col_cuda(cudaStream_t stream, const float* data_im, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* data_col) { // num_axes should be smaller than block size const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * batch_size * height_col * width_col; modulated_deformable_im2col_gpu_kernel <<>>( num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, channels, deformable_group, height_col, width_col, data_col); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); } } void modulated_deformable_col2im_cuda(cudaStream_t stream, const float* data_col, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* grad_im){ const int channel_per_deformable_group = channels / deformable_group; const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; modulated_deformable_col2im_gpu_kernel <<>>( num_kernels, data_col, data_offset, data_mask, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, deformable_group, height_col, width_col, grad_im); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); } } void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, const float* data_col, const float* data_im, const float* data_offset, const float* data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float* grad_offset, float* grad_mask) { const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; modulated_deformable_col2im_coord_gpu_kernel <<>>( num_kernels, data_col, data_im, data_offset, data_mask, channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, grad_offset, grad_mask); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); } }slamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.h000066400000000000000000000121521435750645400264430ustar00rootroot00000000000000 /*! ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** * * COPYRIGHT * * All contributions by the University of California: * Copyright (c) 2014-2017 The Regents of the University of California (Regents) * All rights reserved. * * All other contributions: * Copyright (c) 2014-2017, the respective contributors * All rights reserved. * * Caffe uses a shared copyright model: each contributor holds copyright over * their contributions to Caffe. The project versioning records all such * contribution and copyright details. If a contributor wants to further mark * their specific copyright on a particular contribution, they should indicate * their copyright solely in the commit message of the change when it is * committed. * * LICENSE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this * list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * * CONTRIBUTION AGREEMENT * * By contributing to the BVLC/caffe repository through pull-request, comment, * or otherwise, the contributor releases their content to the * license and copyright terms herein. * ***************** END Caffe Copyright Notice and Disclaimer ******************** * * Copyright (c) 2018 Microsoft * Licensed under The MIT License [see LICENSE for details] * \file modulated_deformable_im2col.h * \brief Function definitions of converting an image to * column matrix based on kernel, padding, dilation, and offset. * These functions are mainly used in deformable convolution operators. * \ref: https://arxiv.org/abs/1811.11168 * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu */ /***************** Adapted by Charles Shang *********************/ #ifndef DCN_V2_IM2COL_CUDA #define DCN_V2_IM2COL_CUDA #ifdef __cplusplus extern "C" { #endif void modulated_deformable_im2col_cuda(cudaStream_t stream, const float *data_im, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *data_col); void modulated_deformable_col2im_cuda(cudaStream_t stream, const float *data_col, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *grad_im); void modulated_deformable_col2im_coord_cuda(cudaStream_t stream, const float *data_col, const float *data_im, const float *data_offset, const float *data_mask, const int batch_size, const int channels, const int height_im, const int width_im, const int height_col, const int width_col, const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, const int deformable_group, float *grad_offset, float *grad_mask); #ifdef __cplusplus } #endif #endifslamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/dcn_v2_psroi_pooling_cuda.cu000066400000000000000000000375661435750645400303410ustar00rootroot00000000000000/*! * Copyright (c) 2017 Microsoft * Licensed under The MIT License [see LICENSE for details] * \file deformable_psroi_pooling.cu * \brief * \author Yi Li, Guodong Zhang, Jifeng Dai */ /***************** Adapted by Charles Shang *********************/ #include #include #include #include #include #include #include #include #include #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) const int CUDA_NUM_THREADS = 1024; inline int GET_BLOCKS(const int N) { return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; } template __device__ T bilinear_interp_cuda( const T *data, const T x, const T y, const int width, const int height) { int x1 = floor(x); int x2 = ceil(x); int y1 = floor(y); int y2 = ceil(y); T dist_x = static_cast(x - x1); T dist_y = static_cast(y - y1); T value11 = data[y1 * width + x1]; T value12 = data[y2 * width + x1]; T value21 = data[y1 * width + x2]; T value22 = data[y2 * width + x2]; T value = (1 - dist_x) * (1 - dist_y) * value11 + (1 - dist_x) * dist_y * value12 + dist_x * (1 - dist_y) * value21 + dist_x * dist_y * value22; return value; } template __global__ void DeformablePSROIPoolForwardKernelCuda( const int count, const T *bottom_data, const T spatial_scale, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const T *bottom_rois, const T *bottom_trans, const int no_trans, const T trans_std, const int sample_per_part, const int output_dim, const int group_size, const int part_size, const int num_classes, const int channels_each_class, T *top_data, T *top_count) { CUDA_KERNEL_LOOP(index, count) { // The output is in order (n, ctop, ph, pw) int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int ctop = (index / pooled_width / pooled_height) % output_dim; int n = index / pooled_width / pooled_height / output_dim; // [start, end) interval for spatial sampling const T *offset_bottom_rois = bottom_rois + n * 5; int roi_batch_ind = offset_bottom_rois[0]; T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; // Force too small ROIs to be 1x1 T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 T roi_height = max(roi_end_h - roi_start_h, 0.1); // Compute w and h at bottom T bin_size_h = roi_height / static_cast(pooled_height); T bin_size_w = roi_width / static_cast(pooled_width); T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); int part_h = floor(static_cast(ph) / pooled_height * part_size); int part_w = floor(static_cast(pw) / pooled_width * part_size); int class_id = ctop / channels_each_class; T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; T wstart = static_cast(pw) * bin_size_w + roi_start_w; wstart += trans_x * roi_width; T hstart = static_cast(ph) * bin_size_h + roi_start_h; hstart += trans_y * roi_height; T sum = 0; int count = 0; int gw = floor(static_cast(pw) * group_size / pooled_width); int gh = floor(static_cast(ph) * group_size / pooled_height); gw = min(max(gw, 0), group_size - 1); gh = min(max(gh, 0), group_size - 1); const T *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; for (int ih = 0; ih < sample_per_part; ih++) { for (int iw = 0; iw < sample_per_part; iw++) { T w = wstart + iw * sub_bin_size_w; T h = hstart + ih * sub_bin_size_h; // bilinear interpolation if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) { continue; } w = min(max(w, 0.), width - 1.); h = min(max(h, 0.), height - 1.); int c = (ctop * group_size + gh) * group_size + gw; T val = bilinear_interp_cuda(offset_bottom_data + c * height * width, w, h, width, height); sum += val; count++; } } top_data[index] = count == 0 ? static_cast(0) : sum / count; top_count[index] = count; } } template __global__ void DeformablePSROIPoolBackwardAccKernelCuda( const int count, const T *top_diff, const T *top_count, const int num_rois, const T spatial_scale, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int output_dim, T *bottom_data_diff, T *bottom_trans_diff, const T *bottom_data, const T *bottom_rois, const T *bottom_trans, const int no_trans, const T trans_std, const int sample_per_part, const int group_size, const int part_size, const int num_classes, const int channels_each_class) { CUDA_KERNEL_LOOP(index, count) { // The output is in order (n, ctop, ph, pw) int pw = index % pooled_width; int ph = (index / pooled_width) % pooled_height; int ctop = (index / pooled_width / pooled_height) % output_dim; int n = index / pooled_width / pooled_height / output_dim; // [start, end) interval for spatial sampling const T *offset_bottom_rois = bottom_rois + n * 5; int roi_batch_ind = offset_bottom_rois[0]; T roi_start_w = static_cast(round(offset_bottom_rois[1])) * spatial_scale - 0.5; T roi_start_h = static_cast(round(offset_bottom_rois[2])) * spatial_scale - 0.5; T roi_end_w = static_cast(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; T roi_end_h = static_cast(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; // Force too small ROIs to be 1x1 T roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 T roi_height = max(roi_end_h - roi_start_h, 0.1); // Compute w and h at bottom T bin_size_h = roi_height / static_cast(pooled_height); T bin_size_w = roi_width / static_cast(pooled_width); T sub_bin_size_h = bin_size_h / static_cast(sample_per_part); T sub_bin_size_w = bin_size_w / static_cast(sample_per_part); int part_h = floor(static_cast(ph) / pooled_height * part_size); int part_w = floor(static_cast(pw) / pooled_width * part_size); int class_id = ctop / channels_each_class; T trans_x = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * trans_std; T trans_y = no_trans ? static_cast(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * trans_std; T wstart = static_cast(pw) * bin_size_w + roi_start_w; wstart += trans_x * roi_width; T hstart = static_cast(ph) * bin_size_h + roi_start_h; hstart += trans_y * roi_height; if (top_count[index] <= 0) { continue; } T diff_val = top_diff[index] / top_count[index]; const T *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; T *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; int gw = floor(static_cast(pw) * group_size / pooled_width); int gh = floor(static_cast(ph) * group_size / pooled_height); gw = min(max(gw, 0), group_size - 1); gh = min(max(gh, 0), group_size - 1); for (int ih = 0; ih < sample_per_part; ih++) { for (int iw = 0; iw < sample_per_part; iw++) { T w = wstart + iw * sub_bin_size_w; T h = hstart + ih * sub_bin_size_h; // bilinear interpolation if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) { continue; } w = min(max(w, 0.), width - 1.); h = min(max(h, 0.), height - 1.); int c = (ctop * group_size + gh) * group_size + gw; // backward on feature int x0 = floor(w); int x1 = ceil(w); int y0 = floor(h); int y1 = ceil(h); T dist_x = w - x0, dist_y = h - y0; T q00 = (1 - dist_x) * (1 - dist_y); T q01 = (1 - dist_x) * dist_y; T q10 = dist_x * (1 - dist_y); T q11 = dist_x * dist_y; int bottom_index_base = c * height * width; atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val); if (no_trans) { continue; } T U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; T U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; T U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; T U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; T diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; diff_x *= roi_width; T diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; diff_y *= roi_height; atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y); } } } } std::tuple dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(bbox.is_cuda(), "rois must be a CUDA tensor"); AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor"); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_trans = no_trans ? 2 : trans.size(1); const int num_bbox = bbox.size(0); AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); auto pooled_height = pooled_size; auto pooled_width = pooled_size; auto out = at::empty({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); long out_size = num_bbox * output_dim * pooled_height * pooled_width; auto top_count = at::zeros({num_bbox, output_dim, pooled_height, pooled_width}, input.options()); const int num_classes = no_trans ? 1 : channels_trans / 2; const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; cudaStream_t stream = at::cuda::getCurrentCUDAStream(); if (out.numel() == 0) { THCudaCheck(cudaGetLastError()); return std::make_tuple(out, top_count); } dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); dim3 block(512); AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "dcn_v2_psroi_pooling_cuda_forward", [&] { DeformablePSROIPoolForwardKernelCuda<<>>( out_size, input.contiguous().data_ptr(), spatial_scale, channels, height, width, pooled_height, pooled_width, bbox.contiguous().data_ptr(), trans.contiguous().data_ptr(), no_trans, trans_std, sample_per_part, output_dim, group_size, part_size, num_classes, channels_each_class, out.data_ptr(), top_count.data_ptr()); }); THCudaCheck(cudaGetLastError()); return std::make_tuple(out, top_count); } std::tuple dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const at::Tensor &top_count, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { AT_ASSERTM(out_grad.is_cuda(), "out_grad must be a CUDA tensor"); AT_ASSERTM(input.is_cuda(), "input must be a CUDA tensor"); AT_ASSERTM(bbox.is_cuda(), "bbox must be a CUDA tensor"); AT_ASSERTM(trans.is_cuda(), "trans must be a CUDA tensor"); AT_ASSERTM(top_count.is_cuda(), "top_count must be a CUDA tensor"); const int batch = input.size(0); const int channels = input.size(1); const int height = input.size(2); const int width = input.size(3); const int channels_trans = no_trans ? 2 : trans.size(1); const int num_bbox = bbox.size(0); AT_ASSERTM(channels == output_dim, "input channels and output channels must equal"); auto pooled_height = pooled_size; auto pooled_width = pooled_size; long out_size = num_bbox * output_dim * pooled_height * pooled_width; const int num_classes = no_trans ? 1 : channels_trans / 2; const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; auto input_grad = at::zeros({batch, channels, height, width}, out_grad.options()); auto trans_grad = at::zeros_like(trans); if (input_grad.numel() == 0) { THCudaCheck(cudaGetLastError()); return std::make_tuple(input_grad, trans_grad); } dim3 grid(std::min(THCCeilDiv(out_size, 512L), 4096L)); dim3 block(512); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); AT_DISPATCH_FLOATING_TYPES(out_grad.scalar_type(), "dcn_v2_psroi_pooling_cuda_backward", [&] { DeformablePSROIPoolBackwardAccKernelCuda<<>>( out_size, out_grad.contiguous().data_ptr(), top_count.contiguous().data_ptr(), num_bbox, spatial_scale, channels, height, width, pooled_height, pooled_width, output_dim, input_grad.contiguous().data_ptr(), trans_grad.contiguous().data_ptr(), input.contiguous().data_ptr(), bbox.contiguous().data_ptr(), trans.contiguous().data_ptr(), no_trans, trans_std, sample_per_part, group_size, part_size, num_classes, channels_each_class); }); THCudaCheck(cudaGetLastError()); return std::make_tuple(input_grad, trans_grad); }slamd202303071458299/src/lib/models/networks/DCNv2/src/cuda/vision.h000066400000000000000000000051551435750645400243430ustar00rootroot00000000000000#pragma once #include at::Tensor dcn_v2_cuda_forward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int deformable_group); std::vector dcn_v2_cuda_backward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const at::Tensor &grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int deformable_group); std::tuple dcn_v2_psroi_pooling_cuda_forward(const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std); std::tuple dcn_v2_psroi_pooling_cuda_backward(const at::Tensor &out_grad, const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const at::Tensor &top_count, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std);slamd202303071458299/src/lib/models/networks/DCNv2/src/dcn_v2.h000066400000000000000000000170071435750645400232720ustar00rootroot00000000000000#pragma once #include "cpu/vision.h" #ifdef WITH_CUDA #include "cuda/vision.h" #endif at::Tensor dcn_v2_forward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, const int dilation_h, const int dilation_w, const int deformable_group) { if (input.is_cuda()) { #ifdef WITH_CUDA return dcn_v2_cuda_forward(input, weight, bias, offset, mask, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, deformable_group); #else AT_ERROR("Not compiled with GPU support"); #endif } else{ return dcn_v2_cpu_forward(input, weight, bias, offset, mask, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, deformable_group); } } std::vector dcn_v2_backward(const at::Tensor &input, const at::Tensor &weight, const at::Tensor &bias, const at::Tensor &offset, const at::Tensor &mask, const at::Tensor &grad_output, int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int deformable_group) { if (input.is_cuda()) { #ifdef WITH_CUDA return dcn_v2_cuda_backward(input, weight, bias, offset, mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, deformable_group); #else AT_ERROR("Not compiled with GPU support"); #endif } else{ return dcn_v2_cpu_backward(input, weight, bias, offset, mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, deformable_group); } } std::tuple dcn_v2_psroi_pooling_forward(const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { if (input.is_cuda()) { #ifdef WITH_CUDA return dcn_v2_psroi_pooling_cuda_forward(input, bbox, trans, no_trans, spatial_scale, output_dim, group_size, pooled_size, part_size, sample_per_part, trans_std); #else AT_ERROR("Not compiled with GPU support"); #endif } else{ return dcn_v2_psroi_pooling_cpu_forward(input, bbox, trans, no_trans, spatial_scale, output_dim, group_size, pooled_size, part_size, sample_per_part, trans_std); } } std::tuple dcn_v2_psroi_pooling_backward(const at::Tensor &out_grad, const at::Tensor &input, const at::Tensor &bbox, const at::Tensor &trans, const at::Tensor &top_count, const int no_trans, const float spatial_scale, const int output_dim, const int group_size, const int pooled_size, const int part_size, const int sample_per_part, const float trans_std) { if (input.is_cuda()) { #ifdef WITH_CUDA return dcn_v2_psroi_pooling_cuda_backward(out_grad, input, bbox, trans, top_count, no_trans, spatial_scale, output_dim, group_size, pooled_size, part_size, sample_per_part, trans_std); #else AT_ERROR("Not compiled with GPU support"); #endif } else{ return dcn_v2_psroi_pooling_cpu_backward(out_grad, input, bbox, trans, top_count, no_trans, spatial_scale, output_dim, group_size, pooled_size, part_size, sample_per_part, trans_std); } }slamd202303071458299/src/lib/models/networks/DCNv2/src/vision.cpp000066400000000000000000000006251435750645400237570ustar00rootroot00000000000000 #include "dcn_v2.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("dcn_v2_forward", &dcn_v2_forward, "dcn_v2_forward"); m.def("dcn_v2_backward", &dcn_v2_backward, "dcn_v2_backward"); m.def("dcn_v2_psroi_pooling_forward", &dcn_v2_psroi_pooling_forward, "dcn_v2_psroi_pooling_forward"); m.def("dcn_v2_psroi_pooling_backward", &dcn_v2_psroi_pooling_backward, "dcn_v2_psroi_pooling_backward"); } slamd202303071458299/src/lib/models/networks/DCNv2/test/000077500000000000000000000000001435750645400221315ustar00rootroot00000000000000slamd202303071458299/src/lib/models/networks/DCNv2/test/test.py000066400000000000000000000173071435750645400234720ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import absolute_import, division, print_function import torch import torch.nn as nn from torch.autograd import gradcheck from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling deformable_groups = 1 N, inC, inH, inW = 2, 2, 4, 4 outC = 2 kH, kW = 3, 3 def conv_identify(weight, bias): weight.data.zero_() bias.data.zero_() o, i, h, w = weight.shape y = h // 2 x = w // 2 for p in range(i): for q in range(o): if p == q: weight.data[q, p, y, x] = 1.0 def check_zero_offset(): conv_offset = nn.Conv2d( inC, deformable_groups * 2 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ).cuda() conv_mask = nn.Conv2d( inC, deformable_groups * 1 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ).cuda() dcn_v2 = DCNv2(inC, outC, (kH, kW), stride=1, padding=1, dilation=1, deformable_groups=deformable_groups).cuda() conv_offset.weight.data.zero_() conv_offset.bias.data.zero_() conv_mask.weight.data.zero_() conv_mask.bias.data.zero_() conv_identify(dcn_v2.weight, dcn_v2.bias) input = torch.randn(N, inC, inH, inW).cuda() offset = conv_offset(input) mask = conv_mask(input) mask = torch.sigmoid(mask) output = dcn_v2(input, offset, mask) output *= 2 d = (input - output).abs().max() if d < 1e-10: print("Zero offset passed") else: print("Zero offset failed") print(input) print(output) def check_gradient_dconv(): input = torch.rand(N, inC, inH, inW).cuda() * 0.01 input.requires_grad = True offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda() * 2 # offset.data.zero_() # offset.data -= 0.5 offset.requires_grad = True mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda() # mask.data.zero_() mask.requires_grad = True mask = torch.sigmoid(mask) weight = torch.randn(outC, inC, kH, kW).cuda() weight.requires_grad = True bias = torch.rand(outC).cuda() bias.requires_grad = True stride = 1 padding = 1 dilation = 1 print( "check_gradient_dconv: ", gradcheck( dcn_v2_conv, (input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups), eps=1e-3, atol=1e-4, rtol=1e-2, ), ) def check_pooling_zero_offset(): input = torch.randn(2, 16, 64, 64).cuda().zero_() input[0, :, 16:26, 16:26] = 1.0 input[1, :, 10:20, 20:30] = 2.0 rois = ( torch.tensor( [ [0, 65, 65, 103, 103], [1, 81, 41, 119, 79], ] ) .cuda() .float() ) pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=True, group_size=1, trans_std=0.0, ).cuda() out = pooling(input, rois, input.new()) s = ", ".join(["%f" % out[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=False, group_size=1, trans_std=0.0, ).cuda() offset = torch.randn(20, 2, 7, 7).cuda().zero_() dout = dpooling(input, rois, offset) s = ", ".join(["%f" % dout[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) def check_gradient_dpooling(): input = torch.randn(2, 3, 5, 5).cuda() * 0.01 N = 4 batch_inds = torch.randint(2, (N, 1)).cuda().float() x = torch.rand((N, 1)).cuda().float() * 15 y = torch.rand((N, 1)).cuda().float() * 15 w = torch.rand((N, 1)).cuda().float() * 10 h = torch.rand((N, 1)).cuda().float() * 10 rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(N, 2, 3, 3).cuda() input.requires_grad = True offset.requires_grad = True spatial_scale = 1.0 / 4 pooled_size = 3 output_dim = 3 no_trans = 0 group_size = 1 trans_std = 0.0 sample_per_part = 4 part_size = pooled_size print( "check_gradient_dpooling:", gradcheck( dcn_v2_pooling, ( input, rois, offset, spatial_scale, pooled_size, output_dim, no_trans, group_size, part_size, sample_per_part, trans_std, ), eps=1e-4, ), ) def example_dconv(): input = torch.randn(2, 64, 128, 128).cuda() # wrap all things (offset and mask) in DCN dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, padding=1, deformable_groups=2).cuda() # print(dcn.weight.shape, input.shape) output = dcn(input) targert = output.new(*output.size()) targert.data.uniform_(-0.01, 0.01) error = (targert - output).mean() error.backward() print(output.shape) def example_dpooling(): input = torch.randn(2, 32, 64, 64).cuda() batch_inds = torch.randint(2, (20, 1)).cuda().float() x = torch.randint(256, (20, 1)).cuda().float() y = torch.randint(256, (20, 1)).cuda().float() w = torch.randint(64, (20, 1)).cuda().float() h = torch.randint(64, (20, 1)).cuda().float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(20, 2, 7, 7).cuda() input.requires_grad = True offset.requires_grad = True # normal roi_align pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=True, group_size=1, trans_std=0.1, ).cuda() # deformable pooling dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, ).cuda() out = pooling(input, rois, offset) dout = dpooling(input, rois, offset) print(out.shape) print(dout.shape) target_out = out.new(*out.size()) target_out.data.uniform_(-0.01, 0.01) target_dout = dout.new(*dout.size()) target_dout.data.uniform_(-0.01, 0.01) e = (target_out - out).mean() e.backward() e = (target_dout - dout).mean() e.backward() def example_mdpooling(): input = torch.randn(2, 32, 64, 64).cuda() input.requires_grad = True batch_inds = torch.randint(2, (20, 1)).cuda().float() x = torch.randint(256, (20, 1)).cuda().float() y = torch.randint(256, (20, 1)).cuda().float() w = torch.randint(64, (20, 1)).cuda().float() h = torch.randint(64, (20, 1)).cuda().float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) # mdformable pooling (V2) dpooling = DCNPooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, deform_fc_dim=1024, ).cuda() dout = dpooling(input, rois) target = dout.new(*dout.size()) target.data.uniform_(-0.1, 0.1) error = (target - dout).mean() error.backward() print(dout.shape) if __name__ == "__main__": example_dconv() example_dpooling() example_mdpooling() check_pooling_zero_offset() # zero offset check if inC == outC: check_zero_offset() check_gradient_dpooling() check_gradient_dconv() # """ # ****** Note: backward is not reentrant error may not be a serious problem, # ****** since the max error is less than 1e-7, # ****** Still looking for what trigger this problem # """ slamd202303071458299/src/lib/models/networks/DCNv2/test/testcpu.py000066400000000000000000000166001435750645400241750ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import absolute_import, division, print_function import torch import torch.nn as nn from torch.autograd import gradcheck from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling deformable_groups = 1 N, inC, inH, inW = 2, 2, 4, 4 outC = 2 kH, kW = 3, 3 def conv_identify(weight, bias): weight.data.zero_() bias.data.zero_() o, i, h, w = weight.shape y = h // 2 x = w // 2 for p in range(i): for q in range(o): if p == q: weight.data[q, p, y, x] = 1.0 def check_zero_offset(): conv_offset = nn.Conv2d( inC, deformable_groups * 2 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ) conv_mask = nn.Conv2d( inC, deformable_groups * 1 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ) dcn_v2 = DCNv2(inC, outC, (kH, kW), stride=1, padding=1, dilation=1, deformable_groups=deformable_groups) conv_offset.weight.data.zero_() conv_offset.bias.data.zero_() conv_mask.weight.data.zero_() conv_mask.bias.data.zero_() conv_identify(dcn_v2.weight, dcn_v2.bias) input = torch.randn(N, inC, inH, inW) offset = conv_offset(input) mask = conv_mask(input) mask = torch.sigmoid(mask) output = dcn_v2(input, offset, mask) output *= 2 d = (input - output).abs().max() if d < 1e-10: print("Zero offset passed") else: print("Zero offset failed") print(input) print(output) def check_gradient_dconv(): input = torch.rand(N, inC, inH, inW) * 0.01 input.requires_grad = True offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW) * 2 # offset.data.zero_() # offset.data -= 0.5 offset.requires_grad = True mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW) # mask.data.zero_() mask.requires_grad = True mask = torch.sigmoid(mask) weight = torch.randn(outC, inC, kH, kW) weight.requires_grad = True bias = torch.rand(outC) bias.requires_grad = True stride = 1 padding = 1 dilation = 1 print( "check_gradient_dconv: ", gradcheck( dcn_v2_conv, (input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups), eps=1e-3, atol=1e-4, rtol=1e-2, ), ) def check_pooling_zero_offset(): input = torch.randn(2, 16, 64, 64).zero_() input[0, :, 16:26, 16:26] = 1.0 input[1, :, 10:20, 20:30] = 2.0 rois = torch.tensor( [ [0, 65, 65, 103, 103], [1, 81, 41, 119, 79], ] ).float() pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=True, group_size=1, trans_std=0.0, ) out = pooling(input, rois, input.new()) s = ", ".join(["%f" % out[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=False, group_size=1, trans_std=0.0, ) offset = torch.randn(20, 2, 7, 7).zero_() dout = dpooling(input, rois, offset) s = ", ".join(["%f" % dout[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) def check_gradient_dpooling(): input = torch.randn(2, 3, 5, 5) * 0.01 N = 4 batch_inds = torch.randint(2, (N, 1)).float() x = torch.rand((N, 1)).float() * 15 y = torch.rand((N, 1)).float() * 15 w = torch.rand((N, 1)).float() * 10 h = torch.rand((N, 1)).float() * 10 rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(N, 2, 3, 3) input.requires_grad = True offset.requires_grad = True spatial_scale = 1.0 / 4 pooled_size = 3 output_dim = 3 no_trans = 0 group_size = 1 trans_std = 0.0 sample_per_part = 4 part_size = pooled_size print( "check_gradient_dpooling:", gradcheck( dcn_v2_pooling, ( input, rois, offset, spatial_scale, pooled_size, output_dim, no_trans, group_size, part_size, sample_per_part, trans_std, ), eps=1e-4, ), ) def example_dconv(): input = torch.randn(2, 64, 128, 128) # wrap all things (offset and mask) in DCN dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, padding=1, deformable_groups=2) # print(dcn.weight.shape, input.shape) output = dcn(input) targert = output.new(*output.size()) targert.data.uniform_(-0.01, 0.01) error = (targert - output).mean() error.backward() print(output.shape) def example_dpooling(): input = torch.randn(2, 32, 64, 64) batch_inds = torch.randint(2, (20, 1)).float() x = torch.randint(256, (20, 1)).float() y = torch.randint(256, (20, 1)).float() w = torch.randint(64, (20, 1)).float() h = torch.randint(64, (20, 1)).float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(20, 2, 7, 7) input.requires_grad = True offset.requires_grad = True # normal roi_align pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=True, group_size=1, trans_std=0.1, ) # deformable pooling dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, ) out = pooling(input, rois, offset) dout = dpooling(input, rois, offset) print(out.shape) print(dout.shape) target_out = out.new(*out.size()) target_out.data.uniform_(-0.01, 0.01) target_dout = dout.new(*dout.size()) target_dout.data.uniform_(-0.01, 0.01) e = (target_out - out).mean() e.backward() e = (target_dout - dout).mean() e.backward() def example_mdpooling(): input = torch.randn(2, 32, 64, 64) input.requires_grad = True batch_inds = torch.randint(2, (20, 1)).float() x = torch.randint(256, (20, 1)).float() y = torch.randint(256, (20, 1)).float() w = torch.randint(64, (20, 1)).float() h = torch.randint(64, (20, 1)).float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) # mdformable pooling (V2) dpooling = DCNPooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, deform_fc_dim=1024, ) dout = dpooling(input, rois) target = dout.new(*dout.size()) target.data.uniform_(-0.1, 0.1) error = (target - dout).mean() error.backward() print(dout.shape) if __name__ == "__main__": example_dconv() example_dpooling() example_mdpooling() check_pooling_zero_offset() # zero offset check if inC == outC: check_zero_offset() check_gradient_dpooling() check_gradient_dconv() # """ # ****** Note: backward is not reentrant error may not be a serious problem, # ****** since the max error is less than 1e-7, # ****** Still looking for what trigger this problem # """ slamd202303071458299/src/lib/models/networks/DCNv2/test/testcuda.py000066400000000000000000000173171435750645400243300ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import absolute_import, division, print_function import torch import torch.nn as nn from torch.autograd import gradcheck from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling deformable_groups = 1 N, inC, inH, inW = 2, 2, 4, 4 outC = 2 kH, kW = 3, 3 def conv_identify(weight, bias): weight.data.zero_() bias.data.zero_() o, i, h, w = weight.shape y = h // 2 x = w // 2 for p in range(i): for q in range(o): if p == q: weight.data[q, p, y, x] = 1.0 def check_zero_offset(): conv_offset = nn.Conv2d( inC, deformable_groups * 2 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ).cuda() conv_mask = nn.Conv2d( inC, deformable_groups * 1 * kH * kW, kernel_size=(kH, kW), stride=(1, 1), padding=(1, 1), bias=True, ).cuda() dcn_v2 = DCNv2(inC, outC, (kH, kW), stride=1, padding=1, dilation=1, deformable_groups=deformable_groups).cuda() conv_offset.weight.data.zero_() conv_offset.bias.data.zero_() conv_mask.weight.data.zero_() conv_mask.bias.data.zero_() conv_identify(dcn_v2.weight, dcn_v2.bias) input = torch.randn(N, inC, inH, inW).cuda() offset = conv_offset(input) mask = conv_mask(input) mask = torch.sigmoid(mask) output = dcn_v2(input, offset, mask) output *= 2 d = (input - output).abs().max() if d < 1e-10: print("Zero offset passed") else: print("Zero offset failed") print(input) print(output) def check_gradient_dconv(): input = torch.rand(N, inC, inH, inW).cuda() * 0.01 input.requires_grad = True offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW).cuda() * 2 # offset.data.zero_() # offset.data -= 0.5 offset.requires_grad = True mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW).cuda() # mask.data.zero_() mask.requires_grad = True mask = torch.sigmoid(mask) weight = torch.randn(outC, inC, kH, kW).cuda() weight.requires_grad = True bias = torch.rand(outC).cuda() bias.requires_grad = True stride = 1 padding = 1 dilation = 1 print( "check_gradient_dconv: ", gradcheck( dcn_v2_conv, (input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups), eps=1e-3, atol=1e-4, rtol=1e-2, ), ) def check_pooling_zero_offset(): input = torch.randn(2, 16, 64, 64).cuda().zero_() input[0, :, 16:26, 16:26] = 1.0 input[1, :, 10:20, 20:30] = 2.0 rois = ( torch.tensor( [ [0, 65, 65, 103, 103], [1, 81, 41, 119, 79], ] ) .cuda() .float() ) pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=True, group_size=1, trans_std=0.0, ).cuda() out = pooling(input, rois, input.new()) s = ", ".join(["%f" % out[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=16, no_trans=False, group_size=1, trans_std=0.0, ).cuda() offset = torch.randn(20, 2, 7, 7).cuda().zero_() dout = dpooling(input, rois, offset) s = ", ".join(["%f" % dout[i, :, :, :].mean().item() for i in range(rois.shape[0])]) print(s) def check_gradient_dpooling(): input = torch.randn(2, 3, 5, 5).cuda().float() * 0.01 N = 4 batch_inds = torch.randint(2, (N, 1)).cuda().float() x = torch.rand((N, 1)).cuda().float() * 15 y = torch.rand((N, 1)).cuda().float() * 15 w = torch.rand((N, 1)).cuda().float() * 10 h = torch.rand((N, 1)).cuda().float() * 10 rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(N, 2, 3, 3).cuda() input.requires_grad = True offset.requires_grad = True spatial_scale = 1.0 / 4 pooled_size = 3 output_dim = 3 no_trans = 0 group_size = 1 trans_std = 0.0 sample_per_part = 4 part_size = pooled_size print( "check_gradient_dpooling:", gradcheck( dcn_v2_pooling, ( input, rois, offset, spatial_scale, pooled_size, output_dim, no_trans, group_size, part_size, sample_per_part, trans_std, ), eps=1e-4, ), ) def example_dconv(): input = torch.randn(2, 64, 128, 128).cuda() # wrap all things (offset and mask) in DCN dcn = DCN(64, 64, kernel_size=(3, 3), stride=1, padding=1, deformable_groups=2).cuda() # print(dcn.weight.shape, input.shape) output = dcn(input) targert = output.new(*output.size()) targert.data.uniform_(-0.01, 0.01) error = (targert - output).mean() error.backward() print(output.shape) def example_dpooling(): input = torch.randn(2, 32, 64, 64).cuda() batch_inds = torch.randint(2, (20, 1)).cuda().float() x = torch.randint(256, (20, 1)).cuda().float() y = torch.randint(256, (20, 1)).cuda().float() w = torch.randint(64, (20, 1)).cuda().float() h = torch.randint(64, (20, 1)).cuda().float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) offset = torch.randn(20, 2, 7, 7).cuda() input.requires_grad = True offset.requires_grad = True # normal roi_align pooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=True, group_size=1, trans_std=0.1, ).cuda() # deformable pooling dpooling = DCNv2Pooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, ).cuda() out = pooling(input, rois, offset) dout = dpooling(input, rois, offset) print(out.shape) print(dout.shape) target_out = out.new(*out.size()) target_out.data.uniform_(-0.01, 0.01) target_dout = dout.new(*dout.size()) target_dout.data.uniform_(-0.01, 0.01) e = (target_out - out).mean() e.backward() e = (target_dout - dout).mean() e.backward() def example_mdpooling(): input = torch.randn(2, 32, 64, 64).cuda() input.requires_grad = True batch_inds = torch.randint(2, (20, 1)).cuda().float() x = torch.randint(256, (20, 1)).cuda().float() y = torch.randint(256, (20, 1)).cuda().float() w = torch.randint(64, (20, 1)).cuda().float() h = torch.randint(64, (20, 1)).cuda().float() rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1) # mdformable pooling (V2) dpooling = DCNPooling( spatial_scale=1.0 / 4, pooled_size=7, output_dim=32, no_trans=False, group_size=1, trans_std=0.1, deform_fc_dim=1024, ).cuda() dout = dpooling(input, rois) target = dout.new(*dout.size()) target.data.uniform_(-0.1, 0.1) error = (target - dout).mean() error.backward() print(dout.shape) if __name__ == "__main__": example_dconv() example_dpooling() example_mdpooling() check_pooling_zero_offset() # zero offset check if inC == outC: check_zero_offset() check_gradient_dpooling() check_gradient_dconv() # """ # ****** Note: backward is not reentrant error may not be a serious problem, # ****** since the max error is less than 1e-7, # ****** Still looking for what trigger this problem # """ slamd202303071458299/src/lib/models/networks/dlav0.py000066400000000000000000000542321435750645400216640ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function import math from os.path import join import torch from torch import nn import torch.utils.model_zoo as model_zoo import numpy as np BatchNorm = nn.BatchNorm2d def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'): return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash)) def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, inplanes, planes, stride=1, dilation=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn1 = BatchNorm(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation) self.bn2 = BatchNorm(planes) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 2 def __init__(self, inplanes, planes, stride=1, dilation=1): super(Bottleneck, self).__init__() expansion = Bottleneck.expansion bottle_planes = planes // expansion self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False) self.bn1 = BatchNorm(bottle_planes) self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = BatchNorm(bottle_planes) self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False) self.bn3 = BatchNorm(planes) self.relu = nn.ReLU(inplace=True) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += residual out = self.relu(out) return out class BottleneckX(nn.Module): expansion = 2 cardinality = 32 def __init__(self, inplanes, planes, stride=1, dilation=1): super(BottleneckX, self).__init__() cardinality = BottleneckX.cardinality # dim = int(math.floor(planes * (BottleneckV5.expansion / 64.0))) # bottle_planes = dim * cardinality bottle_planes = planes * cardinality // 32 self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False) self.bn1 = BatchNorm(bottle_planes) self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation, groups=cardinality) self.bn2 = BatchNorm(bottle_planes) self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False) self.bn3 = BatchNorm(planes) self.relu = nn.ReLU(inplace=True) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += residual out = self.relu(out) return out class Root(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, residual): super(Root, self).__init__() self.conv = nn.Conv2d( in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2) self.bn = BatchNorm(out_channels) self.relu = nn.ReLU(inplace=True) self.residual = residual def forward(self, *x): children = x x = self.conv(torch.cat(x, 1)) x = self.bn(x) if self.residual: x += children[0] x = self.relu(x) return x class Tree(nn.Module): def __init__(self, levels, block, in_channels, out_channels, stride=1, level_root=False, root_dim=0, root_kernel_size=1, dilation=1, root_residual=False): super(Tree, self).__init__() if root_dim == 0: root_dim = 2 * out_channels if level_root: root_dim += in_channels if levels == 1: self.tree1 = block(in_channels, out_channels, stride, dilation=dilation) self.tree2 = block(out_channels, out_channels, 1, dilation=dilation) else: self.tree1 = Tree(levels - 1, block, in_channels, out_channels, stride, root_dim=0, root_kernel_size=root_kernel_size, dilation=dilation, root_residual=root_residual) self.tree2 = Tree(levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, root_kernel_size=root_kernel_size, dilation=dilation, root_residual=root_residual) if levels == 1: self.root = Root(root_dim, out_channels, root_kernel_size, root_residual) self.level_root = level_root self.root_dim = root_dim self.downsample = None self.project = None self.levels = levels if stride > 1: self.downsample = nn.MaxPool2d(stride, stride=stride) if in_channels != out_channels: self.project = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), BatchNorm(out_channels) ) def forward(self, x, residual=None, children=None): children = [] if children is None else children bottom = self.downsample(x) if self.downsample else x residual = self.project(bottom) if self.project else bottom if self.level_root: children.append(bottom) x1 = self.tree1(x, residual) if self.levels == 1: x2 = self.tree2(x1) x = self.root(x2, x1, *children) else: children.append(x1) x = self.tree2(x1, children=children) return x class DLA(nn.Module): def __init__(self, levels, channels, num_classes=1000, block=BasicBlock, residual_root=False, return_levels=False, pool_size=7, linear_root=False): super(DLA, self).__init__() self.channels = channels self.return_levels = return_levels self.num_classes = num_classes self.base_layer = nn.Sequential( nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, bias=False), BatchNorm(channels[0]), nn.ReLU(inplace=True)) self.level0 = self._make_conv_level( channels[0], channels[0], levels[0]) self.level1 = self._make_conv_level( channels[0], channels[1], levels[1], stride=2) self.level2 = Tree(levels[2], block, channels[1], channels[2], 2, level_root=False, root_residual=residual_root) self.level3 = Tree(levels[3], block, channels[2], channels[3], 2, level_root=True, root_residual=residual_root) self.level4 = Tree(levels[4], block, channels[3], channels[4], 2, level_root=True, root_residual=residual_root) self.level5 = Tree(levels[5], block, channels[4], channels[5], 2, level_root=True, root_residual=residual_root) self.avgpool = nn.AvgPool2d(pool_size) self.fc = nn.Conv2d(channels[-1], num_classes, kernel_size=1, stride=1, padding=0, bias=True) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, BatchNorm): m.weight.data.fill_(1) m.bias.data.zero_() def _make_level(self, block, inplanes, planes, blocks, stride=1): downsample = None if stride != 1 or inplanes != planes: downsample = nn.Sequential( nn.MaxPool2d(stride, stride=stride), nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False), BatchNorm(planes), ) layers = [] layers.append(block(inplanes, planes, stride, downsample=downsample)) for i in range(1, blocks): layers.append(block(inplanes, planes)) return nn.Sequential(*layers) def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): modules = [] for i in range(convs): modules.extend([ nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1, padding=dilation, bias=False, dilation=dilation), BatchNorm(planes), nn.ReLU(inplace=True)]) inplanes = planes return nn.Sequential(*modules) def forward(self, x): y = [] x = self.base_layer(x) for i in range(6): x = getattr(self, 'level{}'.format(i))(x) y.append(x) if self.return_levels: return y else: x = self.avgpool(x) x = self.fc(x) x = x.view(x.size(0), -1) return x def load_pretrained_model(self, data='imagenet', name='dla34', hash='ba72cf86'): fc = self.fc if name.endswith('.pth'): model_weights = torch.load(data + name) else: model_url = get_model_url(data, name, hash) model_weights = model_zoo.load_url(model_url) num_classes = len(model_weights[list(model_weights.keys())[-1]]) self.fc = nn.Conv2d( self.channels[-1], num_classes, kernel_size=1, stride=1, padding=0, bias=True) self.load_state_dict(model_weights) self.fc = fc def dla34(pretrained, **kwargs): # DLA-34 model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=BasicBlock, **kwargs) if pretrained: model.load_pretrained_model(data='imagenet', name='dla34', hash='ba72cf86') return model def dla46_c(pretrained=None, **kwargs): # DLA-46-C Bottleneck.expansion = 2 model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 64, 128, 256], block=Bottleneck, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla46_c') return model def dla46x_c(pretrained=None, **kwargs): # DLA-X-46-C BottleneckX.expansion = 2 model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 64, 128, 256], block=BottleneckX, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla46x_c') return model def dla60x_c(pretrained, **kwargs): # DLA-X-60-C BottleneckX.expansion = 2 model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 64, 64, 128, 256], block=BottleneckX, **kwargs) if pretrained: model.load_pretrained_model(data='imagenet', name='dla60x_c', hash='b870c45c') return model def dla60(pretrained=None, **kwargs): # DLA-60 Bottleneck.expansion = 2 model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], block=Bottleneck, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla60') return model def dla60x(pretrained=None, **kwargs): # DLA-X-60 BottleneckX.expansion = 2 model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], block=BottleneckX, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla60x') return model def dla102(pretrained=None, **kwargs): # DLA-102 Bottleneck.expansion = 2 model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024], block=Bottleneck, residual_root=True, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla102') return model def dla102x(pretrained=None, **kwargs): # DLA-X-102 BottleneckX.expansion = 2 model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024], block=BottleneckX, residual_root=True, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla102x') return model def dla102x2(pretrained=None, **kwargs): # DLA-X-102 64 BottleneckX.cardinality = 64 model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024], block=BottleneckX, residual_root=True, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla102x2') return model def dla169(pretrained=None, **kwargs): # DLA-169 Bottleneck.expansion = 2 model = DLA([1, 1, 2, 3, 5, 1], [16, 32, 128, 256, 512, 1024], block=Bottleneck, residual_root=True, **kwargs) if pretrained is not None: model.load_pretrained_model(pretrained, 'dla169') return model def set_bn(bn): global BatchNorm BatchNorm = bn dla.BatchNorm = bn class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, x): return x def fill_up_weights(up): w = up.weight.data f = math.ceil(w.size(2) / 2) c = (2 * f - 1 - f % 2) / (2. * f) for i in range(w.size(2)): for j in range(w.size(3)): w[0, 0, i, j] = \ (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) for c in range(1, w.size(0)): w[c, 0, :, :] = w[0, 0, :, :] class IDAUp(nn.Module): def __init__(self, node_kernel, out_dim, channels, up_factors): super(IDAUp, self).__init__() self.channels = channels self.out_dim = out_dim for i, c in enumerate(channels): if c == out_dim: proj = Identity() else: proj = nn.Sequential( nn.Conv2d(c, out_dim, kernel_size=1, stride=1, bias=False), BatchNorm(out_dim), nn.ReLU(inplace=True)) f = int(up_factors[i]) if f == 1: up = Identity() else: up = nn.ConvTranspose2d( out_dim, out_dim, f * 2, stride=f, padding=f // 2, output_padding=0, groups=out_dim, bias=False) fill_up_weights(up) setattr(self, 'proj_' + str(i), proj) setattr(self, 'up_' + str(i), up) for i in range(1, len(channels)): node = nn.Sequential( nn.Conv2d(out_dim * 2, out_dim, kernel_size=node_kernel, stride=1, padding=node_kernel // 2, bias=False), BatchNorm(out_dim), nn.ReLU(inplace=True)) setattr(self, 'node_' + str(i), node) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, BatchNorm): m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, layers): assert len(self.channels) == len(layers), \ '{} vs {} layers'.format(len(self.channels), len(layers)) layers = list(layers) for i, l in enumerate(layers): upsample = getattr(self, 'up_' + str(i)) project = getattr(self, 'proj_' + str(i)) layers[i] = upsample(project(l)) x = layers[0] y = [] for i in range(1, len(layers)): node = getattr(self, 'node_' + str(i)) x = node(torch.cat([x, layers[i]], 1)) y.append(x) return x, y class DLAUp(nn.Module): def __init__(self, channels, scales=(1, 2, 4, 8, 16), in_channels=None): super(DLAUp, self).__init__() if in_channels is None: in_channels = channels self.channels = channels channels = list(channels) scales = np.array(scales, dtype=int) for i in range(len(channels) - 1): j = -i - 2 setattr(self, 'ida_{}'.format(i), IDAUp(3, channels[j], in_channels[j:], scales[j:] // scales[j])) scales[j + 1:] = scales[j] in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]] def forward(self, layers): layers = list(layers) assert len(layers) > 1 for i in range(len(layers) - 1): ida = getattr(self, 'ida_{}'.format(i)) x, y = ida(layers[-i - 2:]) layers[-i - 1:] = y return x def fill_fc_weights(layers): for m in layers.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.001) # torch.nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu') # torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: nn.init.constant_(m.bias, 0) class DLASeg(nn.Module): def __init__(self, base_name, heads, pretrained=True, down_ratio=4, head_conv=256): super(DLASeg, self).__init__() assert down_ratio in [2, 4, 8, 16] self.heads = heads self.first_level = int(np.log2(down_ratio)) self.base = globals()[base_name]( pretrained=pretrained, return_levels=True) channels = self.base.channels scales = [2 ** i for i in range(len(channels[self.first_level:]))] self.dla_up = DLAUp(channels[self.first_level:], scales=scales) ''' self.fc = nn.Sequential( nn.Conv2d(channels[self.first_level], classes, kernel_size=1, stride=1, padding=0, bias=True) ) ''' for head in self.heads: classes = self.heads[head] if head_conv > 0: fc = nn.Sequential( nn.Conv2d(channels[self.first_level], head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, classes, kernel_size=1, stride=1, padding=0, bias=True)) if 'hm' in head: fc[-1].bias.data.fill_(-2.19) else: fill_fc_weights(fc) else: fc = nn.Conv2d(channels[self.first_level], classes, kernel_size=1, stride=1, padding=0, bias=True) if 'hm' in head: fc.bias.data.fill_(-2.19) else: fill_fc_weights(fc) self.__setattr__(head, fc) ''' up_factor = 2 ** self.first_level if up_factor > 1: up = nn.ConvTranspose2d(classes, classes, up_factor * 2, stride=up_factor, padding=up_factor // 2, output_padding=0, groups=classes, bias=False) fill_up_weights(up) up.weight.requires_grad = False else: up = Identity() self.up = up self.softmax = nn.LogSoftmax(dim=1) for m in self.fc.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, BatchNorm): m.weight.data.fill_(1) m.bias.data.zero_() ''' def forward(self, x): x = self.base(x) x = self.dla_up(x[self.first_level:]) # x = self.fc(x) # y = self.softmax(self.up(x)) ret = {} for head in self.heads: ret[head] = self.__getattr__(head)(x) return [ret] ''' def optim_parameters(self, memo=None): for param in self.base.parameters(): yield param for param in self.dla_up.parameters(): yield param for param in self.fc.parameters(): yield param ''' ''' def dla34up(classes, pretrained_base=None, **kwargs): model = DLASeg('dla34', classes, pretrained_base=pretrained_base, **kwargs) return model def dla60up(classes, pretrained_base=None, **kwargs): model = DLASeg('dla60', classes, pretrained_base=pretrained_base, **kwargs) return model def dla102up(classes, pretrained_base=None, **kwargs): model = DLASeg('dla102', classes, pretrained_base=pretrained_base, **kwargs) return model def dla169up(classes, pretrained_base=None, **kwargs): model = DLASeg('dla169', classes, pretrained_base=pretrained_base, **kwargs) return model ''' def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4): model = DLASeg('dla{}'.format(num_layers), heads, pretrained=True, down_ratio=down_ratio, head_conv=head_conv) return model slamd202303071458299/src/lib/models/networks/large_hourglass.py000066400000000000000000000233261435750645400240370ustar00rootroot00000000000000# ------------------------------------------------------------------------------ # This code is base on # CornerNet (https://github.com/princeton-vl/CornerNet) # Copyright (c) 2018, University of Michigan # Licensed under the BSD 3-Clause License # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import torch import torch.nn as nn class convolution(nn.Module): def __init__(self, k, inp_dim, out_dim, stride=1, with_bn=True): super(convolution, self).__init__() pad = (k - 1) // 2 self.conv = nn.Conv2d(inp_dim, out_dim, (k, k), padding=(pad, pad), stride=(stride, stride), bias=not with_bn) self.bn = nn.BatchNorm2d(out_dim) if with_bn else nn.Sequential() self.relu = nn.ReLU(inplace=True) def forward(self, x): conv = self.conv(x) bn = self.bn(conv) relu = self.relu(bn) return relu class fully_connected(nn.Module): def __init__(self, inp_dim, out_dim, with_bn=True): super(fully_connected, self).__init__() self.with_bn = with_bn self.linear = nn.Linear(inp_dim, out_dim) if self.with_bn: self.bn = nn.BatchNorm1d(out_dim) self.relu = nn.ReLU(inplace=True) def forward(self, x): linear = self.linear(x) bn = self.bn(linear) if self.with_bn else linear relu = self.relu(bn) return relu class residual(nn.Module): def __init__(self, k, inp_dim, out_dim, stride=1, with_bn=True): super(residual, self).__init__() self.conv1 = nn.Conv2d(inp_dim, out_dim, (3, 3), padding=(1, 1), stride=(stride, stride), bias=False) self.bn1 = nn.BatchNorm2d(out_dim) self.relu1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(out_dim, out_dim, (3, 3), padding=(1, 1), bias=False) self.bn2 = nn.BatchNorm2d(out_dim) self.skip = nn.Sequential( nn.Conv2d(inp_dim, out_dim, (1, 1), stride=(stride, stride), bias=False), nn.BatchNorm2d(out_dim) ) if stride != 1 or inp_dim != out_dim else nn.Sequential() self.relu = nn.ReLU(inplace=True) def forward(self, x): conv1 = self.conv1(x) bn1 = self.bn1(conv1) relu1 = self.relu1(bn1) conv2 = self.conv2(relu1) bn2 = self.bn2(conv2) skip = self.skip(x) return self.relu(bn2 + skip) def make_layer(k, inp_dim, out_dim, modules, layer=convolution, **kwargs): layers = [layer(k, inp_dim, out_dim, **kwargs)] for _ in range(1, modules): layers.append(layer(k, out_dim, out_dim, **kwargs)) return nn.Sequential(*layers) def make_layer_revr(k, inp_dim, out_dim, modules, layer=convolution, **kwargs): layers = [] for _ in range(modules - 1): layers.append(layer(k, inp_dim, inp_dim, **kwargs)) layers.append(layer(k, inp_dim, out_dim, **kwargs)) return nn.Sequential(*layers) class MergeUp(nn.Module): def forward(self, up1, up2): return up1 + up2 def make_merge_layer(dim): return MergeUp() # def make_pool_layer(dim): # return nn.MaxPool2d(kernel_size=2, stride=2) def make_pool_layer(dim): return nn.Sequential() def make_unpool_layer(dim): return nn.Upsample(scale_factor=2) def make_kp_layer(cnv_dim, curr_dim, out_dim): return nn.Sequential( convolution(3, cnv_dim, curr_dim, with_bn=False), nn.Conv2d(curr_dim, out_dim, (1, 1)) ) def make_inter_layer(dim): return residual(3, dim, dim) def make_cnv_layer(inp_dim, out_dim): return convolution(3, inp_dim, out_dim) class kp_module(nn.Module): def __init__( self, n, dims, modules, layer=residual, make_up_layer=make_layer, make_low_layer=make_layer, make_hg_layer=make_layer, make_hg_layer_revr=make_layer_revr, make_pool_layer=make_pool_layer, make_unpool_layer=make_unpool_layer, make_merge_layer=make_merge_layer, **kwargs ): super(kp_module, self).__init__() self.n = n curr_mod = modules[0] next_mod = modules[1] curr_dim = dims[0] next_dim = dims[1] self.up1 = make_up_layer( 3, curr_dim, curr_dim, curr_mod, layer=layer, **kwargs ) self.max1 = make_pool_layer(curr_dim) self.low1 = make_hg_layer( 3, curr_dim, next_dim, curr_mod, layer=layer, **kwargs ) self.low2 = kp_module( n - 1, dims[1:], modules[1:], layer=layer, make_up_layer=make_up_layer, make_low_layer=make_low_layer, make_hg_layer=make_hg_layer, make_hg_layer_revr=make_hg_layer_revr, make_pool_layer=make_pool_layer, make_unpool_layer=make_unpool_layer, make_merge_layer=make_merge_layer, **kwargs ) if self.n > 1 else \ make_low_layer( 3, next_dim, next_dim, next_mod, layer=layer, **kwargs ) self.low3 = make_hg_layer_revr( 3, next_dim, curr_dim, curr_mod, layer=layer, **kwargs ) self.up2 = make_unpool_layer(curr_dim) self.merge = make_merge_layer(curr_dim) def forward(self, x): up1 = self.up1(x) max1 = self.max1(x) low1 = self.low1(max1) low2 = self.low2(low1) low3 = self.low3(low2) up2 = self.up2(low3) return self.merge(up1, up2) class exkp(nn.Module): def __init__( self, n, nstack, dims, modules, heads, pre=None, cnv_dim=256, make_tl_layer=None, make_br_layer=None, make_cnv_layer=make_cnv_layer, make_heat_layer=make_kp_layer, make_tag_layer=make_kp_layer, make_regr_layer=make_kp_layer, make_up_layer=make_layer, make_low_layer=make_layer, make_hg_layer=make_layer, make_hg_layer_revr=make_layer_revr, make_pool_layer=make_pool_layer, make_unpool_layer=make_unpool_layer, make_merge_layer=make_merge_layer, make_inter_layer=make_inter_layer, kp_layer=residual ): super(exkp, self).__init__() self.nstack = nstack self.heads = heads curr_dim = dims[0] self.pre = nn.Sequential( convolution(7, 3, 128, stride=2), residual(3, 128, 256, stride=2) ) if pre is None else pre self.kps = nn.ModuleList([ kp_module( n, dims, modules, layer=kp_layer, make_up_layer=make_up_layer, make_low_layer=make_low_layer, make_hg_layer=make_hg_layer, make_hg_layer_revr=make_hg_layer_revr, make_pool_layer=make_pool_layer, make_unpool_layer=make_unpool_layer, make_merge_layer=make_merge_layer ) for _ in range(nstack) ]) self.cnvs = nn.ModuleList([ make_cnv_layer(curr_dim, cnv_dim) for _ in range(nstack) ]) self.inters = nn.ModuleList([ make_inter_layer(curr_dim) for _ in range(nstack - 1) ]) self.inters_ = nn.ModuleList([ nn.Sequential( nn.Conv2d(curr_dim, curr_dim, (1, 1), bias=False), nn.BatchNorm2d(curr_dim) ) for _ in range(nstack - 1) ]) self.cnvs_ = nn.ModuleList([ nn.Sequential( nn.Conv2d(cnv_dim, curr_dim, (1, 1), bias=False), nn.BatchNorm2d(curr_dim) ) for _ in range(nstack - 1) ]) ## keypoint heatmaps for head in heads.keys(): if 'hm' in head: module = nn.ModuleList([ make_heat_layer( cnv_dim, curr_dim, heads[head]) for _ in range(nstack) ]) self.__setattr__(head, module) for heat in self.__getattr__(head): heat[-1].bias.data.fill_(-2.19) else: module = nn.ModuleList([ make_regr_layer( cnv_dim, curr_dim, heads[head]) for _ in range(nstack) ]) self.__setattr__(head, module) self.relu = nn.ReLU(inplace=True) def forward(self, image): # print('image shape', image.shape) inter = self.pre(image) outs = [] for ind in range(self.nstack): kp_, cnv_ = self.kps[ind], self.cnvs[ind] kp = kp_(inter) cnv = cnv_(kp) out = {} for head in self.heads: layer = self.__getattr__(head)[ind] y = layer(cnv) out[head] = y outs.append(out) if ind < self.nstack - 1: inter = self.inters_[ind](inter) + self.cnvs_[ind](cnv) inter = self.relu(inter) inter = self.inters[ind](inter) return outs def make_hg_layer(kernel, dim0, dim1, mod, layer=convolution, **kwargs): layers = [layer(kernel, dim0, dim1, stride=2)] layers += [layer(kernel, dim1, dim1) for _ in range(mod - 1)] return nn.Sequential(*layers) class HourglassNet(exkp): def __init__(self, heads, num_stacks=2): n = 5 dims = [256, 256, 384, 384, 384, 512] modules = [2, 2, 2, 2, 2, 4] super(HourglassNet, self).__init__( n, num_stacks, dims, modules, heads, make_tl_layer=None, make_br_layer=None, make_pool_layer=make_pool_layer, make_hg_layer=make_hg_layer, kp_layer=residual, cnv_dim=256 ) def get_large_hourglass_net(num_layers, heads, head_conv): model = HourglassNet(heads, 2) return model slamd202303071458299/src/lib/models/networks/msra_resnet.py000066400000000000000000000236671435750645400232100ustar00rootroot00000000000000# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # Modified by Xingyi Zhou # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo BN_MOMENTUM = 0.1 model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', } def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class PoseResNet(nn.Module): def __init__(self, block, layers, heads, head_conv, **kwargs): self.inplanes = 64 self.deconv_with_bias = False self.heads = heads super(PoseResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # used for deconv layers self.deconv_layers = self._make_deconv_layer( 3, [256, 256, 256], [4, 4, 4], ) # self.final_layer = [] for head in sorted(self.heads): num_output = self.heads[head] if head_conv > 0: fc = nn.Sequential( nn.Conv2d(256, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, num_output, kernel_size=1, stride=1, padding=0)) else: fc = nn.Conv2d( in_channels=256, out_channels=num_output, kernel_size=1, stride=1, padding=0 ) self.__setattr__(head, fc) # self.final_layer = nn.ModuleList(self.final_layer) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.deconv_layers(x) ret = {} for head in self.heads: ret[head] = self.__getattr__(head)(x) return [ret] def init_weights(self, num_layers, pretrained=True): if pretrained: # print('=> init resnet deconv weights from normal distribution') for _, m in self.deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): # print('=> init {}.weight as normal(0, 0.001)'.format(name)) # print('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): # print('=> init {}.weight as 1'.format(name)) # print('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # print('=> init final conv weights from normal distribution') for head in self.heads: final_layer = self.__getattr__(head) for i, m in enumerate(final_layer.modules()): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # print('=> init {}.weight as normal(0, 0.001)'.format(name)) # print('=> init {}.bias as 0'.format(name)) if m.weight.shape[0] == self.heads[head]: if 'hm' in head: nn.init.constant_(m.bias, -2.19) else: nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) #pretrained_state_dict = torch.load(pretrained) url = model_urls['resnet{}'.format(num_layers)] pretrained_state_dict = model_zoo.load_url(url) print('=> loading pretrained model {}'.format(url)) self.load_state_dict(pretrained_state_dict, strict=False) else: print('=> imagenet pretrained model dose not exist') print('=> please download it first') raise ValueError('imagenet pretrained model does not exist') resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]), 101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3])} def get_pose_net(num_layers, heads, head_conv): block_class, layers = resnet_spec[num_layers] model = PoseResNet(block_class, layers, heads, head_conv=head_conv) model.init_weights(num_layers, pretrained=True) return model slamd202303071458299/src/lib/models/networks/pose_dla_dcn.py000066400000000000000000000424301435750645400232650ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import math import logging import numpy as np from os.path import join import torch from torch import nn import torch.nn.functional as F import torch.utils.model_zoo as model_zoo from .DCNv2.dcn_v2 import DCN BN_MOMENTUM = 0.1 logger = logging.getLogger(__name__) def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'): return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash)) def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, inplanes, planes, stride=1, dilation=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 2 def __init__(self, inplanes, planes, stride=1, dilation=1): super(Bottleneck, self).__init__() expansion = Bottleneck.expansion bottle_planes = planes // expansion self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(bottle_planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(bottle_planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += residual out = self.relu(out) return out class BottleneckX(nn.Module): expansion = 2 cardinality = 32 def __init__(self, inplanes, planes, stride=1, dilation=1): super(BottleneckX, self).__init__() cardinality = BottleneckX.cardinality # dim = int(math.floor(planes * (BottleneckV5.expansion / 64.0))) # bottle_planes = dim * cardinality bottle_planes = planes * cardinality // 32 self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(bottle_planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation, groups=cardinality) self.bn2 = nn.BatchNorm2d(bottle_planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.stride = stride def forward(self, x, residual=None): if residual is None: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += residual out = self.relu(out) return out class Root(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, residual): super(Root, self).__init__() self.conv = nn.Conv2d( in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2) self.bn = nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.residual = residual def forward(self, *x): children = x x = self.conv(torch.cat(x, 1)) x = self.bn(x) if self.residual: x += children[0] x = self.relu(x) return x class Tree(nn.Module): def __init__(self, levels, block, in_channels, out_channels, stride=1, level_root=False, root_dim=0, root_kernel_size=1, dilation=1, root_residual=False): super(Tree, self).__init__() if root_dim == 0: root_dim = 2 * out_channels if level_root: root_dim += in_channels if levels == 1: self.tree1 = block(in_channels, out_channels, stride, dilation=dilation) self.tree2 = block(out_channels, out_channels, 1, dilation=dilation) else: self.tree1 = Tree(levels - 1, block, in_channels, out_channels, stride, root_dim=0, root_kernel_size=root_kernel_size, dilation=dilation, root_residual=root_residual) self.tree2 = Tree(levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, root_kernel_size=root_kernel_size, dilation=dilation, root_residual=root_residual) if levels == 1: self.root = Root(root_dim, out_channels, root_kernel_size, root_residual) self.level_root = level_root self.root_dim = root_dim self.downsample = None self.project = None self.levels = levels if stride > 1: self.downsample = nn.MaxPool2d(stride, stride=stride) if in_channels != out_channels: self.project = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM) ) def forward(self, x, residual=None, children=None): children = [] if children is None else children bottom = self.downsample(x) if self.downsample else x residual = self.project(bottom) if self.project else bottom if self.level_root: children.append(bottom) x1 = self.tree1(x, residual) if self.levels == 1: x2 = self.tree2(x1) x = self.root(x2, x1, *children) else: children.append(x1) x = self.tree2(x1, children=children) return x class DLA(nn.Module): def __init__(self, levels, channels, num_classes=1000, block=BasicBlock, residual_root=False, linear_root=False): super(DLA, self).__init__() self.channels = channels self.num_classes = num_classes self.base_layer = nn.Sequential( nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, bias=False), nn.BatchNorm2d(channels[0], momentum=BN_MOMENTUM), nn.ReLU(inplace=True)) self.level0 = self._make_conv_level( channels[0], channels[0], levels[0]) self.level1 = self._make_conv_level( channels[0], channels[1], levels[1], stride=2) self.level2 = Tree(levels[2], block, channels[1], channels[2], 2, level_root=False, root_residual=residual_root) self.level3 = Tree(levels[3], block, channels[2], channels[3], 2, level_root=True, root_residual=residual_root) self.level4 = Tree(levels[4], block, channels[3], channels[4], 2, level_root=True, root_residual=residual_root) self.level5 = Tree(levels[5], block, channels[4], channels[5], 2, level_root=True, root_residual=residual_root) # for m in self.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) # elif isinstance(m, nn.BatchNorm2d): # m.weight.data.fill_(1) # m.bias.data.zero_() def _make_level(self, block, inplanes, planes, blocks, stride=1): downsample = None if stride != 1 or inplanes != planes: downsample = nn.Sequential( nn.MaxPool2d(stride, stride=stride), nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(planes, momentum=BN_MOMENTUM), ) layers = [] layers.append(block(inplanes, planes, stride, downsample=downsample)) for i in range(1, blocks): layers.append(block(inplanes, planes)) return nn.Sequential(*layers) def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): modules = [] for i in range(convs): modules.extend([ nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1, padding=dilation, bias=False, dilation=dilation), nn.BatchNorm2d(planes, momentum=BN_MOMENTUM), nn.ReLU(inplace=True)]) inplanes = planes return nn.Sequential(*modules) def forward(self, x): y = [] x = self.base_layer(x) for i in range(6): x = getattr(self, 'level{}'.format(i))(x) y.append(x) return y def load_pretrained_model(self, data='imagenet', name='dla34', hash='ba72cf86'): # fc = self.fc if name.endswith('.pth'): model_weights = torch.load(data + name) else: model_url = get_model_url(data, name, hash) model_weights = model_zoo.load_url(model_url) num_classes = len(model_weights[list(model_weights.keys())[-1]]) self.fc = nn.Conv2d( self.channels[-1], num_classes, kernel_size=1, stride=1, padding=0, bias=True) self.load_state_dict(model_weights) # self.fc = fc def dla34(pretrained=True, **kwargs): # DLA-34 model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=BasicBlock, **kwargs) if pretrained: model.load_pretrained_model(data='imagenet', name='dla34', hash='ba72cf86') return model class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, x): return x def fill_fc_weights(layers): for m in layers.modules(): if isinstance(m, nn.Conv2d): if m.bias is not None: nn.init.constant_(m.bias, 0) def fill_up_weights(up): w = up.weight.data f = math.ceil(w.size(2) / 2) c = (2 * f - 1 - f % 2) / (2. * f) for i in range(w.size(2)): for j in range(w.size(3)): w[0, 0, i, j] = \ (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) for c in range(1, w.size(0)): w[c, 0, :, :] = w[0, 0, :, :] class DeformConv(nn.Module): def __init__(self, chi, cho): super(DeformConv, self).__init__() self.actf = nn.Sequential( nn.BatchNorm2d(cho, momentum=BN_MOMENTUM), nn.ReLU(inplace=True) ) self.conv = DCN(chi, cho, kernel_size=(3,3), stride=1, padding=1, dilation=1, deformable_groups=1) def forward(self, x): x = self.conv(x) x = self.actf(x) return x class IDAUp(nn.Module): def __init__(self, o, channels, up_f): super(IDAUp, self).__init__() for i in range(1, len(channels)): c = channels[i] f = int(up_f[i]) proj = DeformConv(c, o) node = DeformConv(o, o) up = nn.ConvTranspose2d(o, o, f * 2, stride=f, padding=f // 2, output_padding=0, groups=o, bias=False) fill_up_weights(up) setattr(self, 'proj_' + str(i), proj) setattr(self, 'up_' + str(i), up) setattr(self, 'node_' + str(i), node) def forward(self, layers, startp, endp): for i in range(startp + 1, endp): upsample = getattr(self, 'up_' + str(i - startp)) project = getattr(self, 'proj_' + str(i - startp)) layers[i] = upsample(project(layers[i])) node = getattr(self, 'node_' + str(i - startp)) layers[i] = node(layers[i] + layers[i - 1]) class DLAUp(nn.Module): def __init__(self, startp, channels, scales, in_channels=None): super(DLAUp, self).__init__() self.startp = startp if in_channels is None: in_channels = channels self.channels = channels channels = list(channels) scales = np.array(scales, dtype=int) for i in range(len(channels) - 1): j = -i - 2 setattr(self, 'ida_{}'.format(i), IDAUp(channels[j], in_channels[j:], scales[j:] // scales[j])) scales[j + 1:] = scales[j] in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]] def forward(self, layers): out = [layers[-1]] # start with 32 for i in range(len(layers) - self.startp - 1): ida = getattr(self, 'ida_{}'.format(i)) ida(layers, len(layers) -i - 2, len(layers)) out.insert(0, layers[-1]) return out class Interpolate(nn.Module): def __init__(self, scale, mode): super(Interpolate, self).__init__() self.scale = scale self.mode = mode def forward(self, x): x = F.interpolate(x, scale_factor=self.scale, mode=self.mode, align_corners=False) return x class DLASeg(nn.Module): def __init__(self, base_name, heads, pretrained, down_ratio, final_kernel, last_level, head_conv, out_channel=0): super(DLASeg, self).__init__() assert down_ratio in [2, 4, 8, 16] self.first_level = int(np.log2(down_ratio)) self.last_level = last_level self.base = globals()[base_name](pretrained=pretrained) channels = self.base.channels scales = [2 ** i for i in range(len(channels[self.first_level:]))] self.dla_up = DLAUp(self.first_level, channels[self.first_level:], scales) if out_channel == 0: out_channel = channels[self.first_level] self.ida_up = IDAUp(out_channel, channels[self.first_level:self.last_level], [2 ** i for i in range(self.last_level - self.first_level)]) self.heads = heads for head in self.heads: classes = self.heads[head] if head_conv > 0: fc = nn.Sequential( nn.Conv2d(channels[self.first_level], head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, classes, kernel_size=final_kernel, stride=1, padding=final_kernel // 2, bias=True)) if 'hm' in head: fc[-1].bias.data.fill_(-2.19) else: fill_fc_weights(fc) else: fc = nn.Conv2d(channels[self.first_level], classes, kernel_size=final_kernel, stride=1, padding=final_kernel // 2, bias=True) if 'hm' in head: fc.bias.data.fill_(-2.19) else: fill_fc_weights(fc) self.__setattr__(head, fc) def forward(self, x): x = self.base(x) x = self.dla_up(x) y = [] for i in range(self.last_level - self.first_level): y.append(x[i].clone()) self.ida_up(y, 0, len(y)) z = {} for head in self.heads: z[head] = self.__getattr__(head)(y[-1]) if head in ["ratio_al", "ratio_bl"]: z[head] = F.sigmoid(z[head]) return [z] def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4): model = DLASeg('dla{}'.format(num_layers), heads, pretrained=True, down_ratio=down_ratio, final_kernel=1, last_level=5, head_conv=head_conv) return model slamd202303071458299/src/lib/models/networks/resnet_dcn.py000066400000000000000000000235061435750645400230020ustar00rootroot00000000000000# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # Modified by Dequan Wang and Xingyi Zhou # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import math import logging import torch import torch.nn as nn from .DCNv2.dcn_v2 import DCN import torch.utils.model_zoo as model_zoo BN_MOMENTUM = 0.1 logger = logging.getLogger(__name__) model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', } def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out def fill_up_weights(up): w = up.weight.data f = math.ceil(w.size(2) / 2) c = (2 * f - 1 - f % 2) / (2. * f) for i in range(w.size(2)): for j in range(w.size(3)): w[0, 0, i, j] = \ (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) for c in range(1, w.size(0)): w[c, 0, :, :] = w[0, 0, :, :] def fill_fc_weights(layers): for m in layers.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.001) # torch.nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu') # torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: nn.init.constant_(m.bias, 0) class PoseResNet(nn.Module): def __init__(self, block, layers, heads, head_conv): self.inplanes = 64 self.heads = heads self.deconv_with_bias = False super(PoseResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # used for deconv layers self.deconv_layers = self._make_deconv_layer( 3, [256, 128, 64], [4, 4, 4], ) for head in self.heads: classes = self.heads[head] if head_conv > 0: fc = nn.Sequential( nn.Conv2d(64, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, classes, kernel_size=1, stride=1, padding=0, bias=True)) if 'hm' in head: fc[-1].bias.data.fill_(-2.19) else: fill_fc_weights(fc) else: fc = nn.Conv2d(64, classes, kernel_size=1, stride=1, padding=0, bias=True) if 'hm' in head: fc.bias.data.fill_(-2.19) else: fill_fc_weights(fc) self.__setattr__(head, fc) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] fc = DCN(self.inplanes, planes, kernel_size=(3,3), stride=1, padding=1, dilation=1, deformable_groups=1) # fc = nn.Conv2d(self.inplanes, planes, # kernel_size=3, stride=1, # padding=1, dilation=1, bias=False) # fill_fc_weights(fc) up = nn.ConvTranspose2d( in_channels=planes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias) fill_up_weights(up) layers.append(fc) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) layers.append(up) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes = planes return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.deconv_layers(x) ret = {} for head in self.heads: ret[head] = self.__getattr__(head)(x) return [ret] def init_weights(self, num_layers): if 1: url = model_urls['resnet{}'.format(num_layers)] pretrained_state_dict = model_zoo.load_url(url) print('=> loading pretrained model {}'.format(url)) self.load_state_dict(pretrained_state_dict, strict=False) print('=> init deconv weights from normal distribution') for name, m in self.deconv_layers.named_modules(): if isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]), 101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3])} def get_pose_net(num_layers, heads, head_conv=256): block_class, layers = resnet_spec[num_layers] model = PoseResNet(block_class, layers, heads, head_conv=head_conv) model.init_weights(num_layers) return model slamd202303071458299/src/lib/models/scatter_gather.py000066400000000000000000000027771435750645400220100ustar00rootroot00000000000000import torch from torch.autograd import Variable from torch.nn.parallel._functions import Scatter, Gather def scatter(inputs, target_gpus, dim=0, chunk_sizes=None): r""" Slices variables into approximately equal chunks and distributes them across given GPUs. Duplicates references to objects that are not variables. Does not support Tensors. """ def scatter_map(obj): if isinstance(obj, Variable): return Scatter.apply(target_gpus, chunk_sizes, dim, obj) assert not torch.is_tensor(obj), "Tensors not supported in scatter." if isinstance(obj, tuple): return list(zip(*map(scatter_map, obj))) if isinstance(obj, list): return list(map(list, zip(*map(scatter_map, obj)))) if isinstance(obj, dict): return list(map(type(obj), zip(*map(scatter_map, obj.items())))) return [obj for targets in target_gpus] return scatter_map(inputs) def scatter_kwargs(inputs, kwargs, target_gpus, dim=0, chunk_sizes=None): r"""Scatter with support for kwargs dictionary""" inputs = scatter(inputs, target_gpus, dim, chunk_sizes) if inputs else [] kwargs = scatter(kwargs, target_gpus, dim, chunk_sizes) if kwargs else [] if len(inputs) < len(kwargs): inputs.extend([() for _ in range(len(kwargs) - len(inputs))]) elif len(kwargs) < len(inputs): kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))]) inputs = tuple(inputs) kwargs = tuple(kwargs) return inputs, kwargs slamd202303071458299/src/lib/models/utils.py000066400000000000000000000030431435750645400201340ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import torch.nn as nn def _sigmoid(x): y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4) return y def _gather_feat(feat, ind, mask=None): dim = feat.size(2) ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim) feat = feat.gather(1, ind) if mask is not None: mask = mask.unsqueeze(2).expand_as(feat) feat = feat[mask] feat = feat.view(-1, dim) return feat def _transpose_and_gather_feat(feat, ind): feat = feat.permute(0, 2, 3, 1).contiguous() feat = feat.view(feat.size(0), -1, feat.size(3)) feat = _gather_feat(feat, ind) return feat def flip_tensor(x): return torch.flip(x, [3]) # tmp = x.detach().cpu().numpy()[..., ::-1].copy() # return torch.from_numpy(tmp).to(x.device) def flip_lr(x, flip_idx): tmp = x.detach().cpu().numpy()[..., ::-1].copy() shape = tmp.shape for e in flip_idx: tmp[:, e[0], ...], tmp[:, e[1], ...] = \ tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy() return torch.from_numpy(tmp.reshape(shape)).to(x.device) def flip_lr_off(x, flip_idx): tmp = x.detach().cpu().numpy()[..., ::-1].copy() shape = tmp.shape tmp = tmp.reshape(tmp.shape[0], 17, 2, tmp.shape[2], tmp.shape[3]) tmp[:, :, 0, :, :] *= -1 for e in flip_idx: tmp[:, e[0], ...], tmp[:, e[1], ...] = \ tmp[:, e[1], ...].copy(), tmp[:, e[0], ...].copy() return torch.from_numpy(tmp.reshape(shape)).to(x.device)slamd202303071458299/src/lib/opts.py000066400000000000000000000444101435750645400165010ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='ctdet', help='ctdet | ddd | multi_pose | exdet') self.parser.add_argument('--dataset', default='coco', help='coco | kitti | coco_hp | pascal') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') self.parser.add_argument('--debug', type=int, default=0, help='level of visualization.' '1: only show the final detection results' '2: show the network output features' '3: use matplot to display' # useful when lunching training with ipython notebook '4: save all visualizations to disk') self.parser.add_argument('--demo', default='', help='path to image/ image folders/ video. ' 'or "webcam"') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=4, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.3, help='visualization threshold.') self.parser.add_argument('--debugger_theme', default='white', choices=['white', 'black']) # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'res_18 | res_101 | resdcn_18 | resdcn_101 |' 'dlav0_34 | dla_34 | hourglass') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '64 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1.25e-4, help='learning rate for batch size 32.') self.parser.add_argument('--lr_step', type=str, default='90,120', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=140, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=32, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=5, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') # test self.parser.add_argument('--flip_test', action='store_true', help='flip data augmentation.') self.parser.add_argument('--test_scales', type=str, default='1', help='multi scale test augmentation.') self.parser.add_argument('--nms', action='store_true', help='run nms in testing.') self.parser.add_argument('--K', type=int, default=100, help='max number of output objects.') self.parser.add_argument('--not_prefetch_test', action='store_true', help='not use parallal data pre-processing.') self.parser.add_argument('--fix_res', action='store_true', help='fix testing resolution or keep ' 'the original resolution') self.parser.add_argument('--keep_res', action='store_true', help='keep the original resolution' ' during validation.') # dataset self.parser.add_argument('--not_rand_crop', action='store_true', help='not use the random crop data augmentation' 'from CornerNet.') self.parser.add_argument('--shift', type=float, default=0.1, help='when not using random crop' 'apply shift augmentation.') self.parser.add_argument('--scale', type=float, default=0.4, help='when not using random crop' 'apply scale augmentation.') self.parser.add_argument('--rotate', type=float, default=0, help='when not using random crop' 'apply rotation augmentation.') self.parser.add_argument('--flip', type = float, default=0.5, help='probability of applying flip augmentation.') self.parser.add_argument('--no_color_aug', action='store_true', help='not use the color augmenation ' 'from CornerNet') # multi_pose self.parser.add_argument('--aug_rot', type=float, default=0, help='probability of applying ' 'rotation augmentation.') # ddd self.parser.add_argument('--aug_ddd', type=float, default=0.5, help='probability of applying crop augmentation.') self.parser.add_argument('--rect_mask', action='store_true', help='for ignored object, apply mask on the ' 'rectangular region or just center point.') self.parser.add_argument('--kitti_split', default='3dop', help='different validation split for kitti: ' '3dop | subcnn') # loss self.parser.add_argument('--mse_loss', action='store_true', help='use mse loss or focal loss to train ' 'keypoint heatmaps.') # ctdet self.parser.add_argument('--reg_loss', default='l1', help='regression loss: sl1 | l1 | l2') self.parser.add_argument('--hm_weight', type=float, default=1, help='loss weight for keypoint heatmaps.') self.parser.add_argument('--off_weight', type=float, default=1, help='loss weight for keypoint local offsets.') self.parser.add_argument('--wh_weight', type=float, default=0.1, help='loss weight for bounding box size.') # multi_pose self.parser.add_argument('--hp_weight', type=float, default=1, help='loss weight for human pose offset.') self.parser.add_argument('--hm_hp_weight', type=float, default=1, help='loss weight for human keypoint heatmap.') # ddd self.parser.add_argument('--dep_weight', type=float, default=1, help='loss weight for depth.') self.parser.add_argument('--dim_weight', type=float, default=1, help='loss weight for 3d bounding box size.') self.parser.add_argument('--rot_weight', type=float, default=1, help='loss weight for orientation.') self.parser.add_argument('--peak_thresh', type=float, default=0.2) # task # ctdet self.parser.add_argument('--norm_wh', action='store_true', help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') self.parser.add_argument('--dense_wh', action='store_true', help='apply weighted regression near center or ' 'just apply regression on center point.') self.parser.add_argument('--cat_spec_wh', action='store_true', help='category specific bounding box size.') self.parser.add_argument('--not_reg_offset', action='store_true', help='not regress local offset.') # exdet self.parser.add_argument('--agnostic_ex', action='store_true', help='use category agnostic extreme points.') self.parser.add_argument('--scores_thresh', type=float, default=0.1, help='threshold for extreme point heatmap.') self.parser.add_argument('--center_thresh', type=float, default=0.1, help='threshold for centermap.') self.parser.add_argument('--aggr_weight', type=float, default=0.0, help='edge aggregation weight.') # multi_pose self.parser.add_argument('--dense_hp', action='store_true', help='apply weighted pose regression near center ' 'or just apply regression on center point.') self.parser.add_argument('--not_hm_hp', action='store_true', help='not estimate human joint heatmap, ' 'directly use the joint offset from center.') self.parser.add_argument('--not_reg_hp_offset', action='store_true', help='not regress local offset for ' 'human joint heatmaps.') self.parser.add_argument('--not_reg_bbox', action='store_true', help='not regression bounding box size.') # ground truth validation self.parser.add_argument('--eval_oracle_hm', action='store_true', help='use ground center heatmap.') self.parser.add_argument('--eval_oracle_wh', action='store_true', help='use ground truth bounding box size.') self.parser.add_argument('--eval_oracle_offset', action='store_true', help='use ground truth local heatmap offset.') self.parser.add_argument('--eval_oracle_kps', action='store_true', help='use ground truth human pose offset.') self.parser.add_argument('--eval_oracle_hmhp', action='store_true', help='use ground truth human joint heatmaps.') self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', help='use ground truth human joint local offset.') self.parser.add_argument('--eval_oracle_dep', action='store_true', help='use ground truth depth.') def parse(self, args=''): if args == '': opt = self.parser.parse_args() else: opt = self.parser.parse_args(args) opt.gpus_str = opt.gpus opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] opt.lr_step = [int(i) for i in opt.lr_step.split(',')] opt.test_scales = [float(i) for i in opt.test_scales.split(',')] opt.fix_res = not opt.keep_res print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') opt.reg_offset = not opt.not_reg_offset opt.reg_bbox = not opt.not_reg_bbox opt.hm_hp = not opt.not_hm_hp opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp if opt.head_conv == -1: # init default head_conv opt.head_conv = 256 if 'dla' in opt.arch else 64 opt.pad = 127 if 'hourglass' in opt.arch else 31 opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 if opt.trainval: opt.val_intervals = 100000000 if opt.debug > 0: opt.num_workers = 0 opt.batch_size = 1 opt.gpus = [opt.gpus[0]] opt.master_batch_size = -1 if opt.master_batch_size == -1: opt.master_batch_size = opt.batch_size // len(opt.gpus) rest_batch_size = (opt.batch_size - opt.master_batch_size) opt.chunk_sizes = [opt.master_batch_size] for i in range(len(opt.gpus) - 1): slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) if i < rest_batch_size % (len(opt.gpus) - 1): slave_chunk_size += 1 opt.chunk_sizes.append(slave_chunk_size) print('training chunk_sizes:', opt.chunk_sizes) opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') opt.data_dir = os.path.join(opt.root_dir, 'data') opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) opt.debug_dir = os.path.join(opt.save_dir, 'debug') print('The output will be saved to ', opt.save_dir) if opt.resume and opt.load_model == '': model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ else opt.save_dir opt.load_model = os.path.join(model_path, 'model_last.pth') return opt def update_dataset_info_and_set_heads(self, opt, dataset): input_h, input_w = dataset.default_resolution opt.mean, opt.std = dataset.mean, dataset.std opt.num_classes = dataset.num_classes # input_h(w): opt.input_h overrides opt.input_res overrides dataset default input_h = opt.input_res if opt.input_res > 0 else input_h input_w = opt.input_res if opt.input_res > 0 else input_w opt.input_h = opt.input_h if opt.input_h > 0 else input_h opt.input_w = opt.input_w if opt.input_w > 0 else input_w opt.output_h = opt.input_h // opt.down_ratio opt.output_w = opt.input_w // opt.down_ratio opt.input_res = max(opt.input_h, opt.input_w) opt.output_res = max(opt.output_h, opt.output_w) if opt.task == 'exdet': # assert opt.dataset in ['coco'] num_hm = 1 if opt.agnostic_ex else opt.num_classes opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, 'hm_b': num_hm, 'hm_r': num_hm, 'hm_c': opt.num_classes} if opt.reg_offset: opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2}) elif opt.task == 'ddd': # assert opt.dataset in ['gta', 'kitti', 'viper'] opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3} if opt.reg_bbox: opt.heads.update( {'wh': 2}) if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'ctdet': # assert opt.dataset in ['pascal', 'coco'] opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.cat_spec_wh else 2 * opt.num_classes} if opt.reg_offset: opt.heads.update({'reg': 2}) elif opt.task == 'multi_pose': # assert opt.dataset in ['coco_hp'] opt.flip_idx = dataset.flip_idx opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34} if opt.reg_offset: opt.heads.update({'reg': 2}) if opt.hm_hp: opt.heads.update({'hm_hp': 17}) if opt.reg_hp_offset: opt.heads.update({'hp_offset': 2}) else: assert 0, 'task not defined!' print('heads', opt.heads) return opt def init(self, args=''): default_dataset_info = { 'ctdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'exdet': {'default_resolution': [512, 512], 'num_classes': 80, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco'}, 'multi_pose': { 'default_resolution': [512, 512], 'num_classes': 1, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'coco_hp', 'num_joints': 17, 'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]}, 'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'dataset': 'kitti'}, } class Struct: def __init__(self, entries): for k, v in entries.items(): self.__setattr__(k, v) opt = self.parse(args) dataset = Struct(default_dataset_info[opt.task]) opt.dataset = dataset.dataset opt = self.update_dataset_info_and_set_heads(opt, dataset) return opt slamd202303071458299/src/lib/trains/000077500000000000000000000000001435750645400164375ustar00rootroot00000000000000slamd202303071458299/src/lib/trains/base_trainer.py000066400000000000000000000075111435750645400214530ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import time import torch from progress.bar import Bar from models.data_parallel import DataParallel from utils.utils import AverageMeter class ModelWithLoss(torch.nn.Module): def __init__(self, model, loss): super(ModelWithLoss, self).__init__() self.model = model self.loss = loss def forward(self, batch): outputs = self.model(batch['input']) loss, loss_stats = self.loss(outputs, batch) return outputs[-1], loss, loss_stats class BaseTrainer(object): def __init__( self, opt, model, optimizer=None): self.opt = opt self.optimizer = optimizer self.loss_stats, self.loss = self._get_losses(opt) self.model_with_loss = ModelWithLoss(model, self.loss) def set_device(self, gpus, chunk_sizes, device): if len(gpus) > 1: self.model_with_loss = DataParallel( self.model_with_loss, device_ids=gpus, chunk_sizes=chunk_sizes).to(device) else: self.model_with_loss = self.model_with_loss.to(device) for state in self.optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.to(device=device, non_blocking=True) def run_epoch(self, phase, epoch, data_loader): model_with_loss = self.model_with_loss if phase == 'train': model_with_loss.train() else: if len(self.opt.gpus) > 1: model_with_loss = self.model_with_loss.module model_with_loss.eval() torch.cuda.empty_cache() opt = self.opt results = {} data_time, batch_time = AverageMeter(), AverageMeter() avg_loss_stats = {l: AverageMeter() for l in self.loss_stats} num_iters = len(data_loader) if opt.num_iters < 0 else opt.num_iters bar = Bar('{}/{}'.format(opt.task, opt.exp_id), max=num_iters) end = time.time() for iter_id, batch in enumerate(data_loader): if iter_id >= num_iters: break data_time.update(time.time() - end) for k in batch: if k != 'meta': batch[k] = batch[k].to(device=opt.device, non_blocking=True) output, loss, loss_stats = model_with_loss(batch) loss = loss.mean() if phase == 'train': self.optimizer.zero_grad() loss.backward() self.optimizer.step() batch_time.update(time.time() - end) end = time.time() Bar.suffix = '{phase}: [{0}][{1}/{2}]|Tot: {total:} |ETA: {eta:} '.format( epoch, iter_id, num_iters, phase=phase, total=bar.elapsed_td, eta=bar.eta_td) for l in avg_loss_stats: avg_loss_stats[l].update( loss_stats[l].mean().item(), batch['input'].size(0)) Bar.suffix = Bar.suffix + '|{} {:.4f} '.format(l, avg_loss_stats[l].avg) if not opt.hide_data_time: Bar.suffix = Bar.suffix + '|Data {dt.val:.3f}s({dt.avg:.3f}s) ' \ '|Net {bt.avg:.3f}s'.format(dt=data_time, bt=batch_time) if opt.print_iter > 0: if iter_id % opt.print_iter == 0: print('{}/{}| {}'.format(opt.task, opt.exp_id, Bar.suffix)) else: bar.next() if opt.debug > 0: self.debug(batch, output, iter_id) if opt.test: self.save_result(output, batch, results) del output, loss, loss_stats bar.finish() ret = {k: v.avg for k, v in avg_loss_stats.items()} ret['time'] = bar.elapsed_td.total_seconds() / 60. return ret, results def debug(self, batch, output, iter_id): raise NotImplementedError def save_result(self, output, batch, results): raise NotImplementedError def _get_losses(self, opt): raise NotImplementedError def val(self, epoch, data_loader): return self.run_epoch('val', epoch, data_loader) def train(self, epoch, data_loader): return self.run_epoch('train', epoch, data_loader)slamd202303071458299/src/lib/trains/eldet.py000066400000000000000000000177151435750645400201210ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import numpy as np from models.losses import FocalLoss from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss from models.decode import eldet_decode from models.utils import _sigmoid from utils.debugger import Debugger from utils.post_process import eldet_post_process from utils.oracle_utils import gen_oracle_map from .base_trainer import BaseTrainer from torch.utils.tensorboard import SummaryWriter class EldetLoss(torch.nn.Module): def __init__(self, opt): super(EldetLoss, self).__init__() self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \ RegLoss() if opt.reg_loss == 'sl1' else None self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \ NormRegL1Loss() if opt.norm_wh else \ RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg self.crit_l1_loss = torch.nn.L1Loss(reduction='elementwise_mean') self.crit_mesloss = torch.nn.MSELoss() self.crit_angle = RegL1Loss4Angle(use_smooth_l1=(opt.reg_loss=='sl1')) self.iou_loss = IoULoss() self.opt = opt def forward(self, outputs, batch): opt = self.opt hm_loss, wh_loss, off_loss, ellipse_loss, iou_loss = 0, 0, 0, 0, 0 ratio_al_loss, ratio_bl_loss, theta_loss, sincos_loss = 0, 0, 0, 0 for s in range(opt.num_stacks): output = outputs[s] if not opt.mse_loss: output['hm'] = _sigmoid(output['hm']) # default # Use ground truth label for validation? if opt.eval_oracle_hm: output['hm'] = batch['hm'] if opt.eval_oracle_wh: output['wh'] = torch.from_numpy(gen_oracle_map( batch['wh'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['wh'].shape[3], output['wh'].shape[2])).to(opt.device) if opt.eval_oracle_offset: output['reg'] = torch.from_numpy(gen_oracle_map( batch['reg'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['reg'].shape[3], output['reg'].shape[2])).to(opt.device) hm_loss += self.crit(output['hm'], batch['hm']) / opt.num_stacks if opt.wh_weight > 0: wh_loss += self.crit_reg( output['l'], batch['reg_mask'], batch['ind'], batch['l']) / opt.num_stacks # if opt.dense_wh: # mask_weight = batch['dense_wh_mask'].sum() + 1e-4 # wh_loss += ( # self.crit_wh(output['wh'] * batch['dense_wh_mask'], # batch['dense_wh'] * batch['dense_wh_mask']) / # mask_weight) / opt.num_stacks # elif opt.cat_spec_wh: # wh_loss += self.crit_wh( # output['wh'], batch['cat_spec_mask'], # batch['ind'], batch['cat_spec_wh']) / opt.num_stacks # else: # wh_loss += self.crit_reg( # output['wh'], batch['reg_mask'], # batch['ind'], batch['wh']) / opt.num_stacks if opt.reg_offset and opt.off_weight > 0: off_loss += self.crit_reg(output['reg'], batch['reg_mask'], batch['ind'], batch['reg']) / opt.num_stacks # ellipse loss # if opt.ellipse_weight > 0: # ellipse_loss += self.crit_l1_loss(output['ratio_al'], batch['ratio_al']) / opt.num_stacks # ratio_al # ellipse_loss += self.crit_l1_loss(output['ratio_ba'], batch['ratio_ba']) / opt.num_stacks # ratio_ba # ellipse_loss += self.crit_l1_loss(output['theta'], batch['theta']) / opt.num_stacks # theta ratio_al_loss = self.crit_reg(output['ratio_al'], batch['reg_mask'], batch['ind'], batch['ratio_al']) / opt.num_stacks # ratio_al ratio_bl_loss = self.crit_reg(output['ratio_bl'], batch['reg_mask'], batch['ind'], batch['ratio_bl']) / opt.num_stacks # ratio_bl theta_loss = opt.theta_weight * self.crit_angle(output['theta'], batch['reg_mask'], batch['ind'], batch['theta']) / opt.num_stacks # theta (where output['theta'] is (1, 1, 128, 128)) sincos_loss = opt.sincos_weight * self.crit_reg(output['sincos'], batch['reg_mask'], batch['ind'], batch['sincos']) / opt.num_stacks # sincos ellipse_loss = ellipse_loss + ratio_al_loss + ratio_bl_loss + theta_loss + sincos_loss # if opt.ellipse_reg_weight > 0: # cons = output['ratio_al'] ** 2 * (1 + output['ratio_ba'] ** 2) cons = output['ratio_al'] ** 2 + output['ratio_bl'] ** 2 ellipse_loss += opt.ellipse_reg_weight * self.crit_mesloss(cons, torch.tensor(1.0).to(opt.device)) # rotated iou loss iou_loss, iou = self.iou_loss(output, batch) # if not opt.iou_weight > 0: # iou_loss = 0 loss = opt.hm_weight * hm_loss + opt.wh_weight * wh_loss + opt.iou_weight * iou_loss + \ opt.off_weight * off_loss + opt.ellipse_weight * ellipse_loss loss_stats = {'loss': loss, 'hm_loss': hm_loss, 'l_loss': wh_loss, 'off_loss': off_loss, 'ellipse_loss': ellipse_loss, 'ratio_al_loss': ratio_al_loss, 'ratio_bl_loss': ratio_bl_loss, 'theta_loss': theta_loss, 'sincos_loss': sincos_loss, 'iou_loss': iou_loss, 'iou': iou} return loss, loss_stats class EldetTrainer(BaseTrainer): def __init__(self, opt, model, optimizer=None): super(EldetTrainer, self).__init__(opt, model, optimizer=optimizer) def _get_losses(self, opt): loss_states = ['loss', 'hm_loss', 'l_loss', 'off_loss', 'ellipse_loss', 'ratio_al_loss', 'ratio_bl_loss', 'theta_loss', 'sincos_loss', 'iou_loss', 'iou'] loss = EldetLoss(opt) return loss_states, loss def debug(self, batch, output, iter_id): opt = self.opt reg = output['reg'] if opt.reg_offset else None dets = eldet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=opt.cat_spec_wh, K=opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets[:, :, :4] *= opt.down_ratio dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2]) dets_gt[:, :, :4] *= opt.down_ratio for i in range(1): debugger = Debugger( dataset=opt.dataset, ipynb=(opt.debug==3), theme=opt.debugger_theme) img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0) img = np.clip((( img * opt.std + opt.mean) * 255.), 0, 255).astype(np.uint8) pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy()) debugger.add_blend_img(img, pred, 'pred_hm') debugger.add_blend_img(img, gt, 'gt_hm') debugger.add_img(img, img_id='out_pred') for k in range(len(dets[i])): if dets[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets[i, k, :4], dets[i, k, -1], dets[i, k, 4], img_id='out_pred') debugger.add_img(img, img_id='out_gt') for k in range(len(dets_gt[i])): if dets_gt[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets_gt[i, k, :4], dets_gt[i, k, -1], dets_gt[i, k, 4], img_id='out_gt') if opt.debug == 4: debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id)) else: debugger.show_all_imgs(pause=True) def save_result(self, output, batch, results): reg = output['reg'] if self.opt.reg_offset else None dets = eldet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets_out = eldet_post_process( dets.copy(), batch['meta']['c'].cpu().numpy(), batch['meta']['s'].cpu().numpy(), output['hm'].shape[2], output['hm'].shape[3], output['hm'].shape[1]) results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]slamd202303071458299/src/lib/trains/eldet_bbox.py000066400000000000000000000126161435750645400211260ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import numpy as np from models.losses import FocalLoss from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss from models.decode import ctdet_decode from models.utils import _sigmoid from utils.debugger import Debugger from utils.post_process import ctdet_post_process from utils.oracle_utils import gen_oracle_map from .base_trainer import BaseTrainer class EldetLoss(torch.nn.Module): def __init__(self, opt): super(EldetLoss, self).__init__() self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \ RegLoss() if opt.reg_loss == 'sl1' else None self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \ NormRegL1Loss() if opt.norm_wh else \ RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg self.opt = opt def forward(self, outputs, batch): opt = self.opt hm_loss, wh_loss, off_loss = 0, 0, 0 for s in range(opt.num_stacks): output = outputs[s] if not opt.mse_loss: output['hm'] = _sigmoid(output['hm']) if opt.eval_oracle_hm: output['hm'] = batch['hm'] if opt.eval_oracle_wh: output['wh'] = torch.from_numpy(gen_oracle_map( batch['wh'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['wh'].shape[3], output['wh'].shape[2])).to(opt.device) if opt.eval_oracle_offset: output['reg'] = torch.from_numpy(gen_oracle_map( batch['reg'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['reg'].shape[3], output['reg'].shape[2])).to(opt.device) hm_loss += self.crit(output['hm'], batch['hm']) / opt.num_stacks if opt.wh_weight > 0: if opt.dense_wh: mask_weight = batch['dense_wh_mask'].sum() + 1e-4 wh_loss += ( self.crit_wh(output['wh'] * batch['dense_wh_mask'], batch['dense_wh'] * batch['dense_wh_mask']) / mask_weight) / opt.num_stacks elif opt.cat_spec_wh: wh_loss += self.crit_wh( output['wh'], batch['cat_spec_mask'], batch['ind'], batch['cat_spec_wh']) / opt.num_stacks else: wh_loss += self.crit_reg( output['wh'], batch['reg_mask'], batch['ind'], batch['wh']) / opt.num_stacks if opt.reg_offset and opt.off_weight > 0: off_loss += self.crit_reg(output['reg'], batch['reg_mask'], batch['ind'], batch['reg']) / opt.num_stacks loss = opt.hm_weight * hm_loss + opt.wh_weight * wh_loss + \ opt.off_weight * off_loss loss_stats = {'loss': loss, 'hm_loss': hm_loss, 'wh_loss': wh_loss, 'off_loss': off_loss} return loss, loss_stats class EldetTrainer(BaseTrainer): def __init__(self, opt, model, optimizer=None): super(EldetTrainer, self).__init__(opt, model, optimizer=optimizer) def _get_losses(self, opt): loss_states = ['loss', 'hm_loss', 'wh_loss', 'off_loss'] loss = EldetLoss(opt) return loss_states, loss def debug(self, batch, output, iter_id): opt = self.opt reg = output['reg'] if opt.reg_offset else None dets = ctdet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=opt.cat_spec_wh, K=opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets[:, :, :4] *= opt.down_ratio dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2]) dets_gt[:, :, :4] *= opt.down_ratio for i in range(1): debugger = Debugger( dataset=opt.dataset, ipynb=(opt.debug==3), theme=opt.debugger_theme) img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0) img = np.clip((( img * opt.std + opt.mean) * 255.), 0, 255).astype(np.uint8) pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy()) debugger.add_blend_img(img, pred, 'pred_hm') debugger.add_blend_img(img, gt, 'gt_hm') debugger.add_img(img, img_id='out_pred') for k in range(len(dets[i])): if dets[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets[i, k, :4], dets[i, k, -1], dets[i, k, 4], img_id='out_pred') debugger.add_img(img, img_id='out_gt') for k in range(len(dets_gt[i])): if dets_gt[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets_gt[i, k, :4], dets_gt[i, k, -1], dets_gt[i, k, 4], img_id='out_gt') if opt.debug == 4: debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id)) else: debugger.show_all_imgs(pause=True) def save_result(self, output, batch, results): reg = output['reg'] if self.opt.reg_offset else None dets = ctdet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets_out = ctdet_post_process( dets.copy(), batch['meta']['c'].cpu().numpy(), batch['meta']['s'].cpu().numpy(), output['hm'].shape[2], output['hm'].shape[3], output['hm'].shape[1]) results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]slamd202303071458299/src/lib/trains/eldet_onlyangle.py000066400000000000000000000150751435750645400221660ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch import numpy as np from models.losses import FocalLoss from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle from models.utils import _sigmoid from utils.debugger import Debugger from utils.post_process import ctdet_post_process from utils.oracle_utils import gen_oracle_map from .base_trainer import BaseTrainer from torch.utils.tensorboard import SummaryWriter class EldetLoss(torch.nn.Module): def __init__(self, opt): super(EldetLoss, self).__init__() self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \ RegLoss() if opt.reg_loss == 'sl1' else None self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \ NormRegL1Loss() if opt.norm_wh else \ RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg self.crit_l1_loss = torch.nn.L1Loss(reduction='elementwise_mean') self.crit_mesloss = torch.nn.MSELoss() self.crit_angle = RegL1Loss4Angle(use_smooth_l1=(opt.reg_loss=='sl1')) self.iou_loss = IoULoss4Angle() self.opt = opt def forward(self, outputs, batch): opt = self.opt hm_loss, wh_loss, off_loss, ellipse_loss, iou_loss = 0, 0, 0, 0, 0 ratio_al_loss, ratio_bl_loss, theta_loss, sincos_loss = 0, 0, 0, 0 for s in range(opt.num_stacks): output = outputs[s] if not opt.mse_loss: output['hm'] = _sigmoid(output['hm']) # default # Use ground truth label for validation? if opt.eval_oracle_hm: output['hm'] = batch['hm'] if opt.eval_oracle_wh: output['wh'] = torch.from_numpy(gen_oracle_map( batch['wh'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['wh'].shape[3], output['wh'].shape[2])).to(opt.device) if opt.eval_oracle_offset: output['reg'] = torch.from_numpy(gen_oracle_map( batch['reg'].detach().cpu().numpy(), batch['ind'].detach().cpu().numpy(), output['reg'].shape[3], output['reg'].shape[2])).to(opt.device) hm_loss += 0 * self.crit(output['hm'], batch['hm']) / opt.num_stacks if opt.wh_weight > 0: wh_loss += 0 * self.crit_reg( output['l'], batch['reg_mask'], batch['ind'], batch['l']) / opt.num_stacks if opt.reg_offset and opt.off_weight > 0: off_loss += 0 * self.crit_reg(output['reg'], batch['reg_mask'], batch['ind'], batch['reg']) / opt.num_stacks # ellipse loss ratio_al_loss = 0 * self.crit_reg(output['ratio_al'], batch['reg_mask'], batch['ind'], batch['ratio_al']) / opt.num_stacks # ratio_al ratio_bl_loss = 0 * self.crit_reg(output['ratio_bl'], batch['reg_mask'], batch['ind'], batch['ratio_bl']) / opt.num_stacks # ratio_bl theta_loss = opt.theta_weight * self.crit_angle(output['theta'], batch['reg_mask'], batch['ind'], batch['theta']) / opt.num_stacks # theta (where output['theta'] is (1, 1, 128, 128)) sincos_loss = opt.sincos_weight * self.crit_reg(output['sincos'], batch['reg_mask'], batch['ind'], batch['sincos']) / opt.num_stacks # sincos ellipse_loss = theta_loss + sincos_loss # rotated iou loss iou_loss, iou = self.iou_loss(output, batch) # if not opt.iou_weight > 0: # iou_loss = 0 loss = opt.iou_weight * iou_loss + opt.ellipse_weight * ellipse_loss loss_stats = {'loss': loss, 'hm_loss': hm_loss, 'l_loss': wh_loss, 'off_loss': off_loss, 'ellipse_loss': ellipse_loss, 'ratio_al_loss': ratio_al_loss, 'ratio_bl_loss': ratio_bl_loss, 'theta_loss': theta_loss, 'sincos_loss': sincos_loss, 'iou_loss': iou_loss, 'iou': iou} return loss, loss_stats class EldetTrainer(BaseTrainer): def __init__(self, opt, model, optimizer=None): super(EldetTrainer, self).__init__(opt, model, optimizer=optimizer) def _get_losses(self, opt): loss_states = ['loss', 'hm_loss', 'l_loss', 'off_loss', 'ellipse_loss', 'ratio_al_loss', 'ratio_bl_loss', 'theta_loss', 'sincos_loss', 'iou_loss', 'iou'] loss = EldetLoss(opt) return loss_states, loss def debug(self, batch, output, iter_id): opt = self.opt reg = output['reg'] if opt.reg_offset else None dets = ctdet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=opt.cat_spec_wh, K=opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets[:, :, :4] *= opt.down_ratio dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2]) dets_gt[:, :, :4] *= opt.down_ratio for i in range(1): debugger = Debugger( dataset=opt.dataset, ipynb=(opt.debug==3), theme=opt.debugger_theme) img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0) img = np.clip((( img * opt.std + opt.mean) * 255.), 0, 255).astype(np.uint8) pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy()) debugger.add_blend_img(img, pred, 'pred_hm') debugger.add_blend_img(img, gt, 'gt_hm') debugger.add_img(img, img_id='out_pred') for k in range(len(dets[i])): if dets[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets[i, k, :4], dets[i, k, -1], dets[i, k, 4], img_id='out_pred') debugger.add_img(img, img_id='out_gt') for k in range(len(dets_gt[i])): if dets_gt[i, k, 4] > opt.center_thresh: debugger.add_coco_bbox(dets_gt[i, k, :4], dets_gt[i, k, -1], dets_gt[i, k, 4], img_id='out_gt') if opt.debug == 4: debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id)) else: debugger.show_all_imgs(pause=True) def save_result(self, output, batch, results): reg = output['reg'] if self.opt.reg_offset else None dets = ctdet_decode( output['hm'], output['wh'], reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) dets_out = ctdet_post_process( dets.copy(), batch['meta']['c'].cpu().numpy(), batch['meta']['s'].cpu().numpy(), output['hm'].shape[2], output['hm'].shape[3], output['hm'].shape[1]) results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]slamd202303071458299/src/lib/trains/train_factory.py000066400000000000000000000010451435750645400216550ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function # from .ctdet import CtdetTrainer # from .ddd import DddTrainer # from .exdet import ExdetTrainer # from .multi_pose import MultiPoseTrainer from .eldet import EldetTrainer # from .eldet_onlyangle import EldetTrainer # print("!!!Warning!!! This setting only regresses on angle") train_factory = { # 'exdet': ExdetTrainer, # 'ddd': DddTrainer, # 'ctdet': CtdetTrainer, # 'multi_pose': MultiPoseTrainer, 'eldet': EldetTrainer } slamd202303071458299/src/lib/utils/000077500000000000000000000000001435750645400162775ustar00rootroot00000000000000slamd202303071458299/src/lib/utils/__init__.py000066400000000000000000000000001435750645400203760ustar00rootroot00000000000000slamd202303071458299/src/lib/utils/ddd_utils.py000066400000000000000000000107041435750645400206260ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 def compute_box_3d(dim, location, rotation_y): # dim: 3 # location: 3 # rotation_y: 1 # return: 8 x 3 c, s = np.cos(rotation_y), np.sin(rotation_y) R = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], dtype=np.float32) l, w, h = dim[2], dim[1], dim[0] x_corners = [l/2, l/2, -l/2, -l/2, l/2, l/2, -l/2, -l/2] y_corners = [0,0,0,0,-h,-h,-h,-h] z_corners = [w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2] corners = np.array([x_corners, y_corners, z_corners], dtype=np.float32) corners_3d = np.dot(R, corners) corners_3d = corners_3d + np.array(location, dtype=np.float32).reshape(3, 1) return corners_3d.transpose(1, 0) def project_to_image(pts_3d, P): # pts_3d: n x 3 # P: 3 x 4 # return: n x 2 pts_3d_homo = np.concatenate( [pts_3d, np.ones((pts_3d.shape[0], 1), dtype=np.float32)], axis=1) pts_2d = np.dot(P, pts_3d_homo.transpose(1, 0)).transpose(1, 0) pts_2d = pts_2d[:, :2] / pts_2d[:, 2:] # import pdb; pdb.set_trace() return pts_2d def compute_orientation_3d(dim, location, rotation_y): # dim: 3 # location: 3 # rotation_y: 1 # return: 2 x 3 c, s = np.cos(rotation_y), np.sin(rotation_y) R = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], dtype=np.float32) orientation_3d = np.array([[0, dim[2]], [0, 0], [0, 0]], dtype=np.float32) orientation_3d = np.dot(R, orientation_3d) orientation_3d = orientation_3d + \ np.array(location, dtype=np.float32).reshape(3, 1) return orientation_3d.transpose(1, 0) def draw_box_3d(image, corners, c=(0, 0, 255)): face_idx = [[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]] for ind_f in range(3, -1, -1): f = face_idx[ind_f] for j in range(4): cv2.line(image, (corners[f[j], 0], corners[f[j], 1]), (corners[f[(j+1)%4], 0], corners[f[(j+1)%4], 1]), c, 2, lineType=cv2.LINE_AA) if ind_f == 0: cv2.line(image, (corners[f[0], 0], corners[f[0], 1]), (corners[f[2], 0], corners[f[2], 1]), c, 1, lineType=cv2.LINE_AA) cv2.line(image, (corners[f[1], 0], corners[f[1], 1]), (corners[f[3], 0], corners[f[3], 1]), c, 1, lineType=cv2.LINE_AA) return image def unproject_2d_to_3d(pt_2d, depth, P): # pts_2d: 2 # depth: 1 # P: 3 x 4 # return: 3 z = depth - P[2, 3] x = (pt_2d[0] * depth - P[0, 3] - P[0, 2] * z) / P[0, 0] y = (pt_2d[1] * depth - P[1, 3] - P[1, 2] * z) / P[1, 1] pt_3d = np.array([x, y, z], dtype=np.float32) return pt_3d def alpha2rot_y(alpha, x, cx, fx): """ Get rotation_y by alpha + theta - 180 alpha : Observation angle of object, ranging [-pi..pi] x : Object center x to the camera center (x-W/2), in pixels rotation_y : Rotation ry around Y-axis in camera coordinates [-pi..pi] """ rot_y = alpha + np.arctan2(x - cx, fx) if rot_y > np.pi: rot_y -= 2 * np.pi if rot_y < -np.pi: rot_y += 2 * np.pi return rot_y def rot_y2alpha(rot_y, x, cx, fx): """ Get rotation_y by alpha + theta - 180 alpha : Observation angle of object, ranging [-pi..pi] x : Object center x to the camera center (x-W/2), in pixels rotation_y : Rotation ry around Y-axis in camera coordinates [-pi..pi] """ alpha = rot_y - np.arctan2(x - cx, fx) if alpha > np.pi: alpha -= 2 * np.pi if alpha < -np.pi: alpha += 2 * np.pi return alpha def ddd2locrot(center, alpha, dim, depth, calib): # single image locations = unproject_2d_to_3d(center, depth, calib) locations[1] += dim[0] / 2 rotation_y = alpha2rot_y(alpha, center[0], calib[0, 2], calib[0, 0]) return locations, rotation_y def project_3d_bbox(location, dim, rotation_y, calib): box_3d = compute_box_3d(dim, location, rotation_y) box_2d = project_to_image(box_3d, calib) return box_2d if __name__ == '__main__': calib = np.array( [[7.070493000000e+02, 0.000000000000e+00, 6.040814000000e+02, 4.575831000000e+01], [0.000000000000e+00, 7.070493000000e+02, 1.805066000000e+02, -3.454157000000e-01], [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 4.981016000000e-03]], dtype=np.float32) alpha = -0.20 tl = np.array([712.40, 143.00], dtype=np.float32) br = np.array([810.73, 307.92], dtype=np.float32) ct = (tl + br) / 2 rotation_y = 0.01 print('alpha2rot_y', alpha2rot_y(alpha, ct[0], calib[0, 2], calib[0, 0])) print('rotation_y', rotation_y)slamd202303071458299/src/lib/utils/debugger.py000066400000000000000000000515231435750645400204430ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d class Debugger(object): def __init__(self, ipynb=False, theme='black', num_classes=-1, dataset=None, down_ratio=4): self.ipynb = ipynb if not self.ipynb: import matplotlib.pyplot as plt self.plt = plt self.imgs = {} self.theme = theme colors = [(color_list[_]).astype(np.uint8) \ for _ in range(len(color_list))] self.colors = np.array(colors, dtype=np.uint8).reshape(len(colors), 1, 1, 3) if self.theme == 'white': self.colors = self.colors.reshape(-1)[::-1].reshape(len(colors), 1, 1, 3) self.colors = np.clip(self.colors, 0., 0.6 * 255).astype(np.uint8) self.dim_scale = 1 if dataset == 'coco_hp': self.names = ['p'] self.num_class = 1 self.num_joints = 17 self.edges = [[0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], [6, 12], [11, 12], [11, 13], [13, 15], [12, 14], [14, 16]] self.ec = [(255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 255), (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 255), (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255)] self.colors_hp = [(255, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255)] elif num_classes == 80 or dataset == 'coco': self.names = coco_class_name elif num_classes == 20 or dataset == 'pascal': self.names = pascal_class_name elif dataset == 'gta': self.names = gta_class_name self.focal_length = 935.3074360871937 self.W = 1920 self.H = 1080 self.dim_scale = 3 elif dataset == 'viper': self.names = gta_class_name self.focal_length = 1158 self.W = 1920 self.H = 1080 self.dim_scale = 3 elif num_classes == 3 or dataset == 'kitti': self.names = kitti_class_name self.focal_length = 721.5377 self.W = 1242 self.H = 375 elif dataset == 'coco_fhd': self.names = fhd_class_name num_classes = len(self.names) self.down_ratio=down_ratio # for bird view self.world_size = 64 self.out_size = 384 def add_img(self, img, img_id='default', revert_color=False): if revert_color: img = 255 - img self.imgs[img_id] = img.copy() def add_mask(self, mask, bg, imgId = 'default', trans = 0.8): self.imgs[imgId] = (mask.reshape( mask.shape[0], mask.shape[1], 1) * 255 * trans + \ bg * (1 - trans)).astype(np.uint8) def show_img(self, pause = False, imgId = 'default'): cv2.imshow('{}'.format(imgId), self.imgs[imgId]) if pause: cv2.waitKey() def add_blend_img(self, back, fore, img_id='blend', trans=0.7): if self.theme == 'white': fore = 255 - fore if fore.shape[0] != back.shape[0] or fore.shape[0] != back.shape[1]: fore = cv2.resize(fore, (back.shape[1], back.shape[0])) if len(fore.shape) == 2: fore = fore.reshape(fore.shape[0], fore.shape[1], 1) self.imgs[img_id] = (back * (1. - trans) + fore * trans) self.imgs[img_id][self.imgs[img_id] > 255] = 255 self.imgs[img_id][self.imgs[img_id] < 0] = 0 self.imgs[img_id] = self.imgs[img_id].astype(np.uint8).copy() ''' # slow version def gen_colormap(self, img, output_res=None): # num_classes = len(self.colors) img[img < 0] = 0 h, w = img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) color_map = np.zeros((output_res[0], output_res[1], 3), dtype=np.uint8) for i in range(img.shape[0]): resized = cv2.resize(img[i], (output_res[1], output_res[0])) resized = resized.reshape(output_res[0], output_res[1], 1) cl = self.colors[i] if not (self.theme == 'white') \ else 255 - self.colors[i] color_map = np.maximum(color_map, (resized * cl).astype(np.uint8)) return color_map ''' def gen_colormap(self, img, output_res=None): img = img.copy() c, h, w = img.shape[0], img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) colors = np.array( self.colors, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) if self.theme == 'white': colors = 255 - colors color_map = (img * colors).max(axis=2).astype(np.uint8) color_map = cv2.resize(color_map, (output_res[0], output_res[1])) return color_map ''' # slow def gen_colormap_hp(self, img, output_res=None): # num_classes = len(self.colors) # img[img < 0] = 0 h, w = img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) color_map = np.zeros((output_res[0], output_res[1], 3), dtype=np.uint8) for i in range(img.shape[0]): resized = cv2.resize(img[i], (output_res[1], output_res[0])) resized = resized.reshape(output_res[0], output_res[1], 1) cl = self.colors_hp[i] if not (self.theme == 'white') else \ (255 - np.array(self.colors_hp[i])) color_map = np.maximum(color_map, (resized * cl).astype(np.uint8)) return color_map ''' def gen_colormap_hp(self, img, output_res=None): c, h, w = img.shape[0], img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) colors = np.array( self.colors_hp, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) if self.theme == 'white': colors = 255 - colors color_map = (img * colors).max(axis=2).astype(np.uint8) color_map = cv2.resize(color_map, (output_res[0], output_res[1])) return color_map def add_rect(self, rect1, rect2, c, conf=1, img_id='default'): cv2.rectangle( self.imgs[img_id], (rect1[0], rect1[1]), (rect2[0], rect2[1]), c, 2) if conf < 1: cv2.circle(self.imgs[img_id], (rect1[0], rect1[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect2[0], rect2[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect1[0], rect2[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect2[0], rect1[1]), int(10 * conf), c, 1) def add_coco_bbox(self, bbox, cat, conf=1, show_txt=True, img_id='default'): bbox = np.array(bbox, dtype=np.int32) # cat = (int(cat) + 1) % 80 cat = int(cat) # print('cat', cat, self.names[cat]) c = self.colors[cat][0][0].tolist() if self.theme == 'white': c = (255 - np.array(c)).tolist() txt = '{}{:.1f}'.format(self.names[cat], conf) font = cv2.FONT_HERSHEY_SIMPLEX cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] cv2.rectangle( self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), c, 2) if show_txt: cv2.rectangle(self.imgs[img_id], (bbox[0], bbox[1] - cat_size[1] - 2), (bbox[0] + cat_size[0], bbox[1] - 2), c, -1) cv2.putText(self.imgs[img_id], txt, (bbox[0], bbox[1] - 2), font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) def add_coco_hp(self, points, img_id='default'): points = np.array(points, dtype=np.int32).reshape(self.num_joints, 2) for j in range(self.num_joints): cv2.circle(self.imgs[img_id], (points[j, 0], points[j, 1]), 3, self.colors_hp[j], -1) for j, e in enumerate(self.edges): if points[e].min() > 0: cv2.line(self.imgs[img_id], (points[e[0], 0], points[e[0], 1]), (points[e[1], 0], points[e[1], 1]), self.ec[j], 2, lineType=cv2.LINE_AA) def add_points(self, points, img_id='default'): num_classes = len(points) # assert num_classes == len(self.colors) for i in range(num_classes): for j in range(len(points[i])): c = self.colors[i, 0, 0] cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, points[i][j][1] * self.down_ratio), 5, (255, 255, 255), -1) cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, points[i][j][1] * self.down_ratio), 3, (int(c[0]), int(c[1]), int(c[2])), -1) def show_all_imgs(self, pause=False, time=0): if not self.ipynb: for i, v in self.imgs.items(): cv2.imshow('{}'.format(i), v) if cv2.waitKey(0 if pause else 1) == 27: import sys sys.exit(0) else: self.ax = None nImgs = len(self.imgs) fig=self.plt.figure(figsize=(nImgs * 10,10)) nCols = nImgs nRows = nImgs // nCols for i, (k, v) in enumerate(self.imgs.items()): fig.add_subplot(1, nImgs, i + 1) if len(v.shape) == 3: self.plt.imshow(cv2.cvtColor(v, cv2.COLOR_BGR2RGB)) else: self.plt.imshow(v) self.plt.show() def save_img(self, imgId='default', path='./cache/debug/'): cv2.imwrite(path + '{}.png'.format(imgId), self.imgs[imgId]) def save_all_imgs(self, path='./cache/debug/', prefix='', genID=False): if genID: try: idx = int(np.loadtxt(path + '/id.txt')) except: idx = 0 prefix=idx np.savetxt(path + '/id.txt', np.ones(1) * (idx + 1), fmt='%d') for i, v in self.imgs.items(): cv2.imwrite(path + '/{}{}.png'.format(prefix, i), v) def remove_side(self, img_id, img): if not (img_id in self.imgs): return ws = img.sum(axis=2).sum(axis=0) l = 0 while ws[l] == 0 and l < len(ws): l+= 1 r = ws.shape[0] - 1 while ws[r] == 0 and r > 0: r -= 1 hs = img.sum(axis=2).sum(axis=1) t = 0 while hs[t] == 0 and t < len(hs): t += 1 b = hs.shape[0] - 1 while hs[b] == 0 and b > 0: b -= 1 self.imgs[img_id] = self.imgs[img_id][t:b+1, l:r+1].copy() def project_3d_to_bird(self, pt): pt[0] += self.world_size / 2 pt[1] = self.world_size - pt[1] pt = pt * self.out_size / self.world_size return pt.astype(np.int32) def add_ct_detection( self, img, dets, show_box=False, show_txt=True, center_thresh=0.5, img_id='det'): # dets: max_preds x 5 self.imgs[img_id] = img.copy() if type(dets) == type({}): for cat in dets: for i in range(len(dets[cat])): if dets[cat][i, 2] > center_thresh: cl = (self.colors[cat, 0, 0]).tolist() ct = dets[cat][i, :2].astype(np.int32) if show_box: w, h = dets[cat][i, -2], dets[cat][i, -1] x, y = dets[cat][i, 0], dets[cat][i, 1] bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], dtype=np.float32) self.add_coco_bbox( bbox, cat - 1, dets[cat][i, 2], show_txt=show_txt, img_id=img_id) else: for i in range(len(dets)): if dets[i, 2] > center_thresh: # print('dets', dets[i]) cat = int(dets[i, -1]) cl = (self.colors[cat, 0, 0] if self.theme == 'black' else \ 255 - self.colors[cat, 0, 0]).tolist() ct = dets[i, :2].astype(np.int32) * self.down_ratio cv2.circle(self.imgs[img_id], (ct[0], ct[1]), 3, cl, -1) if show_box: w, h = dets[i, -3] * self.down_ratio, dets[i, -2] * self.down_ratio x, y = dets[i, 0] * self.down_ratio, dets[i, 1] * self.down_ratio bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], dtype=np.float32) self.add_coco_bbox(bbox, dets[i, -1], dets[i, 2], img_id=img_id) def add_3d_detection( self, image_or_path, dets, calib, show_txt=False, center_thresh=0.5, img_id='det'): if isinstance(image_or_path, np.ndarray): self.imgs[img_id] = image_or_path else: self.imgs[img_id] = cv2.imread(image_or_path) for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] # loc[1] = loc[1] - dim[0] / 2 + dim[0] / 2 / self.dim_scale # dim = dim / self.dim_scale if loc[2] > 1: box_3d = compute_box_3d(dim, loc, rot_y) box_2d = project_to_image(box_3d, calib) self.imgs[img_id] = draw_box_3d(self.imgs[img_id], box_2d, cl) def compose_vis_add( self, img_path, dets, calib, center_thresh, pred, bev, img_id='out'): self.imgs[img_id] = cv2.imread(img_path) # h, w = self.imgs[img_id].shape[:2] # pred = cv2.resize(pred, (h, w)) h, w = pred.shape[:2] hs, ws = self.imgs[img_id].shape[0] / h, self.imgs[img_id].shape[1] / w self.imgs[img_id] = cv2.resize(self.imgs[img_id], (w, h)) self.add_blend_img(self.imgs[img_id], pred, img_id) for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] # loc[1] = loc[1] - dim[0] / 2 + dim[0] / 2 / self.dim_scale # dim = dim / self.dim_scale if loc[2] > 1: box_3d = compute_box_3d(dim, loc, rot_y) box_2d = project_to_image(box_3d, calib) box_2d[:, 0] /= hs box_2d[:, 1] /= ws self.imgs[img_id] = draw_box_3d(self.imgs[img_id], box_2d, cl) self.imgs[img_id] = np.concatenate( [self.imgs[img_id], self.imgs[bev]], axis=1) def add_2d_detection( self, img, dets, show_box=False, show_txt=True, center_thresh=0.5, img_id='det'): self.imgs[img_id] = img for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: bbox = dets[cat][i, 1:5] self.add_coco_bbox( bbox, cat - 1, dets[cat][i, -1], show_txt=show_txt, img_id=img_id) def add_bird_view(self, dets, center_thresh=0.3, img_id='bird'): bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 for cat in dets: cl = (self.colors[cat - 1, 0, 0]).tolist() lc = (250, 152, 12) for i in range(len(dets[cat])): if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] for k in range(4): rect[k] = self.project_3d_to_bird(rect[k]) # cv2.circle(bird_view, (rect[k][0], rect[k][1]), 2, lc, -1) cv2.polylines( bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], True,lc,2,lineType=cv2.LINE_AA) for e in [[0, 1]]: t = 4 if e == [0, 1] else 1 cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), (rect[e[1]][0], rect[e[1]][1]), lc, t, lineType=cv2.LINE_AA) self.imgs[img_id] = bird_view def add_bird_views(self, dets_dt, dets_gt, center_thresh=0.3, img_id='bird'): alpha = 0.5 bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 for ii, (dets, lc, cc) in enumerate( [(dets_gt, (12, 49, 250), (0, 0, 255)), (dets_dt, (250, 152, 12), (255, 0, 0))]): # cc = np.array(lc, dtype=np.uint8).reshape(1, 1, 3) for cat in dets: cl = (self.colors[cat - 1, 0, 0]).tolist() for i in range(len(dets[cat])): if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] for k in range(4): rect[k] = self.project_3d_to_bird(rect[k]) if ii == 0: cv2.fillPoly( bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], lc,lineType=cv2.LINE_AA) else: cv2.polylines( bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], True,lc,2,lineType=cv2.LINE_AA) # for e in [[0, 1], [1, 2], [2, 3], [3, 0]]: for e in [[0, 1]]: t = 4 if e == [0, 1] else 1 cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), (rect[e[1]][0], rect[e[1]][1]), lc, t, lineType=cv2.LINE_AA) self.imgs[img_id] = bird_view kitti_class_name = [ 'p', 'v', 'b' ] gta_class_name = [ 'p', 'v' ] fhd_class_name = [ 'lung', 'heart' ] pascal_class_name = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] coco_class_name = [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] color_list = np.array( [ 1.000, 1.000, 1.000, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494, 0.184, 0.556, 0.466, 0.674, 0.188, 0.301, 0.745, 0.933, 0.635, 0.078, 0.184, 0.300, 0.300, 0.300, 0.600, 0.600, 0.600, 1.000, 0.000, 0.000, 1.000, 0.500, 0.000, 0.749, 0.749, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 1.000, 0.667, 0.000, 1.000, 0.333, 0.333, 0.000, 0.333, 0.667, 0.000, 0.333, 1.000, 0.000, 0.667, 0.333, 0.000, 0.667, 0.667, 0.000, 0.667, 1.000, 0.000, 1.000, 0.333, 0.000, 1.000, 0.667, 0.000, 1.000, 1.000, 0.000, 0.000, 0.333, 0.500, 0.000, 0.667, 0.500, 0.000, 1.000, 0.500, 0.333, 0.000, 0.500, 0.333, 0.333, 0.500, 0.333, 0.667, 0.500, 0.333, 1.000, 0.500, 0.667, 0.000, 0.500, 0.667, 0.333, 0.500, 0.667, 0.667, 0.500, 0.667, 1.000, 0.500, 1.000, 0.000, 0.500, 1.000, 0.333, 0.500, 1.000, 0.667, 0.500, 1.000, 1.000, 0.500, 0.000, 0.333, 1.000, 0.000, 0.667, 1.000, 0.000, 1.000, 1.000, 0.333, 0.000, 1.000, 0.333, 0.333, 1.000, 0.333, 0.667, 1.000, 0.333, 1.000, 1.000, 0.667, 0.000, 1.000, 0.667, 0.333, 1.000, 0.667, 0.667, 1.000, 0.667, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.333, 1.000, 1.000, 0.667, 1.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.143, 0.143, 0.143, 0.286, 0.286, 0.286, 0.429, 0.429, 0.429, 0.571, 0.571, 0.571, 0.714, 0.714, 0.714, 0.857, 0.857, 0.857, 0.000, 0.447, 0.741, 0.50, 0.5, 0 ] ).astype(np.float32) color_list = color_list.reshape((-1, 3)) * 255 slamd202303071458299/src/lib/utils/image.py000066400000000000000000000172551435750645400177450ustar00rootroot00000000000000# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # Modified by Xingyi Zhou # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 import random def flip(img): return img[:, :, ::-1].copy() def transform_preds(coords, center, scale, output_size): target_coords = np.zeros(coords.shape) trans = get_affine_transform(center, scale, 0, output_size, inv=1) for p in range(coords.shape[0]): target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) return target_coords def transform_length(length, center, scale, output_size): trans = get_affine_transform(center, scale, 0, output_size, inv=1) return length * trans[0][0] def get_affine_transform(center, scale, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0): if not isinstance(scale, np.ndarray) and not isinstance(scale, list): scale = np.array([scale, scale], dtype=np.float32) scale_tmp = scale src_w = scale_tmp[0] dst_w = output_size[0] dst_h = output_size[1] rot_rad = np.pi * rot / 180 src_dir = get_dir([0, src_w * -0.5], rot_rad) dst_dir = np.array([0, dst_w * -0.5], np.float32) src = np.zeros((3, 2), dtype=np.float32) dst = np.zeros((3, 2), dtype=np.float32) src[0, :] = center + scale_tmp * shift src[1, :] = center + src_dir + scale_tmp * shift dst[0, :] = [dst_w * 0.5, dst_h * 0.5] dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir src[2:, :] = get_3rd_point(src[0, :], src[1, :]) dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) if inv: trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) else: trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) return trans def affine_transform(pt, t): new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T new_pt = np.dot(t, new_pt) return new_pt[:2] def get_3rd_point(a, b): direct = a - b return b + np.array([-direct[1], direct[0]], dtype=np.float32) def get_dir(src_point, rot_rad): sn, cs = np.sin(rot_rad), np.cos(rot_rad) src_result = [0, 0] src_result[0] = src_point[0] * cs - src_point[1] * sn src_result[1] = src_point[0] * sn + src_point[1] * cs return src_result def crop(img, center, scale, output_size, rot=0): trans = get_affine_transform(center, scale, rot, output_size) dst_img = cv2.warpAffine(img, trans, (int(output_size[0]), int(output_size[1])), flags=cv2.INTER_LINEAR) return dst_img def gaussian_radius(det_size, min_overlap=0.7): height, width = det_size a1 = 1 b1 = (height + width) c1 = width * height * (1 - min_overlap) / (1 + min_overlap) sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1) r1 = (b1 + sq1) / 2 a2 = 4 b2 = 2 * (height + width) c2 = (1 - min_overlap) * width * height sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2) r2 = (b2 + sq2) / 2 a3 = 4 * min_overlap b3 = -2 * min_overlap * (height + width) c3 = (min_overlap - 1) * width * height sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3) r3 = (b3 + sq3) / 2 return min(r1, r2, r3) def gaussian2D(shape, sigma=1): m, n = [(ss - 1.) / 2. for ss in shape] y, x = np.ogrid[-m:m+1,-n:n+1] h = np.exp(-(x * x + y * y) / (2 * sigma * sigma)) h[h < np.finfo(h.dtype).eps * h.max()] = 0 return h def draw_umich_gaussian(heatmap, center, radius, k=1): diameter = 2 * radius + 1 gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6) x, y = int(center[0]), int(center[1]) height, width = heatmap.shape[0:2] left, right = min(x, radius), min(width - x, radius + 1) top, bottom = min(y, radius), min(height - y, radius + 1) masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right] if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap) return heatmap def draw_dense_reg(regmap, heatmap, center, value, radius, is_offset=False): diameter = 2 * radius + 1 gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6) value = np.array(value, dtype=np.float32).reshape(-1, 1, 1) dim = value.shape[0] reg = np.ones((dim, diameter*2+1, diameter*2+1), dtype=np.float32) * value if is_offset and dim == 2: delta = np.arange(diameter*2+1) - radius reg[0] = reg[0] - delta.reshape(1, -1) reg[1] = reg[1] - delta.reshape(-1, 1) x, y = int(center[0]), int(center[1]) height, width = heatmap.shape[0:2] left, right = min(x, radius), min(width - x, radius + 1) top, bottom = min(y, radius), min(height - y, radius + 1) masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] masked_regmap = regmap[:, y - top:y + bottom, x - left:x + right] masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right] masked_reg = reg[:, radius - top:radius + bottom, radius - left:radius + right] if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug idx = (masked_gaussian >= masked_heatmap).reshape( 1, masked_gaussian.shape[0], masked_gaussian.shape[1]) masked_regmap = (1-idx) * masked_regmap + idx * masked_reg regmap[:, y - top:y + bottom, x - left:x + right] = masked_regmap return regmap def draw_msra_gaussian(heatmap, center, sigma): tmp_size = sigma * 3 mu_x = int(center[0] + 0.5) mu_y = int(center[1] + 0.5) w, h = heatmap.shape[0], heatmap.shape[1] ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)] br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)] if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0: return heatmap size = 2 * tmp_size + 1 x = np.arange(0, size, 1, np.float32) y = x[:, np.newaxis] x0 = y0 = size // 2 g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2)) g_x = max(0, -ul[0]), min(br[0], h) - ul[0] g_y = max(0, -ul[1]), min(br[1], w) - ul[1] img_x = max(0, ul[0]), min(br[0], h) img_y = max(0, ul[1]), min(br[1], w) heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum( heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]], g[g_y[0]:g_y[1], g_x[0]:g_x[1]]) return heatmap def grayscale(image): return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) def lighting_(data_rng, image, alphastd, eigval, eigvec): alpha = data_rng.normal(scale=alphastd, size=(3, )) image += np.dot(eigvec, eigval * alpha) def blend_(alpha, image1, image2): image1 *= alpha image2 *= (1 - alpha) image1 += image2 def saturation_(data_rng, image, gs, gs_mean, var): alpha = 1. + data_rng.uniform(low=-var, high=var) blend_(alpha, image, gs[:, :, None]) def brightness_(data_rng, image, gs, gs_mean, var): alpha = 1. + data_rng.uniform(low=-var, high=var) image *= alpha def contrast_(data_rng, image, gs, gs_mean, var): alpha = 1. + data_rng.uniform(low=-var, high=var) blend_(alpha, image, gs_mean) def color_aug(data_rng, image, eig_val, eig_vec): functions = [brightness_, contrast_, saturation_] random.shuffle(functions) gs = grayscale(image) gs_mean = gs.mean() for f in functions: f(data_rng, image, gs, gs_mean, 0.4) lighting_(data_rng, image, 0.1, eig_val, eig_vec) slamd202303071458299/src/lib/utils/oracle_utils.py000066400000000000000000000024451435750645400213430ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import numba @numba.jit(nopython=True, nogil=True) def gen_oracle_map(feat, ind, w, h): # feat: B x maxN x featDim # ind: B x maxN batch_size = feat.shape[0] max_objs = feat.shape[1] feat_dim = feat.shape[2] out = np.zeros((batch_size, feat_dim, h, w), dtype=np.float32) vis = np.zeros((batch_size, h, w), dtype=np.uint8) ds = [(0, 1), (0, -1), (1, 0), (-1, 0)] for i in range(batch_size): queue_ind = np.zeros((h*w*2, 2), dtype=np.int32) queue_feat = np.zeros((h*w*2, feat_dim), dtype=np.float32) head, tail = 0, 0 for j in range(max_objs): if ind[i][j] > 0: x, y = ind[i][j] % w, ind[i][j] // w out[i, :, y, x] = feat[i][j] vis[i, y, x] = 1 queue_ind[tail] = x, y queue_feat[tail] = feat[i][j] tail += 1 while tail - head > 0: x, y = queue_ind[head] f = queue_feat[head] head += 1 for (dx, dy) in ds: xx, yy = x + dx, y + dy if xx >= 0 and yy >= 0 and xx < w and yy < h and vis[i, yy, xx] < 1: out[i, :, yy, xx] = f vis[i, yy, xx] = 1 queue_ind[tail] = xx, yy queue_feat[tail] = f tail += 1 return outslamd202303071458299/src/lib/utils/post_process.py000066400000000000000000000132731435750645400214020ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from .image import transform_preds, transform_length from .ddd_utils import ddd2locrot import pdb def get_pred_depth(depth): return depth def get_alpha(rot): # output: (B, 8) [bin1_cls[0], bin1_cls[1], bin1_sin, bin1_cos, # bin2_cls[0], bin2_cls[1], bin2_sin, bin2_cos] # return rot[:, 0] idx = rot[:, 1] > rot[:, 5] alpha1 = np.arctan2(rot[:, 2], rot[:, 3]) + (-0.5 * np.pi) alpha2 = np.arctan2(rot[:, 6], rot[:, 7]) + ( 0.5 * np.pi) return alpha1 * idx + alpha2 * (1 - idx) def ddd_post_process_2d(dets, c, s, opt): # dets: batch x max_dets x dim # return 1-based class det list ret = [] include_wh = dets.shape[2] > 16 for i in range(dets.shape[0]): top_preds = {} dets[i, :, :2] = transform_preds( dets[i, :, 0:2], c[i], s[i], (opt.output_w, opt.output_h)) classes = dets[i, :, -1] for j in range(opt.num_classes): inds = (classes == j) top_preds[j + 1] = np.concatenate([ dets[i, inds, :3].astype(np.float32), get_alpha(dets[i, inds, 3:11])[:, np.newaxis].astype(np.float32), get_pred_depth(dets[i, inds, 11:12]).astype(np.float32), dets[i, inds, 12:15].astype(np.float32)], axis=1) if include_wh: top_preds[j + 1] = np.concatenate([ top_preds[j + 1], transform_preds( dets[i, inds, 15:17], c[i], s[i], (opt.output_w, opt.output_h)) .astype(np.float32)], axis=1) ret.append(top_preds) return ret def ddd_post_process_3d(dets, calibs): # dets: batch x max_dets x dim # return 1-based class det list ret = [] for i in range(len(dets)): preds = {} for cls_ind in dets[i].keys(): preds[cls_ind] = [] for j in range(len(dets[i][cls_ind])): center = dets[i][cls_ind][j][:2] score = dets[i][cls_ind][j][2] alpha = dets[i][cls_ind][j][3] depth = dets[i][cls_ind][j][4] dimensions = dets[i][cls_ind][j][5:8] wh = dets[i][cls_ind][j][8:10] locations, rotation_y = ddd2locrot( center, alpha, dimensions, depth, calibs[0]) bbox = [center[0] - wh[0] / 2, center[1] - wh[1] / 2, center[0] + wh[0] / 2, center[1] + wh[1] / 2] pred = [alpha] + bbox + dimensions.tolist() + \ locations.tolist() + [rotation_y, score] preds[cls_ind].append(pred) preds[cls_ind] = np.array(preds[cls_ind], dtype=np.float32) ret.append(preds) return ret def ddd_post_process(dets, c, s, calibs, opt): # dets: batch x max_dets x dim # return 1-based class det list dets = ddd_post_process_2d(dets, c, s, opt) dets = ddd_post_process_3d(dets, calibs) return dets def ctdet_post_process(dets, c, s, h, w, num_classes): # dets: batch x max_dets x dim # return 1-based class det dict ret = [] for i in range(dets.shape[0]): top_preds = {} dets[i, :, :2] = transform_preds( dets[i, :, 0:2], c[i], s[i], (w, h)) dets[i, :, 2:4] = transform_preds( dets[i, :, 2:4], c[i], s[i], (w, h)) classes = dets[i, :, -1] for j in range(num_classes): inds = (classes == j) top_preds[j + 1] = np.concatenate([ dets[i, inds, :4].astype(np.float32), dets[i, inds, 4:5].astype(np.float32)], axis=1).tolist() ret.append(top_preds) return ret def eldet_post_process(dets, c, s, h, w, num_classes): # dets: batch x max_dets x dim # dets = [bboxes, cx, cy, l, ratio_al, ratio_ba, theta, scores, clses] # return 1-based class det dict ret = [] for i in range(dets.shape[0]): top_preds = {} dets[i, :, :2] = transform_preds( dets[i, :, 0:2], c[i], s[i], (w, h)) dets[i, :, 2:4] = transform_preds( dets[i, :, 2:4], c[i], s[i], (w, h)) dets[i, :, 4:6] = transform_preds( dets[i, :, 4:6], c[i], s[i], (w, h)) # center (xs, ys) dets[i, :, 6] = transform_length( dets[i, :, 6], c[i], s[i], (w, h)) # l classes = dets[i, :, -1] for j in range(num_classes): inds = (classes == j) top_preds[j + 1] = np.concatenate([ dets[i, inds, :10].astype(np.float32), dets[i, inds, -2:-1].astype(np.float32)], axis=1).tolist() ret.append(top_preds) return ret def eldet_post_process_sincos(dets, c, s, h, w, num_classes): # dets: batch x max_dets x dim # dets = [bboxes, cx, cy, l, ratio_al, ratio_ba, theta, sincos, scores, clses] # return 1-based class det dict ret = [] for i in range(dets.shape[0]): top_preds = {} dets[i, :, :2] = transform_preds( dets[i, :, 0:2], c[i], s[i], (w, h)) dets[i, :, 2:4] = transform_preds( dets[i, :, 2:4], c[i], s[i], (w, h)) dets[i, :, 4:6] = transform_preds( dets[i, :, 4:6], c[i], s[i], (w, h)) # center (xs, ys) dets[i, :, 6] = transform_length( dets[i, :, 6], c[i], s[i], (w, h)) # l classes = dets[i, :, -1] for j in range(num_classes): inds = (classes == j) top_preds[j + 1] = np.concatenate([ dets[i, inds, :12].astype(np.float32), dets[i, inds, -2:-1].astype(np.float32)], axis=1).tolist() ret.append(top_preds) return ret def multi_pose_post_process(dets, c, s, h, w): # dets: batch x max_dets x 40 # return list of 39 in image coord ret = [] for i in range(dets.shape[0]): bbox = transform_preds(dets[i, :, :4].reshape(-1, 2), c[i], s[i], (w, h)) pts = transform_preds(dets[i, :, 5:39].reshape(-1, 2), c[i], s[i], (w, h)) top_preds = np.concatenate( [bbox.reshape(-1, 4), dets[i, :, 4:5], pts.reshape(-1, 34)], axis=1).astype(np.float32).tolist() ret.append({np.ones(1, dtype=np.int32)[0]: top_preds}) return ret slamd202303071458299/src/lib/utils/utils.py000066400000000000000000000010361435750645400200110ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n if self.count > 0: self.avg = self.sum / self.countslamd202303071458299/src/main.py000066400000000000000000000066141435750645400156760ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' import torch import torch.utils.data from fhd_opts import opts # from opts import opts from models.model import create_model, load_model, save_model from models.data_parallel import DataParallel from logger import Logger from datasets.dataset_factory import get_dataset from trains.train_factory import train_factory from torch.utils.tensorboard import SummaryWriter def main(opt): torch.manual_seed(opt.seed) torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test Dataset = get_dataset(opt.dataset, opt.task) opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) logger = Logger(opt) os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu') print('Creating model...') model = create_model(opt.arch, opt.heads, opt.head_conv) optimizer = torch.optim.Adam(model.parameters(), opt.lr) start_epoch = 0 if opt.load_model != '': model, optimizer, start_epoch = load_model( model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step) Trainer = train_factory[opt.task] trainer = Trainer(opt, model, optimizer) trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device) print('Setting up data...') val_loader = torch.utils.data.DataLoader( Dataset(opt, 'val'), batch_size=1, shuffle=False, num_workers=1, pin_memory=True ) if opt.test: _, preds = trainer.val(0, val_loader) val_loader.dataset.run_eval(preds, opt.save_dir) return train_loader = torch.utils.data.DataLoader( Dataset(opt, 'train'), batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, pin_memory=True, drop_last=True ) print('Starting training...') best = 1e10 for epoch in range(start_epoch + 1, opt.num_epochs + 1): mark = epoch if opt.save_all else 'last' log_dict_train, _ = trainer.train(epoch, train_loader) logger.write('epoch: {} |'.format(epoch)) for k, v in log_dict_train.items(): logger.scalar_summary('train_{}'.format(k), v, epoch) logger.write('{} {:8f} | '.format(k, v)) if opt.val_intervals > 0 and epoch % opt.val_intervals == 0: save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)), epoch, model, optimizer) with torch.no_grad(): log_dict_val, preds = trainer.val(epoch, val_loader) for k, v in log_dict_val.items(): logger.scalar_summary('val_{}'.format(k), v, epoch) logger.write('{} {:8f} | '.format(k, v)) if log_dict_val[opt.metric] < best: best = log_dict_val[opt.metric] save_model(os.path.join(opt.save_dir, 'model_best.pth'), epoch, model) else: save_model(os.path.join(opt.save_dir, 'model_last.pth'), epoch, model, optimizer) logger.write('\n') if epoch in opt.lr_step: save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)), epoch, model, optimizer) lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1)) print('Drop LR to', lr) for param_group in optimizer.param_groups: param_group['lr'] = lr logger.close() if __name__ == '__main__': opt = opts().parse() main(opt)slamd202303071458299/src/test.py000066400000000000000000000100301435750645400157140ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths import os import json import cv2 import numpy as np import time from progress.bar import Bar import torch from external.nms import soft_nms # from opts import opts from fhd_opts import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory class PrefetchDataset(torch.utils.data.Dataset): def __init__(self, opt, dataset, pre_process_func): self.images = dataset.images self.load_image_func = dataset.coco.loadImgs self.img_dir = dataset.img_dir self.pre_process_func = pre_process_func self.opt = opt def __getitem__(self, index): img_id = self.images[index] img_info = self.load_image_func(ids=[img_id])[0] img_path = os.path.join(self.img_dir, img_info['file_name']) image = cv2.imread(img_path) images, meta = {}, {} for scale in opt.test_scales: if opt.task == 'ddd': images[scale], meta[scale] = self.pre_process_func( image, scale, img_info['calib']) else: images[scale], meta[scale] = self.pre_process_func(image, scale) return img_id, {'images': images, 'image': image, 'meta': meta} def __len__(self): return len(self.images) def prefetch_test(opt): os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Logger(opt) Detector = detector_factory[opt.task] split = 'val' if not opt.trainval else 'test' dataset = Dataset(opt, split) detector = Detector(opt) data_loader = torch.utils.data.DataLoader( PrefetchDataset(opt, dataset, detector.pre_process), batch_size=1, shuffle=False, num_workers=1, pin_memory=True) results = {} num_iters = len(dataset) bar = Bar('{}'.format(opt.exp_id), max=num_iters) time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge'] avg_time_stats = {t: AverageMeter() for t in time_stats} for ind, (img_id, pre_processed_images) in enumerate(data_loader): ret = detector.run(pre_processed_images) results[img_id.numpy().astype(np.int32)[0]] = ret['results'] Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format( ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td) for t in avg_time_stats: avg_time_stats[t].update(ret[t]) Bar.suffix = Bar.suffix + '|{} {tm.val:.3f}s ({tm.avg:.3f}s) '.format( t, tm = avg_time_stats[t]) bar.next() bar.finish() dataset.run_eval(results, opt.save_dir) def test(opt): os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Logger(opt) Detector = detector_factory[opt.task] split = 'val' if not opt.trainval else 'test' dataset = Dataset(opt, split) detector = Detector(opt) results = {} num_iters = len(dataset) bar = Bar('{}'.format(opt.exp_id), max=num_iters) time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge'] avg_time_stats = {t: AverageMeter() for t in time_stats} for ind in range(num_iters): img_id = dataset.images[ind] img_info = dataset.coco.loadImgs(ids=[img_id])[0] img_path = os.path.join(dataset.img_dir, img_info['file_name']) if opt.task == 'ddd': ret = detector.run(img_path, img_info['calib']) else: ret = detector.run(img_path) results[img_id] = ret['results'] Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format( ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td) for t in avg_time_stats: avg_time_stats[t].update(ret[t]) Bar.suffix = Bar.suffix + '|{} {:.3f} '.format(t, avg_time_stats[t].avg) bar.next() bar.finish() dataset.run_eval(results, opt.save_dir) if __name__ == '__main__': opt = opts().parse() if opt.not_prefetch_test: test(opt) else: prefetch_test(opt)slamd202303071458299/src/tools/000077500000000000000000000000001435750645400155315ustar00rootroot00000000000000slamd202303071458299/src/tools/_init_paths.py000066400000000000000000000003521435750645400204040ustar00rootroot00000000000000import os.path as osp import sys def add_path(path): if path not in sys.path: sys.path.insert(0, path) this_dir = osp.dirname(__file__) # Add lib to PYTHONPATH lib_path = osp.join(this_dir, '../lib') add_path(lib_path) slamd202303071458299/src/tools/calc_coco_overlap.py000066400000000000000000000251651435750645400215510ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import pycocotools.coco as COCO import cv2 import numpy as np from pycocotools import mask as maskUtils ANN_PATH = '../../data/coco/annotations/' IMG_PATH = '../../data/coco/' ANN_FILES = {'train': 'instances_train2017.json', 'val': 'instances_val2017.json'} DEBUG = False RESIZE = True class_name = [ '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] def iou(box1, box2): area1 = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1) area2 = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1) inter = max(min(box1[2], box2[2]) - max(box1[0], box2[0]) + 1, 0) * \ max(min(box1[3], box2[3]) - max(box1[1], box2[1]) + 1, 0) iou = 1.0 * inter / (area1 + area2 - inter) return iou def generate_anchors( stride=16, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2) ): """Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors are centered on stride / 2, have (approximate) sqrt areas of the specified sizes, and aspect ratios as given. """ return _generate_anchors( stride, np.array(sizes, dtype=np.float) / stride, np.array(aspect_ratios, dtype=np.float) ) def _generate_anchors(base_size, scales, aspect_ratios): """Generate anchor (reference) windows by enumerating aspect ratios X scales wrt a reference (0, 0, base_size - 1, base_size - 1) window. """ anchor = np.array([1, 1, base_size, base_size], dtype=np.float) - 1 anchors = _ratio_enum(anchor, aspect_ratios) anchors = np.vstack( [_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])] ) return anchors def _whctrs(anchor): """Return width, height, x center, and y center for an anchor (window).""" w = anchor[2] - anchor[0] + 1 h = anchor[3] - anchor[1] + 1 x_ctr = anchor[0] + 0.5 * (w - 1) y_ctr = anchor[1] + 0.5 * (h - 1) return w, h, x_ctr, y_ctr def _mkanchors(ws, hs, x_ctr, y_ctr): """Given a vector of widths (ws) and heights (hs) around a center (x_ctr, y_ctr), output a set of anchors (windows). """ ws = ws[:, np.newaxis] hs = hs[:, np.newaxis] anchors = np.hstack( ( x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1) ) ) return anchors def _ratio_enum(anchor, ratios): """Enumerate a set of anchors for each aspect ratio wrt an anchor.""" w, h, x_ctr, y_ctr = _whctrs(anchor) size = w * h size_ratios = size / ratios ws = np.round(np.sqrt(size_ratios)) hs = np.round(ws * ratios) anchors = _mkanchors(ws, hs, x_ctr, y_ctr) return anchors def _scale_enum(anchor, scales): """Enumerate a set of anchors for each scale wrt an anchor.""" w, h, x_ctr, y_ctr = _whctrs(anchor) ws = w * scales hs = h * scales anchors = _mkanchors(ws, hs, x_ctr, y_ctr) return anchors def _coco_box_to_bbox(box): bbox = np.array([box[0], box[1], box[0] + box[2], box[1] + box[3]], dtype=np.float32) return bbox def count_agnostic(split): coco = COCO.COCO(ANN_PATH + ANN_FILES[split]) images = coco.getImgIds() cnt = 0 for img_id in images: ann_ids = coco.getAnnIds(imgIds=[img_id]) anns = coco.loadAnns(ids=ann_ids) centers = [] for ann in anns: bbox = ann['bbox'] center = ((bbox[0] + bbox[2] / 2) // 4, (bbox[1] + bbox[3] / 2) // 4) for c in centers: if center[0] == c[0] and center[1] == c[1]: cnt += 1 centers.append(center) print('find {} collisions!'.format(cnt)) def count(split): coco = COCO.COCO(ANN_PATH + ANN_FILES[split]) images = coco.getImgIds() cnt = 0 obj = 0 for img_id in images: ann_ids = coco.getAnnIds(imgIds=[img_id]) anns = coco.loadAnns(ids=ann_ids) centers = [] obj += len(anns) for ann in anns: if ann['iscrowd'] > 0: continue bbox = ann['bbox'] center = ((bbox[0] + bbox[2] / 2) // 4, (bbox[1] + bbox[3] / 2) // 4, ann['category_id'], bbox) for c in centers: if center[0] == c[0] and center[1] == c[1] and center[2] == c[2] and \ iou(_coco_box_to_bbox(bbox), _coco_box_to_bbox(c[3])) < 2:# 0.5: cnt += 1 if DEBUG: file_name = coco.loadImgs(ids=[img_id])[0]['file_name'] img = cv2.imread('{}/{}2017/{}'.format(IMG_PATH, split, file_name)) x1, y1 = int(c[3][0]), int(c[3][1]), x2, y2 = int(c[3][0] + c[3][2]), int(c[3][1] + c[3][3]) cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2, cv2.LINE_AA) x1, y1 = int(center[3][0]), int(center[3][1]), x2, y2 = int(center[3][0] + center[3][2]), int(center[3][1] + center[3][3]) cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2, cv2.LINE_AA) cv2.imshow('img', img) cv2.waitKey() centers.append(center) print('find {} collisions of {} objects!'.format(cnt, obj)) def count_iou(split): coco = COCO.COCO(ANN_PATH + ANN_FILES[split]) images = coco.getImgIds() cnt = 0 obj = 0 for img_id in images: ann_ids = coco.getAnnIds(imgIds=[img_id]) anns = coco.loadAnns(ids=ann_ids) bboxes = [] obj += len(anns) for ann in anns: if ann['iscrowd'] > 0: continue bbox = _coco_box_to_bbox(ann['bbox']).tolist() + [ann['category_id']] for b in bboxes: if iou(b, bbox) > 0.5 and b[4] == bbox[4]: cnt += 1 if DEBUG: file_name = coco.loadImgs(ids=[img_id])[0]['file_name'] img = cv2.imread('{}/{}2017/{}'.format(IMG_PATH, split, file_name)) x1, y1 = int(b[0]), int(b[1]), x2, y2 = int(b[2]), int(b[3]) cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2, cv2.LINE_AA) x1, y1 = int(bbox[0]), int(bbox[1]), x2, y2 = int(bbox[2]), int(bbox[3]) cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2, cv2.LINE_AA) cv2.imshow('img', img) print('cats', class_name[b[4]], class_name[bbox[4]]) cv2.waitKey() bboxes.append(bbox) print('find {} collisions of {} objects!'.format(cnt, obj)) def count_anchor(split): coco = COCO.COCO(ANN_PATH + ANN_FILES[split]) images = coco.getImgIds() cnt = 0 obj = 0 stride = 16 anchor = generate_anchors().reshape(15, 2, 2) miss_s, miss_m, miss_l = 0, 0, 0 N = len(images) print(N, 'images') for ind, img_id in enumerate(images): if ind % 1000 == 0: print(ind, N) anchors = [] ann_ids = coco.getAnnIds(imgIds=[img_id]) anns = coco.loadAnns(ids=ann_ids) obj += len(anns) img_info = coco.loadImgs(ids=[img_id])[0] h, w = img_info['height'], img_info['width'] if RESIZE: if h > w: for i in range(len(anns)): anns[i]['bbox'][0] *= 800 / w anns[i]['bbox'][1] *= 800 / w anns[i]['bbox'][2] *= 800 / w anns[i]['bbox'][3] *= 800 / w h = h * 800 // w w = 800 else: for i in range(len(anns)): anns[i]['bbox'][0] *= 800 / h anns[i]['bbox'][1] *= 800 / h anns[i]['bbox'][2] *= 800 / h anns[i]['bbox'][3] *= 800 / h w = w * 800 // h h = 800 for i in range(w // stride): for j in range(h // stride): ct = np.array([i * stride, j * stride], dtype=np.float32).reshape(1, 1, 2) anchors.append(anchor + ct) anchors = np.concatenate(anchors, axis=0).reshape(-1, 4) anchors[:, 2:4] = anchors[:, 2:4] - anchors[:, 0:2] anchors = anchors.tolist() # import pdb; pdb.set_trace() g = [g['bbox'] for g in anns] iscrowd = [int(o['iscrowd']) for o in anns] ious = maskUtils.iou(anchors,g,iscrowd) for t in range(len(g)): if ious[:, t].max() < 0.5: s = anns[t]['area'] if s < 32 ** 2: miss_s += 1 elif s < 96 ** 2: miss_m += 1 else: miss_l += 1 if DEBUG: file_name = coco.loadImgs(ids=[img_id])[0]['file_name'] img = cv2.imread('{}/{}2017/{}'.format(IMG_PATH, split, file_name)) if RESIZE: img = cv2.resize(img, (w, h)) for t, gt in enumerate(g): if anns[t]['iscrowd'] > 0: continue x1, y1, x2, y2 = _coco_box_to_bbox(gt) cl = (0, 0, 255) if ious[:, t].max() < 0.5 else (0, 255, 0) cv2.rectangle(img, (x1, y1), (x2, y2), cl, 2, cv2.LINE_AA) for k in range(len(anchors)): if ious[k, t] > 0.5: x1, y1, x2, y2 = _coco_box_to_bbox(anchors[k]) cl = (np.array([255, 0, 0]) * ious[k, t]).astype(np.int32).tolist() cv2.rectangle(img, (x1, y1), (x2, y2), cl, 1, cv2.LINE_AA) cv2.imshow('img', img) cv2.waitKey() miss = 0 if len(ious) > 0: miss = (ious.max(axis=0) < 0.5).sum() cnt += miss print('cnt, obj, ratio ', cnt, obj, cnt / obj) print('s, m, l ', miss_s, miss_m, miss_l) # import pdb; pdb.set_trace() def count_size(split): coco = COCO.COCO(ANN_PATH + ANN_FILES[split]) images = coco.getImgIds() cnt = 0 obj = 0 stride = 16 anchor = generate_anchors().reshape(15, 2, 2) cnt_s, cnt_m, cnt_l = 0, 0, 0 N = len(images) print(N, 'images') for ind, img_id in enumerate(images): anchors = [] ann_ids = coco.getAnnIds(imgIds=[img_id]) anns = coco.loadAnns(ids=ann_ids) obj += len(anns) img_info = coco.loadImgs(ids=[img_id])[0] for t in range(len(anns)): if 1: s = anns[t]['area'] if s < 32 ** 2: cnt_s += 1 elif s < 96 ** 2: cnt_m += 1 else: cnt_l += 1 cnt += 1 print('cnt', cnt) print('s, m, l ', cnt_s, cnt_m, cnt_l) # count_iou('train') # count_anchor('train') # count('train') count_size('train') slamd202303071458299/src/tools/convert_hourglass_weight.py000066400000000000000000000016111435750645400232200ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function MODEL_PATH = '../../models/ExtremeNet_500000.pkl' OUT_PATH = '../../models/ExtremeNet_500000.pth' import torch state_dict = torch.load(MODEL_PATH) key_map = {'t_heats': 'hm_t', 'l_heats': 'hm_l', 'b_heats': 'hm_b', \ 'r_heats': 'hm_r', 'ct_heats': 'hm_c', \ 't_regrs': 'reg_t', 'l_regrs': 'reg_l', \ 'b_regrs': 'reg_b', 'r_regrs': 'reg_r'} out = {} for k in state_dict.keys(): changed = False for m in key_map.keys(): if m in k: if 'ct_heats' in k and m == 't_heats': continue new_k = k.replace(m, key_map[m]) out[new_k] = state_dict[k] changed = True print('replace {} to {}'.format(k, new_k)) if not changed: out[k] = state_dict[k] data = {'epoch': 0, 'state_dict': out} torch.save(data, OUT_PATH) slamd202303071458299/src/tools/convert_kitti_to_coco.py000066400000000000000000000134571435750645400225060ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import pickle import json import numpy as np import cv2 DATA_PATH = '../../data/kitti/' DEBUG = False # VAL_PATH = DATA_PATH + 'training/label_val/' import os SPLITS = ['3dop', 'subcnn'] import _init_paths from utils.ddd_utils import compute_box_3d, project_to_image, alpha2rot_y from utils.ddd_utils import draw_box_3d, unproject_2d_to_3d ''' #Values Name Description ---------------------------------------------------------------------------- 1 type Describes the type of object: 'Car', 'Van', 'Truck', 'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram', 'Misc' or 'DontCare' 1 truncated Float from 0 (non-truncated) to 1 (truncated), where truncated refers to the object leaving image boundaries 1 occluded Integer (0,1,2,3) indicating occlusion state: 0 = fully visible, 1 = partly occluded 2 = largely occluded, 3 = unknown 1 alpha Observation angle of object, ranging [-pi..pi] 4 bbox 2D bounding box of object in the image (0-based index): contains left, top, right, bottom pixel coordinates 3 dimensions 3D object dimensions: height, width, length (in meters) 3 location 3D object location x,y,z in camera coordinates (in meters) 1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi] 1 score Only for results: Float, indicating confidence in detection, needed for p/r curves, higher is better. ''' def _bbox_to_coco_bbox(bbox): return [(bbox[0]), (bbox[1]), (bbox[2] - bbox[0]), (bbox[3] - bbox[1])] def read_clib(calib_path): f = open(calib_path, 'r') for i, line in enumerate(f): if i == 2: calib = np.array(line[:-1].split(' ')[1:], dtype=np.float32) calib = calib.reshape(3, 4) return calib cats = ['Pedestrian', 'Car', 'Cyclist', 'Van', 'Truck', 'Person_sitting', 'Tram', 'Misc', 'DontCare'] cat_ids = {cat: i + 1 for i, cat in enumerate(cats)} # cat_info = [{"name": "pedestrian", "id": 1}, {"name": "vehicle", "id": 2}] F = 721 H = 384 # 375 W = 1248 # 1242 EXT = [45.75, -0.34, 0.005] CALIB = np.array([[F, 0, W / 2, EXT[0]], [0, F, H / 2, EXT[1]], [0, 0, 1, EXT[2]]], dtype=np.float32) cat_info = [] for i, cat in enumerate(cats): cat_info.append({'name': cat, 'id': i + 1}) for SPLIT in SPLITS: image_set_path = DATA_PATH + 'ImageSets_{}/'.format(SPLIT) ann_dir = DATA_PATH + 'training/label_2/' calib_dir = DATA_PATH + '{}/calib/' splits = ['train', 'val'] # splits = ['trainval', 'test'] calib_type = {'train': 'training', 'val': 'training', 'trainval': 'training', 'test': 'testing'} for split in splits: ret = {'images': [], 'annotations': [], "categories": cat_info} image_set = open(image_set_path + '{}.txt'.format(split), 'r') image_to_id = {} for line in image_set: if line[-1] == '\n': line = line[:-1] image_id = int(line) calib_path = calib_dir.format(calib_type[split]) + '{}.txt'.format(line) calib = read_clib(calib_path) image_info = {'file_name': '{}.png'.format(line), 'id': int(image_id), 'calib': calib.tolist()} ret['images'].append(image_info) if split == 'test': continue ann_path = ann_dir + '{}.txt'.format(line) # if split == 'val': # os.system('cp {} {}/'.format(ann_path, VAL_PATH)) anns = open(ann_path, 'r') if DEBUG: image = cv2.imread( DATA_PATH + 'images/trainval/' + image_info['file_name']) for ann_ind, txt in enumerate(anns): tmp = txt[:-1].split(' ') cat_id = cat_ids[tmp[0]] truncated = int(float(tmp[1])) occluded = int(tmp[2]) alpha = float(tmp[3]) bbox = [float(tmp[4]), float(tmp[5]), float(tmp[6]), float(tmp[7])] dim = [float(tmp[8]), float(tmp[9]), float(tmp[10])] location = [float(tmp[11]), float(tmp[12]), float(tmp[13])] rotation_y = float(tmp[14]) ann = {'image_id': image_id, 'id': int(len(ret['annotations']) + 1), 'category_id': cat_id, 'dim': dim, 'bbox': _bbox_to_coco_bbox(bbox), 'depth': location[2], 'alpha': alpha, 'truncated': truncated, 'occluded': occluded, 'location': location, 'rotation_y': rotation_y} ret['annotations'].append(ann) if DEBUG and tmp[0] != 'DontCare': box_3d = compute_box_3d(dim, location, rotation_y) box_2d = project_to_image(box_3d, calib) # print('box_2d', box_2d) image = draw_box_3d(image, box_2d) x = (bbox[0] + bbox[2]) / 2 ''' print('rot_y, alpha2rot_y, dlt', tmp[0], rotation_y, alpha2rot_y(alpha, x, calib[0, 2], calib[0, 0]), np.cos( rotation_y - alpha2rot_y(alpha, x, calib[0, 2], calib[0, 0]))) ''' depth = np.array([location[2]], dtype=np.float32) pt_2d = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32) pt_3d = unproject_2d_to_3d(pt_2d, depth, calib) pt_3d[1] += dim[0] / 2 print('pt_3d', pt_3d) print('location', location) if DEBUG: cv2.imshow('image', image) cv2.waitKey() print("# images: ", len(ret['images'])) print("# annotations: ", len(ret['annotations'])) # import pdb; pdb.set_trace() out_path = '{}/annotations/kitti_{}_{}.json'.format(DATA_PATH, SPLIT, split) json.dump(ret, open(out_path, 'w')) slamd202303071458299/src/tools/eval_coco.py000066400000000000000000000012351435750645400200360ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import pycocotools.coco as coco from pycocotools.cocoeval import COCOeval import sys import cv2 import numpy as np import pickle import os this_dir = os.path.dirname(__file__) ANN_PATH = this_dir + '../../data/coco/annotations/instances_val2017.json' print(ANN_PATH) if __name__ == '__main__': pred_path = sys.argv[1] coco = coco.COCO(ANN_PATH) dets = coco.loadRes(pred_path) img_ids = coco.getImgIds() num_images = len(img_ids) coco_eval = COCOeval(coco, dets, "bbox") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() slamd202303071458299/src/tools/eval_coco_hp.py000066400000000000000000000014331435750645400205250ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import pycocotools.coco as coco from pycocotools.cocoeval import COCOeval import sys import cv2 import numpy as np import pickle import os this_dir = os.path.dirname(__file__) ANN_PATH = this_dir + '../../data/coco/annotations/person_keypoints_val2017.json' print(ANN_PATH) if __name__ == '__main__': pred_path = sys.argv[1] coco = coco.COCO(ANN_PATH) dets = coco.loadRes(pred_path) img_ids = coco.getImgIds() num_images = len(img_ids) coco_eval = COCOeval(coco, dets, "keypoints") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() coco_eval = COCOeval(coco, dets, "bbox") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() slamd202303071458299/src/tools/get_kitti.sh000066400000000000000000000004731435750645400200540ustar00rootroot00000000000000mkdir kitti cd kitti wget http://www.cvlibs.net/download.php?file=data_object_image_2.zip wget http://www.cvlibs.net/download.php?file=data_object_label_2.zip wget http://www.cvlibs.net/download.php?file=data_object_calib.zip unzip data_object_image_2.zip unzip data_object_label_2.zip unzip data_object_calib.zip slamd202303071458299/src/tools/get_pascal_voc.sh000066400000000000000000000017461435750645400210460ustar00rootroot00000000000000mkdir voc cd voc wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar tar xvf VOCtrainval_11-May-2012.tar tar xvf VOCdevkit_18-May-2011.tar rm VOCtrainval_06-Nov-2007.tar rm VOCtest_06-Nov-2007.tar rm VOCdevkit_08-Jun-2007.tar rm VOCtrainval_11-May-2012.tar rm VOCdevkit_18-May-2011.tar mkdir images cp VOCdevkit/VOC2007/JPEGImages/* images/ cp VOCdevkit/VOC2012/JPEGImages/* images/ wget https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip unzip PASCAL_VOC.zip rm PASCAL_VOC.zip mv PASCAL_VOC annotations/ cd .. python merge_pascal_json.py slamd202303071458299/src/tools/kitti_eval/000077500000000000000000000000001435750645400176645ustar00rootroot00000000000000slamd202303071458299/src/tools/kitti_eval/README.md000066400000000000000000000014361435750645400211470ustar00rootroot00000000000000# kitti_eval `evaluate_object_3d_offline.cpp`evaluates your KITTI detection locally on your own computer using your validation data selected from KITTI training dataset, with the following metrics: - overlap on image (AP) - oriented overlap on image (AOS) - overlap on ground-plane (AP) - overlap in 3D (AP) Compile `evaluate_object_3d_offline.cpp` with dependency of Boost and Linux `dirent.h` (You should already have it under most Linux). Run the evalutaion by: ./evaluate_object_3d_offline groundtruth_dir result_dir Note that you don't have to detect over all KITTI training data. The evaluator only evaluates samples whose result files exist. ### Updates - June, 2017: * Fixed the bug of detection box filtering based on min height according to KITTI's note on 25.04.2017. slamd202303071458299/src/tools/kitti_eval/evaluate_object_3d.cpp000066400000000000000000001005041435750645400241120ustar00rootroot00000000000000// from https://github.com/prclibo/kitti_eval #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "mail.h" BOOST_GEOMETRY_REGISTER_C_ARRAY_CS(cs::cartesian) typedef boost::geometry::model::polygon > Polygon; using namespace std; /*======================================================================= STATIC EVALUATION PARAMETERS =======================================================================*/ // holds the number of test images on the server const int32_t N_TESTIMAGES = 7518; // easy, moderate and hard evaluation level enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2}; // evaluation metrics: image, ground or 3D enum METRIC{IMAGE=0, GROUND=1, BOX3D=2}; // evaluation parameter const int32_t MIN_HEIGHT[3] = {40, 25, 25}; // minimum height for evaluated groundtruth/detections const int32_t MAX_OCCLUSION[3] = {0, 1, 2}; // maximum occlusion level of the groundtruth used for evaluation const double MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; // maximum truncation level of the groundtruth used for evaluation // evaluated object classes enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2}; const int NUM_CLASS = 3; // parameters varying per class vector CLASS_NAMES; // the minimum overlap required for 2D evaluation on the image/ground plane and 3D evaluation const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.5, 0.25, 0.25}, {0.5, 0.25, 0.25}}; // no. of recall steps that should be evaluated (discretized) const double N_SAMPLE_PTS = 41; // initialize class names void initGlobals () { CLASS_NAMES.push_back("car"); CLASS_NAMES.push_back("pedestrian"); CLASS_NAMES.push_back("cyclist"); } /*======================================================================= DATA TYPES FOR EVALUATION =======================================================================*/ // holding data needed for precision-recall and precision-aos struct tPrData { vector v; // detection score for computing score thresholds double similarity; // orientation similarity int32_t tp; // true positives int32_t fp; // false positives int32_t fn; // false negatives tPrData () : similarity(0), tp(0), fp(0), fn(0) {} }; // holding bounding boxes for ground truth and detections struct tBox { string type; // object type as car, pedestrian or cyclist,... double x1; // left corner double y1; // top corner double x2; // right corner double y2; // bottom corner double alpha; // image orientation tBox (string type, double x1,double y1,double x2,double y2,double alpha) : type(type),x1(x1),y1(y1),x2(x2),y2(y2),alpha(alpha) {} }; // holding ground truth data struct tGroundtruth { tBox box; // object type, box, orientation double truncation; // truncation 0..1 int32_t occlusion; // occlusion 0,1,2 (non, partly, fully) double ry; double t1, t2, t3; double h, w, l; tGroundtruth () : box(tBox("invalild",-1,-1,-1,-1,-10)),truncation(-1),occlusion(-1) {} tGroundtruth (tBox box,double truncation,int32_t occlusion) : box(box),truncation(truncation),occlusion(occlusion) {} tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) : box(tBox(type,x1,y1,x2,y2,alpha)),truncation(truncation),occlusion(occlusion) {} }; // holding detection data struct tDetection { tBox box; // object type, box, orientation double thresh; // detection score double ry; double t1, t2, t3; double h, w, l; tDetection (): box(tBox("invalid",-1,-1,-1,-1,-10)),thresh(-1000) {} tDetection (tBox box,double thresh) : box(box),thresh(thresh) {} tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) : box(tBox(type,x1,y1,x2,y2,alpha)),thresh(thresh) {} }; /*======================================================================= FUNCTIONS TO LOAD DETECTION AND GROUND TRUTH DATA ONCE, SAVE RESULTS =======================================================================*/ vector indices; vector loadDetections(string file_name, bool &compute_aos, vector &eval_image, vector &eval_ground, vector &eval_3d, bool &success) { // holds all detections (ignored detections are indicated by an index vector vector detections; FILE *fp = fopen(file_name.c_str(),"r"); if (!fp) { success = false; return detections; } while (!feof(fp)) { tDetection d; double trash; char str[255]; if (fscanf(fp, "%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", str, &trash, &trash, &d.box.alpha, &d.box.x1, &d.box.y1, &d.box.x2, &d.box.y2, &d.h, &d.w, &d.l, &d.t1, &d.t2, &d.t3, &d.ry, &d.thresh)==16) { // d.thresh = 1; d.box.type = str; detections.push_back(d); // orientation=-10 is invalid, AOS is not evaluated if at least one orientation is invalid if(d.box.alpha == -10) compute_aos = false; // a class is only evaluated if it is detected at least once for (int c = 0; c < NUM_CLASS; c++) { if (!strcasecmp(d.box.type.c_str(), CLASS_NAMES[c].c_str())) { if (!eval_image[c] && d.box.x1 >= 0) eval_image[c] = true; if (!eval_ground[c] && d.t1 != -1000) eval_ground[c] = true; if (!eval_3d[c] && d.t2 != -1000) eval_3d[c] = true; break; } } } } fclose(fp); success = true; return detections; } vector loadGroundtruth(string file_name,bool &success) { // holds all ground truth (ignored ground truth is indicated by an index vector vector groundtruth; FILE *fp = fopen(file_name.c_str(),"r"); if (!fp) { success = false; return groundtruth; } while (!feof(fp)) { tGroundtruth g; char str[255]; if (fscanf(fp, "%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", str, &g.truncation, &g.occlusion, &g.box.alpha, &g.box.x1, &g.box.y1, &g.box.x2, &g.box.y2, &g.h, &g.w, &g.l, &g.t1, &g.t2, &g.t3, &g.ry )==15) { g.box.type = str; groundtruth.push_back(g); } } fclose(fp); success = true; return groundtruth; } void saveStats (const vector &precision, const vector &aos, FILE *fp_det, FILE *fp_ori) { // save precision to file if(precision.empty()) return; for (int32_t i=0; i Polygon toPolygon(const T& g) { using namespace boost::numeric::ublas; using namespace boost::geometry; matrix mref(2, 2); mref(0, 0) = cos(g.ry); mref(0, 1) = sin(g.ry); mref(1, 0) = -sin(g.ry); mref(1, 1) = cos(g.ry); static int count = 0; matrix corners(2, 4); double data[] = {g.l / 2, g.l / 2, -g.l / 2, -g.l / 2, g.w / 2, -g.w / 2, -g.w / 2, g.w / 2}; std::copy(data, data + 8, corners.data().begin()); matrix gc = prod(mref, corners); for (int i = 0; i < 4; ++i) { gc(0, i) += g.t1; gc(1, i) += g.t3; } double points[][2] = {{gc(0, 0), gc(1, 0)},{gc(0, 1), gc(1, 1)},{gc(0, 2), gc(1, 2)},{gc(0, 3), gc(1, 3)},{gc(0, 0), gc(1, 0)}}; Polygon poly; append(poly, points); return poly; } // measure overlap between bird's eye view bounding boxes, parametrized by (ry, l, w, tx, tz) inline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) { using namespace boost::geometry; Polygon gp = toPolygon(g); Polygon dp = toPolygon(d); std::vector in, un; intersection(gp, dp, in); union_(gp, dp, un); double inter_area = in.empty() ? 0 : area(in.front()); double union_area = area(un.front()); double o; if(criterion==-1) // union o = inter_area / union_area; else if(criterion==0) // bbox_a o = inter_area / area(dp); else if(criterion==1) // bbox_b o = inter_area / area(gp); return o; } // measure overlap between 3D bounding boxes, parametrized by (ry, h, w, l, tx, ty, tz) inline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) { using namespace boost::geometry; Polygon gp = toPolygon(g); Polygon dp = toPolygon(d); std::vector in, un; intersection(gp, dp, in); union_(gp, dp, un); double ymax = min(d.t2, g.t2); double ymin = max(d.t2 - d.h, g.t2 - g.h); double inter_area = in.empty() ? 0 : area(in.front()); double inter_vol = inter_area * max(0.0, ymax - ymin); double det_vol = d.h * d.l * d.w; double gt_vol = g.h * g.l * g.w; double o; if(criterion==-1) // union o = inter_vol / (det_vol + gt_vol - inter_vol); else if(criterion==0) // bbox_a o = inter_vol / det_vol; else if(criterion==1) // bbox_b o = inter_vol / gt_vol; return o; } vector getThresholds(vector &v, double n_groundtruth){ // holds scores needed to compute N_SAMPLE_PTS recall values vector t; // sort scores in descending order // (highest score is assumed to give best/most confident detections) sort(v.begin(), v.end(), greater()); // get scores for linearly spaced recall double current_recall = 0; for(int32_t i=0; i >, const vector &det, vector &ignored_gt, vector &dc, vector &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){ // extract ground truth bounding boxes for current evaluation class for(int32_t i=0;iMAX_OCCLUSION[difficulty] || gt[i].truncation>MAX_TRUNCATION[difficulty] || height >, const vector &det, const vector &dc, const vector &ignored_gt, const vector &ignored_det, bool compute_fp, double (*boxoverlap)(tDetection, tGroundtruth, int32_t), METRIC metric, bool compute_aos=false, double thresh=0, bool debug=false){ tPrData stat = tPrData(); const double NO_DETECTION = -10000000; vector delta; // holds angular difference for TPs (needed for AOS evaluation) vector assigned_detection; // holds wether a detection was assigned to a valid or ignored ground truth assigned_detection.assign(det.size(), false); vector ignored_threshold; ignored_threshold.assign(det.size(), false); // holds detections with a threshold lower than thresh if FP are computed // detections with a low score are ignored for computing precision (needs FP) if(compute_fp) for(int32_t i=0; i 0.5) (logical len(det)) =======================================================================*/ int32_t det_idx = -1; double valid_detection = NO_DETECTION; double max_overlap = 0; // search for a possible detection bool assigned_ignored_det = false; for(int32_t j=0; jMIN_OVERLAP[metric][current_class] && det[j].thresh>valid_detection){ det_idx = j; valid_detection = det[j].thresh; } // for computing pr curve values, the candidate with the greatest overlap is considered // if the greatest overlap is an ignored detection (min_height), the overlapping detection is used else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && (overlap>max_overlap || assigned_ignored_det) && ignored_det[j]==0){ max_overlap = overlap; det_idx = j; valid_detection = 1; assigned_ignored_det = false; } else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && valid_detection==NO_DETECTION && ignored_det[j]==1){ det_idx = j; valid_detection = 1; assigned_ignored_det = true; } } /*======================================================================= compute TP, FP and FN =======================================================================*/ // nothing was assigned to this valid ground truth if(valid_detection==NO_DETECTION && ignored_gt[i]==0) { stat.fn++; } // only evaluate valid ground truth <=> detection assignments (considering difficulty level) else if(valid_detection!=NO_DETECTION && (ignored_gt[i]==1 || ignored_det[det_idx]==1)) assigned_detection[det_idx] = true; // found a valid true positive else if(valid_detection!=NO_DETECTION){ // write highest score to threshold vector stat.tp++; stat.v.push_back(det[det_idx].thresh); // compute angular difference of detection and ground truth if valid detection orientation was provided if(compute_aos) delta.push_back(gt[i].box.alpha - det[det_idx].box.alpha); // clean up assigned_detection[det_idx] = true; } } // if FP are requested, consider stuff area if(compute_fp){ // count fp for(int32_t i=0; iMIN_OVERLAP[metric][current_class]){ assigned_detection[j] = true; nstuff++; } } } // FP = no. of all not to ground truth assigned detections - detections assigned to stuff areas stat.fp -= nstuff; // if all orientation values are valid, the AOS is computed if(compute_aos){ vector tmp; // FP have a similarity of 0, for all TP compute AOS tmp.assign(stat.fp, 0); for(int32_t i=0; i0 || stat.fp>0) stat.similarity = accumulate(tmp.begin(), tmp.end(), 0.0); // there was neither a FP nor a TP, so the similarity is ignored in the evaluation else stat.similarity = -1; } } return stat; } /*======================================================================= EVALUATE CLASS-WISE =======================================================================*/ bool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class, const vector< vector > &groundtruth, const vector< vector > &detections, bool compute_aos, double (*boxoverlap)(tDetection, tGroundtruth, int32_t), vector &precision, vector &aos, DIFFICULTY difficulty, METRIC metric) { assert(groundtruth.size() == detections.size()); // init int32_t n_gt=0; // total no. of gt (denominator of recall) vector v, thresholds; // detection scores, evaluated for recall discretization vector< vector > ignored_gt, ignored_det; // index of ignored gt detection for current class/difficulty vector< vector > dontcare; // index of dontcare areas, included in ground truth // for all test images do for (int32_t i=0; i i_gt, i_det; vector dc; // only evaluate objects of current class and ignore occluded, truncated objects cleanData(current_class, groundtruth[i], detections[i], i_gt, dc, i_det, n_gt, difficulty); ignored_gt.push_back(i_gt); ignored_det.push_back(i_det); dontcare.push_back(dc); // compute statistics to get recall values tPrData pr_tmp = tPrData(); pr_tmp = computeStatistics(current_class, groundtruth[i], detections[i], dc, i_gt, i_det, false, boxoverlap, metric); // add detection scores to vector over all images for(int32_t j=0; j pr; pr.assign(thresholds.size(),tPrData()); for (int32_t i=0; i recall; precision.assign(N_SAMPLE_PTS, 0); if(compute_aos) aos.assign(N_SAMPLE_PTS, 0); double r=0; for (int32_t i=0; i vals[],bool is_aos){ char command[1024]; // save plot data to file FILE *fp = fopen((dir_name + "/" + file_name + ".txt").c_str(),"w"); printf("save %s\n", (dir_name + "/" + file_name + ".txt").c_str()); for (int32_t i=0; i<(int)N_SAMPLE_PTS; i++) fprintf(fp,"%f %f %f %f\n",(double)i/(N_SAMPLE_PTS-1.0),vals[0][i],vals[1][i],vals[2][i]); fclose(fp); // create png + eps for (int32_t j=0; j<2; j++) { // open file FILE *fp = fopen((dir_name + "/" + file_name + ".gp").c_str(),"w"); // save gnuplot instructions if (j==0) { fprintf(fp,"set term png size 450,315 font \"Helvetica\" 11\n"); fprintf(fp,"set output \"%s.png\"\n",file_name.c_str()); } else { fprintf(fp,"set term postscript eps enhanced color font \"Helvetica\" 20\n"); fprintf(fp,"set output \"%s.eps\"\n",file_name.c_str()); } // set labels and ranges fprintf(fp,"set size ratio 0.7\n"); fprintf(fp,"set xrange [0:1]\n"); fprintf(fp,"set yrange [0:1]\n"); fprintf(fp,"set xlabel \"Recall\"\n"); if (!is_aos) fprintf(fp,"set ylabel \"Precision\"\n"); else fprintf(fp,"set ylabel \"Orientation Similarity\"\n"); obj_type[0] = toupper(obj_type[0]); fprintf(fp,"set title \"%s\"\n",obj_type.c_str()); // line width int32_t lw = 5; if (j==0) lw = 3; // plot error curve fprintf(fp,"plot "); fprintf(fp,"\"%s.txt\" using 1:2 title 'Easy' with lines ls 1 lw %d,",file_name.c_str(),lw); fprintf(fp,"\"%s.txt\" using 1:3 title 'Moderate' with lines ls 2 lw %d,",file_name.c_str(),lw); fprintf(fp,"\"%s.txt\" using 1:4 title 'Hard' with lines ls 3 lw %d",file_name.c_str(),lw); // close file fclose(fp); // run gnuplot => create png + eps sprintf(command,"cd %s; gnuplot %s",dir_name.c_str(),(file_name + ".gp").c_str()); system(command); } // create pdf and crop sprintf(command,"cd %s; ps2pdf %s.eps %s_large.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str()); system(command); sprintf(command,"cd %s; pdfcrop %s_large.pdf %s.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str()); system(command); sprintf(command,"cd %s; rm %s_large.pdf",dir_name.c_str(),file_name.c_str()); system(command); } bool eval(string result_sha,Mail* mail){ // set some global parameters initGlobals(); // ground truth and result directories string gt_dir = "data/object/label_2"; string result_dir = "results/" + result_sha; string plot_dir = result_dir + "/plot"; // create output directories system(("mkdir " + plot_dir).c_str()); // hold detections and ground truth in memory vector< vector > groundtruth; vector< vector > detections; // holds wether orientation similarity shall be computed (might be set to false while loading detections) // and which labels where provided by this submission bool compute_aos=true; vector eval_image(NUM_CLASS, false); vector eval_ground(NUM_CLASS, false); vector eval_3d(NUM_CLASS, false); // for all images read groundtruth and detections mail->msg("Loading detections..."); for (int32_t i=0; i gt = loadGroundtruth(gt_dir + "/" + file_name,gt_success); vector det = loadDetections(result_dir + "/data/" + file_name, compute_aos, eval_image, eval_ground, eval_3d, det_success); groundtruth.push_back(gt); detections.push_back(det); // check for errors if (!gt_success) { mail->msg("ERROR: Couldn't read: %s of ground truth. Please write me an email!", file_name); return false; } if (!det_success) { mail->msg("ERROR: Couldn't read: %s", file_name); return false; } } mail->msg(" done."); // holds pointers for result files FILE *fp_det=0, *fp_ori=0; // eval image 2D bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_image[c]) { fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection.txt").c_str(), "w"); if(compute_aos) fp_ori = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_orientation.txt").c_str(),"w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[0], aos[0], EASY, IMAGE) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[1], aos[1], MODERATE, IMAGE) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[2], aos[2], HARD, IMAGE)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection", CLASS_NAMES[c], precision, 0); if(compute_aos){ saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_orientation", CLASS_NAMES[c], aos, 1); fclose(fp_ori); } } } // don't evaluate AOS for birdview boxes and 3D boxes compute_aos = false; // eval bird's eye view bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_ground[c]) { fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection_ground.txt").c_str(), "w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[0], aos[0], EASY, GROUND) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[1], aos[1], MODERATE, GROUND) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[2], aos[2], HARD, GROUND)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_ground", CLASS_NAMES[c], precision, 0); } } // eval 3D bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_3d[c]) { fp_det = fopen((result_dir + "/stats_" + CLASS_NAMES[c] + "_detection_3d.txt").c_str(), "w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[0], aos[0], EASY, BOX3D) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[1], aos[1], MODERATE, BOX3D) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[2], aos[2], HARD, BOX3D)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_3d", CLASS_NAMES[c], precision, 0); } } // success return true; } int32_t main (int32_t argc,char *argv[]) { // we need 2 or 4 arguments! if (argc!=2 && argc!=4) { cout << "Usage: ./eval_detection result_sha [user_sha email]" << endl; return 1; } // read arguments string result_sha = argv[1]; // init notification mail Mail *mail; if (argc==4) mail = new Mail(argv[3]); else mail = new Mail(); mail->msg("Thank you for participating in our evaluation!"); // run evaluation if (eval(result_sha,mail)) { mail->msg("Your evaluation results are available at:"); mail->msg("http://www.cvlibs.net/datasets/kitti/user_submit_check_login.php?benchmark=object&user=%s&result=%s",argv[2], result_sha.c_str()); } else { system(("rm -r results/" + result_sha).c_str()); mail->msg("An error occured while processing your results."); mail->msg("Please make sure that the data in your zip archive has the right format!"); } // send mail and exit delete mail; return 0; } slamd202303071458299/src/tools/kitti_eval/evaluate_object_3d_offline000066400000000000000000012215001435750645400250340ustar00rootroot00000000000000ELF>)@@@@8 @ @@@@@88@8@@@uu cc` ccTT@T@DDPtdCCQtdRtdcc((/lib64/ld-linux-x86-64.so.2GNU GNU8NI:?Y[ U /p2L4$d@%LPS$m C _c)c JFHZq+(EL C&|f yIk@"#D  'p /Xd # 63 O  1 ? 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P%>Pf.wYf.%PL$f.^yPX%Pf.d$sf.%eP׃fD$f.|$w*H,fH*f.L$rn$$yDH|$pD^DT$DH$D^DT$|@CI8GCLC8HH(L$H苩L胩H$֟H|$p̟Ht@AWAVAAUATL%v UH-~ SIIL)HHHt 1LLDAHH9uH[]A\A]A^A_Ðf.HHbad numeric conversion: overflowbad numeric conversion: negative overflowbad numeric conversion: positive overflowBoost.Geometry Overlay invalid input exceptionbasic_string::_M_construct null not valid/usr/include/boost/geometry/algorithms/detail/overlay/get_ring.hppid.ring_index >= -1 && id.ring_index < int(boost::size(interior_rings(polygon)))/usr/include/boost/geometry/util/range.hppBoost.Geometry Turn exception: set term png size 450,315 font "Helvetica" 11 set term postscript eps enhanced color font "Helvetica" 20 set ylabel "Orientation Similarity" "%s.txt" using 1:2 title 'Easy' with lines ls 1 lw %d,"%s.txt" using 1:3 title 'Moderate' with lines ls 2 lw %d,"%s.txt" using 1:4 title 'Hard' with lines ls 3 lw %dcd %s; ps2pdf %s.eps %s_large.pdfcd %s; pdfcrop %s_large.pdf %s.pdf%s: __pos (which is %zu) > this->size() (which is %zu)/usr/include/boost/numeric/ublas/functional.hppsize_j == 0 || size_i <= (std::numeric_limits::max) () / size_ji <= ((std::numeric_limits::max) () - j) / size_j/usr/include/boost/numeric/ublas/storage.hppevaluate_object_3d_offline.cpp%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf/usr/include/boost/numeric/ublas/detail/matrix_assign.hppbad rational: zero denominator/usr/include/boost/rational.hppgroundtruth.size() == detections.size()number of files for evaluation: %d ERROR: Couldn't read: %s of ground truth. Please write me an email!Usage: ./eval_detection_3d_offline gt_dir result_dir/usr/lib/sendmail -t -f noreply@cvlibs.netSubject: KITTI Evaluation Benchmark Thank you for participating in our evaluation!Your evaluation results are available at:An error occured while processing your results./usr/include/boost/geometry/algorithms/detail/overlay/get_turn_info.hpp/usr/include/boost/geometry/algorithms/detail/overlay/copy_segments.hppfrom_index < static_cast(boost::size(view))/usr/include/boost/geometry/algorithms/detail/overlay/copy_segment_point.hppseg_id.source_index == 0 || seg_id.source_index == 1/usr/include/boost/geometry/policies/relate/intersection_points.hppdeque::_M_new_elements_at_frontdeque::_M_new_elements_at_back/usr/include/boost/geometry/policies/robustness/get_rescale_policy.hppi < boost::size(rng)%f basic_string::append/wsave %s %f %f %f %f %s AP: %f %f %f .gpset output "%s.png" set output "%s.eps" set size ratio 0.7 set xrange [0:1] set yrange [0:1] set xlabel "Recall" set ylabel "Precision" set title "%s" plot cd %s; gnuplot %scd %s; rm %s_large.pdfbasic_string::substrCheck failed in file at line :bad sizei < size_ibad indexj < size_ji < size_vector::_M_fill_inserttmp.size()==stat.fp+stat.tpdelta.size()==stat.tpcarpedestriancyclistinvalidPedestrianPerson_sittingVanDontCareCarinvalildsize1 == size2bad argumentvector::_M_fill_insertthis->test_invariant()vector::_M_insert_aux/../plotmkdir Loading detections...%06d.txtERROR: Couldn't read: %s done./../stats__detection.txt_orientation.txt_detection_orientation_detection_ground.txt_detection_ground_detection_3d.txt_detection_3d%s evaluation failed.To: %s From: noreply@cvlibs.net rm -r index < info.countindex >= 0 && index < nthis->den > zeror.den > zeroopp.left or rightalignedux/uxiu/iuiu/uxix/iuix/ixii/iiux/iuiu/ix/usr/include/boost/array.hpp(i < N)&&("out of range")ratio.denominator() != 0arrival != 0i <= boost::size(rng)No next IPDead end at startVisit againDead endEndless loop!boost::empty(rng)it != boost::end(rng)factor >= 1kBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBnBkBnBlBkB@nBkBnBnBkBnBnBnB@lBnBnBnBnBnBkB0mBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB`BBBBBBBBPBBBBBBBPBtypename boost::range_reference::type boost::geometry::range::at(const RandomAccessRange&, typename boost::range_size::type) [with RandomAccessRange = std::deque, true, true, std::vector, std::allocator>, std::allocator, true, true, std::vector, std::allocator> > >; typename boost::range_reference::type = const boost::geometry::model::ring, true, true, std::vector, std::allocator>&; typename boost::range_size::type = long unsigned int]static const typename boost::geometry::ring_return_type::type boost::geometry::detail::overlay::get_ring::apply(const boost::geometry::ring_identifier&, const Polygon&) [with Polygon = boost::geometry::model::polygon >; typename boost::geometry::ring_return_type::type = const boost::geometry::model::ring, true, true, std::vector, std::allocator>&]typename boost::range_reference::type boost::geometry::range::front(Range&) [with Range = boost::geometry::model::ring, true, true, std::vector, std::allocator>; typename boost::range_reference::type = boost::geometry::model::d2::point_xy&]typename boost::range_iterator::type boost::geometry::range::erase(Range&, typename boost::range_iterator::type) [with Range = boost::geometry::model::ring, true, true, std::vector, std::allocator>; typename boost::range_iterator::type = __gnu_cxx::__normal_iterator*, std::vector, std::allocator > > >]typename boost::range_iterator::type boost::geometry::range::pos(RandomAccessRange&, typename boost::range_size::type) [with RandomAccessRange = std::deque, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >, std::allocator, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> > > >; typename boost::range_iterator::type = std::_Deque_iterator, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >, boost::geometry::detail::overlay::turn_info, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >&, boost::geometry::detail::overlay::turn_info, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >*>; typename boost::range_size::type = long unsigned int]static void boost::geometry::detail::overlay::collinear::apply(const Point1&, const Point1&, const Point1&, const Point2&, const Point2&, const Point2&, TurnInfo&, const IntersectionInfo&, const DirInfo&, const SidePolicy&) [with Point1 = boost::geometry::model::d2::point_xy; Point2 = boost::geometry::model::d2::point_xy; IntersectionInfo = boost::geometry::segment_intersection_points, boost::geometry::segment_ratio >; DirInfo = boost::geometry::policies::relate::direction_type; SidePolicy = boost::geometry::detail::overlay::side_calculator, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point >; TurnInfo = boost::geometry::detail::overlay::turn_info, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >]static void boost::geometry::detail::overlay::base_turn_handler::assign_point(TurnInfo&, boost::geometry::detail::overlay::method_type, const IntersectionInfo&, unsigned int) [with TurnInfo = boost::geometry::detail::overlay::turn_info, boost::geometry::segment_ratio, boost::geometry::detail::overlay::turn_operation >, boost::array >, 2ul> >; IntersectionInfo = boost::geometry::segment_intersection_points, boost::geometry::segment_ratio >]static void boost::geometry::detail::copy_segments::copy_segments_ring::apply(const Ring&, const SegmentIdentifier&, boost::geometry::signed_index_type, const RobustPolicy&, RangeOut&) [with Ring = boost::geometry::model::ring, true, true, std::vector, std::allocator>; SegmentIdentifier = boost::geometry::segment_identifier; RobustPolicy = boost::geometry::detail::robust_policy, boost::geometry::model::point, double>; RangeOut = boost::geometry::model::ring, true, true, std::vector, std::allocator>; bool Reverse = false; boost::geometry::signed_index_type = long int]static bool boost::geometry::detail::copy_segments::copy_segment_point_range::apply(const Range&, const SegmentIdentifier&, bool, PointOut&) [with Range = boost::geometry::model::ring, true, true, std::vector, std::allocator>; bool Reverse = false; SegmentIdentifier = boost::geometry::segment_identifier; PointOut = boost::geometry::model::d2::point_xy]bool boost::geometry::copy_segment_point(const Geometry1&, const Geometry2&, const SegmentIdentifier&, bool, PointOut&) [with bool Reverse1 = false; bool Reverse2 = false; Geometry1 = boost::geometry::model::polygon >; Geometry2 = boost::geometry::model::polygon >; SegmentIdentifier = boost::geometry::segment_identifier; PointOut = boost::geometry::model::d2::point_xy]const T& boost::array::operator[](boost::array::size_type) const [with T = boost::geometry::detail::overlay::traversal_turn_operation, boost::geometry::segment_ratio >; long unsigned int N = 2ul; boost::array::const_reference = const boost::geometry::detail::overlay::traversal_turn_operation, boost::geometry::segment_ratio >&; boost::array::size_type = long unsigned int]static void boost::geometry::detail::overlay::collinear::apply(const Point1&, const Point1&, const Point1&, const Point2&, const Point2&, const Point2&, TurnInfo&, const IntersectionInfo&, const DirInfo&, const SidePolicy&) [with Point1 = boost::geometry::model::d2::point_xy; Point2 = boost::geometry::model::d2::point_xy; IntersectionInfo = boost::geometry::segment_intersection_points, boost::geometry::segment_ratio >; DirInfo = boost::geometry::policies::relate::direction_type; SidePolicy = boost::geometry::detail::overlay::side_calculator, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point, boost::geometry::model::point >; TurnInfo = boost::geometry::detail::overlay::traversal_turn_info, boost::geometry::segment_ratio >]static void boost::geometry::detail::overlay::base_turn_handler::assign_point(TurnInfo&, boost::geometry::detail::overlay::method_type, const IntersectionInfo&, unsigned int) [with TurnInfo = boost::geometry::detail::overlay::traversal_turn_info, boost::geometry::segment_ratio >; IntersectionInfo = boost::geometry::segment_intersection_points, boost::geometry::segment_ratio >]static void boost::geometry::policies::relate::segments_intersection_points::assign(Point&, const Segment&, const SegmentRatio&, const T&, const T&) [with Point = boost::geometry::model::d2::point_xy; Segment = boost::geometry::model::referring_segment >; SegmentRatio = boost::geometry::segment_ratio; T = double; ReturnType = boost::geometry::segment_intersection_points, boost::geometry::segment_ratio >]bool boost::rational::operator<(const boost::rational&) const [with IntType = long long int]void boost::rational::normalize() [with IntType = long long int]T& boost::array::operator[](boost::array::size_type) [with T = boost::geometry::detail::overlay::traversal_turn_operation, boost::geometry::segment_ratio >; long unsigned int N = 2ul; boost::array::reference = boost::geometry::detail::overlay::traversal_turn_operation, boost::geometry::segment_ratio >&; boost::array::size_type = long unsigned int]typename boost::range_reference::type boost::geometry::range::at(const RandomAccessRange&, typename boost::range_size::type) [with RandomAccessRange = std::vector, true, true, std::vector, std::allocator>, std::allocator, true, true, std::vector, std::allocator> > >; typename boost::range_reference::type = const boost::geometry::model::ring, true, true, std::vector, std::allocator>&; typename boost::range_size::type = long unsigned int]void boost::geometry::detail::get_rescale_policy::scale_box_to_integer_range(const Box&, Point&, RobustPoint&, Factor&) [with Box = boost::geometry::model::box >; Point = boost::geometry::model::d2::point_xy; RobustPoint = boost::geometry::model::point; Factor = double]bool eval_class(FILE*, FILE*, CLASSES, const std::vector >&, const std::vector >&, bool, double (*)(tDetection, tGroundtruth, int32_t), std::vector&, std::vector&, DIFFICULTY, METRIC)tPrData computeStatistics(CLASSES, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, bool, double (*)(tDetection, tGroundtruth, int32_t), METRIC, bool, double, bool)ffffff?????????333333?333333??(N5boost7numeric5ublas12bad_argumentE@cCcN5boost7numeric5ublas8bad_sizeE@cCcN5boost7numeric5ublas9bad_indexE@cCcN5boost7numeric16bad_numeric_castE@c@CcN5boost7numeric17negative_overflowE@cChCN5boost7numeric17positive_overflowE@cChCN5boost8geometry9exceptionE@cC cN5boost12bad_rationalE@c@CcN5boost8geometry19turn_info_exceptionE@cC CN5boost8geometry6detail20self_get_turn_points17self_ip_exceptionE@cC CN5boost8geometry31overlay_invalid_input_exceptionE@c C CC@@ @Cp@@ @(C@@ @hC0@@@@C@@@C`@p@@ C@@!@XC@@ @CP@@@C @0@!@XC@@@<??D@c$@?@.AcAC0AB;H  2` V6.h(MP Xp x h  0 HH xx (HhأXXح @8Xh(`$x(HX,8,(,8H-($->0A1B5D8 DP Dh D D D D XE hE E8 EP Ep E E E F (F HF XF0 xFP Fh F F F F G G 8G0 hG G JLHM@MhNNOhXPQQXR(RSTP8UVWYP(ZxZ[(h\x\X]](_x_xa(fhhjPHklno`8qrxs8wzz@|}H~`88X8X@`0 x h!X`!(!Ȣ!!8"h"Ȩ"Ȫ##HP#x#(#8#X$$((%@%X%X%%8%8&X&&'X'hp'8'((((H) )XH)X)()`**++h++,(8.Xx..x..(/x/H/0P0(1(`1 128P2X2!28$H3'3234 4X5p4(74:5<`5>h?(h?H@@Ȯ@8AAAA8ABx8BBXBCx`CxCDDD0E8Eh E( (FXFFGxhGG'HH5Hh6H>0IFIZIH\ J]`J^JlJo(KrKxsKLHXLȟL8L8@MMzRx *zRx $0FJ w?;*3$"DH;\@;t8;0;(; 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1t@t$Do@hN @V @ ^o@ko@@@z@@@B0@08@8`@` $@ $0$@0$=C= =C=8O CCDCccccccp pcpc 05P1q $slamd202303071458299/src/tools/kitti_eval/evaluate_object_3d_offline.cpp000066400000000000000000001017561435750645400256260ustar00rootroot00000000000000#include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "mail.h" BOOST_GEOMETRY_REGISTER_C_ARRAY_CS(cs::cartesian) typedef boost::geometry::model::polygon > Polygon; using namespace std; /*======================================================================= STATIC EVALUATION PARAMETERS =======================================================================*/ // holds the number of test images on the server const int32_t N_TESTIMAGES = 7518; // easy, moderate and hard evaluation level enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2}; // evaluation metrics: image, ground or 3D enum METRIC{IMAGE=0, GROUND=1, BOX3D=2}; // evaluation parameter const int32_t MIN_HEIGHT[3] = {40, 25, 25}; // minimum height for evaluated groundtruth/detections const int32_t MAX_OCCLUSION[3] = {0, 1, 2}; // maximum occlusion level of the groundtruth used for evaluation const double MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; // maximum truncation level of the groundtruth used for evaluation // evaluated object classes enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2}; const int NUM_CLASS = 3; // parameters varying per class vector CLASS_NAMES; // the minimum overlap required for 2D evaluation on the image/ground plane and 3D evaluation const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.5, 0.25, 0.25}, {0.5, 0.25, 0.25}}; // const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}}; // no. of recall steps that should be evaluated (discretized) const double N_SAMPLE_PTS = 41; // initialize class names void initGlobals () { CLASS_NAMES.push_back("car"); CLASS_NAMES.push_back("pedestrian"); CLASS_NAMES.push_back("cyclist"); } /*======================================================================= DATA TYPES FOR EVALUATION =======================================================================*/ // holding data needed for precision-recall and precision-aos struct tPrData { vector v; // detection score for computing score thresholds double similarity; // orientation similarity int32_t tp; // true positives int32_t fp; // false positives int32_t fn; // false negatives tPrData () : similarity(0), tp(0), fp(0), fn(0) {} }; // holding bounding boxes for ground truth and detections struct tBox { string type; // object type as car, pedestrian or cyclist,... double x1; // left corner double y1; // top corner double x2; // right corner double y2; // bottom corner double alpha; // image orientation tBox (string type, double x1,double y1,double x2,double y2,double alpha) : type(type),x1(x1),y1(y1),x2(x2),y2(y2),alpha(alpha) {} }; // holding ground truth data struct tGroundtruth { tBox box; // object type, box, orientation double truncation; // truncation 0..1 int32_t occlusion; // occlusion 0,1,2 (non, partly, fully) double ry; double t1, t2, t3; double h, w, l; tGroundtruth () : box(tBox("invalild",-1,-1,-1,-1,-10)),truncation(-1),occlusion(-1) {} tGroundtruth (tBox box,double truncation,int32_t occlusion) : box(box),truncation(truncation),occlusion(occlusion) {} tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) : box(tBox(type,x1,y1,x2,y2,alpha)),truncation(truncation),occlusion(occlusion) {} }; // holding detection data struct tDetection { tBox box; // object type, box, orientation double thresh; // detection score double ry; double t1, t2, t3; double h, w, l; tDetection (): box(tBox("invalid",-1,-1,-1,-1,-10)),thresh(-1000) {} tDetection (tBox box,double thresh) : box(box),thresh(thresh) {} tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) : box(tBox(type,x1,y1,x2,y2,alpha)),thresh(thresh) {} }; /*======================================================================= FUNCTIONS TO LOAD DETECTION AND GROUND TRUTH DATA ONCE, SAVE RESULTS =======================================================================*/ vector indices; vector loadDetections(string file_name, bool &compute_aos, vector &eval_image, vector &eval_ground, vector &eval_3d, bool &success) { // holds all detections (ignored detections are indicated by an index vector vector detections; FILE *fp = fopen(file_name.c_str(),"r"); if (!fp) { success = false; return detections; } while (!feof(fp)) { tDetection d; double trash; char str[255]; if (fscanf(fp, "%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", str, &trash, &trash, &d.box.alpha, &d.box.x1, &d.box.y1, &d.box.x2, &d.box.y2, &d.h, &d.w, &d.l, &d.t1, &d.t2, &d.t3, &d.ry, &d.thresh)==16) { // d.thresh = 1; d.box.type = str; detections.push_back(d); // orientation=-10 is invalid, AOS is not evaluated if at least one orientation is invalid if(d.box.alpha == -10) compute_aos = false; // a class is only evaluated if it is detected at least once for (int c = 0; c < NUM_CLASS; c++) { if (!strcasecmp(d.box.type.c_str(), CLASS_NAMES[c].c_str())) { if (!eval_image[c] && d.box.x1 >= 0) eval_image[c] = true; if (!eval_ground[c] && d.t1 != -1000) eval_ground[c] = true; if (!eval_3d[c] && d.t2 != -1000) eval_3d[c] = true; break; } } } } fclose(fp); success = true; return detections; } vector loadGroundtruth(string file_name,bool &success) { // holds all ground truth (ignored ground truth is indicated by an index vector vector groundtruth; FILE *fp = fopen(file_name.c_str(),"r"); if (!fp) { success = false; return groundtruth; } while (!feof(fp)) { tGroundtruth g; char str[255]; if (fscanf(fp, "%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", str, &g.truncation, &g.occlusion, &g.box.alpha, &g.box.x1, &g.box.y1, &g.box.x2, &g.box.y2, &g.h, &g.w, &g.l, &g.t1, &g.t2, &g.t3, &g.ry )==15) { g.box.type = str; groundtruth.push_back(g); } } fclose(fp); success = true; return groundtruth; } void saveStats (const vector &precision, const vector &aos, FILE *fp_det, FILE *fp_ori) { // save precision to file if(precision.empty()) return; for (int32_t i=0; i Polygon toPolygon(const T& g) { using namespace boost::numeric::ublas; using namespace boost::geometry; matrix mref(2, 2); mref(0, 0) = cos(g.ry); mref(0, 1) = sin(g.ry); mref(1, 0) = -sin(g.ry); mref(1, 1) = cos(g.ry); static int count = 0; matrix corners(2, 4); double data[] = {g.l / 2, g.l / 2, -g.l / 2, -g.l / 2, g.w / 2, -g.w / 2, -g.w / 2, g.w / 2}; std::copy(data, data + 8, corners.data().begin()); matrix gc = prod(mref, corners); for (int i = 0; i < 4; ++i) { gc(0, i) += g.t1; gc(1, i) += g.t3; } double points[][2] = {{gc(0, 0), gc(1, 0)},{gc(0, 1), gc(1, 1)},{gc(0, 2), gc(1, 2)},{gc(0, 3), gc(1, 3)},{gc(0, 0), gc(1, 0)}}; Polygon poly; append(poly, points); return poly; } // measure overlap between bird's eye view bounding boxes, parametrized by (ry, l, w, tx, tz) inline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) { using namespace boost::geometry; Polygon gp = toPolygon(g); Polygon dp = toPolygon(d); std::vector in, un; intersection(gp, dp, in); union_(gp, dp, un); double inter_area = in.empty() ? 0 : area(in.front()); double union_area = area(un.front()); double o; if(criterion==-1) // union o = inter_area / union_area; else if(criterion==0) // bbox_a o = inter_area / area(dp); else if(criterion==1) // bbox_b o = inter_area / area(gp); return o; } // measure overlap between 3D bounding boxes, parametrized by (ry, h, w, l, tx, ty, tz) inline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) { using namespace boost::geometry; Polygon gp = toPolygon(g); Polygon dp = toPolygon(d); std::vector in, un; intersection(gp, dp, in); union_(gp, dp, un); double ymax = min(d.t2, g.t2); double ymin = max(d.t2 - d.h, g.t2 - g.h); double inter_area = in.empty() ? 0 : area(in.front()); double inter_vol = inter_area * max(0.0, ymax - ymin); double det_vol = d.h * d.l * d.w; double gt_vol = g.h * g.l * g.w; double o; if(criterion==-1) // union o = inter_vol / (det_vol + gt_vol - inter_vol); else if(criterion==0) // bbox_a o = inter_vol / det_vol; else if(criterion==1) // bbox_b o = inter_vol / gt_vol; return o; } vector getThresholds(vector &v, double n_groundtruth){ // holds scores needed to compute N_SAMPLE_PTS recall values vector t; // sort scores in descending order // (highest score is assumed to give best/most confident detections) sort(v.begin(), v.end(), greater()); // get scores for linearly spaced recall double current_recall = 0; for(int32_t i=0; i >, const vector &det, vector &ignored_gt, vector &dc, vector &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){ // extract ground truth bounding boxes for current evaluation class for(int32_t i=0;iMAX_OCCLUSION[difficulty] || gt[i].truncation>MAX_TRUNCATION[difficulty] || height >, const vector &det, const vector &dc, const vector &ignored_gt, const vector &ignored_det, bool compute_fp, double (*boxoverlap)(tDetection, tGroundtruth, int32_t), METRIC metric, bool compute_aos=false, double thresh=0, bool debug=false){ tPrData stat = tPrData(); const double NO_DETECTION = -10000000; vector delta; // holds angular difference for TPs (needed for AOS evaluation) vector assigned_detection; // holds wether a detection was assigned to a valid or ignored ground truth assigned_detection.assign(det.size(), false); vector ignored_threshold; ignored_threshold.assign(det.size(), false); // holds detections with a threshold lower than thresh if FP are computed // detections with a low score are ignored for computing precision (needs FP) if(compute_fp) for(int32_t i=0; i 0.5) (logical len(det)) =======================================================================*/ int32_t det_idx = -1; double valid_detection = NO_DETECTION; double max_overlap = 0; // search for a possible detection bool assigned_ignored_det = false; for(int32_t j=0; jMIN_OVERLAP[metric][current_class] && det[j].thresh>valid_detection){ det_idx = j; valid_detection = det[j].thresh; } // for computing pr curve values, the candidate with the greatest overlap is considered // if the greatest overlap is an ignored detection (min_height), the overlapping detection is used else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && (overlap>max_overlap || assigned_ignored_det) && ignored_det[j]==0){ max_overlap = overlap; det_idx = j; valid_detection = 1; assigned_ignored_det = false; } else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && valid_detection==NO_DETECTION && ignored_det[j]==1){ det_idx = j; valid_detection = 1; assigned_ignored_det = true; } } /*======================================================================= compute TP, FP and FN =======================================================================*/ // nothing was assigned to this valid ground truth if(valid_detection==NO_DETECTION && ignored_gt[i]==0) { stat.fn++; } // only evaluate valid ground truth <=> detection assignments (considering difficulty level) else if(valid_detection!=NO_DETECTION && (ignored_gt[i]==1 || ignored_det[det_idx]==1)) assigned_detection[det_idx] = true; // found a valid true positive else if(valid_detection!=NO_DETECTION){ // write highest score to threshold vector stat.tp++; stat.v.push_back(det[det_idx].thresh); // compute angular difference of detection and ground truth if valid detection orientation was provided if(compute_aos) delta.push_back(gt[i].box.alpha - det[det_idx].box.alpha); // clean up assigned_detection[det_idx] = true; } } // if FP are requested, consider stuff area if(compute_fp){ // count fp for(int32_t i=0; iMIN_OVERLAP[metric][current_class]){ assigned_detection[j] = true; nstuff++; } } } // FP = no. of all not to ground truth assigned detections - detections assigned to stuff areas stat.fp -= nstuff; // if all orientation values are valid, the AOS is computed if(compute_aos){ vector tmp; // FP have a similarity of 0, for all TP compute AOS tmp.assign(stat.fp, 0); for(int32_t i=0; i0 || stat.fp>0) stat.similarity = accumulate(tmp.begin(), tmp.end(), 0.0); // there was neither a FP nor a TP, so the similarity is ignored in the evaluation else stat.similarity = -1; } } return stat; } /*======================================================================= EVALUATE CLASS-WISE =======================================================================*/ bool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class, const vector< vector > &groundtruth, const vector< vector > &detections, bool compute_aos, double (*boxoverlap)(tDetection, tGroundtruth, int32_t), vector &precision, vector &aos, DIFFICULTY difficulty, METRIC metric) { assert(groundtruth.size() == detections.size()); // init int32_t n_gt=0; // total no. of gt (denominator of recall) vector v, thresholds; // detection scores, evaluated for recall discretization vector< vector > ignored_gt, ignored_det; // index of ignored gt detection for current class/difficulty vector< vector > dontcare; // index of dontcare areas, included in ground truth // for all test images do for (int32_t i=0; i i_gt, i_det; vector dc; // only evaluate objects of current class and ignore occluded, truncated objects cleanData(current_class, groundtruth[i], detections[i], i_gt, dc, i_det, n_gt, difficulty); ignored_gt.push_back(i_gt); ignored_det.push_back(i_det); dontcare.push_back(dc); // compute statistics to get recall values tPrData pr_tmp = tPrData(); pr_tmp = computeStatistics(current_class, groundtruth[i], detections[i], dc, i_gt, i_det, false, boxoverlap, metric); // add detection scores to vector over all images for(int32_t j=0; j pr; pr.assign(thresholds.size(),tPrData()); for (int32_t i=0; i recall; precision.assign(N_SAMPLE_PTS, 0); if(compute_aos) aos.assign(N_SAMPLE_PTS, 0); double r=0; for (int32_t i=0; i vals[],bool is_aos){ char command[1024]; // save plot data to file FILE *fp = fopen((dir_name + "/" + file_name + ".txt").c_str(),"w"); printf("save %s\n", (dir_name + "/" + file_name + ".txt").c_str()); for (int32_t i=0; i<(int)N_SAMPLE_PTS; i++) fprintf(fp,"%f %f %f %f\n",(double)i/(N_SAMPLE_PTS-1.0),vals[0][i],vals[1][i],vals[2][i]); fclose(fp); float sum[3] = {0, 0, 0}; for (int v = 0; v < 3; ++v) for (int i = 0; i < vals[v].size(); i = i + 4) sum[v] += vals[v][i]; printf("%s AP: %f %f %f\n", file_name.c_str(), sum[0] / 11 * 100, sum[1] / 11 * 100, sum[2] / 11 * 100); // create png + eps for (int32_t j=0; j<2; j++) { // open file FILE *fp = fopen((dir_name + "/" + file_name + ".gp").c_str(),"w"); // save gnuplot instructions if (j==0) { fprintf(fp,"set term png size 450,315 font \"Helvetica\" 11\n"); fprintf(fp,"set output \"%s.png\"\n",file_name.c_str()); } else { fprintf(fp,"set term postscript eps enhanced color font \"Helvetica\" 20\n"); fprintf(fp,"set output \"%s.eps\"\n",file_name.c_str()); } // set labels and ranges fprintf(fp,"set size ratio 0.7\n"); fprintf(fp,"set xrange [0:1]\n"); fprintf(fp,"set yrange [0:1]\n"); fprintf(fp,"set xlabel \"Recall\"\n"); if (!is_aos) fprintf(fp,"set ylabel \"Precision\"\n"); else fprintf(fp,"set ylabel \"Orientation Similarity\"\n"); obj_type[0] = toupper(obj_type[0]); fprintf(fp,"set title \"%s\"\n",obj_type.c_str()); // line width int32_t lw = 5; if (j==0) lw = 3; // plot error curve fprintf(fp,"plot "); fprintf(fp,"\"%s.txt\" using 1:2 title 'Easy' with lines ls 1 lw %d,",file_name.c_str(),lw); fprintf(fp,"\"%s.txt\" using 1:3 title 'Moderate' with lines ls 2 lw %d,",file_name.c_str(),lw); fprintf(fp,"\"%s.txt\" using 1:4 title 'Hard' with lines ls 3 lw %d",file_name.c_str(),lw); // close file fclose(fp); // run gnuplot => create png + eps sprintf(command,"cd %s; gnuplot %s",dir_name.c_str(),(file_name + ".gp").c_str()); system(command); } // create pdf and crop sprintf(command,"cd %s; ps2pdf %s.eps %s_large.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str()); system(command); sprintf(command,"cd %s; pdfcrop %s_large.pdf %s.pdf",dir_name.c_str(),file_name.c_str(),file_name.c_str()); system(command); sprintf(command,"cd %s; rm %s_large.pdf",dir_name.c_str(),file_name.c_str()); system(command); } vector getEvalIndices(const string& result_dir) { DIR* dir; dirent* entity; dir = opendir(result_dir.c_str()); if (dir) { while (entity = readdir(dir)) { string path(entity->d_name); int32_t len = path.size(); if (len < 10) continue; int32_t index = atoi(path.substr(len - 10, 10).c_str()); indices.push_back(index); } } return indices; } bool eval(string gt_dir, string result_dir, Mail* mail){ // set some global parameters initGlobals(); // ground truth and result directories // string gt_dir = "data/object/label_2"; // string result_dir = "results/" + result_sha; string plot_dir = result_dir + "/../plot"; // create output directories system(("mkdir " + plot_dir).c_str()); // hold detections and ground truth in memory vector< vector > groundtruth; vector< vector > detections; // holds wether orientation similarity shall be computed (might be set to false while loading detections) // and which labels where provided by this submission bool compute_aos=true; vector eval_image(NUM_CLASS, false); vector eval_ground(NUM_CLASS, false); vector eval_3d(NUM_CLASS, false); // for all images read groundtruth and detections mail->msg("Loading detections..."); std::vector indices = getEvalIndices(result_dir); printf("number of files for evaluation: %d\n", (int)indices.size()); for (int32_t i=0; i gt = loadGroundtruth(gt_dir + "/" + file_name,gt_success); vector det = loadDetections(result_dir + file_name, compute_aos, eval_image, eval_ground, eval_3d, det_success); groundtruth.push_back(gt); detections.push_back(det); // check for errors if (!gt_success) { mail->msg("ERROR: Couldn't read: %s of ground truth. Please write me an email!", file_name); return false; } if (!det_success) { mail->msg("ERROR: Couldn't read: %s", file_name); return false; } } mail->msg(" done."); // holds pointers for result files FILE *fp_det=0, *fp_ori=0; // eval image 2D bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_image[c]) { fp_det = fopen((result_dir + "/../stats_" + CLASS_NAMES[c] + "_detection.txt").c_str(), "w"); if(compute_aos) fp_ori = fopen((result_dir + "/../stats_" + CLASS_NAMES[c] + "_orientation.txt").c_str(),"w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[0], aos[0], EASY, IMAGE) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[1], aos[1], MODERATE, IMAGE) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[2], aos[2], HARD, IMAGE)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection", CLASS_NAMES[c], precision, 0); if(compute_aos){ saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_orientation", CLASS_NAMES[c], aos, 1); fclose(fp_ori); } } } // don't evaluate AOS for birdview boxes and 3D boxes compute_aos = false; // eval bird's eye view bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_ground[c]) { fp_det = fopen((result_dir + "/../stats_" + CLASS_NAMES[c] + "_detection_ground.txt").c_str(), "w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[0], aos[0], EASY, GROUND) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[1], aos[1], MODERATE, GROUND) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[2], aos[2], HARD, GROUND)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_ground", CLASS_NAMES[c], precision, 0); } } // eval 3D bounding boxes for (int c = 0; c < NUM_CLASS; c++) { CLASSES cls = (CLASSES)c; if (eval_3d[c]) { fp_det = fopen((result_dir + "/../stats_" + CLASS_NAMES[c] + "_detection_3d.txt").c_str(), "w"); vector precision[3], aos[3]; if( !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[0], aos[0], EASY, BOX3D) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[1], aos[1], MODERATE, BOX3D) || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[2], aos[2], HARD, BOX3D)) { mail->msg("%s evaluation failed.", CLASS_NAMES[c].c_str()); return false; } fclose(fp_det); saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + "_detection_3d", CLASS_NAMES[c], precision, 0); } } // success return true; } int32_t main (int32_t argc,char *argv[]) { // we need 2 or 4 arguments! if (argc!=3) { cout << "Usage: ./eval_detection_3d_offline gt_dir result_dir" << endl; return 1; } // read arguments string gt_dir = argv[1]; string result_dir = argv[2]; // init notification mail Mail *mail; mail = new Mail(); mail->msg("Thank you for participating in our evaluation!"); // run evaluation if (eval(gt_dir, result_dir, mail)) { mail->msg("Your evaluation results are available at:"); mail->msg(result_dir.c_str()); } else { system(("rm -r " + result_dir + "/../plot").c_str()); mail->msg("An error occured while processing your results."); } // send mail and exit delete mail; return 0; } slamd202303071458299/src/tools/kitti_eval/mail.h000066400000000000000000000014531435750645400207620ustar00rootroot00000000000000#ifndef MAIL_H #define MAIL_H #include #include #include class Mail { public: Mail (std::string email = "") { if (email.compare("")) { mail = popen("/usr/lib/sendmail -t -f noreply@cvlibs.net","w"); fprintf(mail,"To: %s\n", email.c_str()); fprintf(mail,"From: noreply@cvlibs.net\n"); fprintf(mail,"Subject: KITTI Evaluation Benchmark\n"); fprintf(mail,"\n\n"); } else { mail = 0; } } ~Mail() { if (mail) { pclose(mail); } } void msg (const char *format, ...) { va_list args; va_start(args,format); if (mail) { vfprintf(mail,format,args); fprintf(mail,"\n"); } vprintf(format,args); printf("\n"); va_end(args); } private: FILE *mail; }; #endif slamd202303071458299/src/tools/merge_pascal_json.py000066400000000000000000000020421435750645400215540ustar00rootroot00000000000000import json # ANNOT_PATH = '/home/zxy/Datasets/VOC/annotations/' ANNOT_PATH = 'voc/annotations/' OUT_PATH = ANNOT_PATH INPUT_FILES = ['pascal_train2012.json', 'pascal_val2012.json', 'pascal_train2007.json', 'pascal_val2007.json'] OUTPUT_FILE = 'pascal_trainval0712.json' KEYS = ['images', 'type', 'annotations', 'categories'] MERGE_KEYS = ['images', 'annotations'] out = {} tot_anns = 0 for i, file_name in enumerate(INPUT_FILES): data = json.load(open(ANNOT_PATH + file_name, 'r')) print('keys', data.keys()) if i == 0: for key in KEYS: out[key] = data[key] print(file_name, key, len(data[key])) else: out['images'] += data['images'] for j in range(len(data['annotations'])): data['annotations'][j]['id'] += tot_anns out['annotations'] += data['annotations'] print(file_name, 'images', len(data['images'])) print(file_name, 'annotations', len(data['annotations'])) tot_anns = len(out['annotations']) print('tot', len(out['annotations'])) json.dump(out, open(OUT_PATH + OUTPUT_FILE, 'w')) slamd202303071458299/src/tools/reval.py000066400000000000000000000044331435750645400172200ustar00rootroot00000000000000#!/usr/bin/env python # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # Modified by Xingyi Zhou # -------------------------------------------------------- # Reval = re-eval. Re-evaluate saved detections. from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os.path as osp sys.path.insert(0, osp.join(osp.dirname(__file__), 'voc_eval_lib')) from model.test import apply_nms from datasets.pascal_voc import pascal_voc import pickle import os, argparse import numpy as np import json def parse_args(): """ Parse input arguments """ parser = argparse.ArgumentParser(description='Re-evaluate results') parser.add_argument('detection_file', type=str) parser.add_argument('--output_dir', help='results directory', type=str) parser.add_argument('--imdb', dest='imdb_name', help='dataset to re-evaluate', default='voc_2007_test', type=str) parser.add_argument('--matlab', dest='matlab_eval', help='use matlab for evaluation', action='store_true') parser.add_argument('--comp', dest='comp_mode', help='competition mode', action='store_true') parser.add_argument('--nms', dest='apply_nms', help='apply nms', action='store_true') if len(sys.argv) == 1: parser.print_help() sys.exit(1) args = parser.parse_args() return args def from_dets(imdb_name, detection_file, args): imdb = pascal_voc('test', '2007') imdb.competition_mode(args.comp_mode) imdb.config['matlab_eval'] = args.matlab_eval with open(os.path.join(detection_file), 'rb') as f: if 'json' in detection_file: dets = json.load(f) else: dets = pickle.load(f, encoding='latin1') # import pdb; pdb.set_trace() if args.apply_nms: print('Applying NMS to all detections') test_nms = 0.3 nms_dets = apply_nms(dets, test_nms) else: nms_dets = dets print('Evaluating detections') imdb.evaluate_detections(nms_dets) if __name__ == '__main__': args = parse_args() imdb_name = args.imdb_name from_dets(imdb_name, args.detection_file, args) slamd202303071458299/src/tools/vis_pred.py000066400000000000000000000067631435750645400177320ustar00rootroot00000000000000import pycocotools.coco as coco from pycocotools.cocoeval import COCOeval import sys import cv2 import numpy as np import pickle IMG_PATH = '../../data/coco/val2017/' ANN_PATH = '../../data/coco/annotations/instances_val2017.json' DEBUG = True def _coco_box_to_bbox(box): bbox = np.array([box[0], box[1], box[0] + box[2], box[1] + box[3]], dtype=np.int32) return bbox _cat_ids = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90 ] num_classes = 80 _classes = { ind + 1: cat_id for ind, cat_id in enumerate(_cat_ids) } _to_order = {cat_id: ind for ind, cat_id in enumerate(_cat_ids)} coco = coco.COCO(ANN_PATH) CAT_NAMES = [coco.loadCats([_classes[i + 1]])[0]['name'] \ for i in range(num_classes)] COLORS = [((np.random.random((3, )) * 0.6 + 0.4)*255).astype(np.uint8) \ for _ in range(num_classes)] def add_box(image, bbox, sc, cat_id): cat_id = _to_order[cat_id] cat_name = CAT_NAMES[cat_id] cat_size = cv2.getTextSize(cat_name + '0', cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0] color = np.array(COLORS[cat_id]).astype(np.int32).tolist() txt = '{}{:.0f}'.format(cat_name, sc * 10) if bbox[1] - cat_size[1] - 2 < 0: cv2.rectangle(image, (bbox[0], bbox[1] + 2), (bbox[0] + cat_size[0], bbox[1] + cat_size[1] + 2), color, -1) cv2.putText(image, txt, (bbox[0], bbox[1] + cat_size[1] + 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1) else: cv2.rectangle(image, (bbox[0], bbox[1] - cat_size[1] - 2), (bbox[0] + cat_size[0], bbox[1] - 2), color, -1) cv2.putText(image, txt, (bbox[0], bbox[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1) cv2.rectangle(image, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) return image if __name__ == '__main__': dets = [] img_ids = coco.getImgIds() num_images = len(img_ids) for k in range(1, len(sys.argv)): pred_path = sys.argv[k] dets.append(coco.loadRes(pred_path)) # import pdb; pdb.set_trace() for i, img_id in enumerate(img_ids): img_info = coco.loadImgs(ids=[img_id])[0] img_path = IMG_PATH + img_info['file_name'] img = cv2.imread(img_path) gt_ids = coco.getAnnIds(imgIds=[img_id]) gts = coco.loadAnns(gt_ids) gt_img = img.copy() for j, pred in enumerate(gts): bbox = _coco_box_to_bbox(pred['bbox']) cat_id = pred['category_id'] gt_img = add_box(gt_img, bbox, 0, cat_id) for k in range(len(dets)): pred_ids = dets[k].getAnnIds(imgIds=[img_id]) preds = dets[k].loadAnns(pred_ids) pred_img = img.copy() for j, pred in enumerate(preds): bbox = _coco_box_to_bbox(pred['bbox']) sc = pred['score'] cat_id = pred['category_id'] if sc > 0.2: pred_img = add_box(pred_img, bbox, sc, cat_id) cv2.imshow('pred{}'.format(k), pred_img) # cv2.imwrite('vis/{}_pred{}.png'.format(i, k), pred_img) cv2.imshow('gt', gt_img) # cv2.imwrite('vis/{}_gt.png'.format(i), gt_img) cv2.waitKey() # coco_eval.evaluate() # coco_eval.accumulate() # coco_eval.summarize() slamd202303071458299/src/tools/voc_eval_lib/000077500000000000000000000000001435750645400201555ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/LICENSE000066400000000000000000000020541435750645400211630ustar00rootroot00000000000000MIT License Copyright (c) 2017 Xinlei Chen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. slamd202303071458299/src/tools/voc_eval_lib/Makefile000066400000000000000000000001361435750645400216150ustar00rootroot00000000000000all: python setup.py build_ext --inplace rm -rf build clean: rm -rf */*.pyc rm -rf */*.so slamd202303071458299/src/tools/voc_eval_lib/__init__.py000066400000000000000000000000001435750645400222540ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/datasets/000077500000000000000000000000001435750645400217655ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/datasets/__init__.py000066400000000000000000000000001435750645400240640ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/datasets/bbox.pyx000066400000000000000000000033351435750645400234650ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Sergey Karayev # -------------------------------------------------------- cimport cython import numpy as np cimport numpy as np DTYPE = np.float ctypedef np.float_t DTYPE_t def bbox_overlaps( np.ndarray[DTYPE_t, ndim=2] boxes, np.ndarray[DTYPE_t, ndim=2] query_boxes): """ Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ cdef unsigned int N = boxes.shape[0] cdef unsigned int K = query_boxes.shape[0] cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) cdef DTYPE_t iw, ih, box_area cdef DTYPE_t ua cdef unsigned int k, n for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float( (boxes[n, 2] - boxes[n, 0] + 1) * (boxes[n, 3] - boxes[n, 1] + 1) + box_area - iw * ih ) overlaps[n, k] = iw * ih / ua return overlaps slamd202303071458299/src/tools/voc_eval_lib/datasets/ds_utils.py000066400000000000000000000025721435750645400241730ustar00rootroot00000000000000# -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def unique_boxes(boxes, scale=1.0): """Return indices of unique boxes.""" v = np.array([1, 1e3, 1e6, 1e9]) hashes = np.round(boxes * scale).dot(v) _, index = np.unique(hashes, return_index=True) return np.sort(index) def xywh_to_xyxy(boxes): """Convert [x y w h] box format to [x1 y1 x2 y2] format.""" return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1)) def xyxy_to_xywh(boxes): """Convert [x1 y1 x2 y2] box format to [x y w h] format.""" return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1)) def validate_boxes(boxes, width=0, height=0): """Check that a set of boxes are valid.""" x1 = boxes[:, 0] y1 = boxes[:, 1] x2 = boxes[:, 2] y2 = boxes[:, 3] assert (x1 >= 0).all() assert (y1 >= 0).all() assert (x2 >= x1).all() assert (y2 >= y1).all() assert (x2 < width).all() assert (y2 < height).all() def filter_small_boxes(boxes, min_size): w = boxes[:, 2] - boxes[:, 0] h = boxes[:, 3] - boxes[:, 1] keep = np.where((w >= min_size) & (h > min_size))[0] return keep slamd202303071458299/src/tools/voc_eval_lib/datasets/imdb.py000066400000000000000000000222071435750645400232550ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # Modified by Xingyi Zhou # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import PIL # from utils.cython_bbox import bbox_overlaps import numpy as np import scipy.sparse from model.config import cfg def bbox_overlaps(box1, box2): area1 = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1) area2 = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1) inter = max(min(box1[2], box2[2]) - max(box1[0], box2[0]) + 1, 0) * \ max(min(box1[3], box2[3]) - max(box1[1], box2[1]) + 1, 0) iou = 1.0 * inter / (area1 + area2 - inter) return iou class imdb(object): """Image database.""" def __init__(self, name, classes=None): self._name = name self._num_classes = 0 if not classes: self._classes = [] else: self._classes = classes self._image_index = [] self._obj_proposer = 'gt' self._roidb = None self._roidb_handler = self.default_roidb # Use this dict for storing dataset specific config options self.config = {} @property def name(self): return self._name @property def num_classes(self): return len(self._classes) @property def classes(self): return self._classes @property def image_index(self): return self._image_index @property def roidb_handler(self): return self._roidb_handler @roidb_handler.setter def roidb_handler(self, val): self._roidb_handler = val def set_proposal_method(self, method): method = eval('self.' + method + '_roidb') self.roidb_handler = method @property def roidb(self): # A roidb is a list of dictionaries, each with the following keys: # boxes # gt_overlaps # gt_classes # flipped if self._roidb is not None: return self._roidb self._roidb = self.roidb_handler() return self._roidb @property def cache_path(self): cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache')) if not os.path.exists(cache_path): os.makedirs(cache_path) return cache_path @property def num_images(self): return len(self.image_index) def image_path_at(self, i): raise NotImplementedError def default_roidb(self): raise NotImplementedError def evaluate_detections(self, all_boxes, output_dir=None): """ all_boxes is a list of length number-of-classes. Each list element is a list of length number-of-images. Each of those list elements is either an empty list [] or a numpy array of detection. all_boxes[class][image] = [] or np.array of shape #dets x 5 """ raise NotImplementedError def _get_widths(self): return [PIL.Image.open(self.image_path_at(i)).size[0] for i in range(self.num_images)] def append_flipped_images(self): num_images = self.num_images widths = self._get_widths() for i in range(num_images): boxes = self.roidb[i]['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() boxes[:, 0] = widths[i] - oldx2 - 1 boxes[:, 2] = widths[i] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all() entry = {'boxes': boxes, 'gt_overlaps': self.roidb[i]['gt_overlaps'], 'gt_classes': self.roidb[i]['gt_classes'], 'flipped': True} self.roidb.append(entry) self._image_index = self._image_index * 2 def evaluate_recall(self, candidate_boxes=None, thresholds=None, area='all', limit=None): """Evaluate detection proposal recall metrics. Returns: results: dictionary of results with keys 'ar': average recall 'recalls': vector recalls at each IoU overlap threshold 'thresholds': vector of IoU overlap thresholds 'gt_overlaps': vector of all ground-truth overlaps """ # Record max overlap value for each gt box # Return vector of overlap values areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3, '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7} area_ranges = [[0 ** 2, 1e5 ** 2], # all [0 ** 2, 32 ** 2], # small [32 ** 2, 96 ** 2], # medium [96 ** 2, 1e5 ** 2], # large [96 ** 2, 128 ** 2], # 96-128 [128 ** 2, 256 ** 2], # 128-256 [256 ** 2, 512 ** 2], # 256-512 [512 ** 2, 1e5 ** 2], # 512-inf ] assert area in areas, 'unknown area range: {}'.format(area) area_range = area_ranges[areas[area]] gt_overlaps = np.zeros(0) num_pos = 0 for i in range(self.num_images): # Checking for max_overlaps == 1 avoids including crowd annotations # (...pretty hacking :/) max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1) gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) & (max_gt_overlaps == 1))[0] gt_boxes = self.roidb[i]['boxes'][gt_inds, :] gt_areas = self.roidb[i]['seg_areas'][gt_inds] valid_gt_inds = np.where((gt_areas >= area_range[0]) & (gt_areas <= area_range[1]))[0] gt_boxes = gt_boxes[valid_gt_inds, :] num_pos += len(valid_gt_inds) if candidate_boxes is None: # If candidate_boxes is not supplied, the default is to use the # non-ground-truth boxes from this roidb non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0] boxes = self.roidb[i]['boxes'][non_gt_inds, :] else: boxes = candidate_boxes[i] if boxes.shape[0] == 0: continue if limit is not None and boxes.shape[0] > limit: boxes = boxes[:limit, :] overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) _gt_overlaps = np.zeros((gt_boxes.shape[0])) for j in range(gt_boxes.shape[0]): # find which proposal box maximally covers each gt box argmax_overlaps = overlaps.argmax(axis=0) # and get the iou amount of coverage for each gt box max_overlaps = overlaps.max(axis=0) # find which gt box is 'best' covered (i.e. 'best' = most iou) gt_ind = max_overlaps.argmax() gt_ovr = max_overlaps.max() assert (gt_ovr >= 0) # find the proposal box that covers the best covered gt box box_ind = argmax_overlaps[gt_ind] # record the iou coverage of this gt box _gt_overlaps[j] = overlaps[box_ind, gt_ind] assert (_gt_overlaps[j] == gt_ovr) # mark the proposal box and the gt box as used overlaps[box_ind, :] = -1 overlaps[:, gt_ind] = -1 # append recorded iou coverage level gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps)) gt_overlaps = np.sort(gt_overlaps) if thresholds is None: step = 0.05 thresholds = np.arange(0.5, 0.95 + 1e-5, step) recalls = np.zeros_like(thresholds) # compute recall for each iou threshold for i, t in enumerate(thresholds): recalls[i] = (gt_overlaps >= t).sum() / float(num_pos) # ar = 2 * np.trapz(recalls, thresholds) ar = recalls.mean() return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds, 'gt_overlaps': gt_overlaps} def create_roidb_from_box_list(self, box_list, gt_roidb): assert len(box_list) == self.num_images, \ 'Number of boxes must match number of ground-truth images' roidb = [] for i in range(self.num_images): boxes = box_list[i] num_boxes = boxes.shape[0] overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32) if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0: gt_boxes = gt_roidb[i]['boxes'] gt_classes = gt_roidb[i]['gt_classes'] gt_overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) argmaxes = gt_overlaps.argmax(axis=1) maxes = gt_overlaps.max(axis=1) I = np.where(maxes > 0)[0] overlaps[I, gt_classes[argmaxes[I]]] = maxes[I] overlaps = scipy.sparse.csr_matrix(overlaps) roidb.append({ 'boxes': boxes, 'gt_classes': np.zeros((num_boxes,), dtype=np.int32), 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': np.zeros((num_boxes,), dtype=np.float32), }) return roidb @staticmethod def merge_roidbs(a, b): assert len(a) == len(b) for i in range(len(a)): a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes'])) a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], b[i]['gt_classes'])) a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'], b[i]['gt_overlaps']]) a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'], b[i]['seg_areas'])) return a def competition_mode(self, on): """Turn competition mode on or off.""" pass slamd202303071458299/src/tools/voc_eval_lib/datasets/pascal_voc.py000066400000000000000000000261211435750645400244530ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from datasets.imdb import imdb import datasets.ds_utils as ds_utils import xml.etree.ElementTree as ET import numpy as np import scipy.sparse import scipy.io as sio # import utils.cython_bbox import pickle import subprocess import uuid from .voc_eval import voc_eval from model.config import cfg class pascal_voc(imdb): def __init__(self, image_set, year, use_diff=False): name = 'voc_' + year + '_' + image_set if use_diff: name += '_diff' imdb.__init__(self, name) self._year = year self._image_set = image_set self._devkit_path = self._get_default_path() self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) self._classes = ('__background__', # always index 0 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) self._image_ext = '.jpg' self._image_index = self._load_image_set_index() # Default to roidb handler self._roidb_handler = self.gt_roidb self._salt = str(uuid.uuid4()) self._comp_id = 'comp4' # PASCAL specific config options self.config = {'cleanup': True, 'use_salt': True, 'use_diff': use_diff, 'matlab_eval': False, 'rpn_file': None} assert os.path.exists(self._devkit_path), \ 'VOCdevkit path does not exist: {}'.format(self._devkit_path) assert os.path.exists(self._data_path), \ 'Path does not exist: {}'.format(self._data_path) def image_path_at(self, i): """ Return the absolute path to image i in the image sequence. """ return self.image_path_from_index(self._image_index[i]) def image_path_from_index(self, index): """ Construct an image path from the image's "index" identifier. """ image_path = os.path.join(self._data_path, 'JPEGImages', index + self._image_ext) assert os.path.exists(image_path), \ 'Path does not exist: {}'.format(image_path) return image_path def _load_image_set_index(self): """ Load the indexes listed in this dataset's image set file. """ # Example path to image set file: # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', self._image_set + '.txt') assert os.path.exists(image_set_file), \ 'Path does not exist: {}'.format(image_set_file) with open(image_set_file) as f: image_index = [x.strip() for x in f.readlines()] return image_index def _get_default_path(self): """ Return the default path where PASCAL VOC is expected to be installed. """ return os.path.join(cfg.DATA_DIR, 'voc', 'VOCdevkit') def gt_roidb(self): """ Return the database of ground-truth regions of interest. This function loads/saves from/to a cache file to speed up future calls. """ cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') if os.path.exists(cache_file): with open(cache_file, 'rb') as fid: try: roidb = pickle.load(fid) except: roidb = pickle.load(fid, encoding='bytes') print('{} gt roidb loaded from {}'.format(self.name, cache_file)) return roidb gt_roidb = [self._load_pascal_annotation(index) for index in self.image_index] with open(cache_file, 'wb') as fid: pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) print('wrote gt roidb to {}'.format(cache_file)) return gt_roidb def rpn_roidb(self): if int(self._year) == 2007 or self._image_set != 'test': gt_roidb = self.gt_roidb() rpn_roidb = self._load_rpn_roidb(gt_roidb) roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) else: roidb = self._load_rpn_roidb(None) return roidb def _load_rpn_roidb(self, gt_roidb): filename = self.config['rpn_file'] print('loading {}'.format(filename)) assert os.path.exists(filename), \ 'rpn data not found at: {}'.format(filename) with open(filename, 'rb') as f: box_list = pickle.load(f) return self.create_roidb_from_box_list(box_list, gt_roidb) def _load_pascal_annotation(self, index): """ Load image and bounding boxes info from XML file in the PASCAL VOC format. """ filename = os.path.join(self._data_path, 'Annotations', index + '.xml') tree = ET.parse(filename) objs = tree.findall('object') if not self.config['use_diff']: # Exclude the samples labeled as difficult non_diff_objs = [ obj for obj in objs if int(obj.find('difficult').text) == 0] # if len(non_diff_objs) != len(objs): # print 'Removed {} difficult objects'.format( # len(objs) - len(non_diff_objs)) objs = non_diff_objs num_objs = len(objs) boxes = np.zeros((num_objs, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) # "Seg" area for pascal is just the box area seg_areas = np.zeros((num_objs), dtype=np.float32) # Load object bounding boxes into a data frame. for ix, obj in enumerate(objs): bbox = obj.find('bndbox') # Make pixel indexes 0-based x1 = float(bbox.find('xmin').text) - 1 y1 = float(bbox.find('ymin').text) - 1 x2 = float(bbox.find('xmax').text) - 1 y2 = float(bbox.find('ymax').text) - 1 cls = self._class_to_ind[obj.find('name').text.lower().strip()] boxes[ix, :] = [x1, y1, x2, y2] gt_classes[ix] = cls overlaps[ix, cls] = 1.0 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) overlaps = scipy.sparse.csr_matrix(overlaps) return {'boxes': boxes, 'gt_classes': gt_classes, 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': seg_areas} def _get_comp_id(self): comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] else self._comp_id) return comp_id def _get_voc_results_file_template(self): # VOCdevkit/results/VOC2007/Main/_det_test_aeroplane.txt filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt' path = os.path.join( self._devkit_path, 'results', 'VOC' + self._year, 'Main', filename) return path def _write_voc_results_file(self, all_boxes): for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue # print('Writing {} VOC results file'.format(cls)) filename = self._get_voc_results_file_template().format(cls) # print(filename) with open(filename, 'wt') as f: for im_ind, index in enumerate(self.image_index): dets = np.array(all_boxes[cls_ind][im_ind]) if len(dets) == 0: continue # the VOCdevkit expects 1-based indices for k in range(dets.shape[0]): f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. format(index, dets[k, -1], dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1)) def _do_python_eval(self, output_dir=None): annopath = os.path.join( self._devkit_path, 'VOC' + self._year, 'Annotations', '{:s}.xml') imagesetfile = os.path.join( self._devkit_path, 'VOC' + self._year, 'ImageSets', 'Main', self._image_set + '.txt') cachedir = os.path.join(self._devkit_path, 'annotations_cache') aps = [] # The PASCAL VOC metric changed in 2010 use_07_metric = True if int(self._year) < 2010 else False print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No')) if output_dir is not None and not os.path.isdir(output_dir): os.mkdir(output_dir) for i, cls in enumerate(self._classes): if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) rec, prec, ap = voc_eval( filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric, use_diff=self.config['use_diff']) aps += [ap] print(('AP for {} = {:.4f}'.format(cls, ap))) if output_dir is not None: with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f: pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f) print(('Mean AP = {:.4f}'.format(np.mean(aps)))) print('~~~~~~~~') ''' print('Results:') for ap in aps: print(('{:.3f}'.format(ap))) print(('{:.3f}'.format(np.mean(aps)))) print('~~~~~~~~') print('') print('--------------------------------------------------------------') print('Results computed with the **unofficial** Python eval code.') print('Results should be very close to the official MATLAB eval code.') print('Recompute with `./tools/reval.py --matlab ...` for your paper.') print('-- Thanks, The Management') print('--------------------------------------------------------------') ''' def _do_matlab_eval(self, output_dir='output'): print('-----------------------------------------------------') print('Computing results with the official MATLAB eval code.') print('-----------------------------------------------------') path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', 'VOCdevkit-matlab-wrapper') cmd = 'cd {} && '.format(path) cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) cmd += '-r "dbstop if error; ' cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ .format(self._devkit_path, self._get_comp_id(), self._image_set, output_dir) print(('Running:\n{}'.format(cmd))) status = subprocess.call(cmd, shell=True) def evaluate_detections(self, all_boxes, output_dir=None): self._write_voc_results_file(all_boxes) self._do_python_eval(output_dir) if self.config['matlab_eval']: self._do_matlab_eval(output_dir) if self.config['cleanup']: for cls in self._classes: if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) os.remove(filename) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': from datasets.pascal_voc import pascal_voc d = pascal_voc('trainval', '2007') res = d.roidb from IPython import embed; embed() slamd202303071458299/src/tools/voc_eval_lib/datasets/voc_eval.py000066400000000000000000000152221435750645400241370ustar00rootroot00000000000000# -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Bharath Hariharan # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import xml.etree.ElementTree as ET import os import pickle import numpy as np def parse_rec(filename): """ Parse a PASCAL VOC xml file """ tree = ET.parse(filename) objects = [] for obj in tree.findall('object'): obj_struct = {} obj_struct['name'] = obj.find('name').text obj_struct['pose'] = obj.find('pose').text obj_struct['truncated'] = int(obj.find('truncated').text) obj_struct['difficult'] = int(obj.find('difficult').text) bbox = obj.find('bndbox') obj_struct['bbox'] = [int(bbox.find('xmin').text), int(bbox.find('ymin').text), int(bbox.find('xmax').text), int(bbox.find('ymax').text)] objects.append(obj_struct) return objects def voc_ap(rec, prec, use_07_metric=False): """ ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point method (default:False). """ if use_07_metric: # 11 point metric ap = 0. for t in np.arange(0., 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) # print(t, p) ap = ap + p / 11. else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5, use_07_metric=False, use_diff=False): """rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname, [ovthresh], [use_07_metric]) Top level function that does the PASCAL VOC evaluation. detpath: Path to detections detpath.format(classname) should produce the detection results file. annopath: Path to annotations annopath.format(imagename) should be the xml annotations file. imagesetfile: Text file containing the list of images, one image per line. classname: Category name (duh) cachedir: Directory for caching the annotations [ovthresh]: Overlap threshold (default = 0.5) [use_07_metric]: Whether to use VOC07's 11 point AP computation (default False) """ # assumes detections are in detpath.format(classname) # assumes annotations are in annopath.format(imagename) # assumes imagesetfile is a text file with each line an image name # cachedir caches the annotations in a pickle file # first load gt if not os.path.isdir(cachedir): os.mkdir(cachedir) cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) # read list of images with open(imagesetfile, 'r') as f: lines = f.readlines() imagenames = [x.strip() for x in lines] if not os.path.isfile(cachefile): # load annotations recs = {} for i, imagename in enumerate(imagenames): recs[imagename] = parse_rec(annopath.format(imagename)) if i % 100 == 0: print('Reading annotation for {:d}/{:d}'.format( i + 1, len(imagenames))) # save print('Saving cached annotations to {:s}'.format(cachefile)) with open(cachefile, 'wb') as f: pickle.dump(recs, f) else: # load with open(cachefile, 'rb') as f: try: recs = pickle.load(f) except: recs = pickle.load(f, encoding='bytes') # extract gt objects for this class class_recs = {} npos = 0 for imagename in imagenames: R = [obj for obj in recs[imagename] if obj['name'] == classname] bbox = np.array([x['bbox'] for x in R]) if use_diff: difficult = np.array([False for x in R]).astype(np.bool) else: difficult = np.array([x['difficult'] for x in R]).astype(np.bool) det = [False] * len(R) npos = npos + sum(~difficult) class_recs[imagename] = {'bbox': bbox, 'difficult': difficult, 'det': det} # read dets detfile = detpath.format(classname) with open(detfile, 'r') as f: lines = f.readlines() splitlines = [x.strip().split(' ') for x in lines] image_ids = [x[0] for x in splitlines] confidence = np.array([float(x[1]) for x in splitlines]) BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) nd = len(image_ids) tp = np.zeros(nd) fp = np.zeros(nd) if BB.shape[0] > 0: # sort by confidence sorted_ind = np.argsort(-confidence) sorted_scores = np.sort(-confidence) BB = BB[sorted_ind, :] image_ids = [image_ids[x] for x in sorted_ind] # go down dets and mark TPs and FPs for d in range(nd): R = class_recs[image_ids[d]] bb = BB[d, :].astype(float) ovmax = -np.inf BBGT = R['bbox'].astype(float) if BBGT.size > 0: # compute overlaps # intersection ixmin = np.maximum(BBGT[:, 0], bb[0]) iymin = np.maximum(BBGT[:, 1], bb[1]) ixmax = np.minimum(BBGT[:, 2], bb[2]) iymax = np.minimum(BBGT[:, 3], bb[3]) iw = np.maximum(ixmax - ixmin + 1., 0.) ih = np.maximum(iymax - iymin + 1., 0.) inters = iw * ih # union uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + (BBGT[:, 2] - BBGT[:, 0] + 1.) * (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) overlaps = inters / uni ovmax = np.max(overlaps) jmax = np.argmax(overlaps) if ovmax > ovthresh: if not R['difficult'][jmax]: if not R['det'][jmax]: tp[d] = 1. R['det'][jmax] = 1 else: fp[d] = 1. else: fp[d] = 1. # compute precision recall fp = np.cumsum(fp) tp = np.cumsum(tp) rec = tp / float(npos) # avoid divide by zero in case the first detection matches a difficult # ground truth prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) ap = voc_ap(rec, prec, use_07_metric) return rec, prec, ap slamd202303071458299/src/tools/voc_eval_lib/model/000077500000000000000000000000001435750645400212555ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/model/__init__.py000066400000000000000000000000001435750645400233540ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/model/bbox_transform.py000066400000000000000000000047651435750645400246700ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np def bbox_transform(ex_rois, gt_rois): ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights targets_dw = np.log(gt_widths / ex_widths) targets_dh = np.log(gt_heights / ex_heights) targets = np.vstack( (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() return targets def bbox_transform_inv(boxes, deltas): if boxes.shape[0] == 0: return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype) boxes = boxes.astype(deltas.dtype, copy=False) widths = boxes[:, 2] - boxes[:, 0] + 1.0 heights = boxes[:, 3] - boxes[:, 1] + 1.0 ctr_x = boxes[:, 0] + 0.5 * widths ctr_y = boxes[:, 1] + 0.5 * heights dx = deltas[:, 0::4] dy = deltas[:, 1::4] dw = deltas[:, 2::4] dh = deltas[:, 3::4] pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] pred_w = np.exp(dw) * widths[:, np.newaxis] pred_h = np.exp(dh) * heights[:, np.newaxis] pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype) # x1 pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # y1 pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # x2 pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # y2 pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h return pred_boxes def clip_boxes(boxes, im_shape): """ Clip boxes to image boundaries. """ # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) # x2 < im_shape[1] boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) # y2 < im_shape[0] boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) return boxes slamd202303071458299/src/tools/voc_eval_lib/model/config.py000066400000000000000000000254021435750645400230770ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import numpy as np # `pip install easydict` if you don't have it from easydict import EasyDict as edict __C = edict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C # # Training options # __C.TRAIN = edict() # Initial learning rate __C.TRAIN.LEARNING_RATE = 0.001 # Momentum __C.TRAIN.MOMENTUM = 0.9 # Weight decay, for regularization __C.TRAIN.WEIGHT_DECAY = 0.0001 # Factor for reducing the learning rate __C.TRAIN.GAMMA = 0.1 # Step size for reducing the learning rate, currently only support one step __C.TRAIN.STEPSIZE = [30000] # Iteration intervals for showing the loss during training, on command line interface __C.TRAIN.DISPLAY = 10 # Whether to double the learning rate for bias __C.TRAIN.DOUBLE_BIAS = True # Whether to initialize the weights with truncated normal distribution __C.TRAIN.TRUNCATED = False # Whether to have weight decay on bias as well __C.TRAIN.BIAS_DECAY = False # Whether to add ground truth boxes to the pool when sampling regions __C.TRAIN.USE_GT = False # Whether to use aspect-ratio grouping of training images, introduced merely for saving # GPU memory __C.TRAIN.ASPECT_GROUPING = False # The number of snapshots kept, older ones are deleted to save space __C.TRAIN.SNAPSHOT_KEPT = 3 # The time interval for saving tensorflow summaries __C.TRAIN.SUMMARY_INTERVAL = 180 # Scale to use during training (can list multiple scales) # The scale is the pixel size of an image's shortest side __C.TRAIN.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TRAIN.MAX_SIZE = 1000 # Images to use per minibatch __C.TRAIN.IMS_PER_BATCH = 1 # Minibatch size (number of regions of interest [ROIs]) __C.TRAIN.BATCH_SIZE = 128 # Fraction of minibatch that is labeled foreground (i.e. class > 0) __C.TRAIN.FG_FRACTION = 0.25 # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) __C.TRAIN.FG_THRESH = 0.5 # Overlap threshold for a ROI to be considered background (class = 0 if # overlap in [LO, HI)) __C.TRAIN.BG_THRESH_HI = 0.5 __C.TRAIN.BG_THRESH_LO = 0.1 # Use horizontally-flipped images during training? __C.TRAIN.USE_FLIPPED = True # Train bounding-box regressors __C.TRAIN.BBOX_REG = True # Overlap required between a ROI and ground-truth box in order for that ROI to # be used as a bounding-box regression training example __C.TRAIN.BBOX_THRESH = 0.5 # Iterations between snapshots __C.TRAIN.SNAPSHOT_ITERS = 5000 # solver.prototxt specifies the snapshot path prefix, this adds an optional # infix to yield the path: [_]_iters_XYZ.caffemodel __C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn' # Normalize the targets (subtract empirical mean, divide by empirical stddev) __C.TRAIN.BBOX_NORMALIZE_TARGETS = True # Deprecated (inside weights) __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Normalize the targets using "precomputed" (or made up) means and stdevs # (BBOX_NORMALIZE_TARGETS must also be True) __C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True __C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0) __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) # Train using these proposals __C.TRAIN.PROPOSAL_METHOD = 'gt' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation # on zero-padding. # Use RPN to detect objects __C.TRAIN.HAS_RPN = True # IOU >= thresh: positive example __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 # IOU < thresh: negative example __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 # If an anchor satisfied by positive and negative conditions set to negative __C.TRAIN.RPN_CLOBBER_POSITIVES = False # Max number of foreground examples __C.TRAIN.RPN_FG_FRACTION = 0.5 # Total number of examples __C.TRAIN.RPN_BATCHSIZE = 256 # NMS threshold used on RPN proposals __C.TRAIN.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 # Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TRAIN.RPN_POST_NMS_TOP_N = 2000 # Deprecated (outside weights) __C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Give the positive RPN examples weight of p * 1 / {num positives} # and give negatives a weight of (1 - p) # Set to -1.0 to use uniform example weighting __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 # Whether to use all ground truth bounding boxes for training, # For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd'' __C.TRAIN.USE_ALL_GT = True # # Testing options # __C.TEST = edict() # Scale to use during testing (can NOT list multiple scales) # The scale is the pixel size of an image's shortest side __C.TEST.SCALES = (600,) # Max pixel size of the longest side of a scaled input image __C.TEST.MAX_SIZE = 1000 # Overlap threshold used for non-maximum suppression (suppress boxes with # IoU >= this threshold) __C.TEST.NMS = 0.3 # Experimental: treat the (K+1) units in the cls_score layer as linear # predictors (trained, eg, with one-vs-rest SVMs). __C.TEST.SVM = False # Test using bounding-box regressors __C.TEST.BBOX_REG = True # Propose boxes __C.TEST.HAS_RPN = False # Test using these proposals __C.TEST.PROPOSAL_METHOD = 'gt' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TEST.RPN_PRE_NMS_TOP_N = 6000 # Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TEST.RPN_POST_NMS_TOP_N = 300 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) # __C.TEST.RPN_MIN_SIZE = 16 # Testing mode, default to be 'nms', 'top' is slower but better # See report for details __C.TEST.MODE = 'nms' # Only useful when TEST.MODE is 'top', specifies the number of top proposals to select __C.TEST.RPN_TOP_N = 5000 # # ResNet options # __C.RESNET = edict() # Option to set if max-pooling is appended after crop_and_resize. # if true, the region will be resized to a square of 2xPOOLING_SIZE, # then 2x2 max-pooling is applied; otherwise the region will be directly # resized to a square of POOLING_SIZE __C.RESNET.MAX_POOL = False # Number of fixed blocks during training, by default the first of all 4 blocks is fixed # Range: 0 (none) to 3 (all) __C.RESNET.FIXED_BLOCKS = 1 # # MobileNet options # __C.MOBILENET = edict() # Whether to regularize the depth-wise filters during training __C.MOBILENET.REGU_DEPTH = False # Number of fixed layers during training, by default the bottom 5 of 14 layers is fixed # Range: 0 (none) to 12 (all) __C.MOBILENET.FIXED_LAYERS = 5 # Weight decay for the mobilenet weights __C.MOBILENET.WEIGHT_DECAY = 0.00004 # Depth multiplier __C.MOBILENET.DEPTH_MULTIPLIER = 1. # # MISC # # Pixel mean values (BGR order) as a (1, 1, 3) array # We use the same pixel mean for all networks even though it's not exactly what # they were trained with __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # For reproducibility __C.RNG_SEED = 3 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..', '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # Name (or path to) the matlab executable __C.MATLAB = 'matlab' # Place outputs under an experiments directory __C.EXP_DIR = 'default' # Use GPU implementation of non-maximum suppression __C.USE_GPU_NMS = True # Use an end-to-end tensorflow model. # Note: models in E2E tensorflow mode have only been tested in feed-forward mode, # but these models are exportable to other tensorflow instances as GraphDef files. __C.USE_E2E_TF = True # Default pooling mode, only 'crop' is available __C.POOLING_MODE = 'crop' # Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 # Anchor scales for RPN __C.ANCHOR_SCALES = [8,16,32] # Anchor ratios for RPN __C.ANCHOR_RATIOS = [0.5,1,2] # Number of filters for the RPN layer __C.RPN_CHANNELS = 512 def get_output_dir(imdb, weights_filename): """Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def get_output_tb_dir(imdb, weights_filename): """Return the directory where tensorflow summaries are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def _merge_a_into_b(a, b): """Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. """ if type(a) is not edict: return for k, v in a.items(): # a must specify keys that are in b if k not in b: raise KeyError('{} is not a valid config key'.format(k)) # the types must match, too old_type = type(b[k]) if old_type is not type(v): if isinstance(b[k], np.ndarray): v = np.array(v, dtype=b[k].dtype) else: raise ValueError(('Type mismatch ({} vs. {}) ' 'for config key: {}').format(type(b[k]), type(v), k)) # recursively merge dicts if type(v) is edict: try: _merge_a_into_b(a[k], b[k]) except: print(('Error under config key: {}'.format(k))) raise else: b[k] = v def cfg_from_file(filename): """Load a config file and merge it into the default options.""" import yaml with open(filename, 'r') as f: yaml_cfg = edict(yaml.load(f)) _merge_a_into_b(yaml_cfg, __C) def cfg_from_list(cfg_list): """Set config keys via list (e.g., from command line).""" from ast import literal_eval assert len(cfg_list) % 2 == 0 for k, v in zip(cfg_list[0::2], cfg_list[1::2]): key_list = k.split('.') d = __C for subkey in key_list[:-1]: assert subkey in d d = d[subkey] subkey = key_list[-1] assert subkey in d try: value = literal_eval(v) except: # handle the case when v is a string literal value = v assert type(value) == type(d[subkey]), \ 'type {} does not match original type {}'.format( type(value), type(d[subkey])) d[subkey] = value slamd202303071458299/src/tools/voc_eval_lib/model/nms_wrapper.py000066400000000000000000000013161435750645400241650ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from model.config import cfg from nms.gpu_nms import gpu_nms from nms.cpu_nms import cpu_nms def nms(dets, thresh, force_cpu=False): """Dispatch to either CPU or GPU NMS implementations.""" if dets.shape[0] == 0: return [] if cfg.USE_GPU_NMS and not force_cpu: return gpu_nms(dets, thresh, device_id=0) else: return cpu_nms(dets, thresh) slamd202303071458299/src/tools/voc_eval_lib/model/test.py000066400000000000000000000143761435750645400226210ustar00rootroot00000000000000# -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np try: import cPickle as pickle except ImportError: import pickle import os import math from utils.timer import Timer from utils.blob import im_list_to_blob from model.config import cfg, get_output_dir from model.bbox_transform import clip_boxes, bbox_transform_inv # from model.nms_wrapper import nms # need to compile cython nms before import nms nms = None # not needed in pascal evaluation def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors) def _get_blobs(im): """Convert an image and RoIs within that image into network inputs.""" blobs = {} blobs['data'], im_scale_factors = _get_image_blob(im) return blobs, im_scale_factors def _clip_boxes(boxes, im_shape): """Clip boxes to image boundaries.""" # x1 >= 0 boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0) # x2 < im_shape[1] boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1) # y2 < im_shape[0] boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1) return boxes def _rescale_boxes(boxes, inds, scales): """Rescale boxes according to image rescaling.""" for i in range(boxes.shape[0]): boxes[i,:] = boxes[i,:] / scales[int(inds[i])] return boxes def im_detect(sess, net, im): blobs, im_scales = _get_blobs(im) assert len(im_scales) == 1, "Only single-image batch implemented" im_blob = blobs['data'] blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32) _, scores, bbox_pred, rois = net.test_image(sess, blobs['data'], blobs['im_info']) boxes = rois[:, 1:5] / im_scales[0] scores = np.reshape(scores, [scores.shape[0], -1]) bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1]) if cfg.TEST.BBOX_REG: # Apply bounding-box regression deltas box_deltas = bbox_pred pred_boxes = bbox_transform_inv(boxes, box_deltas) pred_boxes = _clip_boxes(pred_boxes, im.shape) else: # Simply repeat the boxes, once for each class pred_boxes = np.tile(boxes, (1, scores.shape[1])) return scores, pred_boxes def apply_nms(all_boxes, thresh): """Apply non-maximum suppression to all predicted boxes output by the test_net method. """ num_classes = len(all_boxes) num_images = len(all_boxes[0]) nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)] for cls_ind in range(num_classes): for im_ind in range(num_images): dets = np.array(all_boxes[cls_ind][im_ind], dtype=np.float32) if len(dets) == 0: continue #print('dets', dets) x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] inds = np.where((x2 > x1) & (y2 > y1))[0] dets = dets[inds,:] if dets == []: continue keep = nms(dets, thresh) if len(keep) == 0: continue nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() return nms_boxes def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.): np.random.seed(cfg.RNG_SEED) """Test a Fast R-CNN network on an image database.""" num_images = len(imdb.image_index) # all detections are collected into: # all_boxes[cls][image] = N x 5 array of detections in # (x1, y1, x2, y2, score) all_boxes = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)] output_dir = get_output_dir(imdb, weights_filename) # timers _t = {'im_detect' : Timer(), 'misc' : Timer()} for i in range(num_images): im = cv2.imread(imdb.image_path_at(i)) _t['im_detect'].tic() scores, boxes = im_detect(sess, net, im) _t['im_detect'].toc() _t['misc'].tic() # skip j = 0, because it's the background class for j in range(1, imdb.num_classes): inds = np.where(scores[:, j] > thresh)[0] cls_scores = scores[inds, j] cls_boxes = boxes[inds, j*4:(j+1)*4] cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \ .astype(np.float32, copy=False) keep = nms(cls_dets, cfg.TEST.NMS) cls_dets = cls_dets[keep, :] all_boxes[j][i] = cls_dets # Limit to max_per_image detections *over all classes* if max_per_image > 0: image_scores = np.hstack([all_boxes[j][i][:, -1] for j in range(1, imdb.num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in range(1, imdb.num_classes): keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] all_boxes[j][i] = all_boxes[j][i][keep, :] _t['misc'].toc() print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \ .format(i + 1, num_images, _t['im_detect'].average_time, _t['misc'].average_time)) det_file = os.path.join(output_dir, 'detections.pkl') with open(det_file, 'wb') as f: pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir) slamd202303071458299/src/tools/voc_eval_lib/nms/000077500000000000000000000000001435750645400207525ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/nms/.gitignore000066400000000000000000000000001435750645400227300ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/nms/__init__.py000066400000000000000000000000001435750645400230510ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/nms/cpu_nms.c000066400000000000000000010726111435750645400225720ustar00rootroot00000000000000/* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #define CYTHON_ABI "0_20_1" #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY #define Py_OptimizeFlag 0 #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; 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if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ const char* function_name); /*proto*/ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact); /*proto*/ static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/ #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/ static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); #if CYTHON_CCOMPLEX #define __Pyx_c_eqf(a, b) ((a)==(b)) #define __Pyx_c_sumf(a, b) ((a)+(b)) #define __Pyx_c_difff(a, b) ((a)-(b)) #define __Pyx_c_prodf(a, b) ((a)*(b)) #define __Pyx_c_quotf(a, b) ((a)/(b)) #define __Pyx_c_negf(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zerof(z) ((z)==(float)0) #define __Pyx_c_conjf(z) (::std::conj(z)) #if 1 #define __Pyx_c_absf(z) (::std::abs(z)) #define __Pyx_c_powf(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zerof(z) ((z)==0) #define __Pyx_c_conjf(z) (conjf(z)) #if 1 #define __Pyx_c_absf(z) (cabsf(z)) #define __Pyx_c_powf(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); #if CYTHON_CCOMPLEX #define __Pyx_c_eq(a, b) ((a)==(b)) #define __Pyx_c_sum(a, b) ((a)+(b)) #define __Pyx_c_diff(a, b) ((a)-(b)) #define __Pyx_c_prod(a, b) ((a)*(b)) #define __Pyx_c_quot(a, b) ((a)/(b)) #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero(z) ((z)==(double)0) #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs(z) (::std::abs(z)) #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero(z) ((z)==0) #define __Pyx_c_conj(z) (conj(z)) #if 1 #define __Pyx_c_abs(z) (cabs(z)) #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); static int __Pyx_check_binary_version(void); #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #else #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #endif #endif static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ typedef struct { int code_line; PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); /*proto*/ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; 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static char __pyx_k_h[] = "h"; static char __pyx_k_i[] = "i"; static char __pyx_k_j[] = "_j"; static char __pyx_k_l[] = "l"; static char __pyx_k_q[] = "q"; static char __pyx_k_w[] = "w"; static char __pyx_k_Zd[] = "Zd"; static char __pyx_k_Zf[] = "Zf"; static char __pyx_k_Zg[] = "Zg"; static char __pyx_k_np[] = "np"; static char __pyx_k_x1[] = "x1"; static char __pyx_k_x2[] = "x2"; static char __pyx_k_y1[] = "y1"; static char __pyx_k_y2[] = "y2"; static char __pyx_k_i_2[] = "_i"; static char __pyx_k_int[] = "int"; static char __pyx_k_ix1[] = "ix1"; static char __pyx_k_ix2[] = "ix2"; static char __pyx_k_iy1[] = "iy1"; static char __pyx_k_iy2[] = "iy2"; static char __pyx_k_j_2[] = "j"; static char __pyx_k_ovr[] = "ovr"; static char __pyx_k_xx1[] = "xx1"; static char __pyx_k_xx2[] = "xx2"; static char __pyx_k_yy1[] = "yy1"; static char __pyx_k_yy2[] = "yy2"; static char __pyx_k_dets[] = "dets"; static char __pyx_k_keep[] = "keep"; static char __pyx_k_main[] = "__main__"; static char __pyx_k_test[] = "__test__"; 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#else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); } static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (none_allowed && obj == Py_None) return 1; else if (exact) { if (likely(Py_TYPE(obj) == type)) return 1; #if PY_MAJOR_VERSION == 2 else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(PyObject_TypeCheck(obj, type))) return 1; } __Pyx_RaiseArgumentTypeInvalid(name, obj, type); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) /* First char was not a digit */ PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; /* not a 'break' in the loop */ } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; #endif result = (*call)(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 Py_LeaveRecursiveCall(); #endif if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (result) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } #if PY_VERSION_HEX < 0x02050000 if (PyClass_Check(type)) { #else if (PyType_Check(type)) { #endif #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *getbuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); if (getbuffer_cobj) { getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } } #endif PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *releasebuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); if (releasebuffer_cobj) { releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ { \ func_type value = func(x); \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ PyErr_SetString(PyExc_OverflowError, \ (is_unsigned && unlikely(value < zero)) ? \ "can't convert negative value to " #target_type : \ "value too large to convert to " #target_type); \ return (target_type) -1; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else /* PY_VERSION_HEX < 0x03030000 */ if (PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_DATA_SIZE(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ return PyUnicode_AsUTF8AndSize(o, length); #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ #endif /* PY_VERSION_HEX < 0x03030000 */ } else #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ #if !CYTHON_COMPILING_IN_PYPY #if PY_VERSION_HEX >= 0x02060000 if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif #if PY_VERSION_HEX < 0x02060000 return PyInt_AsSsize_t(b); #else return PyLong_AsSsize_t(b); #endif } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } #endif /* Py_PYTHON_H */ slamd202303071458299/src/tools/voc_eval_lib/nms/cpu_nms.pyx000066400000000000000000000043011435750645400231560ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np cimport numpy as np cdef inline np.float32_t max(np.float32_t a, np.float32_t b): return a if a >= b else b cdef inline np.float32_t min(np.float32_t a, np.float32_t b): return a if a <= b else b def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] cdef int ndets = dets.shape[0] cdef np.ndarray[np.int_t, ndim=1] suppressed = \ np.zeros((ndets), dtype=np.int) # nominal indices cdef int _i, _j # sorted indices cdef int i, j # temp variables for box i's (the box currently under consideration) cdef np.float32_t ix1, iy1, ix2, iy2, iarea # variables for computing overlap with box j (lower scoring box) cdef np.float32_t xx1, yy1, xx2, yy2 cdef np.float32_t w, h cdef np.float32_t inter, ovr keep = [] for _i in range(ndets): i = order[_i] if suppressed[i] == 1: continue keep.append(i) ix1 = x1[i] iy1 = y1[i] ix2 = x2[i] iy2 = y2[i] iarea = areas[i] for _j in range(_i + 1, ndets): j = order[_j] if suppressed[j] == 1: continue xx1 = max(ix1, x1[j]) yy1 = max(iy1, y1[j]) xx2 = min(ix2, x2[j]) yy2 = min(iy2, y2[j]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (iarea + areas[j] - inter) if ovr >= thresh: suppressed[j] = 1 return keep slamd202303071458299/src/tools/voc_eval_lib/nms/gpu_nms.cpp000066400000000000000000010165351435750645400231410ustar00rootroot00000000000000/* Generated by Cython 0.20.1 on Wed Oct 5 13:15:30 2016 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #define CYTHON_ABI "0_20_1" #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY #define Py_OptimizeFlag 0 #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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(PySequence_GetSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) #endif #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) #else #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_NAMESTR(n) ((char *)(n)) #define __Pyx_DOCSTR(n) ((char *)(n)) #else #define __Pyx_NAMESTR(n) (n) #define __Pyx_DOCSTR(n) (n) #endif #ifndef CYTHON_INLINE #if defined(__GNUC__) #define CYTHON_INLINE __inline__ #elif defined(_MSC_VER) #define CYTHON_INLINE __inline #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_INLINE inline #else #define CYTHON_INLINE #endif #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is a quiet NaN. */ float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #define __PYX_HAVE__nms__gpu_nms #define __PYX_HAVE_API__nms__gpu_nms #include "string.h" #include "stdio.h" #include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "gpu_nms.hpp" #ifdef _OPENMP #include #endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ (sizeof(type) < sizeof(Py_ssize_t)) || \ (sizeof(type) > sizeof(Py_ssize_t) && \ likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX) && \ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ v == (type)PY_SSIZE_T_MIN))) || \ (sizeof(type) == sizeof(Py_ssize_t) && \ (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s) #define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) #define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((char*)s) #define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s) #define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s) #if PY_MAJOR_VERSION < 3 static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return u_end - u - 1; } #else #define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen #endif #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys = NULL; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; sys = PyImport_ImportModule("sys"); if (sys == NULL) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); if (default_encoding == NULL) goto bad; if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { const char* default_encoding_c = PyBytes_AS_STRING(default_encoding); char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (ascii_chars_u == NULL) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } } Py_XDECREF(sys); Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return 0; bad: Py_XDECREF(sys); Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys = NULL; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (sys == NULL) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); if (default_encoding == NULL) goto bad; default_encoding_c = PyBytes_AS_STRING(default_encoding); __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(sys); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(sys); Py_XDECREF(default_encoding); return -1; } #endif #endif #ifdef __GNUC__ /* Test for GCC > 2.95 */ #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* __GNUC__ > 2 ... */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ > 2 ... */ #else /* __GNUC__ */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "gpu_nms.pyx", "__init__.pxd", "type.pxd", }; #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; /* for error messages only */ struct __Pyx_StructField_* fields; size_t size; /* sizeof(type) */ size_t arraysize[8]; 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; 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/* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "/home/xinleic/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; 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This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; /* not a 'break' in the loop */ } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (result) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; #endif result = (*call)(func, arg, kw); #if PY_VERSION_HEX >= 0x02060000 Py_LeaveRecursiveCall(); #endif if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { #if CYTHON_COMPILING_IN_CPYTHON PyMappingMethods* mp; #if PY_MAJOR_VERSION < 3 PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; if (likely(ms && ms->sq_slice)) { if (!has_cstart) { if (_py_start && (*_py_start != Py_None)) { cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstart = 0; } if (!has_cstop) { if (_py_stop && (*_py_stop != Py_None)) { cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstop = PY_SSIZE_T_MAX; } if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { Py_ssize_t l = ms->sq_length(obj); if (likely(l >= 0)) { if (cstop < 0) { cstop += l; if (cstop < 0) cstop = 0; } if (cstart < 0) { cstart += l; if (cstart < 0) cstart = 0; } } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else goto bad; } } return ms->sq_slice(obj, cstart, cstop); } #endif mp = Py_TYPE(obj)->tp_as_mapping; if (likely(mp && mp->mp_subscript)) #endif { PyObject* result; PyObject *py_slice, *py_start, *py_stop; if (_py_slice) { py_slice = *_py_slice; } else { PyObject* owned_start = NULL; PyObject* owned_stop = NULL; if (_py_start) { py_start = *_py_start; } else { if (has_cstart) { owned_start = py_start = PyInt_FromSsize_t(cstart); if (unlikely(!py_start)) goto bad; } else py_start = Py_None; } if (_py_stop) { py_stop = *_py_stop; } else { if (has_cstop) { owned_stop = py_stop = PyInt_FromSsize_t(cstop); if (unlikely(!py_stop)) { Py_XDECREF(owned_start); goto bad; } } else py_stop = Py_None; } py_slice = PySlice_New(py_start, py_stop, Py_None); Py_XDECREF(owned_start); Py_XDECREF(owned_stop); if (unlikely(!py_slice)) goto bad; } #if CYTHON_COMPILING_IN_CPYTHON result = mp->mp_subscript(obj, py_slice); #else result = PyObject_GetItem(obj, py_slice); #endif if (!_py_slice) { Py_DECREF(py_slice); } return result; } PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); bad: return NULL; } static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } #if PY_VERSION_HEX < 0x02050000 if (PyClass_Check(type)) { #else if (PyType_Check(type)) { #endif #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *getbuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); if (getbuffer_cobj) { getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } } #endif PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict) { PyObject *releasebuffer_cobj = PyObject_GetItem( obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); if (releasebuffer_cobj) { releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ { \ func_type value = func(x); \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ PyErr_SetString(PyExc_OverflowError, \ (is_unsigned && unlikely(value < zero)) ? \ "can't convert negative value to " #target_type : \ "value too large to convert to " #target_type); \ return (target_type) -1; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) { const npy_int32 neg_one = (npy_int32) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(npy_int32) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_int32"); return (npy_int32) -1; } return (npy_int32) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(npy_int32)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (npy_int32) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to npy_int32"); return (npy_int32) -1; } if (sizeof(npy_int32) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(npy_int32) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(npy_int32)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(npy_int32) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(npy_int32) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(npy_int32) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long, PyLong_AsLong) } else if (sizeof(npy_int32) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(npy_int32, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else npy_int32 val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (npy_int32) -1; } } else { npy_int32 val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (npy_int32) -1; val = __Pyx_PyInt_As_npy_int32(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(int)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS if (sizeof(digit) <= sizeof(long)) { switch (Py_SIZE(x)) { case 0: return 0; case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; } } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else /* PY_VERSION_HEX < 0x03030000 */ if (PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_DATA_SIZE(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ return PyUnicode_AsUTF8AndSize(o, length); #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ #endif /* PY_VERSION_HEX < 0x03030000 */ } else #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ #if !CYTHON_COMPILING_IN_PYPY #if PY_VERSION_HEX >= 0x02060000 if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif #if PY_VERSION_HEX < 0x02060000 return PyInt_AsSsize_t(b); #else return PyLong_AsSsize_t(b); #endif } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } #endif /* Py_PYTHON_H */ slamd202303071458299/src/tools/voc_eval_lib/nms/gpu_nms.hpp000066400000000000000000000002221435750645400231270ustar00rootroot00000000000000void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, int boxes_dim, float nms_overlap_thresh, int device_id); slamd202303071458299/src/tools/voc_eval_lib/nms/gpu_nms.pyx000066400000000000000000000021261435750645400231650ustar00rootroot00000000000000# -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np cimport numpy as np assert sizeof(int) == sizeof(np.int32_t) cdef extern from "gpu_nms.hpp": void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, np.int32_t device_id=0): cdef int boxes_num = dets.shape[0] cdef int boxes_dim = dets.shape[1] cdef int num_out cdef np.ndarray[np.int32_t, ndim=1] \ keep = np.zeros(boxes_num, dtype=np.int32) cdef np.ndarray[np.float32_t, ndim=1] \ scores = dets[:, 4] cdef np.ndarray[np.int_t, ndim=1] \ order = scores.argsort()[::-1] cdef np.ndarray[np.float32_t, ndim=2] \ sorted_dets = dets[order, :] _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) keep = keep[:num_out] return list(order[keep]) slamd202303071458299/src/tools/voc_eval_lib/nms/nms_kernel.cu000066400000000000000000000117101435750645400234400ustar00rootroot00000000000000// ------------------------------------------------------------------ // Faster R-CNN // Copyright (c) 2015 Microsoft // Licensed under The MIT License [see fast-rcnn/LICENSE for details] // Written by Shaoqing Ren // ------------------------------------------------------------------ #include "gpu_nms.hpp" #include #include #define CUDA_CHECK(condition) \ /* Code block avoids redefinition of cudaError_t error */ \ do { \ cudaError_t error = condition; \ if (error != cudaSuccess) { \ std::cout << cudaGetErrorString(error) << std::endl; \ } \ } while (0) #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) int const threadsPerBlock = sizeof(unsigned long long) * 8; __device__ inline float devIoU(float const * const a, float const * const b) { float left = max(a[0], b[0]), right = min(a[2], b[2]); float top = max(a[1], b[1]), bottom = min(a[3], b[3]); float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); float interS = width * height; float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); return interS / (Sa + Sb - interS); } __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, const float *dev_boxes, unsigned long long *dev_mask) { const int row_start = blockIdx.y; const int col_start = blockIdx.x; // if (row_start > col_start) return; const int row_size = min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); const int col_size = min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); __shared__ float block_boxes[threadsPerBlock * 5]; if (threadIdx.x < col_size) { block_boxes[threadIdx.x * 5 + 0] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; block_boxes[threadIdx.x * 5 + 1] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; block_boxes[threadIdx.x * 5 + 2] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; block_boxes[threadIdx.x * 5 + 3] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; block_boxes[threadIdx.x * 5 + 4] = dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; } __syncthreads(); if (threadIdx.x < row_size) { const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; const float *cur_box = dev_boxes + cur_box_idx * 5; int i = 0; unsigned long long t = 0; int start = 0; if (row_start == col_start) { start = threadIdx.x + 1; } for (i = start; i < col_size; i++) { if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { t |= 1ULL << i; } } const int col_blocks = DIVUP(n_boxes, threadsPerBlock); dev_mask[cur_box_idx * col_blocks + col_start] = t; } } void _set_device(int device_id) { int current_device; CUDA_CHECK(cudaGetDevice(¤t_device)); if (current_device == device_id) { return; } // The call to cudaSetDevice must come before any calls to Get, which // may perform initialization using the GPU. CUDA_CHECK(cudaSetDevice(device_id)); } void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, int boxes_dim, float nms_overlap_thresh, int device_id) { _set_device(device_id); float* boxes_dev = NULL; unsigned long long* mask_dev = NULL; const int col_blocks = DIVUP(boxes_num, threadsPerBlock); CUDA_CHECK(cudaMalloc(&boxes_dev, boxes_num * boxes_dim * sizeof(float))); CUDA_CHECK(cudaMemcpy(boxes_dev, boxes_host, boxes_num * boxes_dim * sizeof(float), cudaMemcpyHostToDevice)); CUDA_CHECK(cudaMalloc(&mask_dev, boxes_num * col_blocks * sizeof(unsigned long long))); dim3 blocks(DIVUP(boxes_num, threadsPerBlock), DIVUP(boxes_num, threadsPerBlock)); dim3 threads(threadsPerBlock); nms_kernel<<>>(boxes_num, nms_overlap_thresh, boxes_dev, mask_dev); std::vector mask_host(boxes_num * col_blocks); CUDA_CHECK(cudaMemcpy(&mask_host[0], mask_dev, sizeof(unsigned long long) * boxes_num * col_blocks, cudaMemcpyDeviceToHost)); std::vector remv(col_blocks); memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); int num_to_keep = 0; for (int i = 0; i < boxes_num; i++) { int nblock = i / threadsPerBlock; int inblock = i % threadsPerBlock; if (!(remv[nblock] & (1ULL << inblock))) { keep_out[num_to_keep++] = i; unsigned long long *p = &mask_host[0] + i * col_blocks; for (int j = nblock; j < col_blocks; j++) { remv[j] |= p[j]; } } } *num_out = num_to_keep; CUDA_CHECK(cudaFree(boxes_dev)); CUDA_CHECK(cudaFree(mask_dev)); } slamd202303071458299/src/tools/voc_eval_lib/nms/py_cpu_nms.py000066400000000000000000000020331435750645400234760ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import numpy as np def py_cpu_nms(dets, thresh): """Pure Python NMS baseline.""" x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= thresh)[0] order = order[inds + 1] return keep slamd202303071458299/src/tools/voc_eval_lib/setup.py000066400000000000000000000124251435750645400216730ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import os from os.path import join as pjoin import numpy as np from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext def find_in_path(name, path): "Find a file in a search path" #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ for dir in path.split(os.pathsep): binpath = pjoin(dir, name) if os.path.exists(binpath): return os.path.abspath(binpath) return None def locate_cuda(): """Locate the CUDA environment on the system Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' and values giving the absolute path to each directory. Starts by looking for the CUDAHOME env variable. If not found, everything is based on finding 'nvcc' in the PATH. """ # first check if the CUDAHOME env variable is in use if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = pjoin(home, 'bin', 'nvcc') else: # otherwise, search the PATH for NVCC default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) if nvcc is None: raise EnvironmentError('The nvcc binary could not be ' 'located in your $PATH. Either add it to your path, or set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home':home, 'nvcc':nvcc, 'include': pjoin(home, 'include'), 'lib64': pjoin(home, 'lib64')} for k, v in cudaconfig.items(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) return cudaconfig CUDA = locate_cuda() # Obtain the numpy include directory. This logic works across numpy versions. try: numpy_include = np.get_include() except AttributeError: numpy_include = np.get_numpy_include() def customize_compiler_for_nvcc(self): """inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler, it's not trivial to get your subclass injected in, and still have the right customizations (i.e. distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route, I have this. Note, it's kindof like a wierd functional subclassing going on.""" # tell the compiler it can processes .cu self.src_extensions.append('.cu') # save references to the default compiler_so and _comple methods default_compiler_so = self.compiler_so super = self._compile # now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): print(extra_postargs) if os.path.splitext(src)[1] == '.cu': # use the cuda for .cu files self.set_executable('compiler_so', CUDA['nvcc']) # use only a subset of the extra_postargs, which are 1-1 translated # from the extra_compile_args in the Extension class postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['gcc'] super(obj, src, ext, cc_args, postargs, pp_opts) # reset the default compiler_so, which we might have changed for cuda self.compiler_so = default_compiler_so # inject our redefined _compile method into the class self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) ext_modules = [ Extension( "utils.cython_bbox", ["utils/bbox.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension( "nms.cpu_nms", ["nms/cpu_nms.pyx"], extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, include_dirs = [numpy_include] ), Extension('nms.gpu_nms', ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], library_dirs=[CUDA['lib64']], libraries=['cudart'], language='c++', runtime_library_dirs=[CUDA['lib64']], # this syntax is specific to this build system # we're only going to use certain compiler args with nvcc and not with gcc # the implementation of this trick is in customize_compiler() below extra_compile_args={'gcc': ["-Wno-unused-function"], 'nvcc': ['-arch=sm_61', '--ptxas-options=-v', '-c', '--compiler-options', "'-fPIC'"]}, include_dirs = [numpy_include, CUDA['include']] ) ] setup( name='tf_faster_rcnn', ext_modules=ext_modules, # inject our custom trigger cmdclass={'build_ext': custom_build_ext}, ) slamd202303071458299/src/tools/voc_eval_lib/utils/000077500000000000000000000000001435750645400213155ustar00rootroot00000000000000slamd202303071458299/src/tools/voc_eval_lib/utils/.gitignore000066400000000000000000000000241435750645400233010ustar00rootroot00000000000000*.c *.cpp *.h *.hpp slamd202303071458299/src/tools/voc_eval_lib/utils/__init__.py000066400000000000000000000003701435750645400234260ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- slamd202303071458299/src/tools/voc_eval_lib/utils/bbox.pyx000066400000000000000000000033351435750645400230150ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Sergey Karayev # -------------------------------------------------------- cimport cython import numpy as np cimport numpy as np DTYPE = np.float ctypedef np.float_t DTYPE_t def bbox_overlaps( np.ndarray[DTYPE_t, ndim=2] boxes, np.ndarray[DTYPE_t, ndim=2] query_boxes): """ Parameters ---------- boxes: (N, 4) ndarray of float query_boxes: (K, 4) ndarray of float Returns ------- overlaps: (N, K) ndarray of overlap between boxes and query_boxes """ cdef unsigned int N = boxes.shape[0] cdef unsigned int K = query_boxes.shape[0] cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) cdef DTYPE_t iw, ih, box_area cdef DTYPE_t ua cdef unsigned int k, n for k in range(K): box_area = ( (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1) ) for n in range(N): iw = ( min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1 ) if iw > 0: ih = ( min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1 ) if ih > 0: ua = float( (boxes[n, 2] - boxes[n, 0] + 1) * (boxes[n, 3] - boxes[n, 1] + 1) + box_area - iw * ih ) overlaps[n, k] = iw * ih / ua return overlaps slamd202303071458299/src/tools/voc_eval_lib/utils/blob.py000066400000000000000000000027401435750645400226100ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Blob helper functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 def im_list_to_blob(ims): """Convert a list of images into a network input. Assumes images are already prepared (means subtracted, BGR order, ...). """ max_shape = np.array([im.shape for im in ims]).max(axis=0) num_images = len(ims) blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), dtype=np.float32) for i in range(num_images): im = ims[i] blob[i, 0:im.shape[0], 0:im.shape[1], :] = im return blob def prep_im_for_blob(im, pixel_means, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im -= pixel_means im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale slamd202303071458299/src/tools/voc_eval_lib/utils/timer.py000066400000000000000000000016641435750645400230160ustar00rootroot00000000000000# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- import time class Timer(object): """A simple timer.""" def __init__(self): self.total_time = 0. self.calls = 0 self.start_time = 0. self.diff = 0. self.average_time = 0. def tic(self): # using time.time instead of time.clock because time time.clock # does not normalize for multithreading self.start_time = time.time() def toc(self, average=True): self.diff = time.time() - self.start_time self.total_time += self.diff self.calls += 1 self.average_time = self.total_time / self.calls if average: return self.average_time else: return self.diff slamd202303071458299/src/tools/voc_eval_lib/utils/visualization.py000066400000000000000000000076601435750645400246010ustar00rootroot00000000000000# -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import range import PIL.Image as Image import PIL.ImageColor as ImageColor import PIL.ImageDraw as ImageDraw import PIL.ImageFont as ImageFont STANDARD_COLORS = [ 'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque', 'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite', 'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan', 'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange', 'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet', 'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite', 'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod', 'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki', 'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue', 'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey', 'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue', 'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime', 'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid', 'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen', 'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin', 'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed', 'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed', 'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple', 'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown', 'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue', 'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow', 'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White', 'WhiteSmoke', 'Yellow', 'YellowGreen' ] NUM_COLORS = len(STANDARD_COLORS) try: FONT = ImageFont.truetype('arial.ttf', 24) except IOError: FONT = ImageFont.load_default() def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4): draw = ImageDraw.Draw(image) (left, right, top, bottom) = (xmin, xmax, ymin, ymax) draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=thickness, fill=color) text_bottom = bottom # Reverse list and print from bottom to top. text_width, text_height = font.getsize(display_str) margin = np.ceil(0.05 * text_height) draw.rectangle( [(left, text_bottom - text_height - 2 * margin), (left + text_width, text_bottom)], fill=color) draw.text( (left + margin, text_bottom - text_height - margin), display_str, fill='black', font=font) return image def draw_bounding_boxes(image, gt_boxes, im_info): num_boxes = gt_boxes.shape[0] gt_boxes_new = gt_boxes.copy() gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2]) disp_image = Image.fromarray(np.uint8(image[0])) for i in range(num_boxes): this_class = int(gt_boxes_new[i, 4]) disp_image = _draw_single_box(disp_image, gt_boxes_new[i, 0], gt_boxes_new[i, 1], gt_boxes_new[i, 2], gt_boxes_new[i, 3], 'N%02d-C%02d' % (i, this_class), FONT, color=STANDARD_COLORS[this_class % NUM_COLORS]) image[0, :] = np.array(disp_image) return image slamd202303071458299/tags000066400000000000000000012125061435750645400144720ustar00rootroot00000000000000!_TAG_FILE_FORMAT 2 /extended format; --format=1 will not append ;" to lines/ !_TAG_FILE_SORTED 1 /0=unsorted, 1=sorted, 2=foldcase/ !_TAG_PROGRAM_AUTHOR Darren Hiebert /dhiebert@users.sourceforge.net/ !_TAG_PROGRAM_NAME Exuberant Ctags // !_TAG_PROGRAM_URL http://ctags.sourceforge.net /official site/ !_TAG_PROGRAM_VERSION 5.8 // ANNOT_PATH src/tools/merge_pascal_json.py /^ANNOT_PATH = 'voc\/annotations\/'$/;" v ANN_FILES src/tools/calc_coco_overlap.py /^ANN_FILES = {'train': 'instances_train2017.json',$/;" v ANN_PATH src/tools/calc_coco_overlap.py /^ANN_PATH = '..\/..\/data\/coco\/annotations\/'$/;" v ANN_PATH src/tools/eval_coco.py /^ANN_PATH = this_dir + '..\/..\/data\/coco\/annotations\/instances_val2017.json'$/;" v ANN_PATH src/tools/eval_coco_hp.py /^ANN_PATH = this_dir + '..\/..\/data\/coco\/annotations\/person_keypoints_val2017.json'$/;" v ANN_PATH src/tools/vis_pred.py /^ANN_PATH = '..\/..\/data\/coco\/annotations\/instances_val2017.json'$/;" v AdaptiveWingLoss src/lib/models/losses.py /^class AdaptiveWingLoss(nn.Module):$/;" c AverageMeter src/evaluate.py /^from utils.utils import AverageMeter$/;" i AverageMeter src/lib/trains/base_trainer.py /^from utils.utils import AverageMeter$/;" i AverageMeter src/lib/utils/utils.py /^class AverageMeter(object):$/;" c AverageMeter src/test.py /^from utils.utils import AverageMeter$/;" i BATCH_SIZE src/lib/models/Rotated_IoU/demo.py /^BATCH_SIZE = 32$/;" v BN_MOMENTUM src/lib/models/networks/msra_resnet.py /^BN_MOMENTUM = 0.1$/;" v BN_MOMENTUM src/lib/models/networks/pose_dla_dcn.py /^BN_MOMENTUM = 0.1$/;" v BN_MOMENTUM src/lib/models/networks/resnet_dcn.py /^BN_MOMENTUM = 0.1$/;" v BOX3D src/tools/kitti_eval/evaluate_object_3d.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: BOX3D src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: Bar src/evaluate.py /^from progress.bar import Bar$/;" i Bar src/lib/detectors/base_detector.py /^from progress.bar import Bar$/;" i Bar src/lib/detectors/eldet.py /^from progress.bar import Bar$/;" i Bar src/lib/detectors/eldet_bbox.py /^from progress.bar import Bar$/;" i Bar src/lib/trains/base_trainer.py /^from progress.bar import Bar$/;" i Bar src/test.py /^from progress.bar import Bar$/;" i BaseDetector src/lib/detectors/base_detector.py /^class BaseDetector(object):$/;" c BaseDetector src/lib/detectors/eldet.py /^from .base_detector import BaseDetector$/;" i BaseDetector src/lib/detectors/eldet_bbox.py /^from .base_detector import BaseDetector$/;" i BaseTrainer src/lib/trains/base_trainer.py /^class BaseTrainer(object):$/;" c BaseTrainer src/lib/trains/eldet.py /^from .base_trainer import BaseTrainer$/;" i BaseTrainer src/lib/trains/eldet_bbox.py /^from .base_trainer import BaseTrainer$/;" i BaseTrainer src/lib/trains/eldet_onlyangle.py /^from .base_trainer import BaseTrainer$/;" i BasicBlock src/lib/models/networks/dlav0.py /^class BasicBlock(nn.Module):$/;" c BasicBlock src/lib/models/networks/msra_resnet.py /^class BasicBlock(nn.Module):$/;" c BasicBlock src/lib/models/networks/pose_dla_dcn.py /^class BasicBlock(nn.Module):$/;" c BasicBlock src/lib/models/networks/resnet_dcn.py /^class BasicBlock(nn.Module):$/;" c BatchNorm src/lib/models/networks/dlav0.py /^BatchNorm = nn.BatchNorm2d$/;" v BinRotLoss src/lib/models/losses.py /^class BinRotLoss(nn.Module):$/;" c Bottleneck src/lib/models/networks/dlav0.py /^class Bottleneck(nn.Module):$/;" c Bottleneck src/lib/models/networks/msra_resnet.py /^class Bottleneck(nn.Module):$/;" c Bottleneck src/lib/models/networks/pose_dla_dcn.py /^class Bottleneck(nn.Module):$/;" c Bottleneck src/lib/models/networks/resnet_dcn.py /^class Bottleneck(nn.Module):$/;" c BottleneckX src/lib/models/networks/dlav0.py /^class BottleneckX(nn.Module):$/;" c BottleneckX src/lib/models/networks/pose_dla_dcn.py /^class BottleneckX(nn.Module):$/;" c BoxDataSet src/lib/models/Rotated_IoU/demo.py /^class BoxDataSet(Dataset):$/;" c BuildExtension src/lib/models/Rotated_IoU/cuda_op/setup.py /^from torch.utils.cpp_extension import BuildExtension, CUDAExtension$/;" i CALIB src/tools/convert_kitti_to_coco.py /^CALIB = np.array([[F, 0, W \/ 2, EXT[0]], [0, F, H \/ 2, EXT[1]], $/;" v CAR src/tools/kitti_eval/evaluate_object_3d.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: CAR src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: CAT_NAMES src/tools/vis_pred.py /^ for i in range(num_classes)]$/;" v CHECK_CONTIGUOUS src/lib/models/Rotated_IoU/cuda_op/utils.h /^#define CHECK_CONTIGUOUS(/;" d CHECK_CUDA src/lib/models/Rotated_IoU/cuda_op/utils.h /^#define CHECK_CUDA(/;" d CHECK_IS_BOOL src/lib/models/Rotated_IoU/cuda_op/utils.h /^#define CHECK_IS_BOOL(/;" d CHECK_IS_FLOAT src/lib/models/Rotated_IoU/cuda_op/utils.h /^#define CHECK_IS_FLOAT(/;" d CHECK_IS_INT src/lib/models/Rotated_IoU/cuda_op/utils.h /^#define CHECK_IS_INT(/;" d CLASSES src/tools/kitti_eval/evaluate_object_3d.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" g file: CLASSES src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" g file: CLASS_NAMES src/tools/kitti_eval/evaluate_object_3d.cpp /^vector CLASS_NAMES;$/;" v CLASS_NAMES src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector CLASS_NAMES;$/;" v COCO src/tools/calc_coco_overlap.py /^import pycocotools.coco as COCO$/;" i COCOFHD src/lib/datasets/dataset/coco_fhd.py /^class COCOFHD(data.Dataset):$/;" c COCOFHD src/lib/datasets/dataset_factory.py /^from .dataset.coco_fhd import COCOFHD$/;" i COCOeval src/lib/datasets/dataset/coco_fhd.py /^from pycocotools.cocoeval import COCOeval$/;" i COCOeval src/tools/eval_coco.py /^from pycocotools.cocoeval import COCOeval$/;" i COCOeval src/tools/eval_coco_hp.py /^from pycocotools.cocoeval import COCOeval$/;" i COCOeval src/tools/vis_pred.py /^from pycocotools.cocoeval import COCOeval$/;" i COLORS src/tools/vis_pred.py /^ for _ in range(num_classes)]$/;" v CUDA src/tools/voc_eval_lib/setup.py /^CUDA = locate_cuda()$/;" v CUDAExtension src/lib/models/Rotated_IoU/cuda_op/setup.py /^from torch.utils.cpp_extension import BuildExtension, CUDAExtension$/;" i CUDAExtension src/lib/models/networks/DCNv2/setup.py /^from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension$/;" i CUDA_CHECK_ERRORS src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h /^# define CUDA_CHECK_ERRORS(/;" d CUDA_HOME src/lib/models/networks/DCNv2/setup.py /^from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension$/;" i CYCLIST src/tools/kitti_eval/evaluate_object_3d.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: CYCLIST src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: CYTHON_ABI src/tools/voc_eval_lib/nms/cpu_nms.c /^#define CYTHON_ABI /;" d file: CYTHON_ABI src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define CYTHON_ABI /;" d file: CYTHON_CCOMPLEX src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_CCOMPLEX /;" d file: CYTHON_CCOMPLEX src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_CCOMPLEX /;" d file: CYTHON_COMPILING_IN_CPYTHON src/tools/voc_eval_lib/nms/cpu_nms.c /^#define CYTHON_COMPILING_IN_CPYTHON /;" d file: CYTHON_COMPILING_IN_CPYTHON src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define CYTHON_COMPILING_IN_CPYTHON /;" d file: CYTHON_COMPILING_IN_PYPY src/tools/voc_eval_lib/nms/cpu_nms.c /^#define CYTHON_COMPILING_IN_PYPY /;" d file: CYTHON_COMPILING_IN_PYPY src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define CYTHON_COMPILING_IN_PYPY /;" d file: CYTHON_FORMAT_SSIZE_T src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_FORMAT_SSIZE_T /;" d file: CYTHON_FORMAT_SSIZE_T src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_FORMAT_SSIZE_T /;" d file: CYTHON_INLINE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_INLINE /;" d file: CYTHON_INLINE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_INLINE$/;" d file: CYTHON_INLINE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_INLINE /;" d file: CYTHON_INLINE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_INLINE$/;" d file: CYTHON_PEP393_ENABLED src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_PEP393_ENABLED /;" d file: CYTHON_PEP393_ENABLED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_PEP393_ENABLED /;" d file: CYTHON_REFNANNY src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_REFNANNY /;" d file: CYTHON_REFNANNY src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_REFNANNY /;" d file: CYTHON_RESTRICT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_RESTRICT /;" d file: CYTHON_RESTRICT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define CYTHON_RESTRICT$/;" d file: CYTHON_RESTRICT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_RESTRICT /;" d file: CYTHON_RESTRICT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define CYTHON_RESTRICT$/;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/cpu_nms.c /^# define CYTHON_UNUSED /;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/cpu_nms.c /^# define CYTHON_UNUSED$/;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/cpu_nms.c /^# define CYTHON_UNUSED /;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/cpu_nms.c /^# define CYTHON_UNUSED$/;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^# define CYTHON_UNUSED /;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^# define CYTHON_UNUSED$/;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^# define CYTHON_UNUSED /;" d file: CYTHON_UNUSED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^# define CYTHON_UNUSED$/;" d file: CYTHON_USE_PYLONG_INTERNALS src/tools/voc_eval_lib/nms/cpu_nms.c /^#define CYTHON_USE_PYLONG_INTERNALS /;" d file: CYTHON_USE_PYLONG_INTERNALS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define CYTHON_USE_PYLONG_INTERNALS /;" d file: CYTHON_WITHOUT_ASSERTIONS src/tools/voc_eval_lib/nms/cpu_nms.c /^#define CYTHON_WITHOUT_ASSERTIONS$/;" d file: CYTHON_WITHOUT_ASSERTIONS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define CYTHON_WITHOUT_ASSERTIONS$/;" d file: CppExtension src/lib/models/networks/DCNv2/setup.py /^from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension$/;" i DATA src/lib/models/Rotated_IoU/demo.py /^DATA = ".\/data"$/;" v DATA_PATH src/tools/convert_kitti_to_coco.py /^DATA_PATH = '..\/..\/data\/kitti\/'$/;" v DCN src/lib/models/networks/DCNv2/dcn_v2.py /^class DCN(DCNv2):$/;" c DCN src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCN src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCN src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCN src/lib/models/networks/pose_dla_dcn.py /^from .DCNv2.dcn_v2 import DCN$/;" i DCN src/lib/models/networks/resnet_dcn.py /^from .DCNv2.dcn_v2 import DCN$/;" i DCNPooling src/lib/models/networks/DCNv2/dcn_v2.py /^class DCNPooling(DCNv2Pooling):$/;" c DCNPooling src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNPooling src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNPooling src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCN_V2_IM2COL_CPU src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.h /^#define DCN_V2_IM2COL_CPU$/;" d DCN_V2_IM2COL_CUDA src/lib/models/networks/DCNv2/src/cuda/dcn_v2_im2col_cuda.h /^#define DCN_V2_IM2COL_CUDA$/;" d DCNv2 src/lib/models/networks/DCNv2/dcn_v2.py /^class DCNv2(nn.Module):$/;" c DCNv2 src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNv2 src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNv2 src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNv2Pooling src/lib/models/networks/DCNv2/dcn_v2.py /^class DCNv2Pooling(nn.Module):$/;" c DCNv2Pooling src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNv2Pooling src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DCNv2Pooling src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i DEBUG src/tools/calc_coco_overlap.py /^DEBUG = False$/;" v DEBUG src/tools/convert_kitti_to_coco.py /^DEBUG = False$/;" v DEBUG src/tools/vis_pred.py /^DEBUG = True$/;" v DECREF src/tools/voc_eval_lib/nms/cpu_nms.c /^ void (*DECREF)(void*, PyObject*, int);$/;" m struct:__anon36 file: DECREF src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void (*DECREF)(void*, PyObject*, int);$/;" m struct:__anon8 file: DIFFICULTY src/tools/kitti_eval/evaluate_object_3d.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" g file: DIFFICULTY src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" g file: DLA src/lib/models/networks/dlav0.py /^class DLA(nn.Module):$/;" c DLA src/lib/models/networks/pose_dla_dcn.py /^class DLA(nn.Module):$/;" c DLASeg src/lib/models/networks/dlav0.py /^class DLASeg(nn.Module):$/;" c DLASeg src/lib/models/networks/pose_dla_dcn.py /^class DLASeg(nn.Module):$/;" c DLAUp src/lib/models/networks/dlav0.py /^class DLAUp(nn.Module):$/;" c DLAUp src/lib/models/networks/pose_dla_dcn.py /^class DLAUp(nn.Module):$/;" c DL_EXPORT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define DL_EXPORT(/;" d file: DL_EXPORT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define DL_EXPORT(/;" d file: DL_IMPORT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define DL_IMPORT(/;" d file: DL_IMPORT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define DL_IMPORT(/;" d file: DTYPE src/tools/voc_eval_lib/datasets/bbox.pyx /^DTYPE = np.float$/;" v DTYPE src/tools/voc_eval_lib/utils/bbox.pyx /^DTYPE = np.float$/;" v DataLoader src/lib/models/Rotated_IoU/demo.py /^from torch.utils.data import DataLoader, Dataset$/;" i DataParallel src/lib/models/data_parallel.py /^def DataParallel(module, device_ids=None, output_device=None, dim=0, chunk_sizes=None):$/;" f DataParallel src/lib/trains/base_trainer.py /^from models.data_parallel import DataParallel$/;" i DataParallel src/main.py /^from models.data_parallel import DataParallel$/;" i Dataset src/evaluate.py /^ Dataset = dataset_factory[opt.dataset]$/;" v Dataset src/lib/datasets/dataset_factory.py /^ class Dataset(dataset_factory[dataset], _sample_factory[task]):$/;" c function:get_dataset Dataset src/lib/models/Rotated_IoU/demo.py /^from torch.utils.data import DataLoader, Dataset$/;" i Debugger src/lib/detectors/base_detector.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/detectors/eldet.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/detectors/eldet_bbox.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/trains/eldet.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/trains/eldet_bbox.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/trains/eldet_onlyangle.py /^from utils.debugger import Debugger$/;" i Debugger src/lib/utils/debugger.py /^class Debugger(object):$/;" c DeformConv src/lib/models/networks/pose_dla_dcn.py /^class DeformConv(nn.Module):$/;" c DeformablePSROIPoolBackwardAccKernelCpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp /^void DeformablePSROIPoolBackwardAccKernelCpu($/;" f DeformablePSROIPoolForwardKernelCpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp /^ void DeformablePSROIPoolForwardKernelCpu($/;" f Detector src/evaluate.py /^ Detector = detector_factory[opt.task]$/;" v EASY src/tools/kitti_eval/evaluate_object_3d.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: EASY src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: ELDetDataset src/lib/datasets/dataset_factory.py /^from .sample.eldet import ELDetDataset$/;" i ELDetDataset src/lib/datasets/sample/eldet.py /^class ELDetDataset(data.Dataset):$/;" c EPSILON src/lib/models/Rotated_IoU/box_intersection_2d.py /^EPSILON = 1e-8$/;" v EPSILON src/lib/models/Rotated_IoU/utiles.py /^EPSILON = 1e-8$/;" v ET src/tools/voc_eval_lib/datasets/pascal_voc.py /^import xml.etree.ElementTree as ET$/;" i ET src/tools/voc_eval_lib/datasets/voc_eval.py /^import xml.etree.ElementTree as ET$/;" i EXT src/tools/convert_kitti_to_coco.py /^EXT = [45.75, -0.34, 0.005]$/;" v EldetBBOXDetector src/lib/detectors/detector_factory.py /^from .eldet_bbox import EldetBBOXDetector$/;" i EldetBBOXDetector src/lib/detectors/eldet_bbox.py /^class EldetBBOXDetector(BaseDetector):$/;" c EldetDetector src/lib/detectors/detector_factory.py /^from .eldet import EldetDetector$/;" i EldetDetector src/lib/detectors/eldet.py /^class EldetDetector(BaseDetector):$/;" c EldetLoss src/lib/trains/eldet.py /^class EldetLoss(torch.nn.Module):$/;" c EldetLoss src/lib/trains/eldet_bbox.py /^class EldetLoss(torch.nn.Module):$/;" c EldetLoss src/lib/trains/eldet_onlyangle.py /^class EldetLoss(torch.nn.Module):$/;" c EldetTrainer src/lib/trains/eldet.py /^class EldetTrainer(BaseTrainer):$/;" c EldetTrainer src/lib/trains/eldet_bbox.py /^class EldetTrainer(BaseTrainer):$/;" c EldetTrainer src/lib/trains/eldet_onlyangle.py /^class EldetTrainer(BaseTrainer):$/;" c EldetTrainer src/lib/trains/train_factory.py /^from .eldet import EldetTrainer$/;" i Extension src/lib/external/setup.py /^from distutils.extension import Extension$/;" i Extension src/tools/voc_eval_lib/setup.py /^from distutils.extension import Extension$/;" i F src/lib/models/decode.py /^import torch.nn.functional as F$/;" i F src/lib/models/losses.py /^import torch.nn.functional as F$/;" i F src/lib/models/networks/pose_dla_dcn.py /^import torch.nn.functional as F$/;" i F src/tools/convert_kitti_to_coco.py /^F = 721$/;" v FONT src/tools/voc_eval_lib/utils/visualization.py /^ FONT = ImageFont.load_default()$/;" v FONT src/tools/voc_eval_lib/utils/visualization.py /^ FONT = ImageFont.truetype('arial.ttf', 24)$/;" v FinishContext src/tools/voc_eval_lib/nms/cpu_nms.c /^ void (*FinishContext)(void**);$/;" m struct:__anon36 file: FinishContext src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void (*FinishContext)(void**);$/;" m struct:__anon8 file: FocalLoss src/lib/models/losses.py /^class FocalLoss(nn.Module):$/;" c FocalLoss src/lib/trains/eldet.py /^from models.losses import FocalLoss$/;" i FocalLoss src/lib/trains/eldet_bbox.py /^from models.losses import FocalLoss$/;" i FocalLoss src/lib/trains/eldet_onlyangle.py /^from models.losses import FocalLoss$/;" i Function src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^from torch.autograd import Function$/;" i Function src/lib/models/networks/DCNv2/dcn_v2.py /^from torch.autograd import Function$/;" i GIVEREF src/tools/voc_eval_lib/nms/cpu_nms.c /^ void (*GIVEREF)(void*, PyObject*, int);$/;" m struct:__anon36 file: GIVEREF src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void (*GIVEREF)(void*, PyObject*, int);$/;" m struct:__anon8 file: GOTREF src/tools/voc_eval_lib/nms/cpu_nms.c /^ void (*GOTREF)(void*, PyObject*, int);$/;" m struct:__anon36 file: GOTREF src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void (*GOTREF)(void*, PyObject*, int);$/;" m struct:__anon8 file: GROUND src/tools/kitti_eval/evaluate_object_3d.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: GROUND src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: Gather src/lib/models/scatter_gather.py /^from torch.nn.parallel._functions import Scatter, Gather$/;" i H src/tools/convert_kitti_to_coco.py /^H = 384 # 375$/;" v HARD src/tools/kitti_eval/evaluate_object_3d.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: HARD src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: HourglassNet src/lib/models/networks/large_hourglass.py /^class HourglassNet(exkp):$/;" c IDAUp src/lib/models/networks/dlav0.py /^class IDAUp(nn.Module):$/;" c IDAUp src/lib/models/networks/pose_dla_dcn.py /^class IDAUp(nn.Module):$/;" c IMAGE src/tools/kitti_eval/evaluate_object_3d.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: IMAGE src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" e enum:METRIC file: IMG_PATH src/tools/calc_coco_overlap.py /^IMG_PATH = '..\/..\/data\/coco\/'$/;" v IMG_PATH src/tools/vis_pred.py /^IMG_PATH = '..\/..\/data\/coco\/val2017\/'$/;" v INCREF src/tools/voc_eval_lib/nms/cpu_nms.c /^ void (*INCREF)(void*, PyObject*, int);$/;" m struct:__anon36 file: INCREF src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void (*INCREF)(void*, PyObject*, int);$/;" m struct:__anon8 file: INPUT_FILES src/tools/merge_pascal_json.py /^INPUT_FILES = ['pascal_train2012.json', 'pascal_val2012.json',$/;" v IS_UNSIGNED src/tools/voc_eval_lib/nms/cpu_nms.c /^#define IS_UNSIGNED(/;" d file: IS_UNSIGNED src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define IS_UNSIGNED(/;" d file: Identity src/lib/models/networks/dlav0.py /^class Identity(nn.Module):$/;" c Identity src/lib/models/networks/pose_dla_dcn.py /^class Identity(nn.Module):$/;" c Image src/evaluate.py /^from PIL import Image$/;" i Image src/tools/voc_eval_lib/utils/visualization.py /^import PIL.Image as Image$/;" i ImageColor src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageColor as ImageColor$/;" i ImageDraw src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageDraw as ImageDraw$/;" i ImageFont src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageFont as ImageFont$/;" i Interpolate src/lib/models/networks/pose_dla_dcn.py /^class Interpolate(nn.Module):$/;" c IoULoss src/lib/models/losses.py /^class IoULoss(nn.Module):$/;" c IoULoss src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i IoULoss4Angle src/lib/models/losses.py /^class IoULoss4Angle(nn.Module):$/;" c IoULoss4Angle src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i IoULossSincos src/lib/models/losses.py /^class IoULossSincos(nn.Module):$/;" c KEYS src/tools/merge_pascal_json.py /^KEYS = ['images', 'type', 'annotations', 'categories']$/;" v L1Loss src/lib/models/losses.py /^class L1Loss(nn.Module):$/;" c L1LossRegression src/lib/models/losses.py /^class L1LossRegression(nn.Module):$/;" c L1LossRegression src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i L1LossRegression src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i LINES src/lib/models/Rotated_IoU/min_enclosing_box.py /^LINES = np.array(LINES).astype(np.int)$/;" v Logger src/evaluate.py /^from logger import Logger$/;" i Logger src/lib/logger.py /^class Logger(object):$/;" c Logger src/main.py /^from logger import Logger$/;" i Logger src/test.py /^from logger import Logger$/;" i MAIL_H src/tools/kitti_eval/mail.h /^#define MAIL_H$/;" d MAX_NUM_VERT_IDX src/lib/models/Rotated_IoU/cuda_op/sort_vert.h /^#define MAX_NUM_VERT_IDX /;" d MAX_OCCLUSION src/tools/kitti_eval/evaluate_object_3d.cpp /^const int32_t MAX_OCCLUSION[3] = {0, 1, 2}; \/\/ maximum occlusion level of the groundtruth used for evaluation$/;" v MAX_OCCLUSION src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const int32_t MAX_OCCLUSION[3] = {0, 1, 2}; \/\/ maximum occlusion level of the groundtruth used for evaluation$/;" v MAX_TRUNCATION src/tools/kitti_eval/evaluate_object_3d.cpp /^const double MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; \/\/ maximum truncation level of the groundtruth used for evaluation$/;" v MAX_TRUNCATION src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const double MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; \/\/ maximum truncation level of the groundtruth used for evaluation$/;" v MERGE_KEYS src/tools/merge_pascal_json.py /^MERGE_KEYS = ['images', 'annotations']$/;" v METRIC src/tools/kitti_eval/evaluate_object_3d.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" g file: METRIC src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum METRIC{IMAGE=0, GROUND=1, BOX3D=2};$/;" g file: MIN_HEIGHT src/tools/kitti_eval/evaluate_object_3d.cpp /^const int32_t MIN_HEIGHT[3] = {40, 25, 25}; \/\/ minimum height for evaluated groundtruth\/detections$/;" v MIN_HEIGHT src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const int32_t MIN_HEIGHT[3] = {40, 25, 25}; \/\/ minimum height for evaluated groundtruth\/detections$/;" v MIN_OVERLAP src/tools/kitti_eval/evaluate_object_3d.cpp /^const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.5, 0.25, 0.25}, {0.5, 0.25, 0.25}};$/;" v MIN_OVERLAP src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.5, 0.25, 0.25}, {0.5, 0.25, 0.25}};$/;" v MODEL_PATH src/tools/convert_hourglass_weight.py /^MODEL_PATH = '..\/..\/models\/ExtremeNet_500000.pkl'$/;" v MODERATE src/tools/kitti_eval/evaluate_object_3d.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: MODERATE src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};$/;" e enum:DIFFICULTY file: Mail src/tools/kitti_eval/mail.h /^ Mail (std::string email = "") {$/;" f class:Mail Mail src/tools/kitti_eval/mail.h /^class Mail {$/;" c MergeUp src/lib/models/networks/large_hourglass.py /^class MergeUp(nn.Module):$/;" c ModelWithLoss src/lib/trains/base_trainer.py /^class ModelWithLoss(torch.nn.Module):$/;" c Module src/lib/models/data_parallel.py /^from torch.nn.modules import Module$/;" i NUM_CLASS src/tools/kitti_eval/evaluate_object_3d.cpp /^const int NUM_CLASS = 3;$/;" v NUM_CLASS src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const int NUM_CLASS = 3;$/;" v NUM_COLORS src/tools/voc_eval_lib/utils/visualization.py /^NUM_COLORS = len(STANDARD_COLORS)$/;" v NUM_EPOCH src/lib/models/Rotated_IoU/demo.py /^NUM_EPOCH = 20$/;" v NUM_TEST src/lib/models/Rotated_IoU/demo.py /^NUM_TEST = 20 * BATCH_SIZE * N_DATA$/;" v NUM_TRAIN src/lib/models/Rotated_IoU/demo.py /^NUM_TRAIN = 200 * BATCH_SIZE * N_DATA$/;" v N_DATA src/lib/models/Rotated_IoU/demo.py /^N_DATA = 128$/;" v N_SAMPLE_PTS src/tools/kitti_eval/evaluate_object_3d.cpp /^const double N_SAMPLE_PTS = 41;$/;" v N_SAMPLE_PTS src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const double N_SAMPLE_PTS = 41;$/;" v N_TESTIMAGES src/tools/kitti_eval/evaluate_object_3d.cpp /^const int32_t N_TESTIMAGES = 7518;$/;" v N_TESTIMAGES src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^const int32_t N_TESTIMAGES = 7518;$/;" v NormRegL1Loss src/lib/models/losses.py /^class NormRegL1Loss(nn.Module):$/;" c NormRegL1Loss src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i NormRegL1Loss src/lib/trains/eldet_bbox.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss$/;" i NormRegL1Loss src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i OUTPUT_FILE src/tools/merge_pascal_json.py /^OUTPUT_FILE = 'pascal_trainval0712.json'$/;" v OUT_PATH src/tools/convert_hourglass_weight.py /^OUT_PATH = '..\/..\/models\/ExtremeNet_500000.pth'$/;" v OUT_PATH src/tools/merge_pascal_json.py /^OUT_PATH = ANNOT_PATH$/;" v PEDESTRIAN src/tools/kitti_eval/evaluate_object_3d.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: PEDESTRIAN src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^enum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};$/;" e enum:CLASSES file: PIL src/tools/voc_eval_lib/datasets/imdb.py /^import PIL$/;" i PIL src/tools/voc_eval_lib/utils/visualization.py /^import PIL.Image as Image$/;" i PIL src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageColor as ImageColor$/;" i PIL src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageDraw as ImageDraw$/;" i PIL src/tools/voc_eval_lib/utils/visualization.py /^import PIL.ImageFont as ImageFont$/;" i POINTS src/lib/models/Rotated_IoU/min_enclosing_box.py /^POINTS = np.array(POINTS).astype(np.int)$/;" v PYBIND11_MODULE src/lib/models/Rotated_IoU/cuda_op/sort_vert.cpp /^PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){$/;" f PYBIND11_MODULE src/lib/models/networks/DCNv2/src/vision.cpp /^PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {$/;" f PY_FORMAT_SIZE_T src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PY_FORMAT_SIZE_T /;" d file: PY_FORMAT_SIZE_T src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PY_FORMAT_SIZE_T /;" d file: PY_LONG_LONG src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PY_LONG_LONG /;" d file: PY_LONG_LONG src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PY_LONG_LONG /;" d file: PY_SSIZE_T_CLEAN src/tools/voc_eval_lib/nms/cpu_nms.c /^#define PY_SSIZE_T_CLEAN$/;" d file: PY_SSIZE_T_CLEAN src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define PY_SSIZE_T_CLEAN$/;" d file: PY_SSIZE_T_MAX src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PY_SSIZE_T_MAX /;" d file: PY_SSIZE_T_MAX src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PY_SSIZE_T_MAX /;" d file: PY_SSIZE_T_MIN src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PY_SSIZE_T_MIN /;" d file: PY_SSIZE_T_MIN src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PY_SSIZE_T_MIN /;" d file: Polygon src/tools/kitti_eval/evaluate_object_3d.cpp /^typedef boost::geometry::model::polygon > Polygon;$/;" t file: Polygon src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^typedef boost::geometry::model::polygon > Polygon;$/;" t file: PoseResNet src/lib/models/networks/msra_resnet.py /^class PoseResNet(nn.Module):$/;" c PoseResNet src/lib/models/networks/resnet_dcn.py /^class PoseResNet(nn.Module):$/;" c PrefetchDataset src/test.py /^class PrefetchDataset(torch.utils.data.Dataset):$/;" c PyBUF_ANY_CONTIGUOUS src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_ANY_CONTIGUOUS /;" d file: PyBUF_ANY_CONTIGUOUS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_ANY_CONTIGUOUS /;" d file: PyBUF_C_CONTIGUOUS src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_C_CONTIGUOUS /;" d file: PyBUF_C_CONTIGUOUS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_C_CONTIGUOUS /;" d file: PyBUF_FORMAT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_FORMAT /;" d file: PyBUF_FORMAT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_FORMAT /;" d file: PyBUF_FULL src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_FULL /;" d file: PyBUF_FULL src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_FULL /;" d file: PyBUF_F_CONTIGUOUS src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_F_CONTIGUOUS /;" d file: PyBUF_F_CONTIGUOUS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_F_CONTIGUOUS /;" d file: PyBUF_INDIRECT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_INDIRECT /;" d file: PyBUF_INDIRECT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_INDIRECT /;" d file: PyBUF_ND src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_ND /;" d file: PyBUF_ND src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_ND /;" d file: PyBUF_RECORDS src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_RECORDS /;" d file: PyBUF_RECORDS src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_RECORDS /;" d file: PyBUF_SIMPLE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_SIMPLE /;" d file: PyBUF_SIMPLE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_SIMPLE /;" d file: PyBUF_STRIDES src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_STRIDES /;" d file: PyBUF_STRIDES src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_STRIDES /;" d file: PyBUF_WRITABLE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBUF_WRITABLE /;" d file: PyBUF_WRITABLE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBUF_WRITABLE /;" d file: PyBaseString_Type src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBaseString_Type /;" d file: PyBaseString_Type src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBaseString_Type /;" d file: PyBoolObject src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBoolObject /;" d file: PyBoolObject src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBoolObject /;" d file: PyBytesObject src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytesObject /;" d file: PyBytesObject src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytesObject /;" d file: PyBytes_AS_STRING src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_AS_STRING /;" d file: PyBytes_AS_STRING src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_AS_STRING /;" d file: PyBytes_AsString src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_AsString /;" d file: PyBytes_AsString src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_AsString /;" d file: PyBytes_AsStringAndSize src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_AsStringAndSize /;" d file: PyBytes_AsStringAndSize src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_AsStringAndSize /;" d file: PyBytes_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_Check /;" d file: PyBytes_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_Check /;" d file: PyBytes_CheckExact src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_CheckExact /;" d file: PyBytes_CheckExact src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_CheckExact /;" d file: PyBytes_Concat src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_Concat /;" d file: PyBytes_Concat src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_Concat /;" d file: PyBytes_ConcatAndDel src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_ConcatAndDel /;" d file: PyBytes_ConcatAndDel src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_ConcatAndDel /;" d file: PyBytes_DecodeEscape src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_DecodeEscape /;" d file: PyBytes_DecodeEscape src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_DecodeEscape /;" d file: PyBytes_FromFormat src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_FromFormat /;" d file: PyBytes_FromFormat src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_FromFormat /;" d file: PyBytes_FromString src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_FromString /;" d file: PyBytes_FromString src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_FromString /;" d file: PyBytes_FromStringAndSize src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_FromStringAndSize /;" d file: PyBytes_FromStringAndSize src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_FromStringAndSize /;" d file: PyBytes_GET_SIZE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_GET_SIZE /;" d file: PyBytes_GET_SIZE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_GET_SIZE /;" d file: PyBytes_Repr src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_Repr /;" d file: PyBytes_Repr src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_Repr /;" d file: PyBytes_Size src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_Size /;" d file: PyBytes_Size src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_Size /;" d file: PyBytes_Type src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyBytes_Type /;" d file: PyBytes_Type src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyBytes_Type /;" d file: PyErr_WarnEx src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyErr_WarnEx(/;" d file: PyErr_WarnEx src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyErr_WarnEx(/;" d file: PyFrozenSet_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyFrozenSet_Check(/;" d file: PyFrozenSet_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyFrozenSet_Check(/;" d file: PyIndex_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyIndex_Check /;" d file: PyIndex_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyIndex_Check /;" d file: PyIntObject src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyIntObject /;" d file: PyIntObject src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyIntObject /;" d file: PyInt_AS_LONG src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AS_LONG /;" d file: PyInt_AS_LONG src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AS_LONG /;" d file: PyInt_AsLong src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AsLong /;" d file: PyInt_AsLong src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AsLong /;" d file: PyInt_AsSsize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AsSsize_t /;" d file: PyInt_AsSsize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AsSsize_t(/;" d file: PyInt_AsSsize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AsSsize_t /;" d file: PyInt_AsSsize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AsSsize_t(/;" d file: PyInt_AsUnsignedLongLongMask src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AsUnsignedLongLongMask /;" d file: PyInt_AsUnsignedLongLongMask src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AsUnsignedLongLongMask /;" d file: PyInt_AsUnsignedLongMask src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_AsUnsignedLongMask /;" d file: PyInt_AsUnsignedLongMask src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_AsUnsignedLongMask /;" d file: PyInt_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_Check(/;" d file: PyInt_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_Check(/;" d file: PyInt_CheckExact src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_CheckExact(/;" d file: PyInt_CheckExact src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_CheckExact(/;" d file: PyInt_FromLong src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromLong /;" d file: PyInt_FromLong src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromLong /;" d file: PyInt_FromSize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromSize_t /;" d file: PyInt_FromSize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromSize_t /;" d file: PyInt_FromSsize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromSsize_t /;" d file: PyInt_FromSsize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromSsize_t(/;" d file: PyInt_FromSsize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromSsize_t /;" d file: PyInt_FromSsize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromSsize_t(/;" d file: PyInt_FromString src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromString /;" d file: PyInt_FromString src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromString /;" d file: PyInt_FromUnicode src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_FromUnicode /;" d file: PyInt_FromUnicode src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_FromUnicode /;" d file: PyInt_Type src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyInt_Type /;" d file: PyInt_Type src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyInt_Type /;" d file: PyMethod_New src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyMethod_New(/;" d file: PyMethod_New src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyMethod_New(/;" d file: PyNumber_Index src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyNumber_Index(/;" d file: PyNumber_Index src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyNumber_Index(/;" d file: PyNumber_Int src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyNumber_Int /;" d file: PyNumber_Int src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyNumber_Int /;" d file: PySet_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PySet_Check(/;" d file: PySet_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PySet_Check(/;" d file: PySet_CheckExact src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PySet_CheckExact(/;" d file: PySet_CheckExact src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PySet_CheckExact(/;" d file: PyStringObject src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyStringObject /;" d file: PyStringObject src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyStringObject /;" d file: PyString_Check src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyString_Check /;" d file: PyString_Check src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyString_Check /;" d file: PyString_CheckExact src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyString_CheckExact /;" d file: PyString_CheckExact src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyString_CheckExact /;" d file: PyString_Type src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyString_Type /;" d file: PyString_Type src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyString_Type /;" d file: PyType_Modified src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyType_Modified(/;" d file: PyType_Modified src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyType_Modified(/;" d file: PyUnicode_FromString src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyUnicode_FromString(/;" d file: PyUnicode_FromString src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyUnicode_FromString(/;" d file: PyVarObject_HEAD_INIT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define PyVarObject_HEAD_INIT(/;" d file: PyVarObject_HEAD_INIT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define PyVarObject_HEAD_INIT(/;" d file: Py_HUGE_VAL src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_HUGE_VAL /;" d file: Py_HUGE_VAL src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_HUGE_VAL /;" d file: Py_OptimizeFlag src/tools/voc_eval_lib/nms/cpu_nms.c /^#define Py_OptimizeFlag /;" d file: Py_OptimizeFlag src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define Py_OptimizeFlag /;" d file: Py_REFCNT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_REFCNT(/;" d file: Py_REFCNT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_REFCNT(/;" d file: Py_SIZE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_SIZE(/;" d file: Py_SIZE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_SIZE(/;" d file: Py_TPFLAGS_CHECKTYPES src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_CHECKTYPES /;" d file: Py_TPFLAGS_CHECKTYPES src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_CHECKTYPES /;" d file: Py_TPFLAGS_HAVE_FINALIZE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_HAVE_FINALIZE /;" d file: Py_TPFLAGS_HAVE_FINALIZE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_HAVE_FINALIZE /;" d file: Py_TPFLAGS_HAVE_INDEX src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_HAVE_INDEX /;" d file: Py_TPFLAGS_HAVE_INDEX src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_HAVE_INDEX /;" d file: Py_TPFLAGS_HAVE_NEWBUFFER src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_HAVE_NEWBUFFER /;" d file: Py_TPFLAGS_HAVE_NEWBUFFER src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_HAVE_NEWBUFFER /;" d file: Py_TPFLAGS_HAVE_VERSION_TAG src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_HAVE_VERSION_TAG /;" d file: Py_TPFLAGS_HAVE_VERSION_TAG src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_HAVE_VERSION_TAG /;" d file: Py_TPFLAGS_IS_ABSTRACT src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TPFLAGS_IS_ABSTRACT /;" d file: Py_TPFLAGS_IS_ABSTRACT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TPFLAGS_IS_ABSTRACT /;" d file: Py_TYPE src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define Py_TYPE(/;" d file: Py_TYPE src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define Py_TYPE(/;" d file: Py_buffer src/tools/voc_eval_lib/nms/cpu_nms.c /^ } Py_buffer;$/;" t typeref:struct:__anon29 file: Py_buffer src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ } Py_buffer;$/;" t typeref:struct:__anon1 file: Py_hash_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef long Py_hash_t;$/;" t file: Py_hash_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef long Py_hash_t;$/;" t file: Py_ssize_t src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef int Py_ssize_t;$/;" t file: Py_ssize_t src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef int Py_ssize_t;$/;" t file: RESIZE src/tools/calc_coco_overlap.py /^RESIZE = True$/;" v RegL1Loss src/lib/models/losses.py /^class RegL1Loss(nn.Module):$/;" c RegL1Loss src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i RegL1Loss src/lib/trains/eldet_bbox.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss$/;" i RegL1Loss src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i RegL1Loss4Angle src/lib/models/losses.py /^class RegL1Loss4Angle(nn.Module):$/;" c RegL1Loss4Angle src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i RegL1Loss4Angle src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i RegLoss src/lib/models/losses.py /^class RegLoss(nn.Module):$/;" c RegLoss src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i RegLoss src/lib/trains/eldet_bbox.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss$/;" i RegLoss src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i RegWeightedL1Loss src/lib/models/losses.py /^class RegWeightedL1Loss(nn.Module):$/;" c RegWeightedL1Loss src/lib/trains/eldet.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss$/;" i RegWeightedL1Loss src/lib/trains/eldet_bbox.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss$/;" i RegWeightedL1Loss src/lib/trains/eldet_onlyangle.py /^from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, L1LossRegression, RegL1Loss4Angle, IoULoss4Angle$/;" i Root src/lib/models/networks/dlav0.py /^class Root(nn.Module):$/;" c Root src/lib/models/networks/pose_dla_dcn.py /^class Root(nn.Module):$/;" c SCALE src/lib/models/Rotated_IoU/demo.py /^SCALE = 0.5$/;" v SPLITS src/tools/convert_kitti_to_coco.py /^SPLITS = ['3dop', 'subcnn'] $/;" v STANDARD_COLORS src/tools/voc_eval_lib/utils/visualization.py /^STANDARD_COLORS = [$/;" v Scatter src/lib/models/scatter_gather.py /^from torch.nn.parallel._functions import Scatter, Gather$/;" i SetupContext src/tools/voc_eval_lib/nms/cpu_nms.c /^ void* (*SetupContext)(const char*, int, const char*);$/;" m struct:__anon36 file: SetupContext src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void* (*SetupContext)(const char*, int, const char*);$/;" m struct:__anon8 file: SortVertices src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^class SortVertices(Function):$/;" c Struct scripts/fhd_opts.py /^ class Struct:$/;" c function:opts.init Struct scripts/jupyter_opts.py /^ class Struct:$/;" c function:opts.init Struct src/lib/fhd_opts.py /^ class Struct:$/;" c function:opts.init Struct src/lib/opts.py /^ class Struct:$/;" c function:opts.init SummaryWriter src/lib/trains/eldet.py /^from torch.utils.tensorboard import SummaryWriter$/;" i SummaryWriter src/lib/trains/eldet_onlyangle.py /^from torch.utils.tensorboard import SummaryWriter$/;" i SummaryWriter src/main.py /^from torch.utils.tensorboard import SummaryWriter$/;" i TOTAL_THREADS src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h /^#define TOTAL_THREADS /;" d Timer src/tools/voc_eval_lib/model/test.py /^from utils.timer import Timer$/;" i Timer src/tools/voc_eval_lib/utils/timer.py /^class Timer(object):$/;" c Tree src/lib/models/networks/dlav0.py /^class Tree(nn.Module):$/;" c Tree src/lib/models/networks/pose_dla_dcn.py /^class Tree(nn.Module):$/;" c USE_TENSORBOARD src/lib/logger.py /^ USE_TENSORBOARD = False$/;" v USE_TENSORBOARD src/lib/logger.py /^USE_TENSORBOARD = True$/;" v Variable src/lib/models/scatter_gather.py /^from torch.autograd import Variable$/;" i W src/tools/convert_kitti_to_coco.py /^W = 1248 # 1242$/;" v X_MAX src/lib/models/Rotated_IoU/demo.py /^X_MAX = 3$/;" v Y_MAX src/lib/models/Rotated_IoU/demo.py /^Y_MAX = 3$/;" v _CUDA_UTILS_H src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h /^#define _CUDA_UTILS_H$/;" d _Complex_I src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define _Complex_I /;" d file: _Complex_I src/tools/voc_eval_lib/nms/cpu_nms.c /^ #undef _Complex_I$/;" d file: _Complex_I src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define _Complex_I /;" d file: _Complex_I src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #undef _Complex_I$/;" d file: _DCNv2 src/lib/models/networks/DCNv2/dcn_v2.py /^class _DCNv2(Function):$/;" c _DCNv2Pooling src/lib/models/networks/DCNv2/dcn_v2.py /^class _DCNv2Pooling(Function):$/;" c _DataParallel src/lib/models/data_parallel.py /^class _DataParallel(Module):$/;" c _USE_MATH_DEFINES src/tools/voc_eval_lib/nms/cpu_nms.c /^#define _USE_MATH_DEFINES$/;" d file: _USE_MATH_DEFINES src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define _USE_MATH_DEFINES$/;" d file: __C src/tools/voc_eval_lib/model/config.py /^__C = edict()$/;" v __PYX_BUF_FLAGS_PACKED_STRUCT src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_BUF_FLAGS_PACKED_STRUCT /;" d file: __PYX_BUF_FLAGS_PACKED_STRUCT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_BUF_FLAGS_PACKED_STRUCT /;" d file: __PYX_BUILD_PY_SSIZE_T src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define __PYX_BUILD_PY_SSIZE_T /;" d file: __PYX_BUILD_PY_SSIZE_T src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define __PYX_BUILD_PY_SSIZE_T /;" d file: __PYX_DEFAULT_STRING_ENCODING src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_DEFAULT_STRING_ENCODING /;" d file: __PYX_DEFAULT_STRING_ENCODING src/tools/voc_eval_lib/nms/cpu_nms.c /^static char* __PYX_DEFAULT_STRING_ENCODING;$/;" v file: __PYX_DEFAULT_STRING_ENCODING src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_DEFAULT_STRING_ENCODING /;" d file: __PYX_DEFAULT_STRING_ENCODING src/tools/voc_eval_lib/nms/gpu_nms.cpp /^static char* __PYX_DEFAULT_STRING_ENCODING;$/;" v file: __PYX_DEFAULT_STRING_ENCODING_IS_ASCII src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII /;" d file: __PYX_DEFAULT_STRING_ENCODING_IS_ASCII src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII /;" d file: __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT /;" d file: __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT /;" d file: __PYX_EXTERN_C src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define __PYX_EXTERN_C /;" d file: __PYX_EXTERN_C src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define __PYX_EXTERN_C /;" d file: __PYX_FORCE_INIT_THREADS src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define __PYX_FORCE_INIT_THREADS /;" d file: __PYX_HAVE_API__nms__cpu_nms src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_HAVE_API__nms__cpu_nms$/;" d file: __PYX_HAVE_API__nms__gpu_nms src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_HAVE_API__nms__gpu_nms$/;" d file: __PYX_HAVE_RT_ImportModule src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_HAVE_RT_ImportModule$/;" d file: __PYX_HAVE_RT_ImportModule src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_HAVE_RT_ImportModule$/;" d file: __PYX_HAVE_RT_ImportType src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_HAVE_RT_ImportType$/;" d file: __PYX_HAVE_RT_ImportType src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_HAVE_RT_ImportType$/;" d file: __PYX_HAVE__nms__cpu_nms src/tools/voc_eval_lib/nms/cpu_nms.c /^#define __PYX_HAVE__nms__cpu_nms$/;" d file: __PYX_HAVE__nms__gpu_nms src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define __PYX_HAVE__nms__gpu_nms$/;" d file: __PYX_NAN 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[$/;" v _classes src/tools/vis_pred.py /^_classes = {$/;" v _clip_boxes src/tools/voc_eval_lib/model/test.py /^def _clip_boxes(boxes, im_shape):$/;" f _coco_box_to_bbox src/lib/datasets/sample/eldet.py /^ def _coco_box_to_bbox(self, box):$/;" m class:ELDetDataset _coco_box_to_bbox src/tools/calc_coco_overlap.py /^def _coco_box_to_bbox(box):$/;" f _coco_box_to_bbox src/tools/vis_pred.py /^def _coco_box_to_bbox(box):$/;" f _compile src/tools/voc_eval_lib/setup.py /^ def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):$/;" f function:customize_compiler_for_nvcc _do_matlab_eval src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _do_matlab_eval(self, output_dir='output'):$/;" m class:pascal_voc _do_python_eval src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _do_python_eval(self, output_dir=None):$/;" m class:pascal_voc _draw_single_box src/tools/voc_eval_lib/utils/visualization.py /^def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, font, color='black', thickness=4):$/;" f _gather_feat src/lib/models/decode.py /^from .utils import _gather_feat, _transpose_and_gather_feat$/;" i _gather_feat src/lib/models/utils.py /^def _gather_feat(feat, ind, mask=None):$/;" f _generate_anchors src/tools/calc_coco_overlap.py /^def _generate_anchors(base_size, scales, aspect_ratios):$/;" f _get_blobs src/tools/voc_eval_lib/model/test.py /^def _get_blobs(im):$/;" f _get_border src/lib/datasets/sample/eldet.py /^ def _get_border(self, border, size):$/;" m class:ELDetDataset _get_comp_id src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _get_comp_id(self):$/;" m class:pascal_voc _get_deconv_cfg src/lib/models/networks/msra_resnet.py /^ def _get_deconv_cfg(self, deconv_kernel, index):$/;" m class:PoseResNet _get_deconv_cfg src/lib/models/networks/resnet_dcn.py /^ def _get_deconv_cfg(self, deconv_kernel, index):$/;" m class:PoseResNet _get_default_path src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _get_default_path(self):$/;" m class:pascal_voc _get_image_blob src/tools/voc_eval_lib/model/test.py /^def _get_image_blob(im):$/;" f _get_losses src/lib/trains/base_trainer.py /^ def _get_losses(self, opt):$/;" m class:BaseTrainer _get_losses src/lib/trains/eldet.py /^ def _get_losses(self, opt):$/;" m class:EldetTrainer _get_losses src/lib/trains/eldet_bbox.py /^ def _get_losses(self, opt):$/;" m class:EldetTrainer _get_losses src/lib/trains/eldet_onlyangle.py /^ def _get_losses(self, opt):$/;" m class:EldetTrainer _get_voc_results_file_template src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _get_voc_results_file_template(self):$/;" m class:pascal_voc _get_widths src/tools/voc_eval_lib/datasets/imdb.py /^ def _get_widths(self):$/;" m class:imdb _h_aggregate src/lib/models/decode.py /^def _h_aggregate(heat, aggr_weight=0.1):$/;" f _init_paths src/demo.py /^import _init_paths$/;" i _init_paths src/main.py /^import _init_paths$/;" i _init_paths src/test.py /^import _init_paths$/;" i _init_paths src/tools/convert_kitti_to_coco.py /^import _init_paths$/;" i _left_aggregate src/lib/models/decode.py /^def _left_aggregate(heat):$/;" f _load_image_set_index src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _load_image_set_index(self):$/;" m class:pascal_voc _load_pascal_annotation src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _load_pascal_annotation(self, index):$/;" m class:pascal_voc _load_rpn_roidb src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _load_rpn_roidb(self, gt_roidb):$/;" m class:pascal_voc _make_conv_level src/lib/models/networks/dlav0.py /^ def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):$/;" m class:DLA _make_conv_level src/lib/models/networks/pose_dla_dcn.py /^ def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):$/;" m class:DLA _make_deconv_layer src/lib/models/networks/msra_resnet.py /^ def _make_deconv_layer(self, num_layers, num_filters, num_kernels):$/;" m class:PoseResNet _make_deconv_layer src/lib/models/networks/resnet_dcn.py /^ def _make_deconv_layer(self, num_layers, num_filters, num_kernels):$/;" m class:PoseResNet _make_layer src/lib/models/networks/msra_resnet.py /^ def _make_layer(self, block, planes, blocks, stride=1):$/;" m class:PoseResNet _make_layer src/lib/models/networks/resnet_dcn.py /^ def _make_layer(self, block, planes, blocks, stride=1):$/;" m class:PoseResNet _make_level src/lib/models/networks/dlav0.py /^ def _make_level(self, block, inplanes, planes, blocks, stride=1):$/;" m class:DLA _make_level src/lib/models/networks/pose_dla_dcn.py /^ def _make_level(self, block, inplanes, planes, blocks, stride=1):$/;" m class:DLA _merge_a_into_b src/tools/voc_eval_lib/model/config.py /^def _merge_a_into_b(a, b):$/;" f _mkanchors src/tools/calc_coco_overlap.py /^def _mkanchors(ws, hs, x_ctr, y_ctr):$/;" f _model_factory src/lib/models/model.py /^_model_factory = {$/;" v _neg_loss src/lib/models/losses.py /^def _neg_loss(pred, gt):$/;" f _nms src/lib/models/decode.py /^def _nms(heat, kernel=3):$/;" f _not_faster_neg_loss src/lib/models/losses.py /^def _not_faster_neg_loss(pred, gt):$/;" f _pair src/lib/models/networks/DCNv2/dcn_v2.py /^from torch.nn.modules.utils import _pair$/;" i _ratio_enum src/tools/calc_coco_overlap.py /^def _ratio_enum(anchor, ratios):$/;" f _reg_loss src/lib/models/losses.py /^def _reg_loss(regr, gt_regr, mask):$/;" f _rescale_boxes src/tools/voc_eval_lib/model/test.py /^def _rescale_boxes(boxes, inds, scales):$/;" f _right_aggregate src/lib/models/decode.py /^def _right_aggregate(heat):$/;" f _sample_factory src/lib/datasets/dataset_factory.py /^_sample_factory = {$/;" v _scale_enum src/tools/calc_coco_overlap.py /^def _scale_enum(anchor, scales):$/;" f _sigmoid src/lib/models/utils.py /^def _sigmoid(x):$/;" f _sigmoid src/lib/trains/eldet.py /^from models.utils import _sigmoid$/;" i _sigmoid src/lib/trains/eldet_bbox.py /^from models.utils import _sigmoid$/;" i _sigmoid src/lib/trains/eldet_onlyangle.py /^from models.utils import _sigmoid$/;" i _slow_neg_loss src/lib/models/losses.py /^def _slow_neg_loss(pred, gt):$/;" f _slow_reg_loss src/lib/models/losses.py /^def _slow_reg_loss(regr, gt_regr, mask):$/;" f _to_float src/lib/datasets/dataset/coco_fhd.py /^ def _to_float(self, x):$/;" m class:COCOFHD _to_order src/tools/vis_pred.py /^_to_order = {cat_id: ind for ind, cat_id in enumerate(_cat_ids)}$/;" v _top_aggregate src/lib/models/decode.py /^def _top_aggregate(heat):$/;" f _topk src/lib/models/decode.py /^def _topk(scores, K=40):$/;" f _topk_channel src/lib/models/decode.py /^def _topk_channel(scores, K=40):$/;" f _transpose_and_gather_feat src/evaluate.py /^from models.losses import _transpose_and_gather_feat$/;" i _transpose_and_gather_feat src/lib/models/decode.py /^from .utils import _gather_feat, _transpose_and_gather_feat$/;" i _transpose_and_gather_feat src/lib/models/losses.py /^from .utils import _transpose_and_gather_feat$/;" i _transpose_and_gather_feat src/lib/models/utils.py /^def _transpose_and_gather_feat(feat, ind):$/;" f _v_aggregate src/lib/models/decode.py /^def _v_aggregate(heat, aggr_weight=0.1):$/;" f _whctrs src/tools/calc_coco_overlap.py /^def _whctrs(anchor):$/;" f _write_voc_results_file src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def _write_voc_results_file(self, all_boxes):$/;" m class:pascal_voc a src/evaluate.py /^ a = ratio_al * l \/ 2$/;" v a_gt src/evaluate.py /^ a_gt = ellipse[2]$/;" v a_gt src/evaluate.py /^ a_gt = ellipse[3]$/;" v absolute_import scripts/fhd_opts.py /^from __future__ import absolute_import$/;" i absolute_import scripts/jupyter_opts.py /^from __future__ import absolute_import$/;" i absolute_import src/demo.py /^from __future__ import absolute_import$/;" i absolute_import src/evaluate.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/datasets/dataset/coco_fhd.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/datasets/dataset_factory.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/datasets/sample/eldet.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/detectors/base_detector.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/detectors/detector_factory.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/detectors/eldet.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/detectors/eldet_bbox.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/fhd_opts.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/logger.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/decode.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/losses.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/model.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/networks/DCNv2/dcn_v2.py /^from __future__ import absolute_import, division, print_function$/;" i absolute_import src/lib/models/networks/DCNv2/test/test.py /^from __future__ import absolute_import, division, print_function$/;" i absolute_import src/lib/models/networks/DCNv2/test/testcpu.py /^from __future__ import absolute_import, division, print_function$/;" i absolute_import src/lib/models/networks/DCNv2/test/testcuda.py /^from __future__ import absolute_import, division, print_function$/;" i absolute_import src/lib/models/networks/dlav0.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/networks/large_hourglass.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/networks/msra_resnet.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/networks/pose_dla_dcn.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/networks/resnet_dcn.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/models/utils.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/opts.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/trains/base_trainer.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/trains/eldet.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/trains/eldet_bbox.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/trains/eldet_onlyangle.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/trains/train_factory.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/ddd_utils.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/debugger.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/image.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/oracle_utils.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/post_process.py /^from __future__ import absolute_import$/;" i absolute_import src/lib/utils/utils.py /^from __future__ import absolute_import$/;" i absolute_import src/main.py /^from __future__ import absolute_import$/;" i absolute_import src/test.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/calc_coco_overlap.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/convert_hourglass_weight.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/convert_kitti_to_coco.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/eval_coco.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/eval_coco_hp.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/reval.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/datasets/ds_utils.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/datasets/imdb.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/datasets/pascal_voc.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/datasets/voc_eval.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/model/bbox_transform.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/model/config.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/model/nms_wrapper.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/model/test.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/utils/blob.py /^from __future__ import absolute_import$/;" i absolute_import src/tools/voc_eval_lib/utils/visualization.py /^from __future__ import absolute_import$/;" i add_2d_detection src/lib/utils/debugger.py /^ def add_2d_detection($/;" m class:Debugger add_3d_detection src/lib/utils/debugger.py /^ def add_3d_detection($/;" m class:Debugger add_bird_view src/lib/utils/debugger.py /^ def add_bird_view(self, dets, center_thresh=0.3, img_id='bird'):$/;" m class:Debugger add_bird_views src/lib/utils/debugger.py /^ def add_bird_views(self, dets_dt, dets_gt, center_thresh=0.3, img_id='bird'):$/;" m class:Debugger add_blend_img src/lib/utils/debugger.py /^ def add_blend_img(self, back, fore, img_id='blend', trans=0.7):$/;" m class:Debugger add_box src/tools/vis_pred.py /^def add_box(image, bbox, sc, cat_id):$/;" f add_coco_bbox src/lib/utils/debugger.py /^ def add_coco_bbox(self, bbox, cat, conf=1, show_txt=True, img_id='default'): $/;" m class:Debugger add_coco_hp src/lib/utils/debugger.py /^ def add_coco_hp(self, points, img_id='default'): $/;" m class:Debugger add_ct_detection src/lib/utils/debugger.py /^ def add_ct_detection($/;" m class:Debugger add_img src/lib/utils/debugger.py /^ def add_img(self, img, img_id='default', revert_color=False):$/;" m class:Debugger add_mask src/lib/utils/debugger.py /^ def add_mask(self, mask, bg, imgId = 'default', trans = 0.8):$/;" m class:Debugger add_path src/_init_paths.py /^def add_path(path):$/;" f add_path src/tools/_init_paths.py /^def add_path(path):$/;" f add_points src/lib/utils/debugger.py /^ def add_points(self, points, img_id='default'):$/;" m class:Debugger add_rect src/lib/utils/debugger.py /^ def add_rect(self, rect1, rect2, c, conf=1, img_id='default'): $/;" m class:Debugger affine_transform src/lib/datasets/sample/eldet.py /^from utils.image import get_affine_transform, affine_transform$/;" i affine_transform src/lib/utils/image.py /^def affine_transform(pt, t):$/;" f agnex_ct_decode src/lib/models/decode.py /^def agnex_ct_decode($/;" f all_except_two src/lib/models/Rotated_IoU/min_enclosing_box.py /^ def all_except_two(o1, o2):$/;" f function:generate_table alpha src/lib/utils/ddd_utils.py /^ alpha = -0.20$/;" v alpha src/tools/convert_kitti_to_coco.py /^ alpha = float(tmp[3])$/;" v alpha src/tools/kitti_eval/evaluate_object_3d.cpp /^ double alpha; \/\/ image orientation$/;" m struct:tBox file: alpha src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double alpha; \/\/ image orientation$/;" m struct:tBox file: alpha2rot_y src/lib/utils/ddd_utils.py /^def alpha2rot_y(alpha, x, cx, fx):$/;" f alpha2rot_y src/tools/convert_kitti_to_coco.py /^from utils.ddd_utils import compute_box_3d, project_to_image, alpha2rot_y$/;" i angle src/evaluate.py /^ angle = theta * 180$/;" v angle_errors src/evaluate.py /^ angle_errors = []$/;" v angle_gt src/evaluate.py /^ angle_gt = theta_gt * 180$/;" v angle_loss src/lib/models/losses.py /^def angle_loss(pred_angle, true_angle, size_average=False, use_smooth_l1=False):$/;" f ann src/evaluate.py /^ ann = anns[class_idx]$/;" v ann src/tools/convert_kitti_to_coco.py /^ ann = {'image_id': image_id,$/;" v ann_dir src/tools/convert_kitti_to_coco.py /^ ann_dir = DATA_PATH + 'training\/label_2\/'$/;" v ann_ids src/evaluate.py /^ ann_ids = dataset.coco.getAnnIds(imgIds=[img_id])$/;" v ann_path src/tools/convert_kitti_to_coco.py /^ ann_path = ann_dir + '{}.txt'.format(line)$/;" v anns src/evaluate.py /^ anns = dataset.coco.loadAnns(ids=ann_ids)$/;" v anns src/tools/convert_kitti_to_coco.py /^ anns = open(ann_path, 'r')$/;" v append_flipped_images src/tools/voc_eval_lib/datasets/imdb.py /^ def append_flipped_images(self):$/;" m class:imdb apply_nms src/tools/reval.py /^from model.test import apply_nms$/;" i apply_nms src/tools/voc_eval_lib/model/test.py /^def apply_nms(all_boxes, thresh):$/;" f argparse scripts/fhd_opts.py /^import argparse$/;" i argparse scripts/jupyter_opts.py /^import argparse$/;" i argparse src/lib/fhd_opts.py /^import argparse$/;" i argparse src/lib/models/Rotated_IoU/demo.py /^import argparse$/;" i argparse src/lib/opts.py /^import argparse$/;" i argparse src/tools/reval.py /^import os, argparse$/;" i args src/tools/reval.py /^ args = parse_args()$/;" v arraysize src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t arraysize[8]; \/* length of array in each dimension *\/$/;" m struct:__anon31 file: arraysize src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t arraysize[8]; \/* length of array in each dimension *\/$/;" m struct:__anon3 file: author src/lib/models/networks/DCNv2/setup.py /^ author="charlesshang",$/;" v b src/evaluate.py /^ b = ratio_bl * l \/ 2$/;" v b_gt src/evaluate.py /^ b_gt = ellipse[2]$/;" v b_gt src/evaluate.py /^ b_gt = ellipse[3]$/;" v backward src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ def backward(ctx, gradout):$/;" m class:SortVertices backward src/lib/models/networks/DCNv2/dcn_v2.py /^ def backward(ctx, grad_output):$/;" m class:_DCNv2 backward src/lib/models/networks/DCNv2/dcn_v2.py /^ def backward(ctx, grad_output):$/;" m class:_DCNv2Pooling bbox src/tools/convert_kitti_to_coco.py /^ bbox = [float(tmp[4]), float(tmp[5]), float(tmp[6]), float(tmp[7])]$/;" v bbox src/tools/vis_pred.py /^ bbox = _coco_box_to_bbox(pred['bbox'])$/;" v bbox src/tools/vis_pred.py /^ bbox = _coco_box_to_bbox(pred['bbox'])$/;" v bbox_ious src/evaluate.py /^ bbox_ious = []$/;" v bbox_overlaps src/tools/voc_eval_lib/datasets/bbox.pyx /^def bbox_overlaps($/;" f bbox_overlaps src/tools/voc_eval_lib/datasets/imdb.py /^def bbox_overlaps(box1, box2):$/;" f bbox_overlaps src/tools/voc_eval_lib/utils/bbox.pyx /^def bbox_overlaps($/;" f bbox_transform src/tools/voc_eval_lib/model/bbox_transform.py /^def bbox_transform(ex_rois, gt_rois):$/;" f bbox_transform_inv src/tools/voc_eval_lib/model/bbox_transform.py /^def bbox_transform_inv(boxes, deltas):$/;" f bbox_transform_inv src/tools/voc_eval_lib/model/test.py /^from model.bbox_transform import clip_boxes, bbox_transform_inv$/;" i bilinear_interp_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp /^T bilinear_interp_cpu($/;" f blend_ src/lib/utils/image.py /^def blend_(alpha, image1, image2):$/;" f box src/tools/kitti_eval/evaluate_object_3d.cpp /^ tBox box; \/\/ object type, box, orientation$/;" m struct:tGroundtruth file: box src/tools/kitti_eval/evaluate_object_3d.cpp /^ tBox box; \/\/ object type, box, orientation$/;" m struct:tDetection file: box src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tBox box; \/\/ object type, box, orientation$/;" m struct:tGroundtruth file: box src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tBox box; \/\/ object type, box, orientation$/;" m struct:tDetection file: box1 src/lib/models/Rotated_IoU/min_enclosing_box.py /^ box1 = [0, 0, 2, 3, np.pi\/6]$/;" v box1_in_box2 src/lib/models/Rotated_IoU/box_intersection_2d.py /^def box1_in_box2(corners1:torch.Tensor, corners2:torch.Tensor):$/;" f box2 src/lib/models/Rotated_IoU/min_enclosing_box.py /^ box2 = [1, 5, 4, 4, -np.pi\/4]$/;" v box2corners src/lib/models/Rotated_IoU/demo.py /^from utiles import box2corners$/;" i box2corners src/lib/models/Rotated_IoU/min_enclosing_box.py /^ from utiles import box2corners$/;" i box2corners src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from utiles import box2corners, box_intersection, box_in_box, box_intersection_area$/;" i box2corners src/lib/models/Rotated_IoU/utiles.py /^def box2corners(x, y, w, h, alpha):$/;" f box2corners src/lib/models/losses.py /^from .Rotated_IoU.utiles import box2corners$/;" i box2corners_th src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def box2corners_th(box:torch.Tensor)-> torch.Tensor:$/;" f box3DOverlap src/tools/kitti_eval/evaluate_object_3d.cpp /^inline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {$/;" f box3DOverlap src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^inline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {$/;" f box3d1 src/lib/models/Rotated_IoU/oriented_iou_loss.py /^ box3d1 = np.array([0,0,0,3,3,3,0])$/;" v box3d2 src/lib/models/Rotated_IoU/oriented_iou_loss.py /^ box3d2 = np.array([1,1,1,2,2,2,np.pi\/3])$/;" v box_0 src/lib/models/Rotated_IoU/test_corner_cases.py /^box_0 = torch.tensor([.0, .0, 2., 2., .0], device=device)$/;" v box_2d src/tools/convert_kitti_to_coco.py /^ box_2d = project_to_image(box_3d, calib)$/;" v box_3d src/tools/convert_kitti_to_coco.py /^ box_3d = compute_box_3d(dim, location, rotation_y)$/;" v box_in_box src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from utiles import box2corners, box_intersection, box_in_box, box_intersection_area$/;" i box_in_box src/lib/models/Rotated_IoU/utiles.py /^def box_in_box(corners1, corners2):$/;" f box_in_box_th src/lib/models/Rotated_IoU/box_intersection_2d.py /^def box_in_box_th(corners1:torch.Tensor, corners2:torch.Tensor):$/;" f box_in_box_th src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from box_intersection_2d import box_intersection_th, box_in_box_th, oriented_box_intersection_2d$/;" i box_intersection src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from utiles import box2corners, box_intersection, box_in_box, box_intersection_area$/;" i box_intersection src/lib/models/Rotated_IoU/utiles.py /^def box_intersection(corners1, corners2):$/;" f box_intersection_area src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from utiles import box2corners, box_intersection, box_in_box, box_intersection_area$/;" i box_intersection_area src/lib/models/Rotated_IoU/utiles.py /^def box_intersection_area(box1, box2):$/;" f box_intersection_th src/lib/models/Rotated_IoU/box_intersection_2d.py /^def box_intersection_th(corners1:torch.Tensor, corners2:torch.Tensor):$/;" f box_intersection_th src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^from box_intersection_2d import box_intersection_th, box_in_box_th, oriented_box_intersection_2d$/;" i br src/lib/utils/ddd_utils.py /^ br = np.array([810.73, 307.92], dtype=np.float32)$/;" v brightness_ src/lib/utils/image.py /^def brightness_(data_rng, image, gs, gs_mean, var):$/;" f buf src/tools/voc_eval_lib/nms/cpu_nms.c /^ void *buf;$/;" m struct:__anon29 file: buf src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void *buf;$/;" m struct:__anon1 file: build_ext src/tools/voc_eval_lib/setup.py /^from Cython.Distutils import build_ext$/;" i build_extensions src/tools/voc_eval_lib/setup.py /^ def build_extensions(self):$/;" m class:custom_build_ext build_vertices src/lib/models/Rotated_IoU/box_intersection_2d.py /^def build_vertices(corners1:torch.Tensor, corners2:torch.Tensor, $/;" f c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;$/;" m struct:__anon56 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;$/;" m struct:__anon48 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; double x; } __Pyx_st_double;$/;" m struct:__anon45 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; float x; } __Pyx_st_float;$/;" m struct:__anon44 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; int x; } __Pyx_st_int;$/;" m struct:__anon42 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; long double x; } __Pyx_st_longdouble;$/;" m struct:__anon46 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; long x; } __Pyx_st_long;$/;" m struct:__anon43 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; short x; } __Pyx_st_short;$/;" m struct:__anon41 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { char c; void *x; } __Pyx_st_void_p;$/;" m struct:__anon47 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { double x; char c; } __Pyx_pad_double;$/;" m struct:__anon53 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { float x; char c; } __Pyx_pad_float;$/;" m struct:__anon52 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { int x; char c; } __Pyx_pad_int;$/;" m struct:__anon50 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { long double x; char c; } __Pyx_pad_longdouble;$/;" m struct:__anon54 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { long x; char c; } __Pyx_pad_long;$/;" m struct:__anon51 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { short x; char c; } __Pyx_pad_short;$/;" m struct:__anon49 file: c src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct { void *x; char c; } __Pyx_pad_void_p;$/;" m struct:__anon55 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;$/;" m struct:__anon28 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;$/;" m struct:__anon20 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; double x; } __Pyx_st_double;$/;" m struct:__anon17 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; float x; } __Pyx_st_float;$/;" m struct:__anon16 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; int x; } __Pyx_st_int;$/;" m struct:__anon14 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; long double x; } __Pyx_st_longdouble;$/;" m struct:__anon18 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; long x; } __Pyx_st_long;$/;" m struct:__anon15 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; short x; } __Pyx_st_short;$/;" m struct:__anon13 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { char c; void *x; } __Pyx_st_void_p;$/;" m struct:__anon19 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { double x; char c; } __Pyx_pad_double;$/;" m struct:__anon25 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { float x; char c; } __Pyx_pad_float;$/;" m struct:__anon24 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { int x; char c; } __Pyx_pad_int;$/;" m struct:__anon22 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { long double x; char c; } __Pyx_pad_longdouble;$/;" m struct:__anon26 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { long x; char c; } __Pyx_pad_long;$/;" m struct:__anon23 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { short x; char c; } __Pyx_pad_short;$/;" m struct:__anon21 file: c src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct { void *x; char c; } __Pyx_pad_void_p;$/;" m struct:__anon27 file: cache_path src/tools/voc_eval_lib/datasets/imdb.py /^ def cache_path(self):$/;" m class:imdb cal_diou src/evaluate.py /^from models.Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_iou$/;" i cal_diou src/lib/models/Rotated_IoU/demo.py /^from oriented_iou_loss import cal_diou, cal_giou$/;" i cal_diou src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_diou(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"):$/;" f cal_diou src/lib/models/losses.py /^from .Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_diou_sincos$/;" i cal_diou_3d src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_diou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, enclosing_type:str="smallest"):$/;" f cal_diou_sincos src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_diou_sincos(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"):$/;" f cal_diou_sincos src/lib/models/losses.py /^from .Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_diou_sincos$/;" i cal_giou src/evaluate.py /^from models.Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_iou$/;" i cal_giou src/lib/models/Rotated_IoU/demo.py /^from oriented_iou_loss import cal_diou, cal_giou$/;" i cal_giou src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_giou(box1:torch.Tensor, box2:torch.Tensor, enclosing_type:str="smallest"):$/;" f cal_giou src/lib/models/losses.py /^from .Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_diou_sincos$/;" i cal_giou_3d src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_giou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, enclosing_type:str="smallest"):$/;" f cal_iou src/evaluate.py /^from models.Rotated_IoU.oriented_iou_loss import cal_diou, cal_giou, cal_iou$/;" i cal_iou src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_iou(box1:torch.Tensor, box2:torch.Tensor):$/;" f cal_iou src/lib/models/Rotated_IoU/test_corner_cases.py /^from oriented_iou_loss import cal_iou$/;" i cal_iou_3d src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_iou_3d(box3d1:torch.Tensor, box3d2:torch.Tensor, verbose=False):$/;" f cal_iou_sincos src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def cal_iou_sincos(box1:torch.Tensor, box2:torch.Tensor):$/;" f calculate_area src/lib/models/Rotated_IoU/box_intersection_2d.py /^def calculate_area(idx_sorted:torch.Tensor, vertices:torch.Tensor):$/;" f calib src/lib/utils/ddd_utils.py /^ calib = np.array($/;" v calib src/tools/convert_kitti_to_coco.py /^ calib = read_clib(calib_path)$/;" v calib_dir src/tools/convert_kitti_to_coco.py /^ calib_dir = DATA_PATH + '{}\/calib\/'$/;" v calib_path src/tools/convert_kitti_to_coco.py /^ calib_path = calib_dir.format(calib_type[split]) + '{}.txt'.format(line)$/;" v calib_type src/tools/convert_kitti_to_coco.py /^ calib_type = {'train': 'training', 'val': 'training', 'trainval': 'training',$/;" v cardinality src/lib/models/networks/dlav0.py /^ cardinality = 32$/;" v class:BottleneckX cardinality src/lib/models/networks/pose_dla_dcn.py /^ cardinality = 32$/;" v class:BottleneckX cat_id src/tools/convert_kitti_to_coco.py /^ cat_id = cat_ids[tmp[0]]$/;" v cat_id src/tools/vis_pred.py /^ cat_id = pred['category_id']$/;" v cat_id src/tools/vis_pred.py /^ cat_id = pred['category_id']$/;" v cat_ids src/tools/convert_kitti_to_coco.py /^cat_ids = {cat: i + 1 for i, cat in enumerate(cats)}$/;" v cat_info src/tools/convert_kitti_to_coco.py /^cat_info = []$/;" v cats src/tools/convert_kitti_to_coco.py /^cats = ['Pedestrian', 'Car', 'Cyclist', 'Van', 'Truck', 'Person_sitting',$/;" v cfg src/tools/voc_eval_lib/datasets/imdb.py /^from model.config import cfg$/;" i cfg src/tools/voc_eval_lib/datasets/pascal_voc.py /^from model.config import cfg$/;" i cfg src/tools/voc_eval_lib/model/config.py /^cfg = __C$/;" v cfg src/tools/voc_eval_lib/model/nms_wrapper.py /^from model.config import cfg$/;" i cfg src/tools/voc_eval_lib/model/test.py /^from model.config import cfg, get_output_dir$/;" i cfg_from_file src/tools/voc_eval_lib/model/config.py /^def cfg_from_file(filename):$/;" f cfg_from_list src/tools/voc_eval_lib/model/config.py /^def cfg_from_list(cfg_list):$/;" f changed src/tools/convert_hourglass_weight.py /^ changed = True$/;" v changed src/tools/convert_hourglass_weight.py /^ changed = False$/;" v check_gradient_dconv src/lib/models/networks/DCNv2/test/test.py /^def check_gradient_dconv():$/;" f check_gradient_dconv src/lib/models/networks/DCNv2/test/testcpu.py /^def check_gradient_dconv():$/;" f check_gradient_dconv src/lib/models/networks/DCNv2/test/testcuda.py /^def check_gradient_dconv():$/;" f check_gradient_dpooling src/lib/models/networks/DCNv2/test/test.py /^def check_gradient_dpooling():$/;" f check_gradient_dpooling src/lib/models/networks/DCNv2/test/testcpu.py /^def check_gradient_dpooling():$/;" f check_gradient_dpooling src/lib/models/networks/DCNv2/test/testcuda.py /^def check_gradient_dpooling():$/;" f check_pooling_zero_offset src/lib/models/networks/DCNv2/test/test.py /^def check_pooling_zero_offset():$/;" f check_pooling_zero_offset src/lib/models/networks/DCNv2/test/testcpu.py /^def check_pooling_zero_offset():$/;" f check_pooling_zero_offset src/lib/models/networks/DCNv2/test/testcuda.py /^def check_pooling_zero_offset():$/;" f check_zero_offset src/lib/models/networks/DCNv2/test/test.py /^def check_zero_offset():$/;" f check_zero_offset src/lib/models/networks/DCNv2/test/testcpu.py /^def check_zero_offset():$/;" f check_zero_offset src/lib/models/networks/DCNv2/test/testcuda.py /^def check_zero_offset():$/;" f class_id src/evaluate.py /^ class_id = class_idx + 1$/;" v class_name src/tools/calc_coco_overlap.py /^class_name = [$/;" v classes src/tools/voc_eval_lib/datasets/imdb.py /^ def classes(self):$/;" m class:imdb cleanData src/tools/kitti_eval/evaluate_object_3d.cpp /^void cleanData(CLASSES current_class, const vector >, const vector &det, vector &ignored_gt, vector &dc, vector &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){$/;" f cleanData src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^void cleanData(CLASSES current_class, const vector >, const vector &det, vector &ignored_gt, vector &dc, vector &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){$/;" f clip_boxes src/tools/voc_eval_lib/model/bbox_transform.py /^def clip_boxes(boxes, im_shape):$/;" f clip_boxes src/tools/voc_eval_lib/model/test.py /^from model.bbox_transform import clip_boxes, bbox_transform_inv$/;" i close src/lib/logger.py /^ def close(self):$/;" m class:Logger cls_angle_error src/evaluate.py /^ cls_angle_error = []$/;" v cls_bbox_iou src/evaluate.py /^ cls_bbox_iou = []$/;" v cls_dice src/evaluate.py /^ cls_dice = []$/;" v cls_iou src/evaluate.py /^ cls_iou = []$/;" v cmdclass src/lib/models/Rotated_IoU/cuda_op/setup.py /^ cmdclass={$/;" v cmdclass src/lib/models/networks/DCNv2/setup.py /^ cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},$/;" v cmdclass src/tools/voc_eval_lib/setup.py /^ cmdclass={'build_ext': custom_build_ext},$/;" v coco src/lib/datasets/dataset/coco_fhd.py /^import pycocotools.coco as coco$/;" i coco src/tools/eval_coco.py /^ coco = coco.COCO(ANN_PATH)$/;" v coco src/tools/eval_coco.py /^import pycocotools.coco as coco$/;" i coco src/tools/eval_coco_hp.py /^ coco = coco.COCO(ANN_PATH)$/;" v coco src/tools/eval_coco_hp.py /^import pycocotools.coco as coco$/;" i coco src/tools/vis_pred.py /^coco = coco.COCO(ANN_PATH)$/;" v coco src/tools/vis_pred.py /^import pycocotools.coco as coco$/;" i coco_class_name src/lib/utils/debugger.py /^coco_class_name = [$/;" v coco_eval src/tools/eval_coco.py /^ coco_eval = COCOeval(coco, dets, "bbox")$/;" v coco_eval src/tools/eval_coco_hp.py /^ coco_eval = COCOeval(coco, dets, "bbox")$/;" v coco_eval src/tools/eval_coco_hp.py /^ coco_eval = COCOeval(coco, dets, "keypoints")$/;" v code_line src/tools/voc_eval_lib/nms/cpu_nms.c /^ int code_line;$/;" m struct:__anon40 file: code_line src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int code_line;$/;" m struct:__anon12 file: code_object src/tools/voc_eval_lib/nms/cpu_nms.c /^ PyCodeObject* code_object;$/;" m struct:__anon40 file: code_object src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ PyCodeObject* code_object;$/;" m struct:__anon12 file: color_aug src/lib/datasets/sample/eldet.py /^from utils.image import flip, color_aug$/;" i color_aug src/lib/utils/image.py /^def color_aug(data_rng, image, eig_val, eig_vec):$/;" f color_list src/lib/utils/debugger.py /^color_list = color_list.reshape((-1, 3)) * 255$/;" v color_list src/lib/utils/debugger.py /^color_list = np.array($/;" v color_maps src/evaluate.py /^ color_maps = [(0, 0, 255), (0, 255, 255), (0, 128, 0), (0, 255, 0)]$/;" v compare_vertices src/lib/models/Rotated_IoU/utiles.py /^def compare_vertices(v1, v2):$/;" f competition_mode src/tools/voc_eval_lib/datasets/imdb.py /^ def competition_mode(self, on):$/;" m class:imdb competition_mode src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def competition_mode(self, on):$/;" m class:pascal_voc compose_vis_add src/lib/utils/debugger.py /^ def compose_vis_add($/;" m class:Debugger computeStatistics src/tools/kitti_eval/evaluate_object_3d.cpp /^tPrData computeStatistics(CLASSES current_class, const vector >,$/;" f computeStatistics src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^tPrData computeStatistics(CLASSES current_class, const vector >,$/;" f compute_bin_loss src/lib/models/losses.py /^def compute_bin_loss(output, target, mask):$/;" f compute_box_3d src/lib/utils/ddd_utils.py /^def compute_box_3d(dim, location, rotation_y):$/;" f compute_box_3d src/lib/utils/debugger.py /^from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d$/;" i compute_box_3d src/tools/convert_kitti_to_coco.py /^from utils.ddd_utils import compute_box_3d, project_to_image, alpha2rot_y$/;" i compute_dice src/evaluate.py /^def compute_dice(pred, label):$/;" f compute_iou src/evaluate.py /^def compute_iou(pred, label):$/;" f compute_orientation_3d src/lib/utils/ddd_utils.py /^def compute_orientation_3d(dim, location, rotation_y):$/;" f compute_res_loss src/lib/models/losses.py /^def compute_res_loss(output, target):$/;" f compute_rot_loss src/lib/models/losses.py /^def compute_rot_loss(output, target_bin, target_res, mask):$/;" f compute_rotated_bbox_iou src/evaluate.py /^def compute_rotated_bbox_iou(ell1, ell2):$/;" f compute_single_class_metric src/evaluate.py /^def compute_single_class_metric(ell1, ell2, shape):$/;" f contrast_ src/lib/utils/image.py /^def contrast_(data_rng, image, gs, gs_mean, var):$/;" f conv3x3 src/lib/models/networks/dlav0.py /^def conv3x3(in_planes, out_planes, stride=1):$/;" f conv3x3 src/lib/models/networks/msra_resnet.py /^def conv3x3(in_planes, out_planes, stride=1):$/;" f conv3x3 src/lib/models/networks/pose_dla_dcn.py /^def conv3x3(in_planes, out_planes, stride=1):$/;" f conv3x3 src/lib/models/networks/resnet_dcn.py /^def conv3x3(in_planes, out_planes, stride=1):$/;" f conv_identify src/lib/models/networks/DCNv2/test/test.py /^def conv_identify(weight, bias):$/;" f conv_identify src/lib/models/networks/DCNv2/test/testcpu.py /^def conv_identify(weight, bias):$/;" f conv_identify src/lib/models/networks/DCNv2/test/testcuda.py /^def conv_identify(weight, bias):$/;" f convert_eval_format src/lib/datasets/dataset/coco_fhd.py /^ def convert_eval_format(self, all_bboxes):$/;" m class:COCOFHD convolution src/lib/models/networks/large_hourglass.py /^class convolution(nn.Module):$/;" c corners1 src/lib/models/Rotated_IoU/min_enclosing_box.py /^ corners1 = box2corners(*box1) # 4, 2$/;" v corners2 src/lib/models/Rotated_IoU/min_enclosing_box.py /^ corners2 = box2corners(*box2) # 4, 2$/;" v count src/tools/calc_coco_overlap.py /^def count(split):$/;" f count src/tools/voc_eval_lib/nms/cpu_nms.c /^ int count;$/;" m struct:__Pyx_CodeObjectCache file: count src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int count;$/;" m struct:__Pyx_CodeObjectCache file: count_agnostic src/tools/calc_coco_overlap.py /^def count_agnostic(split):$/;" f count_anchor src/tools/calc_coco_overlap.py /^def count_anchor(split):$/;" f count_iou src/tools/calc_coco_overlap.py /^def count_iou(split):$/;" f count_size src/tools/calc_coco_overlap.py /^def count_size(split):$/;" f cpu_nms src/tools/voc_eval_lib/model/nms_wrapper.py /^from nms.cpu_nms import cpu_nms$/;" i cpu_nms src/tools/voc_eval_lib/nms/cpu_nms.pyx /^def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):$/;" f create_data src/lib/models/Rotated_IoU/demo.py /^def create_data(num):$/;" f create_model src/lib/detectors/base_detector.py /^from models.model import create_model, load_model$/;" i create_model src/lib/models/model.py /^def create_model(arch, heads, head_conv):$/;" f create_model src/main.py /^from models.model import create_model, load_model, save_model$/;" i create_network src/lib/models/Rotated_IoU/demo.py /^def create_network():$/;" f create_roidb_from_box_list src/tools/voc_eval_lib/datasets/imdb.py /^ def create_roidb_from_box_list(self, box_list, gt_roidb):$/;" m class:imdb crop src/lib/utils/image.py /^def crop(img, center, scale, output_size, rot=0):$/;" f ct src/lib/utils/ddd_utils.py /^ ct = (tl + br) \/ 2$/;" v ctdet_decode src/lib/detectors/eldet_bbox.py /^from models.decode import ctdet_decode$/;" i ctdet_decode src/lib/models/decode.py /^def ctdet_decode(heat, wh, reg=None, cat_spec_wh=False, K=100):$/;" f ctdet_decode src/lib/trains/eldet_bbox.py /^from models.decode import ctdet_decode$/;" i ctdet_post_process src/lib/detectors/eldet_bbox.py /^from utils.post_process import ctdet_post_process$/;" i ctdet_post_process src/lib/trains/eldet_bbox.py /^from utils.post_process import ctdet_post_process$/;" i ctdet_post_process src/lib/trains/eldet_onlyangle.py /^from utils.post_process import ctdet_post_process$/;" i ctdet_post_process src/lib/utils/post_process.py /^def ctdet_post_process(dets, c, s, h, w, num_classes):$/;" f ctr_precision src/evaluate.py /^ ctr_precision = 0$/;" v ctr_precision src/evaluate.py /^ ctr_precision = 1 - abs(ratio_p - ratio_g) \/ ratio_g$/;" v ctr_precisions src/evaluate.py /^ ctr_precisions = []$/;" v custom_build_ext src/tools/voc_eval_lib/setup.py /^class custom_build_ext(build_ext):$/;" c customize_compiler_for_nvcc src/tools/voc_eval_lib/setup.py /^def customize_compiler_for_nvcc(self):$/;" f cv2 src/demo.py /^import cv2$/;" i cv2 src/evaluate.py /^import cv2$/;" i cv2 src/lib/datasets/sample/eldet.py /^import cv2$/;" i cv2 src/lib/detectors/base_detector.py /^import cv2$/;" i cv2 src/lib/detectors/eldet.py /^import cv2$/;" i cv2 src/lib/detectors/eldet_bbox.py /^import cv2$/;" i cv2 src/lib/utils/ddd_utils.py /^import cv2$/;" i cv2 src/lib/utils/debugger.py /^import cv2$/;" i cv2 src/lib/utils/image.py /^import cv2$/;" i cv2 src/test.py /^import cv2$/;" i cv2 src/tools/calc_coco_overlap.py /^import cv2$/;" i cv2 src/tools/convert_kitti_to_coco.py /^import cv2$/;" i cv2 src/tools/eval_coco.py /^import cv2$/;" i cv2 src/tools/eval_coco_hp.py /^import cv2$/;" i cv2 src/tools/vis_pred.py /^import cv2$/;" i cv2 src/tools/voc_eval_lib/model/test.py /^import cv2$/;" i cv2 src/tools/voc_eval_lib/utils/blob.py /^import cv2$/;" i cx_gt src/evaluate.py /^ cx_gt = ellipse[0]$/;" v cy_gt src/evaluate.py /^ cy_gt = ellipse[1]$/;" v cython src/tools/voc_eval_lib/datasets/bbox.pyx /^cimport cython$/;" i cython src/tools/voc_eval_lib/utils/bbox.pyx /^cimport cython$/;" i cythonize src/lib/external/setup.py /^from Cython.Build import cythonize$/;" i d src/tools/voc_eval_lib/datasets/pascal_voc.py /^ d = pascal_voc('trainval', '2007')$/;" v data src/lib/datasets/dataset/coco_fhd.py /^import torch.utils.data as data$/;" i data src/lib/datasets/sample/eldet.py /^import torch.utils.data as data$/;" i data src/main.py /^import torch.utils.data$/;" i data src/tools/convert_hourglass_weight.py /^data = {'epoch': 0,$/;" v data src/tools/merge_pascal_json.py /^ data = json.load(open(ANNOT_PATH + file_name, 'r'))$/;" v data src/tools/voc_eval_lib/nms/cpu_nms.c /^ char *data;$/;" m struct:__anon39 file: data src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char *data;$/;" m struct:__anon11 file: data_parallel src/lib/models/data_parallel.py /^def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):$/;" f dataset src/evaluate.py /^ dataset = Dataset(opt, split)$/;" v dataset_factory src/evaluate.py /^from datasets.dataset_factory import dataset_factory$/;" i dataset_factory src/lib/datasets/dataset_factory.py /^dataset_factory = {$/;" v dataset_factory src/test.py /^from datasets.dataset_factory import dataset_factory$/;" i datasets src/tools/voc_eval_lib/datasets/pascal_voc.py /^import datasets.ds_utils as ds_utils$/;" i dcn_v2_backward src/lib/models/networks/DCNv2/src/dcn_v2.h /^dcn_v2_backward(const at::Tensor &input,$/;" f dcn_v2_conv src/lib/models/networks/DCNv2/dcn_v2.py /^dcn_v2_conv = _DCNv2.apply$/;" v dcn_v2_conv src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_conv src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_conv src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_cpu_backward src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.cpp /^std::vector dcn_v2_cpu_backward(const at::Tensor &input,$/;" f dcn_v2_cpu_forward src/lib/models/networks/DCNv2/src/cpu/dcn_v2_cpu.cpp /^dcn_v2_cpu_forward(const at::Tensor &input,$/;" f dcn_v2_forward src/lib/models/networks/DCNv2/src/dcn_v2.h /^dcn_v2_forward(const at::Tensor &input,$/;" f dcn_v2_pooling src/lib/models/networks/DCNv2/dcn_v2.py /^dcn_v2_pooling = _DCNv2Pooling.apply$/;" v dcn_v2_pooling src/lib/models/networks/DCNv2/test/test.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_pooling src/lib/models/networks/DCNv2/test/testcpu.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_pooling src/lib/models/networks/DCNv2/test/testcuda.py /^from dcn_v2 import DCN, DCNPooling, DCNv2, DCNv2Pooling, dcn_v2_conv, dcn_v2_pooling$/;" i dcn_v2_psroi_pooling_backward src/lib/models/networks/DCNv2/src/dcn_v2.h /^dcn_v2_psroi_pooling_backward(const at::Tensor &out_grad,$/;" f dcn_v2_psroi_pooling_cpu_backward src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp /^dcn_v2_psroi_pooling_cpu_backward(const at::Tensor &out_grad,$/;" f dcn_v2_psroi_pooling_cpu_forward src/lib/models/networks/DCNv2/src/cpu/dcn_v2_psroi_pooling_cpu.cpp /^dcn_v2_psroi_pooling_cpu_forward(const at::Tensor &input,$/;" f dcn_v2_psroi_pooling_forward src/lib/models/networks/DCNv2/src/dcn_v2.h /^dcn_v2_psroi_pooling_forward(const at::Tensor &input,$/;" f ddd2locrot src/lib/utils/ddd_utils.py /^def ddd2locrot(center, alpha, dim, depth, calib):$/;" f ddd2locrot src/lib/utils/post_process.py /^from .ddd_utils import ddd2locrot$/;" i ddd_decode src/lib/models/decode.py /^def ddd_decode(heat, rot, depth, dim, wh=None, reg=None, K=40):$/;" f ddd_post_process src/lib/utils/post_process.py /^def ddd_post_process(dets, c, s, calibs, opt):$/;" f ddd_post_process_2d src/lib/utils/post_process.py /^def ddd_post_process_2d(dets, c, s, opt):$/;" f ddd_post_process_3d src/lib/utils/post_process.py /^def ddd_post_process_3d(dets, calibs):$/;" f debug src/lib/detectors/base_detector.py /^ def debug(self, debugger, images, dets, output, scale=1):$/;" m class:BaseDetector debug src/lib/detectors/eldet.py /^ def debug(self, debugger, images, dets, output, scale=1):$/;" m class:EldetDetector debug src/lib/detectors/eldet_bbox.py /^ def debug(self, debugger, images, dets, output, scale=1):$/;" m class:EldetBBOXDetector debug src/lib/trains/base_trainer.py /^ def debug(self, batch, output, iter_id):$/;" m class:BaseTrainer debug src/lib/trains/eldet.py /^ def debug(self, batch, output, iter_id):$/;" m class:EldetTrainer debug src/lib/trains/eldet_bbox.py /^ def debug(self, batch, output, iter_id):$/;" m class:EldetTrainer debug src/lib/trains/eldet_onlyangle.py /^ def debug(self, batch, output, iter_id):$/;" m class:EldetTrainer decode_label_4iou src/lib/models/decode.py /^def decode_label_4iou(label, K=2): # Decode label for iou loss$/;" f decode_label_4iou src/lib/models/losses.py /^from .decode import decode_output_4iou, decode_label_4iou, decode_output_4iou_sincos$/;" i decode_output_4iou src/lib/models/decode.py /^def decode_output_4iou(output, K=2): # Decode output for iou loss$/;" f decode_output_4iou src/lib/models/losses.py /^from .decode import decode_output_4iou, decode_label_4iou, decode_output_4iou_sincos$/;" i decode_output_4iou_sincos src/lib/models/decode.py /^def decode_output_4iou_sincos(output, K=2): # Decode output for iou loss$/;" f decode_output_4iou_sincos src/lib/models/losses.py /^from .decode import decode_output_4iou, decode_label_4iou, decode_output_4iou_sincos$/;" i default_resolution src/lib/datasets/dataset/coco_fhd.py /^ default_resolution = [512, 512]$/;" v class:COCOFHD default_roidb src/tools/voc_eval_lib/datasets/imdb.py /^ def default_roidb(self):$/;" m class:imdb deformable_groups src/lib/models/networks/DCNv2/test/test.py /^deformable_groups = 1$/;" v deformable_groups src/lib/models/networks/DCNv2/test/testcpu.py /^deformable_groups = 1$/;" v deformable_groups src/lib/models/networks/DCNv2/test/testcuda.py /^deformable_groups = 1$/;" v demo src/demo.py /^def demo(opt):$/;" f depth src/tools/convert_kitti_to_coco.py /^ depth = np.array([location[2]], dtype=np.float32)$/;" v description src/lib/models/networks/DCNv2/setup.py /^ description="deformable convolutional networks",$/;" v detector src/evaluate.py /^ detector = Detector(opt)$/;" v detector_factory src/demo.py /^from detectors.detector_factory import detector_factory$/;" i detector_factory src/evaluate.py /^from detectors.detector_factory import detector_factory$/;" i detector_factory src/lib/detectors/detector_factory.py /^detector_factory = {$/;" v detector_factory src/test.py /^from detectors.detector_factory import detector_factory$/;" i dets src/tools/eval_coco.py /^ dets = coco.loadRes(pred_path)$/;" v dets src/tools/eval_coco_hp.py /^ dets = coco.loadRes(pred_path)$/;" v dets src/tools/vis_pred.py /^ dets = []$/;" v device src/lib/models/Rotated_IoU/test_corner_cases.py /^device = torch.device('cuda:0')$/;" v diameter_gt src/evaluate.py /^ diameter_gt = [1, 1]$/;" v diameter_pred src/evaluate.py /^ diameter_pred = [1, 1]$/;" v dices src/evaluate.py /^ dices = []$/;" v dim src/tools/convert_kitti_to_coco.py /^ dim = [float(tmp[8]), float(tmp[9]), float(tmp[10])]$/;" v diminfo src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_Buf_DimInfo diminfo[8];$/;" m struct:__anon39 file: diminfo src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_Buf_DimInfo diminfo[8];$/;" m struct:__anon11 file: division scripts/fhd_opts.py /^from __future__ import division$/;" i division scripts/jupyter_opts.py /^from __future__ import division$/;" i division src/demo.py /^from __future__ import division$/;" i division src/evaluate.py /^from __future__ import division$/;" i division src/lib/datasets/dataset/coco_fhd.py /^from __future__ import division$/;" i division src/lib/datasets/dataset_factory.py /^from __future__ import division$/;" i division src/lib/datasets/sample/eldet.py /^from __future__ import division$/;" i division src/lib/detectors/base_detector.py /^from __future__ import division$/;" i division src/lib/detectors/detector_factory.py /^from __future__ import division$/;" i division src/lib/detectors/eldet.py /^from __future__ import division$/;" i division src/lib/detectors/eldet_bbox.py /^from __future__ import division$/;" i division src/lib/fhd_opts.py /^from __future__ import division$/;" i division src/lib/logger.py /^from __future__ import division$/;" i division src/lib/models/decode.py /^from __future__ import division$/;" i division src/lib/models/losses.py /^from __future__ import division$/;" i division src/lib/models/model.py /^from __future__ import division$/;" i division src/lib/models/networks/DCNv2/dcn_v2.py /^from __future__ import absolute_import, division, print_function$/;" i division src/lib/models/networks/DCNv2/test/test.py /^from __future__ import absolute_import, division, print_function$/;" i division src/lib/models/networks/DCNv2/test/testcpu.py /^from __future__ import absolute_import, division, print_function$/;" i division src/lib/models/networks/DCNv2/test/testcuda.py /^from __future__ import absolute_import, division, print_function$/;" i division src/lib/models/networks/dlav0.py /^from __future__ import division$/;" i division src/lib/models/networks/large_hourglass.py /^from __future__ import division$/;" i division src/lib/models/networks/msra_resnet.py /^from __future__ import division$/;" i division src/lib/models/networks/pose_dla_dcn.py /^from __future__ import division$/;" i division src/lib/models/networks/resnet_dcn.py /^from __future__ import division$/;" i division src/lib/models/utils.py /^from __future__ import division$/;" i division src/lib/opts.py /^from __future__ import division$/;" i division src/lib/trains/base_trainer.py /^from __future__ import division$/;" i division src/lib/trains/eldet.py /^from __future__ import division$/;" i division src/lib/trains/eldet_bbox.py /^from __future__ import division$/;" i division src/lib/trains/eldet_onlyangle.py /^from __future__ import division$/;" i division src/lib/trains/train_factory.py /^from __future__ import division$/;" i division src/lib/utils/ddd_utils.py /^from __future__ import division$/;" i division src/lib/utils/debugger.py /^from __future__ import division$/;" i division src/lib/utils/image.py /^from __future__ import division$/;" i division src/lib/utils/oracle_utils.py /^from __future__ import division$/;" i division src/lib/utils/post_process.py /^from __future__ import division$/;" i division src/lib/utils/utils.py /^from __future__ import division$/;" i division src/main.py /^from __future__ import division$/;" i division src/test.py /^from __future__ import division$/;" i division src/tools/calc_coco_overlap.py /^from __future__ import division$/;" i division src/tools/convert_hourglass_weight.py /^from __future__ import division$/;" i division src/tools/convert_kitti_to_coco.py /^from __future__ import division$/;" i division src/tools/eval_coco.py /^from __future__ import division$/;" i division src/tools/eval_coco_hp.py /^from __future__ import division$/;" i division src/tools/reval.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/datasets/ds_utils.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/datasets/imdb.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/datasets/pascal_voc.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/datasets/voc_eval.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/model/bbox_transform.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/model/config.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/model/nms_wrapper.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/model/test.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/utils/blob.py /^from __future__ import division$/;" i division src/tools/voc_eval_lib/utils/visualization.py /^from __future__ import division$/;" i dla102 src/lib/models/networks/dlav0.py /^def dla102(pretrained=None, **kwargs): # DLA-102$/;" f dla102x src/lib/models/networks/dlav0.py /^def dla102x(pretrained=None, **kwargs): # DLA-X-102$/;" f dla102x2 src/lib/models/networks/dlav0.py /^def dla102x2(pretrained=None, **kwargs): # DLA-X-102 64$/;" f dla169 src/lib/models/networks/dlav0.py /^def dla169(pretrained=None, **kwargs): # DLA-169$/;" f dla34 src/lib/models/networks/dlav0.py /^def dla34(pretrained, **kwargs): # DLA-34$/;" f dla34 src/lib/models/networks/pose_dla_dcn.py /^def dla34(pretrained=True, **kwargs): # DLA-34$/;" f dla46_c src/lib/models/networks/dlav0.py /^def dla46_c(pretrained=None, **kwargs): # DLA-46-C$/;" f dla46x_c src/lib/models/networks/dlav0.py /^def dla46x_c(pretrained=None, **kwargs): # DLA-X-46-C$/;" f dla60 src/lib/models/networks/dlav0.py /^def dla60(pretrained=None, **kwargs): # DLA-60$/;" f dla60x src/lib/models/networks/dlav0.py /^def dla60x(pretrained=None, **kwargs): # DLA-X-60$/;" f dla60x_c src/lib/models/networks/dlav0.py /^def dla60x_c(pretrained, **kwargs): # DLA-X-60-C$/;" f dmcn_get_coordinate_weight_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w,$/;" f dmcn_get_gradient_weight_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w,$/;" f dmcn_im2col_bilinear_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_width,$/;" f draw_bounding_boxes src/tools/voc_eval_lib/utils/visualization.py /^def draw_bounding_boxes(image, gt_boxes, im_info):$/;" f draw_box_3d src/lib/utils/ddd_utils.py /^def draw_box_3d(image, corners, c=(0, 0, 255)):$/;" f draw_box_3d src/lib/utils/debugger.py /^from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d$/;" i draw_box_3d src/tools/convert_kitti_to_coco.py /^from utils.ddd_utils import draw_box_3d, unproject_2d_to_3d$/;" i draw_dense_reg src/lib/datasets/sample/eldet.py /^from utils.image import draw_dense_reg$/;" i draw_dense_reg src/lib/utils/image.py /^def draw_dense_reg(regmap, heatmap, center, value, radius, is_offset=False):$/;" f draw_msra_gaussian src/lib/datasets/sample/eldet.py /^from utils.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian$/;" i draw_msra_gaussian src/lib/utils/image.py /^def draw_msra_gaussian(heatmap, center, sigma):$/;" f draw_umich_gaussian src/lib/datasets/sample/eldet.py /^from utils.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian$/;" i draw_umich_gaussian src/lib/utils/image.py /^def draw_umich_gaussian(heatmap, center, radius, k=1):$/;" f ds_utils src/tools/voc_eval_lib/datasets/pascal_voc.py /^import datasets.ds_utils as ds_utils$/;" i dtype src/lib/datasets/dataset/coco_fhd.py /^ dtype=np.float32).reshape(1, 1, 3)$/;" v class:COCOFHD dtype src/lib/utils/ddd_utils.py /^ dtype=np.float32)$/;" v dtype src/tools/convert_kitti_to_coco.py /^ dtype=np.float32)$/;" v edict src/tools/voc_eval_lib/model/config.py /^from easydict import EasyDict as edict$/;" i eigenvector_22 src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def eigenvector_22(x:torch.Tensor):$/;" f eldet_decode src/lib/detectors/eldet.py /^from models.decode import eldet_decode, eldet_decode_sincos$/;" i eldet_decode src/lib/models/decode.py /^def eldet_decode(heat, l, ratio_al, ratio_bl, theta, reg=None, cat_spec_wh=False, K=2): # Decode ellipse information$/;" f eldet_decode src/lib/trains/eldet.py /^from models.decode import eldet_decode$/;" i eldet_decode_sincos src/lib/detectors/eldet.py /^from models.decode import eldet_decode, eldet_decode_sincos$/;" i eldet_decode_sincos src/lib/models/decode.py /^def eldet_decode_sincos(heat, l, ratio_al, ratio_bl, theta, sincos, reg=None, cat_spec_wh=False, K=2): # Decode ellipse information with sin and cos$/;" f eldet_post_process src/lib/detectors/eldet.py /^from utils.post_process import eldet_post_process, eldet_post_process_sincos$/;" i eldet_post_process src/lib/trains/eldet.py /^from utils.post_process import eldet_post_process$/;" i eldet_post_process src/lib/utils/post_process.py /^def eldet_post_process(dets, c, s, h, w, num_classes):$/;" f eldet_post_process_sincos src/lib/detectors/eldet.py /^from utils.post_process import eldet_post_process, eldet_post_process_sincos$/;" i eldet_post_process_sincos src/lib/utils/post_process.py /^def eldet_post_process_sincos(dets, c, s, h, w, num_classes):$/;" f ellipse src/evaluate.py /^ ellipse = ann['ellipse']$/;" v embed src/tools/voc_eval_lib/datasets/pascal_voc.py /^ from IPython import embed;$/;" i enc_count src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t new_count, enc_count;$/;" m struct:__anon33 file: enc_count src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t new_count, enc_count;$/;" m struct:__anon5 file: enc_packmode src/tools/voc_eval_lib/nms/cpu_nms.c /^ char enc_packmode;$/;" m struct:__anon33 file: enc_packmode src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char enc_packmode;$/;" m struct:__anon5 file: enc_type src/tools/voc_eval_lib/nms/cpu_nms.c /^ char enc_type;$/;" m struct:__anon33 file: enc_type src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char enc_type;$/;" m struct:__anon5 file: enclosing_box src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def enclosing_box(corners1:torch.Tensor, corners2:torch.Tensor, enclosing_type:str="smallest"):$/;" f enclosing_box_aligned src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def enclosing_box_aligned(corners1:torch.Tensor, corners2:torch.Tensor):$/;" f enclosing_box_pca src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def enclosing_box_pca(corners1:torch.Tensor, corners2:torch.Tensor):$/;" f encoding src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon30 file: encoding src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon2 file: entries src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_CodeObjectCacheEntry* entries;$/;" m struct:__Pyx_CodeObjectCache file: entries src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_CodeObjectCacheEntry* entries;$/;" m struct:__Pyx_CodeObjectCache file: error src/evaluate.py /^ error = compute_single_class_metric(pred_ell, gt_ell, shape)$/;" v etree src/tools/voc_eval_lib/datasets/pascal_voc.py /^import xml.etree.ElementTree as ET$/;" i etree src/tools/voc_eval_lib/datasets/voc_eval.py /^import xml.etree.ElementTree as ET$/;" i eval src/tools/kitti_eval/evaluate_object_3d.cpp /^bool eval(string result_sha,Mail* mail){$/;" f eval src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^bool eval(string gt_dir, string result_dir, Mail* mail){$/;" f eval_class src/tools/kitti_eval/evaluate_object_3d.cpp /^bool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class,$/;" f eval_class src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^bool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class,$/;" f evaluate_detections src/tools/voc_eval_lib/datasets/imdb.py /^ def evaluate_detections(self, all_boxes, output_dir=None):$/;" m class:imdb evaluate_detections src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def evaluate_detections(self, all_boxes, output_dir=None):$/;" m class:pascal_voc evaluate_recall src/tools/voc_eval_lib/datasets/imdb.py /^ def evaluate_recall(self, candidate_boxes=None, thresholds=None,$/;" m class:imdb example_dconv src/lib/models/networks/DCNv2/test/test.py /^def example_dconv():$/;" f example_dconv src/lib/models/networks/DCNv2/test/testcpu.py /^def example_dconv():$/;" f example_dconv src/lib/models/networks/DCNv2/test/testcuda.py /^def example_dconv():$/;" f example_dpooling src/lib/models/networks/DCNv2/test/test.py /^def example_dpooling():$/;" f example_dpooling src/lib/models/networks/DCNv2/test/testcpu.py /^def example_dpooling():$/;" f example_dpooling src/lib/models/networks/DCNv2/test/testcuda.py /^def example_dpooling():$/;" f example_mdpooling src/lib/models/networks/DCNv2/test/test.py /^def example_mdpooling():$/;" f example_mdpooling src/lib/models/networks/DCNv2/test/testcpu.py /^def example_mdpooling():$/;" f example_mdpooling src/lib/models/networks/DCNv2/test/testcuda.py /^def example_mdpooling():$/;" f exclude src/lib/models/networks/DCNv2/setup.py /^ exclude=($/;" v exct_decode src/lib/models/decode.py /^def exct_decode($/;" f exkp src/lib/models/networks/large_hourglass.py /^class exkp(nn.Module):$/;" c exp_name src/evaluate.py /^ exp_name = opt.load_model.split('\/')[-2]$/;" v expansion src/lib/models/networks/dlav0.py /^ expansion = 2$/;" v class:Bottleneck expansion src/lib/models/networks/dlav0.py /^ expansion = 2$/;" v class:BottleneckX expansion src/lib/models/networks/msra_resnet.py /^ expansion = 1$/;" v class:BasicBlock expansion src/lib/models/networks/msra_resnet.py /^ expansion = 4$/;" v class:Bottleneck expansion src/lib/models/networks/pose_dla_dcn.py /^ expansion = 2$/;" v class:Bottleneck expansion src/lib/models/networks/pose_dla_dcn.py /^ expansion = 2$/;" v class:BottleneckX expansion src/lib/models/networks/resnet_dcn.py /^ expansion = 1$/;" v class:BasicBlock expansion src/lib/models/networks/resnet_dcn.py /^ expansion = 4$/;" v class:Bottleneck ext_modules src/lib/external/setup.py /^ ext_modules=cythonize(extensions),$/;" v ext_modules src/lib/models/Rotated_IoU/cuda_op/setup.py /^ ext_modules=[$/;" v ext_modules src/lib/models/networks/DCNv2/setup.py /^ ext_modules=get_extensions(),$/;" v ext_modules src/tools/voc_eval_lib/setup.py /^ ext_modules=ext_modules,$/;" v ext_modules src/tools/voc_eval_lib/setup.py /^ext_modules = [$/;" v extensions src/lib/external/setup.py /^extensions = [$/;" v extra_compile_args src/lib/external/setup.py /^ extra_compile_args=["-Wno-cpp", "-Wno-unused-function"]$/;" v fhd_class_name src/lib/utils/debugger.py /^fhd_class_name = [$/;" v field src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_StructField* field;$/;" m struct:__anon32 file: field src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_StructField* field;$/;" m struct:__anon4 file: fields src/tools/voc_eval_lib/nms/cpu_nms.c /^ struct __Pyx_StructField_* fields;$/;" m struct:__anon31 typeref:struct:__anon31::__Pyx_StructField_ file: fields src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ struct __Pyx_StructField_* fields;$/;" m struct:__anon3 typeref:struct:__anon3::__Pyx_StructField_ file: fill_fc_weights src/lib/models/networks/dlav0.py /^def fill_fc_weights(layers):$/;" f fill_fc_weights src/lib/models/networks/pose_dla_dcn.py /^def fill_fc_weights(layers):$/;" f fill_fc_weights src/lib/models/networks/resnet_dcn.py /^def fill_fc_weights(layers):$/;" f fill_up_weights src/lib/models/networks/dlav0.py /^def fill_up_weights(up):$/;" f fill_up_weights src/lib/models/networks/pose_dla_dcn.py /^def fill_up_weights(up):$/;" f fill_up_weights src/lib/models/networks/resnet_dcn.py /^def fill_up_weights(up):$/;" f filter_small_boxes src/tools/voc_eval_lib/datasets/ds_utils.py /^def filter_small_boxes(boxes, min_size):$/;" f find_in_path src/tools/voc_eval_lib/setup.py /^def find_in_path(name, path):$/;" f find_packages src/lib/models/networks/DCNv2/setup.py /^from setuptools import find_packages, setup$/;" i flags src/lib/models/Rotated_IoU/demo.py /^ flags = parser.parse_args()$/;" v flags src/tools/voc_eval_lib/nms/cpu_nms.c /^ int flags;$/;" m struct:__anon31 file: flags src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int flags;$/;" m struct:__anon3 file: flip src/lib/datasets/sample/eldet.py /^from utils.image import flip, color_aug$/;" i flip src/lib/utils/image.py /^def flip(img):$/;" f flip_lr src/lib/models/utils.py /^def flip_lr(x, flip_idx):$/;" f flip_lr_off src/lib/models/utils.py /^def flip_lr_off(x, flip_idx):$/;" f flip_tensor src/lib/detectors/eldet.py /^from models.utils import flip_tensor$/;" i flip_tensor src/lib/detectors/eldet_bbox.py /^from models.utils import flip_tensor$/;" i flip_tensor src/lib/models/utils.py /^def flip_tensor(x):$/;" f fmt_offset src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t fmt_offset;$/;" m struct:__anon33 file: fmt_offset src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t fmt_offset;$/;" m struct:__anon5 file: fn src/tools/kitti_eval/evaluate_object_3d.cpp /^ int32_t fn; \/\/ false negatives$/;" m struct:tPrData file: fn src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ int32_t fn; \/\/ false negatives$/;" m struct:tPrData file: format src/tools/voc_eval_lib/nms/cpu_nms.c /^ char *format;$/;" m struct:__anon29 file: format src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char *format;$/;" m struct:__anon1 file: forward src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ def forward(ctx, vertices, mask, num_valid):$/;" m class:SortVertices forward src/lib/models/data_parallel.py /^ def forward(self, *inputs, **kwargs):$/;" m class:_DataParallel forward src/lib/models/losses.py /^ def forward(self, out, target):$/;" m class:FocalLoss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, rotbin, rotres):$/;" m class:BinRotLoss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:L1Loss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:NormRegL1Loss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:RegL1Loss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:RegL1Loss4Angle forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:RegLoss forward src/lib/models/losses.py /^ def forward(self, output, mask, ind, target):$/;" m class:RegWeightedL1Loss forward src/lib/models/losses.py /^ def forward(self, output, target):$/;" m class:IoULoss forward src/lib/models/losses.py /^ def forward(self, output, target):$/;" m class:IoULoss4Angle forward src/lib/models/losses.py /^ def forward(self, output, target):$/;" m class:IoULossSincos forward src/lib/models/losses.py /^ def forward(self, output, target):$/;" m class:L1LossRegression forward src/lib/models/losses.py /^ def forward(self, pred, target):$/;" m class:AdaptiveWingLoss forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward($/;" m class:_DCNv2Pooling forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward(ctx, input, offset, mask, weight, bias, stride, padding, dilation, deformable_groups):$/;" m class:_DCNv2 forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward(self, input):$/;" m class:DCN forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward(self, input, offset, mask):$/;" m class:DCNv2 forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward(self, input, rois):$/;" m class:DCNPooling forward src/lib/models/networks/DCNv2/dcn_v2.py /^ def forward(self, input, rois, offset):$/;" m class:DCNv2Pooling forward src/lib/models/networks/dlav0.py /^ def forward(self, *x):$/;" m class:Root forward src/lib/models/networks/dlav0.py /^ def forward(self, layers):$/;" m class:DLAUp forward src/lib/models/networks/dlav0.py /^ def forward(self, layers):$/;" m class:IDAUp forward src/lib/models/networks/dlav0.py /^ def forward(self, x):$/;" m class:DLA forward src/lib/models/networks/dlav0.py /^ def forward(self, x):$/;" m class:DLASeg forward src/lib/models/networks/dlav0.py /^ def forward(self, x):$/;" m class:Identity forward src/lib/models/networks/dlav0.py /^ def forward(self, x, residual=None):$/;" m class:BasicBlock forward src/lib/models/networks/dlav0.py /^ def forward(self, x, residual=None):$/;" m class:Bottleneck forward src/lib/models/networks/dlav0.py /^ def forward(self, x, residual=None):$/;" m class:BottleneckX forward src/lib/models/networks/dlav0.py /^ def forward(self, x, residual=None, children=None):$/;" m class:Tree forward src/lib/models/networks/large_hourglass.py /^ def forward(self, image):$/;" m class:exkp forward src/lib/models/networks/large_hourglass.py /^ def forward(self, up1, up2):$/;" m class:MergeUp forward src/lib/models/networks/large_hourglass.py /^ def forward(self, x):$/;" m class:convolution forward src/lib/models/networks/large_hourglass.py /^ def forward(self, x):$/;" m class:fully_connected forward src/lib/models/networks/large_hourglass.py /^ def forward(self, x):$/;" m class:kp_module forward src/lib/models/networks/large_hourglass.py /^ def forward(self, x):$/;" m class:residual forward src/lib/models/networks/msra_resnet.py /^ def forward(self, x):$/;" m class:BasicBlock forward src/lib/models/networks/msra_resnet.py /^ def forward(self, x):$/;" m class:Bottleneck forward src/lib/models/networks/msra_resnet.py /^ def forward(self, x):$/;" m class:PoseResNet forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, *x):$/;" m class:Root forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, layers):$/;" m class:DLAUp forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, layers, startp, endp):$/;" m class:IDAUp forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x):$/;" m class:DLA forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x):$/;" m class:DLASeg forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x):$/;" m class:DeformConv forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x):$/;" m class:Identity forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x):$/;" m class:Interpolate forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x, residual=None):$/;" m class:BasicBlock forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x, residual=None):$/;" m class:Bottleneck forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x, residual=None):$/;" m class:BottleneckX forward src/lib/models/networks/pose_dla_dcn.py /^ def forward(self, x, residual=None, children=None):$/;" m class:Tree forward src/lib/models/networks/resnet_dcn.py /^ def forward(self, x):$/;" m class:BasicBlock forward src/lib/models/networks/resnet_dcn.py /^ def forward(self, x):$/;" m class:Bottleneck forward src/lib/models/networks/resnet_dcn.py /^ def forward(self, x):$/;" m class:PoseResNet forward src/lib/trains/base_trainer.py /^ def forward(self, batch):$/;" m class:ModelWithLoss forward src/lib/trains/eldet.py /^ def forward(self, outputs, batch):$/;" m class:EldetLoss forward src/lib/trains/eldet_bbox.py /^ def forward(self, outputs, batch):$/;" m class:EldetLoss forward src/lib/trains/eldet_onlyangle.py /^ def forward(self, outputs, batch):$/;" m class:EldetLoss fp src/tools/kitti_eval/evaluate_object_3d.cpp /^ int32_t fp; \/\/ false positives$/;" m struct:tPrData file: fp src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ int32_t fp; \/\/ false positives$/;" m struct:tPrData file: from_dets src/tools/reval.py /^def from_dets(imdb_name, detection_file, args):$/;" f fully_connected src/lib/models/networks/large_hourglass.py /^class fully_connected(nn.Module):$/;" c functools src/lib/models/Rotated_IoU/utiles.py /^import functools$/;" i gather src/lib/models/data_parallel.py /^ def gather(self, outputs, output_device):$/;" m class:_DataParallel gather src/lib/models/data_parallel.py /^from torch.nn.parallel.scatter_gather import gather$/;" i gather_lines_points src/lib/models/Rotated_IoU/min_enclosing_box.py /^def gather_lines_points(corners:torch.Tensor):$/;" f gaussian2D src/lib/utils/image.py /^def gaussian2D(shape, sigma=1):$/;" f gaussian_radius src/lib/datasets/sample/eldet.py /^from utils.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian$/;" i gaussian_radius src/lib/utils/image.py /^def gaussian_radius(det_size, min_overlap=0.7):$/;" f gen_colormap src/lib/utils/debugger.py /^ def gen_colormap(self, img, output_res=None):$/;" m class:Debugger gen_colormap_hp src/lib/utils/debugger.py /^ def gen_colormap_hp(self, img, output_res=None):$/;" m class:Debugger gen_oracle_map src/lib/trains/eldet.py /^from utils.oracle_utils import gen_oracle_map$/;" i gen_oracle_map src/lib/trains/eldet_bbox.py /^from utils.oracle_utils import gen_oracle_map$/;" i gen_oracle_map src/lib/trains/eldet_onlyangle.py /^from utils.oracle_utils import gen_oracle_map$/;" i gen_oracle_map src/lib/utils/oracle_utils.py /^def gen_oracle_map(feat, ind, w, h):$/;" f generate_anchors src/tools/calc_coco_overlap.py /^def generate_anchors($/;" f generate_table src/lib/models/Rotated_IoU/min_enclosing_box.py /^def generate_table():$/;" f getEvalIndices src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector getEvalIndices(const string& result_dir) {$/;" f getThresholds src/tools/kitti_eval/evaluate_object_3d.cpp /^vector getThresholds(vector &v, double n_groundtruth){$/;" f getThresholds src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector getThresholds(vector &v, double n_groundtruth){$/;" f get_3rd_point src/lib/utils/image.py /^def get_3rd_point(a, b):$/;" f get_affine_transform src/lib/datasets/sample/eldet.py /^from utils.image import get_affine_transform, affine_transform$/;" i get_affine_transform src/lib/detectors/base_detector.py /^from utils.image import get_affine_transform$/;" i get_affine_transform src/lib/detectors/eldet.py /^from utils.image import get_affine_transform$/;" i get_affine_transform src/lib/detectors/eldet_bbox.py /^from utils.image import get_affine_transform$/;" i get_affine_transform src/lib/utils/image.py /^def get_affine_transform(center,$/;" f get_alpha src/lib/utils/post_process.py /^def get_alpha(rot):$/;" f get_dataset src/lib/datasets/dataset_factory.py /^def get_dataset(dataset, task):$/;" f get_dataset src/main.py /^from datasets.dataset_factory import get_dataset$/;" i get_dir src/lib/utils/image.py /^def get_dir(src_point, rot_rad):$/;" f get_dla_dcn src/lib/models/model.py /^from .networks.pose_dla_dcn import get_pose_net as get_dla_dcn$/;" i get_dlav0 src/lib/models/model.py /^from .networks.dlav0 import get_pose_net as get_dlav0$/;" i get_extensions src/lib/models/networks/DCNv2/setup.py /^def get_extensions():$/;" f get_large_hourglass_net src/lib/models/model.py /^from .networks.large_hourglass import get_large_hourglass_net$/;" i get_large_hourglass_net src/lib/models/networks/large_hourglass.py /^def get_large_hourglass_net(num_layers, heads, head_conv):$/;" f get_model_url src/lib/models/networks/dlav0.py /^def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'):$/;" f get_model_url src/lib/models/networks/pose_dla_dcn.py /^def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'):$/;" f get_output_dir src/tools/voc_eval_lib/model/config.py /^def get_output_dir(imdb, weights_filename):$/;" f get_output_dir src/tools/voc_eval_lib/model/test.py /^from model.config import cfg, get_output_dir$/;" i get_output_tb_dir src/tools/voc_eval_lib/model/config.py /^def get_output_tb_dir(imdb, weights_filename):$/;" f get_pose_net src/lib/models/model.py /^from .networks.msra_resnet import get_pose_net$/;" i get_pose_net src/lib/models/networks/dlav0.py /^def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4):$/;" f get_pose_net src/lib/models/networks/msra_resnet.py /^def get_pose_net(num_layers, heads, head_conv):$/;" f get_pose_net src/lib/models/networks/pose_dla_dcn.py /^def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4):$/;" f get_pose_net src/lib/models/networks/resnet_dcn.py /^def get_pose_net(num_layers, heads, head_conv=256):$/;" f get_pose_net_dcn src/lib/models/model.py /^from .networks.resnet_dcn import get_pose_net as get_pose_net_dcn$/;" i get_pred_depth src/lib/utils/post_process.py /^def get_pred_depth(depth):$/;" f getbufferproc src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);$/;" t file: getbufferproc src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);$/;" t file: glob src/evaluate.py /^from glob import glob$/;" i glob src/lib/models/networks/DCNv2/setup.py /^import glob$/;" i gpu_nms src/tools/voc_eval_lib/model/nms_wrapper.py /^from nms.gpu_nms import gpu_nms$/;" i gpu_nms src/tools/voc_eval_lib/nms/gpu_nms.pyx /^def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh,$/;" f gradcheck src/lib/models/networks/DCNv2/test/test.py /^from torch.autograd import gradcheck$/;" i gradcheck src/lib/models/networks/DCNv2/test/testcpu.py /^from torch.autograd import gradcheck$/;" i gradcheck src/lib/models/networks/DCNv2/test/testcuda.py /^from torch.autograd import gradcheck$/;" i grayscale src/lib/utils/image.py /^def grayscale(image):$/;" f groundBoxOverlap src/tools/kitti_eval/evaluate_object_3d.cpp /^inline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {$/;" f groundBoxOverlap src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^inline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {$/;" f gt_ell src/evaluate.py /^ gt_ell = [cx_gt, cy_gt, a_gt, b_gt, angle_gt]$/;" v gt_ids src/tools/vis_pred.py /^ gt_ids = coco.getAnnIds(imgIds=[img_id])$/;" v gt_img src/tools/vis_pred.py /^ gt_img = add_box(gt_img, bbox, 0, cat_id)$/;" v gt_img src/tools/vis_pred.py /^ gt_img = img.copy()$/;" v gt_roidb src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def gt_roidb(self):$/;" m class:pascal_voc gta_class_name src/lib/utils/debugger.py /^gta_class_name = [$/;" v gts src/tools/vis_pred.py /^ gts = coco.loadAnns(gt_ids)$/;" v h src/tools/kitti_eval/evaluate_object_3d.cpp /^ double h, w, l;$/;" m struct:tDetection file: h src/tools/kitti_eval/evaluate_object_3d.cpp /^ double h, w, l;$/;" m struct:tGroundtruth file: h src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double h, w, l;$/;" m struct:tDetection file: h src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double h, w, l;$/;" m struct:tGroundtruth file: head src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_BufFmt_StackElem* head;$/;" m struct:__anon33 file: head src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_BufFmt_StackElem* head;$/;" m struct:__anon5 file: help src/lib/models/Rotated_IoU/demo.py /^ help="type of enclosing box. support: aligned (axis-aligned) or pca (rotated) or smallest (rotated). [default: smallest]")$/;" v im_detect src/tools/voc_eval_lib/model/test.py /^def im_detect(sess, net, im):$/;" f im_list_to_blob src/tools/voc_eval_lib/model/test.py /^from utils.blob import im_list_to_blob$/;" i im_list_to_blob src/tools/voc_eval_lib/utils/blob.py /^def im_list_to_blob(ims):$/;" f imag src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef struct { double real, imag; } __pyx_t_double_complex;$/;" m struct:__anon35 file: imag src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef struct { float real, imag; } __pyx_t_float_complex;$/;" m struct:__anon34 file: imag src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef struct { double real, imag; } __pyx_t_double_complex;$/;" m struct:__anon7 file: imag src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef struct { float real, imag; } __pyx_t_float_complex;$/;" m struct:__anon6 file: image src/evaluate.py /^ image = cv2.imread(img_path)$/;" v image src/tools/convert_kitti_to_coco.py /^ image = draw_box_3d(image, box_2d)$/;" v image src/tools/convert_kitti_to_coco.py /^ image = cv2.imread($/;" v imageBoxOverlap src/tools/kitti_eval/evaluate_object_3d.cpp /^inline double imageBoxOverlap(tBox a, tBox b, int32_t criterion=-1){$/;" f imageBoxOverlap src/tools/kitti_eval/evaluate_object_3d.cpp /^inline double imageBoxOverlap(tDetection a, tGroundtruth b, int32_t criterion=-1){$/;" f imageBoxOverlap src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^inline double imageBoxOverlap(tBox a, tBox b, int32_t criterion=-1){$/;" f imageBoxOverlap src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^inline double imageBoxOverlap(tDetection a, tGroundtruth b, int32_t criterion=-1){$/;" f image_ext src/demo.py /^image_ext = ['jpg', 'jpeg', 'png', 'webp']$/;" v image_id src/tools/convert_kitti_to_coco.py /^ image_id = int(line)$/;" v image_index src/tools/voc_eval_lib/datasets/imdb.py /^ def image_index(self):$/;" m class:imdb image_info src/tools/convert_kitti_to_coco.py /^ image_info = {'file_name': '{}.png'.format(line),$/;" v image_path_at src/tools/voc_eval_lib/datasets/imdb.py /^ def image_path_at(self, i):$/;" m class:imdb image_path_at src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def image_path_at(self, i):$/;" m class:pascal_voc image_path_from_index src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def image_path_from_index(self, index):$/;" m class:pascal_voc image_set src/tools/convert_kitti_to_coco.py /^ image_set = open(image_set_path + '{}.txt'.format(split), 'r')$/;" v image_set_path src/tools/convert_kitti_to_coco.py /^ image_set_path = DATA_PATH + 'ImageSets_{}\/'.format(SPLIT)$/;" v image_to_id src/tools/convert_kitti_to_coco.py /^ image_to_id = {}$/;" v imdb src/tools/voc_eval_lib/datasets/imdb.py /^class imdb(object):$/;" c imdb src/tools/voc_eval_lib/datasets/pascal_voc.py /^from datasets.imdb import imdb$/;" i imdb_name src/tools/reval.py /^ imdb_name = args.imdb_name$/;" v img src/evaluate.py /^ img = cv2.ellipse(img, (cx, cy), (int(a), int(b)), angle, 0.0, 360.0, color_maps[class_idx * 2 + 1], thickness=3)$/;" v img src/evaluate.py /^ img = cv2.ellipse(img, (cx_gt, cy_gt), (int(a_gt), int(b_gt)), angle_gt, 0.0, 360.0, (255, 0, 0), thickness=2)$/;" v img src/evaluate.py /^ img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=(125, 0, 0), thickness=1)$/;" v img src/evaluate.py /^ img = cv2.rectangle(img=image, pt1=pt1, pt2=pt2, color=color_maps[class_idx * 2], thickness=2)$/;" v img src/evaluate.py /^ img = Image.fromarray(image)$/;" v img src/tools/vis_pred.py /^ img = cv2.imread(img_path)$/;" v img_id src/evaluate.py /^ img_id = dataset.images[ind]$/;" v img_ids src/tools/eval_coco.py /^ img_ids = coco.getImgIds()$/;" v img_ids src/tools/eval_coco_hp.py /^ img_ids = coco.getImgIds()$/;" v img_ids src/tools/vis_pred.py /^ img_ids = coco.getImgIds()$/;" v img_info src/evaluate.py /^ img_info = dataset.coco.loadImgs(ids=[img_id])[0]$/;" v img_info src/tools/vis_pred.py /^ img_info = coco.loadImgs(ids=[img_id])[0]$/;" v img_path src/evaluate.py /^ img_path = os.path.join(dataset.img_dir, img_info['file_name'])$/;" v img_path src/tools/vis_pred.py /^ img_path = IMG_PATH + img_info['file_name']$/;" v include_dirs src/lib/external/setup.py /^ include_dirs=[numpy.get_include()]$/;" v indices src/tools/kitti_eval/evaluate_object_3d.cpp /^vector indices;$/;" v indices src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector indices;$/;" v init scripts/fhd_opts.py /^ def init(self, args=''):$/;" m class:opts init scripts/jupyter_opts.py /^ def init(self, args=''):$/;" m class:opts init src/lib/fhd_opts.py /^ def init(self, args=''):$/;" m class:opts init src/lib/opts.py /^ def init(self, args=''):$/;" m class:opts initGlobals src/tools/kitti_eval/evaluate_object_3d.cpp /^void initGlobals () {$/;" f initGlobals src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^void initGlobals () {$/;" f init_offset src/lib/models/networks/DCNv2/dcn_v2.py /^ def init_offset(self):$/;" m class:DCN init_weights src/lib/models/networks/msra_resnet.py /^ def init_weights(self, num_layers, pretrained=True):$/;" m class:PoseResNet init_weights src/lib/models/networks/resnet_dcn.py /^ def init_weights(self, num_layers):$/;" m class:PoseResNet initcpu_nms src/tools/voc_eval_lib/nms/cpu_nms.c /^PyMODINIT_FUNC initcpu_nms(void)$/;" f initgpu_nms src/tools/voc_eval_lib/nms/gpu_nms.cpp /^PyMODINIT_FUNC initgpu_nms(void)$/;" f intern src/tools/voc_eval_lib/nms/cpu_nms.c /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon30 file: intern src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon2 file: internal src/tools/voc_eval_lib/nms/cpu_nms.c /^ void *internal;$/;" m struct:__anon29 file: internal src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ void *internal;$/;" m struct:__anon1 file: intersection_poly src/lib/models/Rotated_IoU/utiles.py /^def intersection_poly(corners1, corners2):$/;" f iou src/tools/calc_coco_overlap.py /^def iou(box1, box2):$/;" f ious src/evaluate.py /^ ious = []$/;" v is_complex src/tools/voc_eval_lib/nms/cpu_nms.c /^ int is_complex;$/;" m struct:__anon33 file: is_complex src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int is_complex;$/;" m struct:__anon5 file: is_str src/tools/voc_eval_lib/nms/cpu_nms.c /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon30 file: is_str src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon2 file: is_unicode src/tools/voc_eval_lib/nms/cpu_nms.c /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon30 file: is_unicode src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; \/*proto*\/$/;" m struct:__anon2 file: is_unsigned src/tools/voc_eval_lib/nms/cpu_nms.c /^ char is_unsigned;$/;" m struct:__anon31 file: is_unsigned src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char is_unsigned;$/;" m struct:__anon3 file: is_valid_array src/tools/voc_eval_lib/nms/cpu_nms.c /^ char is_valid_array;$/;" m struct:__anon33 file: is_valid_array src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char is_valid_array;$/;" m struct:__anon5 file: itemsize src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t itemsize;$/;" m struct:__anon29 file: itemsize src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t itemsize;$/;" m struct:__anon1 file: join src/lib/models/networks/dlav0.py /^from os.path import join$/;" i join src/lib/models/networks/pose_dla_dcn.py /^from os.path import join$/;" i json src/evaluate.py /^import json$/;" i json src/lib/datasets/dataset/coco_fhd.py /^import json$/;" i json src/lib/datasets/sample/eldet.py /^import json$/;" i json src/test.py /^import json$/;" i json src/tools/convert_kitti_to_coco.py /^import json$/;" i json src/tools/merge_pascal_json.py /^import json$/;" i json src/tools/reval.py /^import json$/;" i key_map src/tools/convert_hourglass_weight.py /^ 'b_regrs': 'reg_b', 'r_regrs': 'reg_r'}$/;" v kitti_class_name src/lib/utils/debugger.py /^kitti_class_name = [$/;" v kp_module src/lib/models/networks/large_hourglass.py /^class kp_module(nn.Module):$/;" c l src/evaluate.py /^ l = int(ret['results'][class_id][0, 6]) # !attention here!$/;" v l src/tools/kitti_eval/evaluate_object_3d.cpp /^ double h, w, l;$/;" m struct:tDetection file: l src/tools/kitti_eval/evaluate_object_3d.cpp /^ double h, w, l;$/;" m struct:tGroundtruth file: l src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double h, w, l;$/;" m struct:tDetection file: l src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double h, w, l;$/;" m struct:tGroundtruth file: l_gt src/evaluate.py /^ l_gt = 2 * math.sqrt(a_gt ** 2 + b_gt ** 2)$/;" v len src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t len;$/;" m struct:__anon29 file: len src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t len;$/;" m struct:__anon1 file: lib_path src/_init_paths.py /^lib_path = osp.join(this_dir, 'lib')$/;" v lib_path src/evaluate.py /^lib_path = '..\/..\/Rotated_IoU\/'$/;" v lib_path src/evaluate.py /^lib_path = os.path.join('..\/src\/')$/;" v lib_path src/evaluate.py /^lib_path = os.path.join('..\/src\/lib')$/;" v lib_path src/tools/_init_paths.py /^lib_path = osp.join(this_dir, '..\/lib')$/;" v lighting_ src/lib/utils/image.py /^def lighting_(data_rng, image, alphastd, eigval, eigvec):$/;" f likely src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define likely(/;" d file: likely src/tools/voc_eval_lib/nms/cpu_nms.c /^ #define likely(/;" d file: likely src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define likely(/;" d file: likely src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ #define likely(/;" d file: line src/tools/convert_kitti_to_coco.py /^ line = line[:-1]$/;" v line_seg_intersection src/lib/models/Rotated_IoU/utiles.py /^def line_seg_intersection(line1:np.array, line2:np.array):$/;" f literal_eval src/tools/voc_eval_lib/model/config.py /^ from ast import literal_eval$/;" i loadDetections src/tools/kitti_eval/evaluate_object_3d.cpp /^vector loadDetections(string file_name, bool &compute_aos,$/;" f loadDetections src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector loadDetections(string file_name, bool &compute_aos,$/;" f loadGroundtruth src/tools/kitti_eval/evaluate_object_3d.cpp /^vector loadGroundtruth(string file_name,bool &success) {$/;" f loadGroundtruth src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^vector loadGroundtruth(string file_name,bool &success) {$/;" f load_model src/lib/detectors/base_detector.py /^from models.model import create_model, load_model$/;" i load_model src/lib/models/model.py /^def load_model(model, model_path, optimizer=None, resume=False, $/;" f load_model src/main.py /^from models.model import create_model, load_model, save_model$/;" i load_pretrained_model src/lib/models/networks/dlav0.py /^ def load_pretrained_model(self, data='imagenet', name='dla34', hash='ba72cf86'):$/;" m class:DLA load_pretrained_model src/lib/models/networks/pose_dla_dcn.py /^ def load_pretrained_model(self, data='imagenet', name='dla34', hash='ba72cf86'):$/;" m class:DLA locate_cuda src/tools/voc_eval_lib/setup.py /^def locate_cuda():$/;" f location src/tools/convert_kitti_to_coco.py /^ location = [float(tmp[11]), float(tmp[12]), float(tmp[13])]$/;" v logger src/lib/models/networks/pose_dla_dcn.py /^logger = logging.getLogger(__name__)$/;" v logger src/lib/models/networks/resnet_dcn.py /^logger = logging.getLogger(__name__)$/;" v logging src/lib/models/networks/pose_dla_dcn.py /^import logging$/;" i logging src/lib/models/networks/resnet_dcn.py /^import logging$/;" i m src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ m = (torch.rand([8, 1024, 24]) > 0.8).cuda()$/;" v mail src/tools/kitti_eval/mail.h /^ FILE *mail;$/;" m class:Mail main src/lib/models/Rotated_IoU/demo.py /^def main(loss_type:str="giou", enclosing_type:str="aligned"):$/;" f main src/main.py /^def main(opt):$/;" f main src/tools/kitti_eval/evaluate_object_3d.cpp /^int32_t main (int32_t argc,char *argv[]) {$/;" f main src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^int32_t main (int32_t argc,char *argv[]) {$/;" f make_cnv_layer src/lib/models/networks/large_hourglass.py /^def make_cnv_layer(inp_dim, out_dim):$/;" f make_hg_layer src/lib/models/networks/large_hourglass.py /^def make_hg_layer(kernel, dim0, dim1, mod, layer=convolution, **kwargs):$/;" f make_inter_layer src/lib/models/networks/large_hourglass.py /^def make_inter_layer(dim):$/;" f make_kp_layer src/lib/models/networks/large_hourglass.py /^def make_kp_layer(cnv_dim, curr_dim, out_dim):$/;" f make_layer src/lib/models/networks/large_hourglass.py /^def make_layer(k, inp_dim, out_dim, modules, layer=convolution, **kwargs):$/;" f make_layer_revr src/lib/models/networks/large_hourglass.py /^def make_layer_revr(k, inp_dim, out_dim, modules, layer=convolution, **kwargs):$/;" f make_merge_layer src/lib/models/networks/large_hourglass.py /^def make_merge_layer(dim):$/;" f make_pool_layer src/lib/models/networks/large_hourglass.py /^def make_pool_layer(dim):$/;" f make_unpool_layer src/lib/models/networks/large_hourglass.py /^def make_unpool_layer(dim):$/;" f maskUtils src/tools/calc_coco_overlap.py /^from pycocotools import mask as maskUtils$/;" i math src/evaluate.py /^import math$/;" i math src/lib/datasets/sample/eldet.py /^import math$/;" i math src/lib/models/decode.py /^import math$/;" i math src/lib/models/losses.py /^import math$/;" i math src/lib/models/networks/DCNv2/dcn_v2.py /^import math$/;" i math src/lib/models/networks/dlav0.py /^import math$/;" i math src/lib/models/networks/pose_dla_dcn.py /^import math$/;" i math src/lib/models/networks/resnet_dcn.py /^import math$/;" i math src/tools/voc_eval_lib/model/test.py /^import math$/;" i matplotlib src/evaluate.py /^import matplotlib.pyplot as plt$/;" i matplotlib src/lib/models/Rotated_IoU/min_enclosing_box.py /^ import matplotlib.pyplot as plt$/;" i matplotlib src/lib/models/Rotated_IoU/utiles.py /^import matplotlib.pyplot as plt$/;" i matplotlib src/lib/utils/debugger.py /^ import matplotlib.pyplot as plt$/;" i max_count src/tools/voc_eval_lib/nms/cpu_nms.c /^ int max_count;$/;" m struct:__Pyx_CodeObjectCache file: max_count src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int max_count;$/;" m struct:__Pyx_CodeObjectCache file: mean src/lib/datasets/dataset/coco_fhd.py /^ mean = np.array([0.216, 0.216, 0.216],$/;" v class:COCOFHD mean src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ mean = torch.mean(v, dim=2, keepdim=True)$/;" v merge_outputs src/lib/detectors/base_detector.py /^ def merge_outputs(self, detections):$/;" m class:BaseDetector merge_outputs src/lib/detectors/eldet.py /^ def merge_outputs(self, detections):$/;" m class:EldetDetector merge_outputs src/lib/detectors/eldet_bbox.py /^ def merge_outputs(self, detections):$/;" m class:EldetBBOXDetector merge_roidbs src/tools/voc_eval_lib/datasets/imdb.py /^ def merge_roidbs(a, b):$/;" m class:imdb model_urls src/lib/models/networks/msra_resnet.py /^model_urls = {$/;" v model_urls src/lib/models/networks/resnet_dcn.py /^model_urls = {$/;" v model_zoo src/lib/models/networks/dlav0.py /^import torch.utils.model_zoo as model_zoo$/;" i model_zoo src/lib/models/networks/msra_resnet.py /^import torch.utils.model_zoo as model_zoo$/;" i model_zoo src/lib/models/networks/pose_dla_dcn.py /^import torch.utils.model_zoo as model_zoo$/;" i model_zoo src/lib/models/networks/resnet_dcn.py /^import torch.utils.model_zoo as model_zoo$/;" i models src/lib/models/model.py /^import torchvision.models as models$/;" i modulated_deformable_col2im_coord_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_col2im_coord_cpu(const float* data_col, const float* data_im, const float* data_offset, const float* data_mask,$/;" f modulated_deformable_col2im_coord_cpu_kernel src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_col2im_coord_cpu_kernel(const int n, const float *data_col, const float *data_im,$/;" f modulated_deformable_col2im_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_col2im_cpu(const float* data_col, const float* data_offset, const float* data_mask,$/;" f modulated_deformable_col2im_cpu_kernel src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_col2im_cpu_kernel(const int n, const float *data_col, const float *data_offset, const float *data_mask,$/;" f modulated_deformable_im2col_cpu src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_im2col_cpu(const float* data_im, const float* data_offset, const float* data_mask,$/;" f modulated_deformable_im2col_cpu_kernel src/lib/models/networks/DCNv2/src/cpu/dcn_v2_im2col_cpu.cpp /^void modulated_deformable_im2col_cpu_kernel(const int n, const float *data_im, const float *data_offset, const float *data_mask,$/;" f msg src/tools/kitti_eval/mail.h /^ void msg (const char *format, ...) {$/;" f class:Mail multi_pose_decode src/lib/models/decode.py /^def multi_pose_decode($/;" f multi_pose_post_process src/lib/utils/post_process.py /^def multi_pose_post_process(dets, c, s, h, w):$/;" f n src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon30 file: n src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon2 file: name src/lib/external/setup.py /^ name="coco",$/;" v name src/lib/models/Rotated_IoU/cuda_op/setup.py /^ name='sort_vertices',$/;" v name src/lib/models/networks/DCNv2/setup.py /^ name="DCNv2",$/;" v name src/tools/voc_eval_lib/datasets/imdb.py /^ def name(self):$/;" m class:imdb name src/tools/voc_eval_lib/nms/cpu_nms.c /^ const char* name; \/* for error messages only *\/$/;" m struct:__anon31 file: name src/tools/voc_eval_lib/nms/cpu_nms.c /^ const char* name;$/;" m struct:__Pyx_StructField_ file: name src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ const char* name; \/* for error messages only *\/$/;" m struct:__anon3 file: name src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ const char* name;$/;" m struct:__Pyx_StructField_ file: name src/tools/voc_eval_lib/setup.py /^ name='tf_faster_rcnn',$/;" v ndim src/tools/voc_eval_lib/nms/cpu_nms.c /^ int ndim;$/;" m struct:__anon29 file: ndim src/tools/voc_eval_lib/nms/cpu_nms.c /^ int ndim;$/;" m struct:__anon31 file: ndim src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int ndim;$/;" m struct:__anon1 file: ndim src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int ndim;$/;" m struct:__anon3 file: new_count src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t new_count, enc_count;$/;" m struct:__anon33 file: new_count src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t new_count, enc_count;$/;" m struct:__anon5 file: new_k src/tools/convert_hourglass_weight.py /^ new_k = k.replace(m, key_map[m])$/;" v new_packmode src/tools/voc_eval_lib/nms/cpu_nms.c /^ char new_packmode;$/;" m struct:__anon33 file: new_packmode src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ char new_packmode;$/;" m struct:__anon5 file: nms src/lib/external/nms.pyx /^def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):$/;" f nms src/tools/voc_eval_lib/model/nms_wrapper.py /^def nms(dets, thresh, force_cpu=False):$/;" f nms src/tools/voc_eval_lib/model/test.py /^nms = None # not needed in pascal evaluation$/;" v nn src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^from torch import nn$/;" i nn src/lib/models/Rotated_IoU/demo.py /^from torch import nn, optim$/;" i nn src/lib/models/decode.py /^import torch.nn as nn$/;" i nn src/lib/models/decode.py /^import torch.nn.functional as F$/;" i nn src/lib/models/losses.py /^import torch.nn as nn$/;" i nn src/lib/models/losses.py /^import torch.nn.functional as F$/;" i nn src/lib/models/model.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/DCNv2/dcn_v2.py /^from torch import nn$/;" i nn src/lib/models/networks/DCNv2/test/test.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/DCNv2/test/testcpu.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/DCNv2/test/testcuda.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/dlav0.py /^from torch import nn$/;" i nn src/lib/models/networks/large_hourglass.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/msra_resnet.py /^import torch.nn as nn$/;" i nn src/lib/models/networks/pose_dla_dcn.py /^from torch import nn$/;" i nn src/lib/models/networks/pose_dla_dcn.py /^import torch.nn.functional as F$/;" i nn src/lib/models/networks/resnet_dcn.py /^import torch.nn as nn$/;" i nn src/lib/models/utils.py /^import torch.nn as nn$/;" i np src/evaluate.py /^import numpy as np$/;" i np src/lib/datasets/dataset/coco_fhd.py /^import numpy as np$/;" i np src/lib/datasets/sample/eldet.py /^import numpy as np$/;" i np src/lib/detectors/base_detector.py /^import numpy as np$/;" i np src/lib/detectors/eldet.py /^import numpy as np$/;" i np src/lib/detectors/eldet_bbox.py /^import numpy as np$/;" i np src/lib/external/nms.pyx /^cimport numpy as np$/;" i np src/lib/external/nms.pyx /^import numpy as np$/;" i np src/lib/models/Rotated_IoU/demo.py /^import numpy as np$/;" i np src/lib/models/Rotated_IoU/min_enclosing_box.py /^import numpy as np$/;" i np src/lib/models/Rotated_IoU/oriented_iou_loss.py /^import numpy as np$/;" i np src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^import numpy as np$/;" i np src/lib/models/Rotated_IoU/utiles.py /^import numpy as np$/;" i np src/lib/models/networks/dlav0.py /^import numpy as np$/;" i np src/lib/models/networks/large_hourglass.py /^import numpy as np$/;" i np src/lib/models/networks/pose_dla_dcn.py /^import numpy as np$/;" i np src/lib/trains/eldet.py /^import numpy as np$/;" i np src/lib/trains/eldet_bbox.py /^import numpy as np$/;" i np src/lib/trains/eldet_onlyangle.py /^import numpy as np$/;" i np src/lib/utils/ddd_utils.py /^import numpy as np$/;" i np src/lib/utils/debugger.py /^import numpy as np$/;" i np src/lib/utils/image.py /^import numpy as np$/;" i np src/lib/utils/oracle_utils.py /^import numpy as np$/;" i np src/lib/utils/post_process.py /^import numpy as np$/;" i np src/test.py /^import numpy as np$/;" i np src/tools/calc_coco_overlap.py /^import numpy as np$/;" i np src/tools/convert_kitti_to_coco.py /^import numpy as np$/;" i np src/tools/eval_coco.py /^import numpy as np$/;" i np src/tools/eval_coco_hp.py /^import numpy as np$/;" i np src/tools/reval.py /^import numpy as np$/;" i np src/tools/vis_pred.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/datasets/bbox.pyx /^cimport numpy as np$/;" i np src/tools/voc_eval_lib/datasets/bbox.pyx /^import numpy as np$/;" i np src/tools/voc_eval_lib/datasets/ds_utils.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/datasets/imdb.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/datasets/pascal_voc.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/datasets/voc_eval.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/model/bbox_transform.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/model/config.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/model/test.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/nms/cpu_nms.pyx /^cimport numpy as np$/;" i np src/tools/voc_eval_lib/nms/cpu_nms.pyx /^import numpy as np$/;" i np src/tools/voc_eval_lib/nms/gpu_nms.pyx /^cimport numpy as np$/;" i np src/tools/voc_eval_lib/nms/gpu_nms.pyx /^import numpy as np$/;" i np src/tools/voc_eval_lib/nms/py_cpu_nms.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/setup.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/utils/bbox.pyx /^cimport numpy as np$/;" i np src/tools/voc_eval_lib/utils/bbox.pyx /^import numpy as np$/;" i np src/tools/voc_eval_lib/utils/blob.py /^import numpy as np$/;" i np src/tools/voc_eval_lib/utils/visualization.py /^import numpy as np$/;" i num_classes src/lib/datasets/dataset/coco_fhd.py /^ num_classes = 2$/;" v class:COCOFHD num_classes src/tools/vis_pred.py /^num_classes = 80$/;" v num_classes src/tools/voc_eval_lib/datasets/imdb.py /^ def num_classes(self):$/;" m class:imdb num_images src/tools/eval_coco.py /^ num_images = len(img_ids)$/;" v num_images src/tools/eval_coco_hp.py /^ num_images = len(img_ids)$/;" v num_images src/tools/vis_pred.py /^ num_images = len(img_ids)$/;" v num_images src/tools/voc_eval_lib/datasets/imdb.py /^ def num_images(self):$/;" m class:imdb numba src/lib/utils/oracle_utils.py /^import numba$/;" i numpy src/lib/external/setup.py /^import numpy$/;" i numpy_include src/tools/voc_eval_lib/setup.py /^ numpy_include = np.get_include()$/;" v numpy_include src/tools/voc_eval_lib/setup.py /^ numpy_include = np.get_numpy_include()$/;" v nv src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ nv = torch.sum(m.int(), dim=-1).int().cuda()$/;" v obj src/tools/voc_eval_lib/nms/cpu_nms.c /^ PyObject *obj;$/;" m struct:__anon29 file: obj src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ PyObject *obj;$/;" m struct:__anon1 file: occluded src/tools/convert_kitti_to_coco.py /^ occluded = int(tmp[2])$/;" v occlusion src/tools/kitti_eval/evaluate_object_3d.cpp /^ int32_t occlusion; \/\/ occlusion 0,1,2 (non, partly, fully)$/;" m struct:tGroundtruth file: occlusion src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ int32_t occlusion; \/\/ occlusion 0,1,2 (non, partly, fully)$/;" m struct:tGroundtruth file: offset src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t offset;$/;" m struct:__Pyx_StructField_ file: offset src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t offset;$/;" m struct:__Pyx_StructField_ file: offsetof src/tools/voc_eval_lib/nms/cpu_nms.c /^#define offsetof(/;" d file: offsetof src/tools/voc_eval_lib/nms/gpu_nms.cpp /^#define offsetof(/;" d file: once_differentiable src/lib/models/networks/DCNv2/dcn_v2.py /^from torch.autograd.function import once_differentiable$/;" i opt src/demo.py /^ opt = opts().init()$/;" v opt src/evaluate.py /^ opt = opts().parse()$/;" v opt src/evaluate.py /^ opt = opts().update_dataset_info_and_set_heads(opt, Dataset)$/;" v opt src/main.py /^ opt = opts().parse()$/;" v opt src/test.py /^ opt = opts().parse()$/;" v opt_block_config src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h /^inline dim3 opt_block_config(int x, int y){$/;" f opt_n_thread src/lib/models/Rotated_IoU/cuda_op/cuda_utils.h /^inline int opt_n_thread(int work_size){$/;" f optim src/lib/models/Rotated_IoU/demo.py /^from torch import nn, optim$/;" i opts scripts/fhd_opts.py /^class opts(object):$/;" c opts scripts/jupyter_opts.py /^class 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src/tools/voc_eval_lib/datasets/imdb.py /^import os$/;" i os src/tools/voc_eval_lib/datasets/imdb.py /^import os.path as osp$/;" i os src/tools/voc_eval_lib/datasets/pascal_voc.py /^import os$/;" i os src/tools/voc_eval_lib/datasets/voc_eval.py /^import os$/;" i os src/tools/voc_eval_lib/model/config.py /^import os$/;" i os src/tools/voc_eval_lib/model/config.py /^import os.path as osp$/;" i os src/tools/voc_eval_lib/model/test.py /^import os$/;" i os src/tools/voc_eval_lib/setup.py /^import os$/;" i osp src/_init_paths.py /^import os.path as osp$/;" i osp src/tools/_init_paths.py /^import os.path as osp$/;" i osp src/tools/reval.py /^import os.path as osp$/;" i osp src/tools/voc_eval_lib/datasets/imdb.py /^import os.path as osp$/;" i osp src/tools/voc_eval_lib/model/config.py /^import os.path as osp$/;" i out src/tools/convert_hourglass_weight.py /^out = {}$/;" v out src/tools/merge_pascal_json.py /^out = {}$/;" v outC src/lib/models/networks/DCNv2/test/test.py /^outC = 2$/;" v outC src/lib/models/networks/DCNv2/test/testcpu.py /^outC = 2$/;" v outC src/lib/models/networks/DCNv2/test/testcuda.py /^outC = 2$/;" v out_path src/tools/convert_kitti_to_coco.py /^ out_path = '{}\/annotations\/kitti_{}_{}.json'.format(DATA_PATH, SPLIT, split)$/;" v p src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon30 file: p src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon2 file: packages src/lib/models/networks/DCNv2/setup.py /^ packages=find_packages($/;" v parallel_apply src/lib/models/data_parallel.py /^ def parallel_apply(self, replicas, inputs, kwargs):$/;" m class:_DataParallel parallel_apply src/lib/models/data_parallel.py /^from torch.nn.parallel.parallel_apply import parallel_apply$/;" i params2corners_sincos src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def params2corners_sincos(box)-> torch.Tensor:$/;" f params2corners_th src/lib/models/Rotated_IoU/oriented_iou_loss.py /^def params2corners_th(box)-> torch.Tensor:$/;" f parent_offset src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t parent_offset;$/;" m struct:__anon32 file: parent_offset src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t parent_offset;$/;" m struct:__anon4 file: parse scripts/fhd_opts.py /^ def parse(self, args=''):$/;" m class:opts parse scripts/jupyter_opts.py /^ def parse(self, args=''):$/;" m class:opts parse src/lib/fhd_opts.py /^ def parse(self, args=''):$/;" m class:opts parse src/lib/opts.py /^ def parse(self, args=''):$/;" m class:opts parse_args src/tools/reval.py /^def parse_args():$/;" f parse_pred src/lib/models/Rotated_IoU/demo.py /^def parse_pred(pred:torch.Tensor):$/;" f parse_rec src/tools/voc_eval_lib/datasets/voc_eval.py /^def parse_rec(filename):$/;" f parser src/lib/models/Rotated_IoU/demo.py /^ parser = argparse.ArgumentParser()$/;" v pascal_class_name src/lib/utils/debugger.py /^pascal_class_name = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", $/;" v pascal_voc src/tools/reval.py /^from datasets.pascal_voc import pascal_voc$/;" i pascal_voc src/tools/voc_eval_lib/datasets/pascal_voc.py /^ from datasets.pascal_voc import pascal_voc$/;" i pascal_voc src/tools/voc_eval_lib/datasets/pascal_voc.py /^class pascal_voc(imdb):$/;" c pdb src/lib/models/decode.py /^import pdb$/;" i pdb src/lib/models/losses.py /^import pdb$/;" i pdb src/lib/utils/post_process.py /^import pdb$/;" i pickle src/tools/convert_kitti_to_coco.py /^import pickle$/;" i pickle src/tools/eval_coco.py /^import pickle$/;" i pickle src/tools/eval_coco_hp.py /^import pickle$/;" i pickle src/tools/reval.py /^import pickle$/;" i pickle src/tools/vis_pred.py /^import pickle$/;" i pickle src/tools/voc_eval_lib/datasets/pascal_voc.py /^import pickle$/;" i pickle src/tools/voc_eval_lib/datasets/voc_eval.py /^import pickle$/;" i pickle src/tools/voc_eval_lib/model/test.py /^ import cPickle as pickle$/;" i pickle src/tools/voc_eval_lib/model/test.py /^ import pickle$/;" i pjoin src/tools/voc_eval_lib/setup.py /^from os.path import join as pjoin$/;" i plt src/evaluate.py /^import matplotlib.pyplot as plt$/;" i plt src/lib/models/Rotated_IoU/min_enclosing_box.py /^ import matplotlib.pyplot as plt$/;" i plt src/lib/models/Rotated_IoU/utiles.py /^import matplotlib.pyplot as plt$/;" i plt src/lib/utils/debugger.py /^ import matplotlib.pyplot as plt$/;" i point_in_box src/lib/models/Rotated_IoU/utiles.py /^def point_in_box(point, corners):$/;" f point_line_distance_range src/lib/models/Rotated_IoU/min_enclosing_box.py /^def point_line_distance_range(lines:torch.Tensor, points:torch.Tensor):$/;" f point_line_projection_range src/lib/models/Rotated_IoU/min_enclosing_box.py /^def point_line_projection_range(lines:torch.Tensor, points:torch.Tensor):$/;" f post_process src/lib/detectors/base_detector.py /^ def post_process(self, dets, meta, scale=1):$/;" m class:BaseDetector post_process src/lib/detectors/eldet.py /^ def post_process(self, dets, meta, scale=1):$/;" m class:EldetDetector post_process src/lib/detectors/eldet_bbox.py /^ def post_process(self, dets, meta, scale=1):$/;" m class:EldetBBOXDetector pre_process src/lib/detectors/base_detector.py /^ def pre_process(self, image, scale, meta=None):$/;" m class:BaseDetector pred_ell src/evaluate.py /^ pred_ell = [cx, cy, a, b, angle]$/;" v pred_ids src/tools/vis_pred.py /^ pred_ids = dets[k].getAnnIds(imgIds=[img_id])$/;" v pred_img src/tools/vis_pred.py /^ pred_img = add_box(pred_img, bbox, sc, cat_id)$/;" v pred_img src/tools/vis_pred.py /^ pred_img = img.copy()$/;" v pred_path src/tools/eval_coco.py /^ pred_path = sys.argv[1]$/;" v pred_path src/tools/eval_coco_hp.py /^ pred_path = sys.argv[1]$/;" v pred_path src/tools/vis_pred.py /^ pred_path = sys.argv[k]$/;" v preds src/tools/vis_pred.py /^ preds = dets[k].loadAnns(pred_ids)$/;" v prefetch_test src/test.py /^def prefetch_test(opt):$/;" f prep_im_for_blob src/tools/voc_eval_lib/utils/blob.py /^def prep_im_for_blob(im, pixel_means, target_size, max_size):$/;" f print_function scripts/fhd_opts.py /^from __future__ import print_function$/;" i print_function scripts/jupyter_opts.py /^from __future__ import print_function$/;" i print_function src/demo.py /^from __future__ import print_function$/;" i print_function src/evaluate.py /^from __future__ import print_function$/;" i print_function src/lib/datasets/dataset/coco_fhd.py /^from __future__ import print_function$/;" i print_function src/lib/datasets/dataset_factory.py /^from __future__ import print_function$/;" i print_function src/lib/datasets/sample/eldet.py /^from __future__ import print_function$/;" i print_function src/lib/detectors/base_detector.py /^from __future__ import print_function$/;" i print_function src/lib/detectors/detector_factory.py /^from __future__ import print_function$/;" i print_function src/lib/detectors/eldet.py /^from __future__ import print_function$/;" i print_function src/lib/detectors/eldet_bbox.py /^from __future__ import print_function$/;" i print_function src/lib/fhd_opts.py /^from __future__ import print_function$/;" i print_function src/lib/logger.py /^from __future__ import print_function$/;" i print_function src/lib/models/decode.py /^from __future__ import print_function$/;" i print_function src/lib/models/losses.py /^from __future__ import print_function$/;" i print_function src/lib/models/model.py /^from __future__ import print_function$/;" i print_function src/lib/models/networks/DCNv2/dcn_v2.py /^from __future__ import absolute_import, division, print_function$/;" i print_function src/lib/models/networks/DCNv2/test/test.py /^from __future__ import absolute_import, division, print_function$/;" i print_function src/lib/models/networks/DCNv2/test/testcpu.py /^from __future__ import absolute_import, division, print_function$/;" i print_function src/lib/models/networks/DCNv2/test/testcuda.py /^from __future__ import absolute_import, division, print_function$/;" i print_function src/lib/models/networks/dlav0.py /^from __future__ import print_function$/;" i print_function src/lib/models/networks/large_hourglass.py /^from __future__ import print_function$/;" i print_function src/lib/models/networks/msra_resnet.py /^from __future__ import print_function$/;" i print_function src/lib/models/networks/pose_dla_dcn.py /^from __future__ import print_function$/;" i print_function src/lib/models/networks/resnet_dcn.py /^from __future__ import print_function$/;" i print_function src/lib/models/utils.py /^from __future__ import print_function$/;" i print_function src/lib/opts.py /^from __future__ import print_function$/;" i print_function src/lib/trains/base_trainer.py /^from __future__ import print_function$/;" i print_function src/lib/trains/eldet.py /^from __future__ import print_function$/;" i print_function src/lib/trains/eldet_bbox.py /^from __future__ import print_function$/;" i print_function src/lib/trains/eldet_onlyangle.py /^from __future__ import print_function$/;" i print_function src/lib/trains/train_factory.py /^from __future__ import print_function$/;" i print_function src/lib/utils/ddd_utils.py /^from __future__ import print_function$/;" i print_function src/lib/utils/debugger.py /^from __future__ import print_function$/;" i print_function src/lib/utils/image.py /^from __future__ import print_function$/;" i print_function src/lib/utils/oracle_utils.py /^from __future__ import print_function$/;" i print_function src/lib/utils/post_process.py /^from __future__ import print_function$/;" i print_function src/lib/utils/utils.py /^from __future__ import print_function$/;" i print_function src/main.py /^from __future__ import print_function$/;" i print_function src/test.py /^from __future__ import print_function$/;" i print_function src/tools/calc_coco_overlap.py /^from __future__ import print_function$/;" i print_function src/tools/convert_hourglass_weight.py /^from __future__ import print_function$/;" i print_function src/tools/convert_kitti_to_coco.py /^from __future__ import print_function$/;" i print_function src/tools/eval_coco.py /^from __future__ import print_function$/;" i print_function src/tools/eval_coco_hp.py /^from __future__ import print_function$/;" i print_function src/tools/reval.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/datasets/ds_utils.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/datasets/imdb.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/datasets/pascal_voc.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/datasets/voc_eval.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/model/bbox_transform.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/model/config.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/model/nms_wrapper.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/model/test.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/utils/blob.py /^from __future__ import print_function$/;" i print_function src/tools/voc_eval_lib/utils/visualization.py /^from __future__ import print_function$/;" i process src/lib/detectors/base_detector.py /^ def process(self, images, return_time=False):$/;" m class:BaseDetector process src/lib/detectors/eldet.py /^ def process(self, images, return_time=False):$/;" m class:EldetDetector process src/lib/detectors/eldet_bbox.py /^ def process(self, images, return_time=False):$/;" m class:EldetBBOXDetector project_3d_bbox src/lib/utils/ddd_utils.py /^def project_3d_bbox(location, dim, rotation_y, calib):$/;" f project_3d_to_bird src/lib/utils/debugger.py /^ def project_3d_to_bird(self, pt):$/;" m class:Debugger project_to_image src/lib/utils/ddd_utils.py /^def project_to_image(pts_3d, P):$/;" f project_to_image src/lib/utils/debugger.py /^from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d$/;" i project_to_image src/tools/convert_kitti_to_coco.py /^from utils.ddd_utils import compute_box_3d, project_to_image, alpha2rot_y$/;" i pt1 src/evaluate.py /^ pt1 = (int(cx - l \/ 2), int(cy - l \/ 2))$/;" v pt1 src/evaluate.py /^ pt1 = (int(cx_gt - l_gt \/ 2), int(cy_gt - l_gt \/ 2))$/;" v pt1 src/evaluate.py /^ pt1 = (int(ret['results'][class_id][0, 0]), int(ret['results'][class_id][0, 1]))$/;" v pt2 src/evaluate.py /^ pt2 = (int(cx + l \/ 2), int(cy + l \/ 2))$/;" v pt2 src/evaluate.py /^ pt2 = (int(cx_gt + l_gt \/ 2), int(cy_gt + l_gt \/ 2))$/;" v pt2 src/evaluate.py /^ pt2 = (int(ret['results'][class_id][0, 2]), int(ret['results'][class_id][0, 3]))$/;" v pt_2d src/tools/convert_kitti_to_coco.py /^ pt_2d = np.array([(bbox[0] + bbox[2]) \/ 2, (bbox[1] + bbox[3]) \/ 2],$/;" v pt_3d src/tools/convert_kitti_to_coco.py /^ pt_3d = unproject_2d_to_3d(pt_2d, depth, calib)$/;" v py_cpu_nms src/tools/voc_eval_lib/nms/py_cpu_nms.py /^def py_cpu_nms(dets, thresh):$/;" f pybuffer src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_buffer pybuffer;$/;" m struct:__anon38 file: pybuffer src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_buffer pybuffer;$/;" m struct:__anon10 file: pycocotools src/lib/datasets/dataset/coco_fhd.py /^import pycocotools.coco as coco$/;" i pycocotools src/tools/calc_coco_overlap.py /^import pycocotools.coco as COCO$/;" i pycocotools src/tools/eval_coco.py /^import pycocotools.coco as coco$/;" i pycocotools src/tools/eval_coco_hp.py /^import pycocotools.coco as coco$/;" i pycocotools src/tools/vis_pred.py /^import pycocotools.coco as coco$/;" i random src/lib/datasets/sample/eldet.py /^import random$/;" i random src/lib/utils/image.py /^import random$/;" i range src/tools/voc_eval_lib/utils/visualization.py /^from six.moves import range$/;" i ratio_al src/evaluate.py /^ ratio_al = ret['results'][class_id][0, 7]$/;" v ratio_bl src/evaluate.py /^ ratio_bl = ret['results'][class_id][0, 8]$/;" v ratio_p src/evaluate.py /^ ratio_p = 0$/;" v ratios_gt src/evaluate.py /^ ratios_gt = []$/;" v ratios_pred src/evaluate.py /^ ratios_pred = []$/;" v rcbuffer src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_Buffer *rcbuffer;$/;" m struct:__anon39 file: rcbuffer src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_Buffer *rcbuffer;$/;" m struct:__anon11 file: read_clib src/tools/convert_kitti_to_coco.py /^def read_clib(calib_path):$/;" f readonly src/tools/voc_eval_lib/nms/cpu_nms.c /^ int readonly;$/;" m struct:__anon29 file: readonly src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ int readonly;$/;" m struct:__anon1 file: real src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef struct { double real, imag; } __pyx_t_double_complex;$/;" m struct:__anon35 file: real src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef struct { float real, imag; } __pyx_t_float_complex;$/;" m struct:__anon34 file: real src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef struct { double real, imag; } __pyx_t_double_complex;$/;" m struct:__anon7 file: real src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef struct { float real, imag; } __pyx_t_float_complex;$/;" m struct:__anon6 file: refcount src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t refcount;$/;" m struct:__anon38 file: refcount src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t refcount;$/;" m struct:__anon10 file: releasebufferproc src/tools/voc_eval_lib/nms/cpu_nms.c /^ typedef void (*releasebufferproc)(PyObject *, Py_buffer *);$/;" t file: releasebufferproc src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ typedef void (*releasebufferproc)(PyObject *, Py_buffer *);$/;" t file: remove_side src/lib/utils/debugger.py /^ def remove_side(self, img_id, img):$/;" m class:Debugger replicate src/lib/models/data_parallel.py /^ def replicate(self, module, device_ids):$/;" m class:_DataParallel replicate src/lib/models/data_parallel.py /^from torch.nn.parallel.replicate import replicate$/;" i requirements src/lib/models/networks/DCNv2/setup.py /^requirements = ["torch", "torchvision"]$/;" v res src/tools/voc_eval_lib/datasets/pascal_voc.py /^ res = d.roidb$/;" v reset src/lib/utils/utils.py /^ def reset(self):$/;" m class:AverageMeter reset_parameters src/lib/models/networks/DCNv2/dcn_v2.py /^ def reset_parameters(self):$/;" m class:DCNv2 residual src/lib/models/networks/large_hourglass.py /^class residual(nn.Module):$/;" c resnet_spec src/lib/models/networks/msra_resnet.py /^resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]),$/;" v resnet_spec src/lib/models/networks/resnet_dcn.py /^resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]),$/;" v result src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ result = sort_v(v, m, nv)$/;" v ret src/evaluate.py /^ ret = detector.run(img_path)$/;" v ret src/tools/convert_kitti_to_coco.py /^ ret = {'images': [], 'annotations': [], "categories": cat_info}$/;" v roidb src/tools/voc_eval_lib/datasets/imdb.py /^ def roidb(self):$/;" m class:imdb roidb_handler src/tools/voc_eval_lib/datasets/imdb.py /^ def roidb_handler(self):$/;" m class:imdb roidb_handler src/tools/voc_eval_lib/datasets/imdb.py /^ def roidb_handler(self, val):$/;" m class:imdb root src/tools/voc_eval_lib/nms/cpu_nms.c /^ __Pyx_StructField root;$/;" m struct:__anon33 file: root src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ __Pyx_StructField root;$/;" m struct:__anon5 file: rot_y2alpha src/lib/utils/ddd_utils.py /^def rot_y2alpha(rot_y, x, cx, fx):$/;" f rotation_y src/lib/utils/ddd_utils.py /^ rotation_y = 0.01$/;" v rotation_y src/tools/convert_kitti_to_coco.py /^ rotation_y = float(tmp[14])$/;" v rpn_roidb src/tools/voc_eval_lib/datasets/pascal_voc.py /^ def rpn_roidb(self):$/;" m class:pascal_voc run src/lib/detectors/base_detector.py /^ def run(self, image_or_path_or_tensor, meta=None):$/;" m class:BaseDetector run_epoch src/lib/trains/base_trainer.py /^ def run_epoch(self, phase, epoch, data_loader):$/;" m class:BaseTrainer run_eval src/lib/datasets/dataset/coco_fhd.py /^ def run_eval(self, results, save_dir):$/;" m class:COCOFHD ry src/tools/kitti_eval/evaluate_object_3d.cpp /^ double ry;$/;" m struct:tDetection file: ry src/tools/kitti_eval/evaluate_object_3d.cpp /^ double ry;$/;" m struct:tGroundtruth file: ry src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double ry;$/;" m struct:tDetection file: ry src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double ry;$/;" m struct:tGroundtruth file: s src/tools/voc_eval_lib/nms/cpu_nms.c /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon30 file: s src/tools/voc_eval_lib/nms/gpu_nms.cpp /^typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;$/;" m struct:__anon2 file: saturation_ src/lib/utils/image.py /^def saturation_(data_rng, image, gs, gs_mean, var):$/;" f saveAndPlotPlots src/tools/kitti_eval/evaluate_object_3d.cpp /^void saveAndPlotPlots(string dir_name,string file_name,string obj_type,vector vals[],bool is_aos){$/;" f saveAndPlotPlots src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^void saveAndPlotPlots(string dir_name,string file_name,string obj_type,vector vals[],bool is_aos){$/;" f saveStats src/tools/kitti_eval/evaluate_object_3d.cpp /^void saveStats (const vector &precision, const vector &aos, FILE *fp_det, FILE *fp_ori) {$/;" f saveStats src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^void saveStats (const vector &precision, const vector &aos, FILE *fp_det, FILE *fp_ori) {$/;" f save_all_imgs src/lib/utils/debugger.py /^ def save_all_imgs(self, path='.\/cache\/debug\/', prefix='', genID=False):$/;" m class:Debugger save_dataset src/lib/models/Rotated_IoU/demo.py /^def save_dataset():$/;" f save_img src/lib/utils/debugger.py /^ def save_img(self, imgId='default', path='.\/cache\/debug\/'):$/;" m class:Debugger save_model src/lib/models/model.py /^def save_model(path, epoch, model, optimizer=None):$/;" f save_model src/main.py /^from models.model import create_model, load_model, save_model$/;" i save_pred_folder src/evaluate.py /^ save_pred_folder = os.path.join('..\/exp\/prediction\/', exp_name)$/;" v save_result src/lib/trains/base_trainer.py /^ def save_result(self, output, batch, results):$/;" m class:BaseTrainer save_result src/lib/trains/eldet.py /^ def save_result(self, output, batch, results):$/;" m class:EldetTrainer save_result src/lib/trains/eldet_bbox.py /^ def save_result(self, output, batch, results):$/;" m class:EldetTrainer save_result src/lib/trains/eldet_onlyangle.py /^ def save_result(self, output, batch, results):$/;" m class:EldetTrainer save_results src/lib/datasets/dataset/coco_fhd.py /^ def save_results(self, results, save_dir):$/;" m class:COCOFHD sc src/tools/vis_pred.py /^ sc = pred['score']$/;" v scalar_summary src/lib/logger.py /^ def scalar_summary(self, tag, value, step):$/;" m class:Logger scatter src/lib/models/data_parallel.py /^ def scatter(self, inputs, kwargs, device_ids, chunk_sizes):$/;" m class:_DataParallel scatter src/lib/models/scatter_gather.py /^def scatter(inputs, target_gpus, dim=0, chunk_sizes=None):$/;" f scatter_kwargs src/lib/models/data_parallel.py /^from .scatter_gather import scatter_kwargs$/;" i scatter_kwargs src/lib/models/scatter_gather.py /^def scatter_kwargs(inputs, kwargs, target_gpus, dim=0, chunk_sizes=None):$/;" f scatter_map src/lib/models/scatter_gather.py /^ def scatter_map(obj):$/;" f function:scatter scipy src/tools/voc_eval_lib/datasets/imdb.py /^import scipy.sparse$/;" i scipy src/tools/voc_eval_lib/datasets/pascal_voc.py /^import scipy.io as sio$/;" i scipy src/tools/voc_eval_lib/datasets/pascal_voc.py /^import scipy.sparse$/;" i set_bn src/lib/models/networks/dlav0.py /^def set_bn(bn):$/;" f set_device src/lib/trains/base_trainer.py /^ def set_device(self, gpus, chunk_sizes, device):$/;" m class:BaseTrainer set_proposal_method src/tools/voc_eval_lib/datasets/imdb.py /^ def set_proposal_method(self, method):$/;" m class:imdb setup src/lib/external/setup.py /^from distutils.core import setup$/;" i setup src/lib/models/Rotated_IoU/cuda_op/setup.py /^from setuptools import setup$/;" i setup src/lib/models/networks/DCNv2/setup.py /^from setuptools import find_packages, setup$/;" i setup src/tools/voc_eval_lib/setup.py /^from distutils.core import setup$/;" i shape src/evaluate.py /^ shape = image.shape[:2]$/;" v shape src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t *shape;$/;" m struct:__anon29 file: shape src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon37 file: shape src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t *shape;$/;" m struct:__anon1 file: shape src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon9 file: show_all_imgs src/lib/utils/debugger.py /^ def show_all_imgs(self, pause=False, time=0):$/;" m class:Debugger show_img src/lib/utils/debugger.py /^ def show_img(self, pause = False, imgId = 'default'):$/;" m class:Debugger show_results src/lib/detectors/base_detector.py /^ def show_results(self, debugger, image, results):$/;" m class:BaseDetector show_results src/lib/detectors/eldet.py /^ def show_results(self, debugger, image, results):$/;" m class:EldetDetector show_results src/lib/detectors/eldet_bbox.py /^ def show_results(self, debugger, image, results):$/;" m class:EldetBBOXDetector similarity src/tools/kitti_eval/evaluate_object_3d.cpp /^ double similarity; \/\/ orientation similarity$/;" m struct:tPrData file: similarity src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double similarity; \/\/ orientation similarity$/;" m struct:tPrData file: sio src/tools/voc_eval_lib/datasets/pascal_voc.py /^import scipy.io as sio$/;" i size src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t size; \/* sizeof(type) *\/$/;" m struct:__anon31 file: size src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t size; \/* sizeof(type) *\/$/;" m struct:__anon3 file: smallest_bounding_box src/lib/models/Rotated_IoU/min_enclosing_box.py /^def smallest_bounding_box(corners:torch.Tensor, verbose=False):$/;" f smallest_bounding_box src/lib/models/Rotated_IoU/oriented_iou_loss.py /^from .min_enclosing_box import smallest_bounding_box$/;" i soft_nms src/evaluate.py /^from external.nms import soft_nms$/;" i soft_nms src/lib/detectors/eldet.py /^ from external.nms import soft_nms$/;" i soft_nms src/lib/detectors/eldet_bbox.py /^ from external.nms import soft_nms$/;" i soft_nms src/lib/external/nms.pyx /^def soft_nms(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0):$/;" f soft_nms src/test.py /^from external.nms import soft_nms$/;" i soft_nms_39 src/lib/external/nms.pyx /^def soft_nms_39(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0):$/;" f soft_nms_merge src/lib/external/nms.pyx /^def soft_nms_merge(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0, float weight_exp=6):$/;" f sort_indices src/lib/models/Rotated_IoU/box_intersection_2d.py /^def sort_indices(vertices:torch.Tensor, mask:torch.Tensor):$/;" f sort_v src/lib/models/Rotated_IoU/box_intersection_2d.py /^from .cuda_op.cuda_ext import sort_v$/;" i sort_v src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^sort_v = SortVertices.apply$/;" v sort_vertices src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^import sort_vertices$/;" i sort_vertices src/lib/models/Rotated_IoU/cuda_op/sort_vert.cpp /^at::Tensor sort_vertices(at::Tensor vertices, at::Tensor mask, at::Tensor num_valid){$/;" f sparse src/tools/voc_eval_lib/datasets/imdb.py /^import scipy.sparse$/;" i sparse src/tools/voc_eval_lib/datasets/pascal_voc.py /^import scipy.sparse$/;" i split src/evaluate.py /^ split = 'val'$/;" v splits src/tools/convert_kitti_to_coco.py /^ splits = ['train', 'val']$/;" v start src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ start = time.time()$/;" v state_dict src/tools/convert_hourglass_weight.py /^state_dict = torch.load(MODEL_PATH)$/;" v std src/lib/datasets/dataset/coco_fhd.py /^ std = np.array([0.222, 0.222, 0.222],$/;" v class:COCOFHD strides src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t *strides;$/;" m struct:__anon29 file: strides src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon37 file: strides src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t *strides;$/;" m struct:__anon1 file: strides src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon9 file: struct_alignment src/tools/voc_eval_lib/nms/cpu_nms.c /^ size_t struct_alignment;$/;" m struct:__anon33 file: struct_alignment src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ size_t struct_alignment;$/;" m struct:__anon5 file: suboffsets src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t *suboffsets;$/;" m struct:__anon29 file: suboffsets src/tools/voc_eval_lib/nms/cpu_nms.c /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon37 file: suboffsets src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t *suboffsets;$/;" m struct:__anon1 file: suboffsets src/tools/voc_eval_lib/nms/gpu_nms.cpp /^ Py_ssize_t shape, strides, suboffsets;$/;" m struct:__anon9 file: subprocess src/tools/voc_eval_lib/datasets/pascal_voc.py /^import subprocess$/;" i sys scripts/fhd_opts.py /^import sys$/;" i sys scripts/jupyter_opts.py /^import sys$/;" i sys src/_init_paths.py /^import sys$/;" i sys src/evaluate.py /^import sys$/;" i sys src/lib/fhd_opts.py /^import sys$/;" i sys src/lib/logger.py /^import sys$/;" i sys src/lib/models/networks/DCNv2/setup.py /^import sys$/;" i sys src/lib/opts.py /^import sys$/;" i sys src/lib/utils/debugger.py /^ import sys$/;" i sys src/tools/_init_paths.py /^import sys$/;" i sys src/tools/eval_coco.py /^import sys$/;" i sys src/tools/eval_coco_hp.py /^import sys$/;" i sys src/tools/reval.py /^import sys$/;" i sys src/tools/vis_pred.py /^import sys$/;" i t1 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t1 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: t1 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t1 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: t2 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t2 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: t2 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t2 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: t3 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t3 src/tools/kitti_eval/evaluate_object_3d.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: t3 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tDetection file: t3 src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double t1, t2, t3;$/;" m struct:tGroundtruth file: tBox src/tools/kitti_eval/evaluate_object_3d.cpp /^ tBox (string type, double x1,double y1,double x2,double y2,double alpha) :$/;" f struct:tBox tBox src/tools/kitti_eval/evaluate_object_3d.cpp /^struct tBox {$/;" s file: tBox src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tBox (string type, double x1,double y1,double x2,double y2,double alpha) :$/;" f struct:tBox tBox src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^struct tBox {$/;" s file: tDetection src/tools/kitti_eval/evaluate_object_3d.cpp /^ tDetection ():$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d.cpp /^ tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) :$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d.cpp /^ tDetection (tBox box,double thresh) :$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d.cpp /^struct tDetection {$/;" s file: tDetection src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tDetection ():$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) :$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tDetection (tBox box,double thresh) :$/;" f struct:tDetection tDetection src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^struct tDetection {$/;" s file: tGroundtruth src/tools/kitti_eval/evaluate_object_3d.cpp /^ tGroundtruth () :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d.cpp /^ tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d.cpp /^ tGroundtruth (tBox box,double truncation,int32_t occlusion) :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d.cpp /^struct tGroundtruth {$/;" s file: tGroundtruth src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tGroundtruth () :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tGroundtruth (tBox box,double truncation,int32_t occlusion) :$/;" f struct:tGroundtruth tGroundtruth src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^struct tGroundtruth {$/;" s file: tPrData src/tools/kitti_eval/evaluate_object_3d.cpp /^ tPrData () :$/;" f struct:tPrData tPrData src/tools/kitti_eval/evaluate_object_3d.cpp /^struct tPrData {$/;" s file: tPrData src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ tPrData () :$/;" f struct:tPrData tPrData src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^struct tPrData {$/;" s file: tensor1 src/lib/models/Rotated_IoU/min_enclosing_box.py /^ tensor1 = torch.FloatTensor(np.concatenate([corners1, corners2], axis=0))$/;" v tensor1 src/lib/models/Rotated_IoU/oriented_iou_loss.py /^ tensor1 = torch.FloatTensor(box3d1).unsqueeze(0).unsqueeze(0).cuda()$/;" v tensor2 src/lib/models/Rotated_IoU/oriented_iou_loss.py /^ tensor2 = torch.FloatTensor(box3d2).unsqueeze(0).unsqueeze(0).cuda()$/;" v tensorboardX src/lib/logger.py /^ import tensorboardX$/;" i test src/test.py /^def test(opt):$/;" f test_area src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^def test_area():$/;" f test_box2corners src/lib/models/Rotated_IoU/utiles.py /^def test_box2corners():$/;" f test_box_in_box_th src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^def test_box_in_box_th():$/;" f test_box_intersection src/lib/models/Rotated_IoU/utiles.py /^def test_box_intersection(box1, box2):$/;" f test_box_intersection_th src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^def test_box_intersection_th():$/;" f test_intersection_area src/lib/models/Rotated_IoU/utiles.py /^def test_intersection_area(box1, box2):$/;" f test_line_seg_intersection src/lib/models/Rotated_IoU/utiles.py /^def test_line_seg_intersection():$/;" f test_net src/tools/voc_eval_lib/model/test.py /^def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.):$/;" f test_point_in_box src/lib/models/Rotated_IoU/utiles.py /^def test_point_in_box():$/;" f test_same_box src/lib/models/Rotated_IoU/test_corner_cases.py /^def test_same_box():$/;" f test_same_box2 src/lib/models/Rotated_IoU/test_corner_cases.py /^def test_same_box2():$/;" f test_same_edge src/lib/models/Rotated_IoU/test_corner_cases.py /^def test_same_edge():$/;" f test_same_edge_offset src/lib/models/Rotated_IoU/test_corner_cases.py /^def test_same_edge_offset():$/;" f theta src/evaluate.py /^ theta = ret['results'][class_id][0, 9]$/;" v theta_gt src/evaluate.py /^ theta_gt = ellipse[4] + 0.5 * math.pi$/;" v theta_gt src/evaluate.py /^ theta_gt = ellipse[4]$/;" v theta_gt src/evaluate.py /^ theta_gt = theta_gt - 1 if theta_gt > 0.5 else theta_gt + 1 if theta_gt < -0.5 else theta_gt$/;" v theta_gt src/evaluate.py /^ theta_gt = theta_gt \/ math.pi$/;" v this_dir src/_init_paths.py /^this_dir = osp.dirname(__file__)$/;" v this_dir src/tools/_init_paths.py /^this_dir = osp.dirname(__file__)$/;" v this_dir src/tools/eval_coco.py /^this_dir = os.path.dirname(__file__)$/;" v this_dir src/tools/eval_coco_hp.py /^this_dir = os.path.dirname(__file__)$/;" v thresh src/tools/kitti_eval/evaluate_object_3d.cpp /^ double thresh; \/\/ detection score$/;" m struct:tDetection file: thresh src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^ double thresh; \/\/ detection score$/;" m struct:tDetection file: tic src/tools/voc_eval_lib/utils/timer.py /^ def tic(self):$/;" m class:Timer time src/evaluate.py /^import time$/;" i time src/lib/detectors/base_detector.py /^import time$/;" i time src/lib/detectors/eldet.py /^import time$/;" i time src/lib/detectors/eldet_bbox.py /^import time$/;" i time src/lib/logger.py /^import time$/;" i time src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^ import time$/;" i time src/lib/trains/base_trainer.py /^import time$/;" i time src/test.py /^import time$/;" i time src/tools/voc_eval_lib/utils/timer.py /^import time$/;" i time_stats src/demo.py /^time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']$/;" v tl src/lib/utils/ddd_utils.py /^ tl = np.array([712.40, 143.00], dtype=np.float32)$/;" v tmp src/tools/convert_kitti_to_coco.py /^ tmp = txt[:-1].split(' ')$/;" v toPolygon src/tools/kitti_eval/evaluate_object_3d.cpp /^Polygon toPolygon(const T& g) {$/;" f toPolygon src/tools/kitti_eval/evaluate_object_3d_offline.cpp /^Polygon toPolygon(const T& g) {$/;" f toc src/tools/voc_eval_lib/utils/timer.py /^ def toc(self, average=True):$/;" m class:Timer torch src/evaluate.py /^import torch$/;" i torch src/lib/datasets/dataset/coco_fhd.py /^import torch.utils.data as data$/;" i torch src/lib/datasets/sample/eldet.py /^import torch$/;" i torch src/lib/datasets/sample/eldet.py /^import torch.utils.data as data$/;" i torch src/lib/detectors/base_detector.py /^import torch$/;" i torch src/lib/detectors/eldet.py /^import torch$/;" i torch src/lib/detectors/eldet_bbox.py /^import torch$/;" i torch src/lib/logger.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/box_intersection_2d.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/cuda_op/cuda_ext.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/demo.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/min_enclosing_box.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/oriented_iou_loss.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/test_box_intersection_2d.py /^import torch$/;" i torch src/lib/models/Rotated_IoU/test_corner_cases.py /^import torch$/;" i torch src/lib/models/data_parallel.py /^import torch$/;" i torch src/lib/models/decode.py /^import torch$/;" i torch src/lib/models/decode.py /^import torch.nn as nn$/;" i torch src/lib/models/decode.py /^import torch.nn.functional as F$/;" i torch src/lib/models/losses.py /^import torch$/;" i torch src/lib/models/losses.py /^import torch.nn as nn$/;" i torch src/lib/models/losses.py /^import torch.nn.functional as F$/;" i torch src/lib/models/model.py /^import torch$/;" i torch src/lib/models/model.py /^import torch.nn as nn$/;" i torch src/lib/models/networks/DCNv2/dcn_v2.py /^import torch$/;" i torch src/lib/models/networks/DCNv2/setup.py /^import torch$/;" i torch src/lib/models/networks/DCNv2/test/test.py /^import torch$/;" i torch src/lib/models/networks/DCNv2/test/test.py /^import torch.nn as nn$/;" i torch src/lib/models/networks/DCNv2/test/testcpu.py /^import torch$/;" i torch src/lib/models/networks/DCNv2/test/testcpu.py /^import torch.nn as nn$/;" i torch src/lib/models/networks/DCNv2/test/testcuda.py /^import torch$/;" i torch src/lib/models/networks/DCNv2/test/testcuda.py /^import torch.nn as nn$/;" i torch src/lib/models/networks/dlav0.py /^import torch$/;" i torch src/lib/models/networks/dlav0.py /^import torch.utils.model_zoo as model_zoo$/;" i torch src/lib/models/networks/large_hourglass.py /^import torch$/;" i torch src/lib/models/networks/large_hourglass.py /^import torch.nn as nn$/;" i torch src/lib/models/networks/msra_resnet.py /^import torch$/;" i torch src/lib/models/networks/msra_resnet.py 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