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They should be base names, not # paths. ignore=__init__.py # Pickle collected data for later comparisons. persistent=yes # List of plugins (as comma separated values of python modules names) to load, # usually to register additional checkers. load-plugins= [MESSAGES CONTROL] # Enable the message, report, category or checker with the given id(s). You can # either give multiple identifier separated by comma (,) or put this option # multiple time. See also the "--disable" option for examples. enable=indexing-exception,old-raise-syntax # Disable the message, report, category or checker with the given id(s). You # can either give multiple identifiers separated by comma (,) or put this # option multiple times (only on the command line, not in the configuration # file where it should appear only once).You can also use "--disable=all" to # disable everything first and then re-enable specific checks. For example, if # you want to run only the similarities checker, you can use "--disable=all # --enable=similarities". If you want to run only the classes checker, but have # no Warning level messages displayed, use"--disable=all --enable=classes # --disable=W" disable=design,similarities,no-self-use,attribute-defined-outside-init,locally-disabled,star-args,pointless-except,bad-option-value,global-statement,fixme,suppressed-message,useless-suppression,locally-enabled,no-member,no-name-in-module,import-error,unsubscriptable-object,unbalanced-tuple-unpacking,undefined-variable,not-context-manager,arguments-differ,line-too-long,consider-using-with # Set the cache size for astng objects. cache-size=500 [REPORTS] # Set the output format. Available formats are text, parseable, colorized, msvs # (visual studio) and html. You can also give a reporter class, eg # mypackage.mymodule.MyReporterClass. output-format=text # Put messages in a separate file for each module / package specified on the # command line instead of printing them on stdout. Reports (if any) will be # written in a file name "pylint_global.[txt|html]". files-output=no # Tells whether to display a full report or only the messages reports=no # Python expression which should return a note less than 10 (10 is the highest # note). You have access to the variables errors warning, statement which # respectively contain the number of errors / warnings messages and the total # number of statements analyzed. This is used by the global evaluation report # (RP0004). evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) # Add a comment according to your evaluation note. This is used by the global # evaluation report (RP0004). comment=no # Template used to display messages. This is a python new-style format string # used to format the message information. See doc for all details #msg-template= [TYPECHECK] # Tells whether missing members accessed in mixin class should be ignored. A # mixin class is detected if its name ends with "mixin" (case insensitive). ignore-mixin-members=yes # List of classes names for which member attributes should not be checked # (useful for classes with attributes dynamically set). ignored-classes=SQLObject # When zope mode is activated, add a predefined set of Zope acquired attributes # to generated-members. zope=no # List of members which are set dynamically and missed by pylint inference # system, and so shouldn't trigger E0201 when accessed. Python regular # expressions are accepted. generated-members=REQUEST,acl_users,aq_parent # List of decorators that create context managers from functions, such as # contextlib.contextmanager. contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager [VARIABLES] # Tells whether we should check for unused import in __init__ files. init-import=no # A regular expression matching the beginning of the name of dummy variables # (i.e. not used). dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) # List of additional names supposed to be defined in builtins. Remember that # you should avoid to define new builtins when possible. additional-builtins= [BASIC] # Required attributes for module, separated by a comma required-attributes= # List of builtins function names that should not be used, separated by a comma bad-functions=apply,input,reduce # Disable the report(s) with the given id(s). # All non-Google reports are disabled by default. disable-report=R0001,R0002,R0003,R0004,R0101,R0102,R0201,R0202,R0220,R0401,R0402,R0701,R0801,R0901,R0902,R0903,R0904,R0911,R0912,R0913,R0914,R0915,R0921,R0922,R0923 # Regular expression which should only match correct module names module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ # Regular expression which should only match correct module level names const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ # Regular expression which should only match correct class names class-rgx=^_?[A-Z][a-zA-Z0-9]*$ # Regular expression which should only match correct function names function-rgx=^(?:(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ # Regular expression which should only match correct method names method-rgx=^(?:(?P__[a-z0-9_]+__|next)|(?P_{0,2}[A-Z][a-zA-Z0-9]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ # Regular expression which should only match correct instance attribute names attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ # Regular expression which should only match correct argument names argument-rgx=^[a-z][a-z0-9_]*$ # Regular expression which should only match correct variable names variable-rgx=^[a-z][a-z0-9_]*$ # Regular expression which should only match correct attribute names in class # bodies class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ # Regular expression which should only match correct list comprehension / # generator expression variable names inlinevar-rgx=^[a-z][a-z0-9_]*$ # Good variable names which should always be accepted, separated by a comma good-names=main,_ # Bad variable names which should always be refused, separated by a comma bad-names= # Regular expression which should only match function or class names that do # not require a docstring. no-docstring-rgx=(__.*__|main) # Minimum line length for functions/classes that require docstrings, shorter # ones are exempt. docstring-min-length=10 [FORMAT] # Maximum number of characters on a single line. # mindspore: max-line-length=120 (Default: 80) max-line-length=120 # Regexp for a line that is allowed to be longer than the limit. ignore-long-lines=(?x) (^\s*(import|from)\s |\$Id:\s\/\/depot\/.+#\d+\s\$ |^[a-zA-Z_][a-zA-Z0-9_]*\s*=\s*("[^"]\S+"|'[^']\S+') |^\s*\#\ LINT\.ThenChange |^[^#]*\#\ type:\ [a-zA-Z_][a-zA-Z0-9_.,[\] ]*$ |pylint |""" |\# |lambda |(https?|ftp):) # Allow the body of an if to be on the same line as the test if there is no # else. single-line-if-stmt=y # List of optional constructs for which whitespace checking is disabled no-space-check= # Maximum number of lines in a module max-module-lines=99999 # String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 # tab). # mindspore: 4 spaces (Default: 2 spaces) indent-string=' ' [SIMILARITIES] # Minimum lines number of a similarity. min-similarity-lines=4 # Ignore comments when computing similarities. ignore-comments=yes # Ignore docstrings when computing similarities. ignore-docstrings=yes # Ignore imports when computing similarities. ignore-imports=no [MISCELLANEOUS] # List of note tags to take in consideration, separated by a comma. notes= [IMPORTS] # Deprecated modules which should not be used, separated by a comma deprecated-modules=regsub,TERMIOS,Bastion,rexec,sets # Create a graph of every (i.e. internal and external) dependencies in the # given file (report RP0402 must not be disabled) import-graph= # Create a graph of external dependencies in the given file (report RP0402 must # not be disabled) ext-import-graph= # Create a graph of internal dependencies in the given file (report RP0402 must # not be disabled) int-import-graph= [CLASSES] # List of interface methods to ignore, separated by a comma. This is used for # instance to not check methods defines in Zope's Interface base class. ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by # List of method names used to declare (i.e. assign) instance attributes. defining-attr-methods=__init__,__new__,setUp # List of valid names for the first argument in a class method. valid-classmethod-first-arg=cls,class_ # List of valid names for the first argument in a metaclass class method. valid-metaclass-classmethod-first-arg=mcs [DESIGN] # Maximum number of arguments for function / method max-args=5 # Argument names that match this expression will be ignored. Default to name # with leading underscore ignored-argument-names=_.* # Maximum number of locals for function / method body max-locals=15 # Maximum number of return / yield for function / method body max-returns=6 # Maximum number of branch for function / method body max-branches=12 # Maximum number of statements in function / method body max-statements=50 # Maximum number of parents for a class (see R0901). max-parents=7 # Maximum number of attributes for a class (see R0902). max-attributes=7 # Minimum number of public methods for a class (see R0903). min-public-methods=2 # Maximum number of public methods for a class (see R0904). max-public-methods=20 [EXCEPTIONS] # Exceptions that will emit a warning when being caught. Defaults to # "Exception" overgeneral-exceptions=Exception,StandardError,BaseException [AST] # Maximum line length for lambdas short-func-length=1 # List of module members that should be marked as deprecated. # All of the string functions are listed in 4.1.4 Deprecated string functions # in the Python 2.4 docs. deprecated-members=string.atof,string.atoi,string.atol,string.capitalize,string.expandtabs,string.find,string.rfind,string.index,string.rindex,string.count,string.lower,string.split,string.rsplit,string.splitfields,string.join,string.joinfields,string.lstrip,string.rstrip,string.strip,string.swapcase,string.translate,string.upper,string.ljust,string.rjust,string.center,string.zfill,string.replace,sys.exitfunc [DOCSTRING] # List of exceptions that do not need to be mentioned in the Raises section of # a docstring. ignore-exceptions=AssertionError,NotImplementedError,StopIteration,TypeError [TOKENS] # Number of spaces of indent required when the last token on the preceding line # is an open (, [, or {. indent-after-paren=4 [MINDSPORE LINES] # Regexp for a proper copyright notice. copyright=Copyright \d{4} The MindSpore Authors\. +All [Rr]ights [Rr]eserved\.yolov/.github/workflows/000077500000000000000000000000001443434675500156425ustar00rootroot00000000000000yolov/.github/workflows/main.yml000066400000000000000000000021141443434675500173070ustar00rootroot00000000000000# This workflow will install Python dependencies, run tests and lint with a single version of Python # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions name: CI on: push: branches: [ "master" ] pull_request: branches: [ "master" ] permissions: contents: read jobs: ut-test: strategy: matrix: os: [ubuntu-latest] # , macos-latest, windows-latest python: [3.7, 3.8, 3.9] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v3 with: python-version: ${{ matrix.python }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements/cpu_requirements.txt - name: Analysing the code with pylint run: | pylint mindyolo --rcfile=.github/pylint.conf - name: Test with unit test (UT) pytest run: | pytest tests/ut/ - name: Test with system test (ST) pytest run: | pytest tests/st/yolov/.gitignore000066400000000000000000000012431443434675500142350ustar00rootroot00000000000000# MindSpore *.ir kernel_meta/ somas_meta/ trace_code_graph_* # Cmake files CMakeFiles/ cmake_install.cmake CMakeCache.txt Makefile cmake-build-debug # Dynamic libraries *.so *.so.* *.dylib # Static libraries *.la *.lai *.a *.lib # Protocol buffers *_pb2.py *.pb.h *.pb.cc *.pb *_grpc.py # Object files *.o # Editor .vscode .idea/ # Cquery .cquery_cached_index/ compile_commands.json # Ctags and cscope tags TAGS CTAGS GTAGS GRTAGS GSYMS GPATH cscope.* # Python files *__pycache__* .pytest_cache # Mac files *.DS_Store # Test results test_temp_summary_event_file/ *.dot *.dat *.svg *.perf *.info *.ckpt *.shp *.pkl *.pb .clangd # lite opencl compile file *.cl.inc yolov/.ipynb_checkpoints/000077500000000000000000000000001443434675500160365ustar00rootroot00000000000000yolov/.ipynb_checkpoints/log-checkpoint.txt000066400000000000000000000262511443434675500215130ustar00rootroot00000000000000parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=1, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=False, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=8, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='') Please check the above information for the configurations from n params module arguments 0 -1 1 992 [3, 32, 3, 1] 1 -1 1 18688 [32, 64, 3, 2] 2 -1 1 4352 [64, 64, 1, 1] 3 -1 1 20864 [64, 64] 4 -1 1 4352 [64, 64, 1, 1] 5 1 1 4352 [64, 64, 1, 1] 6 [-2, -1] 1 0 [1] 7 -1 1 8448 [128, 64, 1, 1] 8 -1 1 74240 [64, 128, 3, 2] 9 -1 1 8448 [128, 64, 1, 1] 10 -1 2 41728 [64, 64] 11 -1 1 4352 [64, 64, 1, 1] 12 8 1 8448 [128, 64, 1, 1] 13 [-2, -1] 1 0 [1] 14 -1 1 16896 [128, 128, 1, 1] 15 -1 1 295936 [128, 256, 3, 2] 16 -1 1 33280 [256, 128, 1, 1] 17 -1 8 661504 [128, 128] 18 -1 1 16896 [128, 128, 1, 1] 19 15 1 33280 [256, 128, 1, 1] 20 [-2, -1] 1 0 [1] 21 -1 1 66560 [256, 256, 1, 1] 22 -1 1 1181696 [256, 512, 3, 2] 23 -1 1 132096 [512, 256, 1, 1] 24 -1 8 2633728 [256, 256] 25 -1 1 66560 [256, 256, 1, 1] 26 22 1 132096 [512, 256, 1, 1] 27 [-2, -1] 1 0 [1] 28 -1 1 264192 [512, 512, 1, 1] 29 -1 1 4722688 [512, 1024, 3, 2] 30 -1 1 526336 [1024, 512, 1, 1] 31 -1 8 10510336 [512, 512] 32 -1 1 264192 [512, 512, 1, 1] 33 29 1 526336 [1024, 512, 1, 1] 34 [-2, -1] 1 0 [1] 35 -1 1 1052672 [1024, 1024, 1, 1] 36 -1 1 5249024 [1024, 1024, False] 37 -1 1 526336 [1024, 512, 1, 1] 38 -1 1 0 [5, 1] 39 -2 1 0 [9, 1] 40 -3 1 0 [13, 1] 41 [-1, -2, -3, -4] 1 0 [1] 42 -1 1 1050624 [2048, 512, 1, 1] 43 -1 1 4722688 [512, 1024, 3, 1] 44 -1 1 526336 [1024, 512, 1, 1] 45 -1 1 132096 [512, 256, 1, 1] 46 -1 1 0 [None, 2, 'nearest'] 47 28 1 132096 [512, 256, 1, 1] 48 [-2, -1] 1 0 [1] 49 -1 2 2627584 [512, 512, False] 50 -1 1 132096 [512, 256, 1, 1] 51 -1 1 295424 [256, 128, 3, 1] 52 -1 1 0 [None, 2, 'nearest'] 53 21 1 33280 [256, 128, 1, 1] 54 [-2, -1] 1 0 [1] 55 -1 2 658432 [256, 256, False] 56 -1 1 33280 [256, 128, 1, 1] 57 -1 1 295936 [128, 256, 3, 1] 58 -2 1 295936 [128, 256, 3, 2] 2023-05-05 15:22:15,160 [INFO] Dataset Cache file hash/version check success. 2023-05-05 15:22:15,160 [INFO] Load dataset cache from [../coco_val_yolo/val2017.cache.npy] success. Scanning '../coco_val_yolo/val2017.cache.npy' images and labels... 4952 found, 48 missing, 0 empty, 0 cor Scanning '../coco_val_yolo/val2017.cache.npy' images and labels... 4952 found, 48 missing, 0 empty, 0 cor2023-05-05 15:22:15,252 [INFO] Dataloader num parallel workers: [4] albumentations: WARNING ⚠️ version 1.0.3 is required by YOLOv5, but version 0.4.5 is currently installed [WARNING] albumentations load failed albumentations: WARNING ⚠️ version 1.0.3 is required by YOLOv5, but version 0.4.5 is currently installed [WARNING] albumentations load failed albumentations: WARNING ⚠️ version 1.0.3 is required by YOLOv5, but version 0.4.5 is currently installed [WARNING] albumentations load failed albumentations: WARNING ⚠️ version 1.0.3 is required by YOLOv5, but version 0.4.5 is currently installed [WARNING] albumentations load failed [ERROR] ANALYZER(149757,ffffb68acac0,python):2023-05-05-15:23:35.159.359 [mindspore/ccsrc/pipeline/jit/static_analysis/static_analysis.cc:321] EvalCNode] Can not cast to a AbstractFunction from AbstractScalar(Type: Int32, Value: 0, Shape: NoShape). [ERROR] ANALYZER(149757,ffffb68acac0,python):2023-05-05-15:23:35.159.492 [mindspore/ccsrc/pipeline/jit/static_analysis/static_analysis.cc:322] EvalCNode] It's called at: 4577_1_jit_warpper.304:[CNode]305{[0]: ValueNode 0, [1]: [CNode]155} Traceback (most recent call last): File "train.py", line 227, in train(args) File "train.py", line 219, in train test_fn=test_fn, File "/home/ma-user/work/mindyolo_for_yolov4/mindyolo/utils/trainer_factory.py", line 102, in train self.train_step(imgs, labels, cur_step=cur_step, cur_epoch=cur_epoch) File "/home/ma-user/work/mindyolo_for_yolov4/mindyolo/utils/trainer_factory.py", line 169, in train_step loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, True) File "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/mindspore/common/api.py", line 559, in staging_specialize out = _MindsporeFunctionExecutor(func, hash_obj, input_signature, process_obj, jit_config)(*args) File "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/mindspore/common/api.py", line 98, in wrapper results = fn(*arg, **kwargs) File "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/mindspore/common/api.py", line 360, in __call__ phase = self.compile(args_list, self.fn.__name__) File "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/mindspore/common/api.py", line 323, in compile is_compile = self._graph_executor.compile(self.fn, compile_args, phase, True) ValueError: This may be not defined, or it can't be a operator. Please check code. ---------------------------------------------------- - The Traceback of Net Construct Code: ---------------------------------------------------- The function call stack (See file '/home/ma-user/work/mindyolo_for_yolov4/rank_0/om/analyze_fail.dat' for more details. Get instructions about `analyze_fail.dat` at https://www.mindspore.cn/search?inputValue=analyze_fail.dat): ---------------------------------------------------- - C++ Call Stack: (For framework developers) ---------------------------------------------------- mindspore/ccsrc/pipeline/jit/static_analysis/static_analysis.cc:323 EvalCNode yolov/.ipynb_checkpoints/train-checkpoint.py000066400000000000000000000252511443434675500216570ustar00rootroot00000000000000import os import ast import argparse from functools import partial import mindspore as ms from mindyolo.data import COCODataset, create_loader from mindyolo.models import create_loss, create_model from mindyolo.optim import create_group_param, create_lr_scheduler, create_warmup_momentum_scheduler, \ create_optimizer, EMA from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.utils import set_seed, set_default, load_pretrain, freeze_layers from mindyolo.utils.train_step_factory import get_gradreducer, get_loss_scaler, create_train_step_fn from mindyolo.utils.trainer_factory import create_trainer from test import test def get_parser_train(parents=None): parser = argparse.ArgumentParser(description='Train', parents=[parents] if parents else []) parser.add_argument('--device_target', type=str, default='Ascend', help='device target, Ascend/GPU/CPU') parser.add_argument('--save_dir', type=str, default='./runs', help='save dir') parser.add_argument('--device_per_servers', type=int, default=8, help='device number on a server') parser.add_argument('--log_level', type=str, default='INFO', help='save dir') parser.add_argument('--is_parallel', type=ast.literal_eval, default=False, help='Distribute train or not') parser.add_argument('--ms_mode', type=int, default=0, help='Running in GRAPH_MODE(0) or PYNATIVE_MODE(1) (default=0)') parser.add_argument('--ms_amp_level', type=str, default='O0', help='amp level, O0/O1/O2/O3') parser.add_argument('--keep_loss_fp32', type=ast.literal_eval, default=True, help='Whether to maintain loss using fp32/O0-level calculation') parser.add_argument('--ms_loss_scaler', type=str, default='static', help='train loss scaler, static/dynamic/none') parser.add_argument('--ms_loss_scaler_value', type=float, default=1024.0, help='static loss scale value') parser.add_argument('--ms_grad_sens', type=float, default=1024.0, help='gard sens') parser.add_argument('--ms_jit', type=ast.literal_eval, default=True, help='use jit or not') parser.add_argument('--ms_enable_graph_kernel', type=ast.literal_eval, default=False, help='use enable_graph_kernel or not') parser.add_argument('--overflow_still_update', type=ast.literal_eval, default=True, help='overflow still update') parser.add_argument('--ema', type=ast.literal_eval, default=True, help='ema') parser.add_argument('--weight', type=str, default='', help='initial weight path') parser.add_argument('--ema_weight', type=str, default='', help='initial ema weight path') parser.add_argument('--freeze', type=list, default=[], help='Freeze layers: backbone of yolov7=50, first3=0 1 2') parser.add_argument('--epochs', type=int, default=300, help="total train epochs") parser.add_argument('--per_batch_size', type=int, default=32, help='per batch size for each device') parser.add_argument('--img_size', type=list, default=640, help='train image sizes') parser.add_argument('--nbs', type=list, default=64, help='nbs') parser.add_argument('--accumulate', type=int, default=1, help='grad accumulate step, recommended when batch-size is less than 64') parser.add_argument('--auto_accumulate', type=ast.literal_eval, default=False, help='auto accumulate') parser.add_argument('--log_interval', type=int, default=100, help='log interval') parser.add_argument('--single_cls', type=ast.literal_eval, default=False, help='train multi-class data as single-class') parser.add_argument('--sync_bn', type=ast.literal_eval, default=False, help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--keep_checkpoint_max', type=int, default=100) parser.add_argument('--run_eval', type=ast.literal_eval, default=False, help='Whether to run eval during training') parser.add_argument('--rect', type=ast.literal_eval, default=False, help='rectangular training') parser.add_argument('--nms_time_limit', type=float, default=20.0, help='time limit for NMS') parser.add_argument('--recompute', type=ast.literal_eval, default=False, help='Recompute') parser.add_argument('--recompute_layers', type=int, default=0) parser.add_argument('--seed', type=int, default=2, help='set global seed') # args for ModelArts parser.add_argument('--enable_modelarts', type=ast.literal_eval, default=False, help='enable modelarts') parser.add_argument('--data_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--ckpt_url', type=str, default='', help='ModelArts: obs path to checkpoint folder') parser.add_argument('--train_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--data_dir', type=str, default='/cache/data/', help='ModelArts: local device path to dataset folder') parser.add_argument('--ckpt_dir', type=str, default='/cache/pretrain_ckpt/', help='ModelArts: local device path to checkpoint folder') return parser def train(args): # Set Default set_seed(args.seed) set_default(args) main_device = (args.rank % args.rank_size == 0) # Create Network args.network.recompute = args.recompute args.network.recompute_layers = args.recompute_layers network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, sync_bn=args.sync_bn, ) if args.ema and main_device: ema_network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, ) ema = EMA(network, ema_network) else: ema = None load_pretrain(network, args.weight, ema, args.ema_weight) # load pretrain freeze_layers(network, args.freeze) # freeze Layers ms.amp.auto_mixed_precision(network, amp_level=args.ms_amp_level) if ema: ms.amp.auto_mixed_precision(ema.ema, amp_level=args.ms_amp_level) # Create Dataloader dataset = COCODataset( dataset_path=args.data.train_set, img_size=args.img_size, transforms_dict=args.data.train_transforms, is_training=True, augment=True, rect=args.rect, single_cls=args.single_cls, batch_size=args.total_batch_size, stride=max(args.network.stride), ) dataloader = create_loader( dataset=dataset, batch_collate_fn=dataset.train_collate_fn, dataset_column_names=dataset.dataset_column_names, batch_size=args.per_batch_size, epoch_size=1, rank=args.rank, rank_size=args.rank_size, shuffle=True, drop_remainder=True, num_parallel_workers=args.data.num_parallel_workers, python_multiprocessing=True ) steps_per_epoch = dataloader.get_dataset_size() if args.run_eval: eval_dataset = COCODataset( dataset_path=args.data.val_set, img_size=args.img_size, transforms_dict=args.data.test_transforms, is_training=False, augment=False, rect=args.rect, single_cls=args.single_cls, batch_size=args.per_batch_size * 2, stride=max(args.network.stride), ) eval_dataloader = create_loader( dataset=eval_dataset, batch_collate_fn=eval_dataset.test_collate_fn, dataset_column_names=eval_dataset.dataset_column_names, batch_size=args.per_batch_size * 2, epoch_size=1, rank=0, rank_size=1, shuffle=False, drop_remainder=False, num_parallel_workers=args.data.num_parallel_workers, python_multiprocessing=True ) else: eval_dataset, eval_dataloader = None, None # Create Loss loss_fn = create_loss( **args.loss, anchors=args.network.anchors, stride=args.network.stride, nc=args.data.nc ) ms.amp.auto_mixed_precision(loss_fn, amp_level='O0' if args.keep_loss_fp32 else args.ms_amp_level) # Create Optimizer args.optimizer.steps_per_epoch = steps_per_epoch lr = create_lr_scheduler(**args.optimizer) params = create_group_param(params=network.trainable_params(), **args.optimizer) optimizer = create_optimizer(params=params, lr=lr, **args.optimizer) warmup_momentum = create_warmup_momentum_scheduler(**args.optimizer) # Create train_step_fn reducer = get_gradreducer(args.is_parallel, optimizer.parameters) scaler = get_loss_scaler(args.ms_loss_scaler, scale_value=args.ms_loss_scaler_value) train_step_fn = create_train_step_fn( network=network, loss_fn=loss_fn, optimizer=optimizer, loss_ratio=args.rank_size, scaler=scaler, reducer=reducer, overflow_still_update=args.overflow_still_update, ms_jit=args.ms_jit ) # Create test function for run eval while train if args.run_eval: is_coco_dataset = ('coco' in args.data.dataset_name) test_fn = partial( test, dataloader=eval_dataloader, anno_json_path=os.path.join(args.data.val_set[:-len(args.data.val_set.split('/')[-1])], 'annotations/instances_val2017.json'), conf_thres=args.conf_thres, iou_thres=args.iou_thres, nms_time_limit=args.nms_time_limit, is_coco_dataset=is_coco_dataset, imgIds=None if not is_coco_dataset else eval_dataset.imgIds, per_batch_size=args.per_batch_size ) else: test_fn = None # Create Trainer network.set_train(True) optimizer.set_train(True) model_name = os.path.basename(args.config)[:-5] # delete ".yaml" trainer = create_trainer( model_name=model_name, train_step_fn=train_step_fn, scaler=scaler, dataloader=dataloader, network=network, ema=ema, optimizer=optimizer, ) trainer.train( epochs=args.epochs, main_device=main_device, warmup_step=max(round(args.optimizer.warmup_epochs * steps_per_epoch), args.optimizer.min_warmup_step), warmup_momentum=warmup_momentum, accumulate=args.accumulate, overflow_still_update=args.overflow_still_update, keep_checkpoint_max=args.keep_checkpoint_max, log_interval=args.log_interval, loss_item_name=[] if not hasattr(loss_fn, 'loss_item_name') else loss_fn.loss_item_name, save_dir=args.save_dir, enable_modelarts=args.enable_modelarts, train_url=args.train_url, run_eval=args.run_eval, test_fn=test_fn, ) logger.info('Training completed.') if __name__ == '__main__': parser = get_parser_train() args = parse_args(parser) train(args) yolov/CONTRIBUTING.md000066400000000000000000000157651443434675500145140ustar00rootroot00000000000000# MindYOLO contributing guidelines - [MindYOLO contributing guidelines](#mindyolo-contributing-guidelines) - [Contributor License Agreement](#contributor-license-agreement) - [Getting Started](#getting-started) - [Contribution workflow](#contribution-workflow) - [Code style](#code-style) - [Fork-Pull development model](#fork-pull-development-model) - [Report issues](#report-issues) - [Propose PRs](#propose-prs) ## Contributor License Agreement It's required to sign CLA before your first code submission to MindYOLO community. For individual contributor, please refer to [ICLA online document](https://www.mindspore.cn/icla) for the detailed information. ## Getting Started - Fork the repository on [Github](https://github.com/mindspore-lab/mindyolo). - Read the [README.md](README.md). ## Contribution Workflow ### Code style Please follow this style to make MindYOLO easy to review, maintain and develop. - Coding guidelines The *Python* coding style suggested by [Python PEP 8 Coding Style](https://pep8.org/) and *C++* coding style suggested by [Google C++ Coding Guidelines](http://google.github.io/styleguide/cppguide.html) are used in MindYOLO community. The [CppLint](https://github.com/cpplint/cpplint), [CppCheck](http://cppcheck.sourceforge.net), [CMakeLint](https://github.com/cmake-lint/cmake-lint), [CodeSpell](https://github.com/codespell-project/codespell), [Lizard](http://www.lizard.ws), [ShellCheck](https://github.com/koalaman/shellcheck) and [PyLint](https://pylint.org) are used to check the format of codes, installing these plugins in your IDE is recommended. - Unittest guidelines The *Python* unittest style suggested by [pytest](http://www.pytest.org/en/latest/) and *C++* unittest style suggested by [Googletest Primer](https://github.com/google/googletest/blob/master/docs/primer.md) are used in MindYOLO community. The design intent of a testcase should be reflected by its name of comment. - Refactoring guidelines We encourage developers to refactor our code to eliminate the [code smell](https://en.wikipedia.org/wiki/Code_smell). All codes should conform to needs to the coding style and testing style, and refactoring codes are no exception. [Lizard](http://www.lizard.ws) threshold for nloc (lines of code without comments) is 100 and for cnc (cyclomatic complexity number) is 20, when you receive a *Lizard* warning, you have to refactor the code you want to merge. - Document guidelines We use *MarkdownLint* to check the format of markdown documents. MindYOLO CI modifies the following rules based on the default configuration. - MD007 (unordered list indentation): The **indent** parameter is set to **4**, indicating that all content in the unordered list needs to be indented using four spaces. - MD009 (spaces at the line end): The **br_spaces** parameter is set to **2**, indicating that there can be 0 or 2 spaces at the end of a line. - MD029 (sequence numbers of an ordered list): The **style** parameter is set to **ordered**, indicating that the sequence numbers of the ordered list are in ascending order. For details, please refer to [RULES](https://github.com/markdownlint/markdownlint/blob/master/docs/RULES.md). ### Fork-Pull development model - Fork MindYOLO repository Before submitting code to MindYOLO project, please make sure that this project have been forked to your own repository. It means that there will be parallel development between MindYOLO repository and your own repository, so be careful to avoid the inconsistency between them. - Clone the remote repository If you want to download the code to the local machine, `git` is the best way: ```shell # For GitHub git clone https://github.com/{insert_your_forked_repo}/mindyolo.git git remote add upstream https://github.com/mindspore-lab/mindyolo.git ``` - Develop code locally To avoid inconsistency between multiple branches, checking out to a new branch is `SUGGESTED`: ```shell git checkout -b {new_branch_name} origin/master ``` Taking the master branch as an example, MindYOLO may create version branches and downstream development branches as needed, please fix bugs upstream first. Then you can change the code arbitrarily. - Push the code to the remote repository After updating the code, you should push the update in the formal way: ```shell git add . git status # Check the update status git commit -m "Your commit title" git commit -s --amend #Add the concrete description of your commit git push origin {new_branch_name} ``` - Pull a request to MindYOLO repository In the last step, your need to pull a compare request between your new branch and MindYOLO `master` branch. After finishing the pull request, the Jenkins CI will be automatically set up for building test. Your pull request should be merged into the upstream master branch as soon as possible to reduce the risk of merging. ### Report issues A great way to contribute to the project is to send a detailed report when you encounter an issue. We always appreciate a well-written, thorough bug report, and will thank you for it! When reporting issues, refer to this format: - What version of env (MindSpore, os, python, MindYOLO etc) are you using? - Is this a BUG REPORT or FEATURE REQUEST? - What kind of issue is, add the labels to highlight it on the issue dashboard. - What happened? - What you expected to happen? - How to reproduce it?(as minimally and precisely as possible) - Special notes for your reviewers? **Issues advisory:** - **If you find an unclosed issue, which is exactly what you are going to solve,** please put some comments on that issue to tell others you would be in charge of it. - **If an issue is opened for a while,** it's recommended for contributors to precheck before working on solving that issue. - **If you resolve an issue which is reported by yourself,** it's also required to let others know before closing that issue. - **If you want the issue to be responded as quickly as possible,** please try to label it, you can find kinds of labels on [Label List](https://gitee.com/mindspore/community/blob/master/sigs/dx/docs/labels.md) ### Propose PRs - Raise your idea as an *issue* on [GitHub](https://github.com/mindspore-lab/mindyolo/issues) - If it is a new feature that needs lots of design details, a design proposal should also be submitted. - After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR. - None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR. - After PR is sufficiently discussed, it will get merged, abandoned or rejected depending on the outcome of the discussion. **PRs advisory:** - Any irrelevant changes should be avoided. - Make sure your commit history being ordered. - Always keep your branch up with the master branch. - For bug-fix PRs, make sure all related issues being linked. yolov/GETTING_STARTED.md000066400000000000000000000047131443434675500150630ustar00rootroot00000000000000## Getting Started with MindYOLO This document provides a brief intro of the usage of builtin command-line tools in MindYOLO. ### Inference Demo with Pre-trained Models 1. Pick a model and its config file from [model zoo](MODEL_ZOO.md), for example, `./configs/yolov7/yolov7.yaml`. 2. Download the corresponding pretrain checkpoint from the link in the [model zoo](MODEL_ZOO.md) of each model. 3. We provide `infer.py` that is able to demo builtin configs. Run it with: ``` python infer.py \ --config ./configs/yolov7/yolov7.yaml \ --device_target=Ascend \ --weight=MODEL.WEIGHTS \ --image_path /PATH/TO/IMAGE.jpg ``` The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for inference. This command will run the inference and show visualizations in an OpenCV window. For details of the command line arguments, see `infer.py -h` or look at its source code to understand its behavior. Some common arguments are: * To run on cpu, modify device_target to CPU. * The results will be saved in `./detect_results` ### Training & Evaluation in Command Line * Prepare your dataset in YOLO format. If train with COCO dataset, prepare it from yolov5 or darknet.
``` coco/ {train,val}2017.txt annotations/ instances_{train,val}2017.json images/ {train,val}2017/ 00000001.jpg ... # image files that are mentioned in the corresponding train/val2017.txt labels/ {train,val}2017/ 00000001.txt ... # label files that are mentioned in the corresponding train/val2017.txt ```
* To train a model: ``` mpirun --allow-run-as-root -n 8 python train.py e.g.: mpirun --allow-run-as-root -n 8 python train.py \ --config ./configs/yolov7/yolov7.yaml \ --device_target Ascend \ --is_parallel True > log.txt 2>&1 & tail -f ./log.txt ``` * The configs are made for 8 NPU/GPU training. To train on 1 NPU/GPU, you may need to change some parameters, e.g.: ``` python train.py \ --config ./configs/yolov7/yolov7.yaml \ --device_target Ascend \ --per_batch_size 16 ``` * To evaluate a model's performance, use ``` python test.py \ --config ./configs/yolov7/yolov7.yaml \ --device_target Ascend \ --weight=MODEL.WEIGHTS ``` * For more options, see `train/test.py -h`. ### Use MindYOLO APIs in Your Code To be supplemented. yolov/LICENSE.md000066400000000000000000000261351443434675500136600ustar00rootroot00000000000000 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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See the License for the specific language governing permissions and limitations under the License. yolov/MODEL_ZOO.md000066400000000000000000000064301443434675500141610ustar00rootroot00000000000000# MindYOLO Model Zoo and Baselines | Name | Scale | Context | ImageSize | Dataset | Box mAP (%) | Params | FLOPs | Recipe | Download | |--------|-----------|----------|-----------|--------------|-------------|---------|--------|-----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| | YOLOv7 | Tiny | D910x8-G | 640 | MS COCO 2017 | 37.5 | 6.2M | 13.8G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-d8972c94.ckpt) | | YOLOv7 | L | D910x8-G | 640 | MS COCO 2017 | 50.8 | 36.9M | 104.7G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7_300e_mAP508-734ac919.ckpt) | | YOLOv7 | X | D910x8-G | 640 | MS COCO 2017 | 52.4 | 71.3M | 189.9G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-x_300e_mAP524-e2f58741.ckpt) | | YOLOv5 | N | D910x8-G | 640 | MS COCO 2017 | 27.3 | 1.9M | 4.5G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | | YOLOv5 | S | D910x8-G | 640 | MS COCO 2017 | 37.6 | 7.2M | 16.5G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | | YOLOv5 | M | D910x8-G | 640 | MS COCO 2017 | 44.9 | 21.2M | 49.0G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | | YOLOv5 | L | D910x8-G | 640 | MS COCO 2017 | 48.5 | 46.5M | 109.1G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) | | YOLOv3 | Darknet53 | D910x8-G | 640 | MS COCO 2017 | 43.8 | 61.9M | 156.4G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov3/yolov3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP438-4cddcb38.ckpt) |
#### Notes - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Box mAP: Accuracy reported on the validation set. yolov/README.md000066400000000000000000000065141443434675500135320ustar00rootroot00000000000000# MindYOLO

docs GitHub PRs Welcome

MindYOLO is [MindSpore Lab](https://github.com/mindspore-lab)'s software system that implements state-of-the-art YOLO series algorithms, [support list and benchmark](MODEL_ZOO.md). It is written in Python and powered by the [MindSpore](https://mindspore.cn/) deep learning framework. The master branch works with **MindSpore 1.8.1**. ## What is New - 2023/03/30 1. Currently, the models supported by the first release include the basic specifications of YOLOv3/YOLOv5/YOLOv7; 2. Models can be exported to MindIR/AIR format for deployment. 3. ⚠️ The current version is based on the static shape of GRAPH. The dynamic shape of the PYNATIVE will be added later. Please look forward to it. 4. ⚠️ The current version only supports the Ascend platform, and the GPU platform will support it later. ## Benchmark and Model Zoo Seed [MODEL ZOO](MODEL_ZOO.md).
Supported Algorithms - [x] [YOLOv7](configs/yolov7) - [x] [YOLOv5](configs/yolov5) - [x] [YOLOv3](configs/yolov3) - [ ] [YOLOv8](configs/yolov6) - [ ] [YOLOv6](configs/yolov6) - [ ] [YOLOv4](configs/yolov6) - [ ] [YOLOX](configs/yolox)
## Installation ### Dependency - mindspore >= 1.8.1 - numpy >= 1.17.0 - pyyaml >= 5.3 - openmpi 4.0.3 (for distributed mode) To install the dependency, please run ```shell pip install -r requirements.txt ``` MindSpore can be easily installed by following the official [instructions](https://www.mindspore.cn/install) where you can select your hardware platform for the best fit. To run in distributed mode, [openmpi](https://www.open-mpi.org/software/ompi/v4.0/) is required to install. The following instructions assume the desired dependency is fulfilled. ## Getting Started See [GETTING STARTED](GETTING_STARTED.md) ## Learn More about MindYOLO To be supplemented. ## Notes ### How to Contribute We appreciate all contributions including issues and PRs to make MindYOLO better. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline. ### License MindYOLO is released under the [Apache License 2.0](LICENSE.md). ### Acknowledgement MindYOLO is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new realtime object detection methods. ### Citation If you find this project useful in your research, please consider cite: ```latex @misc{MindSpore Object Detection YOLO 2023, title={{MindSpore Object Detection YOLO}:MindSpore Object Detection YOLO Toolbox and Benchmark}, author={MindSpore YOLO Contributors}, howpublished = {\url{https://github.com/mindspore-lab/mindyolo}}, year={2023} } ``` yolov/configs/000077500000000000000000000000001443434675500136755ustar00rootroot00000000000000yolov/configs/.ipynb_checkpoints/000077500000000000000000000000001443434675500174665ustar00rootroot00000000000000yolov/configs/.ipynb_checkpoints/coco-checkpoint.yaml000066400000000000000000000024651443434675500234310ustar00rootroot00000000000000# COCO 2017 dataset http://cocodataset.org data: dataset_name: coco train_set: ../coco_val_yolo/val2017.txt # 118287 images val_set: ./coco/val2017.txt # 5000 images test_set: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 nc: 80 # class names names: [ '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' ] train_transforms: [] test_transforms: []yolov/configs/coco.yaml000066400000000000000000000024551443434675500155120ustar00rootroot00000000000000# COCO 2017 dataset http://cocodataset.org data: dataset_name: coco train_set: ./coco/train2017.txt # 118287 images val_set: ./coco/val2017.txt # 5000 images test_set: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 nc: 80 # class names names: [ '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' ] train_transforms: [] test_transforms: []yolov/configs/yolov3/000077500000000000000000000000001443434675500151305ustar00rootroot00000000000000yolov/configs/yolov3/README.md000066400000000000000000000077771443434675500164310ustar00rootroot00000000000000# YOLOv3 > [YOLOv3: An Incremental Improvement](https://arxiv.org/pdf/1804.02767.pdf) ## Abstract We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster.
## Results
| Name | Scale | Context | ImageSize | Dataset | Box mAP (%) | Params | FLOPs | Recipe | Download | |--------|-----------|----------|-----------|--------------|-------------|---------|--------|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------| | YOLOv3 | Darknet53 | D910x8-G | 640 | MS COCO 2017 | 43.8 | 61.9M | 156.4G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov3/yolov3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP438-4cddcb38.ckpt) |

#### Notes - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Box mAP: Accuracy reported on the validation set. - We referred to a commonly used third-party [YOLOv3](https://github.com/ultralytics/yolov3) implementation. ## Quick Start Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details. ### Training
#### - Distributed Training It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run ```shell # distributed training on multiple GPU/Ascend devices mpirun -n 8 python train.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend --is_parallel True ``` > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. Similarly, you can train the model on multiple GPU devices with the above mpirun command. For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py). **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. #### - Standalone Training If you want to train or finetune the model on a smaller dataset without distributed training, please run: ```shell # standalone training on a CPU/GPU/Ascend device python train.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend ```
### Validation and Test To validate the accuracy of the trained model, you can use `test.py` and parse the checkpoint path with `--weight`. ``` python test.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` ## References [1] Jocher Glenn. YOLOv3 release v9.1. https://github.com/ultralytics/yolov3/releases/tag/v9.1, 2021. [2] Joseph Redmon and Ali Farhadi. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. yolov/configs/yolov3/hyp.scratch.yaml000066400000000000000000000036301443434675500202440ustar00rootroot00000000000000optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) gp_weight_decay: 0.001 # optimizer weight decay 5e-4 momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 1000 # minimum warmup step group_param: yolov5 # group param strategy start_factor: 1.0 end_factor: 0.1 loss: name: YOLOv3Loss box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - { func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.9 } - { func_name: mixup, prob: 0.1, alpha: 8.0, beta: 8.0, needed_mosaic: True } - { func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4 } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: albumentations } - { func_name: fliplr, prob: 0.5 } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } test_transforms: - { func_name: letterbox, scaleup: False } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov3/yolov3.yaml000066400000000000000000000034731443434675500172560ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.yaml', ] weight: './yolov3_backbone.ckpt' per_batch_size: 16 # 16 * 8 = 128 img_size: 640 network: model_name: yolov3 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32] anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [32, 3, 1]], # 0 [-1, 1, ConvNormAct, [64, 3, 2]], # 1-P1/2 [-1, 1, Bottleneck, [64]], [-1, 1, ConvNormAct, [128, 3, 2]], # 3-P2/4 [-1, 2, Bottleneck, [128]], [-1, 1, ConvNormAct, [256, 3, 2]], # 5-P3/8 [-1, 8, Bottleneck, [256]], [-1, 1, ConvNormAct, [512, 3, 2]], # 7-P4/16 [-1, 8, Bottleneck, [512]], [-1, 1, ConvNormAct, [1024, 3, 2]], # 9-P5/32 [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3 head head: [[-1, 1, Bottleneck, [1024, False]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 1]], # 15 (P5/32-large) [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Bottleneck, [512, False]], [-1, 1, Bottleneck, [512, False]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], # 22 (P4/16-medium) [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P3 [-1, 1, Bottleneck, [256, False]], [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [[27, 22, 15], 1, YOLOv3Head, [nc, anchors, stride]], # Detect(P3, P4, P5) ] yolov/configs/yolov4/000077500000000000000000000000001443434675500151315ustar00rootroot00000000000000yolov/configs/yolov4/.ipynb_checkpoints/000077500000000000000000000000001443434675500207225ustar00rootroot00000000000000yolov/configs/yolov4/.ipynb_checkpoints/hyp.scratch-checkpoint.yaml000066400000000000000000000027341443434675500261670ustar00rootroot00000000000000# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) ms_loss_scaler_value: 1.0 ms_grad_sens: 1.0 ema: False optimizer: # lr_scheduler: "cosine_annealing" lr_init: 0.012 start_factor: 1.0 end_factor: 0.0 warmup_epochs: 20 gp_weight_decay: 0.0005 momentum: 0.9 loss_scale: 64 group_param: yolov4 loss: name: YOLOv4Loss box: 10. # box loss gain cls: 1. # cls loss gain obj: 1. # obj loss gain (scale with pixels) ignore_threshold: 0.7 iou_threshold: 0.213 label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - { func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.9 } - { func_name: mixup, prob: 0.1, alpha: 8.0, beta: 8.0, needed_mosaic: True } - { func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4 } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: albumentations } - { func_name: fliplr, prob: 0.5 } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } test_transforms: - { func_name: letterbox, scaleup: False } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov4/.ipynb_checkpoints/hyp.scratch_-checkpoint.yaml000066400000000000000000000032111443434675500263150ustar00rootroot00000000000000# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) ms_loss_scaler_value: 1.0 ms_grad_sens: 1.0 ema: False optimizer: # lr_scheduler: "cosine_annealing" lr_init: 0.012 start_factor: 1.0 end_factor: 0.0 warmup_epochs: 20 gp_weight_decay: 0.0005 momentum: 0.9 loss_scale: 64 group_param: yolov4 loss: name: YOLOv4Loss box: 10. # box loss gain cls: 1. # cls loss gain obj: 1. # obj loss gain (scale with pixels) ignore_threshold: 0.7 iou_threshold: 0.213 bbox_class_loss_coff: [10., 1., 1.] label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - { func_name: get_img_and_label_yolov4 } batch_transform: { hue: 0.1, saturation: 1.5, value: 1.5, jitter: 0.3, resize_rate: 10, multi_scale: [ [ 416, 416 ], [ 448, 448 ], [ 480, 480 ], [ 512, 512 ], [ 544, 544 ], [ 576, 576 ], [ 608, 608 ], [ 640, 640 ], [ 672, 672 ], [ 704, 704 ], [ 736, 736 ] ], anchors: [ [ 12, 16 ], [ 19, 36 ], [ 40, 28 ], [ 36, 75 ], [ 76, 55 ], [ 72, 146 ], [ 142, 110 ], [ 192, 243 ], [ 459, 401 ] ], max_box: 90, each_multiscale: True, mosaic: False, label_smooth: 0.0, label_smooth_factor: 0.1, num_classes: 80 } yolov/configs/yolov4/.ipynb_checkpoints/yolov4-checkpoint.yaml000066400000000000000000000065521443434675500251770ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.yaml', ] #weight: './yolov4_backbone.ckpt' is_parallel: False enable_modelarts: False ckpt_dir: '/cache/yolov4_backbone.ckpt' log_interval: 1 ms_mode: 0 ms_jit: True device_per_servers: 1 iou_thres: 0.6 epochs: 320 per_batch_size: 16 # 16 * 8 = 128 img_size: 608 network: model_name: yolov4 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple img_size: [608, 608] stride: [8, 16, 32] anchors: [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]] # cspdarknet53 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [32, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 2]], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 1, Bottleneck, [64]], [-1, 1, ConvNormAct, [64, 1, 1]], [1, 1, ConvNormAct, [64, 1, 1]], [[-2, -1], 1, Concat, [1] ], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 2]], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 2, Bottleneck, [64]], [-1, 1, ConvNormAct, [64, 1, 1]], [8, 1, ConvNormAct, [64, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 2]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 8, Bottleneck, [128]], [-1, 1, ConvNormAct, [128, 1, 1]], [15, 1, ConvNormAct, [128, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 2]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 8, Bottleneck, [256]], [-1, 1, ConvNormAct, [256, 1, 1]], [22, 1, ConvNormAct, [256, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 2]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 8, Bottleneck, [512]], [-1, 1, ConvNormAct, [512, 1, 1]], [29, 1, ConvNormAct, [512, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1]] ] # YOLOv4 head head: [[-1, 1, Bottleneck, [1024, False]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, nn.MaxPool2d, [5, 1, 'same']], [-2, 1, nn.MaxPool2d, [9, 1, 'same']], [-3, 1, nn.MaxPool2d, [13, 1, 'same']], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [28, 1, ConvNormAct, [256, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [21, 1, ConvNormAct, [128, 1, 1 ]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [256, False]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-2, 1, ConvNormAct, [256, 3, 2]], [[-1, -9], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-2, 1, ConvNormAct, [512, 3, 2]], [[-1, -20], 1, Concat, [1]], [-1, 3, Bottleneck, [1024, False]], [[-1, -4, -9], 1, YOLOv4Head, [nc, anchors]], # Detect(P3, P4, P5) ] yolov/configs/yolov4/hyp.scratch.yaml000066400000000000000000000027341443434675500202510ustar00rootroot00000000000000# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) ms_loss_scaler_value: 1.0 ms_grad_sens: 1.0 ema: False optimizer: # lr_scheduler: "cosine_annealing" lr_init: 0.012 start_factor: 1.0 end_factor: 0.0 warmup_epochs: 20 gp_weight_decay: 0.0005 momentum: 0.9 loss_scale: 64 group_param: yolov4 loss: name: YOLOv4Loss box: 10. # box loss gain cls: 1. # cls loss gain obj: 1. # obj loss gain (scale with pixels) ignore_threshold: 0.7 iou_threshold: 0.213 label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 2 train_transforms: - { func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.9 } - { func_name: mixup, prob: 0.1, alpha: 8.0, beta: 8.0, needed_mosaic: True } - { func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4 } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: albumentations } - { func_name: fliplr, prob: 0.5 } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } test_transforms: - { func_name: letterbox, scaleup: False } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov4/hyp.scratch_silu.yaml000066400000000000000000000027341443434675500213050ustar00rootroot00000000000000# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing) ms_loss_scaler_value: 1.0 ms_grad_sens: 1.0 ema: False optimizer: # lr_scheduler: "cosine_annealing" lr_init: 0.012 start_factor: 1.0 end_factor: 0.0 warmup_epochs: 20 gp_weight_decay: 0.0005 momentum: 0.9 loss_scale: 64 group_param: yolov4 loss: name: YOLOv4Loss box: 10. # box loss gain cls: 1. # cls loss gain obj: 1. # obj loss gain (scale with pixels) ignore_threshold: 0.7 iou_threshold: 0.213 label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - { func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.9 } - { func_name: mixup, prob: 0.1, alpha: 8.0, beta: 8.0, needed_mosaic: True } - { func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4 } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: albumentations } - { func_name: fliplr, prob: 0.5 } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } test_transforms: - { func_name: letterbox, scaleup: False } - { func_name: label_norm, xyxy2xywh_: True } - { func_name: label_pad, padding_size: 160, padding_value: -1 } - { func_name: image_norm, scale: 255. } - { func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov4/yolov4-silu.yaml000066400000000000000000000065461443434675500202360ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch_silu.yaml', ] weight: './yolov4_backbone_new.ckpt' is_parallel: True enable_modelarts: False ckpt_dir: '/cache/yolov4_backbone.ckpt' log_interval: 1 ms_mode: 0 ms_jit: True device_per_servers: 8 iou_thres: 0.6 epochs: 320 per_batch_size: 16 # 16 * 8 = 128 img_size: 608 network: model_name: yolov4 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [32, 16, 8] anchors: [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]] # cspdarknet53 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [32, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 2]], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 1, Bottleneck, [64]], [-1, 1, ConvNormAct, [64, 1, 1]], [1, 1, ConvNormAct, [64, 1, 1]], [[-2, -1], 1, Concat, [1] ], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 2]], [-1, 1, ConvNormAct, [64, 1, 1]], [-1, 2, Residualblock, [64]], [-1, 1, ConvNormAct, [64, 1, 1]], [8, 1, ConvNormAct, [64, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 2]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 8, Residualblock, [128]], [-1, 1, ConvNormAct, [128, 1, 1]], [15, 1, ConvNormAct, [128, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 2]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 8, Residualblock, [256]], [-1, 1, ConvNormAct, [256, 1, 1]], [22, 1, ConvNormAct, [256, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 2]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 4, Residualblock, [512]], [-1, 1, ConvNormAct, [512, 1, 1]], [29, 1, ConvNormAct, [512, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1]] ] # YOLOv4 head head: [[-1, 1, Bottleneck, [1024, False]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, nn.MaxPool2d, [5, 1, 'same']], [-2, 1, nn.MaxPool2d, [9, 1, 'same']], [-3, 1, nn.MaxPool2d, [13, 1, 'same']], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [1024, 3, 1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [28, 1, ConvNormAct, [256, 1, 1]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [21, 1, ConvNormAct, [128, 1, 1 ]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [256, False]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-2, 1, ConvNormAct, [256, 3, 2]], [[-1, -9], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-2, 1, ConvNormAct, [512, 3, 2]], [[-1, -20], 1, Concat, [1]], [-1, 3, Bottleneck, [1024, False]], [[-1, -4, -9], 1, YOLOv4Head, [nc, anchors]], # Detect(P3, P4, P5) ] yolov/configs/yolov4/yolov4.yaml000066400000000000000000000101161443434675500172500ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.yaml', ] weight: './yolov4_backbone.ckpt' per_batch_size: 16 # 16 * 8 = 128 img_size: 608 network: model_name: yolov4 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [32, 16, 8] anchors: [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]] # cspdarknet53 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [32, 3, 1, act=nn.Mish()]], [-1, 1, ConvNormAct, [64, 3, 2, act=nn.Mish()]], [-1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [-1, 1, Bottleneck, [64, act=nn.Mish()]], [-1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [[-2, -1], 1, Concat, [1] ], [-1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [-1, 1, ConvNormAct, [128, 3, 2, act=nn.Mish()]], [-1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [-1, 2, Residualblock, [64, act=nn.Mish()]], [-1, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [8, 1, ConvNormAct, [64, 1, 1, act=nn.Mish()]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1, act=nn.Mish()]], [-1, 1, ConvNormAct, [256, 3, 2, act=nn.Mish()]], [-1, 1, ConvNormAct, [128, 1, 1, act=nn.Mish()]], [-1, 8, Residualblock, [128, act=nn.Mish()]], [-1, 1, ConvNormAct, [128, 1, 1, act=nn.Mish()]], [15, 1, ConvNormAct, [128, 1, 1, act=nn.Mish()]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.Mish()]], [-1, 1, ConvNormAct, [512, 3, 2, act=nn.Mish()]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.Mish()]], [-1, 8, Residualblock, [256, act=nn.Mish()]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.Mish()]], [22, 1, ConvNormAct, [256, 1, 1, act=nn.Mish()]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.Mish()]], [-1, 1, ConvNormAct, [1024, 3, 2, act=nn.Mish()]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.Mish()]], [-1, 4, Residualblock, [512, act=nn.Mish()]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.Mish()]], [29, 1, ConvNormAct, [512, 1, 1, act=nn.Mish()]], [[-2, -1], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1, act=nn.Mish()]] ] # YOLOv4 head head: [[-1, 1, Bottleneck, [1024, False, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, nn.MaxPool2d, [5, 1, 'same']], [-2, 1, nn.MaxPool2d, [9, 1, 'same']], [-3, 1, nn.MaxPool2d, [13, 1, 'same']], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [1024, 3, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [512, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, Upsample, [None, 2, 'nearest']], [28, 1, ConvNormAct, [256, 1, 1, act=nn.LeakyReLU(0.1)]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 3, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, Upsample, [None, 2, 'nearest']], [21, 1, ConvNormAct, [128, 1, 1, act=nn.LeakyReLU(0.1)]], [[-2, -1], 1, Concat, [1]], [-1, 2, Bottleneck, [256, False, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 3, 1, act=nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [256, 3, 2, act=nn.LeakyReLU(0.1)]], [[-1, -9], 1, Concat, [1]], [-1, 2, Bottleneck, [512, False, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 1, 1, act=nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [512, 3, 1, act=nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [512, 3, 2, act=nn.LeakyReLU(0.1)]], [[-1, -20], 1, Concat, [1]], [-1, 3, Bottleneck, [1024, False, act=nn.LeakyReLU(0.1)]], [[-1, -4, -9], 1, YOLOv4Head, [nc, anchors]], # Detect(P3, P4, P5) ] yolov/configs/yolov5/000077500000000000000000000000001443434675500151325ustar00rootroot00000000000000yolov/configs/yolov5/README.md000066400000000000000000000111721443434675500164130ustar00rootroot00000000000000# YOLOv5 ## Abstract YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
## Results
| Name | Scale | Arch | Context | ImageSize | Dataset | Box mAP (%) | Params | FLOPs | Recipe | Download | |--------|-------|----------|----------|-----------|--------------|-------------|--------|--------|-----------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------| | YOLOv5 | N | P5 | D910x8-G | 640 | MS COCO 2017 | 27.3 | 1.9M | 4.5G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | | YOLOv5 | S | P5 | D910x8-G | 640 | MS COCO 2017 | 37.6 | 7.2M | 16.5G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | | YOLOv5 | M | P5 | D910x8-G | 640 | MS COCO 2017 | 44.9 | 21.2M | 49.0G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | | YOLOv5 | L | P5 | D910x8-G | 640 | MS COCO 2017 | 48.5 | 46.5M | 109.1G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov5/yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) |

#### Notes - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Box mAP: Accuracy reported on the validation set. - We refer to the official [YOLOV5](https://github.com/ultralytics/yolov5) to reproduce the P5 series model, and the differences are as follows: 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size is 32. This is different from the official code. ## Quick Start Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details. ### Training
#### - Distributed Training It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run ```shell # distributed training on multiple GPU/Ascend devices mpirun -n 8 python train.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --is_parallel True ``` > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. Similarly, you can train the model on multiple GPU devices with the above mpirun command. For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py). **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. #### - Standalone Training If you want to train or finetune the model on a smaller dataset without distributed training, please run: ```shell # standalone training on a CPU/GPU/Ascend device python train.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend ```
### Validation and Test To validate the accuracy of the trained model, you can use `test.py` and parse the checkpoint path with `--weight`. ``` python test.py --config ./configs/yolov5/yolov5n.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` ## References [1] Jocher Glenn. YOLOv5 release v6.1. https://github.com/ultralytics/yolov5/releases/tag/v6.1, 2022. yolov/configs/yolov5/hyp.scratch-high.yaml000066400000000000000000000037201443434675500211630ustar00rootroot00000000000000#enable_clip_grad: True #bn_eps: 0.00001 #bn_momentum: 0.1 optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 100 # minimum warmup step group_param: yolov5 # group param strategy gp_weight_decay: 0.0005 # group param weight decay 5e-4 start_factor: 1.0 end_factor: 0.1 loss: name: YOLOv5Loss box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight anchor_t: 4.0 # anchor-multiple threshold fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) # iou_t: 0.20 # IoU training threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - {func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.9, copy_paste_prob: 0.1} - {func_name: mixup, prob: 0.1, alpha: 32.0, beta: 32.0, needed_mosaic: True} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: albumentations} - {func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4} - {func_name: fliplr, prob: 0.5} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255.} - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True} test_transforms: - {func_name: letterbox, scaleup: False} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255. } - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov5/hyp.scratch-low.yaml000066400000000000000000000035771443434675500210570ustar00rootroot00000000000000optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 100 # minimum warmup step group_param: yolov5 # group param strategy gp_weight_decay: 0.0005 # group param weight decay 5e-4 start_factor: 1.0 end_factor: 0.01 loss: name: YOLOv5Loss box: 0.05 # box loss gain cls: 0.5 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 1.0 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight anchor_t: 4.0 # anchor-multiple threshold fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) # iou_t: 0.20 # IoU training threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - {func_name: mosaic, prob: 1.0, mosaic9_prob: 0.0, translate: 0.1, scale: 0.5} - {func_name: mixup, prob: 0.0, alpha: 32.0, beta: 32.0, needed_mosaic: True} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: albumentations} - {func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4} - {func_name: fliplr, prob: 0.5} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255.} - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True} test_transforms: - {func_name: letterbox, scaleup: False} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255. } - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov5/yolov5l.yaml000066400000000000000000000035041443434675500174310ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch-high.yaml', ] per_batch_size: 32 # 32 * 8 = 256 img_size: 640 sync_bn: False # backbone/head calculate using fp16, loss fp32 ms_amp_level: O3 keep_loss_fp32: True network: model_name: yolov5 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32] anchors: - [ 10,13, 16,30, 33,23 ] # P3/8 - [ 30,61, 62,45, 59,119 ] # P4/16 - [ 116,90, 156,198, 373,326 ] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [ -1, 1, ConvNormAct, [ 64, 6, 2, 2 ] ], # 0-P1/2 [ -1, 1, ConvNormAct, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 6, C3, [ 256 ] ], [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, ConvNormAct, [ 1024, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 1024 ] ], [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 ] # YOLOv5 v6.0 head head: [ [ -1, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 13 [ -1, 1, ConvNormAct, [ 256, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large) [ [ 17, 20, 23 ], 1, YOLOv5Head, [nc, anchors, stride] ], # Detect(P3, P4, P5) ] yolov/configs/yolov5/yolov5m.yaml000066400000000000000000000035001443434675500174260ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch-high.yaml', ] per_batch_size: 32 # 32 * 8 = 256 img_size: 640 sync_bn: False # backbone/head calculate using fp16, loss fp32 ms_amp_level: O3 keep_loss_fp32: True network: model_name: yolov5 depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple stride: [8, 16, 32] anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [ -1, 1, ConvNormAct, [ 64, 6, 2, 2 ] ], # 0-P1/2 [ -1, 1, ConvNormAct, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 6, C3, [ 256 ] ], [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, ConvNormAct, [ 1024, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 1024 ] ], [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 ] # YOLOv5 v6.0 head head: [ [ -1, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 13 [ -1, 1, ConvNormAct, [ 256, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large) [ [ 17, 20, 23 ], 1, YOLOv5Head, [nc, anchors, stride] ], # Detect(P3, P4, P5) ] yolov/configs/yolov5/yolov5n.yaml000066400000000000000000000033011443434675500174260ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch-low.yaml', ] per_batch_size: 32 # 32 * 8 = 256 img_size: 640 sync_bn: False # backbone/head calculate using fp16, loss fp32 ms_amp_level: O3 keep_loss_fp32: True network: model_name: yolov5 depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple stride: [8, 16, 32] anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, ConvNormAct, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, ConvNormAct, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, ConvNormAct, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, ConvNormAct, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, ConvNormAct, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, ConvNormAct, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, YOLOv5Head, [nc, anchors, stride]], # Detect(P3, P4, P5) ] yolov/configs/yolov5/yolov5s.yaml000066400000000000000000000035011443434675500174350ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch-low.yaml', ] per_batch_size: 32 # 32 * 8 = 256 img_size: 640 sync_bn: False # backbone/head calculate using fp16, loss fp32 ms_amp_level: O3 keep_loss_fp32: True network: model_name: yolov5 depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple stride: [8, 16, 32] anchors: - [ 10,13, 16,30, 33,23 ] # P3/8 - [ 30,61, 62,45, 59,119 ] # P4/16 - [ 116,90, 156,198, 373,326 ] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [ -1, 1, ConvNormAct, [ 64, 6, 2, 2 ] ], # 0-P1/2 [ -1, 1, ConvNormAct, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 6, C3, [ 256 ] ], [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, ConvNormAct, [ 1024, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 1024 ] ], [ -1, 1, SPPF, [ 1024 ] ], # 9 ] # YOLOv5 v6.0 head head: [ [ -1, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 13 [ -1, 1, ConvNormAct, [ 256, 1, 1 ] ], [-1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large) [ [ 17, 20, 23 ], 1, YOLOv5Head, [nc, anchors, stride] ], # Detect(P3, P4, P5) ] yolov/configs/yolov5/yolov5x.yaml000066400000000000000000000035511443434675500174470ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch-high.yaml', ] per_batch_size: 32 # 32 * 8 = 256 img_size: 640 sync_bn: False recompute: True recompute_layers: 1 # backbone/head calculate using fp16, loss fp32 ms_amp_level: O3 keep_loss_fp32: True network: model_name: yolov5 depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple stride: [8, 16, 32] anchors: - [ 10,13, 16,30, 33,23 ] # P3/8 - [ 30,61, 62,45, 59,119 ] # P4/16 - [ 116,90, 156,198, 373,326 ] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [ [ -1, 1, ConvNormAct, [ 64, 6, 2, 2 ] ], # 0-P1/2 [ -1, 1, ConvNormAct, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 6, C3, [ 256 ] ], [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, ConvNormAct, [ 1024, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 1024 ] ], [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 ] # YOLOv5 v6.0 head head: [ [ -1, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 13 [ -1, 1, ConvNormAct, [ 256, 1, 1 ] ], [ -1, 1, Upsample, [None, 2, 'nearest']], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) [ -1, 1, ConvNormAct, [ 256, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) [ -1, 1, ConvNormAct, [ 512, 3, 2 ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large) [ [ 17, 20, 23 ], 1, YOLOv5Head, [nc, anchors, stride] ], # Detect(P3, P4, P5) ] yolov/configs/yolov7/000077500000000000000000000000001443434675500151345ustar00rootroot00000000000000yolov/configs/yolov7/README.md000066400000000000000000000121071443434675500164140ustar00rootroot00000000000000# YOLOv7 > [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/pdf/2207.02696.pdf) ## Abstract YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights.
## Results
| Name | Scale | Arch | Context | ImageSize | Dataset | Box mAP (%) | Params | FLOPs | Recipe | Download | |--------|-------|------|----------|-----------|--------------|-------------|--------|--------|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------| | YOLOv7 | Tiny | P5 | D910x8-G | 640 | MS COCO 2017 | 37.5 | 6.2M | 13.8G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-d8972c94.ckpt) | | YOLOv7 | L | P5 | D910x8-G | 640 | MS COCO 2017 | 50.8 | 36.9M | 104.7G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7_300e_mAP508-734ac919.ckpt) | | YOLOv7 | X | P5 | D910x8-G | 640 | MS COCO 2017 | 52.4 | 71.3M | 189.9G | [yaml](https://github.com/mindspore-lab/mindyolo/blob/master/configs/yolov7/yolov7-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-x_300e_mAP524-e2f58741.ckpt) |

#### Notes - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Box mAP: Accuracy reported on the validation set. - We refer to the official [YOLOV7](https://github.com/WongKinYiu/yolov7) to reproduce the P5 series model, and the differences are as follows: 1. We use 8x NPU(Ascend910) for training, and the single-NPU batch size for tiny/l/x is 16/16/12. This is different from the official code. ## Quick Start Please refer to the [GETTING_STARTED](https://github.com/mindspore-lab/mindyolo/blob/master/GETTING_STARTED.md) in MindYOLO for details. ### Training
#### - Distributed Training It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run ```shell # distributed training on multiple GPU/Ascend devices mpirun -n 8 python train.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend --is_parallel True ``` > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. Similarly, you can train the model on multiple GPU devices with the above mpirun command. For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindyolo/blob/master/mindyolo/utils/config.py). **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. #### - Standalone Training If you want to train or finetune the model on a smaller dataset without distributed training, please run: ```shell # standalone training on a CPU/GPU/Ascend device python train.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend ```
### Validation and Test To validate the accuracy of the trained model, you can use `test.py` and parse the checkpoint path with `--weight`. ``` python test.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt ``` ## References [1] Chien-Yao Wang, Alexey Bochkovskiy, and HongYuan Mark Liao. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696, 2022. yolov/configs/yolov7/hyp.scratch.p5.yaml000066400000000000000000000035511443434675500205750ustar00rootroot00000000000000optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 1000 # minimum warmup step group_param: yolov7 # group param strategy gp_weight_decay: 0.0005 # group param weight decay 5e-4 start_factor: 1.0 end_factor: 0.1 loss: name: YOLOv7Loss box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) anchor_t: 4.0 # anchor-multiple threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - {func_name: mosaic, prob: 1.0, mosaic9_prob: 0.2, translate: 0.2, scale: 0.9} - {func_name: mixup, prob: 0.15, alpha: 8.0, beta: 8.0, needed_mosaic: True} - {func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4} - {func_name: pastein, prob: 0.15, num_sample: 30} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: fliplr, prob: 0.5} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255.} - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True} test_transforms: - {func_name: letterbox, scaleup: False} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255. } - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov7/hyp.scratch.p6.yaml000066400000000000000000000036321443434675500205760ustar00rootroot00000000000000optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 1000 # minimum warmup step group_param: yolov7 # group param strategy gp_weight_decay: 0.0005 # group param weight decay 5e-4 start_factor: 1.0 end_factor: 0.2 # final OneCycleLR learning rate (lr0 * lrf) loss: name: YOLOv7AuxLoss box: 0.05 # box loss gain cls: 0.3 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 0.7 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) anchor_t: 4.0 # anchor-multiple threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - {func_name: mosaic, prob: 1.0, mosaic9_prob: 0.2, translate: 0.2, scale: 0.9} - {func_name: mixup, prob: 0.15, alpha: 8.0, beta: 8.0, needed_mosaic: True} - {func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4} - {func_name: pastein, prob: 0.15, num_sample: 30} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: fliplr, prob: 0.5} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255.} - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True} test_transforms: - {func_name: letterbox, scaleup: False} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255. } - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov7/hyp.scratch.tiny.yaml000066400000000000000000000035521443434675500212350ustar00rootroot00000000000000optimizer: optimizer: momentum lr_init: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) momentum: 0.937 # SGD momentum/Adam beta1 nesterov: True # update gradients with NAG(Nesterov Accelerated Gradient) algorithm loss_scale: 1.0 # loss scale for optimizer warmup_epochs: 3 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr min_warmup_step: 1000 # minimum warmup step group_param: yolov7 # group param strategy gp_weight_decay: 0.0005 # group param weight decay 5e-4 start_factor: 1.0 end_factor: 0.01 loss: name: YOLOv7Loss box: 0.05 # box loss gain cls: 0.5 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 1.0 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) anchor_t: 4.0 # anchor-multiple threshold label_smoothing: 0.0 # label smoothing epsilon data: num_parallel_workers: 4 train_transforms: - {func_name: mosaic, prob: 1.0, mosaic9_prob: 0.2, translate: 0.1, scale: 0.5} - {func_name: mixup, prob: 0.05, alpha: 8.0, beta: 8.0, needed_mosaic: True} - {func_name: hsv_augment, prob: 1.0, hgain: 0.015, sgain: 0.7, vgain: 0.4} - {func_name: pastein, prob: 0.05, num_sample: 30} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: fliplr, prob: 0.5} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255.} - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True} test_transforms: - {func_name: letterbox, scaleup: False} - {func_name: label_norm, xyxy2xywh_: True} - {func_name: label_pad, padding_size: 160, padding_value: -1} - {func_name: image_norm, scale: 255. } - {func_name: image_transpose, bgr2rgb: True, hwc2chw: True } yolov/configs/yolov7/yolov7-d6.yaml000066400000000000000000000166721443434675500176020ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p6.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 1280 sync_bn: True network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32, 64] # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # yolov7 backbone backbone: # [from, number, module, args], [[-1, 1, ReOrg, []], # 0 [-1, 1, ConvNormAct, [96, 3, 1]], # 1-P1/2 [-1, 1, DownC, [192]], # 2-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [192, 1, 1]], # 14 [-1, 1, DownC, [384]], # 15-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], # 27 [-1, 1, DownC, [768]], # 28-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [768, 1, 1]], # 40 [-1, 1, DownC, [1152]], # 41-P5/32 [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [1152, 1, 1]], # 53 [-1, 1, DownC, [1536]], # 54-P6/64 [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [1536, 1, 1]], # 66 ] # yolov7 head head: [[-1, 1, SPPCSPC, [768]], # 67 [-1, 1, ConvNormAct, [576, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [53, 1, ConvNormAct, [576, 1, 1]], # route backbone P5 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [576, 1, 1]], # 83 [-1, 1, ConvNormAct, [384, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [40, 1, ConvNormAct, [384, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], # 99 [-1, 1, ConvNormAct, [192, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [27, 1, ConvNormAct, [192, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [192, 1, 1]], # 115 [-1, 1, DownC, [384]], [[-1, 99], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], # 129 [-1, 1, DownC, [576]], [[-1, 83], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [576, 1, 1]], # 143 [-1, 1, DownC, [768]], [[-1, 67], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], [-1, 1, ConvNormAct, [768, 1, 1]], # 157 [115, 1, ConvNormAct, [384, 3, 1]], [129, 1, ConvNormAct, [768, 3, 1]], [143, 1, ConvNormAct, [1152, 3, 1]], [157, 1, ConvNormAct, [1536, 3, 1]], [115, 1, ConvNormAct, [384, 3, 1]], [99, 1, ConvNormAct, [768, 3, 1]], [83, 1, ConvNormAct, [1152, 3, 1]], [67, 1, ConvNormAct, [1536, 3, 1]], [[158,159,160,161,162,163,164,165], 1, YOLOv7AuxHead, [nc, anchors, stride]], # Detect(P3, P4, P5, P6) ] yolov/configs/yolov7/yolov7-e6.yaml000066400000000000000000000147701443434675500176000ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p6.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 1280 sync_bn: True network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32, 64] # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # yolov7 backbone backbone: # [from, number, module, args], [[-1, 1, ReOrg, []], # 0 [-1, 1, ConvNormAct, [80, 3, 1]], # 1-P1/2 [-1, 1, DownC, [160]], # 2-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 12 [-1, 1, DownC, [320]], # 13-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 23 [-1, 1, DownC, [640]], # 24-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 34 [-1, 1, DownC, [960]], # 35-P5/32 [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [960, 1, 1]], # 45 [-1, 1, DownC, [1280]], # 46-P6/64 [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [1280, 1, 1]], # 56 ] # yolov7 head head: [[-1, 1, SPPCSPC, [640]], # 57 [-1, 1, ConvNormAct, [480, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [45, 1, ConvNormAct, [480, 1, 1]], # route backbone P5 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 71 [-1, 1, ConvNormAct, [320, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [34, 1, ConvNormAct, [320, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 85 [-1, 1, ConvNormAct, [160, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [23, 1, ConvNormAct, [160, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 99 [-1, 1, DownC, [320]], [[-1, 85], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 111 [-1, 1, DownC, [480]], [[-1, 71], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 123 [-1, 1, DownC, [640]], [[-1, 57], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 135 [99, 1, ConvNormAct, [320, 3, 1]], [111, 1, ConvNormAct, [640, 3, 1]], [123, 1, ConvNormAct, [960, 3, 1]], [135, 1, ConvNormAct, [1280, 3, 1]], [99, 1, ConvNormAct, [320, 3, 1]], [85, 1, ConvNormAct, [640, 3, 1]], [71, 1, ConvNormAct, [960, 3, 1]], [57, 1, ConvNormAct, [1280, 3, 1]], [[136,137,138,139,140,141,142,143], 1, YOLOv7AuxHead, [nc, anchors, stride]], # Detect(P3, P4, P5, P6) ] yolov/configs/yolov7/yolov7-e6e.yaml000066400000000000000000000270571443434675500177470ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p6.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 1280 sync_bn: True network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32, 64] # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # yolov7 backbone backbone: # [from, number, module, args], [[-1, 1, ReOrg, []], # 0 [-1, 1, ConvNormAct, [80, 3, 1]], # 1-P1/2 [-1, 1, DownC, [160]], # 2-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 12 [-11, 1, ConvNormAct, [64, 1, 1]], [-12, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 22 [[-1, -11], 1, Shortcut, []], # 23 [-1, 1, DownC, [320]], # 24-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 34 [-11, 1, ConvNormAct, [128, 1, 1]], [-12, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 44 [[-1, -11], 1, Shortcut, []], # 45 [-1, 1, DownC, [640]], # 46-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 56 [-11, 1, ConvNormAct, [256, 1, 1]], [-12, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 66 [[-1, -11], 1, Shortcut, []], # 67 [-1, 1, DownC, [960]], # 68-P5/32 [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [960, 1, 1]], # 78 [-11, 1, ConvNormAct, [384, 1, 1]], [-12, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [960, 1, 1]], # 88 [[-1, -11], 1, Shortcut, []], # 89 [-1, 1, DownC, [1280]], # 90-P6/64 [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [1280, 1, 1]], # 100 [-11, 1, ConvNormAct, [512, 1, 1]], [-12, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [1280, 1, 1]], # 110 [[-1, -11], 1, Shortcut, []], # 111 ] # yolov7 head head: [[-1, 1, SPPCSPC, [640]], # 112 [-1, 1, ConvNormAct, [480, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [89, 1, ConvNormAct, [480, 1, 1]], # route backbone P5 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 126 [-11, 1, ConvNormAct, [384, 1, 1]], [-12, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 136 [[-1, -11], 1, Shortcut, []], # 137 [-1, 1, ConvNormAct, [320, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [67, 1, ConvNormAct, [320, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 151 [-11, 1, ConvNormAct, [256, 1, 1]], [-12, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 161 [[-1, -11], 1, Shortcut, []], # 162 [-1, 1, ConvNormAct, [160, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [45, 1, ConvNormAct, [160, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 176 [-11, 1, ConvNormAct, [128, 1, 1]], [-12, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 186 [[-1, -11], 1, Shortcut, []], # 187 [-1, 1, DownC, [320]], [[-1, 162], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 199 [-11, 1, ConvNormAct, [256, 1, 1]], [-12, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 209 [[-1, -11], 1, Shortcut, []], # 210 [-1, 1, DownC, [480]], [[-1, 137], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 222 [-11, 1, ConvNormAct, [384, 1, 1]], [-12, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [480, 1, 1]], # 232 [[-1, -11], 1, Shortcut, []], # 233 [-1, 1, ConvNormAct, [640, 3, 2]], [ [ -1, 112 ], 1, Concat, [ 1 ] ], [ -1, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -2, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ [ -1, -2, -3, -4, -5, -6, -7, -8 ], 1, Concat, [ 1 ] ], [ -1, 1, ConvNormAct, [ 640, 1, 1 ] ], # 245 [ -11, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -12, 1, ConvNormAct, [ 512, 1, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ -1, 1, ConvNormAct, [ 256, 3, 1 ] ], [ [ -1, -2, -3, -4, -5, -6, -7, -8 ], 1, Concat, [ 1 ] ], [ -1, 1, ConvNormAct, [ 640, 1, 1 ] ], # 255 [[-1, -11], 1, Shortcut, []], # 256 [187, 1, ConvNormAct, [320, 3, 1]], [210, 1, ConvNormAct, [640, 3, 1]], [233, 1, ConvNormAct, [960, 3, 1]], [256, 1, ConvNormAct, [1280, 3, 1]], [186, 1, ConvNormAct, [320, 3, 1]], [161, 1, ConvNormAct, [640, 3, 1]], [136, 1, ConvNormAct, [960, 3, 1]], [112, 1, ConvNormAct, [1280, 3, 1]], [[257,258,259,260,261,262,263,264], 1, YOLOv7AuxHead, [nc, anchors, stride]], # Detect(P3, P4, P5, P6) ] yolov/configs/yolov7/yolov7-tiny.yaml000066400000000000000000000125311443434675500202420ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.tiny.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 640 # image sizes sync_bn: True network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32] # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # yolov7-tiny backbone backbone: # [from, number, module, args] c2, k=1, s=1, p=None, g=1, d=1, act=True [[-1, 1, ConvNormAct, [32, 3, 2, None, 1, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 [-1, 1, ConvNormAct, [64, 3, 2, None, 1, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 [-1, 1, ConvNormAct, [32, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [32, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [32, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [32, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 7 [-1, 1, MP, []], # 8-P3/8 [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 14 [-1, 1, MP, []], # 15-P4/16 [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 21 [-1, 1, MP, []], # 22-P5/32 [-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [256, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 28 ] # yolov7-tiny head head: [[-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, SP, [5]], [-2, 1, SP, [9]], [-3, 1, SP, [13]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -7], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 37 [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, Upsample, [None, 2, 'nearest']], [21, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 47 [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, Upsample, [None, 2, 'nearest']], [14, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [32, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [32, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [32, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [32, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 57 [-1, 1, ConvNormAct, [128, 3, 2, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, 47], 1, Concat, [1]], [-1, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [64, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [64, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 65 [-1, 1, ConvNormAct, [256, 3, 2, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, 37], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-2, 1, ConvNormAct, [128, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [-1, 1, ConvNormAct, [128, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1, None, 1, 1, nn.LeakyReLU(0.1)]], # 73 [57, 1, ConvNormAct, [128, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [65, 1, ConvNormAct, [256, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [73, 1, ConvNormAct, [512, 3, 1, None, 1, 1, nn.LeakyReLU(0.1)]], [[74,75,76], 1, YOLOv7Head, [nc, anchors, stride]], # Detect(P3, P4, P5) ] yolov/configs/yolov7/yolov7-w6.yaml000066400000000000000000000132721443434675500176160ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p6.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 1280 sync_bn: True network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32, 64] # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # yolov7 backbone backbone: # [from, number, module, args] [[-1, 1, ReOrg, []], # 0 [-1, 1, ConvNormAct, [64, 3, 1]], # 1-P1/2 [-1, 1, ConvNormAct, [128, 3, 2]], # 2-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], # 10 [-1, 1, ConvNormAct, [256, 3, 2]], # 11-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], # 19 [-1, 1, ConvNormAct, [512, 3, 2]], # 20-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], # 28 [-1, 1, ConvNormAct, [768, 3, 2]], # 29-P5/32 [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [-1, 1, ConvNormAct, [384, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [768, 1, 1]], # 37 [-1, 1, ConvNormAct, [1024, 3, 2]], # 38-P6/64 [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1]], # 46 ] # yolov7 head head: [[-1, 1, SPPCSPC, [512]], # 47 [-1, 1, ConvNormAct, [384, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [37, 1, ConvNormAct, [384, 1, 1]], # route backbone P5 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], # 59 [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [28, 1, ConvNormAct, [256, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], # 71 [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [19, 1, ConvNormAct, [128, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], # 83 [-1, 1, ConvNormAct, [256, 3, 2]], [[-1, 71], 1, Concat, [1]], # cat [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], # 93 [-1, 1, ConvNormAct, [384, 3, 2]], [[-1, 59], 1, Concat, [1]], # cat [-1, 1, ConvNormAct, [384, 1, 1]], [-2, 1, ConvNormAct, [384, 1, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [-1, 1, ConvNormAct, [192, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [384, 1, 1]], # 103 [-1, 1, ConvNormAct, [512, 3, 2]], [[-1, 47], 1, Concat, [1]], # cat [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], # 113 [83, 1, ConvNormAct, [256, 3, 1]], [93, 1, ConvNormAct, [512, 3, 1]], [103, 1, ConvNormAct, [768, 3, 1]], [113, 1, ConvNormAct, [1024, 3, 1]], [83, 1, ConvNormAct, [320, 3, 1]], [71, 1, ConvNormAct, [640, 3, 1]], [59, 1, ConvNormAct, [960, 3, 1]], [47, 1, ConvNormAct, [1280, 3, 1]], [[114,115,116,117,118,119,120,121], 1, YOLOv7AuxHead, [nc, anchors, stride]], # Detect(P3, P4, P5, P6) ] yolov/configs/yolov7/yolov7-x.yaml000066400000000000000000000126521443434675500175320ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p5.yaml', ] per_batch_size: 12 # 12 * 8 = 96 img_size: 640 sync_bn: True recompute: True recompute_layers: 5 network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32] # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32 # yolov7 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [40, 3, 1]], # 0 [-1, 1, ConvNormAct, [80, 3, 2]], # 1-P1/2 [-1, 1, ConvNormAct, [80, 3, 1]], [-1, 1, ConvNormAct, [160, 3, 2]], # 3-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 13 [-1, 1, MP, []], [-1, 1, ConvNormAct, [160, 1, 1]], [-3, 1, ConvNormAct, [160, 1, 1]], [-1, 1, ConvNormAct, [160, 3, 2]], [[-1, -3], 1, Concat, [1]], # 18-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 28 [-1, 1, MP, []], [-1, 1, ConvNormAct, [320, 1, 1]], [-3, 1, ConvNormAct, [320, 1, 1]], [-1, 1, ConvNormAct, [320, 3, 2]], [[-1, -3], 1, Concat, [1]], # 33-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [1280, 1, 1]], # 43 [-1, 1, MP, []], [-1, 1, ConvNormAct, [640, 1, 1]], [-3, 1, ConvNormAct, [640, 1, 1]], [-1, 1, ConvNormAct, [640, 3, 2]], [[-1, -3], 1, Concat, [1]], # 48-P5/32 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [1280, 1, 1]], # 58 ] # yolov7 head head: [[-1, 1, SPPCSPC, [640]], # 59 [-1, 1, ConvNormAct, [320, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [43, 1, ConvNormAct, [320, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 73 [-1, 1, ConvNormAct, [160, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [28, 1, ConvNormAct, [160, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [160, 1, 1]], # 87 [-1, 1, MP, []], [-1, 1, ConvNormAct, [160, 1, 1]], [-3, 1, ConvNormAct, [160, 1, 1]], [-1, 1, ConvNormAct, [160, 3, 2]], [[-1, -3, 73], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [320, 1, 1]], # 102 [-1, 1, MP, []], [-1, 1, ConvNormAct, [320, 1, 1]], [-3, 1, ConvNormAct, [320, 1, 1]], [-1, 1, ConvNormAct, [320, 3, 2]], [[-1, -3, 59], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [-1, 1, ConvNormAct, [512, 3, 1]], [[-1, -3, -5, -7, -8], 1, Concat, [1]], [-1, 1, ConvNormAct, [640, 1, 1]], # 117 [87, 1, ConvNormAct, [320, 3, 1]], [102, 1, ConvNormAct, [640, 3, 1]], [117, 1, ConvNormAct, [1280, 3, 1]], [[118,119,120], 1, YOLOv7Head, [nc, anchors, stride]], # Detect(P3, P4, P5) ] yolov/configs/yolov7/yolov7.yaml000066400000000000000000000114231443434675500172600ustar00rootroot00000000000000__BASE__: [ '../coco.yaml', './hyp.scratch.p5.yaml', ] per_batch_size: 16 # 16 * 8 = 128 img_size: 640 sync_bn: True recompute: True recompute_layers: 5 network: model_name: yolov7 depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple stride: [8, 16, 32] # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32 # yolov7 backbone backbone: # [from, number, module, args] [[-1, 1, ConvNormAct, [32, 3, 1]], # 0 [-1, 1, ConvNormAct, [64, 3, 2]], # 1-P1/2 [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 2]], # 3-P2/4 [-1, 1, ConvNormAct, [64, 1, 1]], [-2, 1, ConvNormAct, [64, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], # 11 [-1, 1, MP, []], [-1, 1, ConvNormAct, [128, 1, 1]], [-3, 1, ConvNormAct, [128, 1, 1]], # 14 [-1, 1, ConvNormAct, [128, 3, 2]], [[-1, -3], 1, Concat, [1]], # 16-P3/8 [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-1, 1, MP, []], [-1, 1, ConvNormAct, [256, 1, 1]], [-3, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 2]], [[-1, -3], 1, Concat, [1]], # 29-P4/16 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1]], [-1, 1, MP, []], [-1, 1, ConvNormAct, [512, 1, 1]], [-3, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [512, 3, 2]], [[-1, -3], 1, Concat, [1]], # 42-P5/32 [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [1024, 1, 1]], # 50 ] # yolov7 head head: [[-1, 1, SPPCSPC, [512]], # 51 [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [37, 1, ConvNormAct, [256, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-1, 1, Upsample, [None, 2, 'nearest']], [24, 1, ConvNormAct, [128, 1, 1]], # 66/ route backbone P3 [[-1, -2], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], [-2, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [-1, 1, ConvNormAct, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [128, 1, 1]], # 75 [-1, 1, MP, []], [-1, 1, ConvNormAct, [128, 1, 1]], [-3, 1, ConvNormAct, [128, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 2]], [[-1, -3, 63], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], [-2, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [-1, 1, ConvNormAct, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [256, 1, 1]], # 88 [-1, 1, MP, []], [-1, 1, ConvNormAct, [256, 1, 1]], [-3, 1, ConvNormAct, [256, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 2]], [[-1, -3, 51], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], [-2, 1, ConvNormAct, [512, 1, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [-1, 1, ConvNormAct, [256, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, ConvNormAct, [512, 1, 1]], # 101 [75, 1, RepConv, [256, 3, 1]], [88, 1, RepConv, [512, 3, 1]], [101, 1, RepConv, [1024, 3, 1]], [[102,103,104], 1, YOLOv7Head, [nc, anchors, stride]], # Detect(P3, P4, P5) ] yolov/deploy/000077500000000000000000000000001443434675500135415ustar00rootroot00000000000000yolov/deploy/export.py000066400000000000000000000061001443434675500154310ustar00rootroot00000000000000import os import argparse import numpy as np import mindspore as ms from mindspore import context, Tensor, export from mindyolo.models import create_model from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.utils import set_seed def get_parser_export(parents=None): parser = argparse.ArgumentParser(description='Export', parents=[parents] if parents else []) parser.add_argument('--device_target', type=str, default='Ascend', help='device target, Ascend/GPU/CPU') parser.add_argument('--ms_mode', type=int, default=0, help='train mode, graph/pynative') parser.add_argument('--ms_amp_level', type=str, default='O0', help='amp level, O0/O1/O2') parser.add_argument('--weight', type=str, default='yolov7_300.ckpt', help='model.ckpt path(s)') parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--per_batch_size', type=int, default=1, help='size of each image batch') parser.add_argument('--seed', type=int, default=2, help='set global seed') parser.add_argument('--log_level', type=str, default='INFO', help='save dir') parser.add_argument('--nms_time_limit', type=float, default=60.0, help='time limit for NMS') parser.add_argument('--file_format', type=str, default='MINDIR', help='treat as single-class dataset') return parser def set_default_export(args): # Set Context context.set_context(mode=args.ms_mode, device_target=args.device_target, max_call_depth=2000) if args.device_target == "Ascend": context.set_context(device_id=int(os.getenv('DEVICE_ID', 0))) args.rank, args.rank_size = 0, 1 # Set Data args.data.nc = 1 if args.single_cls else int(args.data.nc) # number of classes args.data.names = ['item'] if args.single_cls and len(args.names) != 1 else args.data.names # class names assert len(args.data.names) == args.data.nc, '%g names found for nc=%g dataset in %s' % \ (len(args.data.names), args.data.nc, args.config) # Set Logger logger.setup_logging(logger_name="MindYOLO", log_level="INFO", rank_id=args.rank, device_per_servers=args.rank_size) logger.setup_logging_file(log_dir=os.path.join(args.save_dir, "logs")) def export_weight(args): # Init set_seed(args.seed) set_default_export(args) # Create Network network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, sync_bn=args.sync_bn, checkpoint_path=args.weight ) network.set_train(False) ms.amp.auto_mixed_precision(network, amp_level=args.ms_amp_level) # Export input_arr = Tensor(np.ones([args.per_batch_size, 3, args.img_size, args.img_size]), ms.float32) file_name = os.path.basename(args.config)[:-5] # delete ".yaml" export(network, input_arr, file_name=file_name, file_format=args.file_format) logger.info('Export completed.') if __name__ == '__main__': parser = get_parser_export() args = parse_args(parser) export_weight(args) yolov/deploy/infer.py000066400000000000000000000124641443434675500152250ustar00rootroot00000000000000import os import time from pathlib import Path import numpy as np from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from infer_engine import * from mindyolo.data import COCO80_TO_COCO91_CLASS from mindyolo.data import COCODataset, create_loader from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.metrics import non_max_suppression, scale_coords, xyxy2xywh def detect(nc=80, anchor=(), stride=()): no = nc + 5 nl = len(anchor) na = len(anchor[0]) // 2 anchor_grid = np.array(anchor).reshape(nl, 1, -1, 1, 1, 2) def forward(x): z = () outs = () for i in range(len(x)): out = x[i] bs, _, ny, nx = out.shape out = out.reshape(bs, na, no, ny, nx).transpose(0, 1, 3, 4, 2) outs += (out,) xv, yv = np.meshgrid(np.arange(nx), np.arange(ny)) grid = np.stack((xv, yv), 2).reshape((1, 1, ny, nx, 2)) y = 1 / (1 + np.exp(-out)) y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid) * stride[i] y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] z += (y.reshape(bs, -1, no),) return np.concatenate(z, 1), outs return forward def infer(cfg): # Create Network network = MindXModel("./models/yolov5s.om") de = detect(nc=80, anchor=cfg.network.anchors, stride=cfg.network.stride) dataset = COCODataset( dataset_path=cfg.data.val_set, img_size=cfg.img_size, transforms_dict=cfg.data.test_transforms, is_training=False, augment=False, rect=cfg.rect, single_cls=cfg.single_cls, batch_size=cfg.per_batch_size, stride=max(cfg.network.stride), ) dataloader = create_loader( dataset=dataset, batch_collate_fn=dataset.test_collate_fn, dataset_column_names=dataset.dataset_column_names, batch_size=cfg.per_batch_size, epoch_size=1, rank=0, rank_size=1, shuffle=False, drop_remainder=False, num_parallel_workers=cfg.data.num_parallel_workers, python_multiprocessing=True ) loader = dataloader.create_dict_iterator(output_numpy=True, num_epochs=1) # anno_json_path = os.path.join(self.cfg.data.dataset_dir, self.cfg.data.val_anno_path) dataset_dir = cfg.data.val_set[:-len(cfg.data.val_set.split('/')[-1])] anno_json_path = os.path.join(dataset_dir, 'annotations/instances_val2017.json') coco91class = COCO80_TO_COCO91_CLASS is_coco_dataset = ('coco' in cfg.data.dataset_name) step_num = dataloader.get_dataset_size() sample_num = 0 infer_times = 0. nms_times = 0. result_dicts = [] for i, data in enumerate(loader): imgs, _, paths, ori_shape, pad, hw_scale = data['image'], data['labels'], data['img_files'], \ data['hw_ori'], data['pad'], data['hw_scale'] nb, _, height, width = imgs.shape # Run infer _t = time.time() out = network.infer(imgs) # inference and training outputs out, _ = de(out) infer_times += time.time() - _t # Run NMS t = time.time() out = non_max_suppression(out, conf_thres=cfg.conf_thres, iou_thres=cfg.iou_thres, multi_label=True, time_limit=cfg.nms_time_limit) nms_times += time.time() - t # Statistics pred for si, pred in enumerate(out): path = Path(str(paths[si])) sample_num += 1 if len(pred) == 0: continue # Predictions predn = np.copy(pred) scale_coords(imgs[si].shape[1:], predn[:, :4], ori_shape[si], ratio=hw_scale[si], pad=pad[si]) # native-space pred image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(pred.tolist(), box.tolist()): result_dicts.append({'image_id': image_id, 'category_id': coco91class[int(p[5])] if is_coco_dataset else int(p[5]), 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5)}) print(f"Sample {step_num}/{i + 1}, time cost: {(time.time() - _t) * 1000:.2f} ms.") # Compute mAP try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb anno = COCO(anno_json_path) # init annotations api pred = anno.loadRes(result_dicts) # init predictions api eval = COCOeval(anno, pred, 'bbox') if is_coco_dataset: eval.params.imgIds = [int(Path(im_file).stem) for im_file in dataset.img_files] eval.evaluate() eval.accumulate() eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: logger.warning(f'pycocotools unable to run: {e}') raise e t = tuple(x / sample_num * 1E3 for x in (infer_times, nms_times, infer_times + nms_times)) + \ (height, width, cfg.per_batch_size) # tuple logger.info(f'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g;' % t) return map, map50 if __name__ == '__main__': args = parse_args('val') infer(args) yolov/deploy/infer_engine/000077500000000000000000000000001443434675500161715ustar00rootroot00000000000000yolov/deploy/infer_engine/__init__.py000066400000000000000000000001421443434675500202770ustar00rootroot00000000000000 from .lite import LiteModel from .mindx import MindXModel __all__ = ['LiteModel', 'MindXModel'] yolov/deploy/infer_engine/lite.py000066400000000000000000000015331443434675500175020ustar00rootroot00000000000000import numpy as np from .model_base import ModelBase class LiteModel(ModelBase): def __init__(self, model_path, device_id = 0): super().__init__() self.model_path = model_path self.device_id = device_id def _init_model(self): import mindspore_lite as mslite context = mslite.Context() context.target = ['ascend'] context.ascend.device_id = self.device_id self.model = mslite.Model() self.model.build_from_from_file(self.model_path, mslite.ModelType.MINDIR, context) def infer(self, input): inputs = self.model.get_inputs() self.model.resize(inputs, [list(input.shape)]) inputs[0].set_data_from_numpy(input) outputs = self.model.predict(inputs) outputs = [outputs.get_data_to_numpy() for output in outputs] return outputs yolov/deploy/infer_engine/mindx.py000066400000000000000000000014541443434675500176660ustar00rootroot00000000000000import numpy as np from .model_base import ModelBase class MindXModel(ModelBase): def __init__(self, model_path, device_id=0): super().__init__() self.model_path = model_path self.device_id = device_id self._init_model() def _init_model(self): global base, Tensor from mindx.sdk import base, Tensor, visionDataFormat base.mx_init() self.model = base.model(self.model_path, self.device_id) if not self.model: raise ValueError(f"The model file {self.model_path} load failed.") def infer(self, input): inputs = Tensor(input) outputs = self.model.infer(inputs) list([output.to_host() for output in outputs]) outputs = [np.array(output) for output in outputs] return outputs yolov/deploy/infer_engine/model_base.py000066400000000000000000000012251443434675500206350ustar00rootroot00000000000000from abc import ABCMeta, abstractmethod from typing import List import numpy as np class ModelBase(metaclass=ABCMeta): """ base class for model load and infer """ def __init__(self, *args, **kwargs): super().__init__() self.model = None @abstractmethod def _init_model(self): pass @abstractmethod def infer(self, input: np.numarray) -> List[np.numarray]: """ model inference, just for single input Args: input: np img Returns: """ pass def __del__(self): if hasattr(self, "model") and self.model: del self.model yolov/docs/000077500000000000000000000000001443434675500131755ustar00rootroot00000000000000yolov/docs/en/000077500000000000000000000000001443434675500135775ustar00rootroot00000000000000yolov/docs/en/readme.md000066400000000000000000000000001443434675500153440ustar00rootroot00000000000000yolov/docs/en/tutorials/000077500000000000000000000000001443434675500156255ustar00rootroot00000000000000yolov/docs/en/tutorials/readme.md000066400000000000000000000000001443434675500173720ustar00rootroot00000000000000yolov/docs/zh_cn/000077500000000000000000000000001443434675500142765ustar00rootroot00000000000000yolov/docs/zh_cn/readme.md000066400000000000000000000000001443434675500160430ustar00rootroot00000000000000yolov/docs/zh_cn/tutorials/000077500000000000000000000000001443434675500163245ustar00rootroot00000000000000yolov/docs/zh_cn/tutorials/readme.md000066400000000000000000000000001443434675500200710ustar00rootroot00000000000000yolov/infer.py000066400000000000000000000171341443434675500137300ustar00rootroot00000000000000import os import yaml import ast import argparse import math import time import cv2 import numpy as np from datetime import datetime import mindspore as ms from mindspore import nn, context, Tensor from mindyolo.models import create_model from mindyolo.data import COCO80_TO_COCO91_CLASS from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.metrics import non_max_suppression, scale_coords, xyxy2xywh from mindyolo.utils.utils import set_seed, draw_result def get_parser_infer(parents=None): parser = argparse.ArgumentParser(description='Infer', parents=[parents] if parents else []) parser.add_argument('--device_target', type=str, default='Ascend', help='device target, Ascend/GPU/CPU') parser.add_argument('--ms_mode', type=int, default=0, help='train mode, graph/pynative') parser.add_argument('--ms_amp_level', type=str, default='O0', help='amp level, O0/O1/O2') parser.add_argument('--ms_enable_graph_kernel', type=ast.literal_eval, default=False, help='use enable_graph_kernel or not') parser.add_argument('--weight', type=str, default='yolov7_300.ckpt', help='model.ckpt path(s)') parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--seed', type=int, default=2, help='set global seed') parser.add_argument('--save_dir', type=str, default='./runs_infer', help='save dir') parser.add_argument('--log_level', type=str, default='INFO', help='save dir') parser.add_argument('--conf_thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou_thres', type=float, default=0.65, help='IOU threshold for NMS') parser.add_argument('--nms_time_limit', type=float, default=60.0, help='time limit for NMS') parser.add_argument("--image_path", type=str, help="path to image") parser.add_argument("--save_result", type=ast.literal_eval, default=True, help="whether save the inference result") return parser def set_default_infer(args): # Set Context context.set_context(mode=args.ms_mode, device_target=args.device_target, max_call_depth=2000) if args.device_target == "Ascend": context.set_context(device_id=int(os.getenv('DEVICE_ID', 0))) elif args.device_target == "GPU" and args.ms_enable_graph_kernel: context.set_context(enable_graph_kernel=True) args.rank, args.rank_size = 0, 1 # Set Data args.data.nc = 1 if args.single_cls else int(args.data.nc) # number of classes args.data.names = ['item'] if args.single_cls and len(args.names) != 1 else args.data.names # class names assert len(args.data.names) == args.data.nc, '%g names found for nc=%g dataset in %s' % \ (len(args.data.names), args.data.nc, args.config) # Directories and Save run settings args.save_dir = os.path.join(args.save_dir, datetime.now().strftime("%Y.%m.%d-%H:%M:%S")) os.makedirs(args.save_dir, exist_ok=True) if args.rank % args.rank_size == 0: with open(os.path.join(args.save_dir, "cfg.yaml"), 'w') as f: yaml.dump(vars(args), f, sort_keys=False) # Set Logger logger.setup_logging(logger_name="MindYOLO", log_level="INFO", rank_id=args.rank, device_per_servers=args.rank_size) logger.setup_logging_file(log_dir=os.path.join(args.save_dir, "logs")) def detect( network: nn.Cell, img: np.ndarray, conf_thres: float = 0.25, iou_thres: float = 0.65, nms_time_limit: float = 60.0, img_size: int = 640, stride: int = 32, is_coco_dataset: bool = True ): # Resize h_ori, w_ori = img.shape[:2] # orig hw r = img_size / max(h_ori, w_ori) # resize image to img_size if r != 1: # always resize down, only resize up if training with augmentation interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR img = cv2.resize(img, (int(w_ori * r), int(h_ori * r)), interpolation=interp) h, w = img.shape[:2] if h < img_size or w < img_size: new_h, new_w = math.ceil(h / stride) * stride, math.ceil(w / stride) * stride dh, dw = (new_h - h) / 2, (new_w - w) / 2 top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) # add border # Transpose Norm img = img[:, :, ::-1].transpose(2, 0, 1) / 255. imgs_tensor = Tensor(img[None], ms.float32) # Run infer _t = time.time() out = network(imgs_tensor) # inference and training outputs out = out[0] if isinstance(out, (tuple, list)) else out infer_times = time.time() - _t # Run NMS t = time.time() out = out.asnumpy() out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=True, time_limit=nms_time_limit) nms_times = time.time() - t result_dict = {'category_id': [], 'bbox': [], 'score': []} total_category_ids, total_bboxes, total_scores = [], [], [] for si, pred in enumerate(out): if len(pred) == 0: continue # Predictions predn = np.copy(pred) scale_coords(img.shape[1:], predn[:, :4], (h_ori, w_ori)) # native-space pred box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner category_ids, bboxes, scores = [], [], [] for p, b in zip(pred.tolist(), box.tolist()): category_ids.append(COCO80_TO_COCO91_CLASS[int(p[5])] if is_coco_dataset else int(p[5])) bboxes.append([round(x, 3) for x in b]) scores.append(round(p[4], 5)) total_category_ids.extend(category_ids) total_bboxes.extend(bboxes) total_scores.extend(scores) result_dict['category_id'].extend(total_category_ids) result_dict['bbox'].extend(total_bboxes) result_dict['score'].extend(total_scores) t = tuple(x * 1E3 for x in (infer_times, nms_times, infer_times + nms_times)) + (img_size, img_size, 1) # tuple logger.info(f"Predict result is: {result_dict}") logger.info(f"Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g;" % t) logger.info(f"Detect a image success.") return result_dict def infer(args): # Init set_seed(args.seed) set_default_infer(args) # Create Network network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, sync_bn=False, checkpoint_path=args.weight ) network.set_train(False) ms.amp.auto_mixed_precision(network, amp_level=args.ms_amp_level) # Load Image if isinstance(args.image_path, str) and os.path.isfile(args.image_path): import cv2 img = cv2.imread(args.image_path) else: raise ValueError("Detect: input image file not available.") # Detect is_coco_dataset = ('coco' in args.data.dataset_name) result_dict = detect( network=network, img=img, conf_thres=args.conf_thres, iou_thres=args.iou_thres, nms_time_limit=args.nms_time_limit, img_size=args.img_size, stride=max(max(args.network.stride), 32), is_coco_dataset=is_coco_dataset ) if args.save_result: save_path = os.path.join(args.save_dir, 'detect_results') draw_result(args.image_path, result_dict, args.data.names, save_path=save_path) logger.info('Infer completed.') if __name__ == '__main__': parser = get_parser_infer() args = parse_args(parser) infer(args) yolov/log.txt000066400000000000000000037543101443434675500136040ustar00rootroot00000000000000parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations parse_args: Namespace(accumulate=1, auto_accumulate=False, ckpt_dir='/cache/yolov4_backbone.ckpt', ckpt_url='', config='./configs/yolov4/yolov4.yaml', data={}, data_dir='/cache/data/', data_url='', device_per_servers=8, device_target='Ascend', ema=False, ema_weight='', enable_modelarts=False, epochs=320, freeze=[], img_size=608, iou_thres=0.6, is_parallel=True, keep_checkpoint_max=100, keep_loss_fp32=True, log_interval=1, log_level='INFO', loss={}, ms_amp_level='O0', ms_enable_graph_kernel=False, ms_grad_sens=1.0, ms_jit=True, ms_loss_scaler='static', ms_loss_scaler_value=1.0, ms_mode=0, nbs=64, network={}, nms_time_limit=20.0, optimizer={}, overflow_still_update=True, per_batch_size=16, recompute=False, recompute_layers=0, rect=False, run_eval=False, save_dir='./runs', seed=2, single_cls=False, sync_bn=False, train_url='', weight='./yolov4_backbone.ckpt') Please check the above information for the configurations [WARNING] HCCL_ADPT(3982243,ffffaff44570,python):2023-05-16-18:26:51.038.926 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982240,ffffad344570,python):2023-05-16-18:26:51.040.157 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982241,ffff814d4570,python):2023-05-16-18:26:51.050.965 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982234,ffff96784570,python):2023-05-16-18:26:51.096.784 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982237,ffffa9624570,python):2023-05-16-18:26:51.103.754 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982238,ffff9d854570,python):2023-05-16-18:26:51.109.085 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982239,ffff98f94570,python):2023-05-16-18:26:51.110.260 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 [WARNING] HCCL_ADPT(3982242,ffffb86e4570,python):2023-05-16-18:26:51.112.149 [mindspore/ccsrc/plugin/device/ascend/hal/hccl_adapter/hccl_adapter.cc:47] GenHcclOptions] The environment variable DEPLOY_MODE is not set. Now set to default value 0 from n params module arguments from n params module arguments from n params module arguments from n params module arguments from n params module arguments 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] from n params module arguments 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] from n params module arguments 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] from n params module arguments 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] 6 [-2, -1] 1 0 [1] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] 6 [-2, -1] 1 0 [1] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 0 -1 1 992 [3, 32, 3, 1, None, 1, 1, Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 1 -1 1 18688 [32, 64, 3, 2, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 2 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 3 -1 1 20864 [64, 64, True, [1, 3], [1, 1], Mish<>] 4 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 5 1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 6 [-2, -1] 1 0 [1] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 7 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 8 -1 1 74240 [64, 128, 3, 2, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 9 -1 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 10 -1 2 82944 [64, 64, [1, 3], [1, 1], Mish<>] 11 -1 1 4352 [64, 64, 1, 1, None, 1, 1, Mish<>] 12 8 1 8448 [128, 64, 1, 1, None, 1, 1, Mish<>] 13 [-2, -1] 1 0 [1] 14 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 15 -1 1 295936 [128, 256, 3, 2, None, 1, 1, Mish<>] 16 -1 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 20 [-2, -1] 1 0 [1] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 20 [-2, -1] 1 0 [1] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 17 -1 8 1318912 [128, 128, [1, 3], [1, 1], Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 18 -1 1 16896 [128, 128, 1, 1, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 19 15 1 33280 [256, 128, 1, 1, None, 1, 1, Mish<>] 20 [-2, -1] 1 0 [1] 21 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 22 -1 1 1181696 [256, 512, 3, 2, None, 1, 1, Mish<>] 23 -1 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 24 -1 8 5259264 [256, 256, [1, 3], [1, 1], Mish<>] 25 -1 1 66560 [256, 256, 1, 1, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 26 22 1 132096 [512, 256, 1, 1, None, 1, 1, Mish<>] 27 [-2, -1] 1 0 [1] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 28 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 29 -1 1 4722688 [512, 1024, 3, 2, None, 1, 1, Mish<>] 30 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 31 -1 4 10502144 [512, 512, [1, 3], [1, 1], Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 32 -1 1 264192 [512, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 33 29 1 526336 [1024, 512, 1, 1, None, 1, 1, Mish<>] 34 [-2, -1] 1 0 [1] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 35 -1 1 1052672 [1024, 1024, 1, 1, None, 1, 1, Mish<>] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 38 -1 1 0 [5, 1, 'same'] 39 -2 1 0 [9, 1, 'same'] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 39 -2 1 0 [9, 1, 'same'] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 36 -1 1 5249024 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 38 -1 1 0 [5, 1, 'same'] 37 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 38 -1 1 0 [5, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 39 -2 1 0 [9, 1, 'same'] 40 -3 1 0 [13, 1, 'same'] 41 [-1, -2, -3, -4] 1 0 [1] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 42 -1 1 1050624 [2048, 512, 1, 1, None, 1, 1, LeakyReLU<>] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 46 -1 1 0 [None, 2, 'nearest'] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 48 [-2, -1] 1 0 [1] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 43 -1 1 4722688 [512, 1024, 3, 1, None, 1, 1, LeakyReLU<>] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 44 -1 1 526336 [1024, 512, 1, 1, None, 1, 1, LeakyReLU<>] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 45 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 46 -1 1 0 [None, 2, 'nearest'] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 47 28 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 48 [-2, -1] 1 0 [1] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 49 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 50 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 54 [-2, -1] 1 0 [1] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 51 -1 1 295424 [256, 128, 3, 1, None, 1, 1, LeakyReLU<>] 52 -1 1 0 [None, 2, 'nearest'] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 53 21 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 54 [-2, -1] 1 0 [1] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 55 -1 2 658432 [256, 256, False, [1, 3], [1, 1], LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 56 -1 1 33280 [256, 128, 1, 1, None, 1, 1, LeakyReLU<>] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 59 [-1, -9] 1 0 [1] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 57 -1 1 295936 [128, 256, 3, 1, None, 1, 1, LeakyReLU<>] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 58 -2 1 295936 [128, 256, 3, 2, None, 1, 1, LeakyReLU<>] 59 [-1, -9] 1 0 [1] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 60 -1 2 2627584 [512, 512, False, [1, 3], [1, 1], LeakyReLU<>] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 61 -1 1 132096 [512, 256, 1, 1, None, 1, 1, LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 62 -1 1 1181696 [256, 512, 3, 1, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 63 -2 1 1181696 [256, 512, 3, 2, None, 1, 1, LeakyReLU<>] 64 [-1, -20] 1 0 [1] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] 65 -1 3 15747072 [1024, 1024, False, [1, 3], [1, 1], LeakyReLU<>] 66 [-1, -4, -9] 1 457725 [80, [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]], [1024, 512, 256]] [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.211 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.442 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.510 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.565 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.619 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.gamma is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.672 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.beta is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.723 [mindspore/train/serialization.py:714] model.model.36.conv2.conv.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.773 [mindspore/train/serialization.py:714] model.model.36.conv2.bn.moving_mean is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.822 [mindspore/train/serialization.py:714] model.model.36.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.871 [mindspore/train/serialization.py:714] model.model.36.conv2.bn.gamma is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.918 [mindspore/train/serialization.py:714] model.model.36.conv2.bn.beta is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.499.965 [mindspore/train/serialization.py:714] model.model.37.conv.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.500.012 [mindspore/train/serialization.py:714] model.model.37.bn.moving_mean is not loaded. 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[WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.505.075 [mindspore/train/serialization.py:714] model.model.60.1.conv1.bn.beta is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.505.120 [mindspore/train/serialization.py:714] model.model.60.1.conv2.conv.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.505.164 [mindspore/train/serialization.py:714] model.model.60.1.conv2.bn.moving_mean is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.505.208 [mindspore/train/serialization.py:714] model.model.60.1.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.505.253 [mindspore/train/serialization.py:714] model.model.60.1.conv2.bn.gamma is not loaded. 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[WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.506.768 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.506.812 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.gamma is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.506.857 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.beta is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.506.901 [mindspore/train/serialization.py:714] model.model.65.2.conv1.conv.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.506.944 [mindspore/train/serialization.py:714] model.model.65.2.conv1.bn.moving_mean is not loaded. 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[WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.207 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.252 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.gamma is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.296 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.beta is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.340 [mindspore/train/serialization.py:714] model.model.66.m.0.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.384 [mindspore/train/serialization.py:714] model.model.66.m.0.bias is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.428 [mindspore/train/serialization.py:714] model.model.66.m.1.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.473 [mindspore/train/serialization.py:714] model.model.66.m.1.bias is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.516 [mindspore/train/serialization.py:714] model.model.66.m.2.weight is not loaded. [WARNING] ME(3982241:281472851068272,MainProcess):2023-05-16-18:26:54.507.560 [mindspore/train/serialization.py:714] model.model.66.m.2.bias is not loaded. [WARNING] ME(3982237:281473523533168,MainProcess):2023-05-16-18:26:54.516.286 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982237:281473523533168,MainProcess):2023-05-16-18:26:54.516.473 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. 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[WARNING] ME(3982237:281473523533168,MainProcess):2023-05-16-18:26:54.524.574 [mindspore/train/serialization.py:714] model.model.66.m.2.bias is not loaded. [WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.530.406 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.530.587 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.530.654 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.530.767 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. 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[WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.537.159 [mindspore/train/serialization.py:714] model.model.65.0.conv1.conv.weight is not loaded. [WARNING] ME(3982243:281473633764720,MainProcess):2023-05-16-18:26:54.537.044 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982243:281473633764720,MainProcess):2023-05-16-18:26:54.537.262 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982240:281473587627376,MainProcess):2023-05-16-18:26:54.537.203 [mindspore/train/serialization.py:714] model.model.65.0.conv1.bn.moving_mean is not loaded. 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[WARNING] ME(3982243:281473633764720,MainProcess):2023-05-16-18:26:54.545.505 [mindspore/train/serialization.py:714] model.model.66.m.2.bias is not loaded. [WARNING] ME(3982239:281473248216432,MainProcess):2023-05-16-18:26:54.554.215 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982239:281473248216432,MainProcess):2023-05-16-18:26:54.554.441 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982239:281473248216432,MainProcess):2023-05-16-18:26:54.554.512 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982239:281473248216432,MainProcess):2023-05-16-18:26:54.554.571 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. 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[WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.565.220 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.565.408 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.565.474 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.565.526 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.565.577 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.gamma is not loaded. 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[WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.573.154 [mindspore/train/serialization.py:714] model.model.66.m.0.bias is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.573.197 [mindspore/train/serialization.py:714] model.model.66.m.1.weight is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.573.241 [mindspore/train/serialization.py:714] model.model.66.m.1.bias is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.573.284 [mindspore/train/serialization.py:714] model.model.66.m.2.weight is not loaded. [WARNING] ME(3982234:281473206207856,MainProcess):2023-05-16-18:26:54.573.327 [mindspore/train/serialization.py:714] model.model.66.m.2.bias is not loaded. 2023-05-16 18:26:54,573 [INFO] Pretrain model load from "./yolov4_backbone.ckpt" success. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.615.685 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.615.906 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.615.976 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.616.030 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.616.086 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.gamma is not loaded. 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[WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.622.559 [mindspore/train/serialization.py:714] model.model.65.0.conv1.conv.weight is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.622.605 [mindspore/train/serialization.py:714] model.model.65.0.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.622.650 [mindspore/train/serialization.py:714] model.model.65.0.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.622.695 [mindspore/train/serialization.py:714] model.model.65.0.conv1.bn.gamma is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.622.740 [mindspore/train/serialization.py:714] model.model.65.0.conv1.bn.beta is not loaded. 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[WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.623.456 [mindspore/train/serialization.py:714] model.model.65.2.conv1.conv.weight is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.623.501 [mindspore/train/serialization.py:714] model.model.65.2.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.623.546 [mindspore/train/serialization.py:714] model.model.65.2.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.623.590 [mindspore/train/serialization.py:714] model.model.65.2.conv1.bn.gamma is not loaded. [WARNING] ME(3982238:281473324500336,MainProcess):2023-05-16-18:26:54.623.635 [mindspore/train/serialization.py:714] model.model.65.2.conv1.bn.beta is not loaded. 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[WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.638.649 [mindspore/train/serialization.py:712] For 'load_param_into_net', 181 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.638.872 [mindspore/train/serialization.py:714] model.model.36.conv1.conv.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.638.945 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_mean is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.639.002 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.moving_variance is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.639.057 [mindspore/train/serialization.py:714] model.model.36.conv1.bn.gamma is not loaded. 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[WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.195 [mindspore/train/serialization.py:714] model.model.65.1.conv2.conv.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.240 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.moving_mean is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.286 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.330 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.gamma is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.375 [mindspore/train/serialization.py:714] model.model.65.1.conv2.bn.beta is not loaded. 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[WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.642 [mindspore/train/serialization.py:714] model.model.65.2.conv2.conv.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.687 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.moving_mean is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.732 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.moving_variance is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.776 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.gamma is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.821 [mindspore/train/serialization.py:714] model.model.65.2.conv2.bn.beta is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.864 [mindspore/train/serialization.py:714] model.model.66.m.0.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.909 [mindspore/train/serialization.py:714] model.model.66.m.0.bias is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.952 [mindspore/train/serialization.py:714] model.model.66.m.1.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.646.996 [mindspore/train/serialization.py:714] model.model.66.m.1.bias is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.647.041 [mindspore/train/serialization.py:714] model.model.66.m.2.weight is not loaded. [WARNING] ME(3982242:281473775977840,MainProcess):2023-05-16-18:26:54.647.086 [mindspore/train/serialization.py:714] model.model.66.m.2.bias is not loaded. Scanning '/home/dongliyue/coco_yolo/train2017.cache.npy' images and labels... 117266 found, 1021 missing, 0 empty, 4 corrupted: 100%|██████████| 118287/118287 [00:00s   yolov/mindyolo/__pycache__/version.cpython-37.pyc000066400000000000000000000003111443434675500224160ustar00rootroot00000000000000B ЊSd/@s dZdZdS)z version initz 0.0.1-alphaN)__doc__ __version__rr:/home/ma-user/work/mindyolo_for_yolov4/mindyolo/version.pysyolov/mindyolo/data/000077500000000000000000000000001443434675500150105ustar00rootroot00000000000000yolov/mindyolo/data/__init__.py000066400000000000000000000002741443434675500171240ustar00rootroot00000000000000from .dataset import * from .loader import * from .constants import * from . import dataset, loader, constants __all__ = [] __all__.extend(dataset.__all__) __all__.extend(loader.__all__) yolov/mindyolo/data/__pycache__/000077500000000000000000000000001443434675500172205ustar00rootroot00000000000000yolov/mindyolo/data/__pycache__/__init__.cpython-37.pyc000066400000000000000000000004621443434675500234100ustar00rootroot00000000000000B ЊSd@sLddlTddlTddlTddlmZmZmZgZeejeejdS))*)datasetloader constantsN)rrr__all__extendr r @/home/ma-user/work/mindyolo_for_yolov4/mindyolo/data/__init__.pys  yolov/mindyolo/data/__pycache__/albumentations.cpython-37.pyc000066400000000000000000000072221443434675500246770ustar00rootroot00000000000000B ЊSd@sNddlZddlZddlZddlZGdddZdddZdd d Zd d Z dS)Nc@s eZdZdddZd ddZdS) Albumentationsc Cs8d|_td}yddl}t|jddd|j||dddd |jd d |jd d |jd d |j d d |j dd |j dd |j d dd g}|j ||jddgdd|_t|ddd|DddtdddWnbtk rtdddYnDtk r2}z$t||ddtdddWdd}~XYnXdS)Nzalbumentations: rz1.0.3T)hard)g?g?)g?g(\?g)heightwidthscaleratiopg{Gz?)r K)Z quality_lowerr yolo class_labels)formatZ label_fields)Z bbox_paramsz, css"|]}|jr|ddVqdS)zalways_apply=False, N)r 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yolov/mindyolo/data/__pycache__/dataset.cpython-37.pyc000066400000000000000000000612171443434675500233030ustar00rootroot00000000000000B Ud@sddlZddlZddlZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z m Z ddlmZddlmZddlmZdd lmZd gZx"e jD]Ze jed krPqWGd d d Zd dZdddZddZdddZdS)N)Path)tqdm)ImageExifTags)logger) copy_paste)random_perspective)Albumentations COCODataset Orientationc @s eZdZdZdGdd Zed fd d Zd dZddZddZ ddZ dHddZ dIddZ dJddZ dKdd ZdLd"d#ZdMd%d&Zd'd(Zd)d*ZdNd,d-ZdOd.d/ZdPd2d3ZdQd5d6ZdRd7d8ZdSd9d:Zd;d<Zd=d>Zd?d@ZdAdBZedCdDZedEdFZdS)Tr us Load the COCO dataset (yolo format coco labels) Args: dataset_path (str): dataset label directory for dataset. for example: COCO_ROOT ├── train2017.txt ├── annotations │ └── instances_train2017.json ├── images │ └── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── labels └── train2017 ├── 000000000001.txt └── 000000000002.txt dataset_path (str): ./coco/train2017.txt transforms (list): A list of images data enhancements that apply data 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Applies operations such as transform and batch to the `ms.dataset.Dataset` object created by the `create_dataset` function to get the dataloader. Args: dataset (COCODataset): dataset object created by `create_dataset`. batch_size (int or function): The number of rows each batch is created with. An int or callable object which takes exactly 1 parameter, BatchInfo. drop_remainder (bool, optional): Determines whether to drop the last block whose data row number is less than batch size (default=False). If True, and if there are less than batch_size rows available to make the last batch, then those rows will be dropped and not propagated to the child node. num_parallel_workers (int, optional): Number of workers(threads) to process the dataset in parallel (default=None). python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker processes. This option could be beneficial if the Python operation is computational heavy (default=False). Returns: BatchDataset, dataset batched. iUIz"Dataloader num parallel workers: []rr) column_namesnum_parallel_workersshufflepython_multiprocessing num_shardsshard_id )r r r r ) per_batch_map input_columnsdrop_remainder)cv2 setNumThreadsdeconfigset_seedmultiprocessing cpu_countminintrinfoGeneratorDatasetbatchrepeat) datasetbatch_collate_fndataset_column_names batch_size epoch_sizerank rank_sizer r rr coresdsr)>/home/ma-user/work/mindyolo_for_yolov4/mindyolo/data/loader.pyrs&"     )rrrrTFF) __doc__rrmindspore.datasetr rmindyolo.utilsrZmindyolo.data.yolov4_transformsr__all__rr)r)r)r*s   yolov/mindyolo/data/__pycache__/perspective.cpython-37.pyc000066400000000000000000000070771443434675500242130ustar00rootroot00000000000000B ЊSd@sLddlZddlZddlZddlZdddZdd d Zdd dZdddZdS)N 皙?rrc  Csp|jd|dd} |jd|dd} td} |jd d| d<|jd d| d<td} t| || d<t| || d<td} t| |}td|d |}tj|d |d | dd<td}tt| |tj d |d <tt| |tj d |d<td}td|d|| |d<td|d|| |d<||| | | }|ddks|ddks|tdk r|rtj ||| | fdd}ntj ||dd| | fdd}t |}|rht dd|D}t|df}|rt|}xt|D]\}}tt |df}||ddddf<||j}|r|ddddf|ddddfn|ddddf}t|| | ||<q&Wn^t|ddf}|ddddddddddgf|dd|ddddf<||j}|r@|ddddf|ddddfn|ddddf|d}|ddddddgf}|ddddddgf}t|d|d|d|dfd|j}|ddddgfd| |ddddgf<|ddddgfd| |ddddgf<t|ddddfj||j|r@dndd}||}|||ddddf<||fS)Nr)rr)rr)rr)rrg?)rr)anglecenterscale)rr)rrg?)rrr)ZdsizeZ borderValuecss|]}|VqdS)N)any).0xrrC/home/ma-user/work/mindyolo_for_yolov4/mindyolo/data/perspective.py 1sz%random_perspective..g{Gz?g?)box1box2area_thr)shapenpeyerandomuniformcv2getRotationMatrix2DmathtanpirwarpPerspective warpAffinelenzeros_resample_segments enumerateonesT _segment2boxreshape concatenateminmaxclip_box_candidates)imgtargetssegmentsdegrees translater shear perspectiveborderheightwidthCPRasSr-MnZ use_segmentsnewisegmentxyryrrrrandom_perspectives`    "" 0 B< J4,,0rLcsxt|D]|\}tjddddffddtdtd|tttfddtdDddj||<q W|S)Nrr)axisc s&g|]}tdd|fqS)N)rinterp)rrH)rCrxprr [sz&_resample_segments..r) r+rr0linspacer(arangeranger/r-)r7rFrHr)rCrrPrr*Us "2r*cCsp|j\}}|dk|dk@||k@||k@}||||}}t|rft||||gStdS)Nr)rr)r-rrarrayr1r2r))rIr>r=rrKinsiderrrr._s  r.r缉ؗҜs M yolov/mindyolo/data/__pycache__/yolov4_transforms.cpython-37.pyc000066400000000000000000000410441443434675500253640ustar00rootroot00000000000000B ЊSdc@sdZddlZddlZddlZddlZddlmZddlZd5ddZ d6dd Z d d Z d7d dZ ddZ d8ddZddZddZddZddZddZdd Zd!d"Zd9d'd(Zd)d*Zd+d,ZGd-d.d.Zd/d0Zd1d2Zd3d4ZdS):zPreprocess dataset.N)Image?cCstj|||S)N)nprandomrand)abr I/home/ma-user/work/mindyolo_for_yolov4/mindyolo/data/yolov4_transforms.py_rand sr cCs.|jddks|jddkr$tdt|dddddf|ddddf}t|dddddf|ddddf}tj|||dd||kjdd}tj|ddddf|ddddf|dd}tj|ddddf|ddddf|dd}||dddf||S)aCalculate Intersection-Over-Union(IOU) of two bounding boxes. Parameters ---------- bbox_a : numpy.ndarray An ndarray with shape :math:`(N, 4)`. bbox_b : numpy.ndarray An ndarray with shape :math:`(M, 4)`. offset : float or int, default is 0 The ``offset`` is used to control the whether the width(or height) is computed as (right - left + ``offset``). Note that the offset must be 0 for normalized bboxes, whose ranges are in ``[0, 1]``. Returns ------- numpy.ndarray An ndarray with shape :math:`(N, M)` indicates IOU between each pairs of bounding boxes in `bbox_a` and `bbox_b`. z1Bounding boxes axis 1 must have at least length 4N)axis)shape IndexErrorrmaximumminimumprodall)Zbbox_aZbbox_boffsettlbrZarea_iZarea_aZarea_br r r bbox_ious..&66rcCsRt|tjrt|}|d}tdddg}tdddg}|rN|||}|S)zStatistic normalize images.go@g ףp= ?gv/?gCl?gZd;O?gy&1?g?) isinstancerrarray)imgstatistic_normmeanstdr r r statistic_normalize_img/s   r!r cCs|dkrX|rTt|dkst|\}}}}||kr<||krt |D]0\}}x"t|D]}||krt ||df|d d }t ||df|d d }| |} ||df d }!||ddf| |||| ddf<d| |||| df<|r|d}"|"| |||| ddf<d|| |||| d|!f<nd| |||| d|!f<qWqW||k}#xtt|#jdD]`}xVt|#jdD]B}|#||sVq>x&t|D]}||krzqbt ||df|d d }t ||df|d d }| |} ||df d }!||ddf| |||| ddf<d| |||| df<|rb|d}"|"| |||| ddf<d|| |||| d|!f<nd| |||| d|!f<qbWq>Wq&Wtj |dgtjd}$tj |dgtjd}%tj |dgtjd}&t| dd ddfdg}'t| dd ddfddg}(|(|'dk}(|(jd|kr6|(|$d|(jd<n |(d|}$t| dd ddfdg})t| dd ddfddg}*|*|)dk}*|*jd|kr|*|%d|*jd<n |*d|}%t| d d ddfdg}+t| d d ddfddg},|,|+dk},|,jd|kr*|,|&d|,jd<n |,d|}&| d| d| d |$|%|&fS)u Introduction ------------ 对训练数据的ground truth box进行预处理 Parameters ---------- true_boxes: ground truth box 形状为[boxes, 5], x_min, y_min, x_max, y_max, class_id rr$rr rfloat32)dtypeint32.g@N cs>g|]6}tj|d|dt|dfddqS)rr r9r:)r;)rzerosr%).0l) anchor_mask grid_shapes num_classesr r sz*_preprocess_true_boxes..).rg).r )rg?)rr;)rrrrange expand_dimssizerrargmax enumeratefloorastypeindexr@r:reshape)- true_boxesanchorsin_shaperE max_boxes label_smoothlabel_smooth_factor iou_threshold num_layers input_shapeZboxes_xyZboxes_whZy_trueZ anchors_maxZ anchors_minZ valid_maskZwhZ boxes_maxZ boxes_minZ intersect_minZ intersect_maxZ intersect_whintersect_areabox_areaZ anchor_areaiouZ best_anchortnrBijkcsigmaZthreshold_anchorZ pad_gt_box0Z pad_gt_box1Z pad_gt_box2mask0Zgt_box0mask1gt_box1mask2gt_box2r )rCrDrEr _preprocess_true_boxesys $        $$$ $$$$ *         ric Cst|tjst|}|j\}}t||gtj}|\}}td||||fd}|||ft |}t |dd}t |j dkrtj |dd}tj|||gdd}|tj}||fS)zReshape image.r")r)r*T)rrr=)r)rr fromarrayrJrrr<r-resizer3r!r%rrI concatenaterNr:) image image_sizeZori_wZori_hori_image_shapehwr) image_datar r r _reshape_datas     rscCst| |}tdkr td|n dtd|}tdkrBtd|n dtd|}|dkrbtdt|tj}|d}|d|7<|d|ddkd8<|d|ddkd7<|d|9<|d|9<d||dk<d||dk<|d}|tj}t|tj}|S)zColor distortion.g?r go@).rr).r ).r) r cv2 setNumThreadscvtColorCOLOR_RGB2HSV_FULLrNruint8COLOR_HSV2RGB_FULL)rhuesatval device_numxrrr r r color_distortions$ ""    rcCs |tjS)N) transposerFLIP_LEFT_RIGHT)rr r r filp_pil_image(srcCs4t|jdkr0tj|dd}tj|||gdd}|S)Nrr=)r)r%rrrIrl)rr r r convert_gray_to_color,srcCs"t||}||ko ||kS)N)rminmax)min_ioumax_iouboxcrop_boxr\r r r _is_iou_satisfied_constraint3s rc Cs|r d} nd} dd||fg} x| D]} | \} } | dkrBtj n| } | dkrTtjn| } xt|D]|}t|t|td|d|td|d|}tdd}|r|dkrt||}t||}qt||}t||}n|}|}ttd||}ttd||}|jdkrt|}|ddddgft|t|||ddddgf<|ddddgft|t|||ddddgf<t dd||f}t | | ||tj sqdn| ||||fqdt d qdWq$W| S) z Choose candidate by constraints.))g?N)g333333?N)g?N)gffffff?N)g?N)Nr ))NNrNr g?rr$z!!!! annotation box is less than 1)rinfrHfloatr intrJcopydeepcopyrrnewaxisappend Exception) max_trialinput_winput_himage_wimage_hjitterruse_constraintseach_multiscale constraints candidates constraintrr_Znew_arscaler+r,dxdyt_boxrr r r _choose_candidate_by_constraints8s> 4     88rc Csx|rt|dkr0|tjdt|} n|tjdt|} | \} } }}t|}|ddddgft|t|| |ddddgf<|ddddgft|t|| |ddddgf<|r||ddddgf|ddddgf<|rn|t|dddf|dddfddk|dddf|dddfddk}|t|dddf|dddfd|k|dddf|dddfd|k}d|ddddf|ddddfdk<||dddf|dddf|k<||dddf|dddf|k<|dddf|dddf}|dddf|dddf}|t|dk|dk}|j ddkr||dt|<|| fSqW| st dtj | d gtj d dd||ffS) zCalculate correct boxes.r rNrr$g@gz0all candidates can not satisfied re-correct bboxr9)rr;) r%poprrr'rrr logical_andrrr@float64)rrrrrfliprbox_dataallow_outside_centerrTmosaic candidaterrr,r+rZbox_wZbox_hr r r _correct_bbox_by_candidatesps6    88(X,,,$$   rr#r FTc  Cs>t|tjst|}|j\} } |\}}tj|t||krL|d|}tdk}t |df}t d|||| | ||| d }t |||| | |||d|| d \}}|\}}}}t dd }| ||ft|}td ||fd }||||f|}|rt|}t|}t|}t||||| }t|dd }|tj}||fS) aCrop an image randomly with bounding box constraints. This data augmentation is used in training of Single Shot Multibox Detector [#]_. More details can be found in data augmentation section of the original paper. .. [#] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector. ECCV 2016.Ng?r9F) rrrrrrrrrT) rrrrrrrrrrTrr#)r)RGB)rr)r)rrrjrJrrshuffler%r r@rrr-rkr3newpasterrrrr!rNr:)rmrrrzr{r|image_input_sizerTrr}rrrrrrrrrrrrr,r+r)Z new_imagerrr r r _data_augsV             rc Cs*t||||||||| || d \}} || fS)zPreprocess data function.) rrzr{r|rrTr}rr)r) rmrrzr{r|rrTr input_sizer}rannor r r preprocess_fnsrcCs |j}t||d\}}|||fS)N)rn)Ztest_img_shapers)rmZimg_iddefault_configrror r r reshape_fnsrc@s6eZdZdZd ddZdeddfdd Zd d Zd S)MultiScaleTranszMulti scale transform.TcKsd|_g|_||_||_i|_td|_|j|jd|_tt |j|j|_ ||_ ||_ | |_| |_| |_| |_||_||_||_||_||_| |_||_dS)Nrg.A)seed_num)seed size_list resize_rate dataset_size size_dictrrgenerate_seed_list seed_listrceilresize_count_numr}rRrEmax_boxrUrVr multi_scalerz saturationvaluerr)selfr}rrrRrrzrrrrErUrVrrrkwargsr r r __init__s* zMultiScaleTrans.__init__ig.A)r icCsBg}t|x.t|D]"}t|d|d}||qW|S)Nrr )rrrHr'r)rZ init_seedr seed_rangerrrr r r rs  z"MultiScaleTrans.generate_seed_listc Cs|}t||j}|j||j||j}g}g}|j|ddkrnt |t |j } | |j|<|} |j| } xht ||D]Z\} } t| | |j|j|j|j|j|j| |j|j \} } || ddd|| qWt|t|fS)Nrrr ) get_epoch_numr get_batch_numrrrrrgetrrchoicerziprrzrrrrrr}rrrrrr)rimgsannos batch_info epoch_numsize_idxZseed_keyZret_imgsZ ret_annosnew_sizerrrrr r r __call__s"    zMultiScaleTrans.__call__N)T)__name__ __module__ __qualname____doc__rrrrr r r r rs rc  Csd} x|D]} t| |j||j|j|j|jd\} } }}}}| ||| <| ||| <|||| <|||| <|||| <|| || <| d} q WdS)z%Preprocess true box for multi-thread.r)rQrRrSrErTrUrVr N)ri anchor_scalesrErrUrV)rrrY result_indexbatch_bbox_true_1batch_bbox_true_2batch_bbox_true_3 batch_gt_box1 batch_gt_box2 batch_gt_box3r_r bbox_true_1 bbox_true_2 bbox_true_3rfrhgt_box3r r r thread_batch_preprocess_true_box$s        rc Csg}g}g}g}g}g}g} d} xtdt|| D]} xLt| D]@} |d|d|d|d|d|dq@W|| | | } tjt| ||| ||||||f d}|| |q2Wx| D] }|qWt |t |t |t |t |t |fS)z&Preprocess true box with multi-thread.rrN)targetargs) rHr%r threadingThreadrstartjoinrr)rrrYrrrrrrthreadssteprOrZ step_annor]r r r batch_preprocess_true_box7s4        rc Csg}g}g}g}g}g}xt|D]l} t| |j||j|j|j|jd\} } } } }}|| || || || ||||qWt|t|t|t|t|t|fS)zPreprocess true boxes.)rQrRrSrErTrUrV) rirrErrUrVrrr)rrrYrrrrrrrrrrrfrhrr r r batch_preprocess_true_box_singleXs&       r)rr)r)r )r4r5)r#r FT)rrrrnumpyrPILrrtr rr!r-r3rirsrrrrrrrrrrrrrr r r r s6   ! 0 8+ A 9!yolov/mindyolo/data/albumentations.py000066400000000000000000000072651443434675500204210ustar00rootroot00000000000000import platform import random import numpy as np import pkg_resources as pkg class Albumentations: # Implement Albumentations augmentation https://github.com/ultralytics/yolov5 # YOLOv5 Albumentations class (optional, only used if package is installed) def __init__(self, size=640): self.transform = None prefix = _colorstr('albumentations: ') try: import albumentations as A _check_version(A.__version__, '1.0.3', hard=True) # version requirement T = [ A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), A.Blur(p=0.01), A.MedianBlur(p=0.01), A.ToGray(p=0.01), A.CLAHE(p=0.01), A.RandomBrightnessContrast(p=0.0), A.RandomGamma(p=0.0), A.ImageCompression(quality_lower=75, p=0.0)] # transforms self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) print(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p), flush=True) print("[INFO] albumentations load success", flush=True) except ImportError: # package not installed, skip pass print("[WARNING] package not installed, albumentations load failed", flush=True) except Exception as e: print(f'{prefix}{e}', flush=True) print("[WARNING] albumentations load failed", flush=True) def __call__(self, im, labels, p=1.0): if self.transform and random.random() < p: new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) return im, labels def _check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): # Check version vs. required version current, minimum = (pkg.parse_version(x) for x in (current, minimum)) result = (current == minimum) if pinned else (current >= minimum) # bool s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string if hard: assert result, _emojis(s) # assert min requirements met if verbose and not result: print(s, flush=True) return result def _emojis(string=''): # Return platform-dependent emoji-safe version of string return string.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else string def _colorstr(*input): # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string colors = {'black': '\033[30m', # basic colors 'red': '\033[31m', 'green': '\033[32m', 'yellow': '\033[33m', 'blue': '\033[34m', 'magenta': '\033[35m', 'cyan': '\033[36m', 'white': '\033[37m', 'bright_black': '\033[90m', # bright colors 'bright_red': '\033[91m', 'bright_green': '\033[92m', 'bright_yellow': '\033[93m', 'bright_blue': '\033[94m', 'bright_magenta': '\033[95m', 'bright_cyan': '\033[96m', 'bright_white': '\033[97m', 'end': '\033[0m', # misc 'bold': '\033[1m', 'underline': '\033[4m'} return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] yolov/mindyolo/data/constants.py000066400000000000000000000010661443434675500174010ustar00rootroot00000000000000""" Constant data enhancement parameters of coco dataset """ # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ COCO80_TO_COCO91_CLASS = [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] yolov/mindyolo/data/copypaste.py000066400000000000000000000036321443434675500173750ustar00rootroot00000000000000import random import cv2 import numpy as np def copy_paste(img, labels, segments, probability=0.5): # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) n = len(segments) if probability and n: h, w, c = img.shape # height, width, channels im_new = np.zeros(img.shape, np.uint8) for j in random.sample(range(n), k=round(probability * n)): l, s = labels[j], segments[j] box = w - l[3], l[2], w - l[1], l[4] ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area if (ioa < 0.30).all(): # allow 30% obscuration of existing labels labels = np.concatenate((labels, [[l[0], *box]]), 0) segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) result = cv2.bitwise_and(src1=img, src2=im_new) result = cv2.flip(result, 1) # augment segments (flip left-right) i = result > 0 # pixels to replace # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug return img, labels, segments def bbox_ioa(box1, box2): # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 box2 = box2.transpose() # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] # Intersection area inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 # Intersection over box2 area return inter_area / box2_area yolov/mindyolo/data/dataset.py000066400000000000000000001034331443434675500170130ustar00rootroot00000000000000import os import cv2 import random import copy import glob import hashlib import numpy as np from pathlib import Path from tqdm import tqdm from PIL import Image, ExifTags from mindyolo.utils import logger from .copypaste import copy_paste from .perspective import random_perspective from .albumentations import Albumentations __all__ = ["COCODataset"] # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == 'Orientation': break class COCODataset: """ Load the COCO dataset (yolo format coco labels) Args: dataset_path (str): dataset label directory for dataset. for example: COCO_ROOT ├── train2017.txt ├── annotations │ └── instances_train2017.json ├── images │ └── train2017 │ ├── 000000000001.jpg │ └── 000000000002.jpg └── labels └── train2017 ├── 000000000001.txt └── 000000000002.txt dataset_path (str): ./coco/train2017.txt transforms (list): A list of images data enhancements that apply data enhancements on data set objects in order. """ def __init__(self, dataset_path='', img_size=640, transforms_dict=None, is_training=False, augment=False, rect=False, single_cls=False, batch_size=32, stride=32, pad=0.0 ): self.cache_version = 0.1 self.path = dataset_path self.img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes self.help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' self.img_size = img_size self.augment = augment self.rect = rect self.stride = stride self.transforms_dict = transforms_dict self.is_training = is_training if is_training: self.dataset_column_names = ['image', 'labels', 'img_files'] else: self.dataset_column_names = ['image', 'labels', 'img_files', 'hw_ori', 'hw_scale', 'pad'] try: f = [] # image files for p in self.path if isinstance(self.path, list) else [self.path]: p = Path(p) # os-agnostic if p.is_dir(): # dir f += glob.glob(str(p / '**' / '*.*'), recursive=True) elif p.is_file(): # file with open(p, 'r') as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path else: raise Exception(f'{p} does not exist') self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in self.img_formats]) assert self.img_files, f'No images found' except Exception as e: raise Exception(f'Error loading data from {self.path}: {e}\nSee {self.help_url}') # Check cache self.label_files = self._img2label_paths(self.img_files) # labels cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache.npy') # cached labels if cache_path.is_file(): cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict if cache['version'] == self.cache_version and \ cache['hash'] == self._get_hash(self.label_files + self.img_files): logger.info(f'Dataset Cache file hash/version check success.') logger.info(f'Load dataset cache from [{cache_path}] success.') else: logger.info(f'Dataset cache file hash/version check fail.') logger.info(f'Datset caching now...') cache, exists = self.cache_labels(cache_path), False # cache logger.info(f'Dataset caching success.') else: logger.info(f'No dataset cache available, caching now...') cache, exists = self.cache_labels(cache_path), False # cache logger.info(f'Dataset caching success.') # Display cache nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total if exists: d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" tqdm(None, desc=d, total=n, initial=n) # display cache results assert nf > 0 or not augment, f'No labels in {cache_path}. Can not train without labels. See {self.help_url}' # Read cache cache.pop('hash') # remove hash cache.pop('version') # remove version labels, shapes, self.segments = zip(*cache.values()) self.labels = list(labels) self.img_shapes = np.array(shapes, dtype=np.float64) self.img_files = list(cache.keys()) # update self.label_files = self._img2label_paths(cache.keys()) # update if single_cls: for x in self.labels: x[:, 0] = 0 n = len(labels) # number of images bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index nb = bi[-1] + 1 # number of batches self.batch = bi # batch index of image # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) self.imgs, self.img_hw_ori, self.indices = [None, ] * n, [None, ] * n, range(n) # Rectangular Train/Test if self.rect: # Sort by aspect ratio s = self.img_shapes # wh ar = s[:, 1] / s[:, 0] # aspect ratio irect = ar.argsort() self.img_files = [self.img_files[i] for i in irect] self.label_files = [self.label_files[i] for i in irect] self.labels = [self.labels[i] for i in irect] self.img_shapes = s[irect] # wh ar = ar[irect] # Set training image shapes shapes = [[1, 1]] * nb for i in range(nb): ari = ar[bi == i] mini, maxi = ari.min(), ari.max() if maxi < 1: shapes[i] = [maxi, 1] elif mini > 1: shapes[i] = [1, 1 / mini] self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride self.imgIds = [int(Path(im_file).stem) for im_file in self.img_files] def cache_labels(self, path=Path('./labels.cache')): # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == 'Orientation': break # Cache dataset labels, check images and read shapes x = {} # dict nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) for i, (im_file, lb_file) in enumerate(pbar): try: # verify images im = Image.open(im_file) im.verify() # PIL verify shape = self._exif_size(im) # image size segments = [] # instance segments assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' assert im.format.lower() in self.img_formats, f'invalid image format {im.format}' # verify labels if os.path.isfile(lb_file): nf += 1 # label found with open(lb_file, 'r') as f: l = [x.split() for x in f.read().strip().splitlines()] if any([len(x) > 8 for x in l]): # is segment classes = np.array([x[0] for x in l], dtype=np.float32) segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) l = np.concatenate((classes.reshape(-1, 1), self._segments2boxes(segments)), 1) # (cls, xywh) l = np.array(l, dtype=np.float32) if len(l): assert l.shape[1] == 5, 'labels require 5 columns each' assert (l >= 0).all(), 'negative labels' assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' else: ne += 1 # label empty l = np.zeros((0, 5), dtype=np.float32) else: nm += 1 # label missing l = np.zeros((0, 5), dtype=np.float32) x[im_file] = [l, shape, segments] except Exception as e: nc += 1 print(f'WARNING: Ignoring corrupted image and/or label {im_file}: {e}') pbar.desc = f"Scanning '{path.parent / path.stem}' images and labels... " \ f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" pbar.close() if nf == 0: print(f'WARNING: No labels found in {path}. See {self.help_url}') x['hash'] = self._get_hash(self.label_files + self.img_files) x['results'] = nf, nm, ne, nc, i + 1 x['version'] = self.cache_version # cache version np.save(path, x) # save for next time logger.info(f'New cache created: {path}') return x def __getitem__(self, index): index, image, labels, segment, hw_ori, hw_scale, pad = index, None, None, None, None, None, None for _i, ori_trans in enumerate(self.transforms_dict): _trans = ori_trans.copy() func_name, prob = _trans.pop('func_name'), _trans.pop('prob', 1.0) if random.random() < prob: if func_name == 'mosaic': image, labels = self.mosaic(index, **_trans) elif func_name == 'letterbox': image, hw_ori = self.load_image(index) labels = self.labels[index].copy() new_shape = self.img_size if not self.rect else self.batch_shapes[self.batch[index]] image, labels, hw_ori, hw_scale, pad = self.letterbox(image, labels, hw_ori, new_shape, **_trans) elif func_name == 'albumentations': if getattr(self, 'albumentations', None) is None: self.albumentations = Albumentations(size=self.img_size) image, labels = self.albumentations(image, labels, **_trans) elif func_name == 'get_img_and_label_yolov4': image, labels = self.get_img_and_label_yolov4(index) else: image, labels = getattr(self, func_name)(image, labels, **_trans) image = np.ascontiguousarray(image) if self.is_training: return image, labels, self.img_files[index] else: return image, labels, self.img_files[index], hw_ori, hw_scale, pad def __len__(self): return len(self.img_files) def load_image(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img = self.imgs[index] if img is None: # not cached path = self.img_files[index] img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h_ori, w_ori = img.shape[:2] # orig hw r = self.img_size / max(h_ori, w_ori) # resize image to img_size if r != 1: # always resize down, only resize up if training with augmentation interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR img = cv2.resize(img, (int(w_ori * r), int(h_ori * r)), interpolation=interp) return img, np.array([h_ori, w_ori]) # img, hw_original else: return self.imgs[index], self.img_hw_ori[index] # img, hw_original def load_samples(self, index): # loads images in a 4-mosaic labels4, segments4 = [], [] s = self.img_size mosaic_border = [-s // 2, -s // 2] yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in mosaic_border] # mosaic center x, y indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _ = self.load_image(index) (h, w) = img.shape[:2] # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) segments4.extend(segments) # Concat/clip labels labels4 = np.concatenate(labels4, 0) for x in (labels4[:, 1:], *segments4): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # Augment sample_labels, sample_images, sample_masks = self._sample_segments(img4, labels4, segments4, probability=0.5) return sample_labels, sample_images, sample_masks def mosaic(self, index, mosaic9_prob=0., copy_paste_prob=0.0, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0): assert mosaic9_prob >= 0. and mosaic9_prob <= 1. if random.random() < (1 - mosaic9_prob): return self.mosaic4(index, copy_paste_prob, degrees, translate, scale, shear, perspective) else: return self.mosaic9(index, copy_paste_prob, degrees, translate, scale, shear, perspective) def mosaic4(self, index, copy_paste_prob=0.0, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0): # loads images in a 4-mosaic labels4, segments4 = [], [] s = self.img_size mosaic_border = [-s // 2, -s // 2] yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in mosaic_border] # mosaic center x, y indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _ = self.load_image(index) (h, w) = img.shape[:2] # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) segments4.extend(segments) # Concat/clip labels labels4 = np.concatenate(labels4, 0) for x in (labels4[:, 1:], *segments4): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # Augment img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=copy_paste_prob) img4, labels4 = random_perspective(img4, labels4, segments4, degrees=degrees, translate=translate, scale=scale, shear=shear, perspective=perspective, border=mosaic_border) # border to remove return img4, labels4 def mosaic9(self, index, copy_paste_prob=0.0, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0): # loads images in a 9-mosaic labels9, segments9 = [], [] s = self.img_size mosaic_border = [-s // 2, -s // 2] indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices for i, index in enumerate(indices): # Load image img, _ = self.load_image(index) (h, w) = img.shape[:2] # place img in img9 if i == 0: # center img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles h0, w0 = h, w c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates elif i == 1: # top c = s, s - h, s + w, s elif i == 2: # top right c = s + wp, s - h, s + wp + w, s elif i == 3: # right c = s + w0, s, s + w0 + w, s + h elif i == 4: # bottom right c = s + w0, s + hp, s + w0 + w, s + hp + h elif i == 5: # bottom c = s + w0 - w, s + h0, s + w0, s + h0 + h elif i == 6: # bottom left c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h elif i == 7: # left c = s - w, s + h0 - h, s, s + h0 elif i == 8: # top left c = s - w, s + h0 - hp - h, s, s + h0 - hp padx, pady = c[:2] x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padx, pady) for x in segments] labels9.append(labels) segments9.extend(segments) # Image img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset yc, xc = [int(random.uniform(0, s)) for _ in mosaic_border] # mosaic center x, y img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] # Concat/clip labels labels9 = np.concatenate(labels9, 0) labels9[:, [1, 3]] -= xc labels9[:, [2, 4]] -= yc c = np.array([xc, yc]) # centers segments9 = [x - c for x in segments9] for x in (labels9[:, 1:], *segments9): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # Augment img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=copy_paste_prob) img9, labels9 = random_perspective(img9, labels9, segments9, degrees=degrees, translate=translate, scale=scale, shear=shear, perspective=perspective, border=mosaic_border) # border to remove return img9, labels9 def mixup(self, image, labels, alpha=8.0, beta=8.0, needed_mosaic=True): if needed_mosaic: mosaic_trans = None for _trans in self.transforms_dict: if _trans['func_name'] == 'mosaic': mosaic_trans = _trans.copy() break assert mosaic_trans is not None, "Mixup needed mosaic bug 'mosaic' not in transforms_dict" _, _ = mosaic_trans.pop('func_name'), mosaic_trans.pop('prob', 1.0) image2, labels2 = self.mosaic(random.randint(0, len(self.labels) - 1), **mosaic_trans) else: index2 = random.randint(0, len(self.labels) - 1) image2, _ = self.load_image(index2) labels2 = self.labels[index2] r = np.random.beta(alpha, beta) # mixup ratio, alpha=beta=8.0 image = (image * r + image2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) return image, labels def pastein(self, image, labels, num_sample=30): # load sample sample_labels, sample_images, sample_masks = [], [], [] while len(sample_labels) < num_sample: sample_labels_, sample_images_, sample_masks_ = \ self.load_samples(random.randint(0, len(self.labels) - 1)) sample_labels += sample_labels_ sample_images += sample_images_ sample_masks += sample_masks_ if len(sample_labels) == 0: break # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 h, w = image.shape[:2] # create random masks scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6 # image size fraction for s in scales: if random.random() < 0.2: continue mask_h = random.randint(1, int(h * s)) mask_w = random.randint(1, int(w * s)) # box xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h) box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) if len(labels): ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area else: ioa = np.zeros(1) if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin + 20) and ( ymax > ymin + 20): # allow 30% obscuration of existing labels sel_ind = random.randint(0, len(sample_labels) - 1) hs, ws, cs = sample_images[sel_ind].shape r_scale = min((ymax - ymin) / hs, (xmax - xmin) / ws) r_w = int(ws * r_scale) r_h = int(hs * r_scale) if (r_w > 10) and (r_h > 10): r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h)) r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h)) temp_crop = image[ymin:ymin + r_h, xmin:xmin + r_w] m_ind = r_mask > 0 if m_ind.astype(np.int).sum() > 60: temp_crop[m_ind] = r_image[m_ind] box = np.array([xmin, ymin, xmin + r_w, ymin + r_h], dtype=np.float32) if len(labels): labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0) else: labels = np.array([[sample_labels[sel_ind], *box]]) image[ymin:ymin + r_h, xmin:xmin + r_w] = temp_crop # Modify on the original image return image, labels def hsv_augment(self, image, labels, hgain=0.5, sgain=0.5, vgain=0.5): r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV)) dtype = image.dtype # uint8 x = np.arange(0, 256, dtype=np.int16) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) image = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR) # Modify on the original image return image, labels def fliplr(self, image, labels): # flip left-right image = np.fliplr(image) if len(labels): labels[:, 1] = 1 - labels[:, 1] return image, labels def flipud(self, image, labels): # flip up-down image = np.flipud(image) if len(labels): labels[:, 2] = 1 - labels[:, 2] return image, labels def letterbox(self, image, labels, hw_ori, new_shape, scaleup=False, color=(114, 114, 114)): # Resize and pad image while meeting stride-multiple constraints shape = image.shape[:2] # current shape [height, width] h, w = shape[:] h0, w0 = hw_ori hw_scale = np.array([h / h0, w / w0]) if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) if not scaleup: # only scale down, do not scale up (for better test mAP) r = min(r, 1.0) # Compute padding new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding dw /= 2 # divide padding into 2 sides dh /= 2 hw_pad = np.array([dh, dw]) if shape[::-1] != new_unpad: # resize image = cv2.resize(image, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border # convert labels if labels.size: # normalized xywh to pixel xyxy format labels[:, 1:] = xywhn2xyxy(labels[:, 1:], r * w, r * h, padw=hw_pad[1], padh=hw_pad[0]) return image, labels, hw_ori, hw_scale, hw_pad def label_norm(self, image, labels, xyxy2xywh_=True): if len(labels) == 0: return image, labels if xyxy2xywh_: labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh labels[:, [2, 4]] /= image.shape[0] # normalized height 0-1 labels[:, [1, 3]] /= image.shape[1] # normalized width 0-1 return image, labels def label_pad(self, image, labels, padding_size=160, padding_value=-1): # create fixed label, avoid dynamic shape problem. labels_out = np.full((padding_size, 6), padding_value, dtype=np.float32) nL = len(labels) if nL: labels_out[:min(nL, padding_size), 0:1] = 0. labels_out[:min(nL, padding_size), 1:] = labels[:min(nL, padding_size), :] return image, labels_out def image_norm(self, image, labels, scale=255.): image = image.astype(np.float32, copy=False) image /= scale return image, labels def image_transpose(self, image, labels, bgr2rgb=True, hwc2chw=True): if bgr2rgb: image = image[:, :, ::-1] if hwc2chw: image = image.transpose(2, 0, 1) return image, labels def _sample_segments(self, img, labels, segments, probability=0.5): # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) n = len(segments) sample_labels = [] sample_images = [] sample_masks = [] if probability and n: h, w, c = img.shape # height, width, channels for j in random.sample(range(n), k=round(probability * n)): l, s = labels[j], segments[j] box = l[1].astype(int).clip(0, w - 1), l[2].astype(int).clip(0, h - 1), l[3].astype(int).clip(0, w - 1), \ l[ 4].astype(int).clip(0, h - 1) if (box[2] <= box[0]) or (box[3] <= box[1]): continue sample_labels.append(l[0]) mask = np.zeros(img.shape, np.uint8) cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) sample_masks.append(mask[box[1]:box[3], box[0]:box[2], :]) result = cv2.bitwise_and(src1=img, src2=mask) i = result > 0 # pixels to replace mask[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug sample_images.append(mask[box[1]:box[3], box[0]:box[2], :]) return sample_labels, sample_images, sample_masks def _img2label_paths(self, img_paths): # Define label paths as a function of image paths sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] def _get_hash(self, paths): # Returns a single hash value of a list of paths (files or dirs) size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes h = hashlib.md5(str(size).encode()) # hash sizes h.update(''.join(paths).encode()) # hash paths return h.hexdigest() # return hash def _exif_size(self, img): # Returns exif-corrected PIL size s = img.size # (width, height) try: rotation = dict(img._getexif().items())[orientation] if rotation == 6: # rotation 270 s = (s[1], s[0]) elif rotation == 8: # rotation 90 s = (s[1], s[0]) except: pass return s def _segments2boxes(self, segments): # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) boxes = [] for s in segments: x, y = s.T # segment xy boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy return xyxy2xywh(np.array(boxes)) # cls, xywh @staticmethod def train_collate_fn(imgs, labels, path, batch_info): for i, l in enumerate(labels): l[:, 0] = i # add target image index for build_targets() return np.stack(imgs, 0), np.stack(labels, 0), path @staticmethod def test_collate_fn(imgs, labels, path, hw_ori, hw_scale, pad, batch_info): for i, l in enumerate(labels): l[:, 0] = i # add target image index for build_targets() return np.stack(imgs, 0), np.stack(labels, 0), path, \ np.stack(hw_ori, 0), np.stack(hw_scale, 0), np.stack(pad, 0) def bbox_ioa(box1, box2): # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 box2 = box2.transpose() # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] # Intersection area inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 # Intersection over box2 area return inter_area / box2_area def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = np.copy(x) y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y return y def xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = np.copy(x) y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center y[:, 2] = x[:, 2] - x[:, 0] # width y[:, 3] = x[:, 3] - x[:, 1] # height return y def xyn2xy(x, w=640, h=640, padw=0, padh=0): # Convert normalized segments into pixel segments, shape (n,2) y = np.copy(x) y[:, 0] = w * x[:, 0] + padw # top left x y[:, 1] = h * x[:, 1] + padh # top left y return y yolov/mindyolo/data/loader.py000066400000000000000000000053231443434675500166330ustar00rootroot00000000000000""" Create dataloader """ import cv2 import multiprocessing import mindspore.dataset as de from mindyolo.utils import logger from mindyolo.data.yolov4_transforms import MultiScaleTrans __all__ = ["create_loader"] def create_loader( dataset, batch_collate_fn, dataset_column_names, batch_size, epoch_size=1, rank=0, rank_size=1, num_parallel_workers=8, shuffle=True, drop_remainder=False, python_multiprocessing=False, ): r"""Creates dataloader. Applies operations such as transform and batch to the `ms.dataset.Dataset` object created by the `create_dataset` function to get the dataloader. Args: dataset (COCODataset): dataset object created by `create_dataset`. batch_size (int or function): The number of rows each batch is created with. An int or callable object which takes exactly 1 parameter, BatchInfo. drop_remainder (bool, optional): Determines whether to drop the last block whose data row number is less than batch size (default=False). If True, and if there are less than batch_size rows available to make the last batch, then those rows will be dropped and not propagated to the child node. num_parallel_workers (int, optional): Number of workers(threads) to process the dataset in parallel (default=None). python_multiprocessing (bool, optional): Parallelize Python operations with multiple worker processes. This option could be beneficial if the Python operation is computational heavy (default=False). Returns: BatchDataset, dataset batched. """ cv2.setNumThreads(2) de.config.set_seed(1236517205 + rank) cores = multiprocessing.cpu_count() num_parallel_workers = min(int(cores / rank_size), num_parallel_workers) logger.info(f"Dataloader num parallel workers: [{num_parallel_workers}]") if rank_size > 1: ds = de.GeneratorDataset(dataset, column_names=dataset_column_names, num_parallel_workers=min(8, num_parallel_workers), shuffle=shuffle, python_multiprocessing=python_multiprocessing, num_shards=rank_size, shard_id=rank) else: ds = de.GeneratorDataset(dataset, column_names=dataset_column_names, num_parallel_workers=min(32, num_parallel_workers), shuffle=shuffle, python_multiprocessing=python_multiprocessing) ds = ds.batch(batch_size, per_batch_map=batch_collate_fn, input_columns=dataset_column_names, drop_remainder=drop_remainder) ds = ds.repeat(epoch_size) return ds yolov/mindyolo/data/perspective.py000066400000000000000000000106551443434675500177220ustar00rootroot00000000000000import random import math import cv2 import numpy as np def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): height = img.shape[0] + border[0] * 2 # shape(h,w,c) width = img.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -img.shape[1] / 2 # x translation (pixels) C[1, 2] = -img.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) s = random.uniform(1 - scale, 1.1 + scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) # Transform label coordinates n = len(targets) if n: use_segments = any(x.any() for x in segments) new = np.zeros((n, 4)) if use_segments: # warp segments segments = _resample_segments(segments) # upsample for i, segment in enumerate(segments): xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = _segment2box(xy, width, height) else: # warp boxes xy = np.ones((n * 4, 3)) xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates i = _box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] targets[:, 1:5] = new[i] return img, targets def _resample_segments(segments, n=1000): # Up-sample an (n,2) segment for i, s in enumerate(segments): s = np.concatenate((s, s[0:1, :]), axis=0) x = np.linspace(0, len(s) - 1, n) xp = np.arange(len(s)) segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy return segments def _segment2box(segment, width=640, height=640): # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) x, y, = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy def _box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1] ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates yolov/mindyolo/data/poly.py000066400000000000000000000061401443434675500163460ustar00rootroot00000000000000import cv2 import numpy as np def show_img_with_bbox(data_dict, classes): """ Image and bboxes visualization. If input multiple images, apply on the first image only. Args: record: related data of images classes: all categories of the whole dataset Returns: an image with detection boxes and categories """ img, labels = data_dict['image'][0], data_dict['labels'][0] labels = labels[labels[:, 1] > 0] # filter invalid label category_ids = labels[:, 1] bboxes = labels[:, 2:] categories = [classes[category_id[0]] for category_id in category_ids] bboxes = bboxes[category_ids[:, 0] >= 0] for bbox, category in zip(bboxes, categories): bbox = bbox.astype(np.int32) categories_size = cv2.getTextSize(category + '0', cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0] color = ((np.random.random((3,)) * 0.6 + 0.4) * 255).astype(np.uint8) color = np.array(color).astype(np.int32).tolist() if bbox[1] - categories_size[1] - 3 < 0: cv2.rectangle(img, (bbox[0], bbox[1] + 2), (bbox[0] + categories_size[0], bbox[1] + categories_size[1] + 3), color=color, thickness=-1 ) cv2.putText(img, category, (bbox[0], bbox[1] + categories_size[1] + 3), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1 ) else: cv2.rectangle(img, (bbox[0], bbox[1] - categories_size[1] - 3), (bbox[0] + categories_size[0], bbox[1] - 3), color, thickness=-1 ) cv2.putText(img, category, (bbox[0], bbox[1] - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1 ) cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, thickness=2) return img def show_img_with_poly(data_dict): """ Image and polygons visualization. If input multiple images, apply on the first image only. Args: record: related data of images Returns: an image with polygons """ img, labels, polys = data_dict['image'][0], data_dict['labels'][0], data_dict['segments'][0] labels = labels[labels[:, 1] > 0] # filter invalid label category_ids = labels[:, 1] i = category_ids[:, 0] >= 0 real_polys = [] for j, value in enumerate(i): if value: real_polys.append(polys[j]) for poly in real_polys: poly = poly.astype(np.int32) color = ((np.random.random((3,)) * 0.6 + 0.4) * 255).astype(np.uint8) color = np.array(color).astype(np.int32).tolist() img = cv2.drawContours(img, [poly], -1, color, 2) return img yolov/mindyolo/data/yolov4_transforms.py000066400000000000000000000614311443434675500211010ustar00rootroot00000000000000"""Preprocess dataset.""" import random import threading import copy import numpy as np from PIL import Image import cv2 def _rand(a=0., b=1.): return np.random.rand() * (b - a) + a def bbox_iou(bbox_a, bbox_b, offset=0): """Calculate Intersection-Over-Union(IOU) of two bounding boxes. Parameters ---------- bbox_a : numpy.ndarray An ndarray with shape :math:`(N, 4)`. bbox_b : numpy.ndarray An ndarray with shape :math:`(M, 4)`. offset : float or int, default is 0 The ``offset`` is used to control the whether the width(or height) is computed as (right - left + ``offset``). Note that the offset must be 0 for normalized bboxes, whose ranges are in ``[0, 1]``. Returns ------- numpy.ndarray An ndarray with shape :math:`(N, M)` indicates IOU between each pairs of bounding boxes in `bbox_a` and `bbox_b`. """ if bbox_a.shape[1] < 4 or bbox_b.shape[1] < 4: raise IndexError("Bounding boxes axis 1 must have at least length 4") tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2]) br = np.minimum(bbox_a[:, None, 2:4], bbox_b[:, 2:4]) area_i = np.prod(br - tl + offset, axis=2) * (tl < br).all(axis=2) area_a = np.prod(bbox_a[:, 2:4] - bbox_a[:, :2] + offset, axis=1) area_b = np.prod(bbox_b[:, 2:4] - bbox_b[:, :2] + offset, axis=1) return area_i / (area_a[:, None] + area_b - area_i) def statistic_normalize_img(img, statistic_norm): """Statistic normalize images.""" # img: RGB if isinstance(img, Image.Image): img = np.array(img) img = img/255. # Computed from random subset of ImageNet training images mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) if statistic_norm: img = (img - mean) / std return img def get_interp_method(interp, sizes=()): """ Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp method selection and a auto-estimation method. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic or Bilinear. Args: interp (int): Interpolation method for all resizing operations. - 0: Nearest Neighbors Interpolation. - 1: Bilinear interpolation. - 2: Bicubic interpolation over 4x4 pixel neighborhood. - 3: Nearest Neighbors. Originally it should be Area-based, as we cannot find Area-based, so we use NN instead. Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). - 4: Lanczos interpolation over 8x8 pixel neighborhood. - 9: Cubic for enlarge, area for shrink, bilinear for others. - 10: Random select from interpolation method mentioned above. sizes (tuple): Format should like (old_height, old_width, new_height, new_width), if None provided, auto(9) will return Area(2) anyway. Default: () Returns: int, interp method from 0 to 4. """ if interp == 9: if sizes: assert len(sizes) == 4 oh, ow, nh, nw = sizes if nh > oh and nw > ow: return 2 if nh < oh and nw < ow: return 0 return 1 return 2 if interp == 10: return random.randint(0, 4) if interp not in (0, 1, 2, 3, 4): raise ValueError('Unknown interp method %d' % interp) return interp def pil_image_reshape(interp): """Reshape pil image.""" reshape_type = { 0: Image.NEAREST, 1: Image.BILINEAR, 2: Image.BICUBIC, 3: Image.NEAREST, 4: Image.LANCZOS, } return reshape_type[interp] def _preprocess_true_boxes(true_boxes, anchors, in_shape, num_classes, max_boxes, label_smooth, label_smooth_factor=0.1, iou_threshold=0.213): """ Introduction ------------ 对训练数据的ground truth box进行预处理 Parameters ---------- true_boxes: ground truth box 形状为[boxes, 5], x_min, y_min, x_max, y_max, class_id """ anchors = np.array(anchors) num_layers = anchors.shape[0] // 3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(in_shape, dtype='int32') boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2. # trans to box center point boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] # input_shape is [h, w] true_boxes[..., 0:2] = boxes_xy / input_shape[::-1] true_boxes[..., 2:4] = boxes_wh / input_shape[::-1] # true_boxes = [xywh] grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8] # grid_shape [h, w] y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), dtype='float32') for l in range(num_layers)] # y_true [gridy, gridx] # 这里扩充维度是为了后面应用广播计算每个图中所有box的anchor互相之间的iou anchors = np.expand_dims(anchors, 0) anchors_max = anchors / 2. anchors_min = -anchors_max # 因为之前对box做了padding, 因此需要去除全0行 valid_mask = boxes_wh[..., 0] > 0 wh = boxes_wh[valid_mask] if wh.size != 0: # 为了应用广播扩充维度 wh = np.expand_dims(wh, -2) # wh 的shape为[box_num, 1, 2] # move to original point to compare, and choose the best layer-anchor to set boxes_max = wh / 2. boxes_min = -boxes_max intersect_min = np.maximum(boxes_min, anchors_min) intersect_max = np.minimum(boxes_max, anchors_max) intersect_wh = np.maximum(intersect_max - intersect_min, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) # 找出和ground truth box的iou最大的anchor box, # 然后将对应不同比例的负责该ground turth box 的位置置为ground truth box坐标 best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32') # grid_y j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32') # grid_x k = anchor_mask[l].index(n) c = true_boxes[t, 4].astype('int32') y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4] y_true[l][j, i, k, 4] = 1. # lable-smooth if label_smooth: sigma = label_smooth_factor / (num_classes - 1) y_true[l][j, i, k, 5:] = sigma y_true[l][j, i, k, 5 + c] = 1 - label_smooth_factor else: y_true[l][j, i, k, 5 + c] = 1. threshold_anchor = (iou > iou_threshold) for t in range(threshold_anchor.shape[0]): for n in range(threshold_anchor.shape[1]): if not threshold_anchor[t][n]: continue for l in range(num_layers): if n not in anchor_mask[l]: continue i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32') # grid_y j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32') # grid_x k = anchor_mask[l].index(n) c = true_boxes[t, 4].astype('int32') y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4] y_true[l][j, i, k, 4] = 1. # lable-smooth if label_smooth: sigma = label_smooth_factor / (num_classes - 1) y_true[l][j, i, k, 5:] = sigma y_true[l][j, i, k, 5 + c] = 1 - label_smooth_factor else: y_true[l][j, i, k, 5 + c] = 1. # pad_gt_boxes for avoiding dynamic shape pad_gt_box0 = np.zeros(shape=[max_boxes, 4], dtype=np.float32) pad_gt_box1 = np.zeros(shape=[max_boxes, 4], dtype=np.float32) pad_gt_box2 = np.zeros(shape=[max_boxes, 4], dtype=np.float32) mask0 = np.reshape(y_true[0][..., 4:5], [-1]) gt_box0 = np.reshape(y_true[0][..., 0:4], [-1, 4]) # gt_box [boxes, [x,y,w,h]] gt_box0 = gt_box0[mask0 == 1] # gt_box0: get all boxes which have object if gt_box0.shape[0] < max_boxes: pad_gt_box0[:gt_box0.shape[0]] = gt_box0 else: pad_gt_box0 = gt_box0[:max_boxes] # gt_box0.shape[0]: total number of boxes in gt_box0 # top N of pad_gt_box0 is real box, and after are pad by zero mask1 = np.reshape(y_true[1][..., 4:5], [-1]) gt_box1 = np.reshape(y_true[1][..., 0:4], [-1, 4]) gt_box1 = gt_box1[mask1 == 1] if gt_box1.shape[0] < max_boxes: pad_gt_box1[:gt_box1.shape[0]] = gt_box1 else: pad_gt_box1 = gt_box1[:max_boxes] mask2 = np.reshape(y_true[2][..., 4:5], [-1]) gt_box2 = np.reshape(y_true[2][..., 0:4], [-1, 4]) gt_box2 = gt_box2[mask2 == 1] if gt_box2.shape[0] < max_boxes: pad_gt_box2[:gt_box2.shape[0]] = gt_box2 else: pad_gt_box2 = gt_box2[:max_boxes] return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2 def _reshape_data(image, image_size): """Reshape image.""" if not isinstance(image, Image.Image): image = Image.fromarray(image) ori_w, ori_h = image.size ori_image_shape = np.array([ori_w, ori_h], np.int32) # original image shape fir:H sec:W h, w = image_size interp = get_interp_method(interp=9, sizes=(ori_h, ori_w, h, w)) image = image.resize((w, h), pil_image_reshape(interp)) image_data = statistic_normalize_img(image, statistic_norm=True) if len(image_data.shape) == 2: image_data = np.expand_dims(image_data, axis=-1) image_data = np.concatenate([image_data, image_data, image_data], axis=-1) image_data = image_data.astype(np.float32) return image_data, ori_image_shape def color_distortion(img, hue, sat, val, device_num): """Color distortion.""" hue = _rand(-hue, hue) sat = _rand(1, sat) if _rand() < .5 else 1 / _rand(1, sat) val = _rand(1, val) if _rand() < .5 else 1 / _rand(1, val) if device_num != 1: cv2.setNumThreads(1) x = cv2.cvtColor(img, cv2.COLOR_RGB2HSV_FULL) x = x / 255. x[..., 0] += hue x[..., 0][x[..., 0] > 1] -= 1 x[..., 0][x[..., 0] < 0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x > 1] = 1 x[x < 0] = 0 x = x * 255. x = x.astype(np.uint8) image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB_FULL) return image_data def filp_pil_image(img): return img.transpose(Image.FLIP_LEFT_RIGHT) def convert_gray_to_color(img): if len(img.shape) == 2: img = np.expand_dims(img, axis=-1) img = np.concatenate([img, img, img], axis=-1) return img def _is_iou_satisfied_constraint(min_iou, max_iou, box, crop_box): iou = bbox_iou(box, crop_box) return min_iou <= iou.min() and max_iou >= iou.max() def _choose_candidate_by_constraints(max_trial, input_w, input_h, image_w, image_h, jitter, box, use_constraints, each_multiscale): """Choose candidate by constraints.""" if use_constraints: constraints = ( (0.1, None), (0.3, None), (0.5, None), (0.7, None), (0.9, None), (None, 1), ) else: constraints = ( (None, None), ) # add default candidate candidates = [(0, 0, input_w, input_h)] for constraint in constraints: min_iou, max_iou = constraint min_iou = -np.inf if min_iou is None else min_iou max_iou = np.inf if max_iou is None else max_iou for _ in range(max_trial): # box_data should have at least one box new_ar = float(input_w) / float(input_h) * _rand(1 - jitter, 1 + jitter) / _rand(1 - jitter, 1 + jitter) scale = _rand(0.25, 2) if each_multiscale: if new_ar < 1: nh = int(scale * input_h) nw = int(nh * new_ar) else: nw = int(scale * input_w) nh = int(nw / new_ar) else: nh = input_h nw = input_w dx = int(_rand(0, input_w - nw)) dy = int(_rand(0, input_h - nh)) if box.size > 0: t_box = copy.deepcopy(box) t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(image_w) + dx t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(image_h) + dy crop_box = np.array((0, 0, input_w, input_h)) if not _is_iou_satisfied_constraint(min_iou, max_iou, t_box, crop_box[np.newaxis]): continue else: candidates.append((dx, dy, nw, nh)) else: raise Exception("!!! annotation box is less than 1") return candidates def _correct_bbox_by_candidates(candidates, input_w, input_h, image_w, image_h, flip, box, box_data, allow_outside_center, max_boxes, mosaic): """Calculate correct boxes.""" while candidates: if len(candidates) > 1: # ignore default candidate which do not crop candidate = candidates.pop(np.random.randint(1, len(candidates))) else: candidate = candidates.pop(np.random.randint(0, len(candidates))) dx, dy, nw, nh = candidate t_box = copy.deepcopy(box) t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(image_w) + dx t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(image_h) + dy if flip: t_box[:, [0, 2]] = input_w - t_box[:, [2, 0]] if allow_outside_center: pass else: t_box = t_box[np.logical_and((t_box[:, 0] + t_box[:, 2])/2. >= 0., (t_box[:, 1] + t_box[:, 3])/2. >= 0.)] t_box = t_box[np.logical_and((t_box[:, 0] + t_box[:, 2]) / 2. <= input_w, (t_box[:, 1] + t_box[:, 3]) / 2. <= input_h)] # recorrect x, y for case x,y < 0 reset to zero, after dx and dy, some box can smaller than zero t_box[:, 0:2][t_box[:, 0:2] < 0] = 0 # recorrect w,h not higher than input size t_box[:, 2][t_box[:, 2] > input_w] = input_w t_box[:, 3][t_box[:, 3] > input_h] = input_h box_w = t_box[:, 2] - t_box[:, 0] box_h = t_box[:, 3] - t_box[:, 1] # discard invalid box: w or h smaller than 1 pixel t_box = t_box[np.logical_and(box_w > 1, box_h > 1)] if t_box.shape[0] > 0: # break if number of find t_box box_data[: len(t_box)] = t_box return box_data, candidate if not mosaic: raise Exception('all candidates can not satisfied re-correct bbox') return np.zeros(shape=[max_boxes, 5], dtype=np.float64), (0, 0, nw, nh) def _data_aug(image, box, jitter, hue, sat, val, image_input_size, max_boxes, max_trial=10, device_num=1, mosaic=False, each_multiscale=True): """Crop an image randomly with bounding box constraints. This data augmentation is used in training of Single Shot Multibox Detector [#]_. More details can be found in data augmentation section of the original paper. .. [#] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector. ECCV 2016.""" if not isinstance(image, Image.Image): image = Image.fromarray(image) image_w, image_h = image.size input_h, input_w = image_input_size np.random.shuffle(box) if len(box) > max_boxes: box = box[:max_boxes] flip = _rand() < .5 box_data = np.zeros((max_boxes, 5)) candidates = _choose_candidate_by_constraints(use_constraints=False, max_trial=max_trial, input_w=input_w, input_h=input_h, image_w=image_w, image_h=image_h, jitter=jitter, box=box, each_multiscale=each_multiscale) box_data, candidate = _correct_bbox_by_candidates(candidates=candidates, input_w=input_w, input_h=input_h, image_w=image_w, image_h=image_h, flip=flip, box=box, box_data=box_data, allow_outside_center=True, max_boxes=max_boxes, mosaic=mosaic) dx, dy, nw, nh = candidate interp = get_interp_method(interp=10) image = image.resize((nw, nh), pil_image_reshape(interp)) # place image, gray color as back graoud new_image = Image.new('RGB', (input_w, input_h), (128, 128, 128)) new_image.paste(image, (dx, dy)) image = new_image if flip: image = filp_pil_image(image) image = np.array(image) image = convert_gray_to_color(image) image_data = color_distortion(image, hue, sat, val, device_num) image_data = statistic_normalize_img(image_data, statistic_norm=True) image_data = image_data.astype(np.float32) return image_data, box_data def preprocess_fn(image, box, hue, sat, val, jitter, max_boxes, mosaic, input_size, device_num, each_multiscale): """Preprocess data function.""" image, anno = _data_aug(image, box, jitter=jitter, hue=hue, sat=sat, val=val, image_input_size=input_size, max_boxes=max_boxes, device_num=device_num, mosaic=mosaic, each_multiscale=each_multiscale) return image, anno def reshape_fn(image, img_id, default_config): input_size = default_config.test_img_shape image, ori_image_shape = _reshape_data(image, image_size=input_size) return image, ori_image_shape, img_id class MultiScaleTrans: """Multi scale transform.""" def __init__(self, device_num, dataset_size, resize_rate, anchors, multi_scale, hue, saturation, value, jitter, num_classes, label_smooth, label_smooth_factor, max_box, mosaic, each_multiscale=True, **kwargs): self.seed = 0 self.size_list = [] self.resize_rate = resize_rate self.dataset_size = dataset_size self.size_dict = {} self.seed_num = int(1e6) self.seed_list = self.generate_seed_list(seed_num=self.seed_num) self.resize_count_num = int(np.ceil(self.dataset_size / self.resize_rate)) self.device_num = device_num self.anchors = anchors self.num_classes = num_classes self.max_box = max_box self.label_smooth = label_smooth self.label_smooth_factor = label_smooth_factor self.each_multiscale = each_multiscale self.multi_scale = multi_scale self.hue = hue self.saturation = saturation self.value = value self.jitter = jitter self.mosaic = mosaic def generate_seed_list(self, init_seed=1234, seed_num=int(1e6), seed_range=(1, 1000)): seed_list = [] random.seed(init_seed) for _ in range(seed_num): seed = random.randint(seed_range[0], seed_range[1]) seed_list.append(seed) return seed_list def __call__(self, imgs, annos, batch_info): epoch_num = batch_info.get_epoch_num() size_idx = int(batch_info.get_batch_num() / self.resize_rate) seed_key = self.seed_list[(epoch_num * self.resize_count_num + size_idx) % self.seed_num] ret_imgs = [] ret_annos = [] if self.size_dict.get(seed_key, None) is None: random.seed(seed_key) new_size = random.choice(self.multi_scale) self.size_dict[seed_key] = new_size seed = seed_key input_size = self.size_dict[seed] for img, anno in zip(imgs, annos): img, anno = preprocess_fn(img, anno, self.hue, self.saturation, self.value, self.jitter, self.max_box, self.mosaic, input_size, self.device_num, self.each_multiscale) ret_imgs.append(img.transpose(2, 0, 1).copy()) ret_annos.append(anno) return np.array(ret_imgs), np.array(ret_annos) def thread_batch_preprocess_true_box(annos, default_config, input_shape, result_index, batch_bbox_true_1, batch_bbox_true_2, batch_bbox_true_3, batch_gt_box1, batch_gt_box2, batch_gt_box3): """Preprocess true box for multi-thread.""" i = 0 for anno in annos: bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \ _preprocess_true_boxes(true_boxes=anno, anchors=default_config.anchor_scales, in_shape=input_shape, num_classes=default_config.num_classes, max_boxes=default_config.max_box, label_smooth=default_config.label_smooth, label_smooth_factor=default_config.label_smooth_factor) batch_bbox_true_1[result_index + i] = bbox_true_1 batch_bbox_true_2[result_index + i] = bbox_true_2 batch_bbox_true_3[result_index + i] = bbox_true_3 batch_gt_box1[result_index + i] = gt_box1 batch_gt_box2[result_index + i] = gt_box2 batch_gt_box3[result_index + i] = gt_box3 i = i + 1 def batch_preprocess_true_box(annos, default_config, input_shape): """Preprocess true box with multi-thread.""" batch_bbox_true_1 = [] batch_bbox_true_2 = [] batch_bbox_true_3 = [] batch_gt_box1 = [] batch_gt_box2 = [] batch_gt_box3 = [] threads = [] step = 4 for index in range(0, len(annos), step): for _ in range(step): batch_bbox_true_1.append(None) batch_bbox_true_2.append(None) batch_bbox_true_3.append(None) batch_gt_box1.append(None) batch_gt_box2.append(None) batch_gt_box3.append(None) step_anno = annos[index: index + step] t = threading.Thread(target=thread_batch_preprocess_true_box, args=(step_anno, default_config, input_shape, index, batch_bbox_true_1, batch_bbox_true_2, batch_bbox_true_3, batch_gt_box1, batch_gt_box2, batch_gt_box3)) t.start() threads.append(t) for t in threads: t.join() return np.array(batch_bbox_true_1), np.array(batch_bbox_true_2), np.array(batch_bbox_true_3), \ np.array(batch_gt_box1), np.array(batch_gt_box2), np.array(batch_gt_box3) def batch_preprocess_true_box_single(annos, default_config, input_shape): """Preprocess true boxes.""" batch_bbox_true_1 = [] batch_bbox_true_2 = [] batch_bbox_true_3 = [] batch_gt_box1 = [] batch_gt_box2 = [] batch_gt_box3 = [] for anno in annos: bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \ _preprocess_true_boxes(true_boxes=anno, anchors=default_config.anchor_scales, in_shape=input_shape, num_classes=default_config.num_classes, max_boxes=default_config.max_box, label_smooth=default_config.label_smooth, label_smooth_factor=default_config.label_smooth_factor) batch_bbox_true_1.append(bbox_true_1) batch_bbox_true_2.append(bbox_true_2) batch_bbox_true_3.append(bbox_true_3) batch_gt_box1.append(gt_box1) batch_gt_box2.append(gt_box2) batch_gt_box3.append(gt_box3) return np.array(batch_bbox_true_1), np.array(batch_bbox_true_2), np.array(batch_bbox_true_3), \ np.array(batch_gt_box1), np.array(batch_gt_box2), np.array(batch_gt_box3) yolov/mindyolo/models/000077500000000000000000000000001443434675500153625ustar00rootroot00000000000000yolov/mindyolo/models/.ipynb_checkpoints/000077500000000000000000000000001443434675500211535ustar00rootroot00000000000000yolov/mindyolo/models/.ipynb_checkpoints/model_factory-checkpoint.py000066400000000000000000000133641443434675500265100ustar00rootroot00000000000000import os import math from copy import deepcopy from mindspore import nn, ops, load_checkpoint, load_param_into_net from .registry import is_model, model_entrypoint from .layers import * from .heads import * from mindyolo.utils import logger __all__ = [ 'create_model', 'build_model_from_cfg' ] def create_model( model_name: str, model_cfg: dict = None, in_channels: int = 3, num_classes: int = 80, checkpoint_path: str = '', **kwargs): model_args = dict(cfg=model_cfg, num_classes=num_classes, in_channels=in_channels) kwargs = {k: v for k, v in kwargs.items() if v is not None} if not is_model(model_name): raise RuntimeError(f'Unknown model {model_name}') create_fn = model_entrypoint(model_name) model = create_fn(**model_args, **kwargs) if os.path.exists(checkpoint_path): checkpoint_param = load_checkpoint(checkpoint_path) load_param_into_net(model, checkpoint_param) logger.info(f'create model load checkpoint from [{checkpoint_path}] success.') return model # Tools: build model from yaml cfg def build_model_from_cfg(**kwargs): return Model(**kwargs) class Model(nn.Cell): def __init__(self, model_cfg, in_channels=3, num_classes=80, sync_bn=False): super(Model, self).__init__() self.model, self.save, self.layers_param = parse_model(deepcopy(model_cfg), ch=[in_channels], nc=num_classes, sync_bn=sync_bn) # Recompute if hasattr(model_cfg, 'recompute') and model_cfg.recompute and model_cfg.recompute_layers > 0: for i in range(model_cfg.recompute_layers): self.model[i].recompute() print(f"Turn on recompute, and the results of the first {model_cfg.recompute_layers} layers " f"will be recomputed.") def construct(self, x): y, dt = (), () # outputs for i in range(len(self.model)): m = self.model[i] iol, f, _, _ = self.layers_param[i] # iol: index of layers if not (isinstance(f, int) and f == -1): # if not from previous layer # x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if isinstance(f, int): x = y[f] else: _x = () for j in f: if j == -1: _x += (x,) else: _x += (y[j],) x = _x # print("index m: ", iol) # print if debug on pynative mode, not available on graph mode. x = m(x) # run y += (x if iol in self.save else None,) # save output return x @staticmethod @ops.constexpr def _get_h_w_list(ratio, gs, hw): return tuple([math.ceil(x * ratio / gs) * gs for x in hw]) def parse_model(d, ch, nc, sync_bn=False): # model_dict, input_channels(3) _SYNC_BN = sync_bn if _SYNC_BN: print('Parse model with Sync BN.') print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) anchors, stride, gd, gw = d.anchors, d.stride, d.depth_multiple, d.width_multiple na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out layers_param = [] for i, (f, n, m, args) in enumerate(d.backbone + d.head): # from, number, module, args kwargs = {} m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in (nn.Conv2d, ConvNormAct, RepConv, DownC, SPPCSPC, SPPF, C3, C2f, Bottleneck): c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = math.ceil(c2 * gw / 8) * 8 args = [c1, c2, *args[1:]] if m in (ConvNormAct, RepConv, DownC, SPPCSPC, SPPF, C3, C2f, Bottleneck): kwargs["sync_bn"] = sync_bn if m in (DownC, SPPCSPC, C3, C2f): args.insert(2, n) # number of repeats n = 1 elif m in (nn.BatchNorm2d, nn.SyncBatchNorm): args = [ch[f]] elif m in (Concat,): c2 = sum([ch[x] for x in f]) elif m is Shortcut: c2 = ch[f[0]] elif m in (YOLOv7Head, YOLOv7AuxHead, YOLOv5Head, YOLOv4Head, YOLOv3Head): args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is ReOrg: c2 = ch[f] * 4 else: c2 = ch[f] m_ = nn.SequentialCell([m(*args, **kwargs) for _ in range(n)]) if n > 1 else m(*args, **kwargs) t = str(m) # module type np = sum([x.size for x in m_.get_parameters()]) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params layers_param.append((i, f, t, np)) print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.CellList(layers), sorted(save), layers_param yolov/mindyolo/models/__init__.py000066400000000000000000000013061443434675500174730ustar00rootroot00000000000000from . import heads, layers, losses, yolov7, yolov5, yolov4, yolov3, initializer, model_factory __all__ = [] __all__.extend(heads.__all__) __all__.extend(layers.__all__) __all__.extend(losses.__all__) __all__.extend(yolov7.__all__) __all__.extend(yolov5.__all__) __all__.extend(yolov4.__all__) __all__.extend(yolov3.__all__) __all__.extend(initializer.__all__) __all__.extend(model_factory.__all__) #fixme: since yolov7 is used as both the file and function name, we need to import * after __all__ from .heads import * from .layers import * from .losses import * from .yolov7 import * from .yolov5 import * from .yolov4 import * from .yolov3 import * from .initializer import * from .model_factory import * 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Refer to the base class `models.GoogLeNet` for more details.)rrr)r )rrrrr&rrrr :s__main__) parse_config) create_modelrF) model_namerrrizOutput shape is cCsg|] }|jqSr)shape)rorrrrPsr)r )rN)*numpyr mindsporer!rr model_factoryrregistryr initializerrZheads.yolov7_headrr __all__r default_cfgsCellr r r3mindyolo.utils.configr9Zmindyolo.models.model_factoryr:optnetdatar%hasattrrr&randomrandnfloat32r-outr0listtupleprintrrrrs2       yolov/mindyolo/models/cspdarknet53.py000066400000000000000000000166701443434675500202540ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """DarkNet model.""" import mindspore.nn as nn from mindspore.ops import operations as P class Mish(nn.Cell): """Mish activation method""" def __init__(self): super(Mish, self).__init__() self.mul = P.Mul() self.tanh = P.Tanh() self.softplus = P.Softplus() def construct(self, input_x): res1 = self.softplus(input_x) tanh = self.tanh(res1) output = self.mul(input_x, tanh) return output def conv_block(in_channels, out_channels, kernel_size, stride, dilation=1): """Get a conv2d batchnorm and relu layer""" pad_mode = 'same' padding = 0 return nn.SequentialCell( [nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, pad_mode=pad_mode), nn.BatchNorm2d(out_channels, momentum=0.9, eps=1e-5), Mish() ] ) class ResidualBlock(nn.Cell): """ DarkNet V1 residual block definition. Args: in_channels: Integer. Input channel. out_channels: Integer. Output channel. Returns: Tensor, output tensor. Examples: ResidualBlock(3, 208) """ def __init__(self, in_channels, out_channels): super(ResidualBlock, self).__init__() out_chls = out_channels self.conv1 = conv_block(in_channels, out_chls, kernel_size=1, stride=1) self.conv2 = conv_block(out_chls, out_channels, kernel_size=3, stride=1) self.add = P.Add() def construct(self, x): identity = x out = self.conv1(x) out = self.conv2(out) out = self.add(out, identity) return out class CspDarkNet53(nn.Cell): """ DarkNet V1 network. Args: block: Cell. Block for network. layer_nums: List. Numbers of different layers. in_channels: Integer. Input channel. out_channels: Integer. Output channel. num_classes: Integer. Class number. Default:100. Returns: Tuple, tuple of output tensor,(f1,f2,f3,f4,f5). Examples: DarkNet(ResidualBlock) """ def __init__(self, block, detect=False): super(CspDarkNet53, self).__init__() self.outchannel = 1024 self.detect = detect self.concat = P.Concat(axis=1) self.add = P.Add() self.conv0 = conv_block(3, 32, kernel_size=3, stride=1) self.conv1 = conv_block(32, 64, kernel_size=3, stride=2) self.conv2 = conv_block(64, 64, kernel_size=1, stride=1) self.conv3 = conv_block(64, 32, kernel_size=1, stride=1) self.conv4 = conv_block(32, 64, kernel_size=3, stride=1) self.conv5 = conv_block(64, 64, kernel_size=1, stride=1) self.conv6 = conv_block(64, 64, kernel_size=1, stride=1) self.conv7 = conv_block(128, 64, kernel_size=1, stride=1) self.conv8 = conv_block(64, 128, kernel_size=3, stride=2) self.conv9 = conv_block(128, 64, kernel_size=1, stride=1) self.conv10 = conv_block(64, 64, kernel_size=1, stride=1) self.conv11 = conv_block(128, 64, kernel_size=1, stride=1) self.conv12 = conv_block(128, 128, kernel_size=1, stride=1) self.conv13 = conv_block(128, 256, kernel_size=3, stride=2) self.conv14 = conv_block(256, 128, kernel_size=1, stride=1) self.conv15 = conv_block(128, 128, kernel_size=1, stride=1) self.conv16 = conv_block(256, 128, kernel_size=1, stride=1) self.conv17 = conv_block(256, 256, kernel_size=1, stride=1) self.conv18 = conv_block(256, 512, kernel_size=3, stride=2) self.conv19 = conv_block(512, 256, kernel_size=1, stride=1) self.conv20 = conv_block(256, 256, kernel_size=1, stride=1) self.conv21 = conv_block(512, 256, kernel_size=1, stride=1) self.conv22 = conv_block(512, 512, kernel_size=1, stride=1) self.conv23 = conv_block(512, 1024, kernel_size=3, stride=2) self.conv24 = conv_block(1024, 512, kernel_size=1, stride=1) self.conv25 = conv_block(512, 512, kernel_size=1, stride=1) self.conv26 = conv_block(1024, 512, kernel_size=1, stride=1) self.conv27 = conv_block(1024, 1024, kernel_size=1, stride=1) self.layer2 = self._make_layer(block, 2, in_channel=64, out_channel=64) self.layer3 = self._make_layer(block, 8, in_channel=128, out_channel=128) self.layer4 = self._make_layer(block, 8, in_channel=256, out_channel=256) self.layer5 = self._make_layer(block, 4, in_channel=512, out_channel=512) def _make_layer(self, block, layer_num, in_channel, out_channel): """ Make Layer for DarkNet. :param block: Cell. DarkNet block. :param layer_num: Integer. Layer number. :param in_channel: Integer. Input channel. :param out_channel: Integer. Output channel. :return: SequentialCell, the output layer. Examples: _make_layer(ConvBlock, 1, 128, 256) """ layers = [] darkblk = block(in_channel, out_channel) layers.append(darkblk) for _ in range(1, layer_num): darkblk = block(out_channel, out_channel) layers.append(darkblk) return nn.SequentialCell(layers) def construct(self, x): """construct method""" c1 = self.conv0(x) c2 = self.conv1(c1) #route c3 = self.conv2(c2) c4 = self.conv3(c3) c5 = self.conv4(c4) c6 = self.add(c3, c5) c7 = self.conv5(c6) c8 = self.conv6(c2) c9 = self.concat((c7, c8)) c10 = self.conv7(c9) c11 = self.conv8(c10) #route c12 = self.conv9(c11) c13 = self.layer2(c12) c14 = self.conv10(c13) c15 = self.conv11(c11) c16 = self.concat((c14, c15)) c17 = self.conv12(c16) c18 = self.conv13(c17) #route c19 = self.conv14(c18) c20 = self.layer3(c19) c21 = self.conv15(c20) c22 = self.conv16(c18) c23 = self.concat((c21, c22)) c24 = self.conv17(c23) #output1 c25 = self.conv18(c24) #route c26 = self.conv19(c25) c27 = self.layer4(c26) c28 = self.conv20(c27) c29 = self.conv21(c25) c30 = self.concat((c28, c29)) c31 = self.conv22(c30) #output2 c32 = self.conv23(c31) #route c33 = self.conv24(c32) c34 = self.layer5(c33) c35 = self.conv25(c34) c36 = self.conv26(c32) c37 = self.concat((c35, c36)) c38 = self.conv27(c37) #output3 if self.detect: return c24, c31, c38 return c38 def get_out_channels(self): return self.outchannel yolov/mindyolo/models/heads/000077500000000000000000000000001443434675500164465ustar00rootroot00000000000000yolov/mindyolo/models/heads/.ipynb_checkpoints/000077500000000000000000000000001443434675500222375ustar00rootroot00000000000000yolov/mindyolo/models/heads/.ipynb_checkpoints/__init__-checkpoint.py000066400000000000000000000003351443434675500264760ustar00rootroot00000000000000"""layers init""" from .yolov7_head import * from .yolov5_head import * from .yolov4_head import * from .yolov3_head import * __all__ = [ 'YOLOv7Head', 'YOLOv7AuxHead', 'YOLOv5Head', 'YOLOv3Head', 'YOLOv4Head' ] yolov/mindyolo/models/heads/__init__.py000066400000000000000000000003351443434675500205600ustar00rootroot00000000000000"""layers init""" from .yolov7_head import * from .yolov5_head import * from .yolov4_head import * from .yolov3_head import * __all__ = [ 'YOLOv7Head', 'YOLOv7AuxHead', 'YOLOv5Head', 'YOLOv3Head', 'YOLOv4Head' ] yolov/mindyolo/models/heads/__pycache__/000077500000000000000000000000001443434675500206565ustar00rootroot00000000000000yolov/mindyolo/models/heads/__pycache__/__init__.cpython-37.pyc000066400000000000000000000005661443434675500250530ustar00rootroot00000000000000B Sd@s6dZddlTddlTddlTddlTdddddgZdS) z layers init)*Z YOLOv7HeadZ YOLOv7AuxHeadZ YOLOv5HeadZ YOLOv3HeadZ YOLOv4HeadN)__doc__Z yolov7_headZ yolov5_headZ yolov4_headZ yolov3_head__all__rrH/home/ma-user/work/mindyolo_for_yolov4/mindyolo/models/heads/__init__.pys yolov/mindyolo/models/heads/__pycache__/yolov3_head.cpython-37.pyc000066400000000000000000000067371443434675500255360ustar00rootroot00000000000000B ЊSdl@s^ddlZddlZddlZddlmZddlmZmZm Z m Z ddl m Z Gdddej ZdS)N)nnopsTensor Parameter)loggercsVeZdZdZdfdd ZddZdd d Zed d ej fd d Z eddZ Z S) YOLOv3Headzf YOLOv3 Detect Head, convert the output result to a prediction box based on the anchor point. 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""" def __init__(self, nc=80, anchors=(), stride=(), ch=()): # detection layer super(YOLOv3Head, self).__init__() assert isinstance(anchors, (tuple, list)) and len(anchors) > 0 assert isinstance(stride, (tuple, list)) and len(stride) > 0 assert isinstance(ch, (tuple, list)) and len(ch) > 0 self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors # anchor preprocess anchors = np.array(anchors) stride = np.array(stride) anchors, anchor_grid = self._check_anchor_order(anchors=anchors.reshape((self.nl, -1, 2)), anchor_grid=anchors.reshape((self.nl, 1, -1, 1, 1, 2)), stride=stride) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Parameter(Tensor(stride, ms.int32), requires_grad=False) self.anchors = Parameter(Tensor(anchors, ms.float32), requires_grad=False) # shape(nl,na,2) self.anchor_grid = Parameter(Tensor(anchor_grid, ms.float32), requires_grad=False) # shape(nl,1,na,1,1,2) self.m = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch]) # output conv def construct(self, x): z = () # inference output outs = () for i in range(self.nl): out = self.m[i](x[i]) # conv bs, _, ny, nx = out.shape # (bs,255,20,20) out = out.view(bs, self.na, self.no, ny, nx).transpose((0, 1, 3, 4, 2)) # (bs,3,20,20,85) outs += (out,) if not self.training: # inference grid_tensor = self._make_grid(nx, ny, out.dtype) y = ops.Sigmoid()(out) y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid_tensor) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z += (y.view(bs, -1, self.no),) return outs if self.training else (ops.concat(z, 1), outs) def initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 m = self for mi, s in zip(m.m, m.stride): # from s = s.asnumpy() b = mi.bias.view(m.na, -1).asnumpy() # conv.bias(255) to (3,85) b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else np.log(cf / cf.sum()) # cls mi.bias = ops.assign(mi.bias, Tensor(b, ms.float32).view(-1)) @staticmethod def _make_grid(nx=20, ny=20, dtype=ms.float32): xv, yv = ops.meshgrid((mnp.arange(nx), mnp.arange(ny))) return ops.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).astype(dtype) @staticmethod def _check_anchor_order(anchors, anchor_grid, stride): # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary a = np.prod(anchor_grid, -1).reshape((-1,)) # anchor area da = a[-1] - a[0] # delta a ds = stride[-1] - stride[0] # delta s if np.sign(da) != np.sign(ds): # same order logger.warning('Reversing anchor order') anchors = anchors[::-1, ...] anchor_grid = anchor_grid[::-1, ...] return anchors, anchor_grid yolov/mindyolo/models/heads/yolov4_head.py000066400000000000000000000117271443434675500212450ustar00rootroot00000000000000from mindspore import nn, ops, Tensor import mindspore as ms class YOLOv4Head(nn.Cell): """ YOLOv3 Detect Head, convert the output result to a prediction box based on the anchor point. """ def __init__(self, nc=80, anchors=(), ch=()): # detection layer super(YOLOv4Head, self).__init__() assert isinstance(anchors, (tuple, list)) and len(anchors) > 0 assert isinstance(ch, (tuple, list)) and len(ch) > 0 self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = 3 # number of detection layers self.na = len(anchors) // 3 # number of anchors self.m = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch]) # output conv # prediction on the default anchor boxes self.detect_1 = DetectionBlock('l', anchors, self.no) self.detect_2 = DetectionBlock('m', anchors, self.no) self.detect_3 = DetectionBlock('s', anchors, self.no) def construct(self, x): big_object_output = self.m[0](x[0]) medium_object_output = self.m[1](x[1]) small_object_output = self.m[2](x[2]) bs = small_object_output.shape[0] output_big = self.detect_1(big_object_output) output_me = self.detect_2(medium_object_output) output_small = self.detect_3(small_object_output) if not self.training: big = output_big.view(bs, -1, self.no) me = output_me.view(bs, -1, self.no) small = output_small.view(bs, -1, self.no) return ops.concat((big, me, small), 1), (output_big, output_me, output_small) return output_big, output_me, output_small class DetectionBlock(nn.Cell): """ YOLOv4 detection Network. It will finally output the detection result. Args: scale: Character. config: Configuration. is_training: Bool, Whether train or not, default True. Returns: Tuple, tuple of output tensor,(f1,f2,f3). Examples: DetectionBlock(scale='l',stride=32) """ def __init__(self, scale, anchor_scales, no): super(DetectionBlock, self).__init__() if scale == 's': idx = (6, 7, 8) self.scale_x_y = 1.2 self.offset_x_y = 0.1 self.stride = 8 elif scale == 'm': idx = (3, 4, 5) self.scale_x_y = 1.1 self.offset_x_y = 0.05 self.stride = 16 elif scale == 'l': idx = (0, 1, 2) self.scale_x_y = 1.05 self.offset_x_y = 0.025 self.stride = 32 else: raise KeyError("Invalid scale value for DetectionBlock") self.anchors = Tensor([anchor_scales[i] for i in idx], ms.float32) self.num_anchors_per_scale = 3 self.num_attrib = no self.sigmoid = ops.Sigmoid() def construct(self, x): """construct method""" num_batch = x.shape[0] grid_size = x.shape[2:4] input_shape = [size * self.stride for size in grid_size] input_shape = Tensor(tuple(input_shape[::-1]), ms.float32) # Reshape and transpose the feature to [n, grid_size[0], grid_size[1], 3, num_attrib] prediction = x.view(num_batch, self.num_anchors_per_scale, self.num_attrib, grid_size[0], grid_size[1]) prediction = prediction.transpose((0, 3, 4, 1, 2)) range_x = range(grid_size[1]) range_y = range(grid_size[0]) grid_x = ops.cast(ops.tuple_to_array(range_x), ms.float32) grid_y = ops.cast(ops.tuple_to_array(range_y), ms.float32) # Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid # [batch, gridx, gridy, 1, 1] grid_x = ops.tile(grid_x.view(1, 1, -1, 1, 1), (1, grid_size[0], 1, 1, 1)) grid_y = ops.tile(grid_y.view(1, -1, 1, 1, 1), (1, 1, grid_size[1], 1, 1)) # Shape is [grid_size[0], grid_size[1], 1, 2] grid = ops.concat((grid_x, grid_y), -1) box_xy = prediction[:, :, :, :, :2] box_wh = prediction[:, :, :, :, 2:4] box_confidence = prediction[:, :, :, :, 4:5] box_probs = prediction[:, :, :, :, 5:] # gridsize1 is x # gridsize0 is y box_xy = (self.scale_x_y * self.sigmoid(box_xy) - self.offset_x_y + grid) / \ ops.cast(ops.tuple_to_array((grid_size[1], grid_size[0])), ms.float32) # box_wh is w->h box_wh = ops.exp(box_wh) * self.anchors / input_shape box_confidence = self.sigmoid(box_confidence) box_probs = self.sigmoid(box_probs) if self.training: return prediction, box_xy, box_wh box_xy *= input_shape box_wh *= input_shape return ops.concat((box_xy, box_wh, box_confidence, box_probs), -1)yolov/mindyolo/models/heads/yolov5_head.py000066400000000000000000000052541443434675500212440ustar00rootroot00000000000000import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import nn, ops, Tensor, Parameter class YOLOv5Head(nn.Cell): def __init__(self, nc=80, anchors=(), stride=(), ch=()): # detection layer super(YOLOv5Head, self).__init__() self.stride = stride self.is_export = False self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.anchors = Parameter(Tensor(anchors, ms.float32).view(self.nl, -1, 2), requires_grad=False) # shape(nl,na,2) self.anchor_grid = Parameter(Tensor(anchors, ms.float32).view(self.nl, 1, -1, 1, 1, 2), requires_grad=False) # shape(nl,1,na,1,1,2) self.m = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch]) # output conv def construct(self, x): z = () # inference output outs = () for i in range(self.nl): out = self.m[i](x[i]) # conv if self.is_export: outs += (out,) continue bs, _, ny, nx = out.shape # (bs,255,20,20) out = ops.Transpose()(out.view(bs, self.na, self.no, ny, nx), (0, 1, 3, 4, 2)) # (bs,3,20,20,85) out = out outs += (out,) if not self.training: # inference grid_tensor = self._make_grid(nx, ny, out.dtype) y = ops.Sigmoid()(out) y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid_tensor) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z += (y.view(bs, -1, self.no),) # return outs return outs if self.training or self.is_export else (ops.concat(z, 1), outs) @staticmethod def _make_grid(nx=20, ny=20, dtype=ms.float32): xv, yv = ops.meshgrid((mnp.arange(nx), mnp.arange(ny))) return ops.cast(ops.stack((xv, yv), 2).view((1, 1, ny, nx, 2)), dtype) def convert(self, z): z = ops.concat(z, 1) box = z[:, :, :4] conf = z[:, :, 4:5] score = z[:, :, 5:] score *= conf convert_matrix = get_convert_matrix() box = ops.matmul(box, convert_matrix) return (box, score) @ops.constexpr(reuse_result=True) def get_convert_matrix(): return Tensor(np.array([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]]), dtype=ms.float32) yolov/mindyolo/models/heads/yolov7_head.py000066400000000000000000000227471443434675500212540ustar00rootroot00000000000000import math import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import nn, ops, Tensor, Parameter from mindyolo.utils import logger from ..layers.implicit import ImplicitA, ImplicitM class YOLOv7Head(nn.Cell): """ YOLOv7 Detect Head, convert the output result to a prediction box based on the anchor point. """ def __init__(self, nc=80, anchors=(), stride=(), ch=()): # detection layer super(YOLOv7Head, self).__init__() assert isinstance(anchors, (tuple, list)) and len(anchors) > 0 assert isinstance(stride, (tuple, list)) and len(stride) > 0 assert isinstance(ch, (tuple, list)) and len(ch) > 0 self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors # anchor preprocess anchors = np.array(anchors) stride = np.array(stride) anchors, anchor_grid = self._check_anchor_order(anchors=anchors.reshape((self.nl, -1, 2)), anchor_grid=anchors.reshape((self.nl, 1, -1, 1, 1, 2)), stride=stride) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Parameter(Tensor(stride, ms.int32), requires_grad=False) self.anchors = Parameter(Tensor(anchors, ms.float32), requires_grad=False) # shape(nl,na,2) self.anchor_grid = Parameter(Tensor(anchor_grid, ms.float32), requires_grad=False) # shape(nl,1,na,1,1,2) self.convert_matrix = Parameter(Tensor(np.array([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]]), dtype=ms.float32), requires_grad=False) self.m = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch]) # output conv self.ia = nn.CellList([ImplicitA(x) for x in ch]) self.im = nn.CellList([ImplicitM(self.no * self.na) for _ in ch]) def construct(self, x): z = () # inference output outs = () for i in range(self.nl): out = self.m[i](self.ia[i](x[i])) # conv out = self.im[i](out) bs, _, ny, nx = out.shape # (bs,255,20,20) out = out.view(bs, self.na, self.no, ny, nx).transpose((0, 1, 3, 4, 2)) # (bs,3,20,20,85) outs += (out,) if not self.training: # inference grid_tensor = self._make_grid(nx, ny, out.dtype) # y = ops.sigmoid(out) y = ops.Sigmoid()(out) y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid_tensor) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z += (y.view(bs, -1, self.no),) return outs if self.training else (ops.concat(z, 1), outs) def initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 m = self for mi, s in zip(m.m, m.stride): # from s = s.asnumpy() b = mi.bias.view(m.na, -1).asnumpy() # conv.bias(255) to (3,85) b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else np.log(cf / cf.sum()) # cls mi.bias = ops.assign(mi.bias, Tensor(b, ms.float32).view(-1)) @staticmethod def _make_grid(nx=20, ny=20, dtype=ms.float32): xv, yv = ops.meshgrid((mnp.arange(nx), mnp.arange(ny))) return ops.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).astype(dtype) @staticmethod def _check_anchor_order(anchors, anchor_grid, stride): # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary a = np.prod(anchor_grid, -1).reshape((-1,)) # anchor area da = a[-1] - a[0] # delta a ds = stride[-1] - stride[0] # delta s if np.sign(da) != np.sign(ds): # same order logger.warning('Reversing anchor order') anchors = anchors[::-1, ...] anchor_grid = anchor_grid[::-1, ...] return anchors, anchor_grid class YOLOv7AuxHead(nn.Cell): """ YOLOv7 Detect Aux Head, convert the output result to a prediction box based on the anchor point. """ def __init__(self, nc=80, anchors=(), stride=(), ch=()): # detection layer super(YOLOv7AuxHead, self).__init__() assert isinstance(anchors, (tuple, list)) and len(anchors) > 0 assert isinstance(stride, (tuple, list)) and len(stride) > 0 assert isinstance(ch, (tuple, list)) and len(ch) > 0 self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors # anchor preprocess anchors = np.array(anchors) stride = np.array(stride) anchors, anchor_grid = self._check_anchor_order(anchors=anchors.reshape((self.nl, -1, 2)), anchor_grid=anchors.reshape((self.nl, 1, -1, 1, 1, 2)), stride=stride) anchors /= stride.reshape((-1, 1, 1)) self.stride = Parameter(Tensor(stride, ms.int32), requires_grad=False) self.anchors = Parameter(Tensor(anchors, ms.float32), requires_grad=False) # shape(nl,na,2) self.anchor_grid = Parameter(Tensor(anchor_grid, ms.float32), requires_grad=False) # shape(nl,1,na,1,1,2) self.convert_matrix = Parameter(Tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], dtype=ms.float32), requires_grad=False) self.m = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch[:self.nl]]) # output conv self.m2 = nn.CellList([nn.Conv2d(x, self.no * self.na, 1, pad_mode="valid", has_bias=True) for x in ch[self.nl:]]) # output conv self.ia = nn.CellList([ImplicitA(x) for x in ch[:self.nl]]) self.im = nn.CellList([ImplicitM(self.no * self.na) for _ in ch[:self.nl]]) def construct(self, x): z = () # inference output outs_1 = () outs_2 = () for i in range(self.nl): out1 = self.m[i](self.ia[i](x[i])) # conv out1 = self.im[i](out1) bs, _, ny, nx = out1.shape # x(bs,255,20,20) to x(bs,3,20,20,85) out1 = ops.Transpose()(out1.view(bs, self.na, self.no, ny, nx), (0, 1, 3, 4, 2)) outs_1 += (out1,) out2 = self.m2[i](x[i + self.nl]) out2 = ops.Transpose()(out2.view(bs, self.na, self.no, ny, nx), (0, 1, 3, 4, 2)) outs_2 += (out2,) if not self.training: # inference grid_tensor = self._make_grid(nx, ny, out1.dtype) # y = ops.sigmoid(out1) y = ops.Sigmoid()(out1) y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid_tensor) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z += (y.view(bs, -1, self.no),) outs = outs_1 + outs_2 return outs if self.training else (ops.concat(z, 1), outs_1) def _initialize_aux_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 m = self for mi, mi2, s in zip(m.m, m.m2, m.stride): # from s = s.asnumpy() b = mi.bias.view(m.na, -1).asnumpy() # conv.bias(255) to (3,85) b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else np.log(cf / cf.sum()) # cls mi.bias = ops.assign(mi.bias, Tensor(b, ms.float32).view(-1)) b2 = mi2.bias.view(m.na, -1).asnumpy() # conv.bias(255) to (3,85) b2[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b2[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else np.log(cf / cf.sum()) # cls mi2.bias = ops.assign(mi2.bias, Tensor(b2, ms.float32).view(-1)) @staticmethod def _make_grid(nx=20, ny=20, dtype=ms.float32): xv, yv = ops.meshgrid((mnp.arange(nx), mnp.arange(ny))) return ops.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).astype(dtype) @staticmethod def _check_anchor_order(anchors, anchor_grid, stride): # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary a = np.prod(anchor_grid, -1).reshape((-1,)) # anchor area da = a[-1] - a[0] # delta a ds = stride[-1] - stride[0] # delta s if np.sign(da) != np.sign(ds): # same order logger.warning('Reversing anchor order') anchors = anchors[::-1, ...] anchor_grid = anchor_grid[::-1, ...] return anchors, anchor_grid yolov/mindyolo/models/initializer.py000066400000000000000000000041661443434675500202660ustar00rootroot00000000000000import math from mindspore import nn from mindspore.common import initializer as init __all__ = ['initialize_defult'] def initialize_defult(model): for _, cell in model.cells_and_names(): if isinstance(cell, nn.Conv2d): cell.weight.set_data(init.initializer(init.HeUniform(negative_slope=math.sqrt(5)), cell.weight.shape, cell.weight.dtype)) if cell.bias is not None: fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.shape) bound = 1 / math.sqrt(fan_in) cell.bias.set_data(init.initializer(init.Uniform(bound), cell.bias.shape, cell.bias.dtype)) elif isinstance(cell, nn.Dense): cell.weight.set_data(init.initializer(init.HeUniform(negative_slope=math.sqrt(5)), cell.weight.shape, cell.weight.dtype)) if cell.bias is not None: fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.shape) bound = 1 / math.sqrt(fan_in) cell.bias.set_data(init.initializer(init.Uniform(bound), cell.bias.shape, cell.bias.dtype)) def _calculate_fan_in_and_fan_out(shape): dimensions = len(shape) if dimensions < 2: raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions") num_input_fmaps = shape[1] num_output_fmaps = shape[0] receptive_field_size = 1 if dimensions > 2: # math.prod is not always available, accumulate the product manually # we could use functools.reduce but that is not supported by TorchScript for s in shape[2:]: receptive_field_size *= s fan_in = num_input_fmaps * receptive_field_size fan_out = num_output_fmaps * receptive_field_size return fan_in, fan_out yolov/mindyolo/models/layers/000077500000000000000000000000001443434675500166615ustar00rootroot00000000000000yolov/mindyolo/models/layers/.ipynb_checkpoints/000077500000000000000000000000001443434675500224525ustar00rootroot00000000000000yolov/mindyolo/models/layers/.ipynb_checkpoints/pool-checkpoint.py000066400000000000000000000020661443434675500261260ustar00rootroot00000000000000from mindspore import nn from .utils import autopad class MP(nn.Cell): """ Use the same step size and kernel size for maxpool. """ def __init__(self, k=2): super(MP, self).__init__() self.m = nn.MaxPool2d(kernel_size=k, stride=k) def construct(self, x): return self.m(x) class SP(nn.Cell): """ Use autopad for maxpool. """ def __init__(self, k=3, s=1): super(SP, self).__init__() self.m = MaxPool2d(kernel_size=k, stride=s, padding=k // 2) def construct(self, x): return self.m(x) class MaxPool2d(nn.Cell): """ Maxpool with pad. """ def __init__(self, kernel_size, stride, padding=0): super(MaxPool2d, self).__init__() assert isinstance(padding, int) padding = autopad(kernel_size) self.pad = nn.Pad(paddings=((0, 0), (0, 0), (padding, padding), (padding, padding))) self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride) def construct(self, x): x = self.pad(x) x = self.pool(x) return x yolov/mindyolo/models/layers/__init__.py000066400000000000000000000010011443434675500207620ustar00rootroot00000000000000"""layers init""" from .activation import * from .common import * from .conv import * from .bottleneck import * from .implicit import * from .pool import * from .spp import * from .upsample import * __all__ = ['Swish', 'Shortcut', 'Concat', 'ReOrg', 'Identity', 'ConvNormAct', 'RepConv', 'DownC', 'Bottleneck', 'C3', 'C2f', 'ImplicitA', 'ImplicitM', 'MP', 'SP', 'MaxPool2d', 'SPPCSPC', 'SPPF', 'Upsample', 'Residualblock' ] yolov/mindyolo/models/layers/__pycache__/000077500000000000000000000000001443434675500210715ustar00rootroot00000000000000yolov/mindyolo/models/layers/__pycache__/__init__.cpython-37.pyc000066400000000000000000000011111443434675500252510ustar00rootroot00000000000000B Vd@stdZddlTddlTddlTddlTddlTddlTddlTddlTddddddd d d d d dddddddddgZ dS)z layers init)*ZSwishZShortcutConcatZReOrgIdentityZ ConvNormActZRepConvZDownC BottleneckC3ZC2f ImplicitA ImplicitMMPSP MaxPool2dZSPPCSPCZSPPFZUpsampleZ ResidualblockN) __doc__ activationcommonconv bottleneckimplicitpoolZsppupsample__all__rrJ/home/dongliyue/mindyolo_for_yolov4_new/mindyolo/models/layers/__init__.pys yolov/mindyolo/models/layers/__pycache__/activation.cpython-37.pyc000066400000000000000000000015621443434675500256650ustar00rootroot00000000000000B ЊSdl@s*dZddlmZmZGdddejZdS)z Custom activation operators. )nnopscs*eZdZdZdfdd ZddZZS)Swishue Swish activation function: x * sigmoid(βx). 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The data type is an integer or a tuple of two integers. An integer represents the height and width of the convolution kernel. A tuple of two integers represents the height and width of the convolution kernel respectively. Default: 1. s (Union[int, tuple[int]]): Stride, the movement stride of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the movement step size in both height and width directions. A tuple of two integers represents the movement step size in the height and width directions respectively. Default: 1. p (Union[None, int, tuple[int]]): Padding, the number of padding on the height and width directions of the input. The data type is None or an integer or a tuple of four integers. If `padding` is an None, then padding with autopad. If `padding` is an integer, then the top, bottom, left, and right padding are all equal to `padding`. If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively. The value should be greater than or equal to 0. Default: None. g (int): Group, Splits filter into groups, `c1` and `c2` must be divisible by `group`. If the group is equal to `c1` and `c2`, this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1. d (Union[int, tuple[int]]): Dilation, Dilation size of 2D convolution kernel. The data type is an integer or a tuple of two integers. If :math:`k > 1`, the kernel is sampled every `k` elements. The value of `k` on the height and width directions is in range of [1, H] and [1, W] respectively. Default: 1. act (Union[bool, nn.Cell]): Activation. The data type is bool or nn.Cell. If `act` is True, then the activation function uses nn.SiLU. If `act` is False, do not use activation function. If 'act' is nn.Cell, use the object of this cell as the activation function. Default: True. sync_bn (bool): Whether the BN layer use nn.SyncBatchNorm. 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An integer represents the height and width of the convolution kernel. A tuple of two integers represents the height and width of the convolution kernel respectively. Default: 1. s (Union[int, tuple[int]]): Stride, the movement stride of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the movement step size in both height and width directions. A tuple of two integers represents the movement step size in the height and width directions respectively. Default: 1. p (Union[None, int, tuple[int]]): Padding, the number of padding on the height and width directions of the input. The data type is None or an integer or a tuple of four integers. If `padding` is an None, then padding with autopad. If `padding` is an integer, then the top, bottom, left, and right padding are all equal to `padding`. If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively. The value should be greater than or equal to 0. Default: None. g (int): Group, Splits filter into groups, `c1` and `c2` must be divisible by `group`. If the group is equal to `c1` and `c2`, this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1. act (Union[bool, nn.Cell]): Activation. The data type is bool or nn.Cell. If `act` is True, then the activation function uses nn.SiLU. If `act` is False, do not use activation function. If 'act' is nn.Cell, use the object of this cell as the activation function. Default: True. sync_bn (bool): Whether the BN layer use nn.SyncBatchNorm. 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The main purpose of designing CSPNet is to enable this architecture to achieve a richer gradient combination while reducing the amount of computation. This aim is achieved by partitioning feature map of the base layer into two parts and then merging them through a proposed cross-stage hierarchy. Our main concept is to make the gradient flow propagate through different network paths by splitting the gradient flow. 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Args: scales (tuple[float], optional): a tuple of float. Describe the scale along each dimension. Its length is the same as that of shape of `x`. The numbers in `scales` must all be positive. Only one of `scales` and `sizes` can be specified. sizes (tuple[int], optional): a tuple of int, describes the shape of the output tensor. The numbers in `sizes` must all be positive. Only one of `scales` and `sizes` can be specified. If `sizes` is specified, then set `scales` to 'None' in this operator's input list. It is 1 int elements :math:`(new\_width,)` when `mode` is "linear". It is 2 int elements :math:`(new\_height, new\_width)` when `mode` is "bilinear". mode (string): The method used to interpolate: 'linear' | 'bilinear'. Default is 'linear'. 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""" from mindspore import nn, ops class Swish(nn.Cell): """ Swish activation function: x * sigmoid(βx). If beta equals 1, you can use nn.SiLU instead. """ def __init__(self, beta=1.0): super().__init__() self.beta = beta def construct(self, x): return x * ops.sigmoid(self.beta * x) yolov/mindyolo/models/layers/bottleneck.py000066400000000000000000000064231443434675500213720ustar00rootroot00000000000000from mindspore import nn, ops from .conv import ConvNormAct class Bottleneck(nn.Cell): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, k=(1, 3), g=(1, 1), act=True, e=0.5, momentum=0.97, eps=1e-3, sync_bn=False): # ch_in, ch_out, shortcut, groups, kernels, expand super().__init__() c_ = int(c2 * e) # hidden channels self.conv1 = ConvNormAct(c1, c_, k[0], 1, g=g[0], act=act, momentum=momentum, eps=eps, sync_bn=sync_bn) self.conv2 = ConvNormAct(c_, c2, k[1], 1, g=g[1], act=act, momentum=momentum, eps=eps, sync_bn=sync_bn) self.add = shortcut and c1 == c2 def construct(self, x): if self.add: out = x + self.conv2(self.conv1(x)) else: out = self.conv2(self.conv1(x)) return out class Residualblock(nn.Cell): # Standard bottleneck def __init__(self, c1, c2, k=(1, 3), g=(1, 1), act=True, momentum=0.97, eps=1e-3, sync_bn=False): # ch_in, ch_out, shortcut, groups, kernels, expand super().__init__() self.conv1 = ConvNormAct(c1, c2, k[0], 1, g=g[0], act=act, momentum=momentum, eps=eps, sync_bn=sync_bn) self.conv2 = ConvNormAct(c2, c2, k[1], 1, g=g[1], act=act, momentum=momentum, eps=eps, sync_bn=sync_bn) def construct(self, x): out = x + self.conv2(self.conv1(x)) return out class C3(nn.Cell): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, e=0.5, momentum=0.97, eps=1e-3, sync_bn=False): super(C3, self).__init__() c_ = int(c2 * e) # hidden channels self.conv1 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.conv2 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.conv3 = ConvNormAct(2 * c_, c2, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) # act=FReLU(c2) self.m = nn.SequentialCell( [Bottleneck(c_, c_, shortcut, k=(1, 3), e=1.0, momentum=momentum, eps=eps, sync_bn=sync_bn) for _ in range(n)]) self.concat = ops.Concat(axis=1) def construct(self, x): c1 = self.conv1(x) c2 = self.m(c1) c3 = self.conv2(x) c4 = self.concat((c2, c3)) c5 = self.conv3(c4) return c5 class C2f(nn.Cell): # CSP Bottleneck with 2 convolutions def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, momentum=0.97, eps=1e-3, sync_bn=False): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() _c = int(c2 * e) # hidden channels self.cv1 = ConvNormAct(c1, 2 * _c, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv2 = ConvNormAct((2 + n) * _c, c2, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) # optional act=FReLU(c2) self.m = nn.CellList([ Bottleneck(_c, _c, shortcut, k=(3, 3), g=(1, g), e=1.0, momentum=momentum, eps=eps, sync_bn=sync_bn) for _ in range(n) ]) def construct(self, x): y = () x = self.cv1(x) x_tuple = ops.split(x, axis=1, output_num=2) y += x_tuple for i in range(len(self.m)): m = self.m[i] out = m(y[-1]) y += (out,) return self.cv2(ops.concat(y, axis=1)) yolov/mindyolo/models/layers/common.py000066400000000000000000000017451443434675500205320ustar00rootroot00000000000000from mindspore import nn, ops class Shortcut(nn.Cell): """ Shortcut layer. """ def construct(self, x): if isinstance(x, (tuple, list)) and len(x) == 2: return x[0] + x[1] return x class Concat(nn.Cell): """ Connect tensor in the specified axis. """ def __init__(self, axis=1): super(Concat, self).__init__() self.axis = axis def construct(self, x): return ops.concat(x, self.axis) class ReOrg(nn.Cell): """ Reorganize the input Tensor (b, c, w, h) into a new shape (b, 4c, w/2, h/2). """ def __init__(self): super(ReOrg, self).__init__() def construct(self, x): # in: (b,c,w,h) -> out: (b,4c,w/2,h/2) x1 = x[:, :, ::2, ::2] x2 = x[:, :, 1::2, ::2] x3 = x[:, :, ::2, 1::2] x4 = x[:, :, 1::2, 1::2] out = ops.concat((x1, x2, x3, x4), 1) return out class Identity(nn.Cell): def construct(self, x): return x yolov/mindyolo/models/layers/conv.py000066400000000000000000000202201443434675500201740ustar00rootroot00000000000000from mindspore import nn, ops from .common import Identity from .utils import autopad class ConvNormAct(nn.Cell): """Conv2d + BN + Act Args: c1 (int): In channels, the channel number of the input tensor of the Conv2d layer. c2 (int): Out channels, the channel number of the output tensor of the Conv2d layer. k (Union[int, tuple[int]]): Kernel size, Specifies the height and width of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the height and width of the convolution kernel. A tuple of two integers represents the height and width of the convolution kernel respectively. Default: 1. s (Union[int, tuple[int]]): Stride, the movement stride of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the movement step size in both height and width directions. A tuple of two integers represents the movement step size in the height and width directions respectively. Default: 1. p (Union[None, int, tuple[int]]): Padding, the number of padding on the height and width directions of the input. The data type is None or an integer or a tuple of four integers. If `padding` is an None, then padding with autopad. If `padding` is an integer, then the top, bottom, left, and right padding are all equal to `padding`. If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively. The value should be greater than or equal to 0. Default: None. g (int): Group, Splits filter into groups, `c1` and `c2` must be divisible by `group`. If the group is equal to `c1` and `c2`, this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1. d (Union[int, tuple[int]]): Dilation, Dilation size of 2D convolution kernel. The data type is an integer or a tuple of two integers. If :math:`k > 1`, the kernel is sampled every `k` elements. The value of `k` on the height and width directions is in range of [1, H] and [1, W] respectively. Default: 1. act (Union[bool, nn.Cell]): Activation. The data type is bool or nn.Cell. If `act` is True, then the activation function uses nn.SiLU. If `act` is False, do not use activation function. If 'act' is nn.Cell, use the object of this cell as the activation function. Default: True. sync_bn (bool): Whether the BN layer use nn.SyncBatchNorm. Default: False. """ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True, momentum=0.97, eps=1e-3, sync_bn=False): # ch_in, ch_out, kernel, stride, padding, groups super(ConvNormAct, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, pad_mode="pad", padding=autopad(k, p, d), group=g, dilation=d, has_bias=False) if sync_bn: self.bn = nn.SyncBatchNorm(c2, momentum=momentum, eps=eps) else: self.bn = nn.BatchNorm2d(c2, momentum=momentum, eps=eps) self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Cell) else Identity) def construct(self, x): return self.act(self.bn(self.conv(x))) class RepConv(nn.Cell): """Represented convolution, https://arxiv.org/abs/2101.03697 Args: c1 (int): In channels, the channel number of the input tensor of the Conv2d layer. c2 (int): Out channels, the channel number of the output tensor of the Conv2d layer. k (Union[int, tuple[int]]): Kernel size, Specifies the height and width of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the height and width of the convolution kernel. A tuple of two integers represents the height and width of the convolution kernel respectively. Default: 1. s (Union[int, tuple[int]]): Stride, the movement stride of the 2D convolution kernel. The data type is an integer or a tuple of two integers. An integer represents the movement step size in both height and width directions. A tuple of two integers represents the movement step size in the height and width directions respectively. Default: 1. p (Union[None, int, tuple[int]]): Padding, the number of padding on the height and width directions of the input. The data type is None or an integer or a tuple of four integers. If `padding` is an None, then padding with autopad. If `padding` is an integer, then the top, bottom, left, and right padding are all equal to `padding`. If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively. The value should be greater than or equal to 0. Default: None. g (int): Group, Splits filter into groups, `c1` and `c2` must be divisible by `group`. If the group is equal to `c1` and `c2`, this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1. act (Union[bool, nn.Cell]): Activation. The data type is bool or nn.Cell. If `act` is True, then the activation function uses nn.SiLU. If `act` is False, do not use activation function. If 'act' is nn.Cell, use the object of this cell as the activation function. Default: True. sync_bn (bool): Whether the BN layer use nn.SyncBatchNorm. Default: False. """ def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, momentum=0.97, eps=1e-3, sync_bn=False): super(RepConv, self).__init__() self.groups = g self.in_channels = c1 self.out_channels = c2 assert k == 3 assert autopad(k, p) == 1 padding_11 = autopad(k, p) - k // 2 self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Cell) else Identity) if sync_bn: BatchNorm = nn.SyncBatchNorm else: BatchNorm = nn.BatchNorm2d self.rbr_identity = BatchNorm(num_features=c1, momentum=(1 - 0.03), eps=1e-3) if c2 == c1 and s == 1 else None self.rbr_dense = nn.SequentialCell([ nn.Conv2d(c1, c2, k, s, pad_mode="pad", padding=autopad(k, p), group=g, has_bias=False), BatchNorm(num_features=c2, momentum=momentum, eps=eps), ]) self.rbr_1x1 = nn.SequentialCell( nn.Conv2d(c1, c2, 1, s, pad_mode="pad", padding=padding_11, group=g, has_bias=False), BatchNorm(num_features=c2, momentum=momentum, eps=eps), ) def construct(self, inputs): if self.rbr_identity is None: id_out = 0.0 else: id_out = self.rbr_identity(inputs) return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) def fuse(self): # TODO: The reparameterization function will be developed in subsequent versions pass class DownC(nn.Cell): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, n=1, k=2, momentum=0.97, eps=1e-3, sync_bn=False): super(DownC, self).__init__() c_ = c1 # hidden channels self.cv1 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv2 = ConvNormAct(c_, c2//2, 3, k, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv3 = ConvNormAct(c1, c2//2, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.mp = nn.MaxPool2d(kernel_size=k, stride=k) def construct(self, x): return ops.concat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), axis=1) yolov/mindyolo/models/layers/implicit.py000066400000000000000000000031251443434675500210460ustar00rootroot00000000000000import numpy as np import mindspore as ms from mindspore import nn, Tensor, Parameter class ImplicitA(nn.Cell): """ https://arxiv.org/pdf/2105.04206v1.pdf. Implicit knowledge in YOLOR combined with convolution feature map in addition and multiplication manner: Implicit knowledge in YOLOR can be simplified to a vector by pre-computing at the inference stage. This vector can be combined with the bias and weight of the previous or subsequent convolutional layer. """ def __init__(self, channel, mean=0., std=.02): super(ImplicitA, self).__init__() self.channel = channel self.mean = mean self.std = std self.implicit = Parameter(Tensor(np.random.normal(self.mean, self.std, (1, channel, 1, 1)), ms.float32)) def construct(self, x): return self.implicit + x class ImplicitM(nn.Cell): """ https://arxiv.org/pdf/2105.04206v1.pdf. Implicit knowledge in YOLOR combined with convolution feature map in addition and multiplication manner: Implicit knowledge in YOLOR can be simplified to a vector by pre-computing at the inference stage. This vector can be combined with the bias and weight of the previous or subsequent convolutional layer. """ def __init__(self, channel, mean=0., std=.02): super(ImplicitM, self).__init__() self.channel = channel self.mean = mean self.std = std self.implicit = Parameter(Tensor(np.random.normal(self.mean, self.std, (1, channel, 1, 1)), ms.float32)) def construct(self, x): return self.implicit * x yolov/mindyolo/models/layers/pool.py000066400000000000000000000020661443434675500202100ustar00rootroot00000000000000from mindspore import nn from .utils import autopad class MP(nn.Cell): """ Use the same step size and kernel size for maxpool. """ def __init__(self, k=2): super(MP, self).__init__() self.m = nn.MaxPool2d(kernel_size=k, stride=k) def construct(self, x): return self.m(x) class SP(nn.Cell): """ Use autopad for maxpool. """ def __init__(self, k=3, s=1): super(SP, self).__init__() self.m = MaxPool2d(kernel_size=k, stride=s, padding=k // 2) def construct(self, x): return self.m(x) class MaxPool2d(nn.Cell): """ Maxpool with pad. """ def __init__(self, kernel_size, stride, padding=0): super(MaxPool2d, self).__init__() assert isinstance(padding, int) padding = autopad(kernel_size) self.pad = nn.Pad(paddings=((0, 0), (0, 0), (padding, padding), (padding, padding))) self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride) def construct(self, x): x = self.pad(x) x = self.pool(x) return x yolov/mindyolo/models/layers/spp.py000066400000000000000000000054311443434675500200400ustar00rootroot00000000000000from mindspore import nn, ops from .conv import ConvNormAct from .pool import MaxPool2d class SPPCSPC(nn.Cell): """ CSPNet, https://arxiv.org/pdf/1911.11929v1.pdf. The main purpose of designing CSPNet is to enable this architecture to achieve a richer gradient combination while reducing the amount of computation. This aim is achieved by partitioning feature map of the base layer into two parts and then merging them through a proposed cross-stage hierarchy. Our main concept is to make the gradient flow propagate through different network paths by splitting the gradient flow. In this way, we have confirmed that the propagated gradient information can have a large correlation difference by switching concatenation and transition steps. """ def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13), momentum=0.97, eps=1e-3, sync_bn=False): super(SPPCSPC, self).__init__() c_ = int(2 * c2 * e) # hidden channels self.cv1 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv2 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv3 = ConvNormAct(c_, c_, 3, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv4 = ConvNormAct(c_, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.m = nn.CellList([MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) self.cv5 = ConvNormAct(4 * c_, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv6 = ConvNormAct(c_, c_, 3, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.cv7 = ConvNormAct(2 * c_, c2, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) def construct(self, x): x1 = self.cv4(self.cv3(self.cv1(x))) m_tuple = (x1,) for i in range(len(self.m)): m_tuple += (self.m[i](x1),) y1 = self.cv6(self.cv5(ops.Concat(axis=1)(m_tuple))) y2 = self.cv2(x) return self.cv7(ops.Concat(axis=1)((y1, y2))) class SPPF(nn.Cell): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5, momentum=0.97, eps=1e-3, sync_bn=False): # equivalent to SPP(k=(5, 9, 13)) super(SPPF, self).__init__() c_ = c1 // 2 # hidden channels self.conv1 = ConvNormAct(c1, c_, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.conv2 = ConvNormAct(c_ * 4, c2, 1, 1, momentum=momentum, eps=eps, sync_bn=sync_bn) self.concat = ops.Concat(axis=1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, pad_mode="same") def construct(self, x): x = self.conv1(x) y1 = self.m(x) y2 = self.m(y1) y3 = self.m(y2) y = self.conv2(self.concat((x, y1, y2, y3))) return y yolov/mindyolo/models/layers/upsample.py000066400000000000000000000026761443434675500210740ustar00rootroot00000000000000from mindspore import nn, ops class Upsample(nn.Cell): """ Using the interpolate method specified by `mode` resize the input tensor. Args: scales (tuple[float], optional): a tuple of float. Describe the scale along each dimension. Its length is the same as that of shape of `x`. The numbers in `scales` must all be positive. Only one of `scales` and `sizes` can be specified. sizes (tuple[int], optional): a tuple of int, describes the shape of the output tensor. The numbers in `sizes` must all be positive. Only one of `scales` and `sizes` can be specified. If `sizes` is specified, then set `scales` to 'None' in this operator's input list. It is 1 int elements :math:`(new\_width,)` when `mode` is "linear". It is 2 int elements :math:`(new\_height, new\_width)` when `mode` is "bilinear". mode (string): The method used to interpolate: 'linear' | 'bilinear'. Default is 'linear'. """ def __init__(self, sizes=None, scales=None, mode='nearest'): super(Upsample, self).__init__() self.sizes = sizes self.scales = scales self.mode = mode def construct(self, x): if self.mode == 'nearest' and self.scales: return ops.ResizeNearestNeighbor((x.shape[-2] * self.scales, x.shape[-1] * self.scales))(x) else: return ops.interpolate(x, sizes=self.sizes, scales=self.scales, mode=self.mode) yolov/mindyolo/models/layers/utils.py000066400000000000000000000010441443434675500203720ustar00rootroot00000000000000import math def make_divisible(x, divisor): # Returns x evenly divisible by divisor return math.ceil(x / divisor) * divisor def autopad(k, p=None, d=1): # kernel, padding, dilation # Pad to 'same' shape outputs if d > 1: k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad if isinstance(p, list): assert len(p) == 2 p = (p[0], p[0], p[1], p[1]) return p yolov/mindyolo/models/losses/000077500000000000000000000000001443434675500166725ustar00rootroot00000000000000yolov/mindyolo/models/losses/__init__.py000066400000000000000000000006321443434675500210040ustar00rootroot00000000000000from .yolov3_loss import * from .yolov4_loss import * from .yolov5_loss import * from .yolov7_loss import * from .loss_factory import * from . import yolov3_loss, yolov4_loss, yolov5_loss, yolov7_loss, loss_factory __all__ = [] __all__.extend(yolov3_loss.__all__) __all__.extend(yolov4_loss.__all__) __all__.extend(yolov5_loss.__all__) __all__.extend(yolov7_loss.__all__) __all__.extend(loss_factory.__all__) yolov/mindyolo/models/losses/__pycache__/000077500000000000000000000000001443434675500211025ustar00rootroot00000000000000yolov/mindyolo/models/losses/__pycache__/__init__.cpython-37.pyc000066400000000000000000000006611443434675500252730ustar00rootroot00000000000000B ЊSd@sddlTddlTddlTddlTddlTddlmZmZmZmZmZgZeejeejeejeejeejdS))*) yolov3_loss yolov4_loss yolov5_loss yolov7_loss loss_factoryN)rrrrr__all__extendr r I/home/ma-user/work/mindyolo_for_yolov4/mindyolo/models/losses/__init__.pys    yolov/mindyolo/models/losses/__pycache__/focal_loss.cpython-37.pyc000066400000000000000000000103641443434675500256610ustar00rootroot00000000000000B ЊSdq@sJddlZddlmZmZd ddZGdddejZGdd d ejZdS) N)nnops皙?cCsdd|d|fS)Ng?g?)epsrrK/home/ma-user/work/mindyolo_for_yolov4/mindyolo/models/losses/focal_loss.py smooth_BCEsrcs,eZdZdZd fdd Zd dd ZZS) FocalLossu Focal Loss for Dense Object Detection, https://arxiv.org/pdf/1708.02002v2.pdf Args: bce_weight (Tensor, optional): A rescaling weight applied to the loss of each batch element for BCEWithLogitsLoss. If not None, it can be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. bce_pos_weight (Tensor, optional): A weight of positive examples for BCEWithLogitsLoss. Must be a vector with length equal to the number of classes. If not None, it must be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. gamma: A modulating factor (1 − pt)^gamma to the cross entropy loss, with tunable focusing. Default: 1.5 alpha: An alpha-balanced variant of the focal loss. Default: 0.25 reduction (str): Type of reduction to be applied to loss. The optional values are 'mean', 'sum', and 'none'. If 'none', do not perform reduction. Default: 'mean'. 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Default: None. bce_pos_weight (Tensor, optional): A weight of positive examples. Must be a vector with length equal to the number of classes. If not None, it must be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. reduction (str): Type of reduction to be applied to loss. The optional values are 'mean', 'sum', and 'none'. If 'none', do not perform reduction. Default: 'mean'. r )rrrN)rrrrrrr)rrrr)rrrr;szBCEWithLogitsLoss.__init__cCs|j}||tj|tj}|dk r2||9}|jdkrx|dk rj|||jdd|S||S|jdkr||S||SdS)Nr rr) rrrr r!rrr#r )rr$r%r&r'r(rrrr*Ps & zBCEWithLogitsLoss.construct)NNr )N)r+r,r-rr*r/rr)rrr:sr)r) mindsporer rrrCellr rrrrrs 0yolov/mindyolo/models/losses/__pycache__/iou_loss.cpython-37.pyc000066400000000000000000000105551443434675500253730ustar00rootroot00000000000000B ЊSd@s`ddlZddlZddlmZmZeejejZdZddZ ddZ dd Z d d Z dddZ dS)N)opsTensorHz>cCs@|dddf|dddf|dddf|dddfS)z Return area of boxes. 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yolov/mindyolo/models/losses/focal_loss.py000066400000000000000000000111611443434675500213700ustar00rootroot00000000000000import mindspore as ms from mindspore import nn, ops def smooth_BCE(eps=0.1): # return positive, negative label smoothing BCE targets, https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 return 1.0 - 0.5 * eps, 0.5 * eps class FocalLoss(nn.Cell): """ Focal Loss for Dense Object Detection, https://arxiv.org/pdf/1708.02002v2.pdf Args: bce_weight (Tensor, optional): A rescaling weight applied to the loss of each batch element for BCEWithLogitsLoss. If not None, it can be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. bce_pos_weight (Tensor, optional): A weight of positive examples for BCEWithLogitsLoss. Must be a vector with length equal to the number of classes. If not None, it must be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. gamma: A modulating factor (1 − pt)^gamma to the cross entropy loss, with tunable focusing. Default: 1.5 alpha: An alpha-balanced variant of the focal loss. Default: 0.25 reduction (str): Type of reduction to be applied to loss. The optional values are 'mean', 'sum', and 'none'. If 'none', do not perform reduction. Default: 'mean'. """ def __init__(self, bce_weight=None, bce_pos_weight=None, gamma=1.5, alpha=0.25, reduction="mean"): super(FocalLoss, self).__init__() self.loss_fcn = nn.BCEWithLogitsLoss(weight=bce_weight, pos_weight=bce_pos_weight, reduction="none") self.gamma = gamma self.alpha = alpha self.reduction = reduction # default mean assert self.loss_fcn.reduction == 'none' # required to apply FL to each element def construct(self, pred, true, mask=None): ori_dtype = pred.dtype loss = self.loss_fcn(pred.astype(ms.float32), true.astype(ms.float32)) # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py pred_prob = ops.sigmoid(pred) # prob from logits p_t = true * pred_prob + (1 - true) * (1 - pred_prob) alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = (1.0 - p_t) ** self.gamma loss *= alpha_factor * modulating_factor if mask is not None: loss *= mask if self.reduction == 'mean': if mask is not None: return (loss.sum() / mask.astype(loss.dtype).sum().clip(1, None)).astype(ori_dtype) return loss.mean().astype(ori_dtype) elif self.reduction == 'sum': return loss.sum().astype(ori_dtype) else: # 'none' return loss.astype(ori_dtype) class BCEWithLogitsLoss(nn.Cell): def __init__(self, bce_weight=None, bce_pos_weight=None, reduction="mean"): """ Adds sigmoid activation function to input logits, and uses the given logits to compute binary cross entropy between the logits and the labels. Args: bce_weight (Tensor, optional): A rescaling weight applied to the loss of each batch element. If not None, it can be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. bce_pos_weight (Tensor, optional): A weight of positive examples. Must be a vector with length equal to the number of classes. If not None, it must be broadcast to a tensor with shape of `logits`, data type must be float16 or float32. Default: None. reduction (str): Type of reduction to be applied to loss. The optional values are 'mean', 'sum', and 'none'. If 'none', do not perform reduction. Default: 'mean'. """ super(BCEWithLogitsLoss, self).__init__() self.loss_fcn = nn.BCEWithLogitsLoss(weight=bce_weight, pos_weight=bce_pos_weight, reduction="none") self.reduction = reduction # default mean assert self.loss_fcn.reduction == 'none' # required to apply FL to each element def construct(self, pred, true, mask=None): ori_dtype = pred.dtype loss = self.loss_fcn(pred.astype(ms.float32), true.astype(ms.float32)) if mask is not None: loss *= mask if self.reduction == 'mean': if mask is not None: return (loss.sum() / mask.astype(loss.dtype).sum().clip(1, None)).astype(ori_dtype) return loss.mean().astype(ori_dtype) elif self.reduction == 'sum': return loss.sum().astype(ori_dtype) else: # 'none' return loss.astype(ori_dtype) yolov/mindyolo/models/losses/iou_loss.py000066400000000000000000000133361443434675500211060ustar00rootroot00000000000000import math import mindspore as ms from mindspore import ops, Tensor PI = Tensor(math.pi, ms.float32) EPS = 1e-7 def box_area(box): """ Return area of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box (Tensor[N, 4]) Returns: area (Tensor[N,]) """ return (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]) def batch_box_area(box): """ Return area of batch boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box (Tensor[B, N, 4]) Returns: area (Tensor[B, N]) """ return (box[:, :, 2] - box[:, :, 0]) * (box[:, :, 3] - box[:, :, 1]) def box_iou(box1, box2): # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box1 (Tensor[N, 4]) box2 (Tensor[M, 4]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 """ area1 = box_area(box1) area2 = box_area(box2) expand_size_1 = box2.shape[0] expand_size_2 = box1.shape[0] box1 = ops.tile(ops.expand_dims(box1, 1), (1, expand_size_1, 1)) box2 = ops.tile(ops.expand_dims(box2, 0), (expand_size_2, 1, 1)) # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) # inter = ops.minimum(box1[:, None, 2:], box2[None, :, 2:]) - ops.maximum(box1[:, None, :2], box2[None, :, :2]) inter = ops.minimum(box1[..., 2:], box2[..., 2:]) - ops.maximum(box1[..., :2], box2[..., :2]) inter = inter.clip(0., None) inter = inter[:, :, 0] * inter[:, :, 1] # zhy_test return inter / (area1[:, None] + area2[None, :] - inter).clip(EPS, None) # iou = inter / (area1 + area2 - inter) def batch_box_iou(batch_box1, batch_box2): """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box1 (Tensor[B, N, 4]) box2 (Tensor[B, M, 4]) Returns: iou (Tensor[B, N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 """ area1 = batch_box_area(batch_box1) area2 = batch_box_area(batch_box2) expand_size_1 = batch_box2.shape[1] expand_size_2 = batch_box1.shape[1] batch_box1 = ops.tile(ops.expand_dims(batch_box1, 2), (1, 1, expand_size_1, 1)) batch_box2 = ops.tile(ops.expand_dims(batch_box2, 1), (1, expand_size_2, 1, 1)) # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) inter = ops.minimum(batch_box1[..., 2:], batch_box2[..., 2:]) - \ ops.maximum(batch_box1[..., :2], batch_box2[..., :2]) inter = inter.clip(0., None) inter = inter[:, :, :, 0] * inter[:, :, :, 1] # zhy_test return inter / (area1[:, :, None] + area2[:, None, :] - inter).clip(EPS, None) # iou = inter / (area1 + area2 - inter) def bbox_iou(box1, box2, axis, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): """ Return intersection-over-union (IoU) of boxes. Arguments: box1 (Tensor[N, 4]) box2 (Tensor[N, 4]) xywh (bool): Whether the box format is (x_center, y_center, w, h) or (x1, y1, x2, y2). Default: True. GIoU (bool): Whether to use GIoU. Default: False. DIoU (bool): Whether to use DIoU. Default: False. CIoU (bool): Whether to use CIoU. Default: False. Returns: iou (Tensor[N,]): the IoU values for every element in boxes1 and boxes2 """ # Get the coordinates of bounding boxes if xywh: # transform from xywh to xyxy x1, y1, w1, h1 = ops.split(box1, axis, 4) x2, y2, w2, h2 = ops.split(box2, axis, 4) w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ else: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = ops.split(box1, axis, 4) b2_x1, b2_y1, b2_x2, b2_y2 = ops.split(box2, axis, 4) # Intersection area inter = (ops.minimum(b1_x2, b2_x2) - ops.maximum(b1_x1, b2_x1)).clip(0., None) * \ (ops.minimum(b1_y2, b2_y2) - ops.maximum(b1_y1, b2_y1)).clip(0., None) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps union = w1 * h1 + w2 * h2 - inter + eps # IoU iou = inter / union if CIoU or DIoU or GIoU: cw = ops.maximum(b1_x2, b2_x2) - ops.minimum(b1_x1, b2_x1) # convex (smallest enclosing box) width ch = ops.maximum(b1_y2, b2_y2) - ops.minimum(b1_y1, b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 if DIoU: return iou - rho2 / c2 # DIoU elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 # v = (4 / get_pi(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2) v = (4 / PI.astype(iou.dtype) ** 2) * ops.pow(ops.atan(w2 / (h2 + eps)) - ops.atan(w1 / (h1 + eps)), 2) alpha = v / (v - iou + (1 + eps)) alpha = ops.stop_gradient(alpha) return iou - (rho2 / c2 + v * alpha) # CIoU c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf return iou # IoU yolov/mindyolo/models/losses/label_assignment.py000066400000000000000000000000441443434675500225510ustar00rootroot00000000000000# TODO: General label assign method yolov/mindyolo/models/losses/loss_factory.py000066400000000000000000000005671443434675500217630ustar00rootroot00000000000000from mindyolo.models.registry import is_model, model_entrypoint __all__ = ['create_loss'] def create_loss(name: str, **kwargs): kwargs = {k: v for k, v in kwargs.items() if v is not None} if not is_model(name): raise RuntimeError(f'Unknown loss module {name}') create_fn = model_entrypoint(name) loss_fn = create_fn(**kwargs) return loss_fn yolov/mindyolo/models/losses/yolov3_loss.py000066400000000000000000000237311443434675500215450ustar00rootroot00000000000000import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import ops, nn, Tensor from mindyolo.models.registry import register_model from .focal_loss import FocalLoss, BCEWithLogitsLoss, smooth_BCE from .iou_loss import bbox_iou, batch_box_iou CLIP_VALUE = 1000. EPS = 1e-7 __all__ = [ 'YOLOv3Loss', ] @register_model class YOLOv3Loss(nn.Cell): def __init__( self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs ): super(YOLOv3Loss, self).__init__() self.hyp_box = box self.hyp_obj = obj self.hyp_cls = cls self.hyp_anchor_t = anchor_t self.nc = nc # number of classes self.na = len(anchors[0]) // 2 # number of anchors self.nl = len(anchors) # number of layers stride = np.array(stride) anchors = np.array(anchors).reshape((self.nl, -1, 2)) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Tensor(stride, ms.int32) self.anchors = Tensor(anchors, ms.float32) # shape(nl,na,2) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=label_smoothing) # positive, negative BCE targets # Focal loss g = fl_gamma # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(bce_pos_weight=Tensor([cls_pw], ms.float32), gamma=g), \ FocalLoss(bce_pos_weight=Tensor([obj_pw], ms.float32), gamma=g) else: # Define criteria BCEcls = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([cls_pw]), ms.float32)) BCEobj = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([obj_pw]), ms.float32)) _balance = {3: [4.0, 1.0, 0.4]}.get(self.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.balance = ms.Parameter(Tensor(_balance, ms.float32), requires_grad=False) self.BCEcls, self.BCEobj, self.gr = BCEcls, BCEobj, 1.0 self._off = Tensor([ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m ], dtype=ms.float32) self.loss_item_name = ['loss', 'lbox', 'lobj', 'lcls'] # branch name returned by lossitem for print def construct(self, p, targets, imgs): lcls, lbox, lobj = 0., 0., 0. tcls, tbox, indices, anchors, tmasks = self.build_targets(p, targets) # class, box, (image, anchor, gridj, gridi), anchors, mask tcls, tbox, indices, anchors, tmasks = ops.stop_gradient(tcls), ops.stop_gradient(tbox), \ ops.stop_gradient(indices), ops.stop_gradient(anchors), \ ops.stop_gradient(tmasks) # Losses for layer_index, pi in enumerate(p): # layer index, layer predictions tmask = tmasks[layer_index] b, a, gj, gi = ops.split(indices[layer_index] * tmask[None, :], 0, 4) # image, anchor, gridy, gridx b, a, gj, gi = b.view(-1), a.view(-1), gj.view(-1), gi.view(-1) tobj = ops.zeros(pi.shape[:4], pi.dtype) # target obj n = b.shape[0] # number of targets if n: _meta_pred = pi[b, a, gj, gi] # gather from (bs,na,h,w,nc) pxy, pwh, _, pcls = _meta_pred[:, :2], _meta_pred[:, 2:4], _meta_pred[:, 4:5], _meta_pred[:, 5:] # Regression pxy = ops.Sigmoid()(pxy) * 2 - 0.5 pwh = (ops.Sigmoid()(pwh) * 2) ** 2 * anchors[layer_index] pbox = ops.concat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox, tbox[layer_index], CIoU=True).squeeze() # iou(prediction, target) # iou = iou * tmask # lbox += ((1.0 - iou) * tmask).mean() # iou loss lbox += (((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None)).astype(iou.dtype) # Objectness iou = ops.stop_gradient(iou).clip(0, None).astype(pi.dtype) # tobj[b, a, gj, gi] = iou * tmask # iou ratio tobj[b, a, gj, gi] = ((1.0 - self.gr) + self.gr * ops.stop_gradient(iou).clip(0, None)) * tmask # iou ratio # Classification if self.nc > 1: # cls loss (only if multiple classes) t = ops.fill(pcls.dtype, pcls.shape, self.cn) # targets t[mnp.arange(n), tcls[layer_index]] = self.cp lcls += self.BCEcls(pcls, t, ops.tile(tmask[:, None], (1, t.shape[-1]))) # BCE obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[layer_index] # obj loss lbox *= self.hyp_box lobj *= self.hyp_obj lcls *= self.hyp_cls bs = p[0].shape[0] # batch size loss = lbox + lobj + lcls return loss * bs, ops.stop_gradient(ops.stack((loss * bs * 8, lbox, lobj, lcls))) def build_targets(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) targets = targets.view(-1, 6) mask_t = targets[:, 1] >= 0 na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch, tmasks = (), (), (), (), () gain = ops.ones(7, ms.int32) # normalized to gridspace gain ai = ops.tile(mnp.arange(na).view(-1, 1), (1, nt)) # shape: (na, nt) ai = ops.cast(ai, targets.dtype) targets = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # shape: (na, nt, 7) g = 0.5 # bias off = ops.cast(self._off, targets.dtype) * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = get_tensor(shape, targets.dtype)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain # shape(na,nt,7) # xywhn -> xywh # Matches # if nt: r = t[..., 4:6] / anchors[:, None] # wh ratio j = ops.maximum(r, 1 / r).max(2) < self.hyp_anchor_t # compare # t = t[j] # filter mask_m_t = ops.logical_and(j, mask_t[None, :]).view(-1) t = t.view(-1, 7) # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse jk = ops.logical_and((gxy % 1 < g), (gxy > 1)) lm = ops.logical_and((gxi % 1 < g), (gxi > 1)) j, k = jk[:, 0], jk[:, 1] l, m = lm[:, 0], lm[:, 1] # # original # j = ops.stack((ops.ones_like(j), j, k, l, m)) # shape: (5, *) # t = ops.tile(t, (5, 1, 1)) # shape(5, *, 7) # mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) # t = t.view(-1, 7) # offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) #(1,*,2) + (5,1,2) -> (5,*,2) # offsets = offsets.view(-1, 2) # faster, tag1, tag2 = ops.identity(j), ops.identity(k) tag1, tag2 = ops.tile(tag1[:, None], (1, 2)), ops.tile(tag2[:, None], (1, 2)) j_l = ops.logical_or(j, l).astype(ms.int32) k_m = ops.logical_or(k, m).astype(ms.int32) center = ops.ones_like(j_l) j = ops.stack((center, j_l, k_m)) t = ops.tile(t, (3, 1, 1)) # shape(5, *, 7) t = t.view(-1, 7) mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) offsets_new = ops.zeros((3,) + offsets.shape[1:], offsets.dtype) # offsets_new[0, :, :] = offsets[0, :, :] offsets_new[1:2, :, :] = ops.select(tag1.astype(ms.bool_), offsets[1, :, :], offsets[3, :, :]) offsets_new[2:3, :, :] = ops.select(tag2.astype(ms.bool_), offsets[2, :, :], offsets[4, :, :]) offsets = offsets_new offsets = offsets.view(-1, 2) # Define b, c, gxy, gwh, a = ops.cast(t[:, 0], ms.int32), \ ops.cast(t[:, 1], ms.int32), \ t[:, 2:4], \ t[:, 4:6], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors gij = ops.cast(gxy - offsets, ms.int32) gij = gij[:] gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[3] - 1) gj = gj.clip(0, shape[2] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid tbox += (ops.concat((gxy - gij, gwh), 1),) # box anch += (anchors[a],) # anchors tcls += (c,) # class tmasks += (mask_m_t,) return ops.stack(tcls), \ ops.stack(tbox), \ ops.stack(indices), \ ops.stack(anch), \ ops.stack(tmasks) # class, box, (image, anchor, gridj, gridi), anchors, mask def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = ops.Identity()(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y @ops.constexpr def get_tensor(x, dtype=ms.float32): return Tensor(x, dtype) if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.losses.loss_factory import create_loss cfg = parse_config() loss_fn = create_loss(name='YOLOv7Loss', **cfg.loss, anchors=cfg.network.get('anchors', None), stride=cfg.network.get('stride', None), nc=cfg.data.get('nc', None)) print(f"loss_fn is {loss_fn}") yolov/mindyolo/models/losses/yolov4_loss.py000066400000000000000000000262341443434675500215470ustar00rootroot00000000000000import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import ops, nn, Tensor from mindyolo.models.registry import register_model from .focal_loss import smooth_BCE, BCEWithLogitsLoss from .iou_loss import bbox_iou CLIP_VALUE = 1000. EPS = 1e-7 __all__ = [ 'YOLOv4Loss', ] class ConfidenceLoss(nn.Cell): """Loss for confidence.""" def __init__(self): super(ConfidenceLoss, self).__init__() self.cross_entropy = ops.SigmoidCrossEntropyWithLogits() self.reduce_sum = ops.ReduceSum() def construct(self, object_mask, predict_confidence, ignore_mask): confidence_loss = self.cross_entropy(predict_confidence, object_mask) confidence_loss = object_mask * confidence_loss + (1 - object_mask) * confidence_loss * ignore_mask confidence_loss = self.reduce_sum(confidence_loss, ()) return confidence_loss @register_model class YOLOv4Loss(nn.Cell): def __init__( self, box, obj, cls, label_smoothing, ignore_threshold, iou_threshold, anchors, nc, **kwargs ): super(YOLOv4Loss, self).__init__() self.ignore_threshold = ignore_threshold self.iou = Iou() self.iou_threshold = iou_threshold self.hyp_box = box self.hyp_obj = obj self.hyp_cls = cls self.nc = nc # number of classes anchors = np.array(anchors) self.na = anchors.shape[0] # number of anchors self.nl = 3 # number of layers # anchors = anchors.reshape((self.nl, -1, 2)) self.anchors = Tensor(anchors, ms.float32) # shape(na,2) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=label_smoothing) # positive, negative BCE targets self.BCEobj = ConfidenceLoss() self.BCEcls = BCEWithLogitsLoss(reduction="sum") self.loss_item_name = ['loss', 'lbox', 'lobj', 'lcls'] # branch name returned by lossitem for print self.concat = ops.Concat(axis=-1) self.reduce_max = ops.ReduceMax(keep_dims=False) self.gain_xy = ms.Parameter(ops.ones((1, 7), ms.float32)) self.gain_wh = ms.Parameter(ops.ones((1, 7), ms.float32)) def construct(self, p, targets, imgs): image_shape = imgs.shape gain = get_tensor(image_shape, targets.dtype)[[3, 2]] ori_targets = targets.copy() lcls, lbox, lobj = 0., 0., 0. tcls, tbox, indices, anchors, tmasks = self.build_targets(p, targets, imgs) # class, box, (image, anchor, gridj, gridi), anchors, mask tcls, tbox, indices, anchors, tmasks = ops.stop_gradient(tcls), ops.stop_gradient(tbox), \ ops.stop_gradient(indices), ops.stop_gradient(anchors), \ ops.stop_gradient(tmasks) # Losses for layer_index, yolo_out in enumerate(p): # layer index, layer predictions pi = yolo_out[0] tmask = tmasks[layer_index] b, a, gj, gi = ops.split(indices[layer_index] * tmask[None, :], 0, 4) # image, anchor, gridy, gridx b, a, gj, gi = b.view(-1), a.view(-1), gj.view(-1), gi.view(-1) pi_shape = pi.shape y_true = ops.zeros((pi_shape[0], pi_shape[1], pi_shape[2], pi_shape[3], 1), pi.dtype) y_true[b, gj, gi, a][:, 0] = 1. n = b.shape[0] # number of targets if n: pxy = yolo_out[1][b, gj, gi, a] pwh = yolo_out[2][b, gj, gi, a] _meta_pred = pi[b, gj, gi, a] # gather from (bs,na,h,w,nc) pcls = _meta_pred[:, 5:] # Regression pbox = ops.concat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox, tbox, 1, GIoU=True).squeeze() # iou(prediction, target) # iou = iou * tmask # lbox += ((1.0 - iou) * tmask).mean() # iou loss box_loss_scale = 2 - tbox[:, 2] * tbox[:, 3] / gain[0] / gain[1] lbox += (((1.0 - iou) * tmask * box_loss_scale).sum()).astype(iou.dtype) # Classification if self.nc > 1: # cls loss (only if multiple classes) t = ops.fill(pcls.dtype, pcls.shape, self.cn) # targets t[mnp.arange(n), tcls] = self.cp lcls += self.BCEcls(pcls, t, ops.tile(tmask[:, None], (1, t.shape[-1]))) # BCE gt_box = ori_targets[:, :, 2:] pred_boxes = self.concat((yolo_out[1], yolo_out[2])) gt_shape = ops.Shape()(gt_box) gt_box = ops.Reshape()(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2])) iou = self.iou(ops.ExpandDims()(pred_boxes, -2), gt_box) best_iou = self.reduce_max(iou, -1) ignore_mask = best_iou < self.ignore_threshold ignore_mask = ops.Cast()(ignore_mask, ms.float32) ignore_mask = ops.ExpandDims()(ignore_mask, -1) ignore_mask = ops.stop_gradient(ignore_mask) object_mask = y_true[:, :, :, :, 0:1] lobj += self.BCEobj(object_mask, pi[:, :, :, :, 4:5], ignore_mask) lbox *= self.hyp_box lobj *= self.hyp_obj lcls *= self.hyp_cls bs = p[0][0].shape[0] # batch size loss = lbox + lobj + lcls return loss / bs / 8, ops.stop_gradient(ops.stack((loss / bs, lbox / bs, lobj / bs, lcls / bs))) def build_targets(self, p, targets, imgs): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) image_shape = imgs.shape targets = targets.view(-1, 6) mask_t = targets[:, 1] >= 0 na, nt = self.na, targets.shape[0] # number of anchors, targets indices, anch, tmasks = (), (), () ai = ops.tile(mnp.arange(na).view(-1, 1), (1, nt)) # shape: (na, nt) ai = ops.cast(ai, targets.dtype) targets_9_anchors = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # shape: (na, nt, 7) indices_gain_wh = Tensor(np.array([[0, 4], [0, 5]]), ms.int32) updates = get_tensor(image_shape, targets_9_anchors.dtype)[[3, 2]] # xyxy gain self.gain_wh = ops.ScatterNdUpdate()(self.gain_wh, indices_gain_wh, updates) gain_wh = ops.squeeze(self.gain_wh) # Match targets to anchors t_wh = targets_9_anchors * gain_wh # Matches gt_box = ops.zeros((na, nt, 4), ms.float32) gt_box[..., 2:] = t_wh[..., 4:6] anchor_shapes = ops.zeros((na, 1, 4), ms.float32) anchor_shapes[..., 2:] = ops.ExpandDims()(self.anchors, 1) anch_ious = bbox_iou(gt_box, anchor_shapes, 2).squeeze() j = anch_ious == anch_ious.max(axis=0) l = anch_ious > self.iou_threshold j_l = ops.logical_or(j, l).astype(ms.int32).reshape((self.nl, -1, nt)) anchor_scales = self.anchors.reshape((self.nl, -1, 2)) ai = ops.tile(mnp.arange(na // self.nl).view(-1, 1), (1, nt)) # shape: (na, nt) ai = ops.cast(ai, targets.dtype) targets_3_anchors = ops.concat((ops.tile(targets, (na // self.nl, 1, 1)), ai[:, :, None]), 2) indices_gain_xy = Tensor(np.array([[0, 2], [0, 3]]), ms.int32) for i in range(self.nl): anchors, shape = anchor_scales[i], p[i][0].shape updates = get_tensor(shape, targets_3_anchors.dtype)[[2, 1]] # xyxy gain self.gain_xy = ops.ScatterNdUpdate()(self.gain_xy, indices_gain_xy, updates) gain_xy = ops.squeeze(self.gain_xy) t = targets_3_anchors * gain_xy mask_m_t = (j_l[i] * ops.cast(mask_t[None, :], ms.int32)).view(-1) t = t.view(-1, 7) # Define b, gxy, a = ops.cast(t[:, 0], ms.int32), \ t[:, 2:4], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors gij = ops.cast(gxy, ms.int32) gij = gij[:] gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[2] - 1) gj = gj.clip(0, shape[1] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid anch += (anchors[a],) # anchors tmasks += (mask_m_t,) targets_3_anchors = targets_3_anchors.view(-1, 7) tcls = ops.cast(targets_3_anchors[:, 1], ms.int32) # class tbox = targets_3_anchors[:, 2:6] # box return tcls, \ tbox, \ ops.stack(indices), \ ops.stack(anch), \ ops.stack(tmasks) # class, box, (image, anchor, gridj, gridi), anchors, mask def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = ops.Identity()(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y @ops.constexpr def get_tensor(x, dtype=ms.float32): return Tensor(x, dtype) class Iou(nn.Cell): """Calculate the iou of boxes""" def __init__(self): super(Iou, self).__init__() self.min = ops.Minimum() self.max = ops.Maximum() def construct(self, box1, box2): """ box1: pred_box [batch, gx, gy, anchors, 1, 4] ->4: [x_center, y_center, w, h] box2: gt_box [batch, 1, 1, 1, maxbox, 4] convert to topLeft and rightDown """ box1_xy = box1[:, :, :, :, :, :2] box1_wh = box1[:, :, :, :, :, 2:4] box1_mins = box1_xy - box1_wh / ops.scalar_to_tensor(2.0) # topLeft box1_maxs = box1_xy + box1_wh / ops.scalar_to_tensor(2.0) # rightDown box2_xy = box2[:, :, :, :, :, :2] box2_wh = box2[:, :, :, :, :, 2:4] box2_mins = box2_xy - box2_wh / ops.scalar_to_tensor(2.0) box2_maxs = box2_xy + box2_wh / ops.scalar_to_tensor(2.0) intersect_mins = self.max(box1_mins, box2_mins) intersect_maxs = self.min(box1_maxs, box2_maxs) intersect_wh = self.max(intersect_maxs - intersect_mins, ops.scalar_to_tensor(0.0)) # P.squeeze: for effiecient slice intersect_area = ops.Squeeze(-1)(intersect_wh[:, :, :, :, :, 0:1]) * \ ops.Squeeze(-1)(intersect_wh[:, :, :, :, :, 1:2]) box1_area = ops.Squeeze(-1)(box1_wh[:, :, :, :, :, 0:1]) * ops.Squeeze(-1)(box1_wh[:, :, :, :, :, 1:2]) box2_area = ops.Squeeze(-1)(box2_wh[:, :, :, :, :, 0:1]) * ops.Squeeze(-1)(box2_wh[:, :, :, :, :, 1:2]) iou = intersect_area / (box1_area + box2_area - intersect_area) # iou : [batch, gx, gy, anchors, maxboxes] return iou if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.losses.loss_factory import create_loss cfg = parse_config() loss_fn = create_loss(name='YOLOv7Loss', **cfg.loss, anchors=cfg.network.get('anchors', None), stride=cfg.network.get('stride', None), nc=cfg.data.get('nc', None)) print(f"loss_fn is {loss_fn}") yolov/mindyolo/models/losses/yolov5_loss.py000066400000000000000000000231621443434675500215450ustar00rootroot00000000000000import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import ops, nn, Tensor, Parameter from mindyolo.models.registry import register_model from .focal_loss import FocalLoss, BCEWithLogitsLoss, smooth_BCE from .iou_loss import bbox_iou __all__ = [ 'YOLOv5Loss', ] @register_model class YOLOv5Loss(nn.Cell): # Compute losses def __init__( self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs ): super(YOLOv5Loss, self).__init__() self.sort_obj_iou = False self.hyp_anchor_t = anchor_t self.hyp_box = box self.hyp_obj = obj self.hyp_cls = cls self.nc = nc # number of classes self.na = len(anchors[0]) // 2 # number of anchors self.nl = len(anchors) # number of layers stride = np.array(stride) anchors = np.array(anchors).reshape((self.nl, -1, 2)) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Tensor(stride, ms.int32) self.anchors = Tensor(anchors, ms.float32) # shape(nl,na,2) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=label_smoothing) # positive, negative BCE targets # Focal loss g = fl_gamma # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(bce_pos_weight=Tensor([cls_pw], ms.float32), gamma=g), \ FocalLoss(bce_pos_weight=Tensor([obj_pw], ms.float32), gamma=g) else: # Define criteria BCEcls = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([cls_pw]), ms.float32)) BCEobj = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([obj_pw]), ms.float32)) _balance = {3: [4.0, 1.0, 0.4]}.get(self.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.balance = Parameter(Tensor(_balance, ms.float32), requires_grad=False) self.BCEcls, self.BCEobj, self.gr = BCEcls, BCEobj, 1.0 self._off = Tensor([ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], dtype=ms.float32) self.loss_item_name = ['loss', 'lbox', 'lobj', 'lcls'] # branch name returned by loss for print def scatter_index_tensor(self, x, index): x_tmp = ops.transpose(x.reshape((-1, x.shape[-1])), (1, 0)) res = x_tmp[index].reshape(x.shape[:-1]) return res def construct(self, p, targets, imgs): # predictions, targets lcls, lbox, lobj = 0., 0., 0. tcls, tbox, indices, anchors, tmasks = self.build_targets(p, targets) # class, box, (image, anchor, gridj, gridi), anchors, mask tcls, tbox, indices, anchors, tmasks = ops.stop_gradient(tcls), ops.stop_gradient(tbox), \ ops.stop_gradient(indices), ops.stop_gradient(anchors), \ ops.stop_gradient(tmasks) # Losses for layer_index, pi in enumerate(p): # layer index, layer predictions pi = ops.cast(pi, ms.float32) tmask = tmasks[layer_index] b, a, gj, gi = ops.split(indices[layer_index] * tmask[None, :], 0, 4) # image, anchor, gridy, gridx b, a, gj, gi = b.view(-1), a.view(-1), gj.view(-1), gi.view(-1) tobj = ops.zeros(pi.shape[:4], pi.dtype) # target obj n = b.shape[0] # number of targets if n: _meta_pred = pi[b, a, gj, gi] # gather from (bs,na,h,w,nc) pxy, pwh, _, pcls = _meta_pred[:, :2], _meta_pred[:, 2:4], _meta_pred[:, 4:5], _meta_pred[:, 5:] # Regression pxy = ops.Sigmoid()(pxy) * 2 - 0.5 pwh = (ops.Sigmoid()(pwh) * 2) ** 2 * anchors[layer_index] pbox = ops.concat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox, tbox[layer_index], CIoU=True).squeeze() # iou(prediction, target) lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum() # iou loss # Objectness iou = ops.stop_gradient(iou).clip(0, None) if self.sort_obj_iou: _, j = ops.sort(iou) b, a, gj, gi, iou, tmask = b[j], a[j], gj[j], gi[j], iou[j], tmask[j] if self.gr < 1: iou = (1.0 - self.gr) + self.gr * iou tobj[b, a, gj, gi] = ops.stop_gradient(iou) * tmask # iou ratio # Classification if self.nc > 1: # cls loss (only if multiple classes) t = ops.fill(pcls.dtype, pcls.shape, self.cn) # targets t[mnp.arange(n), tcls[layer_index]] = self.cp lcls += self.BCEcls(pcls, t, ops.tile(tmask[:, None], (1, t.shape[-1]))) # BCE # obji = self.BCEobj(pi[..., 4], tobj) obji = self.BCEobj(self.scatter_index_tensor(pi, 4), tobj) lobj += obji * self.balance[layer_index] # obj loss lbox *= self.hyp_box lobj *= self.hyp_obj lcls *= self.hyp_cls bs = p[0].shape[0] # batch size loss = lbox + lobj + lcls loss_item = ops.stop_gradient(ops.stack((loss, lbox, lobj, lcls))) return loss * bs, loss_item def build_targets(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) targets = targets.view(-1, 6) mask_t = targets[:, 1] >= 0 na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch, tmasks = (), (), (), (), () gain = ops.ones(7, ms.int32) # normalized to gridspace gain ai = ops.tile(mnp.arange(na).view(-1, 1), (1, nt)) # shape: (na, nt) ai = ops.cast(ai, targets.dtype) targets = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # shape: (na, nt, 7) g = 0.5 # bias off = ops.cast(self._off, targets.dtype) * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = get_tensor(shape, targets.dtype)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain # shape(na,nt,7) # xywhn -> xywh # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio j = ops.maximum(r, 1 / r).max(2) < self.hyp_anchor_t # compare mask_m_t = ops.logical_and(j, mask_t[None, :]).view(-1) t = t.view(-1, 7) # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse jk = ops.logical_and((gxy % 1 < g), (gxy > 1)) # .astype(ms.int32) lm = ops.logical_and((gxi % 1 < g), (gxi > 1)) # .astype(ms.int32) j, k = jk[:, 0], jk[:, 1] l, m = lm[:, 0], lm[:, 1] # # Original # j = ops.stack((ops.ones_like(j), j, k, l, m)) # shape: (5, *) # t = ops.tile(t, (5, 1, 1)) # shape(5, *, 7) # t = t.view(-1, 7) # mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) # # t = t.repeat((5, 1, 1))[j] # offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) #(1,*,2) + (5,1,2) -> (5,*,2) # offsets = offsets.view(-1, 2) # faster, tag1, tag2 = ops.identity(j), ops.identity(k) tag1, tag2 = ops.tile(tag1[:, None], (1, 2)), ops.tile(tag2[:, None], (1, 2)) j_l = ops.logical_or(j, l).astype(ms.int32) k_m = ops.logical_or(k, m).astype(ms.int32) center = ops.ones_like(j_l) j = ops.stack((center, j_l, k_m)) t = ops.tile(t, (3, 1, 1)) # shape(5, *, 7) t = t.view(-1, 7) mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) offsets_new = ops.zeros((3,) + offsets.shape[1:], offsets.dtype) offsets_new[1:2, :, :] = ops.select(tag1.astype(ms.bool_), offsets[1, :, :], offsets[3, :, :]) offsets_new[2:3, :, :] = ops.select(tag2.astype(ms.bool_), offsets[2, :, :], offsets[4, :, :]) offsets = offsets_new offsets = offsets.view(-1, 2) # Define b, c, gxy, gwh, a = ops.cast(t[:, 0], ms.int32), \ ops.cast(t[:, 1], ms.int32), \ t[:, 2:4], \ t[:, 4:6], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors gij = ops.cast(gxy - offsets, ms.int32) gij = gij[:] gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[3] - 1) gj = gj.clip(0, shape[2] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid tbox += (ops.concat((gxy - gij, gwh), 1),) # box anch += (anchors[a],) # anchors tcls += (c,) # class tmasks += (mask_m_t,) return ops.stack(tcls), \ ops.stack(tbox), \ ops.stack(indices), \ ops.stack(anch), \ ops.stack(tmasks) # class, box, (image, anchor, gridj, gridi), anchors, mask @ops.constexpr def get_tensor(x, dtype=ms.float32): return Tensor(x, dtype) yolov/mindyolo/models/losses/yolov7_loss.py000066400000000000000000001363731443434675500215600ustar00rootroot00000000000000import numpy as np import mindspore as ms import mindspore.numpy as mnp from mindspore import ops, nn, Tensor, Parameter from mindyolo.models.registry import register_model from .focal_loss import FocalLoss, BCEWithLogitsLoss, smooth_BCE from .iou_loss import bbox_iou, batch_box_iou CLIP_VALUE = 1000. EPS = 1e-7 __all__ = [ 'YOLOv7Loss', 'YOLOv7AuxLoss' ] @register_model class YOLOv7Loss(nn.Cell): def __init__( self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs ): super(YOLOv7Loss, self).__init__() self.hyp_box = box self.hyp_obj = obj self.hyp_cls = cls self.hyp_anchor_t = anchor_t self.nc = nc # number of classes self.na = len(anchors[0]) // 2 # number of anchors self.nl = len(anchors) # number of layers stride = np.array(stride) anchors = np.array(anchors).reshape((self.nl, -1, 2)) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Tensor(stride, ms.int32) self.anchors = Tensor(anchors, ms.float32) # shape(nl,na,2) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=label_smoothing) # positive, negative BCE targets # Focal loss g = fl_gamma # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(bce_pos_weight=Tensor([cls_pw], ms.float32), gamma=g), \ FocalLoss(bce_pos_weight=Tensor([obj_pw], ms.float32), gamma=g) else: # Define criteria BCEcls = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([cls_pw]), ms.float32)) BCEobj = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([obj_pw]), ms.float32)) _balance = {3: [4.0, 1.0, 0.4]}.get(self.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.balance = ms.Parameter(Tensor(_balance, ms.float32), requires_grad=False) self.BCEcls, self.BCEobj, self.gr = BCEcls, BCEobj, 1.0 self._off = Tensor([ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m ], dtype=ms.float32) self.loss_item_name = ['loss', 'lbox', 'lobj', 'lcls'] # branch name returned by lossitem for print def construct(self, p, targets, imgs): lcls, lbox, lobj = 0., 0., 0. bs, as_, gjs, gis, targets, anchors, tmasks = self.build_targets(p, targets, imgs) # bs: (nl, bs*5*na*gt_max) bs, as_, gjs, gis, targets, anchors, tmasks = ops.stop_gradient(bs), ops.stop_gradient(as_), \ ops.stop_gradient(gjs), ops.stop_gradient(gis), \ ops.stop_gradient(targets), ops.stop_gradient(anchors), \ ops.stop_gradient(tmasks) pre_gen_gains = () for pp in p: pre_gen_gains += (get_tensor(pp.shape, targets.dtype)[[3, 2, 3, 2]],) # Losses # for i, pi in enumerate(p): # layer index, layer predictions for i in range(self.nl): # layer index pi = p[i] # layer predictions b, a, gj, gi, tmask = bs[i], as_[i], gjs[i], gis[i], tmasks[i] # image, anchor, gridy, gridx, tmask tobj = ops.zeros_like(pi[..., 0]) # target obj n = b.shape[0] # number of targets ps = pi[b, a, gj, gi] # prediction subset corresponding to targets # Regression grid = ops.stack([gi, gj], axis=1) pxy = ops.Sigmoid()(ps[:, :2]) * 2. - 0.5 pwh = (ops.Sigmoid()(ps[:, 2:4]) * 2) ** 2 * anchors[i] pbox = ops.concat((pxy, pwh), 1) # predicted box selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] selected_tbox[:, :2] -= grid iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True).view(-1) lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None) # iou loss # Objectness tobj[b, a, gj, gi] = ((1.0 - self.gr) + self.gr * ops.stop_gradient(iou).clip(0, None)) * tmask # iou ratio # Classification selected_tcls = ops.cast(targets[i][:, 1], ms.int32) if self.nc > 1: # cls loss (only if multiple classes) t = ops.ones_like(ps[:, 5:]) * self.cn # targets t[mnp.arange(n), selected_tcls] = self.cp lcls += self.BCEcls(ps[:, 5:], t, ops.tile(tmask[:, None], (1, t.shape[1]))) # BCE obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss lbox *= self.hyp_box lobj *= self.hyp_obj lcls *= self.hyp_cls bs = p[0].shape[0] # batch size loss = lbox + lobj + lcls return loss * bs, ops.stop_gradient(ops.stack((loss, lbox, lobj, lcls))) def build_targets(self, p, targets, imgs): indices, anch, tmasks = self.find_3_positive(p, targets) na, n_gt_max = self.na, targets.shape[1] nl, batch_size, img_size = len(p), p[0].shape[0], imgs[0].shape[1] this_target = targets.view(-1, 6) txywh = this_target[:, 2:6] * img_size txyxy = xywh2xyxy(txywh) txyxy = txyxy.view(batch_size, n_gt_max, 4) this_target = this_target.view(batch_size, n_gt_max, 6) this_mask = this_target[:, :, 1] >= 0 # (bs, gt_max) pxyxys = () p_cls = () p_obj = () all_b = () all_a = () all_gj = () all_gi = () all_anch = () all_tmasks = () # for i, pi in enumerate(p): for i in range(self.nl): pi = p[i] _this_indices = indices[i].view(4, 3 * na, batch_size, n_gt_max).transpose(0, 2, 1, 3).view(4, -1) _this_anch = anch[i].view(3 * na, batch_size, n_gt_max * 2).transpose(1, 0, 2).view(-1, 2) _this_mask = tmasks[i].view(3 * na, batch_size, n_gt_max).transpose(1, 0, 2).view(-1) # zhy_test _this_indices *= _this_mask[None, :] _this_anch *= _this_mask[:, None] b, a, gj, gi = ops.split(_this_indices, 0, 4) b, a, gj, gi = b.view(-1), a.view(-1), \ gj.view(-1), gi.view(-1) fg_pred = pi[b, a, gj, gi] p_obj += (fg_pred[:, 4:5].view(batch_size, 3 * na * n_gt_max, 1),) p_cls += (fg_pred[:, 5:].view(batch_size, 3 * na * n_gt_max, -1),) grid = ops.stack((gi, gj), axis=1) pxy = (ops.Sigmoid()(fg_pred[:, :2]) * 2. - 0.5 + grid) * self.stride[i] # / 8. pwh = (ops.Sigmoid()(fg_pred[:, 2:4]) * 2) ** 2 * _this_anch * self.stride[i] # / 8. pxywh = ops.concat((pxy, pwh), axis=-1) pxyxy = xywh2xyxy(pxywh) b, a, gj, gi, pxyxy, _this_anch, _this_mask = b.view(batch_size, -1), a.view(batch_size, -1), \ gj.view(batch_size, -1), gi.view(batch_size, -1), \ pxyxy.view(batch_size, -1, 4), \ _this_anch.view(batch_size, -1, 2), \ _this_mask.view(batch_size, -1) all_b += (b,) all_a += (a,) all_gj += (gj,) all_gi += (gi,) pxyxys += (pxyxy,) all_anch += (_this_anch,) all_tmasks += (_this_mask,) pxyxys = ops.concat(pxyxys, axis=1) # nl * (bs, 5*na*gt_max, 4) -> cat -> (bs, c, 4) # nt = bs * gt_max p_obj = ops.concat(p_obj, axis=1) p_cls = ops.concat(p_cls, axis=1) # nl * (bs, 5*na*gt_max, 80) -> (bs, nl*5*na*gt_max, 80) all_b = ops.concat(all_b, axis=1) # nl * (bs, 5*na*gt_max) -> (bs, nl*5*na*gt_max) all_a = ops.concat(all_a, axis=1) all_gj = ops.concat(all_gj, axis=1) all_gi = ops.concat(all_gi, axis=1) all_anch = ops.concat(all_anch, axis=1) all_tmasks = ops.concat(all_tmasks, axis=1) # (bs, nl*5*na*gt_max) this_mask = all_tmasks[:, None, :] * this_mask[:, :, None] # (bs, gt_max, nl*5*na*gt_max,) # (bs, gt_max, 4), (bs, nl*5*na*gt_max, 4) -> (bs, gt_max, nl*5*na*gt_max) pair_wise_iou = batch_box_iou(txyxy, pxyxys) * this_mask # (bs, gt_max, nl*5*na*gt_max,) pair_wise_iou_loss = -ops.log(pair_wise_iou + EPS) v, _ = ops.top_k(pair_wise_iou, 10) # (bs, gt_max, 10) dynamic_ks = ops.cast(v.sum(-1).clip(1, 10), ms.int32) # (bs, gt_max) # (bs, gt_max, 80) gt_cls_per_image = ops.one_hot(indices=ops.cast(this_target[:, :, 1], ms.int32), depth=self.nc, on_value=ops.ones(1, p_cls.dtype), off_value=ops.zeros(1, p_cls.dtype)) # (bs, gt_max, nl*5*na*gt_max, 80) gt_cls_per_image = ops.tile(ops.expand_dims(ops.cast(gt_cls_per_image, p_cls.dtype), 2), (1, 1, pxyxys.shape[1], 1)) cls_preds_ = ops.sqrt(ops.Sigmoid()(p_cls) * ops.Sigmoid()(p_obj)) cls_preds_ = ops.tile(ops.expand_dims(cls_preds_, 1), (1, n_gt_max, 1, 1)) # (bs, nl*5*na*gt_max, 80) -> (bs, gt_max, nl*5*na*gt_max, 80) y = cls_preds_ pair_wise_cls_loss = ops.binary_cross_entropy_with_logits( ops.log(y / (1 - y) + EPS), gt_cls_per_image, ops.ones(1, cls_preds_.dtype), ops.ones(1, cls_preds_.dtype), reduction="none", ).sum(-1) # (bs, gt_max, nl*5*na*gt_max) cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss cost = cost * this_mask cost += CLIP_VALUE * (1.0 - ops.cast(this_mask, cost.dtype)) sort_cost, sort_idx = ops.top_k(-cost, 10, sorted=True) # (bs, gt_max, 10) sort_cost = -sort_cost pos_idx = ops.stack((mnp.arange(batch_size * n_gt_max), dynamic_ks.view(-1) - 1), -1) pos_v = ops.gather_nd(sort_cost.view(batch_size * n_gt_max, 10), pos_idx).view(batch_size, n_gt_max) matching_matrix = ops.cast(cost <= pos_v[:, :, None], ms.int32) * this_mask ## delete reduplicate match label, one anchor only match one gt cost_argmin = mnp.argmin(cost, axis=1) # (bs, nl*5*na*gt_max) anchor_matching_gt_mask = ops.one_hot(cost_argmin, n_gt_max, ops.ones(1, ms.float16), ops.zeros(1, ms.float16), axis=-1).transpose(0, 2, 1) # (bs, gt_max, nl*5*na*gt_max) matching_matrix = matching_matrix * ops.cast(anchor_matching_gt_mask, matching_matrix.dtype) fg_mask_inboxes = matching_matrix.astype(ms.float16).sum(1) > 0.0 # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) all_tmasks = all_tmasks * ops.cast(fg_mask_inboxes, ms.int32) # (bs, nl*5*na*gt_max) matched_gt_inds = matching_matrix.argmax(1) # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) matched_bs_inds = ops.tile(mnp.arange(batch_size)[:, None], (1, matching_matrix.shape[2])) # (bs, nl*5*na*gt_max) matched_inds = ops.stack((matched_bs_inds.view(-1), matched_gt_inds.view(-1)), 1) # (bs*nl*5*na*gt_max, 2) # zhy_test matched_inds *= all_tmasks.view(-1)[:, None] this_target = ops.gather_nd(this_target, matched_inds) # (bs*nl*5*na*gt_max, 6) # this_target = this_target.view(-1, 6)[matched_gt_inds.view(-1,)] # (bs*nl*5*na*gt_max, 6) # (bs, nl*5*na*gt_max,) -> (bs, nl, 5*na*gt_max) -> (nl, bs*5*na*gt_max) # zhy_test matching_tmasks = all_tmasks.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) matching_bs = all_b.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_as = all_a.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gjs = all_gj.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gis = all_gi.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_targets = this_target.view(batch_size, nl, -1, 6).transpose(1, 0, 2, 3).view(nl, -1, 6) * matching_tmasks[..., None] matching_anchs = all_anch.view(batch_size, nl, -1, 2).transpose(1, 0, 2, 3).view(nl, -1, 2) * matching_tmasks[..., None] return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs, matching_tmasks def find_3_positive(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) targets = targets.view(-1, 6) # (bs, gt_max, 6) -> (bs*gt_max, 6) mask_t = targets[:, 1] >= 0 # (bs*gt_max,) na, nt = self.na, targets.shape[0] # number of anchors, targets indices, anch, tmasks = (), (), () gain = ops.ones(7, ms.int32) # normalized to gridspace gain ai = ops.tile(mnp.arange(na, dtype=targets.dtype).view(na, 1), (1, nt)) # shape: (na, nt) targets = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # (na, nt, 7) g = 0.5 # bias off = ops.cast(self._off, targets.dtype) * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = get_tensor(shape, targets.dtype)[[3, 2, 3, 2]] # xyxy gain # [W, H, W, H] # Match targets to anchors t = targets * gain # (na, nt, 7) # Matches # if nt: r = t[:, :, 4:6] / anchors[:, None, :] # wh ratio j = ops.maximum(r, 1. / r).max(2) < self.hyp_anchor_t # compare # (na, nt) # t = t[j] # filter mask_m_t = ops.logical_and(j, mask_t[None, :]).view(-1) t = t.view(-1, 7) # (na*nt, 7) # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse jk = ops.logical_and((gxy % 1. < g), (gxy > 1.)) lm = ops.logical_and((gxi % 1. < g), (gxi > 1.)) j, k = jk[:, 0], jk[:, 1] l, m = lm[:, 0], lm[:, 1] # original # j = ops.stack((ops.ones_like(j), j, k, l, m)) # shape: (5, *) # t = ops.tile(t, (5, 1, 1)) # shape(5, *, 7) # t = t.view(-1, 7) # mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) # # t = t.repeat((5, 1, 1))[j] # offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) # offsets = offsets.view(-1, 2) # (5*na*nt, 2) # # offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] # faster, tag1, tag2 = ops.tile(j[:, None], (1, 2)), ops.tile(k[:, None], (1, 2)) j_l = ops.logical_or(j, l).astype(ms.int32) k_m = ops.logical_or(k, m).astype(ms.int32) center = ops.ones_like(j_l) j = ops.stack((center, j_l, k_m)) mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) t = ops.tile(t, (3, 1, 1)) # shape(5, *, 7) t = t.view(-1, 7) offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) offsets_new = ops.zeros((3,) + offsets.shape[1:], offsets.dtype) # offsets_new[0, :, :] = offsets[0, :, :] offsets_new[1, :, :] = ops.select(tag1.astype(ms.bool_), offsets[1, ...], offsets[3, ...]) offsets_new[2, :, :] = ops.select(tag2.astype(ms.bool_), offsets[2, ...], offsets[4, ...]) offsets = offsets_new offsets = offsets.view(-1, 2) # Define b, c, gxy, gwh, a = ops.cast(t[:, 0], ms.int32), \ ops.cast(t[:, 1], ms.int32), \ t[:, 2:4], \ t[:, 4:6], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors # b: (5*na*nt,), gxy: (5*na*nt, 2) # gij = gxy - offsets gij = ops.cast(gxy - offsets, ms.int32) gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[3] - 1) gj = gj.clip(0, shape[2] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid anch += (anchors[a],) # anchors tmasks += (mask_m_t,) return indices, anch, tmasks @register_model class YOLOv7AuxLoss(nn.Cell): def __init__( self, box, obj, cls, anchor_t, label_smoothing, fl_gamma, cls_pw, obj_pw, anchors, stride, nc, **kwargs): super(YOLOv7AuxLoss, self).__init__() self.hyp_box = box self.hyp_obj = obj self.hyp_cls = cls self.hyp_anchor_t = anchor_t self.nc = nc # number of classes self.na = len(anchors[0]) // 2 # number of anchors self.nl = len(anchors) # number of layers # modify weight of box/obj/cls when aux_loss self.hyp_box *= 3. / self.nl self.hyp_cls *= self.nc / 80. * 3. / self.nl self.hyp_obj *= 2.0 ** 2 * 3. / self.nl stride = np.array(stride) anchors = np.array(anchors).reshape((self.nl, -1, 2)) anchors = anchors / stride.reshape((-1, 1, 1)) self.stride = Tensor(stride, ms.int32) self.anchors = Tensor(anchors, ms.float32) # shape(nl,na,2) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=label_smoothing) # positive, negative BCE targets # Focal loss g = fl_gamma # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(bce_pos_weight=Tensor([cls_pw], ms.float32), gamma=g), \ FocalLoss(bce_pos_weight=Tensor([obj_pw], ms.float32), gamma=g) else: # Define criteria BCEcls = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([cls_pw]), ms.float32)) BCEobj = BCEWithLogitsLoss(bce_pos_weight=Tensor(np.array([obj_pw]), ms.float32)) _balance = {3: [4.0, 1.0, 0.4]}.get(self.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.balance = ms.Parameter(Tensor(_balance, ms.float32), requires_grad=False) self.BCEcls, self.BCEobj, self.gr = BCEcls, BCEobj, 1.0 self._off = Tensor([ [0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], dtype=ms.float32) self.loss_item_name = ['loss', 'lbox', 'lobj', 'lcls'] # branch name returned by loss for print def construct(self, p, targets, imgs): lcls, lbox, lobj = 0., 0., 0. targets_ori = targets bs, as_, gjs, gis, targets, anchors, tmasks = self.build_targets(p[:self.nl], targets_ori, imgs) # bs: (nl, bs*3*na*gt_max) bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux, tmasks_aux = self.build_targets_2(p[:self.nl], targets_ori, imgs) # bs: (nl, bs*5*na*gt_max) bs, as_, gjs, gis, targets, anchors, tmasks = ops.stop_gradient(bs), ops.stop_gradient(as_), \ ops.stop_gradient(gjs), ops.stop_gradient(gis), \ ops.stop_gradient(targets), ops.stop_gradient(anchors), \ ops.stop_gradient(tmasks) bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux, tmasks_aux = ops.stop_gradient(bs_aux),\ ops.stop_gradient(as_aux_), \ ops.stop_gradient(gjs_aux),\ ops.stop_gradient(gis_aux), \ ops.stop_gradient(targets_aux), \ ops.stop_gradient(anchors_aux), \ ops.stop_gradient(tmasks_aux) pre_gen_gains = () # pre_gen_gains_aux = () for pp in p[:self.nl]: pre_gen_gains += (get_tensor(pp.shape, targets.dtype)[[3, 2, 3, 2]],) # pre_gen_gains_aux += (get_tensor(pp.shape, targets.dtype)[[3, 2, 3, 2]],) # Losses for i in range(self.nl): # layer index pi = p[i] # layer predictions pi_aux = p[i + self.nl] b, a, gj, gi, tmask = bs[i], as_[i], gjs[i], gis[i], tmasks[i] # image, anchor, gridy, gridx, tmask b_aux, a_aux, gj_aux, gi_aux, tmask_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i], tmasks_aux[i] tobj = ops.zeros_like(pi[..., 0]) # target obj tobj_aux = ops.zeros_like(pi_aux[..., 0]) # target obj # 1. Branch1, Compute main branch loss n = b.shape[0] # number of targets ps = pi[b, a, gj, gi] # prediction subset corresponding to targets # 1.1. Regression grid = ops.stack([gi, gj], axis=1) pxy = ops.Sigmoid()(ps[:, :2]) * 2. - 0.5 pwh = (ops.Sigmoid()(ps[:, 2:4]) * 2) ** 2 * anchors[i] pbox = ops.concat((pxy, pwh), 1) # predicted box selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] selected_tbox[:, :2] -= grid iou = bbox_iou(pbox, selected_tbox, xywh=True, CIoU=True).view(-1) lbox += ((1.0 - iou) * tmask).sum() / tmask.astype(iou.dtype).sum().clip(1, None) # iou loss # 1.2. Objectness tobj[b, a, gj, gi] = ((1.0 - self.gr) + self.gr * ops.stop_gradient(iou).clip(0, None)) * tmask # iou ratio obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss # 1.3. Classification selected_tcls = ops.cast(targets[i][:, 1], ms.int32) if self.nc > 1: # cls loss (only if multiple classes) t = ops.ones_like(ps[:, 5:]) * self.cn # targets t[mnp.arange(n), selected_tcls] = self.cp lcls += self.BCEcls(ps[:, 5:], t, ops.tile(tmask[:, None], (1, t.shape[1]))) # BCE # 2. Branch2, Compute Aux branch loss n_aux = b_aux.shape[0] # number of targets ps_aux = pi[b_aux, a_aux, gj_aux, gi_aux] # prediction subset corresponding to targets # 2.1. Regression grid_aux = ops.stack([gi_aux, gj_aux], axis=1) pxy_aux = ops.Sigmoid()(ps_aux[:, :2]) * 2. - 0.5 pwh_aux = (ops.Sigmoid()(ps_aux[:, 2:4]) * 2) ** 2 * anchors_aux[i] pbox_aux = ops.concat((pxy_aux, pwh_aux), 1) # predicted box selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains[i] selected_tbox_aux[:, :2] -= grid_aux iou_aux = bbox_iou(pbox_aux, selected_tbox_aux, xywh=True, CIoU=True).view(-1) lbox += 0.25 * ((1.0 - iou_aux) * tmask_aux).sum() / tmask_aux.astype(iou_aux.dtype).sum().clip(1, None) # iou loss # 1.2. Objectness tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = ((1.0 - self.gr) + self.gr * ops.stop_gradient(iou_aux).clip(0, None)) * tmask_aux # iou ratio obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux) lobj += 0.25 * obji_aux * self.balance[i] # obj loss # 1.3. Classification selected_tcls_aux = ops.cast(targets_aux[i][:, 1], ms.int32) if self.nc > 1: # cls loss (only if multiple classes) t_aux = ops.ones_like(ps_aux[:, 5:]) * self.cn # targets t_aux[mnp.arange(n_aux), selected_tcls_aux] = self.cp lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux, ops.tile(tmask_aux[:, None], (1, t_aux.shape[1]))) # BCE lbox *= self.hyp_box lobj *= self.hyp_obj lcls *= self.hyp_cls bs = p[0].shape[0] # batch size loss = lbox + lobj + lcls return loss * bs, ops.stop_gradient(ops.stack((loss, lbox, lobj, lcls))) def build_targets(self, p, targets, imgs): indices, anch, tmasks = self.find_3_positive(p, targets) na, n_gt_max = self.na, targets.shape[1] nl, batch_size, img_size = len(p), p[0].shape[0], imgs[0].shape[1] this_target = targets.view(-1, 6) txywh = this_target[:, 2:6] * img_size txyxy = xywh2xyxy(txywh) txyxy = txyxy.view(batch_size, n_gt_max, 4) this_target = this_target.view(batch_size, n_gt_max, 6) this_mask = this_target[:, :, 1] >= 0 # (bs, gt_max) pxyxys = () p_cls = () p_obj = () all_b = () all_a = () all_gj = () all_gi = () all_anch = () all_tmasks = () # for i, pi in enumerate(p): for i in range(self.nl): pi = p[i] _this_indices = indices[i].view(4, 3 * na, batch_size, n_gt_max).transpose(0, 2, 1, 3).view(4, -1) _this_anch = anch[i].view(3 * na, batch_size, n_gt_max * 2).transpose(1, 0, 2).view(-1, 2) _this_mask = tmasks[i].view(3 * na, batch_size, n_gt_max).transpose(1, 0, 2).view(-1) # zhy_test _this_indices *= _this_mask[None, :] _this_anch *= _this_mask[:, None] b, a, gj, gi = ops.split(_this_indices, 0, 4) b, a, gj, gi = b.view(-1), a.view(-1), \ gj.view(-1), gi.view(-1) fg_pred = pi[b, a, gj, gi] p_obj += (fg_pred[:, 4:5].view(batch_size, 3 * na * n_gt_max, 1),) p_cls += (fg_pred[:, 5:].view(batch_size, 3 * na * n_gt_max, -1),) grid = ops.stack((gi, gj), axis=1) pxy = (ops.Sigmoid()(fg_pred[:, :2]) * 2. - 0.5 + grid) * self.stride[i] # / 8. pwh = (ops.Sigmoid()(fg_pred[:, 2:4]) * 2) ** 2 * _this_anch * self.stride[i] # / 8. pxywh = ops.concat((pxy, pwh), axis=-1) pxyxy = xywh2xyxy(pxywh) b, a, gj, gi, pxyxy, _this_anch, _this_mask = b.view(batch_size, -1), a.view(batch_size, -1), \ gj.view(batch_size, -1), gi.view(batch_size, -1), \ pxyxy.view(batch_size, -1, 4), \ _this_anch.view(batch_size, -1, 2), \ _this_mask.view(batch_size, -1) all_b += (b,) all_a += (a,) all_gj += (gj,) all_gi += (gi,) pxyxys += (pxyxy,) all_anch += (_this_anch,) all_tmasks += (_this_mask,) pxyxys = ops.concat(pxyxys, axis=1) # nl * (bs, 5*na*gt_max, 4) -> cat -> (bs, c, 4) # nt = bs * gt_max p_obj = ops.concat(p_obj, axis=1) p_cls = ops.concat(p_cls, axis=1) # nl * (bs, 5*na*gt_max, 80) -> (bs, nl*5*na*gt_max, 80) all_b = ops.concat(all_b, axis=1) # nl * (bs, 5*na*gt_max) -> (bs, nl*5*na*gt_max) all_a = ops.concat(all_a, axis=1) all_gj = ops.concat(all_gj, axis=1) all_gi = ops.concat(all_gi, axis=1) all_anch = ops.concat(all_anch, axis=1) all_tmasks = ops.concat(all_tmasks, axis=1) # (bs, nl*5*na*gt_max) this_mask = all_tmasks[:, None, :] * this_mask[:, :, None] # (bs, gt_max, nl*5*na*gt_max,) # (bs, gt_max, 4), (bs, nl*5*na*gt_max, 4) -> (bs, gt_max, nl*5*na*gt_max) pair_wise_iou = batch_box_iou(txyxy, pxyxys) * this_mask # (bs, gt_max, nl*5*na*gt_max,) pair_wise_iou_loss = -ops.log(pair_wise_iou + EPS) # Top 20 iou sum for aux, default 10 v, _ = ops.top_k(pair_wise_iou, 20) # (bs, gt_max, 20) dynamic_ks = ops.cast(v.sum(-1).clip(1, 20), ms.int32) # (bs, gt_max) # (bs, gt_max, 80) gt_cls_per_image = ops.one_hot(indices=ops.cast(this_target[:, :, 1], ms.int32), depth=self.nc, on_value=ops.ones(1, p_cls.dtype), off_value=ops.zeros(1, p_cls.dtype)) # (bs, gt_max, nl*5*na*gt_max, 80) gt_cls_per_image = ops.tile(ops.expand_dims(ops.cast(gt_cls_per_image, p_cls.dtype), 2), (1, 1, pxyxys.shape[1], 1)) cls_preds_ = ops.sqrt(ops.Sigmoid()(p_cls) * ops.Sigmoid()(p_obj)) cls_preds_ = ops.tile(ops.expand_dims(cls_preds_, 1), (1, n_gt_max, 1, 1)) # (bs, nl*5*na*gt_max, 80) -> (bs, gt_max, nl*5*na*gt_max, 80) y = cls_preds_ pair_wise_cls_loss = ops.binary_cross_entropy_with_logits( ops.log(y / (1 - y) + EPS), gt_cls_per_image, ops.ones(1, cls_preds_.dtype), ops.ones(1, cls_preds_.dtype), reduction="none", ).sum(-1) # (bs, gt_max, nl*5*na*gt_max) cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss cost = cost * this_mask cost += CLIP_VALUE * (1.0 - ops.cast(this_mask, cost.dtype)) sort_cost, sort_idx = ops.top_k(-cost, 20, sorted=True) # (bs, gt_max, 20) sort_cost = -sort_cost pos_idx = ops.stack((mnp.arange(batch_size * n_gt_max), dynamic_ks.view(-1) - 1), -1) pos_v = ops.gather_nd(sort_cost.view(batch_size * n_gt_max, 20), pos_idx).view(batch_size, n_gt_max) matching_matrix = ops.cast(cost <= pos_v[:, :, None], ms.int32) * this_mask ## delete reduplicate match label, one anchor only match one gt cost_argmin = mnp.argmin(cost, axis=1) # (bs, nl*5*na*gt_max) anchor_matching_gt_mask = ops.one_hot(cost_argmin, n_gt_max, ops.ones(1, ms.float16), ops.zeros(1, ms.float16), axis=-1).transpose(0, 2, 1) # (bs, gt_max, nl*5*na*gt_max) matching_matrix = matching_matrix * ops.cast(anchor_matching_gt_mask, matching_matrix.dtype) fg_mask_inboxes = matching_matrix.astype(ms.float16).sum(1) > 0.0 # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) all_tmasks = all_tmasks * ops.cast(fg_mask_inboxes, ms.int32) # (bs, nl*5*na*gt_max) matched_gt_inds = matching_matrix.argmax(1) # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) matched_bs_inds = ops.tile(mnp.arange(batch_size)[:, None], (1, matching_matrix.shape[2])) # (bs, nl*5*na*gt_max) matched_inds = ops.stack((matched_bs_inds.view(-1), matched_gt_inds.view(-1)), 1) # (bs*nl*5*na*gt_max, 2) matched_inds *= all_tmasks.view(-1)[:, None] this_target = ops.gather_nd(this_target, matched_inds) # (bs*nl*5*na*gt_max, 6) # this_target = this_target.view(-1, 6)[matched_gt_inds.view(-1,)] # (bs*nl*5*na*gt_max, 6) # (bs, nl*5*na*gt_max,) -> (bs, nl, 5*na*gt_max) -> (nl, bs*5*na*gt_max) matching_tmasks = all_tmasks.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) matching_bs = all_b.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_as = all_a.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gjs = all_gj.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gis = all_gi.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_targets = this_target.view(batch_size, nl, -1, 6).transpose(1, 0, 2, 3).view(nl, -1, 6) * matching_tmasks[..., None] matching_anchs = all_anch.view(batch_size, nl, -1, 2).transpose(1, 0, 2, 3).view(nl, -1, 2) * matching_tmasks[..., None] return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs, matching_tmasks def build_targets_2(self, p, targets, imgs): indices, anch, tmasks = self.find_5_positive(p, targets) na, n_gt_max = self.na, targets.shape[1] nl, batch_size, img_size = len(p), p[0].shape[0], imgs[0].shape[1] this_target = targets.view(-1, 6) txywh = this_target[:, 2:6] * img_size txyxy = xywh2xyxy(txywh) txyxy = txyxy.view(batch_size, n_gt_max, 4) this_target = this_target.view(batch_size, n_gt_max, 6) this_mask = this_target[:, :, 1] >= 0 # (bs, gt_max) pxyxys = () p_cls = () p_obj = () all_b = () all_a = () all_gj = () all_gi = () all_anch = () all_tmasks = () # for i, pi in enumerate(p): for i in range(self.nl): pi = p[i] _this_indices = indices[i].view(4, 5 * na, batch_size, n_gt_max).transpose(0, 2, 1, 3).view(4, -1) _this_anch = anch[i].view(5 * na, batch_size, n_gt_max * 2).transpose(1, 0, 2).view(-1, 2) _this_mask = tmasks[i].view(5 * na, batch_size, n_gt_max).transpose(1, 0, 2).view(-1) # zhy_test _this_indices *= _this_mask[None, :] _this_anch *= _this_mask[:, None] b, a, gj, gi = ops.split(_this_indices, 0, 4) b, a, gj, gi = b.view(-1), a.view(-1), \ gj.view(-1), gi.view(-1) fg_pred = pi[b, a, gj, gi] p_obj += (fg_pred[:, 4:5].view(batch_size, 5 * na * n_gt_max, 1),) p_cls += (fg_pred[:, 5:].view(batch_size, 5 * na * n_gt_max, -1),) grid = ops.stack((gi, gj), axis=1) pxy = (ops.Sigmoid()(fg_pred[:, :2]) * 2. - 0.5 + grid) * self.stride[i] # / 8. pwh = (ops.Sigmoid()(fg_pred[:, 2:4]) * 2) ** 2 * _this_anch * self.stride[i] # / 8. pxywh = ops.concat((pxy, pwh), axis=-1) pxyxy = xywh2xyxy(pxywh) b, a, gj, gi, pxyxy, _this_anch, _this_mask = b.view(batch_size, -1), a.view(batch_size, -1), \ gj.view(batch_size, -1), gi.view(batch_size, -1), \ pxyxy.view(batch_size, -1, 4), \ _this_anch.view(batch_size, -1, 2), \ _this_mask.view(batch_size, -1) all_b += (b,) all_a += (a,) all_gj += (gj,) all_gi += (gi,) pxyxys += (pxyxy,) all_anch += (_this_anch,) all_tmasks += (_this_mask,) pxyxys = ops.concat(pxyxys, axis=1) # nl * (bs, 5*na*gt_max, 4) -> cat -> (bs, c, 4) # nt = bs * gt_max p_obj = ops.concat(p_obj, axis=1) p_cls = ops.concat(p_cls, axis=1) # nl * (bs, 5*na*gt_max, 80) -> (bs, nl*5*na*gt_max, 80) all_b = ops.concat(all_b, axis=1) # nl * (bs, 5*na*gt_max) -> (bs, nl*5*na*gt_max) all_a = ops.concat(all_a, axis=1) all_gj = ops.concat(all_gj, axis=1) all_gi = ops.concat(all_gi, axis=1) all_anch = ops.concat(all_anch, axis=1) all_tmasks = ops.concat(all_tmasks, axis=1) # (bs, nl*5*na*gt_max) this_mask = all_tmasks[:, None, :] * this_mask[:, :, None] # (bs, gt_max, nl*5*na*gt_max,) # (bs, gt_max, 4), (bs, nl*5*na*gt_max, 4) -> (bs, gt_max, nl*5*na*gt_max) pair_wise_iou = batch_box_iou(txyxy, pxyxys) * this_mask # (bs, gt_max, nl*5*na*gt_max,) pair_wise_iou_loss = -ops.log(pair_wise_iou + EPS) # Top 20 iou sum for aux, default 10 v, _ = ops.top_k(pair_wise_iou, 20) # (bs, gt_max, 20) dynamic_ks = ops.cast(v.sum(-1).clip(1, 20), ms.int32) # (bs, gt_max) # (bs, gt_max, 80) gt_cls_per_image = ops.one_hot(indices=ops.cast(this_target[:, :, 1], ms.int32), depth=self.nc, on_value=ops.ones(1, p_cls.dtype), off_value=ops.zeros(1, p_cls.dtype)) # (bs, gt_max, nl*5*na*gt_max, 80) gt_cls_per_image = ops.tile(ops.expand_dims(ops.cast(gt_cls_per_image, p_cls.dtype), 2), (1, 1, pxyxys.shape[1], 1)) cls_preds_ = ops.sqrt(ops.Sigmoid()(p_cls) * ops.Sigmoid()(p_obj)) cls_preds_ = ops.tile(ops.expand_dims(cls_preds_, 1), (1, n_gt_max, 1, 1)) # (bs, nl*5*na*gt_max, 80) -> (bs, gt_max, nl*5*na*gt_max, 80) y = cls_preds_ pair_wise_cls_loss = ops.binary_cross_entropy_with_logits( ops.log(y / (1 - y) + EPS), gt_cls_per_image, ops.ones(1, cls_preds_.dtype), ops.ones(1, cls_preds_.dtype), reduction="none", ).sum(-1) # (bs, gt_max, nl*5*na*gt_max) cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss cost = cost * this_mask cost += CLIP_VALUE * (1.0 - ops.cast(this_mask, cost.dtype)) sort_cost, sort_idx = ops.top_k(-cost, 20, sorted=True) # (bs, gt_max, 20) sort_cost = -sort_cost pos_idx = ops.stack((mnp.arange(batch_size * n_gt_max), dynamic_ks.view(-1) - 1), -1) pos_v = ops.gather_nd(sort_cost.view(batch_size * n_gt_max, 20), pos_idx).view(batch_size, n_gt_max) matching_matrix = ops.cast(cost <= pos_v[:, :, None], ms.int32) * this_mask ## delete reduplicate match label, one anchor only match one gt cost_argmin = mnp.argmin(cost, axis=1) # (bs, nl*5*na*gt_max) anchor_matching_gt_mask = ops.one_hot(cost_argmin, n_gt_max, ops.ones(1, ms.float16), ops.zeros(1, ms.float16), axis=-1).transpose(0, 2, 1) # (bs, gt_max, nl*5*na*gt_max) matching_matrix = matching_matrix * ops.cast(anchor_matching_gt_mask, matching_matrix.dtype) fg_mask_inboxes = matching_matrix.astype(ms.float16).sum( 1) > 0.0 # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) all_tmasks = all_tmasks * ops.cast(fg_mask_inboxes, ms.int32) # (bs, nl*5*na*gt_max) matched_gt_inds = matching_matrix.argmax(1) # (bs, gt_max, nl*5*na*gt_max) -> (bs, nl*5*na*gt_max) matched_bs_inds = ops.tile(mnp.arange(batch_size)[:, None], (1, matching_matrix.shape[2])) # (bs, nl*5*na*gt_max) matched_inds = ops.stack((matched_bs_inds.view(-1), matched_gt_inds.view(-1)), 1) # (bs*nl*5*na*gt_max, 2) matched_inds *= all_tmasks.view(-1)[:, None] this_target = ops.gather_nd(this_target, matched_inds) # (bs*nl*5*na*gt_max, 6) # this_target = this_target.view(-1, 6)[matched_gt_inds.view(-1,)] # (bs*nl*5*na*gt_max, 6) # (bs, nl*5*na*gt_max,) -> (bs, nl, 5*na*gt_max) -> (nl, bs*5*na*gt_max) matching_tmasks = all_tmasks.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) matching_bs = all_b.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_as = all_a.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gjs = all_gj.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_gis = all_gi.view(batch_size, nl, -1).transpose(1, 0, 2).view(nl, -1) * matching_tmasks matching_targets = this_target.view(batch_size, nl, -1, 6).transpose(1, 0, 2, 3).view(nl, -1, 6) * \ matching_tmasks[..., None] matching_anchs = all_anch.view(batch_size, nl, -1, 2).transpose(1, 0, 2, 3).view(nl, -1, 2) * matching_tmasks[..., None] return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs, matching_tmasks def find_3_positive(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) targets = targets.view(-1, 6) # (bs, gt_max, 6) -> (bs*gt_max, 6) mask_t = targets[:, 1] >= 0 # (bs*gt_max,) na, nt = self.na, targets.shape[0] # number of anchors, targets indices, anch, tmasks = (), (), () gain = ops.ones(7, ms.int32) # normalized to gridspace gain ai = ops.tile(mnp.arange(na, dtype=targets.dtype).view(na, 1), (1, nt)) # shape: (na, nt) targets = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # (na, nt, 7) g = 0.5 # bias off = ops.cast(self._off, targets.dtype) * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = get_tensor(shape, targets.dtype)[[3, 2, 3, 2]] # xyxy gain # [W, H, W, H] # Match targets to anchors t = targets * gain # (na, nt, 7) # Matches # if nt: r = t[:, :, 4:6] / anchors[:, None, :] # wh ratio j = ops.maximum(r, 1. / r).max(2) < self.hyp_anchor_t # compare # (na, nt) # t = t[j] # filter mask_m_t = ops.logical_and(j, mask_t[None, :]).view(-1) t = t.view(-1, 7) # (na*nt, 7) # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse jk = ops.logical_and((gxy % 1. < g), (gxy > 1.)) lm = ops.logical_and((gxi % 1. < g), (gxi > 1.)) j, k = jk[:, 0], jk[:, 1] l, m = lm[:, 0], lm[:, 1] # original # j = ops.stack((ops.ones_like(j), j, k, l, m)) # shape: (5, *) # t = ops.tile(t, (5, 1, 1)) # shape(5, *, 7) # t = t.view(-1, 7) # mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) # # t = t.repeat((5, 1, 1))[j] # offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) # offsets = offsets.view(-1, 2) # (5*na*nt, 2) # # offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] # faster, tag1, tag2 = ops.tile(j[:, None], (1, 2)), ops.tile(k[:, None], (1, 2)) j_l = ops.logical_or(j, l).astype(ms.int32) k_m = ops.logical_or(k, m).astype(ms.int32) center = ops.ones_like(j_l) j = ops.stack((center, j_l, k_m)) mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) t = ops.tile(t, (3, 1, 1)) # shape(5, *, 7) t = t.view(-1, 7) offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) offsets_new = ops.zeros((3,) + offsets.shape[1:], offsets.dtype) # offsets_new[0, :, :] = offsets[0, :, :] offsets_new[1, :, :] = ops.select(tag1.astype(ms.bool_), offsets[1, ...], offsets[3, ...]) offsets_new[2, :, :] = ops.select(tag2.astype(ms.bool_), offsets[2, ...], offsets[4, ...]) offsets = offsets_new offsets = offsets.view(-1, 2) # Define b, c, gxy, gwh, a = ops.cast(t[:, 0], ms.int32), \ ops.cast(t[:, 1], ms.int32), \ t[:, 2:4], \ t[:, 4:6], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors # b: (5*na*nt,), gxy: (5*na*nt, 2) # gij = gxy - offsets gij = ops.cast(gxy - offsets, ms.int32) gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[3] - 1) gj = gj.clip(0, shape[2] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid anch += (anchors[a],) # anchors tmasks += (mask_m_t,) return indices, anch, tmasks def find_5_positive(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) targets = targets.view(-1, 6) # (bs, gt_max, 6) -> (bs*gt_max, 6) mask_t = targets[:, 1] >= 0 # (bs*gt_max,) na, nt = self.na, targets.shape[0] # number of anchors, targets indices, anch, tmasks = (), (), () gain = ops.ones(7, ms.int32) # normalized to gridspace gain ai = ops.tile(mnp.arange(na, dtype=targets.dtype).view(na, 1), (1, nt)) # shape: (na, nt) targets = ops.concat((ops.tile(targets, (na, 1, 1)), ai[:, :, None]), 2) # append anchor indices # (na, nt, 7) g = 1.0 # bias off = ops.cast(self._off, targets.dtype) * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = get_tensor(shape, targets.dtype)[[3, 2, 3, 2]] # xyxy gain # [W, H, W, H] # Match targets to anchors t = targets * gain # (na, nt, 7) # Matches r = t[:, :, 4:6] / anchors[:, None, :] # wh ratio j = ops.maximum(r, 1. / r).max(2) < self.hyp_anchor_t # compare # (na, nt) # t = t[j] # filter mask_m_t = ops.logical_and(j, mask_t[None, :]).view(-1) # filter t = t.view(-1, 7) # (na*nt, 7) # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse jk = ops.logical_and((gxy % 1. < g), (gxy > 1.)).astype(ms.int32) lm = ops.logical_and((gxi % 1. < g), (gxi > 1.)).astype(ms.int32) j, k = jk[:, 0], jk[:, 1] l, m = lm[:, 0], lm[:, 1] # original j = ops.stack((ops.ones_like(j), j, k, l, m)) # shape: (5, *) t = ops.tile(t, (5, 1, 1)) # shape(5, *, 7) t = t.view(-1, 7) mask_m_t = (ops.cast(j, ms.int32) * ops.cast(mask_m_t[None, :], ms.int32)).view(-1) offsets = (ops.zeros_like(gxy)[None, :, :] + off[:, None, :]) # (1,*,2) + (5,1,2) -> (5,na*nt,2) offsets = offsets.view(-1, 2) # (5*na*nt, 2) # Define b, c, gxy, gwh, a = ops.cast(t[:, 0], ms.int32), \ ops.cast(t[:, 1], ms.int32), \ t[:, 2:4], \ t[:, 4:6], \ ops.cast(t[:, 6], ms.int32) # (image, class), grid xy, grid wh, anchors # b: (5*na*nt,), gxy: (5*na*nt, 2) # gij = gxy - offsets gij = ops.cast(gxy - offsets, ms.int32) gi, gj = gij[:, 0], gij[:, 1] # grid indices gi = gi.clip(0, shape[3] - 1) gj = gj.clip(0, shape[2] - 1) # Append indices += (ops.stack((b, a, gj, gi), 0),) # image, anchor, grid anch += (anchors[a],) # anchors tmasks += (mask_m_t,) return indices, anch, tmasks def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = ops.Identity()(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y @ops.constexpr def get_tensor(x, dtype=ms.float32): return Tensor(x, dtype) if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.losses.loss_factory import create_loss cfg = parse_config() loss_fn = create_loss(name='YOLOv7Loss', **cfg.loss, anchors=cfg.network.get('anchors', None), stride=cfg.network.get('stride', None), nc=cfg.data.get('nc', None)) print(f"loss_fn is {loss_fn}") yolov/mindyolo/models/model_factory.py000066400000000000000000000134221443434675500205650ustar00rootroot00000000000000import os import math from copy import deepcopy from mindspore import nn, ops, load_checkpoint, load_param_into_net from .registry import is_model, model_entrypoint from .layers import * from .heads import * from mindyolo.utils import logger __all__ = [ 'create_model', 'build_model_from_cfg' ] def create_model( model_name: str, model_cfg: dict = None, in_channels: int = 3, num_classes: int = 80, checkpoint_path: str = '', **kwargs): model_args = dict(cfg=model_cfg, num_classes=num_classes, in_channels=in_channels) kwargs = {k: v for k, v in kwargs.items() if v is not None} if not is_model(model_name): raise RuntimeError(f'Unknown model {model_name}') create_fn = model_entrypoint(model_name) model = create_fn(**model_args, **kwargs) if os.path.exists(checkpoint_path): checkpoint_param = load_checkpoint(checkpoint_path) load_param_into_net(model, checkpoint_param) logger.info(f'create model load checkpoint from [{checkpoint_path}] success.') return model # Tools: build model from yaml cfg def build_model_from_cfg(**kwargs): return Model(**kwargs) class Model(nn.Cell): def __init__(self, model_cfg, in_channels=3, num_classes=80, sync_bn=False): super(Model, self).__init__() self.model, self.save, self.layers_param = parse_model(deepcopy(model_cfg), ch=[in_channels], nc=num_classes, sync_bn=sync_bn) # Recompute if hasattr(model_cfg, 'recompute') and model_cfg.recompute and model_cfg.recompute_layers > 0: for i in range(model_cfg.recompute_layers): self.model[i].recompute() print(f"Turn on recompute, and the results of the first {model_cfg.recompute_layers} layers " f"will be recomputed.") def construct(self, x): y, dt = (), () # outputs for i in range(len(self.model)): m = self.model[i] iol, f, _, _ = self.layers_param[i] # iol: index of layers if not (isinstance(f, int) and f == -1): # if not from previous layer # x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if isinstance(f, int): x = y[f] else: _x = () for j in f: if j == -1: _x += (x,) else: _x += (y[j],) x = _x # print("index m: ", iol) # print if debug on pynative mode, not available on graph mode. x = m(x) # run y += (x if iol in self.save else None,) # save output return x @staticmethod @ops.constexpr def _get_h_w_list(ratio, gs, hw): return tuple([math.ceil(x * ratio / gs) * gs for x in hw]) def parse_model(d, ch, nc, sync_bn=False): # model_dict, input_channels(3) _SYNC_BN = sync_bn if _SYNC_BN: print('Parse model with Sync BN.') print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) anchors, stride, gd, gw = d.anchors, d.stride, d.depth_multiple, d.width_multiple na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out layers_param = [] for i, (f, n, m, args) in enumerate(d.backbone + d.head): # from, number, module, args kwargs = {} m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in (nn.Conv2d, ConvNormAct, RepConv, DownC, SPPCSPC, SPPF, C3, C2f, Bottleneck, Residualblock): c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = math.ceil(c2 * gw / 8) * 8 args = [c1, c2, *args[1:]] if m in (ConvNormAct, RepConv, DownC, SPPCSPC, SPPF, C3, C2f, Bottleneck, Residualblock): kwargs["sync_bn"] = sync_bn if m in (DownC, SPPCSPC, C3, C2f): args.insert(2, n) # number of repeats n = 1 elif m in (nn.BatchNorm2d, nn.SyncBatchNorm): args = [ch[f]] elif m in (Concat,): c2 = sum([ch[x] for x in f]) elif m is Shortcut: c2 = ch[f[0]] elif m in (YOLOv7Head, YOLOv7AuxHead, YOLOv5Head, YOLOv4Head, YOLOv3Head): args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is ReOrg: c2 = ch[f] * 4 else: c2 = ch[f] m_ = nn.SequentialCell([m(*args, **kwargs) for _ in range(n)]) if n > 1 else m(*args, **kwargs) t = str(m) # module type np = sum([x.size for x in m_.get_parameters()]) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params layers_param.append((i, f, t, np)) print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.CellList(layers), sorted(save), layers_param yolov/mindyolo/models/registry.py000066400000000000000000000061501443434675500176060ustar00rootroot00000000000000'''model registry and list''' import sys import fnmatch from collections import defaultdict __all__ = [ 'list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules', 'is_model_pretrained' ] _module_to_models = defaultdict(set) _model_to_module = {} _model_entrypoints = {} _model_has_pretrained = set() def register_model(fn): # lookup containing module mod = sys.modules[fn.__module__] module_name_split = fn.__module__.split('.') module_name = module_name_split[-1] if len(module_name_split) else '' # add model to __all__ in module model_name = fn.__name__ if hasattr(mod, '__all__'): mod.__all__.append(model_name) else: mod.__all__ = [model_name] # add entries to registry dict/sets _model_entrypoints[model_name] = fn _model_to_module[model_name] = module_name _module_to_models[module_name].add(model_name) has_pretrained = False if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs: cfg = mod.default_cfgs[model_name] has_pretrained = 'url' in cfg and cfg['url'] if has_pretrained: _model_has_pretrained.add(model_name) return fn def list_models(filter='', module='', pretrained=False, exclude_filters=''): if module: all_models = list(_module_to_models[module]) else: all_models = _model_entrypoints.keys() if filter: models = [] include_filters = filter if isinstance(filter, (tuple, list)) else [filter] for f in include_filters: include_models = fnmatch.filter(all_models, f) # include these models if include_models: models = set(models).union(include_models) else: models = all_models if exclude_filters: if not isinstance(exclude_filters, (tuple, list)): exclude_filters = [exclude_filters] for xf in exclude_filters: exclude_models = fnmatch.filter(models, xf) # exclude these models if exclude_models: models = set(models).difference(exclude_models) if pretrained: models = _model_has_pretrained.intersection(models) models = sorted(list(models)) return models def is_model(model_name): """ Check if a model name exists """ return model_name in _model_entrypoints def model_entrypoint(model_name): """ Fetch a model entrypoint for specified model name """ return _model_entrypoints[model_name] def list_modules(): """ Return list of module names that contain models / model entrypoints """ modules = _module_to_models.keys() return list(sorted(modules)) def is_model_in_modules(model_name, module_names): """ Check if a model exists within a subset of modules Args: model_name (str) - name of model to check module_names (tuple, list, set) - names of modules to search in """ assert isinstance(module_names, (tuple, list, set)) return any(model_name in _module_to_models[n] for n in module_names) def is_model_pretrained(model_name): return model_name in _model_has_pretrained yolov/mindyolo/models/yolov3.py000066400000000000000000000041541443434675500171730ustar00rootroot00000000000000import numpy as np import mindspore as ms from mindspore import nn, Tensor from .model_factory import build_model_from_cfg from .registry import register_model from .initializer import initialize_defult from .heads.yolov3_head import YOLOv3Head __all__ = [ 'YOLOv3', 'yolov3' ] def _cfg(url='', **kwargs): return { 'url': url, **kwargs } default_cfgs = { 'yolov3': _cfg(url='') } class YOLOv3(nn.Cell): def __init__(self, cfg, in_channels=3, num_classes=None, sync_bn=False): super(YOLOv3, self).__init__() self.cfg = cfg self.stride = Tensor(np.array(cfg.stride), ms.int32) self.stride_max = int(max(self.cfg.stride)) ch, nc = in_channels, num_classes self.nc = nc # override yaml value self.model = build_model_from_cfg(model_cfg=cfg, in_channels=ch, num_classes=nc, sync_bn=sync_bn) self.names = [str(i) for i in range(nc)] # default names self.reset_parameter() def construct(self, x): return self.model(x) def reset_parameter(self): # init default initialize_defult(self) # reset parameter for Detect Head m = self.model.model[-1] if isinstance(m, YOLOv3Head): m.initialize_biases() @register_model def yolov3(cfg, in_channels=3, num_classes=None, **kwargs) -> YOLOv3: """Get GoogLeNet model. Refer to the base class `models.GoogLeNet` for more details.""" model = YOLOv3(cfg=cfg, in_channels=in_channels, num_classes=num_classes, **kwargs) return model if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.model_factory import create_model opt = parse_config() model = create_model(model_name='yolov3', model_cfg=opt.net, num_classes=opt.data.nc, sync_bn=opt.sync_bn if hasattr(opt, 'sync_bn') else False) x = Tensor(np.random.randn(1, 3, 640, 640), ms.float32) out = model(x) out = out[0] if isinstance(out, (list, tuple)) else out print(f"Output shape is {[o.shape for o in out]}") yolov/mindyolo/models/yolov4.py000066400000000000000000000023431443434675500171720ustar00rootroot00000000000000"""YOLOv4 based on DarkNet.""" import mindspore.nn as nn from .initializer import initialize_defult from .registry import register_model from .model_factory import build_model_from_cfg __all__ = [ 'YOLOv4', 'yolov4' ] def _cfg(url='', **kwargs): return { 'url': url, **kwargs } default_cfgs = { 'yolov4': _cfg(url='') } class YOLOv4(nn.Cell): def __init__(self, cfg, in_channels=3, num_classes=None, sync_bn=False): super(YOLOv4, self).__init__() self.cfg = cfg ch, nc = in_channels, num_classes self.nc = nc # override yaml value self.model = build_model_from_cfg(model_cfg=cfg, in_channels=ch, num_classes=nc, sync_bn=sync_bn) self.names = [str(i) for i in range(nc)] # default names self.reset_parameter() def construct(self, x): return self.model(x) def reset_parameter(self): # init default initialize_defult(self) @register_model def yolov4(cfg, in_channels=3, num_classes=None, **kwargs) -> YOLOv4: """Get GoogLeNet model. Refer to the base class `models.GoogLeNet` for more details.""" model = YOLOv4(cfg=cfg, in_channels=in_channels, num_classes=num_classes, **kwargs) return model yolov/mindyolo/models/yolov5.py000066400000000000000000000043771443434675500172040ustar00rootroot00000000000000import numpy as np import mindspore as ms from mindspore import nn, Tensor from .model_factory import build_model_from_cfg from .registry import register_model from .initializer import initialize_defult from .heads.yolov7_head import YOLOv7Head, YOLOv7AuxHead __all__ = [ 'YOLOv5', 'yolov5' ] def _cfg(url='', **kwargs): return { 'url': url, **kwargs } default_cfgs = { 'yolov5': _cfg(url='') } class YOLOv5(nn.Cell): def __init__(self, cfg, in_channels=3, num_classes=None, sync_bn=False): super(YOLOv5, self).__init__() self.cfg = cfg self.stride = Tensor(np.array(cfg.stride), ms.int32) self.stride_max = int(max(self.cfg.stride)) ch, nc = in_channels, num_classes self.nc = nc # override yaml value self.model = build_model_from_cfg(model_cfg=cfg, in_channels=ch, num_classes=nc, sync_bn=sync_bn) self.names = [str(i) for i in range(nc)] # default names self.reset_parameter() def construct(self, x): return self.model(x) def reset_parameter(self): # init default initialize_defult(self) # reset parameter for Detect Head m = self.model.model[-1] if isinstance(m, YOLOv7Head): m.initialize_biases() if isinstance(m, YOLOv7AuxHead): m.initialize_aux_biases() @register_model def yolov5(cfg, in_channels=3, num_classes=None, **kwargs) -> YOLOv5: """Get GoogLeNet model. Refer to the base class `models.GoogLeNet` for more details.""" model = YOLOv5(cfg=cfg, in_channels=in_channels, num_classes=num_classes, **kwargs) return model # TODO: Preset pre-training model for yolov5-n/s/m if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.model_factory import create_model opt = parse_config() model = create_model(model_name='yolov5', model_cfg=opt.net, num_classes=opt.data.nc, sync_bn=opt.sync_bn if hasattr(opt, 'sync_bn') else False) x = Tensor(np.random.randn(1, 3, 640, 640), ms.float32) out = model(x) out = out[0] if isinstance(out, (list, tuple)) else out print(f"Output shape is {[o.shape for o in out]}") yolov/mindyolo/models/yolov7.py000066400000000000000000000044021443434675500171730ustar00rootroot00000000000000import numpy as np import mindspore as ms from mindspore import nn, Tensor from .model_factory import build_model_from_cfg from .registry import register_model from .initializer import initialize_defult from .heads.yolov7_head import YOLOv7Head, YOLOv7AuxHead __all__ = [ 'YOLOv7', 'yolov7' ] def _cfg(url='', **kwargs): return { 'url': url, **kwargs } default_cfgs = { 'yolov7': _cfg(url='') } class YOLOv7(nn.Cell): def __init__(self, cfg, in_channels=3, num_classes=None, sync_bn=False): super(YOLOv7, self).__init__() self.cfg = cfg self.stride = Tensor(np.array(cfg.stride), ms.int32) self.stride_max = int(max(self.cfg.stride)) ch, nc = in_channels, num_classes self.nc = nc # override yaml value self.model = build_model_from_cfg(model_cfg=cfg, in_channels=ch, num_classes=nc, sync_bn=sync_bn) self.names = [str(i) for i in range(nc)] # default names self.reset_parameter() def construct(self, x): return self.model(x) def reset_parameter(self): # init default initialize_defult(self) # reset parameter for Detect Head m = self.model.model[-1] if isinstance(m, YOLOv7Head): m.initialize_biases() if isinstance(m, YOLOv7AuxHead): m.initialize_aux_biases() @register_model def yolov7(cfg, in_channels=3, num_classes=None, **kwargs) -> YOLOv7: """Get GoogLeNet model. Refer to the base class `models.GoogLeNet` for more details.""" model = YOLOv7(cfg=cfg, in_channels=in_channels, num_classes=num_classes, **kwargs) return model # TODO: Preset pre-training model for yolov7-tiny/l/x if __name__ == '__main__': from mindyolo.utils.config import parse_config from mindyolo.models.model_factory import create_model opt = parse_config() model = create_model(model_name='yolov7', model_cfg=opt.net, num_classes=opt.data.nc, sync_bn=opt.sync_bn if hasattr(opt, 'sync_bn') else False) x = Tensor(np.random.randn(1, 3, 640, 640), ms.float32) out = model(x) out = out[0] if isinstance(out, (list, tuple)) else out print(f"Output shape is {[o.shape for o in out]}") yolov/mindyolo/optim/000077500000000000000000000000001443434675500152275ustar00rootroot00000000000000yolov/mindyolo/optim/.ipynb_checkpoints/000077500000000000000000000000001443434675500210205ustar00rootroot00000000000000yolov/mindyolo/optim/.ipynb_checkpoints/group_params-checkpoint.py000066400000000000000000000131661443434675500262250ustar00rootroot00000000000000import numpy as np from .scheduler import cosine_decay_lr, linear_lr __all__ = ['create_group_param'] def create_group_param(params, gp_weight_decay=0.0, **kwargs): """ Create group parameters for optimizer. Args: params: Network parameters gp_weight_decay: Weight decay. Default: 0.0 **kwargs: Others """ if 'group_param' in kwargs: gp_strategy = kwargs['group_param'] if gp_strategy == 'filter_bias_and_bn': return filter_bias_and_bn(params, gp_weight_decay) elif gp_strategy == "yolov7": return group_param_yolov7(params, weight_decay=gp_weight_decay, **kwargs) elif gp_strategy == "yolov5": return group_param_yolov5(params, weight_decay=gp_weight_decay, **kwargs) elif gp_strategy == "yolov4": return group_param_yolov4(params, weight_decay=gp_weight_decay, **kwargs) else: raise NotImplementedError else: return params def filter_bias_and_bn(params, weight_decay): no_decay_params, decay_params = _group_param_common2(params) return [ {'params': decay_params, 'weight_decay': weight_decay}, {'params': no_decay_params}, ] def group_param_yolov4(params, weight_decay, start_factor, end_factor, lr_init, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): pg0, pg1 = _group_param_common2(params) # bias/beta, weight, others lr_pg0, lr_pg1 = [], [] lrs = cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, xi, [0.0, lr_init])) lr_pg1.append(np.interp(i, xi, [0.0, lr_init])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}] return group_params def group_param_yolov5(params, weight_decay, start_factor, end_factor, lr_init, warmup_bias_lr, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): # old: # weight, gamma, bias/beta # new: # bias/beta, weight, others pg0, pg1, pg2 = _group_param_common3(params) lr_pg0, lr_pg1, lr_pg2 = [], [], [] lrs = linear_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, xi, [warmup_bias_lr, _lr])) lr_pg1.append(np.interp(i, xi, [0.0, _lr])) lr_pg2.append(np.interp(i, xi, [0.0, _lr])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) lr_pg2.append(_lr) nbs = 64 weight_decay *= total_batch_size * accumulate / nbs # scale weight_decay group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}, {'params': pg2, 'lr': lr_pg2}] return group_params def group_param_yolov7(params, weight_decay, start_factor, end_factor, lr_init, warmup_bias_lr, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): pg0, pg1, pg2 = _group_param_common3(params) # bias/beta, weight, others lr_pg0, lr_pg1, lr_pg2 = [], [], [] lrs = cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) warmup_bias_steps_first = min(max(round(3 * steps_per_epoch), min_warmup_step), warmup_steps) warmup_bias_lr_first = np.interp(warmup_bias_steps_first, [0, warmup_steps], [0.0, lr_init]) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, [0, warmup_bias_steps_first, warmup_steps], [warmup_bias_lr, warmup_bias_lr_first, _lr])) lr_pg1.append(np.interp(i, xi, [0.0, _lr])) lr_pg2.append(np.interp(i, xi, [0.0, _lr])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) lr_pg2.append(_lr) nbs = 64 weight_decay *= total_batch_size * accumulate / nbs # scale weight_decay group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}, {'params': pg2, 'lr': lr_pg2}] return group_params def _group_param_common2(params): pg0, pg1 = [], [] # optimizer parameter groups for p in params: if 'bias' in p.name or 'beta' in p.name or 'gamma' in p.name: pg0.append(p) else: pg1.append(p) return pg0, pg1 # bias/beta/gamma, others def _group_param_common3(params): pg0, pg1, pg2 = [], [], [] # optimizer parameter groups for p in params: if 'bias' in p.name or 'beta' in p.name: pg0.append(p) elif 'weight' in p.name: pg1.append(p) else: pg2.append(p) return pg0, pg1, pg2 # bias/beta, weight, others yolov/mindyolo/optim/__init__.py000066400000000000000000000004661443434675500173460ustar00rootroot00000000000000from .ema import * from .group_params import * from .scheduler import * from .optim_factory import * from . import ema, group_params, scheduler, optim_factory __all__ = [] __all__.extend(ema.__all__) __all__.extend(group_params.__all__) __all__.extend(scheduler.__all__) __all__.extend(optim_factory.__all__) yolov/mindyolo/optim/__pycache__/000077500000000000000000000000001443434675500174375ustar00rootroot00000000000000yolov/mindyolo/optim/__pycache__/__init__.cpython-37.pyc000066400000000000000000000005631443434675500236310ustar00rootroot00000000000000B ЊSd6@spddlTddlTddlTddlTddlmZmZmZmZgZeejeejeejeejdS))*)ema group_params scheduler optim_factoryN)rrrr__all__extendr r A/home/ma-user/work/mindyolo_for_yolov4/mindyolo/optim/__init__.pys   yolov/mindyolo/optim/__pycache__/ema.cpython-37.pyc000066400000000000000000000043771443434675500226430ustar00rootroot00000000000000B ЊSd @s<ddlZddlmZmZmZmZdgZGdddejZdS)N)nnopsTensor ParameterEMAcsFeZdZdZd fdd ZddZejdd Zejd d Z Z S) ra Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). 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H.?rcs|tt|||_|jdtt||_ tt||_ t t |tj dd|_||_t|_t|_dS)NF) requires_grad)superr__init__ema set_trainmsParameterTuplelistget_parametersweight ema_weightrrfloat32updates decay_valuerAssignassignHyperMap hyper_map)selfmodelZ ema_modeldecayr) __class__.update_paramr ) r assign_addrrrpartialrrdepend)rr'rr&successr)rrupdates    z EMA.updatecCs2t|jd}|tj|j|j}t||}|S)Nr )rr(rrrrrr*)rrr+rrrclone_from_model0s zEMA.clone_from_model)rr) __name__ __module__ __qualname____doc__r rr ms_functionr,r- __classcell__rr)rrrs  ) mindsporer rrrr__all__Cellrrrrrsyolov/mindyolo/optim/__pycache__/group_params.cpython-37.pyc000066400000000000000000000067621443434675500246000ustar00rootroot00000000000000B ЊSdv@s\ddlZddlmZmZdgZdddZddZd d Zd d Z d dZ ddZ ddZ dS)N)cosine_decay_lr linear_lrcreate_group_paramcKsd|kr||d}|dkr"t||S|dkr>t|fd|i|S|dkrZt|fd|i|S|dkrvt|fd|i|Stn|SdS)z Create group parameters for optimizer. Args: params: Network parameters gp_weight_decay: Weight decay. Default: 0.0 **kwargs: Others group_paramfilter_bias_and_bnyolov7 weight_decayyolov5yolov4N)rgroup_param_yolov7group_param_yolov5group_param_yolov4NotImplementedError)paramsZgp_weight_decaykwargsZ gp_strategyrE/home/ma-user/work/mindyolo_for_yolov4/mindyolo/optim/group_params.pyrs  cCst|\}}||dd|igS)N)rr r)_group_param_common2)rr Zno_decay_paramsZ decay_paramsrrrr!s rc  Kst|\} } gg}}t|||| |}tt|| |}d|g}xjt|| D]Z}||}||kr|t||d|g|t||d|gqN||||qNW| |d| ||dg}|S)Nrg)rlr)rrr )rrmaxroundrangeappendnpinterp)rr start_factor end_factorlr_init warmup_epochsmin_warmup_step accumulateepochssteps_per_epochtotal_batch_sizerpg0pg1lr_pg0lr_pg1lrs warmup_stepsxii_lr group_paramsrrrr*s   rc  Ks t|\} }}ggg}}}t|||| | }tt|| |}d|g}xt| | D]|}||}||kr|t||||g|t||d|g|t||d|gqV||||||qVWd}|| ||9}| |d|||d||dg}|S)Nrg@)rr)rrr )_group_param_common3rrrrrrr)rr rrrwarmup_bias_lrr r!r"r#r$r%rr&r'pg2r(r)lr_pg2r*r+r,r-r.nbsr/rrrrEs(    rc  KsBt|\} }}ggg}}}t|||| | }tt|| |}tttd| ||}t|d|gd|g}d|g}xt| | D]}||}||kr|t|d||g|||g|t||d|g|t||d|gq||||||qWd}|| ||9}| |d|||d||dg}|S)Nrgr0)rr)rrr ) r1rrrminrrrr)rr rrrr2r r!r"r#r$r%rr&r'r3r(r)r4r*r+Zwarmup_bias_steps_firstZwarmup_bias_lr_firstr,r-r.r5r/rrrr es0     r cCsTgg}}x@|D]8}d|jks2d|jks2d|jkr>||q||qW||fS)Nbiasbetagamma)namer)rr&r'prrrrs    rcCshggg}}}xL|D]D}d|jks.d|jkr:||qd|jkrP||q||qW|||fS)Nr8r9weight)r;r)rr&r'r3r<rrrr1s    r1)r) numpyr schedulerrr__all__rrrrr rr1rrrrs   # yolov/mindyolo/optim/__pycache__/optim_factory.cpython-37.pyc000066400000000000000000000034311443434675500247460ustar00rootroot00000000000000B ЊSd @s^dZddlZddlmZddlmZddlmZmZdgZde ee e e e e e d ddZ dS)z optim factory N)Optional)nn)load_checkpointload_param_into_netcreate_optimizermomentumMbP??F?) optimizerlr weight_decayrnesterov loss_scalecheckpoint_pathc Ks|} | dkr(tj||||||d}n.| dkrHtj||||||d}ntd| |dr~tj|r~t |} t || |S)aCreates optimizer by name. Args: params: network parameters. optim: optimizer name like 'sgd', 'nesterov', 'momentum'. lr: learning rate, float or lr scheduler. Fixed and dynamic learning rate are supported. Default: 1e-3. weight_decay: weight decay factor. Default: 0. momentum: momentum if the optimizer supports. Default: 0.9. nesterov: Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients. Default: False. loss_scale: A floating point value for the loss scale, which must be larger than 0.0. Default: 1.0. checkpoint_path: Optimizer weight path. Default: ''. Returns: Optimizer object sgd)params learning_raterrrr)rr)rrrr use_nesterovrzInvalid optimizer: z.ckpt) lowerrSGDMomentum ValueErrorendswithospathisfilerr) rr r rrrrrkwargsoptim param_dictr!F/home/ma-user/work/mindyolo_for_yolov4/mindyolo/optim/optim_factory.pyr s(   )rrrr Fr r ) __doc__rtypingr mindsporerrr__all__strfloatboolrr!r!r!r"s  yolov/mindyolo/optim/__pycache__/scheduler.cpython-37.pyc000066400000000000000000000137311443434675500240510ustar00rootroot00000000000000B ЊSd@sVddlZddlZddgZdddZdddZddZddd Zdd d Zdd dZ dS)Ncreate_lr_scheduler create_warmup_momentum_schedulercKs|rtd|n|SdS)z Create lr scheduler for optimizer. Args: lr_init: Initial learning rate lr_scheduler: LR scheduler name like 'linear', 'cos'. **kwargs: Others zNot support lr_scheduler: N)NotImplementedError)lr_initZ lr_schedulerkwargsrB/home/ma-user/work/mindyolo_for_yolov4/mindyolo/optim/scheduler.pyr s cKs*|r"tt|||}t|||SdSdS)a@ Create warmup momentum scheduler. Args: steps_per_epoch: Number of steps in each epoch. momentum (float, optional): Hyperparameter of type float, means momentum for the moving average. It must be at least 0.0. Default: None. warmup_momentum (float, optional): Hyperparameter of type float, means warmup momentum for the moving average. It must be at least 0.0. Default: None. warmup_epochs: Number of epochs for warmup. min_warmup_step: Minimum number of steps for warmup. **kwargs: Others N)maxroundlinear_momentum)steps_per_epochmomentumwarmup_momentum warmup_epochsmin_warmup_stepr warmup_stepsrrrrs c Cs6g}x,t|D] }|t|d|g||gqW|S)z Args: start: Starting value. end: Ending value. total_steps: Number of total step. Returns: momentum_list: A list with length total_steps. r)rangeappendnpinterp)startend total_stepsZ momentum_listirrrr 2s  r c Ksf|dkr |}g}||}||} x@t||D]0} | |} t| ||} ||| | |q.W|S)aM Args: start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = linear_lr(0.1, 0.01, 0.2, 100, 5) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs per epoch: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs: [0.02, 0.0155, 0.011, 0.0065, 0.002] N)rminr) start_factor end_factorrr epochst_maxrlrsstart_lrend_lrrZ epoch_idx multiplierrrr linear_lrDsr#c Ks~|dkr |}g}||}||} d|| } xLt||D]<} | |} t| |} || | dttj| |q:W|S)aI Args: start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = cosine_decay_lr(0.1, 0.01, 0.2, 100, 5) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs: [0.02, 0.0173, 0.011, 0.0046, 0.002] Ng?g?)rrrmathcospi) rrrr rrrrr r!deltarZt_currrrcosine_decay_lrbs  *r(c  s t|trd|gn|}t|ttfr2dt|g}nt|ttfrF|}ntt|tr\|g}n,t|ttfr|ddkr|dd}ntt|t|dkstdt|||||fdd|D} t | dt t|| d<x4t | dD]$} | } t | | || g| <qWS) ar Args: warmup_epochs (Union[int, tuple[int]]): The warmup epochs of the lr scheduler. The data type is an integer or a tuple of integers. An integer represents the warmup epoch size. A tuple of integers represents the warmup epochs interpolation nodes. Like: [0, 12, 24] or 24. warmup_lrs (Union[int, tuple[float]]): The warmup lr of the lr scheduler. The data type is a float or a tuple of float(The last element can be None). A float represents the start warmup lr. A tuple of float represents the warmup lrs interpolation nodes. Like: [0.01, 0.1, 'None'] or [0.01, 0.1] or 0.01. start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. min_warmup_step (int): Minimum warm-up steps. Default: 1000. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = cosine_decay_lr_with_linear_warmup([0, 3], [0.0001, None], 0.1, 0.01, 0.2, 100, 5, min_warmup_step=1) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs every epoch: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs every epoch: [0.0066, 0.0115, 0.0109, 0.0046, 0.002] r)NonenoneNNzKLRScheduler: The length of 'warmup_epochs' and 'warmup_lrs' is inconsistentcsg|]}t|tqSr)rlen).0r)rr rr sz6cosine_decay_lr_with_linear_warmup..) isinstanceintfloatlisttuple ValueErrorr.AssertionErrorr(r rrrr) rZ warmup_lrsrrrr rrrrrr_lrr)rr r"cosine_decay_lr_with_linear_warmups*  r9)N)NNNN)N)N)r)N) r$numpyr__all__rrr r#r(r9rrrrs    !yolov/mindyolo/optim/ema.py000066400000000000000000000040151443434675500163430ustar00rootroot00000000000000import mindspore as ms from mindspore import nn, ops, Tensor, Parameter __all__ = ['EMA'] class EMA(nn.Cell): """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). This is intended to allow functionality like https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage A smoothed version of the weight is necessary for some training schemes to perform well. """ def __init__(self, model, ema_model, decay=0.9999, updates=0): super(EMA, self).__init__() # Create EMA self.ema = ema_model self.ema.set_train(False) self.weight = ms.ParameterTuple(list(model.get_parameters())) self.ema_weight = ms.ParameterTuple(list(ema_model.get_parameters())) self.updates = Parameter(Tensor(updates, ms.float32), requires_grad=False) # number of EMA updates self.decay_value = decay self.assign = ops.Assign() self.hyper_map = ops.HyperMap() def decay(self, x): # decay exponential ramp (to help early epochs) return self.decay_value * (1 - ops.exp(ops.neg(x) / 2000)) @ms.ms_function def update(self): # Update EMA parameters def update_param(d, ema_v, weight): if weight.dtype == ms.int32: return self.assign(ema_v, weight) else: tep_v = ema_v * d return self.assign(ema_v, weight * (1. - d) + tep_v) updates = ops.assign_add(self.updates, 1) d = self.decay(self.updates) success = self.hyper_map(ops.partial(update_param, d), self.ema_weight, self.weight) updates = ops.depend(updates, success) return updates @ms.ms_function def clone_from_model(self): updates = ops.assign_add(self.updates, 1) success = self.hyper_map(ops.assign, self.ema_weight, self.weight) updates = ops.depend(updates, success) return updates yolov/mindyolo/optim/group_params.py000066400000000000000000000131661443434675500203070ustar00rootroot00000000000000import numpy as np from .scheduler import cosine_decay_lr, linear_lr __all__ = ['create_group_param'] def create_group_param(params, gp_weight_decay=0.0, **kwargs): """ Create group parameters for optimizer. Args: params: Network parameters gp_weight_decay: Weight decay. Default: 0.0 **kwargs: Others """ if 'group_param' in kwargs: gp_strategy = kwargs['group_param'] if gp_strategy == 'filter_bias_and_bn': return filter_bias_and_bn(params, gp_weight_decay) elif gp_strategy == "yolov7": return group_param_yolov7(params, weight_decay=gp_weight_decay, **kwargs) elif gp_strategy == "yolov5": return group_param_yolov5(params, weight_decay=gp_weight_decay, **kwargs) elif gp_strategy == "yolov4": return group_param_yolov4(params, weight_decay=gp_weight_decay, **kwargs) else: raise NotImplementedError else: return params def filter_bias_and_bn(params, weight_decay): no_decay_params, decay_params = _group_param_common2(params) return [ {'params': decay_params, 'weight_decay': weight_decay}, {'params': no_decay_params}, ] def group_param_yolov4(params, weight_decay, start_factor, end_factor, lr_init, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): pg0, pg1 = _group_param_common2(params) # bias/beta, weight, others lr_pg0, lr_pg1 = [], [] lrs = cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, xi, [0.0, lr_init])) lr_pg1.append(np.interp(i, xi, [0.0, lr_init])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}] return group_params def group_param_yolov5(params, weight_decay, start_factor, end_factor, lr_init, warmup_bias_lr, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): # old: # weight, gamma, bias/beta # new: # bias/beta, weight, others pg0, pg1, pg2 = _group_param_common3(params) lr_pg0, lr_pg1, lr_pg2 = [], [], [] lrs = linear_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, xi, [warmup_bias_lr, _lr])) lr_pg1.append(np.interp(i, xi, [0.0, _lr])) lr_pg2.append(np.interp(i, xi, [0.0, _lr])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) lr_pg2.append(_lr) nbs = 64 weight_decay *= total_batch_size * accumulate / nbs # scale weight_decay group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}, {'params': pg2, 'lr': lr_pg2}] return group_params def group_param_yolov7(params, weight_decay, start_factor, end_factor, lr_init, warmup_bias_lr, warmup_epochs, min_warmup_step, accumulate, epochs, steps_per_epoch, total_batch_size, **kwargs): pg0, pg1, pg2 = _group_param_common3(params) # bias/beta, weight, others lr_pg0, lr_pg1, lr_pg2 = [], [], [] lrs = cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs) warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) warmup_bias_steps_first = min(max(round(3 * steps_per_epoch), min_warmup_step), warmup_steps) warmup_bias_lr_first = np.interp(warmup_bias_steps_first, [0, warmup_steps], [0.0, lr_init]) xi = [0, warmup_steps] for i in range(epochs * steps_per_epoch): _lr = lrs[i] if i < warmup_steps: lr_pg0.append(np.interp(i, [0, warmup_bias_steps_first, warmup_steps], [warmup_bias_lr, warmup_bias_lr_first, _lr])) lr_pg1.append(np.interp(i, xi, [0.0, _lr])) lr_pg2.append(np.interp(i, xi, [0.0, _lr])) else: lr_pg0.append(_lr) lr_pg1.append(_lr) lr_pg2.append(_lr) nbs = 64 weight_decay *= total_batch_size * accumulate / nbs # scale weight_decay group_params = [{'params': pg0, 'lr': lr_pg0}, {'params': pg1, 'lr': lr_pg1, 'weight_decay': weight_decay}, {'params': pg2, 'lr': lr_pg2}] return group_params def _group_param_common2(params): pg0, pg1 = [], [] # optimizer parameter groups for p in params: if 'bias' in p.name or 'beta' in p.name or 'gamma' in p.name: pg0.append(p) else: pg1.append(p) return pg0, pg1 # bias/beta/gamma, others def _group_param_common3(params): pg0, pg1, pg2 = [], [], [] # optimizer parameter groups for p in params: if 'bias' in p.name or 'beta' in p.name: pg0.append(p) elif 'weight' in p.name: pg1.append(p) else: pg2.append(p) return pg0, pg1, pg2 # bias/beta, weight, others yolov/mindyolo/optim/optim_factory.py000066400000000000000000000042571443434675500204700ustar00rootroot00000000000000''' optim factory ''' import os from typing import Optional from mindspore import nn from mindspore import load_checkpoint, load_param_into_net __all__ = ["create_optimizer"] def create_optimizer( params, optimizer: str = 'momentum', lr: Optional[float] = 1e-3, weight_decay: float = 0, momentum: float = 0.9, nesterov: bool = False, loss_scale: float = 1.0, checkpoint_path: str = '', **kwargs): r"""Creates optimizer by name. Args: params: network parameters. optim: optimizer name like 'sgd', 'nesterov', 'momentum'. lr: learning rate, float or lr scheduler. Fixed and dynamic learning rate are supported. Default: 1e-3. weight_decay: weight decay factor. Default: 0. momentum: momentum if the optimizer supports. Default: 0.9. nesterov: Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients. Default: False. loss_scale: A floating point value for the loss scale, which must be larger than 0.0. Default: 1.0. checkpoint_path: Optimizer weight path. Default: ''. Returns: Optimizer object """ optim = optimizer.lower() if optim == 'sgd': optimizer = nn.SGD(params=params, learning_rate=lr, momentum=momentum, weight_decay=weight_decay, nesterov=nesterov, loss_scale=loss_scale ) elif optim in ['momentum', 'nesterov']: optimizer = nn.Momentum(params=params, learning_rate=lr, momentum=momentum, weight_decay=weight_decay, use_nesterov=nesterov, loss_scale=loss_scale ) else: raise ValueError(f'Invalid optimizer: {optim}') if checkpoint_path.endswith('.ckpt') and os.path.isfile(checkpoint_path): param_dict = load_checkpoint(checkpoint_path) load_param_into_net(optimizer, param_dict) return optimizer yolov/mindyolo/optim/scheduler.py000066400000000000000000000152301443434675500175600ustar00rootroot00000000000000import math import numpy as np __all__ = [ 'create_lr_scheduler', 'create_warmup_momentum_scheduler' ] def create_lr_scheduler(lr_init, lr_scheduler=None, **kwargs): """ Create lr scheduler for optimizer. Args: lr_init: Initial learning rate lr_scheduler: LR scheduler name like 'linear', 'cos'. **kwargs: Others """ if lr_scheduler: # TODO: Add common lr_scheduler raise NotImplementedError(f"Not support lr_scheduler: {lr_scheduler}") else: return lr_init def create_warmup_momentum_scheduler(steps_per_epoch, momentum=None, warmup_momentum=None, warmup_epochs=None, min_warmup_step=None, **kwargs): """ Create warmup momentum scheduler. Args: steps_per_epoch: Number of steps in each epoch. momentum (float, optional): Hyperparameter of type float, means momentum for the moving average. It must be at least 0.0. Default: None. warmup_momentum (float, optional): Hyperparameter of type float, means warmup momentum for the moving average. It must be at least 0.0. Default: None. warmup_epochs: Number of epochs for warmup. min_warmup_step: Minimum number of steps for warmup. **kwargs: Others """ if warmup_momentum: warmup_steps = max(round(warmup_epochs * steps_per_epoch), min_warmup_step) return linear_momentum(warmup_momentum, momentum, warmup_steps) else: return None def linear_momentum(start, end, total_steps): """ Args: start: Starting value. end: Ending value. total_steps: Number of total step. Returns: momentum_list: A list with length total_steps. """ momentum_list = [] for i in range(total_steps): momentum_list.append(np.interp(i, [0, total_steps], [start, end])) return momentum_list def linear_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs, t_max=None, **kwargs): """ Args: start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = linear_lr(0.1, 0.01, 0.2, 100, 5) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs per epoch: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs: [0.02, 0.0155, 0.011, 0.0065, 0.002] """ if t_max is None: t_max = epochs lrs = [] start_lr = lr_init * start_factor end_lr = lr_init * end_factor for i in range(steps_per_epoch * epochs): epoch_idx = i // steps_per_epoch multiplier = min(epoch_idx, t_max) / t_max lrs.append(start_lr + multiplier * (end_lr - start_lr)) return lrs def cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs, t_max=None, **kwargs): """ Args: start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = cosine_decay_lr(0.1, 0.01, 0.2, 100, 5) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs: [0.02, 0.0173, 0.011, 0.0046, 0.002] """ if t_max is None: t_max = epochs lrs = [] start_lr = lr_init * start_factor end_lr = lr_init * end_factor delta = 0.5 * (start_lr - end_lr) for i in range(steps_per_epoch * epochs): t_cur = i // steps_per_epoch t_cur = min(t_cur, t_max) lrs.append(end_lr + delta * (1.0 + math.cos(math.pi * t_cur / t_max))) return lrs def cosine_decay_lr_with_linear_warmup(warmup_epochs, warmup_lrs, start_factor, end_factor, lr_init, steps_per_epoch, epochs, min_warmup_step=1000, t_max=None, **kwargs): """ Args: warmup_epochs (Union[int, tuple[int]]): The warmup epochs of the lr scheduler. The data type is an integer or a tuple of integers. An integer represents the warmup epoch size. A tuple of integers represents the warmup epochs interpolation nodes. Like: [0, 12, 24] or 24. warmup_lrs (Union[int, tuple[float]]): The warmup lr of the lr scheduler. The data type is a float or a tuple of float(The last element can be None). A float represents the start warmup lr. A tuple of float represents the warmup lrs interpolation nodes. Like: [0.01, 0.1, 'None'] or [0.01, 0.1] or 0.01. start_factor: Starting factor. end_factor: Ending factor. lr_init: Initial learning rate. steps_per_epoch: Total number of steps per epoch. epochs: Total number of epochs trained. min_warmup_step (int): Minimum warm-up steps. Default: 1000. t_max: The maximum number of epochs where lr changes. Default: None. Examples: >>> lrs = cosine_decay_lr_with_linear_warmup([0, 3], [0.0001, None], 0.1, 0.01, 0.2, 100, 5, min_warmup_step=1) >>> print(f"lrs len: {len(lrs)}") >>> print(f"lrs every epoch: {[lrs[i] for i in range(len(lrs)) if ((i + 1) % 100 == 0)]}") lrs len: 500 lrs every epoch: [0.0066, 0.0115, 0.0109, 0.0046, 0.002] """ warmup_epochs = [0, warmup_epochs] if isinstance(warmup_epochs, int) else warmup_epochs if isinstance(warmup_epochs, (int, float)): warmup_epochs = [0, int(warmup_epochs)] elif isinstance(warmup_epochs, (list, tuple)): warmup_epochs = warmup_epochs else: raise ValueError if isinstance(warmup_lrs, float): warmup_lrs = [warmup_lrs,] elif isinstance(warmup_lrs, (list, tuple)): if warmup_lrs[-1] in ('None', 'none', None): warmup_lrs = warmup_lrs[:-1] else: raise ValueError assert len(warmup_epochs) == len(warmup_lrs) + 1, \ "LRScheduler: The length of 'warmup_epochs' and 'warmup_lrs' is inconsistent" lrs = cosine_decay_lr(start_factor, end_factor, lr_init, steps_per_epoch, epochs, t_max) warmup_steps = [min(i * steps_per_epoch, len(lrs)) for i in warmup_epochs] warmup_steps[-1] = max(warmup_steps[-1], min(len(lrs), min_warmup_step)) for i in range(warmup_steps[-1]): _lr = lrs[i] lrs[i] = np.interp(i, warmup_steps, warmup_lrs + [_lr,]) return lrs yolov/mindyolo/utils/000077500000000000000000000000001443434675500152375ustar00rootroot00000000000000yolov/mindyolo/utils/.ipynb_checkpoints/000077500000000000000000000000001443434675500210305ustar00rootroot00000000000000yolov/mindyolo/utils/.ipynb_checkpoints/trainer_factory-checkpoint.py000066400000000000000000000253541443434675500267330ustar00rootroot00000000000000import os import types import time from typing import Union import mindspore as ms from mindspore import nn, ops, Tensor from mindyolo.utils import logger from mindyolo.utils.checkpoint_manager import CheckpointManager from mindyolo.utils.modelarts import sync_data __all__ = [ "create_trainer", ] def create_trainer( model_name: str, train_step_fn: types.FunctionType, scaler, network: nn.Cell, ema: nn.Cell, optimizer: nn.Cell, dataloader: ms.dataset.Dataset, ): return Trainer( model_name=model_name, train_step_fn=train_step_fn, scaler=scaler, dataloader=dataloader, network=network, ema=ema, optimizer=optimizer ) class Trainer: def __init__(self, model_name, train_step_fn, scaler, network, ema, optimizer, dataloader): self.model_name = model_name self.train_step_fn = train_step_fn self.scaler = scaler self.dataloader = dataloader self.network = network # for save checkpoint self.ema = ema # for save checkpoint self.optimizer = optimizer # for save checkpoint self.global_step = 0 self.steps_per_epoch = self.dataloader.get_dataset_size() def train( self, epochs: int, main_device: bool, warmup_step: int = 0, warmup_momentum: Union[list, None] = None, accumulate: int = 1, overflow_still_update: bool = False, keep_checkpoint_max: int = 10, log_interval: int = 1, loss_item_name: list = [], save_dir: str = '', enable_modelarts: bool = False, train_url: str = '', run_eval: bool = False, test_fn: types.FunctionType = None, ): # Set Attr self.epochs = epochs self.main_device = main_device self.log_interval = log_interval self.overflow_still_update = overflow_still_update self.loss_item_name = loss_item_name # Directories settings ckpt_save_dir = os.path.join(save_dir, 'weights') sync_lock_dir = os.path.join(save_dir, 'sync_locks') if not enable_modelarts else '/tmp/sync_locks' if main_device: os.makedirs(ckpt_save_dir, exist_ok=True) # save checkpoint path os.makedirs(sync_lock_dir, exist_ok=False) # sync_lock for run_eval # Grad Accumulate self.accumulate_cur_step = 0 self.accumulate_grads = None self.accumulate = accumulate self.accumulate_grads_fn = self._get_accumulate_grads_fn() manager = CheckpointManager(ckpt_save_policy='latest_k') manager_ema = CheckpointManager(ckpt_save_policy='latest_k') if self.ema else None manager_best = CheckpointManager(ckpt_save_policy='top_k') if run_eval else None ckpt_filelist_best = [] self.dataloader = self.dataloader.repeat(epochs) loader = self.dataloader.create_dict_iterator(output_numpy=False, num_epochs=1) s_step_time = time.time() s_epoch_time = time.time() for i, data in enumerate(loader): cur_epoch = (i // self.steps_per_epoch) + 1 cur_step = (i % self.steps_per_epoch) + 1 self.global_step += 1 if self.global_step < warmup_step: if warmup_momentum and isinstance(self.optimizer, (nn.SGD, nn.Momentum)): dtype = self.optimizer.momentum.dtype self.optimizer.momentum = Tensor(warmup_momentum[i], dtype) imgs, labels = data['image'], data['labels'] self.train_step(imgs, labels, cur_step=cur_step, cur_epoch=cur_epoch) # train log if cur_step % self.log_interval == 0: logger.info(f"Epoch {epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"step time: {(time.time() - s_step_time) * 1000 / self.log_interval:.2f} ms") s_step_time = time.time() # run eval per epoch on main device if run_eval and (i + 1) % self.steps_per_epoch == 0: s_eval_time = time.time() sync_lock = os.path.join(sync_lock_dir, "/run_eval_sync.lock" + str(cur_epoch)) # single device run eval only if self.main_device and not os.path.exists(sync_lock): eval_network = self.ema.ema if self.ema else self.network _train_status = eval_network.training eval_network.set_train(False) accuracy = test_fn(network=eval_network) accuracy = accuracy[0] if isinstance(accuracy, (list, tuple)) else accuracy eval_network.set_train(_train_status) save_path_best = os.path.join(ckpt_save_dir, f"best/{self.model_name}-{cur_epoch}_{self.steps_per_epoch}" f"_acc{accuracy:.2f}.ckpt") ckpt_filelist_best = manager_best.save_ckpoint(eval_network, num_ckpt=keep_checkpoint_max, metric=accuracy, save_path=save_path_best) logger.info(f"Epoch {epochs}/{cur_epoch}, eval accuracy: {accuracy:.2f}, " f"run_eval time: {(time.time() - s_eval_time):.2f} s.") try: os.mknod(sync_lock) except IOError: pass # other device wait for lock sign while True: if os.path.exists(sync_lock): break time.sleep(1) # save checkpoint per epoch on main device if self.main_device and (i + 1) % self.steps_per_epoch == 0: # Save Checkpoint ms.save_checkpoint(self.optimizer, os.path.join(ckpt_save_dir, f'optim_{self.model_name}.ckpt'), async_save=True) save_path = os.path.join(ckpt_save_dir, f"{self.model_name}-{cur_epoch}_{self.steps_per_epoch}.ckpt") manager.save_ckpoint(self.network, num_ckpt=keep_checkpoint_max, save_path=save_path) if self.ema: save_path_ema = os.path.join(ckpt_save_dir, f"EMA_{self.model_name}-{cur_epoch}_{self.steps_per_epoch}.ckpt") manager_ema.save_ckpoint(self.ema.ema, num_ckpt=keep_checkpoint_max, save_path=save_path_ema) logger.info(f"Saving model to {save_path}") if enable_modelarts: sync_data(save_path, train_url + "/weights/" + save_path.split("/")[-1]) if self.ema: sync_data(save_path_ema, train_url + "/weights/" + save_path_ema.split("/")[-1]) logger.info( f"Epoch {epochs}/{cur_epoch}, epoch time: {(time.time() - s_epoch_time) / 60:.2f} min.") s_epoch_time = time.time() if enable_modelarts and ckpt_filelist_best: for p in ckpt_filelist_best: sync_data(p, train_url + '/weights/best/' + p.split("/")[-1]) logger.info("End Train.") def train_step(self, imgs, labels, cur_step=0, cur_epoch=0): if self.accumulate == 1: loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, True) if self.ema: self.ema.update() self.scaler.adjust(grads_finite) if not grads_finite and (cur_step % self.log_interval == 0): if self.overflow_still_update: logger.warning(f"overflow, still update, loss scale adjust to {self.scaler.scale_value.asnumpy()}") else: logger.warning(f"overflow, drop step, loss scale adjust to {self.scaler.scale_value.asnumpy()}") else: loss, loss_item, grads, grads_finite = self.train_step_fn(imgs, labels, False) self.scaler.adjust(grads_finite) if grads_finite or self.overflow_still_update: self.accumulate_cur_step += 1 if self.accumulate_grads: self.accumulate_grads = self.accumulate_grads_fn(self.accumulate_grads, grads) # update self.accumulate_grads else: self.accumulate_grads = grads if self.accumulate_cur_step % self.accumulate == 0: self.optimizer(self.accumulate_grads) if self.ema: self.ema.update() logger.info(f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"accumulate: {self.accumulate}, optimizer an accumulate step success.") from mindyolo.utils.all_finite import all_finite if not all_finite(self.accumulate_grads): logger.warning(f"overflow, still update.") # reset accumulate self.accumulate_grads, self.accumulate_cur_step = None, 0 else: logger.warning(f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"accumulate: {self.accumulate}, this step grad overflow, drop. " f"Loss scale adjust to {self.scaler.scale_value.asnumpy()}") # train log if cur_step % self.log_interval == 0: log_string = f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, imgsize {imgs.shape[2:]}" # print loss if len(self.loss_item_name) < len(loss_item): self.loss_item_name += [f'loss_item{i}' for i in range(len(loss_item) - len(self.loss_item_name))] for i in range(len(loss_item)): log_string += f", {self.loss_item_name[i]}: {loss_item[i].asnumpy():.4f}" # print lr if self.optimizer.dynamic_lr: if self.optimizer.is_group_lr: lr_cell = self.optimizer.learning_rate[0] cur_lr = lr_cell(Tensor(self.global_step, ms.int32)).asnumpy().item() else: cur_lr = self.optimizer.learning_rate(Tensor(self.global_step, ms.int32)).asnumpy().item() else: cur_lr = self.optimizer.learning_rate.asnumpy().item() log_string += f", cur_lr: {cur_lr}" logger.info(log_string) def _get_accumulate_grads_fn(self): hyper_map = ops.HyperMap() def accu_fn(g1, g2): g1 = g1 + g2 return g1 def accumulate_grads_fn(accumulate_grads, grads): success = hyper_map(accu_fn, accumulate_grads, grads) return success return accumulate_grads_fn yolov/mindyolo/utils/__init__.py000066400000000000000000000002471443434675500173530ustar00rootroot00000000000000"""Utility Tools""" from .logger import * from .config import * from .checkpoint_manager import * from .modelarts import * from .metrics import * from .utils import * yolov/mindyolo/utils/__pycache__/000077500000000000000000000000001443434675500174475ustar00rootroot00000000000000yolov/mindyolo/utils/__pycache__/__init__.cpython-37.pyc000066400000000000000000000004661443434675500236430ustar00rootroot00000000000000B ЊSd@s8dZddlTddlTddlTddlTddlTddlTdS)z Utility Tools)*N)__doc__loggerconfigZcheckpoint_managerZ modelartsmetricsutilsrrA/home/ma-user/work/mindyolo_for_yolov4/mindyolo/utils/__init__.pys yolov/mindyolo/utils/__pycache__/all_finite.cpython-37.pyc000066400000000000000000000031751443434675500242120ustar00rootroot00000000000000B ЊSd1@sddlZdddZeejdkrddlmZmZeddkZeddkZe Z e Z eddkrze Z e e ZndZ eZeZd d Zdd d Zn dd lmZdS)N2.0.0cCst|d}t|d}xXttt|t|D]>}t||t||krRq4t||t||krndSdSq4Wt|t|krdSt|t|krdSdSdS)z :param v1: version, format like 1.8.1 :param v2: version, format like 1.8.1 :return: v1 v1, return -1/0/1 .rN)strsplitrangeminlenint)v1v2l1l2irC/home/ma-user/work/mindyolo_for_yolov4/mindyolo/utils/all_finite.pycompare_versionsr)contextops device_targetAscendGPUcCs*trt|ddkSt|}|S)Nr) _gpu_targetr FloatStatusisfiniteall)inputsstatusrrr _is_finite+s rcCs`trDtt|}t|}t||}|dk}t|}|Sttt |}t | S)Nr) _ascend_targetrdepend_statusNPUGetFloatStatussumNPUClearFloatStatus _hypermap_partialrstackr)rr get_status status_finite_outputsrrr all_finite1s     r-)r-)r)N) mindsporemsr __version__rr get_contextr rNPUAllocFloatStatusnpu_alloc_float_statusr%npu_clear_float_statusr"r+HyperMapr&Partialr'rr- mindspore.amprrrrs    yolov/mindyolo/utils/__pycache__/checkpoint_manager.cpython-37.pyc000066400000000000000000000122351443434675500257220ustar00rootroot00000000000000B ЊSd@sHdZddlZddlZddlZddlZddlmZdgZ GdddZ dS)zcheckpoint manager N)loggerCheckpointManagerc@speZdZdZdddZeddZeddZd d Zd d Z d dZ ddZ dddZ dddZ dddZdS)ra{ Manage checkpoint files according to ckpt_save_policy of checkpoint. 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)NN)Trs)r r.rNnumpyr r mindsporer rZmindspore.contextr"mindspore.communication.managementrrrmindyolo.utilsrrr^rfrrrrrrrs     :  yolov/mindyolo/utils/all_finite.py000066400000000000000000000034611443434675500177230ustar00rootroot00000000000000import mindspore as ms # TODO: Delete this code when publish def compare_version(v1, v2="2.0.0"): """ :param v1: version, format like 1.8.1 :param v2: version, format like 1.8.1 :return: v1 v1, return -1/0/1 """ l1 = str(v1).split(".") l2 = str(v2).split(".") for i in range(min(len(l1), len(l2))): if int(l1[i]) == int(l2[i]): pass elif int(l1[i]) < int(l2[i]): return -1 else: return 1 if len(l1) == len(l2): return 0 elif len(l1) < len(l2): return -1 else: return 1 if compare_version(ms.__version__) < 0: from mindspore import context, ops _ascend_target = context.get_context("device_target") == "Ascend" _gpu_target = context.get_context("device_target") == "GPU" npu_alloc_float_status = ops.NPUAllocFloatStatus() npu_clear_float_status = ops.NPUClearFloatStatus() if context.get_context("device_target") == "Ascend": _status = npu_alloc_float_status() _ = npu_clear_float_status(_status) else: _status = None _hypermap = ops.HyperMap() _partial = ops.Partial() def _is_finite(inputs): if _gpu_target: return ops.FloatStatus()(inputs)[0] == 0 status = ops.isfinite(inputs) return status.all() def all_finite(inputs, status=None): if _ascend_target: status = ops.depend(_status, inputs) get_status = ops.NPUGetFloatStatus()(status) status = ops.depend(status, get_status) status_finite = status.sum() == 0 _ = ops.NPUClearFloatStatus()(status) return status_finite outputs = _hypermap(_partial(_is_finite), inputs) return ops.stack(outputs).all() else: from mindspore.amp import all_finite yolov/mindyolo/utils/checkpoint_manager.py000066400000000000000000000123131443434675500214320ustar00rootroot00000000000000'''checkpoint manager ''' import os import stat import numpy as np import mindspore as ms from mindyolo.utils import logger __all__ = ['CheckpointManager'] class CheckpointManager: """ Manage checkpoint files according to ckpt_save_policy of checkpoint. Args: ckpt_save_policy (str): Checkpoint saving strategy. The optional values is None, "top_k" or "latest_k". None means to save each checkpoint, top_k means to save K checkpoints with the highest accuracy, and latest_k means saving the latest K checkpoint. Default: None. """ def __init__(self, ckpt_save_policy=None): self._ckpoint_filelist = [] self.ckpt_save_policy = ckpt_save_policy @property def ckpoint_filelist(self): """Get all the related checkpoint files managed here.""" return self._ckpoint_filelist @property def ckpoint_num(self): """Get the number of the related checkpoint files managed here.""" return len(self._ckpoint_filelist) def update_ckpoint_filelist(self, directory, prefix): """Update the checkpoint file list.""" self._ckpoint_filelist = [] files = os.listdir(directory) for filename in files: if os.path.splitext(filename)[-1] == ".ckpt" and filename.startswith(prefix + "-"): mid_name = filename[len(prefix):-5] flag = not (True in [char.isalpha() for char in mid_name]) if flag: self._ckpoint_filelist.append(os.path.join(directory, filename)) def remove_ckpoint_file(self, file_name): """Remove the specified checkpoint file from this checkpoint manager and also from the directory.""" try: os.chmod(file_name, stat.S_IWRITE) os.remove(file_name) except OSError: logger.warning("OSError, failed to remove the older ckpt file %s.", file_name) except ValueError: logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name) def remove_oldest_ckpoint_file(self): """Remove the oldest checkpoint file from this checkpoint manager and also from the directory.""" ckpoint_files = sorted(self._ckpoint_filelist, key=os.path.getmtime) self.remove_ckpoint_file(ckpoint_files[0]) self._ckpoint_filelist.remove(ckpoint_files[0]) def keep_one_ckpoint_per_minutes(self, minutes, cur_time): """Only keep the latest one ckpt file per minutes, remove other files generated in [last_time, cur_time].""" del_list = [] oldest_file = '' oldest_time = cur_time for ck_file in self._ckpoint_filelist: modify_time = os.path.getmtime(ck_file) if cur_time - modify_time < 60 * minutes: del_list.append(ck_file) if modify_time < oldest_time: oldest_time = modify_time oldest_file = ck_file for mv_file in del_list: if mv_file == oldest_file: continue self.remove_ckpoint_file(mv_file) def top_K_checkpoint(self, network, K=10, metric=None, save_path=''): """ Save and return Top K checkpoint address and accuracy. """ last_file = self._ckpoint_filelist[-1] if self._ckpoint_filelist else None if type(metric) is not np.ndarray: metric = metric.asnumpy() if self.ckpoint_num < K or np.greater(metric, last_file[1]): if self.ckpoint_num >= K: delete = K - 1 if delete < 0 or self.ckpoint_num <= delete: return to_delete = self._ckpoint_filelist[delete:] for d in to_delete: self.remove_ckpoint_file(d[0]) self._ckpoint_filelist = self._ckpoint_filelist[:delete] ms.save_checkpoint(network, save_path, async_save=True) self._ckpoint_filelist.append((save_path, float(metric))) self._ckpoint_filelist = sorted(self._ckpoint_filelist, key=lambda x: x[1], reverse=True) def latest_K_checkpoint(self, network, K=10, save_path=''): """ Save latest K checkpoint. """ if K and 0 < K <= self.ckpoint_num: self.remove_oldest_ckpoint_file() ms.save_checkpoint(network, save_path, async_save=True) self._ckpoint_filelist.append(save_path) def save_ckpoint(self, network, num_ckpt=10, metric=None, save_path=''): """ Save checkpoint according to different save strategy. """ if self.ckpt_save_policy is None: ms.save_checkpoint(network, save_path, async_save=True) elif self.ckpt_save_policy == 'top_k': if metric is None: raise ValueError(f"The expected 'metric' is not None, but got: {metric}.") self.top_K_checkpoint(network, K=num_ckpt, metric=metric, save_path=save_path) return self._ckpoint_filelist elif self.ckpt_save_policy == 'latest_k': self.latest_K_checkpoint(network, K=num_ckpt, save_path=save_path) return self._ckpoint_filelist else: raise ValueError(f"The expected 'ckpt_save_policy' is None, top_k or latest_k," f"but got: {self.ckpt_save_policy}.") yolov/mindyolo/utils/config.py000066400000000000000000000102711443434675500170570ustar00rootroot00000000000000import os import yaml import argparse import collections from copy import deepcopy from pprint import pprint, pformat try: collectionsAbc = collections.abc except AttributeError: collectionsAbc = collections __all__ = ['parse_args'] def parse_args(parser): parser_config = argparse.ArgumentParser(description='Config', add_help=False) parser_config.add_argument('-c', '--config', type=str, default='', help='YAML config file specifying default arguments.') args_config, remaining = parser_config.parse_known_args() # Do we have a config file to parse? if args_config.config: cfg, _, _ = load_config(args_config.config) cfg = Config(cfg) parser.set_defaults(**cfg) parser.set_defaults(config=args_config.config) # The main arg parser parses the rest of the args, the usual # defaults will have been overridden if config file specified. args = parser.parse_args(remaining) print("parse_args:") pprint(args) print("Please check the above information for the configurations", flush=True) return args def load_config(file_path): BASE = "__BASE__" assert os.path.splitext(file_path)[-1] in [".yaml", ".yml"], f"[{file_path}] not yaml format." cfg_default, cfg_helper, cfg_choices = _parse_yaml(file_path) # NOTE: cfgs outside have higher priority than cfgs in _BASE_ if BASE in cfg_default: all_base_cfg_default = {} all_base_cfg_helper = {} all_base_cfg_choices = {} base_yamls = list(cfg_default[BASE]) for base_yaml in base_yamls: if base_yaml.startswith("~"): base_yaml = os.path.expanduser(base_yaml) if not base_yaml.startswith('/'): base_yaml = os.path.join(os.path.dirname(file_path), base_yaml) base_cfg_default, base_cfg_helper, base_cfg_choices = load_config(base_yaml) all_base_cfg_default = _merge_config(base_cfg_default, all_base_cfg_default) all_base_cfg_helper = _merge_config(base_cfg_helper, all_base_cfg_helper) all_base_cfg_choices = _merge_config(base_cfg_choices, all_base_cfg_choices) del cfg_default[BASE] return _merge_config(cfg_default, all_base_cfg_default), \ _merge_config(cfg_helper, all_base_cfg_helper), \ _merge_config(cfg_choices, all_base_cfg_choices) return cfg_default, cfg_helper, cfg_choices def _parse_yaml(yaml_path): """ Parse the yaml config file. Args: yaml_path: Path to the yaml config. """ with open(yaml_path, 'r') as fin: try: cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader) cfgs = [x for x in cfgs] if len(cfgs) == 1: cfg = cfgs[0] cfg_helper = {} cfg_choices = {} elif len(cfgs) == 2: cfg, cfg_helper = cfgs cfg_choices = {} elif len(cfgs) == 3: cfg, cfg_helper, cfg_choices = cfgs else: raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml") except: raise ValueError("Failed to parse yaml") return cfg, cfg_helper, cfg_choices def _merge_config(config, base): """Merge config""" new = deepcopy(base) for k, v in config.items(): if (k in new and isinstance(new[k], dict) and isinstance(config[k], collectionsAbc.Mapping)): new[k] = _merge_config(config[k], new[k]) else: new[k] = config[k] return new class Config(dict): """ Configuration namespace. Convert dictionary to members. """ def __init__(self, cfg_dict): super(Config, self).__init__() for k, v in cfg_dict.items(): setattr(self, k, Config(v) if isinstance(v, dict) else v) def __setattr__(self, name, value): self[name] = value def __getattr__(self, name): if name in self: return self[name] else: raise AttributeError(name) def __str__(self): return pformat(self.__dict__) def __repr__(self): return self.__str__() yolov/mindyolo/utils/convert_weight_darknet53.py000066400000000000000000000046561443434675500225330ustar00rootroot00000000000000import os import sys import numpy as np import mindspore as ms from config import parse_args sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) from mindyolo.models import create_model def _load_weight(weights_file): """Loads pre-trained weights.""" if not os.path.isfile(weights_file): raise ValueError(f'"{weights_file}" is not a valid weight file.') with open(weights_file, 'rb') as fp: np.fromfile(fp, dtype=np.int32, count=5) return np.fromfile(fp, dtype=np.float32) def convert_weight(cfg, weights_file='./darknet53.conv.74', output_file='./yolov3_backbone.ckpt'): """Convert weight to mindspore ckpt.""" net = create_model(model_name=cfg.network.model_name, model_cfg=cfg.network) params = net.get_parameters() params = [p for p in params] weights = _load_weight(weights_file) index = 0 param_list = [] weights_num = len(weights) for i in range(0, len(params), 5): weight = params[i] mean = params[i+1] var = params[i+2] gamma = params[i+3] beta = params[i+4] beta_data = weights[index: index+beta.size].reshape(beta.shape) index += beta.size gamma_data = weights[index: index+gamma.size].reshape(gamma.shape) index += gamma.size mean_data = weights[index: index+mean.size].reshape(mean.shape) index += mean.size var_data = weights[index: index + var.size].reshape(var.shape) index += var.size weight_data = weights[index: index+weight.size].reshape(weight.shape) index += weight.size param_list.append({'name': weight.name, 'type': weight.dtype, 'shape': weight.shape, 'data': ms.Tensor(weight_data)}) param_list.append({'name': mean.name, 'type': mean.dtype, 'shape': mean.shape, 'data': ms.Tensor(mean_data)}) param_list.append({'name': var.name, 'type': var.dtype, 'shape': var.shape, 'data': ms.Tensor(var_data)}) param_list.append({'name': gamma.name, 'type': gamma.dtype, 'shape': gamma.shape, 'data': ms.Tensor(gamma_data)}) param_list.append({'name': beta.name, 'type': beta.dtype, 'shape': beta.shape, 'data': ms.Tensor(beta_data)}) if index >= weights_num: break ms.save_checkpoint(param_list, output_file) if __name__ == '__main__': args = parse_args() convert_weight(args) yolov/mindyolo/utils/logger.py000066400000000000000000000114021443434675500170660ustar00rootroot00000000000000"""Custom Logger.""" import os import sys import logging from datetime import datetime __all__ = ['get_logger'] GLOBAL_LOGGER = None class Logger(logging.Logger): """ Logger classes and functions, support print information on console and files. Args: logger_name(str): The name of Logger. In most cases, it can be the name of the network. """ def __init__(self, logger_name="MindYOLO"): super(Logger, self).__init__(logger_name) self.log_level = "INFO" self.rank_id = _get_rank_id() self.device_per_servers = 8 self.formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(message)s') def setup_logging(logger_name="MindYOLO", log_level="INFO", rank_id=None, device_per_servers=8): """Setup logging file.""" logger = get_logger() logger.name = logger_name logger.log_level = log_level if rank_id is not None: logger.rank_id = rank_id logger.device_per_servers = device_per_servers if logger.log_level not in ['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']: raise ValueError(f"Not support log_level: {logger.log_level}, " f"the log_level should be in ['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']") # In the distributed scenario, only one card is printed on the console. if logger.rank_id % logger.device_per_servers == 0: console = logging.StreamHandler(sys.stdout) console.setLevel(logger.log_level) console.setFormatter(logger.formatter) logger.addHandler(console) def setup_logging_file(log_dir="./logs"): """Setup logging file.""" logger = get_logger() if not os.path.exists(log_dir): os.makedirs(log_dir, exist_ok=True) # Generate a file stream based on the log generation time and rank_id log_name = f"{logger.name}_{datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S')}_rank_{logger.rank_id}.log" log_path = os.path.join(log_dir, log_name) file_handler = logging.FileHandler(log_path) file_handler.setLevel(logger.log_level) file_handler.setFormatter(logger.formatter) logger.addHandler(file_handler) def print_args(args): """Print hyper-parameter""" get_logger().info('Args:') args_dict = vars(args) for key in args_dict.keys(): get_logger().info('--> %s: %s', key, args_dict[key]) get_logger().info('') def important_info(msg, *args, **kwargs): """For information that needs to be focused on, add special printing format.""" line_width = 2 important_msg = '\n' important_msg += ('*'*70 + '\n')*line_width important_msg += ('*'*line_width + '\n')*2 important_msg += '*'*line_width + ' '*8 + msg + '\n' important_msg += ('*'*line_width + '\n')*2 important_msg += ('*'*70 + '\n')*line_width get_logger().info(important_msg, *args, **kwargs) def info(msg, *args, **kwargs): """ Log a message with severity 'INFO' on the MindYOLO logger. Examples: >>> from mindyolo import logger >>> logger.setup_logging(logger_name="MindYOLO", log_level="INFO", rank_id=0, device_per_servers=8) >>> logger.setup_logging_file(log_dir="./logs") >>> logger.info("test info") """ get_logger().info(msg, *args, **kwargs) def debug(msg, *args, **kwargs): """Log a message with severity 'DEBUG' on the MindYOLO logger.""" get_logger().debug(msg, *args, **kwargs) def error(msg, *args, **kwargs): """Log a message with severity 'ERROR' on the MindYOLO logger.""" get_logger().error(msg, *args, **kwargs) def warning(msg, *args, **kwargs): """Log a message with severity 'WARNING' on the MindYOLO logger.""" get_logger().warning(msg, *args, **kwargs) def critical(msg, *args, **kwargs): """Log a message with severity 'CRITICAL' on the MindYOLO logger.""" get_logger().critical(msg, *args, **kwargs) def get_level(): """ Get the logger level. Returns: str, the Log level includes 'CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG'. """ # level and glog level mapping dictionary return get_logger().log_level def _get_rank_id(): """Get rank id.""" rank_id = os.getenv('RANK_ID') gpu_rank_id = os.getenv('OMPI_COMM_WORLD_RANK') rank = '0' if rank_id and gpu_rank_id and rank_id != gpu_rank_id: print( f"Environment variables RANK_ID and OMPI_COMM_WORLD_RANK set by different values, RANK_ID={rank_id}, " f"OMPI_COMM_WORLD_RANK={gpu_rank_id}. We will use RANK_ID to get rank id by default.", flush=True) if rank_id: rank = rank_id elif gpu_rank_id: rank = gpu_rank_id return int(rank) def get_logger(): """Get logger instance.""" global GLOBAL_LOGGER if GLOBAL_LOGGER: return GLOBAL_LOGGER GLOBAL_LOGGER = Logger() return GLOBAL_LOGGER yolov/mindyolo/utils/metrics.py000066400000000000000000000164221443434675500172640ustar00rootroot00000000000000import time import numpy as np __all__ = [ 'non_max_suppression', 'scale_coords', 'xyxy2xywh', 'xywh2xyxy' ] def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, time_limit=20.0): """Runs Non-Maximum Suppression (NMS) on inference results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ nc = prediction.shape[2] - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Settings min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height max_det = 300 # maximum number of detections per image max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = time_limit if time_limit > 0 else 1e3 # seconds to quit after redundant = True # require redundant detections multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() output = [np.zeros((0, 6))] * prediction.shape[0] for xi, x in enumerate(prediction): # image index, image inference # Apply constraints # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x = x[xc[xi]] # confidence # If none remain process next image if not x.shape[0]: continue # Compute conf if nc == 1: x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, # so there is no need to multiplicate. else: x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box (center x, center y, width, height) to (x1, y1, x2, y2) box = xywh2xyxy(x[:, :4]) # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:] > conf_thres).nonzero() x = np.concatenate((box[i], x[i, j + 5, None], j[:, None].astype(np.float)), 1) else: # best class only conf, j = x[:, 5:].max(1, keepdim=True) x = np.concatenate((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] # Filter by class if classes is not None: x = x[(x[:, 5:6] == np.array(classes)).any(1)] # Check shape n = x.shape[0] # number of boxes if not n: # no boxes continue elif n > max_nms: # excess boxes x = x[x[:, 4].argsort()[-max_nms:]] # sort by confidence # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = _nms(boxes, scores, iou_thres) # NMS for per sample if i.shape[0] > max_det: # limit detections i = i[:max_det] if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = _box_iou(boxes[i], boxes) > iou_thres # iou matrix # (N, M) weights = iou * scores[None] # box weights # (N, M) @ (M, 4) / (N, 1) x[i, :4] = np.matmul(weights, x[:, :4]) / weights.sum(1, keepdim=True) # merged boxes if redundant: i = i[iou.sum(1) > 1] # require redundancy output[xi] = x[i] if (time.time() - t) > time_limit: print(f'WARNING: Batch NMS time limit {time_limit}s exceeded, this batch ' f'process {xi + 1}/{prediction.shape[0]} sample.') break # time limit exceeded return output def scale_coords(img1_shape, coords, img0_shape, ratio=None, pad=None): # Rescale coords (xyxy) from img1_shape to img0_shape if ratio is None: # calculate from img0_shape ratio = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # ratio = old / new else: ratio = ratio[0] if pad is None: padh, padw = (img1_shape[0] - img0_shape[0] * ratio) / 2, \ (img1_shape[1] - img0_shape[1] * ratio) / 2 else: padh, padw = pad[:] coords[:, [0, 2]] -= padw # x padding coords[:, [1, 3]] -= padh # y padding coords[:, [0, 2]] /= ratio # x rescale coords[:, [1, 3]] /= ratio # y rescale coords = _clip_coords(coords, img0_shape) return coords def _clip_coords(boxes, img_shape): # Clip bounding xyxy bounding boxes to image shape (height, width) boxes[:, 0].clip(0, img_shape[1]) # x1 boxes[:, 1].clip(0, img_shape[0]) # y1 boxes[:, 2].clip(0, img_shape[1]) # x2 boxes[:, 3].clip(0, img_shape[0]) # y2 return boxes def _nms(xyxys, scores, threshold): """Calculate NMS""" s_time = time.time() x1 = xyxys[:, 0] y1 = xyxys[:, 1] x2 = xyxys[:, 2] y2 = xyxys[:, 3] scores = scores # zhy_test # areas = (x2 - x1 + 1) * (y2 - y1 + 1) areas = (x2 - x1) * (y2 - y1) order = scores.argsort()[::-1] reserved_boxes = [] while order.size > 0: i = order[0] reserved_boxes.append(i) max_x1 = np.maximum(x1[i], x1[order[1:]]) max_y1 = np.maximum(y1[i], y1[order[1:]]) min_x2 = np.minimum(x2[i], x2[order[1:]]) min_y2 = np.minimum(y2[i], y2[order[1:]]) # zhy_test # intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1) # intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1) intersect_w = np.maximum(0.0, min_x2 - max_x1) intersect_h = np.maximum(0.0, min_y2 - max_y1) intersect_area = intersect_w * intersect_h ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area + 1e-6) indexes = np.where(ovr <= threshold)[0] order = order[indexes + 1] return np.array(reserved_boxes) def _box_iou(box1, box2): # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box1 ([N, 4]) box2 ([M, 4]) Returns: iou ([N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 """ def box_area(box): # box = 4xn return (box[2] - box[0]) * (box[3] - box[1]) area1 = box_area(box1.T) area2 = box_area(box2.T) # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) inter = (np.minimum(box1[:, None, 2:], box2[:, 2:]) - np.maximum(box1[:, None, :2], box2[:, :2])).clip(0, None).prod(2) return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = np.copy(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y def xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = np.copy(x) y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center y[:, 2] = x[:, 2] - x[:, 0] # width y[:, 3] = x[:, 3] - x[:, 1] # height return y yolov/mindyolo/utils/modelarts.py000066400000000000000000000025021443434675500176020ustar00rootroot00000000000000import os _global_sync_count = 0 __all__ = ['sync_data'] def get_device_id(): device_id = os.getenv('DEVICE_ID', '0') return int(device_id) def get_device_num(): device_num = os.getenv('RANK_SIZE', '1') return int(device_num) def get_rank_id(): global_rank_id = os.getenv('RANK_ID', '0') return int(global_rank_id) def sync_data(from_path, to_path): """ Download data from remote obs to local directory if the first url is remote url and the second one is local path Upload data from local directory to remote obs in contrast. """ import moxing as mox import time global _global_sync_count sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count) _global_sync_count += 1 # Each server contains 8 devices as most. if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock): print("from path: ", from_path) print("to path: ", to_path) mox.file.copy_parallel(from_path, to_path) print("===finish data synchronization===") try: os.mknod(sync_lock) except IOError: pass print("===save flag===") while True: if os.path.exists(sync_lock): break time.sleep(1) print("Finish sync data from {} to {}.".format(from_path, to_path)) yolov/mindyolo/utils/train_step_factory.py000066400000000000000000000047271443434675500215220ustar00rootroot00000000000000import mindspore as ms from mindspore import context, nn, ops __all__ = [ "create_train_step_fn", "get_gradreducer", "get_loss_scaler" ] def get_gradreducer(is_parallel, parameters): if is_parallel: mean = context.get_auto_parallel_context("gradients_mean") degree = context.get_auto_parallel_context("device_num") grad_reducer = nn.DistributedGradReducer(parameters, mean, degree) else: grad_reducer = ops.functional.identity return grad_reducer def get_loss_scaler(ms_loss_scaler='static', scale_value=1024, scale_factor=2, scale_window=2000): if ms_loss_scaler == 'dynamic': from mindspore.amp import DynamicLossScaler loss_scaler = DynamicLossScaler(scale_value=scale_value, scale_factor=scale_factor, scale_window=scale_window) elif ms_loss_scaler == 'static': from mindspore.amp import StaticLossScaler loss_scaler = StaticLossScaler(scale_value=scale_value) elif ms_loss_scaler in ('none', 'None'): from mindspore.amp import StaticLossScaler loss_scaler = StaticLossScaler(1.0) else: raise NotImplementedError(f"Not support ms_loss_scaler: {ms_loss_scaler}") return loss_scaler def create_train_step_fn(network, loss_fn, optimizer, loss_ratio, scaler, reducer, overflow_still_update=False, ms_jit=False): from mindyolo.utils.all_finite import all_finite def forward_func(x, label): pred = network(x) loss, loss_items = loss_fn(pred, label, x) loss *= loss_ratio return scaler.scale(loss), ops.stop_gradient(loss_items) grad_fn = ops.value_and_grad(forward_func, grad_position=None, weights=optimizer.parameters, has_aux=True) def train_step_func(x, label, optimizer_update=True): (loss, loss_items), grads = grad_fn(x, label) grads = reducer(grads) unscaled_grads = scaler.unscale(grads) grads_finite = all_finite(unscaled_grads) if optimizer_update: if grads_finite: loss = ops.depend(loss, optimizer(unscaled_grads)) else: if overflow_still_update: loss = ops.depend(loss, optimizer(unscaled_grads)) return scaler.unscale(loss), loss_items, unscaled_grads, grads_finite @ms.ms_function def jit_warpper(*args): return train_step_func(*args) return train_step_func if not ms_jit else jit_warpper yolov/mindyolo/utils/trainer_factory.py000066400000000000000000000254061443434675500210130ustar00rootroot00000000000000import os import types import time from typing import Union import mindspore as ms from mindspore import nn, ops, Tensor from mindyolo.utils import logger from mindyolo.utils.checkpoint_manager import CheckpointManager from mindyolo.utils.modelarts import sync_data __all__ = [ "create_trainer", ] def create_trainer( model_name: str, train_step_fn: types.FunctionType, scaler, network: nn.Cell, ema: nn.Cell, optimizer: nn.Cell, dataloader: ms.dataset.Dataset, ): return Trainer( model_name=model_name, train_step_fn=train_step_fn, scaler=scaler, dataloader=dataloader, network=network, ema=ema, optimizer=optimizer ) class Trainer: def __init__(self, model_name, train_step_fn, scaler, network, ema, optimizer, dataloader): self.model_name = model_name self.train_step_fn = train_step_fn self.scaler = scaler self.dataloader = dataloader self.network = network # for save checkpoint self.ema = ema # for save checkpoint self.optimizer = optimizer # for save checkpoint self.global_step = 0 self.steps_per_epoch = self.dataloader.get_dataset_size() def train( self, epochs: int, main_device: bool, warmup_step: int = 0, warmup_momentum: Union[list, None] = None, accumulate: int = 1, overflow_still_update: bool = False, keep_checkpoint_max: int = 10, log_interval: int = 1, loss_item_name: list = [], save_dir: str = '', enable_modelarts: bool = False, train_url: str = '', run_eval: bool = False, test_fn: types.FunctionType = None, ): # Set Attr self.epochs = epochs self.main_device = main_device self.log_interval = log_interval self.overflow_still_update = overflow_still_update self.loss_item_name = loss_item_name # Directories settings ckpt_save_dir = os.path.join(save_dir, 'weights') sync_lock_dir = os.path.join(save_dir, 'sync_locks') if not enable_modelarts else '/tmp/sync_locks' if main_device: os.makedirs(ckpt_save_dir, exist_ok=True) # save checkpoint path os.makedirs(sync_lock_dir, exist_ok=False) # sync_lock for run_eval # Grad Accumulate self.accumulate_cur_step = 0 self.accumulate_grads = None self.accumulate = accumulate self.accumulate_grads_fn = self._get_accumulate_grads_fn() manager = CheckpointManager(ckpt_save_policy='latest_k') manager_ema = CheckpointManager(ckpt_save_policy='latest_k') if self.ema else None manager_best = CheckpointManager(ckpt_save_policy='top_k') if run_eval else None ckpt_filelist_best = [] self.dataloader = self.dataloader.repeat(epochs) loader = self.dataloader.create_dict_iterator(output_numpy=False, num_epochs=1) s_step_time = time.time() s_epoch_time = time.time() for i, data in enumerate(loader): cur_epoch = (i // self.steps_per_epoch) + 1 cur_step = (i % self.steps_per_epoch) + 1 self.global_step += 1 if self.global_step < warmup_step: if warmup_momentum and isinstance(self.optimizer, (nn.SGD, nn.Momentum)): dtype = self.optimizer.momentum.dtype self.optimizer.momentum = Tensor(warmup_momentum[i], dtype) imgs, labels = data['image'], data['labels'] self.train_step(imgs, labels, cur_step=cur_step, cur_epoch=cur_epoch) # train log if cur_step % self.log_interval == 0: logger.info(f"Epoch {epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"step time: {(time.time() - s_step_time) * 1000 / self.log_interval:.2f} ms") s_step_time = time.time() # run eval per epoch on main device if run_eval and (i + 1) % self.steps_per_epoch == 0: s_eval_time = time.time() sync_lock = os.path.join(sync_lock_dir, "/run_eval_sync.lock" + str(cur_epoch)) # single device run eval only if self.main_device and not os.path.exists(sync_lock): eval_network = self.ema.ema if self.ema else self.network _train_status = eval_network.training eval_network.set_train(False) accuracy = test_fn(network=eval_network) accuracy = accuracy[0] if isinstance(accuracy, (list, tuple)) else accuracy eval_network.set_train(_train_status) save_path_best = os.path.join(ckpt_save_dir, f"best/{self.model_name}-{cur_epoch}_{self.steps_per_epoch}" f"_acc{accuracy:.2f}.ckpt") ckpt_filelist_best = manager_best.save_ckpoint(eval_network, num_ckpt=keep_checkpoint_max, metric=accuracy, save_path=save_path_best) logger.info(f"Epoch {epochs}/{cur_epoch}, eval accuracy: {accuracy:.2f}, " f"run_eval time: {(time.time() - s_eval_time):.2f} s.") try: os.mknod(sync_lock) except IOError: pass # other device wait for lock sign while True: if os.path.exists(sync_lock): break time.sleep(1) # save checkpoint per epoch on main device if self.main_device and (i + 1) % self.steps_per_epoch == 0: # Save Checkpoint # ms.save_checkpoint(self.optimizer, os.path.join(ckpt_save_dir, f'optim_{self.model_name}.ckpt'), # async_save=True) # save_path = os.path.join(ckpt_save_dir, f"{self.model_name}-{cur_epoch}_{self.steps_per_epoch}.ckpt") # manager.save_ckpoint(self.network, num_ckpt=keep_checkpoint_max, save_path=save_path) # if self.ema: # save_path_ema = os.path.join(ckpt_save_dir, # f"EMA_{self.model_name}-{cur_epoch}_{self.steps_per_epoch}.ckpt") # manager_ema.save_ckpoint(self.ema.ema, num_ckpt=keep_checkpoint_max, save_path=save_path_ema) # logger.info(f"Saving model to {save_path}") # if enable_modelarts: # sync_data(save_path, train_url + "/weights/" + save_path.split("/")[-1]) # if self.ema: # sync_data(save_path_ema, train_url + "/weights/" + save_path_ema.split("/")[-1]) logger.info( f"Epoch {epochs}/{cur_epoch}, epoch time: {(time.time() - s_epoch_time) / 60:.2f} min.") s_epoch_time = time.time() if enable_modelarts and ckpt_filelist_best: for p in ckpt_filelist_best: sync_data(p, train_url + '/weights/best/' + p.split("/")[-1]) logger.info("End Train.") def train_step(self, imgs, labels, cur_step=0, cur_epoch=0): if self.accumulate == 1: loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, True) if self.ema: self.ema.update() self.scaler.adjust(grads_finite) if not grads_finite and (cur_step % self.log_interval == 0): if self.overflow_still_update: logger.warning(f"overflow, still update, loss scale adjust to {self.scaler.scale_value.asnumpy()}") else: logger.warning(f"overflow, drop step, loss scale adjust to {self.scaler.scale_value.asnumpy()}") else: loss, loss_item, grads, grads_finite = self.train_step_fn(imgs, labels, False) self.scaler.adjust(grads_finite) if grads_finite or self.overflow_still_update: self.accumulate_cur_step += 1 if self.accumulate_grads: self.accumulate_grads = self.accumulate_grads_fn(self.accumulate_grads, grads) # update self.accumulate_grads else: self.accumulate_grads = grads if self.accumulate_cur_step % self.accumulate == 0: self.optimizer(self.accumulate_grads) if self.ema: self.ema.update() logger.info(f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"accumulate: {self.accumulate}, optimizer an accumulate step success.") from mindyolo.utils.all_finite import all_finite if not all_finite(self.accumulate_grads): logger.warning(f"overflow, still update.") # reset accumulate self.accumulate_grads, self.accumulate_cur_step = None, 0 else: logger.warning(f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, " f"accumulate: {self.accumulate}, this step grad overflow, drop. " f"Loss scale adjust to {self.scaler.scale_value.asnumpy()}") # train log if cur_step % self.log_interval == 0: log_string = f"Epoch {self.epochs}/{cur_epoch}, Step {self.steps_per_epoch}/{cur_step}, imgsize {imgs.shape[2:]}" # print loss if len(self.loss_item_name) < len(loss_item): self.loss_item_name += [f'loss_item{i}' for i in range(len(loss_item) - len(self.loss_item_name))] for i in range(len(loss_item)): log_string += f", {self.loss_item_name[i]}: {loss_item[i].asnumpy():.4f}" # print lr if self.optimizer.dynamic_lr: if self.optimizer.is_group_lr: lr_cell = self.optimizer.learning_rate[0] cur_lr = lr_cell(Tensor(self.global_step, ms.int32)).asnumpy().item() else: cur_lr = self.optimizer.learning_rate(Tensor(self.global_step, ms.int32)).asnumpy().item() else: cur_lr = self.optimizer.learning_rate.asnumpy().item() log_string += f", cur_lr: {cur_lr}" logger.info(log_string) def _get_accumulate_grads_fn(self): hyper_map = ops.HyperMap() def accu_fn(g1, g2): g1 = g1 + g2 return g1 def accumulate_grads_fn(accumulate_grads, grads): success = hyper_map(accu_fn, accumulate_grads, grads) return success return accumulate_grads_fn yolov/mindyolo/utils/utils.py000066400000000000000000000136471443434675500167640ustar00rootroot00000000000000import random import os import yaml import numpy as np from datetime import datetime import mindspore as ms from mindspore import context from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank, get_group_size from mindyolo.utils import logger def set_seed(seed=2): np.random.seed(seed) random.seed(seed) ms.set_seed(seed) def set_default(args): # Set Context context.set_context(mode=args.ms_mode, device_target=args.device_target, max_call_depth=2000) if args.device_target == "Ascend": device_id = int(os.getenv('DEVICE_ID', 0)) context.set_context(device_id=device_id) elif args.device_target == "GPU" and args.ms_enable_graph_kernel: context.set_context(enable_graph_kernel=True) # Set Parallel if args.is_parallel: init() args.rank, args.rank_size, parallel_mode = get_rank(), get_group_size(), ParallelMode.DATA_PARALLEL context.set_auto_parallel_context(device_num=args.rank_size, parallel_mode=parallel_mode, gradients_mean=True) else: args.rank, args.rank_size = 0, 1 # Set Default args.total_batch_size = args.per_batch_size * args.rank_size args.sync_bn = args.sync_bn and context.get_context("device_target") == "Ascend" and args.rank_size > 1 args.accumulate = max(1, np.round(args.nbs / args.total_batch_size)) if args.auto_accumulate else args.accumulate # optimizer args.optimizer.warmup_epochs = args.optimizer.get('warmup_epochs', 0) args.optimizer.min_warmup_step = args.optimizer.get('min_warmup_step', 0) args.optimizer.epochs = args.epochs args.optimizer.nbs = args.nbs args.optimizer.accumulate = args.accumulate args.optimizer.total_batch_size = args.total_batch_size # data args.data.nc = 1 if args.single_cls else int(args.data.nc) # number of classes args.data.names = ['item'] if args.single_cls and len(args.names) != 1 else args.data.names # class names assert len(args.data.names) == args.data.nc, '%g names found for nc=%g dataset in %s' % \ (len(args.data.names), args.data.nc, args.config) # Directories and Save run settings args.save_dir = os.path.join(args.save_dir, datetime.now().strftime("%Y.%m.%d-%H:%M:%S")) os.makedirs(args.save_dir, exist_ok=True) if args.rank % args.rank_size == 0: with open(os.path.join(args.save_dir, "cfg.yaml"), 'w') as f: yaml.dump(vars(args), f, sort_keys=False) # Set Logger logger.setup_logging(logger_name="MindYOLO", log_level=args.log_level, rank_id=args.rank, device_per_servers=args.rank_size) logger.setup_logging_file(log_dir=os.path.join(args.save_dir, "logs")) # Modelarts: Copy data, from the s3 bucket to the computing node; Reset dataset dir. if args.enable_modelarts: from mindyolo.utils.modelarts import sync_data os.makedirs(args.data_dir, exist_ok=True) sync_data(args.data_url, args.data_dir) sync_data(args.save_dir, args.train_url) if args.ckpt_url: sync_data(args.ckpt_url, args.ckpt_dir) # pretrain ckpt # args.data.dataset_dir = os.path.join(args.data_dir, args.data.dataset_dir) args.data.train_set = os.path.join(args.data_dir, args.data.train_set) args.data.val_set = os.path.join(args.data_dir, args.data.val_set) args.data.test_set = os.path.join(args.data_dir, args.data.test_set) args.weight = args.ckpt_dir if args.ckpt_dir else '' args.ema_weight = os.path.join(args.ckpt_dir, args.ema_weight) if args.ema_weight else '' def load_pretrain(network, weight, ema=None, ema_weight=None): if weight.endswith('.ckpt'): param_dict = ms.load_checkpoint(weight) ms.load_param_into_net(network, param_dict) logger.info(f"Pretrain model load from \"{weight}\" success.") if ema: if ema_weight.endswith('.ckpt'): param_dict_ema = ms.load_checkpoint(ema_weight) ms.load_param_into_net(ema.ema, param_dict_ema) logger.info(f"Ema pretrain model load from \"{ema_weight}\" success.") else: ema.clone_from_model() logger.info("ema_weight not exist, default pretrain weight is currently used.") def freeze_layers(network, freeze=[]): if len(freeze) > 0: freeze = [f'model.{x}.' for x in freeze] # parameter names to freeze (full or partial) for n, p in network.parameters_and_names(): if any(x in n for x in freeze): logger.info('freezing %s' % n) p.requires_grad = False def draw_result(img_path, result_dict, data_names, is_coco_dataset=True, save_path='./detect_results'): import cv2, random from mindyolo.data import COCO80_TO_COCO91_CLASS os.makedirs(save_path, exist_ok=True) save_result_path = os.path.join(save_path, img_path.split('/')[-1]) im = cv2.imread(img_path) category_id, bbox, score = result_dict['category_id'], result_dict['bbox'], result_dict['score'] for i in range(len(bbox)): # draw box x_l, y_t, w, h = bbox[i][:] x_r, y_b = x_l + w, y_t + h x_l, y_t, x_r, y_b = int(x_l), int(y_t), int(x_r), int(y_b) _color = [random.randint(0, 255) for _ in range(3)] cv2.rectangle(im, (x_l, y_t), (x_r, y_b), tuple(_color), 2) # draw label if is_coco_dataset: class_name_index = COCO80_TO_COCO91_CLASS.index(category_id[i]) else: class_name_index = category_id[i] class_name = data_names[class_name_index] # args.data.names[class_name_index] text = f'{class_name}: {score[i]}' (text_w, text_h), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2) cv2.rectangle(im, (x_l, y_t - text_h - baseline), (x_l + text_w, y_t), tuple(_color), -1) cv2.putText(im, text, (x_l, y_t - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2) # save results cv2.imwrite(save_result_path, im) yolov/mindyolo/version.py000066400000000000000000000000571443434675500161400ustar00rootroot00000000000000"""version init""" __version__ = "0.0.1-alpha" yolov/package.sh000066400000000000000000000013111443434675500141700ustar00rootroot00000000000000#!/bin/bash set -e BASEPATH=$(cd "$(dirname $0)"; pwd) OUTPUT_PATH="${BASEPATH}/output" PYTHON=$(which python3) mk_new_dir() { local create_dir="$1" # the target to make if [[ -d "${create_dir}" ]];then rm -rf "${create_dir}" fi mkdir -pv "${create_dir}" } write_checksum() { cd "$OUTPUT_PATH" || exit PACKAGE_LIST=$(ls mindyolo-*.whl) || exit for PACKAGE_NAME in $PACKAGE_LIST; do echo $PACKAGE_NAME sha256sum -b "$PACKAGE_NAME" >"$PACKAGE_NAME.sha256" done } mk_new_dir "${OUTPUT_PATH}" ${PYTHON} ${BASEPATH}/setup.py bdist_wheel mv ${BASEPATH}/dist/*whl ${OUTPUT_PATH} write_checksum echo "------Successfully created mindyolo package------" yolov/requirements.txt000066400000000000000000000002641443434675500155330ustar00rootroot00000000000000# MindYOLO requirements # Usage: pip install -r requirements.txt # Base opencv-python>=4.7.0.68 opencv-python-headless>=4.7.0.68 # Extras albumentations>=1.0.3 pycocotools>=2.0.2yolov/requirements/000077500000000000000000000000001443434675500147705ustar00rootroot00000000000000yolov/requirements/cpu_requirements.txt000066400000000000000000000000521443434675500211200ustar00rootroot00000000000000pyyaml >= 5.3 tqdm mindspore pylint pytestyolov/setup.py000066400000000000000000000024441443434675500137630ustar00rootroot00000000000000#!/usr/bin/env python from setuptools import find_packages, setup exec(open("mindyolo/version.py").read()) setup( name="mindyolo", author="MindSpore Ecosystem", author_email="mindspore-ecosystem@example.com", url="https://github.com/mindspore-lab/mindyolo", project_urls={ "Sources": "https://github.com/mindspore-lab/mindyolo", "Issue Tracker": "https://github.com/mindspore-lab/mindyolo/issues", }, description="A toolbox of vision models and algorithms based on MindSpore.", license="Apache Software License 2.0", include_package_data=True, packages=find_packages(include=["mindyolo", "mindyolo.*"]), install_requires=[ "numpy >= 1.17.0", "PyYAML >= 5.3", "tqdm", ], python_requires=">=3.7", classifiers=[ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", ], test_suite="tests", tests_require=[ "pytest", ], version=__version__, zip_safe=False, ) yolov/test.py000066400000000000000000000240311443434675500135760ustar00rootroot00000000000000import os import yaml import ast import argparse import time import numpy as np from datetime import datetime from pathlib import Path from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval import mindspore as ms from mindspore import nn, context, Tensor from mindyolo.models import create_model from mindyolo.data import COCODataset, create_loader, COCO80_TO_COCO91_CLASS from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.metrics import non_max_suppression, scale_coords, xyxy2xywh from mindyolo.utils.utils import set_seed def get_parser_test(parents=None): parser = argparse.ArgumentParser(description='Test', parents=[parents] if parents else []) parser.add_argument('--device_target', type=str, default='Ascend', help='device target, Ascend/GPU/CPU') parser.add_argument('--ms_mode', type=int, default=0, help='train mode, graph/pynative') parser.add_argument('--ms_amp_level', type=str, default='O0', help='amp level, O0/O1/O2') parser.add_argument('--ms_enable_graph_kernel', type=ast.literal_eval, default=False, help='use enable_graph_kernel or not') parser.add_argument('--weight', type=str, default='yolov7_300.ckpt', help='model.ckpt path(s)') parser.add_argument('--per_batch_size', type=int, default=32, help='size of each image batch') parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--single_cls', type=ast.literal_eval, default=False, help='train multi-class data as single-class') parser.add_argument('--rect', type=ast.literal_eval, default=False, help='rectangular training') parser.add_argument('--nms_time_limit', type=float, default=60.0, help='time limit for NMS') parser.add_argument('--conf_thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--iou_thres', type=float, default=0.65, help='IOU threshold for NMS') parser.add_argument('--seed', type=int, default=2, help='set global seed') parser.add_argument('--log_level', type=str, default='INFO', help='save dir') parser.add_argument('--save_dir', type=str, default='./runs_test', help='save dir') # args for ModelArts parser.add_argument('--enable_modelarts', type=ast.literal_eval, default=False, help='enable modelarts') parser.add_argument('--data_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--ckpt_url', type=str, default='', help='ModelArts: obs path to checkpoint folder') parser.add_argument('--train_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--data_dir', type=str, default='/cache/data/', help='ModelArts: local device path to dataset folder') parser.add_argument('--ckpt_dir', type=str, default='/cache/pretrain_ckpt/', help='ModelArts: local device path to checkpoint folder') return parser def set_default_test(args): # Set Context context.set_context(mode=args.ms_mode, device_target=args.device_target, max_call_depth=2000) if args.device_target == "Ascend": context.set_context(device_id=int(os.getenv('DEVICE_ID', 0))) elif args.device_target == "GPU" and args.ms_enable_graph_kernel: context.set_context(enable_graph_kernel=True) args.rank, args.rank_size = 0, 1 # Set Data args.data.nc = 1 if args.single_cls else int(args.data.nc) # number of classes args.data.names = ['item'] if args.single_cls and len(args.names) != 1 else args.data.names # class names assert len(args.data.names) == args.data.nc, '%g names found for nc=%g dataset in %s' % \ (len(args.data.names), args.data.nc, args.config) # Directories and Save run settings args.save_dir = os.path.join(args.save_dir, datetime.now().strftime("%Y.%m.%d-%H:%M:%S")) os.makedirs(args.save_dir, exist_ok=True) if args.rank % args.rank_size == 0: with open(os.path.join(args.save_dir, "cfg.yaml"), 'w') as f: yaml.dump(vars(args), f, sort_keys=False) # Set Logger logger.setup_logging(logger_name="MindYOLO", log_level="INFO", rank_id=args.rank, device_per_servers=args.rank_size) logger.setup_logging_file(log_dir=os.path.join(args.save_dir, "logs")) # Modelarts: Copy data, from the s3 bucket to the computing node; Reset dataset dir. if args.enable_modelarts: from mindyolo.utils.modelarts import sync_data os.makedirs(args.data_dir, exist_ok=True) sync_data(args.data_url, args.data_dir) sync_data(args.save_dir, args.train_url) if args.ckpt_url: sync_data(args.ckpt_url, args.ckpt_dir) # pretrain ckpt # args.data.dataset_dir = os.path.join(args.data_dir, args.data.dataset_dir) args.data.val_set = os.path.join(args.data_dir, args.data.val_set) args.data.test_set = os.path.join(args.data_dir, args.data.test_set) args.weight = args.ckpt_dir if args.ckpt_dir else '' def test( network: nn.Cell, dataloader: ms.dataset.Dataset, anno_json_path: str, conf_thres: float = 0.001, iou_thres: float = 0.65, nms_time_limit: float = -1., is_coco_dataset: bool = True, imgIds: list = [], per_batch_size: int = -1, ): steps_per_epoch = dataloader.get_dataset_size() loader = dataloader.create_dict_iterator(output_numpy=True, num_epochs=1) coco91class = COCO80_TO_COCO91_CLASS sample_num = 0 infer_times = 0. nms_times = 0. result_dicts = [] for i, data in enumerate(loader): imgs, _, paths, ori_shape, pad, hw_scale = data['image'], data['labels'], data['img_files'], \ data['hw_ori'], data['pad'], data['hw_scale'] nb, _, height, width = imgs.shape imgs = Tensor(imgs, ms.float32) # Run infer _t = time.time() out, _ = network(imgs) # inference and training outputs infer_times += time.time() - _t # Run NMS t = time.time() out = out.asnumpy() out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=True, time_limit=nms_time_limit) nms_times += time.time() - t # Statistics pred for si, pred in enumerate(out): path = Path(str(paths[si])) sample_num += 1 if len(pred) == 0: continue # Predictions predn = np.copy(pred) scale_coords(imgs[si].shape[1:], predn[:, :4], ori_shape[si], ratio=hw_scale[si], pad=pad[si]) # native-space pred image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(pred.tolist(), box.tolist()): result_dicts.append({'image_id': image_id, 'category_id': coco91class[int(p[5])] if is_coco_dataset else int(p[5]), 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5)}) logger.info(f"Sample {steps_per_epoch}/{i + 1}, time cost: {(time.time() - _t) * 1000:.2f} ms.") # Compute mAP try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb anno = COCO(anno_json_path) # init annotations api pred = anno.loadRes(result_dicts) # init predictions api eval = COCOeval(anno, pred, 'bbox') if is_coco_dataset: eval.params.imgIds = imgIds eval.evaluate() eval.accumulate() eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: logger.error(f'pycocotools unable to run: {e}') raise e t = tuple(x / sample_num * 1E3 for x in (infer_times, nms_times, infer_times + nms_times)) + \ (height, width, per_batch_size) # tuple logger.info(f'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g;' % t) return map, map50 def main(args): # Init set_seed(args.seed) set_default_test(args) # Create Network network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, sync_bn=False, checkpoint_path=args.weight ) network.set_train(False) ms.amp.auto_mixed_precision(network, amp_level=args.ms_amp_level) # Create Dataloader dataset_path = args.data.val_set is_coco_dataset = ('coco' in args.data.dataset_name) dataset = COCODataset( dataset_path=dataset_path, img_size=args.img_size, transforms_dict=args.data.test_transforms, is_training=False, augment=False, rect=args.rect, single_cls=args.single_cls, batch_size=args.per_batch_size, stride=max(args.network.stride), ) dataloader = create_loader( dataset=dataset, batch_collate_fn=dataset.test_collate_fn, dataset_column_names=dataset.dataset_column_names, batch_size=args.per_batch_size, epoch_size=1, rank=0, rank_size=1, shuffle=False, drop_remainder=False, num_parallel_workers=args.data.num_parallel_workers, python_multiprocessing=True ) # Run test test( network=network, dataloader=dataloader, anno_json_path=os.path.join(args.data.val_set[:-len(args.data.val_set.split('/')[-1])], 'annotations/instances_val2017.json'), conf_thres=args.conf_thres, iou_thres=args.iou_thres, nms_time_limit=args.nms_time_limit, is_coco_dataset=is_coco_dataset, imgIds=None if not is_coco_dataset else dataset.imgIds, per_batch_size=args.per_batch_size ) logger.info('Testing completed.') if __name__ == '__main__': parser = get_parser_test() args = parse_args(parser) main(args) yolov/tests/000077500000000000000000000000001443434675500134075ustar00rootroot00000000000000yolov/tests/dataset_plots.py000066400000000000000000000024731443434675500166350ustar00rootroot00000000000000import cv2 from mindyolo.data.dataset import COCODataset from mindyolo.data.loader import create_loader from mindyolo.data.poly import show_img_with_bbox from mindyolo.utils.config import parse_args if __name__ == '__main__': cfg = parse_args('eval') dataset = COCODataset( dataset_dir=cfg.data.dataset_dir, image_dir=cfg.data.val_img_dir, anno_path=cfg.data.val_anno_path, img_size=cfg.img_size, transforms_dict=cfg.data.test_transforms, is_training=False, rect=False, batch_size=cfg.per_batch_size * 2, stride=max(cfg.network.stride), ) dataloader = create_loader( dataset=dataset, batch_collate_fn=dataset.test_collate_fn, dataset_column_names=dataset.dataset_column_names, batch_size=cfg.per_batch_size * 2, epoch_size=1, rank=0, rank_size=1, shuffle=False, drop_remainder=False, num_parallel_workers=cfg.data.num_parallel_workers, python_multiprocessing=True ) data_loader = dataloader.create_dict_iterator(output_numpy=True, num_epochs=1) for i, data in enumerate(data_loader): img = show_img_with_bbox(data, cfg.data.names) # img = show_img_with_poly(data) cv2.namedWindow('img', cv2.WINDOW_FREERATIO) cv2.imshow('img', img) cv2.waitKey(0) yolov/tests/st/000077500000000000000000000000001443434675500140355ustar00rootroot00000000000000yolov/tests/st/test_example.py000066400000000000000000000001111443434675500170720ustar00rootroot00000000000000""" Test example """ def test_example(): """ init test example """ yolov/tests/ut/000077500000000000000000000000001443434675500140375ustar00rootroot00000000000000yolov/tests/ut/test_example.py000066400000000000000000000001111443434675500170740ustar00rootroot00000000000000""" Test example """ def test_example(): """ init test example """ yolov/train.py000066400000000000000000000256111443434675500137410ustar00rootroot00000000000000import os import ast import argparse from functools import partial import mindspore as ms from mindyolo.data import COCODataset, create_loader from mindyolo.models import create_loss, create_model from mindyolo.optim import create_group_param, create_lr_scheduler, create_warmup_momentum_scheduler, \ create_optimizer, EMA from mindyolo.utils import logger from mindyolo.utils.config import parse_args from mindyolo.utils.utils import set_seed, set_default, load_pretrain, freeze_layers from mindyolo.utils.train_step_factory import get_gradreducer, get_loss_scaler, create_train_step_fn from mindyolo.utils.trainer_factory import create_trainer from test import test def get_parser_train(parents=None): parser = argparse.ArgumentParser(description='Train', parents=[parents] if parents else []) parser.add_argument('--device_target', type=str, default='Ascend', help='device target, Ascend/GPU/CPU') parser.add_argument('--save_dir', type=str, default='./runs', help='save dir') parser.add_argument('--device_per_servers', type=int, default=8, help='device number on a server') parser.add_argument('--log_level', type=str, default='INFO', help='save dir') parser.add_argument('--is_parallel', type=ast.literal_eval, default=False, help='Distribute train or not') parser.add_argument('--ms_mode', type=int, default=0, help='Running in GRAPH_MODE(0) or PYNATIVE_MODE(1) (default=0)') parser.add_argument('--ms_amp_level', type=str, default='O0', help='amp level, O0/O1/O2/O3') parser.add_argument('--keep_loss_fp32', type=ast.literal_eval, default=True, help='Whether to maintain loss using fp32/O0-level calculation') parser.add_argument('--ms_loss_scaler', type=str, default='static', help='train loss scaler, static/dynamic/none') parser.add_argument('--ms_loss_scaler_value', type=float, default=1024.0, help='static loss scale value') parser.add_argument('--ms_grad_sens', type=float, default=1024.0, help='gard sens') parser.add_argument('--ms_jit', type=ast.literal_eval, default=True, help='use jit or not') parser.add_argument('--ms_enable_graph_kernel', type=ast.literal_eval, default=False, help='use enable_graph_kernel or not') parser.add_argument('--overflow_still_update', type=ast.literal_eval, default=True, help='overflow still update') parser.add_argument('--ema', type=ast.literal_eval, default=True, help='ema') parser.add_argument('--weight', type=str, default='', help='initial weight path') parser.add_argument('--ema_weight', type=str, default='', help='initial ema weight path') parser.add_argument('--freeze', type=list, default=[], help='Freeze layers: backbone of yolov7=50, first3=0 1 2') parser.add_argument('--epochs', type=int, default=300, help="total train epochs") parser.add_argument('--per_batch_size', type=int, default=32, help='per batch size for each device') parser.add_argument('--img_size', type=list, default=640, help='train image sizes') parser.add_argument('--nbs', type=list, default=64, help='nbs') parser.add_argument('--accumulate', type=int, default=1, help='grad accumulate step, recommended when batch-size is less than 64') parser.add_argument('--auto_accumulate', type=ast.literal_eval, default=False, help='auto accumulate') parser.add_argument('--log_interval', type=int, default=100, help='log interval') parser.add_argument('--single_cls', type=ast.literal_eval, default=False, help='train multi-class data as single-class') parser.add_argument('--sync_bn', type=ast.literal_eval, default=False, help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--keep_checkpoint_max', type=int, default=100) parser.add_argument('--run_eval', type=ast.literal_eval, default=False, help='Whether to run eval during training') parser.add_argument('--rect', type=ast.literal_eval, default=False, help='rectangular training') parser.add_argument('--nms_time_limit', type=float, default=20.0, help='time limit for NMS') parser.add_argument('--recompute', type=ast.literal_eval, default=False, help='Recompute') parser.add_argument('--recompute_layers', type=int, default=0) parser.add_argument('--seed', type=int, default=2, help='set global seed') # args for ModelArts parser.add_argument('--enable_modelarts', type=ast.literal_eval, default=False, help='enable modelarts') parser.add_argument('--multi_data_url', type=list, default=[], help='ModelArts: obs path to dataset folder') parser.add_argument('--data_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--ckpt_url', type=str, default='', help='ModelArts: obs path to checkpoint folder') parser.add_argument('--train_url', type=str, default='', help='ModelArts: obs path to dataset folder') parser.add_argument('--pretrain_url', type=list, default=[], help='ModelArts: obs path to dataset folder') parser.add_argument('--data_dir', type=str, default='/cache/data/', help='ModelArts: local device path to dataset folder') parser.add_argument('--ckpt_dir', type=str, default='/cache/pretrain_ckpt/', help='ModelArts: local device path to checkpoint folder') return parser def train(args): # Set Default set_seed(args.seed) set_default(args) main_device = (args.rank % args.rank_size == 0) # Create Network args.network.recompute = args.recompute args.network.recompute_layers = args.recompute_layers network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, sync_bn=args.sync_bn, ) if args.ema and main_device: ema_network = create_model( model_name=args.network.model_name, model_cfg=args.network, num_classes=args.data.nc, ) ema = EMA(network, ema_network) else: ema = None load_pretrain(network, args.weight, ema, args.ema_weight) # load pretrain freeze_layers(network, args.freeze) # freeze Layers ms.amp.auto_mixed_precision(network, amp_level=args.ms_amp_level) if ema: ms.amp.auto_mixed_precision(ema.ema, amp_level=args.ms_amp_level) # Create Dataloader dataset = COCODataset( dataset_path=args.data.train_set, img_size=args.img_size, transforms_dict=args.data.train_transforms, is_training=True, augment=True, rect=args.rect, single_cls=args.single_cls, batch_size=args.total_batch_size, stride=max(args.network.stride), ) dataloader = create_loader( dataset=dataset, batch_collate_fn=dataset.train_collate_fn, dataset_column_names=dataset.dataset_column_names, batch_size=args.per_batch_size, epoch_size=1, rank=args.rank, rank_size=args.rank_size, shuffle=True, drop_remainder=True, num_parallel_workers=args.data.num_parallel_workers, python_multiprocessing=True ) steps_per_epoch = dataloader.get_dataset_size() if args.run_eval: eval_dataset = COCODataset( dataset_path=args.data.val_set, img_size=args.img_size, transforms_dict=args.data.test_transforms, is_training=False, augment=False, rect=args.rect, single_cls=args.single_cls, batch_size=args.per_batch_size * 2, stride=max(args.network.stride), ) eval_dataloader = create_loader( dataset=eval_dataset, batch_collate_fn=eval_dataset.test_collate_fn, dataset_column_names=eval_dataset.dataset_column_names, batch_size=args.per_batch_size * 2, epoch_size=1, rank=0, rank_size=1, shuffle=False, drop_remainder=False, num_parallel_workers=args.data.num_parallel_workers, python_multiprocessing=True ) else: eval_dataset, eval_dataloader = None, None # Create Loss loss_fn = create_loss( **args.loss, anchors=args.network.anchors, stride=args.network.stride, nc=args.data.nc ) ms.amp.auto_mixed_precision(loss_fn, amp_level='O0' if args.keep_loss_fp32 else args.ms_amp_level) # Create Optimizer args.optimizer.steps_per_epoch = steps_per_epoch lr = create_lr_scheduler(**args.optimizer) params = create_group_param(params=network.trainable_params(), **args.optimizer) optimizer = create_optimizer(params=params, lr=lr, **args.optimizer) warmup_momentum = create_warmup_momentum_scheduler(**args.optimizer) # Create train_step_fn reducer = get_gradreducer(args.is_parallel, optimizer.parameters) scaler = get_loss_scaler(args.ms_loss_scaler, scale_value=args.ms_loss_scaler_value) train_step_fn = create_train_step_fn( network=network, loss_fn=loss_fn, optimizer=optimizer, loss_ratio=args.rank_size, scaler=scaler, reducer=reducer, overflow_still_update=args.overflow_still_update, ms_jit=args.ms_jit ) # Create test function for run eval while train if args.run_eval: is_coco_dataset = ('coco' in args.data.dataset_name) test_fn = partial( test, dataloader=eval_dataloader, anno_json_path=os.path.join(args.data.val_set[:-len(args.data.val_set.split('/')[-1])], 'annotations/instances_val2017.json'), conf_thres=args.conf_thres, iou_thres=args.iou_thres, nms_time_limit=args.nms_time_limit, is_coco_dataset=is_coco_dataset, imgIds=None if not is_coco_dataset else eval_dataset.imgIds, per_batch_size=args.per_batch_size ) else: test_fn = None # Create Trainer network.set_train(True) optimizer.set_train(True) model_name = os.path.basename(args.config)[:-5] # delete ".yaml" trainer = create_trainer( model_name=model_name, train_step_fn=train_step_fn, scaler=scaler, dataloader=dataloader, network=network, ema=ema, optimizer=optimizer, ) trainer.train( epochs=args.epochs, main_device=main_device, warmup_step=max(round(args.optimizer.warmup_epochs * steps_per_epoch), args.optimizer.min_warmup_step), warmup_momentum=warmup_momentum, accumulate=args.accumulate, overflow_still_update=args.overflow_still_update, keep_checkpoint_max=args.keep_checkpoint_max, log_interval=args.log_interval, loss_item_name=[] if not hasattr(loss_fn, 'loss_item_name') else loss_fn.loss_item_name, save_dir=args.save_dir, enable_modelarts=args.enable_modelarts, train_url=args.train_url, run_eval=args.run_eval, test_fn=test_fn, ) logger.info('Training completed.') if __name__ == '__main__': parser = get_parser_train() args = parse_args(parser) train(args)