pax_global_header00006660000000000000000000000064141550417040014513gustar00rootroot0000000000000052 comment=8965fb58a6b370d3ca70641cb6aad0363d46dabb model-pivot/000077500000000000000000000000001415504170400133165ustar00rootroot00000000000000model-pivot/.gitignore000066400000000000000000000034561415504170400153160ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__ __pycache__/ *.py[cod] *$py.class *.pyc .empty/ # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ vgg/ *.mat model-pivot/LICENSE000066400000000000000000000022121415504170400143200ustar00rootroot00000000000000 MIT License Copyright (c) Microsoft Corporation. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE model-pivot/README.md000066400000000000000000000020241415504170400145730ustar00rootroot00000000000000# Model-Pivot Model-Pivot is a model conversion and visualization tool to help users inter-operate among different deep learning frameworks. Convert models between PyTorch and Tensorflow. IR is based on the National Information Technology Standardization ```Neural Network Representation and Model Compression Part 1: Convolution Neural Network``. ## Requirments - tensorflow==1.8.0 - pytorch==0.4.0 - torchvision==0.2.0 - protobuf>=3.6.1 - python>=3.6 - flask ## How to deploy visualization on Web If you want to access the deployed web page from an external network, you should first modify the *host* and *port* for the *./visualization/app.py* file. You can deploy it on Web by running: ```shell python app.py ``` ## Model Framework | ResNet50 | Inception V3 | ShuffleNet | FCN | LSTM | :----------:|:----------:|:----------:|:----------:|:----------:|:----------:| TensorFlow | √ | √ | √ | √ | √ | PyTorch | √ | √ | √ | √ | √ | ## Test for Tensorflow and PyTorch ```shell CUDA_VSIBLE_DEVICES=0 python test.py ``` model-pivot/docs/000077500000000000000000000000001415504170400142465ustar00rootroot00000000000000model-pivot/docs/Makefile000066400000000000000000000011761415504170400157130ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) model-pivot/docs/make.bat000066400000000000000000000013741415504170400156600ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd model-pivot/docs/source/000077500000000000000000000000001415504170400155465ustar00rootroot00000000000000model-pivot/docs/source/conf.py000066400000000000000000000013241415504170400170450ustar00rootroot00000000000000# Configuration file for the Sphinx documentation builder. # -- Project information project = 'Model-Pivot' copyright = '2021, Guangyao Chen' author = 'Guangyao Chen' release = '0.1' version = '0.1.0' # -- General configuration extensions = [ 'sphinx.ext.duration', 'sphinx.ext.doctest', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', ] intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), } intersphinx_disabled_domains = ['std'] templates_path = ['_templates'] # -- Options for HTML output html_theme = 'sphinx_rtd_theme' # -- Options for EPUB output epub_show_urls = 'footnote' model-pivot/docs/source/examples.rst000066400000000000000000000012331415504170400201150ustar00rootroot00000000000000Usage ===== .. _installation: Installation ------------ To use Lumache, first install it using pip: .. code-block:: console (.venv) $ pip install lumache Creating recipes ---------------- To retrieve a list of random ingredients, you can use the ``lumache.get_random_ingredients()`` function: .. autofunction:: lumache.get_random_ingredients The ``kind`` parameter should be either ``"meat"``, ``"fish"``, or ``"veggies"``. Otherwise, :py:func:`lumache.get_random_ingredients` will raise an exception. .. autoexception:: lumache.InvalidKindError For example: >>> import lumache >>> lumache.get_random_ingredients() ['shells', 'gorgonzola', 'parsley'] model-pivot/docs/source/index.rst000066400000000000000000000011451415504170400174100ustar00rootroot00000000000000Welcome to Model-Pivot's documentation! =================================== **Model-Pivot** is a model conversion and visualization tool to help users inter-operate among different deep learning frameworks. IR is based on the National Information Technology Standardization ```Neural Network Representation and Model Compression Part 1: Convolution Neural Network``. Check out the :doc:`usage` section for further information, including how to :ref:`installation` the project. .. note:: This project is under active development. Contents -------- .. toctree:: usage model operators examples model-pivot/docs/source/model.rst000066400000000000000000000000751415504170400174020ustar00rootroot00000000000000API === .. autosummary:: :toctree: generated lumache model-pivot/docs/source/operators.rst000066400000000000000000000012331415504170400203150ustar00rootroot00000000000000Usage ===== .. _installation: Installation ------------ To use Lumache, first install it using pip: .. code-block:: console (.venv) $ pip install lumache Creating recipes ---------------- To retrieve a list of random ingredients, you can use the ``lumache.get_random_ingredients()`` function: .. autofunction:: lumache.get_random_ingredients The ``kind`` parameter should be either ``"meat"``, ``"fish"``, or ``"veggies"``. Otherwise, :py:func:`lumache.get_random_ingredients` will raise an exception. .. autoexception:: lumache.InvalidKindError For example: >>> import lumache >>> lumache.get_random_ingredients() ['shells', 'gorgonzola', 'parsley'] model-pivot/docs/source/usage.rst000066400000000000000000000012331415504170400174030ustar00rootroot00000000000000Usage ===== .. _installation: Installation ------------ To use Lumache, first install it using pip: .. code-block:: console (.venv) $ pip install lumache Creating recipes ---------------- To retrieve a list of random ingredients, you can use the ``lumache.get_random_ingredients()`` function: .. autofunction:: lumache.get_random_ingredients The ``kind`` parameter should be either ``"meat"``, ``"fish"``, or ``"veggies"``. 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""" model = InceptionResNetV2(num_classes=num_classes) return model ''' TEST Run this code with: ``` cd $HOME/pretrained-models.pytorch python -m pretrainedmodels.inceptionresnetv2 ``` ''' if __name__ == '__main__': assert inceptionresnetv2(num_classes=10, pretrained=None) print('success') assert inceptionresnetv2(num_classes=1000, pretrained='imagenet') print('success') assert inceptionresnetv2(num_classes=1001, pretrained='imagenet+background') print('success') # fail assert inceptionresnetv2(num_classes=1001, pretrained='imagenet')model-pivot/models/torch/lstm.py000066400000000000000000000014601415504170400172520ustar00rootroot00000000000000import torch import torch.nn as nn SEQ_LEN = 3 BATCH_SIZE = 1 IMAGE_SIZE = 224 INPUT_SIZE = IMAGE_SIZE * IMAGE_SIZE HIDDEN_DIM = 128 class Lstm(nn.Module): def __init__(self): super(Lstm, self).__init__() self.lstm = nn.LSTM(input_size=INPUT_SIZE, hidden_size=HIDDEN_DIM, num_layers=1) def forward(self, data_input): data_input = torch.reshape(data_input, (SEQ_LEN, BATCH_SIZE, INPUT_SIZE)) lstm_output, (hidden_state, cell_state) = self.lstm(data_input) return lstm_output def main(): x = torch.zeros(BATCH_SIZE, SEQ_LEN, IMAGE_SIZE, IMAGE_SIZE) net = Lstm() output = net(x) print(output.shape) print(output) if __name__ == '__main__': main() model-pivot/models/torch/shufflenet.py000066400000000000000000000166231415504170400204450ustar00rootroot00000000000000"""shufflenet in pytorch [1] Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices https://arxiv.org/abs/1707.01083v2 """ import torch import torch.nn as nn class BasicConv2d(nn.Module): def __init__(self, input_channels, output_channels, kernel_size, **kwargs): super().__init__() # print(input_channels, kwargs) self.conv = nn.Conv2d(input_channels, output_channels, kernel_size, **kwargs) self.bn = nn.BatchNorm2d(output_channels) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class ChannelShuffle(nn.Module): def __init__(self, groups): super().__init__() self.groups = groups def forward(self, x): batchsize, channels, height, width = x.data.size() channels_per_group = int(channels / self.groups) #"""suppose a convolutional layer with g groups whose output has #g x n channels; we first reshape the output channel dimension #into (g, n)""" x = x.view(batchsize, self.groups, channels_per_group, height, width) #"""transposing and then flattening it back as the input of next layer.""" x = x.transpose(1, 2).contiguous() x = x.view(batchsize, -1, height, width) return x class DepthwiseConv2d(nn.Module): def __init__(self, input_channels, output_channels, kernel_size, **kwargs): super().__init__() # print(input_channels, kwargs) self.depthwise = nn.Sequential( nn.Conv2d(input_channels, output_channels, kernel_size, **kwargs), nn.BatchNorm2d(output_channels) ) def forward(self, x): return self.depthwise(x) class PointwiseConv2d(nn.Module): def __init__(self, input_channels, output_channels, **kwargs): super().__init__() # print(input_channels, kwargs) self.pointwise = nn.Sequential( nn.Conv2d(input_channels, output_channels, 1, **kwargs), nn.BatchNorm2d(output_channels) ) def forward(self, x): return self.pointwise(x) class ShuffleNetUnit(nn.Module): def __init__(self, input_channels, output_channels, stage, stride, groups): super().__init__() #"""Similar to [9], we set the number of bottleneck channels to 1/4 #of the output channels for each ShuffleNet unit.""" self.bottlneck = nn.Sequential( PointwiseConv2d( input_channels, int(output_channels / 4), groups=groups ), nn.ReLU(inplace=True) ) #"""Note that for Stage 2, we do not apply group convolution on the first pointwise #layer because the number of input channels is relatively small.""" if stage == 2: self.bottlneck = nn.Sequential( PointwiseConv2d( input_channels, int(output_channels / 4), groups=groups ), nn.ReLU(inplace=True) ) self.channel_shuffle = ChannelShuffle(groups) self.depthwise = DepthwiseConv2d( int(output_channels / 4), int(output_channels / 4), 3, groups=int(output_channels / 4), stride=stride, padding=1 ) self.expand = PointwiseConv2d( int(output_channels / 4), output_channels, groups=groups ) self.relu = nn.ReLU(inplace=True) self.fusion = self._add self.shortcut = nn.Sequential() #"""As for the case where ShuffleNet is applied with stride, #we simply make two modifications (see Fig 2 (c)): #(i) add a 3 × 3 average pooling on the shortcut path; #(ii) replace the element-wise addition with channel concatenation, #which makes it easy to enlarge channel dimension with little extra #computation cost. if stride != 1 or input_channels != output_channels: self.shortcut = nn.AvgPool2d(3, stride=2, padding=1) self.expand = PointwiseConv2d( int(output_channels / 4), output_channels - input_channels, groups=groups ) self.fusion = self._cat def _add(self, x, y): return torch.add(x, y) def _cat(self, x, y): return torch.cat([x, y], dim=1) def forward(self, x): shortcut = self.shortcut(x) shuffled = self.bottlneck(x) shuffled = self.channel_shuffle(shuffled) shuffled = self.depthwise(shuffled) shuffled = self.expand(shuffled) output = self.fusion(shortcut, shuffled) output = self.relu(output) return output class ShuffleNet(nn.Module): def __init__(self, num_blocks, num_classes=1000, groups=3): super().__init__() if groups == 1: out_channels = [24, 144, 288, 567] elif groups == 2: out_channels = [24, 200, 400, 800] elif groups == 3: out_channels = [24, 240, 480, 960] elif groups == 4: out_channels = [24, 272, 544, 1088] elif groups == 8: out_channels = [24, 384, 768, 1536] self.conv1 = BasicConv2d(3, out_channels[0], 3, padding=1, stride=1) self.input_channels = out_channels[0] self.stage2 = self._make_stage( ShuffleNetUnit, num_blocks[0], out_channels[1], stride=2, stage=2, groups=groups ) self.stage3 = self._make_stage( ShuffleNetUnit, num_blocks[1], out_channels[2], stride=2, stage=3, groups=groups ) self.stage4 = self._make_stage( ShuffleNetUnit, num_blocks[2], out_channels[3], stride=2, stage=4, groups=groups ) self.avg = nn.AvgPool2d((24, 24)) self.fc = nn.Linear(out_channels[3], num_classes) def forward(self, x): x = self.conv1(x) x = self.stage2(x) x = self.stage3(x) x = self.stage4(x) x = self.avg(x) x = x.view(x.size(0), -1) x = self.fc(x) return x def _make_stage(self, block, num_blocks, output_channels, stride, stage, groups): """make shufflenet stage Args: block: block type, shuffle unit out_channels: output depth channel number of this stage num_blocks: how many blocks per stage stride: the stride of the first block of this stage stage: stage index groups: group number of group convolution Return: return a shuffle net stage """ strides = [stride] + [1] * (num_blocks - 1) stage = [] for stride in strides: stage.append( block( self.input_channels, output_channels, stride=stride, stage=stage, groups=groups ) ) self.input_channels = output_channels return nn.Sequential(*stage) def shufflenet(): return ShuffleNet([4, 8, 4])model-pivot/ox/000077500000000000000000000000001415504170400137445ustar00rootroot00000000000000model-pivot/ox/__init__.py000066400000000000000000000001551415504170400160560ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/common/000077500000000000000000000000001415504170400152345ustar00rootroot00000000000000model-pivot/ox/common/DataStructure/000077500000000000000000000000001415504170400200265ustar00rootroot00000000000000model-pivot/ox/common/DataStructure/__init__.py000066400000000000000000000001551415504170400221400ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/common/DataStructure/emitter.py000066400000000000000000000056121415504170400220550ustar00rootroot00000000000000# ---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # ---------------------------------------------------------------------------------------------- from six import string_types as _string_types import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import ModelDef, NodeDef, GraphDef, DataType class Emitter(object): def __init__(self): self.body_code = str() self.weights_dict = dict() self.used_layers = set() self.weight_loaded = False self.layers_codes = dict() def run(self, dstNetworkPath, dstWeightPath=None, phase='test'): self.save_code(dstNetworkPath, phase) # share functions def add_body(self, indent, codes): if isinstance(codes, _string_types): codes = [codes] for code in codes: self.body_code += (" " * indent) + code + '\n' def _load_weights(self, file_name=None): import numpy as np self.weight_loaded = True try: self.weights_dict = np.load(file_name, allow_pickle=True).item() # made default False in response to CVE-2019-6446 except: self.weights_dict = np.load(file_name, encoding='bytes', allow_pickle=True).item() def parent_variable_name(self, IR_node, path_or_name=[0]): if isinstance(path_or_name, _string_types): path = [IR_node.in_edges.index(path_or_name)] elif isinstance(path_or_name, list): path = path_or_name else: raise ValueError return self.IR_graph.get_parent_variable_name(IR_node.name, path) def _build(self): self.IR_graph.build() def gen_code(self, phase): raise NotImplementedError("do not use base emitter class.") def save_code(self, filepath, phase): code = self.gen_code(phase) with open(filepath, 'w') as fout: fout.write(code) print("Target network code snippet is saved as [{}].".format(filepath)) @staticmethod def save_weights(weights, filename): import numpy as np with open(filename, 'wb') as of: np.save(of, weights) print("Target weights are saved as [{}].".format(filename)) @staticmethod def _image_in_transpose_str(dim): dims = [dim] dims.extend(range(dim)) return ','.join('%s' % id for id in dims) @staticmethod def _image_out_transpose_str(dim): dims = list(range(1, dim + 1)) dims.append(0) return ','.join('%s' % id for id in dims) @staticmethod def _conv_kernel_transpose_str(dim): dims = [dim + 1, dim] dims.extend(range(dim)) return ','.join('%s' % id for id in dims) model-pivot/ox/common/DataStructure/graph.py000066400000000000000000000141261415504170400215050ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections class GraphNode(object): def __init__(self, layer): self.in_edges = list() self.out_edges = list() self.layer = layer self.covered = False self.real_name = self.name @property def name(self): assert False @property def variable_name(self): return self.real_name.replace('/', '_').replace('-', '_').replace('[','_').replace(']','_') @property def real_variable_name(self): return self.real_name.replace('/', '_').replace('-', '_').replace('[','_').replace(']','_') def __str__(self): return self.real_variable_name class Graph(object): def __init__(self, model): # key: layer_name value: keras layer self.layer_map = collections.OrderedDict() self.input_layers = list() self.output_layers = list() self.layer_name_map = collections.OrderedDict() self.topological_sort = list() self.model = model def __str__(self): ret = "" for name, layer in self.layer_map.items(): ret += ("name: %s\n layer: %s\n in_edges: %s\n out_edges: %s\n" % (name, layer, layer.in_edges, layer.out_edges)) return ret def build(self): self._make_input_layers() self._make_output_layers() self._get_topological_sort() def rebuild(self): self._make_input_layers(True) self._make_output_layers() self._get_topological_sort() def _make_input_layers(self, rebuild=False): for name, layer in self.layer_map.items(): layer.left_in_edges = len(layer.in_edges) if len(layer.in_edges) == 0: if rebuild: if not getattr(layer, 'scope', None): self.input_layers.append(name) else: self.input_layers.append(name) def _make_output_layers(self): for name, layer in self.layer_map.items(): if len(layer.out_edges) == 0: self.output_layers.append(name) '''get node by its name or tensor name''' def get_node(self, name): if not name.split(':')[0] in self.layer_map: raise IOError("Graph doesn't have node [%s]." % name.split(':')[0]) return None else: return self.layer_map[name.split(':')[0]] def get_nodes(self): return self.layer_map.values() def get_son(self, name, path, set_flag = False): if name == None: return None current_node = self.get_node(name) for idx in path: if len(current_node.out_edges) <= idx: return None son_name = current_node.out_edges[idx].split(':')[0] current_node = self.get_node(son_name) if set_flag: current_node.covered = True return current_node def get_parent(self, name, path, set_flag = False): if name == None: return None current_node = self.get_node(name) for idx in path: if len(current_node.in_edges) <= idx: return None parent_name = current_node.in_edges[idx].split(':')[0] current_node = self.get_node(parent_name) if set_flag: current_node.covered = True return current_node def get_real_parent_name(self, name, path, set_flag = False): if name == None: return None current_node = self.get_node(name) for idx in path: if len(current_node.in_edges) <= idx: return None parent_name = current_node.in_edges[idx].split(':')[0] current_node = self.get_node(parent_name) if set_flag: current_node.covered = True return self.layer_name_map[current_node.name] def get_parent_variable_name(self, name, path, set_flag = False): if name == None: return None current_node = self.get_node(name) for idx in path: if len(current_node.in_edges) <= idx: return None parent_name = current_node.in_edges[idx].split(':')[0] current_subscriptor = '' if len(current_node.in_edges[idx].split(':'))==1 else '[{}]'.format(current_node.in_edges[idx].split(':')[1]) current_node = self.get_node(parent_name) if set_flag: current_node.covered = True return current_node.real_variable_name + current_subscriptor # private functions def _get_topological_sort(self): self.topological_sort = self.input_layers[:] idx = 0 while idx < len(self.topological_sort): current_node = self.get_node(self.topological_sort[idx]) for next_node in current_node.out_edges: next_node_info = self.get_node(next_node) next_node_info.left_in_edges -= self._check_left_in_edges_num(current_node.name, next_node_info) # one node may connect another node by more than one edge. # next_node_info.left_in_edges -= 1 if next_node_info.left_in_edges == 0: self.topological_sort.append(next_node) idx += 1 def _make_connection(self, src, dst): if (src == dst) or (src not in self.layer_map) or (dst not in self.layer_map): if src.split(':')[0] not in self.layer_map: print ("Warning: Graph Construct a self-loop node {}. Ignored.".format(src)) return # print ('{} --> {}'.format(src, dst)) if not dst in self.layer_map[src.split(':')[0]].out_edges: self.layer_map[src.split(':')[0]].out_edges.append(dst) if not src in self.layer_map[dst].in_edges: self.layer_map[dst.split(':')[0]].in_edges.append(src) def _check_left_in_edges_num(self, in_node_name, node): count = 0 for in_edge in node.in_edges: if in_node_name == in_edge.split(':')[0]: count += 1 return count def remove_node(self, node): del self.layer_name_map[node.name] model-pivot/ox/common/DataStructure/parser.py000066400000000000000000000100771415504170400217010ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- import numpy as np import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import ModelDef, NodeDef, GraphDef, DataType info_model = { 'doc_url': '*', 'contributor_name': '*', 'contributor_email': '*', 'contributor_institute': '*', 'framework_name': '*', 'framework_version': '*', 'model_name': '*', 'model_version': '*', 'version': '*' } class Parser(object): def __init__(self): self.IR_model = ModelDef() self._add_model_info() self.IR_graph = self.IR_model.graph self.weight_loaded = False # name --> (weight_name --> ndarray) self.weights = dict() def run(self, dest_path): op_sets = self.gen_IR() self.save_to_json(dest_path + ".json") self.save_to_proto(dest_path + ".pb") self.save_weights(dest_path + ".npy") return op_sets def _add_model_info(self, model=info_model): self.IR_model.doc_url = model['doc_url'] self.IR_model.framework_name = model['framework_name'] self.IR_model.framework_version = model['framework_version'] self.IR_model.model_name = model['model_name'] self.IR_model.model_version = model['model_version'] self.IR_model.version = model['version'] # Contributor self.IR_model.contributors.name.append(model['contributor_name']) self.IR_model.contributors.email.append(model['contributor_email']) self.IR_model.contributors.institute.append(model['contributor_institute']) @property def src_graph(self): raise NotImplementedError def get_son(self, name, path, set_flag = False): return self.src_graph.get_son(name, path, set_flag) def get_parent(self, name, path, set_flag = False): return self.src_graph.get_parent(name, path, set_flag) def set_weight(self, layer_name, weight_name, data): if not layer_name in self.weights: self.weights[layer_name] = dict() layer = self.weights[layer_name] layer[weight_name] = data def save_to_json(self, filename): import google.protobuf.json_format as json_format json_str = json_format.MessageToJson(self.IR_model, preserving_proto_field_name = True) with open(filename, "w") as of: of.write(json_str) print ("IR network structure is saved as [{}].".format(filename)) return json_str def save_to_proto(self, filename): proto_str = self.IR_model.SerializeToString() with open(filename, 'wb') as of: of.write(proto_str) print ("IR network structure is saved as [{}].".format(filename)) return proto_str def save_weights(self, filename): if self.weight_loaded: with open(filename, 'wb') as of: np.save(of, self.weights) print ("IR weights are saved as [{}].".format(filename)) else: print ("Warning: weights are not loaded.") def convert_inedge(self, source_node, IR_node, start_idx = 0, end_idx = None): if end_idx == None: end_idx = len(source_node.in_edges) for idx in range(start_idx, end_idx): IR_node.input.append(self.src_graph.get_node(source_node.in_edges[idx]).real_name.lstrip('_')) @staticmethod def channel_first_conv_kernel_to_IR(tensor): dim = tensor.ndim tensor = np.transpose(tensor, list(range(2, dim)) + [1, 0]) return tensor @staticmethod def channel_first_shape_to_IR(shape): return [shape[0]] + list(shape[2:]) + [shape[1]] @staticmethod def channel_first_axis_to_IR(index): if index == 0: return 0 elif index == 1: return -1 else: return index - 1 model-pivot/ox/common/IR/000077500000000000000000000000001415504170400155465ustar00rootroot00000000000000model-pivot/ox/common/IR/IR_graph.py000066400000000000000000000106311415504170400176140ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.utils import * from ox.common.IR.graph_pb2 import TensorShape, AttrValue from ox.common.DataStructure.graph import Graph, GraphNode def load_protobuf_from_file(container, filename): with open(filename, 'rb') as fin: file_content = fin.read() # First try to read it as a binary file. try: container.ParseFromString(file_content) print("Parse file [%s] with binary format successfully." % (filename)) return container except Exception as e: # pylint: disable=broad-except print ("Info: Trying to parse file [%s] with binary format but failed with error [%s]." % (filename, str(e))) # Next try to read it as a text file. try: from google.protobuf import text_format text_format.Parse(file_content.decode('UTF-8'), container, allow_unknown_extension=True) print("Parse file [%s] with text format successfully." % (filename)) except text_format.ParseError as e: raise IOError("Cannot parse file %s: %s." % (filename, str(e))) return container class IRGraphNode(GraphNode): @staticmethod def replace_scope(name): return name.replace('/', '_') @property def IR_layer(self): return self.layer @property def name(self): return self.layer.name @property def type(self): return self.layer.op def set_attrs(self, attrs): assign_IRnode_values(self.layer, attrs) def get_attr(self, name, default_value = None): if name in self.layer.attr: attr = self.layer.attr[name] field = attr.WhichOneof('value') val = getattr(attr, field) if field else default_value if not val: return val if isinstance(val, AttrValue.ListValue): if val.ListFields(): return list(val.ListFields()[0][1]) else: return val.ListFields() else: return val.decode('utf-8') if isinstance(val, bytes) else val else: return default_value class IRGraph(Graph): @staticmethod def shapeToStr(tensor_shape, keep_minus_one = False): ret = "" first = True for e in tensor_shape.dim: if e.size != -1 or keep_minus_one: if first == False: ret += ", " ret += str(e.size) first = False return ret def __init__(self, filename): model_all = graph_pb2.ModelDef() # model = graph_pb2.GraphDef() load_protobuf_from_file(model_all, filename) model = model_all.graph super(IRGraph, self).__init__(model) def filter_node(self): self.layer_map = dict(filter(lambda layer: layer[1].in_edges or layer[1].out_edges, self.layer_map.items())) def build(self): for layer in self.model.node: self.layer_map[layer.name] = IRGraphNode(layer) self.layer_name_map[layer.name] = layer.name for i, layer in enumerate(self.model.node): for pred in layer.input: self._make_connection(pred, layer.name) self.filter_node() super(IRGraph, self).build() self.input_layers = list(filter(lambda x: self.layer_map[x].type != 'Constant', self.input_layers)) def rebuild(self): self.input_layers.clear() self.output_layers.clear() self.topological_sort.clear() self.filter_node() super(IRGraph, self).build() self.input_layers = list(filter(lambda x: self.layer_map[x].type != 'Constant', self.input_layers)) def clear_out_scope_node(self): def _clear_list_out_scope(list_): for idx in range(len(list_) -1, -1, -1): node = self.get_node(list_[idx]) if node.type != 'Scope' and node.get_attr('scope'): del list_[idx] _clear_list_out_scope(self.input_layers) _clear_list_out_scope(self.topological_sort) _clear_list_out_scope(self.output_layers) model-pivot/ox/common/IR/__init__.py000066400000000000000000000001551415504170400176600ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/common/IR/graph.proto000066400000000000000000000157211415504170400177420ustar00rootroot00000000000000syntax = "proto3"; // Align with Numpy at https://docs.scipy.org/doc/numpy/user/basics.types.html. // No TensorFlow quantized data types. enum DataType { DT_UNDEFINED = 0; DT_INT8 = 1; DT_INT16 = 2; DT_INT32 = 3; DT_INT64 = 4; DT_UINT8 = 5; DT_UINT16 = 6; DT_UINT32 = 7; DT_UINT64 = 8; DT_FLOAT16 = 9; DT_FLOAT32 = 10; DT_FLOAT64 = 11; DT_COMPLEX64 = 12; DT_COMPLEX128 = 13; DT_BOOL = 14; DT_STRING = 15; } // The protobuf format of the Model message ModelDef { // The version of the IR string version = 1; // The contributors of this model ContributorsList contributors = 8; // The information of the framework or tool that generate this model string framework_name = 2; string framework_version = 3; // The information of the model string model_name = 4; string model_version = 5; // A documentation or paper for this model. The doc url can be avaiable. string doc_url = 6; // The param of this model GraphDef graph = 7; } // The contributors of the model message ContributorsList { // name repeated string name = 1; // email repeated string email = 2; // institute repeated string institute = 3; } message GraphDef { repeated NodeDef node = 1; int32 version = 2; } message NodeDef { // The name given to this operator. Used for naming inputs, // logging, visualization, etc. Unique within a single GraphDef. // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*". string name = 1; // The operation name. There may be custom parameters in attrs. // Op names starting with an underscore are reserved for internal use. string op = 2; // Each input is "node:src_output" with "node" being a string name and // "src_output" indicating which output tensor to use from "node". // Regular inputs may optionally be followed by control inputs that // have the format "node". repeated string input = 3; // Operation-specific graph-construction-time configuration. // Note that this should include all attrs defined in the // corresponding OpDef, including those with a value matching // the default -- this allows the default to change and makes // NodeDefs easier to interpret on their own. However, if an // attr with a default is not specified in this list, the // default will be used. // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and // one of the names from the corresponding OpDef's attr field). // The values must have a type matching the corresponding OpDef // attr's type field. // TODO(josh11b): Add some examples here showing best practices. map attr = 4; }; // Protocol buffer representing the value for an attr used to configure an Op. // Comment indicates the corresponding attr type. Only the field matching the // attr type may be filled. message AttrValue { message ListValue { repeated bytes s = 2; // "list(string)" repeated int64 i = 3 [packed = true]; // "list(int)" repeated float f = 4 [packed = true]; // "list(float)" repeated bool b = 5 [packed = true]; // "list(bool)" repeated DataType type = 6 [packed = true]; // "list(type)" repeated TensorShape shape = 7; // "list(shape)" repeated LiteralTensor tensor = 8; // "list(tensor)" } oneof value { ListValue list = 1; // any "list(...)" bytes s = 2; // "string" int64 i = 3; // "int" float f = 4; // "float" bool b = 5; // "bool" DataType type = 6; // "type" TensorShape shape = 7; // "shape" // delete LiteralTensor tensor = 8; // "tensor" } } // Dimensions of a tensor. message TensorShape { // One dimension of the tensor. message Dim { // Size of the tensor in that dimension. // This value must be >= -1, but values of -1 are reserved for "unknown" // shapes (values of -1 mean "unknown" dimension). Certain wrappers // that work with TensorShape may fail at runtime when deserializing // a TensorShape containing a dim value of -1. int64 size = 1; // Optional name of the tensor dimension. string name = 2; }; // Dimensions of the tensor, such as {"input", 30}, {"output", 40} // for a 30 x 40 2D tensor. If an entry has size-1, this // corresponds to a dimension of unknown size. The names are // optional. // // The order of entries in "dim" matters: It indicates the layout of the // values in the tensor in-memory representation. // // The first entry in "dim" is the outermost dimension used to layout the // values, the last entry is the innermost dimension. This matches the // in-memory layout of RowMajor Eigen tensors. // // If "dim.size()" > 0, "unknown_rank" must be false. repeated Dim dim = 2; // If true, the number of dimensions in the shape is unknown. // If true, "dim.size()" must be 0. bool unknown_rank = 3; }; // Protocol buffer representing a literal tensor value. // As data types cross languages and toolkits differ, we can only cover the shared ones. // Then each toolkit converts literal values to final ones according to type. message LiteralTensor { DataType dtype = 1; // Shape of the tensor. TensorShape tensor_shape = 2; // Only one of the representations below is set, one of "tensor_contents" and // the "xxx_val" attributes. We are not using oneof because as oneofs cannot // contain repeated fields it would require another extra set of messages. // Version number. // // In version 0, if the "repeated xxx" representations contain only one // element, that element is repeated to fill the shape. This makes it easy // to represent a constant Tensor with a single value. int32 version_number = 3; // Serialized raw tensor content from either Tensor::AsProtoTensorContent or // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation // can be used for all tensor types. The purpose of this representation is to // reduce serialization overhead during RPC call by avoiding serialization of // many repeated small items. bytes tensor_content = 4; // DT_INT32, DT_INT16, DT_INT8. repeated int32 int_val = 5 [packed = true]; // DT_UINT32, DT_UINT16, DT_UINT8. repeated int32 uint_val = 6 [packed = true]; // DT_INT64 repeated int64 int64_val = 7 [packed = true]; // DT_UINT64 repeated int64 uint64_val = 8 [packed = true]; // DT_FLOAT16, DT_FLOAT32. repeated float float_val = 9 [packed = true]; // DT_FLOAT64, DT_COMPLEX64, DT_COMPLEX128 (may be truncated) repeated double double_val = 10 [packed = true]; // DT_BOOL repeated bool bool_val = 11 [packed = true]; // DT_STRING repeated bytes string_val = 12; }; model-pivot/ox/common/IR/graph_pb2.py000066400000000000000000001022761415504170400177740ustar00rootroot00000000000000# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: graph.proto from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='graph.proto', package='', syntax='proto3', serialized_options=None, serialized_pb=b'\n\x0bgraph.proto\"\xcd\x01\n\x08ModelDef\x12\x0f\n\x07version\x18\x01 \x01(\t\x12\'\n\x0c\x63ontributors\x18\x08 \x01(\x0b\x32\x11.ContributorsList\x12\x16\n\x0e\x66ramework_name\x18\x02 \x01(\t\x12\x19\n\x11\x66ramework_version\x18\x03 \x01(\t\x12\x12\n\nmodel_name\x18\x04 \x01(\t\x12\x15\n\rmodel_version\x18\x05 \x01(\t\x12\x0f\n\x07\x64oc_url\x18\x06 \x01(\t\x12\x18\n\x05graph\x18\x07 \x01(\x0b\x32\t.GraphDef\"B\n\x10\x43ontributorsList\x12\x0c\n\x04name\x18\x01 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14 DT_STRING = 15 _MODELDEF = _descriptor.Descriptor( name='ModelDef', full_name='ModelDef', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='version', full_name='ModelDef.version', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='contributors', full_name='ModelDef.contributors', index=1, number=8, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='framework_name', full_name='ModelDef.framework_name', index=2, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='framework_version', full_name='ModelDef.framework_version', index=3, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_name', full_name='ModelDef.model_name', index=4, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_version', full_name='ModelDef.model_version', index=5, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='doc_url', full_name='ModelDef.doc_url', index=6, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='graph', full_name='ModelDef.graph', index=7, number=7, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=16, serialized_end=221, ) _CONTRIBUTORSLIST = _descriptor.Descriptor( name='ContributorsList', full_name='ContributorsList', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='ContributorsList.name', index=0, number=1, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='email', full_name='ContributorsList.email', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='institute', full_name='ContributorsList.institute', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=223, serialized_end=289, ) _GRAPHDEF = _descriptor.Descriptor( name='GraphDef', full_name='GraphDef', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='node', full_name='GraphDef.node', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='version', full_name='GraphDef.version', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=291, serialized_end=342, ) _NODEDEF_ATTRENTRY = _descriptor.Descriptor( name='AttrEntry', full_name='NodeDef.AttrEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='NodeDef.AttrEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='NodeDef.AttrEntry.value', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=b'8\001', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=431, serialized_end=486, ) _NODEDEF = _descriptor.Descriptor( name='NodeDef', full_name='NodeDef', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='NodeDef.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='op', full_name='NodeDef.op', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='input', full_name='NodeDef.input', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='attr', full_name='NodeDef.attr', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_NODEDEF_ATTRENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=345, serialized_end=486, ) _ATTRVALUE_LISTVALUE = _descriptor.Descriptor( name='ListValue', full_name='AttrValue.ListValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='s', full_name='AttrValue.ListValue.s', index=0, number=2, type=12, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='i', full_name='AttrValue.ListValue.i', index=1, number=3, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='f', full_name='AttrValue.ListValue.f', index=2, number=4, type=2, cpp_type=6, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='b', full_name='AttrValue.ListValue.b', index=3, number=5, type=8, cpp_type=7, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='type', full_name='AttrValue.ListValue.type', index=4, number=6, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='shape', full_name='AttrValue.ListValue.shape', index=5, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='tensor', full_name='AttrValue.ListValue.tensor', index=6, number=8, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=651, serialized_end=808, ) _ATTRVALUE = _descriptor.Descriptor( name='AttrValue', full_name='AttrValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='list', full_name='AttrValue.list', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='s', full_name='AttrValue.s', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='i', full_name='AttrValue.i', index=2, number=3, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='f', full_name='AttrValue.f', index=3, number=4, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='b', full_name='AttrValue.b', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='type', full_name='AttrValue.type', index=5, number=6, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='shape', full_name='AttrValue.shape', index=6, number=7, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_ATTRVALUE_LISTVALUE, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='value', full_name='AttrValue.value', index=0, containing_type=None, fields=[]), ], serialized_start=489, serialized_end=817, ) _TENSORSHAPE_DIM = _descriptor.Descriptor( name='Dim', full_name='TensorShape.Dim', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='size', full_name='TensorShape.Dim.size', index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='TensorShape.Dim.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=887, serialized_end=920, ) _TENSORSHAPE = _descriptor.Descriptor( name='TensorShape', full_name='TensorShape', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='dim', full_name='TensorShape.dim', index=0, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='unknown_rank', full_name='TensorShape.unknown_rank', index=1, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_TENSORSHAPE_DIM, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=819, serialized_end=920, ) _LITERALTENSOR = _descriptor.Descriptor( name='LiteralTensor', full_name='LiteralTensor', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='dtype', full_name='LiteralTensor.dtype', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='tensor_shape', full_name='LiteralTensor.tensor_shape', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='version_number', full_name='LiteralTensor.version_number', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='tensor_content', full_name='LiteralTensor.tensor_content', index=3, number=4, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='int_val', full_name='LiteralTensor.int_val', index=4, number=5, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='uint_val', full_name='LiteralTensor.uint_val', index=5, number=6, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='int64_val', full_name='LiteralTensor.int64_val', index=6, number=7, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='uint64_val', full_name='LiteralTensor.uint64_val', index=7, number=8, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='float_val', full_name='LiteralTensor.float_val', index=8, number=9, type=2, cpp_type=6, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='double_val', full_name='LiteralTensor.double_val', index=9, number=10, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='bool_val', full_name='LiteralTensor.bool_val', index=10, number=11, type=8, cpp_type=7, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\020\001', file=DESCRIPTOR), _descriptor.FieldDescriptor( name='string_val', full_name='LiteralTensor.string_val', index=11, number=12, type=12, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=923, serialized_end=1227, ) _MODELDEF.fields_by_name['contributors'].message_type = _CONTRIBUTORSLIST _MODELDEF.fields_by_name['graph'].message_type = _GRAPHDEF _GRAPHDEF.fields_by_name['node'].message_type = _NODEDEF _NODEDEF_ATTRENTRY.fields_by_name['value'].message_type = _ATTRVALUE _NODEDEF_ATTRENTRY.containing_type = _NODEDEF _NODEDEF.fields_by_name['attr'].message_type = _NODEDEF_ATTRENTRY _ATTRVALUE_LISTVALUE.fields_by_name['type'].enum_type = _DATATYPE _ATTRVALUE_LISTVALUE.fields_by_name['shape'].message_type = _TENSORSHAPE _ATTRVALUE_LISTVALUE.fields_by_name['tensor'].message_type = _LITERALTENSOR _ATTRVALUE_LISTVALUE.containing_type = _ATTRVALUE _ATTRVALUE.fields_by_name['list'].message_type = _ATTRVALUE_LISTVALUE _ATTRVALUE.fields_by_name['type'].enum_type = _DATATYPE _ATTRVALUE.fields_by_name['shape'].message_type = _TENSORSHAPE _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['list']) _ATTRVALUE.fields_by_name['list'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['s']) _ATTRVALUE.fields_by_name['s'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['i']) _ATTRVALUE.fields_by_name['i'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['f']) _ATTRVALUE.fields_by_name['f'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['b']) _ATTRVALUE.fields_by_name['b'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['type']) _ATTRVALUE.fields_by_name['type'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['shape']) _ATTRVALUE.fields_by_name['shape'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _TENSORSHAPE_DIM.containing_type = _TENSORSHAPE _TENSORSHAPE.fields_by_name['dim'].message_type = _TENSORSHAPE_DIM _LITERALTENSOR.fields_by_name['dtype'].enum_type = _DATATYPE _LITERALTENSOR.fields_by_name['tensor_shape'].message_type = _TENSORSHAPE DESCRIPTOR.message_types_by_name['ModelDef'] = _MODELDEF DESCRIPTOR.message_types_by_name['ContributorsList'] = _CONTRIBUTORSLIST DESCRIPTOR.message_types_by_name['GraphDef'] = _GRAPHDEF DESCRIPTOR.message_types_by_name['NodeDef'] = _NODEDEF DESCRIPTOR.message_types_by_name['AttrValue'] = _ATTRVALUE DESCRIPTOR.message_types_by_name['TensorShape'] = _TENSORSHAPE DESCRIPTOR.message_types_by_name['LiteralTensor'] = _LITERALTENSOR DESCRIPTOR.enum_types_by_name['DataType'] = _DATATYPE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ModelDef = _reflection.GeneratedProtocolMessageType('ModelDef', (_message.Message,), { 'DESCRIPTOR' : _MODELDEF, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:ModelDef) }) _sym_db.RegisterMessage(ModelDef) ContributorsList = _reflection.GeneratedProtocolMessageType('ContributorsList', (_message.Message,), { 'DESCRIPTOR' : _CONTRIBUTORSLIST, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:ContributorsList) }) _sym_db.RegisterMessage(ContributorsList) GraphDef = _reflection.GeneratedProtocolMessageType('GraphDef', (_message.Message,), { 'DESCRIPTOR' : _GRAPHDEF, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:GraphDef) }) _sym_db.RegisterMessage(GraphDef) NodeDef = _reflection.GeneratedProtocolMessageType('NodeDef', (_message.Message,), { 'AttrEntry' : _reflection.GeneratedProtocolMessageType('AttrEntry', (_message.Message,), { 'DESCRIPTOR' : _NODEDEF_ATTRENTRY, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:NodeDef.AttrEntry) }) , 'DESCRIPTOR' : _NODEDEF, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:NodeDef) }) _sym_db.RegisterMessage(NodeDef) _sym_db.RegisterMessage(NodeDef.AttrEntry) AttrValue = _reflection.GeneratedProtocolMessageType('AttrValue', (_message.Message,), { 'ListValue' : _reflection.GeneratedProtocolMessageType('ListValue', (_message.Message,), { 'DESCRIPTOR' : _ATTRVALUE_LISTVALUE, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:AttrValue.ListValue) }) , 'DESCRIPTOR' : _ATTRVALUE, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:AttrValue) }) _sym_db.RegisterMessage(AttrValue) _sym_db.RegisterMessage(AttrValue.ListValue) TensorShape = _reflection.GeneratedProtocolMessageType('TensorShape', (_message.Message,), { 'Dim' : _reflection.GeneratedProtocolMessageType('Dim', (_message.Message,), { 'DESCRIPTOR' : _TENSORSHAPE_DIM, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:TensorShape.Dim) }) , 'DESCRIPTOR' : _TENSORSHAPE, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:TensorShape) }) _sym_db.RegisterMessage(TensorShape) _sym_db.RegisterMessage(TensorShape.Dim) LiteralTensor = _reflection.GeneratedProtocolMessageType('LiteralTensor', (_message.Message,), { 'DESCRIPTOR' : _LITERALTENSOR, '__module__' : 'graph_pb2' # @@protoc_insertion_point(class_scope:LiteralTensor) }) _sym_db.RegisterMessage(LiteralTensor) _NODEDEF_ATTRENTRY._options = None _ATTRVALUE_LISTVALUE.fields_by_name['i']._options = None _ATTRVALUE_LISTVALUE.fields_by_name['f']._options = None _ATTRVALUE_LISTVALUE.fields_by_name['b']._options = None _ATTRVALUE_LISTVALUE.fields_by_name['type']._options = None _LITERALTENSOR.fields_by_name['int_val']._options = None _LITERALTENSOR.fields_by_name['uint_val']._options = None _LITERALTENSOR.fields_by_name['int64_val']._options = None _LITERALTENSOR.fields_by_name['uint64_val']._options = None _LITERALTENSOR.fields_by_name['float_val']._options = None _LITERALTENSOR.fields_by_name['double_val']._options = None _LITERALTENSOR.fields_by_name['bool_val']._options = None # @@protoc_insertion_point(module_scope) model-pivot/ox/common/IR/graph_pb2_old.py000066400000000000000000000715371415504170400206370ustar00rootroot00000000000000# Generated by the protocol buffer compiler. DO NOT EDIT! # source: graph.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='graph.proto', package='', syntax='proto3', serialized_pb=_b('\n\x0bgraph.proto\"3\n\x08GraphDef\x12\x16\n\x04node\x18\x01 \x03(\x0b\x32\x08.NodeDef\x12\x0f\n\x07version\x18\x02 \x01(\x05\"\x8d\x01\n\x07NodeDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02op\x18\x02 \x01(\t\x12\r\n\x05input\x18\x03 \x03(\t\x12 \n\x04\x61ttr\x18\x04 \x03(\x0b\x32\x12.NodeDef.AttrEntry\x1a\x37\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x19\n\x05value\x18\x02 \x01(\x0b\x32\n.AttrValue:\x02\x38\x01\"\xea\x02\n\tAttrValue\x12$\n\x04list\x18\x01 \x01(\x0b\x32\x14.AttrValue.ListValueH\x00\x12\x0b\n\x01s\x18\x02 \x01(\x0cH\x00\x12\x0b\n\x01i\x18\x03 \x01(\x03H\x00\x12\x0b\n\x01\x66\x18\x04 \x01(\x02H\x00\x12\x0b\n\x01\x62\x18\x05 \x01(\x08H\x00\x12\x19\n\x04type\x18\x06 \x01(\x0e\x32\t.DataTypeH\x00\x12\x1d\n\x05shape\x18\x07 \x01(\x0b\x32\x0c.TensorShapeH\x00\x12 \n\x06tensor\x18\x08 \x01(\x0b\x32\x0e.LiteralTensorH\x00\x1a\x9d\x01\n\tListValue\x12\t\n\x01s\x18\x02 \x03(\x0c\x12\r\n\x01i\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\r\n\x01\x66\x18\x04 \x03(\x02\x42\x02\x10\x01\x12\r\n\x01\x62\x18\x05 \x03(\x08\x42\x02\x10\x01\x12\x1b\n\x04type\x18\x06 \x03(\x0e\x32\t.DataTypeB\x02\x10\x01\x12\x1b\n\x05shape\x18\x07 \x03(\x0b\x32\x0c.TensorShape\x12\x1e\n\x06tensor\x18\x08 \x03(\x0b\x32\x0e.LiteralTensorB\x07\n\x05value\"e\n\x0bTensorShape\x12\x1d\n\x03\x64im\x18\x02 \x03(\x0b\x32\x10.TensorShape.Dim\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\t\"\xb0\x02\n\rLiteralTensor\x12\x18\n\x05\x64type\x18\x01 \x01(\x0e\x32\t.DataType\x12\"\n\x0ctensor_shape\x18\x02 \x01(\x0b\x32\x0c.TensorShape\x12\x16\n\x0eversion_number\x18\x03 \x01(\x05\x12\x16\n\x0etensor_content\x18\x04 \x01(\x0c\x12\x13\n\x07int_val\x18\x05 \x03(\x05\x42\x02\x10\x01\x12\x14\n\x08uint_val\x18\x06 \x03(\x05\x42\x02\x10\x01\x12\x15\n\tint64_val\x18\x07 \x03(\x03\x42\x02\x10\x01\x12\x16\n\nuint64_val\x18\x08 \x03(\x03\x42\x02\x10\x01\x12\x15\n\tfloat_val\x18\t \x03(\x02\x42\x02\x10\x01\x12\x16\n\ndouble_val\x18\n \x03(\x01\x42\x02\x10\x01\x12\x14\n\x08\x62ool_val\x18\x0b \x03(\x08\x42\x02\x10\x01\x12\x12\n\nstring_val\x18\x0c \x03(\x0c*\xff\x01\n\x08\x44\x61taType\x12\x10\n\x0c\x44T_UNDEFINED\x10\x00\x12\x0b\n\x07\x44T_INT8\x10\x01\x12\x0c\n\x08\x44T_INT16\x10\x02\x12\x0c\n\x08\x44T_INT32\x10\x03\x12\x0c\n\x08\x44T_INT64\x10\x04\x12\x0c\n\x08\x44T_UINT8\x10\x05\x12\r\n\tDT_UINT16\x10\x06\x12\r\n\tDT_UINT32\x10\x07\x12\r\n\tDT_UINT64\x10\x08\x12\x0e\n\nDT_FLOAT16\x10\t\x12\x0e\n\nDT_FLOAT32\x10\n\x12\x0e\n\nDT_FLOAT64\x10\x0b\x12\x10\n\x0c\x44T_COMPLEX64\x10\x0c\x12\x11\n\rDT_COMPLEX128\x10\r\x12\x0b\n\x07\x44T_BOOL\x10\x0e\x12\r\n\tDT_STRING\x10\x0f\x62\x06proto3') ) _DATATYPE = _descriptor.EnumDescriptor( name='DataType', full_name='DataType', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='DT_UNDEFINED', index=0, number=0, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_INT8', index=1, number=1, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_INT16', index=2, number=2, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_INT32', index=3, number=3, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_INT64', index=4, number=4, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_UINT8', index=5, number=5, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_UINT16', index=6, number=6, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_UINT32', index=7, number=7, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_UINT64', index=8, number=8, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_FLOAT16', index=9, number=9, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_FLOAT32', index=10, number=10, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_FLOAT64', index=11, number=11, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_COMPLEX64', index=12, number=12, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_COMPLEX128', index=13, number=13, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_BOOL', index=14, number=14, options=None, type=None), _descriptor.EnumValueDescriptor( name='DT_STRING', index=15, number=15, options=None, type=None), ], containing_type=None, options=None, serialized_start=988, serialized_end=1243, ) _sym_db.RegisterEnumDescriptor(_DATATYPE) DataType = enum_type_wrapper.EnumTypeWrapper(_DATATYPE) DT_UNDEFINED = 0 DT_INT8 = 1 DT_INT16 = 2 DT_INT32 = 3 DT_INT64 = 4 DT_UINT8 = 5 DT_UINT16 = 6 DT_UINT32 = 7 DT_UINT64 = 8 DT_FLOAT16 = 9 DT_FLOAT32 = 10 DT_FLOAT64 = 11 DT_COMPLEX64 = 12 DT_COMPLEX128 = 13 DT_BOOL = 14 DT_STRING = 15 _GRAPHDEF = _descriptor.Descriptor( name='GraphDef', full_name='GraphDef', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='node', full_name='GraphDef.node', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='version', full_name='GraphDef.version', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=15, serialized_end=66, ) _NODEDEF_ATTRENTRY = _descriptor.Descriptor( name='AttrEntry', full_name='NodeDef.AttrEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='NodeDef.AttrEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='value', full_name='NodeDef.AttrEntry.value', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=155, serialized_end=210, ) _NODEDEF = _descriptor.Descriptor( name='NodeDef', full_name='NodeDef', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='NodeDef.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='op', full_name='NodeDef.op', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='input', full_name='NodeDef.input', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='attr', full_name='NodeDef.attr', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_NODEDEF_ATTRENTRY, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=69, serialized_end=210, ) _ATTRVALUE_LISTVALUE = _descriptor.Descriptor( name='ListValue', full_name='AttrValue.ListValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='s', full_name='AttrValue.ListValue.s', index=0, number=2, type=12, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='i', full_name='AttrValue.ListValue.i', index=1, number=3, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='f', full_name='AttrValue.ListValue.f', index=2, number=4, type=2, cpp_type=6, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='b', full_name='AttrValue.ListValue.b', index=3, number=5, type=8, cpp_type=7, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='type', full_name='AttrValue.ListValue.type', index=4, number=6, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='shape', full_name='AttrValue.ListValue.shape', index=5, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tensor', full_name='AttrValue.ListValue.tensor', index=6, number=8, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=409, serialized_end=566, ) _ATTRVALUE = _descriptor.Descriptor( name='AttrValue', full_name='AttrValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='list', full_name='AttrValue.list', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='s', full_name='AttrValue.s', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='i', full_name='AttrValue.i', index=2, number=3, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='f', full_name='AttrValue.f', index=3, number=4, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='b', full_name='AttrValue.b', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='type', full_name='AttrValue.type', index=5, number=6, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='shape', full_name='AttrValue.shape', index=6, number=7, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tensor', full_name='AttrValue.tensor', index=7, number=8, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_ATTRVALUE_LISTVALUE, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='value', full_name='AttrValue.value', index=0, containing_type=None, fields=[]), ], serialized_start=213, serialized_end=575, ) _TENSORSHAPE_DIM = _descriptor.Descriptor( name='Dim', full_name='TensorShape.Dim', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='size', full_name='TensorShape.Dim.size', index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='name', full_name='TensorShape.Dim.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=645, serialized_end=678, ) _TENSORSHAPE = _descriptor.Descriptor( name='TensorShape', full_name='TensorShape', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='dim', full_name='TensorShape.dim', index=0, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='unknown_rank', full_name='TensorShape.unknown_rank', index=1, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_TENSORSHAPE_DIM, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=577, serialized_end=678, ) _LITERALTENSOR = _descriptor.Descriptor( name='LiteralTensor', full_name='LiteralTensor', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='dtype', full_name='LiteralTensor.dtype', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tensor_shape', full_name='LiteralTensor.tensor_shape', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='version_number', full_name='LiteralTensor.version_number', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='tensor_content', full_name='LiteralTensor.tensor_content', index=3, number=4, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='int_val', full_name='LiteralTensor.int_val', index=4, number=5, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='uint_val', full_name='LiteralTensor.uint_val', index=5, number=6, type=5, cpp_type=1, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='int64_val', full_name='LiteralTensor.int64_val', index=6, number=7, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='uint64_val', full_name='LiteralTensor.uint64_val', index=7, number=8, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='float_val', full_name='LiteralTensor.float_val', index=8, number=9, type=2, cpp_type=6, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='double_val', full_name='LiteralTensor.double_val', index=9, number=10, type=1, cpp_type=5, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='bool_val', full_name='LiteralTensor.bool_val', index=10, number=11, type=8, cpp_type=7, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))), _descriptor.FieldDescriptor( name='string_val', full_name='LiteralTensor.string_val', index=11, number=12, type=12, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=681, serialized_end=985, ) _GRAPHDEF.fields_by_name['node'].message_type = _NODEDEF _NODEDEF_ATTRENTRY.fields_by_name['value'].message_type = _ATTRVALUE _NODEDEF_ATTRENTRY.containing_type = _NODEDEF _NODEDEF.fields_by_name['attr'].message_type = _NODEDEF_ATTRENTRY _ATTRVALUE_LISTVALUE.fields_by_name['type'].enum_type = _DATATYPE _ATTRVALUE_LISTVALUE.fields_by_name['shape'].message_type = _TENSORSHAPE _ATTRVALUE_LISTVALUE.fields_by_name['tensor'].message_type = _LITERALTENSOR _ATTRVALUE_LISTVALUE.containing_type = _ATTRVALUE _ATTRVALUE.fields_by_name['list'].message_type = _ATTRVALUE_LISTVALUE _ATTRVALUE.fields_by_name['type'].enum_type = _DATATYPE _ATTRVALUE.fields_by_name['shape'].message_type = _TENSORSHAPE _ATTRVALUE.fields_by_name['tensor'].message_type = _LITERALTENSOR _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['list']) _ATTRVALUE.fields_by_name['list'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['s']) _ATTRVALUE.fields_by_name['s'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['i']) _ATTRVALUE.fields_by_name['i'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['f']) _ATTRVALUE.fields_by_name['f'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['b']) _ATTRVALUE.fields_by_name['b'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['type']) _ATTRVALUE.fields_by_name['type'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['shape']) _ATTRVALUE.fields_by_name['shape'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _ATTRVALUE.oneofs_by_name['value'].fields.append( _ATTRVALUE.fields_by_name['tensor']) _ATTRVALUE.fields_by_name['tensor'].containing_oneof = _ATTRVALUE.oneofs_by_name['value'] _TENSORSHAPE_DIM.containing_type = _TENSORSHAPE _TENSORSHAPE.fields_by_name['dim'].message_type = _TENSORSHAPE_DIM _LITERALTENSOR.fields_by_name['dtype'].enum_type = _DATATYPE _LITERALTENSOR.fields_by_name['tensor_shape'].message_type = _TENSORSHAPE DESCRIPTOR.message_types_by_name['GraphDef'] = _GRAPHDEF DESCRIPTOR.message_types_by_name['NodeDef'] = _NODEDEF DESCRIPTOR.message_types_by_name['AttrValue'] = _ATTRVALUE DESCRIPTOR.message_types_by_name['TensorShape'] = _TENSORSHAPE DESCRIPTOR.message_types_by_name['LiteralTensor'] = _LITERALTENSOR DESCRIPTOR.enum_types_by_name['DataType'] = _DATATYPE _sym_db.RegisterFileDescriptor(DESCRIPTOR) GraphDef = _reflection.GeneratedProtocolMessageType('GraphDef', (_message.Message,), dict( DESCRIPTOR = _GRAPHDEF, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:GraphDef) )) _sym_db.RegisterMessage(GraphDef) NodeDef = _reflection.GeneratedProtocolMessageType('NodeDef', (_message.Message,), dict( AttrEntry = _reflection.GeneratedProtocolMessageType('AttrEntry', (_message.Message,), dict( DESCRIPTOR = _NODEDEF_ATTRENTRY, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:NodeDef.AttrEntry) )) , DESCRIPTOR = _NODEDEF, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:NodeDef) )) _sym_db.RegisterMessage(NodeDef) _sym_db.RegisterMessage(NodeDef.AttrEntry) AttrValue = _reflection.GeneratedProtocolMessageType('AttrValue', (_message.Message,), dict( ListValue = _reflection.GeneratedProtocolMessageType('ListValue', (_message.Message,), dict( DESCRIPTOR = _ATTRVALUE_LISTVALUE, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:AttrValue.ListValue) )) , DESCRIPTOR = _ATTRVALUE, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:AttrValue) )) _sym_db.RegisterMessage(AttrValue) _sym_db.RegisterMessage(AttrValue.ListValue) TensorShape = _reflection.GeneratedProtocolMessageType('TensorShape', (_message.Message,), dict( Dim = _reflection.GeneratedProtocolMessageType('Dim', (_message.Message,), dict( DESCRIPTOR = _TENSORSHAPE_DIM, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:TensorShape.Dim) )) , DESCRIPTOR = _TENSORSHAPE, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:TensorShape) )) _sym_db.RegisterMessage(TensorShape) _sym_db.RegisterMessage(TensorShape.Dim) LiteralTensor = _reflection.GeneratedProtocolMessageType('LiteralTensor', (_message.Message,), dict( DESCRIPTOR = _LITERALTENSOR, __module__ = 'graph_pb2' # @@protoc_insertion_point(class_scope:LiteralTensor) )) _sym_db.RegisterMessage(LiteralTensor) _NODEDEF_ATTRENTRY.has_options = True _NODEDEF_ATTRENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')) _ATTRVALUE_LISTVALUE.fields_by_name['i'].has_options = True _ATTRVALUE_LISTVALUE.fields_by_name['i']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _ATTRVALUE_LISTVALUE.fields_by_name['f'].has_options = True _ATTRVALUE_LISTVALUE.fields_by_name['f']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _ATTRVALUE_LISTVALUE.fields_by_name['b'].has_options = True _ATTRVALUE_LISTVALUE.fields_by_name['b']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _ATTRVALUE_LISTVALUE.fields_by_name['type'].has_options = True _ATTRVALUE_LISTVALUE.fields_by_name['type']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['int_val'].has_options = True _LITERALTENSOR.fields_by_name['int_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['uint_val'].has_options = True _LITERALTENSOR.fields_by_name['uint_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['int64_val'].has_options = True _LITERALTENSOR.fields_by_name['int64_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['uint64_val'].has_options = True _LITERALTENSOR.fields_by_name['uint64_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['float_val'].has_options = True _LITERALTENSOR.fields_by_name['float_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['double_val'].has_options = True _LITERALTENSOR.fields_by_name['double_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) _LITERALTENSOR.fields_by_name['bool_val'].has_options = True _LITERALTENSOR.fields_by_name['bool_val']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) # @@protoc_insertion_point(module_scope) model-pivot/ox/common/IR/ops.pbtxt000066400000000000000000000606021415504170400174360ustar00rootroot00000000000000op { name: "Add" input_arg { name: "x" type_attr: "T" } input_arg { name: "y" type_attr: "T" } output_arg { name: "z" type_attr: "T" } attr { name: "axis" type: "int" } summary: "Returns x + y element-wise." description: "*NOTE*: `Add` supports broadcasting." } op { name: "BatchNorm" attr { name: "axis" type: "int" description: "The axis that should be normalized (typically the features axis). For instance, after a Convolution2D layer with data_format=\"channels_first\", set axis=1 in BatchNormalization" } attr { name: "momentum" type: "float" default_value { f: 0.99 } description: "Momentum for the moving average." } attr { name: "epsilon" type: "float" default_value { f: 0.0001 } } attr { name: "scale" type: "bool" default_value { b: True } description: "If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer." } attr { name: "bias" type: "bool" default_value { b: True } description: " If True, add offset of beta to normalized tensor. If False, beta is ignored." } attr { name: "data_format" type: "string" default_value { s: "NHWC" } description: "The data format for x and y. Either 'NHWC' (default) or 'NCHW'." } attr { name: "is_training" type: "bool" default_value { b: true } description: "A bool value to indicate the operation is for training (default) or inference." } summary: "tf.layers.batch_normalization" description: "Follow Keras FusedBatchNorm, parameters are [scale], [bias], [mean], [var]." } op { name: "Concat" attr { name: "axis" type: "int" } summary: "Concatenates tensors along one dimension." } op { name: "Crop" attr { name: "border" type: "list(int). A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder)." } summary: "Crop and image to the specified spatial dimensions." } op { name: "Conv" attr { name: "use_bias" type: "bool" default_value { b: true } description: "If use bias" } attr { name: "dilations" type: "list(int)" description: "Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. [1, dilation_depth, dilation_height, dilation_width, 1]" } attr { name: "kernel_shape" description: "The shape of the convolution kernel. [filter_depth, filter_height, filter_width, in_channels, out_channels]" type: "list(int)" } attr { name: "strides" type: "list(int)" description: "1-D tensor of length N. [1, stride_deep, stride_height, stride_width, 1]" } attr { name: "pads" type: "list(int)" description: "1-D tensor of length N*2. [x1_begin, x2_begin...x1_end, x2_end,...]" } attr { name: "group" type: "int" default_value { i: 1 } description: "If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the iith output group channels will be only connected to the iith input group channels." } attr { name: "auto_pad" type: "string" description: "The type of padding algorithm to use. Where SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding." allowed_values { list { s: "SAME_UPPER" s: "SAME_LOWER" s: "VALID" } } } attr { name: "data_format" type: "string" default_value { s: "NHWC" } allowed_values { list { s: "NC" s: "NWC" s: "NCW" s: "NHWC" s: "NCHW" s: "NDHWC" s: "NCDHW" } } } summary: "Computes a (N-2)-D convolution given N-D `input` and `filter` tensors." description: "Parameters are [weights] and [bias]. Given an input tensor of shape `[batch, in_depth, in_height, in_width, in_channels]` and a filter kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following:1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`." } op { name: "ConvTranspose" attr { name: "kernel_shape" description: "Shape [filter_depth, filter_height, filter_width, out_channels, in_channels]. Follow tensorflow conv2d_transpose args." type: "list(int)" } attr { name: "strides" type: "list(int)" description: "1-D tensor of length N. [1, stride_deep, stride_height, stride_width, 1]" } attr { name: "group" type: "int" default_value { i: 1 } description: "If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the iith output group channels will be only connected to the iith input group channels." } attr { name: "auto_pad" type: "string" description: "The type of padding algorithm to use. Where SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding." allowed_values { list { s: "SAME_UPPER" s: "SAME_LOWER" s: "VALID" } } } attr { name: "pads" type: "list(int)" description: "1-D tensor of length N*2. [x1_begin, x2_begin...x1_end, x2_end,...]" } attr { name: "data_format" type: "string" default_value { s: "NHWC" } allowed_values { list { s: "NC" s: "NWC" s: "NCW" s: "NHWC" s: "NCHW" s: "NDHWC" s: "NCDHW" } } } summary: "Computes a (N-2)-D convolution given N-D `input` and `filter` tensors." description: "Given an input tensor of shape `[batch, in_depth, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following:1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`." } op { name: "DataInput" attr { name: "dtype" type: "type" description: "The type of elements in the tensor." } attr { name: "shape" type: "shape" description: "The shape of the tensor." } summary: "Input Data Layer." } op { name: "Div" attr { name: "axis" type: "int" } summary: "Performs element-wise binary division." } op { name: "Dropout" attr { name: "keep_prob" type: "float" } attr { name: "seed" type: "int" } attr { name: "noise_shape" type: "list(int)" } summary: "Dropout Layer" description: "With probability keep_prob, outputs the input element scaled up by 1 / keep_prob, otherwise outputs 0. The scaling is so that the expected sum is unchanged." } op { name: "Exp" input_arg { name: "x" } output_arg { name: "y" } summary: "Computes exponential of x element-wise. y = e^x." } op { name: "Flatten" summary: "Flatten Layer." description: "Not directly mapping in Tensorflow. Flatten tensor with shape [batch_size, k]." } op { name: "FullyConnected" attr { name: "units" type: "int" } attr { name: "use_bias" type: "bool" default_value { b: true } description: "If use bias" } summary: "Y = W * X + b" description: "Not in Tensorflow. Dense operator in Keras. parameters are [weights], [bias]." } op { name: "Gather" input_arg { name: "params" description: "The tensor from which to gather values. Must be at least rank `axis + 1`." } input_arg { name: "indices" description: "Index tensor. Must be in range `[0, params.shape[axis])`." } output_arg { name: "output" description: "Values from `params` gathered from indices given by `indices`, with shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`." } attr { name: "axis" description: "The axis in `params` to gather `indices` from. Defaults to the first dimension. Supports negative indexes." type: "int" } attr { name: "input_dim" description: "For Embedding layer. Size of the input vocabulary." type: "int" } attr { name: "output_dim" description: "For Embedding layer. Dimension of the embedding output vectors." type: "int" } summary: "Gather slices from `params` axis `axis` according to `indices`." description: "`indices` must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`" } op { name: "LeakyRelu" attr { name: "alpha" description: "Slope of the activation function at x < 0." type: "float" } description: "LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function f(x) = alpha * x for x < 0, f(x) = x for x >= 0, is applied to the data tensor elementwise." } op { name: "Log" input_arg { name: "x" } output_arg { name: "y" } summary: "Computes natural logarithm of x element-wise." description: "I.e., y = log_e(x)." } op { name: "Matmul" summary: "Matrix product that behaves like numpy.matmul." } op { name: "Mul" attr { name: "axis" type: "int" } summary: "Performs element-wise binary multiplication." } op { name: "Reciprocal" input_arg { name: "x" } output_arg { name: "y" } summary: "Computes the reciprocal of x element-wise." description: "I.e., (y = 1 / x)." } op { name: "Relu" summary: "Computes rectified linear: `max(features, 0)`." } op { name: "Relu6" summary: "Computes rectified linear 6: `min(max(features, 0), 6)`." } op { name: "Sigmoid" summary: "Computes sigmoid of `x` element-wise." description: "Specifically, `y = 1 / (1 + exp(-x))`." } op { name: "Slice" attr { name: "starts" description: "starts[k] specifies the offset into the kth range specification. The exact dimension this corresponds to will be determined by context. Out-of-bounds values will be silently clamped. If the `k`th bit of `begin_mask` then `begin[k]` is ignored and the full range of the appropriate dimension is used instead. Negative values causes indexing to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`." type: "list(int)" } attr { name: "ends" description: "`ends[i]` is like `begin` with the exception that `end_mask` is used to determine full ranges." type: "list(int)" } attr { name: "strides" description: "`strides[i]` specifies the increment in the `i`th specification after extracting a given element. Negative indices will reverse the original order. Out or range values are clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`" type: "list(int)" } summary: "Return a strided slice from `input`." description: "Note, most python users will want to use the Python `Tensor.__getitem__` or `Variable.__getitem__` rather than this op directly. The goal of this op is to produce a new tensor with a subset of the elements from the `n` dimensional `input` tensor. The subset is chosen using a sequence of `m` sparse range specifications encoded into the arguments of this function. Note, in some cases `m` could be equal to `n`, but this need not be the case. Each range specification entry can be one of the following: - An ellipsis (...). Ellipses are used to imply zero or more dimensions of full-dimension selection and are produced using `ellipsis_mask`. For example, `foo[...]` is the identity slice. - A new axis. This is used to insert a new shape=1 dimension and is produced using `new_axis_mask`. For example, `foo[:, ...]` where `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor. - A range `begin:end:stride`. This is used to specify how much to choose from a given dimension. `stride` can be any integer but 0. `begin` is an integer which represents the index of the first value to select while `end` represents the index of the last value to select. The number of values selected in each dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`. `begin` and `end` can be negative where `-1` is the last element, `-2` is the second to last. `begin_mask` controls whether to replace the explicitly given `begin` with an implicit effective value of `0` if `stride > 0` and `-1` if `stride < 0`. `end_mask` is analogous but produces the number required to create the largest open interval. For example, given a shape `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do not assume this is equivalent to `foo[0:-1]` which has an effective `begin` and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the first dimension of a tensor while dropping the last two (in the original order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`. - A single index. This is used to keep only elements that have a given index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a shape `(6,)` tensor. This is encoded in `begin` and `end` and `shrink_axis_mask`. Each conceptual range specification is encoded in the op\'s argument. This encoding is best understand by considering a non-trivial example. In particular, `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as ``` begin = [1, 2, x, x, 0, x] # x denotes don\'t care (usually 0) end = [2, 4, x, x, -3, x] strides = [1, 1, x, x, -1, 1] begin_mask = 1<<4 | 1 << 5 = 48 end_mask = 1<<5 = 32 ellipsis_mask = 1<<3 = 8 new_axis_mask = 1<<2 4 shrink_axis_mask = 1<<0 ``` In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of the slice becomes (2, 1, 5, 5, 2, 5). Let us walk step by step through each argument specification. 1. The first argument in the example slice is turned into `begin = 1` and `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we also set the appropriate bit in `shrink_axis_mask`. 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have zero bits contributed. 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1 dimension in the final shape. Dummy values are contributed to begin, end and stride, while the new_axis_mask bit is set. 4. `...` grab the full ranges from as many dimensions as needed to fully specify a slice for every dimension of the input shape. 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated with a dimension that has shape `s` is converted to a positive index `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion is done internally so begin, end and strides receive x, -3, and -1. The appropriate begin_mask bit is set to indicate the start range is the full range (ignoring the x). 6. `:` indicates that the entire contents of the corresponding dimension is selected. This is equivalent to `::` or `0::1`. begin, end, and strides receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and `end_mask` are also set. *Requirements*: `0 != strides[i] for i in [0, m)` `ellipsis_mask must be a power of two (only one ellipsis)`" } op { name: "Softmax" attr { name: "dim" type: "int" default_value { i: -1 } } summary: "Computes softmax activations." description: "softmax = exp(logits) / reduce_sum(exp(logits), dim)" } op { name: "Split" attr { name: "axis" type: "int" description: "The dimension along which to split." } attr { name: "split" type: "int" description: "The number of ways to split. Must evenly divide value.shape[split_dim]." has_minimum: true minimum: 1 } summary: "Splits a tensor into `num_split` tensors along one dimension." } op { name: "Tanh" summary: "Computes hyperbolic tangent of `x` element-wise." } op { name: "Elu" attr { name: "alpha" type: "float" default_value { f: 1.0 } } summary: "Computes exponential linear: `exp(features) - alpha` if < 0, `features` otherwise." description: "Not directly supported alpha in Tensorflow. See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](http://arxiv.org/abs/1511.07289)" } op { name: "Pad" attr { name: "pads" type: "list(int)" description: "integer tensor with shape N * 2." } attr { name: "mode" type: "string" allowed_values { list { s: "CONSTANT" s: "REFLECT" s: "SYMMETRIC" } } } attr { name: "constant_values" type: "float" } summary: "Padding Layer." description: "Not directly mapping in Tensorflow. Pads a tensor." } op { name: "Pool" attr { name: "global_pooling" type: "bool" default_value { b: false } description: "From Caffe. If global_pooling then it will pool over the size of the bottom by doing shape.h = bottom->height and shape_w = bottom->width." } attr { name: "dilation_rate" type: "list(int)" description: "Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate." } attr { name: "kernel_shape" description: "Shape `[1, depth, height, wide, 1]`." type: "list(int)" } attr { name: "strides" type: "list(int)" description: "1-D tensor of length N. [1, stride_deep, stride_height, stride_weight, 1]" } attr { name: "pooling_type" type: "string" description: "The type of pooling type to use." allowed_values { list { s: "MAX" s: "AVG" } } } attr { name: "audo_pad" type: "string" description: "The type of padding algorithm to use." allowed_values { list { s: "SAME_UPPER" s: "SAME_LOWER" s: "VALID" } } } attr { name: "pads" type: "list(int)" description: "1-D tensor of length N*2. [x1_begin, x2_begin...x1_end, x2_end,...]" } attr { name: "data_format" type: "string" default_value { s: "NHWC" } allowed_values { list { s: "NC" s: "NWC" s: "NCW" s: "NHWC" s: "NCHW" s: "NDHWC" s: "NCDHW" } } } summary: "Performs an N-D pooling operation." description: "tf.nn.pool defined" } op { name: "Mul" summary: "Returns x * y element-wise." description: "*NOTE*: `Mul` supports broadcasting. More about broadcasting[here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)" } op { name: "Reshape" attr { name: "shape" type: "shape" description: "The target shape of the tensor." } summary: "Reshapes a tensor." } op { name: "RNN" } op { name: "LSTM" } op { name: "GRU" } op { name: "SeparableConvolution" attr { name: "strides" type: "list(int)" description: "1-D of length 4. The stride of the sliding window for each dimension of `input`." } attr { name: "padding" type: "string" description: "The type of padding algorithm to use." allowed_values { list { s: "SAME" s: "VALID" } } } attr { name: "data_format" type: "string" default_value { s: "NHWC" } description: "Specify the data format of the input and output data. With the default format \"NHWC\", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be \"NCHW\", the data storage order of: [batch, channels, height, width]." allowed_values { list { s: "NHWC" s: "NCHW" } } } } op { name: "Sub" input_arg { name: "x" type_attr: "T" } input_arg { name: "y" type_attr: "T" } output_arg { name: "z" type_attr: "T" } attr { name: "axis" type: "int" } summary: "Returns x - y element-wise." description: "*NOTE*: `Add` supports broadcasting." } op { name: "LRN" attr { name: "size" type: "int" description: "0-D. Half-width of the 1-D normalization window." } attr { name: "alpha" type: "float" description: "A scale factor, usually positive." } attr { name: "beta" type: "float" description: "An exponent." } attr { name: "bias" type: "float" description: "An offset (usually positive to avoid dividing by 0)." } summary: "Local Response Normalization." description: "The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `size`. In detail, sqr_sum[a, b, c, d] = sum(input[a, b, c, d - size : d + size + 1] ** 2) output = input / (bias + alpha * sqr_sum) ** beta For details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)." } op { name: "Squeeze" attr { name: "squeeze_dims" type: "list(int)" default_value { list { } } description: "If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1." } summary: "Removes dimensions of size 1 from the shape of a tensor." description: "Given a tensor `input`, this operation returns a tensor of the same type with all dimensions of size 1 removed." } op { name: "DepthwiseConv" attr { name: "strides" type: "list(int)" description: "1-D of length 4. The stride of the sliding window for each dimension of `input`." } attr { name: "padding" type: "string" description: "The type of padding algorithm to use." allowed_values { list { s: "SAME" s: "VALID" } } } attr { name: "data_format" type: "string" default_value { s: "NHWC" } description: "Specify the data format of the input and output data. With the default format \"NHWC\", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be \"NCHW\", the data storage order of: [batch, channels, height, width]." allowed_values { list { s: "NHWC" s: "NCHW" } } } summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors." description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, channel_multiplier]`, containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies a different filter to each input channel (expanding from 1 channel to `channel_multiplier` channels for each), then concatenates the results together. Thus, the output has `in_channels * channel_multiplier` channels. ``` for k in 0..in_channels-1 for q in 0..channel_multiplier-1 output[b, i, j, k * channel_multiplier + q] = sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * filter[di, dj, k, q]``` Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`." }model-pivot/ox/common/__init__.py000066400000000000000000000001551415504170400173460ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/common/utils.py000066400000000000000000000215631415504170400167550ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- from __future__ import division import os import sys import numpy as np from six import text_type, binary_type, integer_types import ox.common.IR.graph_pb2 as graph_pb2 __all__ = ["assign_IRnode_values", "convert_onnx_pad_to_tf", 'convert_tf_pad_to_onnx', 'compute_tf_same_padding', 'is_valid_padding', 'download_file', 'shape_to_list', 'list_to_shape'] def assign_attr_value(attr, val): from ox.common.IR.graph_pb2 import TensorShape '''Assign value to AttrValue proto according to data type.''' if isinstance(val, bool): attr.b = val elif isinstance(val, integer_types): attr.i = val elif isinstance(val, float): attr.f = val elif isinstance(val, binary_type) or isinstance(val, text_type): if hasattr(val, 'encode'): val = val.encode() attr.s = val elif isinstance(val, TensorShape): attr.shape.MergeFromString(val.SerializeToString()) elif isinstance(val, list): if not val: return if isinstance(val[0], integer_types): attr.list.i.extend(val) elif isinstance(val[0], TensorShape): attr.list.shape.extend(val) elif isinstance(val[0], float): attr.list.f.extend(val) else: raise NotImplementedError('AttrValue cannot be of list[{}].'.format(val[0])) elif isinstance(val, np.ndarray): assign_attr_value(attr, val.tolist()) else: pass # raise NotImplementedError('AttrValue cannot be of %s' % type(val)) def assign_IRnode_values(IR_node, val_dict): for name, val in val_dict.items(): assign_attr_value(IR_node.attr[name], val) # For padding def convert_tf_pad_to_onnx(pads): pads = np.reshape(pads, -1).tolist() dims = len(pads) assert dims % 2 == 0 ret = [] for idx in range(0, dims, 2): ret.append(pads[idx]) for idx in range(1, dims, 2): ret.append(pads[idx]) return ret def convert_onnx_pad_to_tf(pads): return np.transpose(np.array(pads).reshape([2, -1])).reshape(-1, 2).tolist() def is_valid_padding(pads): return sum(np.reshape(pads, -1)) == 0 def shape_to_list(shape): return [dim.size for dim in shape.dim] def list_to_shape(shape): ret = graph_pb2.TensorShape() for dim in shape: new_dim = ret.dim.add() new_dim.size = dim return ret def compute_tf_same_padding(input_shape, kernel_shape, strides, data_format='NHWC'): """ Convert [SAME] padding in tensorflow, keras to onnx pads, i.e. [x1_begin, x2_begin...x1_end, x2_end,...] """ # print (input_shape) # print (kernel_shape) # print (strides) if data_format.startswith('NC'): # Not tested input_shape = input_shape[2:] remove_dim = len(strides) - len(input_shape) if remove_dim > 0: strides = strides[remove_dim::] else: input_shape = input_shape[1:-1] remove_dim = len(input_shape) - len(strides) + 1 if remove_dim < 0: strides = strides[1:remove_dim] # print (input_shape) # print (kernel_shape) # print (strides) up_list = [0] down_list = [0] for idx in range(0, len(input_shape)): # kernel_shape[idx] = (kernel_shape[idx] - 1) * dilation_rate + 1 output_shape = (input_shape[idx] + strides[idx] - 1) // strides[idx] this_padding = (output_shape - 1) * strides[idx] + kernel_shape[idx] - input_shape[idx] this_padding = max(0, this_padding) up_list.append(this_padding // 2) down_list.append(this_padding - this_padding // 2) # print ([0] + up_list + [0] + down_list if data_format.startswith('NC') else up_list + [0] + down_list + [0]) # print ('-----------------------------------------------------') return [0] + up_list + [0] + down_list if data_format.startswith('NC') else up_list + [0] + down_list + [0] # network library def sizeof_fmt(num, suffix='B'): for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']: if abs(num) < 1024.0: return "%3.1f %s%s" % (num, unit, suffix) num /= 1024.0 return "%.1f %s%s" % (num, 'Yi', suffix) def _progress_check(count, block_size, total_size): read_size = count * block_size read_size_str = sizeof_fmt(read_size) if total_size > 0: percent = int(count * block_size * 100 / total_size) percent = min(percent, 100) sys.stdout.write("\rprogress: {} downloaded, {}%.".format(read_size_str, percent)) if read_size >= total_size: sys.stdout.write("\n") else: sys.stdout.write("\rprogress: {} downloaded.".format(read_size_str)) sys.stdout.flush() def _single_thread_download(url, file_name): from six.moves import urllib result, _ = urllib.request.urlretrieve(url, file_name, _progress_check) return result def _downloader(start, end, url, filename): import requests headers = {'Range': 'bytes=%d-%d' % (start, end)} r = requests.get(url, headers=headers, stream=True) with open(filename, "r+b") as fp: fp.seek(start) var = fp.tell() fp.write(r.content) def _multi_thread_download(url, file_name, file_size, thread_count): import threading fp = open(file_name, "wb") fp.truncate(file_size) fp.close() part = file_size // thread_count for i in range(thread_count): start = part * i if i == thread_count - 1: end = file_size else: end = start + part t = threading.Thread(target=_downloader, kwargs={'start': start, 'end': end, 'url': url, 'filename': file_name}) t.setDaemon(True) t.start() main_thread = threading.current_thread() for t in threading.enumerate(): if t is main_thread: continue t.join() return file_name def download_file(url, directory='./', local_fname=None, force_write=False, auto_unzip=False, compre_type=''): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not os.path.isdir(directory): os.mkdir(directory) if not local_fname: k = url.rfind('/') local_fname = url[k + 1:] local_fname = os.path.join(directory, local_fname) if os.path.exists(local_fname) and not force_write: print ("File [{}] existed!".format(local_fname)) return local_fname else: print ("Downloading file [{}] from [{}]".format(local_fname, url)) try: import wget ret = wget.download(url, local_fname) print ("") except: ret = _single_thread_download(url, local_fname) if auto_unzip: if ret.endswith(".tar.gz") or ret.endswith(".tgz"): try: import tarfile tar = tarfile.open(ret) for name in tar.getnames(): if not (os.path.realpath(os.path.join(directory, name))+ os.sep).startswith(os.path.realpath(directory) + os.sep): raise ValueError('The decompression path does not match the current path. For more info: https://docs.python.org/3/library/tarfile.html#tarfile.TarFile.extractall') tar.extractall(directory) tar.close() except ValueError: raise except: print("Unzip file [{}] failed.".format(ret)) elif ret.endswith('.zip'): try: import zipfile zip_ref = zipfile.ZipFile(ret, 'r') for name in zip_ref.namelist(): if not (os.path.realpath(os.path.join(directory, name))+ os.sep).startswith(os.path.realpath(directory) + os.sep): raise ValueError('The decompression path does not match the current path. For more info: https://docs.python.org/3/library/zipfile.html?highlight=zipfile#zipfile.ZipFile.extractall') zip_ref.extractall(directory) zip_ref.close() except ValueError: raise except: print("Unzip file [{}] failed.".format(ret)) return ret """ r = requests.head(url) try: file_size = int(r.headers['content-length']) return _multi_thread_download(url, local_fname, file_size, 5) except: # not support multi-threads download return _single_thread_download(url, local_fname) return result """ model-pivot/ox/pytorch/000077500000000000000000000000001415504170400154345ustar00rootroot00000000000000model-pivot/ox/pytorch/__init__.py000066400000000000000000000001551415504170400175460ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/pytorch/pytorch_emitter.py000066400000000000000000001131421415504170400212310ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- import os import numpy as np from six import string_types as _string_types from ox.common.IR.IR_graph import IRGraph, IRGraphNode import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import NodeDef, GraphDef, DataType from ox.common.DataStructure.emitter import Emitter from ox.common.utils import * from ox.rewriter.folder import Folder class PytorchEmitter(Emitter): dtype_map = { graph_pb2.DT_FLOAT16 : "torch.float16", graph_pb2.DT_FLOAT32 : "torch.float32", graph_pb2.DT_FLOAT64 : "torch.float64", graph_pb2.DT_INT16 : "torch.int16", graph_pb2.DT_INT32 : "torch.int32", graph_pb2.DT_INT64 : "torch.int64", graph_pb2.DT_UINT8 : "torch.uint8", graph_pb2.DT_UINT16 : "torch.uint16" } # Base Functions def __init__(self, model): super(PytorchEmitter, self).__init__() if isinstance(model, _string_types): network_path = model else: network_path = model[0] weight_path = model[1] self.init_code = str() self.IR_graph = IRGraph(network_path) self.IR_graph.build() self._load_weights(weight_path) folder = Folder(self.IR_graph, self.weights_dict) folder.fold() def run(self, dstNetworkPath, dstWeightPath = None, phase = 'test'): super(PytorchEmitter, self).run(dstNetworkPath, dstWeightPath, phase) if self.weight_loaded: self.save_weights(self.weights_dict, dstWeightPath) def add_init(self, indent, codes): if isinstance(codes, _string_types): codes = [codes] for code in codes: self.init_code += (" " * indent) + code + '\n' def parent_variable_name(self, IR_node, path=[0], weight_type='weights'): if not IR_node.in_edges and IR_node.name in self.weights_dict.keys(): self.weights_dict[IR_node.name][weight_type] = self.weights_dict[IR_node.name][weight_type] return "torch.from_numpy(__weights_dict['{}']['{}'])".format(IR_node.name, weight_type) return super(PytorchEmitter, self).parent_variable_name(IR_node, path) @property def header_code(self): return """import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import math __weights_dict = dict() def load_weights(weight_file): if weight_file == None: return try: weights_dict = np.load(weight_file, allow_pickle=True).item() except: weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() return weights_dict class KitModel(nn.Module): """ def gen_code(self, phase): self.add_init(1, """ def __init__(self, weight_file): super(KitModel, self).__init__() global __weights_dict __weights_dict = load_weights(weight_file) """) self.add_body(1, "def forward(self, x):") for layer in self.IR_graph.topological_sort: # print(layer) current_node = self.IR_graph.get_node(layer) node_type = current_node.type # print(node_type) if hasattr(self, "emit_" + node_type): func = getattr(self, "emit_" + node_type) line = func(current_node) if line: self.add_body(2, line) else: print("Pytorch Emitter has not supported operator [%s]." % (node_type)) self.emit_UNKNOWN(current_node) self.add_body(2, "return {}".format( ', '.join([self.IR_graph.get_node(name).real_variable_name for name in self.IR_graph.output_layers if self.IR_graph.get_node(name).type != 'Pack']))) self.add_body(0, "") for i in self.used_layers: func = getattr(self, "_layer_" + i) func() self.add_body(0, "") for code in self.layers_codes.values(): self.add_body(0, code) return self.header_code + '\n' + self.init_code + '\n' + self.body_code def _defuse_padding(self, IR_node, extra_str = ""): input_node = self.parent_variable_name(IR_node) if IR_node.get_attr('auto_pad') == 'VALID': return input_node if is_valid_padding(IR_node.get_attr("pads")) == True: return input_node padding = self._convert_padding(IR_node) input_node = IR_node.variable_name + '_pad' self.add_body(2, "{:<15} = F.pad({}, {}{})".format( input_node, self.parent_variable_name(IR_node), padding, extra_str )) return input_node def emit_Conv(self, IR_node): self.used_layers.add('Conv') dim = len(IR_node.get_attr('strides')) - 2 in_channels = IR_node.get_attr('kernel_shape')[-2] filter = IR_node.get_attr('kernel_shape')[-1] kernel = IR_node.get_attr('kernel_shape')[:-2] strides = IR_node.get_attr('strides')[1:-1] if IR_node.type == 'DepthwiseConv': group = in_channels filter *= group else: group = IR_node.get_attr('group', 1) self.add_init(2, "self.{} = self.__conv({}, name='{}', in_channels={}, out_channels={}, kernel_size={}, stride={}, groups={}, bias={})".format( IR_node.variable_name, dim, IR_node.name, in_channels, filter, tuple(kernel), tuple(strides), # padding, group, IR_node.get_attr('use_bias'))) input_node = self._defuse_padding(IR_node) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, input_node) if self.weight_loaded: if IR_node.type == 'DepthwiseConv': self.weights_dict[IR_node.name]['weights'] = np.swapaxes(self.weights_dict[IR_node.name]['weights'], -1, -2) self.weights_dict[IR_node.name]['weights'] = np.transpose(self.weights_dict[IR_node.name]['weights'], [dim + 1, dim] + list(range(0, dim))) return code def emit_ConvTranspose(self, IR_node): self.used_layers.add('ConvTranspose') dim = len(IR_node.get_attr('strides')) - 2 in_channels = IR_node.get_attr('kernel_shape')[-2] filter = IR_node.get_attr('kernel_shape')[-1] kernel = IR_node.get_attr('kernel_shape')[:-2] strides = IR_node.get_attr('strides')[1:-1] if IR_node.type == 'DepthwiseConv': group = in_channels filter *= group else: group = IR_node.get_attr('group', 1) self.add_init(2, "self.{} = self.__conv_transpose({}, name='{}', in_channels={}, out_channels={}, kernel_size={}, stride={}, groups={}, bias={})".format( IR_node.variable_name, dim, IR_node.name, in_channels, filter, tuple(kernel), tuple(strides), # padding, group, IR_node.get_attr('use_bias'))) input_node = self._defuse_padding(IR_node) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, input_node) if self.weight_loaded: if IR_node.type == 'DepthwiseConv': self.weights_dict[IR_node.name]['weights'] = np.swapaxes(self.weights_dict[IR_node.name]['weights'], -1, -2) self.weights_dict[IR_node.name]['weights'] = np.transpose(self.weights_dict[IR_node.name]['weights'], [dim + 1, dim] + list(range(0, dim))) return code @staticmethod def is_ceil_mode(pads): lens = len(pads) for i in range(lens // 2 + 1, lens - 1): if pads[i] == pads[i - lens // 2]: return False else: return True def emit_Pool(self, IR_node): dim = len(IR_node.get_attr('strides')) - 2 if IR_node.get_attr('pooling_type') == "MAX": pool_name = "max_pool{}d".format(dim) # exstr = ", value=float('-Inf')" elif IR_node.get_attr('pooling_type') == "AVG": pool_name = "avg_pool{}d".format(dim) # exstr = "" else: raise ValueError() if IR_node.layer.attr['global_pooling'].b: code = "{:<15} = F.{}(input = {}, kernel_size = {}.size()[2:])".format( IR_node.variable_name, pool_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node) ) return code else: if IR_node.get_attr('pooling_type') == "MAX": # Change to padding defuse input_node = self._defuse_padding(IR_node,", value=float('-inf')") for e in IR_node.get_attr('dilations', []): assert e == 1 pool_size = IR_node.get_attr('kernel_shape')[1:-1] strides = IR_node.get_attr('strides')[1:-1] code = "{:<15} = F.{}({}, kernel_size={}, stride={}, padding={}, ceil_mode={})".format( IR_node.variable_name, pool_name, input_node, tuple(pool_size), tuple(strides), 0, False ) return code elif IR_node.get_attr('pooling_type') == "AVG": for e in IR_node.get_attr('dilations', []): assert e == 1 pool_size = IR_node.get_attr('kernel_shape')[1:-1] strides = IR_node.get_attr('strides')[1:-1] padding = IR_node.get_attr('pads')[1:dim] ceil_mode = self.is_ceil_mode(IR_node.get_attr('pads')) # input_node = self._defuse_padding(IR_node, exstr) code = "{:<15} = F.{}({}, kernel_size={}, stride={}, padding={}, ceil_mode={}, count_include_pad=True)".format( IR_node.variable_name, pool_name, self.parent_variable_name(IR_node), tuple(pool_size), tuple(strides), tuple(padding), ceil_mode ) return code else: raise ValueError() def emit_UNKNOWN(self, IR_node): print(IR_node.name) def emit_DataInput(self, IR_node): # Ignore it in Pytorch IR_node.real_name = 'x' def emit_Dropout(self, IR_node): code = "{:<15} = F.dropout(input = {}, p = {}, training = self.training, inplace = True)".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.layer.attr["keep_prob"].f) return code def check_if_need_transpose(self, IR_node): parent = self.IR_graph.get_parent(IR_node.name, [0]) while parent.type == 'Flatten' or parent.type == 'Dropout': parent = self.IR_graph.get_parent(parent.name, [0]) dim = len(parent.layer.attr['_output_shapes'].list.shape[0].dim) if dim > 2: original_dims = self.weights_dict[IR_node.name]['weights'].shape dims = [i.size for i in parent.layer.attr['_output_shapes'].list.shape[0].dim[1:]] + [-1] self.weights_dict[IR_node.name]['weights'] = np.reshape(self.weights_dict[IR_node.name]['weights'], dims) self.weights_dict[IR_node.name]['weights'] = np.transpose(self.weights_dict[IR_node.name]['weights'], [dim - 2] + list(range(0, dim - 2)) + [dim - 1]) self.weights_dict[IR_node.name]['weights'] = np.reshape(self.weights_dict[IR_node.name]['weights'], original_dims) def emit_FullyConnected(self, IR_node): self.used_layers.add(IR_node.type) in_features = 1 for i in self.IR_graph.get_parent(IR_node.name, [0]).layer.attr['_output_shapes'].list.shape[0].dim[1:]: in_features *= i.size if IR_node.get_attr('in_features') != None: in_features = IR_node.get_attr('in_features') self.add_init(2, "self.{} = self.__dense(name = '{}', in_features = {}, out_features = {}, bias = {})".format( IR_node.variable_name, IR_node.name, in_features, IR_node.layer.attr["units"].i, IR_node.IR_layer.attr["use_bias"].b)) input_node = self.parent_variable_name(IR_node) if len(self.IR_graph.get_parent(IR_node.name, [0]).get_attr('_output_shapes')[0].dim) > 2: input_node = "{}.view({}.size(0), -1)".format(input_node, input_node) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, input_node) if self.weight_loaded: self.check_if_need_transpose(IR_node) self.weights_dict[IR_node.name]['weights'] = np.transpose(self.weights_dict[IR_node.name]['weights'], (1, 0)) return code def emit_Flatten(self, IR_node): parent = self.IR_graph.get_parent(IR_node.name, [0]).real_variable_name code = "{:<15} = {}.view({}.size(0), -1)".format( IR_node.variable_name, parent, parent) return code def emit_Reshape(self, IR_node): shape_list = IR_node.get_attr('shape') if shape_list: shape_str = ','.join([str(int(i)) for i in shape_list]) code = "{:<15} = torch.reshape(input = {}, shape = ({}))".format( IR_node.variable_name, self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name, shape_str) else: code = "{:<15} = torch.reshape(input = {}, shape = ({}))".format( IR_node.variable_name, self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name, self.IR_graph.get_node(IR_node.in_edges[1]).real_variable_name,) # code = "{:<15} = {}.view({})".format( # IR_node.variable_name, # self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name, # self.IR_graph.get_node(IR_node.in_edges[1]).real_variable_name,) return code def emit_Tanh(self, IR_node): code = "{:<15} = F.tanh({})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0])) return code def emit_Relu(self, IR_node): code = "{:<15} = F.relu({})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0])) return code def emit_LeakyRelu(self, IR_node): code = "{:<15} = F.leaky_relu({}, negative_slope={})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0]), IR_node.get_attr('alpha')) return code def emit_Relu6(self, IR_node): code = "{:<15} = F.relu6({})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0])) return code def emit_Softmax(self, IR_node): code = "{:<15} = F.softmax({})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0])) return code def emit_Sigmoid(self, IR_node): code = "{:<15} = F.sigmoid({})".format( IR_node.variable_name, self.parent_variable_name(IR_node) ) return code def emit_Embedding(self, IR_node): self.used_layers.add("Embedding") self.add_init(2, "self.{} = self.__embedding('{}', num_embeddings={}, embedding_dim={})".format( IR_node.variable_name, IR_node.name, IR_node.get_attr('input_dim'), #2-D IR_node.get_attr('output_dim') )) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, "torch.LongTensor(np.array({}))".format(self.parent_variable_name(IR_node)) ) return code def emit_RNNs(self, IR_node, func): raise NotImplementedError() # for Keras if "dropout" in IR_node.IR_layer.attr: dropout_str = ",dropout = {}, recurrent_dropout = {}".format( IR_node.IR_layer.attr['dropout'].f, IR_node.IR_layer.attr['recurrent_dropout'].f) else: dropout_str = "" code = "{:<15} = {}(units = {}, use_bias = {} {})({})".format( IR_node.name, func, IR_node.IR_layer.attr['units'].i, IR_node.IR_layer.attr['use_bias'].b, dropout_str, IR_node.in_edges[0]) return code def emit_LSTM(self, IR_node): return self.emit_RNNs(IR_node, "LSTM") def emit_GRU(self, IR_node): return self.emit_RNNs(IR_node, "GRU") def emit_Add(self, IR_node): code = "{:<15} = {} + {}".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1])) return code def emit_Sub(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, ' - '.join(self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges)))) return code def emit_Mul(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, ' * '.join(self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges)))) return code def emit_MatMul(self, IR_node): code = "{:<15} = torch.matmul({})".format( IR_node.variable_name, ' , '.join('%s' % self.IR_graph.get_node(s).real_variable_name for s in IR_node.in_edges)) return code def emit_Constant(self, IR_node): if IR_node.get_attr('value') is not None: value = IR_node.get_attr('value') # print(value) full_shape = IR_node.get_attr('full_shape') if full_shape is not None: code = "{:<15} = torch.full({}, {})".format( IR_node.variable_name, full_shape, value) return code if not isinstance(value, list): value = [value] if len(value) <= 1: code = "self.{:<15} = torch.autograd.Variable(torch.Tensor({}), requires_grad=False)".format( IR_node.variable_name, value) else: code = "self.{:<15} = {}".format( IR_node.variable_name, value) else: code = "self.{:<15} = torch.autograd.Variable(torch.from_numpy(__weights_dict['{}']['value']), requires_grad=False)".format( IR_node.variable_name, IR_node.name) # self.add_init(2, "self.{:<15} = torch.from_numpy(__weights_dict['{}']['value'])".format( # IR_node.variable_name, # IR_node.name)) IR_node.real_name = "self." + IR_node.variable_name return code def _convert_axis(self, IR_node, axis): ndim = len(self.IR_graph.get_parent(IR_node.name, [0]).get_attr('_output_shapes')[0].dim) if axis == 0: return 0 elif axis == ndim - 1: return 1 else: return axis + 1 def emit_Concat(self, IR_node): axis = self._convert_axis(IR_node, IR_node.get_attr('axis')) code = "{:<15} = torch.cat(({}), {})".format( IR_node.variable_name, ', '.join(self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges))), axis, ) return code def emit_BatchNorm(self, IR_node): self.used_layers.add(IR_node.type) dim = len(IR_node.layer.attr['_output_shapes'].list.shape[0].dim) - 2 output_shape = IR_node.layer.attr['_output_shapes'].list.shape[0] if IR_node.get_attr('data_format', "NHWC") == "NCHW": num_features = output_shape.dim[1].size else: num_features = output_shape.dim[-1].size self.add_init(2, "self.{} = self.__batch_normalization({}, '{}', num_features={}, eps={}, momentum={})".format( IR_node.variable_name, dim, IR_node.name, num_features, IR_node.layer.attr['epsilon'].f, IR_node.layer.attr['momentum'].f, )) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, self.parent_variable_name(IR_node) ) return code def emit_Scale(self, IR_node): self.used_layers.add(IR_node.type) dim = len(IR_node.layer.attr['_output_shapes'].list.shape[0].dim) - 2 self.add_init(2, "self.{} = self.__scale({}, '{}', num_features={})".format( IR_node.variable_name, dim, IR_node.name, IR_node.layer.attr['_output_shapes'].list.shape[0].dim[-1].size )) code = "{:<15} = self.{}({})".format( IR_node.variable_name, IR_node.variable_name, self.parent_variable_name(IR_node) ) return code def emit_Squeeze(self, IR_node): code = "{:<15} = torch.squeeze({})".format( IR_node.variable_name, self.parent_variable_name(IR_node) ) return code @staticmethod def _convert_padding(IR_node): padding = IR_node.get_attr('pads') padding = convert_onnx_pad_to_tf(padding)[1:-1] new_padding = [] for pad in padding: new_padding.insert(0, pad) return tuple(np.array(new_padding).reshape(-1).tolist()) def emit_Pad(self, IR_node): if IR_node.get_attr('mode').lower() == 'constant': mode = "mode = 'constant', value = {}".format(0) elif IR_node.get_attr('mode').lower() == 'reflect': mode = "mode = 'reflect'" elif IR_node.get_attr('mode').upper() == 'SYMMETRIC': mode = "mode = 'replicate'" else: assert False padding = self._convert_padding(IR_node) code = "{:<15} = F.pad({}, {}, {})".format( IR_node.variable_name, self.parent_variable_name(IR_node), padding, mode) return code def emit_ReduceMean(self, IR_node): axes = [self._convert_axis(IR_node, x) for x in IR_node.get_attr('axes')] input_node = self.parent_variable_name(IR_node) codes = [] for axis in sorted(axes, reverse=True): code = "{:<15} = torch.mean({}, {}, {})".format( IR_node.variable_name, input_node, axis, IR_node.get_attr("keepdims") ) codes.append(code) input_node = IR_node.variable_name return codes def emit_LRN(self, IR_node): code = "{:<15} = F.local_response_norm({}, size={}, alpha={}, beta={}, k={})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('size') * 2 - 1, IR_node.get_attr('alpha'), IR_node.get_attr('beta'), IR_node.get_attr('k', 1) ) return code def emit_DepthwiseConv(self, IR_node): return self.emit_Conv(IR_node) def emit_Const(self, IR_node): if 'dtype' in IR_node.layer.attr: dtype_str = "dtype={}".format(self.dtype_map[IR_node.layer.attr['dtype'].type]) if 'int' in dtype_str: code = "{:<15} = torch.tensor({}, {})".format( IR_node.variable_name, IR_node.layer.attr['value'].i, dtype_str) else: code = "{:<15} = torch.tensor({}, {})".format( IR_node.variable_name, IR_node.layer.attr['value'].f, dtype_str) else: dtype_str = "dtype=torch.float32" code = "{:<15} = torch.tensor({}, {})".format( IR_node.variable_name, IR_node.layer.attr['value'].f, dtype_str) return code def emit_Shape(self, IR_node): code = "{:<15} = torch.Tensor(list({}.size()))".format( IR_node.variable_name, self.parent_variable_name(IR_node) ) return code def emit_Pack(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, '[' + ','.join('%s' % self.IR_graph.get_node(s).real_variable_name for s in IR_node.in_edges) + ']', ) return code def emit_Slice(self, IR_node): starts = IR_node.get_attr('starts') if len(starts) > 1: starts = [starts[0], starts[-1]] + starts[1:-1] ends = IR_node.get_attr('ends') if len(ends) > 1: ends = [ends[0], ends[-1]] + ends[1:-1] extra_str = "" for idx, _ in enumerate(starts): if idx: extra_str += ", " extra_str += "{}:".format(starts[idx]) if ends[idx]: extra_str += "{}".format(ends[idx]) shrink_mask = IR_node.get_attr('shrink_axis_mask') if shrink_mask: shrink_str = '' for i in range(shrink_mask): shrink_str += ':,' shrink_str += extra_str # mask = [int(s) for s in bin(shrink_mask)[2:][::-1]] # shrink_str = '[' + ','.join(':' if bit==0 else '0' for bit in mask) + ']' else: shrink_str = extra_str input_from_param = IR_node.get_attr('input_from_param') if input_from_param is not None: self.add_init(2, "self.{:<15} = torch.from_numpy(__weights_dict['{}']['{}'])".format( self.parent_variable_name(IR_node), IR_node.name, input_from_param )) code = "{:<15} = {}[{}]".format( IR_node.variable_name, "self." + self.parent_variable_name(IR_node), shrink_str) else: code = "{:<15} = {}[{}]".format( IR_node.variable_name, self.parent_variable_name(IR_node), shrink_str) return code def emit_Split(self, IR_node): if isinstance(IR_node.get_attr('split'), list): split_str = IR_node.get_attr('split') else: num_split = IR_node.get_attr('split') split_str = "math.ceil({}.shape[{}]/{})".format( self.parent_variable_name(IR_node), IR_node.get_attr('axis'), num_split) code = "{:<15} = torch.split({}, {}, dim={})".format( IR_node.variable_name, self.parent_variable_name(IR_node), split_str, IR_node.get_attr('axis'), ) return code def emit_Unstack(self, IR_node): code = "{:<15} = torch.unbind({}, dim={})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('axis') ) return code def emit_Fill(self, IR_node): code = "{:<15} = torch.full({}.int().numpy().tolist(), {})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('value') ) return code def emit_Gather(self, IR_node): pass def emit_Unsqueeze(self, IR_node): code = "{:<15} = {}.unsqueeze({})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('axes')[0] ) return code def emit_Transpose(self, IR_node): if self.parent_variable_name(IR_node, [1]): code = "{:<15} = {}.permute({})".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1])) else: perm = IR_node.get_attr('perm_list') if perm is None: perm = IR_node.get_attr('perm') if perm is None: code = "{:<15} = {}.transpose(1, 2).contiguous()".format( IR_node.variable_name, self.parent_variable_name(IR_node)) else: code = "{:<15} = {}.permute{}.contiguous()".format( IR_node.variable_name, self.parent_variable_name(IR_node), str(tuple(perm))) else: code = "{:<15} = {}.transpose{}.contiguous()".format( IR_node.variable_name, self.parent_variable_name(IR_node), str(tuple(perm))) # print(perm) return code def emit_Minimum(self, IR_node): code = "{:<15} = torch.min({}, {})".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1])) return code def emit_Maxmum(self, IR_node): code = "{:<15} = torch.max({}, {})".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1])) return code def emit_Square(self, IR_node): code = "{:<15} = {}.pow(2)".format( IR_node.variable_name, self.parent_variable_name(IR_node)) return code def emit_PRelu(self, IR_node): code = "{:<15} = F.prelu({}, torch.from_numpy(__weights_dict['{}']['weights']))".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0]), IR_node.name) if self.weight_loaded: self.weights_dict[IR_node.name]['weights'] = self.weights_dict[IR_node.name]['gamma'] return code def emit_Cast(self, IR_node): dstType = IR_node.get_attr('dstType') if dstType == 'float': dst = 'torch.FloatTensor' elif dstType == 'double': dst = 'torch.DoubleTensor' elif dstType == 'int': dst = 'torch.IntTensor' code = "{:<15} = {}.type({})".format( IR_node.real_variable_name, self.parent_variable_name(IR_node), dst) return code def emit_Scope(self, IR_node): input_vars = [self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges))] code = "{:<15} = self.__{}({})".format( IR_node.real_variable_name, IR_node.pattern, ', '.join(input_vars)) self._gen_scope_code(IR_node) return code def _gen_scope_code(self, scope_node): def _scope_func(scope_name, params, code, return_var): code = """ def __{}({}): {} return {} """.format(scope_name, params, code, ', '.join(return_var)) return code if not self.layers_codes.get(scope_node.pattern, None): body_code = str() for node_name in scope_node.topology_list: node = self.IR_graph.get_node(node_name) node_type = node.type if hasattr(self, "emit_" + node_type): func = getattr(self, "emit_" + node_type) line = func(node) if line != None: body_code += " " + line + '\n' else: print("PytorchEmitter has not supported operator [%s]." % (node_type)) self.emit_UNKNOWN(node) # param_code does not need parameter slice. input_params = scope_node.input_params input_params.insert(0, "self") param_code = ', '.join(input_params) function_code = _scope_func(scope_node.pattern, param_code, body_code, scope_node.return_variables) self.layers_codes[scope_node.pattern] = function_code def _layer_Embedding(self): self.add_body(0,""" @staticmethod def __embedding(name, **kwargs): layer = nn.Embedding(**kwargs) #shape layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['weights'])) return layer """) def _layer_Conv(self): self.add_body(0, """ @staticmethod def __conv(dim, name, **kwargs): if dim == 1: layer = nn.Conv1d(**kwargs) elif dim == 2: layer = nn.Conv2d(**kwargs) elif dim == 3: layer = nn.Conv3d(**kwargs) else: raise NotImplementedError() layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['weights'])) if 'bias' in __weights_dict[name]: layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) return layer""") def _layer_ConvTranspose(self): self.add_body(0, """ @staticmethod def __conv_transpose(dim, name, **kwargs): if dim == 1: layer = nn.ConvTranspose1d(**kwargs) elif dim == 2: layer = nn.ConvTranspose2d(**kwargs) elif dim == 3: layer = nn.ConvTranspose3d(**kwargs) else: raise NotImplementedError() layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['weights'])) if 'bias' in __weights_dict[name]: layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) return layer""") def _layer_FullyConnected(self): self.add_body(0, """ @staticmethod def __dense(name, **kwargs): layer = nn.Linear(**kwargs) layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['weights'])) if 'bias' in __weights_dict[name]: layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) return layer""") def _layer_BatchNorm(self): self.add_body(0, """ @staticmethod def __batch_normalization(dim, name, **kwargs): if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) elif dim == 2: layer = nn.BatchNorm2d(**kwargs) elif dim == 3: layer = nn.BatchNorm3d(**kwargs) else: raise NotImplementedError() if 'scale' in __weights_dict[name]: layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['scale'])) else: layer.weight.data.fill_(1) if 'bias' in __weights_dict[name]: layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) else: layer.bias.data.fill_(0) layer.state_dict()['running_mean'].copy_(torch.from_numpy(__weights_dict[name]['mean'])) layer.state_dict()['running_var'].copy_(torch.from_numpy(__weights_dict[name]['var'])) return layer""") def _layer_Scale(self): self.add_body(0, """ # from torch.nn.parameter import Parameter class _Scale(nn.Module): def __init__(self, num_features, affine=True): super(KitModel._Scale, self).__init__() self.num_features = num_features self.affine = affine self.running_mean = torch.zeros(num_features) self.running_var = torch.ones(num_features) self.training = False self.eps = 1e-5 if self.affine: self.weight = nn.Parameter(torch.Tensor(num_features)) self.bias = nn.Parameter(torch.Tensor(num_features)) else: self.register_parameter('weight', None) self.register_parameter('bias', None) self.reset_parameters() def reset_parameters(self): if self.affine: self.weight.data.uniform_() self.bias.data.zero_() def _check_input_dim(self, input): raise NotImplementedError def forward(self, input): self._check_input_dim(input) return F.batch_norm( input, self.running_mean, self.running_var, self.weight, self.bias, self.training, 0 , self.eps) class Scale1d(_Scale): def _check_input_dim(self, input): if input.dim() != 2 and input.dim() != 3: raise ValueError('expected 2D or 3D input (got {}D input)' .format(input.dim())) class Scale2d(_Scale): def _check_input_dim(self, input): if input.dim() != 4: raise ValueError('expected 4D input (got {}D input)' .format(input.dim())) class Scale3d(_Scale): def _check_input_dim(self, input): if input.dim() != 5: raise ValueError('expected 5D input (got {}D input)' .format(input.dim())) @staticmethod def __scale(dim, name, **kwargs): if dim == 1: layer = KitModel.Scale1d(**kwargs) elif dim == 2: layer = KitModel.Scale2d(**kwargs) elif dim == 3: layer = KitModel.Scale3d(**kwargs) else: raise NotImplementedError() if 'scale' in __weights_dict[name]: layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['scale'])) else: layer.weight.data.fill_(1) if 'bias' in __weights_dict[name]: layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) else: layer.bias.data.fill_(0) return layer""") model-pivot/ox/pytorch/pytorch_graph.py000066400000000000000000000177231415504170400206710ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- from ox.common.DataStructure.graph import GraphNode, Graph import torch import torch.jit import torch.autograd import torch.serialization import contextlib from torch.jit import _unique_state_dict import numpy as np import re DYNAMIC_SHAPE_MARK = "Dynamic" class PytorchGraphNode(GraphNode): def __init__(self, *args): if len(args) != 1: self.consturct_from_fields(args) return layer = args[0] self._name = layer.scopeName() self._kind = layer.kind() import re node_id = re.search(r"[\d]+", layer.__str__()) self.id = node_id.group(0) super(PytorchGraphNode, self).__init__(layer) # print(self._kind) if 'Constant' in self._kind: idx1 = layer.__str__().find('value') idx2 = layer.__str__().find('[') for i in range(idx1, len(layer.__str__())): if layer.__str__()[i] in '[}]': idx2 = i; break val = re.findall(r'\d+\.\d+|\d+|\-\d+\.\d+|\-\d+', layer.__str__()[idx1:idx2]) if 'Long' in layer.__str__(): for i in range(len(val)): val[i] = int(val[i]) else: for i in range(len(val)): val[i] = float(val[i]) if 'LongTensor' in layer.__str__(): # val[0] = -1 self.attrs = {k : val for k in layer.attributeNames()} else: self.attrs = {k : val for k in layer.attributeNames()} else: self.attrs = {k : layer[k] for k in layer.attributeNames()} self.weights_name = '.'.join( re.findall(r'\[([\w\d.]+)\]', self._name) ) def consturct_from_fields(self, args): self._name = args[0] self._kind = args[1] self.id = args[2] super(PytorchGraphNode, self).__init__(layer=None) self.attrs = args[3] if len(args) >= 4 else {} self.weights_name = '.'.join( re.findall(r'\[([\w\d.]+)\]', self._name) ) @property def name(self): name = self._name + self.id # Scopes created in a nested scope may have initial characters # that are illegal as the initial character of an op name # (viz. '-', '\', '/', and '_'). name = name.replace('-','n').replace('\\','n').replace('/','n').replace('_','n').replace('[','n').replace(']','n') return name @property def type(self): return self._kind @property def pytorch_layer(self): return self.layer def __str__(self): return "PytorchGraphNode name: %s, type: %s, attrs: %s" % (self.name, self.type, str(self.attrs)) class PytorchGraph(Graph): def __init__(self, model): # sanity check. super(PytorchGraph, self).__init__(model) self.model = model self.state_dict = _unique_state_dict(self.model) self.shape_dict = dict() @staticmethod def _optimize_graph(graph, aten, export_raw_ir=False): # run dce first to eliminate dead parts of the graph that might have been # left behind by things like symbolic_override torch._C._jit_pass_dce(graph) torch._C._jit_pass_lint(graph) torch._C._jit_pass_peephole(graph) torch._C._jit_pass_lint(graph) if not export_raw_ir: graph = torch._C._jit_pass_onnx(graph, aten) torch._C._jit_pass_lint(graph) torch._C._jit_pass_onnx_peephole(graph) torch._C._jit_pass_lint(graph) torch._C._jit_pass_dce(graph) torch._C._jit_pass_lint(graph) graph = torch._C._jit_pass_canonicalize(graph) torch._C._jit_pass_lint(graph) return graph @staticmethod def get_node_id(node): node_id = re.search(r"[\d]+", node.__str__()) return node_id.group(0) @contextlib.contextmanager def set_training(self, model, mode): r""" A context manager to temporarily set the training mode of 'model' to 'mode', resetting it when we exit the with-block. A no-op if mode is None. """ if mode is None: yield return old_mode = model.training if old_mode != mode: model.train(mode) try: yield finally: if old_mode != mode: model.train(old_mode) def build(self, shape): """ build graph for pytorch 0.4.0 """ import re # construct graph dummy_input = torch.autograd.Variable(torch.randn(shape), requires_grad=False) with self.set_training(self.model, False): trace, output = torch.jit.get_trace_graph(self.model, (dummy_input, )) trace.set_graph(PytorchGraph._optimize_graph(trace.graph(), False)) # nodes nodes = list(trace.graph().nodes()) # input layer # TODO # build each layer for node in nodes: node_id = PytorchGraph.get_node_id(node) node_scope = node.scopeName() node_name = node_scope + node_id node_name = node_name.replace('-','n').replace('\\','n').replace('/','n').replace('_','n').replace('[','n').replace(']','n') output_shape_str = re.findall(r'[^()!]+', node.__str__())[1] # print(node.__str__()) if 'Constant' in node.__str__(): # print(node.__str__()) idx1 = node.__str__().find('value') idx2 = node.__str__().find('[') for i in range(idx1, len(node.__str__())): if node.__str__()[i] in '[}]': idx2 = i; break val = re.findall(r'\d+\.\d+|\d+', node.__str__()[idx1:idx2]) for i in range(len(val)): val[i] = float(val[i]) output_shape = list(np.array(val).shape) else: output_shape = [] for x in output_shape_str.split(', '): x_str = x.replace('!', '') if x_str.isnumeric(): output_shape.append(int(x_str)) else: # RNN or other control flow related node has # Changable output shape so it is undefined output_shape = DYNAMIC_SHAPE_MARK break self.shape_dict[node_name] = output_shape self.layer_map[node_name] = PytorchGraphNode(node) self.layer_name_map[node_name] = node_name # input for node_input in list(node.inputs()): if PytorchGraph.get_node_id(node_input.node()) and node_input.node().scopeName(): node_input_name = node_input.node().scopeName() + PytorchGraph.get_node_id(node_input.node()) node_input_name = node_input_name.replace('-','n').replace('\\','n').replace('/','n').replace('_','n').replace('[','n').replace(']','n') self._make_connection(node_input_name, node_name) # print(node_input_name ,'->', node_name) super(PytorchGraph, self).build() def remove_node(self, node): del self.shape_dict[node.name] del self.layer_map[node.name] del self.layer_name_map[node.name] for in_node_name in node.in_edges: in_node = self.get_node(in_node_name) in_node.out_edges.remove(node.name) for out_node_name in node.out_edges: out_node = self.get_node(out_node_name) out_node.in_edges.remove(node.name) node.in_edges = [] node.out_edges = [] self.input_layers = [] super(PytorchGraph, self).rebuild() model-pivot/ox/pytorch/pytorch_parser.py000066400000000000000000000523671415504170400210670ustar00rootroot00000000000000#---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- import logging import os import numpy as np import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import NodeDef, GraphDef, DataType from ox.common.utils import * from ox.common.DataStructure.parser import Parser from ox.pytorch.pytorch_graph import PytorchGraph from ox.pytorch.rewriter.lstm_rewriter import LstmRewriter import torch import torchvision class PytorchParser(Parser): layer_map = { 'onnx::Conv': 'Conv', 'onnx::Flatten': 'Flatten', 'onnx::Gemm': 'FullyConnected', 'onnx::MaxPool': 'Maxpool', 'onnx::AveragePool': 'Avgpool', 'onnx::Dropout': 'Dropout', 'onnx::BatchNormalization': 'BatchNormalization', 'onnx::Add': 'Add', 'onnx::Concat': 'Concat', 'onnx::Relu': 'Relu', 'onnx::Tanh': 'Tanh', 'onnx::Sigmoid': 'Sigmoid', 'onnx::MatMul': 'MatMul', 'onnx::Mul': 'Mul', 'onnx::Constant': 'Constant', 'onnx::Reshape': 'Reshape', 'aten::reshape': 'Reshape', 'onnx::Transpose': 'Transpose', 'onnx::LogSoftmax': 'LogSoftmax', 'onnx::Slice': 'Slice', 'onnx::Squeeze': 'Squeeze', 'onnx::ConvTranspose': 'ConvTranspose' } ############ # property # ############ @property def src_graph(self): return self.pytorch_graph #################### # Public Functions # #################### def __init__(self, model_file_name, input_shape): super(PytorchParser, self).__init__() if not os.path.exists(model_file_name): print("Pytorch model file [{}] is not found.".format(model_file_name)) assert False # test # cpu: https://github.com/pytorch/pytorch/issues/5286 try: model = torch.load(model_file_name) except: model = torch.load(model_file_name, map_location='cpu') self.weight_loaded = True # Build network graph self.pytorch_graph = PytorchGraph(model) self.input_shape = tuple([1] + input_shape) self.pytorch_graph.build(self.input_shape) lstm_rewriter = LstmRewriter(self.pytorch_graph) self.pytorch_graph = lstm_rewriter.run() self.state_dict = self.pytorch_graph.state_dict self.shape_dict = self.pytorch_graph.shape_dict def gen_IR(self): node_set = set() for layer in self.src_graph.topological_sort: current_node = self.src_graph.get_node(layer) onnx_node_type = current_node.type if onnx_node_type not in PytorchParser.layer_map: logger = logging.getLogger() logger.warning("Warning: onnx_node_type '%s' not in PytorchParser.layer_map" % (onnx_node_type)) continue node_type = PytorchParser.layer_map[onnx_node_type] node_set.add(node_type) if hasattr(self, "rename_" + node_type): # print("rename_" + node_type) func = getattr(self, "rename_" + node_type) func(current_node) else: print('UNKNOWN: ', node_type) self.rename_UNKNOWN(current_node) self.gen_Input() return list(node_set) def _set_output_shape(self, source_node, IR_node): shape = graph_pb2.TensorShape() layer_name = source_node.name shape_pytorch = self.shape_dict[layer_name] new_dim = shape.dim.add() # (batch, C, H, W) & NHWC if len(shape_pytorch) == 4: if shape_pytorch[0] == 1: new_dim.size = -1 else: new_dim.size = shape_pytorch[0] for index in [2, 3, 1]: new_dim = shape.dim.add() dim = shape_pytorch[index] new_dim.size = dim if dim else -1 elif len(shape_pytorch) == 2: if shape_pytorch[0] == 1: new_dim.size = -1 else: new_dim.size = shape_pytorch[0] for _ in range(2): new_dim = shape.dim.add() new_dim.size = 1 new_dim = shape.dim.add() dim = shape_pytorch[1] new_dim.size = dim if dim else -1 IR_node.attr["_output_shapes"].list.shape.extend([shape]) ########## # Layers # ########## def rename_UNKNOWN(self, source_node): print (source_node.layer) print (source_node.layer.data.size()) assert False print("PyTorch parser has not supported operator [%s] with name [%s]." % (source_node.type, source_node.name)) def gen_Input(self): IR_node = self.IR_graph.node.add() IR_node.name = 'input' IR_node.op = "DataInput" for node in self.IR_graph.node: if node.name in self.src_graph.input_layers: if not 'onstant' in self.pytorch_graph.layer_map[node.name]._kind: node.input.append('input') assert len(self.input_shape) == 4 new_dim = IR_node.attr["shape"].shape.dim.add() if self.input_shape[0] == 1: new_dim.size = -1 else: new_dim.size = self.input_shape[0] for index in [2, 3, 1]: new_dim = IR_node.attr["shape"].shape.dim.add() new_dim.size = self.input_shape[index] shape = graph_pb2.TensorShape() new_dim = shape.dim.add() shape_pytorch = self.input_shape if len(shape_pytorch) == 4: if shape_pytorch[0] == 1: new_dim.size = -1 else: new_dim.size = shape_pytorch[0] for index in [2, 3, 1]: new_dim = shape.dim.add() dim = shape_pytorch[index] new_dim.size = dim if dim else -1 elif len(shape_pytorch) == 2: if shape_pytorch[0] == 1: new_dim.size = -1 else: new_dim.size = shape_pytorch[0] for _ in range(2): new_dim = shape.dim.add() new_dim.size = 1 new_dim = shape.dim.add() dim = shape_pytorch[1] new_dim.size = dim if dim else -1 IR_node.attr["_output_shapes"].list.shape.extend([shape]) def rename_Constant(self, source_node): IR_node = self.IR_graph.node.add() IR_node.name = source_node.name IR_node.op = "Constant" assign_IRnode_values(IR_node, source_node.attrs) self._set_output_shape(source_node, IR_node) def rename_Conv(self, source_node): attr = source_node.attrs kwargs = dict() # dilation if 'dilations' in attr: kwargs['dilations'] = [1] + attr['dilations'] + [1] else: kwargs['dilations'] = [1] + [1, 1] + [1] if len(attr['pads']) == 4: kwargs['pads'] = [0] + attr['pads'][0:2] + [0, 0] + attr['pads'][2:] + [0] elif len(attr['pads']) == 2: kwargs['pads'] = ( [0] + attr['pads'][0:2] + [0] ) *2 if 'strides' not in attr: kwargs['strides'] = [1] + [1, 1] + [1] else: kwargs['strides'] = [1] + attr['strides'] + [1] kwargs['group'] = attr['group'] bias_name = '{0}.bias'.format(source_node.weights_name) weights_name = '{0}.weight'.format(source_node.weights_name) weight = self.state_dict[weights_name].numpy() dim = weight.ndim - 2 IR_node = self._convert_identity_operation(source_node, new_op="Conv") weight = np.transpose(weight, list(range(2, dim + 2)) + [1, 0]) self.set_weight(source_node.name, 'weights', weight) kwargs['kernel_shape'] = list(weight.shape) kwargs['kernel_shape'][-2] *= kwargs['group'] # print(kwargs['kernel_shape'], kwargs['group']) # handle bias if bias_name in self.state_dict: bias = self.state_dict[bias_name].numpy() self.set_weight(source_node.name, 'bias', bias) kwargs['use_bias'] = True else: kwargs['use_bias'] = False assign_IRnode_values(IR_node, kwargs) def rename_ConvTranspose(self, source_node): # print('Transpose:', source_node.attrs) # IR_node = self._convert_identity_operation(source_node, new_op="ConvTranspose") attr = source_node.attrs kwargs = dict() # dilations [1, 1] # group 1 # kernel shape [4, 4] # pads [0, 0, 0, 0] # strides [2, 2] # dilation if 'dilations' in attr: kwargs['dilations'] = [1] + attr['dilations'] + [1] else: kwargs['dilations'] = [1] + [1, 1] + [1] if len(attr['pads']) == 4: kwargs['pads'] = [0] + attr['pads'][0:2] + [0, 0] + attr['pads'][2:] + [0] elif len(attr['pads']) == 2: kwargs['pads'] = ( [0] + attr['pads'][0:2] + [0] ) *2 if 'strides' not in attr: kwargs['strides'] = [1] + [1, 1] + [1] else: kwargs['strides'] = [1] + attr['strides'] + [1] kwargs['group'] = attr['group'] bias_name = '{0}.bias'.format(source_node.weights_name) weights_name = '{0}.weight'.format(source_node.weights_name) weight = self.state_dict[weights_name].numpy() dim = weight.ndim - 2 IR_node = self._convert_identity_operation(source_node, new_op="ConvTranspose") weight = np.transpose(weight, list(range(2, dim + 2)) + [1, 0]) self.set_weight(source_node.name, 'weights', weight) kwargs['kernel_shape'] = list(weight.shape) kwargs['kernel_shape'][-2] *= kwargs['group'] # print(kwargs['kernel_shape'], kwargs['group']) # handle bias if bias_name in self.state_dict: bias = self.state_dict[bias_name].numpy() self.set_weight(source_node.name, 'bias', bias) kwargs['use_bias'] = True else: kwargs['use_bias'] = False assign_IRnode_values(IR_node, kwargs) def rename_BatchNormalization(self, source_node): # TODO # output_shape IR_node = self._convert_identity_operation(source_node, new_op="BatchNorm") attr = source_node.attrs # epsilon IR_node.attr['epsilon'].f = attr['epsilon'] bias_name = '{0}.bias'.format(source_node.weights_name) weights_name = '{0}.weight'.format(source_node.weights_name) mean_name = '{0}.running_mean'.format(source_node.weights_name) var_name = '{0}.running_var'.format(source_node.weights_name) if bias_name in self.state_dict: beta = self.state_dict[bias_name].numpy() IR_node.attr['bias'].b = True else: IR_node.attr['bias'].b = False if weights_name in self.state_dict: gamma = self.state_dict[weights_name].numpy() IR_node.attr['scale'].b = True else: IR_node.attr['scale'].b = False mean = self.state_dict[mean_name].numpy() variance = self.state_dict[var_name].numpy() if IR_node.attr['scale'].b: self.set_weight(source_node.name, "scale", gamma) if IR_node.attr['bias'].b: self.set_weight(source_node.name, "bias", beta) # mean self.set_weight(source_node.name, "mean", mean) # var self.set_weight(source_node.name, "var", variance) def rename_Reshape(self, source_node): # print('Reshape:', source_node.attrs, source_node.type) IR_node = self._convert_identity_operation(source_node, new_op="Reshape") if "shape" in source_node.attrs: kwargs = dict() kwargs["shape"] = source_node.attrs["shape"] assign_IRnode_values(IR_node, kwargs) # if len(IR_node.input) > 1: # parent = self.src_graph.get_parent(source_node.name, [1]) # shape_list = parent.attrs # print(list(range(2, 2 + 2)) + [1, 0]) # shape_list = np.transpose(shape_list, list(range(2, 2 + 2)) + [1, 0]) # print(shape_list) # print(self.pytorch_graph.get_node(IR_node.input[1]).attrs) def rename_Transpose(self, source_node): # print('Transpose:', source_node.attrs) IR_node = self._convert_identity_operation(source_node, new_op="Transpose") if 'perm_list' in source_node.attrs: kwargs = dict() kwargs['perm_list'] = source_node.attrs['perm_list'] assign_IRnode_values(IR_node, kwargs) if 'perm' in source_node.attrs: kwargs = dict() kwargs['perm'] = source_node.attrs['perm'] assign_IRnode_values(IR_node, kwargs) def rename_Slice(self, source_node): # print('Transpose:', source_node.attrs) IR_node = self._convert_identity_operation(source_node, new_op="Slice") kwargs = dict() kwargs['shrink_axis_mask'] = source_node.attrs['axes'][0] kwargs['starts'] = source_node.attrs['starts'] kwargs['ends'] = source_node.attrs['ends'] if 'input_from_param' in source_node.attrs: input_from_param = source_node.attrs['input_from_param'] if isinstance(input_from_param, list): concat_numpy = np.array([]) concat_name = "" for param_name in input_from_param: assert isinstance(param_name, str), "set 'input_from_param' as illegal type." concat_name += ("_" + param_name) param_numpy = self.state_dict[param_name].numpy() concat_numpy = np.concatenate((concat_numpy, param_numpy)).astype(param_numpy.dtype) kwargs['input_from_param'] = concat_name self.set_weight(source_node.name, concat_name, concat_numpy) else: param_name = input_from_param assert isinstance(input_from_param, str), "set 'input_from_param' as illegal type." kwargs['input_from_param'] = param_name param_numpy = self.state_dict[param_name].numpy() self.set_weight(source_node.name, param_name, param_numpy) assign_IRnode_values(IR_node, kwargs) def rename_LogSoftmax(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="LogSoftmax") def rename_Relu(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Relu") def rename_Tanh(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Tanh") def rename_Sigmoid(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Sigmoid") def rename_Mul(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Mul") def rename_Maxpool(self, source_node): attr = source_node.attrs kwargs = dict() kwargs['strides'] = [1] + attr['strides'] + [1] if 'dilations' not in attr: kwargs['dilations'] = [1] + [1, 1] + [1] else: kwargs['dilations'] = [1] + attr['dilations'] + [1] kwargs['pads'] = [0] + attr['pads'][0:2] + [0, 0] + attr['pads'][2:] + [0] kwargs['kernel_shape'] = [1] + attr['kernel_shape'] + [1] IR_node = self._convert_identity_operation(source_node, new_op="Pool") kwargs['pooling_type'] = 'MAX' assign_IRnode_values(IR_node, kwargs) def rename_Avgpool(self, source_node): # print('Avgpool:', source_node.attrs) attr = source_node.attrs kwargs = dict() kwargs['strides'] = [1] + attr['strides'] + [1] if 'dilations' not in attr: kwargs['dilations'] = [1] + [1, 1] + [1] else: kwargs['dilations'] = [1] + attr['dilations'] + [1] kwargs['pads'] = [0] + attr['pads'][0:2] + [0, 0] + attr['pads'][2:] + [0] kwargs['kernel_shape'] = [1] + attr['kernel_shape'] + [1] IR_node = self._convert_identity_operation(source_node, new_op="Pool") kwargs['pooling_type'] = 'AVG' assign_IRnode_values(IR_node, kwargs) def rename_Flatten(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Flatten") def rename_FullyConnected(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="FullyConnected") bias_name = '{0}.bias'.format(source_node.weights_name) weights_name = '{0}.weight'.format(source_node.weights_name) W = self.state_dict[weights_name].numpy().transpose() input_channels, output_channels = W.shape # Kit weight tranpose # weight: N x M -> C x H x W x M -> H x W x C x M -> N x M if self.weight_loaded: parent = self.src_graph.get_parent(source_node.name, [0]) while parent.type == 'onnx::Flatten' or parent.type == 'onnx::Dropout': parent = self.src_graph.get_parent(parent.name, [0]) if len(self.shape_dict[parent.name]) == 4: # original_shape = W.shape channel_first_list = self.shape_dict[parent.name][1:] dim = len(channel_first_list) + 1 weight = W.reshape(channel_first_list + [original_shape[1]]) assert dim > 2 weight = weight.transpose(list(range(1, dim-1)) + [0, dim-1]) W = weight.reshape(original_shape) # weights self.set_weight(source_node.name, 'weights', W ) # use_bias if bias_name in self.state_dict: IR_node.attr['use_bias'].b = True bias = self.state_dict[bias_name].numpy() self.set_weight(source_node.name, 'bias', bias ) else: IR_node.attr['use_bias'].b = False # units IR_node.attr['units'].i = output_channels def rename_Dropout(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Dropout') IR_node.attr['keep_prob'].f = source_node.attrs['ratio'] def rename_Concat(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Concat') if source_node.attrs['axis'] == 1: IR_node.attr['axis'].i = len(self.shape_dict[source_node.name]) - 1 else: IR_node.attr['axis'].i = source_node.attrs['axis'] def rename_Add(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Add') def rename_MaxPool2d(self, source_node): self._convert_pooling(source_node) def rename_View(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Reshape') assign_IRnode_values(IR_node, {'shape' : list(source_node.get_attr('new_sizes'))[1:]}) def rename_Addmm(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='FullyConnected') kwargs = dict() # handle weight weight = source_node.get_attr('next_functions')[2][0].next_functions[0][0].variable.data.numpy() weight = np.transpose(weight) kwargs['units'] = weight.shape[1] self.set_weight(source_node.name, 'weights', weight) # handle bias if source_node.get_attr('next_functions')[0][0]: bias = source_node.get_attr('next_functions')[0][0].variable.data.numpy() kwargs['use_bias'] = True self.set_weight(source_node.name, 'bias', weight) assign_IRnode_values(IR_node, kwargs) print(IR_node) def rename_Squeeze(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Squeeze') def rename_MatMul(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='MatMul') #################### # Helper Functions # #################### @staticmethod def _copy_and_reop(source_node, IR_node, new_op = None): if new_op == None: new_op = source_node.type IR_node.name = source_node.name IR_node.op = new_op def _convert_identity_operation(self, source_node, in_edge_count = None, new_op = None): IR_node = self.IR_graph.node.add() PytorchParser._copy_and_reop(source_node, IR_node, new_op) self.convert_inedge(source_node, IR_node, 0, in_edge_count) self._set_output_shape(source_node, IR_node) return IR_node def _convert_pooling(self, source_node): kwargs = dict() kwargs['strides'] = [1] + list(source_node.get_attr('stride')) + [1] kwargs['dilations'] = [1] + list(source_node.get_attr('dilation')) + [1] kwargs['pads'] = ([0] + list(source_node.get_attr('padding')) + [0]) * 2 kwargs['kernel_shape'] = [1] + list(source_node.get_attr('kernel_size')) + [1] IR_node = self._convert_identity_operation(source_node, new_op="Pool") if source_node.name.startswith('Max'): kwargs['pooling_type'] = 'MAX' elif source_node.name.startswith('Avg'): kwargs['pooling_type'] = 'AVG' else: raise ValueError('Unknown pooling type') assign_IRnode_values(IR_node, kwargs) model-pivot/ox/pytorch/rewriter/000077500000000000000000000000001415504170400172775ustar00rootroot00000000000000model-pivot/ox/pytorch/rewriter/lstm_rewriter.py000066400000000000000000000350071415504170400225600ustar00rootroot00000000000000import numpy as np import re from ox.pytorch.pytorch_graph import DYNAMIC_SHAPE_MARK from ox.pytorch.pytorch_graph import PytorchGraphNode class LstmRewriter(object): """ Re-write Pytorch onnx::lstm op into the equivalent detail ops, that is, MatMul, add, Sigmoid, Tanh, etc. The detail document of onnx::lstm is at https://github.com/onnx/onnx/blob/master/docs/Operators.md#LSTM Note: we only handle singe-directional lstm here and use defualt peephole weight (0). The support for those will be TODO. """ def __init__(self, pytorch_graph): self.pytorch_graph = pytorch_graph self.unique_id = 0 def get_next_unique_id(self): self.unique_id += 1 return self.unique_id def run(self): self.rewrite_graph() return self.pytorch_graph def rewrite_graph(self): replace_nodes = [] for layer in self.pytorch_graph.topological_sort: current_node = self.pytorch_graph.get_node(layer) onnx_node_type = current_node.type if onnx_node_type == "onnx::LSTM": replace_nodes.append(current_node) for lstm_id, lstm_node in enumerate(replace_nodes): self.process_lstm(lstm_node, lstm_id) self.pytorch_graph.remove_node(lstm_node) self.remove_useless_node() def remove_useless_node(self): remove_node = [] for layer in self.pytorch_graph.topological_sort: current_node = self.pytorch_graph.get_node(layer) if len(current_node.in_edges) == 0 and len( current_node.out_edges) == 0: remove_node.append(current_node) for node in remove_node: self.pytorch_graph.remove_node(node) def process_lstm(self, lstm_node, lstm_id): """ Inserts detail nodes that are equivalent to lstm node. """ lstm_inputs = lstm_node.in_edges x_name = lstm_inputs[0] x_node = self.pytorch_graph.get_node(x_name) x_shape = self.pytorch_graph.shape_dict[x_name] # x_shape is [seq_len, batch_size, input_size] seq_len = x_shape[0] batch_size = x_shape[1] w_iofg = self.insert_weight_node(lstm_node, lstm_inputs[1], lstm_id, is_param_w=True) r_iofg = self.insert_weight_node(lstm_node, lstm_inputs[2], lstm_id, is_param_w=False) wb_iofg, rb_iofg = self.insert_bias_node(lstm_node, lstm_id) h_last, c_last = self.insert_initial_state_node(lstm_node, batch_size) outputs = [] for i in range(seq_len): # input at i-th step, that is x[i] x_t_node = self.insert_x_t_node(lstm_node, i) # input gates i_t_node = self.insert_iofg_node(lstm_node, x_t_node, w_iofg[0], h_last, r_iofg[0], wb_iofg[0], rb_iofg[0], "Sigmoid") # output gates o_t_node = self.insert_iofg_node(lstm_node, x_t_node, w_iofg[3], h_last, r_iofg[3], wb_iofg[3], rb_iofg[3], "Sigmoid") # forget gates f_t_node = self.insert_iofg_node(lstm_node, x_t_node, w_iofg[1], h_last, r_iofg[1], wb_iofg[1], rb_iofg[1], "Sigmoid") # cell gates g_t_node = self.insert_iofg_node(lstm_node, x_t_node, w_iofg[2], h_last, r_iofg[2], wb_iofg[2], rb_iofg[2], "Tanh") # update states c_t_node = self.insert_update_c_node(lstm_node, c_last, f_t_node, i_t_node, g_t_node) h_t_node = self.insert_update_h_node(lstm_node, o_t_node, c_t_node) h_last = h_t_node c_last = c_t_node outputs.append(h_t_node) hidden_size = lstm_node.attrs["hidden_size"] concat_attrs = {"axis": 0} output_node = self.insert_node(lstm_node.name, "onnx::Concat", [seq_len, batch_size, hidden_size], concat_attrs) for o in outputs: self.pytorch_graph._make_connection(o.name, output_node.name) return output_node, h_last, c_last def insert_x_t_node(self, lstm_node, i): """ Insert x_t_node representing x[i] where x is the lstm_node.in_edges[0] """ lstm_inputs = lstm_node.in_edges x_name = lstm_inputs[0] x_shape = self.pytorch_graph.shape_dict[x_name] num_id = re.search(r"[\d]+", lstm_node.name).group(0) # Slice x from [seq_len, batch_size, input_size] to [1, batch_size, input_size] x_slice_output_shape = x_shape[:] x_slice_output_shape[0] = 1 x_slice_attrs = {"axes": [0], "starts": [i], "ends": [i + 1]} x_slice_node = self.insert_node(lstm_node.name, "onnx::Slice", x_slice_output_shape, x_slice_attrs) self.pytorch_graph._make_connection(x_name, x_slice_node.name) # Squeeze the x_slice from [1, batch_size, input_size] to [batch_size, input_size] x_t_output_shape = x_slice_output_shape[1:] x_t_attrs = {"axes": [0]} x_t_node = self.insert_node(lstm_node.name, "onnx::Squeeze", x_t_output_shape, x_t_attrs) self.pytorch_graph._make_connection(x_slice_node.name, x_t_node.name) return x_t_node def insert_weight_node(self, lstm_node, w_name, lstm_id, is_param_w): """ Slice the input weight with w_name into w[iofg] where weight can be weight or recurrence_weight of lstm. """ hidden_size = lstm_node.attrs["hidden_size"] output_shape = self.pytorch_graph.shape_dict[w_name][:] # make a copy # Slice the weight size from [4 * hidden_size, input_size] to [hidden_size, input_size] if output_shape != DYNAMIC_SHAPE_MARK: output_shape[0] /= 4 w_iofg = [] weight_param_name = "lstm.weight_" if is_param_w: weight_param_name += "ih_" else: weight_param_name += "hh_" weight_param_name += ("l" + str(lstm_id)) for i in range(4): w_attrs = { "axes": [0], "starts": [i * hidden_size], "ends": [(i + 1) * hidden_size], "input_from_param": weight_param_name } w_node = self.insert_node(lstm_node.name, "onnx::Slice", output_shape, w_attrs) self.pytorch_graph._make_connection(w_name, w_node.name) w_iofg.append(w_node) return w_iofg def insert_bias_node(self, lstm_node, lstm_id): lstm_inputs = lstm_node.in_edges hidden_size = lstm_node.attrs["hidden_size"] bias_name = lstm_inputs[3] output_shape = self.pytorch_graph.shape_dict[ bias_name][:] # make a copy # Slice the bias size from [8 * hidden_size] to [hidden_size] if output_shape != DYNAMIC_SHAPE_MARK: output_shape[0] /= 8 sliced_bias = [] for i in range(8): b_attrs = { "axes": [0], "starts": [i * hidden_size], "ends": [(i + 1) * hidden_size], "input_from_param": ["lstm.bias_ih_l" + str(lstm_id), "lstm.bias_hh_l" + str(lstm_id)] } b_node = self.insert_node(lstm_node.name, "onnx::Slice", output_shape, b_attrs) self.pytorch_graph._make_connection(bias_name, b_node.name) sliced_bias.append(b_node) wb_iofg = sliced_bias[0:4] rb_iofg = sliced_bias[4:8] return wb_iofg, rb_iofg def insert_initial_state_node(self, lstm_node, batch_size): lstm_inputs = lstm_node.in_edges hidden_size = lstm_node.attrs["hidden_size"] output_shape = [batch_size, hidden_size] default_attrs = {"value": 0.0, "full_shape": output_shape} if len(lstm_inputs) >= 6: initial_h = self.pytorch_graph.get_node(lstm_inputs[5]) else: initial_h = self.insert_node(lstm_node.name, "onnx::Constant", output_shape, default_attrs) if len(lstm_inputs) >= 7: initial_c = self.pytorch_graph.get_node(lstm_inputs[6]) else: initial_c = self.insert_node(lstm_node.name, "onnx::Constant", output_shape, default_attrs) return initial_h, initial_c def insert_update_h_node(self, lstm_node, o_t_node, c_t_node): """ Updates h in lstm. The formula is "h_t = o_t (.) Tanh(c_t)". """ c_shape = self.pytorch_graph.shape_dict[c_t_node.name][:] tanh_c_node = self.insert_node(lstm_node.name, "onnx::Tanh", c_shape) self.pytorch_graph._make_connection(c_t_node.name, tanh_c_node.name) h_t_node = self.insert_elementwise_op_node(lstm_node, o_t_node, tanh_c_node, "Mul") return h_t_node def insert_update_c_node(self, lstm_node, last_c_node, f_t_node, i_t_node, g_t_node): """ Updates c in lstm. The fomula is "c_t = f_t (.) last_c + i_t (.) g_t" """ mul_c_f_node = self.insert_elementwise_op_node(lstm_node, f_t_node, last_c_node, "Mul") mul_i_g_node = self.insert_elementwise_op_node(lstm_node, i_t_node, g_t_node, "Mul") c_t_node = self.insert_elementwise_op_node(lstm_node, mul_c_f_node, mul_i_g_node, "Add") return c_t_node def insert_iofg_node(self, lstm_node, x_node, w_node, h_node, r_node, wb_node, rb_node, act_func): """ Inserts a node to compute iofg, that is input, or forget, or cell, or output gates of lstm. The fomula is "act_func( (x * w^T + wb) + (h * r^T + rb) )" """ w_mul_add_bias = self.insert_mul_add_bias_node(lstm_node, x_node, w_node, wb_node) r_mul_add_bias = self.insert_mul_add_bias_node(lstm_node, h_node, r_node, rb_node) w_mul_add_bias_shape = self.pytorch_graph.shape_dict[ w_mul_add_bias.name][:] r_mul_add_bias_shape = self.pytorch_graph.shape_dict[ r_mul_add_bias.name][:] add_node = self.insert_elementwise_op_node(lstm_node, w_mul_add_bias, r_mul_add_bias, "Add") add_shape = self.pytorch_graph.shape_dict[add_node.name][:] if act_func.lower() == "sigmoid": output_node = self.insert_node(lstm_node.name, "onnx::Sigmoid", add_shape) self.pytorch_graph._make_connection(add_node.name, output_node.name) elif act_func.lower() == "tanh": output_node = self.insert_node(lstm_node.name, "onnx::Tanh", add_shape) self.pytorch_graph._make_connection(add_node.name, output_node.name) else: raise ValueError("Input '" + str(act_func) + "' is not supported activation function.") return output_node def insert_elementwise_op_node(self, lstm_node, left_node, right_node, op_str): left_shape = self.pytorch_graph.shape_dict[left_node.name][:] right_shape = self.pytorch_graph.shape_dict[right_node.name][:] mul_shape = DYNAMIC_SHAPE_MARK if left_shape != DYNAMIC_SHAPE_MARK: mul_shape = left_shape elif right_shape != DYNAMIC_SHAPE_MARK: mul_shape = right_shape if op_str not in ["Add", "Mul"]: raise ValueError("op_str '" + str(op_str) + "' is not supported elementwise op.") mul_node = self.insert_node(lstm_node.name, "onnx::" + op_str, mul_shape) self.pytorch_graph._make_connection(left_node.name, mul_node.name) self.pytorch_graph._make_connection(right_node.name, mul_node.name) return mul_node def insert_mul_add_bias_node(self, lstm_node, x_node, w_node, b_node): """ Inserts a node representing "x_node * (w_node ^ T) + b_node" """ w_trans_shape = self.pytorch_graph.shape_dict[w_node.name][:] if w_trans_shape != DYNAMIC_SHAPE_MARK: w_trans_shape.reverse() perm_attrs = {"perm_list" : [0, 1]} w_trans_node = self.insert_node(lstm_node.name, "onnx::Transpose", w_trans_shape, perm_attrs) self.pytorch_graph._make_connection(w_node.name, w_trans_node.name) x_shape = self.pytorch_graph.shape_dict[x_node.name][:] if x_shape == DYNAMIC_SHAPE_MARK or w_trans_shape == DYNAMIC_SHAPE_MARK: mul_shape = DYNAMIC_SHAPE_MARK else: mul_shape = [x_shape[0], w_trans_shape[1]] mul_node = self.insert_node(lstm_node.name, "onnx::MatMul", mul_shape) self.pytorch_graph._make_connection(x_node.name, mul_node.name) self.pytorch_graph._make_connection(w_trans_node.name, mul_node.name) add_node = self.insert_elementwise_op_node(lstm_node, mul_node, b_node, "Add") return add_node def insert_node(self, lstm_scope_name, onnx_type, output_shape=DYNAMIC_SHAPE_MARK, attrs={}): """ Utility function to insert a node into lstm scope. """ unique_id = self.get_next_unique_id() assert onnx_type.startswith( "onnx::"), "Input '%s' is not onnx_type" % (onnx_type) name_id = "__" + onnx_type[6:] + "_" + str(unique_id) name = lstm_scope_name + name_id node = PytorchGraphNode(name, onnx_type, name_id, attrs) self.pytorch_graph.shape_dict[node.name] = output_shape self.pytorch_graph.layer_map[node.name] = node self.pytorch_graph.layer_name_map[node.name] = node.name return node model-pivot/ox/pytorch/saver.py000066400000000000000000000006401415504170400171260ustar00rootroot00000000000000import torch def save_model(MainModel, network_filepath, weight_filepath, dump_filepath): model = MainModel.KitModel(weight_filepath) model.eval() torch.save(model, dump_filepath) print('PyTorch model file is saved as [{}], generated by [{}.py] and [{}]. Notice that you may need [{}.py] to load the model back.'.format( dump_filepath, network_filepath, weight_filepath, network_filepath)) model-pivot/ox/pytorch/torch_to_np.py000066400000000000000000000035331415504170400203300ustar00rootroot00000000000000import torchfile import numpy as np model = torchfile.load('kit.model') weights = dict() params = ['weight', 'bias', 'running_mean', 'running_var'] recursive = ['conv_nets'] def save_weight(name, node, level): weights[name] = dict() current_layer = weights[name] for p in params: if hasattr(node, p): func = getattr(node, p) arr = np.array(func) if arr.ndim >= 1: current_layer[p] = arr print (" " * level + "{}.{} shape {} {}".format(name, p, current_layer[p].shape, current_layer[p].dtype)) for p in recursive: if hasattr(node, p): func = getattr(node, p) if func != None: for idx, subnode in enumerate(func): new_name = name + ":{}:{}".format(p, idx) save_weight(new_name, subnode, level + 1) for idx, data in enumerate(model.modules): if data != None: print ("Find layer #{} : {}".format(idx, data._typename)) if hasattr(data, 'search_flag'): print (" name = {}".format(data.search_flag)) if data.modules != None: print (" submodule = {}#".format(len(data.modules))) for idx_j, sub in enumerate(data.modules): print (" layer [{}]".format(sub._typename)) name = data.search_flag + ":" + str(idx_j) save_weight(name, sub, 2) print ("\n") else: pass #print (dir(data)) print ("\n") with open("stylebank.npy", 'wb') as of: np.save(of, weights) print ("-------------------------------------------------") load_weight = np.load('stylebank.npy').item() for i in load_weight: #print (i) for j in load_weight[i]: pass #print (" {} with shape {}".format(j, load_weight[i][j].shape))model-pivot/ox/rewriter/000077500000000000000000000000001415504170400156075ustar00rootroot00000000000000model-pivot/ox/rewriter/__init__.py000066400000000000000000000001541415504170400177200ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_functionmodel-pivot/ox/rewriter/folder.py000066400000000000000000000454341415504170400174460ustar00rootroot00000000000000from ox.common.IR.IR_graph import * import sys import re import numpy as np import collections class Folder(object): """A floder to fold a graph' nodes which has same scope into one node.""" def __init__(self, graph, weights_dict, init_level=0, fold_level_num=0, scope_names=None): """ Initializes a Folder. Args: graph: the graph to be folded. init_level: the start scope level to be folded. fold_level_num: the number of level from init_level to be folded. For example, there are three nodes, whose scope are A/B/X, A/B/Y, A/C/Z. If init_level=0 and fold_level_num=1, then the fold result is A/B and A/C. """ self._graph = graph self._weights_dict = weights_dict self._init_level = init_level self._fold_level_num = fold_level_num self._scope_names = scope_names """fold the graph by compressing the nodes which have same scope into one scope node.""" def fold(self): self.scope_level_name_map = self._get_scope_level_name_dict() # level: scope_name set if self._scope_names: scope_names = self._scope_names else: if not self.scope_level_name_map: return scope_names = self.scope_level_name_map[0] for scope_name in scope_names: level = self._init_level sub_fold_level = self._fold_level_num while sub_fold_level >= 0: self._fold(self._graph.topological_sort, scope_name, level, level + sub_fold_level) sub_fold_level -= 1 # check the same pattern scope node whether have same inputs, outputs and weights. # For those don't have, rename their scope names. self.check_scope() self.check_weights() # clear out scope node, typically input constant node. self._graph.clear_out_scope_node() ''' fold the given node list by squash the nodes whose scope is given scope into one scope node. Args: scope_list: scope node topology list scope_name: the scope name need to be folded level: the scope's level fold_level: the scope's sub scope level need to be folded. ''' def _fold(self, scope_list, scope_name, level, sub_level): top_node = None # get sub_scopes if not self.scope_level_name_map.get(sub_level, 0): raise ValueError("The fold level exceed maxium scope level.") sub_scopes = self.scope_level_name_map[sub_level] for sub_scope in sub_scopes: sub_scope_node_name_list = self._get_scope_name_dict(self._graph.topological_sort, top_level=level, top_scope=scope_name, sub_level=sub_level, sub_scope=sub_scope) for scope_list in sub_scope_node_name_list.values(): top_node = self._graph.get_node(scope_list[-1]) self._create_scope_node( sub_scope, scope_list, top_node) '''get scope_level_name_dict. {level: scope name list(without suffix number)}''' def _get_scope_level_name_dict(self): scope_level_name = collections.OrderedDict() for node in self._graph.get_nodes(): if not node.get_attr('scope'): continue for i, scope_name in enumerate(node.get_attr('scope').split('/')): # decline the suffix number import re if re.search(r'\d', scope_name.split('_')[-1]): scope_name = '_'.join(scope_name.split('_')[:-1]) if scope_name == 'top': continue if scope_level_name.get(i, None): if scope_name not in scope_level_name[i]: scope_level_name[i].append(scope_name) else: scope_level_name[i] = list([scope_name]) # use list to keep sort. return scope_level_name ''' get the dict from required node_list by appointed scope_name and level Args: node_list: current self.topology_sort scope_name_dict: scope_no: node_name, a dict like {scope_name_no: a set of scope node' name} ''' def _get_scope_name_dict(self, node_list, top_level=0, top_scope=None, sub_level=2, sub_scope=None): scope_node_names = collections.OrderedDict() def _insert_scope_node_names_dict(scope_no, node_name): if scope_node_names.get(scope_no, None): scope_node_names[scope_no].append(node_name) else: scope_node_names[scope_no] = list([node_name]) def _get_scope_name_dict_by_cond(cond_top, cond_sub): for node_name in node_list: node = self._graph.get_node(node_name) if not node.get_attr('scope'): continue node_scope = node.get_attr('scope') if cond_top(top_scope, node_scope) and cond_sub(sub_scope, node_scope): if 'True' in cond_top.__name__ and 'True' not in cond_sub.__name__: scope_no = node_scope.split('/')[sub_level] elif 'True' not in cond_top.__name__ and 'True' in cond_sub.__name__: scope_no = node_scope.split('/')[top_level] else: # both not equal True scope_no = node_scope.split( '/')[top_level] + '_' + node_scope.split('/')[sub_level] _insert_scope_node_names_dict(scope_no, node.name) def cond_x_in_y(x, y): return x in y def cond_True(x, y): return True # Obtain nodes where the scope name that satisfies top_level is top_scope and sub_level is sub_scope if top_scope and sub_scope: _get_scope_name_dict_by_cond(cond_x_in_y, cond_x_in_y) # Obtain nodes where the scope name that satisfies in sub_level is sub_scope elif not top_scope and sub_scope: _get_scope_name_dict_by_cond(cond_True, cond_x_in_y) # Obtain nodes where the scope name that satisfies in top_level is top_scope elif top_scope and not sub_scope: _get_scope_name_dict_by_cond(cond_x_in_y, cond_True) # Obtain all nodes grouped by sub_level sub_scope elif top_scope is None and sub_scope is None: top_scopes = self.scope_level_name_map[top_level] for top_scope in top_scopes: # this top_scope will replace the input top_scope _get_scope_name_dict_by_cond(cond_x_in_y, cond_True) return scope_node_names '''get the node names' topology sort of scope nodes''' def _get_scope_nodes_topology_list(self, scope_node_name_set): temp_dict = {} for index, name in enumerate(scope_node_name_set): # cover the node self._graph.get_node(name).covered = True # store idx, node into a dict and sort it later to keep its topology sort. index = self._graph.topological_sort.index(name) temp_dict[name] = index temp_dict = sorted( temp_dict.items(), key=lambda item: item[1]) return [x[0] for x in temp_dict] ''' rebuild the conncetion of the edge around this scope node.''' def _rebuild_scope_edges_and_get_ret_vars(self, scope_node): def _get_index(node ,name): for idx, in_edge in enumerate(node.in_edges): if in_edge.split(':')[0] == name: return idx return_nodes = list() return_variable_names = list() for n_name in scope_node.topology_list: n = self._graph.get_node(n_name) for in_edge in n.in_edges: if not in_edge.split(':')[0] in scope_node.topology_list: if not in_edge in scope_node.in_edges: scope_node.in_edges.append(in_edge) # in_node's out edges replace n_name with scope node name. in_node = self._graph.get_node(in_edge) if n_name in in_node.out_edges: idx = in_node.out_edges.index(n_name) in_node.out_edges.remove(n_name) if scope_node.name not in in_node.out_edges: in_node.out_edges.insert(idx, scope_node.name) for out_edge in n.out_edges: if not out_edge in scope_node.topology_list: out_node = self._graph.get_node(out_edge) parent_node_variable_name = self._graph.get_parent_variable_name(out_edge.split( ':')[0], [_get_index(self._graph.get_node(out_edge), n_name)]) if parent_node_variable_name not in return_variable_names: return_nodes.append(self._graph.get_node(n_name)) return_variable_names.append(parent_node_variable_name) scope_node.out_edges.append(out_edge) # no out nodes means the last node in scope nodes should be returned if not return_nodes: return_nodes.append(self._graph.get_node( scope_node.topology_list[-1])) return_variable_names.append(self._graph.get_node( scope_node.topology_list[-1]).real_variable_name) ret_idx = 0 for ret_node, ret_variable_name in zip(return_nodes, return_variable_names): subscript = '' if len(ret_variable_name.split( '[')) == 1 else ':'+ret_variable_name.split('[')[1].split(']')[0] for out_name in ret_node.out_edges: if not out_name in scope_node.topology_list: out_node = self._graph.get_node(out_name) ret_name = ret_node.name + subscript if ret_name in out_node.in_edges: insert_pos = out_node.in_edges.index(ret_name) insert_name = scope_node.name + \ ':{}'.format(str(ret_idx)) if len( return_variable_names) > 1 else scope_node.name out_node.in_edges.remove(ret_name) out_node.in_edges.insert(insert_pos, insert_name) # if out_node is scope node, replace the scope node's inner topology list node. if out_node.type == 'Scope': for n in out_node.topology_list: n = self._graph.get_node(n) if ret_name in n.in_edges: idx = n.in_edges.index(ret_name) n.in_edges.remove(ret_name) n.in_edges.insert(idx, insert_name) ret_idx += 1 return return_variable_names ''' if the input params include tensor which is multi-output type(e.g. unstack), then we need to check whether this scope function body uses only one of the outputs or multi outputs. if it is the former, feed the selected one(e.g. unstack[0]), otherwise feed all. ''' def _check_and_get_input_params(self, scope_node): def wipe_in_egde_idx(in_name, node): for idx, in_edge in enumerate(node.in_edges): if in_name in in_edge: node.in_edges[idx] = in_edge.split(':')[0] node.in_edges = sorted(set(node.in_edges), key=node.in_edges.index) input_params = list() in_name_dict = collections.OrderedDict() for in_name in scope_node.in_edges: if self._graph.get_node(in_name).variable_name not in input_params: input_params.append(self._graph.get_node( in_name).variable_name) if ':' not in in_name: continue if in_name_dict.get(in_name.split(':')[0], None): in_name_dict[in_name.split(':')[0]].add( in_name.split(':')[1]) else: in_name_dict[in_name.split(':')[0]] = set( [in_name.split(':')[1]]) for in_name, subscript_set in in_name_dict.items(): # the input parameter shoule be sliced when call func. if len(subscript_set) == 1: # modify the in_edges in scope inner nodes. decline the :idx. for n in scope_node.topology_list: n = self._graph.get_node(n) wipe_in_egde_idx(in_name, n) else: # >2 wipe_in_egde_idx(in_name, scope_node) return input_params ''' create a scope node. Args: scope_name: the name of this scope, will be assigned to scope pattern. scope_node_names: node names involved by this scope. top_node: the top node in these scope nodes. ''' def _create_scope_node(self, scope_name, scope_node_names, top_node): # 1. initilize scope node scope_node = self._initialize_scope_node(top_node) # 2. get scope nodes' topology list. scope_node.topology_list = self._get_scope_nodes_topology_list( scope_node_names) scope_node.pattern = scope_name # 3. rebuild the edges connection after folding these scope nodes into one node and # get this scope node's return variables. scope_node.return_variables = self._rebuild_scope_edges_and_get_ret_vars( scope_node) # 4. rebuild graph. self._graph.layer_map[scope_node.name] = scope_node self._graph.layer_name_map[scope_node.name] = scope_node.name self._graph.rebuild() '''initialize a scope node by copying source_node's attrs.''' def _initialize_scope_node(self, source_node): scope_node = self._graph.model.node.add() scope_node.name = source_node.name + '_scope' scope_node.op = 'Scope' scope_node = IRGraphNode(scope_node) kwargs = {} kwargs['scope'] = source_node.get_attr('scope') if 'data_format' in source_node.layer.attr: kwargs['data_format'] = source_node.get_attr('data_format') if '_output_shapes' in source_node.layer.attr: scope_node.layer.attr["_output_shapes"].MergeFromString( source_node.layer.attr['_output_shapes'].SerializeToString()) if 'value' in source_node.layer.attr: kwargs['value'] = source_node.get_attr('value') # RNN-related attrs. if 'input_size' in source_node.layer.attr: kwargs['input_size'] = source_node.get_attr('input_size') if 'num_units' in source_node.layer.attr: kwargs['num_units'] = source_node.get_attr('num_units') if 'fill_size' in source_node.layer.attr: kwargs['fill_size'] = source_node.get_attr('fill_size') if 'fill_value' in source_node.layer.attr: kwargs['fill_value'] = source_node.get_attr('fill_value') assign_IRnode_values(scope_node.layer, kwargs) return scope_node ''' check whether same pattern scope node has same inputs and outputs. For thoese do not have, rename it's pattern by adding serial number suffix. ''' def check_scope(self): name_no_dict = collections.OrderedDict() name_inp_out_dict = collections.OrderedDict() for name, ir_node in self._graph.layer_map.items(): if ir_node.type == 'Scope': #get input params ir_node.input_params = self._check_and_get_input_params(ir_node) origi_pattern = re.sub(r'(_\d+)*$', '', ir_node.pattern) if name_inp_out_dict.get(origi_pattern, None): inps_and_outs = name_inp_out_dict[origi_pattern] exist_Equal = False for inp_out in inps_and_outs: if len(ir_node.input_params) == len(inp_out[0]) and len(ir_node.return_variables)== len(inp_out[1]): exist_Equal = True if inp_out[2]: ir_node.pattern = ir_node.pattern + '_' + str(inp_out[2]) if not exist_Equal: name_inp_out_dict[origi_pattern].append([ir_node.input_params, ir_node.return_variables, name_no_dict.get(origi_pattern, 1)]) ir_node.pattern = ir_node.pattern + '_' + str(name_no_dict.get(origi_pattern, 1)) name_no_dict[origi_pattern] = name_no_dict.get(origi_pattern, 1) + 1 else: name_inp_out_dict[origi_pattern] = [[ir_node.input_params, ir_node.return_variables, 0]] name_no_dict[ir_node.pattern] = name_no_dict.get(origi_pattern, 0) + 1 ''' check whether same pattern scope node has same weights. For thoese do not have, rename it's pattern by adding serial number suffix. ''' def check_weights(self): weight_related_ops = ['FullyConnected'] pattern_weight_op = collections.OrderedDict() name_no_dict = collections.OrderedDict() pattern_weights = collections.OrderedDict() for ir_node_name in self._graph.topological_sort: ir_node = self._graph.get_node(ir_node_name) if ir_node.type == 'Scope': for inner_name in ir_node.topology_list: if self._graph.get_node(inner_name).type in weight_related_ops: if pattern_weight_op.get(ir_node.pattern, None): if self._weights_dict[inner_name]['weights'].any() and pattern_weights.get(ir_node.pattern, None): inner_weights = self._weights_dict[inner_name]['weights'] isExist = False for idx, it in enumerate(pattern_weights[ir_node.pattern]): if np.array_equal(inner_weights, it['weights']): ir_node.pattern = ir_node.pattern + '_'+ str(idx) isExist = True break if isExist: continue pattern_weight_op[ir_node.pattern].add(inner_name) pattern_weights[ir_node.pattern].append(self._weights_dict[inner_name]) ir_node.pattern = ir_node.pattern + '_'+ str(len(pattern_weights[ir_node.pattern])-1) else: pattern_weight_op[ir_node.pattern] = set([inner_name]) if self._weights_dict.get(inner_name, None): pattern_weights[ir_node.pattern] = [self._weights_dict[inner_name]] ir_node.pattern = ir_node.pattern + '_'+ str(name_no_dict.get(ir_node.pattern, 0)) model-pivot/ox/rewriter/graph_matcher.py000066400000000000000000000213401415504170400207650ustar00rootroot00000000000000# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Utilities that match patterns in a Graph.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import itertools from ox.common.DataStructure.graph import Graph class Pattern(object): """The parent class of all patterns (e.g. OpTypePattern and OneofPattern).""" @abc.abstractmethod def match(self, op, tensor): """Returns the result of matching op/tensor against this pattern.""" raise NotImplementedError('Method "match" not implemented.') class OpTypePattern(Pattern): """A tree pattern that matches TF expressions with certain op types.""" def __init__(self, op_type, name=None, inputs=None, ordered_inputs=True): """Initializes an OpTypePattern. Args: op_type: string that specifies the allowed types of the root. It can be (1) an op type, e.g. 'Conv2D', (2) '*', i.e. wildcard, or (3) multiple op types separated by '|', e.g., 'Relu|Relu6'. We could use regex strings, which might be worthwhile when we have many similar TF op types. name: Optional string. The name of the pattern that can be looked up in MatchResult. inputs: Optional list of `Pattern`s or strings that specify the patterns for the inputs of a matching op. If None, this pattern accepts any inputs of a matching op. ordered_inputs: Defaults to True. If False, will match any op that matches a permutation of the inputs. Raises: ValueError: if too many inputs are provided when order_inputs is False. """ self._op_type = op_type self._name = name if inputs is None: inputs = [] if len(inputs) > 8: raise ValueError( 'Only < 8 inputs are allowed when ordered_inputs is False.') self._inputs = [ input_pattern if isinstance(input_pattern, Pattern) else OpTypePattern(input_pattern) for input_pattern in inputs ] self._ordered_inputs = ordered_inputs @property def name(self): return self._name @property def scope_ids(self): return self._scope_ids @property def inputs(self): return self._inputs @property def type(self): return self._op_type def set_op_scope(self, op, scope): op.scope = scope def match(self, op): if self._op_type != '*': if op.type not in self._op_type.split('|'): return None match_result = MatchResult() match_result.add(self, op) if not self._inputs: # If pattern.inputs is empty, skips the rest and accepts all the inputs. return match_result if len(op.in_edges) != len(self._inputs): return None input_patterns_list = [self._inputs] # If order doesn't matter for the inputs, then make sure we match at least # one permutation of the inputs. if not self._ordered_inputs: input_patterns_list = list(itertools.permutations(self._inputs)) for input_patterns in input_patterns_list: match_failed = False for input_op, input_pattern in zip(op.in_nodes, input_patterns): input_match_result = input_pattern.match(input_op) if input_match_result is None: match_failed = True break match_result.merge_from(input_match_result) if not match_failed: return match_result return None class OneofPattern(Pattern): """Matches one of the given sub-patterns.""" def __init__(self, sub_patterns): self._sub_patterns = sub_patterns def match(self, op): for sub_pattern in self._sub_patterns: match_result = sub_pattern.match(op) if match_result is not None: return match_result return None class MatchResult(object): r"""Encapsulates the result of a match done by GraphMatcher. MatchResult contains a map from Pattern to the matching op and tensor. When the matching op has multiple output tensors, the matching tensor is the output tensor used by the matching op of the parent pattern. E.g., when we match graph - + / \y0 y1/ \ x split z | y (nodes are ops; edges are going up) against add_pattern defined as y1_pattern = OpTypePattern('*') z_pattern = OpTypePattern('*') add_pattern = OpTypePattern('+', inputs=[y1_pattern, z_pattern]) the matching op of `y1_pattern` is `split`, and the matching tensor of `y1_pattern` is `y1` not `y0`. """ def __init__(self): self._pattern_to_op = {} self._name_to_pattern = {} def add(self, pattern, op): self._pattern_to_op[pattern] = op if pattern.name is not None: if pattern.name in self._name_to_pattern: raise ValueError( 'Name %s is already bound to another pattern' % pattern.name) self._name_to_pattern[pattern.name] = pattern def _to_pattern(self, pattern_or_name): if isinstance(pattern_or_name, Pattern): return pattern_or_name if isinstance(pattern_or_name, str): if pattern_or_name not in self._name_to_pattern: return None return self._name_to_pattern[pattern_or_name] raise ValueError('pattern_or_name has type %s. Expect Pattern or str.' % type(pattern_or_name)) def _get_op_tensor(self, pattern_or_name): pattern = self._to_pattern(pattern_or_name) if pattern is None: return None if pattern not in self._pattern_to_op: return None return self._pattern_to_op[pattern] def get_op(self, pattern_or_name): op_tensor = self._get_op_tensor(pattern_or_name) return op_tensor if op_tensor else None # def get_tensor(self, pattern_or_name): # op_tensor = self._get_op_tensor(pattern_or_name) # return op_tensor[1] if op_tensor else None def merge_from(self, other_match_result): # pylint: disable=protected-access self._pattern_to_op.update(other_match_result._pattern_to_op) self._name_to_pattern.update(other_match_result._name_to_pattern) # pylint: enable=protected-access class GraphMatcher(object): """Checks if a particular subgraph matches a given pattern.""" def __init__(self, pattern): """Initializes a GraphMatcher. Args: pattern: The `Pattern` against which `GraphMatcher` matches subgraphs. """ self._pattern = pattern def _match_pattern(self, pattern, op): """Returns whether an TF expression rooted at `op` matches `pattern`. If there is a match, adds to `self._match_result` the matching op and tensor with key `pattern`. Args: pattern: An `Pattern`. op: A `tf.Operation` to match against the pattern. tensor: the output `tf.Tensor` of `op` that is used by the matching op of `pattern`'s parent. Can be None if `pattern` is already the root of the pattern tree. Returns: True if an TF expression rooted at `op` matches `pattern`. """ match_result = pattern.match(op) if match_result is None: return False self._match_result.merge_from(match_result) return True def match_op(self, op): """Matches `op` against `self._pattern`. Args: op: `tf.Operation` to match against the pattern. Returns: Returns a `MatchResult` if `op` matches the pattern; otherwise, returns None. """ self._match_result = MatchResult() if not self._match_pattern(self._pattern, op): return None return self._match_result def match_ops(self, ops): """Matches each operation in `ops` against `self._pattern`. Args: ops: collection of `tf.Operation` to match against the pattern. Yields: `MatchResult` for each `tf.Operation` that matches the pattern. """ for op in ops: match_result = self.match_op(op) if match_result: yield match_result def match_graph(self, graph): """Matches each operation in `graph` against `self._pattern`. Args: graph: `tf.Graph` containing operations to match. Yields: `MatchResult` for each `tf.Operation` in `graph` that matches the pattern. """ # Python 3.3.2+ implements `yield from`, but for now: for match_result in self.match_ops(graph.get_nodes()): yield match_result model-pivot/ox/rewriter/rewriter.py000066400000000000000000000072211415504170400200260ustar00rootroot00000000000000from __future__ import division from __future__ import print_function import numpy as np import tensorflow from tensorflow.python.framework import tensor_util from tensorflow.core.framework import attr_value_pb2 import sys from ox.tensorflow.tensorflow_graph import * import ox.common.IR.graph_pb2 as graph_pb2 from ox.rewriter.graph_matcher import * from ox.common.DataStructure import * from tensorflow.core.framework.node_def_pb2 import NodeDef from ox.rewriter.rnn_utils import * class UnitRewriterBase(object): def __init__(self, graph, weights_dict): self._graph = graph self._weights_dict = weights_dict def _rewrite_graph_by_pattern(self, pattern_name, graph_type): pattern = rnn_patterns[graph_type][pattern_name] matcher = GraphMatcher(pattern) match_results = list(matcher.match_ops(self._graph.get_nodes())) scope_names_dict = dict() # name: No. for i in range(len(match_results)): result = match_results[i] top_pattern_name = pattern_name + '_' + str(i) top_pattern = result._name_to_pattern[pattern_name] self.create_scope(result, top_pattern, scope_names_dict) top_op = result._pattern_to_op[top_pattern] top_op.scope = top_op.scope + '/top' # self.store_const_to_top(result) # self.set_top_node_prop(result, pattern_name) self.process_match_result(result, pattern_name) def rewrite_graph(self, pattern_names, graph_type): from six import string_types as _string_types if isinstance(pattern_names, _string_types): pattern_names = [pattern_names] elif not isinstance(pattern_names, list): raise ValueError for pattern_name in pattern_names: self._rewrite_graph_by_pattern(pattern_name, graph_type) def run(self, pattern_names, graph_type): self.rewrite_graph(pattern_names, graph_type) def store_const_to_top(self, match_result): top_node = list(match_result._pattern_to_op.values())[0] kwargs = dict() for pattern, op in match_result._pattern_to_op.items(): if pattern.name and pattern.type == 'Const': if tensor_util.MakeNdarray(op.get_attr('value')).shape == (1, ): kwargs[pattern.name] = np.asscalar(tensor_util.MakeNdarray(op.get_attr('value'))) else: kwargs[pattern.name] = np.squeeze(tensor_util.MakeNdarray(op.get_attr('value'))) if hasattr(top_node, 'kwargs'): top_node.kwargs.update(kwargs) else: top_node.kwargs = kwargs def create_scope(self, result, pattern, scope_names_dict, parent_scope_name=''): op = result._pattern_to_op[pattern] if pattern.name: # Do not include input op. if 'input' in pattern.name.split('/')[-1]: return else: no = scope_names_dict.get(pattern.name, 0) scope_names_dict[pattern.name] = no + 1 if parent_scope_name: current_scope_name = '/'.join([parent_scope_name, pattern.name]) + '_' + str(no) else: current_scope_name = pattern.name + '_' + str(no) else: current_scope_name = parent_scope_name op.scope = current_scope_name for sub_pattern in pattern.inputs: self.create_scope(result, sub_pattern, scope_names_dict, current_scope_name) def set_top_node_prop(self, match_result): raise NotImplementedError def process_match_result(self, match_result, pattern_name): raise NotImplementedError model-pivot/ox/rewriter/rnn_utils.py000066400000000000000000000101761415504170400202030ustar00rootroot00000000000000from ox.rewriter.graph_matcher import * from ox.tensorflow.tensorflow_graph import * import numpy as np '''batch size pattern in tensorflow. Note: do not include _number in name''' static_rnn_batch_size_pattern = OpTypePattern('ExpandDims', name='static_rnn_batch_size', inputs=[ OpTypePattern('StridedSlice', inputs=[ OpTypePattern('Shape', inputs=[ OpTypePattern('*', name='input') ]), OpTypePattern('Const'), OpTypePattern('Const'), OpTypePattern('Const') ]), OpTypePattern('Const') ]) '''rnn h zero pattern in tensorflow.''' static_rnn_h_zero_pattern = OpTypePattern('Fill', name='h_zero', inputs=[ OpTypePattern('ConcatV2|Concat', inputs=[ OpTypePattern('*', name='input'), OpTypePattern('Const', name='fill_size'), OpTypePattern('Const') ]), OpTypePattern('Const', name='fill_value') ]) ''''split pattern in gru cell in tensorflow''' gru_xc_pattern = OpTypePattern('Split', name='xc', inputs=[ OpTypePattern("Const"), # axis for split OpTypePattern("Sigmoid", inputs=[ OpTypePattern("BiasAdd", name="bias_add", inputs=[ OpTypePattern("MatMul", inputs=[ OpTypePattern("ConcatV2|Concat", name="xh"), OpTypePattern("Identity", name='cell_kernel') ]), OpTypePattern("Identity", name='cell_bias') ])]), ]) '''split pattern in lstm cell in tensorflow''' lstm_xc_pattern = OpTypePattern('Split', inputs=[ OpTypePattern("Const"), # axis for split OpTypePattern("BiasAdd", name="bias_add", inputs=[ OpTypePattern("MatMul", inputs=[ OpTypePattern("ConcatV2|Concat", name="xh"), OpTypePattern("*", name="cell_kernel"), ]), OpTypePattern("*", name="cell_bias"), ]), ]) ''''gru cell pattern in tensorflow''' grucell_pattern = \ OpTypePattern('Add', name='gru_cell', inputs=[ OpTypePattern('Mul', inputs=[ gru_xc_pattern, OpTypePattern('*', name='input') ]), OpTypePattern('Mul', inputs=[ OpTypePattern('Sub', inputs=[ OpTypePattern('Const'), gru_xc_pattern ]), OpTypePattern('Tanh', inputs=[ OpTypePattern('BiasAdd', inputs=[ OpTypePattern('MatMul', name='FullyConnect', inputs=[ OpTypePattern('Concat|ConcatV2', inputs=[ OpTypePattern('*', name='input'), OpTypePattern('Mul', inputs=[ gru_xc_pattern, OpTypePattern('*', name='input') ]), OpTypePattern('Const'), ]), OpTypePattern('Identity', name='candidate_kernel') ]), OpTypePattern('Identity', name='candidate_bias') ]) ]) ]) ]) ''''lstm cell pattern in tensorflow''' lstmcell_pattern = \ OpTypePattern('Mul', name='lstm_cell', inputs=[ OpTypePattern("Sigmoid", name="ot", inputs=[lstm_xc_pattern]), OpTypePattern('Tanh', inputs=[ OpTypePattern("Add", name="ct", inputs=[ OpTypePattern("Mul", inputs=[ OpTypePattern("Sigmoid", name="ft", inputs=[ OpTypePattern("Add", inputs=[ lstm_xc_pattern, OpTypePattern("*", name="ft_bias"), ]), ]), OpTypePattern("*", name='input'), ]), OpTypePattern("Mul", inputs=[ OpTypePattern("Sigmoid", name="it", inputs=[lstm_xc_pattern]), OpTypePattern("Tanh", name="gt", inputs=[lstm_xc_pattern]), ]), ]), ]), ]) rnn_patterns = { 'tensorflow': { 'gru_cell': grucell_pattern, 'lstm_cell': lstmcell_pattern, 'h_zero': static_rnn_h_zero_pattern, 'static_rnn_batch_size': static_rnn_batch_size_pattern } # TODO: pytorch, mxnet, keras, cntk }model-pivot/ox/rewriter/utils.py000066400000000000000000000005121415504170400173170ustar00rootroot00000000000000from ox.rewriter.rewriter import UnitRewriterBase from ox.tensorflow.rewriter.gru_rewriter import GRURewriter from ox.tensorflow.rewriter.lstm_rewriter import LSTMRewriter def process_graph(graph, weights): rewriter_list = [GRURewriter, LSTMRewriter] for rewriter in rewriter_list: rewriter(graph, weights).run()model-pivot/ox/tensorflow/000077500000000000000000000000001415504170400161465ustar00rootroot00000000000000model-pivot/ox/tensorflow/__init__.py000066400000000000000000000001551415504170400202600ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_function model-pivot/ox/tensorflow/rewriter/000077500000000000000000000000001415504170400200115ustar00rootroot00000000000000model-pivot/ox/tensorflow/rewriter/__init__.py000066400000000000000000000001541415504170400221220ustar00rootroot00000000000000from __future__ import absolute_import from __future__ import division from __future__ import print_functionmodel-pivot/ox/tensorflow/rewriter/gru_rewriter.py000066400000000000000000000050361415504170400231070ustar00rootroot00000000000000from ox.rewriter.rewriter import UnitRewriterBase import numpy as np import re class GRURewriter(UnitRewriterBase): def __init__(self, graph, weights_dict): return super(GRURewriter, self).__init__(graph, weights_dict) def process_gru_cell(self, match_result): if 'gru_cell' not in match_result._pattern_to_op.keys(): return kwargs = dict() top_node = match_result._pattern_to_op[match_result._name_to_pattern['gru_cell']] w_e = match_result.get_op("cell_kernel") w = self._weights_dict[w_e.name.replace('/read', '')] num_units = w.shape[1]//2 input_size = w.shape[0] - num_units kwargs['num_units'] = num_units kwargs['input_size'] = input_size if hasattr(top_node, 'kwargs'): top_node.kwargs.update(kwargs) else: top_node.kwargs = kwargs def process_rnn_h_zero(self, match_result): if 'h_zero' not in match_result._name_to_pattern.keys(): return kwargs = dict() top_node = match_result._pattern_to_op[match_result._name_to_pattern['h_zero']] fill_size = match_result.get_op('fill_size') fill_value = match_result.get_op('fill_value') kwargs['fill_size'] = fill_size.get_attr('value').int_val[0] kwargs['fill_value'] = fill_value.get_attr('value').float_val[0] if hasattr(top_node, 'kwargs'): top_node.kwargs.update(kwargs) else: top_node.kwargs = kwargs def process_match_result(self, match_result, pattern_name): if pattern_name == 'gru_cell': self.process_gru_cell(match_result) elif pattern_name == 'h_zero': if self.check_match_scope(match_result, 'GRUCellZeroState'): self.process_rnn_h_zero(match_result) '''For some short pattern, to avoid match other pattern, check it's scope''' def check_match_scope(self, match_result, scope_name): ops = match_result._pattern_to_op.values() for op in ops: op_name_splits = op.name.split('/') if len(op_name_splits) < 2: return False if re.sub(r'(_\d+)*$', '', op_name_splits[-2]) != scope_name: if len(op_name_splits) > 2: if re.sub(r'(_\d+)*$', '', op_name_splits[-3]) != scope_name: return False else: return False return True def run(self): return super(GRURewriter, self).run(['gru_cell', 'h_zero'], 'tensorflow')model-pivot/ox/tensorflow/rewriter/lstm_rewriter.py000066400000000000000000000051261415504170400232710ustar00rootroot00000000000000from ox.rewriter.rewriter import UnitRewriterBase import numpy as np import re class LSTMRewriter(UnitRewriterBase): def __init__(self, graph, weights_dict): return super(LSTMRewriter, self).__init__(graph, weights_dict) def process_lstm_cell(self, match_result): if 'lstm_cell' not in match_result._pattern_to_op.keys(): return kwargs = dict() top_node = match_result._pattern_to_op[match_result._name_to_pattern['lstm_cell']] w_e = match_result.get_op("cell_kernel") w = self._weights_dict[w_e.name.replace('/read', '')] num_units = w.shape[1]//4 [wx, wh] = np.split(w, [-1 * num_units]) input_size = wx.shape[0] kwargs['num_units'] = num_units kwargs['input_size'] = input_size if hasattr(top_node, 'kwargs'): top_node.kwargs.update(kwargs) else: top_node.kwargs = kwargs def process_rnn_h_zero(self, match_result): if 'h_zero' not in match_result._name_to_pattern.keys(): return kwargs = dict() top_node = match_result._pattern_to_op[match_result._name_to_pattern['h_zero']] fill_size = match_result.get_op('fill_size') fill_value = match_result.get_op('fill_value') kwargs['fill_size'] = fill_size.get_attr('value').int_val[0] kwargs['fill_value'] = fill_value.get_attr('value').float_val[0] if hasattr(top_node, 'kwargs'): top_node.kwargs.update(kwargs) else: top_node.kwargs = kwargs def process_match_result(self, match_result, pattern_name): if pattern_name == 'lstm_cell': self.process_lstm_cell(match_result) elif pattern_name == 'h_zero': if self.check_match_scope(match_result, 'LSTMCellZeroState'): self.process_rnn_h_zero(match_result) '''For some short pattern, to avoid match other pattern, check it's scope''' def check_match_scope(self, match_result, scope_name): ops = match_result._pattern_to_op.values() for op in ops: op_name_splits = op.name.split('/') if len(op_name_splits) < 2: return False if re.sub(r'(_\d+)*$', '', op_name_splits[-2]) != scope_name: if len(op_name_splits) > 2: if re.sub(r'(_\d+)*$', '', op_name_splits[-3]) != scope_name: return False else: return False return True def run(self): return super(LSTMRewriter, self).run(['lstm_cell', 'h_zero'], 'tensorflow')model-pivot/ox/tensorflow/saver.py000066400000000000000000000026371415504170400176500ustar00rootroot00000000000000import tensorflow as tf def save_model(MainModel, network_filepath, weight_filepath, dump_filepath, dump_tag = 'SERVING'): if dump_tag == 'SERVING': tag_list = [tf.saved_model.tag_constants.SERVING] else: tag_list = [tf.saved_model.tag_constants.TRAINING] res = MainModel.KitModel(weight_filepath) input = res[0] model = res[1:] with tf.Session() as sess: sess.run(tf.global_variables_initializer()) builder = tf.saved_model.builder.SavedModelBuilder(dump_filepath) tensor_info_input = tf.saved_model.utils.build_tensor_info(input) outputs = {'output{}'.format(i): tf.saved_model.utils.build_tensor_info(model[i]) for i in range(len(model))} prediction_signature = ( tf.saved_model.signature_def_utils.build_signature_def( inputs={'input': tensor_info_input}, outputs=outputs, method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME ) ) builder.add_meta_graph_and_variables( sess, tag_list, signature_def_map={ tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature } ) save_path = builder.save() print('Tensorflow file is saved as [{}], generated by [{}.py] and [{}].'.format( save_path, network_filepath, weight_filepath)) model-pivot/ox/tensorflow/tensorflow_emitter.py000066400000000000000000001024751415504170400224640ustar00rootroot00000000000000import os import warnings warnings.filterwarnings("ignore") from ox.common.IR.IR_graph import IRGraph, IRGraphNode import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import NodeDef, GraphDef, DataType from ox.common.DataStructure.emitter import Emitter from ox.common.utils import * from ox.rewriter.folder import Folder class TensorflowEmitter(Emitter): dtype_map = { graph_pb2.DT_FLOAT16 : "tf.float16", graph_pb2.DT_FLOAT32 : "tf.float32", graph_pb2.DT_FLOAT64 : "tf.float64", graph_pb2.DT_INT16 : "tf.int16", graph_pb2.DT_INT32 : "tf.int32", graph_pb2.DT_INT64 : "tf.int64", graph_pb2.DT_UINT8 : "tf.uint8", graph_pb2.DT_UINT16 : "tf.uint16", graph_pb2.DT_STRING : "tf.string" } @property def header_code(self): return """import tensorflow as tf __weights_dict = dict() is_train = {} def load_weights(weight_file): import numpy as np if weight_file == None: return try: weights_dict = np.load(weight_file, allow_pickle=True).item() except: weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() return weights_dict def KitModel(weight_file = None): global __weights_dict __weights_dict = load_weights(weight_file) """.format(self.trainable) def __init__(self, model): super(TensorflowEmitter, self).__init__() from six import string_types as _string_types if isinstance(model, _string_types): network_path = model else: network_path = model[0] self._load_weights(model[1]) self.IR_graph = IRGraph(network_path) super(TensorflowEmitter, self)._build() folder = Folder(self.IR_graph, self.weights_dict) folder.fold() def gen_code(self, phase): self.trainable = (phase == 'train') self.add_body(0, self.header_code) for layer in self.IR_graph.topological_sort: current_node = self.IR_graph.get_node(layer) node_type = current_node.type if hasattr(self, "emit_" + node_type): func = getattr(self, "emit_" + node_type) line = func(current_node) if line != None: self.add_body(1, line) else: print("TensorflowEmitter has not supported operator [%s]." % (node_type)) self.emit_UNKNOWN(current_node) self.add_body(1, "return {}, {}".format( ', '.join([self.IR_graph.get_node(name).real_variable_name for name in self.IR_graph.input_layers if self.IR_graph.get_node(name).type != 'Const' and not self.IR_graph.get_node(name).get_attr('feed_weights')]), ', '.join([self.IR_graph.get_node(name).real_variable_name for name in self.IR_graph.output_layers if self.IR_graph.get_node(name).type != 'Pack' and self.IR_graph.get_node(name).type !='Shape']))) self.add_body(0, "") for i in self.used_layers: func = getattr(self, "_layer_" + i) func() self.add_body(0, "") for code in self.layers_codes.values(): self.add_body(0, code) return self.body_code def parent_variable_name(self, IR_node, path=[0]): if not IR_node.in_edges and IR_node.name in self.weights_dict.keys(): return "tf.constant(__weights_dict['{}']['weights'], name='{}')".format( IR_node.name, IR_node.name) return super(TensorflowEmitter, self).parent_variable_name(IR_node, path) @staticmethod def _shapeToStr(shapes): ret = [dim.size if dim.size != -1 else 'None' for dim in shapes.dim] return ', '.join('%s' % i for i in ret) def emit_Conv(self, IR_node): self.used_layers.add(IR_node.type) strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides')[1:-1]) input_node, padding = self._defuse_padding(IR_node) data_format = IR_node.get_attr('data_format') code = "{:<15} = convolution({}, group={}, strides=[{}], padding='{}', name='{}')".format( IR_node.variable_name, input_node, IR_node.get_attr('group', 1), strides_str, padding, IR_node.name) return code def _defuse_padding(self, IR_node, extra_str=""): auto_pad = IR_node.get_attr('auto_pad') if auto_pad: input_node = self.parent_variable_name(IR_node) if auto_pad == 'VALID': padding = 'VALID' elif auto_pad.startswith("SAME"): padding = 'SAME' else: raise ValueError("Unknown padding type [{}].".format(auto_pad)) return input_node, padding else: padding = IR_node.get_attr("pads") padding = convert_onnx_pad_to_tf(padding) if not is_valid_padding(padding): input_node = IR_node.variable_name + '_pad' self.add_body(1, "{:<15} = tf.pad({}, paddings = {}{})".format( input_node, self.parent_variable_name(IR_node), padding, extra_str )) else: input_node = self.parent_variable_name(IR_node) return input_node, 'VALID' def emit_Constant(self, IR_node): if 'dtype' in IR_node.layer.attr: dtype_str = "{}".format(self.dtype_map[IR_node.layer.attr['dtype'].type]) else: dtype_str = "tf.float32" if 'str' in dtype_str: code = "{:<15} = \"{}\"".format( IR_node.variable_name, IR_node.get_attr('value')) else: # code = "{:<15} = {}".format( # IR_node.variable_name, # IR_node.get_attr('value')) if not isinstance(IR_node.get_attr('value'), list) and IR_node.get_attr('value') is not None: code = "{:<15} = {}".format( IR_node.variable_name, IR_node.get_attr('value')) else: code = "{:<15} = tf.constant({}, dtype={}, name='{}')".format( IR_node.variable_name, "__weights_dict['{}']['value']".format(IR_node.name) if IR_node.get_attr('value')== None else IR_node.get_attr('value'), dtype_str, IR_node.name) return code def emit_Pool(self, IR_node): pooling_type = IR_node.get_attr('pooling_type') if pooling_type == 'MAX': op = 'max_pool' padding_const = ", constant_values=float('-Inf')" elif pooling_type == 'AVG': op = 'avg_pool' padding_const = "" else: raise ValueError("unknown pooling type [{}].".format(pooling_type)) arrlen = len(IR_node.get_attr('strides')) dim_str = '3d' if arrlen == 5 else "" if IR_node.layer.attr['global_pooling'].b: code = "{:<15} = tf.nn.{}{}({}, [1] + {}.get_shape().as_list()[1:-1] + [1], strides = [1] * {}, padding = 'VALID', name = '{}')".format( IR_node.variable_name, op, dim_str, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node), arrlen, IR_node.name) else: dim = len(IR_node.get_attr("strides")) - 2 dilations = IR_node.get_attr('dilations') if dilations: for e in IR_node.get_attr('dilations'): assert e == 1 pool_size = IR_node.get_attr('kernel_shape')[1:-1] strides = IR_node.get_attr('strides')[1:-1] padding = IR_node.get_attr('pads')[1:dim] if pooling_type == "AVG" and pool_size.count(pool_size[0]) == len(pool_size) and strides[0] == 1 and strides.count(strides[0]) == len(strides) and padding.count(padding[0]) == len(padding) and pool_size[0] == padding[0]*2 + 1: kernel_shape_str = ', '.join('%s' % i for i in IR_node.get_attr('kernel_shape')) strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides')) code = "{:<15} = tf.nn.{}{}({}, [{}], [{}], padding='{}', name='{}')".format( IR_node.variable_name, op, dim_str, self.parent_variable_name(IR_node), kernel_shape_str, strides_str, 'SAME', IR_node.name) else: kernel_shape_str = ', '.join('%s' % i for i in IR_node.get_attr('kernel_shape')) strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides')) input_node, padding = self._defuse_padding(IR_node, padding_const) code = "{:<15} = tf.nn.{}{}({}, [{}], [{}], padding='{}', name='{}')".format( IR_node.variable_name, op, dim_str, input_node, kernel_shape_str, strides_str, padding, IR_node.name) return code def emit_UNKNOWN(self, IR_node): print(IR_node.name) def emit_Add(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, ' + '.join('%s' % self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges)))) return code def emit_DataInput(self, IR_node): assert not IR_node.in_edges shape_str = self._shapeToStr(IR_node.layer.attr["shape"].shape) if 'dtype' in IR_node.layer.attr: dtype_str = "{}, ".format(self.dtype_map[IR_node.layer.attr['dtype'].type]) else: dtype_str = "tf.float32," code = "{:<15} = tf.placeholder({} shape = ({}), name = '{}')".format( IR_node.variable_name, dtype_str, shape_str, IR_node.name ) return code def emit_Dropout(self, IR_node): parent = self.IR_graph.get_parent(IR_node.name, [0]) if self.trainable: self.add_body(1, "{:<15} = Dropout(name = '{}', dropout_rate = {})({})".format( IR_node.variable_name, IR_node.name, 1 - IR_node.IR_layer.attr["keep_prob"].f, parent.real_variable_name)) else: IR_node.real_name = parent.real_name def emit_FullyConnected(self, IR_node): if IR_node.name in self.weights_dict and 'weights' in self.weights_dict[IR_node.name]: kernel_str = "kernel_initializer = tf.constant_initializer(__weights_dict['{}']['weights']), ".format(IR_node.name) else: kernel_str = "" if IR_node.name in self.weights_dict and 'bias' in self.weights_dict[IR_node.name]: bias_str = "bias_initializer = tf.constant_initializer(__weights_dict['{}']['bias']), ".format(IR_node.name) else: bias_str = "" # check whether flatten operator should be added parent = self.IR_graph.get_parent(IR_node.name, [0]) parent_shape = shape_to_list(parent.get_attr('_output_shapes')[0]) if len(parent_shape) > 2: # flatten is needed self.add_body(1, "{:<15} = tf.contrib.layers.flatten({})".format( IR_node.variable_name + '_flatten', self.parent_variable_name(IR_node))) code = "{:<15} = tf.layers.dense({}, {}, {}{}use_bias = {})".format( IR_node.variable_name, IR_node.variable_name + '_flatten', IR_node.layer.attr['units'].i, kernel_str, bias_str, IR_node.layer.attr['use_bias'].b) return code else: code = "{:<15} = tf.layers.dense({}, {}, {}{}use_bias = {})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.layer.attr['units'].i, kernel_str, bias_str, IR_node.layer.attr['use_bias'].b) return code def emit_UpSampling2D(self, IR_node): scales = IR_node.get_attr('scales') scales = tuple(scales) code = "{:<15} = tf.keras.layers.UpSampling2D(size={})({})".format( IR_node.variable_name, scales, self.parent_variable_name(IR_node)) return code def emit_Flatten(self, IR_node): #self._emit_unary_operation(IR_node, "contrib.layers.flatten") code = "{:<15} = tf.contrib.layers.flatten({})".format( IR_node.variable_name, self.parent_variable_name(IR_node)) return code def emit_Mul(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, ' * '.join('%s' % self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges)))) return code def emit_Const(self, IR_node): if 'dtype' in IR_node.layer.attr: dtype_str = "dtype={}".format(self.dtype_map[IR_node.layer.attr['dtype'].type]) if 'int' in dtype_str: code = "{:<15} = tf.constant({}, {}, shape=(1,))".format( IR_node.variable_name, IR_node.layer.attr['value'].i, dtype_str) else: code = "{:<15} = tf.constant({}, {}, shape=(1,))".format( IR_node.variable_name, IR_node.layer.attr['value'].f, dtype_str) else: dtype_str = "dtype=tf.float32" code ="{:<15} = tf.constant({}, {}, shape=(1,))".format( IR_node.variable_name, IR_node.layer.attr['value'].f, dtype_str) return code def emit_Transpose(self, IR_node): code ="{:<15} = tf.transpose(a = {}, perm = {})".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0]), self.parent_variable_name(IR_node, [1])) return code def emit_Gather(self, IR_node): variable_str = "tf.convert_to_tensor(__weights_dict['{}']['weights'])".format(IR_node.name) code = "{:<15} = tf.gather(params = {}, indices = {}, axis = {})".format( IR_node.variable_name, variable_str, self.parent_variable_name(IR_node), IR_node.get_attr('axis') ) return code def emit_Unstack(self, IR_node): code = "{:<15} = tf.unstack(value={}, num={}, axis={})".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('num'), IR_node.get_attr('axis') ) return code def emit_Reshape(self, IR_node): code = "{:<15} = tf.reshape({}, [{}], '{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), ', '.join('%s' % i for i in IR_node.get_attr('shape')), IR_node.name) return code def emit_Sub(self, IR_node): code = "{:<15} = {}".format( IR_node.variable_name, ' - '.join('%s' % self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges)))) return code def emit_Div(self, IR_node): code = "{:<15} = tf.div({}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1]), IR_node.name ) return code def _emit_unary_operation(self, IR_node, op_name): code = "{:<15} = tf.{}({}, name = '{}')".format( IR_node.variable_name, op_name, self.parent_variable_name(IR_node), IR_node.name) return code def emit_Tanh(self, IR_node): code = self._emit_unary_operation(IR_node, 'tanh') return code def emit_Elu(self, IR_node): return self._emit_unary_operation(IR_node, 'nn.elu') def emit_Relu(self, IR_node): return self._emit_unary_operation(IR_node, 'nn.relu') def emit_Relu6(self, IR_node): return self._emit_unary_operation(IR_node, 'nn.relu6') def emit_CRelu(self, IR_node): return self._emit_unary_operation(IR_node, 'nn.crelu') def emit_PRelu(self, IR_node): self.used_layers.add(IR_node.type) code = "{:<15} = prelu({}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.name) return code def emit_LeakyRelu(self, IR_node): self.add_body(1, "{:<15} = tf.nn.leaky_relu({}, alpha={}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('alpha'), IR_node.name )) def emit_Softmax(self, IR_node): return self._emit_unary_operation(IR_node, 'nn.softmax') def emit_Sigmoid(self, IR_node): code = self._emit_unary_operation(IR_node, 'sigmoid') return code def emit_Embedding(self, IR_node): variable_str = "tf.convert_to_tensor(__weights_dict['{}']['weights'])".format(IR_node.name) code = "{:<15} = tf.nn.embedding_lookup(params = {}, ids = {})".format( IR_node.variable_name, variable_str, self.parent_variable_name(IR_node)) return code def emit_LSTM(self, IR_node): return self.emit_RNNs(IR_node, "LSTM") def emit_GRU(self, IR_node): return self.emit_RNNs(IR_node, "GRU") def emit_Concat(self, IR_node): code = "{:<15} = tf.concat([{}], {}, name = '{}')".format( IR_node.variable_name, ', '.join(self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges))), IR_node.layer.attr['axis'].i, IR_node.name) return code def emit_BatchNorm(self, IR_node): self.used_layers.add(IR_node.type) code = "{:<15} = batch_normalization({}, variance_epsilon={}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('epsilon'), IR_node.name) return code def emit_Scale(self, IR_node): self.used_layers.add(IR_node.type) code = "{:<15} = scale({}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.name) return code def emit_Pad(self, IR_node): padding = IR_node.get_attr('pads') padding = convert_onnx_pad_to_tf(padding) mode = IR_node.get_attr('mode', 'constant') mode = mode.lower() if mode == 'constant' or mode == 'reflect': mode = mode.upper() elif mode == 'edge': mode = 'SYMMETRIC' else: raise NotImplementedError("Not support padding mode {}.".format(mode)) code = "{:<15} = tf.pad({}, {}, '{}', name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), padding, mode, IR_node.variable_name) return code def emit_Squeeze(self, IR_node): code = "{:<15} = tf.squeeze({}, [{}], name = '{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), ', '.join('%s' % axis for axis in IR_node.layer.attr['axes'].list.i), IR_node.name) return code def emit_ReduceMean(self, IR_node): code = "{:<15} = tf.reduce_mean({}, [{}], {}, name = '{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), ','.join('%s' % i for i in IR_node.get_attr('axes')), IR_node.get_attr('keepdims'), IR_node.name) return code def emit_LRN(self, IR_node): code = "{:<15} = tf.nn.lrn({}, depth_radius={}, bias={}, alpha={}, beta={}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('size') - 1, IR_node.get_attr('bias', 1), IR_node.get_attr('alpha') / (IR_node.get_attr('size') * 2 - 1), IR_node.get_attr('beta'), IR_node.name) return code def emit_SeparableConv(self, IR_node): self.used_layers.add(IR_node.type) strides_str = ', '.join('%s' % i for i in IR_node.get_attr('strides')) input_node, padding = self._defuse_padding(IR_node) code = "{:<15} = separable_convolution({}, strides = [{}], padding = '{}', name = '{}')".format( IR_node.variable_name, input_node, strides_str, padding, IR_node.name) return code def emit_DepthwiseConv(self, IR_node): self.used_layers.add(IR_node.type) strides_str = ', '.join('%s' % i for i in IR_node.layer.attr['strides'].list.i) input_node, padding = self._defuse_padding(IR_node) code = "{:<15} = depthwise_convolution({}, strides = [{}], padding = '{}', name = '{}')".format( IR_node.variable_name, input_node, strides_str, padding, IR_node.name) return code def emit_Crop(self, IR_node): border = IR_node.get_attr('border') assert len(border) == 4 output_shape = IR_node.get_attr('_output_shapes')[0] output_shape = shape_to_list(output_shape) code = "{:<15} = tf.image.crop_to_bounding_box({}, offset_height={}, offset_width={}, target_height={}, target_width={})".format( IR_node.variable_name, self.parent_variable_name(IR_node), border[0], border[1], output_shape[1], output_shape[2]) return code def emit_ConvTranspose(self, IR_node): self.used_layers.add(IR_node.type) output_shape = [1] + shape_to_list(IR_node.get_attr('_output_shapes')[0])[1:] input_node, padding = self._defuse_padding(IR_node) code = "{:<15} = convolution_transpose({}, output_shape={}, strides={}, padding='{}', name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node, [1]), output_shape, IR_node.get_attr('strides'), padding, IR_node.name) return code def emit_Slice(self, IR_node): if IR_node.get_attr('starts') != None: starts = IR_node.get_attr('starts') else: starts = self.parent_variable_name(IR_node, [1]) if IR_node.get_attr('ends') != None: ends = IR_node.get_attr('ends') else: ends = self.parent_variable_name(IR_node, [2]) if IR_node.get_attr('strides') != None: strides = IR_node.get_attr('strides') else: strides = self.parent_variable_name(IR_node, [3]) code = "{:<15} = tf.slice({}, {}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0]), starts, ends, IR_node.name) return code def emit_StridedSlice(self, IR_node): extra_str = "" if IR_node.get_attr('begin_mask'): extra_str += ", begin_mask={}".format(IR_node.get_attr('begin_mask')) if IR_node.get_attr('end_mask') != None: extra_str += ", end_mask={}".format(IR_node.get_attr('end_mask')) if IR_node.get_attr('shrink_axis_mask') != None: extra_str += ", shrink_axis_mask={}".format(IR_node.get_attr('shrink_axis_mask')) if IR_node.get_attr('new_axis_mask')!= None: extra_str += ", new_axis_mask={}".format(IR_node.get_attr('new_axis_mask')) if IR_node.get_attr('starts') != None: starts = IR_node.get_attr('starts') else: starts = self.parent_variable_name(IR_node, [1]) if IR_node.get_attr('ends') != None: ends = IR_node.get_attr('ends') else: ends = self.parent_variable_name(IR_node, [2]) if IR_node.get_attr('strides') != None: strides = IR_node.get_attr('strides') else: strides = self.parent_variable_name(IR_node, [3]) code = "{:<15} = tf.strided_slice({}, {}, {}, {} {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node, [0]), starts, ends, strides, extra_str, IR_node.name) return code def emit_Shape(self, IR_node): # code = "{:<15} = {}".format( # IR_node.variable_name, # self.parent_variable_name(IR_node), # IR_node.name) code = "{:<15} = tf.shape({}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.name) return code # def emit_Less(self, IR_node): # code = "{:<15} = tf.less({}, {}, name='{}')".format( # IR_node.variable_name, # self.parent_variable_name(IR_node, [0]), # self.parent_variable_name(IR_node, [1]), # IR_node.name) # return code # def emit_All(self, IR_node): # code = "{:<15} = tf.reduce_all({}, keep_dims={}, name='{}')".format( # IR_node.variable_name, # self.parent_variable_name(IR_node), # # IR_node.get_attr('axis'), # IR_node.get_attr('keep_dims'), # IR_node.name) # return code # def emit_Assert(self, IR_node): # code = "{:<15} = tf.Assert({}, {}, name='{}')".format( # IR_node.variable_name, # self.parent_variable_name(IR_node, [0]), # self.parent_variable_name(IR_node, [1]), # IR_node.name) # return code def emit_Pack(self, IR_node): code = "{:<15} = tf.stack({}, axis={}, name='{}')".format( IR_node.variable_name, '[' + ','.join('%s' % self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges))) + ']', IR_node.get_attr('axis'), IR_node.name) return code def emit_Split(self, IR_node): code = "{:<15} = tf.split({}, {}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('split'), IR_node.get_attr('axis'), IR_node.name) return code def emit_Unsqueeze(self, IR_node): code = "{:<15} = tf.expand_dims({}, axis={}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('axes')[0], IR_node.name) return code def emit_Fill(self, IR_node): code = "{:<15} = tf.fill({}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.get_attr('value'), IR_node.name) return code def emit_Maxmum(self, IR_node): code = "{:<15} = tf.maxmum({}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1]), IR_node.name ) return code def emit_Minimum(self, IR_node): code = "{:<15} = tf.minimum({}, {}, name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), self.parent_variable_name(IR_node, [1]), IR_node.name ) return code def emit_Scope(self, IR_node): input_vars = [self.parent_variable_name(IR_node, [idx]) for idx in range(len(IR_node.in_edges))] input_vars.append('__weights_dict') code = "{:<15} = _{}({})".format( IR_node.real_variable_name, IR_node.pattern, ', '.join(input_vars)) self._gen_scope_code(IR_node) return code def _gen_scope_code(self, scope_node): def _scope_func(scope_name, params, code, return_var): code = """ def _{}({}): {} return {} """.format(scope_name, params, code, ', '.join(return_var)) return code if not self.layers_codes.get(scope_node.pattern, None): body_code = str() for node_name in scope_node.topology_list: node = self.IR_graph.get_node(node_name) node_type = node.type if hasattr(self, "emit_" + node_type): func = getattr(self, "emit_" + node_type) line = func(node) if line != None: body_code += " " + line + '\n' else: print("TensorflowEmitter has not supported operator [%s]." % (node_type)) self.emit_UNKNOWN(node) # param_code does not need parameter slice. input_params = scope_node.input_params input_params.append("__weights_dict") param_code = ', '.join(input_params) function_code = _scope_func(scope_node.pattern, param_code, body_code, scope_node.return_variables) self.layers_codes[scope_node.pattern] = function_code def _layer_Conv(self): self.add_body(0, """ def convolution(input, name, group, **kwargs): w = tf.Variable(__weights_dict[name]['weights'], trainable=is_train, name=name + "_weight") if group == 1: layer = tf.nn.convolution(input, w, name=name, **kwargs) else: weight_groups = tf.split(w, num_or_size_splits=group, axis=-1) xs = tf.split(input, num_or_size_splits=group, axis=-1) convolved = [tf.nn.convolution(x, weight, name=name, **kwargs) for (x, weight) in zip(xs, weight_groups)] layer = tf.concat(convolved, axis=-1) if 'bias' in __weights_dict[name]: b = tf.Variable(__weights_dict[name]['bias'], trainable=is_train, name=name + "_bias") layer = layer + b return layer""") def _layer_PRelu(self): self.add_body(0, """ def prelu(input, name): gamma = tf.Variable(__weights_dict[name]['gamma'], name=name + "_gamma", trainable=is_train) return tf.maximum(0.0, input) + gamma * tf.minimum(0.0, input) """) def _layer_BatchNorm(self): self.add_body(0, """ def batch_normalization(input, name, **kwargs): mean = tf.Variable(__weights_dict[name]['mean'], name = name + "_mean", trainable = is_train) variance = tf.Variable(__weights_dict[name]['var'], name = name + "_var", trainable = is_train) offset = tf.Variable(__weights_dict[name]['bias'], name = name + "_bias", trainable = is_train) if 'bias' in __weights_dict[name] else None scale = tf.Variable(__weights_dict[name]['scale'], name = name + "_scale", trainable = is_train) if 'scale' in __weights_dict[name] else None return tf.nn.batch_normalization(input, mean, variance, offset, scale, name = name, **kwargs) """) def _layer_Scale(self): self.add_body(0, """ def scale(input, name, **kwargs): mean = tf.Variable(__weights_dict[name]['scale_mean'], name = name + "_mean", trainable = is_train) variance = tf.Variable(__weights_dict[name]['scale_var'], name = name + "_var", trainable = is_train) offset = tf.Variable(__weights_dict[name]['bias'], name = name + "_bias", trainable = is_train) if 'bias' in __weights_dict[name] else None scale = tf.Variable(__weights_dict[name]['scale'], name = name + "_scale", trainable = is_train) if 'scale' in __weights_dict[name] else None return tf.nn.batch_normalization(input, mean, variance, offset, scale, variance_epsilon = 0, name = name) """) def _layer_SeparableConv(self): self.add_body(0, """ def separable_convolution(input, name, **kwargs): depthwise = tf.Variable(__weights_dict[name]['depthwise_filter'], trainable = is_train, name = name + "_df") pointwise = tf.Variable(__weights_dict[name]['pointwise_filter'], trainable = is_train, name = name + "_pf") layer = tf.nn.separable_conv2d(input, depthwise, pointwise, **kwargs) if 'bias' in __weights_dict[name]: b = tf.Variable(__weights_dict[name]['bias'], trainable = is_train, name = name + "_bias") layer = layer + b return layer""") def _layer_DepthwiseConv(self): self.add_body(0, """ def depthwise_convolution(input, name, **kwargs): depthwise = tf.Variable(__weights_dict[name]['weights'], trainable = is_train, name = name + "_df") layer = tf.nn.depthwise_conv2d(input, depthwise, **kwargs) if 'bias' in __weights_dict[name]: b = tf.Variable(__weights_dict[name]['bias'], trainable = is_train, name = name + "_bias") layer = layer + b return layer""") def _layer_ConvTranspose(self): self.add_body(0, """ def convolution_transpose(input, name, **kwargs): w = tf.Variable(__weights_dict[name]['weights'], trainable=is_train, name=name + "_weight") dim = __weights_dict[name]['weights'].ndim - 2 if dim == 2: layer = tf.nn.conv2d_transpose(input, w, **kwargs) elif dim == 3: layer = tf.nn.conv3d_transpose(input, w, **kwargs) else: raise ValueError("Error dim number {} in ConvTranspose".format(dim)) if 'bias' in __weights_dict[name]: b = tf.Variable(__weights_dict[name]['bias'], trainable=is_train, name=name + "_bias") layer = layer + b return layer""") model-pivot/ox/tensorflow/tensorflow_frozenparser.py000066400000000000000000001266661415504170400235430ustar00rootroot00000000000000import warnings warnings.filterwarnings("ignore") import numpy as np import tensorflow from tensorflow.python.framework import tensor_util from tensorflow.core.framework import attr_value_pb2 from ox.tensorflow.tensorflow_graph import TensorflowGraph import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import NodeDef, GraphDef, DataType from ox.common.utils import * from ox.common.DataStructure.parser import Parser from distutils.version import LooseVersion import tempfile import os import shutil class TensorflowParser2(Parser): # skip_prefix = [ # "^", # "train_op", # "save", # "gradients", # "init", # "global_step", # "distort_image", # "Adagrad", # ] skip_prefix = [ ] skip_scope = [ "random_uniform", "Initializer", "optimizer", "weight_loss", "parallel_read", "case" ] skip_type = set([ "L2Loss", "VariableV2", "Const", "Assign", "RandomUniform", "FIFOQueueV2", "Assert", "Unpack", "NextIteration", "TensorArrayV3", "Range", "TensorArrayScatterV3", "TensorArrayReadV3", "TensorArrayWriteV3", # "Switch" "Dequantize", # "RequantizationRange", # "Requantize", "ExpandDims", # "Identity", # "Mean", # "Cast" "Pack", "CheckNumerics", "Where" ]) q_type = set([ "Dequantize", "QuantizeV2", "QuantizedConv2D", "QuantizedReshape", "RequantizationRange" ]) dtype_map = { 0 : graph_pb2.DT_UNDEFINED, 1 : graph_pb2.DT_FLOAT32, 2 : graph_pb2.DT_FLOAT64, 3 : graph_pb2.DT_INT32, 4 : graph_pb2.DT_UINT8, 5 : graph_pb2.DT_INT16, 6 : graph_pb2.DT_INT8, 7 : graph_pb2.DT_STRING, 9 : graph_pb2.DT_INT64, 10: graph_pb2.DT_BOOL } tf_dtype_map = { 0: tensorflow.float32, # set default to be float 1: tensorflow.float32, 2: tensorflow.float64, 3: tensorflow.int32, 4: tensorflow.uint8, 5: tensorflow.int16, 6: tensorflow.int8, 7: tensorflow.string, 9: tensorflow.int64, 10: tensorflow.bool } @property def src_graph(self): return self.tf_graph def __init__(self, frozen_file, inputshape, in_nodes, dest_nodes): if LooseVersion(tensorflow.__version__) < LooseVersion('1.8.0'): raise ImportError( 'Your TensorFlow version %s is outdated. ' 'MMdnn requires tensorflow>=1.8.0' % tensorflow.__version__) super(TensorflowParser2, self).__init__() self.weight_loaded = True # load model files into TensorFlow graph with open(frozen_file, 'rb') as f: serialized = f.read() tensorflow.reset_default_graph() original_gdef = tensorflow.GraphDef() original_gdef.ParseFromString(serialized) in_type_list = {} for n in original_gdef.node: if n.name in in_nodes: in_type_list[n.name] = n.attr['dtype'].type from tensorflow.python.tools import strip_unused_lib from tensorflow.python.framework import dtypes from tensorflow.python.platform import gfile original_gdef = strip_unused_lib.strip_unused( input_graph_def = original_gdef, input_node_names = in_nodes, output_node_names = dest_nodes, placeholder_type_enum = dtypes.float32.as_datatype_enum) # Save it to an output file tempdir = tempfile.mkdtemp() frozen_model_file = os.path.join(tempdir, 'frozen.pb') with gfile.GFile(frozen_model_file, "wb") as f: f.write(original_gdef.SerializeToString()) with open(frozen_model_file, 'rb') as f: serialized = f.read() shutil.rmtree(tempdir) tensorflow.reset_default_graph() model = tensorflow.GraphDef() model.ParseFromString(serialized) output_shape_map = dict() input_shape_map = dict() dtype = tensorflow.float32 with tensorflow.Graph().as_default() as g: input_map = {} for i in range(len(inputshape)): dtype = TensorflowParser2.tf_dtype_map[in_type_list[in_nodes[i]]] if in_type_list[in_nodes[i]] in (0, 1, 2): x = tensorflow.placeholder(dtype, shape=[None] + inputshape[i]) elif in_type_list[in_nodes[i]] in (3, 4, 5, 6, 7): x = tensorflow.placeholder(dtype, shape=inputshape[i]) elif in_type_list[in_nodes[i]] == 10: x = tensorflow.placeholder(dtype) else: raise NotImplementedError input_map[in_nodes[i] + ':0'] = x tensorflow.import_graph_def(model, name='', input_map=input_map) with tensorflow.Session(graph = g) as sess: tempdir = tempfile.mkdtemp() meta_graph_def = tensorflow.train.export_meta_graph(filename=os.path.join(tempdir, 'my-model.meta')) model = meta_graph_def.graph_def shutil.rmtree((tempdir)) self.tf_graph = TensorflowGraph(model) self.tf_graph.build() @staticmethod def _get_scopes(layer_name): return layer_name.split('/') def check_const(self, node): while node: if node.type == "Const": return node elif node.type == "NoOp": return None else: node = self.get_parent(node.name, [0]) def _convert_reduction_operators(self, source_node, new_op = None): IR_node = self._convert_identity_operation(source_node, 1, new_op) # keep dims IR_node.attr['keepdims'].b = source_node.layer.attr['keep_dims'].b # axes axes = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor axes = tensor_util.MakeNdarray(axes) IR_node.attr['axes'].list.i.extend(axes) def _convert_layers_batchnorm(self, source_node): IR_node = self.IR_graph.node.add() TensorflowParser2._copy_and_reop(source_node, IR_node, 'BatchNorm') is_transformed = False test = self.get_parent(source_node.name, [0]) if test.type == 'Mul': is_transformed = True # ssd model is transformed if is_transformed: # Ax - (Au - b) # A input_mul_A = self.get_parent(source_node.name, [0, 1]) tensor_content = input_mul_A.get_attr('value') A_content = tensor_util.MakeNdarray(tensor_content) self.set_weight(source_node.name, 'A', A_content) # b input_sub = self.get_parent(source_node.name, [1]) tensor_content = input_sub.get_attr('value') sub_content = tensor_util.MakeNdarray(tensor_content) # print(sub_content) self.set_weight(source_node.name, 'b', sub_content) input_node = self.get_parent(source_node.name, [0]) IR_node.input.append(input_node.real_name) IR_node.attr["_output_shapes"].list.shape.pop() IR_node.attr["_output_shapes"].MergeFromString(input_node.layer.attr['_output_shapes'].SerializeToString()) else: # epsilon epsilon = self.get_parent(source_node.name, [1]) IR_node.attr['epsilon'].f = epsilon.layer.attr['value'].tensor.float_val[0] # moving variance (var) /read moving_variance = self.get_parent(source_node.name, [0]) if moving_variance.type == 'Identity': moving_variance_read = self.src_graph.get_parent(moving_variance.name, [0]) tensor_content = moving_variance_read.get_attr('value') moving_variance_content = tensor_util.MakeNdarray(tensor_content) self.set_weight(source_node.name, 'var', moving_variance_content) else: print(moving_variance.layer) assert False # gamma (scale) Rsqrt = self.get_son(source_node.name, [0], True) # print(Rsqrt.out_edges) if len(Rsqrt.out_edges) == 2: IR_node.attr['scale'].b = False output_node = self.get_son(Rsqrt.name, [0, 0], True) if output_node.type == 'Sub': output_node = self.get_son(Rsqrt.name, [1, 0], True) Mul = self.get_son(Rsqrt.name, [0], True) else: Mul = self.get_son(Rsqrt.name, [1], True) else: IR_node.attr['scale'].b = True son = self.get_son(Rsqrt.name, [0, 0], True) gamma_from = self.get_parent(son.name, [1, 1], True) gamma = self.check_const(gamma_from) gamma_tensor = gamma.get_attr('value') scale = tensor_util.MakeNdarray(gamma_tensor) self.set_weight(source_node.name, 'scale', scale) output_node = self.get_son(source_node.name, [0, 0, 0, 0], True) if output_node.type == 'Sub': output_node = self.get_son(source_node.name, [0, 0, 0, 0, 0], True) Mul = self.get_son(Rsqrt.name, [0, 0], True) else: Mul = self.get_son(Rsqrt.name, [0, 1], True) # beta (bias) beta = self.get_parent(output_node.name, [1, 0, 0], True).get_attr('value') bias = tensor_util.MakeNdarray(beta) IR_node.attr['bias'].b = True self.set_weight(source_node.name, 'bias', bias) # moving mean (mean) moving_mean = self.get_parent(Mul.name, [0, 0]).get_attr('value') mean = tensor_util.MakeNdarray(moving_mean) self.set_weight(source_node.name, 'mean', mean) # input node assert output_node.type == 'Add' input_node = self.get_parent(output_node.name, [0, 0]) IR_node.input.append(input_node.real_name) IR_node.attr["_output_shapes"].list.shape.pop() IR_node.attr["_output_shapes"].MergeFromString(input_node.layer.attr['_output_shapes'].SerializeToString()) output_node.real_name = source_node.name def _convert_layers_instancenorm(self, source_node): IR_node = self.IR_graph.node.add() TensorflowParser2._copy_and_reop(source_node, IR_node, 'InstanceNorm') # epsilon epsilon = self.get_parent(source_node.name, [1]) epsilon_value = epsilon.get_attr('value').float_val[0] IR_node.attr['epsilon'].f = epsilon_value # beta output_node = self.get_son(source_node.name, [0, 0, 0, 0], True) beta = self.get_parent(output_node.name, [1, 0, 0, 0, 0, 1], True) beta_tensor = beta.get_attr('value') beta = tensor_util.MakeNdarray(beta_tensor) self.set_weight(source_node.name, 'bias', beta) # gamma (scale) IR_node.attr['scale'].b = True son = self.get_son(source_node.name, [0, 0, 0], True) gamma = self.get_parent(son.name, [1, 1, 0, 0, 0, 1], True) gamma_tensor = gamma.get_attr('value') scale = tensor_util.MakeNdarray(gamma_tensor) self.set_weight(source_node.name, 'scale', scale) # output_node = self.get_son(source_node.name, [0, 0, 0, 0], True) assert output_node.type == 'Add' input_node = self.get_parent(output_node.name, [0, 0]) IR_node.input.append(input_node.real_name) output_node.real_name = source_node.name # assert False @classmethod def _skip_node(cls, source_node): if source_node.covered: return True for prefix in cls.skip_prefix: if source_node.name.startswith(prefix): return True scopes = TensorflowParser2._get_scopes(source_node.name) for s in scopes: if s in cls.skip_scope: return True return False def _add_constant_node(self, source_node): parent_ids=range(len(source_node.in_edges)) for idx in parent_ids: s = source_node.in_edges[idx] parent_node = self.tf_graph.get_node(s) if parent_node.type == 'Const': self._rename_Const(parent_node) def _rename_Const(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=0, new_op='Constant') # Constant value = source_node.get_attr('value') if value.float_val: value = value.float_val[0] elif value.int_val: value = value.int_val[0] else: value = tensor_util.MakeNdarray(value).tolist() kwargs = {'value': value} assign_IRnode_values(IR_node, kwargs) def gen_IR(self): for layer in self.src_graph.topological_sort: current_node = self.src_graph.get_node(layer) if self._skip_node(current_node): continue node_type = current_node.type if hasattr(self, "rename_" + node_type): func = getattr(self, "rename_" + node_type) func(current_node) else: self.rename_UNKNOWN(current_node) @staticmethod def tensor_shape_to_list(shapes): if isinstance(shapes, attr_value_pb2.AttrValue): return [dim.size for dim in shapes.shape.dim] else: ret = [] for shape in shapes: this_one = [dim.size for dim in shape.dim] ret.append(this_one) return ret @staticmethod def _copy_and_reop(source_node, IR_node, new_op = None): if new_op == None: new_op = source_node.type IR_node.name = source_node.name IR_node.op = new_op kwargs = {} if 'data_format' in source_node.layer.attr: kwargs['data_format'] = source_node.get_attr('data_format') if 'T' in source_node.layer.attr: if source_node.type not in TensorflowParser2.q_type: if source_node.type == 'Enter': IR_node.attr["dtype"].type = TensorflowParser2.dtype_map[6] else: assert source_node.layer.attr['T'].type in TensorflowParser2.dtype_map, 'type [{}] is unknown.'.format(source_node.layer.attr['dtype'].type) IR_node.attr["dtype"].type = TensorflowParser2.dtype_map[source_node.layer.attr['T'].type] else: # Quantized model type IR_node.attr["dtype"].type = TensorflowParser2.dtype_map[6] if '_output_shapes' in source_node.layer.attr: IR_node.attr["_output_shapes"].MergeFromString(source_node.layer.attr['_output_shapes'].SerializeToString()) if 'paddings' in source_node.layer.attr: IR_node.attr["paddings"].MergeFromString(source_node.layer.attr['paddings'].SerializeToString()) assign_IRnode_values(IR_node, kwargs) def _convert_inedge(self, source_node, IR_node, start_idx = 0, end_idx = None): if end_idx == None: end_idx = len(source_node.in_edges) for idx in range(start_idx, end_idx): IR_node.input.append(self.src_graph.get_node(source_node.in_edges[idx]).real_name) @staticmethod def _copy_shape(source_node, IR_node): assert 'shape' in source_node.layer.attr if source_node.layer.attr['shape'].list.shape: IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['shape'].list.shape[0].SerializeToString()) else: IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['shape'].shape.SerializeToString()) def rename_UNKNOWN(self, source_node): if source_node.type in self.skip_type: return print("Tensorflow has not supported operator [%s] with name [%s]." % (source_node.type, source_node.name)) assert False def rename_NoOp(self, source_node): return def _convert_padding(self, source_node, IR_node, kernel_size): # TODO: Fused conv and pool with padding is different from defused operators input_node = self.get_parent(source_node.name, [0]) input_shape = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] if source_node.get_attr('padding') == 'VALID': dims = len(input_shape) assign_IRnode_values(IR_node, {'auto_pad' : "VALID", 'pads' : [0, 0] * dims}) elif source_node.get_attr('padding') == 'SAME': padding = compute_tf_same_padding( input_shape, kernel_size, source_node.get_attr('strides')) assign_IRnode_values(IR_node, {'auto_pad' : "SAME_LOWER", 'pads' : padding}) else: assert False def _get_bias(self, source_node, IR_node): if not source_node.out_edges: return add_node = self.tf_graph.get_node(source_node.out_edges[0]) if add_node.type != "Add" and add_node.type != "BiasAdd": return variable = self.check_const(self.tf_graph.get_node(add_node.in_edges[1])) #add_bias node if not variable or variable.type != 'Const': return bias_value = variable.get_attr('value') bias = tensor_util.MakeNdarray(bias_value) # assert variable.get_attr('_output_shapes')[0].dim[0].size == IR_node.attr['kernel_shape'].list.i[-1] add_node.real_name = IR_node.name add_node.covered = True IR_node.attr['use_bias'].b = True current_layer = self.weights[source_node.name] current_layer['bias'] = bias def _convert_pooling(self, source_node, pool_type): IR_node = self._convert_identity_operation(source_node, new_op='Pool') kwargs = {} # strides kwargs['strides'] = source_node.get_attr('strides') # window_shape kwargs['kernel_shape'] = source_node.get_attr('ksize') # pool type kwargs['pooling_type'] = pool_type # padding self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][1:-1]) assign_IRnode_values(IR_node, kwargs) def _convert_identity_operation(self, source_node, start_idx = 0, end_idx = None, new_op = None): IR_node = self.IR_graph.node.add() TensorflowParser2._copy_and_reop(source_node, IR_node, new_op) self._convert_inedge(source_node, IR_node, start_idx, end_idx) return IR_node def rename_Relu6(self, source_node): self._convert_identity_operation(source_node, new_op = 'Relu6') def rename_Merge(self, source_node): # In facenet or other newtwork using slim.batch_norm, # There are two BN(train, test) skip switch and merge. source_node.real_name = self.src_graph.get_node(source_node.in_edges[0]).real_name def rename_DepthwiseConv2dNative(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx = 1, new_op = 'DepthwiseConv') kwargs = {} kwargs['strides'] = source_node.get_attr('strides') input_node = self.src_graph.get_parent(source_node.name, [1]) kwargs['kernel_shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][:-2]) weight_node = self.src_graph.get_parent(source_node.name, [1]) weight = self.check_const(weight_node).get_attr('value') weight_content = tensor_util.MakeNdarray(weight) self.set_weight(source_node.name, 'weights', weight_content) assign_IRnode_values(IR_node, kwargs) def rename_BatchNormWithGlobalNormalization(self, source_node): IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=1, new_op='BatchNorm') # epsilon IR_node.attr['epsilon'].f = source_node.get_attr('variance_epsilon') # moving variance (var) /read moving_variance = self.get_parent(source_node.name, [2]) tensor_variance = moving_variance.get_attr('value') moving_variance_content = tensor_util.MakeNdarray(tensor_variance) self.set_weight(source_node.name, 'var', moving_variance_content) # gamma (scale) gamma = self.get_parent(source_node.name, [4]) gamma_value = gamma.get_attr('value') gamma = tensor_util.MakeNdarray(gamma_value) self.set_weight(source_node.name, 'scale', gamma) IR_node.attr['scale'].b = True # beta (bias) beta = self.get_parent(source_node.name, [3]) beta_value = beta.get_attr('value') beta = tensor_util.MakeNdarray(beta_value) self.set_weight(source_node.name, 'bias', beta) IR_node.attr['use_bias'].b = True # moving mean (mean) mean = self.get_parent(source_node.name, [1]) mean_value = mean.get_attr('value') mean = tensor_util.MakeNdarray(mean_value) self.set_weight(source_node.name, 'mean', mean) def rename_Placeholder(self, source_node): if source_node.layer.attr["shape"].shape.unknown_rank == True: return IR_node = self._convert_identity_operation(source_node, new_op='DataInput') TensorflowParser2._copy_shape(source_node, IR_node) IR_node.attr['shape'].shape.dim[0].size = -1 IR_node.attr['_output_shapes'].list.shape[0].dim[0].size = -1 def rename_Mean(self, source_node): # ReduceMean IR_node = self._convert_identity_operation(source_node, start_idx = 0, end_idx = 1, new_op='ReduceMean') # keep dims IR_node.attr['keepdims'].b = source_node.layer.attr['keep_dims'].b # axes axes = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor axes = tensor_util.MakeNdarray(axes) IR_node.attr['axes'].list.i.extend(axes) def rename_Reshape(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx = 1) kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} assign_IRnode_values(IR_node, kwargs) def rename_MirrorPad(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'MirrorPad') input_node = self.src_graph.get_parent(source_node.name, [1]) tensor_content = tensor_util.MakeNdarray(input_node.get_attr('value')).reshape(-1) kwargs = {} kwargs['mode'] = source_node.get_attr('mode') kwargs['pads'] = tensor_content.tolist() assign_IRnode_values(IR_node, kwargs) def rename_Min(self, source_node): IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=1, new_op = 'Min') kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape_0'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node = self.src_graph.get_parent(source_node.name, [1]) kwargs['shape_1'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Max(self, source_node): IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=1, new_op = 'Max') kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape_0'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node = self.src_graph.get_parent(source_node.name, [1]) kwargs['shape_1'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Mul(self, source_node): scopes = self._get_scopes(source_node.name) if len(scopes) >= 2: if scopes[-2] == "batchnorm" or scopes[-2].startswith("Assign"): return self._add_constant_node(source_node) self._convert_identity_operation(source_node) def rename_Add(self, source_node): scopes = self._get_scopes(source_node.name) if len(scopes) > 2: if scopes[-2] == 'batchnorm': if scopes[-3] == 'BatchNorm' or scopes[-3] == 'batch_normalization': self._convert_layers_batchnorm(source_node) elif scopes[-3] == 'InstanceNorm': self._convert_layers_instancenorm(source_node) else: IR_node = self._convert_identity_operation(source_node, new_op = "Add") else: IR_node = self._convert_identity_operation(source_node, new_op = "Add") def rename_Fill(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op="Fill") def rename_Sub(self, source_node): scopes = self._get_scopes(source_node.name) if len(scopes) > 2: if scopes[-2].startswith('Assign') or scopes[-1].startswith('Assign'): return IR_node = self._convert_identity_operation(source_node, end_idx=2, new_op = "Sub") def rename_Sum(self, source_node): IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=1, new_op = 'Sum') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs = {} kwargs['cal_shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node_indices = self.src_graph.get_parent(source_node.name, [1]) indice_value = input_node_indices.get_attr('value') if indice_value.tensor_content: shapes = tensor_util.MakeNdarray(indice_value) c = shapes.tolist() kwargs['sum_indices'] = c else: kwargs['sum_indices'] = input_node_indices.get_attr('value').int_val[0] assign_IRnode_values(IR_node, kwargs) def rename_Rsqrt(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = "Rsqrt") kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Square(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Square') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs = {} kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Sigmoid(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Reciprocal(self, source_node): IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=1, new_op = 'Reciprocal') def rename_Minimum(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Minimum') def rename_Maximum(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Maximum') def rename_RealDiv(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'RealDiv') def rename_Enter(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Enter') def rename_Switch(self, source_node): # Skip the node as merge source_node.real_name = self.src_graph.get_node(source_node.in_edges[0]).real_name def rename_Identity(self, source_node): source_node.real_name = self.src_graph.get_node(source_node.in_edges[0]).real_name def rename_Exp(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Exp') def rename_ResizeBilinear(self, source_node): IR_node = self._convert_identity_operation(source_node,end_idx=1, new_op = 'ResizeBilinear') def rename_Cast(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Cast') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs = {} kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Prod(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Prod') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs = {} kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node_const = self.src_graph.get_parent(source_node.name, [1]) kwargs['const'] = input_node_const.get_attr('value').int_val[0] assign_IRnode_values(IR_node, kwargs) def rename_Shape(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Shape') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs = {} kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Squeeze(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Squeeze') def rename_Gather(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Embedding') W = self.src_graph.get_parent(source_node.name, [0]) W = self.src_graph.get_parent(W.name, [0]) self.set_weight(source_node.name, "weights", self.ckpt_data[W.name]) kwargs = { 'input_dim' : self.ckpt_data[W.name].shape[0], 'output_dim' : self.ckpt_data[W.name].shape[1], 'mask_zero' : False } kwargs['axis'] = 0 # add default assign_IRnode_values(IR_node, kwargs) return IR_node def rename_GatherV2(self, source_node): IR_node = self.rename_Gather(source_node) kwargs = {} kwargs['axis'] = source_node.layer.attr['axis'].i assign_IRnode_values(IR_node, kwargs) def rename_StridedSlice(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'Slice') kwargs = {} kwargs = { 'begin_mask' : source_node.get_attr('begin_mask'), 'end_mask' : source_node.get_attr('end_mask'), } starts = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor starts = tensor_util.MakeNdarray(starts).tolist() kwargs['starts'] = starts ends = self.get_parent(source_node.name, [2]).layer.attr['value'].tensor ends = tensor_util.MakeNdarray(ends).tolist() kwargs['ends'] = ends if self.get_parent(source_node.name, [3]) != None: strides = self.get_parent(source_node.name, [3]).layer.attr['value'].tensor strides = tensor_util.MakeNdarray(strides).tolist() kwargs['strides'] = strides assign_IRnode_values(IR_node, kwargs) def rename_ResizeNearestNeighbor(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node_size = self.src_graph.get_parent(source_node.name, [1]) kwargs['size'] = self.tensor_shape_to_list(input_node_size.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Conv2D(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'Conv') kwargs = {} kwargs['strides'] = source_node.get_attr('strides') kwargs['padding'] = source_node.get_attr('padding') input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] # weights input_node_weight = self.src_graph.get_parent(source_node.name, [1]) tensor_content = self.check_const(input_node_weight).get_attr('value') W = tensor_util.MakeNdarray(tensor_content) kwargs['kernel_shape'] = self.tensor_shape_to_list(input_node_weight.get_attr('_output_shapes'))[0] self.set_weight(source_node.name, 'weights', W) self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][:-2]) assign_IRnode_values(IR_node, kwargs) self._get_bias(source_node, IR_node) def rename_Relu(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} assign_IRnode_values(IR_node, kwargs) def rename_MaxPool(self, source_node): self._convert_pooling(source_node, b'MAX') def rename_AvgPool(self, source_node): self._convert_pooling(source_node, b'AVG') def rename_LRN(self, source_node): IR_node = self._convert_identity_operation(source_node) # alpha IR_node.attr["alpha"].f = float(source_node.get_attr("alpha", "0.0001")) # beta IR_node.attr["beta"].f = float(source_node.get_attr("beta", "0.75")) IR_node.attr["size"].i = source_node.get_attr("depth_radius") IR_node.attr["bias"].f = float(source_node.get_attr("bias")) def rename_Concat(self, source_node): n = len(source_node.in_edges) IR_node = self._convert_identity_operation(source_node, start_idx=1, end_idx=n, new_op='Concat') axis = self.tf_graph.get_parent(source_node.name, [0]) IR_node.attr["axis"].i = axis.get_attr('value').int_val[0] def rename_ConcatV2(self, source_node): n = len(source_node.in_edges) IR_node = self._convert_identity_operation(source_node, start_idx=0, end_idx=n-1, new_op='Concat') axis = self.tf_graph.get_parent(source_node.name, [n-1]) IR_node.attr["axis"].i = axis.get_attr('value').int_val[0] def rename_MatMul(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1) input_weight_node = self.src_graph.get_parent(source_node.name, [1]) weightnode = self.check_const(input_weight_node) weight_value = weightnode.get_attr('value') weight = tensor_util.MakeNdarray(weight_value) self.set_weight(source_node.name, 'weights', weight) units = source_node.layer.attr['_output_shapes'].list.shape[-1].dim[-1].size IR_node.attr['units'].i = units if source_node.out_edges and self.tf_graph.get_node(source_node.out_edges[0]).type == 'BiasAdd': add_node = self.tf_graph.get_node(source_node.out_edges[0]) add_node.covered = True add_node.real_name = source_node.real_name TensorflowParser2._copy_and_reop(source_node, IR_node, 'FullyConnected') variable = self.tf_graph.get_node(add_node.in_edges[1]) #add_bias node biasnode = self.check_const(variable) bias_value = biasnode.get_attr('value') bias = tensor_util.MakeNdarray(bias_value) self.set_weight(source_node.name, 'bias', bias) IR_node.attr['use_bias'].b = True def rename_Softmax(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} IR_node.attr["dim"].i = 1 assign_IRnode_values(IR_node, kwargs) def rename_BiasAdd(self, source_node): # Skip BiasAdd source_node.real_name = self.src_graph.get_node(source_node.in_edges[0]).real_name def rename_QuantizeV2(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'QuantizeV2') TensorflowParser2._copy_shape(source_node, IR_node) def rename_QuantizedRelu(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = "QuantizedRelu") kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} assign_IRnode_values(IR_node, kwargs) def rename_QuantizedReshape(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx = 1) kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} assign_IRnode_values(IR_node, kwargs) def rename_QuantizedConv2D(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'QConv') kwargs = {} kwargs['strides'] = source_node.get_attr('strides') kwargs['padding'] = source_node.get_attr('padding') # weights input_node = self.src_graph.get_parent(source_node.name, [1]) tensor_content = input_node.get_attr('value') W = tensor_util.MakeNdarray(tensor_content) W = W.astype(np.uint8) kwargs['kernel_shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] input_node_minw = self.src_graph.get_parent(source_node.name, [4]) min_W = input_node_minw.get_attr('value').float_val[0] input_node_maxw = self.src_graph.get_parent(source_node.name, [5]) max_W = input_node_maxw.get_attr('value').float_val[0] if source_node.get_attr('Tfilter') == tensorflow.quint8: W = ((max_W - min_W)/255.0) * W + min_W else: assert False, ('Only uint8 weights handled currently by the converter') self.set_weight(source_node.name, 'kernel_weights', W) assign_IRnode_values(IR_node, kwargs) def rename_Requantize(self, source_node): input_node = self.get_parent(source_node.name, [0]) son_node = self.get_son(source_node.name, [0]) son_node.real_name = source_node.name def rename_RequantizationRange(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'RequantizationRange') TensorflowParser2._copy_shape(source_node, IR_node) def rename_ZerosLike(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'ZerosLike') def rename_Rank(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Rank') def rename_Transpose(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'Transpose') input_node_perm = self.get_parent(source_node.name, [1]) # input_node_perm = self.check_const(self.get_parent(source_node.name, [1], True)) tensor_content = input_node_perm.get_attr('value') perm = tensor_util.MakeNdarray(tensor_content).tolist() assign_IRnode_values(IR_node, {'perm' : perm}) def rename_GreaterEqual(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'GreaterEqual') def rename_Greater(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'Greater') def rename_Equal(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Equal') def rename_All(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'All') def rename_LogicalAnd(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Mul') def rename_Pad(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'Pad') kwargs = {} kwargs['mode'] = 'constant' # paddings padding = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor shapes = tensor_util.MakeNdarray(padding) kwargs['pads'] = convert_tf_pad_to_onnx(shapes) assign_IRnode_values(IR_node, kwargs) def rename_FusedBatchNorm(self, source_node): scalenode = self.check_const(self.get_parent(source_node.name, [1], True)) if ':' in source_node.in_edges[1]: # ? scalenode = None if scalenode: scale_value = scalenode.get_attr('value') IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'BatchNorm') # for attr.shape >= 2 for i in range(len(IR_node.attr["_output_shapes"].list.shape)-1): IR_node.attr["_output_shapes"].list.shape.pop() else: # For models built by slim.batch_norm, remove duplicate BN (eg.facenet) return scale = tensor_util.MakeNdarray(scale_value) self.set_weight(source_node.name, 'scale', scale) IR_node.attr['scale'].b = True IR_node.attr['epsilon'].f = source_node.get_attr('epsilon', 0) biasnode = self.check_const(self.get_parent(source_node.name, [2], True)) if biasnode: bias_value = biasnode.get_attr('value') else: innode = self.get_parent(source_node.name, [2], True) name = innode.name.split(':')[0] bias_value = self.check_const(self.src_graph.layer_map[name]).get_attr('value') bias = tensor_util.MakeNdarray(bias_value) self.set_weight(source_node.name, 'bias', bias) IR_node.attr['bias'].b = True meannode = self.check_const(self.get_parent(source_node.name, [3], True)) mean_value = meannode.get_attr('value') mean = tensor_util.MakeNdarray(mean_value) self.set_weight(source_node.name, 'mean', mean) variancenode = self.check_const(self.get_parent(source_node.name, [4], True)) variance_value = variancenode.get_attr('value') variance = tensor_util.MakeNdarray(variance_value) self.set_weight(source_node.name, 'var', variance) def rename_SpaceToBatchND(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'SpaceToBatchND') def rename_BatchToSpaceND(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'BatchToSpaceND') def rename_ArgMax(self, source_node): IR_node = self._convert_identity_operation(source_node, end_idx=1, new_op = 'ArgMax') def rename_Slice(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Slice') def rename_Split(self, source_node): if source_node.get_attr('num_split') == 1: source_node.real_name = self.get_parent(source_node.name, [1]).real_name else: IR_node = self._convert_identity_operation(source_node, start_idx=1, new_op = 'Split') kwargs = { 'axis' : self.get_parent(source_node.name, [0]).layer.attr['value'].tensor.int_val[0], 'split' : source_node.get_attr('num_split') } assign_IRnode_values(IR_node, kwargs) def rename_Tile(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Tile') def rename_Sqrt(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Sqrt') def rename_Tanh(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = {} input_node = self.src_graph.get_parent(source_node.name, [0]) kwargs['shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] assign_IRnode_values(IR_node, kwargs) def rename_Log(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op = 'Log')model-pivot/ox/tensorflow/tensorflow_graph.py000066400000000000000000000054631415504170400221130ustar00rootroot00000000000000import warnings warnings.filterwarnings("ignore") from ox.common.DataStructure.graph import GraphNode, Graph from tensorflow.core.framework.node_def_pb2 import NodeDef from tensorflow.core.framework import attr_value_pb2 class TensorflowGraphNode(GraphNode): def __init__(self, layer): super(TensorflowGraphNode, self).__init__(layer) self.in_nodes = list() self.out_nodes = list() self._scope = str() @property def scope(self): return self._scope @scope.setter def scope(self, scope): self._scope = scope @property def name(self): return self.layer.name @property def type(self): return self.layer.op @property def tf_layer(self): return self.layer def get_attr(self, name, default_value = None): if name in self.layer.attr: attr = self.layer.attr[name] field = attr.WhichOneof('value') val = getattr(attr, field) if field else default_value if isinstance(val, attr_value_pb2.AttrValue.ListValue): return list(val.ListFields()[0][1]) else: return val.decode('utf-8') if isinstance(val, bytes) else val else: return default_value class TensorflowGraph(Graph): multi_tensor_type = [ "Slice", "Split", "Unpack" ] def __init__(self, model): # sanity check. pass super(TensorflowGraph, self).__init__(model) self.model = model def build(self): for i, layer in enumerate(self.model.node): self.layer_map[layer.name] = TensorflowGraphNode(layer) self.layer_name_map[layer.name] = layer.name for pred in layer.input: if pred not in self.layer_map: if not pred.split(':')[0] in self.layer_map: #test new_node = NodeDef() new_node.name = pred new_node.op = "NoOp" self.layer_map[pred] = TensorflowGraphNode(new_node) self.layer_name_map[pred] = pred self.tf_make_connection(pred, layer.name) super(TensorflowGraph, self).build() def tf_make_connection(self, src, dst): if ':' not in src and self.get_node(src).type in self.multi_tensor_type: src += ':0' self._make_connection(src, dst) src_node = self.get_node(src.split(':')[0]) dst_node = self.get_node(dst.split(':')[0]) if not src_node in self.layer_map[dst.split(':')[0]].in_nodes: self.layer_map[dst.split(':')[0]].in_nodes.append(src_node) if not dst_node in self.layer_map[src.split(':')[0]].out_nodes: self.layer_map[src.split(':')[0]].out_nodes.append(dst_node) model-pivot/ox/tensorflow/tensorflow_parser.py000066400000000000000000001166101415504170400223030ustar00rootroot00000000000000import warnings warnings.filterwarnings("ignore") import numpy as np import tensorflow from tensorflow.python.framework import tensor_util from tensorflow.core.framework import attr_value_pb2 from ox.tensorflow.tensorflow_graph import TensorflowGraph import ox.common.IR.graph_pb2 as graph_pb2 from ox.common.IR.graph_pb2 import NodeDef, GraphDef, DataType from ox.common.utils import * from ox.common.DataStructure.parser import Parser from tensorflow.tools.graph_transforms import TransformGraph from ox.rewriter.utils import * import tempfile import os import shutil class TensorflowParser(Parser): skip_prefix = [ "^", "train_op", "save", "gradients", "global_step", "distort_image", "Adagrad", ] skip_scope = [ "random_uniform", "Initializer", "optimizer", "weight_loss", "parallel_read", "case" ] skip_type = set([ "L2Loss", "VariableV2", "Const", "Assign", "RandomUniform", "FIFOQueueV2" ]) dtype_map = { 0 : graph_pb2.DT_UNDEFINED, 1 : graph_pb2.DT_FLOAT32, 2 : graph_pb2.DT_FLOAT64, 3 : graph_pb2.DT_INT32, 4 : graph_pb2.DT_UINT8, 5 : graph_pb2.DT_INT16, 6 : graph_pb2.DT_INT8, 7 : graph_pb2.DT_STRING, 9 : graph_pb2.DT_INT64, 10 : graph_pb2.DT_BOOL, 19 : graph_pb2.DT_FLOAT16 } @property def src_graph(self): return self.tf_graph @staticmethod def _shapeToStr(shapes): return [dim.size if dim.size > 0 else 1 for dim in shapes.dim] @staticmethod def _load_meta(model_network_path): """Load a tensorflow meta file from disk Parameters ---------- model_network_path: str Path where the model network path is (protobuf meta file) Returns ------- model: A tensorflow protobuf file """ from tensorflow.core.protobuf import meta_graph_pb2 from ox.common.IR.IR_graph import load_protobuf_from_file meta_graph = meta_graph_pb2.MetaGraphDef() load_protobuf_from_file(meta_graph, model_network_path) graph = meta_graph.graph_def print ("Tensorflow model file [%s] loaded successfully." % model_network_path) return graph @staticmethod def _load_weights(model_weight_path): """Load a tensorflow checkpoint file from disk Parameters ---------- model_weight_path: str Path where the weight path is (checkpoint file) Returns ------- model: tensor name --> ndarry """ reader = tensorflow.train.NewCheckpointReader(model_weight_path) var_to_shape_map = reader.get_variable_to_shape_map() data = dict() for name in var_to_shape_map: tensor = reader.get_tensor(name) name_seg = name.split("/") if name_seg[-1] == "ExponentialMovingAverage": name = "/".join(name_seg[:-1]) data[name] = tensor print ("Tensorflow checkpoint file [%s] loaded successfully. [%d] variables loaded." % (model_weight_path, len(data))) return data @staticmethod def _get_scopes(layer_name): return layer_name.split('/') def check_const(self, node): while node: if node.type == "Const": return node elif node.type == "NoOp": return None else: node = self.get_parent(node.name, [0]) def _add_constant_node(self, source_node): parent_ids=range(len(source_node.in_edges)) for idx in parent_ids: parent_node = self.tf_graph.get_node(source_node.in_edges[idx]) if parent_node.type == 'Const': self._rename_Const(parent_node) def _rename_Const(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count=0, new_op='Constant') # Constant value = source_node.get_attr('value') if value.float_val: shape = tuple(self.tensor_shape_to_list(value.tensor_shape)) value = np.full(shape, value.float_val[0]) elif value.int_val: shape = tuple(self.tensor_shape_to_list(value.tensor_shape)) value = np.full(shape, value.int_val[0]) else: value = np.array(tensor_util.MakeNdarray(value).tolist()) if value.ndim > 1: self.set_weight(source_node.name, 'value', value) else: kwargs = {'value': value} assign_IRnode_values(IR_node, kwargs) def _convert_reduction_operators(self, source_node, new_op = None): IR_node = self._convert_identity_operation(source_node, 0, 1, new_op) # keep dims IR_node.attr['keepdims'].b = source_node.layer.attr['keep_dims'].b # axes axes = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor axes = tensor_util.MakeNdarray(axes) IR_node.attr['axes'].list.i.extend(axes) def _convert_layers_batchnorm(self, source_node): # name, op IR_node = self.IR_graph.node.add() TensorflowParser._copy_and_reop(source_node, IR_node, 'BatchNorm') # epsilon epsilon = self.get_parent(source_node.name, [1]) IR_node.attr['epsilon'].f = epsilon.layer.attr['value'].tensor.float_val[0] # moving variance (var) moving_variance = self.get_parent(source_node.name, [0, 0]) # print(moving_variance.name) if self.weight_loaded and moving_variance.name in self.ckpt_data.keys(): self.set_weight(source_node.name, 'var', self.ckpt_data[moving_variance.name]) # gamma (scale) gamma = self.get_son(source_node.name, [0, 0], True) gamma = self.get_parent(gamma.name, [1, 0], True) if gamma is None or not gamma.type.startswith('Variable'): IR_node.attr['scale'].b = False output_node = self.get_son(source_node.name, [0, 0, 0], True) else: IR_node.attr['scale'].b = True if self.weight_loaded: self.set_weight(source_node.name, 'scale', self.ckpt_data[gamma.name]) output_node = self.get_son(source_node.name, [0, 0, 0, 0], True) if output_node.type == 'Sub': output_node = self.get_son(source_node.name, [0, 0, 1, 0], True) # mean mean = self.get_parent(output_node.name, [1, 1, 0, 0], True) if self.weight_loaded and mean.name in self.ckpt_data.keys(): self.set_weight(source_node.name, 'mean', self.ckpt_data[mean.name]) # bias bias = self.get_parent(output_node.name, [1, 0, 0], True) if bias is None or not bias.type.startswith('Variable'): IR_node.attr['bias'].b = False else: IR_node.attr['bias'].b = True if self.weight_loaded: self.set_weight(source_node.name, 'bias', self.ckpt_data[bias.name]) # input node assert output_node.type == 'Add' input_node = self.get_parent(output_node.name, [0, 0]) IR_node.input.append(input_node.real_name) # output node output_node.real_name = source_node.name def __init__(self, meta_file, checkpoint_file, dest_nodes, inputShape = None, in_nodes = None): super(TensorflowParser, self).__init__() # load model files into TensorFlow graph if meta_file: model = TensorflowParser._load_meta(meta_file) if checkpoint_file: self.ckpt_data = TensorflowParser._load_weights(checkpoint_file) self.weight_loaded = True # extract subgraph using in_nodes and dest_nodes if in_nodes != None and inputShape != None: from tensorflow.python.tools import strip_unused_lib from tensorflow.python.framework import dtypes from tensorflow.python.platform import gfile model = strip_unused_lib.strip_unused( input_graph_def = model, input_node_names = in_nodes, output_node_names = dest_nodes, placeholder_type_enum = dtypes.float32.as_datatype_enum) input_list = [None] for i in range(len(inputShape)): input_list.append(tensorflow.Dimension(inputShape[i])) tensor_input = tensorflow.TensorShape(input_list) # Build network graph self.tf_graph = TensorflowGraph(model) for node in self.tf_graph.model.node: if node.name in in_nodes: node.attr['shape'].shape.CopyFrom(tensor_input.as_proto()) node.attr['_output_shapes'].list.shape.pop() #unknown_rank pop node.attr['_output_shapes'].list.shape.extend([tensor_input.as_proto()]) # extract subgraph using dest_nodes elif dest_nodes != None: from tensorflow.python.framework.graph_util import extract_sub_graph model = extract_sub_graph(model, dest_nodes) self.tf_graph = TensorflowGraph(model) else: self.tf_graph = TensorflowGraph(model) # Graph Transform transforms = ["fold_constants(ignore_errors=true)"] # Get input node name if not in_nodes: in_nodes = [] for node in model.node: if node.op == 'Placeholder': in_nodes.append(node.name) transformed_graph_def = TransformGraph(model, in_nodes, dest_nodes, transforms) in_type_list = {} in_shape_list = {} for n in transformed_graph_def.node: if n.name in in_nodes: in_type_list[n.name] = n.attr['dtype'].type in_node_shape = n.attr['shape'].shape in_node_shape_str = self._shapeToStr(in_node_shape) in_shape_list[n.name] = in_node_shape_str dtype = tensorflow.float32 with tensorflow.Graph().as_default() as g: input_map = {} for in_node in in_nodes: if in_type_list[in_node] == 1 or in_type_list[in_node] == 0: dtype = tensorflow.float32 elif in_type_list[in_node] == 3: dtype = tensorflow.int32 elif in_type_list[in_node] == 10: dtype = tensorflow.bool x = tensorflow.placeholder(dtype, shape = in_shape_list[in_node]) input_map[in_node] = x tensorflow.import_graph_def(transformed_graph_def, name='', input_map=input_map) with tensorflow.Session(graph = g) as sess: tempdir = tempfile.mkdtemp() meta_graph_def = tensorflow.train.export_meta_graph(filename=os.path.join(tempdir, 'my-model.meta')) model = meta_graph_def.graph_def shutil.rmtree(tempdir) self.tf_graph = TensorflowGraph(model) self.tf_graph.build() process_graph(self.tf_graph, self.ckpt_data) @classmethod def _skip_node(cls, source_node): if source_node.covered: return True for prefix in cls.skip_prefix: if source_node.name.startswith(prefix): return True scopes = TensorflowParser._get_scopes(source_node.name) for s in scopes: if s in cls.skip_scope: return True return False @staticmethod def tensor_shape_to_list(shapes): if isinstance(shapes, attr_value_pb2.AttrValue): return [dim.size for dim in shapes.shape.dim] elif isinstance(shapes, attr_value_pb2.tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2.TensorShapeProto): return [dim.size for dim in shapes.dim] else: ret = [] for shape in shapes: this_one = [dim.size for dim in shape.dim] ret.append(this_one) return ret ''' check current source_node wether has input weights. If it has, set the weights into weight dict and remove the input edge. return edges' index which do not include edge connecting weights ''' def _check_weights(self, source_node, start_edge_id = 0, in_edge_count = None): if in_edge_count == None: in_edge_count = len(source_node.in_edges) - start_edge_id valid_pre_ids = [] for pre_idx in range(start_edge_id, start_edge_id + in_edge_count): pre_node = self.get_parent(source_node.name, [pre_idx]) if pre_node.type == 'Identity' and pre_node.name.split('/')[-1] == 'read': weight_node = self.get_parent(pre_node.name, [0]) assert 'Variable' in weight_node.type self.set_weight(source_node.name, 'weights', self.ckpt_data[weight_node.name]) source_node.feed_weights = True else: valid_pre_ids.append(pre_idx) return valid_pre_ids def _convert_padding(self, source_node, IR_node, kernel_size): # TODO: Fused conv and pool with padding is different from defused operators input_node = self.get_parent(source_node.name, [0]) input_shape = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] if source_node.get_attr('padding') == 'VALID': dims = len(input_shape) assign_IRnode_values(IR_node, {'auto_pad' : "VALID", 'pads' : [0, 0] * dims}) elif source_node.get_attr('padding') == 'SAME': padding = compute_tf_same_padding( input_shape, kernel_size, source_node.get_attr('strides')) assign_IRnode_values(IR_node, {'auto_pad' : "SAME_UPPER", 'pads' : padding}) else: assert False def _convert_pooling(self, source_node, pool_type): IR_node = self._convert_identity_operation(source_node, new_op='Pool') kwargs = {} # strides kwargs['strides'] = source_node.get_attr('strides') # window_shape kwargs['kernel_shape'] = source_node.get_attr('ksize') # pool type kwargs['pooling_type'] = pool_type # padding self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][1:-1]) assign_IRnode_values(IR_node, kwargs) def gen_IR(self): node_set = set() for layer in self.src_graph.topological_sort: current_node = self.src_graph.get_node(layer) if self._skip_node(current_node): # print('***Skip:', current_node.type ) continue node_type = current_node.type node_set.add(node_type) if hasattr(self, "rename_" + node_type): # print("rename_" + node_type) func = getattr(self, "rename_" + node_type) func(current_node) else: # print('***Unknown:', current_node.type) self.rename_UNKNOWN(current_node) return list(node_set) @staticmethod def _copy_and_reop(source_node, IR_node, new_op = None): if new_op == None: new_op = source_node.type IR_node.name = source_node.name IR_node.op = new_op kwargs = {} if 'data_format' in source_node.layer.attr: kwargs["data_format"] = source_node.get_attr('data_format') if 'dtype' in source_node.layer.attr: assert source_node.layer.attr['dtype'].type in TensorflowParser.dtype_map, 'type [{}] is unknown.'.format(source_node.layer.attr['dtype'].type) IR_node.attr["dtype"].type = TensorflowParser.dtype_map[source_node.layer.attr['dtype'].type] if '_output_shapes' in source_node.layer.attr: IR_node.attr["_output_shapes"].MergeFromString(source_node.layer.attr['_output_shapes'].SerializeToString()) if hasattr(source_node, 'feed_weights'): kwargs["feed_weights"] = True if hasattr(source_node, 'kwargs'): kwargs.update(source_node.kwargs) kwargs['scope'] = source_node.scope assign_IRnode_values(IR_node, kwargs) def _convert_inedge(self, source_node, IR_node, start_idx = 0, end_idx = None, in_ids=None): if end_idx == None: end_idx = len(source_node.in_edges) - start_idx if not in_ids: in_ids = range(start_idx, end_idx + start_idx) for idx in in_ids: if ':' in source_node.in_edges[idx]: input_tensor = self.src_graph.get_node(source_node.in_edges[idx]).real_name + ':' + source_node.in_edges[idx].split(':')[1] else: input_tensor = self.src_graph.get_node(source_node.in_edges[idx]).real_name IR_node.input.append(input_tensor) def _get_bias(self, source_node, IR_node): if not source_node.out_edges: return add_node = self.tf_graph.get_node(source_node.out_edges[0]) if add_node.type != "Add" and add_node.type != "BiasAdd": return variable = self.tf_graph.get_node(add_node.in_edges[1]) if variable.type != "Identity": return variable = self.tf_graph.get_node(variable.in_edges[0]) # assert variable.layer.attr['shape'].shape.dim[0].size == IR_node.attr['kernel_shape'].list.i[-1] if self.weight_loaded: assert variable.name in self.ckpt_data current_layer = self.weights[source_node.name] current_layer['bias'] = self.ckpt_data[variable.name] add_node.real_name = IR_node.name add_node.covered = True IR_node.attr['use_bias'].b = True @staticmethod def _copy_shape(source_node, IR_node): assert 'shape' in source_node.layer.attr if source_node.layer.attr['shape'].list.shape: IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['shape'].list.shape[0].SerializeToString()) else: IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['shape'].shape.SerializeToString()) def rename_UNKNOWN(self, source_node): if source_node.type in self.skip_type: return print("TensorflowEmitter has not supported operator [%s] with name [%s]." % (source_node.type, source_node.name)) return def rename_Placeholder(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='DataInput') # shape TensorflowParser._copy_shape(source_node, IR_node) if len(IR_node.attr['shape'].shape.dim)>0 and len(IR_node.attr['_output_shapes'].list.shape)>0 and len(IR_node.attr['_output_shapes'].list.shape[0].dim)>0: IR_node.attr['shape'].shape.dim[0].size = -1 IR_node.attr['_output_shapes'].list.shape[0].dim[0].size = -1 def rename_Conv2D(self, source_node): """ weights: name_weights, name_bias """ IR_node = self._convert_identity_operation(source_node, 0, 1, 'Conv') kwargs = {} # strides kwargs['strides'] = source_node.get_attr('strides') # input[1] : W # filter W = self.tf_graph.get_node(source_node.layer.input[1]) if W.type == 'Const': kwargs['kernel_shape'] = tensor_shape = self.tensor_shape_to_list(W.layer.attr['value'].tensor.tensor_shape) else: W = self.tf_graph.get_node(W.layer.input[0]).layer kwargs['kernel_shape'] = self.tensor_shape_to_list(W.attr['shape']) # padding self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][:-2]) if self.weight_loaded: self.set_weight(source_node.name, 'weights', self.ckpt_data[W.name]) assign_IRnode_values(IR_node, kwargs) # output[0] : B self._get_bias(source_node, IR_node) def _convert_identity_operation(self, source_node, start_edge_id = 0, in_edge_count = None, new_op = None): IR_node = self.IR_graph.node.add() in_ids = self._check_weights(source_node, start_edge_id, in_edge_count) TensorflowParser._copy_and_reop(source_node, IR_node, new_op) self._convert_inedge(source_node, IR_node, start_edge_id, in_edge_count, in_ids) return IR_node def rename_Less(self, source_node): pass def rename_Assert(self, source_node): pass def rename_All(self, source_node): pass def rename_Relu(self, source_node): self._convert_identity_operation(source_node) def rename_Softmax(self, source_node): self._convert_identity_operation(source_node) def rename_Relu6(self, source_node): self._convert_identity_operation(source_node) def rename_Add(self, source_node): if not source_node.covered: scopes = self._get_scopes(source_node.name) if len(scopes) < 3: IR_node = self._convert_identity_operation(source_node) # print(IR_node.input) elif scopes[-2] == 'dropout': # converted [dropout] pass elif scopes[-2] == 'batchnorm': # convert [tf.contrib.layers.batch_norm] self._convert_layers_batchnorm(source_node) else: # normal Add self._add_constant_node(source_node) self._convert_identity_operation(source_node) def rename_Sub(self, source_node): self._add_constant_node(source_node) self._convert_identity_operation(source_node) def rename_Reshape(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 1) kwargs = {'shape' : self.tensor_shape_to_list(source_node.get_attr('_output_shapes'))[0]} assign_IRnode_values(IR_node, kwargs) def rename_Abs(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 1, new_op = 'Abs') def rename_Square(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 1, new_op = 'Square') def rename_MatMul(self, source_node): W = self.tf_graph.get_node(self.tf_graph.get_node(source_node.in_edges[1]).in_edges[0]) if 'Variable' in W.type: """ weights: name_weights, name_bias """ IR_node = self._convert_identity_operation(source_node, in_edge_count = 1) # units units = source_node.layer.attr['_output_shapes'].list.shape[-1].dim[-1].size IR_node.attr['units'].i = units # Weights W = self.tf_graph.get_node(self.tf_graph.get_node(source_node.in_edges[1]).in_edges[0]) if self.weight_loaded: self.set_weight(source_node.name, 'weights', self.ckpt_data[W.name]) if source_node.out_edges and (self.tf_graph.get_node(source_node.out_edges[0]).type == 'Add' or self.tf_graph.get_node(source_node.out_edges[0]).type == 'BiasAdd'): add_node = self.tf_graph.get_node(source_node.out_edges[0]) add_node.covered = True add_node.real_name = source_node.real_name # FullyConnected Layer # name, op TensorflowParser._copy_and_reop(source_node, IR_node, 'FullyConnected') # get Bias B = self.tf_graph.get_node(self.tf_graph.get_node(source_node.out_edges[0]).in_edges[1]).in_edges[0] if self.weight_loaded: self.set_weight(source_node.name, 'bias', self.ckpt_data[B]) IR_node.attr['use_bias'].b = True else: # Matmul Layer TensorflowParser._copy_and_reop(source_node, IR_node, 'FullyConnected') assign_IRnode_values(IR_node, {'use_bias' : False}) else: self._convert_identity_operation(source_node, new_op='MatMul') def rename_RealDiv(self, source_node): scopes = self._get_scopes(source_node.name) # Deal Dropout if len(scopes) > 1 and scopes[-2][:7] == 'dropout': IR_node = self._convert_identity_operation(source_node, in_edge_count = 1, new_op = 'Dropout') # keep prob if 'value' in self.tf_graph.get_node(source_node.layer.input[1]).layer.attr: IR_node.attr['keep_prob'].f = self.tf_graph.get_node(source_node.layer.input[1]).layer.attr['value'].tensor.float_val[0] else: IR_node.attr['keep_prob'].f = 1.0 # Remove nodes # Mul mul_node = self.tf_graph.get_node(source_node.out_edges[0]) assert mul_node.type == "Mul" mul_node.covered = True mul_node.real_name = source_node.name # Floor floor_node = self.tf_graph.get_node(mul_node.in_edges[1]) assert floor_node.type == "Floor" floor_node.covered = True else: # print (source_node) # print (source_node.layer) # assert False self._convert_identity_operation(source_node, new_op='Div') def rename_Floor(self, source_node): scopes = self._get_scopes(source_node.name) assert scopes[-2] == 'dropout' def rename_MaxPool(self, source_node): self._convert_pooling(source_node, b'MAX') def rename_AvgPool(self, source_node): self._convert_pooling(source_node, b'AVG') def rename_Identity(self, source_node): source_node.real_name = self.src_graph.get_node(source_node.in_edges[0]).real_name def rename_Squeeze(self, source_node): IR_node = self._convert_identity_operation(source_node) IR_node.attr['axes'].MergeFromString(source_node.layer.attr['squeeze_dims'].SerializeToString()) def rename_QueueDequeueUpToV2(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 0, new_op = 'DataInput') IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['_output_shapes'].list.shape[0].SerializeToString()) IR_node.attr['shape'].shape.dim[0].size = -1 IR_node.attr['dtype'].type = self.dtype_map[source_node.layer.attr['component_types'].list.type[0]] def rename_QueueDequeueManyV2(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 0, new_op = 'DataInput') IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['_output_shapes'].list.shape[0].SerializeToString()) IR_node.attr['shape'].shape.dim[0].size = -1 IR_node.attr['dtype'].type = self.dtype_map[source_node.layer.attr['component_types'].list.type[0]] # def rename_RandomShuffleQueueV2(self, source_node): # # print(source_node.layer) # IR_node = self._convert_identity_operation(source_node, in_edge_count = 0, new_op = 'DataInput') # # IR_node.attr['shape'].shape.MergeFromString(source_node.layer.attr['_output_shapes'].list.shape[0].SerializeToString()) # # IR_node.attr['shape'].shape.dim[0].size = -1 # IR_node.attr['dtype'].type = self.dtype_map[source_node.layer.attr['component_types'].list.type[0]] def rename_Pad(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count = 1, new_op = 'Pad') kwargs = {} kwargs['mode'] = 'constant' kwargs['constant_values'] = 0.0 # paddings padding = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor shapes = tensor_util.MakeNdarray(padding) kwargs['pads'] = convert_tf_pad_to_onnx(shapes) assign_IRnode_values(IR_node, kwargs) def rename_Mean(self, source_node): self._convert_reduction_operators(source_node, new_op = 'ReduceMean') def rename_ConcatV2(self, source_node): n = len(source_node.in_edges) - 1 self._add_constant_node(source_node) IR_node = self._convert_identity_operation(source_node, in_edge_count = n, new_op = 'Concat') axis = self.tf_graph.get_parent(source_node.name, [n]) IR_node.attr['axis'].i = axis.layer.attr['value'].tensor.int_val[0] def rename_DepthwiseConv2dNative(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count=1, new_op='DepthwiseConv') kwargs = {} kwargs['strides'] = source_node.get_attr('strides') input_node = self.src_graph.get_parent(source_node.name, [1]) kwargs['kernel_shape'] = self.tensor_shape_to_list(input_node.get_attr('_output_shapes'))[0] self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][:-2]) if self.weight_loaded: weight = self.src_graph.get_parent(source_node.name, [1, 0]) self.set_weight(source_node.name, 'weights', self.ckpt_data[weight.name]) assign_IRnode_values(IR_node, kwargs) def rename_FusedBatchNorm(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count=1, new_op='BatchNorm') IR_node.attr['epsilon'].f = source_node.get_attr('epsilon', 0) # gamma (scale) scale = self.get_parent(source_node.name, [1], True) if scale.type == 'Const': value = scale.get_attr('value') shape = value.tensor_shape assert len(shape.dim) == 1 shape = shape.dim[0].size assert len(value.float_val) == 1 value = value.float_val[0] if np.isclose(value, 1.0): IR_node.attr['scale'].b = False else: IR_node.attr['scale'].b = True if self.weight_loaded: self.set_weight(source_node.name, 'scale', np.array([value] * shape)) else: scale = self.get_parent(scale.name, [0], True) if self.weight_loaded: self.set_weight(source_node.name, 'scale', self.ckpt_data[scale.name]) IR_node.attr['scale'].b = True # bias bias = self.get_parent(source_node.name, [2, 0], True) IR_node.attr['bias'].b = True # Mean mean = self.get_parent(source_node.name, [3, 0], True) # Var var = self.get_parent(source_node.name, [4, 0], True) if self.weight_loaded: self.set_weight(source_node.name, 'bias', self.ckpt_data[bias.name]) self.set_weight(source_node.name, 'mean', self.ckpt_data[mean.name]) self.set_weight(source_node.name, 'var', self.ckpt_data[var.name]) def rename_Shape(self, source_node): IR_node = self._convert_identity_operation(source_node, in_edge_count=1, new_op='Shape') def rename_Pack(self, source_node): N = len(source_node.layer.input) in_edge_count = 0 for i in range(N): this_node = self.get_parent(source_node.name, [i]) if this_node.type == 'Const': IR_node = self.IR_graph.node.add() TensorflowParser._copy_and_reop(this_node, IR_node, 'Constant') kwargs = { 'value' : this_node.layer.attr['value'].tensor.int_val[0], } assign_IRnode_values(IR_node, kwargs) # source_node.layer.input.remove(this_node.name) else: in_edge_count += 1 IR_node = self._convert_identity_operation(source_node, new_op='Pack', in_edge_count=in_edge_count) kwargs = { 'axis' : source_node.layer.attr['axis'].i, 'N' : source_node.layer.attr['N'].i } assign_IRnode_values(IR_node, kwargs) def rename_Gather(self, source_node): W = self.src_graph.get_parent(source_node.name, [0]) W = self.src_graph.get_parent(W.name, [0]) if 'Variable' in W.type: IR_node = self._convert_identity_operation(source_node, new_op='Embedding') self.set_weight(source_node.name, "weights", self.ckpt_data[W.name]) kwargs = { 'input_dim' : self.ckpt_data[W.name].shape[0], 'output_dim' : self.ckpt_data[W.name].shape[1], 'mask_zero' : False } kwargs['axis'] = 0 # add default assign_IRnode_values(IR_node, kwargs) else: IR_node = self._convert_identity_operation(source_node, new_op='Gather') return IR_node def rename_GatherV2(self, source_node): IR_node = self.rename_Gather(source_node) kwargs = {} kwargs['axis'] = source_node.layer.attr['axis'].i assign_IRnode_values(IR_node, kwargs) def rename_Transpose(self, source_node): self._add_constant_node(source_node) IR_node = self._convert_identity_operation(source_node) def rename_Sigmoid(self, source_node): self._convert_identity_operation(source_node) def rename_Mul(self, source_node): scale1 = self.get_parent(source_node.name, [1], True) scale2 = self.get_parent(source_node.name, [0], True) if scale1.type == 'Const' or scale2.type == 'Const': self._add_constant_node(source_node) self._convert_identity_operation(source_node) elif scale2.type == 'Identity': scale2 = self.get_parent(scale2.name, [0], True) assert scale2.type == "VariableV2" self.set_weight(source_node.name, 'alpha', self.ckpt_data[scale2.name]) self._convert_identity_operation(source_node) else: self._convert_identity_operation(source_node) ''' tf.unpack has been deprecated with replaced tf.unstack ''' def rename_Unpack(self, source_node): IR_node = self._convert_identity_operation(source_node, new_op='Unstack') kwargs = { 'axis' : source_node.get_attr('axis'), 'num' : source_node.get_attr('num') } assign_IRnode_values(IR_node, kwargs) def rename_Split(self, source_node): if source_node.get_attr('num_split') == 1: for n in source_node.out_nodes: for idx, e in enumerate(n.in_edges): if source_node.name in e: n.in_edges[idx] = e.split(':')[0] source_node.real_name = self.get_parent(source_node.name, [1]).real_name else: IR_node = self._convert_identity_operation(source_node, 1, 1) kwargs = { 'axis' : self.get_parent(source_node.name, [0]).layer.attr['value'].tensor.int_val[0], 'split' : source_node.get_attr('num_split') } assign_IRnode_values(IR_node, kwargs) def rename_StridedSlice(self, source_node): # TODO: Current it is only for slice self._add_constant_node(source_node) if self.get_parent(source_node.name, [1]).type != 'Const': self._add_constant_node(source_node) IR_node = self._convert_identity_operation(source_node, new_op='StridedSlice') return IR_node = self._convert_identity_operation(source_node, in_edge_count=1, new_op='StridedSlice') kwargs = { 'begin_mask' : source_node.get_attr('begin_mask'), 'end_mask' : source_node.get_attr('end_mask'), 'shrink_axis_mask': source_node.get_attr('shrink_axis_mask'), 'new_axis_mask' :source_node.get_attr('new_axis_mask') } starts = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor starts = tensor_util.MakeNdarray(starts).tolist() kwargs['starts'] = starts ends = self.get_parent(source_node.name, [2]).layer.attr['value'].tensor ends = tensor_util.MakeNdarray(ends).tolist() kwargs['ends'] = ends if self.get_parent(source_node.name, [3]) != None: strides = self.get_parent(source_node.name, [3]).layer.attr['value'].tensor strides = tensor_util.MakeNdarray(strides).tolist() kwargs['strides'] = strides assign_IRnode_values(IR_node, kwargs) def rename_Slice(self, source_node): # TODO: Current it is only for slice if self.get_parent(source_node.name, [1]).type != 'Const': self._add_constant_node(source_node) IR_node = self._convert_identity_operation(source_node, new_op='Slice') return IR_node = self._convert_identity_operation(source_node, in_edge_count=1, new_op='Slice') kwargs = {} starts = self.get_parent(source_node.name, [1]).layer.attr['value'].tensor starts = tensor_util.MakeNdarray(starts).tolist() kwargs['starts'] = starts ends = self.get_parent(source_node.name, [2]).layer.attr['value'].tensor ends = tensor_util.MakeNdarray(ends).tolist() kwargs['ends'] = ends assign_IRnode_values(IR_node, kwargs) def rename_LRN(self, source_node): IR_node = self._convert_identity_operation(source_node) kwargs = { "alpha" : source_node.get_attr('alpha') * (source_node.get_attr('depth_radius') * 2 + 1), "beta" : source_node.get_attr('beta'), "bias" : source_node.get_attr('bias'), 'size' : source_node.get_attr('depth_radius') + 1 } assign_IRnode_values(IR_node, kwargs) def rename_Tanh(self, source_node): self._convert_identity_operation(source_node) def rename_ExpandDims(self, source_node): IR_node = self._convert_identity_operation(source_node, 0, 1, new_op='Unsqueeze') ax_node = self.get_parent(source_node.name, [1]) kwargs = { 'axes': [ax_node.layer.attr['value'].tensor.int_val[0]] } assign_IRnode_values(IR_node, kwargs) def rename_Fill(self, source_node): IR_node = self._convert_identity_operation(source_node, 0, 1, new_op='Fill') value_node = self.get_parent(source_node.name, [1]) if value_node.layer.attr['value'].tensor.float_val: IR_node.attr['value'].f = value_node.layer.attr['value'].tensor.float_val[0] elif value_node.layer.attr['value'].tensor.int_val: IR_node.attr['value'].i = value_node.layer.attr['value'].tensor.int_val[0] else: raise NotImplementedError() def rename_Conv2DBackpropInput(self, source_node): """ weights: name_weights, name_bias """ IR_node = self._convert_identity_operation(source_node, new_op = 'ConvTranspose') kwargs = {} # strides kwargs['strides'] = source_node.get_attr('strides') # input[1] : W # filter W = self.tf_graph.get_node(source_node.layer.input[1]) W = self.tf_graph.get_node(W.layer.input[0]).layer kwargs['kernel_shape'] = self.tensor_shape_to_list(W.attr['shape']) # padding self._convert_padding(source_node, IR_node, kwargs['kernel_shape'][:-2]) if self.weight_loaded: self.set_weight(source_node.name, 'weights', self.ckpt_data[W.name]) assign_IRnode_values(IR_node, kwargs) # output[0] : B self._get_bias(source_node, IR_node) def rename_Minimum(self, source_node): self._add_constant_node(source_node) self._convert_identity_operation(source_node) def rename_Maxmum(self, source_node): self._add_constant_node(source_node) self._convert_identity_operation(source_node) def rename_Cast(self, source_node): IR_node = self._convert_identity_operation(source_node) dst = source_node.get_attr('DstT') if dst == 1: dst = 'float' elif dst == 3: dst = 'int' else: raise NotImplementedError kwargs = {'dstType' : dst} assign_IRnode_values(IR_node, kwargs) model-pivot/requirements.txt000066400000000000000000000000411415504170400165750ustar00rootroot00000000000000matplotlib numpy tqdm scipy flaskmodel-pivot/setup.py000066400000000000000000000021371415504170400150330ustar00rootroot00000000000000''' python setup.py sdist bdist_wheel python -m twine upload dist/* ''' from setuptools import find_packages from setuptools import setup requirements = ["torch"] with open("./requirements.txt", "r", encoding="utf-8") as fh: install_requires = fh.read() with open("./README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setup( install_requires=install_requires, name="Model-Pivot", version="0.0.1", author="PKU MLG, PCL, and other contributors", author_email="gychen@pku.edu.cn", description="A model conversion and visualization tool.", long_description=long_description, long_description_content_type="text/markdown", url="https://git.openi.org.cn/OpenI/Model-Pivot", packages=find_packages(), classifiers=[ "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "License :: Other/Proprietary License", "Operating System :: OS Independent", ], python_requires='>=3.6' )model-pivot/test.py000066400000000000000000000206311415504170400146510ustar00rootroot00000000000000import os import sys import imp import numpy as np import warnings warnings.filterwarnings("ignore") # Test for Torch def torch(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() from PIL import Image import torch import torchvision.models as models from torchvision import transforms from torch.autograd import Variable # Torch to IR from ox.pytorch.pytorch_parser import PytorchParser for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'PyTorch', model+'.pth') # test model if 'resnet50' in model: model_eval = models.resnet50() elif 'inception' in model: from models.torch import inception model_eval = inception.inceptionresnetv2(pretrained=False) elif 'shufflenet' in model: from models.torch import shufflenet model_eval = shufflenet.shufflenet() elif 'fcn' in model: from models.torch import fcn model_eval = fcn.FCNs() elif 'lstm' in model: from models.torch import lstm model_eval = lstm.Lstm() model_eval.eval() predict = model_eval(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') results_o[model] = preds torch.save(model_eval, arch_filename) # convert IR_filename = os.path.join(model_path, 'IR', model+'_torch') parser = PytorchParser(arch_filename, inputshape) ops = parser.run(IR_filename) op_sets[model] = ops del parser del PytorchParser # IR to Torch from ox.pytorch.pytorch_emitter import PytorchEmitter for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'IR', model+'_torch.pb') weight_filename = os.path.join(model_path, 'IR', model+'_torch.npy') converted_file = os.path.join(model_path, 'PyTorch', model+'_ox') emitter = PytorchEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', converted_file + '.npy', 'test') model_converted = imp.load_source('PytorchModel', converted_file + '.py').KitModel(converted_file + '.npy') model_converted.eval() predict = model_converted(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of ', model+'_ox : ', np.argmax(preds)) print('\033[0m') results_d[model] = np.mean(np.power(results_o[model] - preds, 2)) del emitter del PytorchEmitter return results_d, op_sets # Test for Tensorflow def tensorflow(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() import tensorflow as tf from PIL import Image image = Image.open(img_path) # Tensorflow to IR from ox.tensorflow.tensorflow_parser import TensorflowParser for model in test_models: arch_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt.meta') weight_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt') # test model if 'resnet50' in model: img = np.array(image.resize((299, 299), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import resnet50 preds = resnet50.test(x, model_path) elif 'inception' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import inception_v3 preds = inception_v3.test(x, model_path) elif 'shufflenet' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import shufflenet preds = shufflenet.test(x, model_path) elif 'fcn' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import fcn preds = fcn.test(x, model_path) elif 'lstm' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import lstm preds = lstm.test(x, model_path) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') preds = np.squeeze(preds) if 'fcn' in model: preds = np.array(preds).astype(np.int32) results_o[model] = preds import tensorflow.contrib.keras as keras keras.backend.clear_session() # parser IR_filename = os.path.join(model_path, 'IR', model+'_tf') parser = TensorflowParser(arch_filename, weight_filename, ["OX_output"]) ops = parser.run(IR_filename) op_sets[model] = ops del parser del TensorflowParser # IR to Tensorflow from ox.tensorflow.tensorflow_emitter import TensorflowEmitter for model in test_models: arch_filename = os.path.join(model_path, 'IR', model+'_tf.pb') weight_filename = os.path.join(model_path, 'IR', model+'_tf.npy') converted_file = os.path.join(model_path, 'tensorflow', model, model+'_ox') emitter = TensorflowEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', None, 'test') # test model if 'resnet' in model: img = image.resize((299, 299), Image.ANTIALIAS) else: img = image.resize((224, 224), Image.ANTIALIAS) img = np.array(img) x = np.expand_dims(img, axis=0) if 'lstm' in model: x = np.reshape(x, (-1, 224 * 224 * 3)) model_converted = imp.load_source('TFModel', converted_file + '.py').KitModel(weight_filename) input_tf, model_tf = model_converted with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) predict = sess.run(model_tf, feed_dict = {input_tf : x}) del model_converted del sys.modules['TFModel'] preds = np.squeeze(predict) if 'fcn' in model: preds = np.array(preds).astype(np.int32) print('\033[1;31;40m') print(' Result of ', model+'_ox : ', np.argmax(preds)) print('\033[0m') results_d[model] = np.mean(np.power(results_o[model] - preds, 2)) del emitter del TensorflowEmitter return results_d, op_sets def mk_dirs(path): if not os.path.exists(path): os.makedirs(path) return True return False if __name__=='__main__': test_models = ['resnet50', 'inception_v3', 'shufflenet', 'fcn', 'lstm'] model_path = './../models' img_path = os.path.join('./', 'elephant.jpg') # mkdirs mk_dirs(os.path.join(model_path, 'IR')) mk_dirs(os.path.join(model_path, 'tensorflow')) mk_dirs(os.path.join(model_path, 'PyTorch')) tf_err, tf_op_sets = tensorflow(test_models, model_path, img_path) torch_err, torch_op_sets = torch(test_models, model_path, img_path) for model in test_models: print('Model: {}'.format(model)) print('- Error: tf ({}) | torch ({})'.format(tf_err[model], torch_err[model])) print('- TF Ops: {}'.format(tf_op_sets[model])) print('- Torch Ops: {}'.format(torch_op_sets[model])) print('\n') model-pivot/visualization/000077500000000000000000000000001415504170400162175ustar00rootroot00000000000000model-pivot/visualization/app.py000066400000000000000000000004011415504170400173440ustar00rootroot00000000000000from flask import Flask, render_template, request app = Flask(__name__) @app.route('/', methods=["GET"]) def index(): return render_template('index.html') if __name__ == '__main__': app.run(host="0.0.0.0", debug=True, threaded=True, port=6006)model-pivot/visualization/static/000077500000000000000000000000001415504170400175065ustar00rootroot00000000000000model-pivot/visualization/static/app.js000066400000000000000000000003551415504170400206270ustar00rootroot00000000000000const express = require('express') const app = express() app.use(express.static('public')) app.get('/', (req, res) => res.sendFile('index.html', {"root": __dirname})) app.listen(8080, () => console.log('vis is listening on port 8000!'))model-pivot/visualization/static/assets/000077500000000000000000000000001415504170400210105ustar00rootroot00000000000000model-pivot/visualization/static/assets/css/000077500000000000000000000000001415504170400216005ustar00rootroot00000000000000model-pivot/visualization/static/assets/css/bootstrap.css000066400000000000000000004027021415504170400243340ustar00rootroot00000000000000/*! * Bootstrap v3.2.0 (http://getbootstrap.com) * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ /*! normalize.css v3.0.1 | MIT License | git.io/normalize */ html { font-family: sans-serif; -webkit-text-size-adjust: 100%; -ms-text-size-adjust: 100%; } body { margin: 0; } article, aside, details, figcaption, figure, footer, header, hgroup, main, nav, section, summary { display: block; } audio, canvas, progress, video { display: inline-block; vertical-align: baseline; } audio:not([controls]) { display: none; height: 0; } [hidden], template { display: none; } a { background: transparent; } a:active, a:hover { outline: 0; } abbr[title] { border-bottom: 1px dotted; } b, strong { font-weight: bold; } dfn { font-style: italic; } h1 { margin: .67em 0; font-size: 2em; } mark { color: #000; background: #ff0; } small { font-size: 80%; } sub, sup { position: relative; font-size: 75%; line-height: 0; vertical-align: baseline; } sup { top: -.5em; } sub { bottom: -.25em; } img { border: 0; } svg:not(:root) { overflow: hidden; } figure { margin: 1em 40px; } hr { height: 0; -webkit-box-sizing: content-box; -moz-box-sizing: content-box; box-sizing: content-box; } pre { overflow: auto; } code, kbd, pre, samp { font-family: monospace, monospace; font-size: 1em; } button, input, optgroup, select, textarea { margin: 0; font: inherit; color: inherit; } button { overflow: visible; } button, select { text-transform: none; } button, html input[type="button"], input[type="reset"], input[type="submit"] { -webkit-appearance: button; cursor: pointer; } button[disabled], html input[disabled] { cursor: default; } button::-moz-focus-inner, input::-moz-focus-inner { padding: 0; border: 0; } input { line-height: normal; } input[type="checkbox"], input[type="radio"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; padding: 0; } input[type="number"]::-webkit-inner-spin-button, input[type="number"]::-webkit-outer-spin-button { height: auto; } input[type="search"] { -webkit-box-sizing: content-box; -moz-box-sizing: content-box; box-sizing: content-box; -webkit-appearance: textfield; } input[type="search"]::-webkit-search-cancel-button, input[type="search"]::-webkit-search-decoration { -webkit-appearance: none; } fieldset { padding: .35em .625em .75em; margin: 0 2px; border: 1px solid #c0c0c0; } legend { padding: 0; border: 0; } textarea { overflow: auto; } optgroup { font-weight: bold; } table { border-spacing: 0; border-collapse: collapse; } td, th { padding: 0; } @media print { * { color: #000 !important; text-shadow: none !important; background: transparent !important; -webkit-box-shadow: none !important; box-shadow: none !important; } a, a:visited { text-decoration: underline; } a[href]:after { content: " (" attr(href) ")"; } abbr[title]:after { content: " (" attr(title) ")"; } a[href^="javascript:"]:after, a[href^="#"]:after { content: ""; } pre, blockquote { border: 1px solid #999; page-break-inside: avoid; } thead { display: table-header-group; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } select { background: #fff !important; } .navbar { display: none; } .table td, .table th { background-color: #fff !important; } .btn > .caret, .dropup > .btn > .caret { border-top-color: #000 !important; } .label { border: 1px solid #000; } .table { border-collapse: collapse !important; } .table-bordered th, .table-bordered td { border: 1px solid #ddd !important; } } @font-face { font-family: 'Glyphicons Halflings'; src: url('../fonts/glyphicons-halflings-regular.eot'); src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), 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"\e155"; } .glyphicon-sort-by-attributes-alt:before { content: "\e156"; } .glyphicon-unchecked:before { content: "\e157"; } .glyphicon-expand:before { content: "\e158"; } .glyphicon-collapse-down:before { content: "\e159"; } .glyphicon-collapse-up:before { content: "\e160"; } .glyphicon-log-in:before { content: "\e161"; } .glyphicon-flash:before { content: "\e162"; } .glyphicon-log-out:before { content: "\e163"; } .glyphicon-new-window:before { content: "\e164"; } .glyphicon-record:before { content: "\e165"; } .glyphicon-save:before { content: "\e166"; } .glyphicon-open:before { content: "\e167"; } .glyphicon-saved:before { content: "\e168"; } .glyphicon-import:before { content: "\e169"; } .glyphicon-export:before { content: "\e170"; } .glyphicon-send:before { content: "\e171"; } .glyphicon-floppy-disk:before { content: "\e172"; } .glyphicon-floppy-saved:before { content: "\e173"; } .glyphicon-floppy-remove:before { content: "\e174"; } .glyphicon-floppy-save:before { content: "\e175"; } .glyphicon-floppy-open:before { content: "\e176"; } .glyphicon-credit-card:before { content: "\e177"; } .glyphicon-transfer:before { content: "\e178"; } .glyphicon-cutlery:before { content: "\e179"; } .glyphicon-header:before { content: "\e180"; } .glyphicon-compressed:before { content: "\e181"; } .glyphicon-earphone:before { content: "\e182"; } .glyphicon-phone-alt:before { content: "\e183"; } .glyphicon-tower:before { content: "\e184"; } .glyphicon-stats:before { content: "\e185"; } .glyphicon-sd-video:before { content: "\e186"; } .glyphicon-hd-video:before { content: "\e187"; } .glyphicon-subtitles:before { content: "\e188"; } .glyphicon-sound-stereo:before { content: "\e189"; } .glyphicon-sound-dolby:before { content: "\e190"; } .glyphicon-sound-5-1:before { content: "\e191"; } .glyphicon-sound-6-1:before { content: "\e192"; } .glyphicon-sound-7-1:before { content: "\e193"; } .glyphicon-copyright-mark:before { content: "\e194"; } .glyphicon-registration-mark:before { content: "\e195"; } .glyphicon-cloud-download:before { content: "\e197"; } .glyphicon-cloud-upload:before { content: "\e198"; } .glyphicon-tree-conifer:before { content: "\e199"; } .glyphicon-tree-deciduous:before { content: "\e200"; } * { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } *:before, *:after { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } html { font-size: 10px; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); } body { font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; line-height: 1.42857143; color: #333; background-color: #fff; } input, button, select, textarea { font-family: inherit; font-size: inherit; line-height: inherit; } a { color: #428bca; text-decoration: none; } a:hover, a:focus { color: #2a6496; text-decoration: underline; } a:focus { outline: thin dotted; outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } figure { margin: 0; } img { vertical-align: middle; } .img-responsive, .thumbnail > img, .thumbnail a > img, .carousel-inner > .item > img, .carousel-inner > .item > a > img { display: block; width: 100% \9; max-width: 100%; height: auto; } .img-rounded { border-radius: 6px; } .img-thumbnail { display: inline-block; width: 100% \9; max-width: 100%; height: auto; padding: 4px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 4px; -webkit-transition: all .2s ease-in-out; -o-transition: all .2s ease-in-out; transition: all .2s ease-in-out; } .img-circle { border-radius: 50%; } hr { margin-top: 20px; margin-bottom: 20px; border: 0; border-top: 1px solid #eee; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { font-family: inherit; font-weight: 500; line-height: 1.1; color: inherit; } h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { font-weight: normal; line-height: 1; color: #777; } h1, .h1, h2, .h2, h3, .h3 { margin-top: 20px; margin-bottom: 10px; } h1 small, .h1 small, h2 small, .h2 small, h3 small, .h3 small, h1 .small, .h1 .small, h2 .small, .h2 .small, h3 .small, .h3 .small { font-size: 65%; } h4, .h4, h5, .h5, h6, .h6 { margin-top: 10px; margin-bottom: 10px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 36px; } h2, .h2 { font-size: 30px; } h3, .h3 { font-size: 24px; } h4, .h4 { font-size: 18px; } h5, .h5 { font-size: 14px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 10px; } .lead { margin-bottom: 20px; font-size: 16px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 21px; } } small, .small { font-size: 85%; } cite { font-style: normal; } mark, .mark { padding: .2em; background-color: #fcf8e3; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777; } .text-primary { color: #428bca; } a.text-primary:hover { color: #3071a9; } .text-success { color: #3c763d; } a.text-success:hover { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover { color: #843534; } .bg-primary { color: #fff; background-color: #428bca; } a.bg-primary:hover { background-color: #3071a9; } .bg-success { background-color: #dff0d8; } a.bg-success:hover { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover { background-color: #e4b9b9; } .page-header { padding-bottom: 9px; margin: 40px 0 20px; border-bottom: 1px solid #eee; } ul, ol { margin-top: 0; margin-bottom: 10px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; margin-left: -5px; list-style: none; } .list-inline > li { display: inline-block; padding-right: 5px; padding-left: 5px; } dl { margin-top: 0; margin-bottom: 20px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 768px) { .dl-horizontal dt { float: left; width: 160px; overflow: hidden; clear: left; text-align: right; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 10px 20px; margin: 0 0 20px; font-size: 17.5px; border-left: 5px solid #eee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; text-align: right; border-right: 5px solid #eee; border-left: 0; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } blockquote:before, blockquote:after { content: ""; } address { margin-bottom: 20px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: Menlo, Monaco, Consolas, "Courier New", monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 4px; } kbd { padding: 2px 4px; font-size: 90%; color: #fff; background-color: #333; border-radius: 3px; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); } kbd kbd { padding: 0; font-size: 100%; -webkit-box-shadow: none; box-shadow: none; } pre { display: block; padding: 9.5px; margin: 0 0 10px; font-size: 13px; line-height: 1.42857143; color: #333; word-break: break-all; word-wrap: break-word; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 4px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { padding-right: 15px; padding-left: 15px; margin-right: auto; margin-left: auto; } @media (min-width: 768px) { .container { width: 750px; } } @media (min-width: 992px) { .container { width: 970px; } } @media (min-width: 1200px) { .container { width: 1170px; } } .container-fluid { padding-right: 15px; padding-left: 15px; margin-right: auto; margin-left: auto; } .row { margin-right: -15px; margin-left: -15px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-right: 15px; padding-left: 15px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0; } } table { background-color: transparent; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 20px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-child(odd) > td, .table-striped > tbody > tr:nth-child(odd) > th { background-color: #f9f9f9; } .table-hover > tbody > tr:hover > td, .table-hover > tbody > tr:hover > th { background-color: #f5f5f5; } table col[class*="col-"] { position: static; display: table-column; float: none; } table td[class*="col-"], table th[class*="col-"] { position: static; display: table-cell; float: none; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 15px; overflow-x: auto; overflow-y: hidden; -webkit-overflow-scrolling: touch; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { min-width: 0; padding: 0; margin: 0; border: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 20px; font-size: 21px; line-height: inherit; color: #333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: thin dotted; outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 14px; line-height: 1.42857143; color: #555; } .form-control { display: block; width: 100%; height: 34px; padding: 6px 12px; font-size: 14px; line-height: 1.42857143; color: #555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 4px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); } .form-control::-moz-placeholder { color: #777; opacity: 1; } .form-control:-ms-input-placeholder { color: #777; } .form-control::-webkit-input-placeholder { color: #777; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { cursor: not-allowed; background-color: #eee; opacity: 1; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } input[type="date"], input[type="time"], input[type="datetime-local"], input[type="month"] { line-height: 34px; line-height: 1.42857143 \0; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg { line-height: 46px; } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; min-height: 20px; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-top: 4px \9; margin-left: -20px; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { display: inline-block; padding-left: 20px; margin-bottom: 0; font-weight: normal; vertical-align: middle; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; } .form-control-static.input-lg, .form-control-static.input-sm { padding-right: 0; padding-left: 0; } .input-sm, .form-horizontal .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 3px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .input-lg, .form-horizontal .form-group-lg .form-control { height: 46px; padding: 10px 16px; font-size: 18px; line-height: 1.33; border-radius: 6px; } select.input-lg { height: 46px; line-height: 46px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 42.5px; } .form-control-feedback { position: absolute; top: 25px; right: 0; z-index: 2; display: block; width: 34px; height: 34px; line-height: 34px; text-align: center; } .input-lg + .form-control-feedback { width: 46px; height: 46px; line-height: 46px; } .input-sm + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; background-color: #dff0d8; border-color: #3c763d; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; background-color: #fcf8e3; border-color: #8a6d3b; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; background-color: #f2dede; border-color: #a94442; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #737373; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { padding-top: 7px; margin-top: 0; margin-bottom: 0; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 27px; } .form-horizontal .form-group { margin-right: -15px; margin-left: -15px; } @media (min-width: 768px) { .form-horizontal .control-label { padding-top: 7px; margin-bottom: 0; text-align: right; } } .form-horizontal .has-feedback .form-control-feedback { top: 0; right: 15px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 14.3px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; } } .btn { display: inline-block; padding: 6px 12px; margin-bottom: 0; font-size: 14px; font-weight: normal; line-height: 1.42857143; text-align: center; white-space: nowrap; vertical-align: middle; cursor: pointer; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; background-image: none; border: 1px solid transparent; border-radius: 4px; } .btn:focus, .btn:active:focus, .btn.active:focus { outline: thin dotted; outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus { color: #333; text-decoration: none; } .btn:active, .btn.active { background-image: none; outline: 0; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { pointer-events: none; cursor: not-allowed; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; opacity: .65; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:hover, .btn-default:focus, .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled, .btn-default[disabled], fieldset[disabled] .btn-default, .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled:active, .btn-default[disabled]:active, fieldset[disabled] .btn-default:active, .btn-default.disabled.active, .btn-default[disabled].active, fieldset[disabled] .btn-default.active { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #428bca; border-color: #357ebd; } .btn-primary:hover, .btn-primary:focus, .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #3071a9; border-color: #285e8e; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled, .btn-primary[disabled], fieldset[disabled] .btn-primary, .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled:active, .btn-primary[disabled]:active, fieldset[disabled] .btn-primary:active, .btn-primary.disabled.active, .btn-primary[disabled].active, fieldset[disabled] .btn-primary.active { background-color: #428bca; border-color: #357ebd; } .btn-primary .badge { color: #428bca; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:hover, .btn-success:focus, .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled, .btn-success[disabled], fieldset[disabled] .btn-success, .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled:active, .btn-success[disabled]:active, fieldset[disabled] .btn-success:active, .btn-success.disabled.active, .btn-success[disabled].active, fieldset[disabled] .btn-success.active { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:hover, .btn-info:focus, .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled, .btn-info[disabled], fieldset[disabled] .btn-info, .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled:active, .btn-info[disabled]:active, fieldset[disabled] .btn-info:active, .btn-info.disabled.active, .btn-info[disabled].active, fieldset[disabled] .btn-info.active { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:hover, .btn-warning:focus, .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled, .btn-warning[disabled], fieldset[disabled] .btn-warning, .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled:active, .btn-warning[disabled]:active, fieldset[disabled] .btn-warning:active, .btn-warning.disabled.active, .btn-warning[disabled].active, fieldset[disabled] .btn-warning.active { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:hover, .btn-danger:focus, .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled, .btn-danger[disabled], fieldset[disabled] .btn-danger, .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled:active, .btn-danger[disabled]:active, fieldset[disabled] .btn-danger:active, .btn-danger.disabled.active, .btn-danger[disabled].active, fieldset[disabled] .btn-danger.active { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { font-weight: normal; color: #428bca; cursor: pointer; border-radius: 0; } .btn-link, .btn-link:active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #2a6496; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 18px; line-height: 1.33; border-radius: 6px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 3px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 3px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity .15s linear; -o-transition: opacity .15s linear; transition: opacity .15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition: height .35s ease; -o-transition: height .35s ease; transition: height .35s ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px solid; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; font-size: 14px; text-align: left; list-style: none; background-color: #fff; -webkit-background-clip: padding-box; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, .15); border-radius: 4px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, .175); box-shadow: 0 6px 12px rgba(0, 0, 0, .175); } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 9px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { color: #262626; text-decoration: none; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; background-color: #428bca; outline: 0; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; cursor: not-allowed; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { right: 0; left: auto; } .dropdown-menu-left { right: auto; left: 0; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777; white-space: nowrap; } .dropdown-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { content: ""; border-top: 0; border-bottom: 4px solid; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 1px; } @media (min-width: 768px) { .navbar-right .dropdown-menu { right: 0; left: auto; } .navbar-right .dropdown-menu-left { right: auto; left: 0; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group > .btn:focus, .btn-group-vertical > .btn:focus { outline: 0; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-top-right-radius: 0; border-bottom-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-top-left-radius: 0; border-bottom-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child > .btn:last-child, .btn-group > .btn-group:first-child > .dropdown-toggle { border-top-right-radius: 0; border-bottom-right-radius: 0; } .btn-group > .btn-group:last-child > .btn:first-child { border-top-left-radius: 0; border-bottom-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-right: 8px; padding-left: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-right: 12px; padding-left: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 4px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-left-radius: 0; border-top-right-radius: 0; border-bottom-left-radius: 4px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-left-radius: 0; border-top-right-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { display: table-cell; float: none; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn > input[type="radio"], [data-toggle="buttons"] > .btn > input[type="checkbox"] { position: absolute; z-index: -1; filter: alpha(opacity=0); opacity: 0; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class*="col-"] { float: none; padding-right: 0; padding-left: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 46px; padding: 10px 16px; font-size: 18px; line-height: 1.33; border-radius: 6px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 46px; line-height: 46px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 3px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 14px; font-weight: normal; line-height: 1; color: #555; text-align: center; background-color: #eee; border: 1px solid #ccc; border-radius: 4px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 3px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 18px; border-radius: 6px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-top-right-radius: 0; border-bottom-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-top-left-radius: 0; border-bottom-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { margin-left: -1px; } .nav { padding-left: 0; margin-bottom: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eee; } .nav > li.disabled > a { color: #777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777; text-decoration: none; cursor: not-allowed; background-color: transparent; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eee; border-color: #428bca; } .nav .nav-divider { height: 1px; margin: 9px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 4px 4px 0 0; } .nav-tabs > li > a:hover { border-color: #eee #eee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555; cursor: default; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { margin-bottom: 5px; text-align: center; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 4px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 4px 4px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 4px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #428bca; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { margin-bottom: 5px; text-align: center; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 4px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 4px 4px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-left-radius: 0; border-top-right-radius: 0; } .navbar { position: relative; min-height: 50px; margin-bottom: 20px; border: 1px solid transparent; } @media (min-width: 768px) { .navbar { border-radius: 4px; } } @media (min-width: 768px) { .navbar-header { float: left; } } .navbar-collapse { padding-right: 15px; padding-left: 15px; overflow-x: visible; -webkit-overflow-scrolling: touch; border-top: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 768px) { .navbar-collapse { width: auto; border-top: 0; -webkit-box-shadow: none; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-right: 0; padding-left: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-width: 480px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: -15px; margin-left: -15px; } @media (min-width: 768px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 768px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; -webkit-transform: translate3d(0, 0, 0); -o-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); } @media (min-width: 768px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; height: 50px; padding: 15px 15px; font-size: 18px; line-height: 20px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } @media (min-width: 768px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: -15px; } } .navbar-toggle { position: relative; float: right; padding: 9px 10px; margin-top: 8px; margin-right: 15px; margin-bottom: 8px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 4px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 768px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 7.5px -15px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 20px; } @media (max-width: 767px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; -webkit-box-shadow: none; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 20px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 768px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 15px; padding-bottom: 15px; } .navbar-nav.navbar-right:last-child { margin-right: -15px; } } @media (min-width: 768px) { .navbar-left { float: left !important; } .navbar-right { float: right !important; } } .navbar-form { padding: 10px 15px; margin-top: 8px; margin-right: -15px; margin-bottom: 8px; margin-left: -15px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 767px) { .navbar-form .form-group { margin-bottom: 5px; } } @media (min-width: 768px) { .navbar-form { width: auto; padding-top: 0; padding-bottom: 0; margin-right: 0; margin-left: 0; border: 0; -webkit-box-shadow: none; box-shadow: none; } .navbar-form.navbar-right:last-child { margin-right: -15px; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-left-radius: 0; border-top-right-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: 8px; margin-bottom: 8px; } .navbar-btn.btn-sm { margin-top: 10px; margin-bottom: 10px; } .navbar-btn.btn-xs { margin-top: 14px; margin-bottom: 14px; } .navbar-text { margin-top: 15px; margin-bottom: 15px; } @media (min-width: 768px) { .navbar-text { float: left; margin-right: 15px; margin-left: 15px; } .navbar-text.navbar-right:last-child { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { color: #555; background-color: #e7e7e7; } @media (max-width: 767px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #777; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #777; } .navbar-inverse .navbar-nav > li > a { color: #777; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { color: #fff; background-color: #080808; } @media (max-width: 767px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #777; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #777; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 20px; list-style: none; background-color: #f5f5f5; border-radius: 4px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { padding: 0 5px; color: #ccc; content: "/\00a0"; } .breadcrumb > .active { color: #777; } .pagination { display: inline-block; padding-left: 0; margin: 20px 0; border-radius: 4px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; margin-left: -1px; line-height: 1.42857143; color: #428bca; text-decoration: none; background-color: #fff; border: 1px solid #ddd; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-top-left-radius: 4px; border-bottom-left-radius: 4px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-top-right-radius: 4px; border-bottom-right-radius: 4px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { color: #2a6496; background-color: #eee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 2; color: #fff; cursor: default; background-color: #428bca; border-color: #428bca; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777; cursor: not-allowed; background-color: #fff; border-color: #ddd; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 18px; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-top-left-radius: 6px; border-bottom-left-radius: 6px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-top-right-radius: 6px; border-bottom-right-radius: 6px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-top-left-radius: 3px; border-bottom-left-radius: 3px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-top-right-radius: 3px; border-bottom-right-radius: 3px; } .pager { padding-left: 0; margin: 20px 0; text-align: center; list-style: none; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777; cursor: not-allowed; background-color: #fff; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #428bca; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #3071a9; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; background-color: #777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } a.list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #428bca; background-color: #fff; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding: 30px; margin-bottom: 30px; color: inherit; background-color: #eee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 21px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron { border-radius: 6px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron { padding-right: 60px; padding-left: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 63px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 20px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 4px; -webkit-transition: all .2s ease-in-out; -o-transition: all .2s ease-in-out; transition: all .2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-right: auto; margin-left: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #428bca; } .thumbnail .caption { padding: 9px; color: #333; } .alert { padding: 15px; margin-bottom: 20px; border: 1px solid transparent; border-radius: 4px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @-o-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { height: 20px; margin-bottom: 20px; overflow: hidden; background-color: #f5f5f5; border-radius: 4px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); } .progress-bar { float: left; width: 0; height: 100%; font-size: 12px; line-height: 20px; color: #fff; text-align: center; background-color: #428bca; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); -webkit-transition: width .6s ease; -o-transition: width .6s ease; transition: width .6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); -webkit-background-size: 40px 40px; background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar[aria-valuenow="1"], .progress-bar[aria-valuenow="2"] { min-width: 30px; } .progress-bar[aria-valuenow="0"] { min-width: 30px; color: #777; background-color: transparent; background-image: none; -webkit-box-shadow: none; box-shadow: none; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); } .media, .media-body { overflow: hidden; zoom: 1; } .media, .media .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media-object { display: block; } .media-heading { margin: 0 0 5px; } .media > .pull-left { margin-right: 10px; } .media > .pull-right { margin-left: 10px; } .media-list { padding-left: 0; list-style: none; } .list-group { padding-left: 0; margin-bottom: 20px; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-left-radius: 4px; border-top-right-radius: 4px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 4px; border-bottom-left-radius: 4px; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } a.list-group-item { color: #555; } a.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, a.list-group-item:focus { color: #555; text-decoration: none; background-color: #f5f5f5; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { color: #777; background-color: #eee; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #428bca; border-color: #428bca; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #e1edf7; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, a.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, a.list-group-item-success.active:hover, a.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, a.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, a.list-group-item-info.active:hover, a.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, a.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, a.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, a.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, a.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 20px; background-color: #fff; border: 1px solid transparent; border-radius: 4px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, .05); box-shadow: 0 1px 1px rgba(0, 0, 0, .05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-left-radius: 3px; border-top-right-radius: 3px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 16px; color: inherit; } .panel-title > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 3px; border-bottom-left-radius: 3px; } .panel > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-left-radius: 3px; border-top-right-radius: 3px; } .panel > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 3px; border-bottom-left-radius: 3px; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-left-radius: 3px; border-top-right-radius: 3px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 3px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 3px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 3px; border-bottom-left-radius: 3px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 3px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 3px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { margin-bottom: 0; border: 0; } .panel-group { margin-bottom: 20px; } .panel-group .panel { margin-bottom: 0; border-radius: 4px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #428bca; } .panel-primary > .panel-heading { color: #fff; background-color: #428bca; border-color: #428bca; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #428bca; } .panel-primary > .panel-heading .badge { color: #428bca; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #428bca; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object { position: absolute; top: 0; bottom: 0; left: 0; width: 100%; height: 100%; border: 0; } .embed-responsive.embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive.embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 4px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, .15); } .well-lg { padding: 24px; border-radius: 6px; } .well-sm { padding: 9px; border-radius: 3px; } .close { float: right; font-size: 21px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; filter: alpha(opacity=20); opacity: .2; } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; filter: alpha(opacity=50); opacity: .5; } button.close { -webkit-appearance: none; padding: 0; cursor: pointer; background: transparent; border: 0; } .modal-open { overflow: hidden; } .modal { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; display: none; overflow: hidden; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transition: -webkit-transform .3s ease-out; -o-transition: -o-transform .3s ease-out; transition: transform .3s ease-out; -webkit-transform: translate3d(0, -25%, 0); -o-transform: translate3d(0, -25%, 0); transform: translate3d(0, -25%, 0); } .modal.in .modal-dialog { -webkit-transform: translate3d(0, 0, 0); -o-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; -webkit-background-clip: padding-box; background-clip: padding-box; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, .2); border-radius: 6px; outline: 0; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, .5); box-shadow: 0 3px 9px rgba(0, 0, 0, .5); } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { filter: alpha(opacity=0); opacity: 0; } .modal-backdrop.in { filter: alpha(opacity=50); opacity: .5; } .modal-header { min-height: 16.42857143px; padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-bottom: 0; margin-left: 5px; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, .5); box-shadow: 0 5px 15px rgba(0, 0, 0, .5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-size: 12px; line-height: 1.4; visibility: visible; filter: alpha(opacity=0); opacity: 0; } .tooltip.in { filter: alpha(opacity=90); opacity: .9; } .tooltip.top { padding: 5px 0; margin-top: -3px; } .tooltip.right { padding: 0 5px; margin-left: 3px; } .tooltip.bottom { padding: 5px 0; margin-top: 3px; } .tooltip.left { padding: 0 5px; margin-left: -3px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; text-decoration: none; background-color: #000; border-radius: 4px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; left: 5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { right: 5px; bottom: 0; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; left: 5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; right: 5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; text-align: left; white-space: normal; background-color: #fff; -webkit-background-clip: padding-box; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, .2); border-radius: 6px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, .2); box-shadow: 0 5px 10px rgba(0, 0, 0, .2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { padding: 8px 14px; margin: 0; font-size: 14px; font-weight: normal; line-height: 18px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 5px 5px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { content: ""; border-width: 10px; } .popover.top > .arrow { bottom: -11px; left: 50%; margin-left: -11px; border-top-color: #999; border-top-color: rgba(0, 0, 0, .25); border-bottom-width: 0; } .popover.top > .arrow:after { bottom: 1px; margin-left: -10px; content: " "; border-top-color: #fff; border-bottom-width: 0; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-right-color: #999; border-right-color: rgba(0, 0, 0, .25); border-left-width: 0; } .popover.right > .arrow:after { bottom: -10px; left: 1px; content: " "; border-right-color: #fff; border-left-width: 0; } .popover.bottom > .arrow { top: -11px; left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999; border-bottom-color: rgba(0, 0, 0, .25); } .popover.bottom > .arrow:after { top: 1px; margin-left: -10px; content: " "; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999; border-left-color: rgba(0, 0, 0, .25); } .popover.left > .arrow:after { right: 1px; bottom: -10px; content: " "; border-right-width: 0; border-left-color: #fff; } .carousel { position: relative; } .carousel-inner { position: relative; width: 100%; overflow: hidden; } .carousel-inner > .item { position: relative; display: none; -webkit-transition: .6s ease-in-out left; -o-transition: .6s ease-in-out left; transition: .6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; bottom: 0; left: 0; width: 15%; font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, .6); filter: alpha(opacity=50); opacity: .5; } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); 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inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); border: 1px solid #fff; border-radius: 10px; } .carousel-indicators .active { width: 12px; height: 12px; margin: 0; background-color: #fff; } .carousel-caption { position: absolute; right: 15%; bottom: 20px; left: 15%; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, .6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -15px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -15px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -15px; } .carousel-caption { right: 20%; left: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-footer:before, .modal-footer:after { display: table; content: " "; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, 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.visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { 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margin-left: .3em; } .fa-spin { -webkit-animation: spin 2s infinite linear; -moz-animation: spin 2s infinite linear; -o-animation: spin 2s infinite linear; animation: spin 2s infinite linear; } @-moz-keyframes spin { 0% { -moz-transform: rotate(0deg); } 100% { -moz-transform: rotate(359deg); } } @-webkit-keyframes spin { 0% { -webkit-transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); } } @-o-keyframes spin { 0% { -o-transform: rotate(0deg); } 100% { -o-transform: rotate(359deg); } } @keyframes spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1); -webkit-transform: rotate(90deg); -moz-transform: rotate(90deg); -ms-transform: rotate(90deg); -o-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2); -webkit-transform: rotate(180deg); -moz-transform: rotate(180deg); -ms-transform: rotate(180deg); -o-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3); -webkit-transform: rotate(270deg); -moz-transform: rotate(270deg); -ms-transform: rotate(270deg); -o-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1); -webkit-transform: scale(-1, 1); -moz-transform: scale(-1, 1); -ms-transform: scale(-1, 1); -o-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1); -webkit-transform: scale(1, -1); -moz-transform: scale(1, -1); -ms-transform: scale(1, -1); -o-transform: scale(1, -1); transform: scale(1, -1); } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #ffffff; } /* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons */ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-square:before, .fa-pied-piper:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } model-pivot/visualization/static/assets/css/style.css000066400000000000000000000111341415504170400234520ustar00rootroot00000000000000 /* ============================================================= GENERAL STYLES ============================================================ */ body { font-family: 'Open Sans', sans-serif; line-height:28px; } .set-radius-zero { border-radius:0px; -moz-border-radius:0px; -webkit-border-radius:0px; } .content-wrapper { margin-top: 40px; min-height:800px; padding-bottom:90px; } .header-line { font-weight:900; padding-bottom:25px; border-bottom:1px solid #eeeeee; text-transform:uppercase; } .pad-botm { padding-bottom:30px; } /* ============================================================= NAVBAR & MENU STYLES ============================================================ */ .right-div { float: right; padding: 20px; } #menu-top a { color:#000; text-decoration:none; font-weight:500; padding: 25px 15px 25px 15px; text-transform:uppercase; } .menu-section { background-color: #f7f7f7; border-bottom:5px solid #9170E4; width:100% } .menu-top-active { background-color:#eeeeee; } .navbar-inverse { background-color: #FFF; border-color: rgba(155, 153, 153, 0.23); } .navbar { min-height: 90px; margin-bottom: 0px; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color:transparent; } .navbar-toggle { background-color: black; border: 1px solid black; } .copyrights{ text-indent:-9999px; height:0; line-height:0; font-size:0; overflow:hidden; } /* ============================================================= DASHBOARD STYLES ============================================================ */ .img-comments { border: 3px double #e1e1e1; height: 60px; } .chat-widget-main { max-height:330px; overflow:auto; } .slide-bdr { border:5px solid #9170E4 } .chat-widget-left:after { top: 100%; left: 10%; border: solid transparent; content: " "; position: absolute; border-top-color: #F70E62; border-width: 15px; margin-left: -15px; } .chat-widget-left { width: 100%; height: auto; padding: 15px; -webkit-border-radius: 5px; -moz-border-radius: 5px; border-radius: 5px; position: relative; border: 1px solid #F70E62; font-size:12px; } /* .chat-widget-right:after { top: 100%; right: 10%; border: solid transparent; content: " "; position: absolute; border-top-color: #5AA8CC; border-width: 15px; margin-left: -15px; } */ .chat-widget-right { position: absolute; width: 120; height: auto; padding: 10px; font-family: simsun; font-size: 14px; -webkit-border-radius: 5px; -moz-border-radius: 5px; border-radius: 5px; /* position: relative; */ border: 1px solid rgb(58, 51, 49); background-color: rgb(42, 43, 56); font-size:12px; } .tooltip{ position: absolute; width: 120; height: auto; font-family: simsun; font-size: 14px; text-align: center; border-style: solid; border-width: 1px; background-color: white; border-radius: 5px; } .node-param-row { } .node-param-section { border-left: 1px solid #52637D; padding-left: 10px; } .node-param-key { color: #91A5BD; } .node-param-value { color: #26BF8C; } .chat-widget-name-left { color: #808080; margin-top: 30px; margin-left: 60px; text-align:left; } .img-left-chat { border:3px double #e1e1e1; float:left; margin-right:30px; } .img-right-chat { border:3px double #e1e1e1; float:right; margin-left:30px; } .chat-widget-main img-right { border:3px double #e1e1e1; float:left; } .chat-widget-name-left h4 { font-weight:normal; font-size:11px; } .chat-widget-name-left h5 { font-weight:normal; font-size:10px; } .chat-widget-name-right h4 { font-weight:normal; font-size:11px; } .chat-widget-name-right h5 { font-weight:normal; font-size:10px; } .chat-widget-name-right { color: #808080; margin-top: 40px; margin-right: 60px; text-align:right; } .recent-users-sec img { max-height:80px; margin:15px; } .back-widget-set { background-color:transparent; } /* ============================================================= FOOTER SECTION STYLES ============================================================ */ .footer-section { padding:25px 50px 25px 50px; color:#000; font-size:13px; background-color: #f7f7f7; text-align:right; border-top:5px solid #9170E4; } .footer-section a, .footer-section a:hover { color:#000; } model-pivot/visualization/static/assets/fonts/000077500000000000000000000000001415504170400221415ustar00rootroot00000000000000model-pivot/visualization/static/assets/fonts/FontAwesome.otf000066400000000000000000002226641415504170400251160ustar00rootroot00000000000000OTTO CFF ;)7OS/2j`cmap;Ƥhead6hhea $hmtx#THmaxpPnameoi`Opost}Z B3 _<Tt ϙ/  P,33sZ3pyrs@  ? ?J ?a ?Qs    ~    .  $     * <Copyright 2014 Adobe Systems Incorporated. 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D~Wz*%4kjL7E9 KX ߔ Z>Q>j7Ogv@5] 4P!6xcj5b¾ϠGj&1╫֦T{`8ƀU(s)- b@% I7wѣYĬjU$)EFϑwUYszxۜbO) 퉰PXI̍U@&GΌճpU-%M`qHY 7] 8|uc7Xy8qF(rLj9/aFy7v*K2~E6x$IENDB`model-pivot/visualization/static/assets/js/000077500000000000000000000000001415504170400214245ustar00rootroot00000000000000model-pivot/visualization/static/assets/js/bootstrap.js000066400000000000000000001664111415504170400240100ustar00rootroot00000000000000/*! * Bootstrap v3.2.0 (http://getbootstrap.com) * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ if (typeof jQuery === 'undefined') { throw new Error('Bootstrap\'s JavaScript requires jQuery') } /* ======================================================================== * Bootstrap: transition.js v3.2.0 * http://getbootstrap.com/javascript/#transitions * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // CSS TRANSITION SUPPORT (Shoutout: http://www.modernizr.com/) // ============================================================ function transitionEnd() { var el = document.createElement('bootstrap') var transEndEventNames = { WebkitTransition : 'webkitTransitionEnd', MozTransition : 'transitionend', OTransition : 'oTransitionEnd otransitionend', transition : 'transitionend' } for (var name in transEndEventNames) { if (el.style[name] !== undefined) { return { end: transEndEventNames[name] } } } return false // explicit for ie8 ( ._.) } // http://blog.alexmaccaw.com/css-transitions $.fn.emulateTransitionEnd = function (duration) { var called = false var $el = this $(this).one('bsTransitionEnd', function () { called = true }) var callback = function () { if (!called) $($el).trigger($.support.transition.end) } setTimeout(callback, duration) return this } $(function () { $.support.transition = transitionEnd() if (!$.support.transition) return $.event.special.bsTransitionEnd = { bindType: $.support.transition.end, delegateType: $.support.transition.end, handle: function (e) { if ($(e.target).is(this)) return e.handleObj.handler.apply(this, arguments) } } }) }(jQuery); /* ======================================================================== * Bootstrap: alert.js v3.2.0 * http://getbootstrap.com/javascript/#alerts * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // ALERT CLASS DEFINITION // ====================== var dismiss = '[data-dismiss="alert"]' var Alert = function (el) { $(el).on('click', dismiss, this.close) } Alert.VERSION = '3.2.0' Alert.prototype.close = function (e) { var $this = $(this) var selector = $this.attr('data-target') if (!selector) { selector = $this.attr('href') selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7 } var $parent = $(selector) if (e) e.preventDefault() if (!$parent.length) { $parent = $this.hasClass('alert') ? $this : $this.parent() } $parent.trigger(e = $.Event('close.bs.alert')) if (e.isDefaultPrevented()) return $parent.removeClass('in') function removeElement() { // detach from parent, fire event then clean up data $parent.detach().trigger('closed.bs.alert').remove() } $.support.transition && $parent.hasClass('fade') ? $parent .one('bsTransitionEnd', removeElement) .emulateTransitionEnd(150) : removeElement() } // ALERT PLUGIN DEFINITION // ======================= function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.alert') if (!data) $this.data('bs.alert', (data = new Alert(this))) if (typeof option == 'string') data[option].call($this) }) } var old = $.fn.alert $.fn.alert = Plugin $.fn.alert.Constructor = Alert // ALERT NO CONFLICT // ================= $.fn.alert.noConflict = function () { $.fn.alert = old return this } // ALERT DATA-API // ============== $(document).on('click.bs.alert.data-api', dismiss, Alert.prototype.close) }(jQuery); /* ======================================================================== * Bootstrap: button.js v3.2.0 * http://getbootstrap.com/javascript/#buttons * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // BUTTON PUBLIC CLASS DEFINITION // ============================== var Button = function (element, options) { this.$element = $(element) this.options = $.extend({}, Button.DEFAULTS, options) this.isLoading = false } Button.VERSION = '3.2.0' Button.DEFAULTS = { loadingText: 'loading...' } Button.prototype.setState = function (state) { var d = 'disabled' var $el = this.$element var val = $el.is('input') ? 'val' : 'html' var data = $el.data() state = state + 'Text' if (data.resetText == null) $el.data('resetText', $el[val]()) $el[val](data[state] == null ? this.options[state] : data[state]) // push to event loop to allow forms to submit setTimeout($.proxy(function () { if (state == 'loadingText') { this.isLoading = true $el.addClass(d).attr(d, d) } else if (this.isLoading) { this.isLoading = false $el.removeClass(d).removeAttr(d) } }, this), 0) } Button.prototype.toggle = function () { var changed = true var $parent = this.$element.closest('[data-toggle="buttons"]') if ($parent.length) { var $input = this.$element.find('input') if ($input.prop('type') == 'radio') { if ($input.prop('checked') && this.$element.hasClass('active')) changed = false else $parent.find('.active').removeClass('active') } if (changed) $input.prop('checked', !this.$element.hasClass('active')).trigger('change') } if (changed) this.$element.toggleClass('active') } // BUTTON PLUGIN DEFINITION // ======================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.button') var options = typeof option == 'object' && option if (!data) $this.data('bs.button', (data = new Button(this, options))) if (option == 'toggle') data.toggle() else if (option) data.setState(option) }) } var old = $.fn.button $.fn.button = Plugin $.fn.button.Constructor = Button // BUTTON NO CONFLICT // ================== $.fn.button.noConflict = function () { $.fn.button = old return this } // BUTTON DATA-API // =============== $(document).on('click.bs.button.data-api', '[data-toggle^="button"]', function (e) { var $btn = $(e.target) if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') Plugin.call($btn, 'toggle') e.preventDefault() }) }(jQuery); /* ======================================================================== * Bootstrap: carousel.js v3.2.0 * http://getbootstrap.com/javascript/#carousel * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // CAROUSEL CLASS DEFINITION // ========================= var Carousel = function (element, options) { this.$element = $(element).on('keydown.bs.carousel', $.proxy(this.keydown, this)) this.$indicators = this.$element.find('.carousel-indicators') this.options = options this.paused = this.sliding = this.interval = this.$active = this.$items = null this.options.pause == 'hover' && this.$element .on('mouseenter.bs.carousel', $.proxy(this.pause, this)) .on('mouseleave.bs.carousel', $.proxy(this.cycle, this)) } Carousel.VERSION = '3.2.0' Carousel.DEFAULTS = { interval: 5000, pause: 'hover', wrap: true } Carousel.prototype.keydown = function (e) { switch (e.which) { case 37: this.prev(); break case 39: this.next(); break default: return } e.preventDefault() } Carousel.prototype.cycle = function (e) { e || (this.paused = false) this.interval && clearInterval(this.interval) this.options.interval && !this.paused && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) return this } Carousel.prototype.getItemIndex = function (item) { this.$items = item.parent().children('.item') return this.$items.index(item || this.$active) } Carousel.prototype.to = function (pos) { var that = this var activeIndex = this.getItemIndex(this.$active = this.$element.find('.item.active')) if (pos > (this.$items.length - 1) || pos < 0) return if (this.sliding) return this.$element.one('slid.bs.carousel', function () { that.to(pos) }) // yes, "slid" if (activeIndex == pos) return this.pause().cycle() return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos])) } Carousel.prototype.pause = function (e) { e || (this.paused = true) if (this.$element.find('.next, .prev').length && $.support.transition) { this.$element.trigger($.support.transition.end) this.cycle(true) } this.interval = clearInterval(this.interval) return this } Carousel.prototype.next = function () { if (this.sliding) return return this.slide('next') } Carousel.prototype.prev = function () { if (this.sliding) return return this.slide('prev') } Carousel.prototype.slide = function (type, next) { var $active = this.$element.find('.item.active') var $next = next || $active[type]() var isCycling = this.interval var direction = type == 'next' ? 'left' : 'right' var fallback = type == 'next' ? 'first' : 'last' var that = this if (!$next.length) { if (!this.options.wrap) return $next = this.$element.find('.item')[fallback]() } if ($next.hasClass('active')) return (this.sliding = false) var relatedTarget = $next[0] var slideEvent = $.Event('slide.bs.carousel', { relatedTarget: relatedTarget, direction: direction }) this.$element.trigger(slideEvent) if (slideEvent.isDefaultPrevented()) return this.sliding = true isCycling && this.pause() if (this.$indicators.length) { this.$indicators.find('.active').removeClass('active') var $nextIndicator = $(this.$indicators.children()[this.getItemIndex($next)]) $nextIndicator && $nextIndicator.addClass('active') } var slidEvent = $.Event('slid.bs.carousel', { relatedTarget: relatedTarget, direction: direction }) // yes, "slid" if ($.support.transition && this.$element.hasClass('slide')) { $next.addClass(type) $next[0].offsetWidth // force reflow $active.addClass(direction) $next.addClass(direction) $active .one('bsTransitionEnd', function () { $next.removeClass([type, direction].join(' ')).addClass('active') $active.removeClass(['active', direction].join(' ')) that.sliding = false setTimeout(function () { that.$element.trigger(slidEvent) }, 0) }) .emulateTransitionEnd($active.css('transition-duration').slice(0, -1) * 1000) } else { $active.removeClass('active') $next.addClass('active') this.sliding = false this.$element.trigger(slidEvent) } isCycling && this.cycle() return this } // CAROUSEL PLUGIN DEFINITION // ========================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.carousel') var options = $.extend({}, Carousel.DEFAULTS, $this.data(), typeof option == 'object' && option) var action = typeof option == 'string' ? option : options.slide if (!data) $this.data('bs.carousel', (data = new Carousel(this, options))) if (typeof option == 'number') data.to(option) else if (action) data[action]() else if (options.interval) data.pause().cycle() }) } var old = $.fn.carousel $.fn.carousel = Plugin $.fn.carousel.Constructor = Carousel // CAROUSEL NO CONFLICT // ==================== $.fn.carousel.noConflict = function () { $.fn.carousel = old return this } // CAROUSEL DATA-API // ================= $(document).on('click.bs.carousel.data-api', '[data-slide], [data-slide-to]', function (e) { var href var $this = $(this) var $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) // strip for ie7 if (!$target.hasClass('carousel')) return var options = $.extend({}, $target.data(), $this.data()) var slideIndex = $this.attr('data-slide-to') if (slideIndex) options.interval = false Plugin.call($target, options) if (slideIndex) { $target.data('bs.carousel').to(slideIndex) } e.preventDefault() }) $(window).on('load', function () { $('[data-ride="carousel"]').each(function () { var $carousel = $(this) Plugin.call($carousel, $carousel.data()) }) }) }(jQuery); /* ======================================================================== * Bootstrap: collapse.js v3.2.0 * http://getbootstrap.com/javascript/#collapse * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // COLLAPSE PUBLIC CLASS DEFINITION // ================================ var Collapse = function (element, options) { this.$element = $(element) this.options = $.extend({}, Collapse.DEFAULTS, options) this.transitioning = null if (this.options.parent) this.$parent = $(this.options.parent) if (this.options.toggle) this.toggle() } Collapse.VERSION = '3.2.0' Collapse.DEFAULTS = { toggle: true } Collapse.prototype.dimension = function () { var hasWidth = this.$element.hasClass('width') return hasWidth ? 'width' : 'height' } Collapse.prototype.show = function () { if (this.transitioning || this.$element.hasClass('in')) return var startEvent = $.Event('show.bs.collapse') this.$element.trigger(startEvent) if (startEvent.isDefaultPrevented()) return var actives = this.$parent && this.$parent.find('> .panel > .in') if (actives && actives.length) { var hasData = actives.data('bs.collapse') if (hasData && hasData.transitioning) return Plugin.call(actives, 'hide') hasData || actives.data('bs.collapse', null) } var dimension = this.dimension() this.$element .removeClass('collapse') .addClass('collapsing')[dimension](0) this.transitioning = 1 var complete = function () { this.$element .removeClass('collapsing') .addClass('collapse in')[dimension]('') this.transitioning = 0 this.$element .trigger('shown.bs.collapse') } if (!$.support.transition) return complete.call(this) var scrollSize = $.camelCase(['scroll', dimension].join('-')) this.$element .one('bsTransitionEnd', $.proxy(complete, this)) .emulateTransitionEnd(350)[dimension](this.$element[0][scrollSize]) } Collapse.prototype.hide = function () { if (this.transitioning || !this.$element.hasClass('in')) return var startEvent = $.Event('hide.bs.collapse') this.$element.trigger(startEvent) if (startEvent.isDefaultPrevented()) return var dimension = this.dimension() this.$element[dimension](this.$element[dimension]())[0].offsetHeight this.$element .addClass('collapsing') .removeClass('collapse') .removeClass('in') this.transitioning = 1 var complete = function () { this.transitioning = 0 this.$element .trigger('hidden.bs.collapse') .removeClass('collapsing') .addClass('collapse') } if (!$.support.transition) return complete.call(this) this.$element [dimension](0) .one('bsTransitionEnd', $.proxy(complete, this)) .emulateTransitionEnd(350) } Collapse.prototype.toggle = function () { this[this.$element.hasClass('in') ? 'hide' : 'show']() } // COLLAPSE PLUGIN DEFINITION // ========================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.collapse') var options = $.extend({}, Collapse.DEFAULTS, $this.data(), typeof option == 'object' && option) if (!data && options.toggle && option == 'show') option = !option if (!data) $this.data('bs.collapse', (data = new Collapse(this, options))) if (typeof option == 'string') data[option]() }) } var old = $.fn.collapse $.fn.collapse = Plugin $.fn.collapse.Constructor = Collapse // COLLAPSE NO CONFLICT // ==================== $.fn.collapse.noConflict = function () { $.fn.collapse = old return this } // COLLAPSE DATA-API // ================= $(document).on('click.bs.collapse.data-api', '[data-toggle="collapse"]', function (e) { var href var $this = $(this) var target = $this.attr('data-target') || e.preventDefault() || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') // strip for ie7 var $target = $(target) var data = $target.data('bs.collapse') var option = data ? 'toggle' : $this.data() var parent = $this.attr('data-parent') var $parent = parent && $(parent) if (!data || !data.transitioning) { if ($parent) $parent.find('[data-toggle="collapse"][data-parent="' + parent + '"]').not($this).addClass('collapsed') $this[$target.hasClass('in') ? 'addClass' : 'removeClass']('collapsed') } Plugin.call($target, option) }) }(jQuery); /* ======================================================================== * Bootstrap: dropdown.js v3.2.0 * http://getbootstrap.com/javascript/#dropdowns * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // DROPDOWN CLASS DEFINITION // ========================= var backdrop = '.dropdown-backdrop' var toggle = '[data-toggle="dropdown"]' var Dropdown = function (element) { $(element).on('click.bs.dropdown', this.toggle) } Dropdown.VERSION = '3.2.0' Dropdown.prototype.toggle = function (e) { var $this = $(this) if ($this.is('.disabled, :disabled')) return var $parent = getParent($this) var isActive = $parent.hasClass('open') clearMenus() if (!isActive) { if ('ontouchstart' in document.documentElement && !$parent.closest('.navbar-nav').length) { // if mobile we use a backdrop because click events don't delegate $('

').insertAfter($(this)).on('click', clearMenus) } var relatedTarget = { relatedTarget: this } $parent.trigger(e = $.Event('show.bs.dropdown', relatedTarget)) if (e.isDefaultPrevented()) return $this.trigger('focus') $parent .toggleClass('open') .trigger('shown.bs.dropdown', relatedTarget) } return false } Dropdown.prototype.keydown = function (e) { if (!/(38|40|27)/.test(e.keyCode)) return var $this = $(this) e.preventDefault() e.stopPropagation() if ($this.is('.disabled, :disabled')) return var $parent = getParent($this) var isActive = $parent.hasClass('open') if (!isActive || (isActive && e.keyCode == 27)) { if (e.which == 27) $parent.find(toggle).trigger('focus') return $this.trigger('click') } var desc = ' li:not(.divider):visible a' var $items = $parent.find('[role="menu"]' + desc + ', [role="listbox"]' + desc) if (!$items.length) return var index = $items.index($items.filter(':focus')) if (e.keyCode == 38 && index > 0) index-- // up if (e.keyCode == 40 && index < $items.length - 1) index++ // down if (!~index) index = 0 $items.eq(index).trigger('focus') } function clearMenus(e) { if (e && e.which === 3) return $(backdrop).remove() $(toggle).each(function () { var $parent = getParent($(this)) var relatedTarget = { relatedTarget: this } if (!$parent.hasClass('open')) return $parent.trigger(e = $.Event('hide.bs.dropdown', relatedTarget)) if (e.isDefaultPrevented()) return $parent.removeClass('open').trigger('hidden.bs.dropdown', relatedTarget) }) } function getParent($this) { var selector = $this.attr('data-target') if (!selector) { selector = $this.attr('href') selector = selector && /#[A-Za-z]/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7 } var $parent = selector && $(selector) return $parent && $parent.length ? $parent : $this.parent() } // DROPDOWN PLUGIN DEFINITION // ========================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.dropdown') if (!data) $this.data('bs.dropdown', (data = new Dropdown(this))) if (typeof option == 'string') data[option].call($this) }) } var old = $.fn.dropdown $.fn.dropdown = Plugin $.fn.dropdown.Constructor = Dropdown // DROPDOWN NO CONFLICT // ==================== $.fn.dropdown.noConflict = function () { $.fn.dropdown = old return this } // APPLY TO STANDARD DROPDOWN ELEMENTS // =================================== $(document) .on('click.bs.dropdown.data-api', clearMenus) .on('click.bs.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) .on('click.bs.dropdown.data-api', toggle, Dropdown.prototype.toggle) .on('keydown.bs.dropdown.data-api', toggle + ', [role="menu"], [role="listbox"]', Dropdown.prototype.keydown) }(jQuery); /* ======================================================================== * Bootstrap: modal.js v3.2.0 * http://getbootstrap.com/javascript/#modals * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // MODAL CLASS DEFINITION // ====================== var Modal = function (element, options) { this.options = options this.$body = $(document.body) this.$element = $(element) this.$backdrop = this.isShown = null this.scrollbarWidth = 0 if (this.options.remote) { this.$element .find('.modal-content') .load(this.options.remote, $.proxy(function () { this.$element.trigger('loaded.bs.modal') }, this)) } } Modal.VERSION = '3.2.0' Modal.DEFAULTS = { backdrop: true, keyboard: true, show: true } Modal.prototype.toggle = function (_relatedTarget) { return this.isShown ? this.hide() : this.show(_relatedTarget) } Modal.prototype.show = function (_relatedTarget) { var that = this var e = $.Event('show.bs.modal', { relatedTarget: _relatedTarget }) this.$element.trigger(e) if (this.isShown || e.isDefaultPrevented()) return this.isShown = true this.checkScrollbar() this.$body.addClass('modal-open') this.setScrollbar() this.escape() this.$element.on('click.dismiss.bs.modal', '[data-dismiss="modal"]', $.proxy(this.hide, this)) this.backdrop(function () { var transition = $.support.transition && that.$element.hasClass('fade') if (!that.$element.parent().length) { that.$element.appendTo(that.$body) // don't move modals dom position } that.$element .show() .scrollTop(0) if (transition) { that.$element[0].offsetWidth // force reflow } that.$element .addClass('in') .attr('aria-hidden', false) that.enforceFocus() var e = $.Event('shown.bs.modal', { relatedTarget: _relatedTarget }) transition ? that.$element.find('.modal-dialog') // wait for modal to slide in .one('bsTransitionEnd', function () { that.$element.trigger('focus').trigger(e) }) .emulateTransitionEnd(300) : that.$element.trigger('focus').trigger(e) }) } Modal.prototype.hide = function (e) { if (e) e.preventDefault() e = $.Event('hide.bs.modal') this.$element.trigger(e) if (!this.isShown || e.isDefaultPrevented()) return this.isShown = false this.$body.removeClass('modal-open') this.resetScrollbar() this.escape() $(document).off('focusin.bs.modal') this.$element .removeClass('in') .attr('aria-hidden', true) .off('click.dismiss.bs.modal') $.support.transition && this.$element.hasClass('fade') ? this.$element .one('bsTransitionEnd', $.proxy(this.hideModal, this)) .emulateTransitionEnd(300) : this.hideModal() } Modal.prototype.enforceFocus = function () { $(document) .off('focusin.bs.modal') // guard against infinite focus loop .on('focusin.bs.modal', $.proxy(function (e) { if (this.$element[0] !== e.target && !this.$element.has(e.target).length) { this.$element.trigger('focus') } }, this)) } Modal.prototype.escape = function () { if (this.isShown && this.options.keyboard) { this.$element.on('keyup.dismiss.bs.modal', $.proxy(function (e) { e.which == 27 && this.hide() }, this)) } else if (!this.isShown) { this.$element.off('keyup.dismiss.bs.modal') } } Modal.prototype.hideModal = function () { var that = this this.$element.hide() this.backdrop(function () { that.$element.trigger('hidden.bs.modal') }) } Modal.prototype.removeBackdrop = function () { this.$backdrop && this.$backdrop.remove() this.$backdrop = null } Modal.prototype.backdrop = function (callback) { var that = this var animate = this.$element.hasClass('fade') ? 'fade' : '' if (this.isShown && this.options.backdrop) { var doAnimate = $.support.transition && animate this.$backdrop = $('
') .appendTo(this.$body) this.$element.on('click.dismiss.bs.modal', $.proxy(function (e) { if (e.target !== e.currentTarget) return this.options.backdrop == 'static' ? this.$element[0].focus.call(this.$element[0]) : this.hide.call(this) }, this)) if (doAnimate) this.$backdrop[0].offsetWidth // force reflow this.$backdrop.addClass('in') if (!callback) return doAnimate ? this.$backdrop .one('bsTransitionEnd', callback) .emulateTransitionEnd(150) : callback() } else if (!this.isShown && this.$backdrop) { this.$backdrop.removeClass('in') var callbackRemove = function () { that.removeBackdrop() callback && callback() } $.support.transition && this.$element.hasClass('fade') ? this.$backdrop .one('bsTransitionEnd', callbackRemove) .emulateTransitionEnd(150) : callbackRemove() } else if (callback) { callback() } } Modal.prototype.checkScrollbar = function () { if (document.body.clientWidth >= window.innerWidth) return this.scrollbarWidth = this.scrollbarWidth || this.measureScrollbar() } Modal.prototype.setScrollbar = function () { var bodyPad = parseInt((this.$body.css('padding-right') || 0), 10) if (this.scrollbarWidth) this.$body.css('padding-right', bodyPad + this.scrollbarWidth) } Modal.prototype.resetScrollbar = function () { this.$body.css('padding-right', '') } Modal.prototype.measureScrollbar = function () { // thx walsh var scrollDiv = document.createElement('div') scrollDiv.className = 'modal-scrollbar-measure' this.$body.append(scrollDiv) var scrollbarWidth = scrollDiv.offsetWidth - scrollDiv.clientWidth this.$body[0].removeChild(scrollDiv) return scrollbarWidth } // MODAL PLUGIN DEFINITION // ======================= function Plugin(option, _relatedTarget) { return this.each(function () { var $this = $(this) var data = $this.data('bs.modal') var options = $.extend({}, Modal.DEFAULTS, $this.data(), typeof option == 'object' && option) if (!data) $this.data('bs.modal', (data = new Modal(this, options))) if (typeof option == 'string') data[option](_relatedTarget) else if (options.show) data.show(_relatedTarget) }) } var old = $.fn.modal $.fn.modal = Plugin $.fn.modal.Constructor = Modal // MODAL NO CONFLICT // ================= $.fn.modal.noConflict = function () { $.fn.modal = old return this } // MODAL DATA-API // ============== $(document).on('click.bs.modal.data-api', '[data-toggle="modal"]', function (e) { var $this = $(this) var href = $this.attr('href') var $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) // strip for ie7 var option = $target.data('bs.modal') ? 'toggle' : $.extend({ remote: !/#/.test(href) && href }, $target.data(), $this.data()) if ($this.is('a')) e.preventDefault() $target.one('show.bs.modal', function (showEvent) { if (showEvent.isDefaultPrevented()) return // only register focus restorer if modal will actually get shown $target.one('hidden.bs.modal', function () { $this.is(':visible') && $this.trigger('focus') }) }) Plugin.call($target, option, this) }) }(jQuery); /* ======================================================================== * Bootstrap: tooltip.js v3.2.0 * http://getbootstrap.com/javascript/#tooltip * Inspired by the original jQuery.tipsy by Jason Frame * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // TOOLTIP PUBLIC CLASS DEFINITION // =============================== var Tooltip = function (element, options) { this.type = this.options = this.enabled = this.timeout = this.hoverState = this.$element = null this.init('tooltip', element, options) } Tooltip.VERSION = '3.2.0' Tooltip.DEFAULTS = { animation: true, placement: 'top', selector: false, template: '', trigger: 'hover focus', title: '', delay: 0, html: false, container: false, viewport: { selector: 'body', padding: 0 } } Tooltip.prototype.init = function (type, element, options) { this.enabled = true this.type = type this.$element = $(element) this.options = this.getOptions(options) this.$viewport = this.options.viewport && $(this.options.viewport.selector || this.options.viewport) var triggers = this.options.trigger.split(' ') for (var i = triggers.length; i--;) { var trigger = triggers[i] if (trigger == 'click') { this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) } else if (trigger != 'manual') { var eventIn = trigger == 'hover' ? 'mouseenter' : 'focusin' var eventOut = trigger == 'hover' ? 'mouseleave' : 'focusout' this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) } } this.options.selector ? (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : this.fixTitle() } Tooltip.prototype.getDefaults = function () { return Tooltip.DEFAULTS } Tooltip.prototype.getOptions = function (options) { options = $.extend({}, this.getDefaults(), this.$element.data(), options) if (options.delay && typeof options.delay == 'number') { options.delay = { show: options.delay, hide: options.delay } } return options } Tooltip.prototype.getDelegateOptions = function () { var options = {} var defaults = this.getDefaults() this._options && $.each(this._options, function (key, value) { if (defaults[key] != value) options[key] = value }) return options } Tooltip.prototype.enter = function (obj) { var self = obj instanceof this.constructor ? obj : $(obj.currentTarget).data('bs.' + this.type) if (!self) { self = new this.constructor(obj.currentTarget, this.getDelegateOptions()) $(obj.currentTarget).data('bs.' + this.type, self) } clearTimeout(self.timeout) self.hoverState = 'in' if (!self.options.delay || !self.options.delay.show) return self.show() self.timeout = setTimeout(function () { if (self.hoverState == 'in') self.show() }, self.options.delay.show) } Tooltip.prototype.leave = function (obj) { var self = obj instanceof this.constructor ? obj : $(obj.currentTarget).data('bs.' + this.type) if (!self) { self = new this.constructor(obj.currentTarget, this.getDelegateOptions()) $(obj.currentTarget).data('bs.' + this.type, self) } clearTimeout(self.timeout) self.hoverState = 'out' if (!self.options.delay || !self.options.delay.hide) return self.hide() self.timeout = setTimeout(function () { if (self.hoverState == 'out') self.hide() }, self.options.delay.hide) } Tooltip.prototype.show = function () { var e = $.Event('show.bs.' + this.type) if (this.hasContent() && this.enabled) { this.$element.trigger(e) var inDom = $.contains(document.documentElement, this.$element[0]) if (e.isDefaultPrevented() || !inDom) return var that = this var $tip = this.tip() var tipId = this.getUID(this.type) this.setContent() $tip.attr('id', tipId) this.$element.attr('aria-describedby', tipId) if (this.options.animation) $tip.addClass('fade') var placement = typeof this.options.placement == 'function' ? this.options.placement.call(this, $tip[0], this.$element[0]) : this.options.placement var autoToken = /\s?auto?\s?/i var autoPlace = autoToken.test(placement) if (autoPlace) placement = placement.replace(autoToken, '') || 'top' $tip .detach() .css({ top: 0, left: 0, display: 'block' }) .addClass(placement) .data('bs.' + this.type, this) this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element) var pos = this.getPosition() var actualWidth = $tip[0].offsetWidth var actualHeight = $tip[0].offsetHeight if (autoPlace) { var orgPlacement = placement var $parent = this.$element.parent() var parentDim = this.getPosition($parent) placement = placement == 'bottom' && pos.top + pos.height + actualHeight - parentDim.scroll > parentDim.height ? 'top' : placement == 'top' && pos.top - parentDim.scroll - actualHeight < 0 ? 'bottom' : placement == 'right' && pos.right + actualWidth > parentDim.width ? 'left' : placement == 'left' && pos.left - actualWidth < parentDim.left ? 'right' : placement $tip .removeClass(orgPlacement) .addClass(placement) } var calculatedOffset = this.getCalculatedOffset(placement, pos, actualWidth, actualHeight) this.applyPlacement(calculatedOffset, placement) var complete = function () { that.$element.trigger('shown.bs.' + that.type) that.hoverState = null } $.support.transition && this.$tip.hasClass('fade') ? $tip .one('bsTransitionEnd', complete) .emulateTransitionEnd(150) : complete() } } Tooltip.prototype.applyPlacement = function (offset, placement) { var $tip = this.tip() var width = $tip[0].offsetWidth var height = $tip[0].offsetHeight // manually read margins because getBoundingClientRect includes difference var marginTop = parseInt($tip.css('margin-top'), 10) var marginLeft = parseInt($tip.css('margin-left'), 10) // we must check for NaN for ie 8/9 if (isNaN(marginTop)) marginTop = 0 if (isNaN(marginLeft)) marginLeft = 0 offset.top = offset.top + marginTop offset.left = offset.left + marginLeft // $.fn.offset doesn't round pixel values // so we use setOffset directly with our own function B-0 $.offset.setOffset($tip[0], $.extend({ using: function (props) { $tip.css({ top: Math.round(props.top), left: Math.round(props.left) }) } }, offset), 0) $tip.addClass('in') // check to see if placing tip in new offset caused the tip to resize itself var actualWidth = $tip[0].offsetWidth var actualHeight = $tip[0].offsetHeight if (placement == 'top' && actualHeight != height) { offset.top = offset.top + height - actualHeight } var delta = this.getViewportAdjustedDelta(placement, offset, actualWidth, actualHeight) if (delta.left) offset.left += delta.left else offset.top += delta.top var arrowDelta = delta.left ? delta.left * 2 - width + actualWidth : delta.top * 2 - height + actualHeight var arrowPosition = delta.left ? 'left' : 'top' var arrowOffsetPosition = delta.left ? 'offsetWidth' : 'offsetHeight' $tip.offset(offset) this.replaceArrow(arrowDelta, $tip[0][arrowOffsetPosition], arrowPosition) } Tooltip.prototype.replaceArrow = function (delta, dimension, position) { this.arrow().css(position, delta ? (50 * (1 - delta / dimension) + '%') : '') } Tooltip.prototype.setContent = function () { var $tip = this.tip() var title = this.getTitle() $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) $tip.removeClass('fade in top bottom left right') } Tooltip.prototype.hide = function () { var that = this var $tip = this.tip() var e = $.Event('hide.bs.' + this.type) this.$element.removeAttr('aria-describedby') function complete() { if (that.hoverState != 'in') $tip.detach() that.$element.trigger('hidden.bs.' + that.type) } this.$element.trigger(e) if (e.isDefaultPrevented()) return $tip.removeClass('in') $.support.transition && this.$tip.hasClass('fade') ? $tip .one('bsTransitionEnd', complete) .emulateTransitionEnd(150) : complete() this.hoverState = null return this } Tooltip.prototype.fixTitle = function () { var $e = this.$element if ($e.attr('title') || typeof ($e.attr('data-original-title')) != 'string') { $e.attr('data-original-title', $e.attr('title') || '').attr('title', '') } } Tooltip.prototype.hasContent = function () { return this.getTitle() } Tooltip.prototype.getPosition = function ($element) { $element = $element || this.$element var el = $element[0] var isBody = el.tagName == 'BODY' return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : null, { scroll: isBody ? document.documentElement.scrollTop || document.body.scrollTop : $element.scrollTop(), width: isBody ? $(window).width() : $element.outerWidth(), height: isBody ? $(window).height() : $element.outerHeight() }, isBody ? { top: 0, left: 0 } : $element.offset()) } Tooltip.prototype.getCalculatedOffset = function (placement, pos, actualWidth, actualHeight) { return placement == 'bottom' ? { top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2 } : placement == 'top' ? { top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2 } : placement == 'left' ? { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth } : /* placement == 'right' */ { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width } } Tooltip.prototype.getViewportAdjustedDelta = function (placement, pos, actualWidth, actualHeight) { var delta = { top: 0, left: 0 } if (!this.$viewport) return delta var viewportPadding = this.options.viewport && this.options.viewport.padding || 0 var viewportDimensions = this.getPosition(this.$viewport) if (/right|left/.test(placement)) { var topEdgeOffset = pos.top - viewportPadding - viewportDimensions.scroll var bottomEdgeOffset = pos.top + viewportPadding - viewportDimensions.scroll + actualHeight if (topEdgeOffset < viewportDimensions.top) { // top overflow delta.top = viewportDimensions.top - topEdgeOffset } else if (bottomEdgeOffset > viewportDimensions.top + viewportDimensions.height) { // bottom overflow delta.top = viewportDimensions.top + viewportDimensions.height - bottomEdgeOffset } } else { var leftEdgeOffset = pos.left - viewportPadding var rightEdgeOffset = pos.left + viewportPadding + actualWidth if (leftEdgeOffset < viewportDimensions.left) { // left overflow delta.left = viewportDimensions.left - leftEdgeOffset } else if (rightEdgeOffset > viewportDimensions.width) { // right overflow delta.left = viewportDimensions.left + viewportDimensions.width - rightEdgeOffset } } return delta } Tooltip.prototype.getTitle = function () { var title var $e = this.$element var o = this.options title = $e.attr('data-original-title') || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) return title } Tooltip.prototype.getUID = function (prefix) { do prefix += ~~(Math.random() * 1000000) while (document.getElementById(prefix)) return prefix } Tooltip.prototype.tip = function () { return (this.$tip = this.$tip || $(this.options.template)) } Tooltip.prototype.arrow = function () { return (this.$arrow = this.$arrow || this.tip().find('.tooltip-arrow')) } Tooltip.prototype.validate = function () { if (!this.$element[0].parentNode) { this.hide() this.$element = null this.options = null } } Tooltip.prototype.enable = function () { this.enabled = true } Tooltip.prototype.disable = function () { this.enabled = false } Tooltip.prototype.toggleEnabled = function () { this.enabled = !this.enabled } Tooltip.prototype.toggle = function (e) { var self = this if (e) { self = $(e.currentTarget).data('bs.' + this.type) if (!self) { self = new this.constructor(e.currentTarget, this.getDelegateOptions()) $(e.currentTarget).data('bs.' + this.type, self) } } self.tip().hasClass('in') ? self.leave(self) : self.enter(self) } Tooltip.prototype.destroy = function () { clearTimeout(this.timeout) this.hide().$element.off('.' + this.type).removeData('bs.' + this.type) } // TOOLTIP PLUGIN DEFINITION // ========================= function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.tooltip') var options = typeof option == 'object' && option if (!data && option == 'destroy') return if (!data) $this.data('bs.tooltip', (data = new Tooltip(this, options))) if (typeof option == 'string') data[option]() }) } var old = $.fn.tooltip $.fn.tooltip = Plugin $.fn.tooltip.Constructor = Tooltip // TOOLTIP NO CONFLICT // =================== $.fn.tooltip.noConflict = function () { $.fn.tooltip = old return this } }(jQuery); /* ======================================================================== * Bootstrap: popover.js v3.2.0 * http://getbootstrap.com/javascript/#popovers * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // POPOVER PUBLIC CLASS DEFINITION // =============================== var Popover = function (element, options) { this.init('popover', element, options) } if (!$.fn.tooltip) throw new Error('Popover requires tooltip.js') Popover.VERSION = '3.2.0' Popover.DEFAULTS = $.extend({}, $.fn.tooltip.Constructor.DEFAULTS, { placement: 'right', trigger: 'click', content: '', template: '' }) // NOTE: POPOVER EXTENDS tooltip.js // ================================ Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype) Popover.prototype.constructor = Popover Popover.prototype.getDefaults = function () { return Popover.DEFAULTS } Popover.prototype.setContent = function () { var $tip = this.tip() var title = this.getTitle() var content = this.getContent() $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) $tip.find('.popover-content').empty()[ // we use append for html objects to maintain js events this.options.html ? (typeof content == 'string' ? 'html' : 'append') : 'text' ](content) $tip.removeClass('fade top bottom left right in') // IE8 doesn't accept hiding via the `:empty` pseudo selector, we have to do // this manually by checking the contents. if (!$tip.find('.popover-title').html()) $tip.find('.popover-title').hide() } Popover.prototype.hasContent = function () { return this.getTitle() || this.getContent() } Popover.prototype.getContent = function () { var $e = this.$element var o = this.options return $e.attr('data-content') || (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) } Popover.prototype.arrow = function () { return (this.$arrow = this.$arrow || this.tip().find('.arrow')) } Popover.prototype.tip = function () { if (!this.$tip) this.$tip = $(this.options.template) return this.$tip } // POPOVER PLUGIN DEFINITION // ========================= function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.popover') var options = typeof option == 'object' && option if (!data && option == 'destroy') return if (!data) $this.data('bs.popover', (data = new Popover(this, options))) if (typeof option == 'string') data[option]() }) } var old = $.fn.popover $.fn.popover = Plugin $.fn.popover.Constructor = Popover // POPOVER NO CONFLICT // =================== $.fn.popover.noConflict = function () { $.fn.popover = old return this } }(jQuery); /* ======================================================================== * Bootstrap: scrollspy.js v3.2.0 * http://getbootstrap.com/javascript/#scrollspy * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // SCROLLSPY CLASS DEFINITION // ========================== function ScrollSpy(element, options) { var process = $.proxy(this.process, this) this.$body = $('body') this.$scrollElement = $(element).is('body') ? $(window) : $(element) this.options = $.extend({}, ScrollSpy.DEFAULTS, options) this.selector = (this.options.target || '') + ' .nav li > a' this.offsets = [] this.targets = [] this.activeTarget = null this.scrollHeight = 0 this.$scrollElement.on('scroll.bs.scrollspy', process) this.refresh() this.process() } ScrollSpy.VERSION = '3.2.0' ScrollSpy.DEFAULTS = { offset: 10 } ScrollSpy.prototype.getScrollHeight = function () { return this.$scrollElement[0].scrollHeight || Math.max(this.$body[0].scrollHeight, document.documentElement.scrollHeight) } ScrollSpy.prototype.refresh = function () { var offsetMethod = 'offset' var offsetBase = 0 if (!$.isWindow(this.$scrollElement[0])) { offsetMethod = 'position' offsetBase = this.$scrollElement.scrollTop() } this.offsets = [] this.targets = [] this.scrollHeight = this.getScrollHeight() var self = this this.$body .find(this.selector) .map(function () { var $el = $(this) var href = $el.data('target') || $el.attr('href') var $href = /^#./.test(href) && $(href) return ($href && $href.length && $href.is(':visible') && [[$href[offsetMethod]().top + offsetBase, href]]) || null }) .sort(function (a, b) { return a[0] - b[0] }) .each(function () { self.offsets.push(this[0]) self.targets.push(this[1]) }) } ScrollSpy.prototype.process = function () { var scrollTop = this.$scrollElement.scrollTop() + this.options.offset var scrollHeight = this.getScrollHeight() var maxScroll = this.options.offset + scrollHeight - this.$scrollElement.height() var offsets = this.offsets var targets = this.targets var activeTarget = this.activeTarget var i if (this.scrollHeight != scrollHeight) { this.refresh() } if (scrollTop >= maxScroll) { return activeTarget != (i = targets[targets.length - 1]) && this.activate(i) } if (activeTarget && scrollTop <= offsets[0]) { return activeTarget != (i = targets[0]) && this.activate(i) } for (i = offsets.length; i--;) { activeTarget != targets[i] && scrollTop >= offsets[i] && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) && this.activate(targets[i]) } } ScrollSpy.prototype.activate = function (target) { this.activeTarget = target $(this.selector) .parentsUntil(this.options.target, '.active') .removeClass('active') var selector = this.selector + '[data-target="' + target + '"],' + this.selector + '[href="' + target + '"]' var active = $(selector) .parents('li') .addClass('active') if (active.parent('.dropdown-menu').length) { active = active .closest('li.dropdown') .addClass('active') } active.trigger('activate.bs.scrollspy') } // SCROLLSPY PLUGIN DEFINITION // =========================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.scrollspy') var options = typeof option == 'object' && option if (!data) $this.data('bs.scrollspy', (data = new ScrollSpy(this, options))) if (typeof option == 'string') data[option]() }) } var old = $.fn.scrollspy $.fn.scrollspy = Plugin $.fn.scrollspy.Constructor = ScrollSpy // SCROLLSPY NO CONFLICT // ===================== $.fn.scrollspy.noConflict = function () { $.fn.scrollspy = old return this } // SCROLLSPY DATA-API // ================== $(window).on('load.bs.scrollspy.data-api', function () { $('[data-spy="scroll"]').each(function () { var $spy = $(this) Plugin.call($spy, $spy.data()) }) }) }(jQuery); /* ======================================================================== * Bootstrap: tab.js v3.2.0 * http://getbootstrap.com/javascript/#tabs * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // TAB CLASS DEFINITION // ==================== var Tab = function (element) { this.element = $(element) } Tab.VERSION = '3.2.0' Tab.prototype.show = function () { var $this = this.element var $ul = $this.closest('ul:not(.dropdown-menu)') var selector = $this.data('target') if (!selector) { selector = $this.attr('href') selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7 } if ($this.parent('li').hasClass('active')) return var previous = $ul.find('.active:last a')[0] var e = $.Event('show.bs.tab', { relatedTarget: previous }) $this.trigger(e) if (e.isDefaultPrevented()) return var $target = $(selector) this.activate($this.closest('li'), $ul) this.activate($target, $target.parent(), function () { $this.trigger({ type: 'shown.bs.tab', relatedTarget: previous }) }) } Tab.prototype.activate = function (element, container, callback) { var $active = container.find('> .active') var transition = callback && $.support.transition && $active.hasClass('fade') function next() { $active .removeClass('active') .find('> .dropdown-menu > .active') .removeClass('active') element.addClass('active') if (transition) { element[0].offsetWidth // reflow for transition element.addClass('in') } else { element.removeClass('fade') } if (element.parent('.dropdown-menu')) { element.closest('li.dropdown').addClass('active') } callback && callback() } transition ? $active .one('bsTransitionEnd', next) .emulateTransitionEnd(150) : next() $active.removeClass('in') } // TAB PLUGIN DEFINITION // ===================== function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.tab') if (!data) $this.data('bs.tab', (data = new Tab(this))) if (typeof option == 'string') data[option]() }) } var old = $.fn.tab $.fn.tab = Plugin $.fn.tab.Constructor = Tab // TAB NO CONFLICT // =============== $.fn.tab.noConflict = function () { $.fn.tab = old return this } // TAB DATA-API // ============ $(document).on('click.bs.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { e.preventDefault() Plugin.call($(this), 'show') }) }(jQuery); /* ======================================================================== * Bootstrap: affix.js v3.2.0 * http://getbootstrap.com/javascript/#affix * ======================================================================== * Copyright 2011-2014 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * ======================================================================== */ +function ($) { 'use strict'; // AFFIX CLASS DEFINITION // ====================== var Affix = function (element, options) { this.options = $.extend({}, Affix.DEFAULTS, options) this.$target = $(this.options.target) .on('scroll.bs.affix.data-api', $.proxy(this.checkPosition, this)) .on('click.bs.affix.data-api', $.proxy(this.checkPositionWithEventLoop, this)) this.$element = $(element) this.affixed = this.unpin = this.pinnedOffset = null this.checkPosition() } Affix.VERSION = '3.2.0' Affix.RESET = 'affix affix-top affix-bottom' Affix.DEFAULTS = { offset: 0, target: window } Affix.prototype.getPinnedOffset = function () { if (this.pinnedOffset) return this.pinnedOffset this.$element.removeClass(Affix.RESET).addClass('affix') var scrollTop = this.$target.scrollTop() var position = this.$element.offset() return (this.pinnedOffset = position.top - scrollTop) } Affix.prototype.checkPositionWithEventLoop = function () { setTimeout($.proxy(this.checkPosition, this), 1) } Affix.prototype.checkPosition = function () { if (!this.$element.is(':visible')) return var scrollHeight = $(document).height() var scrollTop = this.$target.scrollTop() var position = this.$element.offset() var offset = this.options.offset var offsetTop = offset.top var offsetBottom = offset.bottom if (typeof offset != 'object') offsetBottom = offsetTop = offset if (typeof offsetTop == 'function') offsetTop = offset.top(this.$element) if (typeof offsetBottom == 'function') offsetBottom = offset.bottom(this.$element) var affix = this.unpin != null && (scrollTop + this.unpin <= position.top) ? false : offsetBottom != null && (position.top + this.$element.height() >= scrollHeight - offsetBottom) ? 'bottom' : offsetTop != null && (scrollTop <= offsetTop) ? 'top' : false if (this.affixed === affix) return if (this.unpin != null) this.$element.css('top', '') var affixType = 'affix' + (affix ? '-' + affix : '') var e = $.Event(affixType + '.bs.affix') this.$element.trigger(e) if (e.isDefaultPrevented()) return this.affixed = affix this.unpin = affix == 'bottom' ? this.getPinnedOffset() : null this.$element .removeClass(Affix.RESET) .addClass(affixType) .trigger($.Event(affixType.replace('affix', 'affixed'))) if (affix == 'bottom') { this.$element.offset({ top: scrollHeight - this.$element.height() - offsetBottom }) } } // AFFIX PLUGIN DEFINITION // ======================= function Plugin(option) { return this.each(function () { var $this = $(this) var data = $this.data('bs.affix') var options = typeof option == 'object' && option if (!data) $this.data('bs.affix', (data = new Affix(this, options))) if (typeof option == 'string') data[option]() }) } var old = $.fn.affix $.fn.affix = Plugin $.fn.affix.Constructor = Affix // AFFIX NO CONFLICT // ================= $.fn.affix.noConflict = function () { $.fn.affix = old return this } // AFFIX DATA-API // ============== 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$('#dataTables-example').dataTable(); }, custom_fun:function() { /*==================================== WRITE YOUR SCRIPTS BELOW ======================================*/ }, } $(document).ready(function () { mainApp.slide_fun(); mainApp.dataTable_fun(); mainApp.custom_fun(); }); }(jQuery)); model-pivot/visualization/static/assets/js/dataTables/000077500000000000000000000000001415504170400234705ustar00rootroot00000000000000model-pivot/visualization/static/assets/js/dataTables/dataTables.bootstrap.css000066400000000000000000000115221415504170400302630ustar00rootroot00000000000000div.dataTables_length label { float: left; text-align: left; font-weight: normal; } div.dataTables_length select { width: 75px; } div.dataTables_filter label { float: right; font-weight: normal; } div.dataTables_filter input { width: 16em; } div.dataTables_info { padding-top: 8px; } div.dataTables_paginate { float: right; margin: 0; } div.dataTables_paginate ul.pagination { margin: 2px 0; white-space: nowrap; } table.dataTable, table.dataTable td, table.dataTable th { -webkit-box-sizing: content-box; -moz-box-sizing: content-box; box-sizing: content-box; } table.dataTable { clear: both; margin-top: 6px !important; margin-bottom: 6px !important; max-width: none !important; } table.dataTable thead .sorting, table.dataTable thead .sorting_asc, table.dataTable thead .sorting_desc, table.dataTable thead .sorting_asc_disabled, table.dataTable thead .sorting_desc_disabled { cursor: pointer; } table.dataTable thead .sorting { background: url('../images/sort_both.png') no-repeat center right; } table.dataTable thead .sorting_asc { background: url('../images/sort_asc.png') no-repeat center right; } table.dataTable thead .sorting_desc { background: url('../images/sort_desc.png') no-repeat center right; } table.dataTable thead .sorting_asc_disabled { background: url('../images/sort_asc_disabled.png') no-repeat center right; } table.dataTable thead .sorting_desc_disabled { background: url('../images/sort_desc_disabled.png') no-repeat center right; } table.dataTable th:active { outline: none; } /* Scrolling */ div.dataTables_scrollHead table { margin-bottom: 0 !important; border-bottom-left-radius: 0; border-bottom-right-radius: 0; } div.dataTables_scrollHead table thead tr:last-child th:first-child, div.dataTables_scrollHead table thead tr:last-child td:first-child { border-bottom-left-radius: 0 !important; border-bottom-right-radius: 0 !important; } div.dataTables_scrollBody table { margin-top: 0 !important; margin-bottom: 0 !important; border-top: none; } div.dataTables_scrollBody tbody tr:first-child th, div.dataTables_scrollBody tbody tr:first-child td { border-top: none; } div.dataTables_scrollFoot table { margin-top: 0 !important; border-top: none; } /* * TableTools styles */ .table tbody tr.active td, .table tbody tr.active th { color: white; background-color: #08C; } .table tbody tr.active:hover td, .table tbody tr.active:hover th { background-color: #0075b0 !important; } .table tbody tr.active a { color: white; } .table-striped tbody tr.active:nth-child(odd) td, .table-striped tbody tr.active:nth-child(odd) th { background-color: #017ebc; } table.DTTT_selectable tbody tr { cursor: pointer; } div.DTTT .btn { font-size: 12px; color: #333 !important; } div.DTTT .btn:hover { text-decoration: none !important; } ul.DTTT_dropdown.dropdown-menu { z-index: 2003; } ul.DTTT_dropdown.dropdown-menu a { color: #333 !important; /* needed only when demo_page.css is included */ } ul.DTTT_dropdown.dropdown-menu li { position: relative; } ul.DTTT_dropdown.dropdown-menu li:hover a { color: white !important; background-color: #0088cc; } div.DTTT_collection_background { z-index: 2002; } /* TableTools information display */ div.DTTT_print_info.modal { height: 150px; margin-top: -75px; text-align: center; } div.DTTT_print_info h6 { margin: 1em; font-size: 28px; font-weight: normal; line-height: 28px; } div.DTTT_print_info p { font-size: 14px; line-height: 20px; } /* * FixedColumns styles */ div.DTFC_LeftHeadWrapper table, div.DTFC_LeftFootWrapper table, div.DTFC_RightHeadWrapper table, div.DTFC_RightFootWrapper table, table.DTFC_Cloned tr.even { background-color: white; } div.DTFC_RightHeadWrapper table, div.DTFC_LeftHeadWrapper table { margin-bottom: 0 !important; border-top-right-radius: 0 !important; border-bottom-left-radius: 0 !important; border-bottom-right-radius: 0 !important; } div.DTFC_RightHeadWrapper table thead tr:last-child th:first-child, div.DTFC_RightHeadWrapper table thead tr:last-child td:first-child, div.DTFC_LeftHeadWrapper table thead tr:last-child th:first-child, div.DTFC_LeftHeadWrapper table thead tr:last-child td:first-child { border-bottom-left-radius: 0 !important; border-bottom-right-radius: 0 !important; } div.DTFC_RightBodyWrapper table, div.DTFC_LeftBodyWrapper table { margin-bottom: 0 !important; border-top: none; } div.DTFC_RightBodyWrapper tbody tr:first-child th, div.DTFC_RightBodyWrapper tbody tr:first-child td, div.DTFC_LeftBodyWrapper tbody tr:first-child th, div.DTFC_LeftBodyWrapper tbody tr:first-child td { border-top: none; } div.DTFC_RightFootWrapper table, div.DTFC_LeftFootWrapper table { border-top: none; }model-pivot/visualization/static/assets/js/dataTables/dataTables.bootstrap.js000066400000000000000000000222311415504170400301060ustar00rootroot00000000000000/* Set the defaults for DataTables initialisation */ $.extend(true, $.fn.dataTable.defaults, { "sDom": "<'row'<'col-sm-6'l><'col-sm-6'f>r>" + "t" + "<'row'<'col-sm-6'i><'col-sm-6'p>>", "oLanguage": { "sLengthMenu": "_MENU_ records per page" } }); /* Default class modification */ $.extend($.fn.dataTableExt.oStdClasses, { "sWrapper": "dataTables_wrapper form-inline", "sFilterInput": "form-control input-sm", "sLengthSelect": "form-control input-sm" }); // In 1.10 we use the pagination renderers to draw the Bootstrap paging, // rather than custom plug-in if ($.fn.dataTable.Api) { $.fn.dataTable.defaults.renderer = 'bootstrap'; $.fn.dataTable.ext.renderer.pageButton.bootstrap = function(settings, host, idx, buttons, page, pages) { var api = new $.fn.dataTable.Api(settings); var classes = settings.oClasses; var lang = settings.oLanguage.oPaginate; var btnDisplay, btnClass; var attach = function(container, buttons) { var i, ien, node, button; var clickHandler = function(e) { e.preventDefault(); if (e.data.action !== 'ellipsis') { api.page(e.data.action).draw(false); } }; for (i = 0, ien = buttons.length; i < ien; i++) { button = buttons[i]; if ($.isArray(button)) { attach(container, button); } else { btnDisplay = ''; btnClass = ''; switch (button) { case 'ellipsis': btnDisplay = '…'; btnClass = 'disabled'; break; case 'first': btnDisplay = lang.sFirst; btnClass = button + (page > 0 ? '' : ' disabled'); break; case 'previous': btnDisplay = lang.sPrevious; btnClass = button + (page > 0 ? '' : ' disabled'); break; case 'next': btnDisplay = lang.sNext; btnClass = button + (page < pages - 1 ? '' : ' disabled'); break; case 'last': btnDisplay = lang.sLast; btnClass = button + (page < pages - 1 ? '' : ' disabled'); break; default: btnDisplay = button + 1; btnClass = page === button ? 'active' : ''; break; } if (btnDisplay) { node = $('
  • ', { 'class': classes.sPageButton + ' ' + btnClass, 'aria-controls': settings.sTableId, 'tabindex': settings.iTabIndex, 'id': idx === 0 && typeof button === 'string' ? settings.sTableId + '_' + button : null }) .append($('', { 'href': '#' }) .html(btnDisplay) ) .appendTo(container); settings.oApi._fnBindAction( node, { action: button }, clickHandler ); } } } }; attach( $(host).empty().html('
      ').children('ul'), buttons ); } } else { // Integration for 1.9- $.fn.dataTable.defaults.sPaginationType = 'bootstrap'; /* API method to get paging information */ $.fn.dataTableExt.oApi.fnPagingInfo = function(oSettings) { return { "iStart": oSettings._iDisplayStart, "iEnd": oSettings.fnDisplayEnd(), "iLength": oSettings._iDisplayLength, "iTotal": oSettings.fnRecordsTotal(), "iFilteredTotal": oSettings.fnRecordsDisplay(), "iPage": oSettings._iDisplayLength === -1 ? 0 : Math.ceil(oSettings._iDisplayStart / oSettings._iDisplayLength), "iTotalPages": oSettings._iDisplayLength === -1 ? 0 : Math.ceil(oSettings.fnRecordsDisplay() / oSettings._iDisplayLength) }; }; /* Bootstrap style pagination control */ $.extend($.fn.dataTableExt.oPagination, { "bootstrap": { "fnInit": function(oSettings, nPaging, fnDraw) { var oLang = oSettings.oLanguage.oPaginate; var fnClickHandler = function(e) { e.preventDefault(); if (oSettings.oApi._fnPageChange(oSettings, e.data.action)) { fnDraw(oSettings); } }; $(nPaging).append( '' ); var els = $('a', nPaging); $(els[0]).bind('click.DT', { action: "previous" }, fnClickHandler); $(els[1]).bind('click.DT', { action: "next" }, fnClickHandler); }, "fnUpdate": function(oSettings, fnDraw) { var iListLength = 5; var oPaging = oSettings.oInstance.fnPagingInfo(); var an = oSettings.aanFeatures.p; var i, ien, j, sClass, iStart, iEnd, iHalf = Math.floor(iListLength / 2); if (oPaging.iTotalPages < iListLength) { iStart = 1; iEnd = oPaging.iTotalPages; } else if (oPaging.iPage <= iHalf) { iStart = 1; iEnd = iListLength; } else if (oPaging.iPage >= (oPaging.iTotalPages - iHalf)) { iStart = oPaging.iTotalPages - iListLength + 1; iEnd = oPaging.iTotalPages; } else { iStart = oPaging.iPage - iHalf + 1; iEnd = iStart + iListLength - 1; } for (i = 0, ien = an.length; i < ien; i++) { // Remove the middle elements $('li:gt(0)', an[i]).filter(':not(:last)').remove(); // Add the new list items and their event handlers for (j = iStart; j <= iEnd; j++) { sClass = (j == oPaging.iPage + 1) ? 'class="active"' : ''; $('
    • ' + j + '
      • ') .insertBefore($('li:last', an[i])[0]) .bind('click', function(e) { e.preventDefault(); oSettings._iDisplayStart = (parseInt($('a', this).text(), 10) - 1) * oPaging.iLength; fnDraw(oSettings); }); } // Add / remove disabled classes from the static elements if (oPaging.iPage === 0) { $('li:first', an[i]).addClass('disabled'); } else { $('li:first', an[i]).removeClass('disabled'); } if (oPaging.iPage === oPaging.iTotalPages - 1 || oPaging.iTotalPages === 0) { $('li:last', an[i]).addClass('disabled'); } else { $('li:last', an[i]).removeClass('disabled'); } } } } }); } /* * TableTools Bootstrap compatibility * Required TableTools 2.1+ */ if ($.fn.DataTable.TableTools) { // Set the classes that TableTools uses to something suitable for Bootstrap $.extend(true, $.fn.DataTable.TableTools.classes, { "container": "DTTT btn-group", "buttons": { "normal": "btn btn-default", "disabled": "disabled" }, "collection": { "container": "DTTT_dropdown dropdown-menu", "buttons": { "normal": "", "disabled": "disabled" } }, "print": { "info": "DTTT_print_info modal" }, "select": { "row": "active" } }); // Have the collection use a bootstrap compatible dropdown $.extend(true, $.fn.DataTable.TableTools.DEFAULTS.oTags, { "collection": { "container": "ul", "button": "li", "liner": "a" } }); } model-pivot/visualization/static/assets/js/dataTables/jquery.dataTables.js000066400000000000000000014377731415504170400274360ustar00rootroot00000000000000/*! DataTables 1.10.0-dev * ©2008-2013 SpryMedia Ltd - datatables.net/license */ /** * @summary DataTables * @description Paginate, search and order HTML tables * @version 1.10.0-dev * @file jquery.dataTables.js * @author SpryMedia Ltd (www.sprymedia.co.uk) * @contact www.sprymedia.co.uk/contact * @copyright Copyright 2008-2013 SpryMedia Ltd. * * This source file is free software, available under the following license: * MIT license - http://datatables.net/license * * This source file is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the license files for details. * * For details please refer to: http://www.datatables.net */ /*jslint evil: true, undef: true, browser: true */ /*globals $,require,jQuery,define,_selector_run,_selector_opts,_selector_first,_selector_row_indexes,_ext,_Api,_api_register,_api_registerPlural,_re_new_lines,_re_html,_re_formatted_numeric,_empty,_intVal,_isNumber,_isHtml,_htmlNumeric,_pluck,_pluck_order,_range,_stripHtml,_unique,_fnBuildAjax,_fnAjaxUpdate,_fnAjaxParameters,_fnAjaxUpdateDraw,_fnAjaxDataSrc,_fnAddColumn,_fnColumnOptions,_fnAdjustColumnSizing,_fnVisibleToColumnIndex,_fnColumnIndexToVisible,_fnVisbleColumns,_fnGetColumns,_fnColumnTypes,_fnApplyColumnDefs,_fnHungarianMap,_fnCamelToHungarian,_fnLanguageCompat,_fnBrowserDetect,_fnAddData,_fnAddTr,_fnNodeToDataIndex,_fnNodeToColumnIndex,_fnGetRowData,_fnGetCellData,_fnSetCellData,_fnSplitObjNotation,_fnGetObjectDataFn,_fnSetObjectDataFn,_fnGetDataMaster,_fnClearTable,_fnDeleteIndex,_fnInvalidateRow,_fnGetRowElements,_fnCreateTr,_fnBuildHead,_fnDrawHead,_fnDraw,_fnReDraw,_fnAddOptionsHtml,_fnDetectHeader,_fnGetUniqueThs,_fnFeatureHtmlFilter,_fnFilterComplete,_fnFilterCustom,_fnFilterColumn,_fnFilter,_fnFilterCreateSearch,_fnEscapeRegex,_fnFilterData,_fnFeatureHtmlInfo,_fnUpdateInfo,_fnInfoMacros,_fnInitialise,_fnInitComplete,_fnLengthChange,_fnFeatureHtmlLength,_fnFeatureHtmlPaginate,_fnPageChange,_fnFeatureHtmlProcessing,_fnProcessingDisplay,_fnFeatureHtmlTable,_fnScrollDraw,_fnApplyToChildren,_fnCalculateColumnWidths,_fnThrottle,_fnConvertToWidth,_fnScrollingWidthAdjust,_fnGetWidestNode,_fnGetMaxLenString,_fnStringToCss,_fnScrollBarWidth,_fnSortFlatten,_fnSort,_fnSortAria,_fnSortListener,_fnSortAttachListener,_fnSortingClasses,_fnSortData,_fnSaveState,_fnLoadState,_fnSettingsFromNode,_fnLog,_fnMap,_fnBindAction,_fnCallbackReg,_fnCallbackFire,_fnLengthOverflow,_fnRenderer,_fnDataSource,_fnRowAttributes*/ (/** @lends */function( window, document, $, undefined ) { (function( factory ) { "use strict"; // Define as an AMD module if possible if ( typeof define === 'function' && define.amd ) { define( 'datatables', ['jquery'], factory ); } /* Define using browser globals otherwise * Prevent multiple instantiations if the script is loaded twice */ else if ( jQuery && !jQuery.fn.dataTable ) { factory( jQuery ); } } (/** @lends */function( $ ) { "use strict"; /** * DataTables is a plug-in for the jQuery Javascript library. It is a highly * flexible tool, based upon the foundations of progressive enhancement, * which will add advanced interaction controls to any HTML table. For a * full list of features please refer to * [DataTables.net](href="http://datatables.net). * * Note that the `DataTable` object is not a global variable but is aliased * to `jQuery.fn.DataTable` and `jQuery.fn.dataTable` through which it may * be accessed. * * @class * @param {object} [init={}] Configuration object for DataTables. Options * are defined by {@link DataTable.defaults} * @requires jQuery 1.3+ * * @example * // Basic initialisation * $(document).ready( function { * $('#example').dataTable(); * } ); * * @example * // Initialisation with configuration options - in this case, disable * // pagination and sorting. * $(document).ready( function { * $('#example').dataTable( { * "paginate": false, * "sort": false * } ); * } ); */ var DataTable; /* * It is useful to have variables which are scoped locally so only the * DataTables functions can access them and they don't leak into global space. * At the same time these functions are often useful over multiple files in the * core and API, so we list, or at least document, all variables which are used * by DataTables as private variables here. This also ensures that there is no * clashing of variable names and that they can easily referenced for reuse. */ // Defined else where // _selector_run // _selector_opts // _selector_first // _selector_row_indexes var _ext; // DataTable.ext var _Api; // DataTable.Api var _api_register; // DataTable.Api.register var _api_registerPlural; // DataTable.Api.registerPlural var _re_new_lines = /[\r\n]/g; var _re_html = /<.*?>/g; var _re_formatted_numeric = /[',$£€¥%]/g; var _re_date_start = /^[\d\+\-a-zA-Z]/; var _empty = function ( d ) { return !d || d === '-' ? true : false; }; var _intVal = function ( s ) { var integer = parseInt( s, 10 ); return !isNaN(integer) && isFinite(s) ? integer : null; }; var _isNumber = function ( d, formatted ) { if ( formatted && typeof d === 'string' ) { d = d.replace( _re_formatted_numeric, '' ); } return !d || d==='-' || (!isNaN( parseFloat(d) ) && isFinite( d )); }; // A string without HTML in it can be considered to be HTML still var _isHtml = function ( d ) { return !d || typeof d === 'string'; }; var _htmlNumeric = function ( d, formatted ) { if ( _empty( d ) ) { return true; } var html = _isHtml( d ); return ! html ? null : _isNumber( _stripHtml( d ), formatted ) ? true : null; }; var _pluck = function ( a, prop, prop2 ) { var out = []; var i=0, ien=a.length; // Could have the test in the loop for slightly smaller code, but speed // is essential here if ( prop2 !== undefined ) { for ( ; i') .css( { position: 'absolute', top: 0, left: 0, height: 1, width: 1, overflow: 'hidden' } ) .append( $('
      ') .css( { position: 'absolute', top: 1, left: 1, width: 100, overflow: 'scroll' } ) .append( $('
      ') .css( { width: '100%', height: 10 } ) ) ) .appendTo( 'body' ); var test = n.find('.test'); // IE6/7 will oversize a width 100% element inside a scrolling element, to // include the width of the scrollbar, while other browsers ensure the inner // element is contained without forcing scrolling browser.bScrollOversize = test[0].offsetWidth === 100; // In rtl text layout, some browsers (most, but not all) will place the // scrollbar on the left, rather than the right. browser.bScrollbarLeft = test.offset().left !== 1; n.remove(); } /** * Add a column to the list used for the table with default values * @param {object} oSettings dataTables settings object * @param {node} nTh The th element for this column * @memberof DataTable#oApi */ function _fnAddColumn( oSettings, nTh ) { var oDefaults = DataTable.defaults.column; var iCol = oSettings.aoColumns.length; var oCol = $.extend( {}, DataTable.models.oColumn, oDefaults, { "sSortingClass": oSettings.oClasses.sSortable, "sSortingClassJUI": oSettings.oClasses.sSortJUI, "nTh": nTh ? nTh : document.createElement('th'), "sTitle": oDefaults.sTitle ? oDefaults.sTitle : nTh ? nTh.innerHTML : '', "aDataSort": oDefaults.aDataSort ? oDefaults.aDataSort : [iCol], "mData": oDefaults.mData ? oDefaults.mData : iCol } ); oSettings.aoColumns.push( oCol ); /* Add a column specific filter */ if ( oSettings.aoPreSearchCols[ iCol ] === undefined || oSettings.aoPreSearchCols[ iCol ] === null ) { oSettings.aoPreSearchCols[ iCol ] = $.extend( true, {}, DataTable.models.oSearch ); } else { var oPre = oSettings.aoPreSearchCols[ iCol ]; /* Don't require that the user must specify bRegex, bSmart or bCaseInsensitive */ if ( oPre.bRegex === undefined ) { oPre.bRegex = true; } if ( oPre.bSmart === undefined ) { oPre.bSmart = true; } if ( oPre.bCaseInsensitive === undefined ) { oPre.bCaseInsensitive = true; } } /* Use the column options function to initialise classes etc */ _fnColumnOptions( oSettings, iCol, null ); } /** * Apply options for a column * @param {object} oSettings dataTables settings object * @param {int} iCol column index to consider * @param {object} oOptions object with sType, bVisible and bSearchable etc * @memberof DataTable#oApi */ function _fnColumnOptions( oSettings, iCol, oOptions ) { var oCol = oSettings.aoColumns[ iCol ]; var oClasses = oSettings.oClasses; /* User specified column options */ if ( oOptions !== undefined && oOptions !== null ) { // Backwards compatibility _fnCompatCols( oOptions ); // Map camel case parameters to their Hungarian counterparts _fnCamelToHungarian( DataTable.defaults.column, oOptions ); /* Backwards compatibility for mDataProp */ if ( oOptions.mDataProp !== undefined && !oOptions.mData ) { oOptions.mData = oOptions.mDataProp; } oCol._sManualType = oOptions.sType; // `class` is a reserved word in Javascript, so we need to provide // the ability to use a valid name for the camel case input if ( oOptions.className && ! oOptions.sClass ) { oOptions.sClass = oOptions.className; } $.extend( oCol, oOptions ); _fnMap( oCol, oOptions, "sWidth", "sWidthOrig" ); /* iDataSort to be applied (backwards compatibility), but aDataSort will take * priority if defined */ if ( typeof oOptions.iDataSort === 'number' ) { oCol.aDataSort = [ oOptions.iDataSort ]; } _fnMap( oCol, oOptions, "aDataSort" ); } /* Cache the data get and set functions for speed */ var mDataSrc = oCol.mData; var mData = _fnGetObjectDataFn( mDataSrc ); var mRender = oCol.mRender ? _fnGetObjectDataFn( oCol.mRender ) : null; var attrTest = function( src ) { return typeof src === 'string' && src.indexOf('@') !== -1; }; oCol._bAttrSrc = $.isPlainObject( mDataSrc ) && ( attrTest(mDataSrc.sort) || attrTest(mDataSrc.type) || attrTest(mDataSrc.filter) ); oCol.fnGetData = function (oData, sSpecific) { var innerData = mData( oData, sSpecific ); if ( oCol.mRender && (sSpecific && sSpecific !== '') ) { return mRender( innerData, sSpecific, oData ); } return innerData; }; oCol.fnSetData = _fnSetObjectDataFn( mDataSrc ); /* Feature sorting overrides column specific when off */ if ( !oSettings.oFeatures.bSort ) { oCol.bSortable = false; } /* Check that the class assignment is correct for sorting */ var bAsc = $.inArray('asc', oCol.asSorting) !== -1; var bDesc = $.inArray('desc', oCol.asSorting) !== -1; if ( !oCol.bSortable || (!bAsc && !bDesc) ) { oCol.sSortingClass = oClasses.sSortableNone; oCol.sSortingClassJUI = ""; } else if ( bAsc && !bDesc ) { oCol.sSortingClass = oClasses.sSortableAsc; oCol.sSortingClassJUI = oClasses.sSortJUIAscAllowed; } else if ( !bAsc && bDesc ) { oCol.sSortingClass = oClasses.sSortableDesc; oCol.sSortingClassJUI = oClasses.sSortJUIDescAllowed; } } /** * Adjust the table column widths for new data. Note: you would probably want to * do a redraw after calling this function! * @param {object} settings dataTables settings object * @memberof DataTable#oApi */ function _fnAdjustColumnSizing ( settings ) { /* Not interested in doing column width calculation if auto-width is disabled */ if ( settings.oFeatures.bAutoWidth !== false ) { var columns = settings.aoColumns; _fnCalculateColumnWidths( settings ); for ( var i=0 , iLen=columns.length ; i
      ')[0]; /* Check to see if we should append an id and/or a class name to the container */ cNext = aDom[i+1]; if ( cNext == "'" || cNext == '"' ) { sAttr = ""; j = 2; while ( aDom[i+j] != cNext ) { sAttr += aDom[i+j]; j++; } /* Replace jQuery UI constants @todo depreciated */ if ( sAttr == "H" ) { sAttr = oSettings.oClasses.sJUIHeader; } else if ( sAttr == "F" ) { sAttr = oSettings.oClasses.sJUIFooter; } /* The attribute can be in the format of "#id.class", "#id" or "class" This logic * breaks the string into parts and applies them as needed */ if ( sAttr.indexOf('.') != -1 ) { var aSplit = sAttr.split('.'); nNewNode.id = aSplit[0].substr(1, aSplit[0].length-1); nNewNode.className = aSplit[1]; } else if ( sAttr.charAt(0) == "#" ) { nNewNode.id = sAttr.substr(1, sAttr.length-1); } else { nNewNode.className = sAttr; } i += j; /* Move along the position array */ } nInsertNode.appendChild( nNewNode ); nInsertNode = nNewNode; } else if ( cOption == '>' ) { /* End container div */ nInsertNode = nInsertNode.parentNode; } // @todo Move options into their own plugins? else if ( cOption == 'l' && oSettings.oFeatures.bPaginate && oSettings.oFeatures.bLengthChange ) { /* Length */ nTmp = _fnFeatureHtmlLength( oSettings ); iPushFeature = 1; } else if ( cOption == 'f' && oSettings.oFeatures.bFilter ) { /* Filter */ nTmp = _fnFeatureHtmlFilter( oSettings ); iPushFeature = 1; } else if ( cOption == 'r' && oSettings.oFeatures.bProcessing ) { /* pRocessing */ nTmp = _fnFeatureHtmlProcessing( oSettings ); iPushFeature = 1; } else if ( cOption == 't' ) { /* Table */ nTmp = _fnFeatureHtmlTable( oSettings ); iPushFeature = 1; } else if ( cOption == 'i' && oSettings.oFeatures.bInfo ) { /* Info */ nTmp = _fnFeatureHtmlInfo( oSettings ); iPushFeature = 1; } else if ( cOption == 'p' && oSettings.oFeatures.bPaginate ) { /* Pagination */ nTmp = _fnFeatureHtmlPaginate( oSettings ); iPushFeature = 1; } else if ( DataTable.ext.feature.length !== 0 ) { /* Plug-in features */ var aoFeatures = DataTable.ext.feature; for ( var k=0, kLen=aoFeatures.length ; k'; var str = settings.oLanguage.sSearch; str = str.match(/_INPUT_/) ? str.replace('_INPUT_', input) : str+input; var filter = $('
      ', { 'id': ! features.f ? tableId+'_filter' : null, 'class': classes.sFilter } ) .append( $('
    * @param {bool} [redraw=true] redraw the table or not * @returns {array} An array of integers, representing the list of indexes in * aoData ({@link DataTable.models.oSettings}) that have been added to * the table. * @dtopt API * @deprecated Since v1.10 * * @example * // Global var for counter * var giCount = 2; * * $(document).ready(function() { * $('#example').dataTable(); * } ); * * function fnClickAddRow() { * $('#example').dataTable().fnAddData( [ * giCount+".1", * giCount+".2", * giCount+".3", * giCount+".4" ] * ); * * giCount++; * } */ this.fnAddData = function( data, redraw ) { var api = this.api( true ); /* Check if we want to add multiple rows or not */ var rows = $.isArray(data) && ( $.isArray(data[0]) || $.isPlainObject(data[0]) ) ? api.rows.add( data ) : api.row.add( data ); if ( redraw === undefined || redraw ) { api.draw(); } return rows.flatten().toArray(); }; /** * This function will make DataTables recalculate the column sizes, based on the data * contained in the table and the sizes applied to the columns (in the DOM, CSS or * through the sWidth parameter). This can be useful when the width of the table's * parent element changes (for example a window resize). * @param {boolean} [bRedraw=true] Redraw the table or not, you will typically want to * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable( { * "sScrollY": "200px", * "bPaginate": false * } ); * * $(window).bind('resize', function () { * oTable.fnAdjustColumnSizing(); * } ); * } ); */ this.fnAdjustColumnSizing = function ( bRedraw ) { var api = this.api( true ).columns.adjust(); var settings = api.settings()[0]; var scroll = settings.oScroll; if ( bRedraw === undefined || bRedraw ) { api.draw( false ); } else if ( scroll.sX !== "" || scroll.sY !== "" ) { /* If not redrawing, but scrolling, we want to apply the new column sizes anyway */ _fnScrollDraw( settings ); } }; /** * Quickly and simply clear a table * @param {bool} [bRedraw=true] redraw the table or not * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Immediately 'nuke' the current rows (perhaps waiting for an Ajax callback...) * oTable.fnClearTable(); * } ); */ this.fnClearTable = function( bRedraw ) { var api = this.api( true ).clear(); if ( bRedraw === undefined || bRedraw ) { api.draw(); } }; /** * The exact opposite of 'opening' a row, this function will close any rows which * are currently 'open'. * @param {node} nTr the table row to 'close' * @returns {int} 0 on success, or 1 if failed (can't find the row) * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable; * * // 'open' an information row when a row is clicked on * $('#example tbody tr').click( function () { * if ( oTable.fnIsOpen(this) ) { * oTable.fnClose( this ); * } else { * oTable.fnOpen( this, "Temporary row opened", "info_row" ); * } * } ); * * oTable = $('#example').dataTable(); * } ); */ this.fnClose = function( nTr ) { this.api( true ).row( nTr ).child.hide(); }; /** * Remove a row for the table * @param {mixed} target The index of the row from aoData to be deleted, or * the TR element you want to delete * @param {function|null} [callBack] Callback function * @param {bool} [redraw=true] Redraw the table or not * @returns {array} The row that was deleted * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Immediately remove the first row * oTable.fnDeleteRow( 0 ); * } ); */ this.fnDeleteRow = function( target, callback, redraw ) { var api = this.api( true ); var rows = api.rows( target ); var settings = rows.settings()[0]; var data = settings.aoData[ rows[0][0] ]; rows.remove(); if ( callback ) { callback.call( this, settings, data ); } if ( redraw === undefined || redraw ) { api.draw(); } return data; }; /** * Restore the table to it's original state in the DOM by removing all of DataTables * enhancements, alterations to the DOM structure of the table and event listeners. * @param {boolean} [remove=false] Completely remove the table from the DOM * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * // This example is fairly pointless in reality, but shows how fnDestroy can be used * var oTable = $('#example').dataTable(); * oTable.fnDestroy(); * } ); */ this.fnDestroy = function ( remove ) { this.api( true ).destroy( remove ); }; /** * Redraw the table * @param {bool} [complete=true] Re-filter and resort (if enabled) the table before the draw. * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Re-draw the table - you wouldn't want to do it here, but it's an example :-) * oTable.fnDraw(); * } ); */ this.fnDraw = function( complete ) { // Note that this isn't an exact match to the old call to _fnDraw - it takes // into account the new data, but can old position. this.api( true ).draw( ! complete ); }; /** * Filter the input based on data * @param {string} sInput String to filter the table on * @param {int|null} [iColumn] Column to limit filtering to * @param {bool} [bRegex=false] Treat as regular expression or not * @param {bool} [bSmart=true] Perform smart filtering or not * @param {bool} [bShowGlobal=true] Show the input global filter in it's input box(es) * @param {bool} [bCaseInsensitive=true] Do case-insensitive matching (true) or not (false) * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Sometime later - filter... * oTable.fnFilter( 'test string' ); * } ); */ this.fnFilter = function( sInput, iColumn, bRegex, bSmart, bShowGlobal, bCaseInsensitive ) { var api = this.api( true ); if ( iColumn === null || iColumn === undefined ) { api.search( sInput, bRegex, bSmart, bCaseInsensitive ); } else { api.column( iColumn ).search( sInput, bRegex, bSmart, bCaseInsensitive ); } api.draw(); }; /** * Get the data for the whole table, an individual row or an individual cell based on the * provided parameters. * @param {int|node} [src] A TR row node, TD/TH cell node or an integer. If given as * a TR node then the data source for the whole row will be returned. If given as a * TD/TH cell node then iCol will be automatically calculated and the data for the * cell returned. If given as an integer, then this is treated as the aoData internal * data index for the row (see fnGetPosition) and the data for that row used. * @param {int} [col] Optional column index that you want the data of. * @returns {array|object|string} If mRow is undefined, then the data for all rows is * returned. If mRow is defined, just data for that row, and is iCol is * defined, only data for the designated cell is returned. * @dtopt API * @deprecated Since v1.10 * * @example * // Row data * $(document).ready(function() { * oTable = $('#example').dataTable(); * * oTable.$('tr').click( function () { * var data = oTable.fnGetData( this ); * // ... do something with the array / object of data for the row * } ); * } ); * * @example * // Individual cell data * $(document).ready(function() { * oTable = $('#example').dataTable(); * * oTable.$('td').click( function () { * var sData = oTable.fnGetData( this ); * alert( 'The cell clicked on had the value of '+sData ); * } ); * } ); */ this.fnGetData = function( src, col ) { var api = this.api( true ); if ( src !== undefined ) { var type = src.nodeName ? src.nodeName.toLowerCase() : ''; return col !== undefined || type == 'td' || type == 'th' ? api.cell( src, col ).data() : api.row( src ).data(); } return api.data().toArray(); }; /** * Get an array of the TR nodes that are used in the table's body. Note that you will * typically want to use the '$' API method in preference to this as it is more * flexible. * @param {int} [iRow] Optional row index for the TR element you want * @returns {array|node} If iRow is undefined, returns an array of all TR elements * in the table's body, or iRow is defined, just the TR element requested. * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Get the nodes from the table * var nNodes = oTable.fnGetNodes( ); * } ); */ this.fnGetNodes = function( iRow ) { var api = this.api( true ); return iRow !== undefined ? api.row( iRow ).node() : api.rows().nodes().toArray(); }; /** * Get the array indexes of a particular cell from it's DOM element * and column index including hidden columns * @param {node} node this can either be a TR, TD or TH in the table's body * @returns {int} If nNode is given as a TR, then a single index is returned, or * if given as a cell, an array of [row index, column index (visible), * column index (all)] is given. * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * $('#example tbody td').click( function () { * // Get the position of the current data from the node * var aPos = oTable.fnGetPosition( this ); * * // Get the data array for this row * var aData = oTable.fnGetData( aPos[0] ); * * // Update the data array and return the value * aData[ aPos[1] ] = 'clicked'; * this.innerHTML = 'clicked'; * } ); * * // Init DataTables * oTable = $('#example').dataTable(); * } ); */ this.fnGetPosition = function( node ) { var api = this.api( true ); var nodeName = node.nodeName.toUpperCase(); if ( nodeName == 'TR' ) { return api.row( node ).index(); } else if ( nodeName == 'TD' || nodeName == 'TH' ) { var cell = api.cell( node ).index(); return [ cell.row, cell.columnVisible, cell.column ]; } return null; }; /** * Check to see if a row is 'open' or not. * @param {node} nTr the table row to check * @returns {boolean} true if the row is currently open, false otherwise * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable; * * // 'open' an information row when a row is clicked on * $('#example tbody tr').click( function () { * if ( oTable.fnIsOpen(this) ) { * oTable.fnClose( this ); * } else { * oTable.fnOpen( this, "Temporary row opened", "info_row" ); * } * } ); * * oTable = $('#example').dataTable(); * } ); */ this.fnIsOpen = function( nTr ) { return this.api( true ).row( nTr ).child.isShown(); }; /** * This function will place a new row directly after a row which is currently * on display on the page, with the HTML contents that is passed into the * function. This can be used, for example, to ask for confirmation that a * particular record should be deleted. * @param {node} nTr The table row to 'open' * @param {string|node|jQuery} mHtml The HTML to put into the row * @param {string} sClass Class to give the new TD cell * @returns {node} The row opened. Note that if the table row passed in as the * first parameter, is not found in the table, this method will silently * return. * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable; * * // 'open' an information row when a row is clicked on * $('#example tbody tr').click( function () { * if ( oTable.fnIsOpen(this) ) { * oTable.fnClose( this ); * } else { * oTable.fnOpen( this, "Temporary row opened", "info_row" ); * } * } ); * * oTable = $('#example').dataTable(); * } ); */ this.fnOpen = function( nTr, mHtml, sClass ) { return this.api( true ).row( nTr ).child( mHtml, sClass ).show(); }; /** * Change the pagination - provides the internal logic for pagination in a simple API * function. With this function you can have a DataTables table go to the next, * previous, first or last pages. * @param {string|int} mAction Paging action to take: "first", "previous", "next" or "last" * or page number to jump to (integer), note that page 0 is the first page. * @param {bool} [bRedraw=true] Redraw the table or not * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * oTable.fnPageChange( 'next' ); * } ); */ this.fnPageChange = function ( mAction, bRedraw ) { var api = this.api( true ).page( mAction ); if ( bRedraw === undefined || bRedraw ) { api.draw(false); } }; /** * Show a particular column * @param {int} iCol The column whose display should be changed * @param {bool} bShow Show (true) or hide (false) the column * @param {bool} [bRedraw=true] Redraw the table or not * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Hide the second column after initialisation * oTable.fnSetColumnVis( 1, false ); * } ); */ this.fnSetColumnVis = function ( iCol, bShow, bRedraw ) { var api = this.api( true ).column( iCol ).visible( bShow ); if ( bRedraw === undefined || bRedraw ) { api.columns.adjust().draw(); } }; /** * Get the settings for a particular table for external manipulation * @returns {object} DataTables settings object. See * {@link DataTable.models.oSettings} * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * var oSettings = oTable.fnSettings(); * * // Show an example parameter from the settings * alert( oSettings._iDisplayStart ); * } ); */ this.fnSettings = function() { return _fnSettingsFromNode( this[_ext.iApiIndex] ); }; /** * Sort the table by a particular column * @param {int} iCol the data index to sort on. Note that this will not match the * 'display index' if you have hidden data entries * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Sort immediately with columns 0 and 1 * oTable.fnSort( [ [0,'asc'], [1,'asc'] ] ); * } ); */ this.fnSort = function( aaSort ) { this.api( true ).order( aaSort ).draw(); }; /** * Attach a sort listener to an element for a given column * @param {node} nNode the element to attach the sort listener to * @param {int} iColumn the column that a click on this node will sort on * @param {function} [fnCallback] callback function when sort is run * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * * // Sort on column 1, when 'sorter' is clicked on * oTable.fnSortListener( document.getElementById('sorter'), 1 ); * } ); */ this.fnSortListener = function( nNode, iColumn, fnCallback ) { this.api( true ).order.listener( nNode, iColumn, fnCallback ); }; /** * Update a table cell or row - this method will accept either a single value to * update the cell with, an array of values with one element for each column or * an object in the same format as the original data source. The function is * self-referencing in order to make the multi column updates easier. * @param {object|array|string} mData Data to update the cell/row with * @param {node|int} mRow TR element you want to update or the aoData index * @param {int} [iColumn] The column to update, give as null or undefined to * update a whole row. * @param {bool} [bRedraw=true] Redraw the table or not * @param {bool} [bAction=true] Perform pre-draw actions or not * @returns {int} 0 on success, 1 on error * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * oTable.fnUpdate( 'Example update', 0, 0 ); // Single cell * oTable.fnUpdate( ['a', 'b', 'c', 'd', 'e'], $('tbody tr')[0] ); // Row * } ); */ this.fnUpdate = function( mData, mRow, iColumn, bRedraw, bAction ) { var api = this.api( true ); if ( iColumn === undefined || iColumn === null ) { api.row( mRow ).data( mData ); } else { api.cell( mRow, iColumn ).data( mData ); } if ( bAction === undefined || bAction ) { api.columns.adjust(); } if ( bRedraw === undefined || bRedraw ) { api.draw(); } return 0; }; /** * Provide a common method for plug-ins to check the version of DataTables being used, in order * to ensure compatibility. * @param {string} sVersion Version string to check for, in the format "X.Y.Z". Note that the * formats "X" and "X.Y" are also acceptable. * @returns {boolean} true if this version of DataTables is greater or equal to the required * version, or false if this version of DataTales is not suitable * @method * @dtopt API * @deprecated Since v1.10 * * @example * $(document).ready(function() { * var oTable = $('#example').dataTable(); * alert( oTable.fnVersionCheck( '1.9.0' ) ); * } ); */ this.fnVersionCheck = _ext.fnVersionCheck; /* * This is really a good bit rubbish this method of exposing the internal methods * publicly... - To be fixed in 2.0 using methods on the prototype */ /** * Create a wrapper function for exporting an internal functions to an external API. * @param {string} fn API function name * @returns {function} wrapped function * @memberof DataTable#internal */ function _fnExternApiFunc (fn) { return function() { var args = [_fnSettingsFromNode( this[DataTable.ext.iApiIndex] )].concat( Array.prototype.slice.call(arguments) ); return DataTable.ext.internal[fn].apply( this, args ); }; } /** * Reference to internal functions for use by plug-in developers. Note that * these methods are references to internal functions and are considered to be * private. If you use these methods, be aware that they are liable to change * between versions. * @namespace */ this.oApi = this.internal = { _fnExternApiFunc: _fnExternApiFunc, _fnBuildAjax: _fnBuildAjax, _fnAjaxUpdate: _fnAjaxUpdate, _fnAjaxParameters: _fnAjaxParameters, _fnAjaxUpdateDraw: _fnAjaxUpdateDraw, _fnAjaxDataSrc: _fnAjaxDataSrc, _fnAddColumn: _fnAddColumn, _fnColumnOptions: _fnColumnOptions, _fnAdjustColumnSizing: _fnAdjustColumnSizing, _fnVisibleToColumnIndex: _fnVisibleToColumnIndex, _fnColumnIndexToVisible: _fnColumnIndexToVisible, _fnVisbleColumns: _fnVisbleColumns, _fnGetColumns: _fnGetColumns, _fnColumnTypes: _fnColumnTypes, _fnApplyColumnDefs: _fnApplyColumnDefs, _fnHungarianMap: _fnHungarianMap, _fnCamelToHungarian: _fnCamelToHungarian, _fnLanguageCompat: _fnLanguageCompat, _fnBrowserDetect: _fnBrowserDetect, _fnAddData: _fnAddData, _fnAddTr: _fnAddTr, _fnNodeToDataIndex: _fnNodeToDataIndex, _fnNodeToColumnIndex: _fnNodeToColumnIndex, _fnGetRowData: _fnGetRowData, _fnGetCellData: _fnGetCellData, _fnSetCellData: _fnSetCellData, _fnSplitObjNotation: _fnSplitObjNotation, _fnGetObjectDataFn: _fnGetObjectDataFn, _fnSetObjectDataFn: _fnSetObjectDataFn, _fnGetDataMaster: _fnGetDataMaster, _fnClearTable: _fnClearTable, _fnDeleteIndex: _fnDeleteIndex, _fnInvalidateRow: _fnInvalidateRow, _fnGetRowElements: _fnGetRowElements, _fnCreateTr: _fnCreateTr, _fnBuildHead: _fnBuildHead, _fnDrawHead: _fnDrawHead, _fnDraw: _fnDraw, _fnReDraw: _fnReDraw, _fnAddOptionsHtml: _fnAddOptionsHtml, _fnDetectHeader: _fnDetectHeader, _fnGetUniqueThs: _fnGetUniqueThs, _fnFeatureHtmlFilter: _fnFeatureHtmlFilter, _fnFilterComplete: _fnFilterComplete, _fnFilterCustom: _fnFilterCustom, _fnFilterColumn: _fnFilterColumn, _fnFilter: _fnFilter, _fnFilterCreateSearch: _fnFilterCreateSearch, _fnEscapeRegex: _fnEscapeRegex, _fnFilterData: _fnFilterData, _fnFeatureHtmlInfo: _fnFeatureHtmlInfo, _fnUpdateInfo: _fnUpdateInfo, _fnInfoMacros: _fnInfoMacros, _fnInitialise: _fnInitialise, _fnInitComplete: _fnInitComplete, _fnLengthChange: _fnLengthChange, _fnFeatureHtmlLength: _fnFeatureHtmlLength, _fnFeatureHtmlPaginate: _fnFeatureHtmlPaginate, _fnPageChange: _fnPageChange, _fnFeatureHtmlProcessing: _fnFeatureHtmlProcessing, _fnProcessingDisplay: _fnProcessingDisplay, _fnFeatureHtmlTable: _fnFeatureHtmlTable, _fnScrollDraw: _fnScrollDraw, _fnApplyToChildren: _fnApplyToChildren, _fnCalculateColumnWidths: _fnCalculateColumnWidths, _fnThrottle: _fnThrottle, _fnConvertToWidth: _fnConvertToWidth, _fnScrollingWidthAdjust: _fnScrollingWidthAdjust, _fnGetWidestNode: _fnGetWidestNode, _fnGetMaxLenString: _fnGetMaxLenString, _fnStringToCss: _fnStringToCss, _fnScrollBarWidth: _fnScrollBarWidth, _fnSortFlatten: _fnSortFlatten, _fnSort: _fnSort, _fnSortAria: _fnSortAria, _fnSortListener: _fnSortListener, _fnSortAttachListener: _fnSortAttachListener, _fnSortingClasses: _fnSortingClasses, _fnSortData: _fnSortData, _fnSaveState: _fnSaveState, _fnLoadState: _fnLoadState, _fnSettingsFromNode: _fnSettingsFromNode, _fnLog: _fnLog, _fnMap: _fnMap, _fnBindAction: _fnBindAction, _fnCallbackReg: _fnCallbackReg, _fnCallbackFire: _fnCallbackFire, _fnLengthOverflow: _fnLengthOverflow, _fnRenderer: _fnRenderer, _fnDataSource: _fnDataSource, _fnRowAttributes: _fnRowAttributes }; $.extend( DataTable.ext.internal, this.internal ); for ( var fn in DataTable.ext.internal ) { if ( fn ) { this[fn] = _fnExternApiFunc(fn); } } var _that = this; var emptyInit = options === undefined; var len = this.length; if ( emptyInit ) { options = {}; } this.each(function() { // For each initialisation we want to give it a clean initialisation // object that can be bashed around var o = {}; var oInit = len > 1 ? // optimisation for single table case _fnExtend( o, options, true ) : options; /*global oInit,_that,emptyInit*/ var i=0, iLen, j, jLen, k, kLen; var sId = this.getAttribute( 'id' ); var bInitHandedOff = false; var defaults = DataTable.defaults; /* Sanity check */ if ( this.nodeName.toLowerCase() != 'table' ) { _fnLog( null, 0, 'Non-table node initialisation ('+this.nodeName+')', 2 ); return; } /* Backwards compatibility for the defaults */ _fnCompatOpts( defaults ); _fnCompatCols( defaults.column ); /* Convert the camel-case defaults to Hungarian */ _fnCamelToHungarian( defaults, defaults, true ); _fnCamelToHungarian( defaults.column, defaults.column, true ); /* Setting up the initialisation object */ _fnCamelToHungarian( defaults, oInit ); /* Check to see if we are re-initialising a table */ var allSettings = DataTable.settings; for ( i=0, iLen=allSettings.length ; i').appendTo(this); } oSettings.nTHead = thead[0]; var tbody = $(this).children('tbody'); if ( tbody.length === 0 ) { tbody = $('').appendTo(this); } oSettings.nTBody = tbody[0]; var tfoot = $(this).children('tfoot'); if ( tfoot.length === 0 && captions.length > 0 && (oSettings.oScroll.sX !== "" || oSettings.oScroll.sY !== "") ) { // If we are a scrolling table, and no footer has been given, then we need to create // a tfoot element for the caption element to be appended to tfoot = $('').appendTo(this); } if ( tfoot.length === 0 || tfoot.children().length === 0 ) { $(this).addClass( oSettings.oClasses.sNoFooter ); } else if ( tfoot.length > 0 ) { oSettings.nTFoot = tfoot[0]; _fnDetectHeader( oSettings.aoFooter, oSettings.nTFoot ); } /* Check if there is data passing into the constructor */ if ( oInit.aaData ) { for ( i=0 ; i 1 ) { value = init; isSet = true; } for ( var i=0, ien=this.length ; i 1 ) { value = init; isSet = true; } for ( var i=this.length-1 ; i>=0 ; i-- ) { if ( ! this.hasOwnProperty(i) ) { continue; } value = isSet ? fn( value, this[i], i, this ) : this[i]; isSet = true; } return value; }, reverse: __arrayProto.reverse, // Object with rows, columns and opts selector: null, shift: __arrayProto.shift, sort: __arrayProto.sort, // ? name - order? splice: __arrayProto.splice, toArray: function () { return __arrayProto.slice.call( this ); }, to$: function () { return $( this ); }, toJQuery: function () { return $( this ); }, unique: function () { return new _Api( this.context, _unique(this) ); }, unshift: __arrayProto.unshift }; _Api.extend = function ( scope, obj, ext ) { // Only extend API instances and static properties of the API if ( ! obj || ( ! (obj instanceof _Api) && ! obj.__dt_wrapper ) ) { return; } var i, ien, j, jen, struct, inner, methodScoping = function ( fn, struc ) { return function () { var ret = fn.apply( scope, arguments ); // Method extension _Api.extend( ret, ret, struc.methodExt ); return ret; }; }; for ( i=0, ien=ext.length ; i 0 ) { return ctx[0].json; } // else return undefined; } ); /** * Reload tables from the Ajax data source. Note that this function will * automatically re-draw the table when the remote data has been loaded. * * @param {boolean} [reset=true] Reset (default) or hold the current paging * position. A full re-sort and re-filter is performed when this method is * called, which is why the pagination reset is the default action. * @returns {DataTables.Api} this */ _api_register( 'ajax.reload()', function ( callback, resetPaging ) { return this.iterator( 'table', function (settings) { __reload( settings, resetPaging===false, callback ); } ); } ); /** * Get the current Ajax URL. Note that this returns the URL from the first * table in the current context. * * @return {string} Current Ajax source URL *//** * Set the Ajax URL. Note that this will set the URL for all tables in the * current context. * * @param {string} url URL to set. * @returns {DataTables.Api} this */ _api_register( 'ajax.url()', function ( url ) { var ctx = this.context; if ( url === undefined ) { // get if ( ctx.length === 0 ) { return undefined; } ctx = ctx[0]; return ctx.ajax ? $.isPlainObject( ctx.ajax ) ? ctx.ajax.url : ctx.ajax : ctx.sAjaxSource; } // set return this.iterator( 'table', function ( settings ) { if ( $.isPlainObject( settings.ajax ) ) { settings.ajax.url = url; } else { settings.ajax = url; } // No need to consider sAjaxSource here since DataTables gives priority // to `ajax` over `sAjaxSource`. So setting `ajax` here, renders any // value of `sAjaxSource` redundant. } ); } ); /** * Load data from the newly set Ajax URL. Note that this method is only * available when `ajax.url()` is used to set a URL. Additionally, this method * has the same effect as calling `ajax.reload()` but is provided for * convenience when setting a new URL. Like `ajax.reload()` it will * automatically redraw the table once the remote data has been loaded. * * @returns {DataTables.Api} this */ _api_register( 'ajax.url().load()', function ( callback, resetPaging ) { // Same as a reload, but makes sense to present it for easy access after a // url change return this.iterator( 'table', function ( ctx ) { __reload( ctx, resetPaging===false, callback ); } ); } ); var _selector_run = function ( selector, select ) { var out = [], res, a, i, ien, j, jen; if ( ! $.isArray( selector ) ) { selector = [ selector ]; } for ( i=0, ien=selector.length ; i 0 ) { // Assign the first element to the first item in the instance // and truncate the instance and context inst[0] = inst[i]; inst.length = 1; inst.context = [ inst.context[i] ]; return inst; } } // Not found - return an empty instance inst.length = 0; return inst; }; var _selector_row_indexes = function ( settings, opts ) { var i, ien, tmp, a=[], displayFiltered = settings.aiDisplay, displayMaster = settings.aiDisplayMaster; var search = opts.search, // none, applied, removed order = opts.order, // applied, current, index (original - compatibility with 1.9) page = opts.page; // all, current // Current page implies that order=current and fitler=applied, since it is // fairly senseless otherwise, regardless of what order and search actually // are if ( page == 'current' ) { for ( i=settings._iDisplayStart, ien=settings.fnDisplayEnd() ; i').find('td').html( r ).parent(); } $('td', r).addClass( k )[0].colSpan = _fnVisbleColumns( ctx ); rows.push( r[0] ); }; if ( $.isArray( data ) || data instanceof $ ) { for ( var i=0, ien=data.length ; i 0 ) { // On each draw, insert the required elements into the document table.on('draw.DT_details', function () { table.find('tbody tr').each( function () { // Look up the row index for each row and append open row var rowIdx = _fnNodeToDataIndex( settings, this ); var row = settings.aoData[ rowIdx ]; if ( row._detailsShow ) { row._details.insertAfter( this ); } } ); } ); // Column visibility change - update the colspan table.on( 'column-visibility.DT_details', function ( e, settings, idx, vis ) { // Update the colspan for the details rows (note, only if it already has // a colspan) var row, visible = _fnVisbleColumns( settings ); for ( var i=0, ien=settings.aoData.length ; i=0 count from left, <0 count from right) * "{integer}:visIdx" - visible column index (i.e. translate to column index) (>=0 count from left, <0 count from right) * "{integer}:visible" - alias for {integer}:visIdx (>=0 count from left, <0 count from right) * "{string}:name" - column name * "{string}" - jQuery selector on column header nodes * */ // can be an array of these items, comma separated list, or an array of comma // separated lists var __re_column_selector = /^(.*):(name|visIdx|visible)$/; var __column_selector = function ( settings, selector, opts ) { var columns = settings.aoColumns, names = _pluck( columns, 'sName' ), nodes = _pluck( columns, 'nTh' ); return _selector_run( selector, function ( s ) { var selInt = _intVal( s ); if ( s === '' ) { // All columns return _range( settings.aoColumns.length ); } else if ( selInt !== null ) { // Integer selector return [ selInt >= 0 ? selInt : // Count from left columns.length + selInt // Count from right (+ because its a negative value) ]; } else { var match = s.match( __re_column_selector ); if ( match ) { switch( match[2] ) { case 'visIdx': case 'visible': var idx = parseInt( match[1], 10 ); // Visible index given, convert to column index if ( idx < 0 ) { // Counting from the right var visColumns = $.map( columns, function (col,i) { return col.bVisible ? i : null; } ); return [ visColumns[ visColumns.length + idx ] ]; } // Counting from the left return [ _fnVisibleToColumnIndex( settings, idx ) ]; case 'name': // match by name. `names` is column index complete and in order return $.map( names, function (name, i) { return name === match[1] ? i : null; } ); } } else { // jQuery selector on the TH elements for the columns return $( nodes ) .filter( s ) .map( function () { return $.inArray( this, nodes ); // `nodes` is column index complete and in order } ) .toArray(); } } } ); }; var __setColumnVis = function ( settings, column, vis ) { var cols = settings.aoColumns, col = cols[ column ], data = settings.aoData, row, cells, i, ien, tr; // Get if ( vis === undefined ) { return col.bVisible; } // Set // No change if ( col.bVisible === vis ) { return; } if ( vis ) { // Insert column // Need to decide if we should use appendChild or insertBefore var insertBefore = $.inArray( true, _pluck(cols, 'bVisible'), column+1 ); for ( i=0, ien=data.length ; i iThat; } return true; }; /** * Check if a `` node is a DataTable table already or not. * * @param {node|jquery|string} table Table node, jQuery object or jQuery * selector for the table to test. Note that if more than more than one * table is passed on, only the first will be checked * @returns {boolean} true the table given is a DataTable, or false otherwise * @static * @dtopt API-Static * * @example * if ( ! $.fn.DataTable.isDataTable( '#example' ) ) { * $('#example').dataTable(); * } */ DataTable.isDataTable = DataTable.fnIsDataTable = function ( table ) { var t = $(table).get(0); var is = false; $.each( DataTable.settings, function (i, o) { if ( o.nTable === t || o.nScrollHead === t || o.nScrollFoot === t ) { is = true; } } ); return is; }; /** * Get all DataTable tables that have been initialised - optionally you can * select to get only currently visible tables. * * @param {boolean} [visible=false] Flag to indicate if you want all (default) * or visible tables only. * @returns {array} Array of `table` nodes (not DataTable instances) which are * DataTables * @static * @dtopt API-Static * * @example * $.each( $.fn.dataTable.tables(true), function () { * $(table).DataTable().columns.adjust(); * } ); */ DataTable.tables = DataTable.fnTables = function ( visible ) { return jQuery.map( DataTable.settings, function (o) { if ( !visible || (visible && $(o.nTable).is(':visible')) ) { return o.nTable; } } ); }; /** * */ _api_register( '$()', function ( selector, opts ) { var rows = this.rows( opts ).nodes(), // Get all rows jqRows = $(rows); return $( [].concat( jqRows.filter( selector ).toArray(), jqRows.find( selector ).toArray() ) ); } ); // jQuery functions to operate on the tables $.each( [ 'on', 'one', 'off' ], function (i, key) { _api_register( key+'()', function ( /* event, handler */ ) { var args = Array.prototype.slice.call(arguments); // Add the `dt` namespace automatically if it isn't already present if ( args[0].indexOf( '.dt' ) === -1 ) { args[0] += '.dt'; } var inst = $( this.tables().nodes() ); inst[key].apply( inst, args ); return this; } ); } ); _api_register( 'clear()', function () { return this.iterator( 'table', function ( settings ) { _fnClearTable( settings ); } ); } ); _api_register( 'settings()', function () { return new _Api( this.context, this.context ); } ); _api_register( 'data()', function () { return this.iterator( 'table', function ( settings ) { return _pluck( settings.aoData, '_aData' ); } ).flatten(); } ); _api_register( 'destroy()', function ( remove ) { remove = remove || false; return this.iterator( 'table', function ( settings ) { var orig = settings.nTableWrapper.parentNode; var classes = settings.oClasses; var table = settings.nTable; var tbody = settings.nTBody; var thead = settings.nTHead; var tfoot = settings.nTFoot; var jqTable = $(table); var jqTbody = $(tbody); var jqWrapper = $(settings.nTableWrapper); var rows = $.map( settings.aoData, function (r) { return r.nTr; } ); var i, ien; // Flag to note that the table is currently being destroyed - no action // should be taken settings.bDestroying = true; // Fire off the destroy callbacks for plug-ins etc _fnCallbackFire( settings, "aoDestroyCallback", "destroy", [settings] ); // If not being removed from the document, make all columns visible if ( ! remove ) { new _Api( settings ).columns().visible( true ); } // Blitz all DT events jqWrapper.unbind('.DT').find(':not(tbody *)').unbind('.DT'); $(window).unbind('.DT-'+settings.sInstance); // When scrolling we had to break the table up - restore it if ( table != thead.parentNode ) { jqTable.children('thead').remove(); jqTable.append( thead ); } if ( tfoot && table != tfoot.parentNode ) { jqTable.children('tfoot').remove(); jqTable.append( tfoot ); } // Remove the DataTables generated nodes, events and classes jqTable.remove(); jqWrapper.remove(); settings.aaSorting = []; settings.aaSortingFixed = []; _fnSortingClasses( settings ); $( rows ).removeClass( settings.asStripeClasses.join(' ') ); $('th, td', thead).removeClass( classes.sSortable+' '+ classes.sSortableAsc+' '+classes.sSortableDesc+' '+classes.sSortableNone ); if ( settings.bJUI ) { $('th span.'+classes.sSortIcon+ ', td span.'+classes.sSortIcon, thead).remove(); $('th, td', thead).each( function () { var wrapper = $('div.'+classes.sSortJUIWrapper, this); $(this).append( wrapper.contents() ); wrapper.remove(); } ); } if ( ! remove ) { // insertBefore acts like appendChild if !arg[1] orig.insertBefore( table, settings.nTableReinsertBefore ); } // Add the TR elements back into the table in their original order jqTbody.children().detach(); jqTbody.append( rows ); // Restore the width of the original table - was read from the style property, // so we can restore directly to that jqTable .css( 'width', settings.sDestroyWidth ) .removeClass( classes.sTable ); // If the were originally stripe classes - then we add them back here. // Note this is not fool proof (for example if not all rows had stripe // classes - but it's a good effort without getting carried away ien = settings.asDestroyStripes.length; if ( ien ) { jqTbody.children().each( function (i) { $(this).addClass( settings.asDestroyStripes[i % ien] ); } ); } /* Remove the settings object from the settings array */ var idx = $.inArray( settings, DataTable.settings ); if ( idx !== -1 ) { DataTable.settings.splice( idx, 1 ); } } ); } ); /** * Version string for plug-ins to check compatibility. Allowed format is * `a.b.c-d` where: a:int, b:int, c:int, d:string(dev|beta|alpha). `d` is used * only for non-release builds. See http://semver.org/ for more information. * @member * @type string * @default Version number */ DataTable.version = "1.10.0-dev"; /** * Private data store, containing all of the settings objects that are * created for the tables on a given page. * * Note that the `DataTable.settings` object is aliased to * `jQuery.fn.dataTableExt` through which it may be accessed and * manipulated, or `jQuery.fn.dataTable.settings`. * @member * @type array * @default [] * @private */ DataTable.settings = []; /** * Object models container, for the various models that DataTables has * available to it. These models define the objects that are used to hold * the active state and configuration of the table. * @namespace */ DataTable.models = {}; /** * Template object for the way in which DataTables holds information about * search information for the global filter and individual column filters. * @namespace */ DataTable.models.oSearch = { /** * Flag to indicate if the filtering should be case insensitive or not * @type boolean * @default true */ "bCaseInsensitive": true, /** * Applied search term * @type string * @default Empty string */ "sSearch": "", /** * Flag to indicate if the search term should be interpreted as a * regular expression (true) or not (false) and therefore and special * regex characters escaped. * @type boolean * @default false */ "bRegex": false, /** * Flag to indicate if DataTables is to use its smart filtering or not. * @type boolean * @default true */ "bSmart": true }; /** * Template object for the way in which DataTables holds information about * each individual row. This is the object format used for the settings * aoData array. * @namespace */ DataTable.models.oRow = { /** * TR element for the row * @type node * @default null */ "nTr": null, /** * Array of TD elements for each row. This is null until the row has been * created. * @type array nodes * @default [] */ "anCells": null, /** * Data object from the original data source for the row. This is either * an array if using the traditional form of DataTables, or an object if * using mData options. The exact type will depend on the passed in * data from the data source, or will be an array if using DOM a data * source. * @type array|object * @default [] */ "_aData": [], /** * Sorting data cache - this array is ostensibly the same length as the * number of columns (although each index is generated only as it is * needed), and holds the data that is used for sorting each column in the * row. We do this cache generation at the start of the sort in order that * the formatting of the sort data need be done only once for each cell * per sort. This array should not be read from or written to by anything * other than the master sorting methods. * @type array * @default null * @private */ "_aSortData": null, /** * Per cell filtering data cache. As per the sort data cache, used to * increase the performance of the filtering in DataTables * @type array * @default null * @private */ "_aFilterData": null, /** * Filtering data cache. This is the same as the cell filtering cache, but * in this case a string rather than an array. This is easily computed with * a join on `_aFilterData`, but is provided as a cache so the join isn't * needed on every search (memory traded for performance) * @type array * @default null * @private */ "_sFilterRow": null, /** * Cache of the class name that DataTables has applied to the row, so we * can quickly look at this variable rather than needing to do a DOM check * on className for the nTr property. * @type string * @default Empty string * @private */ "_sRowStripe": "", /** * Denote if the original data source was from the DOM, or the data source * object. This is used for invalidating data, so DataTables can * automatically read data from the original source, unless uninstructed * otherwise. * @type string * @default null * @private */ "src": null }; /** * Template object for the column information object in DataTables. This object * is held in the settings aoColumns array and contains all the information that * DataTables needs about each individual column. * * Note that this object is related to {@link DataTable.defaults.column} * but this one is the internal data store for DataTables's cache of columns. * It should NOT be manipulated outside of DataTables. Any configuration should * be done through the initialisation options. * @namespace */ DataTable.models.oColumn = { /** * A list of the columns that sorting should occur on when this column * is sorted. That this property is an array allows multi-column sorting * to be defined for a column (for example first name / last name columns * would benefit from this). The values are integers pointing to the * columns to be sorted on (typically it will be a single integer pointing * at itself, but that doesn't need to be the case). * @type array */ "aDataSort": null, /** * Define the sorting directions that are applied to the column, in sequence * as the column is repeatedly sorted upon - i.e. the first value is used * as the sorting direction when the column if first sorted (clicked on). * Sort it again (click again) and it will move on to the next index. * Repeat until loop. * @type array */ "asSorting": null, /** * Flag to indicate if the column is searchable, and thus should be included * in the filtering or not. * @type boolean */ "bSearchable": null, /** * Flag to indicate if the column is sortable or not. * @type boolean */ "bSortable": null, /** * Flag to indicate if the column is currently visible in the table or not * @type boolean */ "bVisible": null, /** * Store for manual type assignment using the `column.type` option. This * is held in store so we can manipulate the column's `sType` property. * @type string * @default null * @private */ "_sManualType": null, /** * Flag to indicate if HTML5 data attributes should be used as the data * source for filtering or sorting. True is either are. * @type boolean * @default false * @private */ "_bAttrSrc": false, /** * Developer definable function that is called whenever a cell is created (Ajax source, * etc) or processed for input (DOM source). This can be used as a compliment to mRender * allowing you to modify the DOM element (add background colour for example) when the * element is available. * @type function * @param {element} nTd The TD node that has been created * @param {*} sData The Data for the cell * @param {array|object} oData The data for the whole row * @param {int} iRow The row index for the aoData data store * @default null */ "fnCreatedCell": null, /** * Function to get data from a cell in a column. You should never * access data directly through _aData internally in DataTables - always use * the method attached to this property. It allows mData to function as * required. This function is automatically assigned by the column * initialisation method * @type function * @param {array|object} oData The data array/object for the array * (i.e. aoData[]._aData) * @param {string} sSpecific The specific data type you want to get - * 'display', 'type' 'filter' 'sort' * @returns {*} The data for the cell from the given row's data * @default null */ "fnGetData": null, /** * Function to set data for a cell in the column. You should never * set the data directly to _aData internally in DataTables - always use * this method. It allows mData to function as required. This function * is automatically assigned by the column initialisation method * @type function * @param {array|object} oData The data array/object for the array * (i.e. aoData[]._aData) * @param {*} sValue Value to set * @default null */ "fnSetData": null, /** * Property to read the value for the cells in the column from the data * source array / object. If null, then the default content is used, if a * function is given then the return from the function is used. * @type function|int|string|null * @default null */ "mData": null, /** * Partner property to mData which is used (only when defined) to get * the data - i.e. it is basically the same as mData, but without the * 'set' option, and also the data fed to it is the result from mData. * This is the rendering method to match the data method of mData. * @type function|int|string|null * @default null */ "mRender": null, /** * Unique header TH/TD element for this column - this is what the sorting * listener is attached to (if sorting is enabled.) * @type node * @default null */ "nTh": null, /** * Unique footer TH/TD element for this column (if there is one). Not used * in DataTables as such, but can be used for plug-ins to reference the * footer for each column. * @type node * @default null */ "nTf": null, /** * The class to apply to all TD elements in the table's TBODY for the column * @type string * @default null */ "sClass": null, /** * When DataTables calculates the column widths to assign to each column, * it finds the longest string in each column and then constructs a * temporary table and reads the widths from that. The problem with this * is that "mmm" is much wider then "iiii", but the latter is a longer * string - thus the calculation can go wrong (doing it properly and putting * it into an DOM object and measuring that is horribly(!) slow). Thus as * a "work around" we provide this option. It will append its value to the * text that is found to be the longest string for the column - i.e. padding. * @type string */ "sContentPadding": null, /** * Allows a default value to be given for a column's data, and will be used * whenever a null data source is encountered (this can be because mData * is set to null, or because the data source itself is null). * @type string * @default null */ "sDefaultContent": null, /** * Name for the column, allowing reference to the column by name as well as * by index (needs a lookup to work by name). * @type string */ "sName": null, /** * Custom sorting data type - defines which of the available plug-ins in * afnSortData the custom sorting will use - if any is defined. * @type string * @default std */ "sSortDataType": 'std', /** * Class to be applied to the header element when sorting on this column * @type string * @default null */ "sSortingClass": null, /** * Class to be applied to the header element when sorting on this column - * when jQuery UI theming is used. * @type string * @default null */ "sSortingClassJUI": null, /** * Title of the column - what is seen in the TH element (nTh). * @type string */ "sTitle": null, /** * Column sorting and filtering type * @type string * @default null */ "sType": null, /** * Width of the column * @type string * @default null */ "sWidth": null, /** * Width of the column when it was first "encountered" * @type string * @default null */ "sWidthOrig": null }; /* * Developer note: The properties of the object below are given in Hungarian * notation, that was used as the interface for DataTables prior to v1.10, however * from v1.10 onwards the primary interface is camel case. In order to avoid * breaking backwards compatibility utterly with this change, the Hungarian * version is still, internally the primary interface, but is is not documented * - hence the @name tags in each doc comment. This allows a Javascript function * to create a map from Hungarian notation to camel case (going the other direction * would require each property to be listed, which would at around 3K to the size * of DataTables, while this method is about a 0.5K hit. * * Ultimately this does pave the way for Hungarian notation to be dropped * completely, but that is a massive amount of work and will break current * installs (therefore is on-hold until v2). */ /** * Initialisation options that can be given to DataTables at initialisation * time. * @namespace */ DataTable.defaults = { /** * An array of data to use for the table, passed in at initialisation which * will be used in preference to any data which is already in the DOM. This is * particularly useful for constructing tables purely in Javascript, for * example with a custom Ajax call. * @type array * @default null * * @dtopt Option * @name DataTable.defaults.data * * @example * // Using a 2D array data source * $(document).ready( function () { * $('#example').dataTable( { * "data": [ * ['Trident', 'Internet Explorer 4.0', 'Win 95+', 4, 'X'], * ['Trident', 'Internet Explorer 5.0', 'Win 95+', 5, 'C'], * ], * "columns": [ * { "title": "Engine" }, * { "title": "Browser" }, * { "title": "Platform" }, * { "title": "Version" }, * { "title": "Grade" } * ] * } ); * } ); * * @example * // Using an array of objects as a data source (`data`) * $(document).ready( function () { * $('#example').dataTable( { * "data": [ * { * "engine": "Trident", * "browser": "Internet Explorer 4.0", * "platform": "Win 95+", * "version": 4, * "grade": "X" * }, * { * "engine": "Trident", * "browser": "Internet Explorer 5.0", * "platform": "Win 95+", * "version": 5, * "grade": "C" * } * ], * "columns": [ * { "title": "Engine", "data": "engine" }, * { "title": "Browser", "data": "browser" }, * { "title": "Platform", "data": "platform" }, * { "title": "Version", "data": "version" }, * { "title": "Grade", "data": "grade" } * ] * } ); * } ); */ "aaData": null, /** * If ordering is enabled, then DataTables will perform a first pass sort on * initialisation. You can define which column(s) the sort is performed * upon, and the sorting direction, with this variable. The `sorting` array * should contain an array for each column to be sorted initially containing * the column's index and a direction string ('asc' or 'desc'). * @type array * @default [[0,'asc']] * * @dtopt Option * @name DataTable.defaults.order * * @example * // Sort by 3rd column first, and then 4th column * $(document).ready( function() { * $('#example').dataTable( { * "order": [[2,'asc'], [3,'desc']] * } ); * } ); * * // No initial sorting * $(document).ready( function() { * $('#example').dataTable( { * "order": [] * } ); * } ); */ "aaSorting": [[0,'asc']], /** * This parameter is basically identical to the `sorting` parameter, but * cannot be overridden by user interaction with the table. What this means * is that you could have a column (visible or hidden) which the sorting * will always be forced on first - any sorting after that (from the user) * will then be performed as required. This can be useful for grouping rows * together. * @type array * @default null * * @dtopt Option * @name DataTable.defaults.orderFixed * * @example * $(document).ready( function() { * $('#example').dataTable( { * "orderFixed": [[0,'asc']] * } ); * } ) */ "aaSortingFixed": [], /** * DataTables can be instructed to load data to display in the table from a * Ajax source. This option defines how that Ajax call is made and where to. * * The `ajax` property has three different modes of operation, depending on * how it is defined. These are: * * * `string` - Set the URL from where the data should be loaded from. * * `object` - Define properties for `jQuery.ajax`. * * `function` - Custom data get function * * `string` * -------- * * As a string, the `ajax` property simply defines the URL from which * DataTables will load data. * * `object` * -------- * * As an object, the parameters in the object are passed to * [jQuery.ajax](http://api.jquery.com/jQuery.ajax/) allowing fine control * of the Ajax request. DataTables has a number of default parameters which * you can override using this option. Please refer to the jQuery * documentation for a full description of the options available, although * the following parameters provide additional options in DataTables or * require special consideration: * * * `data` - As with jQuery, `data` can be provided as an object, but it * can also be used as a function to manipulate the data DataTables sends * to the server. The function takes a single parameter, an object of * parameters with the values that DataTables has readied for sending. An * object may be returned which will be merged into the DataTables * defaults, or you can add the items to the object that was passed in and * not return anything from the function. This supersedes `fnServerParams` * from DataTables 1.9-. * * * `dataSrc` - By default DataTables will look for the property `data` (or * `aaData` for compatibility with DataTables 1.9-) when obtaining data * from an Ajax source or for server-side processing - this parameter * allows that property to be changed. You can use Javascript dotted * object notation to get a data source for multiple levels of nesting, or * it my be used as a function. As a function it takes a single parameter, * the JSON returned from the server, which can be manipulated as * required, with the returned value being that used by DataTables as the * data source for the table. This supersedes `sAjaxDataProp` from * DataTables 1.9-. * * * `success` - Should not be overridden it is used internally in * DataTables. To manipulate / transform the data returned by the server * use `ajax.dataSrc`, or use `ajax` as a function (see below). * * `function` * ---------- * * As a function, making the Ajax call is left up to yourself allowing * complete control of the Ajax request. Indeed, if desired, a method other * than Ajax could be used to obtain the required data, such as Web storage * or an AIR database. * * The function is given four parameters and no return is required. The * parameters are: * * 1. _object_ - Data to send to the server * 2. _function_ - Callback function that must be executed when the required * data has been obtained. That data should be passed into the callback * as the only parameter * 3. _object_ - DataTables settings object for the table * * Note that this supersedes `fnServerData` from DataTables 1.9-. * * @type string|object|function * @default null * * @dtopt Option * @name DataTable.defaults.ajax * @since 1.10.0 * * @example * // Get JSON data from a file via Ajax. * // Note DataTables expects data in the form `{ data: [ ...data... ] }` by default). * $('#example').dataTable( { * "ajax": "data.json" * } ); * * @example * // Get JSON data from a file via Ajax, using `dataSrc` to change * // `data` to `tableData` (i.e. `{ tableData: [ ...data... ] }`) * $('#example').dataTable( { * "ajax": { * "url": "data.json", * "dataSrc": "tableData" * } * } ); * * @example * // Get JSON data from a file via Ajax, using `dataSrc` to read data * // from a plain array rather than an array in an object * $('#example').dataTable( { * "ajax": { * "url": "data.json", * "dataSrc": "" * } * } ); * * @example * // Manipulate the data returned from the server - add a link to data * // (note this can, should, be done using `render` for the column - this * // is just a simple example of how the data can be manipulated). * $('#example').dataTable( { * "ajax": { * "url": "data.json", * "dataSrc": function ( json ) { * for ( var i=0, ien=json.length ; iView message'; * } * return json; * } * } * } ); * * @example * // Add data to the request * $('#example').dataTable( { * "ajax": { * "url": "data.json", * "data": function ( d ) { * return { * "extra_search": $('#extra').val() * }; * } * } * } ); * * @example * // Send request as POST * $('#example').dataTable( { * "ajax": { * "url": "data.json", * "type": "POST" * } * } ); * * @example * // Get the data from localStorage (could interface with a form for * // adding, editing and removing rows). * $('#example').dataTable( { * "ajax": function (data, callback, settings) { * callback( * JSON.parse( localStorage.getItem('dataTablesData') ) * ); * } * } ); */ "ajax": null, /** * This parameter allows you to readily specify the entries in the length drop * down menu that DataTables shows when pagination is enabled. It can be * either a 1D array of options which will be used for both the displayed * option and the value, or a 2D array which will use the array in the first * position as the value, and the array in the second position as the * displayed options (useful for language strings such as 'All'). * * Note that the `pageLength` property will be automatically set to the * first value given in this array, unless `pageLength` is also provided. * @type array * @default [ 10, 25, 50, 100 ] * * @dtopt Option * @name DataTable.defaults.lengthMenu * * @example * $(document).ready( function() { * $('#example').dataTable( { * "lengthMenu": [[10, 25, 50, -1], [10, 25, 50, "All"]] * } ); * } ); */ "aLengthMenu": [ 10, 25, 50, 100 ], /** * The `columns` option in the initialisation parameter allows you to define * details about the way individual columns behave. For a full list of * column options that can be set, please see * {@link DataTable.defaults.column}. Note that if you use `columns` to * define your columns, you must have an entry in the array for every single * column that you have in your table (these can be null if you don't which * to specify any options). * @member * * @name DataTable.defaults.column */ "aoColumns": null, /** * Very similar to `columns`, `columnDefs` allows you to target a specific * column, multiple columns, or all columns, using the `targets` property of * each object in the array. This allows great flexibility when creating * tables, as the `columnDefs` arrays can be of any length, targeting the * columns you specifically want. `columnDefs` may use any of the column * options available: {@link DataTable.defaults.column}, but it _must_ * have `targets` defined in each object in the array. Values in the `targets` * array may be: *
      *
    • a string - class name will be matched on the TH for the column
      • *
    • 0 or a positive integer - column index counting from the left
      • *
    • a negative integer - column index counting from the right
      • *
    • the string "_all" - all columns (i.e. assign a default)
      • *
    * @member * * @name DataTable.defaults.columnDefs */ "aoColumnDefs": null, /** * Basically the same as `search`, this parameter defines the individual column * filtering state at initialisation time. The array must be of the same size * as the number of columns, and each element be an object with the parameters * `search` and `escapeRegex` (the latter is optional). 'null' is also * accepted and the default will be used. * @type array * @default [] * * @dtopt Option * @name DataTable.defaults.searchCols * * @example * $(document).ready( function() { * $('#example').dataTable( { * "searchCols": [ * null, * { "search": "My filter" }, * null, * { "search": "^[0-9]", "escapeRegex": false } * ] * } ); * } ) */ "aoSearchCols": [], /** * An array of CSS classes that should be applied to displayed rows. This * array may be of any length, and DataTables will apply each class * sequentially, looping when required. * @type array * @default null Will take the values determined by the `oClasses.stripe*` * options * * @dtopt Option * @name DataTable.defaults.stripeClasses * * @example * $(document).ready( function() { * $('#example').dataTable( { * "stripeClasses": [ 'strip1', 'strip2', 'strip3' ] * } ); * } ) */ "asStripeClasses": null, /** * Enable or disable automatic column width calculation. This can be disabled * as an optimisation (it takes some time to calculate the widths) if the * tables widths are passed in using `columns`. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.autoWidth * * @example * $(document).ready( function () { * $('#example').dataTable( { * "autoWidth": false * } ); * } ); */ "bAutoWidth": true, /** * Deferred rendering can provide DataTables with a huge speed boost when you * are using an Ajax or JS data source for the table. This option, when set to * true, will cause DataTables to defer the creation of the table elements for * each row until they are needed for a draw - saving a significant amount of * time. * @type boolean * @default false * * @dtopt Features * @name DataTable.defaults.deferRender * * @example * $(document).ready( function() { * $('#example').dataTable( { * "ajax": "sources/arrays.txt", * "deferRender": true * } ); * } ); */ "bDeferRender": false, /** * Replace a DataTable which matches the given selector and replace it with * one which has the properties of the new initialisation object passed. If no * table matches the selector, then the new DataTable will be constructed as * per normal. * @type boolean * @default false * * @dtopt Options * @name DataTable.defaults.destroy * * @example * $(document).ready( function() { * $('#example').dataTable( { * "srollY": "200px", * "paginate": false * } ); * * // Some time later.... * $('#example').dataTable( { * "filter": false, * "destroy": true * } ); * } ); */ "bDestroy": false, /** * Enable or disable filtering of data. Filtering in DataTables is "smart" in * that it allows the end user to input multiple words (space separated) and * will match a row containing those words, even if not in the order that was * specified (this allow matching across multiple columns). Note that if you * wish to use filtering in DataTables this must remain 'true' - to remove the * default filtering input box and retain filtering abilities, please use * {@link DataTable.defaults.dom}. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.searching * * @example * $(document).ready( function () { * $('#example').dataTable( { * "searching": false * } ); * } ); */ "bFilter": true, /** * Enable or disable the table information display. This shows information * about the data that is currently visible on the page, including information * about filtered data if that action is being performed. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.info * * @example * $(document).ready( function () { * $('#example').dataTable( { * "info": false * } ); * } ); */ "bInfo": true, /** * Enable jQuery UI ThemeRoller support (required as ThemeRoller requires some * slightly different and additional mark-up from what DataTables has * traditionally used). * @type boolean * @default false * * @dtopt Features * @name DataTable.defaults.jQueryUI * * @example * $(document).ready( function() { * $('#example').dataTable( { * "jQueryUI": true * } ); * } ); */ "bJQueryUI": false, /** * Allows the end user to select the size of a formatted page from a select * menu (sizes are 10, 25, 50 and 100). Requires pagination (`paginate`). * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.lengthChange * * @example * $(document).ready( function () { * $('#example').dataTable( { * "lengthChange": false * } ); * } ); */ "bLengthChange": true, /** * Enable or disable pagination. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.paging * * @example * $(document).ready( function () { * $('#example').dataTable( { * "paging": false * } ); * } ); */ "bPaginate": true, /** * Enable or disable the display of a 'processing' indicator when the table is * being processed (e.g. a sort). This is particularly useful for tables with * large amounts of data where it can take a noticeable amount of time to sort * the entries. * @type boolean * @default false * * @dtopt Features * @name DataTable.defaults.processing * * @example * $(document).ready( function () { * $('#example').dataTable( { * "processing": true * } ); * } ); */ "bProcessing": false, /** * Retrieve the DataTables object for the given selector. Note that if the * table has already been initialised, this parameter will cause DataTables * to simply return the object that has already been set up - it will not take * account of any changes you might have made to the initialisation object * passed to DataTables (setting this parameter to true is an acknowledgement * that you understand this). `destroy` can be used to reinitialise a table if * you need. * @type boolean * @default false * * @dtopt Options * @name DataTable.defaults.retrieve * * @example * $(document).ready( function() { * initTable(); * tableActions(); * } ); * * function initTable () * { * return $('#example').dataTable( { * "scrollY": "200px", * "paginate": false, * "retrieve": true * } ); * } * * function tableActions () * { * var table = initTable(); * // perform API operations with oTable * } */ "bRetrieve": false, /** * When vertical (y) scrolling is enabled, DataTables will force the height of * the table's viewport to the given height at all times (useful for layout). * However, this can look odd when filtering data down to a small data set, * and the footer is left "floating" further down. This parameter (when * enabled) will cause DataTables to collapse the table's viewport down when * the result set will fit within the given Y height. * @type boolean * @default false * * @dtopt Options * @name DataTable.defaults.scrollCollapse * * @example * $(document).ready( function() { * $('#example').dataTable( { * "scrollY": "200", * "scrollCollapse": true * } ); * } ); */ "bScrollCollapse": false, /** * Configure DataTables to use server-side processing. Note that the * `ajax` parameter must also be given in order to give DataTables a * source to obtain the required data for each draw. * @type boolean * @default false * * @dtopt Features * @dtopt Server-side * @name DataTable.defaults.serverSide * * @example * $(document).ready( function () { * $('#example').dataTable( { * "serverSide": true, * "ajax": "xhr.php" * } ); * } ); */ "bServerSide": false, /** * Enable or disable sorting of columns. Sorting of individual columns can be * disabled by the `sortable` option for each column. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.ordering * * @example * $(document).ready( function () { * $('#example').dataTable( { * "ordering": false * } ); * } ); */ "bSort": true, /** * Enable or display DataTables' ability to sort multiple columns at the * same time (activated by shift-click by the user). * @type boolean * @default true * * @dtopt Options * @name DataTable.defaults.orderMulti * * @example * // Disable multiple column sorting ability * $(document).ready( function () { * $('#example').dataTable( { * "orderMulti": false * } ); * } ); */ "bSortMulti": true, /** * Allows control over whether DataTables should use the top (true) unique * cell that is found for a single column, or the bottom (false - default). * This is useful when using complex headers. * @type boolean * @default false * * @dtopt Options * @name DataTable.defaults.orderCellsTop * * @example * $(document).ready( function() { * $('#example').dataTable( { * "orderCellsTop": true * } ); * } ); */ "bSortCellsTop": false, /** * Enable or disable the addition of the classes `sorting\_1`, `sorting\_2` and * `sorting\_3` to the columns which are currently being sorted on. This is * presented as a feature switch as it can increase processing time (while * classes are removed and added) so for large data sets you might want to * turn this off. * @type boolean * @default true * * @dtopt Features * @name DataTable.defaults.orderClasses * * @example * $(document).ready( function () { * $('#example').dataTable( { * "orderClasses": false * } ); * } ); */ "bSortClasses": true, /** * Enable or disable state saving. When enabled HTML5 `localStorage` will be * used to save table display information such as pagination information, * display length, filtering and sorting. As such when the end user reloads * the page the display display will match what thy had previously set up. * * Due to the use of `localStorage` the default state saving is not supported * in IE6 or 7. If state saving is required in those browsers, use * `stateSaveCallback` to provide a storage solution such as cookies. * @type boolean * @default false * * @dtopt Features * @name DataTable.defaults.stateSave * * @example * $(document).ready( function () { * $('#example').dataTable( { * "stateSave": true * } ); * } ); */ "bStateSave": false, /** * This function is called when a TR element is created (and all TD child * elements have been inserted), or registered if using a DOM source, allowing * manipulation of the TR element (adding classes etc). * @type function * @param {node} row "TR" element for the current row * @param {array} data Raw data array for this row * @param {int} dataIndex The index of this row in the internal aoData array * * @dtopt Callbacks * @name DataTable.defaults.createdRow * * @example * $(document).ready( function() { * $('#example').dataTable( { * "createdRow": function( row, data, dataIndex ) { * // Bold the grade for all 'A' grade browsers * if ( data[4] == "A" ) * { * $('td:eq(4)', row).html( 'A' ); * } * } * } ); * } ); */ "fnCreatedRow": null, /** * This function is called on every 'draw' event, and allows you to * dynamically modify any aspect you want about the created DOM. * @type function * @param {object} settings DataTables settings object * * @dtopt Callbacks * @name DataTable.defaults.drawCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "drawCallback": function( settings ) { * alert( 'DataTables has redrawn the table' ); * } * } ); * } ); */ "fnDrawCallback": null, /** * Identical to fnHeaderCallback() but for the table footer this function * allows you to modify the table footer on every 'draw' event. * @type function * @param {node} foot "TR" element for the footer * @param {array} data Full table data (as derived from the original HTML) * @param {int} start Index for the current display starting point in the * display array * @param {int} end Index for the current display ending point in the * display array * @param {array int} display Index array to translate the visual position * to the full data array * * @dtopt Callbacks * @name DataTable.defaults.footerCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "footerCallback": function( tfoot, data, start, end, display ) { * tfoot.getElementsByTagName('th')[0].innerHTML = "Starting index is "+start; * } * } ); * } ) */ "fnFooterCallback": null, /** * When rendering large numbers in the information element for the table * (i.e. "Showing 1 to 10 of 57 entries") DataTables will render large numbers * to have a comma separator for the 'thousands' units (e.g. 1 million is * rendered as "1,000,000") to help readability for the end user. This * function will override the default method DataTables uses. * @type function * @member * @param {int} toFormat number to be formatted * @returns {string} formatted string for DataTables to show the number * * @dtopt Callbacks * @name DataTable.defaults.formatNumber * * @example * // Format a number using a single quote for the separator (note that * // this can also be done with the language.infoThousands option) * $(document).ready( function() { * $('#example').dataTable( { * "formatNumber": function ( toFormat ) { * return toFormat.toString().replace( * /\B(?=(\d{3})+(?!\d))/g, "'" * ); * }; * } ); * } ); */ "fnFormatNumber": function ( toFormat ) { return toFormat.toString().replace( /\B(?=(\d{3})+(?!\d))/g, this.oLanguage.sInfoThousands ); }, /** * This function is called on every 'draw' event, and allows you to * dynamically modify the header row. This can be used to calculate and * display useful information about the table. * @type function * @param {node} head "TR" element for the header * @param {array} data Full table data (as derived from the original HTML) * @param {int} start Index for the current display starting point in the * display array * @param {int} end Index for the current display ending point in the * display array * @param {array int} display Index array to translate the visual position * to the full data array * * @dtopt Callbacks * @name DataTable.defaults.headerCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "fheaderCallback": function( head, data, start, end, display ) { * head.getElementsByTagName('th')[0].innerHTML = "Displaying "+(end-start)+" records"; * } * } ); * } ) */ "fnHeaderCallback": null, /** * The information element can be used to convey information about the current * state of the table. Although the internationalisation options presented by * DataTables are quite capable of dealing with most customisations, there may * be times where you wish to customise the string further. This callback * allows you to do exactly that. * @type function * @param {object} oSettings DataTables settings object * @param {int} start Starting position in data for the draw * @param {int} end End position in data for the draw * @param {int} max Total number of rows in the table (regardless of * filtering) * @param {int} total Total number of rows in the data set, after filtering * @param {string} pre The string that DataTables has formatted using it's * own rules * @returns {string} The string to be displayed in the information element. * * @dtopt Callbacks * @name DataTable.defaults.infoCallback * * @example * $('#example').dataTable( { * "infoCallback": function( settings, start, end, max, total, pre ) { * return start +" to "+ end; * } * } ); */ "fnInfoCallback": null, /** * Called when the table has been initialised. Normally DataTables will * initialise sequentially and there will be no need for this function, * however, this does not hold true when using external language information * since that is obtained using an async XHR call. * @type function * @param {object} settings DataTables settings object * @param {object} json The JSON object request from the server - only * present if client-side Ajax sourced data is used * * @dtopt Callbacks * @name DataTable.defaults.initComplete * * @example * $(document).ready( function() { * $('#example').dataTable( { * "initComplete": function(settings, json) { * alert( 'DataTables has finished its initialisation.' ); * } * } ); * } ) */ "fnInitComplete": null, /** * Called at the very start of each table draw and can be used to cancel the * draw by returning false, any other return (including undefined) results in * the full draw occurring). * @type function * @param {object} settings DataTables settings object * @returns {boolean} False will cancel the draw, anything else (including no * return) will allow it to complete. * * @dtopt Callbacks * @name DataTable.defaults.preDrawCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "preDrawCallback": function( settings ) { * if ( $('#test').val() == 1 ) { * return false; * } * } * } ); * } ); */ "fnPreDrawCallback": null, /** * This function allows you to 'post process' each row after it have been * generated for each table draw, but before it is rendered on screen. This * function might be used for setting the row class name etc. * @type function * @param {node} row "TR" element for the current row * @param {array} data Raw data array for this row * @param {int} displayIndex The display index for the current table draw * @param {int} displayIndexFull The index of the data in the full list of * rows (after filtering) * * @dtopt Callbacks * @name DataTable.defaults.rowCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "rowCallback": function( row, data, displayIndex, displayIndexFull ) { * // Bold the grade for all 'A' grade browsers * if ( data[4] == "A" ) { * $('td:eq(4)', row).html( 'A' ); * } * } * } ); * } ); */ "fnRowCallback": null, /** * __Deprecated__ The functionality provided by this parameter has now been * superseded by that provided through `ajax`, which should be used instead. * * This parameter allows you to override the default function which obtains * the data from the server so something more suitable for your application. * For example you could use POST data, or pull information from a Gears or * AIR database. * @type function * @member * @param {string} source HTTP source to obtain the data from (`ajax`) * @param {array} data A key/value pair object containing the data to send * to the server * @param {function} callback to be called on completion of the data get * process that will draw the data on the page. * @param {object} settings DataTables settings object * * @dtopt Callbacks * @dtopt Server-side * @name DataTable.defaults.serverData * * @deprecated 1.10. Please use `ajax` for this functionality now. */ "fnServerData": null, /** * __Deprecated__ The functionality provided by this parameter has now been * superseded by that provided through `ajax`, which should be used instead. * * It is often useful to send extra data to the server when making an Ajax * request - for example custom filtering information, and this callback * function makes it trivial to send extra information to the server. The * passed in parameter is the data set that has been constructed by * DataTables, and you can add to this or modify it as you require. * @type function * @param {array} data Data array (array of objects which are name/value * pairs) that has been constructed by DataTables and will be sent to the * server. In the case of Ajax sourced data with server-side processing * this will be an empty array, for server-side processing there will be a * significant number of parameters! * @returns {undefined} Ensure that you modify the data array passed in, * as this is passed by reference. * * @dtopt Callbacks * @dtopt Server-side * @name DataTable.defaults.serverParams * * @deprecated 1.10. Please use `ajax` for this functionality now. */ "fnServerParams": null, /** * Load the table state. With this function you can define from where, and how, the * state of a table is loaded. By default DataTables will load from `localStorage` * but you might wish to use a server-side database or cookies. * @type function * @member * @param {object} settings DataTables settings object * @return {object} The DataTables state object to be loaded * * @dtopt Callbacks * @name DataTable.defaults.stateLoadCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateLoadCallback": function (settings) { * var o; * * // Send an Ajax request to the server to get the data. Note that * // this is a synchronous request. * $.ajax( { * "url": "/state_load", * "async": false, * "dataType": "json", * "success": function (json) { * o = json; * } * } ); * * return o; * } * } ); * } ); */ "fnStateLoadCallback": function ( settings ) { try { return JSON.parse( localStorage.getItem('DataTables_'+settings.sInstance+'_'+window.location.pathname) ); } catch (e) {} }, /** * Callback which allows modification of the saved state prior to loading that state. * This callback is called when the table is loading state from the stored data, but * prior to the settings object being modified by the saved state. Note that for * plug-in authors, you should use the `stateLoadParams` event to load parameters for * a plug-in. * @type function * @param {object} settings DataTables settings object * @param {object} data The state object that is to be loaded * * @dtopt Callbacks * @name DataTable.defaults.stateLoadParams * * @example * // Remove a saved filter, so filtering is never loaded * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateLoadParams": function (settings, data) { * data.oSearch.sSearch = ""; * } * } ); * } ); * * @example * // Disallow state loading by returning false * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateLoadParams": function (settings, data) { * return false; * } * } ); * } ); */ "fnStateLoadParams": null, /** * Callback that is called when the state has been loaded from the state saving method * and the DataTables settings object has been modified as a result of the loaded state. * @type function * @param {object} settings DataTables settings object * @param {object} data The state object that was loaded * * @dtopt Callbacks * @name DataTable.defaults.stateLoaded * * @example * // Show an alert with the filtering value that was saved * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateLoaded": function (settings, data) { * alert( 'Saved filter was: '+data.oSearch.sSearch ); * } * } ); * } ); */ "fnStateLoaded": null, /** * Save the table state. This function allows you to define where and how the state * information for the table is stored By default DataTables will use `localStorage` * but you might wish to use a server-side database or cookies. * @type function * @member * @param {object} settings DataTables settings object * @param {object} data The state object to be saved * * @dtopt Callbacks * @name DataTable.defaults.stateSaveCallback * * @example * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateSaveCallback": function (settings, data) { * // Send an Ajax request to the server with the state object * $.ajax( { * "url": "/state_save", * "data": data, * "dataType": "json", * "method": "POST" * "success": function () {} * } ); * } * } ); * } ); */ "fnStateSaveCallback": function ( settings, data ) { try { localStorage.setItem( 'DataTables_'+settings.sInstance+'_'+window.location.pathname, JSON.stringify(data) ); } catch (e) {} }, /** * Callback which allows modification of the state to be saved. Called when the table * has changed state a new state save is required. This method allows modification of * the state saving object prior to actually doing the save, including addition or * other state properties or modification. Note that for plug-in authors, you should * use the `stateSaveParams` event to save parameters for a plug-in. * @type function * @param {object} settings DataTables settings object * @param {object} data The state object to be saved * * @dtopt Callbacks * @name DataTable.defaults.stateSaveParams * * @example * // Remove a saved filter, so filtering is never saved * $(document).ready( function() { * $('#example').dataTable( { * "stateSave": true, * "stateSaveParams": function (settings, data) { * data.oSearch.sSearch = ""; * } * } ); * } ); */ "fnStateSaveParams": null, /** * Duration for which the saved state information is considered valid. After this period * has elapsed the state will be returned to the default. * Value is given in seconds. * @type int * @default 7200 (2 hours) * * @dtopt Options * @name DataTable.defaults.stateDuration * * @example * $(document).ready( function() { * $('#example').dataTable( { * "stateDuration": 60*60*24; // 1 day * } ); * } ) */ "iStateDuration": 7200, /** * When enabled DataTables will not make a request to the server for the first * page draw - rather it will use the data already on the page (no sorting etc * will be applied to it), thus saving on an XHR at load time. `deferLoading` * is used to indicate that deferred loading is required, but it is also used * to tell DataTables how many records there are in the full table (allowing * the information element and pagination to be displayed correctly). In the case * where a filtering is applied to the table on initial load, this can be * indicated by giving the parameter as an array, where the first element is * the number of records available after filtering and the second element is the * number of records without filtering (allowing the table information element * to be shown correctly). * @type int | array * @default null * * @dtopt Options * @name DataTable.defaults.deferLoading * * @example * // 57 records available in the table, no filtering applied * $(document).ready( function() { * $('#example').dataTable( { * "serverSide": true, * "ajax": "scripts/server_processing.php", * "deferLoading": 57 * } ); * } ); * * @example * // 57 records after filtering, 100 without filtering (an initial filter applied) * $(document).ready( function() { * $('#example').dataTable( { * "serverSide": true, * "ajax": "scripts/server_processing.php", * "deferLoading": [ 57, 100 ], * "search": { * "search": "my_filter" * } * } ); * } ); */ "iDeferLoading": null, /** * Number of rows to display on a single page when using pagination. If * feature enabled (`lengthChange`) then the end user will be able to override * this to a custom setting using a pop-up menu. * @type int * @default 10 * * @dtopt Options * @name DataTable.defaults.pageLength * * @example * $(document).ready( function() { * $('#example').dataTable( { * "pageLength": 50 * } ); * } ) */ "iDisplayLength": 10, /** * Define the starting point for data display when using DataTables with * pagination. Note that this parameter is the number of records, rather than * the page number, so if you have 10 records per page and want to start on * the third page, it should be "20". * @type int * @default 0 * * @dtopt Options * @name DataTable.defaults.displayStart * * @example * $(document).ready( function() { * $('#example').dataTable( { * "displayStart": 20 * } ); * } ) */ "iDisplayStart": 0, /** * By default DataTables allows keyboard navigation of the table (sorting, paging, * and filtering) by adding a `tabindex` attribute to the required elements. This * allows you to tab through the controls and press the enter key to activate them. * The tabindex is default 0, meaning that the tab follows the flow of the document. * You can overrule this using this parameter if you wish. Use a value of -1 to * disable built-in keyboard navigation. * @type int * @default 0 * * @dtopt Options * @name DataTable.defaults.tabIndex * * @example * $(document).ready( function() { * $('#example').dataTable( { * "tabIndex": 1 * } ); * } ); */ "iTabIndex": 0, /** * Classes that DataTables assigns to the various components and features * that it adds to the HTML table. This allows classes to be configured * during initialisation in addition to through the static * {@link DataTable.ext.oStdClasses} object). * @namespace * @name DataTable.defaults.classes */ "oClasses": {}, /** * All strings that DataTables uses in the user interface that it creates * are defined in this object, allowing you to modified them individually or * completely replace them all as required. * @namespace * @name DataTable.defaults.language */ "oLanguage": { /** * Strings that are used for WAI-ARIA labels and controls only (these are not * actually visible on the page, but will be read by screenreaders, and thus * must be internationalised as well). * @namespace * @name DataTable.defaults.language.aria */ "oAria": { /** * ARIA label that is added to the table headers when the column may be * sorted ascending by activing the column (click or return when focused). * Note that the column header is prefixed to this string. * @type string * @default : activate to sort column ascending * * @dtopt Language * @name DataTable.defaults.language.aria.sortAscending * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "aria": { * "sortAscending": " - click/return to sort ascending" * } * } * } ); * } ); */ "sSortAscending": ": activate to sort column ascending", /** * ARIA label that is added to the table headers when the column may be * sorted descending by activing the column (click or return when focused). * Note that the column header is prefixed to this string. * @type string * @default : activate to sort column ascending * * @dtopt Language * @name DataTable.defaults.language.aria.sortDescending * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "aria": { * "sortDescending": " - click/return to sort descending" * } * } * } ); * } ); */ "sSortDescending": ": activate to sort column descending" }, /** * Pagination string used by DataTables for the built-in pagination * control types. * @namespace * @name DataTable.defaults.language.paginate */ "oPaginate": { /** * Text to use when using the 'full_numbers' type of pagination for the * button to take the user to the first page. * @type string * @default First * * @dtopt Language * @name DataTable.defaults.language.paginate.first * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "paginate": { * "first": "First page" * } * } * } ); * } ); */ "sFirst": "First", /** * Text to use when using the 'full_numbers' type of pagination for the * button to take the user to the last page. * @type string * @default Last * * @dtopt Language * @name DataTable.defaults.language.paginate.last * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "paginate": { * "last": "Last page" * } * } * } ); * } ); */ "sLast": "Last", /** * Text to use for the 'next' pagination button (to take the user to the * next page). * @type string * @default Next * * @dtopt Language * @name DataTable.defaults.language.paginate.next * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "paginate": { * "next": "Next page" * } * } * } ); * } ); */ "sNext": "Next", /** * Text to use for the 'previous' pagination button (to take the user to * the previous page). * @type string * @default Previous * * @dtopt Language * @name DataTable.defaults.language.paginate.previous * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "paginate": { * "previous": "Previous page" * } * } * } ); * } ); */ "sPrevious": "Previous" }, /** * This string is shown in preference to `zeroRecords` when the table is * empty of data (regardless of filtering). Note that this is an optional * parameter - if it is not given, the value of `zeroRecords` will be used * instead (either the default or given value). * @type string * @default No data available in table * * @dtopt Language * @name DataTable.defaults.language.emptyTable * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "emptyTable": "No data available in table" * } * } ); * } ); */ "sEmptyTable": "No data available in table", /** * This string gives information to the end user about the information * that is current on display on the page. The following tokens can be * used in the string and will be dynamically replaced as the table * display updates. This tokens can be placed anywhere in the string, or * removed as needed by the language requires: * * * `\_START\_` - Display index of the first record on the current page * * `\_END\_` - Display index of the last record on the current page * * `\_TOTAL\_` - Number of records in the table after filtering * * `\_MAX\_` - Number of records in the table without filtering * * `\_PAGE\_` - Current page number * * `\_PAGES\_` - Total number of pages of data in the table * * @type string * @default Showing _START_ to _END_ of _TOTAL_ entries * * @dtopt Language * @name DataTable.defaults.language.info * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "info": "Showing page _PAGE_ of _PAGES_" * } * } ); * } ); */ "sInfo": "Showing _START_ to _END_ of _TOTAL_ entries", /** * Display information string for when the table is empty. Typically the * format of this string should match `info`. * @type string * @default Showing 0 to 0 of 0 entries * * @dtopt Language * @name DataTable.defaults.language.infoEmpty * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "infoEmpty": "No entries to show" * } * } ); * } ); */ "sInfoEmpty": "Showing 0 to 0 of 0 entries", /** * When a user filters the information in a table, this string is appended * to the information (`info`) to give an idea of how strong the filtering * is. The variable _MAX_ is dynamically updated. * @type string * @default (filtered from _MAX_ total entries) * * @dtopt Language * @name DataTable.defaults.language.infoFiltered * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "infoFiltered": " - filtering from _MAX_ records" * } * } ); * } ); */ "sInfoFiltered": "(filtered from _MAX_ total entries)", /** * If can be useful to append extra information to the info string at times, * and this variable does exactly that. This information will be appended to * the `info` (`infoEmpty` and `infoFiltered` in whatever combination they are * being used) at all times. * @type string * @default Empty string * * @dtopt Language * @name DataTable.defaults.language.infoPostFix * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "infoPostFix": "All records shown are derived from real information." * } * } ); * } ); */ "sInfoPostFix": "", /** * DataTables has a build in number formatter (`formatNumber`) which is used * to format large numbers that are used in the table information. By * default a comma is used, but this can be trivially changed to any * character you wish with this parameter. * @type string * @default , * * @dtopt Language * @name DataTable.defaults.language.infoThousands * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "infoThousands": "'" * } * } ); * } ); */ "sInfoThousands": ",", /** * Detail the action that will be taken when the drop down menu for the * pagination length option is changed. The '_MENU_' variable is replaced * with a default select list of 10, 25, 50 and 100, and can be replaced * with a custom select box if required. * @type string * @default Show _MENU_ entries * * @dtopt Language * @name DataTable.defaults.language.lengthMenu * * @example * // Language change only * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "lengthMenu": "Display _MENU_ records" * } * } ); * } ); * * @example * // Language and options change * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "lengthMenu": 'Display records' * } * } ); * } ); */ "sLengthMenu": "Show _MENU_ entries", /** * When using Ajax sourced data and during the first draw when DataTables is * gathering the data, this message is shown in an empty row in the table to * indicate to the end user the the data is being loaded. Note that this * parameter is not used when loading data by server-side processing, just * Ajax sourced data with client-side processing. * @type string * @default Loading... * * @dtopt Language * @name DataTable.defaults.language.loadingRecords * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "loadingRecords": "Please wait - loading..." * } * } ); * } ); */ "sLoadingRecords": "Loading...", /** * Text which is displayed when the table is processing a user action * (usually a sort command or similar). * @type string * @default Processing... * * @dtopt Language * @name DataTable.defaults.language.processing * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "processing": "DataTables is currently busy" * } * } ); * } ); */ "sProcessing": "Processing...", /** * Details the actions that will be taken when the user types into the * filtering input text box. The variable "_INPUT_", if used in the string, * is replaced with the HTML text box for the filtering input allowing * control over where it appears in the string. If "_INPUT_" is not given * then the input box is appended to the string automatically. * @type string * @default Search: * * @dtopt Language * @name DataTable.defaults.language.search * * @example * // Input text box will be appended at the end automatically * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "search": "Filter records:" * } * } ); * } ); * * @example * // Specify where the filter should appear * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "search": "Apply filter _INPUT_ to table" * } * } ); * } ); */ "sSearch": "Search:", /** * All of the language information can be stored in a file on the * server-side, which DataTables will look up if this parameter is passed. * It must store the URL of the language file, which is in a JSON format, * and the object has the same properties as the oLanguage object in the * initialiser object (i.e. the above parameters). Please refer to one of * the example language files to see how this works in action. * @type string * @default Empty string - i.e. disabled * * @dtopt Language * @name DataTable.defaults.language.url * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "url": "http://www.sprymedia.co.uk/dataTables/lang.txt" * } * } ); * } ); */ "sUrl": "", /** * Text shown inside the table records when the is no information to be * displayed after filtering. `emptyTable` is shown when there is simply no * information in the table at all (regardless of filtering). * @type string * @default No matching records found * * @dtopt Language * @name DataTable.defaults.language.zeroRecords * * @example * $(document).ready( function() { * $('#example').dataTable( { * "language": { * "zeroRecords": "No records to display" * } * } ); * } ); */ "sZeroRecords": "No matching records found" }, /** * This parameter allows you to have define the global filtering state at * initialisation time. As an object the `search` parameter must be * defined, but all other parameters are optional. When `regex` is true, * the search string will be treated as a regular expression, when false * (default) it will be treated as a straight string. When `smart` * DataTables will use it's smart filtering methods (to word match at * any point in the data), when false this will not be done. * @namespace * @extends DataTable.models.oSearch * * @dtopt Options * @name DataTable.defaults.search * * @example * $(document).ready( function() { * $('#example').dataTable( { * "search": {"search": "Initial search"} * } ); * } ) */ "oSearch": $.extend( {}, DataTable.models.oSearch ), /** * __Deprecated__ The functionality provided by this parameter has now been * superseded by that provided through `ajax`, which should be used instead. * * By default DataTables will look for the property `data` (or `aaData` for * compatibility with DataTables 1.9-) when obtaining data from an Ajax * source or for server-side processing - this parameter allows that * property to be changed. You can use Javascript dotted object notation to * get a data source for multiple levels of nesting. * @type string * @default data * * @dtopt Options * @dtopt Server-side * @name DataTable.defaults.ajaxDataProp * * @deprecated 1.10. Please use `ajax` for this functionality now. */ "sAjaxDataProp": "data", /** * __Deprecated__ The functionality provided by this parameter has now been * superseded by that provided through `ajax`, which should be used instead. * * You can instruct DataTables to load data from an external * source using this parameter (use aData if you want to pass data in you * already have). Simply provide a url a JSON object can be obtained from. * @type string * @default null * * @dtopt Options * @dtopt Server-side * @name DataTable.defaults.ajaxSource * * @deprecated 1.10. Please use `ajax` for this functionality now. */ "sAjaxSource": null, /** * This initialisation variable allows you to specify exactly where in the * DOM you want DataTables to inject the various controls it adds to the page * (for example you might want the pagination controls at the top of the * table). DIV elements (with or without a custom class) can also be added to * aid styling. The follow syntax is used: *
      *
    • The following options are allowed: *
        *
      • 'l' - Length changing
        • *
      • 'f' - Filtering input
        • *
      • 't' - The table!
        • *
      • 'i' - Information
        • *
      • 'p' - Pagination
        • *
      • 'r' - pRocessing
        • *
      *
      • *
    • The following constants are allowed: *
        *
      • 'H' - jQueryUI theme "header" classes ('fg-toolbar ui-widget-header ui-corner-tl ui-corner-tr ui-helper-clearfix')
        • *
      • 'F' - jQueryUI theme "footer" classes ('fg-toolbar ui-widget-header ui-corner-bl ui-corner-br ui-helper-clearfix')
        • *
      *
      • *
    • The following syntax is expected: *
        *
      • '<' and '>' - div elements
        • *
      • '<"class" and '>' - div with a class
        • *
      • '<"#id" and '>' - div with an ID
        • *
      *
      • *
    • Examples: *
        *
      • '<"wrapper"flipt>'
        • *
      • '<lf<t>ip>'
        • *
      *
      • *
    * @type string * @default lfrtip (when `jQueryUI` is false) or * <"H"lfr>t<"F"ip> (when `jQueryUI` is true) * * @dtopt Options * @name DataTable.defaults.dom * * @example * $(document).ready( function() { * $('#example').dataTable( { * "dom": '<"top"i>rt<"bottom"flp><"clear">' * } ); * } ); */ "sDom": "lfrtip", /** * DataTables features four different built-in options for the buttons to * display for pagination control: * * * `simple` - 'Previous' and 'Next' buttons only * * 'simple_numbers` - 'Previous' and 'Next' buttons, plus page numbers * * `full` - 'First', 'Previous', 'Next' and 'Last' buttons * * `full_numbers` - 'First', 'Previous', 'Next' and 'Last' buttons, plus * page numbers * * Further methods can be added using {@link DataTable.ext.oPagination}. * @type string * @default simple_numbers * * @dtopt Options * @name DataTable.defaults.pagingType * * @example * $(document).ready( function() { * $('#example').dataTable( { * "pagingType": "full_numbers" * } ); * } ) */ "sPaginationType": "simple_numbers", /** * Enable horizontal scrolling. When a table is too wide to fit into a * certain layout, or you have a large number of columns in the table, you * can enable x-scrolling to show the table in a viewport, which can be * scrolled. This property can be `true` which will allow the table to * scroll horizontally when needed, or any CSS unit, or a number (in which * case it will be treated as a pixel measurement). Setting as simply `true` * is recommended. * @type boolean|string * @default blank string - i.e. disabled * * @dtopt Features * @name DataTable.defaults.scrollX * * @example * $(document).ready( function() { * $('#example').dataTable( { * "scrollX": true, * "scrollCollapse": true * } ); * } ); */ "sScrollX": "", /** * This property can be used to force a DataTable to use more width than it * might otherwise do when x-scrolling is enabled. For example if you have a * table which requires to be well spaced, this parameter is useful for * "over-sizing" the table, and thus forcing scrolling. This property can by * any CSS unit, or a number (in which case it will be treated as a pixel * measurement). * @type string * @default blank string - i.e. disabled * * @dtopt Options * @name DataTable.defaults.scrollXInner * * @example * $(document).ready( function() { * $('#example').dataTable( { * "scrollX": "100%", * "scrollXInner": "110%" * } ); * } ); */ "sScrollXInner": "", /** * Enable vertical scrolling. Vertical scrolling will constrain the DataTable * to the given height, and enable scrolling for any data which overflows the * current viewport. This can be used as an alternative to paging to display * a lot of data in a small area (although paging and scrolling can both be * enabled at the same time). This property can be any CSS unit, or a number * (in which case it will be treated as a pixel measurement). * @type string * @default blank string - i.e. disabled * * @dtopt Features * @name DataTable.defaults.scrollY * * @example * $(document).ready( function() { * $('#example').dataTable( { * "scrollY": "200px", * "paginate": false * } ); * } ); */ "sScrollY": "", /** * __Deprecated__ The functionality provided by this parameter has now been * superseded by that provided through `ajax`, which should be used instead. * * Set the HTTP method that is used to make the Ajax call for server-side * processing or Ajax sourced data. * @type string * @default GET * * @dtopt Options * @dtopt Server-side * @name DataTable.defaults.serverMethod * * @deprecated 1.10. Please use `ajax` for this functionality now. */ "sServerMethod": "GET", /** * DataTables makes use of renderers when displaying HTML elements for * a table. These renderers can be added or modified by plug-ins to * generate suitable mark-up for a site. For example the Bootstrap * integration plug-in for DataTables uses a paging button renderer to * display pagination buttons in the mark-up required by Bootstrap. * * For further information about the renderers available see * DataTable.ext.renderer * @type string|object * @default null * * @name DataTable.defaults.renderer * */ "renderer": null }; _fnHungarianMap( DataTable.defaults ); /* * Developer note - See note in model.defaults.js about the use of Hungarian * notation and camel case. */ /** * Column options that can be given to DataTables at initialisation time. * @namespace */ DataTable.defaults.column = { /** * Define which column(s) an order will occur on for this column. This * allows a column's ordering to take multiple columns into account when * doing a sort or use the data from a different column. For example first * name / last name columns make sense to do a multi-column sort over the * two columns. * @type array|int * @default null Takes the value of the column index automatically * * @name DataTable.defaults.column.orderData * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "orderData": [ 0, 1 ], "targets": [ 0 ] }, * { "orderData": [ 1, 0 ], "targets": [ 1 ] }, * { "orderData": 2, "targets": [ 2 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "orderData": [ 0, 1 ] }, * { "orderData": [ 1, 0 ] }, * { "orderData": 2 }, * null, * null * ] * } ); * } ); */ "aDataSort": null, "iDataSort": -1, /** * You can control the default ordering direction, and even alter the * behaviour of the sort handler (i.e. only allow ascending ordering etc) * using this parameter. * @type array * @default [ 'asc', 'desc' ] * * @name DataTable.defaults.column.orderSequence * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "orderSequence": [ "asc" ], "targets": [ 1 ] }, * { "orderSequence": [ "desc", "asc", "asc" ], "targets": [ 2 ] }, * { "orderSequence": [ "desc" ], "targets": [ 3 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * null, * { "orderSequence": [ "asc" ] }, * { "orderSequence": [ "desc", "asc", "asc" ] }, * { "orderSequence": [ "desc" ] }, * null * ] * } ); * } ); */ "asSorting": [ 'asc', 'desc' ], /** * Enable or disable filtering on the data in this column. * @type boolean * @default true * * @name DataTable.defaults.column.searchable * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "searchable": false, "targets": [ 0 ] } * ] } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "searchable": false }, * null, * null, * null, * null * ] } ); * } ); */ "bSearchable": true, /** * Enable or disable ordering on this column. * @type boolean * @default true * * @name DataTable.defaults.column.orderable * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "orderable": false, "targets": [ 0 ] } * ] } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "orderable": false }, * null, * null, * null, * null * ] } ); * } ); */ "bSortable": true, /** * Enable or disable the display of this column. * @type boolean * @default true * * @name DataTable.defaults.column.visible * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "visible": false, "targets": [ 0 ] } * ] } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "visible": false }, * null, * null, * null, * null * ] } ); * } ); */ "bVisible": true, /** * Developer definable function that is called whenever a cell is created (Ajax source, * etc) or processed for input (DOM source). This can be used as a compliment to mRender * allowing you to modify the DOM element (add background colour for example) when the * element is available. * @type function * @param {element} td The TD node that has been created * @param {*} cellData The Data for the cell * @param {array|object} rowData The data for the whole row * @param {int} row The row index for the aoData data store * @param {int} col The column index for aoColumns * * @name DataTable.defaults.column.createdCell * @dtopt Columns * * @example * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [3], * "createdCell": function (td, cellData, rowData, row, col) { * if ( cellData == "1.7" ) { * $(td).css('color', 'blue') * } * } * } ] * }); * } ); */ "fnCreatedCell": null, /** * This parameter has been replaced by `data` in DataTables to ensure naming * consistency. `dataProp` can still be used, as there is backwards * compatibility in DataTables for this option, but it is strongly * recommended that you use `data` in preference to `dataProp`. * @name DataTable.defaults.column.dataProp */ /** * This property can be used to read data from any data source property, * including deeply nested objects / properties. `data` can be given in a * number of different ways which effect its behaviour: * * * `integer` - treated as an array index for the data source. This is the * default that DataTables uses (incrementally increased for each column). * * `string` - read an object property from the data source. There are * three 'special' options that can be used in the string to alter how * DataTables reads the data from the source object: * * `.` - Dotted Javascript notation. Just as you use a `.` in * Javascript to read from nested objects, so to can the options * specified in `data`. For example: `browser.version` or * `browser.name`. If your object parameter name contains a period, use * `\\` to escape it - i.e. `first\\.name`. * * `[]` - Array notation. DataTables can automatically combine data * from and array source, joining the data with the characters provided * between the two brackets. For example: `name[, ]` would provide a * comma-space separated list from the source array. If no characters * are provided between the brackets, the original array source is * returned. * * `()` - Function notation. Adding `()` to the end of a parameter will * execute a function of the name given. For example: `browser()` for a * simple function on the data source, `browser.version()` for a * function in a nested property or even `browser().version` to get an * object property if the function called returns an object. Note that * function notation is recommended for use in `render` rather than * `data` as it is much simpler to use as a renderer. * * `null` - use the original data source for the row rather than plucking * data directly from it. This action has effects on two other * initialisation options: * * `defaultContent` - When null is given as the `data` option and * `defaultContent` is specified for the column, the value defined by * `defaultContent` will be used for the cell. * * `render` - When null is used for the `data` option and the `render` * option is specified for the column, the whole data source for the * row is used for the renderer. * * `function` - the function given will be executed whenever DataTables * needs to set or get the data for a cell in the column. The function * takes three parameters: * * Parameters: * * `{array|object}` The data source for the row * * `{string}` The type call data requested - this will be 'set' when * setting data or 'filter', 'display', 'type', 'sort' or undefined * when gathering data. Note that when `undefined` is given for the * type DataTables expects to get the raw data for the object back< * * `{*}` Data to set when the second parameter is 'set'. * * Return: * * The return value from the function is not required when 'set' is * the type of call, but otherwise the return is what will be used * for the data requested. * * Note that `data` is a getter and setter option. If you just require * formatting of data for output, you will likely want to use `render` which * is simply a getter and thus simpler to use. * * Note that prior to DataTables 1.9.2 `data` was called `mDataProp`. The * name change reflects the flexibility of this property and is consistent * with the naming of mRender. If 'mDataProp' is given, then it will still * be used by DataTables, as it automatically maps the old name to the new * if required. * * @type string|int|function|null * @default null Use automatically calculated column index * * @name DataTable.defaults.column.data * @dtopt Columns * * @example * // Read table data from objects * // JSON structure for each row: * // { * // "engine": {value}, * // "browser": {value}, * // "platform": {value}, * // "version": {value}, * // "grade": {value} * // } * $(document).ready( function() { * $('#example').dataTable( { * "ajaxSource": "sources/objects.txt", * "columns": [ * { "data": "engine" }, * { "data": "browser" }, * { "data": "platform" }, * { "data": "version" }, * { "data": "grade" } * ] * } ); * } ); * * @example * // Read information from deeply nested objects * // JSON structure for each row: * // { * // "engine": {value}, * // "browser": {value}, * // "platform": { * // "inner": {value} * // }, * // "details": [ * // {value}, {value} * // ] * // } * $(document).ready( function() { * $('#example').dataTable( { * "ajaxSource": "sources/deep.txt", * "columns": [ * { "data": "engine" }, * { "data": "browser" }, * { "data": "platform.inner" }, * { "data": "platform.details.0" }, * { "data": "platform.details.1" } * ] * } ); * } ); * * @example * // Using `data` as a function to provide different information for * // sorting, filtering and display. In this case, currency (price) * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": function ( source, type, val ) { * if (type === 'set') { * source.price = val; * // Store the computed dislay and filter values for efficiency * source.price_display = val=="" ? "" : "$"+numberFormat(val); * source.price_filter = val=="" ? "" : "$"+numberFormat(val)+" "+val; * return; * } * else if (type === 'display') { * return source.price_display; * } * else if (type === 'filter') { * return source.price_filter; * } * // 'sort', 'type' and undefined all just use the integer * return source.price; * } * } ] * } ); * } ); * * @example * // Using default content * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": null, * "defaultContent": "Click to edit" * } ] * } ); * } ); * * @example * // Using array notation - outputting a list from an array * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": "name[, ]" * } ] * } ); * } ); * */ "mData": null, /** * This property is the rendering partner to `data` and it is suggested that * when you want to manipulate data for display (including filtering, * sorting etc) without altering the underlying data for the table, use this * property. `render` can be considered to be the the read only companion to * `data` which is read / write (then as such more complex). Like `data` * this option can be given in a number of different ways to effect its * behaviour: * * * `integer` - treated as an array index for the data source. This is the * default that DataTables uses (incrementally increased for each column). * * `string` - read an object property from the data source. There are * three 'special' options that can be used in the string to alter how * DataTables reads the data from the source object: * * `.` - Dotted Javascript notation. Just as you use a `.` in * Javascript to read from nested objects, so to can the options * specified in `data`. For example: `browser.version` or * `browser.name`. If your object parameter name contains a period, use * `\\` to escape it - i.e. `first\\.name`. * * `[]` - Array notation. DataTables can automatically combine data * from and array source, joining the data with the characters provided * between the two brackets. For example: `name[, ]` would provide a * comma-space separated list from the source array. If no characters * are provided between the brackets, the original array source is * returned. * * `()` - Function notation. Adding `()` to the end of a parameter will * execute a function of the name given. For example: `browser()` for a * simple function on the data source, `browser.version()` for a * function in a nested property or even `browser().version` to get an * object property if the function called returns an object. * * `object` - use different data for the different data types requested by * DataTables ('filter', 'display', 'type' or 'sort'). The property names * of the object is the data type the property refers to and the value can * defined using an integer, string or function using the same rules as * `render` normally does. Note that an `_` option _must_ be specified. * This is the default value to use if you haven't specified a value for * the data type requested by DataTables. * * `function` - the function given will be executed whenever DataTables * needs to set or get the data for a cell in the column. The function * takes three parameters: * * Parameters: * * {array|object} The data source for the row (based on `data`) * * {string} The type call data requested - this will be 'filter', * 'display', 'type' or 'sort'. * * {array|object} The full data source for the row (not based on * `data`) * * Return: * * The return value from the function is what will be used for the * data requested. * * @type string|int|function|object|null * @default null Use the data source value. * * @name DataTable.defaults.column.render * @dtopt Columns * * @example * // Create a comma separated list from an array of objects * $(document).ready( function() { * $('#example').dataTable( { * "ajaxSource": "sources/deep.txt", * "columns": [ * { "data": "engine" }, * { "data": "browser" }, * { * "data": "platform", * "render": "[, ].name" * } * ] * } ); * } ); * * @example * // Execute a function to obtain data * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": null, // Use the full data source object for the renderer's source * "render": "browserName()" * } ] * } ); * } ); * * @example * // As an object, extracting different data for the different types * // This would be used with a data source such as: * // { "phone": 5552368, "phone_filter": "5552368 555-2368", "phone_display": "555-2368" } * // Here the `phone` integer is used for sorting and type detection, while `phone_filter` * // (which has both forms) is used for filtering for if a user inputs either format, while * // the formatted phone number is the one that is shown in the table. * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": null, // Use the full data source object for the renderer's source * "render": { * "_": "phone", * "filter": "phone_filter", * "display": "phone_display" * } * } ] * } ); * } ); * * @example * // Use as a function to create a link from the data source * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "data": "download_link", * "render": function ( data, type, full ) { * return 'Download'; * } * } ] * } ); * } ); */ "mRender": null, /** * Change the cell type created for the column - either TD cells or TH cells. This * can be useful as TH cells have semantic meaning in the table body, allowing them * to act as a header for a row (you may wish to add scope='row' to the TH elements). * @type string * @default td * * @name DataTable.defaults.column.cellType * @dtopt Columns * * @example * // Make the first column use TH cells * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ { * "targets": [ 0 ], * "cellType": "th" * } ] * } ); * } ); */ "sCellType": "td", /** * Class to give to each cell in this column. * @type string * @default Empty string * * @name DataTable.defaults.column.class * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "class": "my_class", "targets": [ 0 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "class": "my_class" }, * null, * null, * null, * null * ] * } ); * } ); */ "sClass": "", /** * When DataTables calculates the column widths to assign to each column, * it finds the longest string in each column and then constructs a * temporary table and reads the widths from that. The problem with this * is that "mmm" is much wider then "iiii", but the latter is a longer * string - thus the calculation can go wrong (doing it properly and putting * it into an DOM object and measuring that is horribly(!) slow). Thus as * a "work around" we provide this option. It will append its value to the * text that is found to be the longest string for the column - i.e. padding. * Generally you shouldn't need this! * @type string * @default Empty string * * @name DataTable.defaults.column.contentPadding * @dtopt Columns * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * null, * null, * null, * { * "contentPadding": "mmm" * } * ] * } ); * } ); */ "sContentPadding": "", /** * Allows a default value to be given for a column's data, and will be used * whenever a null data source is encountered (this can be because `data` * is set to null, or because the data source itself is null). * @type string * @default null * * @name DataTable.defaults.column.defaultContent * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { * "data": null, * "defaultContent": "Edit", * "targets": [ -1 ] * } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * null, * null, * null, * { * "data": null, * "defaultContent": "Edit" * } * ] * } ); * } ); */ "sDefaultContent": null, /** * This parameter is only used in DataTables' server-side processing. It can * be exceptionally useful to know what columns are being displayed on the * client side, and to map these to database fields. When defined, the names * also allow DataTables to reorder information from the server if it comes * back in an unexpected order (i.e. if you switch your columns around on the * client-side, your server-side code does not also need updating). * @type string * @default Empty string * * @name DataTable.defaults.column.name * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "name": "engine", "targets": [ 0 ] }, * { "name": "browser", "targets": [ 1 ] }, * { "name": "platform", "targets": [ 2 ] }, * { "name": "version", "targets": [ 3 ] }, * { "name": "grade", "targets": [ 4 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "name": "engine" }, * { "name": "browser" }, * { "name": "platform" }, * { "name": "version" }, * { "name": "grade" } * ] * } ); * } ); */ "sName": "", /** * Defines a data source type for the ordering which can be used to read * real-time information from the table (updating the internally cached * version) prior to ordering. This allows ordering to occur on user * editable elements such as form inputs. * @type string * @default std * * @name DataTable.defaults.column.orderDataType * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "orderDataType": "dom-text", "targets": [ 2, 3 ] }, * { "type": "numeric", "targets": [ 3 ] }, * { "orderDataType": "dom-select", "targets": [ 4 ] }, * { "orderDataType": "dom-checkbox", "targets": [ 5 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * null, * null, * { "orderDataType": "dom-text" }, * { "orderDataType": "dom-text", "type": "numeric" }, * { "orderDataType": "dom-select" }, * { "orderDataType": "dom-checkbox" } * ] * } ); * } ); */ "sSortDataType": "std", /** * The title of this column. * @type string * @default null Derived from the 'TH' value for this column in the * original HTML table. * * @name DataTable.defaults.column.title * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "title": "My column title", "targets": [ 0 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "title": "My column title" }, * null, * null, * null, * null * ] * } ); * } ); */ "sTitle": null, /** * The type allows you to specify how the data for this column will be * ordered. Four types (string, numeric, date and html (which will strip * HTML tags before ordering)) are currently available. Note that only date * formats understood by Javascript's Date() object will be accepted as type * date. For example: "Mar 26, 2008 5:03 PM". May take the values: 'string', * 'numeric', 'date' or 'html' (by default). Further types can be adding * through plug-ins. * @type string * @default null Auto-detected from raw data * * @name DataTable.defaults.column.type * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "type": "html", "targets": [ 0 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "type": "html" }, * null, * null, * null, * null * ] * } ); * } ); */ "sType": null, /** * Defining the width of the column, this parameter may take any CSS value * (3em, 20px etc). DataTables applies 'smart' widths to columns which have not * been given a specific width through this interface ensuring that the table * remains readable. * @type string * @default null Automatic * * @name DataTable.defaults.column.width * @dtopt Columns * * @example * // Using `columnDefs` * $(document).ready( function() { * $('#example').dataTable( { * "columnDefs": [ * { "width": "20%", "targets": [ 0 ] } * ] * } ); * } ); * * @example * // Using `columns` * $(document).ready( function() { * $('#example').dataTable( { * "columns": [ * { "width": "20%" }, * null, * null, * null, * null * ] * } ); * } ); */ "sWidth": null }; _fnHungarianMap( DataTable.defaults.column ); /** * DataTables settings object - this holds all the information needed for a * given table, including configuration, data and current application of the * table options. DataTables does not have a single instance for each DataTable * with the settings attached to that instance, but rather instances of the * DataTable "class" are created on-the-fly as needed (typically by a * $().dataTable() call) and the settings object is then applied to that * instance. * * Note that this object is related to {@link DataTable.defaults} but this * one is the internal data store for DataTables's cache of columns. It should * NOT be manipulated outside of DataTables. Any configuration should be done * through the initialisation options. * @namespace * @todo Really should attach the settings object to individual instances so we * don't need to create new instances on each $().dataTable() call (if the * table already exists). It would also save passing oSettings around and * into every single function. However, this is a very significant * architecture change for DataTables and will almost certainly break * backwards compatibility with older installations. This is something that * will be done in 2.0. */ DataTable.models.oSettings = { /** * Primary features of DataTables and their enablement state. * @namespace */ "oFeatures": { /** * Flag to say if DataTables should automatically try to calculate the * optimum table and columns widths (true) or not (false). * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bAutoWidth": null, /** * Delay the creation of TR and TD elements until they are actually * needed by a driven page draw. This can give a significant speed * increase for Ajax source and Javascript source data, but makes no * difference at all fro DOM and server-side processing tables. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bDeferRender": null, /** * Enable filtering on the table or not. Note that if this is disabled * then there is no filtering at all on the table, including fnFilter. * To just remove the filtering input use sDom and remove the 'f' option. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bFilter": null, /** * Table information element (the 'Showing x of y records' div) enable * flag. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bInfo": null, /** * Present a user control allowing the end user to change the page size * when pagination is enabled. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bLengthChange": null, /** * Pagination enabled or not. Note that if this is disabled then length * changing must also be disabled. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bPaginate": null, /** * Processing indicator enable flag whenever DataTables is enacting a * user request - typically an Ajax request for server-side processing. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bProcessing": null, /** * Server-side processing enabled flag - when enabled DataTables will * get all data from the server for every draw - there is no filtering, * sorting or paging done on the client-side. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bServerSide": null, /** * Sorting enablement flag. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bSort": null, /** * Multi-column sorting * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bSortMulti": null, /** * Apply a class to the columns which are being sorted to provide a * visual highlight or not. This can slow things down when enabled since * there is a lot of DOM interaction. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bSortClasses": null, /** * State saving enablement flag. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bStateSave": null }, /** * Scrolling settings for a table. * @namespace */ "oScroll": { /** * When the table is shorter in height than sScrollY, collapse the * table container down to the height of the table (when true). * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bCollapse": null, /** * Width of the scrollbar for the web-browser's platform. Calculated * during table initialisation. * @type int * @default 0 */ "iBarWidth": 0, /** * Viewport width for horizontal scrolling. Horizontal scrolling is * disabled if an empty string. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string */ "sX": null, /** * Width to expand the table to when using x-scrolling. Typically you * should not need to use this. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string * @deprecated */ "sXInner": null, /** * Viewport height for vertical scrolling. Vertical scrolling is disabled * if an empty string. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string */ "sY": null }, /** * Language information for the table. * @namespace * @extends DataTable.defaults.oLanguage */ "oLanguage": { /** * Information callback function. See * {@link DataTable.defaults.fnInfoCallback} * @type function * @default null */ "fnInfoCallback": null }, /** * Browser support parameters * @namespace */ "oBrowser": { /** * Indicate if the browser incorrectly calculates width:100% inside a * scrolling element (IE6/7) * @type boolean * @default false */ "bScrollOversize": false, /** * Determine if the vertical scrollbar is on the right or left of the * scrolling container - needed for rtl language layout, although not * all browsers move the scrollbar (Safari). * @type boolean * @default false */ "bScrollbarLeft": false }, "ajax": null, /** * Array referencing the nodes which are used for the features. The * parameters of this object match what is allowed by sDom - i.e. *
      *
    • 'l' - Length changing
      • *
    • 'f' - Filtering input
      • *
    • 't' - The table!
      • *
    • 'i' - Information
      • *
    • 'p' - Pagination
      • *
    • 'r' - pRocessing
      • *
    * @type array * @default [] */ "aanFeatures": [], /** * Store data information - see {@link DataTable.models.oRow} for detailed * information. * @type array * @default [] */ "aoData": [], /** * Array of indexes which are in the current display (after filtering etc) * @type array * @default [] */ "aiDisplay": [], /** * Array of indexes for display - no filtering * @type array * @default [] */ "aiDisplayMaster": [], /** * Store information about each column that is in use * @type array * @default [] */ "aoColumns": [], /** * Store information about the table's header * @type array * @default [] */ "aoHeader": [], /** * Store information about the table's footer * @type array * @default [] */ "aoFooter": [], /** * Store the applied global search information in case we want to force a * research or compare the old search to a new one. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @namespace * @extends DataTable.models.oSearch */ "oPreviousSearch": {}, /** * Store the applied search for each column - see * {@link DataTable.models.oSearch} for the format that is used for the * filtering information for each column. * @type array * @default [] */ "aoPreSearchCols": [], /** * Sorting that is applied to the table. Note that the inner arrays are * used in the following manner: *
      *
    • Index 0 - column number
      • *
    • Index 1 - current sorting direction
      • *
    * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type array * @todo These inner arrays should really be objects */ "aaSorting": null, /** * Sorting that is always applied to the table (i.e. prefixed in front of * aaSorting). * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type array * @default [] */ "aaSortingFixed": [], /** * Classes to use for the striping of a table. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type array * @default [] */ "asStripeClasses": null, /** * If restoring a table - we should restore its striping classes as well * @type array * @default [] */ "asDestroyStripes": [], /** * If restoring a table - we should restore its width * @type int * @default 0 */ "sDestroyWidth": 0, /** * Callback functions array for every time a row is inserted (i.e. on a draw). * @type array * @default [] */ "aoRowCallback": [], /** * Callback functions for the header on each draw. * @type array * @default [] */ "aoHeaderCallback": [], /** * Callback function for the footer on each draw. * @type array * @default [] */ "aoFooterCallback": [], /** * Array of callback functions for draw callback functions * @type array * @default [] */ "aoDrawCallback": [], /** * Array of callback functions for row created function * @type array * @default [] */ "aoRowCreatedCallback": [], /** * Callback functions for just before the table is redrawn. A return of * false will be used to cancel the draw. * @type array * @default [] */ "aoPreDrawCallback": [], /** * Callback functions for when the table has been initialised. * @type array * @default [] */ "aoInitComplete": [], /** * Callbacks for modifying the settings to be stored for state saving, prior to * saving state. * @type array * @default [] */ "aoStateSaveParams": [], /** * Callbacks for modifying the settings that have been stored for state saving * prior to using the stored values to restore the state. * @type array * @default [] */ "aoStateLoadParams": [], /** * Callbacks for operating on the settings object once the saved state has been * loaded * @type array * @default [] */ "aoStateLoaded": [], /** * Cache the table ID for quick access * @type string * @default Empty string */ "sTableId": "", /** * The TABLE node for the main table * @type node * @default null */ "nTable": null, /** * Permanent ref to the thead element * @type node * @default null */ "nTHead": null, /** * Permanent ref to the tfoot element - if it exists * @type node * @default null */ "nTFoot": null, /** * Permanent ref to the tbody element * @type node * @default null */ "nTBody": null, /** * Cache the wrapper node (contains all DataTables controlled elements) * @type node * @default null */ "nTableWrapper": null, /** * Indicate if when using server-side processing the loading of data * should be deferred until the second draw. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean * @default false */ "bDeferLoading": false, /** * Indicate if all required information has been read in * @type boolean * @default false */ "bInitialised": false, /** * Information about open rows. Each object in the array has the parameters * 'nTr' and 'nParent' * @type array * @default [] */ "aoOpenRows": [], /** * Dictate the positioning of DataTables' control elements - see * {@link DataTable.model.oInit.sDom}. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string * @default null */ "sDom": null, /** * Which type of pagination should be used. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string * @default two_button */ "sPaginationType": "two_button", /** * The state duration (for `stateSave`) in seconds. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type int * @default 0 */ "iStateDuration": 0, /** * Array of callback functions for state saving. Each array element is an * object with the following parameters: *
      *
    • function:fn - function to call. Takes two parameters, oSettings * and the JSON string to save that has been thus far created. Returns * a JSON string to be inserted into a json object * (i.e. '"param": [ 0, 1, 2]')
      • *
    • string:sName - name of callback
      • *
    * @type array * @default [] */ "aoStateSave": [], /** * Array of callback functions for state loading. Each array element is an * object with the following parameters: *
      *
    • function:fn - function to call. Takes two parameters, oSettings * and the object stored. May return false to cancel state loading
      • *
    • string:sName - name of callback
      • *
    * @type array * @default [] */ "aoStateLoad": [], /** * State that was loaded. Useful for back reference * @type object * @default null */ "oLoadedState": null, /** * Source url for AJAX data for the table. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string * @default null */ "sAjaxSource": null, /** * Property from a given object from which to read the table data from. This * can be an empty string (when not server-side processing), in which case * it is assumed an an array is given directly. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string */ "sAjaxDataProp": null, /** * Note if draw should be blocked while getting data * @type boolean * @default true */ "bAjaxDataGet": true, /** * The last jQuery XHR object that was used for server-side data gathering. * This can be used for working with the XHR information in one of the * callbacks * @type object * @default null */ "jqXHR": null, /** * JSON returned from the server in the last Ajax request * @type object * @default undefined */ "json": undefined, /** * Function to get the server-side data. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type function */ "fnServerData": null, /** * Functions which are called prior to sending an Ajax request so extra * parameters can easily be sent to the server * @type array * @default [] */ "aoServerParams": [], /** * Send the XHR HTTP method - GET or POST (could be PUT or DELETE if * required). * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type string */ "sServerMethod": null, /** * Format numbers for display. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type function */ "fnFormatNumber": null, /** * List of options that can be used for the user selectable length menu. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type array * @default [] */ "aLengthMenu": null, /** * Counter for the draws that the table does. Also used as a tracker for * server-side processing * @type int * @default 0 */ "iDraw": 0, /** * Indicate if a redraw is being done - useful for Ajax * @type boolean * @default false */ "bDrawing": false, /** * Draw index (iDraw) of the last error when parsing the returned data * @type int * @default -1 */ "iDrawError": -1, /** * Paging display length * @type int * @default 10 */ "_iDisplayLength": 10, /** * Paging start point - aiDisplay index * @type int * @default 0 */ "_iDisplayStart": 0, /** * Server-side processing - number of records in the result set * (i.e. before filtering), Use fnRecordsTotal rather than * this property to get the value of the number of records, regardless of * the server-side processing setting. * @type int * @default 0 * @private */ "_iRecordsTotal": 0, /** * Server-side processing - number of records in the current display set * (i.e. after filtering). Use fnRecordsDisplay rather than * this property to get the value of the number of records, regardless of * the server-side processing setting. * @type boolean * @default 0 * @private */ "_iRecordsDisplay": 0, /** * Flag to indicate if jQuery UI marking and classes should be used. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bJUI": null, /** * The classes to use for the table * @type object * @default {} */ "oClasses": {}, /** * Flag attached to the settings object so you can check in the draw * callback if filtering has been done in the draw. Deprecated in favour of * events. * @type boolean * @default false * @deprecated */ "bFiltered": false, /** * Flag attached to the settings object so you can check in the draw * callback if sorting has been done in the draw. Deprecated in favour of * events. * @type boolean * @default false * @deprecated */ "bSorted": false, /** * Indicate that if multiple rows are in the header and there is more than * one unique cell per column, if the top one (true) or bottom one (false) * should be used for sorting / title by DataTables. * Note that this parameter will be set by the initialisation routine. To * set a default use {@link DataTable.defaults}. * @type boolean */ "bSortCellsTop": null, /** * Initialisation object that is used for the table * @type object * @default null */ "oInit": null, /** * Destroy callback functions - for plug-ins to attach themselves to the * destroy so they can clean up markup and events. * @type array * @default [] */ "aoDestroyCallback": [], /** * Get the number of records in the current record set, before filtering * @type function */ "fnRecordsTotal": function () { return _fnDataSource( this ) == 'ssp' ? this._iRecordsTotal * 1 : this.aiDisplayMaster.length; }, /** * Get the number of records in the current record set, after filtering * @type function */ "fnRecordsDisplay": function () { return _fnDataSource( this ) == 'ssp' ? this._iRecordsDisplay * 1 : this.aiDisplay.length; }, /** * Get the display end point - aiDisplay index * @type function */ "fnDisplayEnd": function () { var len = this._iDisplayLength, start = this._iDisplayStart, calc = start + len, records = this.aiDisplay.length, features = this.oFeatures, paginate = features.bPaginate; if ( features.bServerSide ) { return paginate === false || len === -1 ? start + records : Math.min( start+len, this._iRecordsDisplay ); } else { return ! paginate || calc>records || len===-1 ? records : calc; } }, /** * The DataTables object for this table * @type object * @default null */ "oInstance": null, /** * Unique identifier for each instance of the DataTables object. If there * is an ID on the table node, then it takes that value, otherwise an * incrementing internal counter is used. * @type string * @default null */ "sInstance": null, /** * tabindex attribute value that is added to DataTables control elements, allowing * keyboard navigation of the table and its controls. */ "iTabIndex": 0, /** * DIV container for the footer scrolling table if scrolling */ "nScrollHead": null, /** * DIV container for the footer scrolling table if scrolling */ "nScrollFoot": null, /** * Last applied sort * @type array * @default [] */ "aLastSort": [], /** * Stored plug-in instances * @type object * @default {} */ "oPlugins": {} }; /** * Extension object for DataTables that is used to provide all extension * options. * * Note that the `DataTable.ext` object is available through * `jQuery.fn.dataTable.ext` where it may be accessed and manipulated. It is * also aliased to `jQuery.fn.dataTableExt` for historic reasons. * @namespace * @extends DataTable.models.ext */ /** * DataTables extensions * * This namespace acts as a collection area for plug-ins that can be used to * extend DataTables capabilities. Indeed many of the build in methods * use this method to provide their own capabilities (sorting methods for * example). * * Note that this namespace is aliased to `jQuery.fn.dataTableExt` for legacy * reasons * * @namespace */ DataTable.ext = _ext = { /** * Element class names * * @type object * @default {} */ classes: {}, /** * Error reporting. * * How should DataTables report an error. Can take the value 'alert' or * 'throw' * * @type string * @default alert */ errMode: "alert", /** * Feature plug-ins. * * This is an array of objects which describe the feature plug-ins that are * available to DataTables. These feature plug-ins are then available for * use through the `dom` initialisation option. * * Each feature plug-in is described by an object which must have the * following properties: * * * `fnInit` - function that is used to initialise the plug-in, * * `cFeature` - a character so the feature can be enabled by the `dom` * instillation option. This is case sensitive. * * The `fnInit` function has the following input parameters: * * 1. `{object}` DataTables settings object: see * {@link DataTable.models.oSettings} * * And the following return is expected: * * * {node|null} The element which contains your feature. Note that the * return may also be void if your plug-in does not require to inject any * DOM elements into DataTables control (`dom`) - for example this might * be useful when developing a plug-in which allows table control via * keyboard entry * * @type array * * @example * $.fn.dataTable.ext.features.push( { * "fnInit": function( oSettings ) { * return new TableTools( { "oDTSettings": oSettings } ); * }, * "cFeature": "T" * } ); */ feature: [], /** * Row searching. * * This method of searching is complimentary to the default type based * searching, and a lot more comprehensive as it allows you complete control * over the searching logic. Each element in this array is a function * (parameters described below) that is called for every row in the table, * and your logic decides if it should be included in the searching data set * or not. * * Searching functions have the following input parameters: * * 1. `{object}` DataTables settings object: see * {@link DataTable.models.oSettings} * 2. `{array|object}` Data for the row to be processed (same as the * original format that was passed in as the data source, or an array * from a DOM data source * 3. `{int}` Row index ({@link DataTable.models.oSettings.aoData}), which * can be useful to retrieve the `TR` element if you need DOM interaction. * * And the following return is expected: * * * {boolean} Include the row in the searched result set (true) or not * (false) * * Note that as with the main search ability in DataTables, technically this * is "filtering", since it is subtractive. However, for consistency in * naming we call it searching here. * * @type array * @default [] * * @example * // The following example shows custom search being applied to the * // fourth column (i.e. the data[3] index) based on two input values * // from the end-user, matching the data in a certain range. * $.fn.dataTable.ext.search.push( * function( settings, data, dataIndex ) { * var min = document.getElementById('min').value * 1; * var max = document.getElementById('max').value * 1; * var version = data[3] == "-" ? 0 : data[3]*1; * * if ( min == "" && max == "" ) { * return true; * } * else if ( min == "" && version < max ) { * return true; * } * else if ( min < version && "" == max ) { * return true; * } * else if ( min < version && version < max ) { * return true; * } * return false; * } * ); */ search: [], /** * Internal functions, exposed for used in plug-ins. * * Please note that you should not need to use the internal methods for * anything other than a plug-in (and even then, try to avoid if possible). * The internal function may change between releases. * * @type object * @default {} */ internal: {}, /** * Legacy configuration options. Enable and disable legacy options that * are available in DataTables. * * @type object */ legacy: { /** * Enable / disable DataTables 1.9 compatible server-side processing * requests * * @type boolean * @default false */ ajax: false }, /** * Pagination plug-in methods. * * Each entry in this object is a function and defines which buttons should * be shown by the pagination rendering method that is used for the table: * {@link DataTable.ext.renderer.pageButton}. The renderer addresses how the * buttons are displayed in the document, while the functions here tell it * what buttons to display. This is done by returning an array of button * descriptions (what each button will do). * * Pagination types (the four built in options and any additional plug-in * options defined here) can be used through the `paginationType` * initialisation parameter. * * The functions defined take two parameters: * * 1. `{int} page` The current page index * 2. `{int} pages` The number of pages in the table * * Each function is expected to return an array where each element of the * array can be one of: * * * `first` - Jump to first page when activated * * `last` - Jump to last page when activated * * `previous` - Show previous page when activated * * `next` - Show next page when activated * * `{int}` - Show page of the index given * * `{array}` - A nested array containing the above elements to add a * containing 'DIV' element (might be useful for styling). * * Note that DataTables v1.9- used this object slightly differently whereby * an object with two functions would be defined for each plug-in. That * ability is still supported by DataTables 1.10+ to provide backwards * compatibility, but this option of use is now decremented and no longer * documented in DataTables 1.10+. * * @type object * @default {} * * @example * // Show previous, next and current page buttons only * $.fn.dataTableExt.oPagination.current = function ( page, pages ) { * return [ 'previous', page, 'next' ]; * }; */ pager: {}, renderer: { pageButton: {}, header: {} }, /** * Ordering plug-ins - custom data source * * The extension options for ordering of data available here is complimentary * to the default type based ordering that DataTables typically uses. It * allows much greater control over the the data that is being used to * order a column, but is necessarily therefore more complex. * * This type of ordering is useful if you want to do ordering based on data * live from the DOM (for example the contents of an 'input' element) rather * than just the static string that DataTables knows of. * * The way these plug-ins work is that you create an array of the values you * wish to be ordering for the column in question and then return that * array. The data in the array much be in the index order of the rows in * the table (not the currently ordering order!). Which order data gathering * function is run here depends on the `dt-init columns.orderDataType` * parameter that is used for the column (if any). * * The functions defined take two parameters: * * 1. `{object}` DataTables settings object: see * {@link DataTable.models.oSettings} * 2. `{int}` Target column index * * Each function is expected to return an array: * * * `{array}` Data for the column to be ordering upon * * @type array * * @example * // Ordering using `input` node values * $.fn.dataTable.ext.order['dom-text'] = function ( settings, col ) * { * return this.api().column( col, {order:'index'} ).nodes().map( function ( td, i ) { * return $('input', td).val(); * } ); * } */ order: {}, /** * Type based plug-ins. * * Each column in DataTables has a type assigned to it, either by automatic * detection or by direct assignment using the `type` option for the column. * The type of a column will effect how it is ordering and search (plug-ins * can also make use of the column type if required). * * @namespace */ type: { /** * Type detection functions. * * The functions defined in this object are used to automatically detect * a column's type, making initialisation of DataTables super easy, even * when complex data is in the table. * * The functions defined take a single parameter: * * 1. `{*}` Data from the column cell to be analysed * * Each function is expected to return: * * * `{string|null}` Data type detected, or null if unknown (and thus * pass it on to the other type detection functions. * * @type array * * @example * // Currency type detection plug-in: * $.fn.dataTable.ext.type.detect.push( * function ( data ) { * // Check the numeric part * if ( ! $.isNumeric( data.substring(1) ) ) { * return null; * } * * // Check prefixed by currency * if ( data.charAt(0) == '$' || data.charAt(0) == '£' ) { * return 'currency'; * } * return null; * } * ); */ detect: [], /** * Type based search formatting. * * The type based searching functions can be used to pre-format the * data to be search on. For example, it can be used to strip HTML * tags or to de-format telephone numbers for numeric only searching. * * Note that is a search is not defined for a column of a given type, * no search formatting will be performed. * * Pre-processing of searching data plug-ins - When you assign the sType * for a column (or have it automatically detected for you by DataTables * or a type detection plug-in), you will typically be using this for * custom sorting, but it can also be used to provide custom searching * by allowing you to pre-processing the data and returning the data in * the format that should be searched upon. This is done by adding * functions this object with a parameter name which matches the sType * for that target column. This is the corollary of afnSortData * for searching data. * * The functions defined take a single parameter: * * 1. `{*}` Data from the column cell to be prepared for searching * * Each function is expected to return: * * * `{string|null}` Formatted string that will be used for the searching. * * @type object * @default {} * * @example * $.fn.dataTable.ext.type.search['title-numeric'] = function ( d ) { * return d.replace(/\n/g," ").replace( /<.*?>/g, "" ); * } */ search: {}, /** * Type based ordering. * * The column type tells DataTables what ordering to apply to the table * when a column is sorted upon. The order for each type that is defined, * is defined by the functions available in this object. * * Each ordering option can be described by three properties added to * this object: * * * `{type}-pre` - Pre-formatting function * * `{type}-asc` - Ascending order function * * `{type}-desc` - Descending order function * * All three can be used together, only `{type}-pre` or only * `{type}-asc` and `{type}-desc` together. It is generally recommended * that only `{type}-pre` is used, as this provides the optimal * implementation in terms of speed, although the others are provided * for compatibility with existing Javascript sort functions. * * `{type}-pre`: Functions defined take a single parameter: * * 1. `{*}` Data from the column cell to be prepared for ordering * * And return: * * * `{*}` Data to be sorted upon * * `{type}-asc` and `{type}-desc`: Functions are typical Javascript sort * functions, taking two parameters: * * 1. `{*}` Data to compare to the second parameter * 2. `{*}` Data to compare to the first parameter * * And returning: * * * `{*}` Ordering match: <0 if first parameter should be sorted lower * than the second parameter, ===0 if the two parameters are equal and * >0 if the first parameter should be sorted height than the second * parameter. * * @type object * @default {} * * @example * // Numeric ordering of formatted numbers with a pre-formatter * $.extend( $.fn.dataTable.ext.type.order, { * "string-pre": function(x) { * a = (a === "-" || a === "") ? 0 : a.replace( /[^\d\-\.]/g, "" ); * return parseFloat( a ); * } * } ); * * @example * // Case-sensitive string ordering, with no pre-formatting method * $.extend( $.fn.dataTable.ext.order, { * "string-case-asc": function(x,y) { * return ((x < y) ? -1 : ((x > y) ? 1 : 0)); * }, * "string-case-desc": function(x,y) { * return ((x < y) ? 1 : ((x > y) ? -1 : 0)); * } * } ); */ order: {} }, /** * Unique DataTables instance counter * * @type int * @private */ _unique: 0, // // Depreciated // The following properties are retained for backwards compatiblity only. // The should not be used in new projects and will be removed in a future // version // /** * Version check function. * @type function * @depreciated Since 1.10 */ fnVersionCheck: DataTable.fnVersionCheck, /** * Index for what 'this' index API functions should use * @type int * @deprecated Since v1.10 */ iApiIndex: 0, /** * jQuery UI class container * @type object * @deprecated Since v1.10 */ oJUIClasses: {}, /** * Software version * @type string * @deprecated Since v1.10 */ sVersion: DataTable.version }; // // Backwards compatibility. Alias to pre 1.10 Hungarian notation counter parts // $.extend( _ext, { afnFiltering: _ext.filter, aTypes: _ext.type.detect, ofnSearch: _ext.type.search, oSort: _ext.type.order, afnSortData: _ext.order, aoFeatures: _ext.feature, oApi: _ext.internal, oStdClasses: _ext.classes, oPagination: _ext.pager } ); $.extend( DataTable.ext.classes, { "sTable": "dataTable", "sNoFooter": "no-footer", /* Paging buttons */ "sPageButton": "paginate_button", "sPageButtonActive": "current", "sPageButtonDisabled": "disabled", /* Striping classes */ "sStripeOdd": "odd", "sStripeEven": "even", /* Empty row */ "sRowEmpty": "dataTables_empty", /* Features */ "sWrapper": "dataTables_wrapper", "sFilter": "dataTables_filter", "sInfo": "dataTables_info", "sPaging": "dataTables_paginate paging_", /* Note that the type is postfixed */ "sLength": "dataTables_length", "sProcessing": "dataTables_processing", /* Sorting */ "sSortAsc": "sorting_asc", "sSortDesc": "sorting_desc", "sSortable": "sorting", /* Sortable in both directions */ "sSortableAsc": "sorting_asc_disabled", "sSortableDesc": "sorting_desc_disabled", "sSortableNone": "sorting_disabled", "sSortColumn": "sorting_", /* Note that an int is postfixed for the sorting order */ /* Filtering */ "sFilterInput": "", /* Page length */ "sLengthSelect": "", /* Scrolling */ "sScrollWrapper": "dataTables_scroll", "sScrollHead": "dataTables_scrollHead", "sScrollHeadInner": "dataTables_scrollHeadInner", "sScrollBody": "dataTables_scrollBody", "sScrollFoot": "dataTables_scrollFoot", "sScrollFootInner": "dataTables_scrollFootInner", /* Misc */ "sHeaderTH": "", "sFooterTH": "", // Deprecated "sSortJUIAsc": "", "sSortJUIDesc": "", "sSortJUI": "", "sSortJUIAscAllowed": "", "sSortJUIDescAllowed": "", "sSortJUIWrapper": "", "sSortIcon": "", "sJUIHeader": "", "sJUIFooter": "" } ); (function() { // Reused strings for better compression. Closure compiler appears to have a // weird edge case where it is trying to expand strings rather than use the // variable version. This results in about 200 bytes being added, for very // little preference benefit since it this run on script load only. var _empty = ''; _empty = ''; var _stateDefault = _empty + 'ui-state-default'; var _sortIcon = _empty + 'css_right ui-icon ui-icon-'; var _headerFooter = _empty + 'fg-toolbar ui-toolbar ui-widget-header ui-helper-clearfix'; $.extend( DataTable.ext.oJUIClasses, DataTable.ext.classes, { /* Full numbers paging buttons */ "sPageButton": "fg-button ui-button "+_stateDefault, "sPageButtonActive": "ui-state-disabled", "sPageButtonDisabled": "ui-state-disabled", /* Features */ "sPaging": "dataTables_paginate fg-buttonset ui-buttonset fg-buttonset-multi "+ "ui-buttonset-multi paging_", /* Note that the type is postfixed */ /* Sorting */ "sSortAsc": _stateDefault+" sorting_asc", "sSortDesc": _stateDefault+" sorting_desc", "sSortable": _stateDefault+" sorting", "sSortableAsc": _stateDefault+" sorting_asc_disabled", "sSortableDesc": _stateDefault+" sorting_desc_disabled", "sSortableNone": _stateDefault+" sorting_disabled", "sSortJUIAsc": _sortIcon+"triangle-1-n", "sSortJUIDesc": _sortIcon+"triangle-1-s", "sSortJUI": _sortIcon+"carat-2-n-s", "sSortJUIAscAllowed": _sortIcon+"carat-1-n", "sSortJUIDescAllowed": _sortIcon+"carat-1-s", "sSortJUIWrapper": "DataTables_sort_wrapper", "sSortIcon": "DataTables_sort_icon", /* Scrolling */ "sScrollHead": "dataTables_scrollHead "+_stateDefault, "sScrollFoot": "dataTables_scrollFoot "+_stateDefault, /* Misc */ "sHeaderTH": _stateDefault, "sFooterTH": _stateDefault, "sJUIHeader": _headerFooter+" ui-corner-tl ui-corner-tr", "sJUIFooter": _headerFooter+" ui-corner-bl ui-corner-br" } ); }()); var extPagination = DataTable.ext.pager; function _numbers ( page, pages ) { var numbers = [], buttons = extPagination.numbers_length, half = Math.floor( buttons / 2 ), i = 1; if ( pages <= buttons ) { numbers = _range( 0, pages ); } else if ( page <= half ) { numbers = _range( 0, buttons-2 ); numbers.push( 'ellipsis' ); numbers.push( pages-1 ); } else if ( page >= pages - 1 - half ) { numbers = _range( pages-(buttons-2), pages ); numbers.splice( 0, 0, 'ellipsis' ); // no unshift in ie6 numbers.splice( 0, 0, 0 ); } else { numbers = _range( page-1, page+2 ); numbers.push( 'ellipsis' ); numbers.push( pages-1 ); numbers.splice( 0, 0, 'ellipsis' ); numbers.splice( 0, 0, 0 ); } numbers.DT_el = 'span'; return numbers; } $.extend( extPagination, { simple: function ( page, pages ) { return [ 'previous', 'next' ]; }, full: function ( page, pages ) { return [ 'first', 'previous', 'next', 'last' ]; }, simple_numbers: function ( page, pages ) { return [ 'previous', _numbers(page, pages), 'next' ]; }, full_numbers: function ( page, pages ) { return [ 'first', 'previous', _numbers(page, pages), 'next', 'last' ]; }, // For testing and plug-ins to use _numbers: _numbers, numbers_length: 7 } ); $.extend( true, DataTable.ext.renderer, { pageButton: { _: function ( settings, host, idx, buttons, page, pages ) { var classes = settings.oClasses; var lang = settings.oLanguage.oPaginate; var btnDisplay, btnClass; var attach = function( container, buttons ) { var i, ien, node, button; var clickHandler = function ( e ) { _fnPageChange( settings, e.data.action, true ); }; for ( i=0, ien=buttons.length ; i' ) .appendTo( container ); attach( inner, button ); } else { btnDisplay = ''; btnClass = ''; switch ( button ) { case 'ellipsis': container.append('…'); break; case 'first': btnDisplay = lang.sFirst; btnClass = button + (page > 0 ? '' : ' '+classes.sPageButtonDisabled); break; case 'previous': btnDisplay = lang.sPrevious; btnClass = button + (page > 0 ? '' : ' '+classes.sPageButtonDisabled); break; case 'next': btnDisplay = lang.sNext; btnClass = button + (page < pages-1 ? '' : ' '+classes.sPageButtonDisabled); break; case 'last': btnDisplay = lang.sLast; btnClass = button + (page < pages-1 ? '' : ' '+classes.sPageButtonDisabled); break; default: btnDisplay = button + 1; btnClass = page === button ? classes.sPageButtonActive : ''; break; } if ( btnDisplay ) { node = $('', { 'class': classes.sPageButton+' '+btnClass, 'aria-controls': settings.sTableId, 'tabindex': settings.iTabIndex, 'id': idx === 0 && typeof button === 'string' ? settings.sTableId +'_'+ button : null } ) .html( btnDisplay ) .appendTo( container ); _fnBindAction( node, {action: button}, clickHandler ); } } } }; attach( $(host).empty(), buttons ); } } } ); var __numericReplace = function ( d, re1, re2 ) { if ( !d || d === '-' ) { return -Infinity; } if ( d.replace ) { if ( re1 ) { d = d.replace( re1, '' ); } if ( re2 ) { d = d.replace( re2, '' ); } } return d * 1; }; $.extend( DataTable.ext.type.order, { // Dates "date-pre": function ( d ) { return Date.parse( d ) || 0; }, // Plain numbers "numeric-pre": function ( d ) { return __numericReplace( d ); }, // Formatted numbers "numeric-fmt-pre": function ( d ) { return __numericReplace( d, _re_formatted_numeric ); }, // HTML numeric "html-numeric-pre": function ( d ) { return __numericReplace( d, _re_html ); }, // HTML numeric, formatted "html-numeric-fmt-pre": function ( d ) { return __numericReplace( d, _re_html, _re_formatted_numeric ); }, // html "html-pre": function ( a ) { return a.replace ? a.replace( /<.*?>/g, "" ).toLowerCase() : a+''; }, // string "string-pre": function ( a ) { return typeof a === 'string' ? a.toLowerCase() : ! a || ! a.toString ? '' : a.toString(); }, // string-asc and -desc are retained only for compatibility with the old // sort methods "string-asc": function ( x, y ) { return ((x < y) ? -1 : ((x > y) ? 1 : 0)); }, "string-desc": function ( x, y ) { return ((x < y) ? 1 : ((x > y) ? -1 : 0)); } } ); // Built in type detection. See model.ext.aTypes for information about // what is required from this methods. $.extend( DataTable.ext.type.detect, [ // Plain numbers - first since V8 detects some plain numbers as dates // e.g. Date.parse('55') (but not all, e.g. Date.parse('22')...). function ( d ) { return _isNumber( d ) ? 'numeric' : null; }, // Dates (only those recognised by the browser's Date.parse) function ( d ) { // V8 will remove any unknown characters at the start of the expression, // leading to false matches such as `$245.12` being a valid date. See // forum thread 18941 for detail. if ( d && ! _re_date_start.test(d) ) { return null; } var parsed = Date.parse(d); return (parsed !== null && !isNaN(parsed)) || _empty(d) ? 'date' : null; }, // Formatted numbers function ( d ) { return _isNumber( d, true ) ? 'numeric-fmt' : null; }, // HTML numeric function ( d ) { return _htmlNumeric( d ) ? 'html-numeric' : null; }, // HTML numeric, formatted function ( d ) { return _htmlNumeric( d, true ) ? 'html-numeric-fmt' : null; }, // HTML (this is strict checking - there much be html) function ( d ) { return _empty( d ) || (typeof d === 'string' && d.indexOf('<') !== -1) ? 'html' : null; } ] ); // Filter formatting functions. See model.ext.ofnSearch for information about // what is required from these methods. $.extend( DataTable.ext.type.search, { html: function ( data ) { return _empty(data) ? '' : typeof data === 'string' ? data .replace( _re_new_lines, " " ) .replace( _re_html, "" ) : ''; }, string: function ( data ) { return _empty(data) ? '' : typeof data === 'string' ? data.replace( _re_new_lines, " " ) : data; } } ); $.extend( true, DataTable.ext.renderer, { header: { _: function ( settings, cell, column, idx, classes ) { // No additional mark-up required // Attach a sort listener to update on sort $(settings.nTable).on( 'order.dt', function ( e, settings, sorting, columns ) { cell .removeClass( column.sSortingClass +' '+ classes.sSortAsc +' '+ classes.sSortDesc ) .addClass( columns[ idx ] == 'asc' ? classes.sSortAsc : columns[ idx ] == 'desc' ? classes.sSortDesc : column.sSortingClass ); } ); }, jqueryui: function ( settings, cell, column, idx, classes ) { $('
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This may be useful for binding events or * performing calculations when the table is altered at all. * @name DataTable#draw.dt * @event * @param {event} e jQuery event object * @param {object} o DataTables settings object {@link DataTable.models.oSettings} */ /** * Search event, fired when the searching applied to the table (using the * built-in global search, or column filters) is altered. * @name DataTable#search.dt * @event * @param {event} e jQuery event object * @param {object} o DataTables settings object {@link DataTable.models.oSettings} */ /** * Page change event, fired when the paging of the table is altered. * @name DataTable#page.dt * @event * @param {event} e jQuery event object * @param {object} o DataTables settings object {@link DataTable.models.oSettings} */ /** * Order event, fired when the ordering applied to the table is altered. * @name DataTable#order.dt * @event * @param {event} e jQuery event object * @param {object} o DataTables settings object {@link DataTable.models.oSettings} */ /** * DataTables initialisation complete event, fired when the table is fully * drawn, including Ajax data loaded, if Ajax data is required. * @name DataTable#init.dt * @event * @param {event} e jQuery event object * @param {object} oSettings DataTables settings object * @param {object} json The JSON object request from the server - only * present if client-side Ajax sourced data is used */ /** * State save event, fired when the table has changed state a new state save * is required. This event allows modification of the state saving object * prior to actually doing the save, including addition or other state * properties (for plug-ins) or modification of a DataTables core property. * @name DataTable#stateSaveParams.dt * @event * @param {event} e jQuery event object * @param {object} oSettings DataTables settings object * @param {object} json The state information to be saved */ /** * State load event, fired when the table is loading state from the stored * data, but prior to the settings object being modified by the saved state * - allowing modification of the saved state is required or loading of * state for a plug-in. * @name DataTable#stateLoadParams.dt * @event * @param {event} e jQuery event object * @param {object} oSettings DataTables settings object * @param {object} json The saved state information */ /** * State loaded event, fired when state has been loaded from stored data and * the settings object has been modified by the loaded data. * @name DataTable#stateLoaded.dt * @event * @param {event} e jQuery event object * @param {object} oSettings DataTables settings object * @param {object} json The saved state information */ /** * Processing event, fired when DataTables is doing some kind of processing * (be it, order, searcg or anything else). It can be used to indicate to * the end user that there is something happening, or that something has * finished. * @name DataTable#processing.dt * @event * @param {event} e jQuery event object * @param {object} oSettings DataTables settings object * @param {boolean} bShow Flag for if DataTables is doing processing or not */ /** * Ajax (XHR) event, fired whenever an Ajax request is completed from a * request to made to the server for new data. This event is called before * DataTables processed the returned data, so it can also be used to pre- * process the data returned from the server, if needed. * * Note that this trigger is called in `fnServerData`, if you override * `fnServerData` and which to use this event, you need to trigger it in you * success function. * @name DataTable#xhr.dt * @event * @param {event} e jQuery event object * @param {object} o DataTables settings object {@link DataTable.models.oSettings} * @param {object} json JSON returned from the server * * @example * // Use a custom property returned from the server in another DOM element * $('#table').dataTable().on('xhr.dt', function (e, settings, json) { * $('#status').html( json.status ); * } ); * * @example * // Pre-process the data returned from the server * $('#table').dataTable().on('xhr.dt', function (e, settings, json) { * for ( var i=0, ien=json.aaData.length ; i\n\n\n\n\n\n',DEFAULT_PREVIEW:'
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