[MO] Updating MO to detect TF 2.X OD API models (#6983)
* updated FasterRCNN and SSD analysis patterns * updated tf od api conditions * updated ssd patterns * added more ssd topologies * move preprocessor to tf od api condition * update TF OD API conditions * refactoring * specify data type
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Yegor Kruglov
GitHub
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@ -16,62 +16,73 @@ class TensorFlowObjectDetectionAPIAnalysis(AnalyzeAction):
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"""
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"""
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graph_condition = [lambda graph: graph.graph['fw'] == 'tf']
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graph_condition = [lambda graph: graph.graph['fw'] == 'tf']
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model_scopes = [('MaskRCNN', ['Preprocessor',
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'FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStageBoxPredictor_1',
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'SecondStageFeatureExtractor_1',
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]),
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('RFCN', ['Preprocessor',
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'FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStageBoxPredictor/map',
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'SecondStageBoxPredictor/map_1',
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'SecondStagePostprocessor',
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]),
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('FasterRCNN', ['Preprocessor',
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'FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStagePostprocessor',
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]),
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('SSD', ['Preprocessor',
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'FeatureExtractor',
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'Postprocessor',
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]),
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]
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file_patterns = {'MaskRCNN': 'mask_rcnn_support.*\\.json',
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file_patterns = {'MaskRCNN': 'mask_rcnn_support.*\\.json',
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'RFCN': 'rfcn_support.*\\.json',
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'RFCN': 'rfcn_support.*\\.json',
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'FasterRCNN': 'faster_rcnn_support.*\\.json',
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'FasterRCNN': 'faster_rcnn_support.*\\.json',
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'SSD': 'ssd.*_support.*\\.json',
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'SSD': 'ssd.*_support.*\\.json',
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}
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}
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model_scopes = {'MaskRCNN': (['FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStageBoxPredictor_1',
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'SecondStageFeatureExtractor_1',
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],),
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'RFCN': (['FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStageBoxPredictor/map',
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'SecondStageBoxPredictor/map_1',
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'SecondStagePostprocessor',
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],),
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'FasterRCNN': (['FirstStageFeatureExtractor',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'SecondStagePostprocessor',
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],
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['FirstStageRPNFeatures',
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'FirstStageBoxPredictor',
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'SecondStagePostprocessor',
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'mask_rcnn_keras_box_predictor',
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],),
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'SSD': ([('FeatureExtractor', 'ssd_mobile_net_v2keras_feature_extractor',
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'ssd_mobile_net_v1fpn_keras_feature_extractor',
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'ssd_mobile_net_v2fpn_keras_feature_extractor', 'ResNet50V1_FPN', 'ResNet101V1_FPN',
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'ResNet152V1_FPN'
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),
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'Postprocessor']
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),
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}
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def analyze(self, graph: Graph):
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def analyze(self, graph: Graph):
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if any([name not in graph.nodes() for name in ['image_tensor', 'detection_classes', 'detection_boxes',
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tf_1_names = ['image_tensor', 'detection_classes', 'detection_boxes', 'detection_scores',
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'detection_scores']]):
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('Preprocessor', 'map')]
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tf_1_cond = all([graph_contains_scope(graph, scope) for scope in tf_1_names])
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tf_2_names = ['input_tensor', 'output_control_node', 'Identity_', ('Preprocessor', 'map')]
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tf_2_cond = all([graph_contains_scope(graph, scope) for scope in tf_2_names])
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if not tf_1_cond and not tf_2_cond:
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log.debug('The model does not contain nodes that must exist in the TF OD API models')
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log.debug('The model does not contain nodes that must exist in the TF OD API models')
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return None, None
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return None, None
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for flavor, scopes in __class__.model_scopes:
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for flavor, scopes_tuple in self.model_scopes.items():
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if all([graph_contains_scope(graph, scope) for scope in scopes]):
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for scopes in scopes_tuple:
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result = dict()
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if all([graph_contains_scope(graph, scope) for scope in scopes]):
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result['flavor'] = flavor
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result = dict()
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result['mandatory_parameters'] = {'tensorflow_use_custom_operations_config':
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result['flavor'] = flavor
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files_by_pattern(get_mo_root_dir() + '/extensions/front/tf',
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result['mandatory_parameters'] = {'tensorflow_use_custom_operations_config':
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__class__.file_patterns[flavor],
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files_by_pattern(get_mo_root_dir() + '/extensions/front/tf',
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add_prefix=True),
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__class__.file_patterns[flavor],
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'tensorflow_object_detection_api_pipeline_config': None,
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add_prefix=True),
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}
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'tensorflow_object_detection_api_pipeline_config': None,
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message = "Your model looks like TensorFlow Object Detection API Model.\n" \
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}
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"Check if all parameters are specified:\n" \
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message = "Your model looks like TensorFlow Object Detection API Model.\n" \
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"\t--tensorflow_use_custom_operations_config\n" \
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"Check if all parameters are specified:\n" \
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"\t--tensorflow_object_detection_api_pipeline_config\n" \
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"\t--transformations_config\n" \
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"\t--input_shape (optional)\n" \
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"\t--tensorflow_object_detection_api_pipeline_config\n" \
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"\t--reverse_input_channels (if you convert a model to use with the Inference Engine sample applications)\n" \
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"\t--input_shape (optional)\n" \
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"Detailed information about conversion of this model can be found at\n" \
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"\t--reverse_input_channels (if you convert a model to use with the Inference Engine sample applications)\n" \
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"https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html"
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"Detailed information about conversion of this model can be found at\n" \
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return {'model_type': {'TF_OD_API': result}}, message
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"https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html"
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return {'model_type': {'TF_OD_API': result}}, message
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return None, None
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return None, None
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@ -112,13 +112,14 @@ class AnalysisCollectorAnchor(AnalyzeAction):
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pass
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pass
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def graph_contains_scope(graph: Graph, scope: str):
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def graph_contains_scope(graph: Graph, scope: [str, tuple]):
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"""
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"""
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Checks whether the graph contains node(s) which name starts with "scope" string.
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Checks whether the graph contains node(s) which name includes "scope" string.
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:param graph: graph to check
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:param graph: graph to check
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:param scope: string defining the scope
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:param scope: string or tuple with strings defining the scope
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:return: the result of the check (True/False)
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:return: the result of the check (True/False)
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"""
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"""
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if scope[-1] != '/':
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if type(scope) is str:
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scope += '/'
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return any([node.soft_get('name').find(scope) != -1 for node in graph.get_op_nodes()])
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return any([node.soft_get('name').startswith(scope) for node in graph.get_op_nodes()])
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else:
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return any([graph_contains_scope(graph, s) for s in scope])
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