96 lines
4.4 KiB
Python
96 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import argparse
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import os
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import sys
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try:
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import tensorflow.compat.v1 as tf_v1
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# disable eager execution of TensorFlow 2 environment immediately
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tf_v1.disable_eager_execution()
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except ImportError:
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import tensorflow as tf_v1
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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unlikely_output_types = ['Const', 'Assign', 'NoOp', 'Parameter', 'Assert']
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def children(op_name: str, graph: tf_v1.Graph):
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op = graph.get_operation_by_name(op_name)
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return set(op for out in op.outputs for op in out.consumers())
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def summarize_graph(graph_def):
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placeholders = dict()
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outputs = list()
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graph = tf_v1.Graph()
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with graph.as_default(): # pylint: disable=not-context-manager
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tf_v1.import_graph_def(graph_def, name='')
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for node in graph.as_graph_def().node: # pylint: disable=no-member
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if node.op == 'Placeholder':
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node_dict = dict()
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node_dict['type'] = tf_v1.DType(node.attr['dtype'].type).name
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node_dict['shape'] = str(tf_v1.TensorShape(node.attr['shape'].shape)).replace(' ', '').replace('?', '-1')
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placeholders[node.name] = node_dict
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if len(children(node.name, graph)) == 0:
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if node.op not in unlikely_output_types and node.name.split('/')[-1] not in unlikely_output_types:
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outputs.append(node.name)
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result = dict()
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result['inputs'] = placeholders
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result['outputs'] = outputs
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return result
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if __name__ == "__main__": # pragma: no cover
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sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
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from mo.front.tf.loader import load_tf_graph_def
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_model", type=str, help="Path to tensorflow model", default="")
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parser.add_argument('--input_model_is_text', dest='text',
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help='TensorFlow*: treat the input model file as a text protobuf format. If not specified, '
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'the Model Optimizer treats it as a binary file by default.', action='store_true',
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default=False)
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parser.add_argument('--input_meta', action='store_true',
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help='TensorFlow*: treat the input model file as a meta graph def format', default=False)
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parser.add_argument("--input_checkpoint", type=str, help='TensorFlow variables file to load.', default="")
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parser.add_argument('--saved_model_dir', type=str, default="", help="TensorFlow saved_model_dir")
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parser.add_argument('--saved_model_tags', type=str, default="",
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help="Group of tag(s) of the MetaGraphDef to load, in string \
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format, separated by ','. For tag-set contains multiple tags, all tags must be passed in.")
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argv = parser.parse_args()
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if not argv.input_model and not argv.saved_model_dir:
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print("[ ERROR ] Please, provide --input_model and --input_model_is_text if needed or --input_dir for saved "
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"model directory")
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sys.exit(1)
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if argv.input_model and argv.saved_model_dir:
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print("[ ERROR ] Both keys were provided --input_model and --input_dir. Please, provide only one of them")
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sys.exit(1)
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graph_def, _ = load_tf_graph_def(graph_file_name=argv.input_model, is_binary=not argv.text,
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checkpoint=argv.input_checkpoint,
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model_dir=argv.saved_model_dir, saved_model_tags=argv.saved_model_tags)
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summary = summarize_graph(graph_def)
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print("{} input(s) detected:".format(len(summary['inputs'])))
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for input in summary['inputs']:
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print("Name: {}, type: {}, shape: {}".format(input, summary['inputs'][input]['type'],
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summary['inputs'][input]['shape']))
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print("{} output(s) detected:".format(len(summary['outputs'])))
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print(*summary['outputs'], sep="\n")
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