wehub-resource-sync
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
# copyright (c) 2022 paddlepaddle authors. all rights reserved.
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#
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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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#
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# http://www.apache.org/licenses/license-2.0
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#
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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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import argparse
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import os
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import sys
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import unittest
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import paddle
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from paddle.base.framework import IrGraph
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from paddle.framework import core
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paddle.enable_static()
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--model_path', type=str, default='', help='A path to a model.'
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)
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parser.add_argument(
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'--save_graph_dir',
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type=str,
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default='',
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help='A path to save the graph.',
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)
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parser.add_argument(
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'--save_graph_name',
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type=str,
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default='',
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help='A name to save the graph. Default - name from model path will be used',
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)
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test_args, args = parser.parse_known_args(namespace=unittest)
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return test_args, sys.argv[:1] + args
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def generate_dot_for_model(model_path, save_graph_dir, save_graph_name):
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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inference_scope = paddle.static.global_scope()
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with paddle.static.scope_guard(inference_scope):
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if os.path.exists(os.path.join(model_path, '__model__')):
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.io.load_inference_model(
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model_path, exe, model_filename='__model__'
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)
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else:
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.load_inference_model(
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model_path,
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exe,
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model_filename='model',
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params_filename='params',
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)
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graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
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if not os.path.exists(save_graph_dir):
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os.makedirs(save_graph_dir)
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model_name = os.path.basename(os.path.normpath(save_graph_dir))
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if save_graph_name == '':
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save_graph_name = model_name
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graph.draw(save_graph_dir, save_graph_name, graph.all_op_nodes())
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print(
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f"Success! Generated dot and pdf files for {model_name} model, that can be found at {save_graph_dir} named {save_graph_name}.\n"
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)
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if __name__ == '__main__':
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global test_args
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test_args, remaining_args = parse_args()
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generate_dot_for_model(
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test_args.model_path,
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test_args.save_graph_dir,
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test_args.save_graph_name,
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)
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