122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
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# Copyright (c) 2020 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, '..', '..', '..'))
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sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))
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def set_paddle_flags(**kwargs):
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for key, value in kwargs.items():
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if os.environ.get(key, None) is None:
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os.environ[key] = str(value)
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# NOTE(paddle-dev): All of these flags should be
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# set before `import paddle`. Otherwise, it would
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# not take any effect.
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set_paddle_flags(
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FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
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)
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import json
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import cv2
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from paddle import fluid
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import paddleslim as slim
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from copy import deepcopy
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from eval_det_utils import eval_det_run
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from tools import program
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from ppocr.utils.utility import initial_logger
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from ppocr.data.reader_main import reader_main
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from ppocr.utils.save_load import init_model
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from ppocr.utils.character import CharacterOps
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from ppocr.utils.utility import create_module
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from ppocr.data.reader_main import reader_main
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logger = initial_logger()
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def get_pruned_params(program):
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params = []
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for param in program.global_block().all_parameters():
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if len(
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param.shape
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) == 4 and 'depthwise' not in param.name and 'transpose' not in param.name:
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params.append(param.name)
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return params
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def main():
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config = program.load_config(FLAGS.config)
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program.merge_config(FLAGS.opt)
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logger.info(config)
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# check if set use_gpu=True in paddlepaddle cpu version
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use_gpu = config['Global']['use_gpu']
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program.check_gpu(use_gpu)
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alg = config['Global']['algorithm']
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assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
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if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
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config['Global']['char_ops'] = CharacterOps(config['Global'])
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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startup_prog = fluid.Program()
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eval_program = fluid.Program()
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eval_build_outputs = program.build(
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config, eval_program, startup_prog, mode='test')
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eval_fetch_name_list = eval_build_outputs[1]
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eval_fetch_varname_list = eval_build_outputs[2]
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eval_program = eval_program.clone(for_test=True)
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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init_model(config, eval_program, exe)
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eval_reader = reader_main(config=config, mode="eval")
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eval_info_dict = {'program':eval_program,\
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'reader':eval_reader,\
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'fetch_name_list':eval_fetch_name_list,\
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'fetch_varname_list':eval_fetch_varname_list}
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eval_args = dict()
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eval_args = {'exe': exe, 'config': config, 'eval_info_dict': eval_info_dict}
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metrics = eval_det_run(eval_args)
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print("Baseline: {}".format(metrics))
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params = get_pruned_params(eval_program)
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print('Start to analyze')
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sens_0 = slim.prune.sensitivity(
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eval_program,
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place,
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params,
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eval_det_run,
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sensitivities_file="sensitivities_0.data",
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pruned_ratios=[0.1],
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eval_args=eval_args,
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criterion='geometry_median')
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if __name__ == '__main__':
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parser = program.ArgsParser()
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FLAGS = parser.parse_args()
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main()
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