147 lines
5.2 KiB
Python
147 lines
5.2 KiB
Python
# 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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import numpy as np
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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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import tools.program as program
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from paddle import fluid
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from ppocr.utils.utility import initial_logger
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logger = 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 initial_logger
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from paddleslim.prune import Pruner, save_model
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from paddleslim.analysis import flops
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from paddleslim.core.graph_wrapper import *
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from paddleslim.prune import load_sensitivities, get_ratios_by_loss, merge_sensitive
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logger = initial_logger()
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skip_list = [
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'conv10_linear_weights', 'conv11_linear_weights', 'conv12_expand_weights',
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'conv12_linear_weights', 'conv12_se_2_weights', 'conv13_linear_weights',
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'conv2_linear_weights', 'conv4_linear_weights', 'conv5_expand_weights',
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'conv5_linear_weights', 'conv5_se_2_weights', 'conv6_linear_weights',
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'conv7_linear_weights', 'conv8_expand_weights', 'conv8_linear_weights',
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'conv9_expand_weights', 'conv9_linear_weights'
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]
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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_program = fluid.Program()
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train_program = fluid.Program()
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train_build_outputs = program.build(
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config, train_program, startup_program, mode='train')
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train_loader = train_build_outputs[0]
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train_fetch_name_list = train_build_outputs[1]
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train_fetch_varname_list = train_build_outputs[2]
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train_opt_loss_name = train_build_outputs[3]
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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_program, mode='eval')
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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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train_reader = reader_main(config=config, mode="train")
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train_loader.set_sample_list_generator(train_reader, places=place)
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eval_reader = reader_main(config=config, mode="eval")
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exe = fluid.Executor(place)
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exe.run(startup_program)
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# compile program for multi-devices
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init_model(config, train_program, exe)
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sen = load_sensitivities("sensitivities_0.data")
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for i in skip_list:
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if i in sen.keys():
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sen.pop(i)
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back_bone_list = ['conv' + str(x) for x in range(1, 5)]
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for i in back_bone_list:
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for key in list(sen.keys()):
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if i + '_' in key:
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sen.pop(key)
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ratios = get_ratios_by_loss(sen, 0.03)
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logger.info("FLOPs before pruning: {}".format(flops(eval_program)))
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pruner = Pruner(criterion='geometry_median')
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print("ratios: {}".format(ratios))
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pruned_val_program, _, _ = pruner.prune(
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eval_program,
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fluid.global_scope(),
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params=ratios.keys(),
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ratios=ratios.values(),
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place=place,
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only_graph=True)
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pruned_program, _, _ = pruner.prune(
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train_program,
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fluid.global_scope(),
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params=ratios.keys(),
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ratios=ratios.values(),
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place=place)
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logger.info("FLOPs after pruning: {}".format(flops(pruned_val_program)))
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train_compile_program = program.create_multi_devices_program(
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pruned_program, train_opt_loss_name)
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train_info_dict = {'compile_program':train_compile_program,\
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'train_program':pruned_program,\
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'reader':train_loader,\
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'fetch_name_list':train_fetch_name_list,\
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'fetch_varname_list':train_fetch_varname_list}
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eval_info_dict = {'program':pruned_val_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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if alg in ['EAST', 'DB']:
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program.train_eval_det_run(
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config, exe, train_info_dict, eval_info_dict, is_slim="prune")
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else:
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program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
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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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