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> 运行示例前请先安装develop版本PaddleSlim
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# 模型裁剪压缩教程
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## 概述
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该示例使用PaddleSlim提供的[裁剪压缩API](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)对OCR模型进行压缩。
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在阅读该示例前，建议您先了解以下内容：
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- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
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- [PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)
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## 安装PaddleSlim
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可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim。
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## 敏感度分析训练
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进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析：
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```bash
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python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
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```
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## 裁剪模型与fine-tune
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```bash
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python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
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```
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## 评估并导出
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在得到裁剪训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
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```bash
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python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
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```
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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(os.path.abspath(__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 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.utils.save_load import init_model
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from paddleslim.prune import load_model
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def main():
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startup_prog, eval_program, place, config, _ = program.preprocess()
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feeded_var_names, target_vars, fetches_var_name = program.build_export(
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config, eval_program, startup_prog)
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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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if config['Global']['checkpoints'] is not None:
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path = config['Global']['checkpoints']
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else:
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path = config['Global']['pretrain_weights']
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load_model(exe, eval_program, path)
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save_inference_dir = config['Global']['save_inference_dir']
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if not os.path.exists(save_inference_dir):
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os.makedirs(save_inference_dir)
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fluid.io.save_inference_model(
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dirname=save_inference_dir,
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feeded_var_names=feeded_var_names,
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main_program=eval_program,
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target_vars=target_vars,
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executor=exe,
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model_filename='model',
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params_filename='params')
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print("inference model saved in {}/model and {}/params".format(
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save_inference_dir, save_inference_dir))
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print("save success, output_name_list:", fetches_var_name)
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if __name__ == '__main__':
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main()
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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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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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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_pruning=True)
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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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# 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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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 tools.eval_utils.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 eval_function(eval_args, mode='eval'):
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exe = eval_args['exe']
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config = eval_args['config']
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eval_info_dict = eval_args['eval_info_dict']
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metrics = eval_det_run(exe, config, eval_info_dict, mode=mode)
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return metrics['hmean']
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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_function(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_function,
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sensitivities_file="sensitivities_0.data",
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pruned_ratios=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
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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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@ -241,7 +241,11 @@ def create_multi_devices_program(program, loss_var_name, for_quant=False):
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return compile_program
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def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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def train_eval_det_run(config,
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exe,
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train_info_dict,
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eval_info_dict,
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is_pruning=False):
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train_batch_id = 0
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log_smooth_window = config['Global']['log_smooth_window']
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epoch_num = config['Global']['epoch_num']
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@ -297,7 +301,14 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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best_batch_id = train_batch_id
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best_epoch = epoch
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save_path = save_model_dir + "/best_accuracy"
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save_model(train_info_dict['train_program'], save_path)
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if is_pruning:
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import paddleslim as slim
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slim.prune.save_model(
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exe, train_info_dict['train_program'],
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save_path)
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else:
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save_model(train_info_dict['train_program'],
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save_path)
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strs = 'Test iter: {}, metrics:{}, best_hmean:{:.6f}, best_epoch:{}, best_batch_id:{}'.format(
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train_batch_id, metrics, best_eval_hmean, best_epoch,
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best_batch_id)
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@ -308,10 +319,20 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
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train_loader.reset()
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if epoch == 0 and save_epoch_step == 1:
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save_path = save_model_dir + "/iter_epoch_0"
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save_model(train_info_dict['train_program'], save_path)
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if is_pruning:
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import paddleslim as slim
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slim.prune.save_model(exe, train_info_dict['train_program'],
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save_path)
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else:
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save_model(train_info_dict['train_program'], save_path)
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if epoch > 0 and epoch % save_epoch_step == 0:
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save_path = save_model_dir + "/iter_epoch_%d" % (epoch)
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save_model(train_info_dict['train_program'], save_path)
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if is_pruning:
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import paddleslim as slim
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slim.prune.save_model(exe, train_info_dict['train_program'],
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save_path)
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else:
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save_model(train_info_dict['train_program'], save_path)
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return
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