139 lines
4.8 KiB
Python
139 lines
4.8 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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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
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sys.path.append(
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os.path.abspath(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 program
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import paddle
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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, load_params
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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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from paddleslim.quant import quant_aware, convert
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from paddle.fluid.layer_helper import LayerHelper
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from eval_utils.eval_det_utils import eval_det_run
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from eval_utils.eval_rec_utils import eval_rec_run
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from eval_utils.eval_cls_utils import eval_cls_run
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def main():
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# 1. quantization configs
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quant_config = {
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# weight quantize type, default is 'channel_wise_abs_max'
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'weight_quantize_type': 'channel_wise_abs_max',
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# activation quantize type, default is 'moving_average_abs_max'
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'activation_quantize_type': 'moving_average_abs_max',
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# weight quantize bit num, default is 8
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'weight_bits': 8,
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# activation quantize bit num, default is 8
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'activation_bits': 8,
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# ops of name_scope in not_quant_pattern list, will not be quantized
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'not_quant_pattern': ['skip_quant'],
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# ops of type in quantize_op_types, will be quantized
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'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
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# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
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'dtype': 'int8',
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# window size for 'range_abs_max' quantization. defaulf is 10000
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'window_size': 10000,
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# The decay coefficient of moving average, default is 0.9
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'moving_rate': 0.9,
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}
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# Run code with static graph mode.
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try:
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paddle.enable_static()
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except:
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pass
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startup_prog, eval_program, place, config, alg_type = 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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eval_program = quant_aware(
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eval_program, place, quant_config, scope=None, for_test=True)
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init_model(config, eval_program, exe)
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# 2. Convert the program before save inference program
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# The dtype of eval_program's weights is float32, but in int8 range.
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eval_program = convert(eval_program, place, quant_config, scope=None)
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eval_fetch_name_list = fetches_var_name
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eval_fetch_varname_list = [v.name for v in target_vars]
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eval_reader = reader_main(config=config, mode="eval")
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quant_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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if alg_type == 'det':
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final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
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elif alg_type == 'cls':
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final_metrics = eval_cls_run(exe, quant_info_dict)
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else:
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final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
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print(final_metrics)
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# 3. Save inference model
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model_path = "./quant_model"
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if not os.path.isdir(model_path):
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os.makedirs(model_path)
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fluid.io.save_inference_model(
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dirname=model_path,
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feeded_var_names=feeded_var_names,
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target_vars=target_vars,
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executor=exe,
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main_program=eval_program,
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model_filename=model_path + '/model',
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params_filename=model_path + '/params')
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print("model saved as {}".format(model_path))
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if __name__ == '__main__':
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main()
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