142 lines
5.4 KiB
Python
Executable File
142 lines
5.4 KiB
Python
Executable File
# 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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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.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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import argparse
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import paddle
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from paddle.jit import to_static
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.utils.save_load import init_model
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from ppocr.utils.logging import get_logger
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from tools.program import load_config, merge_config, ArgsParser
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from ppocr.metrics import build_metric
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import tools.program as program
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from paddleslim.dygraph.quant import QAT
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from ppocr.data import build_dataloader
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def export_single_model(quanter, model, infer_shape, save_path, logger):
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quanter.save_quantized_model(
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model,
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save_path,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None] + infer_shape, dtype='float32')
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])
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logger.info('inference QAT model is saved to {}'.format(save_path))
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def main():
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############################################################################################################
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# 1. quantization configs
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############################################################################################################
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quant_config = {
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# weight preprocess type, default is None and no preprocessing is performed.
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'weight_preprocess_type': None,
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# activation preprocess type, default is None and no preprocessing is performed.
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'activation_preprocess_type': None,
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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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# 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. default 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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# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
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'quantizable_layer_type': ['Conv2D', 'Linear'],
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}
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FLAGS = ArgsParser().parse_args()
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config = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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logger = get_logger()
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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config['Global'])
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# build model
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# for rec algorithm
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if hasattr(post_process_class, 'character'):
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char_num = len(getattr(post_process_class, 'character'))
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if config['Architecture']["algorithm"] in ["Distillation",
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]: # distillation model
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for key in config['Architecture']["Models"]:
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config['Architecture']["Models"][key]["Head"][
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'out_channels'] = char_num
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else: # base rec model
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config['Architecture']["Head"]['out_channels'] = char_num
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model = build_model(config['Architecture'])
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# get QAT model
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quanter = QAT(config=quant_config)
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quanter.quantize(model)
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init_model(config, model)
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model.eval()
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# build metric
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eval_class = build_metric(config['Metric'])
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# build dataloader
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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use_srn = config['Architecture']['algorithm'] == "SRN"
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model_type = config['Architecture']['model_type']
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# start eval
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metric = program.eval(model, valid_dataloader, post_process_class,
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eval_class, model_type, use_srn)
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logger.info('metric eval ***************')
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for k, v in metric.items():
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logger.info('{}:{}'.format(k, v))
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infer_shape = [3, 32, 100] if config['Architecture'][
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'model_type'] != "det" else [3, 640, 640]
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save_path = config["Global"]["save_inference_dir"]
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arch_config = config["Architecture"]
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if arch_config["algorithm"] in ["Distillation", ]: # distillation model
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for idx, name in enumerate(model.model_name_list):
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sub_model_save_path = os.path.join(save_path, name, "inference")
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export_single_model(quanter, model.model_list[idx], infer_shape,
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sub_model_save_path, logger)
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else:
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save_path = os.path.join(save_path, "inference")
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export_single_model(quanter, model, infer_shape, save_path, logger)
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if __name__ == "__main__":
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config, device, logger, vdl_writer = program.preprocess()
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main()
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