96 lines
3.6 KiB
Python
Executable File
96 lines
3.6 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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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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def main():
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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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config['Architecture']["Head"]['out_channels'] = char_num
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model = build_model(config['Architecture'])
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init_model(config, model, logger)
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model.eval()
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save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
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if config['Architecture']['algorithm'] == "SRN":
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max_text_length = config['Architecture']['Head']['max_text_length']
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other_shape = [
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paddle.static.InputSpec(
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shape=[None, 1, 64, 256], dtype='float32'), [
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paddle.static.InputSpec(
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shape=[None, 256, 1],
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dtype="int64"), paddle.static.InputSpec(
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shape=[None, max_text_length, 1], dtype="int64"),
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paddle.static.InputSpec(
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shape=[None, 8, max_text_length, max_text_length],
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dtype="int64"), paddle.static.InputSpec(
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shape=[None, 8, max_text_length, max_text_length],
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dtype="int64")
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]
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]
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model = to_static(model, input_spec=other_shape)
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else:
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infer_shape = [3, -1, -1]
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if config['Architecture']['model_type'] == "rec":
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infer_shape = [3, 32, -1] # for rec model, H must be 32
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if 'Transform' in config['Architecture'] and config['Architecture'][
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'Transform'] is not None and config['Architecture'][
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'Transform']['name'] == 'TPS':
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logger.info(
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'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
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)
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infer_shape[-1] = 100
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model = to_static(
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model,
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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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paddle.jit.save(model, save_path)
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logger.info('inference model is saved to {}'.format(save_path))
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if __name__ == "__main__":
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main()
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