mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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83 lines
2.5 KiB
Python
Executable File
83 lines
2.5 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
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("-c", "--config", help="configuration file to use")
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parser.add_argument(
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"-o", "--output_path", type=str, default='./output/infer/')
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return parser.parse_args()
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class Model(paddle.nn.Layer):
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def __init__(self, model):
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super(Model, self).__init__()
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self.pre_model = model
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# Please modify the 'shape' according to actual needs
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@to_static(input_spec=[
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paddle.static.InputSpec(
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shape=[None, 3, 640, 640], dtype='float32')
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])
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def forward(self, inputs):
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x = self.pre_model(inputs)
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return x
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def main():
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FLAGS = parse_args()
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config = load_config(FLAGS.config)
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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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model = Model(model)
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save_path = '{}/{}'.format(FLAGS.output_path,
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config['Architecture']['model_type'])
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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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