115 lines
3.6 KiB
Python
Executable File
115 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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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 numpy as np
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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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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 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.utility import create_module
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from ppocr.utils.utility import get_image_file_list
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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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# check_gpu(use_gpu)
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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rec_model = create_module(config['Architecture']['function'])(params=config)
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startup_prog = fluid.Program()
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, startup_prog):
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with fluid.unique_name.guard():
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_, outputs = rec_model(mode="test")
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fetch_name_list = list(outputs.keys())
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fetch_varname_list = [outputs[v].name for v in fetch_name_list]
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eval_prog = eval_prog.clone(for_test=True)
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exe.run(startup_prog)
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init_model(config, eval_prog, exe)
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blobs = reader_main(config, 'test')()
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infer_img = config['Global']['infer_img']
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infer_list = get_image_file_list(infer_img)
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max_img_num = len(infer_list)
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if len(infer_list) == 0:
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logger.info("Can not find img in infer_img dir.")
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for i in range(max_img_num):
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logger.info("infer_img:%s" % infer_list[i])
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img = next(blobs)
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predict = exe.run(program=eval_prog,
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feed={"image": img},
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fetch_list=fetch_varname_list,
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return_numpy=False)
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scores = np.array(predict[0])
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label = np.array(predict[1])
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if len(label.shape) != 1:
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label, scores = scores, label
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logger.info('\t scores: {}'.format(scores))
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logger.info('\t label: {}'.format(label))
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# save for inference model
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target_var = []
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for key, values in outputs.items():
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target_var.append(values)
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fluid.io.save_inference_model(
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"./output",
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feeded_var_names=['image'],
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target_vars=target_var,
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executor=exe,
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main_program=eval_prog,
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model_filename="model",
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params_filename="params")
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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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