133 lines
4.4 KiB
Python
Executable File
133 lines
4.4 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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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(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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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 paddle.fluid.contrib.model_stat import summary
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def main():
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# build train program
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train_build_outputs = program.build(
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config, train_program, startup_program, mode='train')
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train_loader = train_build_outputs[0]
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train_fetch_name_list = train_build_outputs[1]
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train_fetch_varname_list = train_build_outputs[2]
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train_opt_loss_name = train_build_outputs[3]
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model_average = train_build_outputs[-1]
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# build eval program
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eval_program = fluid.Program()
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eval_build_outputs = program.build(
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config, eval_program, startup_program, mode='eval')
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eval_fetch_name_list = eval_build_outputs[1]
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eval_fetch_varname_list = eval_build_outputs[2]
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eval_program = eval_program.clone(for_test=True)
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# initialize train reader
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train_reader = reader_main(config=config, mode="train")
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train_loader.set_sample_list_generator(train_reader, places=place)
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# initialize eval reader
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eval_reader = reader_main(config=config, mode="eval")
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exe = fluid.Executor(place)
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exe.run(startup_program)
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# compile program for multi-devices
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train_compile_program = program.create_multi_devices_program(
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train_program, train_opt_loss_name)
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# dump mode structure
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if config['Global']['debug']:
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if train_alg_type == 'rec' and 'attention' in config['Global'][
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'loss_type']:
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logger.warning('Does not suport dump attention...')
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else:
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summary(train_program)
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init_model(config, train_program, exe)
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train_info_dict = {'compile_program':train_compile_program,\
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'train_program':train_program,\
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'reader':train_loader,\
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'fetch_name_list':train_fetch_name_list,\
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'fetch_varname_list':train_fetch_varname_list,\
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'model_average': model_average}
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eval_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 train_alg_type == 'det':
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program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
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elif train_alg_type == 'rec':
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program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
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else:
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program.train_eval_cls_run(config, exe, train_info_dict, eval_info_dict)
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def test_reader():
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logger.info(config)
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train_reader = reader_main(config=config, mode="train")
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import time
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starttime = time.time()
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count = 0
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try:
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for data in train_reader():
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count += 1
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if count % 1 == 0:
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batch_time = time.time() - starttime
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starttime = time.time()
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logger.info("reader:", count, len(data), batch_time)
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except Exception as e:
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logger.info(e)
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logger.info("finish reader: {}, Success!".format(count))
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if __name__ == '__main__':
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startup_program, train_program, place, config, train_alg_type = program.preprocess(
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)
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main()
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# test_reader()
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