134 lines
4.5 KiB
Python
Executable File
134 lines
4.5 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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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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import yaml
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import paddle
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import paddle.distributed as dist
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paddle.seed(2)
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from ppocr.data import build_dataloader
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from ppocr.modeling.architectures import build_model
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from ppocr.losses import build_loss
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from ppocr.optimizer import build_optimizer
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from ppocr.postprocess import build_post_process
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from ppocr.metrics import build_metric
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from ppocr.utils.save_load import init_model
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import tools.program as program
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dist.get_world_size()
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def main(config, device, logger, vdl_writer):
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# init dist environment
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if config['Global']['distributed']:
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dist.init_parallel_env()
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global_config = config['Global']
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# build dataloader
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train_dataloader = build_dataloader(config, 'Train', device, logger)
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if len(train_dataloader) == 0:
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logger.error(
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"No Images in train dataset, please ensure\n" +
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"\t1. The images num in the train label_file_list should be larger than or equal with batch size.\n"
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+
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"\t2. The annotation file and path in the configuration file are provided normally."
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)
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return
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if config['Eval']:
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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else:
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valid_dataloader = None
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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global_config)
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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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if config['Global']['distributed']:
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model = paddle.DataParallel(model)
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# build loss
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loss_class = build_loss(config['Loss'])
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# build optim
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optimizer, lr_scheduler = build_optimizer(
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config['Optimizer'],
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epochs=config['Global']['epoch_num'],
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step_each_epoch=len(train_dataloader),
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parameters=model.parameters())
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# build metric
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eval_class = build_metric(config['Metric'])
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# load pretrain model
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pre_best_model_dict = init_model(config, model, optimizer)
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logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
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if valid_dataloader is not None:
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logger.info('valid dataloader has {} iters'.format(
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len(valid_dataloader)))
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# start train
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program.train(config, train_dataloader, valid_dataloader, device, model,
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loss_class, optimizer, lr_scheduler, post_process_class,
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eval_class, pre_best_model_dict, logger, vdl_writer)
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def test_reader(config, device, logger):
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loader = build_dataloader(config, 'Train', device, logger)
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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 loader():
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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: {}, {}, {}".format(
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count, len(data[0]), 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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config, device, logger, vdl_writer = program.preprocess(is_train=True)
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main(config, device, logger, vdl_writer)
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# test_reader(config, device, logger)
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