201 lines
7.8 KiB
Python
201 lines
7.8 KiB
Python
# 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.insert(0, 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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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 load_model
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from ppocr.utils.utility import set_seed
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from ppocr.modeling.architectures import apply_to_static
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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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if config['Architecture']['Models'][key]['Head'][
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'name'] == 'MultiHead': # for multi head
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if config['PostProcess'][
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'name'] == 'DistillationSARLabelDecode':
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char_num = char_num - 2
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# update SARLoss params
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assert list(config['Loss']['loss_config_list'][-1].keys())[
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0] == 'DistillationSARLoss'
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config['Loss']['loss_config_list'][-1][
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'DistillationSARLoss']['ignore_index'] = char_num + 1
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out_channels_list = {}
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out_channels_list['CTCLabelDecode'] = char_num
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out_channels_list['SARLabelDecode'] = char_num + 2
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config['Architecture']['Models'][key]['Head'][
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'out_channels_list'] = out_channels_list
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else:
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config['Architecture']["Models"][key]["Head"][
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'out_channels'] = char_num
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elif config['Architecture']['Head'][
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'name'] == 'MultiHead': # for multi head
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if config['PostProcess']['name'] == 'SARLabelDecode':
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char_num = char_num - 2
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# update SARLoss params
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assert list(config['Loss']['loss_config_list'][1].keys())[
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0] == 'SARLoss'
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if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
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config['Loss']['loss_config_list'][1]['SARLoss'] = {
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'ignore_index': char_num + 1
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}
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else:
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config['Loss']['loss_config_list'][1]['SARLoss'][
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'ignore_index'] = char_num + 1
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out_channels_list = {}
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out_channels_list['CTCLabelDecode'] = char_num
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out_channels_list['SARLabelDecode'] = char_num + 2
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config['Architecture']['Head'][
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'out_channels_list'] = out_channels_list
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else: # base rec model
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config['Architecture']["Head"]['out_channels'] = char_num
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if config['PostProcess']['name'] == 'SARLabelDecode': # for SAR model
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config['Loss']['ignore_index'] = char_num - 1
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model = build_model(config['Architecture'])
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use_sync_bn = config["Global"].get("use_sync_bn", False)
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if use_sync_bn:
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model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
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logger.info('convert_sync_batchnorm')
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model = apply_to_static(model, config, logger)
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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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model=model)
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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 = load_model(config, model, optimizer,
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config['Architecture']["model_type"])
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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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use_amp = config["Global"].get("use_amp", False)
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amp_level = config["Global"].get("amp_level", 'O2')
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if use_amp:
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AMP_RELATED_FLAGS_SETTING = {
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'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
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'FLAGS_max_inplace_grad_add': 8,
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}
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paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
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scale_loss = config["Global"].get("scale_loss", 1.0)
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use_dynamic_loss_scaling = config["Global"].get(
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"use_dynamic_loss_scaling", False)
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scaler = paddle.amp.GradScaler(
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init_loss_scaling=scale_loss,
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use_dynamic_loss_scaling=use_dynamic_loss_scaling)
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if amp_level == "O2":
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model, optimizer = paddle.amp.decorate(
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models=model, optimizers=optimizer, level=amp_level, master_weight=True)
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else:
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scaler = None
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if config['Global']['distributed']:
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model = paddle.DataParallel(model)
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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, scaler,amp_level)
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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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seed = config['Global']['seed'] if 'seed' in config['Global'] else 1024
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set_seed(seed)
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main(config, device, logger, vdl_writer)
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# test_reader(config, device, logger)
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