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159 lines
6.0 KiB
159 lines
6.0 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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CRNN-Seq2Seq-OCR train.
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"""
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import os
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import argparse
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import datetime
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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from mindspore.common import set_seed
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from mindspore import Tensor
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from mindspore import context
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from mindspore.communication.management import init
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from mindspore.train.callback import ModelCheckpoint
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from mindspore.train.callback import CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.config import config
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from src.dataset import create_ocr_train_dataset
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from src.logger import get_logger
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from src.attention_ocr import AttentionOCR, AttentionOCRWithLossCell, TrainingWrapper
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set_seed(1)
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def parse_args():
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"""Parse train arguments."""
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parser = argparse.ArgumentParser('mindspore CRNN-Seq2Seq-OCR training')
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# device related
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parser.add_argument("--device_target", type=str, default="Ascend",
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help="device where the code will be implemented.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default: 0.")
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# distributed related
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parser.add_argument('--is_distributed', type=int, default=0,
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help='Distribute train or not, 1 for yes, 0 for no. Default: 0')
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parser.add_argument('--rank_id', type=int, default=0, help='Local rank of distributed. Default: 0')
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parser.add_argument('--device_num', type=int, default=1, help='World size of device. Default: 1')
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#dataset related
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parser.add_argument('--mindrecord_file', type=str, default='', help='Train dataset directory.')
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# logging related
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parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
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parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
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parser.add_argument('--pre_checkpoint_path', type=str, default='', help='Checkpoint save location.')
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parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')
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parser.add_argument('--is_save_on_master', type=int, default=0,
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help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 0')
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args, _ = parser.parse_known_args()
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# logger
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args.outputs_dir = os.path.join(args.ckpt_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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return args
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def train():
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"""Train function."""
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args = parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if args.is_distributed:
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rank = args.rank_id
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device_num = args.device_num
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context.set_auto_parallel_context(device_num=device_num,
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parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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init()
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else:
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rank = 0
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device_num = 1
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# Logger
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args.logger = get_logger(args.outputs_dir, rank)
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args.rank_save_ckpt_flag = 0
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if args.is_save_on_master:
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if rank == 0:
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args.rank_save_ckpt_flag = 1
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else:
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args.rank_save_ckpt_flag = 1
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# DATASET
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dataset = create_ocr_train_dataset(args.mindrecord_file,
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config.batch_size,
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rank_size=device_num,
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rank_id=rank)
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args.steps_per_epoch = dataset.get_dataset_size()
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args.logger.info('Finish loading dataset')
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if not args.ckpt_interval:
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args.ckpt_interval = args.steps_per_epoch
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args.logger.save_args(args)
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network = AttentionOCR(config.batch_size,
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int(config.img_width / 4),
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config.encoder_hidden_size,
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config.decoder_hidden_size,
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config.decoder_output_size,
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config.max_length,
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config.dropout_p)
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if args.pre_checkpoint_path:
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param_dict = load_checkpoint(args.pre_checkpoint_path)
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load_param_into_net(network, param_dict)
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network = AttentionOCRWithLossCell(network, config.max_length)
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lr = Tensor(config.lr, mstype.float32)
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opt = nn.Adam(network.trainable_params(), lr, beta1=config.adam_beta1, beta2=config.adam_beta2,
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loss_scale=config.loss_scale)
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network = TrainingWrapper(network, opt, sens=config.loss_scale)
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args.logger.info('Finished get network')
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callback = [TimeMonitor(data_size=1), LossMonitor()]
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if args.rank_save_ckpt_flag:
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ckpt_config = CheckpointConfig(save_checkpoint_steps=args.steps_per_epoch,
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keep_checkpoint_max=config.keep_checkpoint_max)
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save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(rank) + '/')
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ckpt_cb = ModelCheckpoint(config=ckpt_config,
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directory=save_ckpt_path,
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prefix="crnn_seq2seq_ocr")
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callback.append(ckpt_cb)
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model = Model(network)
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model.train(config.num_epochs, dataset, callbacks=callback, dataset_sink_mode=False)
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args.logger.info('==========Training Done===============')
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if __name__ == "__main__":
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train()
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