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100 lines
4.4 KiB
100 lines
4.4 KiB
# Copyright 2020 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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"""Warpctc training"""
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import os
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import math as m
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import argparse
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common import set_seed
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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.nn.wrap import WithLossCell
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from mindspore.train.callback import TimeMonitor, LossMonitor, CheckpointConfig, ModelCheckpoint
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from mindspore.communication.management import init, get_group_size, get_rank
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from src.loss import CTCLoss
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from src.config import config as cf
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from src.dataset import create_dataset
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from src.warpctc import StackedRNN, StackedRNNForGPU
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from src.warpctc_for_train import TrainOneStepCellWithGradClip
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from src.lr_schedule import get_lr
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set_seed(1)
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parser = argparse.ArgumentParser(description="Warpctc training")
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parser.add_argument("--run_distribute", action='store_true', help="Run distribute, default is false.")
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path, default is None')
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parser.add_argument('--platform', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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help='Running platform, choose from Ascend, GPU, and default is Ascend.')
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parser.set_defaults(run_distribute=False)
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform)
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if args_opt.platform == 'Ascend':
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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if __name__ == '__main__':
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lr_scale = 1
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if args_opt.run_distribute:
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init()
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if args_opt.platform == 'Ascend':
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device_num = int(os.environ.get("RANK_SIZE"))
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rank = int(os.environ.get("RANK_ID"))
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else:
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device_num = get_group_size()
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rank = get_rank()
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context.reset_auto_parallel_context()
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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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else:
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device_num = 1
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rank = 0
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max_captcha_digits = cf.max_captcha_digits
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input_size = m.ceil(cf.captcha_height / 64) * 64 * 3
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path, batch_size=cf.batch_size,
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num_shards=device_num, shard_id=rank, device_target=args_opt.platform)
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step_size = dataset.get_dataset_size()
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# define lr
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lr_init = cf.learning_rate if not args_opt.run_distribute else cf.learning_rate * device_num * lr_scale
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lr = get_lr(cf.epoch_size, step_size, lr_init)
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loss = CTCLoss(max_sequence_length=cf.captcha_width,
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max_label_length=max_captcha_digits,
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batch_size=cf.batch_size)
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if args_opt.platform == 'Ascend':
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net = StackedRNN(input_size=input_size, batch_size=cf.batch_size, hidden_size=cf.hidden_size)
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else:
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net = StackedRNNForGPU(input_size=input_size, batch_size=cf.batch_size, hidden_size=cf.hidden_size)
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opt = nn.SGD(params=net.trainable_params(), learning_rate=lr, momentum=cf.momentum)
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net = WithLossCell(net, loss)
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net = TrainOneStepCellWithGradClip(net, opt).set_train()
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# define model
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model = Model(net)
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# define callbacks
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callbacks = [LossMonitor(), TimeMonitor(data_size=step_size)]
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if cf.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=cf.save_checkpoint_steps,
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keep_checkpoint_max=cf.keep_checkpoint_max)
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save_ckpt_path = os.path.join(cf.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
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ckpt_cb = ModelCheckpoint(prefix="warpctc", directory=save_ckpt_path, config=config_ck)
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callbacks.append(ckpt_cb)
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model.train(cf.epoch_size, dataset, callbacks=callbacks)
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