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95 lines
4.2 KiB
95 lines
4.2 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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# less 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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import os
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import argparse
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import ast
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import mindspore
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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 import Tensor, Model, context
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
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from src.dataset import create_dataset
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from src.unet3d_model import UNet3d
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from src.config import config as cfg
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from src.lr_schedule import dynamic_lr
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from src.loss import SoftmaxCrossEntropyWithLogits
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, \
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device_id=device_id)
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mindspore.set_seed(1)
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def get_args():
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parser = argparse.ArgumentParser(description='Train the UNet3D on images and target masks')
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parser.add_argument('--data_url', dest='data_url', type=str, default='', help='image data directory')
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parser.add_argument('--seg_url', dest='seg_url', type=str, default='', help='seg data directory')
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parser.add_argument('--run_distribute', dest='run_distribute', type=ast.literal_eval, default=False, \
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help='Run distribute, default: false')
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return parser.parse_args()
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def train_net(data_dir,
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seg_dir,
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run_distribute,
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config=None):
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if run_distribute:
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init()
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rank_id = get_rank()
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rank_size = get_group_size()
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parallel_mode = ParallelMode.DATA_PARALLEL
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context.set_auto_parallel_context(parallel_mode=parallel_mode,
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device_num=rank_size,
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gradients_mean=True)
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else:
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rank_id = 0
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rank_size = 1
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train_dataset = create_dataset(data_path=data_dir, seg_path=seg_dir, config=config, \
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rank_size=rank_size, rank_id=rank_id, is_training=True)
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train_data_size = train_dataset.get_dataset_size()
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print("train dataset length is:", train_data_size)
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network = UNet3d(config=config)
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loss = SoftmaxCrossEntropyWithLogits()
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lr = Tensor(dynamic_lr(config, train_data_size), mstype.float32)
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optimizer = nn.Adam(params=network.trainable_params(), learning_rate=lr)
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scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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network.set_train()
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model = Model(network, loss_fn=loss, optimizer=optimizer, loss_scale_manager=scale_manager)
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time_cb = TimeMonitor(data_size=train_data_size)
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loss_cb = LossMonitor()
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ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix='{}'.format(config.model),
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directory='./ckpt_{}/'.format(device_id),
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config=ckpt_config)
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callbacks_list = [loss_cb, time_cb, ckpoint_cb]
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print("============== Starting Training ==============")
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model.train(config.epoch_size, train_dataset, callbacks=callbacks_list)
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print("============== End Training ==============")
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if __name__ == '__main__':
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args = get_args()
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print("Training setting:", args)
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train_net(data_dir=args.data_url,
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seg_dir=args.seg_url,
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run_distribute=args.run_distribute,
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config=cfg)
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