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106 lines
4.2 KiB
106 lines
4.2 KiB
4 years ago
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# 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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# 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 logging
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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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from mindspore import Model, context
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from mindspore.communication.management import init, get_group_size
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
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from mindspore.context import ParallelMode
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from src.unet import UNet
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from src.data_loader import create_dataset
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from src.loss import CrossEntropyWithLogits
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from src.utils import StepLossTimeMonitor
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from src.config import cfg_unet
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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, device_id=device_id)
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mindspore.set_seed(1)
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def train_net(data_dir,
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cross_valid_ind=1,
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epochs=400,
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batch_size=16,
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lr=0.0001,
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run_distribute=False,
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cfg=None):
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if run_distribute:
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init()
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group_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=group_size,
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gradients_mean=False)
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net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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criterion = CrossEntropyWithLogits()
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train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True, cross_valid_ind, run_distribute)
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train_data_size = train_dataset.get_dataset_size()
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print("dataset length is:", train_data_size)
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ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
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keep_checkpoint_max=cfg['keep_checkpoint_max'])
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ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
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directory='./ckpt_{}/'.format(device_id),
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config=ckpt_config)
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optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
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loss_scale=cfg['loss_scale'])
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loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
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model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
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print("============== Starting Training ==============")
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model.train(1, train_dataset, callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
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dataset_sink_mode=False)
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print("============== End Training ==============")
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def get_args():
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parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
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help='data directory')
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parser.add_argument('-t', '--run_distribute', type=ast.literal_eval,
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default=False, help='Run distribute, default: false.')
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return parser.parse_args()
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if __name__ == '__main__':
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logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
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args = get_args()
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print("Training setting:", args)
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epoch_size = cfg_unet['epochs'] if not args.run_distribute else cfg_unet['distribute_epochs']
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train_net(data_dir=args.data_url,
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cross_valid_ind=cfg_unet['cross_valid_ind'],
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epochs=epoch_size,
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batch_size=cfg_unet['batchsize'],
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lr=cfg_unet['lr'],
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run_distribute=args.run_distribute,
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cfg=cfg_unet)
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