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137 lines
6.0 KiB
137 lines
6.0 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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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, get_rank
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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 mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.unet_medical import UNetMedical
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from src.unet_nested import NestedUNet, UNet
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from src.data_loader import create_dataset, create_cell_nuclei_dataset
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from src.loss import CrossEntropyWithLogits, MultiCrossEntropyWithLogits
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from src.utils import StepLossTimeMonitor, filter_checkpoint_parameter_by_list
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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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rank = 0
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group_size = 1
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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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rank = get_rank()
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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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if cfg['model'] == 'unet_medical':
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net = UNetMedical(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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elif cfg['model'] == 'unet_nested':
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net = NestedUNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'], use_deconv=cfg['use_deconv'],
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use_bn=cfg['use_bn'], use_ds=cfg['use_ds'])
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elif cfg['model'] == 'unet_simple':
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net = UNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'])
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else:
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raise ValueError("Unsupported model: {}".format(cfg['model']))
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if cfg['resume']:
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param_dict = load_checkpoint(cfg['resume_ckpt'])
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if cfg['transfer_training']:
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filter_checkpoint_parameter_by_list(param_dict, cfg['filter_weight'])
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load_param_into_net(net, param_dict)
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if 'use_ds' in cfg and cfg['use_ds']:
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criterion = MultiCrossEntropyWithLogits()
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else:
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criterion = CrossEntropyWithLogits()
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if 'dataset' in cfg and cfg['dataset'] == "Cell_nuclei":
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repeat = 10
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dataset_sink_mode = True
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per_print_times = 0
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train_dataset = create_cell_nuclei_dataset(data_dir, cfg['img_size'], repeat, batch_size,
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is_train=True, augment=True, split=0.8, rank=rank,
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group_size=group_size)
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else:
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repeat = epochs
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dataset_sink_mode = False
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per_print_times = 1
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train_dataset, _ = create_dataset(data_dir, repeat, batch_size, True, cross_valid_ind, run_distribute,
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cfg["crop"], cfg['img_size'])
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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_{}_adam'.format(cfg['model']),
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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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callbacks = [StepLossTimeMonitor(batch_size=batch_size, per_print_times=per_print_times), ckpoint_cb]
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model.train(int(epochs / repeat), train_dataset, callbacks=callbacks, dataset_sink_mode=dataset_sink_mode)
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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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