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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- import os
- import argparse
- import logging
- import ast
-
- import mindspore
- import mindspore.nn as nn
- from mindspore import Model, context
- from mindspore.communication.management import init, get_group_size, get_rank
- from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
- from mindspore.context import ParallelMode
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.unet_medical import UNetMedical
- from src.unet_nested import NestedUNet, UNet
- from src.data_loader import create_dataset, create_cell_nuclei_dataset
- from src.loss import CrossEntropyWithLogits, MultiCrossEntropyWithLogits
- from src.utils import StepLossTimeMonitor, UnetEval, TempLoss, apply_eval, filter_checkpoint_parameter_by_list, dice_coeff
- from src.config import cfg_unet
- from src.eval_callback import EvalCallBack
-
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
-
- mindspore.set_seed(1)
-
- def train_net(args_opt,
- cross_valid_ind=1,
- epochs=400,
- batch_size=16,
- lr=0.0001,
- cfg=None):
- rank = 0
- group_size = 1
- data_dir = args_opt.data_url
- run_distribute = args_opt.run_distribute
- if run_distribute:
- init()
- group_size = get_group_size()
- rank = get_rank()
- parallel_mode = ParallelMode.DATA_PARALLEL
- context.set_auto_parallel_context(parallel_mode=parallel_mode,
- device_num=group_size,
- gradients_mean=False)
- need_slice = False
- if cfg['model'] == 'unet_medical':
- net = UNetMedical(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
- elif cfg['model'] == 'unet_nested':
- net = NestedUNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'], use_deconv=cfg['use_deconv'],
- use_bn=cfg['use_bn'], use_ds=cfg['use_ds'])
- need_slice = cfg['use_ds']
- elif cfg['model'] == 'unet_simple':
- net = UNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'])
- else:
- raise ValueError("Unsupported model: {}".format(cfg['model']))
-
- if cfg['resume']:
- param_dict = load_checkpoint(cfg['resume_ckpt'])
- if cfg['transfer_training']:
- filter_checkpoint_parameter_by_list(param_dict, cfg['filter_weight'])
- load_param_into_net(net, param_dict)
-
- if 'use_ds' in cfg and cfg['use_ds']:
- criterion = MultiCrossEntropyWithLogits()
- else:
- criterion = CrossEntropyWithLogits()
- if 'dataset' in cfg and cfg['dataset'] == "Cell_nuclei":
- repeat = cfg['repeat']
- dataset_sink_mode = True
- per_print_times = 0
- train_dataset = create_cell_nuclei_dataset(data_dir, cfg['img_size'], repeat, batch_size,
- is_train=True, augment=True, split=0.8, rank=rank,
- group_size=group_size)
- valid_dataset = create_cell_nuclei_dataset(data_dir, cfg['img_size'], 1, 1, is_train=False,
- eval_resize=cfg["eval_resize"], split=0.8,
- python_multiprocessing=False)
- else:
- repeat = cfg['repeat']
- dataset_sink_mode = False
- per_print_times = 1
- train_dataset, valid_dataset = create_dataset(data_dir, repeat, batch_size, True, cross_valid_ind,
- run_distribute, cfg["crop"], cfg['img_size'])
- train_data_size = train_dataset.get_dataset_size()
- print("dataset length is:", train_data_size)
- ckpt_config = CheckpointConfig(save_checkpoint_steps=train_data_size,
- keep_checkpoint_max=cfg['keep_checkpoint_max'])
- ckpoint_cb = ModelCheckpoint(prefix='ckpt_{}_adam'.format(cfg['model']),
- directory='./ckpt_{}/'.format(device_id),
- config=ckpt_config)
-
- optimizer = nn.Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=cfg['weight_decay'],
- loss_scale=cfg['loss_scale'])
-
- loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(cfg['FixedLossScaleManager'], False)
-
- model = Model(net, loss_fn=criterion, loss_scale_manager=loss_scale_manager, optimizer=optimizer, amp_level="O3")
-
- print("============== Starting Training ==============")
- callbacks = [StepLossTimeMonitor(batch_size=batch_size, per_print_times=per_print_times), ckpoint_cb]
- if args_opt.run_eval:
- eval_model = Model(UnetEval(net, need_slice=need_slice), loss_fn=TempLoss(),
- metrics={"dice_coeff": dice_coeff(cfg_unet, False)})
- eval_param_dict = {"model": eval_model, "dataset": valid_dataset, "metrics_name": args_opt.eval_metrics}
- eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=args_opt.eval_interval,
- eval_start_epoch=args_opt.eval_start_epoch, save_best_ckpt=True,
- ckpt_directory='./ckpt_{}/'.format(device_id), besk_ckpt_name="best.ckpt",
- metrics_name=args_opt.eval_metrics)
- callbacks.append(eval_cb)
- model.train(int(epochs / repeat), train_dataset, callbacks=callbacks, dataset_sink_mode=dataset_sink_mode)
- print("============== End Training ==============")
-
-
- def get_args():
- parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
- help='data directory')
- parser.add_argument('-t', '--run_distribute', type=ast.literal_eval,
- default=False, help='Run distribute, default: false.')
- parser.add_argument("--run_eval", type=ast.literal_eval, default=False,
- help="Run evaluation when training, default is False.")
- parser.add_argument("--save_best_ckpt", type=ast.literal_eval, default=True,
- help="Save best checkpoint when run_eval is True, default is True.")
- parser.add_argument("--eval_start_epoch", type=int, default=0,
- help="Evaluation start epoch when run_eval is True, default is 0.")
- parser.add_argument("--eval_interval", type=int, default=1,
- help="Evaluation interval when run_eval is True, default is 1.")
- parser.add_argument("--eval_metrics", type=str, default="dice_coeff", choices=("dice_coeff", "iou"),
- help="Evaluation metrics when run_eval is True, support [dice_coeff, iou], "
- "default is dice_coeff.")
-
- return parser.parse_args()
-
-
- if __name__ == '__main__':
- logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
- args = get_args()
- print("Training setting:", args)
-
- epoch_size = cfg_unet['epochs'] if not args.run_distribute else cfg_unet['distribute_epochs']
- train_net(args_opt=args,
- cross_valid_ind=cfg_unet['cross_valid_ind'],
- epochs=epoch_size,
- batch_size=cfg_unet['batchsize'],
- lr=cfg_unet['lr'],
- cfg=cfg_unet)
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