# 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)