| from src.models.build_sam3D import sam_model_registry3D |
| from src.dataset.dataloader import Dataset_promise, Dataloader_promise |
| import torchio as tio |
| from torch.nn.parallel import DistributedDataParallel as DDP |
| from torch.utils.data.distributed import DistributedSampler |
| import torch |
| def get_dataloader(args, split='', use_small=False): |
| transforms_list = [tio.ToCanonical(), tio.Resample(1), ] |
| if split == 'train': |
| transforms_list.append(tio.RandomFlip(axes=(0, 1, 2))) |
|
|
| transforms = tio.Compose(transforms_list) |
|
|
| dataset = Dataset_promise( |
| data=args.data, |
| data_dir=args.data_dir, |
| split=split, |
| transform=transforms, |
| image_size=args.image_size, |
| args=args, |
| ) |
|
|
| batch_size = args.batch_size if split == 'train' else 1 |
|
|
| if split == 'train': |
| train_sampler = None |
| shuffle = True |
| if args.ddp: |
| train_sampler = DistributedSampler(dataset) |
| shuffle = False |
| else: |
| train_sampler = None |
| shuffle = False |
|
|
| pin_memory = True |
| if split != 'train' and args.data == 'lits': |
| pin_memory = False |
|
|
| dataloader = Dataloader_promise( |
| dataset=dataset, |
| sampler=train_sampler, |
| batch_size=batch_size, |
| shuffle=shuffle, |
| num_workers=args.num_workers, |
| pin_memory=pin_memory, |
| ) |
| return dataloader |
|
|
|
|
|
|
| def build_model(args, checkpoint=None): |
| sam_model = sam_model_registry3D[args.model_type](checkpoint=checkpoint, args=args).to(args.device) |
| if args.ddp: |
| sam_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(sam_model) |
| sam_model = DDP(sam_model, device_ids=[args.rank], output_device=args.rank) |
| return sam_model |
|
|
