| import torch.utils.data as data |
| import torch |
| import logging |
| from timm.data.distributed_sampler import RepeatAugSampler |
| from .xray_transform import build_transform_xray, build_seg_transform |
| from .chest_dset import MedFMC_Chest, ShenZhenCXR |
| from .vindr import VinDrNew |
| from .rsna import RSNA, RSNA_3Class, RSNASegmentDataset |
| from .mimic import MIMIC_Pretrain |
| from .world_model_transform import build_world_model_transform |
| from .xray_transform_add import build_world_model_dual_transform |
| from .nih import NIH |
| from .chexpert import CheXpert |
| from .siim import SIIM |
|
|
| def build_dataset(args, split='train', ten_crop=False): |
| transform = build_transform_xray(args, is_train=(split == 'train'), ten_crop=ten_crop) |
| if args.dataset == 'vindr_new': |
| logging.info(f'Dataset VinDr New: Pct={args.data_pct}') |
| dataset = VinDrNew(transform=transform, data_pct=args.data_pct, split=split, dataset_seed=args.dataset_seed) |
| elif args.dataset == 'rsna': |
| logging.info(f'Dataset RSNA: Pct={args.data_pct}') |
| dataset = RSNA(transform=transform, data_pct=args.data_pct, split=split) |
| elif args.dataset == 'rsna_3cls': |
| dataset = RSNA_3Class(transform=transform, data_pct=args.data_pct, split=split) |
| elif args.dataset == 'medfmc': |
| logging.info(f'Dataset MedFMC: {args.fmc_shots} Shots, No.{args.fmc_exp_num}') |
| dataset = MedFMC_Chest(exp_num=args.fmc_exp_num, shots=args.fmc_shots, split=split, transform=transform) |
| elif args.dataset == 'nih': |
| logging.info(f'Dataset NIH: Pct={args.data_pct}') |
| dataset = NIH(split=split, data_pct=args.data_pct, transform=transform, return_label=True) |
| elif args.dataset == 'shenzhen': |
| logging.info(f'Dataset ShenZhen') |
| dataset = ShenZhenCXR(split=split, transform=transform) |
| elif args.dataset == 'chex': |
| dataset = CheXpert(transform=transform, return_label=True, data_pct=args.data_pct, split=split) |
| else: |
| raise NotImplementedError |
| if args.dataset_cat > 1 and split == 'train': |
| logging.info(f'Concat Dataset x{args.dataset_cat}') |
| dataset = data.ConcatDataset([dataset for _ in range(args.dataset_cat)]) |
| dataset.num_classes = dataset.datasets[0].num_classes |
| return dataset |
|
|
|
|
| def build_seg_dataset(args, split='train'): |
| transform = build_seg_transform(args, is_train=(split=='train')) |
| if args.dataset == 'siim': |
| dataset = SIIM(transform=transform, split=split, data_pct=args.data_pct, balance=args.dataset_balance) |
| elif args.dataset == 'rsna': |
| dataset = RSNASegmentDataset(split=split, transform=transform, data_pct=args.data_pct) |
| else: |
| raise NotImplementedError |
| if args.dataset_cat > 1 and split == 'train': |
| logging.info(f'Concat Dataset x{args.dataset_cat}') |
| dataset = data.ConcatDataset([dataset for _ in range(args.dataset_cat)]) |
| dataset.num_classes = dataset.datasets[0].num_classes |
| return dataset |
|
|
| def build_pretrain_dataset(args, ssl_type): |
| if ssl_type == 'iwm': |
| logging.info('Build World Model Transform') |
| transform = build_world_model_transform(args) |
| elif 'iwm_dual' in ssl_type: |
| transform = build_world_model_dual_transform(args) |
| else: |
| transform = build_transform_xray(args, is_train=True) |
| |
| if args.dataset == 'mimic': |
| logging.info('Use MIMIC dataset') |
| dataset = MIMIC_Pretrain(transform=transform, split='all', data_pct=args.data_pct, preload=args.preload_dataset, data_postfix=args.data_postfix, include_lateral=args.include_lateral) |
| else: |
| logging.info(f'Use Hybrid dataset {args.dataset}') |
| d_list = [] |
| if 'nih' in args.dataset: |
| d_list.append(NIH(transform=transform, data_pct=args.data_pct, return_label=False)) |
| if 'chex' in args.dataset: |
| d_list.append(CheXpert(transform=transform, data_pct=args.data_pct, return_label=False, include_lateral=args.include_lateral)) |
| if 'mimic' in args.dataset: |
| d_list.append(MIMIC_Pretrain(transform=transform, split='all', data_pct=args.data_pct, data_postfix=args.data_postfix, include_lateral=args.include_lateral)) |
| dataset = data.ConcatDataset(d_list) |
| logging.info(f'Total Images: {len(dataset)}') |
| return dataset |
|
|
| def get_loader(args, dataset, is_train=True, persistent_workers=True, custom_bs=None): |
| if is_train: |
| if args.num_aug_repeats: |
| sampler = RepeatAugSampler(dataset, num_repeats=args.num_aug_repeats) |
| logging.info(f'Use Repeated Aug Sampler: {args.num_aug_repeats}') |
| else: |
| num_tasks = args.world_size |
| global_rank = args.rank |
| sampler = data.DistributedSampler( |
| dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True |
| ) |
| else: |
| sampler = data.SequentialSampler(dataset) |
| |
| this_bs = args.batch_size if is_train else args.batch_size * 2 |
| this_bs = custom_bs if custom_bs is not None else this_bs |
| return data.DataLoader( |
| dataset, sampler=sampler, |
| batch_size=this_bs, |
| num_workers=args.num_workers, |
| pin_memory=args.pin_mem, |
| drop_last=is_train, |
| persistent_workers=persistent_workers |
| ) |
|
|
| def get_infinite_loader(args, dataset, batch_size, custom_iter=None): |
| |
| |
| |
| num_iters = custom_iter if custom_iter is not None else args.num_iters |
| num_samples = batch_size * num_iters |
| if args.data_seed is not None: |
| generator = torch.Generator() |
| generator.manual_seed(args.data_seed) |
| logging.info(f'Use Data Seed {args.data_seed}') |
| else: |
| generator = None |
| logging.info(f'No Data Seed') |
|
|
| sampler = data.RandomSampler(dataset, replacement=(not args.data_no_replace), num_samples=num_samples, generator=generator) |
| |
| sampler = data.BatchSampler(sampler, batch_size, drop_last=True) |
| return data.DataLoader( |
| dataset, |
| batch_sampler=sampler, |
| num_workers=args.num_workers, |
| pin_memory=args.pin_mem, |
| persistent_workers=True |
| ) |
