# Copyright 2020 - 2021 MONAI Consortium # 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 math import os import copy import numpy as np import torch from monai import data, transforms from monai.data import load_decathlon_datalist class Sampler(torch.utils.data.Sampler): def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, make_even=True): if num_replicas is None: if not torch.distributed.is_available(): raise RuntimeError("Requires distributed package to be available") num_replicas = torch.distributed.get_world_size() if rank is None: if not torch.distributed.is_available(): raise RuntimeError("Requires distributed package to be available") rank = torch.distributed.get_rank() self.shuffle = shuffle self.make_even = make_even self.dataset = dataset self.num_replicas = num_replicas self.rank = rank self.epoch = 0 self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) self.total_size = self.num_samples * self.num_replicas indices = list(range(len(self.dataset))) self.valid_length = len(indices[self.rank : self.total_size : self.num_replicas]) def __iter__(self): if self.shuffle: g = torch.Generator() g.manual_seed(self.epoch) indices = torch.randperm(len(self.dataset), generator=g).tolist() else: indices = list(range(len(self.dataset))) if self.make_even: if len(indices) < self.total_size: if self.total_size - len(indices) < len(indices): indices += indices[: (self.total_size - len(indices))] else: extra_ids = np.random.randint(low=0, high=len(indices), size=self.total_size - len(indices)) indices += [indices[ids] for ids in extra_ids] assert len(indices) == self.total_size indices = indices[self.rank : self.total_size : self.num_replicas] self.num_samples = len(indices) return iter(indices) def __len__(self): return self.num_samples def set_epoch(self, epoch): self.epoch = epoch ######################################################################### # BTCV Dataset ######################################################################### def get_loader_btcv(args): data_dir = args.data_dir datalist_json = os.path.join(data_dir, args.json_list) # transforms for training train_transform = transforms.Compose( [ transforms.LoadImaged(keys=["image", "label"]), transforms.AddChanneld(keys=["image", "label"]), transforms.Orientationd(keys=["image", "label"], axcodes="RAS"), transforms.Spacingd(keys=["image", "label"], pixdim=(args.space_x, args.space_y, args.space_z), mode=("bilinear", "nearest")), transforms.ScaleIntensityRanged(keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True), transforms.CropForegroundd(keys=["image", "label"], source_key="image"), transforms.RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(args.roi_x, args.roi_y, args.roi_z), pos=1, neg=1, num_samples=4, image_key="image", image_threshold=0, ), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=0), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=1), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=2), transforms.RandRotate90d(keys=["image", "label"], prob=args.RandRotate90d_prob, max_k=3), transforms.RandScaleIntensityd(keys="image", factors=0.1, prob=args.RandScaleIntensityd_prob), transforms.RandShiftIntensityd(keys="image", offsets=0.1, prob=args.RandShiftIntensityd_prob), transforms.ToTensord(keys=["image", "label"]), ] ) # transforms for validation/test mode val_transform = transforms.Compose( [ transforms.LoadImaged(keys=["image", "label"]), transforms.AddChanneld(keys=["image", "label"]), transforms.Orientationd(keys=["image", "label"], axcodes="RAS"), transforms.Spacingd(keys=["image", "label"], pixdim=(args.space_x, args.space_y, args.space_z), mode=("bilinear", "nearest")), transforms.ScaleIntensityRanged(keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True), transforms.CropForegroundd(keys=["image", "label"], source_key="image"), transforms.ToTensord(keys=["image", "label"]), ] ) if args.gen_train_adv_mode: print('Loader: Mode = Generate Train-Adv Images ...') files = load_decathlon_datalist(datalist_json, True, "training", base_dir=data_dir) dataset = data.Dataset(data=files, transform=val_transform) sampler = Sampler(dataset, shuffle=False) if args.distributed else None loader = data.DataLoader( dataset, batch_size=1, shuffle=False, num_workers=args.workers, sampler=sampler, pin_memory=True, persistent_workers=True, ) elif args.test_mode or args.gen_val_adv_mode: print('\nLoader: Mode = Clean Validation Files ...') test_files = load_decathlon_datalist(datalist_json, True, "validation", base_dir=data_dir) test_ds = data.Dataset(data=test_files, transform=val_transform) test_sampler = Sampler(test_ds, shuffle=False) if args.distributed else None test_loader = data.DataLoader( test_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=test_sampler, pin_memory=True, persistent_workers=True, ) loader = test_loader else: print('\nLoader: Mode = Clean Train+Test Files ...') datalist = load_decathlon_datalist(datalist_json, True, "training", base_dir=data_dir) if args.use_normal_dataset: train_ds = data.Dataset(data=datalist, transform=train_transform) else: train_ds = data.CacheDataset(data=datalist, transform=train_transform, cache_num=24, cache_rate=1.0, num_workers=args.workers) train_sampler = Sampler(train_ds) if args.distributed else None train_loader = data.DataLoader( train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, sampler=train_sampler, pin_memory=True, persistent_workers=True, ) val_files = load_decathlon_datalist(datalist_json, True, "validation", base_dir=data_dir) val_ds = data.Dataset(data=val_files, transform=val_transform) val_sampler = Sampler(val_ds, shuffle=False) if args.distributed else None val_loader = data.DataLoader( val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True, persistent_workers=True, ) loader = [train_loader, val_loader] return loader ######################################################################### # ACDC Dataset ######################################################################### def get_loader_acdc(args): data_dir = args.data_dir datalist_json = os.path.join(data_dir, args.json_list) train_transform = transforms.Compose( [ transforms.LoadImaged(keys=["image", "label"]), transforms.AddChanneld(keys=["image", "label"]), transforms.Orientationd(keys=["image", "label"], axcodes="RAS"), transforms.Spacingd(keys=["image", "label"], pixdim=(args.space_x, args.space_y, args.space_z), mode=("bilinear", "nearest")), transforms.ScaleIntensityRanged(keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True), transforms.CropForegroundd(keys=["image", "label"], source_key="image"), transforms.ResizeWithPadOrCropd(keys=["image", "label"],spatial_size=(args.roi_x, args.roi_y, args.roi_z), mode='constant', value=0), transforms.RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(args.roi_x, args.roi_y, args.roi_z), pos=1, neg=1, num_samples=4, image_key="image", image_threshold=0, # allow_smaller=True, ), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=0), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=1), transforms.RandFlipd(keys=["image", "label"], prob=args.RandFlipd_prob, spatial_axis=2), transforms.RandRotate90d(keys=["image", "label"], prob=args.RandRotate90d_prob, max_k=3), transforms.RandScaleIntensityd(keys="image", factors=0.1, prob=args.RandScaleIntensityd_prob), transforms.RandShiftIntensityd(keys="image", offsets=0.1, prob=args.RandShiftIntensityd_prob), transforms.ToTensord(keys=["image", "label"]), ] ) val_transform = transforms.Compose( [ transforms.LoadImaged(keys=["image", "label"]), transforms.AddChanneld(keys=["image", "label"]), transforms.Orientationd(keys=["image", "label"], axcodes="RAS"), transforms.Spacingd(keys=["image", "label"], pixdim=(args.space_x, args.space_y, args.space_z), mode=("bilinear", "nearest")), transforms.ResizeWithPadOrCropd(keys=["image", "label"],spatial_size=(args.roi_x, args.roi_y, args.roi_z) , mode='constant', value=0), transforms.ScaleIntensityRanged(keys=["image"], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True), transforms.CropForegroundd(keys=["image", "label"], source_key="image"), transforms.ToTensord(keys=["image", "label"]), ] ) if args.gen_train_adv_mode: print('Loader: Mode = Generate Train-Adv Images ...') datalist = load_decathlon_datalist(datalist_json, True, "training", base_dir=data_dir) if args.use_normal_dataset: train_ds = data.Dataset(data=datalist, transform=val_transform) else: train_ds = data.CacheDataset(data=datalist, transform=val_transform, cache_num=24, cache_rate=1.0, num_workers=args.workers) train_sampler = Sampler(train_ds) if args.distributed else None train_loader = data.DataLoader( train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, sampler=train_sampler, pin_memory=True, persistent_workers=True, ) loader = train_loader elif args.test_mode or args.gen_val_adv_mode: print('\nLoader: Mode = Clean Validation Files ...') test_files = load_decathlon_datalist(datalist_json, True, "validation", base_dir=data_dir) test_ds = data.Dataset(data=test_files, transform=val_transform) test_sampler = Sampler(test_ds, shuffle=False) if args.distributed else None test_loader = data.DataLoader( test_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=test_sampler, pin_memory=True, persistent_workers=True, ) loader = test_loader else: print('\nLoader: Mode = Clean Train+Test Files ...') datalist = load_decathlon_datalist(datalist_json, True, "training", base_dir=data_dir) if args.use_normal_dataset: train_ds = data.Dataset(data=datalist, transform=train_transform) else: train_ds = data.CacheDataset( data=datalist, transform=train_transform, cache_num=24, cache_rate=1.0, num_workers=args.workers ) train_sampler = Sampler(train_ds) if args.distributed else None train_loader = data.DataLoader( train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, sampler=train_sampler, pin_memory=True, persistent_workers=True, ) val_files = load_decathlon_datalist(datalist_json, True, "validation", base_dir=data_dir) val_ds = data.Dataset(data=val_files, transform=val_transform) val_sampler = Sampler(val_ds, shuffle=False) if args.distributed else None val_loader = data.DataLoader( val_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=val_sampler, pin_memory=True, persistent_workers=True, ) loader = [train_loader, val_loader] return loader