Use *d transforms for dictionary-based datasets (recommended for large 3D data):
# Random Rotation (applies to image AND label with same params) RandRotated( keys=["image", "label"], range_x=np.pi / 12, mode=["bilinear", "nearest"], # Bilinear for image, nearest for mask prob=0.5 ), monai data augmentation
dataset = CacheDataset(data=data_list, transform=train_transform, cache_rate=1.0) loader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4) range_x=np.pi / 12
# Apply the data augmentation pipeline augmented_dataset = [] for img, label in dataset: for transform in transforms: img, label = transform(img, label) augmented_dataset.append((img, label)) # Bilinear for image