src/transforms.py

"""Image transforms for training, validation, and test splits."""

from torchvision import transforms

IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
IMG_SIZE = 224


def get_train_transforms(augment_level="standard"):
    if augment_level == "mild":
        return transforms.Compose([
            transforms.Resize((IMG_SIZE, IMG_SIZE)),
            transforms.RandomHorizontalFlip(),
            transforms.RandomResizedCrop(IMG_SIZE, scale=(0.8, 1.0)),
            transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.1),
            transforms.ToTensor(),
            transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
            transforms.RandomErasing(p=0.1),
        ])
    return transforms.Compose([
        transforms.Resize((IMG_SIZE, IMG_SIZE)),
        transforms.RandomHorizontalFlip(),
        transforms.RandomResizedCrop(IMG_SIZE, scale=(0.8, 1.0)),
        transforms.ToTensor(),
        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
    ])


def get_eval_transforms():
    return transforms.Compose([
        transforms.Resize((IMG_SIZE, IMG_SIZE)),
        transforms.ToTensor(),
        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
    ])