Fairness-Generalization/training/transform.py at main · Purdue-M2/Fairness-Generalization · GitHub

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from torchvision import transforms

xception_default_data_transforms = {
    'train': transforms.Compose([
        transforms.Resize((299, 299)),
        transforms.ToTensor(),
        transforms.Normalize([0.5]*3, [0.5]*3)
    ]),
    'val': transforms.Compose([
        transforms.Resize((299, 299)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
    'test': transforms.Compose([
        transforms.Resize((299, 299)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
}

fair_df_default_data_transforms = {
    'train': transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5]*3, [0.5]*3)
    ]),
    'val': transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
    'test': transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
}


resnet_default_data_transforms = {
    'train': transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.5]*3, [0.5]*3)
    ]),
    'val': transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
    'test': transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.5] * 3, [0.5] * 3)
    ]),
}