Transformation (Albumentations)

Albumentations

L’ho usata perché è migliore delle transformation integrate di PyTorch sia in velocità che in varietà.

Configurazione della transformation

Supponiamo di aver usato la seguente transformation per il training:

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transformation = A.Compose(
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    [
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        A.Resize(224, 224),
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        A.CenterCrop(100, 100),
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        A.HorizontalFlip(p=0.5),
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        A.OneOf(
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            [
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                A.MotionBlur(p=0.2),
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                A.MedianBlur(blur_limit=3, p=0.2),
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                A.Blur(blur_limit=3, p=0.2),
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            ],
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            p=1,
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        ),
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        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ),
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        albumentations.pytorch.transforms.ToTensorV2(),
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    ]
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)

Allora durante l’inference bisogna applicare lo stesso ridimensionamento, crop e normalizzazione:

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transformation = A.Compose(
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    [
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        A.Resize(224, 224),
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        A.CenterCrop(100, 100),
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        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ),
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        albumentations.pytorch.transforms.ToTensorV2(),
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    ]
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)

Quando applicare le transformation

Ho passato la transformation come parametro alla creazione del dataset e l’ho applicata in __getitem__.

Transformation usate

Training

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transformation = A.Compose(
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    [
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        A.Resize(224, 224),
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        A.HorizontalFlip(p=0.5),
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        A.OneOf([A.GaussNoise()], p=0.4),
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        A.OneOf(
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            [
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                A.MotionBlur(p=0.2),
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                A.MedianBlur(blur_limit=3, p=0.2),
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                A.Blur(blur_limit=3, p=0.2),
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            ],
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            p=1,
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        ),
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        A.OneOf(
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            [
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                A.HueSaturationValue(p=0.5),
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                A.RGBShift(p=0.5),
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                A.ChannelShuffle(p=0.5),
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            ],
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            p=1,
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        ),
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        A.ShiftScaleRotate(
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            shift_limit=0.2,
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            scale_limit=0.2,
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            rotate_limit=10,
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            border_mode=0,
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            p=0.4,
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        ),
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        A.CoarseDropout(p=0.5),
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        A.ColorJitter(p=0.3),
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        A.RandomBrightnessContrast(p=0.7),
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        # A.Rotate(limit=(-10, 10), p=0.4),
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        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ),
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        albumentations.pytorch.transforms.ToTensorV2(),
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    ]
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)

TTA

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   A.GaussNoise(var_limit=(20.0, 60.0),p=1),
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    A.MedianBlur(blur_limit=9, p=1),
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    A.Blur(blur_limit=9, p=1),
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    A.HueSaturationValue(hue_shift_limit=40, sat_shift_limit=40, val_shift_limit=40,p=1),
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    A.RGBShift(r_shift_limit=50, g_shift_limit=50, b_shift_limit=50,p=1),
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    A.ChannelDropout(p=1),
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    A.ChannelShuffle(p=1),
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    A.CoarseDropout(p=1),
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    A.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5,p=1),
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    A.RandomBrightnessContrast(brightness_limit=0.5, contrast_limit=0.5,p=1),
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    A.ShiftScaleRotate(
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            shift_limit=0.2,
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            scale_limit=0.2,
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            rotate_limit=10,
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            border_mode=0,
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            p=1,
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    ),