utils/mytransforms.py [182:210]:
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                return i, j, h, w

        # Fallback to central crop
        in_ratio = float(width) / float(height)
        if in_ratio < min(ratio):
            w = width
            h = int(round(w / min(ratio)))
        elif in_ratio > max(ratio):
            h = height
            w = int(round(h * max(ratio)))
        else:  # whole image
            w = width
            h = height
        i = (height - h) // 2
        j = (width - w) // 2
        return i, j, h, w

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Image to be cropped and resized.

        Returns:
            PIL Image or Tensor: Randomly cropped and resized image.
        """
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)

    def __repr__(self):
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utils/mytransforms.py [362:390]:
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                return i, j, h, w

        # Fallback to central crop
        in_ratio = float(width) / float(height)
        if in_ratio < min(ratio):
            w = width
            h = int(round(w / min(ratio)))
        elif in_ratio > max(ratio):
            h = height
            w = int(round(h * max(ratio)))
        else:  # whole image
            w = width
            h = height
        i = (height - h) // 2
        j = (width - w) // 2
        return i, j, h, w

    def forward(self, img):
        """
        Args:
            img (PIL Image or Tensor): Image to be cropped and resized.

        Returns:
            PIL Image or Tensor: Randomly cropped and resized image.
        """
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)

    def __repr__(self):
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