Delete dataset/transforms.py with huggingface_hub

dataset/transforms.py

@@ -1,133 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-def crop(image, i, j, h, w):
-    """
-    Args:
-        image (torch.tensor): Image to be cropped. Size is (C, H, W)
-    """
-    if len(image.size()) != 3:
-        raise ValueError("image should be a 3D tensor")
-    return image[..., i : i + h, j : j + w]
-
-def resize(image, target_size, interpolation_mode):
-    if len(target_size) != 2:
-        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
-    return F.interpolate(image.unsqueeze(0), size=target_size, mode=interpolation_mode, align_corners=False).squeeze(0)
-
-def resize_scale(image, target_size, interpolation_mode):
-    if len(target_size) != 2:
-        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
-    H, W = image.size(-2), image.size(-1)
-    scale_ = target_size[0] / min(H, W)
-    return F.interpolate(image.unsqueeze(0), scale_factor=scale_, mode=interpolation_mode, align_corners=False).squeeze(0)
-
-def resized_crop(image, i, j, h, w, size, interpolation_mode="bilinear"):
-    """
-    Do spatial cropping and resizing to the image
-    Args:
-        image (torch.tensor): Image to be cropped. Size is (C, H, W)
-        i (int): i in (i,j) i.e coordinates of the upper left corner.
-        j (int): j in (i,j) i.e coordinates of the upper left corner.
-        h (int): Height of the cropped region.
-        w (int): Width of the cropped region.
-        size (tuple(int, int)): height and width of resized image
-    Returns:
-        image (torch.tensor): Resized and cropped image. Size is (C, H, W)
-    """
-    if len(image.size()) != 3:
-        raise ValueError("image should be a 3D torch.tensor")
-    image = crop(image, i, j, h, w)
-    image = resize(image, size, interpolation_mode)
-    return image
-
-def center_crop(image, crop_size):
-    if len(image.size()) != 3:
-        raise ValueError("image should be a 3D torch.tensor")
-    h, w = image.size(-2), image.size(-1)
-    th, tw = crop_size
-    if h < th or w < tw:
-        raise ValueError("height and width must be no smaller than crop_size")
-    i = int(round((h - th) / 2.0))
-    j = int(round((w - tw) / 2.0))
-    return crop(image, i, j, th, tw)
-
-def center_crop_using_short_edge(image):
-    if len(image.size()) != 3:
-        raise ValueError("image should be a 3D torch.tensor")
-    h, w = image.size(-2), image.size(-1)
-    if h < w:
-        th, tw = h, h
-        i = 0
-        j = int(round((w - tw) / 2.0))
-    else:
-        th, tw = w, w
-        i = int(round((h - th) / 2.0))
-        j = 0
-    return crop(image, i, j, th, tw)
-
-class CenterCropResizeImage:
-    """
-    Resize the image while maintaining aspect ratio, and then crop it to the desired size.
-    The resizing is done such that the area of padding/cropping is minimized.
-    """
-    def __init__(self, size, interpolation_mode="bilinear"):
-        if isinstance(size, tuple):
-            if len(size) != 2:
-                raise ValueError(f"Size should be a tuple (height, width), instead got {size}")
-            self.size = size
-        else:
-            self.size = (size, size)
-        self.interpolation_mode = interpolation_mode
-
-    def __call__(self, image):
-        """
-        Args:
-            image (torch.Tensor): Image to be resized and cropped. Size is (C, H, W)
-        
-        Returns:
-            torch.Tensor: Resized and cropped image. Size is (C, target_height, target_width)
-        """
-        target_height, target_width = self.size
-        target_aspect = target_width / target_height
-
-        # Get current image shape and aspect ratio
-        _, height, width = image.shape
-        height, width = float(height), float(width)
-        current_aspect = width / height
-
-        # Calculate crop dimensions
-        if current_aspect > target_aspect:
-            # Image is wider than target, crop width
-            crop_height = height
-            crop_width = height * target_aspect
-        else:
-            # Image is taller than target, crop height
-            crop_height = width / target_aspect
-            crop_width = width
-
-        # Calculate crop coordinates (center crop)
-        y1 = (height - crop_height) / 2
-        x1 = (width - crop_width) / 2
-
-        # Perform the crop
-        cropped_image = crop(image, int(y1), int(x1), int(crop_height), int(crop_width))
-
-        # Resize the cropped image to the target size
-        resized_image = resize(cropped_image, self.size, self.interpolation_mode)
-
-        return resized_image
-    
-# Example usage
-if __name__ == "__main__":
-    # Create a sample image tensor
-    sample_image = torch.rand(3, 480, 640)  # (C, H, W)
-
-    # Initialize the transform
-    transform = CenterCropResizeImage(size=(224, 224), interpolation_mode="bilinear")
-
-    # Apply the transform
-    transformed_image = transform(sample_image)
-
-    print(f"Original image shape: {sample_image.shape}")
-    print(f"Transformed image shape: {transformed_image.shape}")