Upload processor

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoImageProcessor": "processor.CondViTProcessor",
+    "AutoProcessor": "processor.CondViTProcessor"
+  },
+  "bkg_color": 255,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CondViTProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "input_resolution": 224
+}

processor.py

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+from transformers.image_processing_utils import ImageProcessingMixin, BatchFeature
+
+from torchvision.transforms import transforms as tf
+import torchvision.transforms.functional as F
+from PIL import Image
+import torch
+
+
+class CondViTProcessor(ImageProcessingMixin):
+    def __init__(
+        self,
+        bkg_color=255,
+        input_resolution=224,
+        image_mean=(0.48145466, 0.4578275, 0.40821073),
+        image_std=(0.26862954, 0.26130258, 0.27577711),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.bkg_color = bkg_color
+        self.input_resolution = input_resolution
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def square_pad(self, image):
+        max_wh = max(image.size)
+        p_left, p_top = [(max_wh - s) // 2 for s in image.size]
+        p_right, p_bottom = [
+            max_wh - (s + pad) for s, pad in zip(image.size, [p_left, p_top])
+        ]
+        padding = (p_left, p_top, p_right, p_bottom)
+        return F.pad(image, padding, self.bkg_color, "constant")
+
+    def process_img(self, image):
+        img = self.square_pad(image)
+        img = F.resize(img, self.input_resolution)
+        img = F.to_tensor(img)
+        img = F.normalize(img, self.image_mean, self.image_std)
+        return img
+
+    def __call__(self, images, texts):
+        """
+        Parameters
+        ----------
+        images : Union[Image.Image, List[Image.Image]]
+            Image or list of images to process
+        texts : Union[str, List[str]]
+            Text or list of texts to process. Pass through, no operation is performed.
+
+        Returns
+        -------
+        BatchFeature
+            pixel_values : torch.Tensor
+                Processed image tensor (B C H W)
+            texts : Union[str, List[str]]
+        """
+        # Single Image
+        if isinstance(images, Image.Image):
+            images = BatchFeature(
+                data={"pixel_values": self.process_img(images), "texts": texts}
+            )
+
+        return BatchFeature(
+            data={
+                "pixel_values": torch.stack([self.process_img(img) for img in images]),
+                "texts": texts,
+            }
+        )