Upload processor

preprocessor_config.json

@@ -1,4 +1,7 @@
 {
+  "auto_map": {
+    "AutoImageProcessor": "preprocessor_lenet.LeNetProcessor"
+  },
   "crop_size": null,
   "data_format": "channels_first",
   "default_to_square": true,

preprocessor_lenet.py

@@ -0,0 +1,36 @@
+from transformers import BaseImageProcessorFast, BatchFeature
+from transformers.image_transforms import (
+    normalize, 
+)
+from transformers.image_utils import (
+    ImageInput, 
+    to_numpy_array,
+)
+
+class LeNetProcessor(BaseImageProcessorFast):
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        return_tensors=None,
+        **kwargs
+    ) -> BatchFeature:
+        
+        if not isinstance(images, list):
+            images = [images]
+
+        images = [to_numpy_array(img) for img in images]
+
+        processed_images = []
+        for img in images:
+            processed_images.append(normalize(img, mean=0.1307, std=0.3081))
+
+        data = {"pixel_values": processed_images}
+        return BatchFeature(data=data, tensor_type=return_tensors)