Upload processor

image_processing_efficientnet.py

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+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from configuration_efficientnet import MODEL_NAMES
+from timm import create_model
+from timm.data import resolve_data_config
+from timm.data.transforms_factory import create_transform
+
+class EfficientNetImageProcessor(BaseImageProcessor):
+    model_input_names = ["pixel_values"]
+
+    def __init__(self, 
+                 model_name: str,
+                 **kwargs
+                 ):
+        super().__init__(**kwargs)
+
+        self.model_name = model_name
+        self.config = resolve_data_config({}, model=create_model(model_name, pretrained=False))
+        
+
+    def preprocess(self, image):
+        transforms = create_transform(**self.config)
+        data = {'pixel_values': transforms(image).unsqueeze(0)}
+        return BatchFeature(data=data)
+
+__all__ = [
+    "EfficientNetImageProcessor"
+]

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoImageProcessor": "image_processing_efficientnet.EfficientNetImageProcessor"
+  },
+  "config": {
+    "crop_mode": "center",
+    "crop_pct": 0.9,
+    "input_size": [
+      3,
+      240,
+      240
+    ],
+    "interpolation": "bicubic",
+    "mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "std": [
+      0.5,
+      0.5,
+      0.5
+    ]
+  },
+  "image_processor_type": "EfficientNetImageProcessor",
+  "model_name": "efficientnet_b1"
+}