Upload processor

image_processing_efficientnet.py

@@ -1,27 +1,26 @@
-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"
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+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"
 ]