Update models.py

models.py

@@ -4,56 +4,18 @@ import torchvision
 import torchvision.transforms as T
 from PIL import Image
 
-class DinoVisionTransformerClassifier(nn.Module):
-    def __init__(self, num_classes):
-        super(DinoVisionTransformerClassifier, self).__init__()
+class LinearClassifier(torch.nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(LinearClassifier, self).__init__()
+        num_classes = len(index_to_species.keys())
 
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        # Workaround to bypass HTTP Error 403 rate limit exceeded 
-        torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
-        self.model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitb14_lc")
-        
-        self.model.linear_head = nn.Sequential(
-            nn.Linear(3840, 512, bias=True), 
+        self.linear_head = nn.Sequential(
+            nn.Linear(input_dim, 512, bias=True), 
             nn.ReLU(),
             nn.Linear(512, 256, bias=True),
             nn.ReLU(),
-            nn.Linear(256, num_classes, bias=True)
+            nn.Linear(256, output_dim, bias=True)
         )
 
-        self.model.to(self.device)
-
-        self.transform_image = T.Compose([
-            T.Resize((224, 224)),
-            T.ToTensor(),
-            T.Normalize(mean=[0.485, 0.456, 0.406],
-                                std=[0.229, 0.224, 0.225])
-        ])
-
-        self.model_name = "dinov2"
-
-    def load_image_from_filepath(self, img: str) -> torch.Tensor:
-        """
-        Load an image as filepath and return a tensor that can be used as an input to model.
-        """
-        img = Image.open(img).convert('RGB')
-
-        transformed_img = self.transform_image(img)[:3].unsqueeze(0).to(self.device)
-
-        return transformed_img
-
-    def load_image_from_pillowimage(self, img: Image.Image) -> torch.Tensor:
-        """
-        Load an image as Pillow Image and return a tensor that can be used as an input to model.
-        """
-        transformed_img = self.transform_image(img)[:3].unsqueeze(0).to(self.device)
-
-        return transformed_img
-
     def forward(self, x):
-        if isinstance(x, str):
-            x = self.load_image_from_filepath(x)
-        if isinstance(x, Image.Image):
-            x = self.load_image_from_pillowimage(x)
-        return self.model(x)
+        return self.linear_head(x)