Update model.py

corrected wrong torchvision import

model.py

@@ -1,31 +1,31 @@
-import torch
-from torchvision import transforms
-from torch import nn
-from torch import torchvision
-def create_vit_model(num_classes: int = 3,
-                     seed: int = 42):
-    """Creates a ViT-B/16 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of target classes. Defaults to 3.
-        seed (int, optional): random seed value for output layer. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): ViT-B/16 feature extractor model.
-        transforms (torchvision.transforms): ViT-B/16 image transforms.
-    """
-    # Create ViT_B_16 pretrained weights, transforms and model
-    weights = torchvision.models.ViT_B_16_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.vit_b_16(weights=weights)
-
-    # Freeze all layers in model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head to suit our needs (this will be trainable)
-    torch.manual_seed(seed)
-    model.heads = nn.Sequential(nn.Linear(in_features=768,  # keep this the same as original model
-                                          out_features=num_classes))  # update to reflect target number of classes
-
+import torch
+from torchvision import transforms
+from torch import nn
+import torchvision
+def create_vit_model(num_classes: int = 3,
+                     seed: int = 42):
+    """Creates a ViT-B/16 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of target classes. Defaults to 3.
+        seed (int, optional): random seed value for output layer. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): ViT-B/16 feature extractor model.
+        transforms (torchvision.transforms): ViT-B/16 image transforms.
+    """
+    # Create ViT_B_16 pretrained weights, transforms and model
+    weights = torchvision.models.ViT_B_16_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.vit_b_16(weights=weights)
+
+    # Freeze all layers in model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head to suit our needs (this will be trainable)
+    torch.manual_seed(seed)
+    model.heads = nn.Sequential(nn.Linear(in_features=768,  # keep this the same as original model
+                                          out_features=num_classes))  # update to reflect target number of classes
+
     return model, transforms