model.py

model.py

@@ -0,0 +1,34 @@
+
+import torch
+import torchvision
+
+from torch import nn
+
+
+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