Create model.py

model.py

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+import torch
+import torchvision
+from torchvision import transforms
+import torch.nn as nn
+from torchvision.models import mobilenet_v2
+
+# Load MobileNetV2 with pre-trained weights
+
+
+def create_effnetb2_model(num_classes:int=4, 
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    
+    transforms = transforms.Compose([
+        transforms.Resize((224, 224)), # 1. Reshape all images to 224x224 (though some models may require different sizes)
+        transforms.ToTensor(), # 2. Turn image values to between 0 & 1
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)
+                         std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),
+                                                    ])
+    model = mobilenet_v2(pretrained=True)
+
+    # Freeze all layers in base model
+    # Freeze all base layers by setting requires_grad attribute to False
+    for param in model.parameters():
+        param.requires_grad = False
+    
+    # Since we're creating a new layer with random weights (torch.nn.Linear),
+    # let's set the seeds
+    torch.manual_seed(42)
+    
+    # Update the classifier head to suit our problem
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.2, inplace=True),
+        nn.Linear(in_features=model.classifier[1].in_features,  # Accessing the last layer of the classifier
+                  out_features=num_classes,
+                  bias=True)
+    )
+    
+    return model, transforms
+