Pytorch newbie here! I am trying to fine-tune a VGG16 model to predict 3 different classes. Part of my work involves converting FC layers to CONV layers. However, the values of my predictions don’t fall between 0 to 2 (the 3 classes).
Can someone point me to a good resource on how to compute the correct dimensions for the final layer?
Here are the original fC layers of VGG16:
(classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )

My code for converting FC layers to CONV:
 def convert_fc_to_conv(self, fc_layers):
        # Replace first FC layer with CONV layer
        fc = fc_layers[0].state_dict()
        in_ch = 512
        out_ch = fc["weight"].size(0)
        first_conv = nn.Conv2d(512, out_ch, kernel_size=(1, 1), stride=(1, 1))

        conv_list = [first_conv]
        for idx, layer in enumerate(fc_layers[1:]):
            if isinstance(layer, nn.Linear):
                fc = layer.state_dict()
                in_ch = fc["weight"].size(1)
                out_ch = fc["weight"].size(0)
                if idx == len(fc_layers)-4:
                    in_ch = 3
                conv = nn.Conv2d(out_ch, in_ch, kernel_size=(1, 1), stride=(1, 1))
                conv_list += [conv]
            else:
                conv_list += [layer]
            gc.collect()
        
        avg_pool = nn.AvgPool2d(kernel_size=2, stride=1, ceil_mode=False)
        conv_list += [avg_pool, nn.Softmax()]
        top_layers = nn.Sequential(*conv_list)  
        return top_layers

Final model architecture:
    Model(
    (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))
    
    (classifier): Sequential(
    (0): Conv2d(512, 4096, kernel_size=(1, 1), stride=(1, 1))
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Conv2d(4096, 3, kernel_size=(1, 1), stride=(1, 1))
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): AvgPool2d(kernel_size=2, stride=1, padding=0)
    (7): Softmax()
  )
)

Summary of the model:
            Conv2d-1         [-1, 64, 224, 224]           1,792
              ReLU-2         [-1, 64, 224, 224]               0
            Conv2d-3         [-1, 64, 224, 224]          36,928
              ReLU-4         [-1, 64, 224, 224]               0
         MaxPool2d-5         [-1, 64, 112, 112]               0
            Conv2d-6        [-1, 128, 112, 112]          73,856
              ReLU-7        [-1, 128, 112, 112]               0
            Conv2d-8        [-1, 128, 112, 112]         147,584
              ReLU-9        [-1, 128, 112, 112]               0
        MaxPool2d-10          [-1, 128, 56, 56]               0
           Conv2d-11          [-1, 256, 56, 56]         295,168
             ReLU-12          [-1, 256, 56, 56]               0
           Conv2d-13          [-1, 256, 56, 56]         590,080
             ReLU-14          [-1, 256, 56, 56]               0
           Conv2d-15          [-1, 256, 56, 56]         590,080
             ReLU-16          [-1, 256, 56, 56]               0
        MaxPool2d-17          [-1, 256, 28, 28]               0
           Conv2d-18          [-1, 512, 28, 28]       1,180,160
             ReLU-19          [-1, 512, 28, 28]               0
           Conv2d-20          [-1, 512, 28, 28]       2,359,808
             ReLU-21          [-1, 512, 28, 28]               0
           Conv2d-22          [-1, 512, 28, 28]       2,359,808
             ReLU-23          [-1, 512, 28, 28]               0
        MaxPool2d-24          [-1, 512, 14, 14]               0
           Conv2d-25          [-1, 512, 14, 14]       2,359,808
             ReLU-26          [-1, 512, 14, 14]               0
           Conv2d-27          [-1, 512, 14, 14]       2,359,808
             ReLU-28          [-1, 512, 14, 14]               0
           Conv2d-29          [-1, 512, 14, 14]       2,359,808
             ReLU-30          [-1, 512, 14, 14]               0
        MaxPool2d-31            [-1, 512, 7, 7]               0
           Conv2d-32           [-1, 4096, 7, 7]       2,101,248
             ReLU-33           [-1, 4096, 7, 7]               0
          Dropout-34           [-1, 4096, 7, 7]               0
           Conv2d-35              [-1, 3, 7, 7]          12,291
             ReLU-36              [-1, 3, 7, 7]               0
          Dropout-37              [-1, 3, 7, 7]               0
        AvgPool2d-38              [-1, 3, 6, 6]               0
          Softmax-39              [-1, 3, 6, 6]               0