Trouble understanding behaviour of modified VGG16 forward method (Pytorch)
I have modified VGG16 in pytorch to insert things like BN and dropout within the feature extractor. By chance I now noticed something strange when I changed the definition of the forward method from:
def forward(self, x):
    x = self.model(x)
    return x

to:
def forward(self, x):
    x = self.model.features(x)
    x = self.model.avgpool(x)
    x = self.model.classifier(x)
    return x

In the second method I am now getting an error that the sizes of the matrices don't match
(mat1 dim 1 must match mat2 dim 0)
Below is the entire code For the editted version of VGG that I've been using.
class Vgg(nn.Module):
    
    def __init__(self, n_classes, bias= None, dropout = 0.3):
        
        super().__init__()
        
        self.model = models.vgg16()
        #self.bn64 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        #self.bn128 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        #self.bn256 = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        #self.bn512 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        
        # change to allow 4 channels input
        self.model.features[0] = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))

        # remove/edit some of the first layers to make it more similar to Resnet
        del self.model.features[2]
        del self.model.features[2]
        del self.model.features[-1]
        self.model.features[2] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        
        # add dropout
        for m in self.model.modules():
            if isinstance(m, nn.Dropout):
                m.p = dropout
            else:
                pass
            
        self.dropout = nn.Dropout(p=dropout)
        self.r = nn.ReLU(inplace=True)
        
        modules = nn.Sequential(*[self.model.features[0],
                                  #self.bn64,
                                  self.model.features[1:3],
                                  #self.bn64,
                                  self.model.features[3:5],
                                  #self.dropout, 
                                  
                                  self.model.features[5],
                                  #self.bn128,
                                  self.model.features[6:8],
                                  #self.bn128,
                                  self.model.features[8:10],
                                  #self.dropout, 
                                  
                                  self.model.features[10],
                                  #self.bn256,
                                  self.model.features[11:13],
                                  #self.bn256,
                                  self.model.features[13:15],
                                  #self.bn256,
                                  self.model.features[15:17],
                                  #self.dropout,
                                  
                                  self.model.features[17],
                                  #self.bn512,
                                  self.model.features[18:20],
                                  #self.bn512,
                                  self.model.features[20:22],
                                  #self.bn512,
                                  self.model.features[22:24],
                                  #self.dropout,
                                  
                                  self.model.features[24],
                                  #self.bn512,
                                  self.model.features[25:27],
                                  #self.bn512,
                                  self.model.features[27:29],
                                  #self.bn512,
                                  #self.dropout
                                  nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
                                 ])
        
        
        self.model.features = modules
        
        # change the pooling layer
        self.model.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        
        # set output to correct num classes
        self.model.classifier = nn.Linear(in_features=512, out_features=n_classes, bias=True)
        
        # use predefined bias
        if bias is not None:
            assert isinstance(bias, torch.Tensor), 'bias must be tensor'
            self.model.classifier.bias = nn.Parameter(bias)
        
    def forward(self, x):
        x = self.model.features(x)
        x = self.model.avgpool(x)
        x = self.model.classifier(x)
        return x

I know it is very ugly and hacky looking. I have tried to re-write it but the re-written version does not work either for some reason, and I am assuming that this current issue is related to that as well. I think that the input is not being fed through the forward method the way I think it is. My assumption is that calling x = self.model(x) does not run 'x' through all of the editted layers I have made, otherwise I would get the same behaviour with the two version of the forward method above. But my question is then, what is happening when I call self.model(x) in forward? Is it running the input through the original vgg16 from pytorch? Because when I print self.model in my console it shows the changes I made to the architecture of self.model.features as well as self.model.avgpool and self.model.classifier.
Edit:
Below is the entire trace of the error. Some extra information. t is a class I made to deal with the training steps (so looping through training and validation modes, ect)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-7-0b3983ae9702&gt; in &lt;module&gt;
     77                   print_cl_met = True
     78                   )
---&gt; 79 model = t.run()
     80 t.save_to_json()
     81 print(np.max(np.array(t.f1_tracker)))

/home/stevea/treesat/TreeSat/TreeSat/trainers/basetrainer.py in run(self)
    342             for phase in ['training', 'testing']:
    343                 self.phase = phase
--&gt; 344                 self.model_mode()
    345 
    346                 if self.phase == 'testing':

/home/stevea/treesat/TreeSat/TreeSat/trainers/basetrainer.py in model_mode(self)
    154         if self.phase == 'training':
    155             print('*********TRAINING PHASE*********')
--&gt; 156             self.trainModel()
    157         else:
    158             print('*********VALIDATION PHASE*********')

/home/stevea/treesat/TreeSat/TreeSat/trainers/basetrainer.py in trainModel(self)
    234         # loop through all batches to perform an epoch
    235         for loaded in self.loaders[self.phase]:
--&gt; 236             epoch_loss = self.train_step(loaded, epoch_loss)
    237 
    238         mean_loss = np.mean(np.array(epoch_loss))

/home/stevea/treesat/TreeSat/TreeSat/trainers/basetrainer.py in train_step(self, loaded, epoch_loss)
    262 
    263         # process batch through network
--&gt; 264         self.out = self.model(self.img_batch.float())
    265 
    266         # get loss value

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/home/stevea/treesat/TreeSat/TreeSat/models/vgg.py in forward(self, x)
    143         x = self.model.features(x)
    144         x = self.model.avgpool(x)
--&gt; 145         x = self.model.classifier(x)
    146         return x
    147 

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py in forward(self, input)
     92 
     93     def forward(self, input: Tensor) -&gt; Tensor:
---&gt; 94         return F.linear(input, self.weight, self.bias)
     95 
     96     def extra_repr(self) -&gt; str:

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
   1751     if has_torch_function_variadic(input, weight):
   1752         return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
-&gt; 1753     return torch._C._nn.linear(input, weight, bias)
   1754 
   1755 

RuntimeError: mat1 dim 1 must match mat2 dim 0

I also tried to print off the shape of x at each step of the forward method:
def forward(self, x):
        x = self.model.features(x)
        print(x.shape)
        x = self.model.avgpool(x)
        print(x.shape)
        x = self.model.classifier(x)
        print(x.shape)
        return x

And it shows me that the shapes seem to be fine as the classifier should be taking in 512 features:
torch.Size([64, 512, 4, 4])
torch.Size([64, 512, 1, 1])


I can't run your code, but I believe the issue is because linear layers expect 2d data input (as it is really a matrix multiplication), while you provide 4d input (with dims 2 and 3 of size 1).
Please try squeeze
def forward(self, x):
        x = self.model.features(x)
        x = self.model.avgpool(x)
        x = torch.squeeze(x)
        x = self.model.classifier(x)
        return x


For less hacky-looking code in the squeeze part, see torch einops.
