I have used a predefined resnet101 and trained it. I am now trying to implement gradcam for which I have dissected the model to insert a hook after the last convolution layer.

‘’’

class my_resnet101(nn.Module):

def **init**(self):

super(my_resnet101, self).**init**()

self.resnet = model

```
# disect the network to access its last convolutional layer
self.part1 = nn.Sequential(*list(model.children())[:-2])
# get the max pool of the features stem
self.part2 = nn.Sequential(*list(model.children())[-2:])
self.gradients = None
# hook for the gradients of the activations
def activations_hook(self, grad):
self.gradients = grad
def forward(self, x):
x = self.part1(x)
# register the hook
h = x.register_hook(self.activations_hook)
# apply the remaining pooling
x = self.part2(x)
return x
# method for the gradient extraction
def get_activations_gradient(self):
return self.gradients
# method for the activation exctraction
def get_activations(self, x):
return self.features_conv(x)
```

‘’’

Now when I do pred = resnet(img), i get the error **mat1 and mat2 shapes cannot be multiplied (2048x1 and 2048x3)**. The dimension of img are 1*3*48*48. Below is a screenshot of the part of the model where I am engineering this customization. The model has been trained with images of 3 channels and so there should not be this discrepancy. Not sure what am I missing here.