Hi I am trying to use the same code as mentioned in one of the post of this discussion forum for visualizing feature map but I am kind of getting this error "hook_result = hook(self, input, result)
TypeError: ‘NoneType’ object is not callable"
Below is the code
activation = {}
    def get_activation(name):
        def hook(model, input, output):
            activation[name] = output.detach()
            return hook
        
    model.dc.register_forward_hook(get_activation('dc'))
    
    dataiter = iter(trainloader)
    images,labels = dataiter.next()
    imshow(images[0],labels[0])
    print(images[0].unsqueeze_(0).shape)
    
    output = model(images[0].unsqueeze_(0))
    print(output.shape)

    
    act = activation['dc'].squeeze()
    fig, ax = plt.subplots(act.size(0))
    for idx in range(act.size(0)):
        ax[idx].imshow(act[idx])

Model
class NormalCNN(nn.Module):
    def __init__(self, args, classes):
        super(NormalCNN, self).__init__()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.args = args
        self.class_num = 10
        self.classes = classes
        
        resnet18 = models.resnet18(pretrained=True)
        
        self.backbone = nn.Sequential(*list(resnet18.children())[0:5])
        self.dc = nn.Conv2d(64, 1 , kernel_size = 1, stride=1, padding=1)
        
        self.mlp = nn.Sequential(
                nn.Linear(64, 64),
                nn.ReLU(),
                nn.Linear(64, 10))

        ### ----------------------------------------------

    def forward(self, imgs):

        v = self.backbone(imgs)
            
        v_out = self.dc(v)
        v_out = v_out/8
            
        out = nn.Upsample(size=(8, 8), mode='bilinear')(v_out)
        out = out.view(-1,1*8*8)
        cls_scores = self.mlp(out)
        
        return cls_scores # Dim: [batch_size, 10]