I tried to implement the densenet architecture trained on CIFAR and SVHN, which has 100 layers and 3 dense blocks, by changing parameters of densenet code in torchvision package.
Although, I could print my net architecture, I got an NotImplementedError in the very beginning when I ran the code.


NotImplementedError                       Traceback (most recent call last)
 in ()
16         print(r_x.size())
17         t_y = torch.cat([r_y,l_y],1)
—> 18         output3 = net(r_x,l_x)
19         loss = loss_func(output3, t_y)
20         acc_loss += loss.data[0]
/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
222         for hook in self._forward_pre_hooks.values():
223             hook(self, input)
–> 224         result = self.forward(*input, **kwargs)
225         for hook in self._forward_hooks.values():
226             hook_result = hook(self, input, result)
/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in forward(self, *input)
63         Should be overriden by all subclasses.
64         “”"
—> 65         raise NotImplementedError
66
67     def register_buffer(self, name, tensor):
NotImplementedError:

I have no idea what is going on.
Could anyone help me out with this?
Thanks a lot!!