Possible bug in resnet18_snr ?

[mmrbulbul]

There seems to be dimension mismatch in the model.

Steps to produce:

model = resnet18_snr()
model(torch.randn(2, 3, 256, 128))

Error log:

~/Documents/Codes/re_id/deep-person-reid/torchreid/models/resnet_SNR.py in forward(self, x)
    276 
    277 
--> 278         x_4 = self.avgpool(x_4)
    279         x_4 = x_4.view(x_4.size(0), -1)
    280 

~/Documents/Codes/re_id/test/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1050                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051             return forward_call(*input, **kwargs)
   1052         # Do not call functions when jit is used
   1053         full_backward_hooks, non_full_backward_hooks = [], []

~/Documents/Codes/re_id/test/lib/python3.8/site-packages/torch/nn/modules/pooling.py in forward(self, input)
    613 
    614     def forward(self, input: Tensor) -> Tensor:
--> 615         return F.avg_pool2d(input, self.kernel_size, self.stride,
    616                             self.padding, self.ceil_mode, self.count_include_pad, self.divisor_override)
    617 

RuntimeError: Given input size: (512x8x4). Calculated output size: (512x2x-2). Output size is too small