Hi, I’m currently using Blitz to create a 3D bayesian Unet and when trying to train the model I get the following error:

`Input type (torch.cuda.FloatTensor) and bias type (torch.FloatTensor) should be the same`

I have searched around similar topics, I found one for the same error which didn’t help, and several regarding a similar message but about the weights, which was also of no help for this situation (both model and data are going for the same cuda device).

The model is defined here: model - Pastebin.com

And the training loop is here: loop - Pastebin.com

The definition of the BayesianConv3d layer can be found here.

Full error trace below.

Any help regarding this would be greatly appreciated.

```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-12-d8ddbcf20a6e> in <module>
19 #print("labels: ", labels.shape)
20 optimizer.zero_grad()
---> 21 outputs = model(inputs)
22 #loss = loss_function(outputs, labels)
23 loss = model.sample_elbo(inputs=inputs,
~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~/Desktop/bayesian/BUNet3D.py in forward(self, x)
115
116 def forward(self, x):
--> 117 x1 = self.convd1(x)
118 x2 = self.convd2(x1)
119 x3 = self.convd3(x2)
~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~/Desktop/bayesian/BUNet3D.py in forward(self, x)
42 if not self.first:
43 x = self.maxpool(x)
---> 44 x = self.conv1(x)
45 x = self.bn1(x)
46 #x = self.bn1(self.conv1(x))
~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~/miniconda3/lib/python3.8/site-packages/blitz/modules/conv_bayesian_layer.py in forward(self, x)
366 self.log_prior = self.weight_prior_dist.log_prior(w) + b_log_prior
367
--> 368 return F.conv3d(input=x,
369 weight=w,
370 bias=b,
RuntimeError: Input type (torch.cuda.FloatTensor) and bias type (torch.FloatTensor) should be the same
```