Hi, I’m currently using uconvlstm and when trying to train the model I get the following error:
Input type (double) and bias type (fload) should be the same
I have searched for similar issues but not sure where to start.
Full stack issue below
EPOCH 1/10
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-16-e53f2e9fca6d> in <module>
6
7 model.train()
----> 8 train_metrics = iterate(
9 model,
10 data_loader=train_loader,
11 frames
<ipython-input-11-95ffd484195c> in iterate(model, data_loader, criterion, optimizer, mode, device)
60 elif mode != 'val':
61 optimizer.zero_grad()
---> 62 out = model(x, batch_positions=dates)
63 val_accuracy.append(get_accuracy(out, y))
64 else:
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-3-603af17d624d> in forward(self, input, batch_positions)
369 ) # BxT pad mask
370
--> 371 out = self.in_conv.smart_forward(input)
372
373 feature_maps = [out]
<ipython-input-3-603af17d624d> in smart_forward(self, input)
32 * self.pad_value
33 )
---> 34 temp[~pad_mask] = self.forward(out[~pad_mask])
35 out = temp
36 else:
<ipython-input-3-603af17d624d> in forward(self, input)
110
111 def forward(self, input):
--> 112 return self.conv(input)
113
114
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-3-603af17d624d> in forward(self, input)
89
90 def forward(self, input):
---> 91 return self.conv(input)
92
93
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/container.py in forward(self, input)
202 def forward(self, input):
203 for module in self:
--> 204 input = module(input)
205 return input
206
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py in forward(self, input)
461
462 def forward(self, input: Tensor) -> Tensor:
--> 463 return self._conv_forward(input, self.weight, self.bias)
464
465 class Conv3d(_ConvNd):
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
454 def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
455 if self.padding_mode != 'zeros':
--> 456 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
457 weight, bias, self.stride,
458 _pair(0), self.dilation, self.groups)
RuntimeError: Input type (double) and bias type (float) should be the same`
Any help regarding this would be greatly appreciated.