Update to pytorch-0.4

[sylvchev]

Using pytorch-0.4.1 to run this code, I encountered several problems. I'm creating this pull request to propose a solution to these issues. One blocking issue is related with generate_fast() that raise a RuntimeError: the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (2) I also modified to code to avoid warnings:

• nn.functional.tanh and sigmoid are deprecated in favor of torch.tanh and sigmoid
• scalar should be accessed with item()
• Variable are deprecated in favor of requires_grad args

Unfortunately, I could not verify that these modifications are functional on pytorch < 0.4. I'll try to install pytorch 0.3 on another system and see if it is backward compatible.

Thank you very much for this nice code, I was a huge help to understand WaveNet! I hope I could contribute to this project.

[bobchennan]

Works for me on 0.4

[gecko17k]

The code in demo.ipynb requires saber dataset. Where is this dataset?

I switched to the only available dataset in this repo: bach/chaconne, but then I get an error message that ends with :

" RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight' " Here is the error message in full:

RuntimeError Traceback (most recent call last)

in () 11 tic = time.time() 12 trainer.train(batch_size=8, ---> 13 epochs=20) 14 toc = time.time() 15 print('Training took {} seconds.'.format(toc - tic)) /media/disk1/vince/PythonCodes/waveNet/torch/pytorch-wavenet/wavenet_training.py in train(self, batch_size, epochs, continue_training_at_step) 68 x = Variable(x.type(self.dtype)) 69 target = Variable(target.view(-1).type(self.ltype)) ---> 70 output = self.model(x) 71 loss = F.cross_entropy(output.squeeze(), target.squeeze()) 72 self.optimizer.zero_grad() ~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 489 result = self._slow_forward(*input, **kwargs) 490 else: --> 491 result = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook(self, input, result) /media/disk1/vince/PythonCodes/waveNet/torch/pytorch-wavenet/wavenet_model.py in forward(self, input) 186 def forward(self, input): 187 x = self.wavenet(input, --> 188 dilation_func=self.wavenet_dilate) 189 190 # reshape output /media/disk1/vince/PythonCodes/waveNet/torch/pytorch-wavenet/wavenet_model.py in wavenet(self, input, dilation_func) 125 def wavenet(self, input, dilation_func): 126 --> 127 x = self.start_conv(input) 128 skip = 0 129 ~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 489 result = self._slow_forward(*input, **kwargs) 490 else: --> 491 result = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook(self, input, result) ~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input) 174 def forward(self, input): 175 return F.conv1d(input, self.weight, self.bias, self.stride, --> 176 self.padding, self.dilation, self.groups) 177 178 RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'weight' Thanks.

[gecko17k]

Turns out the solution is to put the arguments dtype=dtype, ltype=ltype into the function WaveNetTrainer(), like so:

trainer = WavenetTrainer(model=model,
                           dataset=data,
                           lr=0.001,
                           weight_decay=0.0,
                           gradient_clipping=None,
                           snapshot_path='snapshots',
#                            snapshot_name='saber_model',                                                    
                           snapshot_name='bach_model',
                           snapshot_interval=100000,
                           dtype=dtype,
                           ltype=ltype, 
                           )

Credit to @ianmktu.

[pathnirvana]

Also you need to move the model to cuda if use_cuda: model.cuda()