srijarkoroy / artistyle Goto Github PK

RuntimeError                              Traceback (most recent call last)
Cell In[4], line 2
      1 content_img, style_img = ('./results/input/content.jpg', './results/input/style.jpg')
----> 2 result = nst_res.run_nst(content_img, style_img)

File ~/autodl-tmp/ArtiStyle/nst/neural_style_transfer.py:219, in NST.run_nst(self, content_path, style_path)
    215 content_img, style_img, input_img = self.image_loader(content_path, style_path)
    217 ''' building the style transfer model '''
--> 219 model, style_loss, content_loss = self.style_model_and_losses(self.cnn, self.cnn_normalization_mean, self.cnn_normalization_std, content_img, style_img)
    221 ''' using LBFGS optimizer to run gradient descent on the input image in order to minimise the content and the style losses '''
    223 optimizer = optim.LBFGS([input_img.requires_grad_()])

File ~/autodl-tmp/ArtiStyle/nst/neural_style_transfer.py:191, in NST.style_model_and_losses(self, cnn, cnn_normalization_mean, cnn_normalization_std, content_img, style_img)
    189 ''' adding style loss '''
    190 if name in style_layers:
--> 191     target_feature = model(style_img).detach()
    192     styl_loss = StyleLoss(target_feature)
    193     model.add_module('style_loss_{}'.format(i), styl_loss)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py:889, in Module._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(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/container.py:119, in Sequential.forward(self, input)
    117 def forward(self, input):
    118     for module in self:
--> 119         input = module(input)
    120     return input

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py:889, in Module._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(),
    892         self._forward_hooks.values()):
    893     hook_result = hook(self, input, result)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py:399, in Conv2d.forward(self, input)
    398 def forward(self, input: Tensor) -> Tensor:
--> 399     return self._conv_forward(input, self.weight, self.bias)

File ~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py:395, in Conv2d._conv_forward(self, input, weight, bias)
    391 if self.padding_mode != 'zeros':
    392     return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    393                     weight, bias, self.stride,
    394                     _pair(0), self.dilation, self.groups)
--> 395 return F.conv2d(input, weight, bias, self.stride,
    396                 self.padding, self.dilation, self.groups)

RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED