Google Colab notebook is not working

[charlescho64]

@madhawav
What is the result of checking Google Colab notebook?

In Google Colab notebook I checked the following way, but without success.

1. python==3.7.13 torch==1.7.1+cu110 torchvision==0.8.2+cu110
   --> Failed building wheel for torch-scatter, torch-sparse, but Successfully built torch-geometric
   --> I can't proceed any further.

2. python==3.6.9 torch==1.8.0+cu111 torchvision==0.9.0+cu111
   --> Successfully installed torch-scatter-2.0.9,Successfully installed torch-sparse-0.6.13,Successfully installed torch-geometric-2.0.4
   --> Fail to import torch_sparse,torch_geometric
   --> I can't proceed any further.

3. python==3.7.13 torch==1.11.0+cu113 torchvision==0.12.0+cu113
   -->Successfully installed torch-scatter,torch_geometric,torch_sparse
   -->No module named 'torchvision.models.utils' in /plan2scene/code/src/plan2scene/texture_gen/nets/vgg.py. I got this error and fixed it with torch.hub because 'torchvision.models.utils' is deprecated in torch 1.11.0.
   --> When data is uploaded in"Task:Upload rectified surface crops extracted from photos." step, the photo_file_name directory is created under rectified_crops and copied. After moving the data to rectified_crops, you can see the texture in the step of "Task: Let's preview the data you have provided."
   --> "# Compute texture embeddings for observed surfaces (Code adapted from ./code/scripts/preprocessing/fill_room_embeddigs.py)" step have error below like.
   --> I can't proceed any further.

/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
[<ipython-input-31-42fcee0d7001>](https://localhost:8080/#) in <module>()
      4       for candidate_key, image_description in room.surface_textures[surface].items():
      5         image = image_description.image
----> 6         emb, loss = tg_predictor.predict_embs([image])
      7         room.surface_embeddings[surface][candidate_key] = emb
      8         room.surface_losses[surface][candidate_key] = loss

10 frames
[/content/plan2scene/code/src/plan2scene/texture_gen/predictor.py](https://localhost:8080/#) in predict_embs(self, sample_image_crops)
     81             predictor_result = self.predict(unsigned_images.to(self.conf.device),
     82                                             unsigned_hsv_images.to(self.conf.device),
---> 83                                             self.get_position(), combined_emb=None, train=False)
     84 
     85             # Compute loss between synthesized texture and conditioned image

[/content/plan2scene/code/src/plan2scene/texture_gen/predictor.py](https://localhost:8080/#) in predict(self, unsigned_images, unsigned_hsv_images, sample_pos, train, combined_emb)
    272             network_input, base_color = self._compute_network_input(unsigned_images, unsigned_hsv_images, additional_params)
    273             network_out, network_emb, substance_out = self.net(network_input, sample_pos.to(self.conf.device),
--> 274                                                                self.seed)
    275         else:
    276             # Predict using the combined_emb. Skip encoder.

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/content/plan2scene/code/src/plan2scene/texture_gen/nets/neural_texture/texture_gen.py](https://localhost:8080/#) in forward(self, image_gt, position, seed, weights_bottleneck)
     87 
     88         input_mlp = torch.cat([z_encoding, noise], dim=1)
---> 89         image_out = self.decoder(input_mlp)
     90         image_out = torch.tanh(image_out)
     91 

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/content/plan2scene/code/src/plan2scene/texture_gen/nets/neural_texture/mlp.py](https://localhost:8080/#) in forward(self, input)
     32     def forward(self, input):
     33 
---> 34         input_z = self.first_conv(input)
     35         output = input_z
     36         for idx, block in enumerate(self.res_blocks):

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/content/plan2scene/code/src/plan2scene/texture_gen/nets/core_modules/standard_block.py](https://localhost:8080/#) in forward(self, input, style)
     67             output = self.norm(output, style)
     68         else:
---> 69             output = self.layer(input)
     70 
     71             # output = self.norm(output)

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py](https://localhost:8080/#) in forward(self, input)
    445 
    446     def forward(self, input: Tensor) -> Tensor:
--> 447         return self._conv_forward(input, self.weight, self.bias)
    448 
    449 class Conv3d(_ConvNd):

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py](https://localhost:8080/#) in _conv_forward(self, input, weight, bias)
    442                             _pair(0), self.dilation, self.groups)
    443         return F.conv2d(input, weight, bias, self.stride,
--> 444                         self.padding, self.dilation, self.groups)
    445 
    446     def forward(self, input: Tensor) -> Tensor:

TypeError: conv2d() received an invalid combination of arguments - got (Tensor, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Tensor, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Tensor, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)

Could you recheck colab notebook?

Originally posted by @charlescho64 in #28 (comment)