@madhawav
What is the result of checking Google Colab notebook?
In Google Colab notebook I checked the following way, but without success.
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.
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.
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 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
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py 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/module.py 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/module.py 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/module.py 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 = [], []
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)
@madhawav What is the result of checking Google Colab notebook?
In Google Colab notebook I checked the following way, but without success.
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.
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.
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.
10 frames /content/plan2scene/code/src/plan2scene/texture_gen/predictor.py 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 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 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 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 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 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 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 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 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 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 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:
Could you recheck colab notebook?
Originally posted by @charlescho64 in https://github.com/3dlg-hcvc/plan2scene/issues/28#issuecomment-1110426761