A bug with torch

[akira2025]

Hi guys, I have a bug when running your code. when I run in Registration part. I have a problem below.
I also try with my machine but also have same problem. I hope you can give me some suggestion.
Thank you so much

---

OSError Traceback (most recent call last)

in ()
6 import requests
7 import torch
----> 8 from torch_points3d.applications.pretrained_api import PretainedRegistry
9 from torch_points3d.core.data_transform import GridSampling3D, AddFeatByKey, AddOnes, Random3AxisRotation
10 from torch_points3d.datasets.registration.pair import Pair

8 frames

/usr/local/lib/python3.7/dist-packages/torch_points3d/applications/pretrained_api.py in ()
5
6 # Import building function for model and dataset
----> 7 from torch_points3d.datasets.dataset_factory import instantiate_dataset
8 from torch_points3d.models.model_factory import instantiate_model
9

/usr/local/lib/python3.7/dist-packages/torch_points3d/datasets/dataset_factory.py in ()
4 import logging
5
----> 6 from torch_points3d.datasets.base_dataset import BaseDataset
7
8 log = logging.getLogger(name)

/usr/local/lib/python3.7/dist-packages/torch_points3d/datasets/base_dataset.py in ()
6 import numpy as np
7 import torch
----> 8 import torch_geometric
9 from torch_geometric.transforms import Compose, FixedPoints
10 import copy

/usr/local/lib/python3.7/dist-packages/torch_geometric/init.py in ()
3
4 from .debug import is_debug_enabled, debug, set_debug
----> 5 import torch_geometric.data
6 import torch_geometric.transforms
7 import torch_geometric.utils

/usr/local/lib/python3.7/dist-packages/torch_geometric/data/init.py in ()
----> 1 from .data import Data
2 from .temporal import TemporalData
3 from .batch import Batch
4 from .dataset import Dataset
5 from .in_memory_dataset import InMemoryDataset

/usr/local/lib/python3.7/dist-packages/torch_geometric/data/data.py in ()
6 import torch
7 import torch_geometric
----> 8 from torch_sparse import coalesce, SparseTensor
9 from torch_geometric.utils import (contains_isolated_nodes,
10 contains_self_loops, is_undirected)

/usr/local/lib/python3.7/dist-packages/torch_sparse/init.py in ()
13 ]:
14 torch.ops.load_library(importlib.machinery.PathFinder().find_spec(
---> 15 f'{library}_{suffix}', [osp.dirname(file)]).origin)
16
17 if torch.cuda.is_available(): # pragma: no cover

/usr/local/lib/python3.7/dist-packages/torch/_ops.py in load_library(self, path)
102 # static (global) initialization code in order to register custom
103 # operators with the JIT.
--> 104 ctypes.CDLL(path)
105 self.loaded_libraries.add(path)
106

/usr/lib/python3.7/ctypes/init.py in init(self, name, mode, handle, use_errno, use_last_error)
362
363 if handle is None:
--> 364 self._handle = _dlopen(self._name, mode)
365 else:
366 self._handle = handle

OSError: /usr/local/lib/python3.7/dist-packages/torch_sparse/_convert_cuda.so: undefined symbol: _ZNK2at6Tensor6deviceEv

[humanpose1]

Hi @akira2025 ,

The bug with torch is solved! The problem was that the version of torch-sparse and torch were incompatible.
But now there is a new bug with torchsparse. I opened an issue in there repo.
Regards,

[trinhnhut-gif]

hi humanposes1
I also have a bug when testing today. I think the problem is torchsparse in new_coor line
I am using torch 1.8.1+cuda 10.2
Thank you.

---

IndexError Traceback (most recent call last)

in ()
4 with torch.no_grad():
5 model.set_input(data_s, "cuda")
----> 6 feat_s = model.forward()
7 model.set_input(data_t, "cuda")
8 feat_t = model.forward()

12 frames

/usr/local/lib/python3.7/dist-packages/torch_points3d/models/registration/ms_svconv3d.py in forward(self, *args, **kwargs)
252
253 def forward(self, *args, **kwargs):
--> 254 self.output, outputs = self.apply_nn(self.input)
255 if self.match is None:
256 return self.output

/usr/local/lib/python3.7/dist-packages/torch_points3d/models/registration/ms_svconv3d.py in apply_nn(self, input)
242 for i in range(len(self.grid_size)):
243 self.unet.set_grid_size(self.grid_size[i])
--> 244 out = self.unet(input.clone())
245 out.x = out.x / (torch.norm(out.x, p=2, dim=1, keepdim=True) + 1e-20)
246 outputs.append(out)

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _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(),

/usr/local/lib/python3.7/dist-packages/torch_points3d/models/registration/ms_svconv3d.py in forward(self, data, **kwargs)
82 d, cluster = self._prepare_data(inp)
83 d.x = self.pre_mlp(d.x)
---> 84 d = self.unet.forward(d)
85 inp_post_mlp = d.x[cluster]
86 if self.add_pos:

/usr/local/lib/python3.7/dist-packages/torch_points3d/applications/sparseconv3d.py in forward(self, data, *args, **kwargs)
196 stack_down = []
197 for i in range(len(self.down_modules) - 1):
--> 198 data = self.down_modulesi
199 stack_down.append(data)
200

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _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(),

/usr/local/lib/python3.7/dist-packages/torch_points3d/modules/SparseConv3d/modules.py in forward(self, x)
137
138 def forward(self, x):
--> 139 out = self.conv_in(x)
140 if self.blocks:
141 out = self.blocks(out)

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _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(),

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
117 def forward(self, input):
118 for module in self:
--> 119 input = module(input)
120 return input
121

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _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(),

/usr/local/lib/python3.7/dist-packages/torch_points3d/core/data_transform/../../../torchsparse/nn/modules/conv.py in forward(self, inputs)
86 stride=self.stride,
87 dilation=self.dilation,
---> 88 transpose=self.t)
89
90

/usr/local/lib/python3.7/dist-packages/torch_points3d/core/data_transform/../../../torchsparse/nn/functional/conv.py in conv3d(inputs, kernel, kernel_size, bias, stride, dilation, transpose)
131 kOffset = kRegion.get_kernel_offset().to(features.device)
132 new_coords = spF.spdownsample(coords, stride, kernel_size,
--> 133 cur_stride)
134 hash_query = spF.sphash(new_coords, kOffset)
135 hash_target = spF.sphash(coords)

/usr/local/lib/python3.7/dist-packages/torch_points3d/core/data_transform/../../../torchsparse/nn/functional/downsample.py in spdownsample(coords, ratio, kernel_size, tensor_stride)
67 # filter out duplicates
68 new_coords = torch.unique(new_coords, dim=0)
---> 69 new_coords = new_coords[:, [1, 2, 3, 0]]
70 return new_coords

IndexError: too many indices for tensor of dimension 1

[humanpose1]

Sorry for the trouble. Now I've fixed the problem, Actually I've downgraded the version of torchsparse.