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YuliangXiu / ICON

[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals
https://icon.is.tue.mpg.de
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Trouble getting ICON results #13

Closed Samiepapa closed 2 years ago

Samiepapa commented 2 years ago

After installing all packages, I got the results successfully for PIFu and PaMIR. I faced the runtime error when trying to get the ICON demo result. Could you guide what setting was wrong?

$ python infer.py -cfg ../configs/icon-filter.yaml -gpu 0 -in_dir ../examples -out_dir ../results
Traceback (most recent call last):
  File "infer.py", line 304, in <module>
    verts_pr, faces_pr, _ = model.test_single(in_tensor)
  File "./ICON/apps/ICON.py", line 738, in test_single
    sdf = self.reconEngine(opt=self.cfg,
  File "./.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 148, in forward
    return self._forward_faster(**kwargs)
  File "../lib/common/seg3d_lossless.py", line 170, in _forward_faster
    occupancys = self.batch_eval(coords, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 139, in batch_eval
    occupancys = self.query_func(**kwargs, points=coords2D)
  File "../lib/common/train_util.py", line 338, in query_func
    preds = netG.query(features=features,
  File "../lib/net/HGPIFuNet.py", line 285, in query
    smpl_sdf, smpl_norm, smpl_cmap, smpl_ind = cal_sdf_batch(
  File "../lib/dataset/mesh_util.py", line 231, in cal_sdf_batch
    residues, normals, pts_cmap, pts_ind = func(
  File "./.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "./.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/mesh_distance.py", line 79, in forward
    output = self.search_tree(triangles, points)
  File "./.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "./.virtualenvs/icon/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "./.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 109, in forward
    output = BVHFunction.apply(
  File "./.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 42, in forward
    outputs = bvh_distance_queries_cuda.distance_queries(
RuntimeError: after reduction step 1: cudaErrorInvalidDevice: invalid device ordinal
Samiepapa commented 2 years ago

For the reference, my nvidia version info is as follows. NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6

The Cuda version was set to 11.5.

$ sudo update-alternatives --config cuda
There are 10 choices for the alternative cuda (providing /usr/local/cuda).

  Selection    Path                  Priority   Status
------------------------------------------------------------
* 0            /usr/local/cuda-11.5   115       auto mode

From the previous issues mentioned here, I've changed the CUDA version to 11.0. I faced the different issue as follows.

$ python infer.py -cfg ../configs/icon-filter.yaml -gpu 0 -in_dir ../examples -out_dir ../results
Using /home/yongilcho/.cache/torch_extensions as PyTorch extensions root...
Detected CUDA files, patching ldflags
Emitting ninja build file /home/yongilcho/.cache/torch_extensions/voxelize_cuda/build.ninja...
Building extension module voxelize_cuda...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/2] /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output voxelize_cuda.cuda.o.d -DTORCH_EXTENSION_NAME=voxelize_cuda -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include -isystem /home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -isystem /home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/TH -isystem /home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /home/test/anaconda3/include/python3.8 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_86,code=sm_86 --compiler-options '-fPIC' -std=c++14 -c /home/yongilcho/reposit/ICON/lib/neural_voxelization_layer/cuda/voxelize_cuda.cu -o voxelize_cuda.cuda.o
FAILED: voxelize_cuda.cuda.o
YuliangXiu commented 2 years ago

When did you git clone the code? I updated a new version 9 hours ago. For now, the voxelize_cuda is installed by pip. Please check out requirements.txt. Also, after changing the CUDA version, remember to install the suitable PyTorch compatible with such CUDA.

Samiepapa commented 2 years ago

Thanks for your quick feedback. I will update the code and check it again. Anyway, I can find the pythorch (1.8.2) install site, https://pytorch.org/get-started/locally/, only for CUDA11.1.

pip3 install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

Could you know any other install command for CUDA11.0 ?

Samiepapa commented 2 years ago

I faced one issue when installing all packages in "requirements.txt." When setting cuda version to be 11.0, I can not install the packages due to some errors. 

Building wheel for bvh-distance-queries (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: /home/test/.virtualenvs/icon/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-_j98v0fe/setup.py'uild-_j98v0fe/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(bdist_wheel -d /tmp/pip-wheel-a0u0j7ca
       cwd: /tmp/pip-req-build-_j98v0fe/
  Complete output (180 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.8
  creating build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/bvh_search_tree.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/__init__.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/mesh_distance.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  running build_ext
  building 'bvh_distance_queries_cuda' extension
  creating /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8
  creating /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src
  Emitting ninja build file /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/build.ninja...
  Compiling objects...
  Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
  [1/2] /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cualenvs/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/includeon/lib/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -Iinclude -Icuda-samples/nvs/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/ib/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/nclude -I/home/test/anaconda3/include/python3.8 -c -c /tmp/pip-req-build-_j98v0fe/src/bvh_cuda_op.cu -o /tmp/pip-req-build-_j98v0fe/build/temp.linux-xDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-TIMINGS=0 -DDEBUG_PRINT=0 -DERROR_CHECKING=1 -DNUM_THREADS=256 -DPROFILING=0 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_UILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=bvh_distance_queries_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=
  FAILED: /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cuda_op.o
  /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cuda_ops/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/ho/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -Iinclude -Icuda-samples/Commonon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/yhon3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/y -I/home/test/anaconda3/include/python3.8 -c -c /tmp/pip-req-build-_j98v0fe/src/bvh_cuda_op.cu -o /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-optionS=0 -DDEBUG_PRINT=0 -DERROR_CHECKING=1 -DNUM_THREADS=256 -DPROFILING=0 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIBBI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=bvh_distance_queries_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=comput
  nvcc fatal   : Unsupported gpu architecture 'compute_86'

Finally, I succeeded to install all packages in requirements.txt with Cuda version 11.5. After installing all packages, I can see the same issue mentioned first even though I changed the cuda version 11.0.

Traceback (most recent call last):
  File "infer.py", line 310, in <module>
    verts_pr, faces_pr, _ = model.test_single(in_tensor)
  File "/home/test/reposit/ICON/apps/ICON.py", line 738, in test_single
    sdf = self.reconEngine(opt=self.cfg,
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 148, in forward
    return self._forward_faster(**kwargs)
  File "../lib/common/seg3d_lossless.py", line 170, in _forward_faster
    occupancys = self.batch_eval(coords, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 139, in batch_eval
    occupancys = self.query_func(**kwargs, points=coords2D)
  File "../lib/common/train_util.py", line 338, in query_func
    preds = netG.query(features=features,
  File "../lib/net/HGPIFuNet.py", line 285, in query
    smpl_sdf, smpl_norm, smpl_cmap, smpl_ind = cal_sdf_batch(
  File "../lib/dataset/mesh_util.py", line 255, in cal_sdf_batch
    residues, normals, pts_cmap, pts_ind = func(
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/mesh_distance.py", line 79, in forward
    output = self.search_tree(triangles, points)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 109, in forward
    output = BVHFunction.apply(
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 42, in forward
    outputs = bvh_distance_queries_cuda.distance_queries(
RuntimeError: after reduction step 1: cudaErrorInvalidDevice: invalid device ordinal
YuliangXiu commented 2 years ago

Thanks for your quick feedback. I will update the code and check it again. Anyway, I can find the pythorch (1.8.2) install site, https://pytorch.org/get-started/locally/, only for CUDA11.1.

pip3 install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

Could you know any other install command for CUDA11.0 ?

cudatookit==11.1 can works well on cuda 11.0, no worry

YuliangXiu commented 2 years ago

I faced one issue when installing all packages in "requirements.txt." When setting cuda version to be 11.0, I can not install the packages due to some errors.

Building wheel for bvh-distance-queries (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: /home/test/.virtualenvs/icon/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-_j98v0fe/setup.py'uild-_j98v0fe/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(bdist_wheel -d /tmp/pip-wheel-a0u0j7ca
       cwd: /tmp/pip-req-build-_j98v0fe/
  Complete output (180 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-3.8
  creating build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/bvh_search_tree.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/__init__.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  copying bvh_distance_queries/mesh_distance.py -> build/lib.linux-x86_64-3.8/bvh_distance_queries
  running build_ext
  building 'bvh_distance_queries_cuda' extension
  creating /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8
  creating /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src
  Emitting ninja build file /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/build.ninja...
  Compiling objects...
  Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
  [1/2] /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cualenvs/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/includeon/lib/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -Iinclude -Icuda-samples/nvs/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/ib/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/nclude -I/home/test/anaconda3/include/python3.8 -c -c /tmp/pip-req-build-_j98v0fe/src/bvh_cuda_op.cu -o /tmp/pip-req-build-_j98v0fe/build/temp.linux-xDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-TIMINGS=0 -DDEBUG_PRINT=0 -DERROR_CHECKING=1 -DNUM_THREADS=256 -DPROFILING=0 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_UILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=bvh_distance_queries_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=
  FAILED: /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cuda_op.o
  /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-3.8/src/bvh_cuda_ops/icon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/ho/python3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -Iinclude -Icuda-samples/Commonon/lib/python3.8/site-packages/torch/include -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/yhon3.8/site-packages/torch/include/TH -I/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/y -I/home/test/anaconda3/include/python3.8 -c -c /tmp/pip-req-build-_j98v0fe/src/bvh_cuda_op.cu -o /tmp/pip-req-build-_j98v0fe/build/temp.linux-x86_64-HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-optionS=0 -DDEBUG_PRINT=0 -DERROR_CHECKING=1 -DNUM_THREADS=256 -DPROFILING=0 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIBBI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=bvh_distance_queries_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=comput
  nvcc fatal   : Unsupported gpu architecture 'compute_86'

Finally, I succeeded to install all packages in requirements.txt with Cuda version 11.5. After installing all packages, I can see the same issue mentioned first even though I changed the cuda version 11.0.

Traceback (most recent call last):
  File "infer.py", line 310, in <module>
    verts_pr, faces_pr, _ = model.test_single(in_tensor)
  File "/home/test/reposit/ICON/apps/ICON.py", line 738, in test_single
    sdf = self.reconEngine(opt=self.cfg,
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 148, in forward
    return self._forward_faster(**kwargs)
  File "../lib/common/seg3d_lossless.py", line 170, in _forward_faster
    occupancys = self.batch_eval(coords, **kwargs)
  File "../lib/common/seg3d_lossless.py", line 139, in batch_eval
    occupancys = self.query_func(**kwargs, points=coords2D)
  File "../lib/common/train_util.py", line 338, in query_func
    preds = netG.query(features=features,
  File "../lib/net/HGPIFuNet.py", line 285, in query
    smpl_sdf, smpl_norm, smpl_cmap, smpl_ind = cal_sdf_batch(
  File "../lib/dataset/mesh_util.py", line 255, in cal_sdf_batch
    residues, normals, pts_cmap, pts_ind = func(
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/mesh_distance.py", line 79, in forward
    output = self.search_tree(triangles, points)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 109, in forward
    output = BVHFunction.apply(
  File "/home/test/.virtualenvs/icon/lib/python3.8/site-packages/bvh_distance_queries/bvh_search_tree.py", line 42, in forward
    outputs = bvh_distance_queries_cuda.distance_queries(
RuntimeError: after reduction step 1: cudaErrorInvalidDevice: invalid device ordinal

Probably, you could have a loot at

Google Colab 
</a>

I showed the full process to set it up in Ubuntu with anaconda.

Current BVH only supports CUDA<=11.0, this is a version modified from torch-mesh-isect, I have no idea when Vassilis will update it to support the latest CUDA.

Samiepapa commented 2 years ago

Okay. Thanks a lot. I will check the Colab setting. BTW, Is it limited to Ubuntu18.04 like Colab ?

YuliangXiu commented 2 years ago

Okay. Thanks a lot. I will check the Colab setting. BTW, Is it limited to Ubuntu18.04 like Colab ?

Nope, it works well on Ubuntu 20.04.

jaymefosa commented 2 years ago

getting the same issue with various versions of Pytorch and Cuda 11.0

bvh samples seem to run for the most part (some other issues with Kornia arise)

YuliangXiu commented 2 years ago

@jaymefosa

Here is the PyTorch version I used for ICON image

Please re-install (uninstall, install) the bvh or PyTorch3D if you changed the version of PyTorch, because these libs are dependent on PyTorch.

guayabas commented 2 years ago

Another hint to solve the CUDA version issue is to define the environmental variable TORCH_CUDA_ARCH_LIST="8.0" (in .bashrc for example) before installing pytorch-related packages

cuda 11.0 requires arch 8.0 (https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/) but python setuptools can give arch 8.6 as default if using cuda 11.1 breaking things between pytorch3d, bvh, etc. which is that annoying 'invalid device ordinal'

YuliangXiu commented 2 years ago

@jaymefosa @Samiepapa, I have replaced the bvh-distance-queries with PyTorch3D+Kaolin, thus you don't need to install it anymore. Also, the CUDA version is not limited to 11.0.