[Installation] build from source succeed, while encounter errors in running.

[zouyuanpeng]

Is there an existing issue for this?

• [X] I have searched the existing issues

Have you followed all the steps in the FAQ?

• [X] I have tried the steps in the FAQ.

Current Behavior

I bulid torchsparse from source(for I may modify some source codes in future), using FORCE_CUDA=1 python setup.py bdist_wheel, it succeed(see Full Error Log below), while when I use it in SPVNAS, it encounter errors. How can I build torchsparse project from source?

Traceback (most recent call last): File "evaluate.py", line 149, in main() File "evaluate.py", line 126, in main outputs = model(inputs) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/conda/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 886, in forward output = self.module(*inputs[0], **kwargs[0]) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/conda/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward return self.module(*inputs[0], **kwargs[0]) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/zouyuanpeng/spvnas/core/models/semantic_kitti/spvcnn.py", line 189, in forward x0 = initial_voxelize(z, self.pres, self.vres) File "/home/zouyuanpeng/spvnas/core/models/utils.py", line 15, in initial_voxelize pc_hash = F.sphash(torch.floor(new_float_coord).int()) File "/opt/conda/lib/python3.7/site-packages/torch/cuda/amp/autocast_mode.py", line 94, in decorate_fwd return fwd(*args, **kwargs) File "/home/zouyuanpeng/torchsparse-master/torchsparse/nn/functional/hash.py", line 43, in forward return sphash_code(coords,offset) File "/home/zouyuanpeng/torchsparse-master/torchsparse/nn/functional/hash.py", line 19, in sphash_code return torchsparse.backend.hash_cuda(coords) AttributeError: module 'torchsparse.backend' has no attribute 'hash_cuda'

Primary job terminated normally, but 1 process returned a non-zero exit code. Per user-direction, the job has been aborted.

---

mpirun detected that one or more processes exited with non-zero status, thus causing the job to be terminated. The first process to do so was:

Process name: [[27469,1],0] Exit code: 1

Error Line

AttributeError: module 'torchsparse.backend' has no attribute 'hash_cuda'

Environment: In docker,

- GCC: 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)
- NVCC:  Cuda compilation tools, release 11.3, V11.3.109
- PyTorch: 1.10
- PyTorch CUDA: 11.3

Full Error Log

FORCE_CUDA=1 python setup.py bdist_wheel running bdist_wheel /opt/conda/lib/python3.7/site-packages/torch/utils/cpp_extension.py:381: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend. warnings.warn(msg.format('we could not find ninja.')) running build running build_py creating build creating build/lib.linux-x86_64-3.7 creating build/lib.linux-x86_64-3.7/torchsparse copying torchsparse/init.py -> build/lib.linux-x86_64-3.7/torchsparse copying torchsparse/operators.py -> build/lib.linux-x86_64-3.7/torchsparse copying torchsparse/tensor.py -> build/lib.linux-x86_64-3.7/torchsparse copying torchsparse/version.py -> build/lib.linux-x86_64-3.7/torchsparse creating build/lib.linux-x86_64-3.7/torchsparse/backbones copying torchsparse/backbones/init.py -> build/lib.linux-x86_64-3.7/torchsparse/backbones copying torchsparse/backbones/resnet.py -> build/lib.linux-x86_64-3.7/torchsparse/backbones copying torchsparse/backbones/unet.py -> build/lib.linux-x86_64-3.7/torchsparse/backbones creating build/lib.linux-x86_64-3.7/torchsparse/nn copying torchsparse/nn/init.py -> build/lib.linux-x86_64-3.7/torchsparse/nn creating build/lib.linux-x86_64-3.7/torchsparse/utils copying torchsparse/utils/init.py -> build/lib.linux-x86_64-3.7/torchsparse/utils copying torchsparse/utils/collate.py -> build/lib.linux-x86_64-3.7/torchsparse/utils copying torchsparse/utils/quantize.py -> build/lib.linux-x86_64-3.7/torchsparse/utils copying torchsparse/utils/utils.py -> build/lib.linux-x86_64-3.7/torchsparse/utils creating build/lib.linux-x86_64-3.7/torchsparse/backbones/modules copying torchsparse/backbones/modules/init.py -> build/lib.linux-x86_64-3.7/torchsparse/backbones/modules copying torchsparse/backbones/modules/blocks.py -> build/lib.linux-x86_64-3.7/torchsparse/backbones/modules creating build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/init.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/activation.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/conv.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/count.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/crop.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/devoxelize.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/downsample.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/hash.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/pooling.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/query.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional copying torchsparse/nn/functional/voxelize.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/functional creating build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/init.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/activation.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/bev.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/conv.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/crop.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/norm.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules copying torchsparse/nn/modules/pooling.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/modules creating build/lib.linux-x86_64-3.7/torchsparse/nn/utils copying torchsparse/nn/utils/init.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/utils copying torchsparse/nn/utils/apply.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/utils copying torchsparse/nn/utils/kernel.py -> build/lib.linux-x86_64-3.7/torchsparse/nn/utils running build_ext building 'torchsparse.backend' extension creating build/temp.linux-x86_64-3.7 creating build/temp.linux-x86_64-3.7/torchsparse creating build/temp.linux-x86_64-3.7/torchsparse/backend creating build/temp.linux-x86_64-3.7/torchsparse/backend/convolution creating build/temp.linux-x86_64-3.7/torchsparse/backend/devoxelize creating build/temp.linux-x86_64-3.7/torchsparse/backend/hash creating build/temp.linux-x86_64-3.7/torchsparse/backend/hashmap creating build/temp.linux-x86_64-3.7/torchsparse/backend/others creating build/temp.linux-x86_64-3.7/torchsparse/backend/voxelize gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/pybind_cuda.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/pybind_cuda.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/convolution/convolution_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/convolution/convolution_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/convolution/convolution_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/convolution/convolution_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 torchsparse/backend/convolution/convolution_cuda.cu: In lambda function: torchsparse/backend/convolution/convolution_cuda.cu:138:45: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/convolution/convolution_cuda.cu:138:100: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/convolution/convolution_cuda.cu: In lambda function: torchsparse/backend/convolution/convolution_cuda.cu:153:45: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/convolution/convolution_cuda.cu:153:100: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/convolution/convolution_cuda.cu: In lambda function: torchsparse/backend/convolution/convolution_cuda.cu:238:45: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/convolution/convolution_cuda.cu:238:100: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/convolution/convolution_cuda.cu: In lambda function: torchsparse/backend/convolution/convolution_cuda.cu:248:45: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/convolution/convolution_cuda.cu:248:100: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/convolution/convolution_cuda.cu: In lambda function: torchsparse/backend/convolution/convolution_cuda.cu:266:45: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/convolution/convolution_cuda.cu:266:100: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/devoxelize/devoxelize_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/devoxelize/devoxelize_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/devoxelize/devoxelize_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/devoxelize/devoxelize_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 torchsparse/backend/devoxelize/devoxelize_cuda.cu: In lambda function: torchsparse/backend/devoxelize/devoxelize_cuda.cu:70:42: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/devoxelize/devoxelize_cuda.cu:70:97: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/devoxelize/devoxelize_cuda.cu: In lambda function: torchsparse/backend/devoxelize/devoxelize_cuda.cu:90:46: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/devoxelize/devoxelize_cuda.cu:90:101: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/hash/hash_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/hash/hash_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/hash/hash_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/hash/hash_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/hashmap/hashmap_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/hashmap/hashmap_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/hashmap/hashmap_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/hashmap/hashmap_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="_gcc" -DPYBIND11_STDLIB="_libstdcpp" -DPYBIND11_BUILD_ABI="_cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 torchsparse/backend/hashmap/hashmap_cuda.cu(28): warning: argument is incompatible with corresponding format string conversion

gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/others/count_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/others/count_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/others/count_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/others/count_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS_ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/others/query_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/others/query_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/others/query_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/others/query_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 gcc -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/voxelize/voxelize_cpu.cpp -o build/temp.linux-x86_64-3.7/torchsparse/backend/voxelize/voxelize_cpu.o -g -O3 -fopenmp -lgomp -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /usr/local/cuda/bin/nvcc -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/opt/conda/include/python3.7m -c torchsparse/backend/voxelize/voxelize_cuda.cu -o build/temp.linux-x86_64-3.7/torchsparse/backend/voxelize/voxelize_cuda.o -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS -D__CUDA_NO_BFLOAT16_CONVERSIONS -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="_gcc" -DPYBIND11_STDLIB="_libstdcpp" -DPYBIND11_BUILD_ABI="_cxxabi1011" -DTORCH_EXTENSION_NAME=backend -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_70,code=compute_70 -gencode=arch=compute_70,code=sm_70 -std=c++14 torchsparse/backend/voxelize/voxelize_cuda.cu: In lambda function: torchsparse/backend/voxelize/voxelize_cuda.cu:53:44: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/voxelize/voxelize_cuda.cu:53:99: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ torchsparse/backend/voxelize/voxelize_cuda.cu: In lambda function: torchsparse/backend/voxelize/voxelize_cuda.cu:72:46: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:194:1: note: declared here DeprecatedTypeProperties & type() const { ^ ~~ torchsparse/backend/voxelize/voxelize_cuda.cu:72:101: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [-Wdeprecated-declarations] AT_DISPATCH_FLOATING_TYPES_AND_HALF( ^ /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Dispatch.h:176:1: note: declared here inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { ^~~~~~~~~~~ g++ -pthread -shared -B /opt/conda/compiler_compat -L/opt/conda/lib -Wl,-rpath=/opt/conda/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/torchsparse/backend/pybind_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/convolution/convolution_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/convolution/convolution_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/devoxelize/devoxelize_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/devoxelize/devoxelize_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/hash/hash_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/hash/hash_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/hashmap/hashmap_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/hashmap/hashmap_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/others/count_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/others/count_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/others/query_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/others/query_cuda.o build/temp.linux-x86_64-3.7/torchsparse/backend/voxelize/voxelize_cpu.o build/temp.linux-x86_64-3.7/torchsparse/backend/voxelize/voxelize_cuda.o -L/opt/conda/lib/python3.7/site-packages/torch/lib -L/usr/local/cuda/lib64 -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda_cu -ltorch_cuda_cpp -o build/lib.linux-x86_64-3.7/torchsparse/backend.cpython-37m-x86_64-linux-gnu.so installing to build/bdist.linux-x86_64/wheel running install running install_lib creating build/bdist.linux-x86_64 creating build/bdist.linux-x86_64/wheel creating build/bdist.linux-x86_64/wheel/torchsparse copying build/lib.linux-x86_64-3.7/torchsparse/init.py -> build/bdist.linux-x86_64/wheel/torchsparse creating build/bdist.linux-x86_64/wheel/torchsparse/backbones copying build/lib.linux-x86_64-3.7/torchsparse/backbones/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/backbones creating build/bdist.linux-x86_64/wheel/torchsparse/backbones/modules copying build/lib.linux-x86_64-3.7/torchsparse/backbones/modules/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/backbones/modules copying build/lib.linux-x86_64-3.7/torchsparse/backbones/modules/blocks.py -> build/bdist.linux-x86_64/wheel/torchsparse/backbones/modules copying build/lib.linux-x86_64-3.7/torchsparse/backbones/resnet.py -> build/bdist.linux-x86_64/wheel/torchsparse/backbones copying build/lib.linux-x86_64-3.7/torchsparse/backbones/unet.py -> build/bdist.linux-x86_64/wheel/torchsparse/backbones copying build/lib.linux-x86_64-3.7/torchsparse/backend.cpython-37m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/torchsparse creating build/bdist.linux-x86_64/wheel/torchsparse/nn copying build/lib.linux-x86_64-3.7/torchsparse/nn/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn creating build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/activation.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/conv.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/count.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/crop.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/devoxelize.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/downsample.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/hash.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/pooling.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/query.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional copying build/lib.linux-x86_64-3.7/torchsparse/nn/functional/voxelize.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/functional creating build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/activation.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/bev.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/conv.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/crop.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/norm.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules copying build/lib.linux-x86_64-3.7/torchsparse/nn/modules/pooling.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/modules creating build/bdist.linux-x86_64/wheel/torchsparse/nn/utils copying build/lib.linux-x86_64-3.7/torchsparse/nn/utils/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/utils copying build/lib.linux-x86_64-3.7/torchsparse/nn/utils/apply.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/utils copying build/lib.linux-x86_64-3.7/torchsparse/nn/utils/kernel.py -> build/bdist.linux-x86_64/wheel/torchsparse/nn/utils copying build/lib.linux-x86_64-3.7/torchsparse/operators.py -> build/bdist.linux-x86_64/wheel/torchsparse copying build/lib.linux-x86_64-3.7/torchsparse/tensor.py -> build/bdist.linux-x86_64/wheel/torchsparse creating build/bdist.linux-x86_64/wheel/torchsparse/utils copying build/lib.linux-x86_64-3.7/torchsparse/utils/init.py -> build/bdist.linux-x86_64/wheel/torchsparse/utils copying build/lib.linux-x86_64-3.7/torchsparse/utils/collate.py -> build/bdist.linux-x86_64/wheel/torchsparse/utils copying build/lib.linux-x86_64-3.7/torchsparse/utils/quantize.py -> build/bdist.linux-x86_64/wheel/torchsparse/utils copying build/lib.linux-x86_64-3.7/torchsparse/utils/utils.py -> build/bdist.linux-x86_64/wheel/torchsparse/utils copying build/lib.linux-x86_64-3.7/torchsparse/version.py -> build/bdist.linux-x86_64/wheel/torchsparse running install_egg_info running egg_info writing torchsparse.egg-info/PKG-INFO writing dependency_links to torchsparse.egg-info/dependency_links.txt writing top-level names to torchsparse.egg-info/top_level.txt reading manifest file 'torchsparse.egg-info/SOURCES.txt' adding license file 'LICENSE' writing manifest file 'torchsparse.egg-info/SOURCES.txt' Copying torchsparse.egg-info to build/bdist.linux-x86_64/wheel/torchsparse-1.4.0-py3.7.egg-info running install_scripts adding license file "LICENSE" (matched pattern "LICEN[CS]E*") creating build/bdist.linux-x86_64/wheel/torchsparse-1.4.0.dist-info/WHEEL creating 'dist/torchsparse-1.4.0-cp37-cp37m-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it adding 'torchsparse/init.py' adding 'torchsparse/backend.cpython-37m-x86_64-linux-gnu.so' adding 'torchsparse/operators.py' adding 'torchsparse/tensor.py' adding 'torchsparse/version.py' adding 'torchsparse/backbones/init.py' adding 'torchsparse/backbones/resnet.py' adding 'torchsparse/backbones/unet.py' adding 'torchsparse/backbones/modules/init.py' adding 'torchsparse/backbones/modules/blocks.py' adding 'torchsparse/nn/init.py' adding 'torchsparse/nn/functional/init.py' adding 'torchsparse/nn/functional/activation.py' adding 'torchsparse/nn/functional/conv.py' adding 'torchsparse/nn/functional/count.py' adding 'torchsparse/nn/functional/crop.py' adding 'torchsparse/nn/functional/devoxelize.py' adding 'torchsparse/nn/functional/downsample.py' adding 'torchsparse/nn/functional/hash.py' adding 'torchsparse/nn/functional/pooling.py' adding 'torchsparse/nn/functional/query.py' adding 'torchsparse/nn/functional/voxelize.py' adding 'torchsparse/nn/modules/init.py' adding 'torchsparse/nn/modules/activation.py' adding 'torchsparse/nn/modules/bev.py' adding 'torchsparse/nn/modules/conv.py' adding 'torchsparse/nn/modules/crop.py' adding 'torchsparse/nn/modules/norm.py' adding 'torchsparse/nn/modules/pooling.py' adding 'torchsparse/nn/utils/init.py' adding 'torchsparse/nn/utils/apply.py' adding 'torchsparse/nn/utils/kernel.py' adding 'torchsparse/utils/init.py' adding 'torchsparse/utils/collate.py' adding 'torchsparse/utils/quantize.py' adding 'torchsparse/utils/utils.py' adding 'torchsparse-1.4.0.dist-info/LICENSE' adding 'torchsparse-1.4.0.dist-info/METADATA' adding 'torchsparse-1.4.0.dist-info/WHEEL' adding 'torchsparse-1.4.0.dist-info/top_level.txt' adding 'torchsparse-1.4.0.dist-info/RECORD' removing build/bdist.linux-x86_64/wheel

[Xiuyu-Li]

Hi,

From the error traceback it seems like torchsparse used in the code was directly imported from /home/zouyuanpeng/torchsparse-master/torchsparse/. Can you try making torchsparse imported from /opt/conda/lib/python3.7/site-packages/torchsparse (which should be the location for the installed wheel) and see if this error still preserves?

[zouyuanpeng]

Hi,

From the error traceback it seems like torchsparse used in the code was directly imported from /home/zouyuanpeng/torchsparse-master/torchsparse/. Can you try making torchsparse imported from /opt/conda/lib/python3.7/site-packages/torchsparse (which should be the location for the installed wheel) and see if this error still preserves?

Hi Xiuyu, thinks for your reply, I find that there has no folders "torchsparse" in my "/opt/conda/lib/python3.7/site-packages/", while another folder "torchsparse-1.4.0-py3.7-linux-x86_64.egg" in it. Files should be in "/opt/conda/lib/python3.7/site-packages/torchsparse" are in "/opt/conda/lib/python3.7/site-packages/torchsparse-1.4.0-py3.7-linux-x86_64.egg/torchsparse/", see below(sorry for that I can't upload pics):

root@9ef3107263e8:/home/zouyuanpeng/spvnas# ls /opt/conda/lib/python3.7/site-packages/ | grep torchsparse torchsparse-1.4.0-py3.7-linux-x86_64.egg root@9ef3107263e8:/home/zouyuanpeng/spvnas# ls /opt/conda/lib/python3.7/site-packages/torchsparse-1.4.0-py3.7-linux-x86_64.egg/ EGG-INFO torchsparse root@9ef3107263e8:/home/zouyuanpeng/spvnas# ls /opt/conda/lib/python3.7/site-packages/torchsparse-1.4.0-py3.7-linux-x86_64.egg/torchsparse/ init.py pycache backbones backend.cpython-37m-x86_64-linux-gnu.so backend.py nn operators.py tensor.py utils version.py

Is there any problems about my installation? or maybe I can import torchsparse from "/opt/conda/lib/python3.7/site-packages/torchsparse-1.4.0-py3.7-linux-x86_64.egg/torchsparse/"? and how?

[Xiuyu-Li]

How did you install torchsparse after building the wheel? Can you try python -m pip install dist/torchsparse-1.4.0-cp37-cp37-linux_x86_64.whl --force-reinstall after running FORCE_CUDA=1 python setup.py bdist_wheel?

[zhijian-liu]

Or could you maybe try FORCE_CUDA=1 pip install -v -e .?

[zhijian-liu]

I'm closing this issue due to its inactivity. However, please feel free to reopen it if your problem persists.