torchvision.ops.batched_nms() crashes with pytorch 1.9.0 and torchvision 0.10.0

🐛 Bug

with the just released pytorch 1.9.0 and torchvision 0.10.0 torchvision.ops.batched_nms() crashes on my machine with the following error:

RuntimeError: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.__version__ and your torchvision version with torchvision.__version__ and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.

Since both are of the current version, I guess they should be compatible (they are not yet listed in the compatibility matrix).

To Reproduce

Steps to reproduce the behavior:

this example code shows the behavior on my machine:

import torch as th
import torchvision as tv

boxes = th.zeros(1000, 4)
scores = th.zeros(1000)
idxs = th.zeros(1000)

tv.ops.batched_nms(boxes, scores, idxs, 0.5)

Expected behavior

This should not result in an error.

Environment

Collecting environment information… PyTorch version: 1.9.0 Is debug build: False CUDA used to build PyTorch: 10.2 ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.5 LTS (x86_64) GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.27

Python version: 3.9 (64-bit runtime) Python platform: Linux-4.15.0-144-generic-x86_64-with-glibc2.27 Is CUDA available: True CUDA runtime version: Could not collect GPU models and configuration: GPU 0: Tesla V100-SXM2-32GB GPU 1: Tesla V100-SXM2-32GB

Nvidia driver version: 460.32.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A

Versions of relevant libraries: [pip3] numpy==1.20.2 [pip3] torch==1.9.0 [pip3] torchaudio==0.9.0a0+33b2469 [pip3] torchvision==0.10.0 [conda] blas 1.0 mkl
[conda] cudatoolkit 10.2.89 h8f6ccaa_8 conda-forge [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] mkl 2021.2.0 h726a3e6_389 conda-forge [conda] mkl-service 2.4.0 py39h3811e60_0 conda-forge [conda] mkl_fft 1.3.0 py39h42c9631_2
[conda] mkl_random 1.2.2 py39hde0f152_0 conda-forge [conda] numpy 1.20.2 py39h2d18471_0
[conda] numpy-base 1.20.2 py39hfae3a4d_0
[conda] pytorch 1.9.0 py3.9_cuda10.2_cudnn7.6.5_0 pytorch [conda] torchaudio 0.9.0 py39 pytorch [conda] torchvision 0.10.0 py39_cu102 pytorch

Additional context

[ChouCHou-y]

with the just released pytorch 1.9.0 and torchvision 0.10.0 torchvision.ops.batched_nms() crashes on my machine with the following error:

RuntimeError: Couldn’t load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.version and your torchvision version with torchvision.version and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.

how to solve？please

[malfet]

I think difference can be explained by presence/absence of conda-forge in ones .condarc. I got the repro after removing conda-forge dependency, but than fixed it by enabling it in the install command as follows:

conda create -n test python=3.9 pytorch torchvision cudatoolkit=11.1 -c pytorch -c nvidia -c conda-forge