I got a another error on AGX Orin r35.1. I’m running inside of the default container: l4t-pytorch:r35.1.0-pth1.11-py3. I tried on l4t-pytorch:r35.1.0-pth1.12-py3 too but no success.
What’s wrong ? Any tips ?
Fusing layers...
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
Adding AutoShape...
Traceback (most recent call last):
File "test_yolo.py", line 10, in <module>
results = model(img)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/root/.cache/torch/hub/ultralytics_yolov5_master/models/common.py", line 642, in forward
y = non_max_suppression(y if self.dmb else y[0],
File "/root/.cache/torch/hub/ultralytics_yolov5_master/utils/general.py", line 885, in non_max_suppression
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
File "/usr/local/lib/python3.8/dist-packages/torchvision-0.13.0a0+da3794e-py3.8-linux-aarch64.egg/torchvision/ops/boxes.py", line 41, in nms
return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
File "/usr/local/lib/python3.8/dist-packages/torch/_ops.py", line 142, in __call__
return self._op(*args, **kwargs or {})
**RuntimeError: CUDA error: no kernel image is available for execution on the device**
**CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.**
**For debugging consider passing CUDA_LAUNCH_BLOCKING=1.**
My env:
-----------------------
python3
Python 3.8.10 (default, Jun 22 2022, 20:18:18) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> import cv2
>>> cv2.__version__
'4.5.0'
>>> import torch
>>> print("PyTorch: "+torch.__version__)
PyTorch: 1.12.0a0+8a1a93a9.nv22.5
>>> import torchvision
>>> print("PyTorch: "+torchvision.__version__)
PyTorch: 0.13.0a0+da3794e
/usr/src/app/yolov5# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_May__4_00:02:26_PDT_2022
Cuda compilation tools, release 11.4, V11.4.239
Build cuda_11.4.r11.4/compiler.31294910_0/
pip3 list
Package Version
----------------------- ------------------------
absl-py 1.2.0
appdirs 1.4.4
asttokens 2.0.8
backcall 0.2.0
cachetools 5.2.0
certifi 2022.6.15
cffi 1.15.1
chardet 3.0.4
charset-normalizer 2.1.0
cmake 3.22.3
cycler 0.11.0
Cython 0.29.32
dbus-python 1.2.16
decorator 5.1.1
distro 1.7.0
executing 1.0.0
fonttools 4.37.1
google-auth 2.11.0
google-auth-oauthlib 0.4.6
graphsurgeon 0.4.6
grpcio 1.48.1
idna 3.3
importlib-metadata 4.12.0
ipython 8.4.0
jedi 0.18.1
kiwisolver 1.4.4
Mako 1.2.1
Markdown 3.4.1
MarkupSafe 2.1.1
matplotlib 3.5.3
matplotlib-inline 0.1.6
ninja 1.10.2.3
numpy 1.23.2
oauthlib 3.2.0
packaging 21.3
pandas 1.4.4
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.2.0
pip 20.0.2
platformdirs 2.5.2
prompt-toolkit 3.0.31
protobuf 3.19.4
psutil 5.9.1
ptyprocess 0.7.0
pure-eval 0.2.2
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.21
pycuda 2022.1
Pygments 2.13.0
PyGObject 3.36.0
pyparsing 3.0.9
PySoundFile 0.9.0.post1
python-apt 2.0.0+ubuntu0.20.4.7
python-dateutil 2.8.2
pytools 2022.1.12
pytz 2022.2.1
PyYAML 6.0
requests 2.28.1
requests-oauthlib 1.3.1
requests-unixsocket 0.2.0
rsa 4.9
scikit-build 0.15.0
scipy 1.9.1
seaborn 0.11.2
setuptools 45.2.0
six 1.14.0
stack-data 0.5.0
tensorboard 2.10.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorrt 8.4.1.5
thop 0.1.1.post2207130030
torch 1.12.0a0+8a1a93a9.nv22.5
torchaudio 0.12.0+2e13884
torchvision 0.13.0a0+da3794e
tqdm 4.64.0
traitlets 5.3.0
typing-extensions 4.3.0
uff 0.6.9
urllib3 1.26.11
wcwidth 0.2.5
Werkzeug 2.2.2
wheel 0.34.2
zipp 3.8.1