Cuda illegal memory access when running inference on *.engine

[Lick]

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Question

When performing inference with *.engine I receive this error

Loading model.engine for TensorRT inference...
[01/16/2022-04:18:26] [TRT] [I] [MemUsageChange] Init CUDA: CPU +426, GPU +0, now: CPU 520, GPU 3258 (MiB)
[01/16/2022-04:18:26] [TRT] [I] Loaded engine size: 28 MiB
[01/16/2022-04:18:26] [TRT] [W] TensorRT was linked against cuBLAS/cuBLAS LT 11.6.3 but loaded cuBLAS/cuBLAS LT 11.3.0
[01/16/2022-04:18:26] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +634, GPU +266, now: CPU 1193, GPU 3552 (MiB)
[01/16/2022-04:18:27] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +126, GPU +58, now: CPU 1319, GPU 3610 (MiB)
[01/16/2022-04:18:27] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +26, now: CPU 0, GPU 26 (MiB)
[01/16/2022-04:18:31] [TRT] [W] TensorRT was linked against cuBLAS/cuBLAS LT 11.6.3 but loaded cuBLAS/cuBLAS LT 11.3.0
[01/16/2022-04:18:31] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +1, GPU +10, now: CPU 5022, GPU 5368 (MiB)
[01/16/2022-04:18:31] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 5022, GPU 5376 (MiB)
[01/16/2022-04:18:31] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +36, now: CPU 0, GPU 62 (MiB)
Adding AutoShape...
[01/16/2022-04:18:32] [TRT] [E] 1: [convolutionRunner.cpp::executeConv::511] Error Code 1: Cudnn (CUDNN_STATUS_EXECUTION_FAILED)
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/root/.cache/torch/hub/ultralytics_yolov5_master/models/common.py", line 539, in forward
t.append(time_sync())
File "/root/.cache/torch/hub/ultralytics_yolov5_master/utils/torch_utils.py", line 91, in time_sync
torch.cuda.synchronize()
File "/usr/local/lib/python3.8/dist-packages/torch/cuda/init.py", line 493, in synchronize
return torch._C._cuda_synchronize()
RuntimeError: CUDA error: an illegal memory access was encountered
[01/16/2022-04:18:32] [TRT] [E] 1: [defaultAllocator.cpp::deallocate::35] Error Code 1: Cuda Runtime (an illegal memory access was encountered)

Code:

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled= False
model = torch.hub.load('ultralytics/yolov5', 'custom', 'model.engine', force_reload=True)
model.to(device)
model.half()

image = open("find.png", 'rb').read()
original_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
resized_image = cv2.resize(original_image, (320, 320))
with torch.inference_mode():
    results = model(resized_image, size=320)

Additional

Name: torch
Version: 1.10.1+cu113

Cuda compilation tools, release 11.1, V11.1.105
Build cuda_11.1.TC455_06.29190527_0

[github-actions]

👋 Hello @Lick, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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[glenn-jocher]

@Lick thanks for the bug report! Yes we reproduced this recently and have an open issue on it in #6260. It's confusing because the *.engine exports work correctly with detect.py, but encounter this error with PyTorch Hub. If you have any insights into the problem please let us know.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@Lick good news 😃! Your original issue may now be fixed ✅ in PR #7560 implementing a TRT PyTorch Hub inference fix by @youngjae-avikus.

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!