run onnx model in opencv

[hrshovon]

❔Question

Hello all,
I trained yolov5 on my own data and created onnx model using the export script. The coreml model failed but onnx model was created successfully. But when I try to import this model in opencv(4.3.0-dev and 4.4.0-dev), I get the following error:
`cv2.error: OpenCV(4.4.0-dev) /tmp/ocv_bld/opencv/modules/dnn/src/onnx/onnx_importer.cpp:562: error: (-2:Unspecified error) in function 'void cv::dnn::dnn4_v20200609::ONNXImporter::populateNet(cv::dnn::dnn4_v20200609::Net)'

Slice layer only supports steps = 1 (expected: 'countNonZero(step_blob != 1) == 0'), where
'countNonZero(step_blob != 1)' is 1
must be equal to
'0' is 0`
How can I solve this?

[github-actions]

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[zsy9512]

I also meet this error,have you solved this now ？or, do you have any ohter way to run the yolov5s.onnx model ?thanks

[hrshovon]

I have not received any reply from the developer no unfortunately no. as for running model, I guess now pytorch might be the only option.

[erheng-dawnlight]

Is there any solution? I also encountered this issue. The original code worked well with yolov3. Thanks.

[zsy9512]

Is there any solution? I also encountered this issue. The original code worked well with yolov3. Thanks.

You can try on onnxruntime C++ ，I find that is ok when I use onnx1.6 and opset_version=11 to export the model.May you can refrence this sample https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp

[erheng-dawnlight]

Thanks @zsy9512 . We finally served the onnx model using tersorrt.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[ihmc3jn09hk]

Same issue. The exported file does not work.
Any solution for OpenCV ?

[hfzarslan]

Same issue any solution please

[glenn-jocher]

@ihmc3jn09hk @hfzarslan for running YOLOv5 in a local python project PyTorch Hub is the recommended method. See PyTorch Hub tutorial for more details:
https://docs.ultralytics.com/yolov5

import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).autoshape()  # for PIL/cv2/np inputs and NMS

# Images
img1 = Image.open('zidane.jpg')
img2 = Image.open('bus.jpg')
imgs = [img1, img2]  # batched list of images

# Inference
prediction = model(imgs, size=640)  # includes NMS

[ihmc3jn09hk]

@ihmc3jn09hk @hfzarslan for running YOLOv5 in a local python project PyTorch Hub is the recommended method. See PyTorch Hub tutorial for more details:
https://docs.ultralytics.com/yolov5

import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).autoshape()  # for PIL/cv2/np inputs and NMS

# Images
img1 = Image.open('zidane.jpg')
img2 = Image.open('bus.jpg')
imgs = [img1, img2]  # batched list of images

# Inference
prediction = model(imgs, size=640)  # includes NMS

Hi @glenn-jocher , thank you for your suggestions. But I think the question of this thread is either

1. the exportation of the yolov5 model into other NN model frameworks supported by opencv or,
2. a Pytorch C++ implementation of yolov5.

[glenn-jocher]

@ihmc3jn09hk hmm ok I see. I don't have much experience with ML models in opencv or C++, but perhaps the torchscript export is the easiest way to get a model into C?

ONNX export should work also, though in its current open-source form it lacks the Detect() layer outputs, and would require additional handling that would have to be provided separately.