Issue in torchscript model inference

[sourabhyadav]

🐛 Bug

I am facing the below issue when I try to load a saved torchscript model:

To Reproduce (REQUIRED)

Model saving was done using export.py file:

 # Update model
    for k, m in model.named_modules():
        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
        if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
            m.act = Hardswish()  # assign activation
        # if isinstance(m, models.yolo.Detect):
        #     m.forward = m.forward_export  # assign forward (optional)
    model.model[-1].export = True  # set Detect() layer export=True
    y = model(img)  # dry run

    # TorchScript export
    try:
        print('\nStarting TorchScript export with torch %s...' % torch.__version__)
        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
        ts = torch.jit.trace(model, img)
        ts.save(f)
        print('TorchScript export success, saved as %s' % f)
    except Exception as e:
        print('TorchScript export failure: %s' % e)

Run:

python3 models/export.py --weights yolov5m.pt --img-size 640

Model loading is done like this:

self.model = torch.jit.load(weights, map_location=self.device)

Model loading seems fine. But the issue comes when we try to inference the model: Output:

Traceback (most recent call last):
  File "face_recognition.py", line 84, in <module>
    embed_for_analysis()
  File "face_recognition.py", line 80, in embed_for_analysis
    inference.main(args, logger)
  File "/data/sourabh/Releases/smart_vision/facenet_master/tycoai_vision/inference.py", line 81, in main
    camera_object_list, args.face_detector, logger=logger, args=args)
  File "/data/sourabh/Releases/smart_vision/facenet_master/tycoai_vision/analyze_frames.py", line 37, in detect_and_track_faces
    logger=logger, removed_tracks=removed_tracks, args=args)
  File "/data/sourabh/Releases/smart_vision/facenet_master/tycoai_vision/analyze_frames.py", line 120, in detect_normalize_by_face_detector
    bounding_boxes = detection_network.detect(np.stack(frames_downsampled))
  File "/data/sourabh/Releases/smart_vision/facenet_master/tycoai_vision/face_detector/yolov5/yolov5_model.py", line 176, in detect
    inf_out, _ = self.model(imgs, augment=False)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
RuntimeError: forward() expected at most 2 argument(s) but received 3 argument(s). Declaration: forward(__torch__.models.yolo.Model self, Tensor x) -> (Tensor[])

Environment

• OS: Ubuntu 18.04
• GPU: Nvidia RTX 2080 Ti
• Cuda: 10.1

Is am I missing something here? Please guide me.

[glenn-jocher]

@sourabhyadav we don't provide support for torchscript loading or inference, only export.

[sourabhyadav]

@glenn-jocher Ok I will raise it as a question to the community,

[zhiqwang]

Hi @sourabhyadav I have a custom implementation of the loading and inference with torchscript, maybe you could check it in here.

[nobody-cheng]

are you fix？？

[pugovka91]

Hi @sourabhyadav I have a custom implementation of the loading and inference with torchscript, maybe you could check it in here.

@zhiqwang if it's possible, could you please give a link to your implementation of the loading and inference with torchscript (current link is not available any more)? I need to speed up my inference of custom yolov5 model, but I'm new to CV and I don't know, how to implement it myself(

[zhiqwang]

Hi @pugovka91 , the notebook has a python interface of loading and inference with torchscript, and you can check this if you wanna the C++ interface.

[pugovka91]

Hi @pugovka91 , the notebook has a python interface of loading and inference with torchscript, and you can check this if you wanna the C++ interface. @zhiqwang Thanks a lot! I’ll try it.

[pugovka91]

Hello @zhiqwang, it’s a bit out of topic, but I wanted to ask, if it’s possible to make detection with augment flag using yolort-model? Thanks a lot!

[zhiqwang]

Hi @pugovka91 ,

I'm not sure I understand your meaning correctly. Did you mean the Test-Time Augmentation (TTA), If that's the feature you're concerned about, we don't have this feature implemented yet in yolort.

[pugovka91]

@zhiqwang yes, exactly) will be waiting for this feature implementation, thank you!