Comments (4)
This is reserved for the target torchscript model to be generated. Fell free to delete this line! If you want to save the generated model, you can do something as following:
model_script.save(export_script_name)
ths
ultralytics/yolov5 code
model.model[-1].export = False # set
python models/export.py --weights ./runs/train/exp5/weights/best.pt --img 640 --batch 1
it work
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Hi @nobody-cheng ,
Are you interested in the python side or the cpp side?
If you want to use python, you could check this notebook, and if you are interested in the Cpp backend, you could check the unit-test, and we provided an C++ example of how to infer with the transformed torchscript model, but this example is a little outdated.
BTW the upstream PyTorch have refactored their interfaces of torchscript recently, I've only updated the unit-test to PyTorch 1.8. The example in deployment only works in PyTorch 1.7.x. I will update the example in deployment to PyTorch 1.8 soon.
from yolort.
Hi @nobody-cheng ,
Are you interested in the python side or the cpp side?
If you want to use python, you could check this notebook, and if you are interested in the Cpp backend, you could check the unit-test, and we provided an C++ example of how to infer with the transformed torchscript model, but this example is a little outdated.
BTW the upstream PyTorch have refactored their interfaces of torchscript recently, I've only updated the unit-test to PyTorch 1.8. The example in deployment only works in PyTorch 1.7.x. I will update the example in deployment to PyTorch 1.8 soon.
# TorchScript export
print(f'Starting TorchScript export with torch {torch.__version__}...')
export_script_name = './checkpoints/yolov5/yolov5s.torchscript.pt' # >>>>>>> ** Variable is not called **
model_script = torch.jit.script(model)
model_script.eval()
model_script = model_script.to(device)
x = img[None]
out = model(x)
out_script = model_script(x)
export_script_name Variable is not called
from yolort.
Hi @nobody-cheng ,
This is reserved for the target torchscript model to be generated. Fell free to delete this line! If you want to save the generated model, you can do something as following:
model_script.save(export_script_name)
from yolort.
Related Issues (20)
- I'm unable to convert yolov5 ultralytics to an engine or onnx file HOT 10
- AttributeError is reported after installation on Windows system
- `numpy` does not support newline delimiter from version 1.23
- zlibwapi.dll (solved)
- No module named 'yolort.utils.update_module_state' while saving the Yolo Model HOT 6
- Dynamic batch dimension not working with ONNX export HOT 1
- module 'yolort' has no attribute 'utils' HOT 4
- Can't load custom trained model HOT 2
- Unexpected side effect on matplotlib's backend HOT 2
- Remove `NestedTensor` from pre-processing
- Loading pre-trained model is not supported for num_classes != 80 HOT 1
- Can bbox coordinates be negative in yolo output? HOT 6
- Remove `ComputeLoss` from TorchScript graph
- SpeedUp with microsoft/nni HOT 3
- Can not export to ONNX model. AttributeError: 'NoneType' object has no attribute 'shape' HOT 7
- CLI tool for exporting models.: error: the following arguments are required: --checkpoint_path HOT 16
- Is it correct to subtract x_offset twice when performing bbox scale as post-processing? HOT 1
- If put yolov5 onnx exported from ultralytics into export_engine api, the postprocess speed slows down in cpp deploy. HOT 8
- SetCriterion's forward() incompatible with P6 models. Can't train P6 models.
- [defaultAllocator.cpp::deallocate::42] Error Code 1: Cuda Runtime (invalid argument) Segmentation fault (core dumped)
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