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## Deploy YOLO-World |
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- [x] ONNX export |
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- [x] ONNX demo |
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- [ ] TensorRT |
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- [ ] TFLite |
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We provide several ways to deploy YOLO-World with ONNX or TensorRT |
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### Priliminaries |
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```bash |
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pip install supervision onnx onnxruntime onnxsim |
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``` |
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### Export ONNX on Gradio Demo |
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start the `demo.py` and you can modify the texts in the demo and output the ONNX model. |
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```bash |
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python demo.py path/to/config path/to/weights |
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``` |
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### Export YOLO-World to ONNX models |
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You can also use [`export_onnx.py`](../deploy/export_onnx.py) to obtain the ONNX model. You might specify the `--custom-text` with your own `Text JSON` for your custom prompts. The format of `Text JSON` can be found in [`docs/data`](../docs/data.md). |
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```bash |
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PYTHONPATH=./ python deploy/export_onnx.py path/to/config path/to/weights --custom-text path/to/customtexts --opset 11 |
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``` |
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If you don't want to include `NMS` or "post-processing" into the ONNX model, you can add `--without-nms` |
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```bash |
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PYTHONPATH=./ python deploy/export_onnx.py path/to/config path/to/weights --custom-text path/to/customtexts --opset 11 --without-nms |
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``` |
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If you want to quantize YOLO-World with ONNX model, you'd better remove `NMS` and `bbox_decoder` by adding `--without-bbox-decoder` |
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```bash |
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PYTHONPATH=./ python deploy/export_onnx.py path/to/config path/to/weights --custom-text path/to/customtexts --opset 11 --without-bbox-decoder |
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``` |
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**Running ONNX demo** |
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```bash |
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python deploy/onnx_demo.py path/to/model.onnx path/to/images path/to/texts |
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``` |
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### Export YOLO-World to TensorRT models |
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coming soon. |
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### FAQ |
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**Q1**. `RuntimeError: Exporting the operator einsum to ONNX opset version 11 is not supported. Support for this operator was added in version 12, try exporting with this version.` |
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**A:** This error arises because YOLO-World adopts `einsum` for matrix multiplication while it is not supported by `opset 11`. You can set the `--opset` from `11` to `12` if your device supports or change the `einsum` to normal `permute/reshape/multiplication` by set `use_einsum=False` in the `MaxSigmoidCSPLayerWithTwoConv` and `YOLOWorldHeadModule`. You can refer to the [sample config](../configs/pretrain/yolo_world_v2_m_vlpan_bn_noeinsum_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) without einsum. |
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