210 lines
4.3 KiB
Markdown
210 lines
4.3 KiB
Markdown
# RTMDet (MMYOLO) usage
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* [Convert model](#convert-model)
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* [Compile the lib](#compile-the-lib)
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* [Edit the config_infer_primary_rtmdet file](#edit-the-config_infer_primary_rtmdet-file)
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* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
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* [Testing the model](#testing-the-model)
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##
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### Convert model
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#### 1. Download the RTMDet (MMYOLO) repo and install the requirements
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```
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git clone https://github.com/open-mmlab/mmyolo.git
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cd mmyolo
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pip3 install openmim
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mim install "mmengine>=0.6.0"
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mim install "mmcv>=2.0.0rc4,<2.1.0"
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mim install "mmdet>=3.0.0,<4.0.0"
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pip3 install -r requirements/albu.txt
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mim install -v -e .
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pip3 install onnx onnxslim onnxruntime
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```
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**NOTE**: It is recommended to use Python virtualenv.
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#### 2. Copy conversor
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Copy the `export_rtmdet.py` file from `DeepStream-Yolo/utils` directory to the `mmyolo` folder.
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#### 3. Download the model
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Download the `pth` file from [RTMDet (MMYOLO)](https://github.com/open-mmlab/mmyolo/tree/main/configs/rtmdet) releases (example for RTMDet-s*)
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```
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wget https://download.openmmlab.com/mmrazor/v1/rtmdet_distillation/kd_s_rtmdet_m_neck_300e_coco/kd_s_rtmdet_m_neck_300e_coco_20230220_140647-446ff003.pth
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```
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**NOTE**: You can use your custom model.
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#### 4. Convert model
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Generate the ONNX model file (example for RTMDet-s*)
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```
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python3 export_rtmdet.py -w kd_s_rtmdet_m_neck_300e_coco_20230220_140647-446ff003.pth -c configs/rtmdet/distillation/kd_s_rtmdet_m_neck_300e_coco.py --dynamic
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```
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**NOTE**: To change the inference size (defaut: 640)
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```
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-s SIZE
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--size SIZE
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-s HEIGHT WIDTH
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--size HEIGHT WIDTH
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```
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Example for 1280
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```
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-s 1280
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```
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or
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```
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-s 1280 1280
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```
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**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
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```
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--simplify
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```
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**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
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```
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--dynamic
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```
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**NOTE**: To use static batch-size (example for batch-size = 4)
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```
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--batch 4
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```
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**NOTE**: If you are using the DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 17.
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```
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--opset 12
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```
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#### 5. Copy generated files
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Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.
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##
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### Compile the lib
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1. Open the `DeepStream-Yolo` folder and compile the lib
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2. Set the `CUDA_VER` according to your DeepStream version
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```
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export CUDA_VER=XY.Z
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```
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* x86 platform
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```
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DeepStream 7.1 = 12.6
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 = 12.1
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DeepStream 6.2 = 11.8
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DeepStream 6.1.1 = 11.7
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DeepStream 6.1 = 11.6
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DeepStream 6.0.1 / 6.0 = 11.4
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DeepStream 5.1 = 11.1
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```
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* Jetson platform
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```
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DeepStream 7.1 = 12.6
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DeepStream 7.0 / 6.4 = 12.2
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DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
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DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
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```
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3. Make the lib
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```
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make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Edit the config_infer_primary_rtmdet file
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Edit the `config_infer_primary_rtmdet.txt` file according to your model (example for RTMDet-s* with 80 classes)
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```
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[property]
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...
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onnx-file=kd_s_rtmdet_m_neck_300e_coco_20230220_140647-446ff003.pth.onnx
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...
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num-detected-classes=80
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...
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parse-bbox-func-name=NvDsInferParseYolo
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...
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```
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**NOTE**: The **RTMDet (MMYOLO)** resizes the input with center padding. To get better accuracy, use
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```
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[property]
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...
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maintain-aspect-ratio=1
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symmetric-padding=1
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...
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```
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**NOTE**: The **RTMDet (MMYOLO)** uses BGR color format for the image input. It is important to change the `model-color-format` according to the trained values.
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```
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[property]
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...
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model-color-format=1
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...
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```
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**NOTE**: The **RTMDet (MMYOLO)** uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
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Default: `mean = 0.485, 0.456, 0.406` and `std = 0.229, 0.224, 0.225`
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```
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[property]
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...
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net-scale-factor=0.0173520735727919486
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offsets=103.53;116.28;123.675
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...
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```
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##
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### Edit the deepstream_app_config file
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```
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...
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[primary-gie]
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...
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config-file=config_infer_primary_rtmdet.txt
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```
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##
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### Testing the model
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```
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deepstream-app -c deepstream_app_config.txt
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```
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**NOTE**: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).
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**NOTE**: For more information about custom models configuration (`batch-size`, `network-mode`, etc), please check the [`docs/customModels.md`](customModels.md) file.
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