Add YOLOX support

2023-01-30 23:59:51 -03:00
parent f9c7a4dfca
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+# YOLOX usage
+
+**NOTE**: The yaml file is not required.
+
+* [Convert model](#convert-model)
+* [Compile the lib](#compile-the-lib)
+* [Edit the config_infer_primary_yolox file](#edit-the-config_infer_primary_yolox-file)
+* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
+* [Testing the model](#testing-the-model)
+
+##
+
+### Convert model
+
+#### 1. Download the YOLOX repo and install the requirements
+
+```
+git clone https://github.com/Megvii-BaseDetection/YOLOX
+cd YOLOX
+pip3 install -r requirements.txt
+```
+
+**NOTE**: It is recommended to use Python virtualenv.
+
+#### 2. Copy conversor
+
+Copy the `gen_wts_yolox.py` file from `DeepStream-Yolo/utils` directory to the `YOLOX` folder.
+
+#### 3. Download the model
+
+Download the `pth` file from [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/releases) releases (example for YOLOX-s standard)
+
+```
+wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth
+```
+
+**NOTE**: You can use your custom model, but it is important to keep the YOLO model reference (`yolox_`) in you `cfg` and `weights`/`wts` filenames to generate the engine correctly.
+
+#### 4. Convert model
+
+Generate the `cfg` and `wts` files (example for YOLOX-s standard)
+
+```
+python3 gen_wts_yolox.py -w yolox_s.pth -e exps/default/yolox_s.py
+```
+
+#### 5. Copy generated files
+
+Copy the generated `cfg` and `wts` files to the `DeepStream-Yolo` folder.
+
+##
+
+### Compile the lib
+
+Open the `DeepStream-Yolo` folder and compile the lib
+
+* DeepStream 6.1.1 on x86 platform
+
+  ```
+  CUDA_VER=11.7 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.1 on x86 platform
+
+  ```
+  CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.0.1 / 6.0 on x86 platform
+
+  ```
+  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.1.1 / 6.1 on Jetson platform
+
+  ```
+  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+* DeepStream 6.0.1 / 6.0 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
+##
+
+### Edit the config_infer_primary_yolox file
+
+Edit the `config_infer_primary_yolox.txt` file according to your model (example for YOLOX-s standard)
+
+```
+[property]
+...
+custom-network-config=yolox_s.cfg
+model-file=yolox_s.wts
+...
+```
+
+**NOTE**: If you use the **legacy** model, you should edit the `config_infer_primary_yolox_legacy.txt` file.
+
+**NOTE**: The **YOLOX standard** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
+
+```
+net-scale-factor=0
+```
+
+**NOTE**: The **YOLOX legacy** uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
+
+Default: `mean = 0.485, 0.456, 0.406` and `std = 0.229, 0.224, 0.225`
+
+```
+net-scale-factor=0.0173520735727919486
+offsets=123.675;116.28;103.53
+```
+
+##
+
+### Edit the deepstream_app_config file
+
+```
+...
+[primary-gie]
+...
+config-file=config_infer_primary_yolox.txt
+```
+
+**NOTE**: If you use the **legacy** model, you should edit it to `config_infer_primary_yolox_legacy.txt`.
+
+##
+
+### Testing the model
+
+```
+deepstream-app -c deepstream_app_config.txt
+```