260 lines
4.5 KiB
Markdown
260 lines
4.5 KiB
Markdown
# YOLO-NAS usage
|
|
|
|
**NOTE**: The yaml file is not required.
|
|
|
|
* [Convert model](#convert-model)
|
|
* [Compile the lib](#compile-the-lib)
|
|
* [Edit the config_infer_primary_yolonas file](#edit-the-config_infer_primary_yolonas-file)
|
|
* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
|
|
* [Testing the model](#testing-the-model)
|
|
|
|
##
|
|
|
|
### Convert model
|
|
|
|
#### 1. Download the YOLO-NAS repo and install the requirements
|
|
|
|
```
|
|
git clone https://github.com/Deci-AI/super-gradients.git
|
|
cd super-gradients
|
|
pip3 install -r requirements.txt
|
|
python3 setup.py install
|
|
pip3 install onnx onnxsim onnxruntime
|
|
```
|
|
|
|
**NOTE**: It is recommended to use Python virtualenv.
|
|
|
|
#### 2. Copy conversor
|
|
|
|
Copy the `export_yolonas.py` file from `DeepStream-Yolo/utils` directory to the `super-gradients` folder.
|
|
|
|
#### 3. Download the model
|
|
|
|
Download the `pth` file from [YOLO-NAS](https://sghub.deci.ai/) releases (example for YOLO-NAS S)
|
|
|
|
```
|
|
wget https://sghub.deci.ai/models/yolo_nas_s_coco.pth
|
|
```
|
|
|
|
**NOTE**: You can use your custom model.
|
|
|
|
#### 4. Convert model
|
|
|
|
Generate the ONNX model file (example for YOLO-NAS S)
|
|
|
|
```
|
|
python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --dynamic
|
|
```
|
|
|
|
**NOTE**: Model names
|
|
|
|
```
|
|
-m yolo_nas_s
|
|
```
|
|
|
|
or
|
|
|
|
```
|
|
-m yolo_nas_m
|
|
```
|
|
|
|
or
|
|
|
|
```
|
|
-m yolo_nas_l
|
|
```
|
|
|
|
**NOTE**: Number of classes (example for 80 classes)
|
|
|
|
```
|
|
-n 80
|
|
```
|
|
|
|
or
|
|
|
|
```
|
|
--classes 80
|
|
```
|
|
|
|
**NOTE**: To change the inference size (defaut: 640)
|
|
|
|
```
|
|
-s SIZE
|
|
--size SIZE
|
|
-s HEIGHT WIDTH
|
|
--size HEIGHT WIDTH
|
|
```
|
|
|
|
Example for 1280
|
|
|
|
```
|
|
-s 1280
|
|
```
|
|
|
|
or
|
|
|
|
```
|
|
-s 1280 1280
|
|
```
|
|
|
|
**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
|
|
|
|
```
|
|
--simplify
|
|
```
|
|
|
|
**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
|
|
|
|
```
|
|
--dynamic
|
|
```
|
|
|
|
**NOTE**: To use implicit batch-size (example for batch-size = 4)
|
|
|
|
```
|
|
--batch 4
|
|
```
|
|
|
|
**NOTE**: If you are using the DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 14.
|
|
|
|
```
|
|
--opset 12
|
|
```
|
|
|
|
#### 5. Copy generated file
|
|
|
|
Copy the generated ONNX model file to the `DeepStream-Yolo` folder.
|
|
|
|
##
|
|
|
|
### Compile the lib
|
|
|
|
Open the `DeepStream-Yolo` folder and compile the lib
|
|
|
|
* DeepStream 6.3 on x86 platform
|
|
|
|
```
|
|
CUDA_VER=12.1 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* DeepStream 6.2 on x86 platform
|
|
|
|
```
|
|
CUDA_VER=11.8 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* 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 5.1 on x86 platform
|
|
|
|
```
|
|
CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 on Jetson platform
|
|
|
|
```
|
|
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
* DeepStream 6.0.1 / 6.0 / 5.1 on Jetson platform
|
|
|
|
```
|
|
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
|
|
```
|
|
|
|
##
|
|
|
|
### Edit the config_infer_primary_yolonas file
|
|
|
|
Edit the `config_infer_primary_yolonas.txt` file according to your model (example for YOLO-NAS S with 80 classes)
|
|
|
|
```
|
|
[property]
|
|
...
|
|
onnx-file=yolo_nas_s_coco.onnx
|
|
...
|
|
num-detected-classes=80
|
|
...
|
|
parse-bbox-func-name=NvDsInferParseYoloE
|
|
...
|
|
```
|
|
|
|
**NOTE**: If you are using a **custom** model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
|
|
|
|
**NOTE**: The **YOLO-NAS** resizes the input with left/top padding. To get better accuracy, use
|
|
|
|
```
|
|
[property]
|
|
...
|
|
maintain-aspect-ratio=1
|
|
symmetric-padding=0
|
|
...
|
|
```
|
|
|
|
**NOTE**: The **pre-trained YOLO-NAS** uses zero mean normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
|
|
|
|
```
|
|
[property]
|
|
...
|
|
net-scale-factor=0.0039215697906911373
|
|
...
|
|
```
|
|
|
|
**NOTE**: The **custom YOLO-NAS** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
|
|
|
|
```
|
|
[property]
|
|
...
|
|
net-scale-factor=1
|
|
...
|
|
```
|
|
|
|
**NOTE**: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line
|
|
|
|
```
|
|
[property]
|
|
...
|
|
force-implicit-batch-dim=1
|
|
...
|
|
```
|
|
|
|
##
|
|
|
|
### Edit the deepstream_app_config file
|
|
|
|
```
|
|
...
|
|
[primary-gie]
|
|
...
|
|
config-file=config_infer_primary_yolonas.txt
|
|
```
|
|
|
|
##
|
|
|
|
### Testing the model
|
|
|
|
```
|
|
deepstream-app -c deepstream_app_config.txt
|
|
```
|
|
|
|
**NOTE**: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).
|
|
|
|
**NOTE**: For more information about custom models configuration (`batch-size`, `network-mode`, etc), please check the [`docs/customModels.md`](customModels.md) file.
|