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Update to YOLOv5 5.0

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Updated files for YOLOv5 5.0
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Marcos Luciano
2021-05-03 20:22:42 -03:00
parent d0f0b66a1c
commit 170f9cac74
11 changed files with 284 additions and 395 deletions
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YOLOv5.md
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@@ -1,9 +1,9 @@
# YOLOv5
NVIDIA DeepStream SDK 5.1 configuration for YOLOv5 models
NVIDIA DeepStream SDK 5.1 configuration for YOLOv5 5.0 models
Thanks [DanaHan](https://github.com/DanaHan/Yolov5-in-Deepstream-5.0), [wang-xinyu](https://github.com/wang-xinyu/tensorrtx) and [Ultralytics](https://github.com/ultralytics/yolov5)
Thanks [wang-xinyu](https://github.com/wang-xinyu/tensorrtx) and [Ultralytics](https://github.com/ultralytics/yolov5)
Supported version: YOLOv5 3.0/3.1
Supported version: YOLOv5 5.0
##
@@ -16,53 +16,15 @@ Supported version: YOLOv5 3.0/3.1
##
### Requirements
* Python3
```
sudo apt-get install python3 python3-dev python3-pip
pip3 install --upgrade pip
```
* [TensorRTX](https://github.com/wang-xinyu/tensorrtx/blob/master/tutorials/install.md)
* OpenCV Python
```
sudo apt-get install libopencv-dev
pip3 install opencv-python
```
* Matplotlib
```
pip3 install matplotlib
```
* [Ultralytics](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)
* Matplotlib (for Jetson plataform)
```
sudo apt-get install python3-matplotlib
```
* Scipy
```
pip3 install scipy
```
* tqdm
```
pip3 install tqdm
```
* Pandas
```
pip3 install pandas
```
* seaborn
```
pip3 install seaborn
```
* PyTorch
```
pip3 install torch torchvision
```
* PyTorch (for Jetson plataform)
```
wget https://nvidia.box.com/shared/static/9eptse6jyly1ggt9axbja2yrmj6pbarc.whl -O torch-1.6.0-cp36-cp36m-linux_aarch64.whl
@@ -84,20 +46,13 @@ sudo python3 setup.py install
### Convert PyTorch model to wts file
1. Download repositories
```
git clone https://github.com/DanaHan/Yolov5-in-Deepstream-5.0.git yolov5converter
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov5.git
```
Note: checkout TensorRTX repo to 3.0/3.1 YOLOv5 version
2. Download latest YoloV5 (YOLOv5s, YOLOv5m, YOLOv5l or YOLOv5x) weights to yolov5 folder (example for YOLOv5s)
```
cd tensorrtx
git checkout '6d0f5cb'
```
2. Download latest YoloV5 (YOLOv5s, YOLOv5m, YOLOv5l or YOLOv5x) weights to yolov5/weights directory (example for YOLOv5s)
```
wget https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt -P yolov5/weights/
wget https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt -P yolov5/
```
3. Copy gen_wts.py file (from tensorrtx/yolov5 folder) to yolov5 (ultralytics) folder
@@ -108,36 +63,15 @@ cp tensorrtx/yolov5/gen_wts.py yolov5/gen_wts.py
4. Generate wts file
```
cd yolov5
python3 gen_wts.py
python3 gen_wts.py yolov5s.pt
```
yolov5s.wts file will be generated in yolov5 folder
<br />
Note: if you want to generate wts file to another YOLOv5 model (YOLOv5m, YOLOv5l or YOLOv5x), edit get_wts.py file changing yolov5s to your model name
```
model = torch.load('weights/yolov5s.pt', map_location=device)['model'].float() # load to FP32
model.to(device).eval()
f = open('yolov5s.wts', 'w')
```
##
### Convert wts file to TensorRT model
1. Replace yololayer files from tensorrtx/yolov5 folder to yololayer and hardswish files from yolov5converter
```
mv yolov5converter/yololayer.cu tensorrtx/yolov5/yololayer.cu
mv yolov5converter/yololayer.h tensorrtx/yolov5/yololayer.h
```
2. Move generated yolov5s.wts file to tensorrtx/yolov5 folder (example for YOLOv5s)
```
cp yolov5/yolov5s.wts tensorrtx/yolov5/yolov5s.wts
```
3. Build tensorrtx/yolov5
1. Build tensorrtx/yolov5
```
cd tensorrtx/yolov5
mkdir build
@@ -146,12 +80,17 @@ cmake ..
make
```
4. Convert to TensorRT model (yolov5s.engine file will be generated in tensorrtx/yolov5/build folder)
2. Move generated yolov5s.wts file to tensorrtx/yolov5 folder (example for YOLOv5s)
```
sudo ./yolov5 -s
cp yolov5/yolov5s.wts tensorrtx/yolov5/build/yolov5s.wts
```
5. Create a custom yolo folder and copy generated files (example for YOLOv5s)
3. Convert to TensorRT model (yolov5s.engine file will be generated in tensorrtx/yolov5/build folder)
```
sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
```
4. Create a custom yolo folder and copy generated file (example for YOLOv5s)
```
mkdir /opt/nvidia/deepstream/deepstream-5.1/sources/yolo
cp yolov5s.engine /opt/nvidia/deepstream/deepstream-5.1/sources/yolo/yolov5s.engine
@@ -159,15 +98,13 @@ cp yolov5s.engine /opt/nvidia/deepstream/deepstream-5.1/sources/yolo/yolov5s.eng
<br />
Note: by default, yolov5 script generate model with batch size = 1, FP16 mode and s model.
Note: by default, yolov5 script generate model with batch size = 1 and FP16 mode.
```
#define USE_FP16 // comment out this if want to use FP32
#define USE_FP32 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.4
#define CONF_THRESH 0.5
#define BATCH_SIZE 1
#define NET s // s m l x
```
Edit yolov5.cpp file before compile if you want to change this parameters.
@@ -179,7 +116,7 @@ Edit yolov5.cpp file before compile if you want to change this parameters.
sudo chmod -R 777 /opt/nvidia/deepstream/deepstream-5.1/sources/
```
2. Donwload [my external/yolov5 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5) and move files to created yolo folder
2. Donwload [my external/yolov5-5.0 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5-5.0) and move files to created yolo folder
3. Compile lib
@@ -198,7 +135,7 @@ CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
##
### Testing model
Use my edited [deepstream_app_config.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5/deepstream_app_config.txt) and [config_infer_primary.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5/config_infer_primary.txt) files available in [my external/yolov5 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5)
Use my edited [deepstream_app_config.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5-5.0/deepstream_app_config.txt) and [config_infer_primary.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5-5.0/config_infer_primary.txt) files available in [my external/yolov5-5.0 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5-5.0)
Run command
```