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wang-xinyu 2023-01-17 15:58:37 +08:00
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yolov5/README.md
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@ -50,7 +50,7 @@ Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.
## Config
- Choose the YOLOv5 sub-model n/s/m/l/x/n6/s6/m6/l6/x6 from command line arguments.
- Other configs please check src/config.h
- Other configs please check [src/config.h](src/config.h)
## Build and Run
@ -59,41 +59,45 @@ Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.
1. generate .wts from pytorch with .pt, or download .wts from model zoo
```
// clone code according to above #Different versions of yolov5
// download https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
cp {tensorrtx}/yolov5/gen_wts.py {ultralytics}/yolov5
cd {ultralytics}/yolov5
git clone -b v7.0 https://github.com/ultralytics/yolov5.git
git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git
cd yolov5/
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
cp [PATH-TO-TENSORRTX]/yolov5/gen_wts.py .
python gen_wts.py -w yolov5s.pt -o yolov5s.wts
// a file 'yolov5s.wts' will be generated.
# A file 'yolov5s.wts' will be generated.
```
2. build tensorrtx/yolov5 and run
```
cd {tensorrtx}/yolov5/
// update CLASS_NUM in yololayer.h if your model is trained on custom dataset
cd [PATH-TO-TENSORRTX]/yolov5/
# Update kNumClass in src/config.h if your model is trained on custom dataset
mkdir build
cd build
cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/build
cp [PATH-TO-ultralytics-yolov5]/yolov5s.wts .
cmake ..
make
./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file
./yolov5_det -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov5s
# For example yolov5s
./yolov5_det -s yolov5s.wts yolov5s.engine s
./yolov5_det -d yolov5s.engine ../images
// For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
# For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
./yolov5_det -s yolov5_custom.wts yolov5.engine c 0.17 0.25
./yolov5_det -d yolov5.engine ../images
```
3. check the images generated, as follows. _zidane.jpg and _bus.jpg
3. Check the images generated, _zidane.jpg and _bus.jpg
4. optional, load and run the tensorrt model in python
4. Optional, load and run the tensorrt model in Python
```
// install python-tensorrt, pycuda, etc.
// ensure the yolov5s.engine and libmyplugins.so have been built
// Install python-tensorrt, pycuda, etc.
// Ensure the yolov5s.engine and libmyplugins.so have been built
python yolov5_det_trt.py
// Another version of python script, which is using CUDA Python instead of pycuda.