optimize yolov5 cmdline args

Author: wang-xinyu

yolov5/README.md

@@ -2,32 +2,34 @@
 
 The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
 
+## Different versions of yolov5
+
 Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0, v3.1 and v4.0.
 
-- For yolov5 v4.0, please visit [yolov5 release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0), and use the latest commit of this repo.
-- For yolov5 v3.0 and v3.1, please visit [yolov5 release v3.0](https://github.com/ultralytics/yolov5/releases/tag/v3.0) and [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), and checkout commit ['6d0f5cb'](https://github.com/wang-xinyu/tensorrtx/commit/6d0f5cbf4745bc00b69aad54a905383fb906f103) of this repo.
-- For yolov5 v2.0, please visit [yolov5 release v2.0](https://github.com/ultralytics/yolov5/releases/tag/v2.0), and checkout commit ['5cfa444'](https://github.com/wang-xinyu/tensorrtx/commit/5cfa4445170eabaa54acd5ad7f469ef65a8763f1) of this repo.
-- For yolov5 v1.0, please visit [yolov5 release v1.0](https://github.com/ultralytics/yolov5/releases/tag/v1.0), and checkout commit ['f09aa3b'](https://github.com/wang-xinyu/tensorrtx/commit/f09aa3bbebf4d4d37b6d3b32a1d39e1f2678a07b) of this repo.
+- For yolov5 v4.0, download .pt from [yolov5 release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0), `git clone -b v4.0 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git`.
+- For yolov5 v3.1, download .pt from [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), `git clone -b v3.1 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git && git checkout 6d0f5cbf4745bc00b69aad54a905383fb906f103`.
+- For yolov5 v3.0, download .pt from [yolov5 release v3.0](https://github.com/ultralytics/yolov5/releases/tag/v3.0), `git clone -b v3.0 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git && git checkout 6d0f5cbf4745bc00b69aad54a905383fb906f103`.
+- For yolov5 v2.0, download .pt from [yolov5 release v2.0](https://github.com/ultralytics/yolov5/releases/tag/v2.0), `git clone -b v2.0 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git && git checkout 5cfa4445170eabaa54acd5ad7f469ef65a8763f1`.
+- For yolov5 v1.0, download .pt from [yolov5 release v1.0](https://github.com/ultralytics/yolov5/releases/tag/v1.0), `git clone -b v1.0 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git && git checkout f09aa3bbebf4d4d37b6d3b32a1d39e1f2678a07b`.
 
 ## Config
 
 - Choose the model s/m/l/x by `NET` macro in yolov5.cpp
 - Input shape defined in yololayer.h
 - Number of classes defined in yololayer.h, **DO NOT FORGET TO ADAPT THIS, If using your own model**
-- FP16/FP32 can be selected by the macro in yolov5.cpp
+- INT8/FP16/FP32 can be selected by the macro in yolov5.cpp, **INT8 need more steps, pls follow `How to Run` first and then go the `INT8 Quantization` below**
 - GPU id can be selected by the macro in yolov5.cpp
 - NMS thresh in yolov5.cpp
 - BBox confidence thresh in yolov5.cpp
 - Batch size in yolov5.cpp
 
 ## How to Run, yolov5s as example
 
-1. generate yolov5s.wts from pytorch with yolov5s.pt, or download .wts from model zoo
+1. generate .wts from pytorch with .pt, or download .wts from model zoo
 
 ```
-git clone https://github.com/wang-xinyu/tensorrtx.git
-git clone https://github.com/ultralytics/yolov5.git
-// download its weights 'yolov5s.pt'
+// git clone src code according to `Different versions of yolov5` above
+// download https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5s.pt
 // copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5
 // ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
 // go to ultralytics/yolov5
@@ -40,18 +42,19 @@ python gen_wts.py
 ```
 // put yolov5s.wts into tensorrtx/yolov5
 // go to tensorrtx/yolov5
-// ensure the macro NET in yolov5.cpp is s
 // update CLASS_NUM in yololayer.h if your model is trained on custom dataset
 mkdir build
 cd build
 cmake ..
 make
-sudo ./yolov5 -s 0.33 0.50   // serialize yolov5s model to plan file i.e. 'yolov5.engine'
-sudo ./yolov5 -s 0.67 0.75   // serialize yolov5m model to plan file i.e. 'yolov5.engine'
-sudo ./yolov5 -s 1.0 1.0   // serialize yolov5l model to plan file i.e. 'yolov5.engine'
-sudo ./yolov5 -s 1.33 1.25   // serialize yolov5x model to plan file i.e. 'yolov5.engine'
-sudo ./yolov5 -s 0.17 0.25   // serialize your own yolov5 model with depth_multiple and width_multiple which you set in '.yaml' file when training the model. i.e. depth = 0.17, width = 0.25
-sudo ./yolov5 -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.
+sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x or c gd gw]  // serialize model to plan file
+sudo ./yolov5 -d [.engine] [image folder]  // deserialize and run inference, the images in [image folder] will be processed.
+// For example yolov5s
+sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
+sudo ./yolov5 -d yolov5s.engine ../samples
+// For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
+sudo ./yolov5 -s yolov5_custom.wts yolov5.engine c 0.17 0.25
+sudo ./yolov5 -d yolov5.engine ../samples
 ```
 
 3. check the images generated, as follows. _zidane.jpg and _bus.jpg

yolov5/samples

@@ -0,0 +1 @@
+../yolov3-spp/samples/