doc(ncnn): update README (Megvii-BaseDetection#1519)

doc(ncnn): update README

Author: xin-li-67

demo/ncnn/README.md

@@ -0,0 +1,8 @@
+# YOLOX-ncnn
+
+Compile files of YOLOX object detection base on [ncnn](https://github.com/Tencent/ncnn).  
+YOLOX is included in ncnn now, you could also try building from ncnn, it's better.
+
+## Acknowledgement
+
+* [ncnn](https://github.com/Tencent/ncnn)

demo/ncnn/cpp/README.md

@@ -1,21 +1,20 @@
 # YOLOX-CPP-ncnn
 
 Cpp file compile of YOLOX object detection base on [ncnn](https://github.com/Tencent/ncnn).  
-YOLOX is included in ncnn now, you could also try building from ncnn, it's better.
 
 ## Tutorial
 
 ### Step1
 Clone [ncnn](https://github.com/Tencent/ncnn) first, then please following [build tutorial of ncnn](https://github.com/Tencent/ncnn/wiki/how-to-build) to build on your own device.
 
 ### Step2
-Use provided tools to generate onnx file.
+First, we try the original onnx2ncnn solution by using provided tools to generate onnx file.
 For example, if you want to generate onnx file of yolox-s, please run the following command:
 ```shell
 cd <path of yolox>
 python3 tools/export_onnx.py -n yolox-s
 ```
-Then, a yolox.onnx file is generated.
+Then a yolox.onnx file is generated.
 
 ### Step3
 Generate ncnn param and bin file.
@@ -25,14 +24,15 @@ cd build/tools/ncnn
 ./onnx2ncnn yolox.onnx model.param model.bin
 ```
 
-Since Focus module is not supported in ncnn. Warnings like:
+Since Focus module is not supported in ncnn. You will see warnings like:
 ```shell
-Unsupported slice step ! 
+Unsupported slice step!
 ```
-will be printed. However, don't  worry!  C++ version of Focus layer is already implemented in yolox.cpp.
+However, don't worry on this as a C++ version of Focus layer is already implemented in yolox.cpp.
 
 ### Step4
-Open **model.param**, and modify it.
+Open **model.param**, and modify it. For more information on the ncnn param and model file structure, please take a look at this [wiki](https://github.com/Tencent/ncnn/wiki/param-and-model-file-structure).
+
 Before (just an example):
 ```
 295 328
@@ -49,7 +49,7 @@ Crop             Slice_39                 1 1 677 682 -23309=1,1 -23310=1,214748
 Concat           Concat_40                4 1 652 672 662 682 683 0=0
 ...
 ```
-* Change first number for 295 to 295 - 9 = 286(since we will remove 10 layers and add 1 layers, total layers number should minus 9). 
+* Change first number for 295 to 295 - 9 = 286 (since we will remove 10 layers and add 1 layers, total layers number should minus 9). 
 * Then remove 10 lines of code from Split to Concat, but remember the last but 2nd number: 683.
 * Add YoloV5Focus layer After Input (using previous number 683):
 ```
@@ -71,14 +71,34 @@ Use ncnn_optimize to generate new param and bin:
 ```
 
 ### Step6
-Copy or Move yolox.cpp file into ncnn/examples, modify the CMakeList.txt, then build yolox
+Copy or Move yolox.cpp file into ncnn/examples, modify the CMakeList.txt to add our implementation, then build.
 
 ### Step7
 Inference image with executable file yolox, enjoy the detect result:
 ```shell
 ./yolox demo.jpg
 ```
 
+### Bounus Solution:
+As ncnn has released another model conversion tool called [pnnx](https://zhuanlan.zhihu.com/p/427620428) which directly finishs the pytorch2ncnn process via torchscript, we can also try on this.
+
+```shell
+# take yolox-s as an example
+python3 tools/export_torchscript.py -n yolox-s -c /path/to/your_checkpoint_files
+```
+Then a `yolox.torchscript.pt` will be generated. Copy this file to your pnnx build directory (pnnx also provides pre-built packages [here](https://github.com/pnnx/pnnx/releases/tag/20220720)).
+
+```shell
+# suppose you put the yolox.torchscript.pt in a seperate folder
+./pnnx yolox/yolox.torchscript.pt inputshape=[1,3,640,640]
+# for zsh users, please use inputshape='[1,3,640,640]'
+```
+Still, as ncnn does not support `slice` op as we mentioned in [Step3](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ncnn/cpp#step3). You will still see the warnings during this process.
+
+Then multiple pnnx related files will be genreated in your yolox folder. Use `yolox.torchscript.ncnn.param` and `yolox.torchscript.ncnn.bin` as your converted model. 
+
+Then we can follow back to our [Step4](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ncnn/cpp#step4) for the rest of our implementation.
+
 ## Acknowledgement
 
 * [ncnn](https://github.com/Tencent/ncnn)