e73bffcd25
* tensorrt-yolov9 * format change * change format * Update block.cpp * format: add space * update block.cpp: add space * update config.h --------- Co-authored-by: Wang Xinyu <wangxinyu_es@163.com>
107 lines
3.3 KiB
Markdown
107 lines
3.3 KiB
Markdown
# yolov9
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The Pytorch implementation is [WongKinYiu/yolov9](https://github.com/WongKinYiu/yolov9).
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## Contributors
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## Progress
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- [x] YOLOv9-c:
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- [x] FP32
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- [x] FP16
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- [x] INT8
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- [x] YOLOv9-e:
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- [x] FP32
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- [x] FP16
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- [x] INT8
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## Requirements
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- TensorRT 8.0+
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- OpenCV 3.4.0+
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## Speed Test
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The speed test is done on a desktop with R7-5700G CPU and RTX 4060Ti GPU. The input size is 640x640. The FP32, FP16 and INT8 models are tested. The time only includes the inference time, not includes the pre-processing and post-processing. The time is the average of 1000 times inference.
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| frame | Model | FP32 | FP16 | INT8 |
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| --- | --- | --- | --- | --- |
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| pytorch | YOLOv9-c | - | 15.5ms | - |
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| pytorch | YOLOv9-e | - | 19.7ms | - |
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| tensorrt | YOLOv9-c | 13.5ms | 4.6ms | 3.0ms |
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| tensorrt | YOLOv9-e | 8.3ms | 3.2ms | 2.15ms |
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YOLOv9-e is faster than YOLOv9-c in tensorrt, because the YOLOv9-e requires fewer layers of inference.
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```
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YOLOv9-c:
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[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]] # [A3, A4, A5, P3, P4, P5]
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YOLOv9-e:
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[[35, 32, 29, 42, 45, 48], 1, DualDDetect, [nc]]
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```
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In DualDDetect, the A3, A4, A5, P3, P4, P5 are the output of the backbone. The first 3 layers are used for the inference of the final result.
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The YOLOv9-c requires 37 layers of inference, but YOLOv9-e requires 35 layers of inference.
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## How to Run, yolov9 as example
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1. generate .wts from pytorch with .pt, or download .wts from model zoo
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```
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// download https://github.com/WongKinYiu/yolov9
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cp {tensorrtx}/yolov9/gen_wts.py {yolov9}/yolov9
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cd {yolov9}/yolov9
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python gen_wts.py
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// a file 'yolov9.wts' will be generated.
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```
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2. build tensorrtx/yolov9 and run
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```
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cd {tensorrtx}/yolov9/
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// update kNumClass in config.h if your model is trained on custom dataset
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mkdir build
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cd build
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cp {ultralytics}/ultralytics/yolov9.wts {tensorrtx}/yolov9/build
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cmake ..
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make
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sudo ./yolov9 -s [.wts] [.engine] [c/e] // serialize model to plan file
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sudo ./yolov9 -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
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// For example yolov9
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sudo ./yolov9 -s yolov9-c.wts yolov9-c.engine c
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sudo ./yolov9 -d yolov9-c.engine ../images
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```
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3. check the images generated, as follows. _zidane.jpg and _bus.jpg
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4. optional, load and run the tensorrt model in python
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```
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// install python-tensorrt, pycuda, etc.
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// ensure the yolov9.engine and libmyplugins.so have been built
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python yolov9_trt.py
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```
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# INT8 Quantization
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1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
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2. unzip it in yolov8/build
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3. set the macro `USE_INT8` in config.h and change the path of calibration images in config.h, such as 'gCalibTablePath="./coco_calib/";'
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4. serialize the model and test
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg" height="360px;">
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</p>
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## More Information
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See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
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