Awesome
TensorRT-CenterNet
demo (GT 1070)
Performance
- support Deform Conv v2.
- no nms.
- support fp32 fp16 int8 mode.
Eval Result
| model | GPU | mode | AP<sup>trt</sup>/AP<sup>paper</sup> | AP<sup>trt</sup><sub>50</sub> | AP<sup>trt</sup><sub>75</sub> | AP<sup>trt</sup><sub>S</sub> | AP<sup>trt</sup><sub>M</sub> | AP<sup>trt</sup><sub>L</sub> |
|---|
| ctdet_coco_dla_2x | gtx 1070 | float32 | 0.365/0.374 | 0.543 | 0.390 | 0.164 | 0.398 | 0.536 |
| ctdet_coco_dlav0_1x | gtx 1070 | float32 | 0.324/-- | 0.511 | 0.343 | 0.140 | 0.350 | 0.476 |
| ctdet_coco_dlav0_1x | gtx 1070 | int8 | 0.295/-- | 0.468 | 0.311 | 0.123 | 0.318 | 0.446 |
| ctdet_coco_resdcn101 | gtx 1070 | float32 | 0.332/0.346 | 0.516 | 0.349 | 0.115 | 0.367 | 0.531 |
| ctdet_coco_resdcn18 | gtx 1070 | float32 | 0.277/0.281 | 0.448 | 0.286 | 0.083 | 0.290 | 0.454 |
| ctdet_coco_resdcn18 | gtx 1070 | int8 | 0.242/0.281 | 0.401 | 0.250 | 0.061 | 0.255 | 0.409 |
notes
- cocoval2017 test AP with no augmentation.
- input_szie = 512x512
- thresh = 0.01
- maxpool kernel_size = 3
- calib_img_list.txt : random sample 700 images from COCO2017/val2017
Enviroments
- gtx 1070
pytorch 1.0-1.1
ubuntu 1604
TensorRT 5.0
onnx-tensorrt v5.0
cuda 9.0
- jetson TX2
jetpack 4.2
Models
- Convert CenterNet model to onnx. See here for details.
- Use netron to observe whether the output of the converted onnx model is (hm, reg, wh)
Example
git clone https://github.com/CaoWGG/TensorRT-CenterNet.git
cd TensorRT-CenterNet
mkdir build
cd build && cmake .. && make
cd ..
##ctdet | config include/ctdetConfig.h
## float32
./buildEngine -i model/ctdet_coco_dla_2x.onnx -o model/ctdet_coco_dla_2x.engine
./runDet -e model/ctdet_coco_dla_2x.engine -i test.jpg -c test.h264
##cthelmet | config include/ctdetConfig.h
## flaot32
./buildEngine -i model/ctdet_helmet.onnx -o model/ctdet_helmet.engine -m 0
./runDet -e model/ctdet_helmet.engine -i test.jpg -c test.h264
## int8
./buildEngine -i model/ctdet_helmet.onnx -o model/ctdet_helmet.engine -m 2 -c calib_img_list.txt
./runDet -e model/ctdet_helmet.engine -i test.jpg -c test.h264
##centerface | config include/ctdetConfig.h
./buildEngine -i model/centerface.onnx -o model/centerface.engine
./runDet -e model/centerface.engine -i test.jpg -c test.h264
## run eval_coco.py | conifg your cocodaset and ctdet_coco engine
python3 eval_coco.py model/ctdet_coco_dla_2x.engine
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