The pipeline in this sample is as below:
| Steps | Backend | Time |
|---|---|---|
| OpenCV load image + preprocessing | CPU | N/A |
| MatX reformat to cuDLA input format | GPU | 0.7ms |
| cuDLA inference | DLA | 4.5ms |
| MatX reformat to FP16 planar format | GPU | 0.7ms |
| Post-process(decode_box, nms) | GPU | 0.1-0.2ms |
| Get final bbox data | CPU | N/A |
.
├── cudla_context_hybrid.cpp # cuDLA inference context in hybrid mode [cuDLA inference]
├── cudla_context_hybrid.h # cuDLA inference context in hybrid mode [cuDLA inference]
├── cudla_context_standalone.cpp # cuDLA inference context in standalone mode [cuDLA inference]
├── cudla_context_standalone.h # cuDLA inference context in standalone mode [cuDLA inference]
├── decode_nms.cu # Decode bbox and NMS [post-processing]
├── decode_nms.h # Decode bbox and NMS [post-processing]
├── matx_reformat # data reformat for cuDLA inputs/outputs [pre-processing] and [post-processing]
│ ├── build_matx_reformat.sh
│ ├── CMakeLists.txt
│ ├── MatX
│ ├── matx_reformat.cu
│ ├── matx_reformat.h
│ ├── README.md
│ └── test.cpp
├── validate_coco.cpp # Validation app, main function [jpg --> OpenCV decode]
├── yolov5.cpp # Yolov5 pipeline [pre-processing --> Inference(cuDLA) --> post-processing]
└── yolov5.h # Yolov5 pipeline [pre-processing --> Inference(cuDLA) --> post-processing]