mirror of https://github.com/open-mmlab/mmpose
117 lines
7.9 KiB
Markdown
117 lines
7.9 KiB
Markdown
# RTMPose Benchmarks
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简体中文 | [English](./README.md)
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欢迎社区用户在不同硬件设备上进行推理速度测试，贡献到本项目目录下。
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当前已测试：
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- CPU
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- Intel i7-11700
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- GPU
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- NVIDIA GeForce 1660 Ti
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- NVIDIA GeForce RTX 3090
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- Nvidia Jetson
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- AGX Orin
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- Orin NX
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- ARM
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- Snapdragon 865
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### 人体 2d 关键点 (17 Keypoints)
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### Model Info
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| Config | Input Size | AP<sup><br>(COCO) | Params(M) | FLOPS(G) |
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| :-------------------------------------------------------------------------------: | :--------: | :---------------: | :-------: | :------: |
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| [RTMPose-t](../rtmpose/body_2d_keypoint/rtmpose-tiny_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 |
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| [RTMPose-s](../rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 |
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| [RTMPose-m](../rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 |
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| [RTMPose-l](../rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 |
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| [RTMPose-m](../rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 |
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| [RTMPose-l](../rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 |
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### Speed Benchmark
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图中所示为模型推理时间，单位毫秒。
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| Config | Input Size | ORT<sup><br>(i7-11700) | TRT-FP16<sup><br>(GTX 1660Ti) | TRT-FP16<sup><br>(RTX 3090) | ncnn-FP16<sup><br>(Snapdragon 865) | TRT-FP16<sup><br>(Jetson AGX Orin) | TRT-FP16<sup><br>(Jetson Orin NX) |
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| :---------: | :--------: | :--------------------: | :---------------------------: | :-------------------------: | :--------------------------------: | :--------------------------------: | :-------------------------------: |
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| [RTMPose-t](../rtmpose/body_2d_keypoint/rtmpose-tiny_8xb256-420e_coco-256x192.py) | 256x192 | 3.20 | 1.06 | 0.98 | 9.02 | 1.63 | 1.97 |
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| [RTMPose-s](../rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 4.48 | 1.39 | 1.12 | 13.89 | 1.85 | 2.18 |
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| [RTMPose-m](../rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 11.06 | 2.29 | 1.18 | 26.44 | 2.72 | 3.35 |
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| [RTMPose-l](../rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 18.85 | 3.46 | 1.37 | 45.37 | 3.67 | 4.78 |
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| [RTMPose-m](../rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 24.78 | 3.66 | 1.20 | 26.44 | 3.45 | 5.08 |
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| [RTMPose-l](../rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | - | 6.05 | 1.74 | - | 4.93 | 7.23 |
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### 人体全身 2d 关键点 (133 Keypoints)
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### Model Info
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| Config | Input Size | Whole AP | Whole AR | FLOPS(G) |
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| :------------------------------------------------------------------------------------------- | :--------: | :------: | :------: | :------: |
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| [RTMPose-m](../rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 60.4 | 66.7 | 2.22 |
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| [RTMPose-l](../rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 63.2 | 69.4 | 4.52 |
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| [RTMPose-l](../rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 67.0 | 72.3 | 10.07 |
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### Speed Benchmark
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- 图中所示为模型推理时间，单位毫秒。
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- 来自不同社区用户的测试数据用 `|` 分隔开。
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| Config | Input Size | ORT<sup><br>(i7-11700) | TRT-FP16<sup><br>(GTX 1660Ti) | TRT-FP16<sup><br>(RTX 3090) | TRT-FP16<sup><br>(Jetson AGX Orin) | TRT-FP16<sup><br>(Jetson Orin NX) |
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| :-------------------------------------------- | :--------: | :--------------------: | :---------------------------: | :-------------------------: | :--------------------------------: | :-------------------------------: |
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| [RTMPose-m](../rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 13.50 | 4.00 | 1.17 \| 1.84 | 2.79 | 3.51 |
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| [RTMPose-l](../rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 23.41 | 5.67 | 1.44 \| 2.61 | 3.80 | 4.95 |
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| [RTMPose-l](../rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 44.58 | 7.68 | 1.75 \| 4.24 | 5.08 | 7.20 |
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## 如何测试推理速度
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我们使用 MMDeploy 提供的 `tools/profiler.py` 脚本进行模型测速。
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用户需要准备一个存放测试图片的文件夹`./test_images`，profiler 将随机从该目录下抽取图片用于模型测速。
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```shell
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python tools/profiler.py \
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configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py \
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{RTMPOSE_PROJECT}/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py \
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../test_images \
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--model {WORK_DIR}/end2end.onnx \
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--shape 256x192 \
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--device cpu \
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--warmup 50 \
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--num-iter 200
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```
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The result is as follows:
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```shell
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01/30 15:06:35 - mmengine - INFO - [onnxruntime]-70 times per count: 8.73 ms, 114.50 FPS
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01/30 15:06:36 - mmengine - INFO - [onnxruntime]-90 times per count: 9.05 ms, 110.48 FPS
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01/30 15:06:37 - mmengine - INFO - [onnxruntime]-110 times per count: 9.87 ms, 101.32 FPS
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01/30 15:06:37 - mmengine - INFO - [onnxruntime]-130 times per count: 9.99 ms, 100.10 FPS
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01/30 15:06:38 - mmengine - INFO - [onnxruntime]-150 times per count: 10.39 ms, 96.29 FPS
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01/30 15:06:39 - mmengine - INFO - [onnxruntime]-170 times per count: 10.77 ms, 92.86 FPS
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01/30 15:06:40 - mmengine - INFO - [onnxruntime]-190 times per count: 10.98 ms, 91.05 FPS
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01/30 15:06:40 - mmengine - INFO - [onnxruntime]-210 times per count: 11.19 ms, 89.33 FPS
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01/30 15:06:41 - mmengine - INFO - [onnxruntime]-230 times per count: 11.16 ms, 89.58 FPS
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01/30 15:06:42 - mmengine - INFO - [onnxruntime]-250 times per count: 11.06 ms, 90.41 FPS
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----- Settings:
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+------------+---------+
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| batch size | 1 |
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| shape | 256x192 |
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| iterations | 200 |
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| warmup | 50 |
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+------------+---------+
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----- Results:
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+--------+------------+---------+
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| Stats | Latency/ms | FPS |
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+--------+------------+---------+
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| Mean | 11.060 | 90.412 |
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| Median | 11.852 | 84.375 |
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| Min | 7.812 | 128.007 |
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| Max | 13.690 | 73.044 |
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+--------+------------+---------+
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```
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If you want to learn more details of profiler, you can refer to the [Profiler Docs](https://mmdeploy.readthedocs.io/en/latest/02-how-to-run/useful_tools.html#profiler).
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