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PaddleOCR

PaddlePaddle / PaddleOCR
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PaddleOCR
未验证 提交 791712e2 编写于 作者: X xiaoting 提交者： GitHub
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update c++ img and qps result (#6267) 

* update c++ img and qps result

* update c++ img and qps result
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Showing with 66 addition and 69 deletion
deploy/pdserving/README.md
浏览文件 @ 791712e2
......@@ -163,43 +163,41 @@ The recognition model is the same.
The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23:
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 62.0:
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
2022-05-12 03:56:46,461 ==================== TRACER ======================
2022-05-12 03:56:46,860 Op(det):
2022-05-12 03:56:46,860 in[80.32286641221374 ms]
2022-05-12 03:56:46,860 prep[74.27364885496183 ms]
2022-05-12 03:56:46,860 midp[33.41587786259542 ms]
2022-05-12 03:56:46,860 postp[20.935980916030534 ms]
2022-05-12 03:56:46,860 out[1.551145038167939 ms]
2022-05-12 03:56:46,860 idle[0.3889510617728378]
2022-05-12 03:56:46,860 Op(rec):
2022-05-12 03:56:46,860 in[15.46498846153846 ms]
2022-05-12 03:56:46,861 prep[22.565715384615384 ms]
2022-05-12 03:56:46,861 midp[91.42518076923076 ms]
2022-05-12 03:56:46,861 postp[11.678453846153847 ms]
2022-05-12 03:56:46,861 out[1.1200576923076924 ms]
2022-05-12 03:56:46,861 idle[0.11658723106110291]
2022-05-12 03:56:46,862 DAGExecutor:
2022-05-12 03:56:46,862 Query count[620]
2022-05-12 03:56:46,862 QPS[62.0 q/s]
2022-05-12 03:56:46,862 Succ[0.4193548387096774]
2022-05-12 03:56:46,862 Latency:
2022-05-12 03:56:46,863 ave[165.54603709677417 ms]
2022-05-12 03:56:46,863 .50[77.863 ms]
2022-05-12 03:56:46,863 .60[158.414 ms]
2022-05-12 03:56:46,863 .70[237.28 ms]
2022-05-12 03:56:46,863 .80[316.022 ms]
2022-05-12 03:56:46,863 .90[424.416 ms]
2022-05-12 03:56:46,863 .95[515.566 ms]
2022-05-12 03:56:46,863 .99[762.256 ms]
2022-05-12 03:56:46,863 Channel (server worker num[10]):
2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0]
2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
<a name="C++"></a>
......
deploy/pdserving/README_CN.md
浏览文件 @ 791712e2
......@@ -162,42 +162,41 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv3_rec_infer/ \
预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到23左右：
在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到62左右：
```
2021-05-13 03:42:36,895 ==================== TRACER ======================
2021-05-13 03:42:36,975 Op(rec):
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
2021-05-13 03:42:36,976 idle[0.16254943864471572]
2021-05-13 03:42:36,976 Op(det):
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
2021-05-13 03:42:36,976 prep[69.5980625 ms]
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
2021-05-13 03:42:36,977 idle[0.7477961159203756]
2021-05-13 03:42:36,977 DAGExecutor:
2021-05-13 03:42:36,977 Query count[224]
2021-05-13 03:42:36,977 QPS[22.4 q/s]
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
2021-05-13 03:42:36,977 Error req[169, 170]
2021-05-13 03:42:36,977 Latency:
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
2021-05-13 03:42:36,977 .50[172.651 ms]
2021-05-13 03:42:36,977 .60[187.904 ms]
2021-05-13 03:42:36,977 .70[245.675 ms]
2021-05-13 03:42:36,977 .80[526.684 ms]
2021-05-13 03:42:36,977 .90[854.596 ms]
2021-05-13 03:42:36,977 .95[1722.728 ms]
2021-05-13 03:42:36,977 .99[3990.292 ms]
2021-05-13 03:42:36,978 Channel (server worker num[10]):
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
2022-05-12 03:56:46,461 ==================== TRACER ======================
2022-05-12 03:56:46,860 Op(det):
2022-05-12 03:56:46,860 in[80.32286641221374 ms]
2022-05-12 03:56:46,860 prep[74.27364885496183 ms]
2022-05-12 03:56:46,860 midp[33.41587786259542 ms]
2022-05-12 03:56:46,860 postp[20.935980916030534 ms]
2022-05-12 03:56:46,860 out[1.551145038167939 ms]
2022-05-12 03:56:46,860 idle[0.3889510617728378]
2022-05-12 03:56:46,860 Op(rec):
2022-05-12 03:56:46,860 in[15.46498846153846 ms]
2022-05-12 03:56:46,861 prep[22.565715384615384 ms]
2022-05-12 03:56:46,861 midp[91.42518076923076 ms]
2022-05-12 03:56:46,861 postp[11.678453846153847 ms]
2022-05-12 03:56:46,861 out[1.1200576923076924 ms]
2022-05-12 03:56:46,861 idle[0.11658723106110291]
2022-05-12 03:56:46,862 DAGExecutor:
2022-05-12 03:56:46,862 Query count[620]
2022-05-12 03:56:46,862 QPS[62.0 q/s]
2022-05-12 03:56:46,862 Succ[0.4193548387096774]
2022-05-12 03:56:46,862 Latency:
2022-05-12 03:56:46,863 ave[165.54603709677417 ms]
2022-05-12 03:56:46,863 .50[77.863 ms]
2022-05-12 03:56:46,863 .60[158.414 ms]
2022-05-12 03:56:46,863 .70[237.28 ms]
2022-05-12 03:56:46,863 .80[316.022 ms]
2022-05-12 03:56:46,863 .90[424.416 ms]
2022-05-12 03:56:46,863 .95[515.566 ms]
2022-05-12 03:56:46,863 .99[762.256 ms]
2022-05-12 03:56:46,863 Channel (server worker num[10]):
2022-05-12 03:56:46,864 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
2022-05-12 03:56:46,864 chl1(In: ['det'], Out: ['rec']) size[2/0]
2022-05-12 03:56:46,865 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
<a name="C++"></a>
......
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