add the en benchmark doc

docs/module_usage/instructions/benchmark.en.md

@@ -0,0 +1,78 @@
+# Model Inference Benchmark
+
+PaddleX support to benchmark model inference. Just set the related flags:
+
+* `PADDLE_PDX_INFER_BENCHMARK`: `True` means enable benchmark. `False` by default;
+* `PADDLE_PDX_INFER_BENCHMARK_WARMUP`: Number of warmup. Using random data to infer before testing benchmark if `input` is set to `None`. `0` by default;
+* `PADDLE_PDX_INFER_BENCHMARK_DATA_SIZE`: The size of randomly generated data. Valid only when `input` is set to `None`. `224` by default;
+* `PADDLE_PDX_INFER_BENCHMARK_ITER`: Number of benchmark testing using random data. Valid only when `input` is set to `None`. `10` by default;
+* `PADDLE_PDX_INFER_BENCHMARK_OUTPUT`: The directory to save benchmark result. `None` by default, that means not save.
+
+The example is as follows：
+
+```bash
+PADDLE_PDX_INFER_BENCHMARK=True \
+PADDLE_PDX_INFER_BENCHMARK_WARMUP=5 \
+PADDLE_PDX_INFER_BENCHMARK_DATA_SIZE=320 \
+PADDLE_PDX_INFER_BENCHMARK_ITER=10 \
+PADDLE_PDX_INFER_BENCHMARK_OUTPUT=./benchmark \
+python main.py \
+    -c ./paddlex/configs/object_detection/PicoDet-XS.yaml \
+    -o Global.mode=predict \
+    -o Predict.model_dir=None \
+    -o Predict.batch_size=2 \
+    -o Predict.input=None
+```
+
+The benchmark infomation would be print:
+
+```
++----------------+-----------------+-----------------+------------------------+
+|   Component    | Total Time (ms) | Number of Calls | Avg Time Per Call (ms) |
++----------------+-----------------+-----------------+------------------------+
+|    ReadCmp     |   99.60412979   |        10       |       9.96041298       |
+|     Resize     |   17.01641083   |        20       |       0.85082054       |
+|   Normalize    |   44.61312294   |        20       |       2.23065615       |
+|   ToCHWImage   |    0.03385544   |        20       |       0.00169277       |
+|    Copy2GPU    |   13.46874237   |        10       |       1.34687424       |
+|     Infer      |   71.31743431   |        10       |       7.13174343       |
+|    Copy2CPU    |    0.39076805   |        10       |       0.03907681       |
+| DetPostProcess |    0.36168098   |        20       |       0.01808405       |
++----------------+-----------------+-----------------+------------------------+
++-------------+-----------------+---------------------+----------------------------+
+|    Stage    | Total Time (ms) | Number of Instances | Avg Time Per Instance (ms) |
++-------------+-----------------+---------------------+----------------------------+
+|  PreProcess |   161.26751900  |          20         |         8.06337595         |
+|  Inference  |   85.17694473   |          20         |         4.25884724         |
+| PostProcess |    0.36168098   |          20         |         0.01808405         |
+|   End2End   |   256.90770149  |          20         |        12.84538507         |
+|    WarmUp   |  5412.37807274  |          10         |        541.23780727        |
++-------------+-----------------+---------------------+----------------------------+
+```
+
+The first table show the benchmark infomation by each component(`Component`), include `Total Time` (unit is "ms"), `Number of Calls` and `Avg Time Per Call`  (unit is "ms"). `Avg Time Per Call` is `Total Time` devide by `Number of Calls`. It should be noted that the `Number of Calls` is the number of times the component has been called.
+
+And the second table show the benchmark infomation by different stages: `WarmUp`, `PreProcess`, `Inference`, `PostProcess` and `End2End`. Different from the first table, `Number of Instances` is the number of instances (samples), not the number of calls.
+
+Meanwhile, the benchmark infomation would be saved to local files (`detail.csv` and `summary.csv`) if you set `PADDLE_PDX_INFER_BENCHMARK_OUTPUT`:
+
+```csv
+Component,Total Time (ms),Number of Calls,Avg Time Per Call (ms)
+ReadCmp,99.60412979125977,10,9.960412979125977
+Resize,17.01641082763672,20,0.8508205413818359
+Normalize,44.61312294006348,20,2.230656147003174
+ToCHWImage,0.033855438232421875,20,0.0016927719116210938
+Copy2GPU,13.468742370605469,10,1.3468742370605469
+Infer,71.31743431091309,10,7.131743431091309
+Copy2CPU,0.39076805114746094,10,0.039076805114746094
+DetPostProcess,0.3616809844970703,20,0.018084049224853516
+```
+
+```csv
+Stage,Total Time (ms),Number of Instances,Avg Time Per Instance (ms)
+PreProcess,161.26751899719238,20,8.06337594985962
+Inference,85.17694473266602,20,4.258847236633301
+PostProcess,0.3616809844970703,20,0.018084049224853516
+End2End,256.90770149230957,20,12.845385074615479
+WarmUp,5412.3780727386475,10,541.2378072738647
+```

docs/module_usage/instructions/benchmark.md

@@ -5,7 +5,7 @@ PaddleX 支持统计模型推理耗时，需通过环境变量进行设置，具
 * `PADDLE_PDX_INFER_BENCHMARK`：设置为 `True` 时则开启 Benchmark，默认为 `False`；
 * `PADDLE_PDX_INFER_BENCHMARK_WARMUP`：设置 warm up，在开始测试前，使用随机数据循环迭代 n 次，默认为 `0`；
 * `PADDLE_PDX_INFER_BENCHMARK_DATA_SIZE`： 设置随机数据的尺寸，默认为 `224`；
-* `PADDLE_PDX_INFER_BENCHMARK_ITER`：使用随机数据进行 Benchmark 测试的循环次数，仅当输入数据为 `None` 时，将使用随机数据进行测试；
+* `PADDLE_PDX_INFER_BENCHMARK_ITER`：使用随机数据进行 Benchmark 测试的循环次数，仅当输入数据为 `None` 时，将使用随机数据进行测试，默认为 `10`；
 * `PADDLE_PDX_INFER_BENCHMARK_OUTPUT`：用于设置保存的目录，如 `./benchmark`，默认为 `None`，表示不保存 Benchmark 指标；
 
 使用示例如下：