> CPU: `12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz`
> CPU: `12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz`
> GPU: `Tesla P4`
> GPU: `Tesla P4`
> cuDNN: `v7`
> cuDNN: `v7`
## Counterpart of anakin :
## anakin 对比对象:
The counterpart of **`Anakin`** is the acknowledged high performance inference engine **`NVIDIA TensorRT 3`** , The models which TensorRT 3 doesn't support we use the custom plugins to support.
The following convolutional neural networks are tested with both `Anakin` and `TenorRT3`.
> 注意在性能测试之前，请先将测试model通过 `External Converter` 工具转换为Anakin model
You can use pretrained caffe model or the model trained by youself.
> 对这些model，本文在GPU上进行单线程单GPU卡的性能测试。
> Please note that you should transform caffe model or others into anakin model with the help of [`external converter ->`](../docs/Manual/Converter_en.md)
-[Vgg16](#1)*caffe model 可以在[这儿](https://gist.github.com/jimmie33/27c1c0a7736ba66c2395)下载*
-[Yolo](#2)*caffe model 可以在[这儿](https://github.com/hojel/caffe-yolo-model)下载*
-[Resnet50](#3)*caffe model 可以在[这儿](https://github.com/KaimingHe/deep-residual-networks#models)下载*
-[Vgg16](#1)*caffe model can be found [here->](https://gist.github.com/jimmie33/27c1c0a7736ba66c2395)*
-[Resnet101](#4)*caffe model 可以在[这儿](https://github.com/KaimingHe/deep-residual-networks#models)下载*
-[Yolo](#2)*caffe model can be found [here->](https://github.com/hojel/caffe-yolo-model)*
-[Mobilenet v1](#5)*caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
-[Resnet50](#3)*caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
-[Mobilenet v2](#6)*caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
-[Resnet101](#4)*caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
-[RNN](#7)*暂不支持*
-[Mobilenet v1](#5)*caffe model can be found [here->](https://github.com/shicai/MobileNet-Caffe)*
-[Mobilenet v2](#6)*caffe model can be found [here->](https://github.com/shicai/MobileNet-Caffe)*
-[RNN](#7)*not support yet*
We tested them on single-GPU with single-thread.
### <span id = '1'>VGG16 </span>
### <span id = '1'>VGG16 </span>
...
@@ -162,9 +157,9 @@ We tested them on single-GPU with single-thread.
...
@@ -162,9 +157,9 @@ We tested them on single-GPU with single-thread.
| 8 | 421 | 351 |
| 8 | 421 | 351 |
| 32 | 637 | 551 |
| 32 | 637 | 551 |
## How to run those Benchmark models?
## How to run those Benchmark models
> 1. At first, you should parse the caffe model with [`external converter`](https://github.com/PaddlePaddle/Anakin/blob/b95f31e19993a192e7428b4fcf852b9fe9860e5f/docs/Manual/Converter_en.md).
1. 首先, 使用[External Converter](./convert_paddle_to_anakin.html)对caffe model 进行转换
> 2. Switch to *source_root/benchmark/CNN* directory. Use 'mkdir ./models' to create ./models and put anakin models into this file.
> 3. Use command 'sh run.sh', we will create files in logs to save model log with different batch size. Finally, model latency summary will be displayed on the screen.
3. 运行脚本 `sh run.sh`，运行结束后，该模型的运行时间将会显示到终端上
> 4. If you want to get more detailed information with op time, you can modify CMakeLists.txt with setting `ENABLE_OP_TIMER` to `YES`, then recompile and run. You will find detailed information in model log file.
