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Revert "Add Anakin Doc (PaddlePaddle#83)"
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18 changes: 9 additions & 9 deletions doc/fluid/advanced_usage/deploy/anakin_arm_benchmark.md
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### <span id = '11'> mobilenetv1 </span>

|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|:---: | :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:|
|麒麟960|107.7ms|61.1ms|38.2ms|152.8ms|85.2ms|51.9ms|152.6ms|nan|nan|
|高通835|105.7ms|63.1ms|~~46.8ms~~|152.7ms|87.0ms|~~92.7ms~~|146.9ms|nan|nan|
|高通653|120.3ms|64.2ms|46.6ms|202.5ms|117.6ms|84.8ms|158.6ms|nan|nan|
|高通653|120.3ms|64.2ms|46.6ms|202.5ms|117.6ms|84.8ms|158.6ms|nan|nan|

### <span id = '22'> mobilenetv2 </span>

|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|:---: | :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:|
|麒麟960|93.1ms|53.9ms|34.8ms|144.4ms|84.3ms|55.3ms|100.6ms|nan|nan|
|高通835|93.0ms|55.6ms|41.1ms|139.1ms|88.4ms|58.1ms|95.2ms|nan|nan|
|高通653|106.6ms|64.2ms|48.0ms|199.9ms|125.1ms|98.9ms|108.5ms|nan|nan|

### <span id = '33'> mobilenet-ssd </span>

|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|platform | Anakin (1) | Anakin (2) | Anakin (4) | ncnn (1) | ncnn (2) | ncnn (4) | TFlite (1) | TFlite (2) | TFlite (4)|
|:---: | :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:|
|麒麟960|213.9ms|120.5ms|74.5ms|307.9ms|166.5ms|104.2ms|nan|nan|nan|
|高通835|213.0ms|125.7ms|~~98.4ms~~|292.9ms|177.9ms|~~167.8ms~~|nan|nan|nan|
|高通653|236.0ms|129.6ms|96.0ms|377.7ms|228.9ms|165.0ms|nan|nan|nan

## How to run those Benchmark models?

1. 首先, 使用[External Converter](./convert_paddle_to_anakin.md)对caffe model 进行转换
2. 然后将转换后的Anakin model和编译好的benchmark_arm 二进制文件通过'adb push'命令上传至测试机
3. 接着在测试机含有Anakin model的目录中运行'./benchmark_arm ./ anakin_model.anakin.bin 1 10 10 1' 命令
4. 最后，终端显示器上将会打印该模型的运行时间
5. 其中运行命令的参数个数和含义可以通过运行'./benchmark_arm'看到
1. 首先, 使用[External Converter](../docs/Manual/Converter_en.md)对caffe model 进行转换
2. 然后将转换后的Anakin model和编译好的benchmark_arm 二进制文件通过'adb push'命令上传至测试机
3. 接着在测试机含有Anakin model的目录中运行'./benchmark_arm ./ anakin_model.anakin.bin 1 10 10 1' 命令
4. 最后，终端显示器上将会打印该模型的运行时间
5. 其中运行命令的参数个数和含义可以通过运行'./benchmark_arm'看到
14 changes: 2 additions & 12 deletions doc/fluid/advanced_usage/deploy/anakin_example.md
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# Anakin 运行模型示例

# Example
Anakin目前只支持NCHW的格式

示例文件在test/framework/net下

## 在NV的GPU上运行CNN模型

示例文件为打开example_nv_cnn_net.cpp，整体流程如下：

- 将模型的的path设置为anakin模型的路径，初始化NV平台的图对象。 anakin模型可以通过转换器转化caffe或Paddle的模型得到
- 将模型的的path设置为anakin模型的路径，初始化NV平台的图对象。 anakin模型可以通过转换器转化caffe或fluid的模型得到
- 根据模型设置网络图的输入尺寸，进行图优化
- 根据优化后的网络图初始化网络执行器
- 取出网络的输入tensor，将数据拷贝到输入tensor
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以NV平台为例演示Anakin框架的使用方法，注意编译时需要打开GPU编译开关

## 在X86上运行RNN模型

示例文件为example_x86_rnn_net.cpp

整体流程与在NV的GPU上运行CNN模型相似，不同之处如下：

- 使用X86标识初始化图对象和网络执行器对象
- rnn模型的输入尺寸是可变的，初始化图时的输入维度是维度的最大值，输入维度N代表总的词的个数。还需要设置输入tensor的seq_offset来标示这些词是如何划分为句子的,如{0,5,12}表示共有12个词，其中第0到第4个词是第一句话，第5到第11个词是第二句话

以X86平台为例演示Anakin框架的使用方法，注意编译时需要打开X86编译开关

## 在NV的GPU上使用Anakin的线程池运行CNN模型

示例文件为example_nv_cnn_net_multi_thread.cpp ，示例使用worker的同步预测接口

整体流程与在NV的GPU上运行CNN模型相似，不同之处如下：

- 用模型地址和线程池大小初始化worker对象
- 将输入tensor注入任务队列,获得输出tensor
41 changes: 23 additions & 18 deletions doc/fluid/advanced_usage/deploy/anakin_gpu_benchmark.md
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# Anakin GPU 性能测试
# Anakin GPU Benchmark

## 环境:
## Machine:

> CPU: `12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz`
> GPU: `Tesla P4`
> cuDNN: `v7`

## anakin 对比对象:
## Counterpart of anakin :

**`Anakin`** 将与高性能的推理引擎 **`NVIDIA TensorRT 3`** 进行比较
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.

## Benchmark Model

> 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
> 对这些model，本文在GPU上进行单线程单GPU卡的性能测试。
The following convolutional neural networks are tested with both `Anakin` and `TenorRT3`.
You can use pretrained caffe model or the model trained by youself.

- [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*
- [Resnet101](#4) *caffe model 可以在[这儿](https://github.com/KaimingHe/deep-residual-networks#models)下载*
- [Mobilenet v1](#5) *caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
- [Mobilenet v2](#6) *caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
- [RNN](#7) *暂不支持*
> 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 can be found [here->](https://gist.github.com/jimmie33/27c1c0a7736ba66c2395)*
- [Yolo](#2) *caffe model can be found [here->](https://github.com/hojel/caffe-yolo-model)*
- [Resnet50](#3) *caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
- [Resnet101](#4) *caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
- [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>

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| 8 | 421 | 351 |
| 32 | 637 | 551 |

## How to run those Benchmark models
## How to run those Benchmark models?

1. 首先, 使用[External Converter](./convert_paddle_to_anakin.md)对caffe model 进行转换
2. 然后跳转至 *source_root/benchmark/CNN* 目录下，使用 'mkdir ./models'创建存放模型的目录，并将转换好的Anakin模型放在该目录下
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. 如果你想获取每层OP的运行时间，你只用将 CMakeLists.txt 中的`ENABLE_OP_TIMER` 设置为 `YES` 即可
> 1. At first, you should parse the caffe model with [`external converter`](https://github.com/PaddlePaddle/Anakin/blob/b95f31e19993a192e7428b4fcf852b9fe9860e5f/docs/Manual/Converter_en.md).
> 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.
> 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.
92 changes: 0 additions & 92 deletions doc/fluid/advanced_usage/deploy/anakin_parser_design.md
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