Add anakin and mobile doc (PaddlePaddle#30)

* Update 3rdparty

* Remove submodule

* Add anakin doc

* Add anakin dev docs

* Add mobile doc

* Follow comments

* Remove mobile

* Polish mobile doc

.gitmodules

@@ -4,6 +4,9 @@
 [submodule "book"]
 	path = book
 	url = https://github.com/PaddlePaddle/book.git
-[submodule "source/anakin"]
-	path = source/anakin
-	url = https://github.com/PaddlePaddle/Anakin
+[submodule "anakin"]
+	path = anakin
+	url = https://github.com/PaddlePaddle/Anakin.git
+[submodule "mobile"]
+	path = mobile
+	url = https://github.com/PaddlePaddle/paddle-mobile.git

source/advanced_usage/deploy/anakin_arm_benchmark.md

@@ -0,0 +1,57 @@
+# Anakin ARM 性能测试
+
+## 测试环境和参数:
++ 测试模型Mobilenetv1, mobilenetv2, mobilenet-ssd
++ 采用android ndk交叉编译，gcc 4.9，enable neon， ABI： armveabi-v7a with neon -mfloat-abi=softfp
++ 测试平台
+   - 荣耀v9(root): 处理器:麒麟960, 4 big cores in 2.36GHz, 4 little cores in 1.8GHz
+   - nubia z17:处理器:高通835, 4 big cores in 2.36GHz, 4 little cores in 1.9GHz
+   - 360 N5:处理器:高通653, 4 big cores in 1.8GHz, 4 little cores in 1.4GHz
++ 多线程：openmp
++ 时间：warmup10次，运行10次取均值
++ ncnn版本：来源于github的master branch中commits ID：307a77f04be29875f40d337cfff6df747df09de6（msg:convert            LogisticRegressionOutput)版本
++ TFlite版本：来源于github的master branch中commits ID：65c05bc2ac19f51f7027e66350bc71652662125c（msg:Removed unneeded file copy that was causing failure in Pi builds)版本
+
+在BenchMark中本文将使用**`ncnn`**、**`TFlite`**和**`Anakin`**进行性能对比分析
+
+## BenchMark model
+
+> 注意在性能测试之前，请先将测试model通过[External Converter](#10003)转换为Anakin model
+> 对这些model，本文在ARM上进行多线程的单batch size测试。
+
+- [Mobilenet v1](#11)  *caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
+- [Mobilenet v2](#22)  *caffe model 可以在[这儿](https://github.com/shicai/MobileNet-Caffe)下载*
+- [mobilenet-ssd](#33)  *caffe model 可以在[这儿](https://github.com/chuanqi305/MobileNet-SSD)下载*
+
+### <span id = '11'> mobilenetv1 </span>
+
+   |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| 
+
+### <span id = '22'> mobilenetv2 </span>
+
+   |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)| 
+   |:---: | :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:| :---:|
+   |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](../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
+

source/advanced_usage/deploy/anakin_example.md

@@ -0,0 +1 @@
+../../../anakin/examples/example_introduction_cn.md

source/advanced_usage/deploy/anakin_gpu_benchmark.md

@@ -0,0 +1,175 @@
+# Anakin GPU Benchmark
+
+## Machine:
+
+>  CPU: `12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz`
+>  GPU: `Tesla P4`
+>  cuDNN: `v7`
+
+
+## Counterpart of anakin  :
+
+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
+
+The following convolutional neural networks are tested with both `Anakin` and `TenorRT3`.
+ You can use pretrained caffe model or the model trained by youself.
+
+> 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>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 8.8690 | 8.2815 |
+| 2 | 15.5344 | 13.9116 |
+| 4 | 26.6000 | 21.8747 |
+| 8 | 49.8279 | 40.4076 |
+| 32 | 188.6270 | 163.7660 |
+
+- GPU Memory Used (`MB`)
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 963 | 997 |
+| 2 | 965 | 1039 |
+| 4 | 991 | 1115 |
+| 8 | 1067 | 1269 |
+| 32 | 1715 | 2193 |
+
+
+### <span id = '2'>Yolo </span>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 16.4596| 15.2124 |
+| 2 | 26.6347| 25.0442 |
+| 4 | 43.3695| 43.5017 |
+| 8 | 80.9139 | 80.9880 |
+| 32 | 293.8080| 310.8810 |
+
+- GPU Memory Used (`MB`)
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 1569 | 1775 |
+| 2 | 1649 | 1815 |
+| 4 | 1709 | 1887 |
+| 8 | 1731 | 2031 |
+| 32 | 2253 | 2907 |
+
+### <span id = '3'> Resnet50 </span>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 4.2459   |  4.1061 |
+| 2 |  6.2627  |  6.5159 |
+| 4 | 10.1277  | 11.3327 |
+| 8 | 17.8209  | 20.6680 |
+| 32 | 65.8582 | 77.8858 |
+
+- GPU Memory Used (`MB`)
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 531  | 503 |
+| 2 | 543  | 517 |
+| 4 | 583 | 541 |
+| 8 | 611 | 589 |
+| 32 |  809 | 879 |
+
+### <span id = '4'> Resnet101 </span>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 7.5562 | 7.0837 |
+| 2 | 11.6023 | 11.4079 |
+| 4 | 18.3650 | 20.0493 |
+| 8 | 32.7632 | 36.0648 |
+| 32 | 123.2550 | 135.4880 |
+
+- GPU Memory Used (`MB)`
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 701  | 683 |
+| 2 | 713  | 697 |
+| 4 | 793 | 721 |
+| 8 | 819 | 769 |
+| 32 | 1043 | 1059 |
+
+###  <span id = '5'> MobileNet V1 </span>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 45.5156  |  1.3947 |
+| 2 |  46.5585  |  2.5483 |
+| 4 | 48.4242  | 4.3404 |
+| 8 |  52.7957 |  8.1513 |
+| 32 | 83.2519 | 31.3178 |
+
+- GPU Memory Used (`MB`)
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 329  | 283 |
+| 2 | 345  | 289 |
+| 4 | 371 | 299 |
+| 8 | 393 | 319 |
+| 32 |  531 | 433 |
+
+###  <span id = '6'> MobileNet V2</span>
+
+- Latency (`ms`) of different batch
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 65.6861 | 2.9842 |
+| 2 | 66.6814 | 4.7472 |
+| 4 | 69.7114 | 7.4163 |
+| 8 | 76.1092 | 12.8779 |
+| 32 | 124.9810 | 47.2142 |
+
+- GPU Memory Used (`MB`)
+
+| BatchSize | TensorRT | Anakin |
+| --- | --- | --- |
+| 1 | 341 | 293 |
+| 2 | 353 | 301 |
+| 4 | 385 | 319 |
+| 8 | 421 | 351 |
+| 32 | 637 | 551 |
+
+## 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).
+> 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.
+
+
+
+
+

source/advanced_usage/deploy/anakin_tutorial.md

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+../../../anakin/docs/Manual/Tutorial_ch.md

source/advanced_usage/deploy/convert_paddle_to_anakin.md

@@ -0,0 +1 @@
+../../../anakin/docs/Manual/Converter_ch.md

source/advanced_usage/deploy/how_to_add_anakin_op.md

@@ -0,0 +1 @@
+../../../anakin/docs/Manual/addCustomOp.md

source/advanced_usage/deploy/how_to_support_new_device_in_anakin.md

@@ -0,0 +1 @@
+../../../anakin/docs/Manual/addCustomDevice.md

source/advanced_usage/deploy/images

@@ -0,0 +1 @@
+../../../mobile/doc/images/

source/advanced_usage/deploy/index.rst

@@ -1,15 +1,45 @@
-########
-预测部署
-########
-
-服务端
-######
+服务器端部署 - 原生引擎
+#######################
 
 ..  toctree::
     :maxdepth: 2
 
+    build_and_install_lib_cn.rst
     native_inference_engine.rst
 
+服务器端部署 - Anakin
+#####################
+
+
+使用文档
+~~~~~~~~
+
+.. toctree::
+   :maxdepth: 1
+
+   install_anakin.md
+   convert_paddle_to_anakin.md
+   run_anakin_on_arm.md
+   anakin_tutorial.md
+   anakin_example.md
+   anakin_gpu_benchmark.md
+   anakin_arm_benchmark.md
+
+开发文档
+~~~~~~~~
+
+.. toctree::
+   :maxdepth: 1
+
+   how_to_add_anakin_op.md
+   how_to_support_new_device_in_anakin.md
+
+移动端部署
+##########
+
+.. toctree::
+   :maxdepth: 2
 
-移动端
-######
+   mobile_build.md
+   mobile_design.md
+   mobile_dev.md

source/advanced_usage/deploy/install_anakin.md

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+../../../anakin/docs/Manual/INSTALL_ch.md

source/advanced_usage/deploy/mobile_build.md

@@ -0,0 +1 @@
+../../../mobile/doc/build.md

source/advanced_usage/deploy/mobile_design.md

@@ -0,0 +1 @@
+../../../mobile/doc/design_doc.md

source/advanced_usage/deploy/mobile_dev.md

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+../../../mobile/doc/development_doc.md

source/advanced_usage/deploy/run_anakin_on_arm.md

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+../../../anakin/docs/Manual/run_on_arm_ch.md