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# On-Device Inference
<!-- TOC -->
- [On-Device Inference](#on-device-inference)
- [Overview](#overview)
- [Compilation Method](#compilation-method)
- [Use of On-Device Inference](#use-of-on-device-inference)
- [Generating an On-Device Model File](#generating-an-on-device-model-file)
- [Implementing On-Device Inference](#implementing-on-device-inference)
<!-- /TOC -->
<a href="https://gitee.com/mindspore/docs/blob/r0.6/tutorials/source_en/advanced_use/on_device_inference.md" target="_blank"><img src="../_static/logo_source.png"></a>
## Overview
MindSpore Lite is a lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. This tutorial describes how to use and compile MindSpore Lite.
![](./images/on_device_inference_frame.jpg)
Figure 1 On-device inference frame diagram
Mindspore Lite's framework consists of frontend, IR, backend, Lite, RT and micro.
- Frontend: It used for model generation. Users can use the model building interface to build models, or transform third-party models into mindspore models.
- IR: It includes the tensor definition, operator prototype definition and graph definition of mindspore. The back-end optimization is based on IR.
- Backend: Graph optimization and high level optimization are independent of hardware, such as operator fusion and constant folding, while low level optimization is related to hardware; quantization includes weight quantization, activation value quantization and other post training quantization methods.
- Lite RT: In the inference runtime, session provides the external interface, kernel registry is operator registry, scheduler is operator heterogeneous scheduler and executor is operator executor. Lite RT and shares with Micro the underlying infrastructure layers such as operator library, memory allocation, runtime thread pool and parallel primitives.
- Micro: Code Gen generates .c files according to the model, and infrastructure such as the underlying operator library is shared with Lite RT.
## Compilation Method
You need to compile the MindSpore Lite by yourself. This section describes how to perform cross compilation in the Ubuntu environment.
The environment requirements are as follows:
- Hardware requirements
- Memory: 1 GB or above
- Hard disk space: 10 GB or above
- System requirements
- System is limited on Linux
- Recommend system: Ubuntu = 18.04.02LTS
- Software dependencies
- [cmake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) >= 5.4
- [autoconf](http://ftp.gnu.org/gnu/autoconf/) 2.69
- [LLVM 8.0.0](http://releases.llvm.org/8.0.0/clang+llvm-8.0.0-x86_64-linux-gnu-ubuntu-16.04.tar.xz)
- [Android_NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
- numpy >= 1.16
- decorator
- scipy
> `numpy decorator scipy` can be installed through `pip`. The reference command is as: `pip3 install numpy==1.16 decorator scipy`.
The compilation procedure is as follows:
1. Configure environment variables.
```bash
export LLVM_PATH={$LLVM_PATH}/clang+llvm-8.0.0-x86_64-linux-gnu-ubuntu-16.04/bin/llvm-config #Set the LLVM path.
export ANDROID_NDK={$NDK_PATH}/android-ndk-r20b #Set the NDK path.
```
2. Download source code from the code repository.
```bash
git clone https://gitee.com/mindspore/mindspore.git -b r0.6
```
3. Run the following command in the root directory of the source code to compile MindSpore Lite.
```bash
cd mindspore/lite
sh build.sh
```
4. Obtain the compilation result.
Go to the `lite/build` directory of the source code to view the generated documents. Then you can use various tools after changing directory.
## Use of On-Device Inference
When MindSpore is used to perform model inference in the APK project of an app, preprocessing input is required before model inference. For example, before an image is converted into the tensor format required by MindSpore inference, the image needs to be resized. After MindSpore completes model inference, postprocess the model inference result and sends the processed output to the app.
This section describes how to use MindSpore to perform model inference. The setup of an APK project and pre- and post-processing of model inference are not described here.
To perform on-device model inference using MindSpore, perform the following steps.
### Generating an On-Device Model File
1. After training is complete, load the generated checkpoint file to the defined network.
```python
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
```
2. Call the `export` API to export the `.pb` model file on the device.
```python
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
```
Take the LeNet network as an example. The generated on-device model file is `lenet.pb`. The complete sample code `lenet.py` is as follows:
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
3. Calling MindSpore convert tool named `converter_lite`, convert model file (`.pb`) to on_device inference model file (`.ms`).
```
./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
```
Result:
```
INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
```
This means that the model has been successfully converted to the mindspore on_device inference model.
### Implementing On-Device Inference
Use the `.ms` model file and image data as input to create a session and implement inference on the device.
![](./images/side_infer_process.jpg)
1. Load the `.ms` model file to the memory buffer. The ReadFile function needs to be implemented by users, according to the [C++ tutorial](http://www.cplusplus.com/doc/tutorial/files/).
```cpp
// Read Model File
std::string model_path = "./lenet.ms";
ReadFile(model_path.c_str(), &model_size, buf);
// Import Model
auto model = lite::Model::Import(content, size);
meta_graph.reset();
content = nullptr;
auto context = new lite::Context;
context->cpuBindMode = lite::NO_BIND;
context->deviceCtx.type = lite::DT_CPU;
context->threadNum = 4;
```
2. Call the `CreateSession` API to get a session.
```cpp
// Create Session
auto session = session::LiteSession::CreateSession(context);
ASSERT_NE(nullptr, session);
```
3. Call the `CompileGraph` API in previous `Session` and transport model.
```cpp
// Compile Graph
auto ret = session->CompileGraph(model.get());
ASSERT_EQ(lite::RET_OK, ret);
```
4. Call the `GetInputs` API to get input `tensor`, then set graph information as `data`, `data` will be used in to perform model inference.
```cpp
auto inputs = session->GetInputs();
ASSERT_EQ(inputs.size(), 1);
auto inTensor = inputs.front();
ASSERT_NE(nullptr, inTensor);
(void)inTensor->MutableData();
```
5. Call the `RunGraph` API in the `Session` to perform inference.
```cpp
// Run Graph
ret = session->RunGraph();
ASSERT_EQ(lite::RET_OK, ret);
```
6. Call the `GetOutputs` API to obtain the output.
```cpp
// Get Outputs
auto outputs = session->GetOutputs();
```
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tutorials/source_en/index.rst
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...@@ -51,13 +51,6 @@ MindSpore Tutorials ...@@ -51,13 +51,6 @@ MindSpore Tutorials
advanced_use/graph_kernel_fusion advanced_use/graph_kernel_fusion
advanced_use/quantization_aware advanced_use/quantization_aware
.. toctree::
:glob:
:maxdepth: 1
:caption: Usage on Device
advanced_use/on_device_inference
.. toctree:: .. toctree::
:glob: :glob:
:maxdepth: 1 :maxdepth: 1
......
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# 端侧推理
<!-- TOC -->
- [端侧推理](#端侧推理)
- [概述](#概述)
- [编译方法](#编译方法)
- [端侧推理使用](#端侧推理使用)
- [生成端侧模型文件](#生成端侧模型文件)
- [在端侧实现推理](#在端侧实现推理)
<!-- /TOC -->
<a href="https://gitee.com/mindspore/docs/blob/r0.6/tutorials/source_zh_cn/advanced_use/on_device_inference.md" target="_blank"><img src="../_static/logo_source.png"></a>
## 概述
MindSpore Lite是一个轻量级的深度神经网络推理引擎，提供了将MindSpore训练出的模型在端侧进行推理的功能。本教程介绍MindSpore Lite的编译方法和使用指南。
![](./images/on_device_inference_frame.jpg)
图1：端侧推理架构图
MindSpore Lite的框架主要由Frontend、IR、Backend、Lite RT、Micro构成。
- Frontend：用于模型的生成，用户可以使用模型构建接口构建模型，或者将第三方模型转化为MindSpore模型。
- IR：包含MindSpore的Tensor定义、算子原型定义、图定义，后端优化基于IR进行。
- Backend：包含图优化，量化等功能。图优化分为两部分：high-level优化与硬件无关，如算子融合、常量折叠等，low-level优化与硬件相关；量化，包括权重量化、激活值量化等多种训练后量化手段。
- Lite RT：推理运行时，由session提供对外接口，kernel registry为算子注册器，scheduler为算子异构调度器，executor为算子执行器。Lite RT与Micro共享底层的算子库、内存分配、运行时线程池、并行原语等基础设施层。
- Micro：Code-Gen根据模型生成.c文件，底层算子库等基础设施与Lite RT共用。
## 编译方法
用户需要自行编译，这里介绍在Ubuntu环境下进行交叉编译的具体步骤。
环境要求如下：
- 硬件要求
- 内存1GB以上
- 硬盘空间10GB以上
- 系统要求
- 系统环境仅支持Linux
- 推荐系统：Ubuntu = 18.04.02LTS
- 软件依赖
- [cmake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) >= 5.4
- [autoconf](http://ftp.gnu.org/gnu/autoconf/) 2.69
- [LLVM 8.0.0](http://releases.llvm.org/8.0.0/clang+llvm-8.0.0-x86_64-linux-gnu-ubuntu-16.04.tar.xz)
- [Android_NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
- numpy >= 1.16
- decorator
- scipy
> `numpy decorator scipy`可以通过`pip`安装，参考命令：`pip3 install numpy==1.16 decorator scipy`。
编译步骤如下：
1. 配置环境变量。
```bash
export LLVM_PATH={$LLVM_PATH}/clang+llvm-8.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/llvm-config #设定llvm路径
export ANDROID_NDK={$NDK_PATH}/android-ndk-r20b #设定ndk路径
```
2. 从代码仓下载源码。
```bash
git clone https://gitee.com/mindspore/mindspore.git -b r0.6
```
3. 在源码根目录下，执行如下命令编译MindSpore Lite。
```bash
cd mindspore/lite
sh build.sh
```
4. 获取编译结果。
进入源码的`lite/build`目录，可查看编译后生成的文件。进入相对应的文件夹下执行命令，就可以使用MindSpore Lite的多种功能。
## 端侧推理使用
在APP的APK工程中使用MindSpore进行模型推理前，需要对输入进行必要的前处理，比如将图片转换成MindSpore推理要求的`tensor`格式、对图片进行`resize`等处理。在MindSpore完成模型推理后，对模型推理的结果进行后处理，并将处理的输出发送给APP应用。
本章主要描述用户如何使用MindSpore进行模型推理，APK工程的搭建和模型推理的前后处理，不在此列举。
MindSpore进行端侧模型推理的步骤如下。
### 生成端侧模型文件
1. 加载训练完毕所生成的CheckPoint文件至定义好的网络中。
```python
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
```
2. 调用`export`接口，导出模型文件(`.pb`)。
```python
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
```
以LeNet网络为例，生成的端侧模型文件为`lenet.pb`，完整示例代码`lenet.py`如下。
```python
import os
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
class LeNet(nn.Cell):
def __init__(self):
super(LeNet, self).__init__()
self.relu = P.ReLU()
self.batch_size = 32
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = P.Reshape()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.reshape(output, (self.batch_size, -1))
output = self.fc1(output)
output = self.relu(output)
output = self.fc2(output)
output = self.relu(output)
output = self.fc3(output)
return output
if __name__ == '__main__':
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
seed = 0
np.random.seed(seed)
origin_data = np.random.uniform(low=0, high=255, size=(32, 1, 32, 32)).astype(np.float32)
origin_data.tofile("lenet.bin")
input_data = Tensor(origin_data)
net = LeNet()
ckpt_file_path = "path_to/lenet.ckpt"
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
print("export model success.")
else:
print("checkpoint file does not exist.")
```
3. 调用MindSpore端侧转化工具`converter_lite`工具，将模型文件(`.pb`)转换为端侧模型文件(`.ms`)。
```
./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
```
结果显示为：
```
INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
```
这表示已经成功将模型转化为MindSpore端侧模型。
### 在端侧实现推理
将`.ms`模型文件和图片数据作为输入，创建`session`在端侧实现推理。
![](./images/side_infer_process.jpg)
图2：端侧推理时序图
1. 读取MindSpore端侧模型文件信息。ReadFile函数功能需要用户参考[C++教程](http://www.cplusplus.com/doc/tutorial/files/)自行实现。
```cpp
// Read Model File
std::string model_path = "./lenet.ms";
ReadFile(model_path.c_str(), &model_size, buf);
// Import Model
auto model = lite::Model::Import(content, size);
meta_graph.reset();
content = nullptr;
auto context = new lite::Context;
context->cpuBindMode = lite::NO_BIND;
context->deviceCtx.type = lite::DT_CPU;
context->threadNum = 4;
```
2. 调用`CreateSession`接口创建`Session`。
```cpp
// Create Session
auto session = session::LiteSession::CreateSession(context);
ASSERT_NE(nullptr, session);
```
3. 调用`Session`中的`CompileGraph`方法，传入模型。
```cpp
// Compile Graph
auto ret = session->CompileGraph(model.get());
ASSERT_EQ(lite::RET_OK, ret);
```
4. 调用`Session`中的`GetInputs`方法，获取输入`Tensor`，获取图片信息设置为`data`，`data`即为用于推理的输入数据。
```cpp
auto inputs = session->GetInputs();
ASSERT_EQ(inputs.size(), 1);
auto inTensor = inputs.front();
ASSERT_NE(nullptr, inTensor);
(void)inTensor->MutableData();
```
5. 调用`Session`中的`RunGraph`接口执行推理。
```cpp
// Run Graph
ret = session->RunGraph();
ASSERT_EQ(lite::RET_OK, ret);
```
6. 调用`GetOutputs`接口获取输出。
```cpp
// Get Outputs
auto outputs = session->GetOutputs();
```
tutorials/source_zh_cn/index.rst
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...@@ -64,7 +64,6 @@ MindSpore教程 ...@@ -64,7 +64,6 @@ MindSpore教程
:caption: 端云使用 :caption: 端云使用
advanced_use/use_on_the_cloud advanced_use/use_on_the_cloud
advanced_use/on_device_inference
.. toctree:: .. toctree::
:glob: :glob:
......
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