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Paddle-Lite
未验证 提交 2b9ea6ea 编写于 作者: Z zhangyang0701 提交者： GitHub
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Merge pull request #2 from PaddlePaddle/develop 

Update to the latest one.
上级 711beed5 4be6a2c1
隐藏空白更改
内联 并排
Showing with 4125 addition and 2917 deletion
CMakeLists.txt
浏览文件 @ 2b9ea6ea
cmake_minimum_required(VERSION 3.0)
cmake_minimum_required(VERSION 3.6)
project(paddle-mobile)
option(DEBUGING "enable debug mode" ON)
option(USE_OPENMP "openmp support" OFF)
option(USE_OPENMP "openmp support" ON)
option(USE_EXCEPTION "use std exception" ON)
option(LOG_PROFILE "log profile" ON)
# select the platform to build
option(CPU "armv7 with neon" ON)
option(MALI_GPU "mali gpu" OFF)
option(FPGA "fpga" OFF)
set(DEBUGING ON)
if (ARM_LINUX)
include("${CMAKE_CURRENT_LIST_DIR}/tools/arm-platform.cmake")
endif ()
option(QUANTI "quantification" OFF)
file(GLOB_RECURSE PADDLE_MOBILE_CC src/*.cc src/*.cpp src/*.c src/*.mm)
file(GLOB_RECURSE PADDLE_MOBILE_H src/*.h)
include_directories(src/)
if(IS_IOS)
set(CMAKE_CXX_FLAGS "-mfpu=neon -marm -fobjc-abi-version=2 -fobjc-arc -std=gnu++11 -stdlib=libc++ -O3 -s -isysroot ${CMAKE_OSX_SYSROOT} ${CMAKE_CXX_FLAGS}")
else()
set(CMAKE_CXX_FLAGS "-std=c++14 -O3 -s ${CMAKE_CXX_FLAGS}")
endif()
if (DEBUGING)
message(STATUS "debug")
set(CMAKE_BUILD_TYPE Release)
set(CMAKE_CXX_FLAGS_RELEASE "-DNDEBUG")
add_definitions(-DPADDLE_MOBILE_DEBUG)
else ()
set(CMAKE_BUILD_TYPE Release)
set(CMAKE_CXX_FLAGS_RELEASE "-DNDEBUG")
add_definitions(-fvisibility=hidden -fvisibility-inlines-hidden)
endif ()
if (USE_EXCEPTION)
message(STATUS "use exception")
add_definitions(-DENABLE_EXCEPTION)
add_definitions(-fexceptions)
else()
add_definitions(-fno-exceptions)
endif ()
if (LOG_PROFILE)
add_definitions(-DPADDLE_MOBILE_PROFILE)
endif()
if(USE_OPENMP AND NOT IS_IOS)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
add_definitions(-DPADDLE_MOBILE_USE_OPENMP)
endif()
# platform control
if (ARM_LINUX)
include("${CMAKE_CURRENT_LIST_DIR}/tools/arm-platform.cmake")
endif ()
if (CPU)
add_definitions(-DPADDLE_MOBILE_CPU)
else()
list(REMOVE_ITEM PADDLE_MOBILE_CC ./src/operators/kernel/arm/*.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ./src/operators/kernel/arm/*.cc)
list(REMOVE_ITEM PADDLE_MOBILE_CC ./src/operators/kernel/arm/*.cpp)
file(GLOB_RECURSE _tmp_list src/operators/kernel/arm/*.cpp src/operators/kernel/arm/*.cc)
foreach(f ${_tmp_list})
list(REMOVE_ITEM PADDLE_MOBILE_CC ${f})
endforeach()
file(GLOB_RECURSE _tmp_list_h src/operators/kernel/arm/*.h)
foreach(f ${_tmp_list_h})
list(REMOVE_ITEM PADDLE_MOBILE_H ${f})
endforeach()
endif()
if (MALI_GPU)
......@@ -55,55 +96,32 @@ endif()
if(FPGA)
add_definitions(-DPADDLE_MOBILE_FPGA)
else()
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/fpga/*.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/fpga/*.cc)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/fpga/*.cpp)
file(GLOB_RECURSE _tmp_list src/operators/kernel/fpga/*.cpp src/operators/kernel/fpga/*.cc)
foreach(f ${_tmp_list})
list(REMOVE_ITEM PADDLE_MOBILE_CC ${f})
endforeach()
file(GLOB_RECURSE _tmp_list_h src/operators/kernel/fpga/*.h)
foreach(f ${_tmp_list_h})
list(REMOVE_ITEM PADDLE_MOBILE_H ${f})
endforeach()
endif()
set(CMAKE_CXX_FLAGS "-std=c++14 -O3 -s ${CMAKE_CXX_FLAGS}")
if (DEBUGING)
message(STATUS "debug")
set(CMAKE_BUILD_TYPE Debug)
set(CMAKE_CXX_FLAGS_DEBUG "-g -DNDEBUG")
add_definitions(-DPADDLE_MOBILE_DEBUG)
if (ANDROID_NDK_TOOLCHAIN_INCLUDED)
add_definitions(-DARMV7)
if (ANDROID_NDK_TOOLCHAIN_INCLUDED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -llog")
endif ()
else ()
set(CMAKE_BUILD_TYPE Release)
set(CMAKE_CXX_FLAGS_RELEASE "-DNDEBUG")
add_definitions(-fvisibility=hidden -fvisibility-inlines-hidden)
endif ()
if (USE_EXCEPTION)
message(STATUS "use exception")
add_definitions(-DENABLE_EXCEPTION)
add_definitions(-fexceptions)
else()
add_definitions(-fno-exceptions)
endif ()
if (LOG_PROFILE)
add_definitions(-DPADDLE_MOBILE_PROFILE)
endif()
if(USE_OPENMP)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
add_definitions(-DPADDLE_MOBILE_USE_OPENMP)
endif()
if (NOT ANDROID_NDK_TOOLCHAIN_INCLUDED)
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/jni/paddle_mobile_jni.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/jni/paddle_mobile_jni.cpp)
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/math/math_func_neon.h)
endif ()
include_directories(src/)
if (IS_IOS)
else()
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/PaddleMobile.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/PaddleMobile.mm)
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/op_symbols.h)
endif ()
set(CMAKE_VERBOSE_MAKEFILE ON)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
......@@ -114,26 +132,28 @@ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY build)
# NET default
set(NET "defult" CACHE STRING "select net type")
set_property(CACHE NET PROPERTY STRINGS "defult" "googlenet" "mobilenet" "yolo" "squeezenet")
include("${CMAKE_CURRENT_LIST_DIR}/tools/op.cmake")
if (IS_IOS)
add_library(paddle-mobile STATIC ${PADDLE_MOBILE_CC} ${PADDLE_MOBILE_H})
else()
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/PaddleMobile.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/PaddleMobile.mm)
list(REMOVE_ITEM PADDLE_MOBILE_H ${CMAKE_CURRENT_SOURCE_DIR}/src/ios_io/op_symbols.h)
endif ()
# build library
if (ANDROID_NDK_TOOLCHAIN_INCLUDED)
list(REMOVE_DUPLICATES CMAKE_CXX_FLAGS)
add_library(paddle-mobile SHARED ${PADDLE_MOBILE_CC} ${PADDLE_MOBILE_H})
elseif(IS_IOS)
add_library(paddle-mobile STATIC ${PADDLE_MOBILE_CC} ${PADDLE_MOBILE_H})
else ()
add_library(paddle-mobile SHARED ${PADDLE_MOBILE_CC} ${PADDLE_MOBILE_H})
endif ()
# unit test
if(DEBUGING)
add_subdirectory(test)
if(IS_IOS)
else()
add_subdirectory(test)
endif()
endif()
if (QUANTI)
add_subdirectory(tools/quantification)
endif ()
Dockerfile
浏览文件 @ 2b9ea6ea
......@@ -32,6 +32,7 @@ RUN cd /tmp && curl -O http://mirrors.neusoft.edu.cn/android/repository/android-
RUN curl -O https://mms-res.cdn.bcebos.com/cmake-3.10.3-Linux-x86_64.tar.gz && \
tar xzf cmake-3.10.3-Linux-x86_64.tar.gz && \
mv cmake-3.10.3-Linux-x86_64 /opt/cmake-3.10 && \
mv /usr/bin/cmake /usr/bin/cmake.bak && ln -s /opt/cmake-3.10/bin/cmake /usr/bin/cmake
mv /usr/bin/cmake /usr/bin/cmake.bak && ln -s /opt/cmake-3.10/bin/cmake /usr/bin/cmake && \
mv /usr/bin/ccmake /usr/bin/ccmake.bak && ln -s /opt/cmake-3.10/bin/ccmake /usr/bin/ccmake
RUN cd /opt && unzip /tmp/android-ndk-r17b-linux-x86_64.zip
ENV NDK_ROOT /opt/android-ndk-r17b
README.md
浏览文件 @ 2b9ea6ea
......@@ -26,8 +26,11 @@ Paddle-Moible是PaddlePaddle组织下的项目，是一个致力于嵌入式平
- **ARM CPU**
![](http://mms-graph.bj.bcebos.com/paddle-mobile%2F2018_07_18.png)
arm cpu是paddle-mobile的主要支持方向，cpu的通用性一直是其优势。嵌入式深度学习，需要大量的cpu汇编实现。我们正在紧锣密鼓的编码，为的是能充分硬件的每一点加速能力。
arm cpu的优化工作还在进行中，现在使用了常规的cpu优化。在arm a73上paddle-mobile现在单核运行一次mobilenet 1.0是160+ms，显然这不是我们的最终目标，我们正在用大量的汇编改写，后续性能仍会有巨大提升空间。
arm cpu的优化工作还在进行中，现在使用了常规的cpu优化。在arm a73上paddle-mobile arm-v7现在单核运行一次mobilenet1.0是110+ms，显然这不是我们的最终目标，我们正在用大量的汇编改写，后续性能仍会有巨大提升空间, 目前只支持armv7, 未来我们也会支持armv8。
- **Mali GPU**
......@@ -66,7 +69,7 @@ Paddle-Moible是PaddlePaddle组织下的项目，是一个致力于嵌入式平
### 开发文档
开发文档主要是关于编译、运行等问题。做为开发者，它可以和贡献文档共同结合使用。
[开发文档]()https://github.com/PaddlePaddle/paddle-mobile/blob/develop/doc/development_doc.md
[开发文档链接](https://github.com/PaddlePaddle/paddle-mobile/blob/develop/doc/development_doc.md)
### 贡献文档
- [贡献文档链接](https://github.com/PaddlePaddle/paddle-mobile/blob/develop/CONTRIBUTING.md)
......@@ -86,10 +89,10 @@ ONNX全称为“Open Neural Network Exchange”，即“开放的神经网络切
目前，百度也在做onnx支持工作。相关转换项目在这里：[paddle-onnx](https://github.com/PaddlePaddle/paddle-onnx)。
![](http://otkwwi4x8.bkt.clouddn.com/2018-07-03-15305875853057.jpg)
![](http://7xop3k.com1.z0.glb.clouddn.com/15311951836000.jpg)
### 4. 部分测试模型下载
[下载链接](https://mms-mis.cdn.bcebos.com/paddle-mobile/models.zip)
### 4. 部分测试模型和测试图片下载
[下载链接](http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip)
## 问题解决
......
demo/ReadMe.md 0 → 100644
浏览文件 @ 2b9ea6ea
## 如何运行demo
- Android demo下载路径
http://mms-graph.bj.bcebos.com/paddle-mobile%2FPaddleMobile_Android.zip
- iOS demo下载路径：
http://mms-graph.bj.bcebos.com/paddle-mobile%2FPaddleMobileDemo_iOS.zip
在demo目录下执行下载demo的脚本
```
sh getDemo.sh
```
demo工程就下载解压到当前目录中了。
\ No newline at end of file
demo/android/PaddleMobile_Android/.gitignore 已删除 100644 → 0
浏览文件 @ 711beed5
*.iml
.gradle
/local.properties
/.idea/workspace.xml
/.idea/libraries
.DS_Store
/build
/captures
.externalNativeBuild
demo/android/PaddleMobile_Android/app/build.gradle 已删除 100644 → 0
浏览文件 @ 711beed5
apply plugin: 'com.android.application'
android {
compileSdkVersion 21
defaultConfig {
applicationId "com.baidu.paddle"
minSdkVersion 15
targetSdkVersion 21
versionCode 1
versionName "1.0"
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
}
}
}
dependencies {
implementation fileTree(dir: 'libs', include: ['*.jar'])
implementation 'com.android.support:appcompat-v7:21.0.3'
}
demo/android/PaddleMobile_Android/app/proguard-rules.pro 已删除 100644 → 0
浏览文件 @ 711beed5
# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile
demo/android/PaddleMobile_Android/app/src/main/AndroidManifest.xml 已删除 100644 → 0
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
package="com.baidu.paddle">
<!-- 往SDCard写入数据权限 -->
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
<uses-permission android:name="android.permission.CAMERA" />
<application
android:allowBackup="true"
android:icon="@mipmap/ic_launcher"
android:label="@string/app_name"
android:supportsRtl="true"
android:theme="@style/AppTheme" >
<activity
android:name=".MainActivity"
android:label="@string/app_name">
<intent-filter>
<action android:name="android.intent.action.MAIN" />
<category android:name="android.intent.category.LAUNCHER" />
</intent-filter>
</activity>
</application>
</manifest>
demo/android/PaddleMobile_Android/app/src/main/java/com/baidu/paddle/MainActivity.java 已删除 100755 → 0
浏览文件 @ 711beed5
/*
* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
* documentation files (the "Software"), to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and
* to permit persons to whom the Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all copies or substantial portions of
* the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
* THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
* CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*/
package com.baidu.paddle;
import android.app.Activity;
import android.content.Context;
import android.content.Intent;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.net.Uri;
import android.os.AsyncTask;
import android.os.Bundle;
import android.os.Environment;
import android.provider.MediaStore;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileOutputStream;
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStream;
import static android.graphics.Color.blue;
import static android.graphics.Color.green;
import static android.graphics.Color.red;
import static com.baidu.paddle.MainActivity.TYPE.googlenet;
public class MainActivity extends Activity {
public static final int TAKE_PHOTO_REQUEST_CODE = 1001;
private Context mContext = null;
private int inputSize = 224;
enum TYPE {
googlenet
}
private TYPE type = googlenet;
private ImageView imageView;
private TextView tvSpeed;
private Button button;
private Bitmap bmp;
static {
try {
System.loadLibrary("paddle-mobile");
} catch (SecurityException e) {
e.printStackTrace();
} catch (UnsatisfiedLinkError e) {
e.printStackTrace();
} catch (NullPointerException e) {
e.printStackTrace();
}
}
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
mContext = this;
setContentView(R.layout.main_activity);
init();
}
private void init() {
imageView = (ImageView) findViewById(R.id.imageView);
tvSpeed = (TextView) findViewById(R.id.tv_speed);
button = (Button) findViewById(R.id.button);
button.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
if (!isHasSdCard()) {
Toast.makeText(mContext, R.string.sdcard_not_available,
Toast.LENGTH_LONG).show();
return;
}
Intent intent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
// save pic in sdcard
Uri imageUri = Uri.fromFile(getTempImage());
intent.putExtra(MediaStore.EXTRA_OUTPUT, imageUri);
startActivityForResult(intent, TAKE_PHOTO_REQUEST_CODE);
}
});
Button bt_load = (Button) findViewById(R.id.bt_load);
bt_load.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
String assetPath = "pml_demo";
String sdcardPath = Environment.getExternalStorageDirectory()
+ File.separator + assetPath + File.separator + type;
PML.load(sdcardPath);
}
});
Button bt_clear = (Button) findViewById(R.id.bt_clear);
bt_clear.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
PML.clear();
}
});
String assetPath = "pml_demo";
String sdcardPath = Environment.getExternalStorageDirectory()
+ File.separator + assetPath;
copyFilesFromAssets(this, assetPath, sdcardPath);
}
public void copyFilesFromAssets(Context context, String oldPath, String newPath) {
try {
String[] fileNames = context.getAssets().list(oldPath);
if (fileNames.length > 0) {
// directory
File file = new File(newPath);
file.mkdirs();
// copy recursivelyC
for (String fileName : fileNames) {
copyFilesFromAssets(context, oldPath + "/" + fileName,
newPath + "/" + fileName);
}
} else {
// file
InputStream is = context.getAssets().open(oldPath);
FileOutputStream fos = new FileOutputStream(new File(newPath));
byte[] buffer = new byte[1024];
int byteCount;
while ((byteCount = is.read(buffer)) != -1) {
fos.write(buffer, 0, byteCount);
}
fos.flush();
is.close();
fos.close();
}
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
public File getTempImage() {
if (Environment.getExternalStorageState().equals(
Environment.MEDIA_MOUNTED)) {
File tempFile = new File(Environment.getExternalStorageDirectory(), "temp.jpg");
try {
tempFile.createNewFile();
} catch (IOException e) {
e.printStackTrace();
}
return tempFile;
}
return null;
}
public void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
switch (requestCode) {
case TAKE_PHOTO_REQUEST_CODE:
if (resultCode == RESULT_OK) {
DetectionTask detectionTask = new DetectionTask();
detectionTask.execute(getTempImage().getPath());
}
break;
default:
break;
}
}
/**
* draw rect on imageView
*
* @param bitmap
* @param predicted
* @param viewWidth
* @param viewHeight
*/
private void drawRect(Bitmap bitmap, float[] predicted, int viewWidth, int viewHeight) {
Canvas canvas = new Canvas(bitmap);
canvas.drawBitmap(bitmap, 0, 0, null);
if (type == googlenet) {
Paint paint = new Paint();
paint.setColor(Color.RED);
paint.setStyle(Paint.Style.STROKE);
paint.setStrokeWidth(3.0f);
float x1 = 0;
float x2 = 0;
float y1 = 0;
float y2 = 0;
// the googlenet result sequence is (left top right top bottom)
x1 = (predicted[0] * viewWidth / 224);
y1 = (predicted[1] * viewHeight / 224);
x2 = (predicted[2] * viewWidth / 224);
y2 = (predicted[3] * viewHeight / 224);
canvas.drawRect(x1, y1, x2, y2, paint);
}
imageView.setImageBitmap(bitmap);
}
float getMaxIndex(float[] predicted) {
float max = 0;
int index = 0;
for (int i = 0; i < predicted.length; i++) {
if (predicted[i] > max) {
max = predicted[i];
index = i;
}
}
return index;
}
public float[] getScaledMatrix(Bitmap bitmap, int desWidth,
int desHeight) {
float[] dataBuf = new float[3 * desWidth * desHeight];
int rIndex;
int gIndex;
int bIndex;
int[] pixels = new int[desWidth * desHeight];
Bitmap bm = Bitmap.createScaledBitmap(bitmap, desWidth, desHeight, false);
bm.getPixels(pixels, 0, desWidth, 0, 0, desWidth, desHeight);
int j = 0;
int k = 0;
for (int i = 0; i < pixels.length; i ++) {
int clr = pixels[i];
j = i / desHeight;
k = i % desWidth;
rIndex = j * desWidth + k;
gIndex = rIndex + desHeight * desWidth;
bIndex = gIndex + desHeight * desWidth;
dataBuf[rIndex] = (float)((clr & 0x00ff0000)>> 16) -148;
dataBuf[gIndex] = (float)((clr & 0x0000ff00)>> 8) - 148;
dataBuf[bIndex] = (float)((clr & 0x000000ff)) -148;
}
if (bm.isRecycled()) {
bm.recycle();
}
return dataBuf;
}
/**
* check whether sdcard is mounted
*
* @return
*/
public boolean isHasSdCard() {
if (Environment.getExternalStorageState().equals(
Environment.MEDIA_MOUNTED)) {
return true;
} else {
return false;
}
}
public void dumpData(float[] results, String filename) {
try {
File writename = new File(filename);
writename.createNewFile();
BufferedWriter out = new BufferedWriter(new FileWriter(writename));
for (float result : results) {
out.write(result + " ");
}
out.flush();
out.close();
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* scale bitmap in case of OOM
*
* @param ctx
* @param filePath
* @return
*/
public Bitmap getScaleBitmap(Context ctx, String filePath) {
BitmapFactory.Options opt = new BitmapFactory.Options();
opt.inJustDecodeBounds = true;
BitmapFactory.decodeFile(filePath, opt);
int bmpWidth = opt.outWidth;
int bmpHeight = opt.outHeight;
int maxSize = 500;
opt.inSampleSize = 1;
while (true) {
if (bmpWidth / opt.inSampleSize < maxSize || bmpHeight / opt.inSampleSize < maxSize) {
break;
}
opt.inSampleSize *= 2;
}
opt.inJustDecodeBounds = false;
Bitmap bmp = BitmapFactory.decodeFile(filePath, opt);
return bmp;
}
@Override
public void onBackPressed() {
super.onBackPressed();
Log.d("mdl", "mdl clear");
// clear mdl
PML.clear();
}
class DetectionTask extends AsyncTask<String, Void, float[]> {
private long time;
public DetectionTask() {
super();
}
@Override
protected void onPreExecute() {
super.onPreExecute();
if (type == googlenet) {
inputSize = 224;
}
}
@Override
protected void onPostExecute(float[] result) {
super.onPostExecute(result);
try {
Bitmap src = Bitmap.createScaledBitmap(bmp, imageView.getWidth(),
imageView.getHeight(), false);
drawRect(src, result, imageView.getWidth(), imageView.getHeight());
tvSpeed.setText("detection cost：" + time + "ms");
} catch (Exception e) {
e.printStackTrace();
}
}
@Override
protected void onProgressUpdate(Void... values) {
super.onProgressUpdate(values);
}
@Override
protected void onCancelled() {
super.onCancelled();
}
@Override
protected float[] doInBackground(String... strings) {
bmp = getScaleBitmap(mContext, strings[0]);
float[] inputData = getScaledMatrix(bmp, inputSize, inputSize);
float[] result = null;
try {
long start = System.currentTimeMillis();
result = PML.predict(inputData);
long end = System.currentTimeMillis();
time = end - start;
} catch (Exception e) {
e.printStackTrace();
}
return result;
}
}
}
demo/android/PaddleMobile_Android/app/src/main/java/com/baidu/paddle/PML.java 已删除 100644 → 0
浏览文件 @ 711beed5
package com.baidu.paddle;
public class PML {
/**
* Load
* @param modelPath
* @return
*/
public static native boolean load(String modelPath);
/**
* object detection
*
* @param buf
* @return
*/
public static native float[] predict(float[] buf);
public static native void clear();
}
demo/android/PaddleMobile_Android/app/src/main/res/drawable/ic_launcher_background.xml 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="utf-8"?>
<vector xmlns:android="http://schemas.android.com/apk/res/android"
android:width="108dp"
android:height="108dp"
android:viewportHeight="108"
android:viewportWidth="108">
<path
android:fillColor="#26A69A"
android:pathData="M0,0h108v108h-108z" />
<path
android:fillColor="#00000000"
android:pathData="M9,0L9,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,0L19,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M29,0L29,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M39,0L39,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M49,0L49,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M59,0L59,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M69,0L69,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M79,0L79,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M89,0L89,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M99,0L99,108"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,9L108,9"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,19L108,19"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,29L108,29"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,39L108,39"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,49L108,49"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,59L108,59"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,69L108,69"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,79L108,79"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,89L108,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M0,99L108,99"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,29L89,29"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,39L89,39"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,49L89,49"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,59L89,59"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,69L89,69"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M19,79L89,79"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M29,19L29,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M39,19L39,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M49,19L49,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M59,19L59,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M69,19L69,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
<path
android:fillColor="#00000000"
android:pathData="M79,19L79,89"
android:strokeColor="#33FFFFFF"
android:strokeWidth="0.8" />
</vector>
demo/android/PaddleMobile_Android/app/src/main/res/layout/main_activity.xml 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
android:orientation="vertical" android:layout_width="match_parent"
android:layout_height="match_parent">
<LinearLayout
android:id="@+id/ll_bottom"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="horizontal"
android:layout_alignParentBottom="true"
android:background="@android:color/background_light">
<TextView
android:id="@+id/tv_speed"
android:layout_width="match_parent"
android:layout_weight="1"
android:layout_height="wrap_content"
android:textColor="@android:color/background_dark"
android:text="@string/time_cost"/>
<Button
android:id="@+id/button"
android:layout_width="match_parent"
android:layout_height="50dp"
android:text="take photo"
android:layout_weight="1"
/>
</LinearLayout>
<LinearLayout
android:id="@+id/ll_test"
android:layout_above="@id/ll_bottom"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="horizontal">
<Button
android:id="@+id/bt_load"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:layout_weight="1"
android:text="load"/>
<Button
android:layout_width="0dp"
android:layout_height="wrap_content"
android:id="@+id/bt_clear"
android:layout_weight="1"
android:text="clear"/>
</LinearLayout>
<ImageView
android:id="@+id/imageView"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:scaleType="centerInside"
android:layout_above="@id/ll_test"
android:background="@android:color/background_light"/>
</RelativeLayout>
\ No newline at end of file
demo/android/PaddleMobile_Android/app/src/main/res/values/colors.xml 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="utf-8"?>
<resources>
<color name="colorPrimary">#3F51B5</color>
<color name="colorPrimaryDark">#303F9F</color>
<color name="colorAccent">#FF4081</color>
</resources>
demo/android/PaddleMobile_Android/app/src/main/res/values/strings.xml 已删除 100644 → 0
浏览文件 @ 711beed5
<resources>
<string name="app_name">PaddleMobile_Android</string>
<string name="sdcard_not_available">sdcard not available</string>
<string name="time_cost">detection cost:</string>
</resources>
demo/android/PaddleMobile_Android/app/src/main/res/values/styles.xml 已删除 100644 → 0
浏览文件 @ 711beed5
<resources>
<!-- Base application theme. -->
<style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
<!-- Customize your theme here. -->
<item name="colorPrimary">@color/colorPrimary</item>
<item name="colorPrimaryDark">@color/colorPrimaryDark</item>
<item name="colorAccent">@color/colorAccent</item>
</style>
</resources>
demo/android/PaddleMobile_Android/build.gradle 已删除 100644 → 0
浏览文件 @ 711beed5
// Top-level build file where you can add configuration options common to all sub-projects/modules.
buildscript {
repositories {
google()
jcenter()
}
dependencies {
classpath 'com.android.tools.build:gradle:3.1.3'
// NOTE: Do not place your application dependencies here; they belong
// in the individual module build.gradle files
}
}
allprojects {
repositories {
google()
jcenter()
}
}
task clean(type: Delete) {
delete rootProject.buildDir
}
demo/android/PaddleMobile_Android/gradle.properties 已删除 100644 → 0
浏览文件 @ 711beed5
# Project-wide Gradle settings.
# IDE (e.g. Android Studio) users:
# Gradle settings configured through the IDE *will override*
# any settings specified in this file.
# For more details on how to configure your build environment visit
# http://www.gradle.org/docs/current/userguide/build_environment.html
# Specifies the JVM arguments used for the daemon process.
# The setting is particularly useful for tweaking memory settings.
org.gradle.jvmargs=-Xmx1536m
android.injected.testOnly=false
# When configured, Gradle will run in incubating parallel mode.
# This option should only be used with decoupled projects. More details, visit
# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
# org.gradle.parallel=true
demo/android/PaddleMobile_Android/gradle/wrapper/gradle-wrapper.properties 已删除 100644 → 0
浏览文件 @ 711beed5
#Mon Jul 02 13:58:58 CST 2018
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-4.4-all.zip
demo/android/PaddleMobile_Android/gradlew 已删除 100755 → 0
浏览文件 @ 711beed5
#!/usr/bin/env bash
##############################################################################
##
## Gradle start up script for UN*X
##
##############################################################################
# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
DEFAULT_JVM_OPTS=""
APP_NAME="Gradle"
APP_BASE_NAME=`basename "$0"`
# Use the maximum available, or set MAX_FD != -1 to use that value.
MAX_FD="maximum"
warn ( ) {
echo "$*"
}
die ( ) {
echo
echo "$*"
echo
exit 1
}
# OS specific support (must be 'true' or 'false').
cygwin=false
msys=false
darwin=false
case "`uname`" in
CYGWIN* )
cygwin=true
;;
Darwin* )
darwin=true
;;
MINGW* )
msys=true
;;
esac
# Attempt to set APP_HOME
# Resolve links: $0 may be a link
PRG="$0"
# Need this for relative symlinks.
while [ -h "$PRG" ] ; do
ls=`ls -ld "$PRG"`
link=`expr "$ls" : '.*-> \(.*\)$'`
if expr "$link" : '/.*' > /dev/null; then
PRG="$link"
else
PRG=`dirname "$PRG"`"/$link"
fi
done
SAVED="`pwd`"
cd "`dirname \"$PRG\"`/" >/dev/null
APP_HOME="`pwd -P`"
cd "$SAVED" >/dev/null
CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
# Determine the Java command to use to start the JVM.
if [ -n "$JAVA_HOME" ] ; then
if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
# IBM's JDK on AIX uses strange locations for the executables
JAVACMD="$JAVA_HOME/jre/sh/java"
else
JAVACMD="$JAVA_HOME/bin/java"
fi
if [ ! -x "$JAVACMD" ] ; then
die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
Please set the JAVA_HOME variable in your environment to match the
location of your Java installation."
fi
else
JAVACMD="java"
which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
Please set the JAVA_HOME variable in your environment to match the
location of your Java installation."
fi
# Increase the maximum file descriptors if we can.
if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
MAX_FD_LIMIT=`ulimit -H -n`
if [ $? -eq 0 ] ; then
if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
MAX_FD="$MAX_FD_LIMIT"
fi
ulimit -n $MAX_FD
if [ $? -ne 0 ] ; then
warn "Could not set maximum file descriptor limit: $MAX_FD"
fi
else
warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
fi
fi
# For Darwin, add options to specify how the application appears in the dock
if $darwin; then
GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
fi
# For Cygwin, switch paths to Windows format before running java
if $cygwin ; then
APP_HOME=`cygpath --path --mixed "$APP_HOME"`
CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
JAVACMD=`cygpath --unix "$JAVACMD"`
# We build the pattern for arguments to be converted via cygpath
ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
SEP=""
for dir in $ROOTDIRSRAW ; do
ROOTDIRS="$ROOTDIRS$SEP$dir"
SEP="|"
done
OURCYGPATTERN="(^($ROOTDIRS))"
# Add a user-defined pattern to the cygpath arguments
if [ "$GRADLE_CYGPATTERN" != "" ] ; then
OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
fi
# Now convert the arguments - kludge to limit ourselves to /bin/sh
i=0
for arg in "$@" ; do
CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option
if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition
eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
else
eval `echo args$i`="\"$arg\""
fi
i=$((i+1))
done
case $i in
(0) set -- ;;
(1) set -- "$args0" ;;
(2) set -- "$args0" "$args1" ;;
(3) set -- "$args0" "$args1" "$args2" ;;
(4) set -- "$args0" "$args1" "$args2" "$args3" ;;
(5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
(6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
(7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
(8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
(9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
esac
fi
# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
function splitJvmOpts() {
JVM_OPTS=("$@")
}
eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"
demo/android/PaddleMobile_Android/gradlew.bat 已删除 100644 → 0
浏览文件 @ 711beed5
@if "%DEBUG%" == "" @echo off
@rem ##########################################################################
@rem
@rem Gradle startup script for Windows
@rem
@rem ##########################################################################
@rem Set local scope for the variables with windows NT shell
if "%OS%"=="Windows_NT" setlocal
@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
set DEFAULT_JVM_OPTS=
set DIRNAME=%~dp0
if "%DIRNAME%" == "" set DIRNAME=.
set APP_BASE_NAME=%~n0
set APP_HOME=%DIRNAME%
@rem Find java.exe
if defined JAVA_HOME goto findJavaFromJavaHome
set JAVA_EXE=java.exe
%JAVA_EXE% -version >NUL 2>&1
if "%ERRORLEVEL%" == "0" goto init
echo.
echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
echo.
echo Please set the JAVA_HOME variable in your environment to match the
echo location of your Java installation.
goto fail
:findJavaFromJavaHome
set JAVA_HOME=%JAVA_HOME:"=%
set JAVA_EXE=%JAVA_HOME%/bin/java.exe
if exist "%JAVA_EXE%" goto init
echo.
echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
echo.
echo Please set the JAVA_HOME variable in your environment to match the
echo location of your Java installation.
goto fail
:init
@rem Get command-line arguments, handling Windowz variants
if not "%OS%" == "Windows_NT" goto win9xME_args
if "%@eval[2+2]" == "4" goto 4NT_args
:win9xME_args
@rem Slurp the command line arguments.
set CMD_LINE_ARGS=
set _SKIP=2
:win9xME_args_slurp
if "x%~1" == "x" goto execute
set CMD_LINE_ARGS=%*
goto execute
:4NT_args
@rem Get arguments from the 4NT Shell from JP Software
set CMD_LINE_ARGS=%$
:execute
@rem Setup the command line
set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
@rem Execute Gradle
"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
:end
@rem End local scope for the variables with windows NT shell
if "%ERRORLEVEL%"=="0" goto mainEnd
:fail
rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
rem the _cmd.exe /c_ return code!
if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
exit /b 1
:mainEnd
if "%OS%"=="Windows_NT" endlocal
:omega
demo/getDemo.sh 0 → 100644
浏览文件 @ 2b9ea6ea
#!/usr/bin/env bash
wget http://mms-graph.bj.bcebos.com/paddle-mobile%2FPaddleMobile_Android.zip
wget http://mms-graph.bj.bcebos.com/paddle-mobile%2FPaddleMobileDemo_iOS.zip
unzip paddle-mobile%2FPaddleMobile_Android.zip
unzip paddle-mobile%2FPaddleMobileDemo_iOS.zip
rm -rf paddle-mobile%2FPaddleMobile_Android.zip
rm -rf paddle-mobile%2FPaddleMobileDemo_iOS.zip
rm -rf __MACOSX
\ No newline at end of file
demo/ios/PaddleMobileDemo/PaddleMobileDemo.xcodeproj/project.pbxproj 已删除 100644 → 0
浏览文件 @ 711beed5
// !$*UTF8*$!
{
archiveVersion = 1;
classes = {
};
objectVersion = 50;
objects = {
/* Begin PBXBuildFile section */
FC12E93320EB6B2800807EF4 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = FC12E93220EB6B2800807EF4 /* AppDelegate.m */; };
FC12E93620EB6B2800807EF4 /* ViewController.m in Sources */ = {isa = PBXBuildFile; fileRef = FC12E93520EB6B2800807EF4 /* ViewController.m */; };
FC12E93920EB6B2800807EF4 /* Main.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = FC12E93720EB6B2800807EF4 /* Main.storyboard */; };
FC12E93B20EB6B2900807EF4 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = FC12E93A20EB6B2900807EF4 /* Assets.xcassets */; };
FC12E93E20EB6B2900807EF4 /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = FC12E93C20EB6B2900807EF4 /* LaunchScreen.storyboard */; };
FC12E94120EB6B2900807EF4 /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = FC12E94020EB6B2900807EF4 /* main.m */; };
FC12E94A20EB6B6800807EF4 /* libpaddle-mobile.a in Frameworks */ = {isa = PBXBuildFile; fileRef = FC12E94820EB6B6800807EF4 /* libpaddle-mobile.a */; };
FC12E94D20EB6BBB00807EF4 /* libstdc++.tbd in Frameworks */ = {isa = PBXBuildFile; fileRef = FC12E94C20EB6BBB00807EF4 /* libstdc++.tbd */; };
FC12E95120EB6BED00807EF4 /* params in Resources */ = {isa = PBXBuildFile; fileRef = FC12E94F20EB6BED00807EF4 /* params */; };
FC12E95220EB6BED00807EF4 /* model in Resources */ = {isa = PBXBuildFile; fileRef = FC12E95020EB6BED00807EF4 /* model */; };
FC12E95420EB6C0D00807EF4 /* apple.jpg in Resources */ = {isa = PBXBuildFile; fileRef = FC12E95320EB6C0D00807EF4 /* apple.jpg */; };
/* End PBXBuildFile section */
/* Begin PBXFileReference section */
FC12E92E20EB6B2800807EF4 /* PaddleMobileDemo.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = PaddleMobileDemo.app; sourceTree = BUILT_PRODUCTS_DIR; };
FC12E93120EB6B2800807EF4 /* AppDelegate.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
FC12E93220EB6B2800807EF4 /* AppDelegate.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = AppDelegate.m; sourceTree = "<group>"; };
FC12E93420EB6B2800807EF4 /* ViewController.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = ViewController.h; sourceTree = "<group>"; };
FC12E93520EB6B2800807EF4 /* ViewController.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = ViewController.m; sourceTree = "<group>"; };
FC12E93820EB6B2800807EF4 /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/Main.storyboard; sourceTree = "<group>"; };
FC12E93A20EB6B2900807EF4 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
FC12E93D20EB6B2900807EF4 /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/LaunchScreen.storyboard; sourceTree = "<group>"; };
FC12E93F20EB6B2900807EF4 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
FC12E94020EB6B2900807EF4 /* main.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = main.m; sourceTree = "<group>"; };
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demo/ios/PaddleMobileDemo/PaddleMobileDemo.xcodeproj/project.xcworkspace/contents.xcworkspacedata 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:PaddleMobileDemo.xcodeproj">
</FileRef>
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demo/ios/PaddleMobileDemo/PaddleMobileDemo.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist 已删除 100644 → 0
浏览文件 @ 711beed5
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demo/ios/PaddleMobileDemo/PaddleMobileDemo.xcodeproj/xcuserdata/liuruilong.xcuserdatad/xcschemes/xcschememanagement.plist 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
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</dict>
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demo/ios/PaddleMobileDemo/PaddleMobileDemo/AppDelegate.m 已删除 100644 → 0
浏览文件 @ 711beed5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import "AppDelegate.h"
@interface AppDelegate ()
@end
@implementation AppDelegate
- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
// Override point for customization after application launch.
return YES;
}
- (void)applicationWillResignActive:(UIApplication *)application {
// Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state.
// Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game.
}
- (void)applicationDidEnterBackground:(UIApplication *)application {
// Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later.
// If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits.
}
- (void)applicationWillEnterForeground:(UIApplication *)application {
// Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background.
}
- (void)applicationDidBecomeActive:(UIApplication *)application {
// Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface.
}
- (void)applicationWillTerminate:(UIApplication *)application {
// Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:.
}
@end
demo/ios/PaddleMobileDemo/PaddleMobileDemo/Assets.xcassets/AppIcon.appiconset/Contents.json 已删除 100644 → 0
浏览文件 @ 711beed5
{
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},
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],
"info" : {
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"author" : "xcode"
}
}
\ No newline at end of file
demo/ios/PaddleMobileDemo/PaddleMobileDemo/Assets.xcassets/Contents.json 已删除 100644 → 0
浏览文件 @ 711beed5
{
"info" : {
"version" : 1,
"author" : "xcode"
}
}
\ No newline at end of file
demo/ios/PaddleMobileDemo/PaddleMobileDemo/Base.lproj/LaunchScreen.storyboard 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" systemVersion="17A277" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
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</document>
demo/ios/PaddleMobileDemo/PaddleMobileDemo/Base.lproj/Main.storyboard 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="UTF-8"?>
<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" systemVersion="17A277" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="BYZ-38-t0r">
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</document>
demo/ios/PaddleMobileDemo/PaddleMobileDemo/Info.plist 已删除 100644 → 0
浏览文件 @ 711beed5
<?xml version="1.0" encoding="UTF-8"?>
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<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
<key>CFBundleInfoDictionaryVersion</key>
<string>6.0</string>
<key>CFBundleName</key>
<string>$(PRODUCT_NAME)</string>
<key>CFBundlePackageType</key>
<string>APPL</string>
<key>CFBundleShortVersionString</key>
<string>1.0</string>
<key>CFBundleVersion</key>
<string>1</string>
<key>LSRequiresIPhoneOS</key>
<true/>
<key>UILaunchStoryboardName</key>
<string>LaunchScreen</string>
<key>UIMainStoryboardFile</key>
<string>Main</string>
<key>UIRequiredDeviceCapabilities</key>
<array>
<string>armv7</string>
</array>
<key>UISupportedInterfaceOrientations</key>
<array>
<string>UIInterfaceOrientationPortrait</string>
<string>UIInterfaceOrientationLandscapeLeft</string>
<string>UIInterfaceOrientationLandscapeRight</string>
</array>
<key>UISupportedInterfaceOrientations~ipad</key>
<array>
<string>UIInterfaceOrientationPortrait</string>
<string>UIInterfaceOrientationPortraitUpsideDown</string>
<string>UIInterfaceOrientationLandscapeLeft</string>
<string>UIInterfaceOrientationLandscapeRight</string>
</array>
</dict>
</plist>
demo/ios/PaddleMobileDemo/PaddleMobileDemo/PaddleMobile/PaddleMobile.h 已删除 100644 → 0
浏览文件 @ 711beed5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <CoreImage/CoreImage.h>
#import <Foundation/Foundation.h>
@interface PaddleMobile : NSObject
- (instancetype)init;
- (BOOL)load:(NSString *)modelPath andWeightsPath:(NSString *)weighsPath;
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim means:(NSArray<NSNumber *> *)means scale:(float)scale;
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim;
- (void)clear;
@end
demo/ios/PaddleMobileDemo/PaddleMobileDemo/ViewController.m 已删除 100644 → 0
浏览文件 @ 711beed5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import "PaddleMobile.h"
#import "ViewController.h"
@interface ViewController ()
@end
@implementation ViewController
- (void)viewDidLoad {
[super viewDidLoad];
PaddleMobile *pam = [[PaddleMobile alloc] init];
NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model" ofType:nil];
NSString *paramPath = [[NSBundle mainBundle] pathForResource:@"params" ofType:nil];
if (modelPath.length == 0 || paramPath.length == 0) {
NSLog(@" need model and param");
return;
}
if ([pam load:modelPath andWeightsPath:paramPath]) {
NSLog(@"load success");
UIImage *inputImage = [UIImage imageNamed:@"apple.jpg"];
if (!inputImage) {
NSLog(@" input image is nil");
return;
}
NSDate *beforeDate = [NSDate date];
NSArray *res = [pam predict:inputImage.CGImage dim:@[@1, @3, @224, @224] means:@[@148, @148, @148] scale:1.0];
NSLog(@"res: %@", res);
NSLog(@"elapsed time: %f", [[NSDate date] timeIntervalSinceDate:beforeDate]);
}
}
@end
demo/ios/PaddleMobileDemo/PaddleMobileDemo/main.m 已删除 100644 → 0
浏览文件 @ 711beed5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <UIKit/UIKit.h>
#import "AppDelegate.h"
int main(int argc, char * argv[]) {
@autoreleasepool {
return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class]));
}
}
doc/build.md
浏览文件 @ 2b9ea6ea
......@@ -19,9 +19,14 @@ paddle-mobile dev 33b146787711 45 hours ago 372MB
```
$ docker run -it --mount type=bind,source=$PWD,target=/paddle-mobile paddle-mobile:dev
root@5affd29d4fc5:/ # cd /paddle-mobile
###
### paddle-mobile 支持 arm 架构下的各种平台，包括 android 以及 linux 等，可以使用不同的
### toolchain 文件生成满足需要的 makefile
###
# 生成构建 android 产出的 Makefile
root@5affd29d4fc5:/ # rm CMakeCache.txt
root@5affd29d4fc5:/ # cmake -DCMAKE_TOOLCHAIN_FILE=tools/toolchains/arm-android-neon.cmake
# 生成构建 linux 产出的 Makefile
root@5affd29d4fc5:/ # rm CMakeCache.txt
root@5affd29d4fc5:/ # cmake -DCMAKE_TOOLCHAIN_FILE=tools/toolchains/arm-linux-gnueabi.cmake
......@@ -56,4 +61,4 @@ root@5affd29d4fc5:/ # make
构架产出可以在 host 机器上查看，在 paddle-mobile 的目录下，build 以及 test/build 下，可以使用 adb 指令或者 scp 传输到 device 上执行
## 不使用 docker
不使用 docker 的方法，可以直接用 cmake 生成 makefile 后构建。使用 ndk 构建 android 应用需要正确设置 NDK_ROOT。构建 linux 应用需要安装 arm-linux-gnueabi-gcc 或者类似的交叉编译工具，可能需要设置 CC，CXX 环境变量，或者在 tools/toolchains/ 中修改 arm-linux-gnueabi.cmake，或者增加自己需要的 toolchain file。
\ No newline at end of file
不使用 docker 的方法，可以直接用 cmake 生成 makefile 后构建。使用 ndk 构建 android 应用需要正确设置 NDK_ROOT。构建 linux 应用需要安装 arm-linux-gnueabi-gcc 或者类似的交叉编译工具，可能需要设置 CC，CXX 环境变量，或者在 tools/toolchains/ 中修改 arm-linux-gnueabi.cmake，或者增加自己需要的 toolchain file。
doc/development_doc.md
浏览文件 @ 2b9ea6ea
......@@ -4,9 +4,11 @@
## 编译
### 一. 使用 build.sh 编译
```sh
# 在 paddle-mobile 目录下:
cd tools
sh build.sh ios
# 如果只想编译某个特定模型的 op, 则需执行以下命令
......@@ -16,10 +18,14 @@ sh build.sh ios googlenet
cd ../build/release/ios/build
```
#### 常见问题:
1. No iOS SDK's found in default search path ...
### 二. 集成
这个问题是因为 tools/ios-cmake/ios.toolchain.cmake 找不到你最近使用的 iOS SDK 路径, 所以需要自己进行指定,
以我当前的环境为例: 在 tools/ios-cmake/ios.toolchain.cmake 143行前添加我本地的 iOS SDK 路径: set(CMAKE_IOS_SDK_ROOT "/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk")
#### 如使用 oc 接口
## 集成
```
将上一步生成的:
......@@ -28,7 +34,11 @@ libpaddle-mobile.a
/src/ios_io/ 下的
PaddleMobile.h
```
拖入工程, 接口如下:
拖入工程
#### oc 接口
接口如下:
```
/*
......@@ -133,11 +143,16 @@ root@5affd29d4fc5:/ # make
##### 下载Android NDK
从源码交叉编译paddle-mobile,用户需要提前准备好交叉编译环境。Android平台使用的C/C++交叉编译工具链是[Android NDK](https://developer.android.com/ndk/)，用户可以自行前往下载，也可以通过以下命令获取：
- Mac平台
```
wget https://dl.google.com/android/repository/android-ndk-r17b-darwin-x86_64.zip
unzip android-ndk-r17b-darwin-x86_64.zip
```
- Linux平台
```
wget https://dl.google.com/android/repository/android-ndk-r17b-linux-x86_64.zip
unzip android-ndk-r17b-linux-x86_64.zip
```
##### 设置环境变量
......@@ -182,27 +197,51 @@ which to test :
##部署
Android应用可通过JNI接口调用底层C/C++，paddle-mobile对外提供的JNI接口如下：
##### 1 load接口 加载模型参数
##### 1 load接口 加载模型参数
- 用于加载参数文件分散的模型
```
/*
*@param modelPath 模型文件路径
*@return jboolean
*/
JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
jclass thiz,
jstring modelPath);
/**
* Load seperated parameters
* @param modelDir
* @return
*/
public static native boolean load(String modelDir);
```
- 用于加载参数文件合并的模型文件
```
/**
* Load combined parameters
* @param modelPath
* @param paramPath
* @return
*/
public static native boolean loadCombined(String modelPath,String paramPath);
```
##### 2 predict接口 执行预测
- 接受预处理过的RGB数组的predict接口
```
/**
*@param buf 输入数据
*@return 输出数据
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predict(
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
JNIEnv *env, jclass thiz, jfloatArray buf);
```
- 接受原始yuv数据的predict接口
```
/**
*
* @param buf yuv420格式的字节数组
* @param imgWidth yuv数据的宽
* @param imgHeight yuv数据的高
* @param ddims 输入数据的形状
* @param meanValues 模型训练时各通道的均值
* @return
*/
public static native float[] predictYuv(byte[] buf, int imgWidth, int imgHeight, int[] ddims, float[]meanValues);
```
##### 3 clear接口 销毁实例、清理内存操作
```
......
src/common/types.cpp
浏览文件 @ 2b9ea6ea
......@@ -24,6 +24,8 @@ const std::string G_OP_TYPE_CONCAT = "concat";
const std::string G_OP_TYPE_ELEMENTWISE_ADD = "elementwise_add";
const std::string G_OP_TYPE_FUSION_CONV_ADD_RELU = "fusion_conv_add_relu";
const std::string G_OP_TYPE_FUSION_CONV_ADD_BN_RELU = "fusion_conv_add_bn_relu";
const std::string G_OP_TYPE_FUSION_DWCONV_BN_RELU = "fusion_dwconv_bn_relu";
const std::string G_OP_TYPE_FUSION_CONV_BN_RELU = "fusion_conv_bn_relu";
const std::string G_OP_TYPE_FC = "fusion_fc";
const std::string G_OP_TYPE_FUSION_CONV_ADD = "fusion_conv_add";
const std::string G_OP_TYPE_LRN = "lrn";
......@@ -47,6 +49,8 @@ std::unordered_map<
std::string, std::pair<std::vector<std::string>, std::vector<std::string>>>
op_input_output_key = {
{G_OP_TYPE_CONV, {{"Input"}, {"Output"}}},
{G_OP_TYPE_FUSION_DWCONV_BN_RELU, {{"Input"}, {"Out"}}},
{G_OP_TYPE_FUSION_CONV_BN_RELU, {{"Input"}, {"Out"}}},
{G_OP_TYPE_FUSION_CONV_ADD, {{"Input"}, {"Out"}}},
{G_OP_TYPE_RELU, {{"X"}, {"Out"}}},
{G_OP_TYPE_SOFTMAX, {{"X"}, {"Out"}}},
......
src/common/types.h
浏览文件 @ 2b9ea6ea
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
namespace paddle_mobile {
......@@ -81,6 +82,8 @@ extern const std::string G_OP_TYPE_FUSION_CONV_ADD_RELU;
extern const std::string G_OP_TYPE_FC;
extern const std::string G_OP_TYPE_FUSION_CONV_ADD;
extern const std::string G_OP_TYPE_FUSION_CONV_ADD_BN_RELU;
extern const std::string G_OP_TYPE_FUSION_DWCONV_BN_RELU;
extern const std::string G_OP_TYPE_FUSION_CONV_BN_RELU;
extern const std::string G_OP_TYPE_LRN;
extern const std::string G_OP_TYPE_MUL;
......
src/common/variant.h
浏览文件 @ 2b9ea6ea
......@@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <cstdlib>
#include "common/enforce.h"
#include "common/log.h"
......@@ -82,7 +84,7 @@ struct Variant {
if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else {
PADDLE_MOBILE_THROW_EXCEPTION(" bad cast in variant ");
PADDLE_MOBILE_THROW_EXCEPTION(" bad cast in variant");
exit(0);
}
}
......
src/framework/attribute.h
浏览文件 @ 2b9ea6ea
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <cstdlib>
#include <string>
#include <typeinfo>
#include <unordered_map>
......
src/framework/data_layout.h
浏览文件 @ 2b9ea6ea
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <cctype>
#include <cstdlib>
#include <string>
namespace paddle_mobile {
......
src/framework/ddim.h
浏览文件 @ 2b9ea6ea
......@@ -14,9 +14,11 @@ limitations under the License. */
#pragma once
#include <cstdlib>
#include <initializer_list>
#include <typeinfo>
#include <vector>
#include "common/enforce.h"
#include "common/variant.h"
#include "dim.h"
......
src/framework/dim.h
浏览文件 @ 2b9ea6ea
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <cstdlib>
#include "common/enforce.h"
namespace paddle_mobile {
namespace framework {
......
src/framework/operator.cpp
浏览文件 @ 2b9ea6ea
......@@ -28,6 +28,16 @@ vector<string> OperatorBase<Dtype>::GetOutKeys() const {
return it->second.second;
}
template <typename Dtype>
vector<string> OperatorBase<Dtype>::GetInputKeys() const {
auto it = op_input_output_key.find(type_);
if (it == op_input_output_key.end()) {
DLOG << type_ << " has no outputs";
return {};
}
return it->second.first;
}
template <typename Dtype>
OperatorBase<Dtype>::OperatorBase(const std::string &type,
const VariableNameMap &inputs,
......@@ -49,6 +59,11 @@ template <typename Dtype>
void OperatorBase<Dtype>::Run() const {
RunImpl();
#ifdef PADDLE_MOBILE_DEBUG
vector<string> input_keys = GetInputKeys();
for (const auto key : input_keys) {
Tensor *input = GetVarValue<framework::LoDTensor>(key, inputs_, *scope_);
DLOG << type_ << " input- " << key << "=" << *input;
}
vector<string> output_keys = GetOutKeys();
for (const auto key : output_keys) {
Tensor *out_ = GetVarValue<framework::LoDTensor>(key, outputs_, *scope_);
......
src/framework/operator.h
浏览文件 @ 2b9ea6ea
......@@ -61,6 +61,7 @@ class OperatorBase {
virtual ~OperatorBase() {}
void Run() const;
std::vector<string> GetOutKeys() const;
std::vector<string> GetInputKeys() const;
virtual void RunImpl() const = 0;
virtual void Init() = 0;
......@@ -118,6 +119,10 @@ class OperatorWithKernel : public OperatorBase<Dtype> {
virtual void InferShape() const = 0;
void Init() {
// for (auto i : this->inputs_) {
// DLOG << i.first;
// DLOG << i.second;
// }
PADDLE_MOBILE_ENFORCE(kernel_.Init(&param_), " %s kernel init failed",
this->type_.c_str());
}
......@@ -146,7 +151,7 @@ class OpKernelBase {
}
#endif
virtual void Compute(const P &para) const = 0;
virtual bool Init(P *para) { return true; };
virtual bool Init(P *para) { return true; }
virtual ~OpKernelBase() = default;
private:
......
src/framework/program/program-optimize/fusion_op_register.h
浏览文件 @ 2b9ea6ea
......@@ -42,8 +42,17 @@ class FusionOpRegister {
matchers_[matcher->Type()] = shared_matcher;
}
const std::map<std::string, std::shared_ptr<FusionOpMatcher>> Matchers() {
return matchers_;
const std::vector<std::shared_ptr<FusionOpMatcher>> Matchers() {
std::vector<std::shared_ptr<FusionOpMatcher>> matchers;
for (const auto& match : matchers_) {
matchers.push_back(match.second);
}
std::sort(matchers.begin(), matchers.end(),
[](std::shared_ptr<FusionOpMatcher> first,
std::shared_ptr<FusionOpMatcher> second) {
return first->BeginNode().Depth() > second->BeginNode().Depth();
});
return matchers;
}
private:
......
src/framework/program/program-optimize/node.cpp
浏览文件 @ 2b9ea6ea
......@@ -44,23 +44,6 @@ bool Node::operator==(const Node &in) {
return true;
}
std::vector<std::shared_ptr<framework::OpDesc>> Node::OpDescs(int size) {
std::vector<std::shared_ptr<framework::OpDesc>> op_descs;
OpDescs(size - 1, &op_descs);
return op_descs;
}
void Node::OpDescs(int index,
std::vector<std::shared_ptr<framework::OpDesc>> *op_desc) {
if (index == 0) {
return;
}
op_desc->push_back(this->op_desc_);
for (auto &output : outputs_) {
output->OpDescs(index, op_desc);
}
}
std::shared_ptr<Node> Node::To(int size) {
std::shared_ptr<Node> node = std::make_shared<Node>();
this->To(size - 1, node);
......
src/framework/program/program-optimize/node.h
浏览文件 @ 2b9ea6ea
......@@ -47,13 +47,10 @@ class Node {
std::map<std::string, std::vector<std::pair<std::string, std::string>>>
change,
std::vector<std::shared_ptr<Node>> *removed_nodes);
std::vector<std::shared_ptr<framework::OpDesc>> OpDescs(int size);
std::shared_ptr<framework::OpDesc> OpDescOfNode() { return op_desc_; }
std::string Type() { return type_; }
private:
void OpDescs(int size,
std::vector<std::shared_ptr<framework::OpDesc>> *op_desc);
void To(int index, std::shared_ptr<Node>);
void Folder(
std::shared_ptr<framework::OpDesc> op_desc,
......
src/framework/program/program-optimize/program_optimize.cpp
浏览文件 @ 2b9ea6ea
......@@ -78,9 +78,8 @@ std::shared_ptr<ProgramDesc> ProgramOptimize::FusionOptimize(
}
for (auto &registed : FusionOpRegister::Instance()->Matchers()) {
std::string fusion_type = registed.first;
std::shared_ptr<FusionOpMatcher> matcher = registed.second;
// DLOG << " registed node \n " << matcher->BeginNode();
std::string fusion_type = registed->Type();
std::shared_ptr<FusionOpMatcher> matcher = registed;
auto match_vector = type_map[matcher->BeginType()];
......
src/framework/program/program.h
浏览文件 @ 2b9ea6ea
......@@ -30,6 +30,7 @@ class Program {
std::string model_path;
std::string para_path;
bool combined = false;
bool quantification = false;
private:
};
......
src/framework/tensor.h
浏览文件 @ 2b9ea6ea
......@@ -152,7 +152,7 @@ class Tensor {
if (holder_ != nullptr) {
holder_->set_type(type);
}
PADDLE_MOBILE_ENFORCE(numel() >= 0, "the Tensor'snumel must >=0.")
PADDLE_MOBILE_ENFORCE(numel() >= 0, "the Tensor's numel must >=0.")
int64_t size = numel() * SizeOfType(type);
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
......
src/io/executor.cpp
浏览文件 @ 2b9ea6ea
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "io/executor.h"
#include <operators/math/gemm.h>
#include <algorithm>
#include <vector>
#include "common/enforce.h"
......@@ -153,7 +154,7 @@ void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
tensor->Resize(framework::make_ddim(desc.Dims()));
void *memory = tensor;
void *memory = nullptr;
int type_size = 0;
switch (desc.DataType()) {
case framework::VARTYPE_TYPE_FP16:
......@@ -178,11 +179,25 @@ void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
default:
break;
}
for (int n = 0; n < memory_size * type_size; ++n) {
static_cast<char *>(memory)[n] = (*data)[n];
if (program_.quantification) {
float min_value;
float max_value;
memcpy(&min_value, *data, sizeof(float));
memcpy(&max_value, *data + sizeof(float), sizeof(float));
*data += 2 * sizeof(float);
const float factor = (max_value - min_value) / 255.0;
uint8_t *uint8_data = (uint8_t *)(*data);
for (int k = 0; k < memory_size; ++k) {
static_cast<float *>(memory)[k] = uint8_data[k] * factor + min_value;
}
*data += (memory_size * sizeof(uint8_t));
} else {
for (int n = 0; n < memory_size * type_size; ++n) {
static_cast<char *>(memory)[n] = (*data)[n];
}
(*data) += (sizeof(char) * memory_size * type_size);
}
(*data) += (sizeof(char) * memory_size * type_size);
}
template <typename Dtype, Precision P>
......
src/io/loader.cpp
浏览文件 @ 2b9ea6ea
......@@ -44,26 +44,29 @@ static size_t ReadBuffer(const char *file_name, uint8_t **out) {
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &dirname, bool optimize, bool can_add_split) {
auto program =
this->LoadProgram(dirname + "/__model__", optimize, can_add_split);
const std::string &dirname, bool optimize, bool quantification,
bool can_add_split) {
auto program = this->LoadProgram(dirname + "/__model__", optimize,
quantification, can_add_split);
program.model_path = dirname;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &model_path, const std::string &para_path,
bool optimize) {
const std::string &model_path, const std::string &para_path, bool optimize,
bool quantification) {
auto program = this->LoadProgram(model_path, optimize);
program.para_path = para_path;
program.combined = true;
program.quantification = quantification;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::LoadProgram(
const std::string &model_path, bool optimize, bool can_add_split) {
const std::string &model_path, bool optimize, bool quantification,
bool can_add_split) {
std::string model_filename = model_path;
PaddleMobile__Framework__Proto__ProgramDesc *c_program;
uint8_t *buf = NULL;
......@@ -82,6 +85,7 @@ const framework::Program<Dtype, P> Loader<Dtype, P>::LoadProgram(
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
program.quantification = quantification;
auto scope = std::make_shared<framework::Scope>();
program.scope = scope;
......
src/io/loader.h
浏览文件 @ 2b9ea6ea
......@@ -30,6 +30,7 @@ class Loader {
* */
const framework::Program<Dtype, P> Load(const std::string &dirname,
bool optimize = false,
bool quantification = false,
bool can_add_split = false);
/*
......@@ -38,11 +39,13 @@ class Loader {
* */
const framework::Program<Dtype, P> Load(const std::string &model_path,
const std::string &para_path,
bool optimize = false);
bool optimize = false,
bool quantification = false);
private:
const framework::Program<Dtype, P> LoadProgram(const std::string &model_path,
bool optimize = false,
bool quantification = false,
bool can_add_split = false);
};
......
src/io/paddle_mobile.cpp
浏览文件 @ 2b9ea6ea
......@@ -16,9 +16,17 @@ limitations under the License. */
namespace paddle_mobile {
template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::SetThreadNum(int num) {
#ifdef _OPENMP
// omp_set_dynamic(0);
omp_set_num_threads(num);
#endif
};
template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::Load(const std::string &dirname, bool optimize,
int batch_size) {
bool quantification, int batch_size) {
if (loader_.get() == nullptr) {
loader_ = std::make_shared<Loader<Dtype, P>>();
} else {
......@@ -27,7 +35,7 @@ bool PaddleMobile<Dtype, P>::Load(const std::string &dirname, bool optimize,
if (executor_.get() == nullptr) {
executor_ = std::make_shared<Executor<Dtype, P>>(
loader_->Load(dirname, optimize), batch_size, optimize);
loader_->Load(dirname, optimize, quantification), batch_size, optimize);
} else {
LOG(kLOG_INFO) << "executor inited";
}
......@@ -38,7 +46,7 @@ bool PaddleMobile<Dtype, P>::Load(const std::string &dirname, bool optimize,
template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::Load(const std::string &model_path,
const std::string &para_path, bool optimize,
int batch_size) {
bool quantification, int batch_size) {
if (loader_.get() == nullptr) {
loader_ = std::make_shared<Loader<Dtype, P>>();
} else {
......@@ -47,7 +55,8 @@ bool PaddleMobile<Dtype, P>::Load(const std::string &model_path,
if (executor_.get() == nullptr) {
executor_ = std::make_shared<Executor<Dtype, P>>(
loader_->Load(model_path, para_path, optimize), batch_size, optimize);
loader_->Load(model_path, para_path, optimize, quantification),
batch_size, optimize);
} else {
LOG(kLOG_INFO) << "executor inited";
}
......@@ -81,7 +90,9 @@ PaddleMobile<Dtype, P>::~PaddleMobile() {
}
template class PaddleMobile<CPU, Precision::FP32>;
template class PaddleMobile<FPGA, Precision::FP32>;
template class PaddleMobile<GPU_MALI, Precision::FP32>;
} // namespace paddle_mobile
src/io/paddle_mobile.h
浏览文件 @ 2b9ea6ea
......@@ -17,6 +17,9 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#ifdef _OPENMP
#include <omp.h>
#endif // _OPENMP
#include "common/types.h"
#include "framework/tensor.h"
......@@ -36,14 +39,19 @@ class PaddleMobile {
* @b 加载分开形式的 fluid 模型
* */
bool Load(const std::string &dirname, bool optimize = false,
int batch_size = 1);
bool quantification = false, int batch_size = 1);
/*
* @b load combine format fluid mode
* @b 加载结合在一起格式的模型
* */
bool Load(const std::string &model_path, const std::string &para_path,
bool optimize = false, int batch_size = 1);
bool optimize = false, bool quantification = false,
int batch_size = 1);
/*
* @b 设置线程数, 当 cmake 中开启 openmp 时生效
* */
void SetThreadNum(int num);
/*
* @b to predict
......
src/ios_io/PaddleMobile.h
浏览文件 @ 2b9ea6ea
......@@ -19,10 +19,34 @@
@interface PaddleMobile : NSObject
/*
创建对象
*/
- (instancetype)init;
/*
load 模型, 开辟内存
*/
- (BOOL)load:(NSString *)modelPath andWeightsPath:(NSString *)weighsPath;
/*
加载散开形式的模型, 需传入模型的目录
*/
- (BOOL)load:(NSString *)modelAndWeightPath;
/*
进行预测, means 和 scale 为训练模型时的预处理参数, 如训练时没有做这些预处理则直接使用 predict
*/
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim means:(NSArray<NSNumber *> *)means scale:(float)scale;
/*
进行预测
*/
- (NSArray *)predict:(CGImageRef)image dim:(NSArray<NSNumber *> *)dim;
/*
清理内存
*/
- (void)clear;
@end
src/ios_io/PaddleMobile.mm
浏览文件 @ 2b9ea6ea
......@@ -62,6 +62,15 @@ static std::mutex shared_mutex;
}
}
- (BOOL)load:(NSString *)modelAndWeightPath{
std::string model_path_str = std::string([modelAndWeightPath UTF8String]);
if (loaded_ = pam_->Load(model_path_str)) {
return YES;
} else {
return NO;
}
}
-(void)preprocess:(const UInt8 *)input output:(float *)output imageWidth:(int)imageWidth imageHeight:(int)imageHeight imageChannels:(int)imageChannels means:(NSArray<NSNumber *> *)means scale:(float)scale dim:(std::vector<int64_t>)dim{
if (means == nil) {
means = @[@0, @0, @0];
......
src/jni/paddle_mobile_jni.cpp
浏览文件 @ 2b9ea6ea
......@@ -15,6 +15,7 @@ limitations under the License. */
#ifdef ANDROID
#include "paddle_mobile_jni.h"
#include <cmath>
#include "common/log.h"
#include "framework/tensor.h"
#include "io/paddle_mobile.h"
......@@ -60,8 +61,26 @@ JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
optimize);
}
JNIEXPORT jfloatArray JNICALL
Java_com_baidu_paddle_PML_predict(JNIEnv *env, jclass thiz, jfloatArray buf) {
JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_loadCombined(
JNIEnv *env, jclass thiz, jstring modelPath, jstring paramPath) {
ANDROIDLOGI("loadCombined invoked");
bool optimize = true;
return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
jstring2cppstring(env, paramPath),
optimize);
}
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
JNIEnv *env, jclass thiz, jfloatArray buf, jintArray ddims) {
ANDROIDLOGI("predictImage invoked");
jsize ddim_size = env->GetArrayLength(ddims);
if (ddim_size != 4) {
ANDROIDLOGE("ddims size not equal to 4");
}
jint *ddim_ptr = env->GetIntArrayElements(ddims, NULL);
framework::DDim ddim = framework::make_ddim(
{ddim_ptr[0], ddim_ptr[1], ddim_ptr[2], ddim_ptr[3]});
int length = framework::product(ddim);
jfloatArray result = NULL;
int count = 0;
float *dataPointer = nullptr;
......@@ -69,17 +88,102 @@ Java_com_baidu_paddle_PML_predict(JNIEnv *env, jclass thiz, jfloatArray buf) {
dataPointer = env->GetFloatArrayElements(buf, NULL);
}
framework::Tensor input;
framework::DDim ddim = framework::make_ddim({1, 3, 224, 224});
input.Resize(ddim);
auto input_ptr = input.mutable_data<float>();
for (int i = 0; i < framework::product(ddim); i++) {
for (int i = 0; i < length; i++) {
input_ptr[i] = dataPointer[i];
}
auto output = shared_paddle_mobile_instance->Predict(input);
count = output->numel();
result = env->NewFloatArray(count);
env->SetFloatArrayRegion(result, 0, count, output->data<float>());
ANDROIDLOGI("predict finished");
env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
ANDROIDLOGI("predictImage finished");
return result;
}
inline int yuv_to_rgb(int y, int u, int v, float *r, float *g, float *b) {
int r1 = (int)(y + 1.370705 * (v - 128));
int g1 = (int)(y - 0.698001 * (u - 128) - 0.703125 * (v - 128));
int b1 = (int)(y + 1.732446 * (u - 128));
r1 = (int)fminf(255, fmaxf(0, r1));
g1 = (int)fminf(255, fmaxf(0, g1));
b1 = (int)fminf(255, fmaxf(0, b1));
*r = r1;
*g = g1;
*b = b1;
return 0;
}
void convert_nv21_to_matrix(uint8_t *nv21, float *matrix, int width, int height,
int targetWidth, int targetHeight, float *means) {
const uint8_t *yData = nv21;
const uint8_t *vuData = nv21 + width * height;
const int yRowStride = width;
const int vuRowStride = width;
float scale_x = width * 1.0 / targetWidth;
float scale_y = height * 1.0 / targetHeight;
for (int j = 0; j < targetHeight; ++j) {
int y = j * scale_y;
const uint8_t *pY = yData + y * yRowStride;
const uint8_t *pVU = vuData + (y >> 1) * vuRowStride;
for (int i = 0; i < targetWidth; ++i) {
int x = i * scale_x;
const int offset = ((x >> 1) << 1);
float r = 0;
float g = 0;
float b = 0;
yuv_to_rgb(pY[x], pVU[offset + 1], pVU[offset], &r, &g, &b);
int r_index = j * targetWidth + i;
int g_index = r_index + targetWidth * targetHeight;
int b_index = g_index + targetWidth * targetHeight;
matrix[r_index] = r - means[0];
matrix[g_index] = g - means[1];
matrix[b_index] = b - means[2];
}
}
}
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
JNIEnv *env, jclass thiz, jbyteArray yuv_, jint imgwidth, jint imgHeight,
jintArray ddims, jfloatArray meanValues) {
ANDROIDLOGI("predictYuv invoked");
jsize ddim_size = env->GetArrayLength(ddims);
if (ddim_size != 4) {
ANDROIDLOGE("ddims size not equal to 4");
}
jint *ddim_ptr = env->GetIntArrayElements(ddims, NULL);
framework::DDim ddim = framework::make_ddim(
{ddim_ptr[0], ddim_ptr[1], ddim_ptr[2], ddim_ptr[3]});
int length = framework::product(ddim);
float matrix[length];
jbyte *yuv = env->GetByteArrayElements(yuv_, NULL);
float *meansPointer = nullptr;
if (nullptr != meanValues) {
meansPointer = env->GetFloatArrayElements(meanValues, NULL);
}
convert_nv21_to_matrix((uint8_t *)yuv, matrix, imgwidth, imgHeight, ddim[3],
ddim[2], meansPointer);
jfloatArray result = NULL;
int count = 0;
framework::Tensor input;
input.Resize(ddim);
auto input_ptr = input.mutable_data<float>();
for (int i = 0; i < length; i++) {
input_ptr[i] = matrix[i];
}
auto output = shared_paddle_mobile_instance->Predict(input);
count = output->numel();
result = env->NewFloatArray(count);
env->SetFloatArrayRegion(result, 0, count, output->data<float>());
env->ReleaseByteArrayElements(yuv_, yuv, 0);
env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
env->ReleaseFloatArrayElements(meanValues, meansPointer, 0);
ANDROIDLOGI("predictYuv finished");
return result;
}
......
src/jni/paddle_mobile_jni.h
浏览文件 @ 2b9ea6ea
......@@ -22,11 +22,29 @@ extern "C" {
namespace paddle_mobile {
namespace jni {
/**
* load model & params of the net for android
* load separated model for android
*/
JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
jclass thiz,
jstring modelPath);
/**
* load combined model for android
*/
JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_loadCombined(
JNIEnv *env, jclass thiz, jstring modelPath, jstring paramPath);
/**
* object detection for anroid
*/
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
JNIEnv *env, jclass thiz, jfloatArray buf, jintArray ddims);
/**
* object detection for anroid
*/
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
JNIEnv *env, jclass thiz, jbyteArray yuv, jint imgwidth, jint imgHeight,
jintArray ddims, jfloatArray meanValues);
/**
* object detection for anroid
......
src/operators/batchnorm_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -26,7 +26,7 @@ void BatchNormOp<Dtype, T>::InferShape() const {
auto x_dims = this->param_.InputX()->dims();
this->param_.OutputY()->Resize(x_dims);
}
template class BatchNormOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/box_coder_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -47,7 +47,7 @@ void BoxCoderOp<Dtype, T>::InferShape() const {
this->param_.OutputBox()->Resize(framework::make_ddim(
{input_targetbox_dims[0], input_priorbox_dims[0], 4}));
}
template class BoxCoderOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/concat_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -56,7 +56,6 @@ void ConcatOp<Dtype, T>::InferShape() const {
this->param_.Out()->Resize(out_dims);
}
template class ConcatOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/conv_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -48,8 +48,6 @@ void ConvOp<Dtype, T>::InferShape() const {
this->param_.Output()->Resize(ddim);
}
template class ConvOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/depthwise_conv_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -49,8 +49,6 @@ void DepthwiseConvOp<Dtype, T>::InferShape() const {
this->param_.Output()->Resize(ddim);
}
template class DepthwiseConvOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/dropout_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -22,7 +22,7 @@ void DropoutOp<Dtype, T>::InferShape() const {
auto input_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(input_dims);
}
template class DropoutOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/elementwise_add_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -24,7 +24,7 @@ void ElementwiseAddOp<Dtype, T>::InferShape() const {
auto x_dim = this->param_.InputX()->dims();
this->param_.Out()->Resize(x_dim);
}
template class ElementwiseAddOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/feed_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -14,10 +14,7 @@ limitations under the License. */
#include "feed_op.h"
namespace paddle_mobile {
namespace operators {
template class FeedOp<CPU, float>;
}
namespace operators {}
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
......
src/operators/fetch_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -14,10 +14,7 @@ limitations under the License. */
#include "fetch_op.h"
namespace paddle_mobile {
namespace operators {
template class FetchOp<CPU, float>;
}
namespace operators {}
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
......
src/operators/fusion_conv_add.cpp
浏览文件 @ 2b9ea6ea
......@@ -45,7 +45,6 @@ void FusionConvAddOp<Dtype, T>::InferShape() const {
this->param_.Output()->Resize(ddim);
}
template class FusionConvAddOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/fusion_conv_add.h
浏览文件 @ 2b9ea6ea
......@@ -36,8 +36,6 @@ class FusionConvAddMatcher : public framework::FusionOpMatcher {
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
vector<std::shared_ptr<framework::OpDesc>> origin_descs =
node->OpDescs(node_.Depth());
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}}}, removed_nodes);
}
......
src/operators/fusion_conv_add_bn_relu_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -44,7 +44,7 @@ void FusionConvAddBNReluOp<Dtype, T>::InferShape() const {
framework::DDim ddim = framework::make_ddim(output_shape);
this->param_.Output()->Resize(ddim);
}
template class FusionConvAddBNReluOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/fusion_conv_add_bn_relu_op.h
浏览文件 @ 2b9ea6ea
......@@ -39,8 +39,6 @@ class FusionConvAddBNReluMatcher : public framework::FusionOpMatcher {
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
vector<std::shared_ptr<framework::OpDesc>> origin_descs =
node->OpDescs(node_.Depth());
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}},
{G_OP_TYPE_BATCHNORM,
......@@ -79,11 +77,11 @@ class FusionConvAddBNReluOp
#ifdef PADDLE_MOBILE_CPU
//#ifndef FUSION_CONV_ADD_BN_RELU_REGISTER
// static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
// new FusionConvAddBNReluMatcher());
//#define FUSION_CONV_ADD_BN_RELU_REGISTER
//#endif
#ifndef FUSION_CONV_ADD_BN_RELU_REGISTER
static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
new FusionConvAddBNReluMatcher());
#define FUSION_CONV_ADD_BN_RELU_REGISTER
#endif
#endif
......
src/operators/fusion_conv_bn_relu_op.cpp 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_CONVBNRELU_OP
#include "operators/fusion_conv_bn_relu_op.h"
#include "operators/math/conv_func.h"
namespace paddle_mobile {
namespace operators {
template <typename Dtype, typename T>
void FusionConvBNReluOp<Dtype, T>::InferShape() const {
auto in_dims = this->param_.Input()->dims();
auto filter_dims = this->param_.Filter()->dims();
const std::vector<int> &strides = this->param_.Strides();
std::vector<int> paddings = this->param_.Paddings();
int groups = this->param_.Groups();
std::vector<int> dilations = this->param_.Dilations();
PADDLE_MOBILE_ENFORCE((in_dims.size() == filter_dims.size() &&
dilations.size() == paddings.size() &&
paddings.size() == strides.size()),
"ConvParam is not suitable");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(
math::ConvOutputSize(in_dims[i + 2], filter_dims[i + 2], dilations[i],
paddings[i], strides[i]));
}
framework::DDim ddim = framework::make_ddim(output_shape);
this->param_.Output()->Resize(ddim);
}
} // namespace operators
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU(fusion_conv_bn_relu, ops::FusionConvBNReluOp);
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
#endif
src/operators/fusion_conv_bn_relu_op.h 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_CONVBNRELU_OP
#pragma once
#include <string>
#include <vector>
#include "framework/operator.h"
#include "framework/program/program-optimize/fusion_op_register.h"
#include "operators/kernel/conv_bn_relu_kernel.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
using std::string;
using std::vector;
class FusionConvBNReluMatcher : public framework::FusionOpMatcher {
public:
FusionConvBNReluMatcher() {
node_ = framework::Node(G_OP_TYPE_CONV);
node_ > std::make_shared<framework::Node>(G_OP_TYPE_BATCHNORM) >
std::make_shared<framework::Node>(G_OP_TYPE_RELU);
}
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_BATCHNORM,
{{"Scale", "Scale"},
{"Mean", "Mean"},
{"Bias", "Bias"},
{"Variance", "Variance"}}}},
removed_nodes);
}
std::string Type() { return G_OP_TYPE_FUSION_CONV_BN_RELU; }
};
template <typename DeviceType, typename T>
class FusionConvBNReluOp : public framework::OperatorWithKernel<
DeviceType, FusionConvBNReluParam,
operators::ConvBNReluKernel<DeviceType, T>> {
public:
FusionConvBNReluOp(const string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<
DeviceType, FusionConvBNReluParam,
operators::ConvBNReluKernel<DeviceType, T>>(type, inputs, outputs,
attrs, scope) {}
using framework::OperatorWithKernel<
DeviceType, FusionConvBNReluParam,
operators::ConvBNReluKernel<DeviceType, T>>::OperatorWithKernel;
void InferShape() const override;
protected:
};
#ifdef PADDLE_MOBILE_CPU
#ifndef FUSION_CONV_BN_RELU_REGISTER
static framework::FusionOpRegistrar fusion_conv_bn_relu_registrar(
new FusionConvBNReluMatcher());
#define FUSION_CONV_BN_RELU_REGISTER
#endif
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
} // namespace operators
} // namespace paddle_mobile
#ifdef PADDLE_MOBILE_CPU
USE_OP_CPU(fusion_conv_bn_relu);
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
#endif
src/operators/fusion_dwconv_bn_relu_op.cpp 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_DWCONVBNRELU_OP
#include "operators/fusion_dwconv_bn_relu_op.h"
#include "operators/math/conv_func.h"
namespace paddle_mobile {
namespace operators {
template <typename Dtype, typename T>
void FusionDWConvBNReluOp<Dtype, T>::InferShape() const {
auto in_dims = this->param_.Input()->dims();
auto filter_dims = this->param_.Filter()->dims();
const std::vector<int> &strides = this->param_.Strides();
std::vector<int> paddings = this->param_.Paddings();
int groups = this->param_.Groups();
std::vector<int> dilations = this->param_.Dilations();
PADDLE_MOBILE_ENFORCE((in_dims.size() == filter_dims.size() &&
dilations.size() == paddings.size() &&
paddings.size() == strides.size()),
"ConvParam is not suitable");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(
math::ConvOutputSize(in_dims[i + 2], filter_dims[i + 2], dilations[i],
paddings[i], strides[i]));
}
framework::DDim ddim = framework::make_ddim(output_shape);
this->param_.Output()->Resize(ddim);
}
} // namespace operators
} // namespace paddle_mobile
namespace ops = paddle_mobile::operators;
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU(fusion_dwconv_bn_relu, ops::FusionDWConvBNReluOp);
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
#endif
src/operators/fusion_dwconv_bn_relu_op.h 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_DWCONVBNRELU_OP
#pragma once
#include <string>
#include <vector>
#include "framework/operator.h"
#include "framework/program/program-optimize/fusion_op_register.h"
#include "op_param.h"
#include "operators/kernel/dwconv_bn_relu_kernel.h"
namespace paddle_mobile {
namespace operators {
using std::string;
using std::vector;
class FusionDWConvBNReluMatcher : public framework::FusionOpMatcher {
public:
FusionDWConvBNReluMatcher() {
node_ = framework::Node(G_OP_TYPE_DEPTHWISE_CONV);
node_ > std::make_shared<framework::Node>(G_OP_TYPE_BATCHNORM) >
std::make_shared<framework::Node>(G_OP_TYPE_RELU);
}
void FolderNodes(
framework::Node *node,
std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
node->Folder(node_.Depth(), Type(),
{{G_OP_TYPE_BATCHNORM,
{{"Scale", "Scale"},
{"Mean", "Mean"},
{"Bias", "Bias"},
{"Variance", "Variance"}}}},
removed_nodes);
}
std::string Type() { return G_OP_TYPE_FUSION_DWCONV_BN_RELU; }
};
template <typename DeviceType, typename T>
class FusionDWConvBNReluOp : public framework::OperatorWithKernel<
DeviceType, FusionDWConvBNReluParam,
operators::DWConvBNReluKernel<DeviceType, T>> {
public:
FusionDWConvBNReluOp(const string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<
DeviceType, FusionDWConvBNReluParam,
operators::DWConvBNReluKernel<DeviceType, T>>(type, inputs, outputs,
attrs, scope) {}
using framework::OperatorWithKernel<
DeviceType, FusionDWConvBNReluParam,
operators::DWConvBNReluKernel<DeviceType, T>>::OperatorWithKernel;
void InferShape() const override;
protected:
};
#ifdef PADDLE_MOBILE_CPU
#ifndef FUSION_DWCONV_BN_RELU_REGISTER
static framework::FusionOpRegistrar fusion_dwconv_bn_relu_registrar(
new FusionDWConvBNReluMatcher());
#define FUSION_DWCONV_BN_RELU_REGISTER
#endif
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#ifndef FUSION_DWCONV_BN_RELU_REGISTER
static framework::FusionOpRegistrar fusion_dwconv_bn_relu_registrar(
new FusionDWConvBNReluMatcher());
#define FUSION_DWCONV_BN_RELU_REGISTER
#endif
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
} // namespace operators
} // namespace paddle_mobile
#ifdef PADDLE_MOBILE_CPU
USE_OP_CPU(fusion_dwconv_bn_relu);
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
#endif
src/operators/fusion_fc_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -50,7 +50,6 @@ void FusionFcOp<Dtype, T>::InferShape() const {
this->param_.Out()->Resize(ddim);
}
template class FusionFcOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/im2sequence_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -47,8 +47,6 @@ void Im2SequenceOp<Dtype, T>::InferShape() const {
this->param_.Output()->Resize(ddim);
}
template class Im2SequenceOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/kernel/arm/conv_add_bn_relu_kernel.cpp
浏览文件 @ 2b9ea6ea
......@@ -15,7 +15,7 @@ limitations under the License. */
#ifdef FUSION_CONVADDBNRELU_OP
#include "operators/kernel/conv_add_bn_relu_kernel.h"
#include "operators/kernel/central-arm-func/conv_add_bn_relu_func.h"
#include "operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h"
namespace paddle_mobile {
namespace operators {
......
src/operators/kernel/arm/conv_bn_relu_kernel.cpp 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_CONVBNRELU_OP
#include "operators/kernel/conv_bn_relu_kernel.h"
#include "operators/kernel/central-arm-func/conv_bn_relu_arm_func.h"
namespace paddle_mobile {
namespace operators {
template <>
bool ConvBNReluKernel<CPU, float>::Init(FusionConvBNReluParam *param) {
const Tensor *mean = param->InputMean();
const Tensor *variance = param->InputVariance();
const Tensor *scale = param->InputScale();
const Tensor *bias = param->InputBias();
const float epsilon = param->Epsilon();
// DLOG << "variance: " << *variance;
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
const int C = mean->numel();
float inv_std_ptr[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor *new_scale = new Tensor();
Tensor *new_bias = new Tensor();
auto new_scale_ptr = new_scale->mutable_data<float>({C});
auto new_bias_ptr = new_bias->mutable_data<float>({C});
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
}
param->SetNewScale(new_scale);
param->SetNewBias(new_bias);
return true;
}
template <>
void ConvBNReluKernel<CPU, float>::Compute(
const FusionConvBNReluParam &param) const {
ConvBNReluCompute<float>(param);
}
template class ConvBNReluKernel<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/arm/dwconv_bn_relu_kernel.cpp 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_DWCONVBNRELU_OP
#include "operators/kernel/dwconv_bn_relu_kernel.h"
#include "operators/kernel/central-arm-func/dwconv_bn_relu_arm_func.h"
namespace paddle_mobile {
namespace operators {
template <>
bool DWConvBNReluKernel<CPU, float>::Init(FusionDWConvBNReluParam *param) {
const Tensor *mean = param->InputMean();
const Tensor *variance = param->InputVariance();
const Tensor *scale = param->InputScale();
const Tensor *bias = param->InputBias();
const float epsilon = param->Epsilon();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
const int C = mean->numel();
float inv_std_ptr[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor *new_scale = new Tensor();
Tensor *new_bias = new Tensor();
auto new_scale_ptr = new_scale->mutable_data<float>({C});
auto new_bias_ptr = new_bias->mutable_data<float>({C});
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
}
param->SetNewScale(new_scale);
param->SetNewBias(new_bias);
return true;
}
template <>
void DWConvBNReluKernel<CPU, float>::Compute(
const FusionDWConvBNReluParam &param) const {
DWConvBNReluCompute<float>(param);
}
template class DWConvBNReluKernel<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/arm/sigmoid_kernel.cpp
浏览文件 @ 2b9ea6ea
......@@ -16,7 +16,7 @@ limitations under the License. */
#include "../sigmoid_kernel.h"
#include "../central-arm-func/sigmoid_arm_func.h"
#if __ARM_NEON
#ifdef __ARM_NEON
#include "../../math/math_func_neon.h"
#endif
#include <cmath>
......
src/operators/kernel/central-arm-func/batchnorm_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -54,7 +54,40 @@ void BatchnormCompute(const BatchNormParam &param) {
int HXW = H * W;
#ifdef ARMV7
#if __ARM_NEON
#if __aarch64__
float *inv_std_ptr = new float[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor new_scale;
auto new_scale_ptr = new_scale.mutable_data<float>(framework::make_ddim({C}));
Tensor new_bias;
auto new_bias_ptr = new_bias.mutable_data<float>(framework::make_ddim({C}));
/// ((x - est_mean) * (inv_var) * scale + bias equal to
/// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
{
for (int n = 0; n < N; n++) {
for (int h = 0; h < H; h++) {
int tmp_index = n * stride0 + i * stride1 + h * stride2;
for (int w = 0; w < W; w++) {
int index = tmp_index + w;
out_ptr[index] =
input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i];
}
}
}
}
}
delete[] inv_std_ptr;
#else
if (HXW > 32) {
int NXC = N * C;
float *inv_std_ptr = new float[NXC * 4];
......@@ -229,6 +262,7 @@ void BatchnormCompute(const BatchNormParam &param) {
delete[] inv_std_ptr;
}
#endif
#else
float *inv_std_ptr = new float[C];
for (int i = 0; i < C; i++) {
......
src/operators/kernel/central-arm-func/conv_add_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -124,9 +124,15 @@ void ConvAddCompute(const FusionConvAddParam &param) {
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3) {
math::DepthwiseConv3x3(param.Input(), param.Strides(), param.Paddings(),
param.Filter(), param.Bias(), param.Output(), true);
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
// math::DepthwiseConv3x3(param.Input(), param.Strides(),
// param.Paddings(),
// param.Filter(), param.Bias(),
// param.Output(), false);
math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(), param.Output(),
*param.Bias(), true);
} else {
ConvAddBasic(param);
}
......
src/operators/kernel/central-arm-func/conv_add_bn_relu_func.h → src/operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -15,6 +15,8 @@ limitations under the License. */
#ifdef FUSION_CONVADDBNRELU_OP
#pragma once
#include <vector>
#include "operators/math/depthwise_conv_3x3.h"
#include "operators/op_param.h"
......@@ -23,16 +25,9 @@ namespace operators {
void ConvAddBNReluBasic(const FusionConvAddBNReluParam &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor bias = *param.Bias();
Tensor new_bias = *param.NewBias();
Tensor new_scale = *param.NewScale();
auto new_bias_ptr = new_bias.data<float>();
auto new_scale_ptr = new_scale.data<float>();
int axis = param.Axis();
Tensor *output = param.Output();
math::expand_bias(bias, axis, output->dims());
output->ShareDataWith(bias);
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
......@@ -106,20 +101,10 @@ void ConvAddBNReluBasic(const FusionConvAddBNReluParam &param) {
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
static_cast<float>(1), &out_slice,
static_cast<float>(0));
}
}
/// todo : use neon in special case instead of 2for(300ms)
auto output_ptr = output->data<float>();
for (int c = 0; c < output_matrix_shape[0]; c++) {
int start = c * output_matrix_shape[1];
for (int j = 0; j < output_matrix_shape[1]; j++) {
output_ptr[start + j] =
output_ptr[start + j] * new_scale_ptr[c] + new_bias_ptr[c];
output_ptr[start + j] =
output_ptr[start + j] < 0 ? 0 : output_ptr[start + j];
math::matmulWithBn<float>(
filter_slice, false, col_matrix, false, static_cast<float>(1),
&out_slice, static_cast<float>(0), true, &new_scale, &new_bias, g);
}
}
}
......@@ -133,14 +118,17 @@ void ConvAddBNReluCompute(const FusionConvAddBNReluParam &param) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 1);
param.NewBias(), true);
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 1);
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
math::DepthwiseConvAddBNRelu3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), true);
} else {
ConvAddBNReluBasic(param);
}
......
src/operators/kernel/central-arm-func/conv_bn_relu_arm_func.h 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_CONVBNRELU_OP
#pragma once
#include <vector>
#include "operators/math/depthwise_conv_3x3.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
void ConvBNReluBasic(const FusionConvBNReluParam &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor new_bias = *param.NewBias();
Tensor new_scale = *param.NewScale();
Tensor *output = param.Output();
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand =
math::IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmulWithBn<float>(
filter_slice, false, col_matrix, false, static_cast<float>(1),
&out_slice, static_cast<float>(0), true, &new_scale, &new_bias, g);
}
}
}
template <typename P>
void ConvBNReluCompute(const FusionConvBNReluParam &param) {
if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), true);
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
math::DepthwiseConvAddBNRelu3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), true);
} else {
ConvBNReluBasic(param);
}
}
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/central-arm-func/depthwise_conv_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -37,8 +37,12 @@ void DepthwiseConvCompute(const ConvParam &param) {
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
math::DepthwiseConv3x3(param.Input(), param.Strides(), param.Paddings(),
param.Filter(), &Bias, param.Output(), false);
// math::DepthwiseConv3x3(param.Input(), param.Strides(),
// param.Paddings(),
// param.Filter(), &Bias, param.Output(), false);
math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(), param.Output(),
Bias, false);
} else {
ConvBasic(param);
}
......
src/operators/kernel/central-arm-func/dwconv_bn_relu_arm_func.h 0 → 100644
浏览文件 @ 2b9ea6ea
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef FUSION_DWCONVBNRELU_OP
#pragma once
#include <vector>
#include "operators/math/depthwise_conv_3x3.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
void DWConvBNReluBasic(const FusionDWConvBNReluParam &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor new_bias = *param.NewBias();
Tensor new_scale = *param.NewScale();
Tensor *output = param.Output();
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand =
math::IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmulWithBn<float>(
filter_slice, false, col_matrix, false, static_cast<float>(1),
&out_slice, static_cast<float>(0), true, &new_scale, &new_bias, g);
}
}
}
template <typename P>
void DWConvBNReluCompute(const FusionDWConvBNReluParam &param) {
if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), true);
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
math::DepthwiseConvAddBNRelu3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), true);
} else {
DWConvBNReluBasic(param);
}
}
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/central-arm-func/pool_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -76,15 +76,20 @@ void PoolCompute(const PoolParam &param) {
}
} else if (ksize[0] == 2 && ksize[0] == ksize[1]) {
#ifndef IOS
#if __ARM_NEON
#if __aarch64__
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
#else
if (pooling_type == "max") {
math::Pool2x2Max(strides, paddings, in_x, out);
} else if (pooling_type == "avg") {
math::Pool2x2Avg(strides, paddings, in_x, out);
}
#endif
#else
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
#endif
#endif // __ARM_NEON
} else {
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
}
......
src/operators/kernel/central-arm-func/sigmoid_arm_func.h
浏览文件 @ 2b9ea6ea
......@@ -14,8 +14,10 @@ limitations under the License. */
#ifdef SIGMOID_OP
#pragma once
#include <cmath>
#include "operators/op_param.h"
#if __ARM_NEON
#ifdef __ARM_NEON
#include <arm_neon.h>
#include "operators/math/math_func_neon.h"
#endif
......@@ -24,7 +26,7 @@ namespace paddle_mobile {
namespace operators {
using framework::DDim;
void sigmoid(const Tensor *X, Tensor *Y) {
#if __ARM_NEON
#ifdef __ARM_NEON
const float *input = X->data<float>();
float *output = Y->mutable_data<float>();
const DDim &dDim = X->dims();
......@@ -66,6 +68,7 @@ void sigmoid(const Tensor *X, Tensor *Y) {
input_outer_ptr++;
}
}
#else
#endif
}
......
src/operators/kernel/conv_add_kernel.h
浏览文件 @ 2b9ea6ea
......@@ -17,7 +17,7 @@ limitations under the License. */
#pragma once
#include <vector>
#if __ARM_NEON
#ifdef __ARM_NEON
#include <arm_neon.h>
#endif
#include "common/common.h"
......
demo/ios/PaddleMobileDemo/PaddleMobileDemo/AppDelegate.h → src/operators/kernel/conv_bn_relu_kernel.h
浏览文件 @ 2b9ea6ea
......@@ -12,12 +12,34 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <UIKit/UIKit.h>
#pragma once
@interface AppDelegate : UIResponder <UIApplicationDelegate>
#ifdef FUSION_CONVBNRELU_OP
@property (strong, nonatomic) UIWindow *window;
#include <vector>
#include "framework/ddim.h"
#include "framework/operator.h"
#include "operators/math/conv_func.h"
#include "operators/math/im2col.h"
#include "operators/math/math_function.h"
#include "operators/math/vol2col.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
@end
using framework::DDim;
using framework::OpKernelBase;
template <typename DeviceType, typename T>
class ConvBNReluKernel
: public OpKernelBase<DeviceType, FusionConvBNReluParam> {
public:
void Compute(const FusionConvBNReluParam &param) const;
bool Init(FusionConvBNReluParam *param);
};
} // namespace operators
} // namespace paddle_mobile
#endif
demo/ios/PaddleMobileDemo/PaddleMobileDemo/ViewController.h → src/operators/kernel/dwconv_bn_relu_kernel.h
浏览文件 @ 2b9ea6ea
......@@ -12,10 +12,34 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#import <UIKit/UIKit.h>
#pragma once
@interface ViewController : UIViewController
#ifdef FUSION_DWCONVBNRELU_OP
#include <vector>
#include "framework/ddim.h"
#include "framework/operator.h"
#include "operators/math/conv_func.h"
#include "operators/math/im2col.h"
#include "operators/math/math_function.h"
#include "operators/math/vol2col.h"
#include "operators/op_param.h"
@end
namespace paddle_mobile {
namespace operators {
using framework::DDim;
using framework::OpKernelBase;
template <typename DeviceType, typename T>
class DWConvBNReluKernel
: public OpKernelBase<DeviceType, FusionDWConvBNReluParam> {
public:
void Compute(const FusionDWConvBNReluParam &param) const;
bool Init(FusionDWConvBNReluParam *param);
};
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/lrn_kernel.h
浏览文件 @ 2b9ea6ea
......@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#ifdef LRN_OP
#ifdef _OPENMP
#include <omp.h>
#endif
#include "framework/operator.h"
#include "operators/op_param.h"
......@@ -47,6 +49,7 @@ struct LRNFunctor {
std::fill(sqr_buffer_ptr, sqr_buffer_ptr + sqr_buffer.numel(), 0.0);
for (int a = 0; a < N; a++) {
#pragma parallel for
for (int b = 0; b < C; b++) {
for (int index = start; index < end; index++) {
int channel = b + index;
......
src/operators/lrn_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -24,7 +24,7 @@ void LrnOp<Dtype, T>::InferShape() const {
auto x_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(x_dims);
}
template class LrnOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/conv_func.h
浏览文件 @ 2b9ea6ea
......@@ -14,7 +14,7 @@ limitations under the License. */
#pragma once
#if __ARM_NEON
#ifdef __ARM_NEON
#include <arm_neon.h>
#endif
......@@ -49,7 +49,7 @@ inline void expand_bias(Tensor &bias, int axis, const DDim &dDim) {
auto new_ptr = bias.mutable_data<float>();
int axis_size = dDim[axis];
#if __ARM_NEON
#ifdef __ARM_NEON
for (int i = 0; i < outer_size; ++i) {
int inner_num = inner_size >> 4;
int remain = inner_size - (inner_num << 4);
......
src/operators/math/depthwise_conv_3x3.cpp
浏览文件 @ 2b9ea6ea
......@@ -12,7 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "operators/math/depthwise_conv_3x3.h"
#if __ARM_NEON
#include <arm_neon.h>
#endif
#include <vector>
namespace paddle_mobile {
......@@ -21,7 +23,6 @@ namespace math {
void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
vector<int> paddings, const Tensor *filter, Tensor *bias,
Tensor *output, bool if_bias) {
#if __ARM_NEON
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
......@@ -179,7 +180,27 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
}
} else {
#if defined(ARMV17)
#if __ARM_NEON
#if __aarch64__
const float32x4_t data1 = vld1q_f32(pos1);
const float32x4_t data2 = vld1q_f32(pos2);
const float32x4_t data3 = vld1q_f32(pos3);
const float32x4_t v_filter1 = vld1q_f32(filter1);
const float32x4_t v_filter2 = vld1q_f32(filter2);
const float32x4_t v_filter3 = vld1q_f32(filter3);
float32x4_t mula = vmulq_f32(data1, v_filter1);
mula = vmlaq_f32(mula, data2, v_filter2);
mula = vmlaq_f32(mula, data3, v_filter3);
float32x2_t res = vpadd_f32(
vget_high_f32(vsetq_lane_f32(0, mula, 3)), vget_low_f32(mula));
res = vpadd_f32(res, res);
if (if_bias) {
output_data[ph * output_width + pw] += vget_lane_f32(res, 0);
} else {
output_data[ph * output_width + pw] = vget_lane_f32(res, 0);
}
#else
asm volatile(
"vld1.32 {q1}, [%[pos1]] \n\t"
......@@ -207,26 +228,10 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
[filter2] "r"(filter2), [filter3] "r"(filter3),
[output_ptr] "r"(output_ptr), [zero] "r"(zero)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6");
#endif // __aarch64__
#else
const float32x4_t data1 = vld1q_f32(pos1);
const float32x4_t data2 = vld1q_f32(pos2);
const float32x4_t data3 = vld1q_f32(pos3);
const float32x4_t v_filter1 = vld1q_f32(filter1);
const float32x4_t v_filter2 = vld1q_f32(filter2);
const float32x4_t v_filter3 = vld1q_f32(filter3);
float32x4_t mula = vmulq_f32(data1, v_filter1);
mula = vmlaq_f32(mula, data2, v_filter2);
mula = vmlaq_f32(mula, data3, v_filter3);
float32x2_t res = vpadd_f32(
vget_high_f32(vsetq_lane_f32(0, mula, 3)), vget_low_f32(mula));
res = vpadd_f32(res, res);
if (if_bias) {
output_data[ph * output_width + pw] += vget_lane_f32(res, 0);
} else {
output_data[ph * output_width + pw] = vget_lane_f32(res, 0);
}
#endif
#endif // __ARM_NEON
}
}
}
......@@ -237,11 +242,11 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#endif
}
void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor *bias, bool if_bias) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
float *output_data = output->data<float>();
......@@ -511,11 +516,13 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
filter_data_tmp += 9;
}
}
#endif
}
void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
float *output_data = output->data<float>();
......@@ -813,11 +820,14 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
filter_data_tmp += 9;
}
}
#endif
}
void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu) {
#if __ARM_NEON
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
......@@ -1009,7 +1019,448 @@ void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#endif
}
void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor bias, bool if_bias) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
float *output_data = output->data<float>();
const float *bias_data = bias.data<float>();
const int in_h = static_cast<int>(input->dims()[2]);
const int in_w = static_cast<int>(input->dims()[3]);
const int out_h = static_cast<int>(output->dims()[2]);
const int out_w = static_cast<int>(output->dims()[3]);
const int out_l = out_h;
const int in_l = in_h;
const int inhxw = in_h * in_w;
const int outhxw = out_h * out_w;
const int if_pad = in_l - 1 == (out_l - 1) * 2 ? 1 : 0;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const float *input_row_ptr;
float *output_row_ptr;
const int w_times = (out_w - 2) / 3;
float32x4_t vbias = vdupq_n_f32(0.0);
float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1];
float32x4_t elewise_res0, elewise_res1, elewise_res2, res3;
int out2in_mid;
float32x4_t zero = vdupq_n_f32(0.0);
for (int b = batch_size; b > 0; --b) {
const float *filter_data_tmp = filter_data;
for (int j = 0; j < c; ++j) {
auto output_data_tmp = output_data + j * out_h * out_w;
auto input_data_tmp = input_data + j * in_h * in_w;
auto input_const = input_data_tmp;
if (if_bias) {
vbias = vdupq_n_f32(bias_data[j]);
}
float w00 = filter_data_tmp[0];
float w01 = filter_data_tmp[1];
float w02 = filter_data_tmp[2];
float w10 = filter_data_tmp[3];
float w11 = filter_data_tmp[4];
float w12 = filter_data_tmp[5];
float w20 = filter_data_tmp[6];
float w21 = filter_data_tmp[7];
float w22 = filter_data_tmp[8];
int h_mid = 0;
for (; h_mid < out_h - 1; h_mid++) {
input_row_ptr = input_data_tmp + 1 + h_mid * 2 * in_w;
output_row_ptr = output_data_tmp + 1 + h_mid * out_w;
for (int w4 = 0; w4 < w_times + 1; w4++) {
if (h_mid == 0) {
elewise_res1 = zero;
elewise_res0 = zero;
elewise_res2 = zero;
} else {
elewise_res1 = vmulq_n_f32(input_buff_bottom[w4].val[1], w01);
elewise_res0 = vmulq_n_f32(input_buff_bottom[w4].val[0], w00);
elewise_res2 = vmulq_n_f32(input_buff_bottom[w4].val[0], w02);
}
input_buff_mid = vld2q_f32(input_row_ptr);
input_buff_bottom[w4] = vld2q_f32(input_row_ptr + in_w);
elewise_res1 = vmlaq_n_f32(elewise_res1, input_buff_mid.val[1], w11);
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
vmlaq_n_f32(elewise_res0, input_buff_bottom[w4].val[0], w20);
elewise_res2 =
vmlaq_n_f32(elewise_res2, input_buff_bottom[w4].val[0], w22);
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vaddq_f32(res3, vbias);
vst1q_f32(output_row_ptr, res3);
input_row_ptr += 6;
output_row_ptr += 3;
}
}
clock();
input_row_ptr = input_data_tmp + 1 + h_mid * 2 * in_w;
output_row_ptr = output_data_tmp + 1 + h_mid * out_w;
for (int w4 = 0; w4 < w_times + 1; w4++) {
elewise_res1 = vmulq_n_f32(input_buff_bottom[w4].val[1], w01);
elewise_res0 = vmulq_n_f32(input_buff_bottom[w4].val[0], w00);
elewise_res2 = vmulq_n_f32(input_buff_bottom[w4].val[0], w02);
input_buff_mid = vld2q_f32(input_row_ptr);
input_buff_bottom[w4] = vld2q_f32(input_row_ptr + in_w);
elewise_res1 = vmlaq_n_f32(elewise_res1, input_buff_mid.val[1], w11);
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
if (!if_pad) {
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
vmlaq_n_f32(elewise_res0, input_buff_bottom[w4].val[0], w20);
elewise_res2 =
vmlaq_n_f32(elewise_res2, input_buff_bottom[w4].val[0], w22);
}
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vaddq_f32(res3, vbias);
if ((w4 != w_times)) {
vst1q_f32(output_row_ptr, res3);
} else {
if (out_l - 2 - w_times * 3 == 1) {
vst1q_lane_f32(output_row_ptr, res3, 0);
} else if (out_l - 2 - w_times * 3 == 2) {
vst1q_lane_f32(output_row_ptr, res3, 0);
vst1q_lane_f32(output_row_ptr + 1, res3, 1);
}
}
input_row_ptr += 6;
output_row_ptr += 3;
}
output_data_tmp[0] = input_const[0] * w11 + input_const[1] * w12 +
input_const[in_l] * w21 +
input_const[in_l + 1] * w22;
out2in_mid = (out_l - 1) * 2;
output_data_tmp[out_l - 1] =
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_l - 1) * 2 * in_w;
output_data_tmp[out_l * (out_l - 1)] =
w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] + w12 * input_const[out2in_mid + 1] +
(1 - if_pad) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_l - 1) * 2 * in_w + (out_l - 1) * 2;
output_data_tmp[out_l * out_l - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
(1 - if_pad) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
if (if_bias) {
output_data_tmp[0] += bias_data[j];
output_data_tmp[out_l - 1] += bias_data[j];
output_data_tmp[out_l * (out_l - 1)] += bias_data[j];
output_data_tmp[out_l * out_l - 1] += bias_data[j];
}
for (int i = 1; i < out_h - 1; i++) {
out2in_mid = i * 2 * in_w;
output_data_tmp[i * out_l] = w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] +
w12 * input_const[out2in_mid + 1] +
w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1];
out2in_mid = i * 2 * in_w + (out_l - 1) * 2;
output_data_tmp[i * out_l + out_l - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
if (if_bias) {
output_data_tmp[i * out_l] += bias_data[j];
output_data_tmp[i * out_l + out_l - 1] += bias_data[j];
}
}
filter_data_tmp += 9;
}
input_data += inhxw * c;
output_data += outhxw * c;
}
#endif
}
void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
float *output_data = output->data<float>();
const float *newscale_data = new_scale->data<float>();
const float *newbias_data = new_bias->data<float>();
float32x4_t vnewbias = vdupq_n_f32(0.0);
float32x4_t vnewscale = vdupq_n_f32(1.0);
const int in_h = static_cast<int>(input->dims()[2]);
const int in_w = static_cast<int>(input->dims()[3]);
const int out_h = static_cast<int>(output->dims()[2]);
const int out_w = static_cast<int>(output->dims()[3]);
const int out_l = out_h;
const int in_l = in_h;
const int inhxw = in_h * in_w;
const int outhxw = out_h * out_w;
const int if_pad = in_l - 1 == (out_l - 1) * 2 ? 1 : 0;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const float *input_row_ptr;
float *output_row_ptr;
const int w_times = (out_w - 2) / 3;
float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1];
float32x4_t elewise_res0, elewise_res1, elewise_res2, res3;
int out2in_mid;
float32x4_t zero = vdupq_n_f32(0.0);
for (int b = batch_size; b > 0; --b) {
const float *filter_data_tmp = filter_data;
for (int j = 0; j < c; ++j) {
auto output_data_tmp = output_data + j * out_h * out_w;
auto input_data_tmp = input_data + j * in_h * in_w;
auto input_const = input_data_tmp;
vnewbias = vdupq_n_f32(newbias_data[j]);
vnewscale = vdupq_n_f32(newscale_data[j]);
float w00 = filter_data_tmp[0];
float w01 = filter_data_tmp[1];
float w02 = filter_data_tmp[2];
float w10 = filter_data_tmp[3];
float w11 = filter_data_tmp[4];
float w12 = filter_data_tmp[5];
float w20 = filter_data_tmp[6];
float w21 = filter_data_tmp[7];
float w22 = filter_data_tmp[8];
int h_mid = 0;
for (; h_mid < out_h - 1; h_mid++) {
input_row_ptr = input_data_tmp + 1 + h_mid * 2 * in_w;
output_row_ptr = output_data_tmp + 1 + h_mid * out_w;
for (int w4 = 0; w4 < w_times + 1; w4++) {
if (h_mid == 0) {
elewise_res1 = zero;
elewise_res0 = zero;
elewise_res2 = zero;
} else {
elewise_res1 = vmulq_n_f32(input_buff_bottom[w4].val[1], w01);
elewise_res0 = vmulq_n_f32(input_buff_bottom[w4].val[0], w00);
elewise_res2 = vmulq_n_f32(input_buff_bottom[w4].val[0], w02);
}
input_buff_mid = vld2q_f32(input_row_ptr);
input_buff_bottom[w4] = vld2q_f32(input_row_ptr + in_w);
elewise_res1 = vmlaq_n_f32(elewise_res1, input_buff_mid.val[1], w11);
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
vmlaq_n_f32(elewise_res0, input_buff_bottom[w4].val[0], w20);
elewise_res2 =
vmlaq_n_f32(elewise_res2, input_buff_bottom[w4].val[0], w22);
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vmlaq_f32(vnewbias, vnewscale, res3);
if (if_relu) {
res3 = vmaxq_f32(res3, zero);
}
vst1q_f32(output_row_ptr, res3);
input_row_ptr += 6;
output_row_ptr += 3;
}
}
clock();
input_row_ptr = input_data_tmp + 1 + h_mid * 2 * in_w;
output_row_ptr = output_data_tmp + 1 + h_mid * out_w;
for (int w4 = 0; w4 < w_times + 1; w4++) {
elewise_res1 = vmulq_n_f32(input_buff_bottom[w4].val[1], w01);
elewise_res0 = vmulq_n_f32(input_buff_bottom[w4].val[0], w00);
elewise_res2 = vmulq_n_f32(input_buff_bottom[w4].val[0], w02);
input_buff_mid = vld2q_f32(input_row_ptr);
input_buff_bottom[w4] = vld2q_f32(input_row_ptr + in_w);
elewise_res1 = vmlaq_n_f32(elewise_res1, input_buff_mid.val[1], w11);
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
if (!if_pad) {
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
vmlaq_n_f32(elewise_res0, input_buff_bottom[w4].val[0], w20);
elewise_res2 =
vmlaq_n_f32(elewise_res2, input_buff_bottom[w4].val[0], w22);
}
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vmlaq_f32(vnewbias, vnewscale, res3);
if (if_relu) {
res3 = vmaxq_f32(res3, zero);
}
if ((w4 != w_times)) {
vst1q_f32(output_row_ptr, res3);
} else {
if (out_l - 2 - w_times * 3 == 1) {
vst1q_lane_f32(output_row_ptr, res3, 0);
} else if (out_l - 2 - w_times * 3 == 2) {
vst1q_lane_f32(output_row_ptr, res3, 0);
vst1q_lane_f32(output_row_ptr + 1, res3, 1);
}
}
input_row_ptr += 6;
output_row_ptr += 3;
}
output_data_tmp[0] = input_const[0] * w11 + input_const[1] * w12 +
input_const[in_l] * w21 +
input_const[in_l + 1] * w22;
out2in_mid = (out_l - 1) * 2;
output_data_tmp[out_l - 1] =
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_l - 1) * 2 * in_w;
output_data_tmp[out_l * (out_l - 1)] =
w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] + w12 * input_const[out2in_mid + 1] +
(1 - if_pad) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_l - 1) * 2 * in_w + (out_l - 1) * 2;
output_data_tmp[out_l * out_l - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
(1 - if_pad) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
output_data_tmp[0] =
output_data_tmp[0] * newscale_data[j] + newbias_data[j];
output_data_tmp[out_l - 1] =
output_data_tmp[out_l - 1] * newscale_data[j] + newbias_data[j];
output_data_tmp[out_l * (out_l - 1)] =
output_data_tmp[out_l * (out_l - 1)] * newscale_data[j] +
newbias_data[j];
output_data_tmp[out_l * out_l - 1] =
output_data_tmp[out_l * out_l - 1] * newscale_data[j] +
newbias_data[j];
if (if_relu) {
output_data_tmp[0] = output_data_tmp[0] < 0 ? 0 : output_data_tmp[0];
output_data_tmp[out_l - 1] =
output_data_tmp[out_l - 1] < 0 ? 0 : output_data_tmp[out_l - 1];
output_data_tmp[out_l * (out_l - 1)] =
output_data_tmp[out_l * (out_l - 1)] < 0
? 0
: output_data_tmp[out_l * (out_l - 1)];
output_data_tmp[out_l * out_l - 1] =
output_data_tmp[out_l * out_l - 1] < 0
? 0
: output_data_tmp[out_l * out_l - 1];
}
for (int i = 1; i < out_h - 1; i++) {
out2in_mid = i * 2 * in_w;
output_data_tmp[i * out_l] = w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] +
w12 * input_const[out2in_mid + 1] +
w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1];
out2in_mid = i * 2 * in_w + (out_l - 1) * 2;
output_data_tmp[i * out_l + out_l - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
output_data_tmp[i * out_l] =
output_data_tmp[i * out_l] * newscale_data[j] + newbias_data[j];
output_data_tmp[i * out_l + out_l - 1] =
output_data_tmp[i * out_l + out_l - 1] * newscale_data[j] +
newbias_data[j];
if (if_relu) {
output_data_tmp[i * out_l] =
output_data_tmp[i * out_l] < 0 ? 0 : output_data_tmp[i * out_l];
output_data_tmp[i * out_l + out_l - 1] =
output_data_tmp[i * out_l + out_l - 1] < 0
? 0
: output_data_tmp[i * out_l + out_l - 1];
}
}
filter_data_tmp += 9;
}
input_data += inhxw * c;
output_data += outhxw * c;
}
#endif
}
} // namespace math
} // namespace operators
} // namespace paddle_mobile
src/operators/math/depthwise_conv_3x3.h
浏览文件 @ 2b9ea6ea
......@@ -38,6 +38,11 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu);
void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor bias, bool if_bias);
void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu);
} // namespace math
} // namespace operators
} // namespace paddle_mobile
src/operators/math/gemm.cpp
浏览文件 @ 2b9ea6ea
......@@ -15,16 +15,25 @@ limitations under the License. */
#include "operators/math/gemm.h"
#include "common/log.h"
#include "memory/t_malloc.h"
#ifndef X86
#if __ARM_NEON
#include <arm_neon.h>
#endif
#ifdef _OPENMP
#include <omp.h>
#endif
namespace paddle_mobile {
namespace operators {
namespace math {
alignas(64) float packedA[MC * KC];
alignas(64) float packedB[KC * NC];
alignas(64) float ab[MR * NR];
int MC = 0;
int KC = 0;
int NC = 0;
float *packedA;
float *packedB;
float *packedC;
float *zero;
/*
// 将A矩阵分块复制到连续内存(ColMajor)
void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
float *buffer) {
......@@ -52,35 +61,6 @@ void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
}
}
// 将A矩阵分块复制到连续内存(RowMajor)
void PackMatrixA_(int m, int k, int m_tail, const float *A, int lda,
float *buffer) {
int i, j;
const float *Ai, *Ai1, *Ai2, *Ai3;
for (i = 0; i < m - m_tail; i += MR) {
Ai = &A(i, 0);
Ai1 = &A(i + 1, 0);
Ai2 = &A(i + 2, 0);
Ai3 = &A(i + 3, 0);
for (int j = 0; j < k; ++j) {
*buffer++ = *Ai++;
*buffer++ = *Ai1++;
*buffer++ = *Ai2++;
*buffer++ = *Ai3++;
}
}
if (m_tail != 0) {
for (j = 0; j < k; ++j) {
for (i = m - m_tail; i < m; ++i) {
*buffer++ = A(i, j);
}
for (i = m; i < m + (MR - m_tail); ++i) {
*buffer++ = 0;
}
}
}
}
// 将B矩阵分块复制到连续内存(ColMajor)
void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
float *buffer) {
......@@ -109,39 +89,90 @@ void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
}
}
}
*/
// 将A矩阵分块复制到连续内存(RowMajor)
void PackMatrixA_(int m, int k, int m_tail, const float *A, int lda,
float *buffer) {
const float *a0, *a1, *a2, *a3;
for (int i = 0; i < m - m_tail; i += MR) {
a0 = A + i * lda;
a1 = A + (i + 1) * lda;
a2 = A + (i + 2) * lda;
a3 = A + (i + 3) * lda;
for (int j = 0; j < k; ++j) {
*buffer++ = *a0++;
*buffer++ = *a1++;
*buffer++ = *a2++;
*buffer++ = *a3++;
}
}
int i = m - m_tail;
a0 = &A(i, 0);
a1 = a0 + lda;
a2 = a0 + 2 * lda;
a3 = a0 + 3 * lda;
if (m_tail != 0) {
if (m_tail <= 3) {
a3 = zero;
}
if (m_tail <= 2) {
a2 = zero;
}
if (m_tail <= 1) {
a1 = zero;
}
for (int j = 0; j < k; ++j) {
*buffer++ = *a0++;
*buffer++ = *a1++;
*buffer++ = *a2++;
*buffer++ = *a3++;
}
}
}
// 将B矩阵分块复制到连续内存(RowMajor)
void PackMatrixB_(int k, int n, int n_tail, const float *B, int ldb,
float *buffer) {
int i, j;
const float *Bij;
for (j = 0; j < n - n_tail; j += NR) {
#ifdef ARMV7
for (i = 0; i < k; ++i) {
Bij = &B(i, j);
const float *b0;
for (int j = 0; j < n - n_tail; j += NR) {
for (int i = 0; i < k; ++i) {
b0 = &B(i, j);
#if __ARM_NEON
#if __aarch64__
asm volatile(
"vld1.32 {q0}, [%[Bij]] \n\t"
"vst1.32 {q0}, [%[buffer]]! \n\t"
"prfm pldl1keep, [%[b0]] \n\t"
"ld1 {v0.4s, v1.4s}, [%[b0]] \n\t"
"st1 {v0.4s, v1.4s}, [%[buffer]], #32 \n\t"
: [buffer] "+r"(buffer)
: [Bij] "r"(Bij)
: "memory", "q0");
}
: [b0] "r"(b0)
: "memory", "v0", "v1");
#else
for (i = 0; i < k; ++i) {
Bij = &B(i, j);
*buffer++ = *Bij;
*buffer++ = *(Bij + 1);
*buffer++ = *(Bij + 2);
*buffer++ = *(Bij + 3);
asm volatile(
"pld [%[b0]] \n\t"
"vld1.32 {q0, q1}, [%[b0]] \n\t"
"vst1.32 {q0, q1}, [%[buffer]]! \n\t"
: [buffer] "+r"(buffer)
: [b0] "r"(b0)
: "memory", "q0", "q1");
#endif // __aarch64__
#else
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
*buffer++ = *b0++;
#endif // __ARM_NEON
}
#endif
}
if (n_tail != 0) {
for (i = 0; i < k; ++i) {
Bij = &B(i, n - n_tail);
for (int i = 0; i < k; ++i) {
b0 = &B(i, n - n_tail);
for (int j = n - n_tail; j < n; ++j) {
*buffer++ = *Bij++;
*buffer++ = *b0++;
}
for (int j = n; j < n + (NR - n_tail); ++j) {
*buffer++ = 0;
......@@ -151,67 +182,57 @@ void PackMatrixB_(int k, int n, int n_tail, const float *B, int ldb,
}
// 分块矩阵乘法
void InnerKernel(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc,
int first_time) {
int m_block = (m + MR - 1) / MR * MR;
int n_block = (n + NR - 1) / NR * NR;
int m_tail = m % MR;
int n_tail = n % NR;
void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
float beta, float *c, float *C, int ldc, bool relu) {
#pragma omp parallel for
for (int j = 0; j < nc; j += NR) {
for (int i = 0; i < mc; i += MR) {
// AddDot4x4(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
AddDot4x8(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
}
}
if (first_time) {
PackMatrixB_(k, n, n_tail, B, ldb, packedB);
if (alpha != 1) {
WriteWithAlphaBeta(mc, nc, c, C, ldc);
return;
}
PackMatrixA_(m, k, m_tail, A, lda, packedA);
int i, j, mc, nc;
// B 取 4 列, 打包预热
for (j = 0; j < n_block; j += NR) {
nc = (n - j) < NR ? n_tail : NR;
// A 取 4 行，打包预热
for (i = 0; i < m_block; i += MR) {
mc = (m - i) < MR ? m_tail : MR;
AddDot4x4(k, alpha, &packedA[i * k], 4, &packedB[j * k], k, beta,
&C(i, j), ldc, mc, nc);
}
if (beta == 0) {
WriteBasic(mc, nc, c, C, ldc);
return;
}
if (beta == 1 && !relu) {
WriteWithAdd(mc, nc, c, C, ldc);
return;
}
if (beta == 1 && relu) {
WriteWithAddRelu(mc, nc, c, C, ldc);
return;
}
}
// 分块矩阵乘法
void InnerKernel_relu(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc,
int first_time, bool relu = false) {
int m_block = (m + MR - 1) / MR * MR;
int n_block = (n + NR - 1) / NR * NR;
int m_tail = m % MR;
int n_tail = n % NR;
if (first_time) {
PackMatrixB_(k, n, n_tail, B, ldb, packedB);
}
PackMatrixA_(m, k, m_tail, A, lda, packedA);
int i, j, mc, nc;
// B 取 4 列, 打包预热
for (j = 0; j < n_block; j += NR) {
nc = (n - j) < NR ? n_tail : NR;
// A 取 4 行，打包预热
for (i = 0; i < m_block; i += MR) {
mc = (m - i) < MR ? m_tail : MR;
AddDot4x4_relu(k, alpha, &packedA[i * k], 4, &packedB[j * k], k, beta,
&C(i, j), ldc, mc, nc, relu);
void InnerKernelWithBn(int mc, int nc, float alpha, const float *a,
const float *b, float beta, float *c, float *C, int ldc,
bool relu, float *new_scale, float *new_bias) {
#pragma omp parallel for
for (int j = 0; j < nc; j += NR) {
for (int i = 0; i < mc; i += MR) {
// AddDot4x4(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
AddDot4x8(KC, a + i * KC, b + j * KC, c + i * NC + j, NC);
}
}
if (relu) {
WriteWithBnRelu(mc, nc, c, C, ldc, new_scale, new_bias);
} else {
WriteWithBn(mc, nc, c, C, ldc, new_scale, new_bias);
}
}
// 计算一个更小的 4 * 4 的 C 矩阵分块
#if defined(IOS)
void AddDot4x4(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc) {
#if __ARM_NEON
#if __aarch64__
void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc) {
// init C
float32x4_t cv0 = vdupq_n_f32(0.0);
float32x4_t cv1 = vdupq_n_f32(0.0);
......@@ -238,252 +259,281 @@ void AddDot4x4(int k, float alpha, const float *a, int lda, const float *b,
a += MR;
b += NR;
}
float32x4x4_t cv = {cv0, cv1, cv2, cv3};
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
C(i, j) = 0.0;
} else if (beta != 1.0) {
C(i, j) *= beta;
}
if (j == 0) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 0);
} else if (j == 1) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 1);
} else if (j == 2) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 2);
} else if (j == 3) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 3);
}
}
}
vst1q_f32(c, cv0);
vst1q_f32(c + ldc, cv1);
vst1q_f32(c + 2 * ldc, cv2);
vst1q_f32(c + 3 * ldc, cv3);
// float32x4x4_t cv = {cv0, cv1, cv2, cv3};
}
void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc,
bool relu = false) {
void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc) {
// init C
float32x4_t cv0 = vdupq_n_f32(0.0);
float32x4_t cv1 = vdupq_n_f32(0.0);
float32x4_t cv2 = vdupq_n_f32(0.0);
float32x4_t cv3 = vdupq_n_f32(0.0);
float32x4_t cv4 = vdupq_n_f32(0.0);
float32x4_t cv5 = vdupq_n_f32(0.0);
float32x4_t cv6 = vdupq_n_f32(0.0);
float32x4_t cv7 = vdupq_n_f32(0.0);
float32x4_t av;
float32x4_t bv;
float32x4_t bv0;
float32x4_t bv1;
float32x2_t av01;
float32x2_t av23;
for (int p = 0; p < k; p += 1) {
av = vld1q_f32(a);
bv = vld1q_f32(b);
bv0 = vld1q_f32(b);
bv1 = vld1q_f32(b + 4);
av01 = vget_low_f32(av);
cv0 = vmlaq_lane_f32(cv0, bv, av01, 0);
cv1 = vmlaq_lane_f32(cv1, bv, av01, 1);
cv0 = vmlaq_lane_f32(cv0, bv0, av01, 0);
cv1 = vmlaq_lane_f32(cv1, bv1, av01, 0);
cv2 = vmlaq_lane_f32(cv2, bv0, av01, 1);
cv3 = vmlaq_lane_f32(cv3, bv1, av01, 1);
av23 = vget_high_f32(av);
cv2 = vmlaq_lane_f32(cv2, bv, av23, 0);
cv3 = vmlaq_lane_f32(cv3, bv, av23, 1);
cv4 = vmlaq_lane_f32(cv4, bv0, av23, 0);
cv5 = vmlaq_lane_f32(cv5, bv1, av23, 0);
cv6 = vmlaq_lane_f32(cv6, bv0, av23, 1);
cv7 = vmlaq_lane_f32(cv7, bv1, av23, 1);
a += MR;
b += NR;
}
float32x4x4_t cv = {cv0, cv1, cv2, cv3};
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
C(i, j) = 0.0;
} else if (beta != 1.0) {
C(i, j) *= beta;
vst1q_f32(c, cv0);
vst1q_f32(c + 4, cv1);
vst1q_f32(c + ldc, cv2);
vst1q_f32(c + ldc + 4, cv3);
vst1q_f32(c + 2 * ldc, cv4);
vst1q_f32(c + 2 * ldc + 4, cv5);
vst1q_f32(c + 3 * ldc, cv6);
vst1q_f32(c + 3 * ldc + 4, cv7);
}
// 分块矩阵乘法结果回写
// C = A * B
void WriteBasic(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 4;
int _nc1 = nc % 4;
float *c_ptr, *C_ptr;
float32x4_t cv;
for (int i = 0; i < mc; ++i) {
c_ptr = c + i * NC;
C_ptr = C + i * ldc;
for (int j = 0; j < nc1; ++j) {
cv = vld1q_f32(c_ptr);
vst1q_f32(C_ptr, cv);
c_ptr += 4;
C_ptr += 4;
}
if (_nc1 != 0) {
cv = vld1q_f32(c_ptr);
if (_nc1 >= 1) {
vst1q_lane_f32(C_ptr, cv, 0);
C_ptr++;
}
if (j == 0) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 0);
} else if (j == 1) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 1);
} else if (j == 2) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 2);
} else if (j == 3) {
C(i, j) += alpha * vgetq_lane_f32(cv.val[i], 3);
if (_nc1 >= 2) {
vst1q_lane_f32(C_ptr, cv, 1);
C_ptr++;
}
if (C(i, j) < 0) {
C(i, j) = 0;
if (_nc1 >= 3) {
vst1q_lane_f32(C_ptr, cv, 2);
}
}
}
}
#elif defined(ARMV7)
void AddDot4x4(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc) {
int kc1 = k / 4, kc2 = k % 4;
int bytes_ldc = 4 * ldc;
int flag_alpha = (alpha == 1.0) ? 1 : 2;
int flag_beta;
if (beta == 0.0) {
flag_beta = 0;
} else if (beta == 1.0) {
flag_beta = 1;
} else {
flag_beta = 2;
// C = alpha * A * B + beta * C
void WriteWithAlphaBeta(int mc, int nc, float *c, float *C, int ldc) {}
// C = A * B + C
void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 4;
int _nc1 = nc % 4;
float *c_ptr, *C_ptr;
float32x4_t cv;
float32x4_t cv1;
for (int i = 0; i < mc; ++i) {
c_ptr = c + i * NC;
C_ptr = C + i * ldc;
for (int j = 0; j < nc1; ++j) {
cv = vld1q_f32(c_ptr);
cv1 = vld1q_f32(C_ptr);
cv = vaddq_f32(cv, cv1);
vst1q_f32(C_ptr, cv);
c_ptr += 4;
C_ptr += 4;
}
if (_nc1 != 0) {
cv = vld1q_f32(c_ptr);
cv1 = vld1q_f32(C_ptr);
cv = vaddq_f32(cv, cv1);
if (_nc1 >= 1) {
vst1q_lane_f32(C_ptr, cv, 0);
C_ptr++;
}
if (_nc1 >= 2) {
vst1q_lane_f32(C_ptr, cv, 1);
C_ptr++;
}
if (_nc1 >= 3) {
vst1q_lane_f32(C_ptr, cv, 2);
}
}
}
asm volatile(
"pld [%[a]] \n\t"
"pld [%[b]] \n\t"
"vmov.f32 q10, #0.0 \n\t"
"vmov.f32 q11, #0.0 \n\t"
"vmov.f32 q12, #0.0 \n\t"
"vmov.f32 q13, #0.0 \n\t"
"subs %[kc1], %[kc1], #1 \n\t"
"blt end_kc1_%= \n\t"
"loop_kc1_%=: \n\t"
"pld [%[a], #64] \n\t"
"pld [%[b], #64] \n\t"
"vld1.32 {q0, q1}, [%[a]]! \n\t"
"vld1.32 {q2, q3}, [%[b]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q2, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q2, d1[1] \n\t"
"vmla.f32 q10, q3, d2[0] \n\t"
"vmla.f32 q11, q3, d2[1] \n\t"
"vmla.f32 q12, q3, d3[0] \n\t"
"vmla.f32 q13, q3, d3[1] \n\t"
"vld1.32 {q0, q1}, [%[a]]! \n\t"
"vld1.32 {q2, q3}, [%[b]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q2, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q2, d1[1] \n\t"
"vmla.f32 q10, q3, d2[0] \n\t"
"vmla.f32 q11, q3, d2[1] \n\t"
"vmla.f32 q12, q3, d3[0] \n\t"
"vmla.f32 q13, q3, d3[1] \n\t"
"subs %[kc1], %[kc1], #1 \n\t"
"bge loop_kc1_%= \n\t"
"end_kc1_%=: \n\t"
"subs %[kc2], %[kc2], #1 \n\t"
"blt end_kc2_%= \n\t"
"loop_kc2_%=: \n\t"
"vld1.32 {q0}, [%[a]]! \n\t"
"vld1.32 {q1}, [%[b]]! \n\t"
"vmla.f32 q10, q1, d0[0] \n\t"
"vmla.f32 q11, q1, d0[1] \n\t"
"vmla.f32 q12, q1, d1[0] \n\t"
"vmla.f32 q13, q1, d1[1] \n\t"
"subs %[kc2], %[kc2], #1 \n\t"
"bge loop_kc2_%= \n\t"
"end_kc2_%=: \n\t"
}
"cmp %[mc], #4 \n\t"
"bne temp_%= \n\t"
"cmp %[nc], #4 \n\t"
"bne temp_%= \n\t"
"vmov.f32 d8[0], %[alpha] \n\t"
"vmov.f32 d8[1], %[beta] \n\t"
"cmp %[flag_alpha], #1 \n\t"
"bne alpha_%= \n\t"
"alpha_%=: \n\t"
"vmul.f32 q10, q10, d8[0] \n\t"
"vmul.f32 q11, q11, d8[0] \n\t"
"vmul.f32 q12, q12, d8[0] \n\t"
"vmul.f32 q13, q13, d8[0] \n\t"
"beta_%=: \n\t"
"cmp %[flag_beta], #0 \n\t"
"beq memory_%= \n\t"
"mov r4, %[C] \n\t"
"mov r6, %[bytes_ldc]\n\t"
"vld1.32 {q0}, [r4], r6 \n\t"
"vld1.32 {q1}, [r4], r6 \n\t"
"vld1.32 {q2}, [r4], r6 \n\t"
"vld1.32 {q3}, [r4] \n\t"
"cmp %[flag_beta], #1 \n\t"
"beq beta_eq1_%= \n\t"
"bne beta_ne1_%= \n\t"
"beta_eq1_%=: \n\t"
"vadd.f32 q10, q10, q0 \n\t"
"vadd.f32 q11, q11, q1 \n\t"
"vadd.f32 q12, q12, q2 \n\t"
"vadd.f32 q13, q13, q3 \n\t"
"b memory_%= \n\t"
"beta_ne1_%=: \n\t"
"vmla.f32 q10, q0, d8[1] \n\t"
"vmla.f32 q11, q1, d8[1] \n\t"
"vmla.f32 q12, q2, d8[1] \n\t"
"vmla.f32 q13, q3, d8[1] \n\t"
"memory_%=: \n\t"
"mov r5, %[C] \n\t"
"mov r6, %[bytes_ldc]\n\t"
"vst1.32 {q10}, [r5], r6 \n\t"
"vst1.32 {q11}, [r5], r6 \n\t"
"vst1.32 {q12}, [r5], r6 \n\t"
"vst1.32 {q13}, [r5] \n\t"
"b end_%= \n\t"
// C = A * B + C, relu(C)
void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 4;
int _nc1 = nc % 4;
float *c_ptr, *C_ptr;
float32x4_t cv;
float32x4_t cv1;
float32x4_t zero = vdupq_n_f32(0.0);
for (int i = 0; i < mc; ++i) {
c_ptr = c + i * NC;
C_ptr = C + i * ldc;
for (int j = 0; j < nc1; ++j) {
cv = vld1q_f32(c_ptr);
cv1 = vld1q_f32(C_ptr);
cv = vaddq_f32(cv, cv1);
cv = vmaxq_f32(cv, zero);
vst1q_f32(C_ptr, cv);
c_ptr += 4;
C_ptr += 4;
}
if (_nc1 != 0) {
cv = vld1q_f32(c_ptr);
cv1 = vld1q_f32(C_ptr);
cv = vaddq_f32(cv, cv1);
cv = vmaxq_f32(cv, zero);
if (_nc1 >= 1) {
vst1q_lane_f32(C_ptr, cv, 0);
C_ptr++;
}
if (_nc1 >= 2) {
vst1q_lane_f32(C_ptr, cv, 1);
C_ptr++;
}
if (_nc1 >= 3) {
vst1q_lane_f32(C_ptr, cv, 2);
}
}
}
}
"temp_%=: \n\t"
"vst1.32 {q10, q11}, [%[ab]]!\n\t"
"vst1.32 {q12, q13}, [%[ab]] \n\t"
"end_%=: \n\t"
:
: [a] "r"(a), [b] "r"(b), [C] "r"(C), [ab] "r"(ab), [kc1] "r"(kc1),
[kc2] "r"(kc2), [mc] "r"(mc), [nc] "r"(nc), [alpha] "r"(alpha),
[beta] "r"(beta), [bytes_ldc] "r"(bytes_ldc),
[flag_alpha] "r"(flag_alpha), [flag_beta] "r"(flag_beta)
: "memory", "q0", "q1", "q2", "q3", "q4", "q10", "q11", "q12", "q13");
if (mc != MR || nc != NR) {
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
if (alpha != 1.0) {
C(i, j) = alpha * ab[i * MR + j];
} else {
C(i, j) = ab[i * MR + j];
}
} else {
if (beta != 1.0) {
C(i, j) *= beta;
}
if (alpha != 1.0) {
C(i, j) += alpha * ab[i * MR + j];
} else {
C(i, j) += ab[i * MR + j];
}
}
// C = A * B, batchnorm(C)
void WriteWithBn(int mc, int nc, float *c, float *C, int ldc, float *new_scale,
float *new_bias) {
int nc1 = nc / 4;
int _nc1 = nc % 4;
float *c_ptr, *C_ptr;
float32x4_t cv;
float32x4_t cv1;
float32x4_t bias;
float32x2_t scale;
for (int i = 0; i < mc; ++i) {
c_ptr = c + i * NC;
C_ptr = C + i * ldc;
bias = vld1q_dup_f32(new_bias);
scale = vld1_dup_f32(new_scale);
new_bias++;
new_scale++;
float scale0 = vget_lane_f32(scale, 0);
for (int j = 0; j < nc1; ++j) {
cv = vld1q_f32(c_ptr);
cv = vmlaq_n_f32(bias, cv, scale0);
vst1q_f32(C_ptr, cv);
c_ptr += 4;
C_ptr += 4;
}
if (_nc1 != 0) {
cv = vld1q_f32(c_ptr);
cv = vmlaq_n_f32(bias, cv, scale0);
if (_nc1 >= 1) {
vst1q_lane_f32(C_ptr, cv, 0);
C_ptr++;
}
if (_nc1 >= 2) {
vst1q_lane_f32(C_ptr, cv, 1);
C_ptr++;
}
if (_nc1 >= 3) {
vst1q_lane_f32(C_ptr, cv, 2);
C_ptr++;
}
}
}
}
void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc,
bool relu = false) {
int kc1 = k / 4, kc2 = k % 4;
int bytes_ldc = 4 * ldc;
int flag_alpha = (alpha == 1.0) ? 1 : 2;
int flag_beta;
if (beta == 0.0) {
flag_beta = 0;
} else if (beta == 1.0) {
flag_beta = 1;
} else {
flag_beta = 2;
// C = A * B, batchnorm(C), relu(C)
void WriteWithBnRelu(int mc, int nc, float *c, float *C, int ldc,
float *new_scale, float *new_bias) {
int nc1 = nc / 4;
int _nc1 = nc % 4;
float *c_ptr, *C_ptr;
float32x4_t cv;
float32x4_t bias;
float32x2_t scale;
float32x4_t zero = vdupq_n_f32(0.0);
for (int i = 0; i < mc; ++i) {
c_ptr = c + i * NC;
C_ptr = C + i * ldc;
bias = vld1q_dup_f32(new_bias);
scale = vld1_dup_f32(new_scale);
new_bias++;
new_scale++;
float scale0 = vget_lane_f32(scale, 0);
for (int j = 0; j < nc1; ++j) {
cv = vld1q_f32(c_ptr);
cv = vmlaq_n_f32(bias, cv, scale0);
cv = vmaxq_f32(cv, zero);
vst1q_f32(C_ptr, cv);
c_ptr += 4;
C_ptr += 4;
}
if (_nc1 != 0) {
cv = vld1q_f32(c_ptr);
cv = vmlaq_n_f32(bias, cv, scale0);
cv = vmaxq_f32(cv, zero);
if (_nc1 >= 1) {
vst1q_lane_f32(C_ptr, cv, 0);
C_ptr++;
}
if (_nc1 >= 2) {
vst1q_lane_f32(C_ptr, cv, 1);
C_ptr++;
}
if (_nc1 >= 3) {
vst1q_lane_f32(C_ptr, cv, 2);
}
}
}
}
#else
void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc) {
const float *a_ptr, *b_ptr;
a_ptr = a;
b_ptr = b;
int kc1 = k / 4;
int kc2 = k % 4;
int step = 4 * ldc;
asm volatile(
"pld [%[a]] \n\t"
"pld [%[b]] \n\t"
"pld [%[a_ptr]] \n\t"
"pld [%[b_ptr]] \n\t"
"vmov.f32 q10, #0.0 \n\t"
"vmov.f32 q11, #0.0 \n\t"
"vmov.f32 q12, #0.0 \n\t"
......@@ -492,20 +542,10 @@ void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
"subs %[kc1], %[kc1], #1 \n\t"
"blt end_kc1_%= \n\t"
"loop_kc1_%=: \n\t"
"pld [%[a], #64] \n\t"
"pld [%[b], #64] \n\t"
"vld1.32 {q0, q1}, [%[a]]! \n\t"
"vld1.32 {q2, q3}, [%[b]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q2, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q2, d1[1] \n\t"
"vmla.f32 q10, q3, d2[0] \n\t"
"vmla.f32 q11, q3, d2[1] \n\t"
"vmla.f32 q12, q3, d3[0] \n\t"
"vmla.f32 q13, q3, d3[1] \n\t"
"vld1.32 {q0, q1}, [%[a]]! \n\t"
"vld1.32 {q2, q3}, [%[b]]! \n\t"
"pld [%[a_ptr], #64] \n\t"
"pld [%[b_ptr], #64] \n\t"
"vld1.32 {q0, q1}, [%[a_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[b_ptr]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q2, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
......@@ -514,6 +554,16 @@ void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
"vmla.f32 q11, q3, d2[1] \n\t"
"vmla.f32 q12, q3, d3[0] \n\t"
"vmla.f32 q13, q3, d3[1] \n\t"
"vld1.32 {q4, q5}, [%[a_ptr]]! \n\t"
"vld1.32 {q6, q7}, [%[b_ptr]]! \n\t"
"vmla.f32 q10, q6, d8[0] \n\t"
"vmla.f32 q11, q6, d8[1] \n\t"
"vmla.f32 q12, q6, d9[0] \n\t"
"vmla.f32 q13, q6, d9[1] \n\t"
"vmla.f32 q10, q7, d10[0] \n\t"
"vmla.f32 q11, q7, d10[1] \n\t"
"vmla.f32 q12, q7, d11[0] \n\t"
"vmla.f32 q13, q7, d11[1] \n\t"
"subs %[kc1], %[kc1], #1 \n\t"
"bge loop_kc1_%= \n\t"
"end_kc1_%=: \n\t"
......@@ -521,8 +571,8 @@ void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
"subs %[kc2], %[kc2], #1 \n\t"
"blt end_kc2_%= \n\t"
"loop_kc2_%=: \n\t"
"vld1.32 {q0}, [%[a]]! \n\t"
"vld1.32 {q1}, [%[b]]! \n\t"
"vld1.32 {q0}, [%[a_ptr]]! \n\t"
"vld1.32 {q1}, [%[b_ptr]]! \n\t"
"vmla.f32 q10, q1, d0[0] \n\t"
"vmla.f32 q11, q1, d0[1] \n\t"
"vmla.f32 q12, q1, d1[0] \n\t"
......@@ -531,290 +581,23 @@ void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
"bge loop_kc2_%= \n\t"
"end_kc2_%=: \n\t"
"cmp %[mc], #4 \n\t"
"bne temp_%= \n\t"
"cmp %[nc], #4 \n\t"
"bne temp_%= \n\t"
"vmov.f32 d8[0], %[alpha] \n\t"
"vmov.f32 d8[1], %[beta] \n\t"
"cmp %[flag_alpha], #1 \n\t"
"bne alpha_%= \n\t"
"alpha_%=: \n\t"
"vmul.f32 q10, q10, d8[0] \n\t"
"vmul.f32 q11, q11, d8[0] \n\t"
"vmul.f32 q12, q12, d8[0] \n\t"
"vmul.f32 q13, q13, d8[0] \n\t"
"beta_%=: \n\t"
"cmp %[flag_beta], #0 \n\t"
"beq memory_%= \n\t"
"mov r4, %[C] \n\t"
"mov r6, %[bytes_ldc]\n\t"
"vld1.32 {q0}, [r4], r6 \n\t"
"vld1.32 {q1}, [r4], r6 \n\t"
"vld1.32 {q2}, [r4], r6 \n\t"
"vld1.32 {q3}, [r4] \n\t"
"cmp %[flag_beta], #1 \n\t"
"beq beta_eq1_%= \n\t"
"bne beta_ne1_%= \n\t"
"beta_eq1_%=: \n\t"
"vadd.f32 q10, q10, q0 \n\t"
"vadd.f32 q11, q11, q1 \n\t"
"vadd.f32 q12, q12, q2 \n\t"
"vadd.f32 q13, q13, q3 \n\t"
"b memory_%= \n\t"
"beta_ne1_%=: \n\t"
"vmla.f32 q10, q0, d8[1] \n\t"
"vmla.f32 q11, q1, d8[1] \n\t"
"vmla.f32 q12, q2, d8[1] \n\t"
"vmla.f32 q13, q3, d8[1] \n\t"
"memory_%=: \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vmax.f32 q12, q12, q14 \n\t"
"vmax.f32 q13, q13, q14 \n\t"
"mov r5, %[C] \n\t"
"mov r6, %[bytes_ldc]\n\t"
"mov r5, %[c] \n\t"
"mov r6, %[step] \n\t"
"vst1.32 {q10}, [r5], r6 \n\t"
"vst1.32 {q11}, [r5], r6 \n\t"
"vst1.32 {q12}, [r5], r6 \n\t"
"vst1.32 {q13}, [r5] \n\t"
"b end_%= \n\t"
"temp_%=: \n\t"
"vst1.32 {q10, q11}, [%[ab]]!\n\t"
"vst1.32 {q12, q13}, [%[ab]] \n\t"
"end_%=: \n\t"
:
: [a] "r"(a), [b] "r"(b), [C] "r"(C), [ab] "r"(ab), [kc1] "r"(kc1),
[kc2] "r"(kc2), [mc] "r"(mc), [nc] "r"(nc), [alpha] "r"(alpha),
[beta] "r"(beta), [bytes_ldc] "r"(bytes_ldc),
[flag_alpha] "r"(flag_alpha), [flag_beta] "r"(flag_beta)
: "memory", "q0", "q1", "q2", "q3", "q4", "q10", "q11", "q12", "q13",
"q14");
if (mc != MR || nc != NR) {
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
if (alpha != 1.0) {
C(i, j) = alpha * ab[i * MR + j];
} else {
C(i, j) = ab[i * MR + j];
}
} else {
if (beta != 1.0) {
C(i, j) *= beta;
}
if (alpha != 1.0) {
C(i, j) += alpha * ab[i * MR + j];
} else {
C(i, j) += ab[i * MR + j];
}
}
if (relu) {
if (C(i, j) < 0) {
C(i, j) = 0;
}
}
}
}
}
}
#else
void AddDot4x4(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc) {
float c[16] = {0};
float reg_a0, reg_a1, reg_a2, reg_a3, reg_b0, reg_b1, reg_b2, reg_b3;
for (int p = 0; p < k; p += 1) {
reg_b0 = *b++;
reg_b1 = *b++;
reg_b2 = *b++;
reg_b3 = *b++;
reg_a0 = *a++;
reg_a1 = *a++;
reg_a2 = *a++;
reg_a3 = *a++;
// first row
c[0] += reg_a0 * reg_b0;
c[1] += reg_a0 * reg_b1;
c[2] += reg_a0 * reg_b2;
c[3] += reg_a0 * reg_b3;
// second row
c[4] += reg_a1 * reg_b0;
c[5] += reg_a1 * reg_b1;
c[6] += reg_a1 * reg_b2;
c[7] += reg_a1 * reg_b3;
// third row
c[8] += reg_a2 * reg_b0;
c[9] += reg_a2 * reg_b1;
c[10] += reg_a2 * reg_b2;
c[11] += reg_a2 * reg_b3;
// fourth row
c[12] += reg_a3 * reg_b0;
c[13] += reg_a3 * reg_b1;
c[14] += reg_a3 * reg_b2;
c[15] += reg_a3 * reg_b3;
}
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
C(i, j) = 0.0;
} else if (beta != 1.0) {
C(i, j) *= beta;
}
if (alpha != 1.0) {
C(i, j) += alpha * c[i * MR + j];
} else {
C(i, j) += c[i * MR + j];
}
}
}
}
void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc,
bool relu) {
float c[16] = {0};
float reg_a0, reg_a1, reg_a2, reg_a3, reg_b0, reg_b1, reg_b2, reg_b3;
for (int p = 0; p < k; p += 1) {
reg_b0 = *b++;
reg_b1 = *b++;
reg_b2 = *b++;
reg_b3 = *b++;
reg_a0 = *a++;
reg_a1 = *a++;
reg_a2 = *a++;
reg_a3 = *a++;
// first row
c[0] += reg_a0 * reg_b0;
c[1] += reg_a0 * reg_b1;
c[2] += reg_a0 * reg_b2;
c[3] += reg_a0 * reg_b3;
// second row
c[4] += reg_a1 * reg_b0;
c[5] += reg_a1 * reg_b1;
c[6] += reg_a1 * reg_b2;
c[7] += reg_a1 * reg_b3;
// third row
c[8] += reg_a2 * reg_b0;
c[9] += reg_a2 * reg_b1;
c[10] += reg_a2 * reg_b2;
c[11] += reg_a2 * reg_b3;
// fourth row
c[12] += reg_a3 * reg_b0;
c[13] += reg_a3 * reg_b1;
c[14] += reg_a3 * reg_b2;
c[15] += reg_a3 * reg_b3;
}
int i, j;
for (i = 0; i < mc; ++i) {
for (j = 0; j < nc; ++j) {
if (beta == 0.0) {
C(i, j) = 0.0;
} else if (beta != 1.0) {
C(i, j) *= beta;
}
if (alpha != 1.0) {
C(i, j) += alpha * c[i * MR + j];
} else {
C(i, j) += c[i * MR + j];
}
if (relu) {
if (C(i, j) < 0) {
C(i, j) = 0;
}
}
}
}
}
#endif
// 32位 float 矩阵乘法
void sgemm(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc) {
int i, j, p, mc, nc, kc;
float beta_;
#ifdef ARMV7
if (m == 1) {
VectorKernel(1, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
return;
}
#endif
for (j = 0; j < n; j += NC) {
nc = s_min(n - j, NC);
for (p = 0; p < k; p += KC) {
kc = s_min(k - p, KC);
for (i = 0; i < m; i += MC) {
mc = s_min(m - i, MC);
if (p != 0) {
beta_ = 1.0;
} else {
beta_ = beta;
}
InnerKernel(mc, nc, kc, alpha, &A(i, p), lda, &B(p, j), ldb, beta_,
&C(i, j), ldc, i == 0);
}
}
}
}
void sgemm_relu(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc) {
int i, j, p, mc, nc, kc;
float beta_;
for (j = 0; j < n; j += NC) {
nc = s_min(n - j, NC);
for (p = 0; p < k; p += KC) {
kc = s_min(k - p, KC);
for (i = 0; i < m; i += MC) {
mc = s_min(m - i, MC);
if (p != 0) {
beta_ = 1.0;
} else {
beta_ = beta;
}
if (p + KC >= k) {
InnerKernel_relu(mc, nc, kc, alpha, &A(i, p), lda, &B(p, j), ldb,
beta_, &C(i, j), ldc, i == 0, true);
} else {
InnerKernel(mc, nc, kc, alpha, &A(i, p), lda, &B(p, j), ldb, beta_,
&C(i, j), ldc, i == 0);
}
}
}
}
: [a_ptr] "r"(a_ptr), [b_ptr] "r"(b_ptr), [c] "r"(c), [kc1] "r"(kc1),
[kc2] "r"(kc2), [step] "r"(step)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q10", "q11", "q12", "q13");
}
#ifdef ARMV7
/*
void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc) {
const float *B, int ldb, float beta, float *C, int ldc,
bool relu) {
float *bufferC = static_cast<float *>(memory::Alloc(sizeof(float) * n));
const float *a0, *b0, *b1, *b2, *b3;
......@@ -1016,17 +799,1175 @@ void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
}
}
c0 = bufferC;
C0 = C;
for (int i = 0; i < n; i++) {
if (beta == 1.0) {
*C0++ += *c0++;
} else {
*C0++ = *c0++;
}
if (alpha != 1) {
VecWriteWithAlphaBeta(n, bufferC, C, ldc);
return;
}
if (beta == 0) {
VecWriteBasic(n, bufferC, C, ldc);
return;
}
if (beta == 1 && !relu) {
VecWriteWithAdd(n, bufferC, C, ldc);
return;
}
if (beta == 1 && relu) {
VecWriteWithAddRelu(n, bufferC, C, ldc);
return;
}
}
#endif
void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
int lda, const float *B, int ldb, float beta, float *C,
int ldc, bool relu, float *new_scale, float *new_bias) {
float *bufferC = static_cast<float *>(memory::Alloc(sizeof(float) * n));
const float *a0, *b0, *b1, *b2, *b3;
float *c0, *C0;
int volatile kc1 = k / 4;
int volatile kc2 = k % 4;
int volatile nc1 = n / 16;
int _nc1 = n % 16;
int volatile nc2 = _nc1 / 4;
int volatile nc3 = _nc1 % 4;
for (int i = 0; i < kc1; i++) {
a0 = A + i * 4;
b0 = B + i * 4 * ldb;
b1 = b0 + ldb;
b2 = b1 + ldb;
b3 = b2 + ldb;
c0 = bufferC;
asm volatile(
"pld [%[a0], #16] \n\t"
"vld1.32 {q0}, [%[a0]] \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"cmp %[i], #0 \n\t"
"beq i_eq0_%= \n\t"
"bne i_ne0_%= \n\t"
"i_eq0_%=: \n\t"
"vmov.f32 q10, #0.0 \n\t"
"vmov.f32 q11, #0.0 \n\t"
"vmov.f32 q12, #0.0 \n\t"
"vmov.f32 q13, #0.0 \n\t"
"b gemm_nc1_%= \n\t"
"i_ne0_%=: \n\t"
"pld [%[c0], #64] \n\t"
"vld1.32 {q10, q11}, [%[c0]]! \n\t"
"vld1.32 {q12, q13}, [%[c0]] \n\t"
"sub %[c0], %[c0], #32 \n\t"
"gemm_nc1_%=: \n\t"
"pld [%[b0], #64] \n\t"
"vld1.32 {q2, q3}, [%[b0]]! \n\t"
"vld1.32 {q4, q5}, [%[b0]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q3, d0[0] \n\t"
"vmla.f32 q12, q4, d0[0] \n\t"
"vmla.f32 q13, q5, d0[0] \n\t"
"pld [%[b1], #64] \n\t"
"vld1.32 {q2, q3}, [%[b1]]! \n\t"
"vld1.32 {q4, q5}, [%[b1]]! \n\t"
"vmla.f32 q10, q2, d0[1] \n\t"
"vmla.f32 q11, q3, d0[1] \n\t"
"vmla.f32 q12, q4, d0[1] \n\t"
"vmla.f32 q13, q5, d0[1] \n\t"
"pld [%[b2], #64] \n\t"
"vld1.32 {q2, q3}, [%[b2]]! \n\t"
"vld1.32 {q4, q5}, [%[b2]]! \n\t"
"vmla.f32 q10, q2, d1[0] \n\t"
"vmla.f32 q11, q3, d1[0] \n\t"
"vmla.f32 q12, q4, d1[0] \n\t"
"vmla.f32 q13, q5, d1[0] \n\t"
"pld [%[b3], #64] \n\t"
"vld1.32 {q2, q3}, [%[b3]]! \n\t"
"vld1.32 {q4, q5}, [%[b3]]! \n\t"
"vmla.f32 q10, q2, d1[1] \n\t"
"vmla.f32 q11, q3, d1[1] \n\t"
"vmla.f32 q12, q4, d1[1] \n\t"
"vmla.f32 q13, q5, d1[1] \n\t"
"vst1.32 {q10, q11}, [%[c0]]! \n\t"
"vst1.32 {q12, q13}, [%[c0]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"cmp %[i], #0 \n\t"
"beq ii_eq0_%= \n\t"
"bne ii_ne0_%= \n\t"
"ii_eq0_%=: \n\t"
"vmov.f32 q10, #0.0 \n\t"
"b gemm_nc2_%= \n\t"
"ii_ne0_%=: \n\t"
"pld [%[c0], #16] \n\t"
"vld1.32 {q10}, [%[c0]] \n\t"
"gemm_nc2_%=: \n\t"
"pld [%[b0], #16] \n\t"
"vld1.32 {q2}, [%[b0]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"pld [%[b1], #16] \n\t"
"vld1.32 {q3}, [%[b1]]! \n\t"
"vmla.f32 q10, q3, d0[1] \n\t"
"pld [%[b2], #16] \n\t"
"vld1.32 {q4}, [%[b2]]! \n\t"
"vmla.f32 q10, q4, d1[0] \n\t"
"pld [%[b3], #16] \n\t"
"vld1.32 {q5}, [%[b3]]! \n\t"
"vmla.f32 q10, q5, d1[1] \n\t"
"vst1.32 {q10}, [%[c0]]! \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
: [b0] "+r"(b0), [b1] "+r"(b1), [b2] "+r"(b2), [b3] "+r"(b3),
[c0] "+r"(c0)
: [a0] "r"(a0), [i] "r"(i), [nc1] "r"(nc1), [nc2] "r"(nc2)
: "memory", "q0", "q2", "q3", "q4", "q5", "q10", "q11", "q12", "q13");
for (int j = 0; j < nc3; j++) {
if (i == 0) {
*c0 = (*a0) * (*b0++);
} else {
*c0 += (*a0) * (*b0++);
}
*c0 += (*(a0 + 1)) * (*b1++);
*c0 += (*(a0 + 2)) * (*b2++);
*c0 += (*(a0 + 3)) * (*b3++);
c0++;
}
}
for (int i = 0; i < kc2; ++i) {
a0 = A + 4 * kc1 + i;
b0 = B + (4 * kc1 + i) * ldb;
c0 = bufferC;
asm volatile(
"pld [%[a0], #16] \n\t"
"vld1.32 {d0}, [%[a0]] \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"pld [%[c0], #64] \n\t"
"vld1.32 {q10, q11}, [%[c0]]! \n\t"
"vld1.32 {q12, q13}, [%[c0]] \n\t"
"sub %[c0], %[c0], #32 \n\t"
"gemm_nc1_%=: \n\t"
"pld [%[b0], #64] \n\t"
"vld1.32 {q2, q3}, [%[b0]]! \n\t"
"vld1.32 {q4, q5}, [%[b0]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vmla.f32 q11, q3, d0[0] \n\t"
"vmla.f32 q12, q4, d0[0] \n\t"
"vmla.f32 q13, q5, d0[0] \n\t"
"vst1.32 {q10, q11}, [%[c0]]! \n\t"
"vst1.32 {q12, q13}, [%[c0]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"pld [%[c0], #16] \n\t"
"vld1.32 {q10}, [%[c0]] \n\t"
"gemm_nc2_%=: \n\t"
"vld1.32 {q2}, [%[b0]]! \n\t"
"vmla.f32 q10, q2, d0[0] \n\t"
"vst1.32 {q10}, [%[c0]]! \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
: [b0] "+r"(b0), [b1] "+r"(b1), [b2] "+r"(b2), [b3] "+r"(b3),
[c0] "+r"(c0)
: [a0] "r"(a0), [nc1] "r"(nc1), [nc2] "r"(nc2)
: "memory", "q0", "q2", "q3", "q4", "q5", "q10", "q11", "q12", "q13");
for (int j = 0; j < nc3; j++) {
*c0 += (*a0) * (*b0++);
c0++;
}
}
if (relu) {
VecWriteWithBnRelu(n, bufferC, C, ldc, new_scale, new_bias);
} else {
VecWriteWithBn(n, bufferC, C, ldc, new_scale, new_bias);
}
}
*/
void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc) {
const float *a_ptr, *b_ptr;
a_ptr = a;
b_ptr = b;
int kc1 = k / 4;
int kc2 = k % 4;
int step = 4 * ldc;
asm volatile(
"pld [%[a_ptr]] \n\t"
"pld [%[b_ptr]] \n\t"
"vmov.f32 q8, #0.0 \n\t"
"vmov.f32 q9, #0.0 \n\t"
"vmov.f32 q10, #0.0 \n\t"
"vmov.f32 q11, #0.0 \n\t"
"vmov.f32 q12, #0.0 \n\t"
"vmov.f32 q13, #0.0 \n\t"
"vmov.f32 q14, #0.0 \n\t"
"vmov.f32 q15, #0.0 \n\t"
"subs %[kc1], %[kc1], #1 \n\t"
"blt end_kc1_%= \n\t"
"loop_kc1_%=: \n\t"
"pld [%[a_ptr], #64] \n\t"
"pld [%[b_ptr], #64] \n\t"
"vld1.32 {q0, q1}, [%[a_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[b_ptr]]! \n\t"
"vld1.32 {q4, q5}, [%[b_ptr]]! \n\t"
"vmla.f32 q8, q2, d0[0] \n\t"
"vmla.f32 q9, q3, d0[0] \n\t"
"vmla.f32 q10, q2, d0[1] \n\t"
"vmla.f32 q11, q3, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q3, d1[0] \n\t"
"vmla.f32 q14, q2, d1[1] \n\t"
"vmla.f32 q15, q3, d1[1] \n\t"
"vmla.f32 q8, q4, d2[0] \n\t"
"vmla.f32 q9, q5, d2[0] \n\t"
"vmla.f32 q10, q4, d2[1] \n\t"
"vmla.f32 q11, q5, d2[1] \n\t"
"vmla.f32 q12, q4, d3[0] \n\t"
"vmla.f32 q13, q5, d3[0] \n\t"
"vmla.f32 q14, q4, d3[1] \n\t"
"vmla.f32 q15, q5, d3[1] \n\t"
"pld [%[b_ptr], #64] \n\t"
"vld1.32 {q0, q1}, [%[a_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[b_ptr]]! \n\t"
"vld1.32 {q4, q5}, [%[b_ptr]]! \n\t"
"vmla.f32 q8, q2, d0[0] \n\t"
"vmla.f32 q9, q3, d0[0] \n\t"
"vmla.f32 q10, q2, d0[1] \n\t"
"vmla.f32 q11, q3, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q3, d1[0] \n\t"
"vmla.f32 q14, q2, d1[1] \n\t"
"vmla.f32 q15, q3, d1[1] \n\t"
"vmla.f32 q8, q4, d2[0] \n\t"
"vmla.f32 q9, q5, d2[0] \n\t"
"vmla.f32 q10, q4, d2[1] \n\t"
"vmla.f32 q11, q5, d2[1] \n\t"
"vmla.f32 q12, q4, d3[0] \n\t"
"vmla.f32 q13, q5, d3[0] \n\t"
"vmla.f32 q14, q4, d3[1] \n\t"
"vmla.f32 q15, q5, d3[1] \n\t"
"subs %[kc1], %[kc1], #1 \n\t"
"bge loop_kc1_%= \n\t"
"end_kc1_%=: \n\t"
"subs %[kc2], %[kc2], #1 \n\t"
"blt end_kc2_%= \n\t"
"loop_kc2_%=: \n\t"
"vld1.32 {q0}, [%[a_ptr]]! \n\t"
"vld1.32 {q2, q3}, [%[b_ptr]]! \n\t"
"vmla.f32 q8, q2, d0[0] \n\t"
"vmla.f32 q9, q3, d0[0] \n\t"
"vmla.f32 q10, q2, d0[1] \n\t"
"vmla.f32 q11, q3, d0[1] \n\t"
"vmla.f32 q12, q2, d1[0] \n\t"
"vmla.f32 q13, q3, d1[0] \n\t"
"vmla.f32 q14, q2, d1[1] \n\t"
"vmla.f32 q15, q3, d1[1] \n\t"
"subs %[kc2], %[kc2], #1 \n\t"
"bge loop_kc2_%= \n\t"
"end_kc2_%=: \n\t"
"mov r5, %[c] \n\t"
"mov r6, %[step] \n\t"
"vst1.32 {q8, q9}, [r5], r6 \n\t"
"vst1.32 {q10, q11}, [r5], r6 \n\t"
"vst1.32 {q12, q13}, [r5], r6 \n\t"
"vst1.32 {q14, q15}, [r5] \n\t"
:
: [a_ptr] "r"(a_ptr), [b_ptr] "r"(b_ptr), [c] "r"(c), [kc1] "r"(kc1),
[kc2] "r"(kc2), [step] "r"(step)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q8", "q9",
"q10", "q11", "q12", "q13", "q14", "q15");
}
// C = A * B
void WriteBasic(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 16;
int _nc1 = nc % 16;
int step = 4 * ldc;
int step1 = 4 * (NC - 16 * nc1);
int volatile m = mc;
float *volatile c_ptr, *volatile C_ptr;
float *C0, *c0;
c_ptr = c;
C_ptr = C;
if (nc1 > 0) {
asm volatile(
"subs %[mc], %[mc], #1 \n\t"
"blt end_mc_%= \n\t"
"loop_mc_%=: \n\t"
"mov r6, %[C_ptr] \n\t"
"mov r5, %[nc1] \n\t"
"subs r5, r5, #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c_ptr]]! \n\t"
"vst1.32 {q0, q1}, [r6]! \n\t"
"vld1.32 {q2, q3}, [%[c_ptr]]! \n\t"
"vst1.32 {q2, q3}, [r6]! \n\t"
"subs r5, r5, #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"add %[C_ptr], %[C_ptr], %[step] \n\t"
"add %[c_ptr], %[c_ptr], %[step1] \n\t"
"subs %[mc], %[mc], #1 \n\t"
"bge loop_mc_%= \n\t"
"end_mc_%=: \n\t"
:
: [C_ptr] "r"(C_ptr), [c_ptr] "r"(c_ptr), [mc] "r"(m), [nc1] "r"(nc1),
[step] "r"(step), [step1] "r"(step1)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3");
}
if (_nc1 != 0) {
for (int i = 0; i < mc; i++) {
C0 = C_ptr + nc1 * 16 + i * ldc;
c0 = c_ptr + nc1 * 16 + i * NC;
for (int j = 0; j < _nc1; j++) {
*C0++ = *c0++;
}
}
}
}
// C = alpha * A * B + beta * C
void WriteWithAlphaBeta(int mc, int nc, float *c, float *C, int ldc) {}
// C = A * B + C
void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 16;
int _nc1 = nc % 16;
int step = 4 * ldc;
int step1 = 4 * (NC - 16 * nc1);
int volatile m = mc;
float *volatile c_ptr, *volatile C_ptr;
float *C0, *c0;
c_ptr = c;
C_ptr = C;
if (nc1 > 0) {
asm volatile(
"subs %[mc], %[mc], #1 \n\t"
"blt end_mc_%= \n\t"
"loop_mc_%=: \n\t"
"mov r6, %[C_ptr] \n\t"
"mov r5, %[nc1] \n\t"
"subs r5, r5, #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [r6] \n\t"
"vld1.32 {q2, q3}, [%[c_ptr]]! \n\t"
"vadd.f32 q10, q0, q2 \n\t"
"vadd.f32 q11, q1, q3 \n\t"
"vst1.32 {q10, q11}, [r6]! \n\t"
"vld1.32 {q4, q5}, [r6] \n\t"
"vld1.32 {q6, q7}, [%[c_ptr]]! \n\t"
"vadd.f32 q12, q4, q6 \n\t"
"vadd.f32 q13, q5, q7 \n\t"
"vst1.32 {q12, q13}, [r6]! \n\t"
"subs r5, r5, #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"add %[C_ptr], %[C_ptr], %[step] \n\t"
"add %[c_ptr], %[c_ptr], %[step1] \n\t"
"subs %[mc], %[mc], #1 \n\t"
"bge loop_mc_%= \n\t"
"end_mc_%=: \n\t"
:
: [C_ptr] "r"(C_ptr), [c_ptr] "r"(c_ptr), [mc] "r"(m), [nc1] "r"(nc1),
[step] "r"(step), [step1] "r"(step1)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q10", "q11", "q12", "q13");
}
if (_nc1 != 0) {
for (int i = 0; i < mc; i++) {
C0 = C_ptr + nc1 * 16 + i * ldc;
c0 = c_ptr + nc1 * 16 + i * NC;
for (int j = 0; j < _nc1; j++) {
*C0++ += *c0++;
}
}
}
}
// C = A * B + C, relu(C)
void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc) {
int nc1 = nc / 16;
int _nc1 = nc % 16;
int step = 4 * ldc;
int step1 = 4 * (NC - 16 * nc1);
int volatile m = mc;
float *volatile c_ptr, *volatile C_ptr;
float *C0, *c0;
c_ptr = c;
C_ptr = C;
if (nc1 > 0) {
asm volatile(
"vmov.f32 q14, #0.0 \n\t"
"subs %[mc], %[mc], #1 \n\t"
"blt end_mc_%= \n\t"
"loop_mc_%=: \n\t"
"mov r6, %[C_ptr] \n\t"
"mov r5, %[nc1] \n\t"
"subs r5, r5, #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [r6] \n\t"
"vld1.32 {q2, q3}, [%[c_ptr]]! \n\t"
"vadd.f32 q10, q0, q2 \n\t"
"vadd.f32 q11, q1, q3 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vst1.32 {q10, q11}, [r6]! \n\t"
"vld1.32 {q4, q5}, [r6] \n\t"
"vld1.32 {q6, q7}, [%[c_ptr]]! \n\t"
"vadd.f32 q12, q4, q6 \n\t"
"vadd.f32 q13, q5, q7 \n\t"
"vmax.f32 q12, q12, q14 \n\t"
"vmax.f32 q13, q13, q14 \n\t"
"vst1.32 {q12, q13}, [r6]! \n\t"
"subs r5, r5, #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"add %[C_ptr], %[C_ptr], %[step] \n\t"
"add %[c_ptr], %[c_ptr], %[step1] \n\t"
"subs %[mc], %[mc], #1 \n\t"
"bge loop_mc_%= \n\t"
"end_mc_%=: \n\t"
:
: [C_ptr] "r"(C_ptr), [c_ptr] "r"(c_ptr), [mc] "r"(m), [nc1] "r"(nc1),
[step] "r"(step), [step1] "r"(step1)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q10", "q11", "q12", "q13");
}
if (_nc1 != 0) {
for (int i = 0; i < mc; i++) {
C0 = C_ptr + nc1 * 16 + i * ldc;
c0 = c_ptr + nc1 * 16 + i * NC;
for (int j = 0; j < _nc1; j++) {
*C0 += *c0;
if (*C0 < 0) {
*C0 = 0;
}
C0++;
c0++;
}
}
}
}
// C = A * B, batchnorm(C)
void WriteWithBn(int mc, int nc, float *c, float *C, int ldc, float *scale,
float *bias) {
if (nc < 4) {
for (int i = 0; i < mc; ++i) {
for (int j = 0; j < nc; ++j) {
*C = (*c) * (*scale) + (*bias);
C++;
c++;
}
C += (ldc - nc);
c += (NC - nc);
scale++;
bias++;
}
return;
}
int volatile nc1 = nc / 16;
int _nc1 = nc % 16;
int volatile nc2 = _nc1 / 4;
int volatile nc3 = 16 - 4 * (_nc1 % 4);
int volatile step = 4 * (ldc - nc);
int volatile step1 = 4 * (NC - nc);
asm volatile(
"subs %[mc], %[mc], #1 \n\t"
"blt end_mc_%= \n\t"
"loop_mc_%=: \n\t"
"mov r5, %[nc1] \n\t"
"mov r6, %[nc2] \n\t"
"vld1.32 {d0}, [%[scale]] \n\t"
"vld1.32 {d1}, [%[bias]] \n\t"
"vdup.32 q1, d0[0] \n\t"
"vdup.32 q2, d1[0] \n\t"
"subs r5, r5, #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q3, q4}, [%[c]]! \n\t"
"vmul.f32 q10, q3, q1 \n\t"
"vmul.f32 q11, q4, q1 \n\t"
"vadd.f32 q10, q10, q2 \n\t"
"vadd.f32 q11, q11, q2 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q5, q6}, [%[c]]! \n\t"
"vmul.f32 q12, q5, q1 \n\t"
"vmul.f32 q13, q6, q1 \n\t"
"vadd.f32 q12, q12, q2 \n\t"
"vadd.f32 q13, q13, q2 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs r5, r5, #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs r6, r6, #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"vld1.32 {q7}, [%[c]]! \n\t"
"vmul.f32 q10, q7, q1 \n\t"
"vadd.f32 q10, q10, q2 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"subs r6, r6, #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
"cmp %[nc3], #16 \n\t"
"beq end_nc3_%= \n\t"
"sub %[c], %[c], %[nc3] \n\t"
"sub %[C], %[C], %[nc3] \n\t"
"vld1.32 {q8}, [%[c]]! \n\t"
"vmul.f32 q11, q8, q1 \n\t"
"vadd.f32 q11, q11, q2 \n\t"
"vst1.32 {q11}, [%[C]]! \n\t"
"end_nc3_%=: \n\t"
"add %[scale], %[scale], #4 \n\t"
"add %[bias], %[bias], #4 \n\t"
"add %[c], %[c], %[step1] \n\t"
"add %[C], %[C], %[step] \n\t"
"subs %[mc], %[mc], #1 \n\t"
"bge loop_mc_%= \n\t"
"end_mc_%=: \n\t"
:
: [C] "r"(C), [c] "r"(c), [mc] "r"(mc), [nc1] "r"(nc1), [nc2] "r"(nc2),
[nc3] "r"(nc3), [step] "r"(step), [step1] "r"(step1),
[scale] "r"(scale), [bias] "r"(bias)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q10", "q11", "q12", "q13");
}
// C = A * B, batchnorm(C), relu(C)
void WriteWithBnRelu(int mc, int nc, float *c, float *C, int ldc, float *scale,
float *bias) {
if (nc < 4) {
for (int i = 0; i < mc; ++i) {
for (int j = 0; j < nc; ++j) {
*C = (*c) * (*scale) + (*bias);
if (*C < 0) {
*C = 0;
}
C++;
c++;
}
C += (ldc - nc);
c += (NC - nc);
scale++;
bias++;
}
return;
}
int nc1 = nc / 16;
int _nc1 = nc % 16;
int nc2 = _nc1 / 4;
int nc3 = 16 - 4 * (_nc1 % 4);
int step = 4 * (ldc - nc);
int step1 = 4 * (NC - nc);
asm volatile(
"vmov.f32 q14, #0.0 \n\t"
"subs %[mc], %[mc], #1 \n\t"
"blt end_mc_%= \n\t"
"loop_mc_%=: \n\t"
"mov r5, %[nc1] \n\t"
"mov r6, %[nc2] \n\t"
"vld1.32 {d0}, [%[scale]] \n\t"
"vld1.32 {d1}, [%[bias]] \n\t"
"vdup.32 q1, d0[0] \n\t"
"vdup.32 q2, d1[0] \n\t"
"subs r5, r5, #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q3, q4}, [%[c]]! \n\t"
"vmul.f32 q10, q3, q1 \n\t"
"vmul.f32 q11, q4, q1 \n\t"
"vadd.f32 q10, q10, q2 \n\t"
"vadd.f32 q11, q11, q2 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q5, q6}, [%[c]]! \n\t"
"vmul.f32 q12, q5, q1 \n\t"
"vmul.f32 q13, q6, q1 \n\t"
"vadd.f32 q12, q12, q2 \n\t"
"vadd.f32 q13, q13, q2 \n\t"
"vmax.f32 q12, q12, q14 \n\t"
"vmax.f32 q13, q13, q14 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs r5, r5, #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs r6, r6, #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"vld1.32 {q7}, [%[c]]! \n\t"
"vmul.f32 q10, q7, q1 \n\t"
"vadd.f32 q10, q10, q2 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"subs r6, r6, #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
"cmp %[nc3], #16 \n\t"
"beq end_nc3_%= \n\t"
"sub %[c], %[c], %[nc3] \n\t"
"sub %[C], %[C], %[nc3] \n\t"
"vld1.32 {q8}, [%[c]]! \n\t"
"vmul.f32 q11, q8, q1 \n\t"
"vadd.f32 q11, q11, q2 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vst1.32 {q11}, [%[C]]! \n\t"
"end_nc3_%=: \n\t"
"add %[scale], %[scale], #4 \n\t"
"add %[bias], %[bias], #4 \n\t"
"add %[c], %[c], %[step1] \n\t"
"add %[C], %[C], %[step] \n\t"
"subs %[mc], %[mc], #1 \n\t"
"bge loop_mc_%= \n\t"
"end_mc_%=: \n\t"
:
: [C] "r"(C), [c] "r"(c), [mc] "r"(mc), [nc1] "r"(nc1), [nc2] "r"(nc2),
[nc3] "r"(nc3), [step] "r"(step), [step1] "r"(step1),
[scale] "r"(scale), [bias] "r"(bias)
: "memory", "r5", "r6", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q10", "q11", "q12", "q13", "q14");
}
/*
// C = A * B
void VecWriteBasic(int n, float *c, float *C, int ldc) {
int nc1 = n / 16;
int _nc1 = n % 16;
int nc2 = _nc1 / 4;
int nc3 = 16 - 4 * (_nc1 % 4);
asm volatile(
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c]]! \n\t"
"vst1.32 {q0, q1}, [%[C]]! \n\t"
"vld1.32 {q2, q3}, [%[c]]! \n\t"
"vst1.32 {q2, q3}, [%[C]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"vld1.32 {q4}, [%[c]]! \n\t"
"vst1.32 {q4}, [%[C]]! \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
"cmp %[nc3], #16 \n\t"
"beq end_nc3_%= \n\t"
"sub %[c], %[c], %[nc3] \n\t"
"sub %[C], %[C], %[nc3] \n\t"
"vld1.32 {q5}, [%[c]]! \n\t"
"vst1.32 {q5}, [%[C]]! \n\t"
"end_nc3_%=: \n\t"
:
: [C] "r"(C), [c] "r"(c), [nc1] "r"(nc1), [nc2] "r"(nc2), [nc3] "r"(nc3)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5");
}
// C = alpha * A * B + beta * C
void VecWriteWithAlphaBeta(int n, float *c, float *C, int ldc) {}
// C = A * B + C
void VecWriteWithAdd(int n, float *c, float *C, int ldc) {
int nc1 = n / 16;
int _nc1 = n % 16;
asm volatile(
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c]]! \n\t"
"vld1.32 {q2, q3}, [%[C]] \n\t"
"vadd.f32 q10, q0, q2 \n\t"
"vadd.f32 q11, q1, q3 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q4, q5}, [%[c]]! \n\t"
"vld1.32 {q6, q7}, [%[C]] \n\t"
"vadd.f32 q12, q4, q6 \n\t"
"vadd.f32 q13, q5, q7 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
: [C] "+r"(C), [c] "+r"(c)
: [nc1] "r"(nc1)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q10",
"q11", "q12", "q13");
if (_nc1 != 0) {
for (int j = 0; j < _nc1; j++) {
*C++ += *c++;
}
}
}
// C = A * B + C, relu(C)
void VecWriteWithAddRelu(int n, float *c, float *C, int ldc) {
int nc1 = n / 16;
int _nc1 = n % 16;
asm volatile(
"vmov.f32 q14, #0.0 \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c]]! \n\t"
"vld1.32 {q2, q3}, [%[C]] \n\t"
"vadd.f32 q10, q0, q2 \n\t"
"vadd.f32 q11, q1, q3 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q4, q5}, [%[c]]! \n\t"
"vld1.32 {q6, q7}, [%[C]] \n\t"
"vadd.f32 q12, q4, q6 \n\t"
"vadd.f32 q13, q5, q7 \n\t"
"vmax.f32 q12, q12, q14 \n\t"
"vmax.f32 q13, q13, q14 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
: [C] "+r"(C), [c] "+r"(c)
: [nc1] "r"(nc1)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q10",
"q11", "q12", "q13");
if (_nc1 != 0) {
for (int j = 0; j < _nc1; j++) {
*C += *c;
if (*C < 0) {
*C = 0;
}
C++;
c++;
}
}
}
// C = A * B, batchnorm(C)
void VecWriteWithBn(int n, float *c, float *C, int ldc, float *scale,
float *bias) {
int nc1 = n / 16;
int _nc1 = n % 16;
int nc2 = _nc1 / 4;
int nc3 = 16 - 4 * (_nc1 % 4);
asm volatile(
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c]]! \n\t"
"vld1.32 {q2, q3}, [%[scale]]! \n\t"
"vld1.32 {q10, q11}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q2 \n\t"
"vmla.f32 q11, q1, q3 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q4, q5}, [%[c]]! \n\t"
"vld1.32 {q6, q7}, [%[scale]]! \n\t"
"vld1.32 {q12, q13}, [%[bias]]! \n\t"
"vmla.f32 q12, q4, q6 \n\t"
"vmla.f32 q13, q5, q7 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"vld1.32 {q0}, [%[c]]! \n\t"
"vld1.32 {q1}, [%[scale]]! \n\t"
"vld1.32 {q10}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q1 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
"cmp %[nc3], #16 \n\t"
"beq end_nc3_%= \n\t"
"sub %[c], %[c], %[nc3] \n\t"
"sub %[scale], %[scale], %[nc3] \n\t"
"sub %[bias], %[bias], %[nc3] \n\t"
"sub %[C], %[C], %[nc3] \n\t"
"vld1.32 {q0}, [%[c]]! \n\t"
"vld1.32 {q1}, [%[scale]]! \n\t"
"vld1.32 {q10}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q1 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"end_nc3_%=: \n\t"
:
: [C] "r"(C), [c] "r"(c), [nc1] "r"(nc1), [nc2] "r"(nc2), [nc3]
"r"(nc3), [scale] "r"(scale), [bias] "r"(bias) : "memory", "q0", "q1", "q2",
"q3", "q4", "q5", "q6", "q7", "q10", "q11", "q12", "q13");
}
// C = A * B, batchnorm(C), relu(C)
void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *scale,
float *bias) {
int nc1 = n / 16;
int _nc1 = n % 16;
int nc2 = _nc1 / 4;
int nc3 = 16 - 4 * (_nc1 % 4);
asm volatile(
"vmov.f32 q14, #0.0 \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"blt end_nc1_%= \n\t"
"loop_nc1_%=: \n\t"
"vld1.32 {q0, q1}, [%[c]]! \n\t"
"vld1.32 {q2, q3}, [%[scale]]! \n\t"
"vld1.32 {q10, q11}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q2 \n\t"
"vmla.f32 q11, q1, q3 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vmax.f32 q11, q11, q14 \n\t"
"vst1.32 {q10, q11}, [%[C]]! \n\t"
"vld1.32 {q4, q5}, [%[c]]! \n\t"
"vld1.32 {q6, q7}, [%[scale]]! \n\t"
"vld1.32 {q12, q13}, [%[bias]]! \n\t"
"vmla.f32 q12, q4, q6 \n\t"
"vmla.f32 q13, q5, q7 \n\t"
"vmax.f32 q12, q12, q14 \n\t"
"vmax.f32 q13, q13, q14 \n\t"
"vst1.32 {q12, q13}, [%[C]]! \n\t"
"subs %[nc1], %[nc1], #1 \n\t"
"bge loop_nc1_%= \n\t"
"end_nc1_%=: \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"blt end_nc2_%= \n\t"
"loop_nc2_%=: \n\t"
"vld1.32 {q0}, [%[c]]! \n\t"
"vld1.32 {q1}, [%[scale]]! \n\t"
"vld1.32 {q10}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q1 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"subs %[nc2], %[nc2], #1 \n\t"
"bge loop_nc2_%= \n\t"
"end_nc2_%=: \n\t"
"cmp %[nc3], #16 \n\t"
"beq end_nc3_%= \n\t"
"sub %[c], %[c], %[nc3] \n\t"
"sub %[scale], %[scale], %[nc3] \n\t"
"sub %[bias], %[bias], %[nc3] \n\t"
"sub %[C], %[C], %[nc3] \n\t"
"vld1.32 {q0}, [%[c]]! \n\t"
"vld1.32 {q1}, [%[scale]]! \n\t"
"vld1.32 {q10}, [%[bias]]! \n\t"
"vmla.f32 q10, q0, q1 \n\t"
"vmax.f32 q10, q10, q14 \n\t"
"vst1.32 {q10}, [%[C]]! \n\t"
"end_nc3_%=: \n\t"
:
: [C] "r"(C), [c] "r"(c), [nc1] "r"(nc1), [nc2] "r"(nc2), [nc3]
"r"(nc3), [scale] "r"(scale), [bias] "r"(bias) : "memory", "q0", "q1", "q2",
"q3", "q4", "q5", "q6", "q7", "q10", "q11", "q12", "q13", "q14");
}
*/
#endif // __aarch64__
#else
void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc) {
float *c0, *c1, *c2, *c3;
c0 = c;
c1 = c + ldc;
c2 = c + 2 * ldc;
c3 = c + 3 * ldc;
for (int p = 0; p < k; p += 1) {
// first row
c0[0] += a[0] * b[0];
c0[1] += a[0] * b[1];
c0[2] += a[0] * b[2];
c0[3] += a[0] * b[3];
// second row
c1[0] += a[1] * b[0];
c1[1] += a[1] * b[1];
c1[2] += a[1] * b[2];
c1[3] += a[1] * b[3];
// third row
c2[0] += a[2] * b[0];
c2[1] += a[2] * b[1];
c2[2] += a[2] * b[2];
c2[3] += a[2] * b[3];
// fourth row
c3[0] += a[3] * b[0];
c3[1] += a[3] * b[1];
c3[2] += a[3] * b[2];
c3[3] += a[3] * b[3];
a += 4;
b += 4;
}
}
#endif // __ARM_NEON
// 32位 float 矩阵乘法
void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc, bool relu) {
// L1 data cache is 32 kib (Per Contex-A57, Contex-A72, Contex-A73)
// L2 cache is 0.5~4 Mib (Contex-A72 cluster)
int L1 = 30 * 1024;
int L2 = 1 * 1024 * 1024;
KC = k;
MC = L2 / (2 * KC * sizeof(float));
NC = MC;
// make sure MC is multiple of 4, and NC is multiple of 8
int mblock_num = (m + MC - 1) / MC;
MC = (m + mblock_num - 1) / mblock_num;
MC = (MC + 4 - 1) / 4 * 4;
// DLOG << "mblock_num = " << mblock_num << ", MC = " << MC << "\n";
int nblock_num = (n + NC - 1) / NC;
NC = (n + nblock_num - 1) / nblock_num;
NC = (NC + 8 - 1) / 8 * 8;
// DLOG << "nblock_num = " << nblock_num << ", NC = " << NC << "\n";
packedA = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * MC * KC));
packedB = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * KC * NC));
packedC = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * MC * NC));
zero = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * KC));
for (int l = 0; l < KC; ++l) {
zero[l] = 0;
}
int mc, nc;
for (int j = 0; j < n; j += NC) {
nc = s_min(n - j, NC);
PackMatrixB_(KC, nc, nc % NR, &B(0, j), ldb, packedB);
for (int i = 0; i < m; i += MC) {
mc = s_min(m - i, MC);
PackMatrixA_(mc, KC, mc % MR, &A(i, 0), lda, packedA);
InnerKernel(mc, nc, alpha, packedA, packedB, beta, packedC, &C(i, j), ldc,
relu);
}
}
paddle_mobile::memory::Free(packedA);
paddle_mobile::memory::Free(packedB);
paddle_mobile::memory::Free(packedC);
paddle_mobile::memory::Free(zero);
}
void SgemmWithBn(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc,
bool relu, float *new_scale, float *new_bias) {
// L1 data cache is 32 kib (Per Contex-A57, Contex-A72, Contex-A73)
// L2 cache is 0.5~4 Mib (Contex-A72 cluster)
int L1 = 30 * 1024;
int L2 = 1 * 1024 * 1024;
KC = k;
MC = L2 / (2 * KC * sizeof(float));
NC = MC;
// make sure MC is multiple of 4, and NC is multiple of 8
int mblock_num = (m + MC - 1) / MC;
MC = (m + mblock_num - 1) / mblock_num;
MC = (MC + 4 - 1) / 4 * 4;
// DLOG << "mblock_num = " << mblock_num << ", MC = " << MC << "\n";
int nblock_num = (n + NC - 1) / NC;
NC = (n + nblock_num - 1) / nblock_num;
NC = (NC + 8 - 1) / 8 * 8;
// DLOG << "nblock_num = " << nblock_num << ", NC = " << NC << "\n";
packedA = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * MC * KC));
packedB = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * KC * NC));
packedC = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * MC * NC));
zero = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * KC));
for (int l = 0; l < KC; ++l) {
zero[l] = 0;
}
int mc, nc;
for (int j = 0; j < n; j += NC) {
nc = s_min(n - j, NC);
PackMatrixB_(KC, nc, nc % NR, &B(0, j), ldb, packedB);
for (int i = 0; i < m; i += MC) {
mc = s_min(m - i, MC);
PackMatrixA_(mc, KC, mc % MR, &A(i, 0), lda, packedA);
InnerKernelWithBn(mc, nc, alpha, packedA, packedB, beta, packedC,
&C(i, j), ldc, relu, new_scale + i, new_bias + i);
}
}
paddle_mobile::memory::Free(packedA);
paddle_mobile::memory::Free(packedB);
paddle_mobile::memory::Free(packedC);
paddle_mobile::memory::Free(zero);
}
} // namespace math
} // namespace operators
......
src/operators/math/gemm.h
浏览文件 @ 2b9ea6ea
......@@ -19,12 +19,8 @@ limitations under the License. */
#define B(i, j) B[(i)*ldb + (j)]
#define C(i, j) C[(i)*ldc + (j)]
// 分块计算的块大小，mc 与 kc 分别对应分块计算时的 m 与 k
#define MC 128
#define KC 128
#define NC 1024
#define MR 4
#define NR 4
#define NR 8
#define s_min(i, j) ((i) < (j) ? (i) : (j))
......@@ -32,6 +28,7 @@ namespace paddle_mobile {
namespace operators {
namespace math {
/*
// 将 A 矩阵分块复制到连续内存(ColMajor)
void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
float *buffer);
......@@ -39,6 +36,7 @@ void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
// 将 B 矩阵分块复制到连续内存(ColMajor)
void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
float *buffer);
*/
// 将 A 矩阵分块复制到连续内存(RowMajor)
void PackMatrixA_(int m, int k, int m_tail, const float *A, int lda,
......@@ -49,28 +47,69 @@ void PackMatrixB_(int k, int n, int n_tail, const float *B, int ldb,
float *buffer);
// 分块矩阵乘法
void InnerKernel(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc,
int first_time);
void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
float beta, float *c, float *C, int ldc, bool relu);
void InnerKernelWithBn(int mc, int nc, float alpha, const float *a,
const float *b, float beta, float *c, float *C, int ldc,
bool relu, float *new_scale, float *new_bias);
/*
// 向量矩阵乘法 (M = 1)
void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc);
// 计算一个更小的 4 * 4 的 C 矩阵分块
void AddDot4x4(int k, float alpha, const float *A, int lda, const float *B,
int ldb, float beta, float *C, int ldc, int mc, int nc);
void AddDot4x4_relu(int k, float alpha, const float *a, int lda, const float *b,
int ldb, float beta, float *C, int ldc, int mc, int nc,
bool relu);
const float *B, int ldb, float beta, float *C, int ldc,
bool relu);
void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
int lda, const float *B, int ldb, float beta, float *C,
int ldc, bool relu, float *new_scale, float *new_bias);
*/
// 计算一个更小的 C 矩阵分块
void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc);
void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc);
// 分块矩阵乘法结果回写
// C = A * B
void WriteBasic(int mc, int nc, float *c, float *C, int ldc);
// C = alpha * A * B + beta * C
void WriteWithAlphaBeta(int mc, int nc, float *c, float *C, int ldc);
// C = A * B + C
void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc);
// C = A * B + C, relu(C)
void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc);
// C = A * B, batchnorm(C)
void WriteWithBn(int mc, int nc, float *c, float *C, int ldc, float *new_scale,
float *new_bias);
// C = A * B, batchnorm(C), relu(C)
void WriteWithBnRelu(int mc, int nc, float *c, float *C, int ldc,
float *new_scale, float *new_bias);
/*
// 向量矩阵乘法结果回写
// C = A * B
void VecWriteBasic(int n, float *c, float *C, int ldc);
// C = alpha * A * B + beta * C
void VecWriteWithAlphaBeta(int n, float *c, float *C, int ldc);
// C = A * B + C
void VecWriteWithAdd(int n, float *c, float *C, int ldc);
// C = A * B + C, relu(C)
void VecWriteWithAddRelu(int n, float *c, float *C, int ldc);
// C = A * B, batchnorm(C)
void VecWriteWithBn(int n, float *c, float *C, int ldc, float *new_scale,
float *new_bias);
// C = A * B, batchnorm(C), relu(C)
void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
float *new_bias);
*/
// 32位 float 矩阵乘法
void sgemm(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc);
void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc, bool relu);
void sgemm_relu(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc);
// 32位 float 矩阵乘法, 并对结果进行 batchnrom
void SgemmWithBn(int m, int n, int k, float alpha, const float *A, int lda,
const float *B, int ldb, float beta, float *C, int ldc,
bool relu, float *new_scale, float *new_bias);
// 64位 double 矩阵乘法
void dgemm(int m, int n, int k, float alpha, const double *A, int lda,
......
src/operators/math/im2col.cpp
浏览文件 @ 2b9ea6ea
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "operators/math/im2col.h"
#include <vector>
#ifdef __ARM_NEON
#include "arm_neon.h"
#include <arm_neon.h>
#endif
#include "common/types.h"
namespace paddle_mobile {
......@@ -69,7 +69,7 @@ class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
int channels_col = im_channels * filter_height * filter_width;
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
#ifdef __ARM_NEON
#if __ARM_NEON
const int osize = col_height;
const int isize = im_height;
bool pad1 = padding[0] > 0;
......
src/operators/math/math_function.cpp
浏览文件 @ 2b9ea6ea
......@@ -39,22 +39,18 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
int K = (!trans_a) ? dim_a[1] : dim_a[0];
if (relu) {
sgemm_relu(M, N, K, alpha, matrix_a.data<float>(), K,
matrix_b.data<float>(), N, beta, matrix_out->data<float>(), N);
} else {
sgemm(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(), N,
beta, matrix_out->data<float>(), N);
}
Sgemm(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(), N,
beta, matrix_out->data<float>(), N, relu);
}
template <>
void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
double alpha, framework::Tensor *matrix_out, double beta,
bool relu) {
void matmulWithBn<float>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
float alpha, framework::Tensor *matrix_out, float beta,
bool relu, framework::Tensor *new_scale,
framework::Tensor *new_bias, int group) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
......@@ -71,7 +67,12 @@ void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
int K = (!trans_a) ? dim_a[1] : dim_a[0];
SgemmWithBn(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(),
N, beta, matrix_out->data<float>(), N, relu,
new_scale->data<float>() + group,
new_bias->data<float>() + group);
}
} // namespace math
......
src/operators/math/math_function.h
浏览文件 @ 2b9ea6ea
......@@ -26,6 +26,13 @@ template <typename T>
void matmul(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b, T alpha,
framework::Tensor *matrix_out, T beta, bool relu = false);
template <typename T>
void matmulWithBn(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b, T alpha,
framework::Tensor *matrix_out, T beta, bool relu,
framework::Tensor *new_scale, framework::Tensor *new_bias,
int group);
} // namespace math
} // namespace operators
} // namespace paddle_mobile
src/operators/math/pool_2x2.cpp
浏览文件 @ 2b9ea6ea
......@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#ifdef POOL_OP
#include "pool_2x2.h"
#include "operators/math/pool_2x2.h"
#include <algorithm>
#include <vector>
namespace paddle_mobile {
namespace operators {
......@@ -23,8 +25,8 @@ void Pool2x2Max(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output) {
#if __ARM_NEON
#ifdef ARMV7
#if __aarch64__
#else
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
......@@ -93,7 +95,7 @@ void Pool2x2Max(vector<int> strides, vector<int> paddings, const Tensor *input,
output_data += output_batch_stride;
}
#endif
#else
#endif
}
......@@ -101,7 +103,8 @@ void Pool2x2Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output) {
#if __ARM_NEON
#ifdef ARMV7
#if __aarch64__
#else
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
......@@ -171,12 +174,9 @@ void Pool2x2Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#else
// TODO(): to imp other asm
#endif
#else
#endif
}
......
src/operators/math/pool_2x2.h
浏览文件 @ 2b9ea6ea
......@@ -17,7 +17,7 @@ limitations under the License. */
#pragma once
#include "framework/tensor.h"
#if __ARM_NEON
#ifdef __ARM_NEON
#include <arm_neon.h>
#endif // __ARM_NEON
namespace paddle_mobile {
......
src/operators/math/pool_3x3.cpp
浏览文件 @ 2b9ea6ea
......@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#ifdef POOL_OP
#include "pool_3x3.h"
#ifdef _OPENMP
#include <omp.h>
#endif
#include "framework/tensor.h"
#include "operators/math/pool_3x3.h"
#if __ARM_NEON
#include <arm_neon.h>
#endif // __ARM_NEON
......@@ -40,46 +43,52 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
const int w_out = output->dims()[3];
const int outputdata_channel_stride = h_out * w_out;
const int inputdata_channel_stride = h_in * w_in;
const int input_batch_stride = output_channels * inputdata_channel_stride;
const int output_batch_stride = output_channels * outputdata_channel_stride;
float *out_data = output->data<float>();
const float *input_data = input->data<float>();
const float coef = 1.0 / 9.0;
for (int k = 0; k < batch_size; ++k) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
const float *input_seg = input_data + c * inputdata_channel_stride;
float *output_seg = out_data + c * outputdata_channel_stride;
// four corner point
out_data[0] = (input_data[0] + input_data[1] + input_data[w_in] +
input_data[w_in + 1]) *
coef;
out_data[w_out - 1] =
(input_data[w_in - 2] + input_data[w_in - 1] +
input_data[w_in * 2 - 2] + input_data[2 * w_in - 1]) *
output_seg[0] = (input_seg[0] + input_seg[1] + input_seg[w_in] +
input_seg[w_in + 1]) *
coef;
output_seg[w_out - 1] =
(input_seg[w_in - 2] + input_seg[w_in - 1] + input_seg[w_in * 2 - 2] +
input_seg[2 * w_in - 1]) *
coef;
out_data[(h_out - 1) * w_out] =
(input_data[(h_in - 2) * w_in] + input_data[(h_in - 2) * w_in + 1] +
input_data[(h_in - 1) * w_in] + input_data[(h_in - 1) * w_in + 1]) *
output_seg[(h_out - 1) * w_out] =
(input_seg[(h_in - 2) * w_in] + input_seg[(h_in - 2) * w_in + 1] +
input_seg[(h_in - 1) * w_in] + input_seg[(h_in - 1) * w_in + 1]) *
coef;
out_data[h_out * w_out - 1] =
(input_data[h_in * w_in - 1] + input_data[h_in * w_in - 2] +
input_data[(h_in - 1) * w_in - 1] +
input_data[(h_in - 1) * w_in - 2]) *
output_seg[h_out * w_out - 1] =
(input_seg[h_in * w_in - 1] + input_seg[h_in * w_in - 2] +
input_seg[(h_in - 1) * w_in - 1] +
input_seg[(h_in - 1) * w_in - 2]) *
coef;
// left side & right side
for (int i = 1; i < h_in - 1; ++i) {
out_data[i * w_out] =
(input_data[i * w_in - w_in] + input_data[i * w_in - w_in + 1] +
input_data[i * w_in] + input_data[i * w_in + 1] +
input_data[i * w_in + w_in] + input_data[i * w_in + w_in + 1]) *
output_seg[i * w_out] =
(input_seg[i * w_in - w_in] + input_seg[i * w_in - w_in + 1] +
input_seg[i * w_in] + input_seg[i * w_in + 1] +
input_seg[i * w_in + w_in] + input_seg[i * w_in + w_in + 1]) *
coef;
out_data[i * w_out + w_out - 1] =
(input_data[i * w_in - w_in + w_in - 2] +
input_data[i * w_in - w_in + 1 + w_in - 2] +
input_data[i * w_in + w_in - 2] +
input_data[i * w_in + 1 + w_in - 2] +
input_data[i * w_in + w_in + w_in - 2] +
input_data[i * w_in + w_in + 1 + w_in - 2]) *
output_seg[i * w_out + w_out - 1] =
(input_seg[i * w_in - w_in + w_in - 2] +
input_seg[i * w_in - w_in + 1 + w_in - 2] +
input_seg[i * w_in + w_in - 2] +
input_seg[i * w_in + 1 + w_in - 2] +
input_seg[i * w_in + w_in + w_in - 2] +
input_seg[i * w_in + w_in + 1 + w_in - 2]) *
coef;
}
// top 1 row & bottom 1 row
const float *input_tmp = input_data;
const float *input_tmp = input_seg;
float32x4_t in0, in1, in2, in3, in4, in5, in6, in7, tmp0, tmp1, tmp2,
tmp3, tmp4, tmp5, sum, out0;
......@@ -90,7 +99,7 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
in4 = vld1q_f32(input_tmp_end);
in6 = vld1q_f32(input_tmp_end + w_in);
int c_mid = w_out - 2;
auto output_ptr = out_data + 1;
auto output_ptr = output_seg + 1;
for (; c_mid > 3; c_mid -= 4) {
in1 = vld1q_f32(input_tmp + 4);
in3 = vld1q_f32(input_tmp + w_in + 4);
......@@ -135,8 +144,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
in6 = in7;
}
// top right remain
float32x4_t pad0 = vdupq_n_f32(input_data[w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[2 * w_in - 1]);
float32x4_t pad0 = vdupq_n_f32(input_seg[w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_seg[2 * w_in - 1]);
tmp0 = vextq_f32(in0, pad0, 1);
tmp1 = vextq_f32(in0, pad0, 2);
......@@ -163,8 +172,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
}
// bottom_right remain
float32x4_t pad2 = vdupq_n_f32(input_data[(h_in - 1) * w_in - 1]);
float32x4_t pad3 = vdupq_n_f32(input_data[h_in * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_seg[(h_in - 1) * w_in - 1]);
float32x4_t pad3 = vdupq_n_f32(input_seg[h_in * w_in - 1]);
tmp0 = vextq_f32(in4, pad2, 1);
tmp1 = vextq_f32(in4, pad2, 2);
......@@ -191,8 +200,8 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
}
// mid
for (int j = 0; j < h_out - 2; ++j) {
output_ptr = out_data + w_out * (j + 1) + 1;
input_tmp = input_data + j * w_in;
output_ptr = output_seg + w_out * (j + 1) + 1;
input_tmp = input_seg + j * w_in;
in0 = vld1q_f32(input_tmp);
in2 = vld1q_f32(input_tmp + w_in);
......@@ -228,9 +237,9 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
in4 = in5;
}
// mid remain
float32x4_t pad0 = vdupq_n_f32(input_data[(j + 1) * w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
float32x4_t pad0 = vdupq_n_f32(input_seg[(j + 1) * w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
tmp0 = vextq_f32(in0, pad0, 1);
tmp1 = vextq_f32(in0, pad0, 2);
......@@ -261,9 +270,11 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
}
}
}
input_data += inputdata_channel_stride;
out_data += outputdata_channel_stride;
// input_data += inputdata_channel_stride;
// out_data += outputdata_channel_stride;
}
input_data += input_batch_stride;
out_data += output_batch_stride;
}
#endif
}
......@@ -282,44 +293,50 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
const int w_out = output->dims()[3];
const int outputdata_channel_stride = h_out * w_out;
const int inputdata_channel_stride = h_in * w_in;
const int input_batch_stride = output_channels * inputdata_channel_stride;
const int output_batch_stride = output_channels * outputdata_channel_stride;
float *out_data = output->data<float>();
const float *input_data = input->data<float>();
for (int k = 0; k < batch_size; ++k) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
const float *input_seg = input_data + c * inputdata_channel_stride;
float *output_seg = out_data + c * outputdata_channel_stride;
// four corner point
out_data[0] = std::max(std::max(input_data[0], input_data[1]),
std::max(input_data[w_in], input_data[w_in + 1]));
out_data[w_out - 1] = std::max(
std::max(input_data[w_in - 2], input_data[w_in - 1]),
std::max(input_data[w_in * 2 - 2], input_data[2 * w_in - 1]));
out_data[(h_out - 1) * w_out] =
std::max(std::max(input_data[(h_in - 2) * w_in],
input_data[(h_in - 2) * w_in + 1]),
std::max(input_data[(h_in - 1) * w_in],
input_data[(h_in - 1) * w_in + 1]));
out_data[h_out * w_out - 1] = std::max(
std::max(input_data[(h_in - 1) * w_in - 1],
input_data[(h_in - 1) * w_in - 2]),
std::max(input_data[h_in * w_in - 1], input_data[h_in * w_in - 2]));
output_seg[0] = std::max(std::max(input_seg[0], input_seg[1]),
std::max(input_seg[w_in], input_seg[w_in + 1]));
output_seg[w_out - 1] =
std::max(std::max(input_seg[w_in - 2], input_seg[w_in - 1]),
std::max(input_seg[w_in * 2 - 2], input_seg[2 * w_in - 1]));
output_seg[(h_out - 1) * w_out] =
std::max(std::max(input_seg[(h_in - 2) * w_in],
input_seg[(h_in - 2) * w_in + 1]),
std::max(input_seg[(h_in - 1) * w_in],
input_seg[(h_in - 1) * w_in + 1]));
output_seg[h_out * w_out - 1] = std::max(
std::max(input_seg[(h_in - 1) * w_in - 1],
input_seg[(h_in - 1) * w_in - 2]),
std::max(input_seg[h_in * w_in - 1], input_seg[h_in * w_in - 2]));
// left side & right side
for (int i = 1; i < h_in - 1; ++i) {
float max1 = std::max(input_data[i * w_in - w_in],
input_data[i * w_in - w_in + 1]);
float max2 = std::max(input_data[i * w_in], input_data[i * w_in + 1]);
float max3 = std::max(input_data[i * w_in + w_in],
input_data[i * w_in + w_in + 1]);
out_data[i * w_out] = std::max(std::max(max1, max2), max3);
max1 = std::max(input_data[i * w_in - w_in + w_in - 2],
input_data[i * w_in - w_in + 1 + w_in - 2]);
max2 = std::max(input_data[i * w_in + w_in - 2],
input_data[i * w_in + 1 + w_in - 2]);
max3 = std::max(input_data[i * w_in + w_in + w_in - 2],
input_data[i * w_in + w_in + 1 + w_in - 2]);
out_data[i * w_out + w_out - 1] = std::max(std::max(max1, max2), max3);
float max1 = std::max(input_seg[i * w_in - w_in],
input_seg[i * w_in - w_in + 1]);
float max2 = std::max(input_seg[i * w_in], input_seg[i * w_in + 1]);
float max3 = std::max(input_seg[i * w_in + w_in],
input_seg[i * w_in + w_in + 1]);
output_seg[i * w_out] = std::max(std::max(max1, max2), max3);
max1 = std::max(input_seg[i * w_in - w_in + w_in - 2],
input_seg[i * w_in - w_in + 1 + w_in - 2]);
max2 = std::max(input_seg[i * w_in + w_in - 2],
input_seg[i * w_in + 1 + w_in - 2]);
max3 = std::max(input_seg[i * w_in + w_in + w_in - 2],
input_seg[i * w_in + w_in + 1 + w_in - 2]);
output_seg[i * w_out + w_out - 1] =
std::max(std::max(max1, max2), max3);
}
// top 1 row & bottom 1 row
const float *input_tmp = input_data;
const float *input_tmp = input_seg;
float32x4_t in0, in1, in2, in3, in4, in5, in6, in7, tmp0, tmp1, tmp2,
tmp3, tmp4, tmp5, max;
......@@ -329,7 +346,7 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
in4 = vld1q_f32(input_tmp_end);
in6 = vld1q_f32(input_tmp_end + w_in);
int c_mid = w_out - 2;
auto output_ptr = out_data + 1;
auto output_ptr = output_seg + 1;
for (; c_mid > 3; c_mid -= 4) {
in1 = vld1q_f32(input_tmp + 4);
in3 = vld1q_f32(input_tmp + w_in + 4);
......@@ -373,8 +390,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
in6 = in7;
}
// top right remain
float32x4_t pad0 = vdupq_n_f32(input_data[w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[2 * w_in - 1]);
float32x4_t pad0 = vdupq_n_f32(input_seg[w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_seg[2 * w_in - 1]);
tmp0 = vextq_f32(in0, pad0, 1);
tmp1 = vextq_f32(in0, pad0, 2);
......@@ -400,8 +417,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
}
// bottom_right remain
float32x4_t pad2 = vdupq_n_f32(input_data[(h_in - 1) * w_in - 1]);
float32x4_t pad3 = vdupq_n_f32(input_data[h_in * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_seg[(h_in - 1) * w_in - 1]);
float32x4_t pad3 = vdupq_n_f32(input_seg[h_in * w_in - 1]);
tmp0 = vextq_f32(in4, pad2, 1);
tmp1 = vextq_f32(in4, pad2, 2);
......@@ -427,8 +444,8 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
}
// mid
for (int j = 0; j < h_out - 2; ++j) {
output_ptr = out_data + (j + 1) * w_out + 1;
input_tmp = input_data + j * w_in;
output_ptr = output_seg + (j + 1) * w_out + 1;
input_tmp = input_seg + j * w_in;
in0 = vld1q_f32(input_tmp);
in2 = vld1q_f32(input_tmp + w_in);
......@@ -463,9 +480,9 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
in4 = in5;
}
// mid remain
float32x4_t pad0 = vdupq_n_f32(input_data[(j + 1) * w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[(j + 2) * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_data[(j + 3) * w_in - 1]);
float32x4_t pad0 = vdupq_n_f32(input_seg[(j + 1) * w_in - 1]);
float32x4_t pad1 = vdupq_n_f32(input_seg[(j + 2) * w_in - 1]);
float32x4_t pad2 = vdupq_n_f32(input_seg[(j + 3) * w_in - 1]);
tmp0 = vextq_f32(in0, pad0, 1);
tmp1 = vextq_f32(in0, pad0, 2);
......@@ -495,10 +512,14 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
}
}
}
input_data += inputdata_channel_stride;
out_data += outputdata_channel_stride;
// input_data += inputdata_channel_stride;
// out_data += outputdata_channel_stride;
}
input_data += input_batch_stride;
out_data += output_batch_stride;
}
#else
#endif
}
......@@ -515,11 +536,11 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int _kernel_size = 3;
const int stride_height = strides[0];
const int stride_width = strides[1];
const int padding_height = paddings[0];
const int padding_width = paddings[1];
// const int _kernel_size = 3;
const int stride = strides[0];
// const int stride_width = strides[1];
const int padding = paddings[0];
// const int padding_width = paddings[1];
const float negative_max = -INT_MAX;
const int input_channel_stride = input_height * input_width;
const int output_channel_stride = output_height * output_width;
......@@ -529,38 +550,52 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
const float *pos1, *pos2, *pos3, *output_ptr;
const float *pos1, *output_ptr;
int hstart, wstart, hend, wend;
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
const float *input_seg = input_data + c * input_channel_stride;
float *output_seg = output_data + c * output_channel_stride;
for (int ph = 0; ph < output_height; ph++) {
for (int pw = 0; pw < output_width; pw++) {
hstart = ph * stride_height - padding_height;
wstart = pw * stride_width - padding_width;
hend = min(hstart + _kernel_size, input_height + padding_height);
wend = min(wstart + _kernel_size, input_width + padding_width);
int hstart = ph * stride - padding;
int wstart = pw * stride - padding;
int hend = min(hstart + 3, input_height + padding);
int wend = min(wstart + 3, input_width + padding);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
hend = min(hend, input_height);
wend = min(wend, input_width);
pos1 = input_data + hstart * input_width + wstart;
pos2 = input_data + (hstart + 1) * input_width + wstart;
pos3 = input_data + (hstart + 2) * input_width + wstart;
output_ptr = output_data + ph * output_width + pw;
const float *pos1 = input_seg + hstart * input_width + wstart;
const float *pos2 = input_seg + (hstart + 1) * input_width + wstart;
const float *pos3 = input_seg + (hstart + 2) * input_width + wstart;
output_ptr = output_seg + ph * output_width + pw;
if (hend - hstart != 3 || wend - wstart != 3) {
float max_value = -INT_MAX;
for (int h = hstart; h < hend; h++) {
for (int w = wstart; w < wend; w++) {
float value = input_data[h * input_width + w];
float value = input_seg[h * input_width + w];
if (value > max_value) {
max_value = value;
}
}
}
output_data[ph * output_width + pw] = max_value;
output_seg[ph * output_width + pw] = max_value;
} else {
#if defined(ARMV7)
#if __aarch64__
const float32x4_t data1 = vld1q_f32(pos1);
const float32x4_t data2 = vld1q_f32(pos1 + input_width);
const float32x4_t data3 = vld1q_f32(pos1 + 2 * input_width);
const float32x4_t max_data =
vmaxq_f32(vmaxq_f32(data1, data2), data3);
float32x2_t res =
vpmax_f32(vget_high_f32(vsetq_lane_f32(-INT_MAX, max_data, 3)),
vget_low_f32(max_data));
res = vpmax_f32(res, res);
output_seg[ph * output_width + pw] = vget_lane_f32(res, 0);
#else
asm volatile(
"vld1.32 {q1}, [%[pos1]] \n\t"
"vld1.32 {q2}, [%[pos2]] \n\t"
......@@ -572,27 +607,14 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
"vpmax.f32 d7, d6, d6 \n\t"
"vst1.32 {d7[0]},[%[output_ptr]] \n\t"
:
: [input_data] "r"(input_data), [pos1] "r"(pos1),
: [input_seg] "r"(input_seg), [pos1] "r"(pos1),
[pos2] "r"(pos2), [pos3] "r"(pos3),
[output_ptr] "r"(output_ptr), [negative_max] "r"(negative_max)
: "memory", "q1", "q2", "q3", "q4");
#else
const float32x4_t data1 = vld1q_f32(pos1);
const float32x4_t data2 = vld1q_f32(pos2);
const float32x4_t data3 = vld1q_f32(pos3);
const float32x4_t max_data =
vmaxq_f32(vmaxq_f32(data1, data3), data2);
float32x2_t res =
vpmax_f32(vget_high_f32(vsetq_lane_f32(-INT_MAX, max_data, 3)),
vget_low_f32(max_data));
res = vpmax_f32(res, res);
output_data[ph * output_width + pw] = vget_lane_f32(res, 0);
#endif
}
}
}
input_data += input_channel_stride;
output_data += output_channel_stride;
}
input_data += input_batch_stride;
output_data += output_batch_stride;
......@@ -613,11 +635,8 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int _kernel_size = 3;
const int stride_height = strides[0];
const int stride_width = strides[1];
const int padding_height = paddings[0];
const int padding_width = paddings[1];
const int stride = strides[0];
const int padding = paddings[0];
const int input_channel_stride = input_height * input_width;
const int output_channel_stride = output_height * output_width;
......@@ -631,33 +650,36 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
const float *input_seg = input_data + c * input_channel_stride;
float *output_seg = output_data + c * output_channel_stride;
for (int ph = 0; ph < output_height; ph++) {
for (int pw = 0; pw < output_width; pw++) {
int hstart = ph * stride_height - padding_height;
int wstart = pw * stride_width - padding_width;
int hend = min(hstart + _kernel_size, input_height + padding_height);
int wend = min(wstart + _kernel_size, input_width + padding_width);
int hstart = ph * stride - padding;
int wstart = pw * stride - padding;
int hend = min(hstart + 3, input_height + padding);
int wend = min(wstart + 3, input_width + padding);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
hend = min(hend, input_height);
wend = min(wend, input_width);
const float *pos1 = input_data + hstart * input_width + wstart;
const float *pos2 = input_data + (hstart + 1) * input_width + wstart;
const float *pos3 = input_data + (hstart + 2) * input_width + wstart;
const float *output_ptr = output_data + ph * output_width + pw;
const float *pos1 = input_seg + hstart * input_width + wstart;
const float *pos2 = input_seg + (hstart + 1) * input_width + wstart;
const float *pos3 = input_seg + (hstart + 2) * input_width + wstart;
float *output_ptr = output_seg + ph * output_width + pw;
if (hend - hstart != 3 || wend - wstart != 3) {
float sum = 0;
for (int h = hstart; h < hend; h++) {
for (int w = wstart; w < wend; w++) {
sum += input_data[h * input_width + w];
sum += input_seg[h * input_width + w];
}
}
output_data[ph * output_width + pw] = sum / 9.0;
output_seg[ph * output_width + pw] = sum / 9.0;
} else {
#if defined(ARMV7)
#if __aarch64__
#else
asm volatile(
"vld1.32 {q1}, [%[pos1]] \n\t"
"vld1.32 {q2}, [%[pos2]] \n\t"
......@@ -671,12 +693,12 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
"vmul.f32 d6,d7 \n\t"
"vst1.32 {d6[0]},[%[output_ptr]] \n\t"
:
: [input_data] "r"(input_data), [pos1] "r"(pos1),
: [input_seg] "r"(input_seg), [pos1] "r"(pos1),
[pos2] "r"(pos2), [pos3] "r"(pos3),
[output_ptr] "r"(output_ptr), [zero] "r"(zero),
[nine_ptr] "r"(nine_ptr)
: "memory", "r6", "q1", "q2", "q3", "q4");
#else
#endif
const float32x4_t data1 = vld1q_f32(pos1);
const float32x4_t data2 = vld1q_f32(pos2);
const float32x4_t data3 = vld1q_f32(pos3);
......@@ -686,17 +708,15 @@ void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
vpadd_f32(vget_high_f32(vsetq_lane_f32(0, sum_data, 3)),
vget_low_f32(sum_data));
res = vpadd_f32(res, res);
output_data[ph * output_width + pw] = vget_lane_f32(res, 0) / 9.0;
#endif
output_seg[ph * output_width + pw] = vget_lane_f32(res, 0) / 9.0;
}
}
}
input_data += input_channel_stride;
output_data += output_channel_stride;
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#else
#endif
}
} // namespace math
......
src/operators/math/pool_3x3.h
浏览文件 @ 2b9ea6ea
......@@ -15,6 +15,9 @@ limitations under the License. */
#ifdef POOL_OP
#pragma once
#ifdef _OPENMP
#include <omp.h>
#endif
#include <algorithm>
#include <vector>
#include "framework/tensor.h"
......
src/operators/math/pooling.cpp
浏览文件 @ 2b9ea6ea
......@@ -16,6 +16,9 @@ limitations under the License. */
#include "pooling.h"
#include "common/types.h"
#ifdef _OPENMP
#include <omp.h>
#endif
namespace paddle_mobile {
namespace operators {
......@@ -57,8 +60,8 @@ class PoolFunctor<CPU, PoolProcess, T> {
T *output_data = output->mutable_data<T>();
for (int i = 0; i < batch_size; i++) {
// #pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
#pragma omp parallel for
for (int ph = 0; ph < output_height; ++ph) {
int hstart = ph * stride_height - padding_height;
int hend = std::min(hstart + ksize_height, input_height);
......
src/operators/math/pooling.h
浏览文件 @ 2b9ea6ea
......@@ -65,7 +65,7 @@ class PoolFunctor {
const std::vector<int> &paddings, PoolProcess pool_compute,
framework::Tensor *output);
};
}
} // namespace math
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/softmax.cpp
浏览文件 @ 2b9ea6ea
......@@ -16,7 +16,7 @@ limitations under the License. */
#include "operators/math/softmax.h"
#include "common/types.h"
#if __ARM_NEON
#ifdef __ARM_NEON
#include <math.h>
#include <algorithm>
#include "operators/math/math_func_neon.h"
......@@ -29,7 +29,7 @@ using framework::DDim;
using framework::Tensor;
template <typename T>
class SoftmaxFuntor<CPU, T> {
#if __ARM_NEON
#ifdef __ARM_NEON
void sum(float *input, float *sumptr, int inner_size, int outter_size) {
float32x4_t acc = vdupq_n_f32(0);
float sum_ = 0;
......@@ -135,6 +135,7 @@ class SoftmaxFuntor<CPU, T> {
}
}
}
#else
#endif // ARM_NEON
public:
......@@ -144,7 +145,7 @@ class SoftmaxFuntor<CPU, T> {
framework::Tensor sub_X = X->Slice(i, i + 1);
framework::Tensor sub_Y = Y->Slice(i, i + 1);
#if __ARM_NEON
#ifdef __ARM_NEON
SoftmaxCacl(&sub_X, &sub_Y);
#endif
}
......
src/operators/mul_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -50,7 +50,7 @@ void MulOp<Dtype, T>::InferShape() const {
framework::DDim ddim = framework::make_ddim(output_dims);
this->param_.Out()->Resize(ddim);
}
template class MulOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/multiclass_nms_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -34,7 +34,7 @@ void MultiClassNMSOp<Dtype, T>::InferShape() const {
// pre size, will change in Compute.
this->param_.Out()->Resize(framework::make_ddim({input_bboxes_dims[1], 6}));
}
template class MultiClassNMSOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/op_param.h
浏览文件 @ 2b9ea6ea
......@@ -371,7 +371,7 @@ class BatchNormParam : OpParam {
input_variance_ = InputVarianceFrom<LoDTensor>(inputs, scope);
epsilon_ = GetAttr<float>("epsilon", attrs);
momentum_ = GetAttr<float>("momentum", attrs);
is_test_ = GetAttr<bool>("is_test", attrs);
// is_test_ = GetAttr<bool>("is_test", attrs);
}
const Tensor *InputX() const { return input_x_; }
......@@ -1059,6 +1059,165 @@ class FusionConvAddBNReluParam : public OpParam {
Print &operator<<(Print &printer, const FusionConvAddParam &conv_param);
#endif
#ifdef FUSION_DWCONVBNRELU_OP
class FusionDWConvBNReluParam : public OpParam {
public:
FusionDWConvBNReluParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope) {
filter_ = FilterFrom<LoDTensor>(inputs, scope);
input_ = InputFrom<LoDTensor>(inputs, scope);
output_ = OutFrom<LoDTensor>(outputs, scope);
strides_ = GetAttr<vector<int>>("strides", attrs);
paddings_ = GetAttr<vector<int>>("paddings", attrs);
dilations_ = GetAttr<vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
input_bias_ = InputBiasFrom<LoDTensor>(inputs, scope);
input_mean_ = InputMeanFrom<LoDTensor>(inputs, scope);
input_scale_ = InputScaleFrom<LoDTensor>(inputs, scope);
input_variance_ = InputVarianceFrom<LoDTensor>(inputs, scope);
epsilon_ = GetAttr<float>("epsilon", attrs);
momentum_ = GetAttr<float>("momentum", attrs);
// is_test_ = GetAttr<bool>("is_test", attrs);
}
const Tensor *Input() const { return input_; }
const Tensor *Filter() const { return filter_; }
Tensor *Output() const { return output_; }
const vector<int> &Strides() const { return strides_; }
const vector<int> &Paddings() const { return paddings_; }
const vector<int> &Dilations() const { return dilations_; }
const int &Groups() const { return groups; }
const Tensor *InputBias() const { return input_bias_; }
const Tensor *InputMean() const { return input_mean_; }
const Tensor *InputScale() const { return input_scale_; }
const Tensor *InputVariance() const { return input_variance_; }
const float &Epsilon() const { return epsilon_; }
const float &Momentum() const { return momentum_; }
const bool &IsTest() const { return is_test_; }
void SetNewScale(Tensor *new_scale) { new_scale_ = new_scale; }
void SetNewBias(Tensor *new_bias) { new_bias_ = new_bias; }
const Tensor *NewScale() const { return new_scale_; }
const Tensor *NewBias() const { return new_bias_; }
protected:
Tensor *input_;
Tensor *output_;
Tensor *filter_;
vector<int> strides_;
vector<int> paddings_;
vector<int> dilations_;
int groups;
Tensor *input_bias_;
Tensor *input_mean_;
Tensor *input_scale_;
Tensor *input_variance_;
float epsilon_;
float momentum_;
bool is_test_;
Tensor *new_bias_;
Tensor *new_scale_;
};
Print &operator<<(Print &printer, const FusionConvAddParam &conv_param);
#endif
#ifdef FUSION_CONVBNRELU_OP
class FusionConvBNReluParam : public OpParam {
public:
FusionConvBNReluParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope) {
filter_ = FilterFrom<LoDTensor>(inputs, scope);
input_ = InputFrom<LoDTensor>(inputs, scope);
output_ = OutFrom<LoDTensor>(outputs, scope);
strides_ = GetAttr<vector<int>>("strides", attrs);
paddings_ = GetAttr<vector<int>>("paddings", attrs);
dilations_ = GetAttr<vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
input_bias_ = InputBiasFrom<LoDTensor>(inputs, scope);
input_mean_ = InputMeanFrom<LoDTensor>(inputs, scope);
input_scale_ = InputScaleFrom<LoDTensor>(inputs, scope);
input_variance_ = InputVarianceFrom<LoDTensor>(inputs, scope);
epsilon_ = GetAttr<float>("epsilon", attrs);
momentum_ = GetAttr<float>("momentum", attrs);
// is_test_ = GetAttr<bool>("is_test", attrs);
}
const Tensor *Input() const { return input_; }
const Tensor *Filter() const { return filter_; }
Tensor *Output() const { return output_; }
const vector<int> &Strides() const { return strides_; }
const vector<int> &Paddings() const { return paddings_; }
const vector<int> &Dilations() const { return dilations_; }
const int &Groups() const { return groups; }
const Tensor *InputBias() const { return input_bias_; }
const Tensor *InputMean() const { return input_mean_; }
const Tensor *InputScale() const { return input_scale_; }
const Tensor *InputVariance() const { return input_variance_; }
const float &Epsilon() const { return epsilon_; }
const float &Momentum() const { return momentum_; }
const bool &IsTest() const { return is_test_; }
void SetNewScale(Tensor *new_scale) { new_scale_ = new_scale; }
void SetNewBias(Tensor *new_bias) { new_bias_ = new_bias; }
const Tensor *NewScale() const { return new_scale_; }
const Tensor *NewBias() const { return new_bias_; }
protected:
Tensor *input_;
Tensor *output_;
Tensor *filter_;
vector<int> strides_;
vector<int> paddings_;
vector<int> dilations_;
int groups;
Tensor *input_bias_;
Tensor *input_mean_;
Tensor *input_scale_;
Tensor *input_variance_;
float epsilon_;
float momentum_;
bool is_test_;
Tensor *new_bias_;
Tensor *new_scale_;
};
#endif
#ifdef IM2SEQUENCE_OP
class Im2SequenceParam : public OpParam {
public:
......
src/operators/pool_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -54,7 +54,7 @@ void PoolOp<DeviceType, T>::InferShape() const {
}
this->param_.Output()->Resize(framework::make_ddim(output_shape));
}
template class PoolOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/prelu_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -23,7 +23,7 @@ void PReluOp<Dtype, T>::InferShape() const {
auto input_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(input_dims);
}
template class PReluOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/prior_box_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -44,7 +44,7 @@ void PriorBoxOp<Dtype, T>::InferShape() const {
this->param_.OutputBoxes()->Resize(framework::make_ddim(dim_vec));
this->param_.OutputVariances()->Resize(framework::make_ddim(dim_vec));
}
template class PriorBoxOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/relu_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -23,7 +23,7 @@ void ReluOp<Dtype, T>::InferShape() const {
auto input_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(input_dims);
}
template class ReluOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/reshape_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -27,7 +27,7 @@ void ReshapeOp<Dtype, T>::InferShape() const {
auto out_dims = ValidateShape(shape, input_x_dims);
this->param_.Out()->Resize(out_dims);
}
template class ReshapeOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/resize_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -24,7 +24,7 @@ void ResizeOp<Dtype, T>::InferShape() const {
auto out_dims = CalOutputShape(this->param_);
this->param_.Out()->Resize(out_dims);
}
template class ResizeOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/scale_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -24,7 +24,7 @@ void ScaleOp<Dtype, T>::InferShape() const {
auto input_dims = this->param_.InputX()->dims();
this->param_.Out()->Resize(input_dims);
}
template class ScaleOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/sigmoid_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -22,7 +22,7 @@ template <typename DeviceType, typename T>
void SigmoidOp<DeviceType, T>::InferShape() const {
this->param_.Out()->Resize(this->param_.InputX()->dims());
}
template class SigmoidOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/slice_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -23,7 +23,7 @@ template <typename Dtype, typename T>
void SliceOp<Dtype, T>::InferShape() const {
/// todo: add InputShape() detection.
}
template class SliceOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/softmax_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -22,7 +22,7 @@ template <typename DeviceType, typename T>
void SoftmaxOp<DeviceType, T>::InferShape() const {
this->param_.Out()->Resize(this->param_.InputX()->dims());
}
template class SoftmaxOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/transpose_op.cpp
浏览文件 @ 2b9ea6ea
......@@ -47,7 +47,7 @@ void TransposeOp<Dtype, T>::InferShape() const {
}
this->param_.Out()->Resize(out_dims);
}
template class TransposeOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
test/common/test_gemm.cpp
浏览文件 @ 2b9ea6ea
......@@ -52,8 +52,9 @@ int main() {
}
auto time1 = time();
paddle_mobile::operators::math::sgemm(m, n, k, 0.9, a, lda, b, ldb, 0.3, c,
ldc);
// paddle_mobile::operators::math::Sgemm(m, n, k, 0.9, a, lda, b, ldb, 0.3,
// c,
// ldc);
auto time2 = time();
DLOG << "gemm cost :" << time_diff(time1, time2) << "ms\n";
for (int i = 0; i < m * n; ++i) {
......
test/framework/test_load.cpp
浏览文件 @ 2b9ea6ea
......@@ -19,7 +19,9 @@ int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
// ../../../test/models/googlenet
// ../../../test/models/mobilenet
auto program = loader.Load(g_googlenet, true);
// auto program = loader.Load(g_googlenet, true);
auto program = loader.Load(g_mobilenet_ssd, true);
// auto program = loader.Load(g_googlenet_combine + "/model",
// g_googlenet_combine +
// "/params", true);
......
test/net/test_googlenet.cpp
浏览文件 @ 2b9ea6ea
......@@ -17,25 +17,21 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
paddle_mobile.SetThreadNum(4);
bool optimize = true;
auto time1 = time();
// auto program = loader.Load(g_googlenet, optimize);
auto program = loader.Load(g_googlenet_combine + "/model",
g_googlenet_combine + "/params", optimize);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time2) << "ms\n";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, optimize);
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
auto time3 = time();
if (paddle_mobile.Load(g_googlenet, optimize)) {
auto time2 = time();
DLOG << "load cost: " << time_diff(time1, time1) << "ms";
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
auto time3 = time();
auto vec_result = paddle_mobile.Predict(input, dims);
auto time4 = time();
for (int i = 0; i < 10; ++i) {
executor.Predict(input, dims);
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms\n";
return 0;
}
test/net/test_mobilenet+ssd.cpp
浏览文件 @ 2b9ea6ea
......@@ -12,16 +12,20 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <fstream>
#include <iostream>
#include "../test_helper.h"
#include "../test_include.h"
int main() {
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
paddle_mobile.SetThreadNum(4);
auto time1 = time();
if (paddle_mobile.Load(g_mobilenet_ssd, true)) {
auto isok = paddle_mobile.Load(g_mobilenet_ssd_gesture + "/model",
g_mobilenet_ssd_gesture + "/params", true);
// auto isok = paddle_mobile.Load(g_mobilenet_ssd, false);
if (isok) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::cout << "load cost :" << time_diff(time1, time2) << "ms" << std::endl;
std::vector<int64_t> dims{1, 3, 300, 300};
Tensor input_tensor;
......@@ -33,7 +37,8 @@ int main() {
auto time3 = time();
paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
std::cout << "predict cost :" << time_diff(time3, time4) << "ms"
<< std::endl;
}
return 0;
}
test/net/test_mobilenet.cpp
浏览文件 @ 2b9ea6ea
......@@ -12,16 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <fstream>
#include <iostream>
#include "../test_helper.h"
#include "../test_include.h"
int main() {
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
paddle_mobile.SetThreadNum(4);
auto time1 = time();
if (paddle_mobile.Load(g_mobilenet, true)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
std::vector<int64_t> dims{1, 3, 224, 224};
Tensor input_tensor;
......@@ -34,7 +35,8 @@ int main() {
auto vec_result = paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
std::cout << "predict cost :" << time_diff(time3, time4) << "ms"
<< std::endl;
}
return 0;
......
test/test_helper.h
浏览文件 @ 2b9ea6ea
......@@ -16,6 +16,8 @@ limitations under the License. */
#include <fstream>
#include <random>
#include <string>
#include <vector>
#include "common/common.h"
#include "common/log.h"
......@@ -23,6 +25,8 @@ limitations under the License. */
#include "framework/tensor.h"
static const std::string g_mobilenet_ssd = "../models/mobilenet+ssd";
static const std::string g_mobilenet_ssd_gesture =
"../models/mobilenet+ssd_gesture";
static const std::string g_squeezenet = "../models/squeezenet";
static const std::string g_googlenet = "../models/googlenet";
static const std::string g_mobilenet = "../models/mobilenet";
......@@ -62,9 +66,9 @@ void GetInput(const std::string &input_name, std::vector<T> *input,
size *= dim;
}
T *input_ptr = (T *)malloc(sizeof(T) * size);
T *input_ptr = reinterpret_cast<T *>(malloc(sizeof(T) * size));
std::ifstream in(input_name, std::ios::in | std::ios::binary);
in.read((char *)(input_ptr), size * sizeof(T));
in.read(reinterpret_cast<char *>(input_ptr), size * sizeof(T));
in.close();
for (int i = 0; i < size; ++i) {
input->push_back(input_ptr[i]);
......@@ -79,6 +83,6 @@ void GetInput(const std::string &input_name,
T *input_ptr = input->mutable_data<T>(dims);
std::ifstream in(input_name, std::ios::in | std::ios::binary);
in.read((char *)(input_ptr), input->numel() * sizeof(T));
in.read(reinterpret_cast<char *>(input_ptr), input->numel() * sizeof(T));
in.close();
}
tools/build.sh
浏览文件 @ 2b9ea6ea
......@@ -38,7 +38,8 @@ build_for_android() {
fi
if [ -z "$PLATFORM" ]; then
PLATFORM="arm-v7a" # Users could choose "arm-v8a" or other platforms from the command line.
PLATFORM="arm-v7a" # Users could choose "arm-v8a" platform.
# PLATFORM="arm-v8a"
fi
if [ "${PLATFORM}" = "arm-v7a" ]; then
......@@ -92,33 +93,33 @@ build_for_ios() {
# rm -rf "../build"
PLATFORM="ios"
MODE="Release"
BUILD_DIR=../build/release/"${PLATFORM}"
# IOS_ARCH="armv7"
# IOS_ARCH="armv7s"
IOS_ARCH="arm64" # Users could choose "armv7" or "armv7s" platforms.
BUILD_DIR=../build/release/"${PLATFORM}"/"${IOS_ARCH}"
TOOLCHAIN_FILE="./tools/ios-cmake/ios.toolchain.cmake"
C_FLAGS="-fobjc-abi-version=2 -fobjc-arc -isysroot ${CMAKE_OSX_SYSROOT}"
CXX_FLAGS="-fobjc-abi-version=2 -fobjc-arc -std=gnu++14 -stdlib=libc++ -isysroot ${CMAKE_OSX_SYSROOT}"
mkdir -p "${BUILD_DIR}"
if [ $# -eq 1 ]; then
cmake .. \
-B"${BUILD_DIR}" \
-DCMAKE_BUILD_TYPE="${MODE}" \
-DCMAKE_TOOLCHAIN_FILE="${TOOLCHAIN_FILE}" \
-DIOS_PLATFORM=OS \
-DCMAKE_C_FLAGS="${C_FLAGS}" \
-DCMAKE_CXX_FLAGS="${CXX_FLAGS}" \
-DIOS_ARCH="${IOS_ARCH}" \
-DCMAKE_TOOLCHAIN_FILE="${TOOLCHAIN_FILE}" \
-DNET=$1 \
-DIS_IOS="true"
else
cmake .. \
-B"${BUILD_DIR}" \
-DCMAKE_BUILD_TYPE="${MODE}" \
-DCMAKE_TOOLCHAIN_FILE="${TOOLCHAIN_FILE}" \
-DIOS_PLATFORM=OS \
-DCMAKE_C_FLAGS="${C_FLAGS}" \
-DCMAKE_CXX_FLAGS="${CXX_FLAGS}" \
-DIOS_ARCH="${IOS_ARCH}" \
-DCMAKE_TOOLCHAIN_FILE="${TOOLCHAIN_FILE}" \
-DIS_IOS="true"
fi
cd "${BUILD_DIR}"
make -j 8
cp ../../../src/ios_io/PaddleMobile.h ./build/PaddleMobile.h
cd ./build
# 生成符号表
ranlib *.a
......
tools/ios-cmake/ios.toolchain.cmake
浏览文件 @ 2b9ea6ea
......@@ -159,7 +159,6 @@ set (CMAKE_OSX_SYSROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Sysroot used for iOS su
# set the architecture for iOS
if (${IOS_PLATFORM} STREQUAL "OS")
set (IOS_ARCH armv7 armv7s arm64)
elseif (${IOS_PLATFORM} STREQUAL "SIMULATOR")
set (IOS_ARCH i386)
elseif (${IOS_PLATFORM} STREQUAL "SIMULATOR64")
......
tools/net.awk 0 → 100644
浏览文件 @ 2b9ea6ea
BEGIN {
print "digraph {"
}
/op:/ {
id++
op = $NF
opname = op "_" id
print opname "[\"label\"=\"" op " [" id "]" "\"]"
}
/input/ {
type = "input"
}
/output/ {
type = "output"
}
/argument/ {
if (type == "output") {
output[$NF] = opname
} else if (type == "input") {
if (output[$NF]) {
print output[$NF] " -> " opname
}
}
}
END {
print "}"
}
tools/op.cmake
浏览文件 @ 2b9ea6ea
......@@ -64,6 +64,8 @@ else ()
set(TRANSPOSE_OP ON)
set(FUSION_CONVADD_RELU_OP ON)
set(FUSION_CONVADDBNRELU_OP ON)
set(FUSION_DWCONVBNRELU_OP ON)
set(FUSION_CONVBNRELU_OP ON)
set(PRELU_OP ON)
set(RESIZE_OP ON)
set(SCALE_OP ON)
......@@ -155,6 +157,14 @@ endif()
if (FUSION_CONVADDBNRELU_OP)
add_definitions(-DFUSION_CONVADDBNRELU_OP)
endif()
if (FUSION_DWCONVBNRELU_OP)
add_definitions(-DFUSION_DWCONVBNRELU_OP)
endif()
if (FUSION_CONVBNRELU_OP)
add_definitions(-DFUSION_CONVBNRELU_OP)
endif()
if (PRELU_OP)
add_definitions(-DPRELU_OP)
endif()
......
tools/quantification/CMakeLists.txt 0 → 100644
浏览文件 @ 2b9ea6ea
set(dir ${CMAKE_CURRENT_SOURCE_DIR})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${dir}/build")
ADD_EXECUTABLE(convert convert.cpp)
target_link_libraries(convert paddle-mobile)
\ No newline at end of file
tools/quantification/convert.cpp 0 → 100644
浏览文件 @ 2b9ea6ea
#include "io/paddle_mobile.h"
#include <cstdlib>
using std::string;
static const std::string g_googlenet_combine = "../models/googlenet_combine";
static const std::string g_googlenet = "../models/googlenet";
using paddle_mobile::Executor;
using paddle_mobile::framework::Program;
char *Get_binary_data(std::string filename) {
FILE *file = fopen(filename.c_str(), "rb");
PADDLE_MOBILE_ENFORCE(file != nullptr, "can't open file: %s ",
filename.c_str());
fseek(file, 0, SEEK_END);
int64_t size = ftell(file);
PADDLE_MOBILE_ENFORCE(size > 0, "size is too small");
rewind(file);
char *data = new char[size];
size_t bytes_read = fread(data, 1, size, file);
PADDLE_MOBILE_ENFORCE(bytes_read == size,
"read binary file bytes do not match with fseek");
DLOG << "Get_binary_data end";
fclose(file);
return data;
}
void LoadWithDump(const paddle_mobile::framework::VarDesc var_desc,
paddle_mobile::framework::LoDTensor *tensor, char **data, FILE *out_file) {
// 1. version
uint32_t version = *reinterpret_cast<uint32_t *>(*data);
// write version
fwrite(&version, sizeof(uint32_t), 1, out_file );
(*data) += sizeof(uint32_t);
// 2 Lod information
uint64_t *lod_level_ptr = new uint64_t();
memcpy(lod_level_ptr, (*data), sizeof(uint64_t));
uint64_t lod_level = 0;
// write lod Information
fwrite(&lod_level, sizeof(uint64_t), 1, out_file);
delete lod_level_ptr;
(*data) += sizeof(uint64_t);
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size = *reinterpret_cast<uint64_t *>(*data);
// write lod size
fwrite(&size, sizeof(uint64_t), 1, out_file);
(*data) += sizeof(uint64_t);
std::vector<size_t> tmp(size / sizeof(size_t));
for (int k = 0; k < tmp.size(); ++k) {
tmp[k] = *reinterpret_cast<size_t *>(*data);
(*data) += sizeof(size_t);
}
// write lod size vector
fwrite(&tmp, sizeof(size_t), tmp.size(), out_file );
lod[i] = tmp;
}
// 3. tensor version
uint32_t tensor_version = *reinterpret_cast<uint32_t *>(*data);
// write tensor version
fwrite(&tensor_version, sizeof(uint32_t), 1, out_file);
(*data) += sizeof(uint32_t);
// 4. tensor desc
int32_t size = *reinterpret_cast<int32_t *>(*data);
// write tensor desc
fwrite(&size, sizeof(int32_t), 1, out_file);
(*data) += sizeof(int32_t);
std::unique_ptr<char[]> buf(new char[size]);
for (int m = 0; m < size; ++m) {
buf.get()[m] = (*data)[m];
}
fwrite(buf.get(), sizeof(char), size, out_file);
(*data) += (sizeof(char) * size);
const paddle_mobile::framework::TensorDesc &desc = var_desc.Tensor_desc();
int memory_size = 1;
for (auto l : desc.Dims()) {
memory_size *= l;
}
tensor->Resize(paddle_mobile::framework::make_ddim(desc.Dims()));
void *memory = tensor;
int type_size = 0;
switch (desc.DataType()) {
case paddle_mobile::framework::VARTYPE_TYPE_FP16:
type_size = 2;
break;
case paddle_mobile::framework::VARTYPE_TYPE_FP32:
type_size = 4;
memory = tensor->mutable_data<float>();
break;
case paddle_mobile::framework::VARTYPE_TYPE_FP64:
type_size = 8;
break;
case paddle_mobile::framework::VARTYPE_TYPE_INT32:
type_size = 4;
break;
case paddle_mobile::framework::VARTYPE_TYPE_INT64:
type_size = 8;
break;
case paddle_mobile::framework::VARTYPE_TYPE_BOOL:
type_size = 1;
break;
default:
break;
}
for (int n = 0; n < memory_size * type_size; ++n) {
static_cast<char *>(memory)[n] = (*data)[n];
}
(*data) += (sizeof(char) * memory_size * type_size);
// for float 32
float min_value = std::numeric_limits<float>::max();
float max_value = std::numeric_limits<float>::min();
for (int k = 0; k < memory_size; ++k) {
min_value = std::min(min_value, static_cast<float *> (memory)[k]);
max_value = std::max(max_value, static_cast<float *> (memory)[k]);
}
fwrite(&min_value, sizeof(float), 1, out_file);
fwrite(&max_value, sizeof(float), 1, out_file);
for (int g = 0; g < memory_size; ++g) {
float value = static_cast<float *> (memory)[g];
uint8_t factor = (uint8_t) round((value - min_value) / (max_value - min_value) * 255);
fwrite(&factor, sizeof(uint8_t), 1, out_file);
}
}
void quantificate_combined(std::string model_path, std::string param_path, std::string param_min_path){
paddle_mobile::Loader<paddle_mobile::CPU,paddle_mobile::Precision::FP32 > loader;
bool optimize = true;
auto program = loader.Load(model_path, param_path, optimize);
char *origin_data = Get_binary_data(program.para_path);
char *data = origin_data;
FILE *out_file = fopen(param_min_path.c_str(), "wb");
for (const auto &block : program.originProgram->Blocks()) {
for (const auto &var_desc : block->Vars()) {
auto var = program.scope->Var(var_desc->Name());
if(var_desc ->Persistable()) {
auto tensor = var->template GetMutable<paddle_mobile::framework::LoDTensor>();
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
continue;
}
LoadWithDump(*var_desc, tensor, &data,out_file);
}
}
}
fclose(out_file);
delete origin_data;
}
void quantificate_seperated(std::string model_dir, std::string param_min_path) {
paddle_mobile::Loader<paddle_mobile::CPU,paddle_mobile::Precision::FP32 > loader;
bool optimize = true;
auto program = loader.Load(model_dir, optimize);
std::string shell_command = "mkdir "+param_min_path;
system(shell_command.c_str());
for (const auto &block : program.originProgram->Blocks()) {
for (const auto &var_desc : block->Vars()) {
auto var = program.scope->Var(var_desc->Name());
if(var_desc ->Persistable()) {
auto tensor = var->template GetMutable<paddle_mobile::framework::LoDTensor>();
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
continue;
}
std::string file_name = param_min_path +"/"+ var_desc->Name();
FILE *out_file = fopen(file_name.c_str(), "wb");
char *origin_data =
Get_binary_data(program.model_path + "/" + var_desc->Name());
char *data = origin_data;
LoadWithDump(*var_desc, tensor, &data,out_file);
delete origin_data;
fclose(out_file);
}
}
}
}
int main() {
std::string filename = "params_min";
std::string model_path = g_googlenet_combine + "/model";
std::string param_path = g_googlenet_combine + "/params";
std::string dirname = "param_min_dir";
std::string model_dir = g_googlenet;
// quantificate_combined(model_path, param_path,filename);
quantificate_seperated(model_dir, dirname);
return 0;
}
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