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[操作系统]学习笔记TF066:TensorFlow移动端应用,iOS、Android系统实践

TensorFlow对Android、iOS、树莓派都提供移动端支持。

移动端应用原理。移动端、嵌入式设备应用深度学习方式,一模型运行在云端服务器,向服务器发送请求,接收服务器响应;二在本地运行模型,PC训练模型,放到移动端预测。向服务端请求数据可行性差,移动端资源稀缺。本地运行实时性更好。加速计算,内存空间和速度优化。精简模型,节省内存空间,加快计算速度。加快框架执行速度,优化模型复杂度和每步计算速度。
精简模型,用更低权得精度,量化(quantization)、权重剪枝(weight pruning,剪小权重连接,把所有权值连接低于阈值的从网络移除)。加速框架执行,优化矩阵通用乘法(GEMM)运算,影响卷积层(先数据im2col运行,再GEMM运算)和全连接层。im2col,索引图像块重排列为矩阵列。先将大矩阵重叠划分多个子矩阵,每个子矩阵序列化成向量,得到另一个矩阵。

量化(quantitative)。《How to Quantize Neural Networks with TensorFlow》https://www.tensorflow.org/performance/quantization 。离散化。用比32位浮点数更少空间存储、运行模型,TensorFlow量化实现屏蔽存储、运行细节。神经网络预测,浮点影响速度,量化加快速度,保持较高精度。减小模型文件大小。存储模型用8位整数,加载模型运算转换回32位浮点数。降低预测过程计算资源。神经网络噪声健壮笥强,量化精度损失不会危害整体准确度。训练,反向传播需要计算梯度,不能用低精度格式直接训练。PC训练浮点数模型,转8位,移动端用8位模型预测。
量化示例。GoogleNet模型转8位模型例子。下载训练好GoogleNet模型 。

bazel build tensorflow/tools/quantization:quantization_graph
bazel-bin/tensorflow/tools/quantization/quantization_graph \
--input=/tmp/classify_image_graph_def.pb \
--output_node_names="softmax" --output=/tmp/quantized_graph.pb \
--mode=eightbit

生成量化后模型大小只有原来的1/4。执行:

bazel build tensorflow/examples/label_image:label_image
bazel-bin/tensorflow/examples/label_image/label_image \
--image=/tmp/cropped_panda.jpg \
--graph=/tmp/quantized_graph.pb \
--labels=/tmp/imagenet_synset_to_human_label_map.txt \
--input_width=299 \
--input_height=299 \
--input_mean=128 \
--input_std=128 \
--input_layer="Mul:0" \
--output_layer="softmax:0"

量化过程实现。预测操作转换成等价8位版本操作实现。原始Relu操作,输入、输出浮点数。量化Relu操作,根据输入浮点数计算最大值、最小值,进入量化(Quantize)操作输入数据转换8位。保证输出层输入数据准确性,需要反量化(Dequantize)操作,权重转回32位精度,保证预测准确性。整个模型前向传播用8位整数支行,最后一层加反量化层,8位转回32位输出层输入。每个量化操作后执行反量化操作。

量化数据表示。浮点数转8位表示,是压缩问题。权重、经过激活函数处理上层输出,是分布在一个范围内的值。量化过程,找出最大值、最小值,将浮点数线性分布,做线性扩展。

优化矩阵乘法运算。谷歌开源小型独立低精度通用矩阵乘法(General Matrix to Matrix Multiplication,GEMM)库 gemmlowp。https://github.com/google/gemmlowp 。

iOS系统实践。

环境准备。操作系统Mac OS X,集成开发工具Xcode 7.3以上版本。编译TensorFlow核心静态库。tensorflow/contrib/makefiles/download_depencies.sh 。依赖库下载到tensorflow/contrib/makefile/downloads目录。eigen #C++开源矩阵计算工具。gemmlowp #小型独立低精度通用矩阵乘法(GEMM)库。googletest #谷歌开源C++测试框架。protobuf #谷歌开源数据交换格式协议。re2 #谷歌开源正则表达式库。

编译演示程度,运行。tensorflow/contrib/makefile/build_all_iso.sh。编译生成静态库,tensorflow/contrib/makefile/gen/lib:ios_ARM64、ios_ARMV7、ios_ARMV7S、ios_I386、ios_X86_64、libtensorflow-core.a。Xcode模拟器或iOS设备运行APP预测示例。TensorFlow iOS示例。https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios/ 。3个目录。benchmark目录是预测基准示例。simple目录是图片预测示例。camera目录是视频流实时预测示例。下载Inception V1模型,能识别1000类图片,https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip 。解压模型,复制到benchmark、simple、camera的data目录。运行目录下xcodeproj文件。选择iPhone 7 Plus模拟器,点击运行标志,编译完成点击Run Model按钮。预测结果见Xcode 控制台。

自定义模型编译、运行。https://github.com/tensorflow/tensorflow/blob/15b1cf025da5c6ac2bcf4d4878ee222fca3aec4a/tensorflow/docs_src/tutorials/image_retraining.md 。下载花卉数据 。郁金香(tulips)、玫瑰(roses)、浦公英(dandelion)、向日葵(sunflowers)、雏菊(daisy)5种花卉文件目录,各800张图片。
训练原始模型。下载预训练Inception V3模型 。

python tensorflow/examples/image_retraining/retrain.py \
--bottlenectk_dir=/tmp/bottlenecks/ \
--how_many_training_steps 10 \
--model_dir=/tmp/inception \
--output_graph=/tmp/retrained_graph.pb \
--output_labels=/tmp/retrained_labels.txt \
--image_dir /tmp/flower_photos

训练完成,/tmp目录有模型文件retrained_graph.pb、标签文件上retrained_labels.txt。“瓶颈”(bottlenecks)文件,描述实际分类最终输出层前一层(倒数第二层)。倒数第二层训练很好,瓶颈值是有意义紧凑图像摘要,包含足够信息使分类选择。第一次训练,retrain.py文件代码先分析所有图片,计算每张图片瓶颈值存储下来。每张图片被使用多次,不必重复计算。

编译iOS支持模型。https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ 。原始模型到iOS模型,先去掉iOS系统不支持操作,优化模型,再将模型量化,权重变8位常数,缩小模型,最后模型内存映射。
去掉iOS系统不支持操作,优化模型。iOS版本TensorFlow仅支持预测阶段常见没有大外部依赖关系操作。支持操作列表:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/makefile/tf_op_files.txt 。DecodeJpeg不支持,JPEG格式图片解码,依赖libjpeg。从摄像头实时识别花卉种类,直接处理相机图像缓冲区,不存JPEG文件再解码。预训练模型Inception V3 从图片数据集训练,包含DecodeJpeg操作。输入数据直接提供(feed)Decode后Mul操作,绕过Decode操作。优化加速预测,显式批处理规范化(explicit batch normalization)操作合并到卷积权重,减少计算次数。

bazel build tensorflow/python/tools:optimize_for_inference
bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=/tmp/retrained_graph.pb \
--output=/tmp/optimized_graph.pb \
--input_names=Mul \
--output_names=final_result \

label_image命令预测:

bazel-bin/tensorflow/examples/label_image/label_image \
--output_layer=final_result \
--labels=/tmp/output_labels.txt \
--image=/tmp/flower_photos/daisy/5547758_eea9edfd54_n.jpg
--graph=/tmp/output_graph.pb \
--input_layer=Mul \
--input_mean=128 \
--input_std=128 \

量化模型。苹果系统在.ipa包分发应用程度,所有应用程度资源都用zip压缩。模型权重从浮点数转整数(范围0~255),损失准确度,小于1%。

bazel build tensorflow/tools/quantization:quantization_graph
bazel-bin/tensorflow/tools/quantization/quantization_graph \
--input=/tmp/optimized_graph.pb \
--output=/tmp/rounded_graph.pb \
--output_node_names=final_result \
--mode=weights_rounded

内存映射 memory mapping。物理内存映射到进程地址空间内,应用程序直接用输入/输出地址空间,提高读写效率。模型全部一次性加载到内存缓冲区,会对iOS RAM施加过大压力,操作系统会杀死内存占用过多程序。模型权值缓冲区只读,可映射到内存。重新排列模型,权重分部分逐块从主GraphDef加载到内存。

bazel build tensorflow/contrib/util:convert_graphdef_memmapped_format
bazel-bin/tensorflow/contrib/util/convert_graphdef_memmapped_format \
--in_graph=/tmp/rounded_graph.pb \
--out_graph=/tmp/mmapped_graph.pb

生成iOS工程文件运行。视频流实进预测演示程序例子。https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios/camera 。模型文件、标记文件复制到data目录。修改CameraExampleViewController.mm,更改加载模型文件名称、输入图片尺寸、操作节点名字、缩放像素大小。

#import <AssertMacros.h>#import <AssetsLibrary/AssetsLibrary.h>#import <CoreImage/CoreImage.h>#import <ImageIO/ImageIO.h>#import "CameraExampleViewController.h"#include <sys/time.h>#include "tensorflow_utils.h"// If you have your own model, modify this to the file name, and make sure// you've added the file to your app resources too.static NSString* model_file_name = @"tensorflow_inception_graph";static NSString* model_file_type = @"pb";// This controls whether we'll be loading a plain GraphDef proto, or a// file created by the convert_graphdef_memmapped_format utility that wraps a// GraphDef and parameter file that can be mapped into memory from file to// reduce overall memory usage.const bool model_uses_memory_mapping = false;// If you have your own model, point this to the labels file.static NSString* labels_file_name = @"imagenet_comp_graph_label_strings";static NSString* labels_file_type = @"txt";// These dimensions need to match those the model was trained with.// 以下尺寸需要和模型训练时相匹配const int wanted_input_width =299;// 224;const int wanted_input_height = 299;//224;const int wanted_input_channels = 3;const float input_mean = 128.0f;//117.0f;const float input_std = 128.0f;//1.0f;const std::string input_layer_name = "Mul";//"input";const std::string output_layer_name = "final_result";//"softmax1";static void *AVCaptureStillImageIsCapturingStillImageContext = &AVCaptureStillImageIsCapturingStillImageContext;@interface CameraExampleViewController (InternalMethods)- (void)setupAVCapture;- (void)teardownAVCapture;@end@implementation CameraExampleViewController- (void)setupAVCapture { NSError *error = nil; session = [AVCaptureSession new]; if ([[UIDevice currentDevice] userInterfaceIdiom] ==  UIUserInterfaceIdiomPhone) [session setSessionPreset:AVCaptureSessionPreset640x480]; else [session setSessionPreset:AVCaptureSessionPresetPhoto]; AVCaptureDevice *device =  [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo]; AVCaptureDeviceInput *deviceInput =  [AVCaptureDeviceInput deviceInputWithDevice:device error:&error]; assert(error == nil); isUsingFrontFacingCamera = NO; if ([session canAddInput:deviceInput]) [session addInput:deviceInput]; stillImageOutput = [AVCaptureStillImageOutput new]; [stillImageOutput  addObserver:self  forKeyPath:@"capturingStillImage"   options:NSKeyValueObservingOptionNew   context:(void *)(AVCaptureStillImageIsCapturingStillImageContext)]; if ([session canAddOutput:stillImageOutput]) [session addOutput:stillImageOutput]; videoDataOutput = [AVCaptureVideoDataOutput new]; NSDictionary *rgbOutputSettings = [NSDictionary  dictionaryWithObject:[NSNumber numberWithInt:kCMPixelFormat_32BGRA]     forKey:(id)kCVPixelBufferPixelFormatTypeKey]; [videoDataOutput setVideoSettings:rgbOutputSettings]; [videoDataOutput setAlwaysDiscardsLateVideoFrames:YES]; videoDataOutputQueue =  dispatch_queue_create("VideoDataOutputQueue", DISPATCH_QUEUE_SERIAL); [videoDataOutput setSampleBufferDelegate:self queue:videoDataOutputQueue]; if ([session canAddOutput:videoDataOutput]) [session addOutput:videoDataOutput]; [[videoDataOutput connectionWithMediaType:AVMediaTypeVideo] setEnabled:YES]; previewLayer = [[AVCaptureVideoPreviewLayer alloc] initWithSession:session]; [previewLayer setBackgroundColor:[[UIColor blackColor] CGColor]]; [previewLayer setVideoGravity:AVLayerVideoGravityResizeAspect]; CALayer *rootLayer = [previewView layer]; [rootLayer setMasksToBounds:YES]; [previewLayer setFrame:[rootLayer bounds]]; [rootLayer addSublayer:previewLayer]; [session startRunning]; if (error) { NSString *title = [NSString stringWithFormat:@"Failed with error %d", (int)[error code]]; UIAlertController *alertController =  [UIAlertController alertControllerWithTitle:title           message:[error localizedDescription]          preferredStyle:UIAlertControllerStyleAlert]; UIAlertAction *dismiss =  [UIAlertAction actionWithTitle:@"Dismiss" style:UIAlertActionStyleDefault handler:nil]; [alertController addAction:dismiss]; [self presentViewController:alertController animated:YES completion:nil]; [self teardownAVCapture]; }}- (void)teardownAVCapture { [stillImageOutput removeObserver:self forKeyPath:@"isCapturingStillImage"]; [previewLayer removeFromSuperlayer];}- (void)observeValueForKeyPath:(NSString *)keyPath      ofObject:(id)object      change:(NSDictionary *)change      context:(void *)context { if (context == AVCaptureStillImageIsCapturingStillImageContext) { BOOL isCapturingStillImage =  [[change objectForKey:NSKeyValueChangeNewKey] boolValue]; if (isCapturingStillImage) {  // do flash bulb like animation  flashView = [[UIView alloc] initWithFrame:[previewView frame]];  [flashView setBackgroundColor:[UIColor whiteColor]];  [flashView setAlpha:0.f];  [[[self view] window] addSubview:flashView];  [UIView animateWithDuration:.4f      animations:^{       [flashView setAlpha:1.f];      }]; } else {  [UIView animateWithDuration:.4f   animations:^{   [flashView setAlpha:0.f];   }   completion:^(BOOL finished) {   [flashView removeFromSuperview];   flashView = nil;   }]; } }}- (AVCaptureVideoOrientation)avOrientationForDeviceOrientation: (UIDeviceOrientation)deviceOrientation { AVCaptureVideoOrientation result =  (AVCaptureVideoOrientation)(deviceOrientation); if (deviceOrientation == UIDeviceOrientationLandscapeLeft) result = AVCaptureVideoOrientationLandscapeRight; else if (deviceOrientation == UIDeviceOrientationLandscapeRight) result = AVCaptureVideoOrientationLandscapeLeft; return result;}- (IBAction)takePicture:(id)sender { if ([session isRunning]) { [session stopRunning]; [sender setTitle:@"Continue" forState:UIControlStateNormal]; flashView = [[UIView alloc] initWithFrame:[previewView frame]]; [flashView setBackgroundColor:[UIColor whiteColor]]; [flashView setAlpha:0.f]; [[[self view] window] addSubview:flashView]; [UIView animateWithDuration:.2f  animations:^{   [flashView setAlpha:1.f];  }  completion:^(BOOL finished) {   [UIView animateWithDuration:.2f    animations:^{    [flashView setAlpha:0.f];    }    completion:^(BOOL finished) {    [flashView removeFromSuperview];    flashView = nil;    }];  }]; } else { [session startRunning]; [sender setTitle:@"Freeze Frame" forState:UIControlStateNormal]; }}+ (CGRect)videoPreviewBoxForGravity:(NSString *)gravity       frameSize:(CGSize)frameSize      apertureSize:(CGSize)apertureSize { CGFloat apertureRatio = apertureSize.height / apertureSize.width; CGFloat viewRatio = frameSize.width / frameSize.height; CGSize size = CGSizeZero; if ([gravity isEqualToString:AVLayerVideoGravityResizeAspectFill]) { if (viewRatio > apertureRatio) {  size.width = frameSize.width;  size.height =   apertureSize.width * (frameSize.width / apertureSize.height); } else {  size.width =   apertureSize.height * (frameSize.height / apertureSize.width);  size.height = frameSize.height; } } else if ([gravity isEqualToString:AVLayerVideoGravityResizeAspect]) { if (viewRatio > apertureRatio) {  size.width =   apertureSize.height * (frameSize.height / apertureSize.width);  size.height = frameSize.height; } else {  size.width = frameSize.width;  size.height =   apertureSize.width * (frameSize.width / apertureSize.height); } } else if ([gravity isEqualToString:AVLayerVideoGravityResize]) { size.width = frameSize.width; size.height = frameSize.height; } CGRect videoBox; videoBox.size = size; if (size.width < frameSize.width) videoBox.origin.x = (frameSize.width - size.width) / 2; else videoBox.origin.x = (size.width - frameSize.width) / 2; if (size.height < frameSize.height) videoBox.origin.y = (frameSize.height - size.height) / 2; else videoBox.origin.y = (size.height - frameSize.height) / 2; return videoBox;}- (void)captureOutput:(AVCaptureOutput *)captureOutputdidOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer  fromConnection:(AVCaptureConnection *)connection { CVPixelBufferRef pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer); CFRetain(pixelBuffer); [self runCNNOnFrame:pixelBuffer]; CFRelease(pixelBuffer);}- (void)runCNNOnFrame:(CVPixelBufferRef)pixelBuffer { assert(pixelBuffer != NULL); OSType sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer); int doReverseChannels; if (kCVPixelFormatType_32ARGB == sourcePixelFormat) { doReverseChannels = 1; } else if (kCVPixelFormatType_32BGRA == sourcePixelFormat) { doReverseChannels = 0; } else { assert(false); // Unknown source format } const int sourceRowBytes = (int)CVPixelBufferGetBytesPerRow(pixelBuffer); const int image_width = (int)CVPixelBufferGetWidth(pixelBuffer); const int fullHeight = (int)CVPixelBufferGetHeight(pixelBuffer); CVPixelBufferLockFlags unlockFlags = kNilOptions; CVPixelBufferLockBaseAddress(pixelBuffer, unlockFlags); unsigned char *sourceBaseAddr =  (unsigned char *)(CVPixelBufferGetBaseAddress(pixelBuffer)); int image_height; unsigned char *sourceStartAddr; if (fullHeight <= image_width) { image_height = fullHeight; sourceStartAddr = sourceBaseAddr; } else { image_height = image_width; const int marginY = ((fullHeight - image_width) / 2); sourceStartAddr = (sourceBaseAddr + (marginY * sourceRowBytes)); } const int image_channels = 4; assert(image_channels >= wanted_input_channels); tensorflow::Tensor image_tensor(  tensorflow::DT_FLOAT,  tensorflow::TensorShape(   {1, wanted_input_height, wanted_input_width, wanted_input_channels})); auto image_tensor_mapped = image_tensor.tensor<float, 4>(); tensorflow::uint8 *in = sourceStartAddr; float *out = image_tensor_mapped.data(); for (int y = 0; y < wanted_input_height; ++y) { float *out_row = out + (y * wanted_input_width * wanted_input_channels); for (int x = 0; x < wanted_input_width; ++x) {  const int in_x = (y * image_width) / wanted_input_width;  const int in_y = (x * image_height) / wanted_input_height;  tensorflow::uint8 *in_pixel =   in + (in_y * image_width * image_channels) + (in_x * image_channels);  float *out_pixel = out_row + (x * wanted_input_channels);  for (int c = 0; c < wanted_input_channels; ++c) {  out_pixel[c] = (in_pixel[c] - input_mean) / input_std;  } } } CVPixelBufferUnlockBaseAddress(pixelBuffer, unlockFlags); if (tf_session.get()) { std::vector<tensorflow::Tensor> outputs; tensorflow::Status run_status = tf_session->Run(  {{input_layer_name, image_tensor}}, {output_layer_name}, {}, &outputs); if (!run_status.ok()) {  LOG(ERROR) << "Running model failed:" << run_status; } else {  tensorflow::Tensor *output = &outputs[0];  auto predictions = output->flat<float>();  NSMutableDictionary *newValues = [NSMutableDictionary dictionary];  for (int index = 0; index < predictions.size(); index += 1) {  const float predictionValue = predictions(index);  if (predictionValue > 0.05f) {   std::string label = labels[index % predictions.size()];   NSString *labelObject = [NSString stringWithUTF8String:label.c_str()];   NSNumber *valueObject = [NSNumber numberWithFloat:predictionValue];   [newValues setObject:valueObject forKey:labelObject];  }  }  dispatch_async(dispatch_get_main_queue(), ^(void) {  [self setPredictionValues:newValues];  }); } } CVPixelBufferUnlockBaseAddress(pixelBuffer, 0);}- (void)dealloc { [self teardownAVCapture];}// use front/back camera- (IBAction)switchCameras:(id)sender { AVCaptureDevicePosition desiredPosition; if (isUsingFrontFacingCamera) desiredPosition = AVCaptureDevicePositionBack; else desiredPosition = AVCaptureDevicePositionFront; for (AVCaptureDevice *d in  [AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo]) { if ([d position] == desiredPosition) {  [[previewLayer session] beginConfiguration];  AVCaptureDeviceInput *input =   [AVCaptureDeviceInput deviceInputWithDevice:d error:nil];  for (AVCaptureInput *oldInput in [[previewLayer session] inputs]) {  [[previewLayer session] removeInput:oldInput];  }  [[previewLayer session] addInput:input];  [[previewLayer session] commitConfiguration];  break; } } isUsingFrontFacingCamera = !isUsingFrontFacingCamera;}- (void)didReceiveMemoryWarning { [super didReceiveMemoryWarning];}- (void)viewDidLoad { [super viewDidLoad]; square = [UIImage imageNamed:@"squarePNG"]; synth = [[AVSpeechSynthesizer alloc] init]; labelLayers = [[NSMutableArray alloc] init]; oldPredictionValues = [[NSMutableDictionary alloc] init]; tensorflow::Status load_status; if (model_uses_memory_mapping) { load_status = LoadMemoryMappedModel(  model_file_name, model_file_type, &tf_session, &tf_memmapped_env); } else { load_status = LoadModel(model_file_name, model_file_type, &tf_session); } if (!load_status.ok()) { LOG(FATAL) << "Couldn't load model: " << load_status; } tensorflow::Status labels_status =  LoadLabels(labels_file_name, labels_file_type, &labels); if (!labels_status.ok()) { LOG(FATAL) << "Couldn't load labels: " << labels_status; } [self setupAVCapture];}- (void)viewDidUnload { [super viewDidUnload];}- (void)viewWillAppear:(BOOL)animated { [super viewWillAppear:animated];}- (void)viewDidAppear:(BOOL)animated { [super viewDidAppear:animated];}- (void)viewWillDisappear:(BOOL)animated { [super viewWillDisappear:animated];}- (void)viewDidDisappear:(BOOL)animated { [super viewDidDisappear:animated];}- (BOOL)shouldAutorotateToInterfaceOrientation: (UIInterfaceOrientation)interfaceOrientation { return (interfaceOrientation == UIInterfaceOrientationPortrait);}- (BOOL)prefersStatusBarHidden { return YES;}- (void)setPredictionValues:(NSDictionary *)newValues { const float decayValue = 0.75f; const float updateValue = 0.25f; const float minimumThreshold = 0.01f; NSMutableDictionary *decayedPredictionValues =  [[NSMutableDictionary alloc] init]; for (NSString *label in oldPredictionValues) { NSNumber *oldPredictionValueObject =  [oldPredictionValues objectForKey:label]; const float oldPredictionValue = [oldPredictionValueObject floatValue]; const float decayedPredictionValue = (oldPredictionValue * decayValue); if (decayedPredictionValue > minimumThreshold) {  NSNumber *decayedPredictionValueObject =   [NSNumber numberWithFloat:decayedPredictionValue];  [decayedPredictionValues setObject:decayedPredictionValueObject         forKey:label]; } } oldPredictionValues = decayedPredictionValues; for (NSString *label in newValues) { NSNumber *newPredictionValueObject = [newValues objectForKey:label]; NSNumber *oldPredictionValueObject =  [oldPredictionValues objectForKey:label]; if (!oldPredictionValueObject) {  oldPredictionValueObject = [NSNumber numberWithFloat:0.0f]; } const float newPredictionValue = [newPredictionValueObject floatValue]; const float oldPredictionValue = [oldPredictionValueObject floatValue]; const float updatedPredictionValue =  (oldPredictionValue + (newPredictionValue * updateValue)); NSNumber *updatedPredictionValueObject =  [NSNumber numberWithFloat:updatedPredictionValue]; [oldPredictionValues setObject:updatedPredictionValueObject forKey:label]; } NSArray *candidateLabels = [NSMutableArray array]; for (NSString *label in oldPredictionValues) { NSNumber *oldPredictionValueObject =  [oldPredictionValues objectForKey:label]; const float oldPredictionValue = [oldPredictionValueObject floatValue]; if (oldPredictionValue > 0.05f) {  NSDictionary *entry = @{  @"label" : label,  @"value" : oldPredictionValueObject  };  candidateLabels = [candidateLabels arrayByAddingObject:entry]; } } NSSortDescriptor *sort =  [NSSortDescriptor sortDescriptorWithKey:@"value" ascending:NO]; NSArray *sortedLabels = [candidateLabels  sortedArrayUsingDescriptors:[NSArray arrayWithObject:sort]]; const float leftMargin = 10.0f; const float topMargin = 10.0f; const float valueWidth = 48.0f; const float valueHeight = 26.0f; const float labelWidth = 246.0f; const float labelHeight = 26.0f; const float labelMarginX = 5.0f; const float labelMarginY = 5.0f; [self removeAllLabelLayers]; int labelCount = 0; for (NSDictionary *entry in sortedLabels) { NSString *label = [entry objectForKey:@"label"]; NSNumber *valueObject = [entry objectForKey:@"value"]; const float value = [valueObject floatValue]; const float originY =  (topMargin + ((labelHeight + labelMarginY) * labelCount)); const int valuePercentage = (int)roundf(value * 100.0f); const float valueOriginX = leftMargin; NSString *valueText = [NSString stringWithFormat:@"%d%%", valuePercentage]; [self addLabelLayerWithText:valueText      originX:valueOriginX      originY:originY       width:valueWidth       height:valueHeight      alignment:kCAAlignmentRight]; const float labelOriginX = (leftMargin + valueWidth + labelMarginX); [self addLabelLayerWithText:[label capitalizedString]      originX:labelOriginX      originY:originY       width:labelWidth       height:labelHeight      alignment:kCAAlignmentLeft]; if ((labelCount == 0) && (value > 0.5f)) {  [self speak:[label capitalizedString]]; } labelCount += 1; if (labelCount > 4) {  break; } }}- (void)removeAllLabelLayers { for (CATextLayer *layer in labelLayers) { [layer removeFromSuperlayer]; } [labelLayers removeAllObjects];}- (void)addLabelLayerWithText:(NSString *)text      originX:(float)originX      originY:(float)originY      width:(float)width      height:(float)height     alignment:(NSString *)alignment { CFTypeRef font = (CFTypeRef) @"Menlo-Regular"; const float fontSize = 20.0f; const float marginSizeX = 5.0f; const float marginSizeY = 2.0f; const CGRect backgroundBounds = CGRectMake(originX, originY, width, height); const CGRect textBounds =  CGRectMake((originX + marginSizeX), (originY + marginSizeY),     (width - (marginSizeX * 2)), (height - (marginSizeY * 2))); CATextLayer *background = [CATextLayer layer]; [background setBackgroundColor:[UIColor blackColor].CGColor]; [background setOpacity:0.5f]; [background setFrame:backgroundBounds]; background.cornerRadius = 5.0f; [[self.view layer] addSublayer:background]; [labelLayers addObject:background]; CATextLayer *layer = [CATextLayer layer]; [layer setForegroundColor:[UIColor whiteColor].CGColor]; [layer setFrame:textBounds]; [layer setAlignmentMode:alignment]; [layer setWrapped:YES]; [layer setFont:font]; [layer setFontSize:fontSize]; layer.contentsScale = [[UIScreen mainScreen] scale]; [layer setString:text]; [[self.view layer] addSublayer:layer]; [labelLayers addObject:layer];}- (void)setPredictionText:(NSString *)text withDuration:(float)duration { if (duration > 0.0) { CABasicAnimation *colorAnimation =  [CABasicAnimation animationWithKeyPath:@"foregroundColor"]; colorAnimation.duration = duration; colorAnimation.fillMode = kCAFillModeForwards; colorAnimation.removedOnCompletion = NO; colorAnimation.fromValue = (id)[UIColor darkGrayColor].CGColor; colorAnimation.toValue = (id)[UIColor whiteColor].CGColor; colorAnimation.timingFunction =  [CAMediaTimingFunction functionWithName:kCAMediaTimingFunctionLinear]; [self.predictionTextLayer addAnimation:colorAnimation         forKey:@"colorAnimation"]; } else { self.predictionTextLayer.foregroundColor = [UIColor whiteColor].CGColor; } [self.predictionTextLayer removeFromSuperlayer]; [[self.view layer] addSublayer:self.predictionTextLayer]; [self.predictionTextLayer setString:text];}- (void)speak:(NSString *)words { if ([synth isSpeaking]) { return; } AVSpeechUtterance *utterance =  [AVSpeechUtterance speechUtteranceWithString:words]; utterance.voice = [AVSpeechSynthesisVoice voiceWithLanguage:@"en-US"]; utterance.rate = 0.75 * AVSpeechUtteranceDefaultSpeechRate; [synth speakUtterance:utterance];}@end

 

连上iPhone手机,双击tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj编译运行。手机安装好APP,打开APP,找到玫瑰花识别。训练迭代次数10000次后,识别率99%以上。模拟器打包,生成打包工程文件位于/Users/libinggen/Library/Developer/Xcode/DeriveData/camera_example-dhfdsdfesfmrwtfb1fpfkfjsdfhdskf/Build/Products/Debug-iphoneos。打开CameraExample.app,有可执行文件CameraExample、资源文件模型文件mmapped_graph.pb、标记文件retrained_labels.txt。

Android系统实践。

环境准备。MacBook Pro。Oracle官网下载JDK1.8版本 。jdk-8u111-macosx-x64.dmg。双击安装。设置Java环境变量:

JAVA_HOME='/usr/libexec/java_home'
export JAVA_HOME

搭建Android SDK环境。Android官网下载Android SDK,https://developer.android.com 。25.0.2版本。android-sdk_r25.0.2-macosx.zip。解压到~/Library/Android/sdk目录。build-tools、extras、patcher、platform-tools #各版本SDK 根据API Level划分SDK版本、platforms、sources、system-images、temp #临时文件夹 在SDK更新安装时用到、tools #各版本通用SDK工具 有adb、aapt、aidl、dx文件。
搭建Android NDK环境。Android官网下载Android NDK Mac OS X版本,https://developer.android.com/ndk/downloads/index.html 。android-ndk-r13b-darwin-x86_64.zip文件。解压,CHANGELOG.md、build、ndk-build、ndk-depends、ndk-gdb、ndk-stack、ndk-which、platforms、prebuilt、python-packages、shader-tools、simpleperf、source.properties、sources、toolchains。搭建Bazel。brew安装bazel:

brew install bazel

更新bazel:

brew upgrade bazel

编译演示程序运行。修改tensorflow-1.1.0根目录WORKSPACE文件。android_sdk_repository、android_ndk_repository配置改为用户自己安装目录、版本。

android_sdk_repository( name = "androidsdk", api_level = 25, build_tools_version = "25.0.2", # Replace with path to Android SDK on your system path = "~/Library/Android/sdk")android_ndk_repository( name = "androidndk", api_level = 23, path = "~/Downloads/android-ndk-r13b")

 

在根目录用bazel构建:

bazel build // tensorflow/examples/android:tensorflow_demo

编译成功,默认在tensorflow-1.1.0/bazel-bin/tensorflow/examples/android目录生成TensorFlow演示程序。
运行。生成apk文件传输到手机,手机摄像头看效果。Android 6.0.1。开启“开发者模式”。手机用数据线与计算机相连,进入SDK所在目录,进入platform-tools文件夹,找到adb命令,执行:

./adb install tensorflow-0.12/bazel-bin/tensorflow/examples/android/tensorflow_demo.apk

tensorflow_demo.apk自动安装到手机。打开TF Detec App。App 调起手机摄像头,摄像头返回数据流实时监测。

自定义模型编译运行。训练原始模型、编译Android系统支持模型、生成Android apk文件运行。
训练原始模型、编译Android系统支持模型。用项目根目录tensorflow/python/tools/optimize_for_inference.py、tensorflow/tools/quantization/quantize_graph.py、tensorflow/contrib/util/convert_graphdef_memmapped_format.cc对模型优化。将第一步生成原始模型文件retrained_graph.pb、标记文件retrained_labels.txt放在tensorflow/examples/android/assets目录。修改tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java要加载模型文件名称,输入图片尺寸、操作节点名字、缩放像素大小。

package org.tensorflow.demo;import android.content.res.AssetManager;import android.graphics.Bitmap;import android.os.Trace;import android.util.Log;import java.io.BufferedReader;import java.io.IOException;import java.io.InputStreamReader;import java.util.ArrayList;import java.util.Comparator;import java.util.List;import java.util.PriorityQueue;import java.util.Vector;import org.tensorflow.Operation;import org.tensorflow.contrib.android.TensorFlowInferenceInterface;/** A classifier specialized to label images using TensorFlow. */public class TensorFlowImageClassifier implements Classifier { private static final String TAG = "TensorFlowImageClassifier"; // Only return this many results with at least this confidence. private static final int MAX_RESULTS = 3; private static final float THRESHOLD = 0.1f; // Config values. private String inputName; private String outputName; private int inputSize; private int imageMean; private float imageStd; // Pre-allocated buffers. private Vector<String> labels = new Vector<String>(); private int[] intValues; private float[] floatValues; private float[] outputs; private String[] outputNames; private boolean logStats = false; private TensorFlowInferenceInterface inferenceInterface; private TensorFlowImageClassifier() {} /** * Initializes a native TensorFlow session for classifying images. * * @param assetManager The asset manager to be used to load assets. * @param modelFilename The filepath of the model GraphDef protocol buffer. * @param labelFilename The filepath of label file for classes. * @param inputSize The input size. A square image of inputSize x inputSize is assumed. * @param imageMean The assumed mean of the image values. * @param imageStd The assumed std of the image values. * @param inputName The label of the image input node. * @param outputName The label of the output node. * @throws IOException */ public static Classifier create(  AssetManager assetManager,  String modelFilename,  String labelFilename,  int inputSize,  int imageMean,  float imageStd,  String inputName,  String outputName) { TensorFlowImageClassifier c = new TensorFlowImageClassifier(); c.inputName = inputName; c.outputName = outputName; // Read the label names into memory. // TODO(andrewharp): make this handle non-assets. String actualFilename = labelFilename.split("file:///android_asset/")[1]; Log.i(TAG, "Reading labels from: " + actualFilename); BufferedReader br = null; try {  br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));  String line;  while ((line = br.readLine()) != null) {  c.labels.add(line);  }  br.close(); } catch (IOException e) {  throw new RuntimeException("Problem reading label file!" , e); } c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename); // The shape of the output is [N, NUM_CLASSES], where N is the batch size. final Operation operation = c.inferenceInterface.graphOperation(outputName); final int numClasses = (int) operation.output(0).shape().size(1); Log.i(TAG, "Read " + c.labels.size() + " labels, output layer size is " + numClasses); // Ideally, inputSize could have been retrieved from the shape of the input operation. Alas, // the placeholder node for input in the graphdef typically used does not specify a shape, so it // must be passed in as a parameter. c.inputSize = inputSize; c.imageMean = imageMean; c.imageStd = imageStd; // Pre-allocate buffers. c.outputNames = new String[] {outputName}; c.intValues = new int[inputSize * inputSize]; c.floatValues = new float[inputSize * inputSize * 3]; c.outputs = new float[numClasses]; return c; } @Override public List<Recognition> recognizeImage(final Bitmap bitmap) { // Log this method so that it can be analyzed with systrace. Trace.beginSection("recognizeImage"); Trace.beginSection("preprocessBitmap"); // Preprocess the image data from 0-255 int to normalized float based // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); for (int i = 0; i < intValues.length; ++i) {  final int val = intValues[i];  floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;  floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;  floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd; } Trace.endSection(); // Copy the input data into TensorFlow. Trace.beginSection("feed"); inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); Trace.endSection(); // Run the inference call. Trace.beginSection("run"); inferenceInterface.run(outputNames, logStats); Trace.endSection(); // Copy the output Tensor back into the output array. Trace.beginSection("fetch"); inferenceInterface.fetch(outputName, outputs); Trace.endSection(); // Find the best classifications. PriorityQueue<Recognition> pq =  new PriorityQueue<Recognition>(   3,   new Comparator<Recognition>() {    @Override    public int compare(Recognition lhs, Recognition rhs) {    // Intentionally reversed to put high confidence at the head of the queue.    return Float.compare(rhs.getConfidence(), lhs.getConfidence());    }   }); for (int i = 0; i < outputs.length; ++i) {  if (outputs[i] > THRESHOLD) {  pq.add(   new Recognition(    "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));  } } final ArrayList<Recognition> recognitions = new ArrayList<Recognition>(); int recognitionsSize = Math.min(pq.size(), MAX_RESULTS); for (int i = 0; i < recognitionsSize; ++i) {  recognitions.add(pq.poll()); } Trace.endSection(); // "recognizeImage" return recognitions; } @Override public void enableStatLogging(boolean logStats) { this.logStats = logStats; } @Override public String getStatString() { return inferenceInterface.getStatString(); } @Override public void close() { inferenceInterface.close(); }}

 

重新编译apk,连接Android手机,安装apk:

bazel buld //tensorflow/examples/android:tensorflow_demo
adb install -r -g bazel-bin/tensorflow/examples/android/tensorflow_demo.apk

树莓派实践。

Tensorflow可以在树莓派(Raspberry Pi)运行。树莓派,只有信用卡大小微型电脑,系统基于Linux,有音频、视频功能。应用,输入1万张自己的面部图片,在树莓派训练人脸识别模型,教会它认识你,你进入家门后,帮你开灯、播放音乐各种功能。树莓派编译方法和直接在Linux环境上用相似。

参考资料:
《TensorFlow技术解析与实战》

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