在这篇文章中，我们将为您详细介绍学习笔记TF066:TensorFlow移动端应用，iOS、Android系统实践的内容，并且讨论关于tensorflow移动端的相关问题。此外，我们还会涉及一些关于A
在这篇文章中，我们将为您详细介绍学习笔记TF066:TensorFlow移动端应用，iOS、Android系统实践的内容，并且讨论关于tensorflow 移动端的相关问题。此外，我们还会涉及一些关于Apple 不再在 iOS 16.2 发布之前签署 iOS 16.1 和 iOS 16.1.1、C++ write and read file via fstream in ios::out,ios::in,ios::app mode、iOS 11.0-iOS 14.3越狱后可以解除吗？如何解除iOS 11.0-iOS 14.3越狱、iOS 15.6.1 验证通道已关闭，iOS 16还能降级吗？的知识，以帮助您更全面地了解这个主题。
本文目录一览：- 学习笔记TF066:TensorFlow移动端应用，iOS、Android系统实践（tensorflow 移动端）
- Apple 不再在 iOS 16.2 发布之前签署 iOS 16.1 和 iOS 16.1.1
- C++ write and read file via fstream in ios::out,ios::in,ios::app mode
- iOS 11.0-iOS 14.3越狱后可以解除吗？如何解除iOS 11.0-iOS 14.3越狱
- iOS 15.6.1 验证通道已关闭，iOS 16还能降级吗？
学习笔记TF066:TensorFlow移动端应用，iOS、Android系统实践（tensorflow 移动端）
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，http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz 。
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 http://download.tensorflow.org/example_images/flower_photos.tgz 。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 http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz 。
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](https://my.oschina.net/u/996807) 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 *)captureOutput
didOutputSampleBuffer:(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。http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html 。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》
欢迎推荐上海机器学习工作机会，我的微信：qingxingfengzi
Apple 不再在 iOS 16.2 发布之前签署 iOS 16.1 和 iOS 16.1.1
继 ios 16.1.2 于 11 月 30 日发布后，apple 现已停止签署 ios 16.1 和 ios 16.1.1。
Apple 不再签署 iOS 16.1 和 iOS 16.1.1
iOS 16.1于 10 月发布，具有多项新功能和增强功能，例如 iCloud 共享照片库、适用于 iPhone 用户的 Fitness+、Live Activities 等。在11月份发布的iOS 16.1.1修复了缺陷并改进了安全性。
然后，在 11 月 30 日，Apple 发布了 iOS 16.1.2，以增强 iPhone 14 的崩溃检测功能，并提高无线运营商的兼容性。这是目前正式提供给用户的最新iOS版本。
与此同时，苹果即将在未来几天向公众发布iOS 16.2 。该更新将添加新的 Freeform 应用程序、对 Home 应用程序的改进、面向 iPhone 14 Pro 用户的新的永远在线选项、Apple Music Sing 等。
经常有越狱的iPhone和iPad用户恢复到旧版本的iOS。目前还没有任何迹象显示正在开发适用于 iOS 16 的越狱工具。将 Apple 设备恢复到以前版本的 iOS 有时也会对升级到最新版本的 iOS 后遇到重大错误的用户有所帮助。
从 iOS 16 降级到 iOS 15
即使您无法轻松恢复到iOS 16.1版本，仍有可能将您的设备降级至iOS 15版本以上。Apple正在为使用iOS 15.7.1的用户提供安全更新，导致此情况发生。如果想将 iPhone 或 iPad 降级，就必须使用 Mac 或 PC。
这不是苹果第一次提供让用户继续使用旧版 iOS 的选项。去年，一旦 iOS 15 可用， 用户可以选择在 iOS 14 上停留更长时间 ，而苹果仍在为其发布安全更新。然而， 该公司在几个月后取消了这个选项。
目前尚不清楚 iOS 15.7.1 作为 iOS 16 的替代选项将保留多长时间。
以上就是Apple 不再在 iOS 16.2 发布之前签署 iOS 16.1 和 iOS 16.1.1的详细内容，更多请关注php中文网其它相关文章！
C++ write and read file via fstream in ios::out,ios::in,ios::app mode
#include <iostream>
#include <uuid/uuid.h>
#include <ostream>
#include <istream>
#include <fstream>
#include <iostream>
using namespace std;
void retrieveUuid(char *uuidValue);
void writeFile2();
void readFile3();
int main()
{
writeFile2();
readFile3();
return 0;
}
void readFile3()
{
fstream wFile;
wFile.open("log3.txt",ios::app|ios::in|ios::out);
if(!wFile.is_open())
{
cout<<"Create or open log3.txt Failed!"<<endl;
}
string uuidValue;
int num=0;
while(getline(wFile,uuidValue))
{
cout<<"Id="<<++num<<",value="<<uuidValue<<endl;
}
wFile.close();
printf("Finished!\n");
}
void writeFile2()
{
fstream wFile;
wFile.open("log3.txt",ios::app|ios::out|ios::in);
if(!wFile.is_open())
{
cout<<"Create or open log3.txt Failed!"<<endl;
}
char *uuidValue=(char*)malloc(40);
for(int i=0;i<10000;i++)
{
retrieveUuid(uuidValue);
wFile<<uuidValue<<endl;
}
free(uuidValue);
wFile.close();
}
void retrieveUuid(char *uuidValue)
{
uuid_t newUUID;
uuid_generate(newUUID);
uuid_unparse(newUUID,uuidValue);
}
Complile and run
g++ -g -std=c++2a h2.cpp -o h2 -luuid
Run the ./h2 command
./h2
iOS 11.0-iOS 14.3越狱后可以解除吗？如何解除iOS 11.0-iOS 14.3越狱
就目前来说，iOS 11.0-iOS 14.3越狱还不是很稳定，会出现白苹果，卡顿，程序崩溃等各种现象，大家可以等待稳定版本出来之后再进行越狱，那已经进行越狱的用户如何取消现有的越狱呢？以下是解除iOS 11.0-iOS 14.3越狱方法教程。
1.打开设备桌面，进入unc0ver，点击左上角设置，把下图中Restore RootFS选项开关打开；
2.回到桌面，再次打开unc0ver，如下图，点击Restore RootFS按钮执行操作；
3.运行到如下图时，选择“ok"；
4.等待运行完毕，即可解除越狱。
iOS 15.6.1 验证通道已关闭，iOS 16还能降级吗？
苹果 于 8 月 17 日给大家发布了iOS 15.6.1正式版，修复了整体错误 Bug 并改进了性能。苹果还通过更新修复了两个主要的安全漏洞，这两个漏洞都允许应用程序在未经用户同意的情况下以内核权限执行任意代码。然而，随着 iOS 16 正式版发布，苹果现已经关闭了 iOS 15.6.1 的验证通道，用户无法再将 iPhone、iPad 和 iPod touch 软件降级到 iOS 15.6.1，这对等待越狱工具的用户有直接的影响。
iOS 16发布不久，很多老机型设备升级后效果不理想，而今iOS 15.6.1 验证通道关闭，升级iOS 16后不稳定怎么办？升级iOS 16后还能降级吗？
iOS 15.6.1正式版推送以后，苹果还给大家推送了iOS 15.7 正式版，该版本留给暂时不愿意升级 iOS 16 正式版的用户。也就是说用户若觉得iOS 16不好用，仍有办法从 iOS 16 降级到 iOS 15。
iOS 15.7 正式版修复了联系人、地图、Safari、Safari 扩展、快捷方式和 WebKit 的安全问题，还解决了多个内核漏洞。苹果表示其中一个内核漏洞已被积极利用，非常适合旧机型养老。
如何降级到iOS 15？
可直接使用爱思助手一键刷机降级到iOS 15，方法如下：
下载安装最新版爱思助手 ，用数据线把移动设备连接到电脑；点击上方“智能刷机”进入到“一键刷机”界面，在可刷版本中选择iOS 15.7，点“立即刷机”即可。
总结
以上是小编为你收集整理的iOS 15.6.1 验证通道已关闭，iOS 16还能降级吗？全部内容。
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今天关于学习笔记TF066:TensorFlow移动端应用，iOS、Android系统实践和tensorflow 移动端的分享就到这里，希望大家有所收获，若想了解更多关于Apple 不再在 iOS 16.2 发布之前签署 iOS 16.1 和 iOS 16.1.1、C++ write and read file via fstream in ios::out,ios::in,ios::app mode、iOS 11.0-iOS 14.3越狱后可以解除吗？如何解除iOS 11.0-iOS 14.3越狱、iOS 15.6.1 验证通道已关闭，iOS 16还能降级吗？等相关知识，可以在本站进行查询。
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