手撕coreML之yolov2 object detection物体检测（含源代码）

一些闲话：

　　前面我有篇博客 https://www.cnblogs.com/riddick/p/10434339.html ，大致说了下如何将pytorch训练的.pth模型转换为mlmodel，部署在IOS端进行前向预测。只是介绍了下类接口，并没有示例，因此有可能会陷入没有demo你说个p的境地。因此，今天就拿实际的模型来说上一说。

　　其实coreML的demo，github上有很多，但是大部分都是用swift写的，而对于从C/C++语言过来的同学来说，Objective-C或许会更容易看懂一些。所以这次就以yolov2实现的object detection为例，创建Objective-C工程并用真机调试，来实现前向预测（并且附源代码）。

　　当然，为了偷懒起见，模型并不是我训练的，模型来自这里：https://github.com/syshen/YOLO-CoreML 。该仓库使用swift实现的，有兴趣的可以对比着看。yolov2的mlmodel模型文件，请看上面仓库的readMe中这句话：

execute download.sh to download the pre-trained model % sh download.sh

闲话少说，进入正题：

一、创建xcode工程，选择编程语言为Objective-C。将模型添加到xcode工程中，我将模型名字改为yoloModel，并且量化到了16bit。当然使用原始模型200多MB也完全OK。

　　手撕coreML之yolov2 object detection物体检测（含源代码）

二、模型添加到工程后，会自动生成yoloModel类头文件，如下：

//
// yoloModel.h
//
// This file was automatically generated and should not be edited.
//

#import <Foundation/Foundation.h>
#import <CoreML/CoreML.h>
#include <stdint.h>

NS_ASSUME_NONNULL_BEGIN


/// Model Prediction Input Type
API_AVAILABLE(macos(10.13.2), ios(11.2), watchos(4.2), tvos(11.2)) __attribute__((visibility("hidden")))
@interface yoloModelInput : NSObject<MLFeatureProvider>

/// input__0 as color (kCVPixelFormatType_32BGRA) image buffer, 608 pixels wide by 608 pixels high
@property (readwrite, nonatomic) CVPixelBufferRef input__0;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithInput__0:(CVPixelBufferRef)input__0;
@end


/// Model Prediction Output Type
API_AVAILABLE(macos(10.13.2), ios(11.2), watchos(4.2), tvos(11.2)) __attribute__((visibility("hidden")))
@interface yoloModelOutput : NSObject<MLFeatureProvider>

/// output__0 as 425 x 19 x 19 3-dimensional array of doubles
@property (readwrite, nonatomic, strong) MLMultiArray * output__0;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithOutput__0:(MLMultiArray *)output__0;
@end


/// Class for model loading and prediction
API_AVAILABLE(macos(10.13.2), ios(11.2), watchos(4.2), tvos(11.2)) __attribute__((visibility("hidden")))
@interface yoloModel : NSObject
@property (readonly, nonatomic, nullable) MLModel * model;
- (nullable instancetype)init;
- (nullable instancetype)initWithContentsOfURL:(NSURL *)url error:(NSError * _Nullable * _Nullable)error;
- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable * _Nullable)error API_AVAILABLE(macos(10.14), ios(12.0), watchos(5.0), tvos(12.0)) __attribute__((visibility("hidden")));
- (nullable instancetype)initWithContentsOfURL:(NSURL *)url configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable * _Nullable)error API_AVAILABLE(macos(10.14), ios(12.0), watchos(5.0), tvos(12.0)) __attribute__((visibility("hidden")));

/**
    Make a prediction using the standard interface
    @param input an instance of yoloModelInput to predict from
    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
    @return the prediction as yoloModelOutput
*/
- (nullable yoloModelOutput *)predictionFromFeatures:(yoloModelInput *)input error:(NSError * _Nullable * _Nullable)error;

/**
    Make a prediction using the standard interface
    @param input an instance of yoloModelInput to predict from
    @param options prediction options
    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
    @return the prediction as yoloModelOutput
*/
- (nullable yoloModelOutput *)predictionFromFeatures:(yoloModelInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable * _Nullable)error;

/**
    Make a prediction using the convenience interface
    @param input__0 as color (kCVPixelFormatType_32BGRA) image buffer, 608 pixels wide by 608 pixels high:
    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
    @return the prediction as yoloModelOutput
*/
- (nullable yoloModelOutput *)predictionFromInput__0:(CVPixelBufferRef)input__0 error:(NSError * _Nullable * _Nullable)error;

/**
    Batch prediction
    @param inputArray array of yoloModelInput instances to obtain predictions from
    @param options prediction options
    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
    @return the predictions as NSArray<yoloModelOutput *>
*/
- (nullable NSArray<yoloModelOutput *> *)predictionsFromInputs:(NSArray<yoloModelInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable * _Nullable)error API_AVAILABLE(macos(10.14), ios(12.0), watchos(5.0), tvos(12.0)) __attribute__((visibility("hidden")));
@end

NS_ASSUME_NONNULL_END

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