coreml之通过URL加载模型

在xcode中使用mlmodel模型，之前说的最简单的方法是将模型拖进工程中即可，xcode会自动生成有关模型的前向预测接口，这种方式非常简单，但是更新模型就很不方便。

今天说下另外一种通过URL加载mlmodel的方式。具体可以查阅apple开发者官方文档 https://developer.apple.com/documentation/coreml/mlmodel：

流程如下：

1.提供mlmodel的文件所在路径model_path

NSString *model_path = "path_to/.mlmodel"

2.将NSSting类型转换为NSURL，并根据路径对模型进行编译（编译出的为.mlmodelc 文件， 这是一个临时文件，如果需要，可以将其保存到一个固定位置：https://developer.apple.com/documentation/coreml/core_ml_api/downloading_and_compiling_a_model_on_the_user_s_device）

NSURL *url = [NSURL fileURLWithPath:model_path isDirectory:FALSE];
NSURL *compile_url = [MLModel compileModelAtURL:url error:&error];

3.根据编译后模型所在路径，加载模型，类型为MLModel

MLModel *compiled_model = [MLModel modelWithContentsOfURL:compile_url configuration:model_config error:&error];

4.需要注意的是采用动态编译方式，coreml只是提供了一种代理方式MLFeatureProvider，类似于C++中的虚函数。因此需要自己重写模型输入和获取模型输出的类接口（该类继承自MLFeatureProvider）。如下自己封装的MLModelInput和MLModelOutput类。MLModelInput类可以根据模型的输入名称InputName，传递data给模型。而MLModelOutput可以根据不同的输出名称featureName获取预测结果。

这个是头文件：

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

NS_ASSUME_NONNULL_BEGIN

/// Model Prediction Input Type
API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLModelInput : NSObject<MLFeatureProvider>

//the input name,default is image
@property (nonatomic, strong) NSString *inputName;

//data as color (kCVPixelFormatType_32BGRA) image buffer
@property (readwrite, nonatomic) CVPixelBufferRef data;

- (instancetype)init NS_UNAVAILABLE;

- (instancetype)initWithData:(CVPixelBufferRef)data inputName:(NSString *)inputName;

@end

API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLModelOutput : NSObject<MLFeatureProvider>

//the output name, defalut is feature
@property (nonatomic, strong) NSString *outputName;

// feature as multidimensional array of doubles
@property (readwrite, nonatomic) MLMultiArray *feature;

- (instancetype)init NS_UNAVAILABLE;

- (instancetype)initWithFeature:(MLMultiArray *)feature;
@end

NS_ASSUME_NONNULL_END

这个是类方法实现的文件：

@implementation MLModelInput

- (instancetype)initWithData:(CVPixelBufferRef)data inputName:(nonnull NSString *)inputName {
    if (self) {
        _data = data;
        _inputName = inputName;
    }
    return self;
}

- (NSSet<NSString *> *)featureNames {
    return [NSSet setWithArray:@[self.inputName]];
}

- (nullable MLFeatureValue *)featureValueForName:(nonnull NSString *)featureName {
    if ([featureName isEqualToString:self.inputName]) {
        return [MLFeatureValue featureValueWithPixelBuffer:_data];
    }
    return nil;
}

@end

@implementation MLModelOutput

- (instancetype)initWithFeature:(MLMultiArray *)feature{
    if (self) {
        _feature = feature;
        _outputName = DefalutOutputValueName;
    }
    return self;
}

- (NSSet<NSString *> *)featureNames{
    return [NSSet setWithArray:@[self.outputName]];
}

- (nullable MLFeatureValue *)featureValueForName:(nonnull NSString *)featureName {
    if ([featureName isEqualToString:self.outputName]) {
        return [MLFeatureValue featureValueWithMultiArray:_feature];
    }
    return nil;
}

@end

5. 模型预测，获取预测结果。上面这两个类接口写完后，就可以整理输入数据为CvPixelBuffer，然后通过获取模型描述MLModelDescription得到输入名称，根据输入名称创建MLModelInput，预测，然后再根据MLModelOutput中的featureNames获取对应的预测输出数据，类型为MLMultiArray：

MLModelDescription *model_description = compiled_model.modelDescription;
NSDictionary *dict = model_description.inputDescriptionsByName;
NSArray<NSString *> *feature_names = [dict allKeys];
NSString *input_feature_name = feature_names[];
NSError *error;
MLModelInput *model_input = [[MLModelInput alloc] initWithData:buffer inputName:input_feature_name];
id<MLFeatureProvider> model_output = [compiled_model predictionFromFeatures:model_input options:option error:&error];

NSSet<NSString *> *out_feature_names = [model_output featureNames];
NSArray<NSString *> *name_list = [out_feature_names allObjects];
NSUInteger size = [name_list count];
std::vector<MLMultiArray *> feature_list;
for (NSUInteger i = 0; i < size; i++) {
    NSString *name = [name_list objectAtIndex:i];
    MLMultiArray *feature = [model_output featureValueForName:name].multiArrayValue;
    feature_list.push_back(feature);
}

6.读取MLMultiArray中的预测结果数据做后续处理..