image.png
image.png
如上图几个模型会对应生成相应的m文件，采用下面的方法可以只用一个m文件来加载多个模型
#import <Foundation/Foundation.h>
#import <CoreML/CoreML.h>
#import <stdint.h>
NS_ASSUME_NONNULL_BEGIN
/// Model Prediction Input Type
API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLCoreModelInput : NSObject<MLFeatureProvider>
//the input name,default is image
@property (nonatomic, strong) NSString *inputName;
//data as color (kCVPixelFormatType_32BGRA) image buffer, 224 pixels wide by 224 pixels high
@property (readwrite, nonatomic) CVPixelBufferRef data;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithData:(CVPixelBufferRef)data;
@end
API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLCoreModelOutput : NSObject<MLFeatureProvider>
//the output name, defalut is prob
@property (nonatomic, strong) NSString *outputName;
// prob as multidimensional array of doubles
@property (readwrite, nonatomic) MLMultiArray *prob;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithProb:(MLMultiArray *)prob;
@end
/// Model Prediction Output Type
API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLCoreModelMapOutput : NSObject<MLFeatureProvider>
//the output value name, defalut is prob
@property (nonatomic, strong) NSString *outputValueName;
//the output label name, defalut is classLabel
@property (nonatomic, strong) NSString *outputLabelName;
/// prob as dictionary of strings to doubles
@property (readwrite, nonatomic) NSDictionary<NSString *, NSNumber *> * prob;
/// classLabel as string value
@property (readwrite, nonatomic) NSString * classLabel;
- (instancetype)init NS_UNAVAILABLE;
- (instancetype)initWithProb:(NSDictionary<NSString *, NSNumber *> *)prob classLabel:(NSString *)classLabel;
@end
// Class for model loading and prediction
API_AVAILABLE(macos(10.13), ios(11.0), watchos(4.0), tvos(11.0))
@interface MLCoreModel : NSObject
@property (readonly, nonatomic, nullable) MLModel * model;
//the input name,default is image
@property (nonatomic, strong) NSString *inputNodeName;
//the output value name, defalut is prob
@property (nonatomic, strong) NSString *outputValueName;
//the output label name, defalut is classLabel
@property (nonatomic, strong) NSString *outputLabelName;
- (nullable instancetype)initWithContentsOfURL:(NSURL *)url error:(NSError * _Nullable * _Nullable)error;
/**
Make a prediction using the standard interface
@param input an instance of ResnetNSFWInput 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 ResnetNSFWOutput
*/
- (nullable MLCoreModelOutput *)predictionFromFeatures:(MLCoreModelInput *)input error:(NSError * _Nullable * _Nullable)error;
/**
Make a prediction using the standard interface
@param input an instance of ResnetNSFWInput 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 MLCoreModelMapOutput
*/
- (nullable MLCoreModelMapOutput *)predictionMapFromFeatures:(MLCoreModelInput *)input error:(NSError * _Nullable * _Nullable)error;
/// All models can predict on a specific set of input features.
- (nullable id<MLFeatureProvider>)prediction:(MLCoreModelInput *)input
error:(NSError **)error;
/**
Make a prediction using the convenience interface
@param data as color (kCVPixelFormatType_32BGRA) image buffer, 224 pixels wide by 224 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 ResnetNSFWOutput
*/
- (nullable MLCoreModelOutput *)predictionFromData:(CVPixelBufferRef)data error:(NSError * _Nullable * _Nullable)error;
/**
Make a prediction using the convenience interface
@param data as color (kCVPixelFormatType_32BGRA) image buffer, 224 pixels wide by 224 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 MLCoreModelMapOutput
*/
- (nullable MLCoreModelMapOutput *)predictionMapFromData:(CVPixelBufferRef)data error:(NSError * _Nullable * _Nullable)error;
@end
NS_ASSUME_NONNULL_END
#import "MLCoreModel.h"
#define DefalutInputName @"image"
#define DefalutOutputValueName @"prob"
#define DefalutOutputLabelName @"classLabel"
@implementation MLCoreModelInput
- (instancetype)initWithData:(CVPixelBufferRef)data {
if (self) {
_data = data;
_inputName = DefalutInputName;
}
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 MLCoreModelOutput
- (instancetype)initWithProb:(MLMultiArray *)prob{
if (self) {
_prob = prob;
_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:_prob];
}
return nil;
}
@end
@implementation MLCoreModelMapOutput
- (instancetype)initWithProb:(NSDictionary<NSString *, NSNumber *> *)prob classLabel:(NSString *)classLabel {
if (self) {
_prob = prob;
_classLabel = classLabel;
_outputValueName = DefalutOutputValueName;
_outputLabelName = DefalutOutputLabelName;
}
return self;
}
- (NSSet<NSString *> *)featureNames {
return [NSSet setWithArray:@[self.outputValueName, self.outputLabelName]];
}
- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
if ([featureName isEqualToString:self.outputValueName]) {
return [MLFeatureValue featureValueWithDictionary:_prob error:nil];
}
if ([featureName isEqualToString:self.outputLabelName]) {
return [MLFeatureValue featureValueWithString:_classLabel];
}
return nil;
}
@end
@implementation MLCoreModel
- (nullable instancetype)initWithContentsOfURL:(NSURL *)url error:(NSError * _Nullable * _Nullable)error{
self = [super init];
if (!self) { return nil; }
_model = [MLModel modelWithContentsOfURL:url error:error];
if (_model == nil) {
return nil;
}
_outputValueName = DefalutOutputValueName;
_outputLabelName = DefalutOutputLabelName;
_inputNodeName = DefalutInputName;
return self;
}
- (nullable MLCoreModelOutput *)predictionFromFeatures:(MLCoreModelInput *)input error:(NSError * _Nullable * _Nullable)error{
id<MLFeatureProvider> outFeatures = [_model predictionFromFeatures:input error:error];
MLCoreModelOutput * result = [[MLCoreModelOutput alloc] initWithProb:[outFeatures featureValueForName:self.outputValueName].multiArrayValue];
return result;
}
- (nullable id<MLFeatureProvider>)prediction:(MLCoreModelInput *)input
error:(NSError **)error
{
id<MLFeatureProvider> outFeatures = [_model predictionFromFeatures:input error:error];
return outFeatures;
}
- (nullable MLCoreModelMapOutput *)predictionMapFromFeatures:(MLCoreModelInput *)input error:(NSError * _Nullable * _Nullable)error{
id<MLFeatureProvider> outFeatures = [_model predictionFromFeatures:input error:error];
MLCoreModelMapOutput * result = [[MLCoreModelMapOutput alloc] initWithProb:(NSDictionary<NSString *, NSNumber *> *)[outFeatures featureValueForName:self.outputValueName].dictionaryValue classLabel:[outFeatures featureValueForName:self.outputLabelName].stringValue];
return result;
}
- (nullable MLCoreModelOutput *)predictionFromData:(CVPixelBufferRef)data error:(NSError * _Nullable * _Nullable)error{
MLCoreModelInput *input_ = [[MLCoreModelInput alloc] initWithData:data];
input_.inputName = self.inputNodeName;
return [self predictionFromFeatures:input_ error:error];
}
- (nullable MLCoreModelMapOutput *)predictionMapFromData:(CVPixelBufferRef)data error:(NSError * _Nullable * _Nullable)error{
MLCoreModelInput *input_ = [[MLCoreModelInput alloc] initWithData:data];
input_.inputName = self.inputNodeName;
return [self predictionMapFromFeatures:input_ error:error];
}
@end
使用模型的时候设置一下inputNodeName，outputValueName
self.coreModel = [[MLCoreModel alloc] initWithContentsOfURL:[NSURL URLWithString:saveURL] error:nil];
if (self.outputLabelsName) self.coreModel.outputLabelName = self.outputLabelsName;
if (self.outputValueName) self.coreModel.outputValueName = self.outputValueName;
模型具有多个输出示例如下：
NSError *error;
_model_pnet =[[MLCoreModel alloc]initWithContentsOfURL:[NSURL URLWithString:saveURL] error:&error];
NSError *error;
MLCoreModelInput *input = [[MLCoreModelInput alloc] initWithData:pixelBuffer];
input.inputName = @"data";
id<MLFeatureProvider> outFeatures = [_model_pnet prediction:input error:&error];
MLMultiArray *conv4_2 = [outFeatures featureValueForName:@"conv4-2"].multiArrayValue;
MLMultiArray * prob1 = [outFeatures featureValueForName:@"prob1"].multiArrayValue;
