机器学习迁移模型到IOS

saver.save(sess, MODEL_SAVEPATH, global_step=50)

if i %SAVE_EPOCH == 0:
    tf.train.write_graph(sess.graph, MODEL_SAVE_DIR, 'model.pbtxt')

with open(TF_MODEL_FILE, 'rb') as f:
    serialized = f.read()
tf.reset_default_graph()
original_gdef = tf.GraphDef()
original_gdef.ParseFromString(serialized)
##2 可以在这里查看到全部的图信息(前提是你有转换成功)
with tf.Graph().as_default() as g:
    tf.import_graph_def(original_gdef, name='')
    ops = g.get_operations()
    try:
        for i in range(10000):
            print('op id {} : op name: {}, op type: "{}"'.format(str(i),ops[i].name, ops[i].type))
    except:
        print("全部节点已打印完毕.")
        pass

input_node_names = ['import/Mul', 'BottleneckInputPlaceholder']   # 本来以为是import/DecodeJpeg/contents, 实际上是Mul(tfcoreml-git上说的)
output_node_names = ['import/pool_3/_reshape','final_train_ops/softMax_last']  # 想要保存的节点 , 'final_train_ops/softMax_last'
gdef = strip_unused_lib.strip_unused(
        input_graph_def = original_gdef,
        input_node_names = input_node_names,
        output_node_names = output_node_names,
        placeholder_type_enum = dtypes.float32.as_datatype_enum)
with gfile.GFile(FROZEN_MODEL_FILE, "wb") as f:
    f.write(gdef.SerializeToString())

input_tensor_shapes = {
                        "import/Mul:0":[1,299,299,3],          # batch size is 1
                        "BottleneckInputPlaceholder:0":[1,2048],
                        }
output_tensor_names = ['import/pool_3/_reshape:0','final_train_ops/softMax_last:0']
# Call the converter. This may take a while
coreml_model = tfcoreml.convert(
        tf_model_path=FROZEN_MODEL_FILE,
        mlmodel_path=COREML_MODEL_FILE,
        input_name_shape_dict=input_tensor_shapes,
        output_feature_names=output_tensor_names,
        image_input_names = ['import/Mul:0'],
        red_bias = -1,
        green_bias = -1,
        blue_bias = -1,
        image_scale = 2.0/255.0)

poo3_frist = sess.run(poo3, feed_dict={inpiut_x: image})   # 按照模型的顺序要, 先喂给它图片, 然后图片提取到瓶颈的tensor
result     = sess.run(predict, feed_dict={change_input:poo3_frist}) # 瓶颈的tensor再转入input传入, 得到我们最后的predict

- (instancetype)initWithBottleneckInputPlaceholder__0:(MLMultiArray *)BottleneckInputPlaceholder__0 import__Mul__0:(CVPixelBufferRef)import__Mul__0;

- (inception_v3 *)tfModel {
    if (!_tfModel) {
        // 1 加载模型, 本身代码会调用init的时候, 方法会调用initWithContentsOfURL, 找到inception文件进行初始化
        _tfModel = [[inception_v3 alloc] init];
    }
    return _tfModel;
}

- (NSString *)predictWithFoodImage:(UIImage *)foodImage
{
    // step1: 标准为size, 转为可传入的参数.
    UIImage *img = [foodImage scaleToSize:CGSizeMake(299, 299)];             // 转换为可传参的图片大小
    CVPixelBufferRef refImage = [[UIImage new] pixelBufferFromCGImage:img];  // 转换为可传参的类型
    // step2.1: 由于一开始是没有BottleneckInputPlaceholder, 直接0值初始一个传入
    MLMultiArray *holder = [[MLMultiArray alloc] initWithShape:@[@2048] dataType:MLMultiArrayDataTypeDouble error:nil];
    // step2.2: 启动预测, 预测完成后得到import__pool_3___reshape__0
    inception_v3Output *output = [self.tfModel predictionFromBottleneckInputPlaceholder__0:holder import__Mul__0:refImage error:nil];
    // step3: 从第二步, 完整得到了想要的BottleneckInputPlaceholder, 直接代入, 图片也代入.
    inception_v3Output *output1 = [self.tfModel predictionFromBottleneckInputPlaceholder__0:output.import__pool_3___reshape__0 import__Mul__0:refImage error:nil];
    // step4: 从final_train_ops__softMax_last__0提取预测结果
    MLMultiArray *__final = output1.final_train_ops__softMax_last__0;
    return [self poAccu:__final];
}

//触发事件：拍照
- (void)addCamera
{
    UIImagePickerController *picker = [[UIImagePickerController alloc] init];
    picker.delegate = self;
    picker.allowsEditing = YES; //可编辑
    //判断是否可以打开照相机
    if ([UIImagePickerController isSourceTypeAvailable:UIImagePickerControllerSourceTypeCamera]) {
        //摄像头
        picker.sourceType = UIImagePickerControllerSourceTypeCamera;
    } else { //否则打开照片库
        picker.sourceType = UIImagePickerControllerSourceTypePhotoLibrary;
    }
    [self presentViewController:picker animated:YES completion:nil];
}

#pragma mark - UIImagePickerControllerDelegate
//拍摄完成后要执行的代理方法
- (void)imagePickerController:(UIImagePickerController *)picker didFinishPickingMediaWithInfo:(NSDictionary *)info
{
    NSString *mediaType = [info objectForKey:UIImagePickerControllerMediaType];
    if ([mediaType isEqualToString:@"public.image"]) {
        //得到照片
        UIImage *image = [info objectForKey:UIImagePickerControllerOriginalImage];
        image = [image scaleToSize:self.imageView.frame.size];
        self.imageView.image = image;
        // 异步处理, 不要占用主线程:
        dispatch_async(dispatch_queue_create(0, 0), ^{
            NSString *preString = [self.prediction predictWithFoodImage:image];
            dispatch_async(dispatch_get_main_queue(), ^{
                self.preLabel.text = preString;
            });
        });
    }
    [self dismissViewControllerAnimated:YES completion:nil];
}

//进入拍摄页面点击取消按钮
- (void)imagePickerControllerDidCancel:(UIImagePickerController *)picker
{
    [self dismissViewControllerAnimated:YES completion:nil];

}