C++-ONNX：用onnxruntime部署自己的模型【Pytorch导出.onnx】【Tensorflow导出.onnx】【C++用onnxruntime框架部署并推理】

客官°小女子只卖身不卖艺 2023-09-25 13:04 74阅读 0赞

微软联合Facebook等在2017年搞了个深度学习以及机器学习模型的格式标准–ONNX，旨在将所有模型格式统一为一致，更方便地实现模型部署。现在大多数的深度学习框架都支持ONNX模型转出并提供相应的导出接口。

ONNXRuntime(Open Neural Network Exchange)是微软推出的一款针对ONNX模型格式的推理框架，用户可以非常便利的用其运行一个onnx模型。ONNXRuntime支持多种运行后端包括CPU，GPU，TensorRT，DML等。可以说ONNXRuntime是对ONNX模型最原生的支持，只要掌握模型导出的相应操作，便能对将不同框架的模型进行部署，提高开发效率。

利用onnx和onnxruntime实现[pytorch][]深度框架使用C++推理进行服务器部署，模型推理的性能是比python快很多的。

利用C++ ONNXruntime部署自己的模型，这里用[Keras][]搭建好的一个网络模型来举例，转换为onnx的文件，在C++上进行部署，另外可以利用tensorRT加速。

Github地址：[https://github.com/zouyuelin/SLAM\_Learning\_notes/tree/main/PoseEstimation][https_github.com_zouyuelin_SLAM_Learning_notes_tree_main_PoseEstimation]

### 1、下载 ###

GitHub下载地址：

[Releases · microsoft/onnxruntime · GitHub][Releases _ microsoft_onnxruntime _ GitHub]

### 2、解压 ###

下载的onnxruntime是直接编译好的库文件，直接放在自定义的文件夹中即可。在CMakeLists.txt中引入onnxruntime的头文件、库文件即可。

# 引入头文件
    include_directories(......../onnxruntime/include)
    # 引入库文件
    link_directories(......../onnxruntime/lib)

## 一、模型的准备 ##

### 1、Pytorch导出.onnx模型 ###

首先，利用pytorch自带的`torch.onnx`模块导出 `.onnx`模型文件，具体查看该部分[pytorch官方文档][pytorch 1]，主要流程如下：

import torch
    checkpoint = torch.load(model_path)
    model = ModelNet(params)
    model.load_state_dict(checkpoint['model'])
    model.eval()
    
    input_x_1 = torch.randn(10,20)
    input_x_2 = torch.randn(1,20,5)
    output, mask = model(input_x_1, input_x_2)
    
    torch.onnx.export(model,
                     (input_x_1, input_x_2),
                     'model.onnx',
                     input_names = ['input','input_mask'],
                     output_names = ['output','output_mask'],
                     opset_version=11,
                     verbose = True,
                     dynamic_axes={'input':{1,'seqlen'}, 'input_mask':{1:'seqlen',2:'time'},'output_mask':{0:'time'}})

torch.onnx.export参数在文档里面都有，opset\_version对应的版本很重要，dynamic\_axes是对输入和输出对应维度可以进行动态设置，不设置的话输入和输出的Tensor 的 shape是不能改变的，如果输入固定就不需要加。

导出的模型可否顺利使用可以先使用python进行检测

import onnxruntime as ort
    import numpy as np
    ort_session = ort.InferenceSession('model.onnx')
    outputs = ort_session.run(None,{'input':np.random.randn(10,20),'input_mask':np.random.randn(1,20,5)})
    # 由于设置了dynamic_axes,支持对应维度的变化
    outputs = ort_session.run(None,{'input':np.random.randn(10,5),'input_mask':np.random.randn(1,26,2)})
    # outputs 为 包含'output'和'output_mask'的list
    
    import onnx
    model = onnx.load('model.onnx')
    onnx.checker.check_model(model)

如果没有异常代表导出的模型没有问题，目前torch.onnx.export只能对部分支持的Tensor操作进行识别，详情参考Supported operators，对于包括transformer等基本的模型都是没有问题的，如果出现ATen等问题，你就需要对模型不支持的Tensor操作进行改进，以免影响C++对该模型的使用。

## 2、Tensorflow Keras导出.onnx模型 ##

搭建网络模型训练：  
[tensorflow keras 搭建相机位姿估计网络–例][tensorflow keras]  
网络的输入输出为：

**网络的输入：** \[image\_ref , image\_cur\]  
**网络的输出:** \[tx , ty , tz , roll , pitch , yaw\]

训练的模型位置：`kerasTempModel\`，一定要用`model.save()`的方式，不能用`model.save_model()`  
在onnxruntime调用需要onnx模型，这里需要将keras的模型转换为onnx模型；

安装转换的工具：

pip install tf2onnx

安装完后运行：

python -m tf2onnx.convert --saved-model kerasTempModel --output "model.onnx" --opset 14

tip:这里设置 opset 版本为14 的优化效率目前亲测是最好的，推理速度比版本 11 、12更快。

运行完以后在终端最后会告诉你网络模型的输入和输出：

2022-01-21 15:48:00,766 - INFO - 
    2022-01-21 15:48:00,766 - INFO - Successfully converted TensorFlow model kerasTempModel to ONNX
    2022-01-21 15:48:00,766 - INFO - Model inputs: ['input1', 'input2']
    2022-01-21 15:48:00,766 - INFO - Model outputs: ['Output']
    2022-01-21 15:48:00,766 - INFO - ONNX model is saved at model.onnx

模型有两个输入，输入节点名分别为`['input1', 'input2']`，输出节点名为`['Output']`。

当然也可以不用具体知道节点名，在onnxruntime中可以通过打印来查看模型的输入输出节点名。

#### 2.1、数据集的处理 ####

数据集的格式：

# image_ref  image_cur  tx  ty  tz  roll(x) pitch(y) yaw(z)
    0 images/0.jpg images/1.jpg 0.000999509 -0.00102794 0.00987293 0.00473228 -0.0160252 -0.0222079 
    1 images/1.jpg images/2.jpg -0.00544488 -0.00282174 0.00828871 -0.00271557 -0.00770117 -0.0195182 
    2 images/2.jpg images/3.jpg -0.0074375 -0.00368121 0.0114751 -0.00721246 -0.0103843 -0.0171883 
    3 images/3.jpg images/4.jpg -0.00238111 -0.00371362 0.0120466 -0.0081171 -0.0149111 -0.0198595 
    4 images/4.jpg images/5.jpg 0.000965841 -0.00520437 0.0135452 -0.0141721 -0.0126401 -0.0182697 
    5 images/5.jpg images/6.jpg -0.00295753 -0.00340146 0.0144557 -0.013633 -0.00463747 -0.0143332

通过load\_image映射数据集到 TF tensor

class datasets:
        def __init__(self, datasetsPath:str):
            self.dataPath = datasetsPath
            self.dim = 512
            self.epochs = 40
            self.batch_size = 8
            self.train_percent = 0.92
            self.learning_rate = 2e-4
            self.model_path = 'kerasTempModel/'
            self.posetxt = os.path.join(self.dataPath,'pose.txt') 
    
            self.GetTheImagesAndPose()
            self.buildTrainData()
    
        def GetTheImagesAndPose(self):
            self.poselist = []
            with open(self.posetxt,'r') as f:
                for line in f.readlines():
                    line = line.strip()
                    line = line.split(' ')
                    line.remove(line[0])
                    self.poselist.append(line)
                    # im1 im2 tx,ty,tz,roll,pitch,yaw
            #打乱数据集
            length = np.shape(self.poselist)[0]
            train_num =int(length * self.train_percent) 
            test_num = length - train_num
            randomPoses = np.array(random.sample(self.poselist,length)) #取出所有数据集
            self.train_pose_list = randomPoses[0:train_num,:]
            self.test_pose_list = randomPoses[train_num:length+1,:]
            print(f"The size of train pose list is : {np.shape(self.train_pose_list)[0]}")
            print(f"The size of test pose list is : {np.shape(self.test_pose_list)[0]}")
    
        def load_image(self,index:tf.Tensor):
    
            img_ref= img_ref = tf.io.read_file(index[0])
            img_ref = tf.image.decode_jpeg(img_ref) #此处为jpeg格式
            img_ref = tf.image.resize(img_ref,(self.dim,self.dim))/255.0
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_ref = tf.cast(img_ref,tf.float32)
    
            img_cur = img_cur = tf.io.read_file(index[1])
            img_cur = tf.image.decode_jpeg(img_cur) #此处为jpeg格式
            img_cur = tf.image.resize(img_cur,(self.dim,self.dim))/255.0
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_cur = tf.cast(img_cur,tf.float32)
            
            pose = tf.strings.to_number(index[2:8],tf.float32)
            
            return (img_ref,img_cur),(pose)
            
        def buildTrainData(self):
            '''
            for example:\\
            >>> poses = dataset.y_train.take(20)\\
            >>> imgs = dataset.x1_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> imgs = dataset.x2_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> print(np.array(list(poses.as_numpy_iterator()))[19]) \\
            '''
            self.traindata = tf.data.Dataset.from_tensor_slices(self.train_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)#.cache() 
               
            self.testdata = tf.data.Dataset.from_tensor_slices(self.test_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)

#### 2.2、网络模型的搭建 ####

**简单的模型**

# 模型
    def model(dim):
        First = K.layers.Input(shape=(dim,dim,3),name="input1")
        Second = K.layers.Input(shape=(dim,dim,3),name="input2")
    
        x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(First)
        x1 = K.layers.Conv2D(512,kernel_size=(3,3), strides=2,padding='same')(x1)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.ReLU()(x1)
        x1 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x1)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.ReLU()(x1)
        x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x1)
    
        x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(Second)
        x2 = K.layers.Conv2D(512,kernel_size=(3,3), strides=2,padding='same')(x2)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.ReLU()(x2)
        x2 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x2)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.ReLU()(x2)
        x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x2)
    
        x = K.layers.concatenate([x1,x2])
        x = K.layers.Conv2D(256,kernel_size=(3,3), strides=1,padding='same',
                            activation='relu')(x)
        x = K.layers.BatchNormalization()(x)
        x = K.layers.ReLU()(x)
        x = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x)
        x = K.layers.Conv2D(128,kernel_size=(3,3), strides=1,padding='same',
                            activation='relu')(x)
        x = K.layers.BatchNormalization()(x)
        x = K.layers.ReLU()(x)
        x = K.layers.Flatten()(x)
        x = K.layers.Dense(1024)(x)
        x = K.layers.Dense(6,name='Output')(x)
        poseModel = K.Model([First,Second],x)
    
        return poseModel
    #损失函数
    def loss_fn(y_true,y_pre):
        loss_value = K.backend.mean(K.backend.square(y_true-y_pre))
        return loss_value
    
    # 学习率下降回调函数
    class learningDecay(K.callbacks.Callback):
        def __init__(self,schedule=None,alpha=1,verbose=0):
            super().__init__()
            self.schedule = schedule
            self.verbose = verbose
            self.alpha = alpha
        def on_epoch_begin(self, epoch, logs=None):
            lr = float(K.backend.get_value(self.model.optimizer.lr))
            if self.schedule != None:
                lr = self.schedule(epoch,lr)
            else:
                if epoch != 0:
                    lr = lr*self.alpha
            K.backend.set_value(self.model.optimizer.lr,K.backend.get_value(lr))
            if self.verbose > 0:
                print(f"Current learning rate is {lr}")
    # 学习率计划
    def scheduler(epoch, lr):
        if epoch < 10:
            return lr
        else:
            return lr * tf.math.exp(-0.1)

#### **resnet34为主骨** ####

#-------resnet 34-------------
    def conv_block(inputs, 
            neuron_num, 
            kernel_size,  
            use_bias, 
            padding= 'same',
            strides= (1, 1),
            with_conv_short_cut = False):
        conv1 = K.layers.Conv2D(
            neuron_num,
            kernel_size = kernel_size,
            activation= 'relu',
            strides= strides,
            use_bias= use_bias,
            padding= padding
        )(inputs)
        conv1 = K.layers.BatchNormalization(axis = 1)(conv1)
    
        conv2 = K.layers.Conv2D(
            neuron_num,
            kernel_size= kernel_size,
            activation= 'relu',
            use_bias= use_bias,
            padding= padding)(conv1)
        conv2 = K.layers.BatchNormalization(axis = 1)(conv2)
    
        if with_conv_short_cut:
            inputs = K.layers.Conv2D(
                neuron_num, 
                kernel_size= kernel_size,
                strides= strides,
                use_bias= use_bias,
                padding= padding
                )(inputs)
            return K.layers.add([inputs, conv2])
    
        else:
            return K.layers.add([inputs, conv2])
    
    def ResNet34(inputs,namescope = ""):
        x = K.layers.ZeroPadding2D((3, 3))(inputs)
    
        # Define the converlutional block 1
        x = K.layers.Conv2D(64, kernel_size= (7, 7), strides= (2, 2), padding= 'valid')(x)
        x = K.layers.BatchNormalization(axis= 1)(x)
        x = K.layers.MaxPooling2D(pool_size= (3, 3), strides= (2, 2), padding= 'same')(x)
    
        # Define the converlutional block 2
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
    
        # Define the converlutional block 3
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True)
    
        # Define the converlutional block 4
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
    
        # Define the converltional block 5
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True)
        x = K.layers.AveragePooling2D(pool_size=(7, 7))(x)
        return x
    
    
    def model(dim_w,dim_h):
        First = K.layers.Input(shape=(dim_w,dim_h,3),name="input1")
        Second = K.layers.Input(shape=(dim_w,dim_h,3),name="input2")
    
        # x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(First)
        x1 = K.layers.Conv2D(128,kernel_size=(3,3), strides=2,padding='same')(First)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.LeakyReLU()(x1)
        # x1 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x1)
        # x1 = K.layers.BatchNormalization()(x1)
        # x1 = K.layers.ReLU()(x1)
        x1 = ResNet34(x1,"x1")
    
        # x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(Second)
        x2 = K.layers.Conv2D(128,kernel_size=(3,3), strides=2,padding='same')(Second)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.LeakyReLU()(x2)
        # x2 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x2)
        # x2 = K.layers.BatchNormalization()(x2)
        # x2 = K.layers.ReLU()(x2)
        x2 = ResNet34(x2,"x2")
    
        x = K.layers.concatenate([x1,x2])
    
        x = K.layers.Flatten()(x)
        x = K.layers.Dense(6,name='Output')(x)
        poseModel = K.Model([First,Second],x)
    
        return poseModel
    
    def loss_fn(y_true,y_pre):
        loss_value_translation = K.backend.square(y_true[-1,0:3]-y_pre[-1,0:3])
        loss_value_rotation = 1/5.7*K.backend.square(y_true[-1,3:6]-y_pre[-1,3:6])
        loss_value = K.backend.mean(loss_value_translation + loss_value_rotation)
    
        # loss_value = K.backend.mean(K.backend.square(y_true-y_pre))
        # tf.print(y_pre)
        return loss_value

#### 2.3、模型的训练 ####

`build()`函数用来编译模型，建立回调函数；

`train_fit()` 函数是利用keras 的 fit函数来训练；

`train_gradient()` 函数是利用 `apply_gradients`函数训练，可以实时对loss和梯度进行监控，灵活性强；

`save_model()` 函数是保存模型的函数，可以有多种保存的方式，利用`model.save()` 可以保存为h5文件和tf格式的模型。

class Posenet:
        def __init__(self,dataset:datasets):
            self.dataset = dataset
            self.build()
    
        def build(self):
            self.poseModel = model(self.dataset.dim)
            self.poseModel.summary()
            self.optm = K.optimizers.RMSprop(1e-4,momentum=0.9) #,decay=1e-5/self.dataset.epochs
            self.decayCallback = learningDecay(schedule = None,alpha = 0.99,verbose = 1)
            decayCallbackScheduler = K.callbacks.LearningRateScheduler(scheduler)
            self.callbacks = [decayCallbackScheduler]
    
            try:
                print("************************loading the model weights***********************************")
                self.poseModel.load_weights("model.h5")
            except:
                pass
    
        def train_fit(self):
            self.poseModel.compile(optimizer=self.optm,loss=loss_fn,metrics=['accuracy'])
            self.poseModel.fit(self.dataset.traindata,
                                validation_data=self.dataset.testdata,
                                epochs=self.dataset.epochs,
                                callbacks=[self.decayCallback],
                                verbose=1)
        
        def train_gradient(self):
            for step in range(self.dataset.epochs):
                loss = 0
                val_loss = 0
                lr = float(self.optm.lr)
                tf.print(">>> [Epoch is %s/%s]"%(step,self.dataset.epochs))
                for (x1,x2),y in self.dataset.traindata:
                    with tf.GradientTape() as tape:
                        prediction = self.poseModel([x1,x2])
                        # y = tf.cast(y,dtype=prediction.dtype)
                        loss = loss_fn(y,prediction)
                    gradients = tape.gradient(loss,self.poseModel.trainable_variables)
                    self.optm.apply_gradients(zip(gradients,self.poseModel.trainable_variables))
                # 测试
                for (x1,x2),y in self.dataset.testdata:
                    prediction = self.poseModel([x1,x2])
                    val_loss = loss_fn(y,prediction)
                tf.print("The loss is %s,the learning rate is : %s, test loss is %s]"%(np.array(loss),lr,val_loss))
                K.backend.set_value(self.optm.lr,K.backend.get_value(lr*0.99))
    
        def save_model(self):
            '''
            利用 save 函数来保存，可以保存为h5文件，也可以保存为文件夹的形式，推荐保存第二种，再使用tf2onnx转onnx
            >>> python -m tf2onnx.convert --saved-model kerasTempModel --output "model.onnx" --opset 14
            '''
            self.poseModel.save("model.h5")
            self.poseModel.save(self.dataset.model_path)
            # self.poseModel.save_weights("model.h5") #只保存权重，没有保存结构
            # tf.saved_model.save(self.poseModel,'tf2TempModel') #这种保存方式不再使用了

**主函数：**

if __name__ == "__main__":
        dataset = datasets("images")
        posenet = Posenet(dataset)
        posenet.train_fit()
        # posenet.train_gradient() #利用 apply_gradient的方式训练
        posenet.save_model()

#### 2.4、加载保存的模型 ####

保存的模型文件夹为： `kerasTempModel\`

model = K.models.load_model(dataset.model_path,compile=False)

测试模型：

output = model([img_ref,img_cur])

#### 2.5、完整代码 ####

import argparse
    import tensorflow as tf
    import tensorflow.keras as K
    import numpy as np
    import cv2 as cv
    import os
    import time
    import random
    from tensorflow.keras import optimizers
    from tensorflow.keras import callbacks
    
    class datasets:
        def __init__(self, datasetsPath:str):
            self.dataPath = datasetsPath
            self.dim = 512
            self.epochs = 40
            self.batch_size = 8
            self.train_percent = 0.92
            self.learning_rate = 2e-4
            self.model_path = 'kerasTempModel/'
            self.posetxt = os.path.join(self.dataPath,'pose.txt') 
    
            self.GetTheImagesAndPose()
            self.buildTrainData()
    
        def GetTheImagesAndPose(self):
            self.poselist = []
            with open(self.posetxt,'r') as f:
                for line in f.readlines():
                    line = line.strip()
                    line = line.split(' ')
                    line.remove(line[0])
                    self.poselist.append(line)
                    # im1 im2 tx,ty,tz,roll,pitch,yaw
            #打乱数据集
            length = np.shape(self.poselist)[0]
            train_num =int(length * self.train_percent) 
            test_num = length - train_num
            randomPoses = np.array(random.sample(self.poselist,length)) #取出所有数据集
            self.train_pose_list = randomPoses[0:train_num,:]
            self.test_pose_list = randomPoses[train_num:length+1,:]
            print(f"The size of train pose list is : {np.shape(self.train_pose_list)[0]}")
            print(f"The size of test pose list is : {np.shape(self.test_pose_list)[0]}")
    
        def load_image(self,index:tf.Tensor):
    
            img_ref= img_ref = tf.io.read_file(index[0])
            img_ref = tf.image.decode_jpeg(img_ref) #此处为jpeg格式
            img_ref = tf.image.resize(img_ref,(self.dim,self.dim))/255.0
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_ref = tf.cast(img_ref,tf.float32)
    
            img_cur = img_cur = tf.io.read_file(index[1])
            img_cur = tf.image.decode_jpeg(img_cur) #此处为jpeg格式
            img_cur = tf.image.resize(img_cur,(self.dim,self.dim))/255.0
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_cur = tf.cast(img_cur,tf.float32)
            pose = tf.strings.to_number(index[2:8],tf.float32)
            return (img_ref,img_cur),(pose)
    
        def buildTrainData(self):
            '''
            for example:\\
            >>> poses = dataset.y_train.take(20)\\
            >>> imgs = dataset.x1_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> imgs = dataset.x2_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> print(np.array(list(poses.as_numpy_iterator()))[19]) \\
            '''
            self.traindata = tf.data.Dataset.from_tensor_slices(self.train_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)#.cache() 
            self.testdata = tf.data.Dataset.from_tensor_slices(self.test_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)
    
    def model(dim):
        First = K.layers.Input(shape=(dim,dim,3),name="input1")
        Second = K.layers.Input(shape=(dim,dim,3),name="input2")
    
        x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(First)
        x1 = K.layers.Conv2D(512,kernel_size=(3,3), strides=2,padding='same')(x1)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.ReLU()(x1)
        x1 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x1)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.ReLU()(x1)
        x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x1)
    
        x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(Second)
        x2 = K.layers.Conv2D(512,kernel_size=(3,3), strides=2,padding='same')(x2)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.ReLU()(x2)
        x2 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x2)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.ReLU()(x2)
        x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x2)
    
        x = K.layers.concatenate([x1,x2])
        x = K.layers.Conv2D(256,kernel_size=(3,3), strides=1,padding='same',
                            activation='relu')(x)
        x = K.layers.BatchNormalization()(x)
        x = K.layers.ReLU()(x)
        x = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(x)
        x = K.layers.Conv2D(128,kernel_size=(3,3), strides=1,padding='same',
                            activation='relu')(x)
        x = K.layers.BatchNormalization()(x)
        x = K.layers.ReLU()(x)
        x = K.layers.Flatten()(x)
        x = K.layers.Dense(1024)(x)
        x = K.layers.Dense(6,name='Output')(x)
        poseModel = K.Model([First,Second],x)
    
        return poseModel
    
    def loss_fn(y_true,y_pre):
        loss_value = K.backend.mean(K.backend.square(y_true-y_pre))
        return loss_value
    
    
    class learningDecay(K.callbacks.Callback):
        def __init__(self,schedule=None,alpha=1,verbose=0):
            super().__init__()
            self.schedule = schedule
            self.verbose = verbose
            self.alpha = alpha
        def on_epoch_begin(self, epoch, logs=None):
            lr = float(K.backend.get_value(self.model.optimizer.lr))
            if self.schedule != None:
                lr = self.schedule(epoch,lr)
            else:
                if epoch != 0:
                    lr = lr*self.alpha
            K.backend.set_value(self.model.optimizer.lr,K.backend.get_value(lr))
            if self.verbose > 0:
                print(f"Current learning rate is {lr}")
    
    def scheduler(epoch, lr):
        if epoch < 10:
            return lr
        else:
            return lr * tf.math.exp(-0.1) 
    
    class Posenet:
        def __init__(self,dataset:datasets):
            self.dataset = dataset
            self.build()
    
        def build(self):
            self.poseModel = model(self.dataset.dim)
            self.poseModel.summary()
            self.optm = K.optimizers.RMSprop(1e-4,momentum=0.9) #,decay=1e-5/self.dataset.epochs
            self.decayCallback = learningDecay(schedule = None,alpha = 0.99,verbose = 1)
            decayCallbackScheduler = K.callbacks.LearningRateScheduler(scheduler)
            self.callbacks = [decayCallbackScheduler]
    
            try:
                print("************************loading the model weights***********************************")
                self.poseModel.load_weights("model.h5")
            except:
                pass
    
        def train_fit(self):
            self.poseModel.compile(optimizer=self.optm,loss=loss_fn,metrics=['accuracy'])
            self.poseModel.fit(self.dataset.traindata,
                                validation_data=self.dataset.testdata,
                                epochs=self.dataset.epochs,
                                callbacks=[self.decayCallback],
                                verbose=1)
        
        def train_gradient(self):
            for step in range(self.dataset.epochs):
                loss = 0
                val_loss = 0
                lr = float(self.optm.lr)
                tf.print(">>> [Epoch is %s/%s]"%(step,self.dataset.epochs))
                for (x1,x2),y in self.dataset.traindata:
                    with tf.GradientTape() as tape:
                        prediction = self.poseModel([x1,x2])
                        # y = tf.cast(y,dtype=prediction.dtype)
                        loss = loss_fn(y,prediction)
                    gradients = tape.gradient(loss,self.poseModel.trainable_variables)
                    self.optm.apply_gradients(zip(gradients,self.poseModel.trainable_variables))
                # 测试
                for (x1,x2),y in self.dataset.testdata:
                    prediction = self.poseModel([x1,x2])
                    val_loss = loss_fn(y,prediction)
                tf.print("The loss is %s,the learning rate is : %s, test loss is %s]"%(np.array(loss),lr,val_loss))
                K.backend.set_value(self.optm.lr,K.backend.get_value(lr*0.99))
    
        def save_model(self):
            '''
            利用 save 函数来保存，可以保存为h5文件，也可以保存为文件夹的形式，推荐保存第二种，再使用tf2onnx转onnx
            >>> python -m tf2onnx.convert --saved-model kerasTempModel --output "model.onnx" --opset 14
            '''
            self.poseModel.save("model.h5")
            self.poseModel.save(self.dataset.model_path)
            # self.poseModel.save_weights("model.h5") #只保存权重，没有保存结构
            # tf.saved_model.save(self.poseModel,'tf2TempModel') #这种保存方式不再使用了
    
    if __name__ == "__main__":
        dataset = datasets("images")
        posenet = Posenet(dataset)
        posenet.train_fit()
        # posenet.train_gradient() #利用 apply_gradient的方式训练
        posenet.save_model()

改进：

import argparse
    import tensorflow as tf
    import tensorflow.keras as K
    import numpy as np
    import cv2 as cv
    import os
    import time
    import sys
    import random
    from tensorflow.keras import optimizers
    from tensorflow.keras import callbacks
    from tensorflow.python.keras.saving.save import save_model
    
    class datasets:
        def __init__(self, datasetsPath:str):
            self.dataPath = datasetsPath
            self.dim_w = 512
            self.dim_h = 512
            self.epochs = 200
            self.batch_size = 8
            self.train_percent = 0.92
            self.learning_rate = 2e-4
            self.model_path = 'kerasTempModel/'
            self.posetxt = os.path.join(self.dataPath,'pose.txt') 
    
            self.GetTheImagesAndPose()
            self.buildTrainData()
    
        def GetTheImagesAndPose(self):
            self.poselist = []
            with open(self.posetxt,'r') as f:
                for line in f.readlines():
                    line = line.strip()
                    line = line.split(' ')
                    line.remove(line[0])
                    self.poselist.append(line)
                    # im1 im2 tx,ty,tz,roll,pitch,yaw
            #打乱数据集
            length = np.shape(self.poselist)[0]
            train_num =int(length * self.train_percent) 
            test_num = length - train_num
            randomPoses = np.array(random.sample(self.poselist,length)) #取出所有数据集
            self.train_pose_list = randomPoses[0:train_num,:]
            self.test_pose_list = randomPoses[train_num:length+1,:]
            print(f"The size of train pose list is : {np.shape(self.train_pose_list)[0]}")
            print(f"The size of test pose list is : {np.shape(self.test_pose_list)[0]}")
    
        def load_image(self,index:tf.Tensor):
    
            img_ref= img_ref = tf.io.read_file(index[0])
            img_ref = tf.image.decode_jpeg(img_ref) #此处为jpeg格式
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_ref = tf.image.resize(img_ref,(self.dim_w,self.dim_h))/255.0
            img_ref = tf.cast(img_ref,tf.float32)
    
            img_cur = img_cur = tf.io.read_file(index[1])
            img_cur = tf.image.decode_jpeg(img_cur) #此处为jpeg格式
            img_cur = tf.image.resize(img_cur,(self.dim_w,self.dim_h))/255.0
            #img = tf.reshape(img,[self.dim,self.dim,3])
            img_cur = tf.cast(img_cur,tf.float32)
            pose = tf.strings.to_number(index[2:8],tf.float32)
            return (img_ref,img_cur),(pose)
    
        def buildTrainData(self):
            '''
            for example:\\
            >>> poses = dataset.y_train.take(20)\\
            >>> imgs = dataset.x1_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> imgs = dataset.x2_train.take(40)\\
            >>> print(np.array(list(imgs.as_numpy_iterator()))[39]) \\
            >>> print(np.array(list(poses.as_numpy_iterator()))[19]) \\
            '''
            self.traindata = tf.data.Dataset.from_tensor_slices(self.train_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)#.cache() 
            self.testdata = tf.data.Dataset.from_tensor_slices(self.test_pose_list) \
               .map(self.load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \
               .shuffle(500)\
               .repeat(10)\
               .batch(self.batch_size) \
               .prefetch(tf.data.experimental.AUTOTUNE)
    #-------resnet 34-------------
    def conv_block(inputs, 
            neuron_num, 
            kernel_size,  
            use_bias, 
            padding= 'same',
            strides= (1, 1),
            with_conv_short_cut = False):
        conv1 = K.layers.Conv2D(
            neuron_num,
            kernel_size = kernel_size,
            activation= 'relu',
            strides= strides,
            use_bias= use_bias,
            padding= padding
        )(inputs)
        conv1 = K.layers.BatchNormalization(axis = 1)(conv1)
    
        conv2 = K.layers.Conv2D(
            neuron_num,
            kernel_size= kernel_size,
            activation= 'relu',
            use_bias= use_bias,
            padding= padding)(conv1)
        conv2 = K.layers.BatchNormalization(axis = 1)(conv2)
    
        if with_conv_short_cut:
            inputs = K.layers.Conv2D(
                neuron_num, 
                kernel_size= kernel_size,
                strides= strides,
                use_bias= use_bias,
                padding= padding
                )(inputs)
            return K.layers.add([inputs, conv2])
    
        else:
            return K.layers.add([inputs, conv2])
    
    def ResNet34(inputs,namescope = ""):
        x = K.layers.ZeroPadding2D((3, 3))(inputs)
    
        # Define the converlutional block 1
        x = K.layers.Conv2D(64, kernel_size= (7, 7), strides= (2, 2), padding= 'valid')(x)
        x = K.layers.BatchNormalization(axis= 1)(x)
        x = K.layers.MaxPooling2D(pool_size= (3, 3), strides= (2, 2), padding= 'same')(x)
    
        # Define the converlutional block 2
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 64, kernel_size= (3, 3), use_bias= True)
    
        # Define the converlutional block 3
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 128, kernel_size= (3, 3), use_bias= True)
    
        # Define the converlutional block 4
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 256, kernel_size= (3, 3), use_bias= True)
    
        # Define the converltional block 5
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True, strides= (2, 2), with_conv_short_cut= True)
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True)
        x = conv_block(x, neuron_num= 512, kernel_size= (3, 3), use_bias= True)
        x = K.layers.AveragePooling2D(pool_size=(7, 7))(x)
        return x
    
    
    def model(dim_w,dim_h):
        First = K.layers.Input(shape=(dim_w,dim_h,3),name="input1")
        Second = K.layers.Input(shape=(dim_w,dim_h,3),name="input2")
    
        # x1 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(First)
        x1 = K.layers.Conv2D(128,kernel_size=(3,3), strides=2,padding='same')(First)
        x1 = K.layers.BatchNormalization()(x1)
        x1 = K.layers.LeakyReLU()(x1)
        # x1 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x1)
        # x1 = K.layers.BatchNormalization()(x1)
        # x1 = K.layers.ReLU()(x1)
        x1 = ResNet34(x1,"x1")
    
        # x2 = K.layers.MaxPool2D(pool_size=(2,2),strides=2)(Second)
        x2 = K.layers.Conv2D(128,kernel_size=(3,3), strides=2,padding='same')(Second)
        x2 = K.layers.BatchNormalization()(x2)
        x2 = K.layers.LeakyReLU()(x2)
        # x2 = K.layers.Conv2D(256,kernel_size=(3,3), strides=2,padding='same')(x2)
        # x2 = K.layers.BatchNormalization()(x2)
        # x2 = K.layers.ReLU()(x2)
        x2 = ResNet34(x2,"x2")
    
        x = K.layers.concatenate([x1,x2])
    
        x = K.layers.Flatten()(x)
        x = K.layers.Dense(6,name='Output')(x)
        poseModel = K.Model([First,Second],x)
    
        return poseModel
    
    def loss_fn(y_true,y_pre):
        loss_value_translation = K.backend.square(y_true[-1,0:3]-y_pre[-1,0:3])
        loss_value_rotation = 1/5.7*K.backend.square(y_true[-1,3:6]-y_pre[-1,3:6])
        loss_value = K.backend.mean(loss_value_translation + loss_value_rotation)
    
        # loss_value = K.backend.mean(K.backend.square(y_true-y_pre))
        # tf.print(y_pre)
        return loss_value
    
    
    class learningDecay(K.callbacks.Callback):
        def __init__(self,schedule=None,alpha=1,verbose=0):
            super().__init__()
            self.schedule = schedule
            self.verbose = verbose
            self.alpha = alpha
        def on_epoch_begin(self, epoch, logs=None):
            lr = float(K.backend.get_value(self.model.optimizer.lr))
            if self.schedule != None:
                lr = self.schedule(epoch,lr)
            else:
                if epoch >= 30:
                    lr = lr*self.alpha
            K.backend.set_value(self.model.optimizer.lr,K.backend.get_value(lr))
            if self.verbose > 0:
                print(f"Current learning rate is {lr}")
            #save the model
            if epoch % 20 == 0 and epoch != 0:
                self.model.save("model.h5")
    
    def scheduler(epoch, lr):
        if epoch < 10:
            return lr
        else:
            return lr * tf.math.exp(-0.1) 
    
    class Posenet:
        def __init__(self,dataset:datasets):
            self.dataset = dataset
            self.build()
    
        def build(self):
            self.poseModel = model(self.dataset.dim_w,self.dataset.dim_h)
            self.poseModel.summary()
            self.optm = K.optimizers.RMSprop(1e-4,momentum=0.9) #,decay=1e-5/self.dataset.epochs
            # self.optm = K.optimizers.Adam(1e-4)
            self.decayCallback = learningDecay(schedule = None,alpha = 0.99,verbose = 1)
            decayCallbackScheduler = K.callbacks.LearningRateScheduler(scheduler)
            self.callbacks = [decayCallbackScheduler]
    
            try:
                print("************************loading the model weights***********************************")
                self.poseModel.load_weights("model.h5")
            except:
                pass
    
        def train_fit(self):
            self.poseModel.compile(optimizer=self.optm,loss=loss_fn,metrics=['accuracy'])
            self.poseModel.fit(self.dataset.traindata,
                                validation_data=self.dataset.testdata,
                                epochs=self.dataset.epochs,
                                callbacks=[self.decayCallback],
                                verbose=1)
        
        def train_gradient(self):
            for step in range(self.dataset.epochs):
                loss = 0
                val_loss = 0
                index = 0
                lr = float(self.optm.lr)
                tf.print(">>> [Epoch is %s/%s]"%(step,self.dataset.epochs))
                for (x1,x2),y in self.dataset.traindata:
                    with tf.GradientTape() as tape:
                        prediction = self.poseModel([x1,x2])
                        # y = tf.cast(y,dtype=prediction.dtype)
                        loss = loss + loss_fn(y,prediction)
                    gradients = tape.gradient(loss,self.poseModel.trainable_variables)
                    self.optm.apply_gradients(zip(gradients,self.poseModel.trainable_variables))
    
                    index = index + 1
                    sys.stdout.write('--------train loss is %.5f-----'%(loss/float(index)))
                    sys.stdout.write('\r')
                    sys.stdout.flush()
    
                index_val = 0
                # 测试
                for (x1,x2),y in self.dataset.testdata:
                    prediction = self.poseModel([x1,x2])
                    val_loss = val_loss + loss_fn(y,prediction)
                    index_val = index_val + 1
                tf.print("The loss is %s,the learning rate is : %s, test loss is %s]"%(np.array(loss/float(index)),lr,val_loss/float(index_val)))
                K.backend.set_value(self.optm.lr,K.backend.get_value(lr*0.99))
                if step%40==0:
                    self.save_model()
    
        def save_model(self):
            '''
            利用 save 函数来保存，可以保存为h5文件，也可以保存为文件夹的形式，推荐保存第二种，再使用tf2onnx转onnx
            >>> python -m tf2onnx.convert --saved-model kerasTempModel --output "model.onnx" --opset 14
            '''
            self.poseModel.save("model.h5")
            self.poseModel.save(self.dataset.model_path)
            # self.poseModel.save_weights("model.h5") #只保存权重，没有保存结构
            # tf.saved_model.save(self.poseModel,'tf2TempModel') #这种保存方式不再使用了
    
            
    def test(dataset):
        im1 = cv.imread("imagesNDI/0.jpg")
        im1 = cv.resize(im1,(512,512))
        im1 = np.array(im1,np.float).reshape((1,512,512,3))/255.0
        im2 = cv.imread("imagesNDI/1.jpg")
        im2 = cv.resize(im2,(512,512))
        im2 = np.array(im2,np.float).reshape((1,512,512,3))/255.0
        posemodel = K.models.load_model(dataset.model_path,compile=False)
        pose = posemodel([im1,im2])
        print(np.array(pose))
    
    if __name__ == "__main__":
        dataset = datasets("images")
        posenet = Posenet(dataset)
        posenet.train_fit()
        # posenet.train_gradient() #利用 apply_gradient的方式训练
        posenet.save_model()
        test(dataset)

## 二、配置ONNXruntime ##

`CMakeLists.txt`:  
首先需要设置你的ONNXRUNTIME 的安装位置：

#******onnxruntime*****
    set(ONNXRUNTIME_ROOT_PATH /path to your onnxruntime-master)
    set(ONNXRUNTIME_INCLUDE_DIRS ${ONNXRUNTIME_ROOT_PATH}/include/onnxruntime
                                 ${ONNXRUNTIME_ROOT_PATH}/onnxruntime
                                 ${ONNXRUNTIME_ROOT_PATH}/include/onnxruntime/core/session/)
    set(ONNXRUNTIME_LIB ${ONNXRUNTIME_ROOT_PATH}/build/Linux/Release/libonnxruntime.so)

C++ main.cpp中  
头文件：

## 三、模型推理流程 ##

总体来看，整个ONNXRuntime的运行可以分为三个阶段：

*  Session构造；
 *  模型加载与初始化；
 *  运行；

![057d48458cc21dff5860de1073a1403f.png][]

### 1、第1阶段：Session构造 ###

构造阶段即创建一个InferenceSession对象。在python前端构建Session对象时，python端会通过[http://onnxruntime\_pybind\_state.cc][http_onnxruntime_pybind_state.cc]调用C++中的InferenceSession类构造函数，得到一个InferenceSession对象。

InferenceSession构造阶段会进行各个成员的初始化，成员包括负责OpKernel管理的KernelRegistryManager对象，持有Session配置信息的SessionOptions对象，负责图分割的GraphTransformerManager，负责log管理的LoggingManager等。当然，这个时候InferenceSession就是一个空壳子，只完成了对成员对象的初始构建。

### 2、第2阶段：模型加载与初始化 ###

在完成InferenceSession对象的构造后，会将onnx模型加载到InferenceSession中并进行进一步的初始化。

#### 2.1. 模型加载 ####

模型加载时，会在C++后端会调用对应的Load()函数，InferenceSession一共提供了8种Load函数。包读从url，ModelProto，void\* model data，model istream等读取ModelProto。InferenceSession会对ModelProto进行解析然后持有其对应的Model成员。

#### 2.2. Providers注册 ####

在Load函数结束后，InferenceSession会调用两个函数：RegisterExecutionProviders()和sess->Initialize();

RegisterExecutionProviders函数会完成ExecutionProvider的注册工作。这里解释一下ExecutionProvider，ONNXRuntime用Provider表示不同的运行设备比如CUDAProvider等。目前ONNXRuntimev1.0支持了包括CPU，CUDA，TensorRT，MKL等七种Providers。通过调用sess->RegisterExecutionProvider()函数，InferenceSession通过一个list持有当前运行环境中支持的ExecutionProviders。

#### 2.3. InferenceSession初始化 ####

即sess->Initialize()，这时InferenceSession会根据自身持有的model和execution providers进行进一步的初始化（在第一阶段Session构造时仅仅持有了空壳子成员变量）。该步骤是InferenceSession初始化的核心，一系列核心操作如内存分配，model partition，kernel注册等都会在这个阶段完成。

1.  首先，session会根据level注册 graph optimization transformers，并通过GraphTransformerManager成员进行持有。
2.  接下来session会进行OpKernel注册，OpKernel即定义的各个node对应在不同运行设备上的计算逻辑。这个过程会将持有的各个ExecutionProvider上定义的所有node对应的Kernel注册到session中，session通过KernelRegistryManager成员进行持有和管理。
3.  然后session会对Graph进行图变换，包括插入copy节点，cast节点等。
4.  接下来是model partition，也就是根运行设备对graph进行切分，决定每个node运行在哪个provider上。
5.  最后，为每个node创建ExecutePlan，运行计划主要包含了各个op的执行顺序，内存申请管理，内存复用管理等操作。

### 3、第3阶段：模型运行 ###

模型运行即InferenceSession每次读入一个batch的数据并进行计算得到模型的最终输出。然而其实绝大多数的工作早已经在InferenceSession初始化阶段完成。细看下源码就会发现run阶段主要是顺序调用各个node的对应OpKernel进行计算。

## 四、模型的部署 ##

和其他所有主流框架相同，ONNXRuntime最常用的语言是python，而实际负责执行框架运行的则是C++。

下面就是C++通过onnxruntime对.onnx模型的使用，参考官方样例和常见问题写的模型多输入多输出的情况，部分参数可以参考样例或者查官方API文档。

### 1. 模型的初始化设置 ###

//模型位置
        string model_path = "../model.onnx";
    	//初始化设置ONNXRUNTIME 的环境
        Ort::Env env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, "PoseEstimate");
        Ort::SessionOptions session_options;
        //TensorRT加速开启，CUDA加速开启
        OrtSessionOptionsAppendExecutionProvider_Tensorrt(session_options, 0); //tensorRT
        OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0);
        session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
        Ort::AllocatorWithDefaultOptions allocator;
        //加载ONNX模型
        Ort::Session session(env, model_path.c_str(), session_options);
        Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);

打印模型信息：`printModelInfo`函数

void printModelInfo(Ort::Session &session, Ort::AllocatorWithDefaultOptions &allocator)
    {
        //输出模型输入节点的数量
        size_t num_input_nodes = session.GetInputCount();
        size_t num_output_nodes = session.GetOutputCount();
        cout<<"Number of input node is:"<<num_input_nodes<<endl;
        cout<<"Number of output node is:"<<num_output_nodes<<endl;
    
        //获取输入输出维度
        for(auto i = 0; i<num_input_nodes;i++)
        {
            std::vector<int64_t> input_dims = session.GetInputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
            cout<<endl<<"input "<<i<<" dim is: ";
            for(auto j=0; j<input_dims.size();j++)
                cout<<input_dims[j]<<" ";
        }
        for(auto i = 0; i<num_output_nodes;i++)
        {
            std::vector<int64_t> output_dims = session.GetOutputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
            cout<<endl<<"output "<<i<<" dim is: ";
            for(auto j=0; j<output_dims.size();j++)
                cout<<output_dims[j]<<" ";
        }
        //输入输出的节点名
        cout<<endl;//换行输出
        for(auto i = 0; i<num_input_nodes;i++)
            cout<<"The input op-name "<<i<<" is:"<<session.GetInputName(i, allocator)<<endl;
        for(auto i = 0; i<num_output_nodes;i++)
            cout<<"The output op-name "<<i<<" is:"<<session.GetOutputName(i, allocator)<<endl;
    
        //input_dims_2[0] = input_dims_1[0] = output_dims[0] = 1;//batch size = 1
    }

函数应用：

//打印模型的信息
    printModelInfo(session,allocator);

输出结果：

Number of input node is:2
    Number of output node is:1
    
    input 0 dim is: -1 512 512 3 
    input 1 dim is: -1 512 512 3 
    output 0 dim is: -1 6 
    The input op-name 0 is:input1
    The input op-name 1 is:input2
    The output op-name 0 is:Output

如果事先不知道网络，通过打印信息这时候就可以定义全局变量：

//输入网络的维度
    static constexpr const int width = 512;
    static constexpr const int height = 512;
    static constexpr const int channel = 3;
    std::array<int64_t, 4> input_shape_{ 1,height, width,channel};

### 2、构建推理 ###

#### 2.1 构建推理函数computPoseDNN()步骤 ####

1.  **对应用Opencv输入的Mat图像进行resize**
    
        Mat Input_1,Input_2;
            resize(img_1,Input_1,Size(512,512));
            resize(img_2,Input_2,Size(512,512));
2.  **指定input和output的节点名，当然也可以定义在全局变量中，这里为了方便置入函数中**
    
        std::vector<const char*> input_node_names = {"input1","input2"};
            std::vector<const char*> output_node_names = {"Output"};
3.  **分配image\_ref和image\_cur的内存，用指针数组存储，这里长度为 512 \* 512 \* 3，因为不能直接把Mat矩阵输入，所以需要数组来存储图像数据，然后再转ONNXRUNTIME专有的tensor类型即可：**
    
        std::array<float, width * height *channel> input_image_1{};
            std::array<float, width * height *channel> input_image_2{};
            float* input_1 =  input_image_1.data();
            float* input_2 =  input_image_2.data();
    
    这里float类型根据自己网络需要来，也有可能是double, 可以利用下面的代码输出网络类型：
    
        cout<<session.GetInputTypeInfo(i).GetTensorTypeAndShapeInfo().GetElementType();
    
    上面的c++代码会输出索引，对应下面的数据类型：
    
        typedef enum ONNXTensorElementDataType {
          ONNX_TENSOR_ELEMENT_DATA_TYPE_UNDEFINED,
          ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT,   // maps to c type float
          ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8,   // maps to c type uint8_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8,    // maps to c type int8_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16,  // maps to c type uint16_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16,   // maps to c type int16_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32,   // maps to c type int32_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64,   // maps to c type int64_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING,  // maps to c++ type std::string
          ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL,
          ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16,
          ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE,      // maps to c type double
          ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32,      // maps to c type uint32_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64,      // maps to c type uint64_t
          ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX64,   // complex with float32 real and imaginary components
          ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX128,  // complex with float64 real and imaginary components
          ONNX_TENSOR_ELEMENT_DATA_TYPE_BFLOAT16     // Non-IEEE floating-point format based on IEEE754 single-precision
        } ONNXTensorElementDataType;
    
    例如，如果cout 输出 1，那么网络输出类型就是 float;
4.  **利用循环对float的数组进行赋值：这里可以是 CHW 或者 HWC 的格式：你在训练中很可能对数据进行了归一化处理，比如除以了255.0，这里数据还原就需要除以255.0**
    
        for (int i = 0; i < Input_1.rows; i++) {
                for (int j = 0; j < Input_1.cols; j++) {
                    for (int c = 0; c < 3; c++)
                    {
                        //NHWC 格式
                        if(c==0)
                            input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+2]/255.0;
                        if(c==1)
                            input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+1]/255.0;
                        if(c==2)
                            input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+0]/255.0;
                        //NCHW 格式
        //                if (c == 0)
        //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 2]/255.0;
        //                if (c == 1)
        //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 1]/255.0;
        //                if (c == 2)
        //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 0]/255.0;
        
        
                    }
                }
            }
            for (int i = 0; i < Input_2.rows; i++) {
                for (int j = 0; j < Input_2.cols; j++) {
                    for (int c = 0; c < 3; c++)
                    {
                        //NHWC 格式
                        if(c==0)
                            input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+2]/255.0;
                        if(c==1)
                            input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+1]/255.0;
                        if(c==2)
                            input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+0]/255.0;
                    }
                }
            }
5.  **这里由于不同网络可能有多个输入节点和多个输出节点，这里需要用std::vector来定义Ort 的tensor；利用两个输入数据创建两个tensor：**
    
        std::vector<Ort::Value> input_tensors;
            input_tensors.push_back(Ort::Value::CreateTensor<float>(
                    memory_info, input_1, input_image_1.size(), input_shape_.data(), input_shape_.size()));
            input_tensors.push_back(Ort::Value::CreateTensor<float>(
                    memory_info, input_2, input_image_2.size(), input_shape_.data(), input_shape_.size()));
    
    其中 input\_shape\_就是输入的维度：
    
        std::array<int64_t, 4> input_shape_{ 1,512, 512,3};
6.  **前向推理：同样定义输出的tensor也为 vector，保证通用性**
    
        std::vector<Ort::Value> output_tensors;
        
            output_tensors = session.Run(Ort::RunOptions { nullptr },
                                            input_node_names.data(), //输入节点名
                                            input_tensors.data(),     //input tensors
                                            input_tensors.size(),     //2
                                            output_node_names.data(), //输出节点名
                                            output_node_names.size()); //1
7.  **输出结果获取：由于本例输出只有一个维度，所以只需要 `output_tensors[0]`即可取出结果：**
    
        float* output = output_tensors[0].GetTensorMutableData<float>();
    
    **之后再进行位姿重构：**
    
        Eigen::Vector3d t(output[0],output[1],output[2]);
            Eigen::Vector3d r(output[3],output[4],output[5]);
        
            // 初始化旋转向量
            Eigen::AngleAxisd R_z(r[2], Eigen::Vector3d(0,0,1));
            Eigen::AngleAxisd R_y(r[1], Eigen::Vector3d(0,1,0));
            Eigen::AngleAxisd R_x(r[0], Eigen::Vector3d(1,0,0));
            // 转换为旋转矩阵，x y z的顺式
            Eigen::Matrix3d R_matrix_xyz  = R_z.toRotationMatrix()*R_y.toRotationMatrix()*R_x.toRotationMatrix();
            return Sophus::SE3(R_matrix_xyz,t);

#### 2.2 函数具体代码 ####

Sophus::SE3 computePoseDNN(Mat img_1, Mat img_2, Ort::Session &session,Ort::MemoryInfo &memory_info)
    {
        Mat Input_1,Input_2;
        resize(img_1,Input_1,Size(512,512));
        resize(img_2,Input_2,Size(512,512));
        
        std::vector<const char*> input_node_names = {"input1","input2"};
        std::vector<const char*> output_node_names = {"Output"};
    
        //将图像存储到数组中，BGR--->RGB
        std::array<float, width * height *channel> input_image_1{};
        std::array<float, width * height *channel> input_image_2{};
    
        float* input_1 =  input_image_1.data();
        float* input_2 =  input_image_2.data();
    
        for (int i = 0; i < Input_1.rows; i++) {
            for (int j = 0; j < Input_1.cols; j++) {
                for (int c = 0; c < 3; c++)
                {
                    //NHWC 格式
                    if(c==0)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+2]/255.0;
                    if(c==1)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+1]/255.0;
                    if(c==2)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+0]/255.0;
                    //NCHW 格式
    //                if (c == 0)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 2]/255.0;
    //                if (c == 1)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 1]/255.0;
    //                if (c == 2)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 0]/255.0;
    
    
                }
            }
        }
        for (int i = 0; i < Input_2.rows; i++) {
            for (int j = 0; j < Input_2.cols; j++) {
                for (int c = 0; c < 3; c++)
                {
                    //NHWC 格式
                    if(c==0)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+2]/255.0;
                    if(c==1)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+1]/255.0;
                    if(c==2)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+0]/255.0;
                }
            }
        }
    
        std::vector<Ort::Value> input_tensors;
        input_tensors.push_back(Ort::Value::CreateTensor<float>(
                memory_info, input_1, input_image_1.size(), input_shape_.data(), input_shape_.size()));
        input_tensors.push_back(Ort::Value::CreateTensor<float>(
                memory_info, input_2, input_image_2.size(), input_shape_.data(), input_shape_.size()));
    
        std::vector<Ort::Value> output_tensors;
    
        output_tensors = session.Run(Ort::RunOptions { nullptr },
                                        input_node_names.data(), //输入节点名
                                        input_tensors.data(),     //input tensors
                                        input_tensors.size(),     //2
                                        output_node_names.data(), //输出节点名
                                        output_node_names.size()); //1
    
    //    cout<<output_tensors.size()<<endl;//输出的维度
        float* output = output_tensors[0].GetTensorMutableData<float>();
        Eigen::Vector3d t(output[0],output[1],output[2]);
        Eigen::Vector3d r(output[3],output[4],output[5]);
    
        // 初始化旋转向量
        Eigen::AngleAxisd R_z(r[2], Eigen::Vector3d(0,0,1));
        Eigen::AngleAxisd R_y(r[1], Eigen::Vector3d(0,1,0));
        Eigen::AngleAxisd R_x(r[0], Eigen::Vector3d(1,0,0));
        // 转换为旋转矩阵
        Eigen::Matrix3d R_matrix_xyz  = R_z.toRotationMatrix()*R_y.toRotationMatrix()*R_x.toRotationMatrix();
    
        return Sophus::SE3(R_matrix_xyz,t);

##  ##

## 五、示例应用 ##

#include <core/session/onnxruntime_cxx_api.h>
    #include <core/providers/cuda/cuda_provider_factory.h>
    #include <core/session/onnxruntime_c_api.h>
    #include <core/providers/tensorrt/tensorrt_provider_factory.h>
    
    #include <opencv2/opencv.hpp>
    #include <sophus/se3.h>
    
    #include <iostream>
    
    Sophus::SE3 computePoseDNN(Mat img_1, Mat img_2, Ort::Session &session, Ort::MemoryInfo &memory_info);
    
    //输入网络的维度
    static constexpr const int width = 512;
    static constexpr const int height = 512;
    static constexpr const int channel = 3;
    std::array<int64_t, 4> input_shape_{ 1,height, width,channel};
    
    using namespace cv;
    using namespace std;
    int main()
    {
    	//模型位置
        string model_path = "../model.onnx";
    
        Ort::Env env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, "PoseEstimate");
        Ort::SessionOptions session_options;
        //CUDA加速开启
        OrtSessionOptionsAppendExecutionProvider_Tensorrt(session_options, 0); //tensorRT
        OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0);
        session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
        Ort::AllocatorWithDefaultOptions allocator;
        //加载ONNX模型
        Ort::Session session(env, model_path.c_str(), session_options);
        Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
        //打印模型的信息
        printModelInfo(session,allocator);
    
    	Mat img_1 = imread("/path_to_your_img1",IMREAD_COLOR);
        Mat img_2 = imread("/path_to_your_img2",IMREAD_COLOR);
        Sophus::SE3 pose = computePoseDNN(img_1,img_2,session,memory_info);
        
    }
    Sophus::SE3 computePoseDNN(Mat img_1, Mat img_2, Ort::Session &session,Ort::MemoryInfo &memory_info)
    {
        Mat Input_1,Input_2;
        resize(img_1,Input_1,Size(512,512));
        resize(img_2,Input_2,Size(512,512));
        std::vector<const char*> input_node_names = {"input1","input2"};
        std::vector<const char*> output_node_names = {"Output"};
    
        //将图像存储到uchar数组中，BGR--->RGB
        std::array<float, width * height *channel> input_image_1{};
        std::array<float, width * height *channel> input_image_2{};
    
        float* input_1 =  input_image_1.data();
        float* input_2 =  input_image_2.data();
    
        for (int i = 0; i < Input_1.rows; i++) {
            for (int j = 0; j < Input_1.cols; j++) {
                for (int c = 0; c < 3; c++)
                {
                    //NHWC 格式
                    if(c==0)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+2]/255.0;
                    if(c==1)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+1]/255.0;
                    if(c==2)
                        input_1[i*Input_1.cols*3+j*3+c] = Input_1.ptr<uchar>(i)[j*3+0]/255.0;
                    //NCHW 格式
    //                if (c == 0)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 2]/255.0;
    //                if (c == 1)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 1]/255.0;
    //                if (c == 2)
    //                     input_1[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + 0]/255.0;
    
    
                }
            }
        }
        for (int i = 0; i < Input_2.rows; i++) {
            for (int j = 0; j < Input_2.cols; j++) {
                for (int c = 0; c < 3; c++)
                {
                    //NHWC 格式
                    if(c==0)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+2]/255.0;
                    if(c==1)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+1]/255.0;
                    if(c==2)
                        input_2[i*Input_2.cols*3+j*3+c] = Input_2.ptr<uchar>(i)[j*3+0]/255.0;
                }
            }
        }
    
        std::vector<Ort::Value> input_tensors;
        input_tensors.push_back(Ort::Value::CreateTensor<float>(
                memory_info, input_1, input_image_1.size(), input_shape_.data(), input_shape_.size()));
        input_tensors.push_back(Ort::Value::CreateTensor<float>(
                memory_info, input_2, input_image_2.size(), input_shape_.data(), input_shape_.size()));
                
        std::vector<Ort::Value> output_tensors;
        output_tensors = session.Run(Ort::RunOptions { nullptr },
                                        input_node_names.data(), //输入节点名
                                        input_tensors.data(),     //input tensors
                                        input_tensors.size(),     //2
                                        output_node_names.data(), //输出节点名
                                        output_node_names.size()); //1
    
    //    cout<<output_tensors.size()<<endl;//输出的维度
        float* output = output_tensors[0].GetTensorMutableData<float>();
        Eigen::Vector3d t(output[0],output[1],output[2]);
        Eigen::Vector3d r(output[3],output[4],output[5]);
    
        // 初始化旋转向量，绕z轴旋转，y轴，x轴；
        Eigen::AngleAxisd R_z(r[2], Eigen::Vector3d(0,0,1));
        Eigen::AngleAxisd R_y(r[1], Eigen::Vector3d(0,1,0));
        Eigen::AngleAxisd R_x(r[0], Eigen::Vector3d(1,0,0));
        // 转换为旋转矩阵
        Eigen::Matrix3d R_matrix_xyz  = R_z.toRotationMatrix()*R_y.toRotationMatrix()*R_x.toRotationMatrix();
    
        return Sophus::SE3(R_matrix_xyz,t);
    }
    
    void printModelInfo(Ort::Session &session, Ort::AllocatorWithDefaultOptions &allocator)
    {
        //输出模型输入节点的数量
        size_t num_input_nodes = session.GetInputCount();
        size_t num_output_nodes = session.GetOutputCount();
        cout<<"Number of input node is:"<<num_input_nodes<<endl;
        cout<<"Number of output node is:"<<num_output_nodes<<endl;
    
        //获取输入输出维度
        for(auto i = 0; i<num_input_nodes;i++)
        {
            std::vector<int64_t> input_dims = session.GetInputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
            cout<<endl<<"input "<<i<<" dim is: ";
            for(auto j=0; j<input_dims.size();j++)
                cout<<input_dims[j]<<" ";
        }
        for(auto i = 0; i<num_output_nodes;i++)
        {
            std::vector<int64_t> output_dims = session.GetOutputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
            cout<<endl<<"output "<<i<<" dim is: ";
            for(auto j=0; j<output_dims.size();j++)
                cout<<output_dims[j]<<" ";
        }
        //输入输出的节点名
        cout<<endl;//换行输出
        for(auto i = 0; i<num_input_nodes;i++)
            cout<<"The input op-name "<<i<<" is:"<<session.GetInputName(i, allocator)<<endl;
        for(auto i = 0; i<num_output_nodes;i++)
            cout<<"The output op-name "<<i<<" is:"<<session.GetOutputName(i, allocator)<<endl;
    }

[C++ 上用 ONNXruntime 部署自己的模型\_机器人学渣的博客-CSDN博客\_c++ onnxruntime][C_ _ ONNXruntime _-CSDN_c_ onnxruntime]

[tensorflow keras 搭建相机位姿估计网络--例\_机器人学渣的博客-CSDN博客\_位姿估计网络][tensorflow keras]

[C++ 上用 ONNXruntime 部署自己的模型\_机器人学渣的博客-CSDN博客][C_ _ ONNXruntime _-CSDN_c_ onnxruntime]

[ONNXRuntime学习笔记(四) - Lee-zq - 博客园][ONNXRuntime_ - Lee-zq -]

[onnxruntime c++ 代码搜集\_落花逐流水的博客-CSDN博客][onnxruntime c_ _-CSDN]

[onnxruntime调用AI模型的python和C++编程\_Arnold-FY-Chen的博客-CSDN博客][onnxruntime_AI_python_C_Arnold-FY-Chen_-CSDN]

[pytorch]: https://so.csdn.net/so/search?q=pytorch&spm=1001.2101.3001.7020
[Keras]: https://so.csdn.net/so/search?q=Keras&spm=1001.2101.3001.7020
[https_github.com_zouyuelin_SLAM_Learning_notes_tree_main_PoseEstimation]: https://github.com/zouyuelin/SLAM_Learning_notes/tree/main/PoseEstimation
[Releases _ microsoft_onnxruntime _ GitHub]: https://github.com/microsoft/onnxruntime/releases
[pytorch 1]: https://pytorch.org/docs/stable/onnx.html#id4
[tensorflow keras]: https://blog.csdn.net/qq_42995327/article/details/122622359
[057d48458cc21dff5860de1073a1403f.png]: https://img-blog.csdnimg.cn/img_convert/057d48458cc21dff5860de1073a1403f.png
[http_onnxruntime_pybind_state.cc]: https://link.zhihu.com/?target=http%3A//onnxruntime_pybind_state.cc
[C_ _ ONNXruntime _-CSDN_c_ onnxruntime]: https://blog.csdn.net/qq_42995327/article/details/122622222
[ONNXRuntime_ - Lee-zq -]: https://www.cnblogs.com/lee-zq/p/16219963.html
[onnxruntime c_ _-CSDN]: https://blog.csdn.net/juluwangriyue/article/details/118265373
[onnxruntime_AI_python_C_Arnold-FY-Chen_-CSDN]: https://blog.csdn.net/XCCCCZ/article/details/110356437