在实际使用过程中，使用python速度不够快，并且不太好嵌入到c++程序中，因此可以把pytorch训练的模型转成onnx模型，然后使用opencv进行调用。

所需要用到的库有：

opencv

1.完整的程序如下

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
import numpy as np
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import LambdaLR
import os
import re
from PIL import Imagecur_pwd_path = os.getcwd()def getBestModuleFilename(browser):file_name = browser             #"tf_logs/save_module"filenames = os.listdir(file_name)pattern = r"d+"result = []for i in range(len(filenames)):rst = int(filenames[i][10:-4])result.append(rst)val = max(result)index = result.index(val)file_best = filenames[index]print(file_best)return file_besttensor = torch.randn(3,3)
bTensor = type(tensor) == torch.Tensor
print(bTensor)
print("tensor is on ", tensor.device)
#数据转到GPU
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
if torch.cuda.is_available():tensor = tensor.to(device)print("tensor is on ",tensor.device)
#数据转到CPU
if tensor.device == 'cuda:0':tensor = tensor.to(torch.device("cpu"))print("tensor is on", tensor.device)
if tensor.device == "cpu":tensor = tensor.to(torch.device("cuda:0"))print("tensor is on", tensor.device)trainning_data = datasets.MNIST(root="data",train=True,transform=ToTensor(),download=True)
print(len(trainning_data))
test_data = datasets.MNIST(root="data",train=True,transform=ToTensor(),download=False)train_loader = DataLoader(trainning_data, batch_size=64,shuffle=True)
test_loader = DataLoader(test_data, batch_size=64,shuffle=True)print(len(train_loader)) #分成了多少个batch
print(len(trainning_data)) #总共多少个图像
# for x, y in train_loader:
#     print(x.shape)
#     print(y.shape)class MinistNet(nn.Module):def __init__(self):super().__init__()# self.flat = nn.Flatten()self.conv1 = nn.Conv2d(1,1,3,1,1)self.hideLayer1 = nn.Linear(28*28,256)self.hideLayer2 = nn.Linear(256,10)def forward(self,x):x= self.conv1(x)x = x.view(-1,28*28)x = self.hideLayer1(x)x = torch.sigmoid(x)x = self.hideLayer2(x)# x = nn.Sigmoid(x)return xmodel_path = "E:\\TOOLE\\slam_evo\\pythonProject\\tf_logs\\save_module\\ckpt_best_10.pth"
img_path = "E:\\TOOLE\\slam_evo\\pythonProject\\2.jpg"
img = Image.open(img_path)
test_model = MinistNet()
test_model1 = torch.load(model_path)
test_model.load_state_dict(test_model1["net"])test_model.eval()
test_model.to("cuda")transform =torchvision.transforms.Compose([
torchvision.transforms.Grayscale(),
torchvision.transforms.ToTensor()
])img = transform(img)
img = torch.unsqueeze(img, 0)
img = img.to("cuda")result = test_model(img)
result = result.to("cpu")
val,index = torch.max(result,dim=1)
print(index)model = MinistNet()
model = model.to(device)
cuda = next(model.parameters()).device
print(model)
criterion = nn.CrossEntropyLoss()
optimer = torch.optim.RMSprop(model.parameters(),lr= 0.001)scheduler_1 = LambdaLR(optimer, lr_lambda=lambda epoch: 1/(epoch+1))num_epoches =10
min_loss_val = 100000
Resume = Falsedef train():global min_loss_valstart_epoch = -1if Resume == False:start_epoch = 0else:#找到数字最大的pth文件path_checkpoint = r'tf_logs/'+"save_module"best_path_checkpoint = getBestModuleFilename(path_checkpoint)if(best_path_checkpoint == ""):returnelse:checkpointResume = torch.load(path_checkpoint)start_epoch = checkpointResume["epoch"]model.load_state_dict(checkpointResume["net"])optimer.load_state_dict(checkpointResume["optimizer"])scheduler_1.load_state_dict(checkpointResume["lr_schedule"])train_losses = []train_acces = []eval_losses = []eval_acces = []#训练model.train()tensorboard_ind =0;for epoch in range(num_epoches):batchsizeNum = 0train_loss = 0train_acc = 0train_correct = 0for x,y in train_loader:# print(epoch)# print(x.shape)# print(y.shape)x = x.to('cuda')y = y.to('cuda')bte = type(x)==torch.Tensorbte1 = type(y)==torch.TensorA = x.deviceB = y.devicepred_y = model(x)loss = criterion(pred_y,y)optimer.zero_grad()loss.backward()optimer.step()loss_val = loss.item()batchsizeNum = batchsizeNum +1train_acc += (pred_y.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()tensorboard_ind += 1train_losses.append(train_loss / len(trainning_data))train_acces.append(train_acc / len(trainning_data))#测试test_loss_value = 0model.eval()with torch.no_grad():num_batch = len(test_data)numSize = len(test_data)test_loss, test_correct = 0,0for x,y in test_loader:x = x.to(device)y = y.to(device)pred_y = model(x)test_loss += criterion(pred_y, y).item()test_correct += (pred_y.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchtest_correct /= numSizeeval_losses.append(test_loss)eval_acces.append(test_correct)test_loss_value = test_lossprint("test result:",100 * test_correct,"%  avg loss:",test_loss)scheduler_1.step()#设置checkpointif epoch > int(num_epoches/3) and test_loss_value < min_loss_val:min_loss_val = test_loss_valuecheckpoint = {"epoch": epoch,"net": model.state_dict(),"optimizer":optimer.state_dict(),"lr_schedule":scheduler_1.state_dict()}if not os.path.isdir(r'tf_logs/' + "save_module"):os.makedirs("tf_logs/" + "save_module")PATH = r'tf_logs/'+"save_module" + "/ckpt_best_%s.pth"%(str(epoch+1))torch.save(checkpoint, PATH)def test_singleFrame():model_path = "E:\\TOOLE\\slam_evo\\pythonProject\\tf_logs\\save_module\\ckpt_best_10.pth"img_path = "E:\\TOOLE\\slam_evo\\pythonProject\\1.jpg"img =Image.open(img_path)test_model = MinistNet()test_model = torch.load(model_path)test_model.to("cuda")transform=ToTensor()img = transform(img)img.to("cuda")result = test_model(img)val, index = torch.max(result)print(index)# Press the green button in the gutter to run the script.if __name__ == '__main__':train()#保存onnxmodel.cpu()model.eval()x= torch.randn(1,1,28,28)torch.onnx.export(model,x,"model.onnx")

2.训练并保存模型

        if epoch > int(num_epoches/3) and test_loss_value < min_loss_val:min_loss_val = test_loss_valuecheckpoint = {"epoch": epoch,"net": model.state_dict(),"optimizer":optimer.state_dict(),"lr_schedule":scheduler_1.state_dict()}if not os.path.isdir(r'tf_logs/' + "save_module"):os.makedirs("tf_logs/" + "save_module")PATH = r'tf_logs/'+"save_module" + "/ckpt_best_%s.pth"%(str(epoch+1))torch.save(checkpoint, PATH)

3.加载并测试模型

model_path = "E:\\TOOLE\\slam_evo\\pythonProject\\tf_logs\\save_module\\ckpt_best_10.pth"
img_path = "E:\\TOOLE\\slam_evo\\pythonProject\\2.jpg"
img = Image.open(img_path)
test_model = MinistNet()
test_model1 = torch.load(model_path)
test_model.load_state_dict(test_model1["net"])test_model.eval()
test_model.to("cuda")transform =torchvision.transforms.Compose([
torchvision.transforms.Grayscale(),
torchvision.transforms.ToTensor()
])img = transform(img)
img = torch.unsqueeze(img, 0)
img = img.to("cuda")result = test_model(img)
result = result.to("cpu")
val,index = torch.max(result,dim=1)
print(index)

结果如下：

按照第0，1来数数，tensor([1])刚好就是2.

4.保存onnx模型

if __name__ == '__main__':train()#保存onnxmodel.cpu()model.eval()x= torch.randn(1,1,28,28)torch.onnx.export(model,x,"model.onnx")

5.使用C++加opencv实现minist手写数字的识别

// test_onnm.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
//#include<ostream>
#include<opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>#include <iostream>using namespace std;
using namespace cv;
using namespace dnn;int main()
{std::cout << "Hello World!\n";//cv::dnn::Net net = cv::dnn::readTensorFromONNX();cv::dnn::Net net = cv::dnn::readNetFromONNX("E:\\TOOLE\\slam_evo\\pythonProject\\model.onnx");if (net.empty()){std::cout << "加载onnx模型失败" << std::endl;return -1;}net.setPreferableBackend(DNN_BACKEND_OPENCV);net.setPreferableTarget(DNN_TARGET_CPU);cv::Mat img = cv::imread("E:\\TOOLE\\slam_evo\\pythonProject\\1.jpg",cv::IMREAD_GRAYSCALE);if(img.cols != 28 || img.rows != 28){return -1;}cv::Mat blob;float scaleFactor = 1 / 255.0;blobFromImage(img, blob, scaleFactor, Size(), Scalar(), true, false, CV_32F);net.setInput(blob);cv::Mat predict = net.forward();for (int i = 0; i < predict.total(); i++){std::cout << predict.at<float>(i) << "  ";}std::cout << std::endl;double minVal, maxVal;Point minLoc, maxLoc;// 查找最大值和最小值及其位置minMaxLoc(predict, &minVal, &maxVal, &minLoc, &maxLoc);cout << maxVal << "    " << maxLoc.x<<"   "<< maxLoc.y << "\n";return 0;}

结果展示：