python人机交互界面_pytorch搭建PyQt5界面实战：ResNet-18实现CLFAR-10图像分类，并利用PyQt5进行人机界面显示...

pytorch实战：ResNet-18实现CLFAR-10图像分类，并利用PyQt5进行人机界面显示

实验环境：

1.pytorch-1.6.0

2.python-3.7.9

3.window-10

4.pycharm

5.pyqt5(相应的QT Designer及工具包)

CLFAR-10的数据集

作为一个初学者，在官网下载CLFAR-10的数据集下载速度不仅慢，而且不是常用的图片格式，这里是转换后的数据集，有需要的可以直接百度云盘提取。

链接：https://pan.baidu.com/s/1l7wvWLCscPcGoKzRjggjRA

提取码：ht88

ResNet-18网络：

ResNet全名Residual Network残差网络。残差网络是由何凯明所提出的，他的《Deep Residual Learning for Image Recognition》获得了当年CVPR最佳论文。他提出的深度残差网络在2015年可以说是洗刷了图像方面的各大比赛，以绝对优势取得了多个比赛的冠军。而且它在保证网络精度的前提下，将网络的深度达到了152层，后来又进一步加到1000的深度。我们这里用到的是一个18 层的残差网络。

网络结构如下：

残差学习：一个构建单元

在pytorch上搭建ResNet-18模型

一、新建resnet.py文件

代码如下：

import torch.nn as nn

import torch.nn.functional as F

class ResidualBlock(nn.Module):

def __init__(self, inchannel, outchannel, stride=1):

super(ResidualBlock, self).__init__()

self.left = nn.Sequential(

nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),

nn.BatchNorm2d(outchannel),

nn.ReLU(inplace=True),

nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),

nn.BatchNorm2d(outchannel)

)

self.shortcut = nn.Sequential()

if stride != 1 or inchannel != outchannel:

self.shortcut = nn.Sequential(

nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),

nn.BatchNorm2d(outchannel)

)

def forward(self, x):

out = self.left(x)

out += self.shortcut(x)

out = F.relu(out)

return out

class ResNet(nn.Module):

def __init__(self, ResidualBlock, num_classes=10):

super(ResNet, self).__init__()

self.inchannel = 64

self.conv1 = nn.Sequential(

nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),

nn.BatchNorm2d(64),

nn.ReLU(),

)

self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)

self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)

self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)

self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)

self.fc = nn.Linear(512, num_classes)

def make_layer(self, block, channels, num_blocks, stride):

strides = [stride] + [1] * (num_blocks - 1) #strides=[1,1]

layers = []

for stride in strides:

layers.append(block(self.inchannel, channels, stride))

self.inchannel = channels

return nn.Sequential(*layers)

def forward(self, x):

out = self.conv1(x)

out = self.layer1(out)

out = self.layer2(out)

out = self.layer3(out)

out = self.layer4(out)

out = F.avg_pool2d(out, 4)

out = out.view(out.size(0), -1)

out = self.fc(out)

return out

def ResNet18():

return ResNet(ResidualBlock)

一开始没看懂下面代码的意思，后来看懂模型结构发现是真香，大家细品。

self.shortcut = nn.Sequential()

if stride != 1 or inchannel != outchannel:

self.shortcut = nn.Sequential(

nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),

nn.BatchNorm2d(outchannel)

)

二、新建train.py文件

代码如下：

import torch

import torch.nn as nn

import torch.optim as optim

import torchvision

import torchvision.transforms as transforms

import argparse

from resnet import ResNet18

# 定义是否使用GPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 参数设置,使得我们能够手动输入命令行参数，就是让风格变得和Linux命令行差不多

parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')

parser.add_argument('--outf', default='./model/', help='folder to output images and model checkpoints') #输出结果保存路径

parser.add_argument('--net', default='./model/Resnet18.pth', help="path to net (to continue training)") #恢复训练时的模型路径

args = parser.parse_args()

# 超参数设置

EPOCH = 200 #遍历数据集次数

pre_epoch = 0 # 定义已经遍历数据集的次数

BATCH_SIZE = 128 #批处理尺寸(batch_size)

LR = 0.001 #学习率

# 准备数据集并预处理

transform_train = transforms.Compose([

transforms.RandomCrop(32, padding=4), #先四周填充0，在吧图像随机裁剪成32*32

transforms.RandomHorizontalFlip(), #图像一半的概率翻转，一半的概率不翻转

transforms.ToTensor(),

transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #R,G,B每层的归一化用到的均值和方差

])

transform_test = transforms.Compose([

transforms.ToTensor(),

transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),

])

trainset = torchvision.datasets.ImageFolder(root='E:\\CLFAR-10+pyqt5\\data\\train', transform=transform_train) #训练数据集

trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2) #生成一个个batch进行批训练，组成batch的时候顺序打乱取

testset = torchvision.datasets.ImageFolder(root='E:\\CLFAR-10+pyqt5\\data\\test', transform=transform_test)

testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=True, num_workers=2)

# Cifar-10的标签

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# 模型定义-ResNet

net = ResNet18().to(device)

# 定义损失函数和优化方式

criterion = nn.CrossEntropyLoss() #损失函数为交叉熵，多用于多分类问题

optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4) #优化方式为mini-batch momentum-SGD，并采用L2正则化（权重衰减）

# 训练

if __name__ == "__main__":

best_acc = 85 #2 初始化best test accuracy

print("Start Training, Resnet-18!") # 定义遍历数据集的次数

with open("acc.txt", "w") as f:

with open("log.txt", "w")as f2:

for epoch in range(pre_epoch, EPOCH):

print('\nEpoch: %d' % (epoch + 1))

net.train()

sum_loss = 0.0

correct = 0.0

total = 0.0

for i, data in enumerate(trainloader, 0):

# 准备数据

length = len(trainloader)

inputs, labels = data

inputs, labels = inputs.to(device), labels.to(device)

optimizer.zero_grad()

# forward + backward

outputs = net(inputs)

loss = criterion(outputs, labels)

loss.backward()

optimizer.step()

# 每训练1个batch打印一次loss和准确率

sum_loss += loss.item()

_, predicted = torch.max(outputs.data, 1)

total += labels.size(0)

correct += predicted.eq(labels.data).cpu().sum()

print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '

% (epoch + 1, (i + 1 + epoch * length), sum_loss / (i + 1), 100. * correct / total))

f2.write('%03d %05d |Loss: %.03f | Acc: %.3f%% '

% (epoch + 1, (i + 1 + epoch * length), sum_loss / (i + 1), 100. * correct / total))

f2.write('\n')

f2.flush()

# 每训练完一个epoch测试一下准确率

print("Waiting Test!")

with torch.no_grad():

correct = 0

total = 0

for data in testloader:

net.eval()

images, labels = data

images, labels = images.to(device), labels.to(device)

outputs = net(images)

# 取得分最高的那个类 (outputs.data的索引号)

_, predicted = torch.max(outputs.data, 1)

total += labels.size(0)

correct += (predicted == labels).sum().item()

# result = torch.floor_divide(correct, total)

# print('测试分类准确率为：%.3f%%' % (100 * result))

acc = 100 * correct / total

print('测试分类准确率为：%.3f%%' % (acc))

# 将每次测试结果实时写入acc.txt文件中

print('Saving model......')

torch.save(net.state_dict(), '%s/net_%03d.pth' % (args.outf, epoch + 1))

f.write("EPOCH=%03d,Accuracy= %.3f%%" % (epoch + 1, acc))

f.write('\n')

f.flush()

# 记录最佳测试分类准确率并写入best_acc.txt文件中

if acc > best_acc:

f3 = open("best_acc.txt", "w")

f3.write("EPOCH=%d,best_acc= %.3f%%" % (epoch + 1, acc))

f3.close()

best_acc = acc

print("Training Finished, TotalEPOCH=%d" % EPOCH)

将训练过程记录在 log.txt中，将每个epoch的测试精度放在acc.txt中，最后通过if语句将最高精度记录在best_acc.txt中，best_acc.txt中保存的是最高测试准确率所对应的epoch。每次epoch的权重保存在model文件夹下

三、新建predict文件

为了让模型和PyQt5结合，写个预测脚本方便GUI文件调用

代码如下：

import torch

import torchvision.transforms as transforms

from resnet import ResNet18

from PIL import Image

def predict_(img):

data_transform = transforms.Compose([

transforms.ToTensor(),

transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),

])

#img =Image.open('E:\CLFAR-10+pyqt5\4.jpg')

img = data_transform(img)

img = torch.unsqueeze(img, dim=0)

model = ResNet18()

model_weight_pth = 'E:\\CLFAR-10+pyqt5\\model\\net_200.pth'

model.load_state_dict(torch.load(model_weight_pth))

model.eval()

classes = {'0': '飞机', '1': '汽车', '2': '鸟', '3': '猫', '4': '鹿', '5': '狗', '6': '青蛙', '7': '马', '8': '船', '9': '卡车'}

with torch.no_grad():

output = torch.squeeze(model(img))

print(output)

predict = torch.softmax(output, dim=0)

predict_cla = torch.argmax(predict).numpy()

return classes[str(predict_cla)], predict[predict_cla].item()

在上述训练过程完成后，通过查看best_acc.txt查看测试精度最好的一次所对应的epoch，在预测脚本中使用精度最高的epoch所对应的权重

model_weight_pth = 'E:\\CLFAR-10+pyqt5\\model\\net_200.pth'

model.load_state_dict(torch.load(model_weight_pth))

接下来测试一下预测代码：

打印一下output

img = Image.open('E:\\CLFAR-10+pyqt5\data\\test\\bird\\25.jpg')

net = predict_(img)

print(net)

结果：

tensor([ 1.2775, -3.7718, 6.0837, -0.4484, -4.9533, 3.0170, -4.3821, 3.7511,

1.8174, -2.6302])

('鸟', 0.8564958572387695)

tensor([19.7340, -4.3800, -3.0140, -3.5426, -2.8213, -2.6680, -3.8995, -4.8666,

4.2137, 0.3724])

Process finished with exit code 0

预测正确！

四、新建GUI.py文件

这里就是建立界面了，代码如下：

from PyQt5.QtWidgets import (QWidget,QLCDNumber,QSlider,QMainWindow,

QGridLayout,QApplication,QPushButton, QLabel, QLineEdit)

from PyQt5.QtGui import *

from PyQt5.QtCore import *

from PyQt5.QtWidgets import *

import sys

from PyQt5.QtCore import Qt

from predict import predict_

from PIL import Image

class Ui_example(QWidget):

def __init__(self):

super().__init__()

self.layout = QGridLayout(self)

self.label_image = QLabel(self)

self.label_predict_result = QLabel('识别结果',self)

self.label_predict_result_display = QLabel(self)

self.label_predict_acc = QLabel('识别准确率',self)

self.label_predict_acc_display = QLabel(self)

self.button_search_image = QPushButton('选择图片',self)

self.button_run = QPushButton('运行',self)

self.setLayout(self.layout)

self.initUi()

def initUi(self):

self.layout.addWidget(self.label_image,1,1,3,2)

self.layout.addWidget(self.button_search_image,1,3,1,2)

self.layout.addWidget(self.button_run,3,3,1,2)

self.layout.addWidget(self.label_predict_result,4,3,1,1)

self.layout.addWidget(self.label_predict_result_display,4,4,1,1)

self.layout.addWidget(self.label_predict_acc,5,3,1,1)

self.layout.addWidget(self.label_predict_acc_display,5,4,1,1)

self.button_search_image.clicked.connect(self.openimage)

self.button_run.clicked.connect(self.run)

self.setGeometry(300,300,300,300)

self.setWindowTitle('CLFAR-10十分类')

self.show()

def openimage(self):

global fname

imgName, imgType = QFileDialog.getOpenFileName(self, "选择图片", "", "*.jpg;;*.png;;All Files(*)")

jpg = QPixmap(imgName).scaled(self.label_image.width(), self.label_image.height())

self.label_image.setPixmap(jpg)

fname = imgName

def run(self):

global fname

file_name = str(fname)

img = Image.open(file_name)

a, b = predict_(img)

self.label_predict_result_display.setText(a)

self.label_predict_acc_display.setText(str(b))

if __name__ == '__main__':

app = QApplication(sys.argv)

ex = Ui_example()

sys.exit(app.exec_())

结果演示

遇到的问题

我学习中遇到的一些问题，通过百度和博客解决了。

1》nn.Sequential(*layers)为什么需要加一个星号？

答：如果星号加在了是实参上，代表的是将输入迭代器拆成一个个元素。

2》net.train()和net.eval()区别？

答：使用PyTorch进行训练和测试时一定注意要把实例化的模型指定train/eval，eval()时，框架会自动把BN和DropOut固定住，不会取平均，而是用训练好的值，不然的话，一旦test的batch_size过小，很容易就会被BN层导致生成图片颜色失真极大。原因就是对于BN层来说，它在训练过程中，是对每一个batch去一个样本均值和方差，然后使用滑动指数平均所有的batch的均值和方差来近似整个样本的均值和方差。对于测试阶段，我们固定我们样本和方差，bn相当于一个线性的映射关系。所以说对于pytorch来说，在训练阶段我们net.train相当于打开滑动指数平均按钮，不断的更新；测试阶段我们关闭它，相当于一个线性映射关系。

3》correct += predicted.eq(labels.data).cpu().sum()是什么意思？

答：correct += predicted.eq(labels.data).cpu().sum()其实和correct += (predicted == labels).sum().item()是一个意思，.item()返回的是一个具体值，而.data返回的是一个tensor，要注意item()不能丢，不然返回的是tensor，而tensor不能相加。

希望对大家有所帮助。

总结和引用

这篇文章大概算是我两个月来初学pytorch的总结，后面大概要去看tensorflow了。