pytorch+PyQt5實戰：ResNet-18實作CLFAR-10影像分類，并利用PyQt5進行人機界面顯示

實驗環境：

1.pytorch-1.6.0
2.python-3.7.9
3.window-10
4.pycharm
5.pyqt5(相應的QT Designer及工具包)

CLFAR-10的資料集

作為一個初學者，在官網下載CLFAR-10的資料集下載速度不僅慢，而且不是常用的圖片格式，這里是轉換后的資料集，有需要的可以直接百度云盤提取，
鏈接：https://pan.baidu.com/s/1l7wvWLCscPcGoKzRjggjRA
提取碼：ht88

ResNet-18網路：

ResNet全名Residual Network殘差網路，殘差網路是由何凱明所提出的，他的《Deep Residual Learning for Image Recognition》獲得了當年CVPR最佳論文，他提出的深度殘差網路在2015年可以說是洗刷了影像方面的各大比賽，以絕對優勢取得了多個比賽的冠軍，而且它在保證網路精度的前提下，將網路的深度達到了152層，后來又進一步加到1000的深度，我們這里用到的是一個18 層的殘差網路，
網路結構如下：

殘差學習：一個構建單元

在pytorch上搭建ResNet-18模型

一、新建resnet.py檔案

代碼如下：

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)

一開始沒看懂下面代碼的意思，后來看懂模型結構發現是真香，大家細品，

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檔案

代碼如下：

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

# 定義是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 引數設定,使得我們能夠手動輸入命令列引數，就是讓風格變得和Linux命令列差不多
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--outf', default='./model/', help='folder to output images and model checkpoints') #輸出結果保存路徑
parser.add_argument('--net', default='./model/Resnet18.pth', help="path to net (to continue training)")  #恢復訓練時的模型路徑
args = parser.parse_args()

# 超引數設定
EPOCH = 200  #遍歷資料集次數
pre_epoch = 0  # 定義已經遍歷資料集的次數
BATCH_SIZE = 128      #批處理尺寸(batch_size)
LR = 0.001        #學習率



# 準備資料集并預處理
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),  #先四周填充0，在吧影像隨機裁剪成32*32
    transforms.RandomHorizontalFlip(),  #影像一半的概率翻轉，一半的概率不翻轉
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #R,G,B每層的歸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) #訓練資料集
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)   #生成一個個batch進行批訓練，組成batch的時候順序打亂取

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

# 模型定義-ResNet
net = ResNet18().to(device)

# 定義損失函式和優化方式
criterion = nn.CrossEntropyLoss()  #損失函式為交叉熵，多用于多分類問題
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4) #優化方式為mini-batch momentum-SGD，并采用L2正則化（權重衰減）

# 訓練
if __name__ == "__main__":
    best_acc = 85  #2 初始化best test accuracy
    print("Start Training, Resnet-18!")  # 定義遍歷資料集的次數
    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):
                    # 準備資料
                    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()

                    # 每訓練1個batch列印一次loss和準確率
                    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()

                # 每訓練完一個epoch測驗一下準確率
                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)
                        # 取得分最高的那個類 (outputs.data的索引號)
                        _, predicted = torch.max(outputs.data, 1)
                        total += labels.size(0)
                        correct += (predicted == labels).sum().item()
                        # result = torch.floor_divide(correct, total)
                    # print('測驗分類準確率為：%.3f%%' % (100 * result))
                    acc = 100 * correct / total
                    print('測驗分類準確率為：%.3f%%' % (acc))
                    # 將每次測驗結果實時寫入acc.txt檔案中
                    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()
                    # 記錄最佳測驗分類準確率并寫入best_acc.txt檔案中
                    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)
         

將訓練程序記錄在 log.txt中，將每個epoch的測驗精度放在acc.txt中，最后通過if陳述句將最高精度記錄在best_acc.txt中，best_acc.txt中保存的是最高測驗準確率所對應的epoch，每次epoch的權重保存在model檔案夾下

三、新建predict檔案

為了讓模型和PyQt5結合，寫個預測腳本方便GUI檔案呼叫
代碼如下：

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': '飛機', '1': '汽車', '2': '鳥', '3': '貓', '4': '鹿', '5': '狗', '6': '青蛙', '7': '馬', '8': '船', '9': '卡車'}
    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()

在上述訓練程序完成后，通過查看best_acc.txt查看測驗精度最好的一次所對應的epoch，在預測腳本中使用精度最高的epoch所對應的權重

model_weight_pth = 'E:\\CLFAR-10+pyqt5\\model\\net_200.pth'
model.load_state_dict(torch.load(model_weight_pth))

接下來測驗一下預測代碼：
列印一下output

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

結果：

tensor([ 1.2775, -3.7718,  6.0837, -0.4484, -4.9533,  3.0170, -4.3821,  3.7511,
         1.8174, -2.6302])
('鳥', 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

預測正確！

四、新建GUI.py檔案

這里就是建立界面了，代碼如下：

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('識別結果',self)
        self.label_predict_result_display = QLabel(self)
        self.label_predict_acc = QLabel('識別準確率',self)
        self.label_predict_acc_display = QLabel(self)

        self.button_search_image = QPushButton('選擇圖片',self)
        self.button_run = QPushButton('運行',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-10十分類')
        self.show()

    def openimage(self):
        global fname
        imgName, imgType = QFileDialog.getOpenFileName(self, "選擇圖片", "", "*.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_())

結果演示

遇到的問題

我學習中遇到的一些問題，通過百度和博客解決了，

1》nn.Sequential(*layers)為什么需要加一個星號？

答：如果星號加在了是實參上，代表的是將輸入迭代器拆成一個個元素，

2》net.train()和net.eval()區別？

答：使用PyTorch進行訓練和測驗時一定注意要把實體化的模型指定train/eval，eval()時，框架會自動把BN和DropOut固定住，不會取平均，而是用訓練好的值，不然的話，一旦test的batch_size過小，很容易就會被BN層導致生成圖片顏色失真極大，原因就是對于BN層來說，它在訓練程序中，是對每一個batch去一個樣本均值和方差，然后使用滑動指數平均所有的batch的均值和方差來近似整個樣本的均值和方差，對于測驗階段，我們固定我們樣本和方差，bn相當于一個線性的映射關系，所以說對于pytorch來說，在訓練階段我們net.train相當于打開滑動指數平均按鈕，不斷的更新；測驗階段我們關閉它，相當于一個線性映射關系，

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

答：correct += predicted.eq(labels.data).cpu().sum()其實和correct += (predicted == labels).sum().item()是一個意思，.item()回傳的是一個具體值，而.data回傳的是一個tensor，要注意item()不能丟，不然回傳的是tensor，而tensor不能相加，

如果對大家的學習有所幫助，希望大家幫我點個贊，讓我覺得我的分享是有價值的，也歡迎大家和我交流

總結和參考

這篇文章大概算是我兩個月來初學pytorch的總結，后面大概要去看tensorflow了，

參考文章：
1.Pytorch實戰2：ResNet-18實作Cifar-10影像分類（測驗集分類準確率95.170%）

2.PYQT5+Pytorch的貓狗分類（從資料集制作->網路模型搭建和訓練->界面演示）