seo发布网站,营销网络建设四个阶段,网站建设公司实力,ios微信上的pdf乱码#x1f3c6; 作者简介#xff1a;席万里 ⚡ 个人网站#xff1a;https://dahua.bloggo.chat/ ✍️ 一名后端开发小趴菜#xff0c;同时略懂Vue与React前端技术#xff0c;也了解一点微信小程序开发。 #x1f37b; 对计算机充满兴趣#xff0c;愿意并且希望学习更多的技… 作者简介席万里 ⚡ 个人网站https://dahua.bloggo.chat/ ✍️ 一名后端开发小趴菜同时略懂Vue与React前端技术也了解一点微信小程序开发。 对计算机充满兴趣愿意并且希望学习更多的技术接触更多的大神提高自己的编程思维和解决问题的能力。 文章目录 作品演示代码1.train_and_test.py2、view.py可视化界面 作品演示 代码
采用模型VGG16、ALEXNet、Resnet18训练测试。python版本3.10.11 。
数据集和鲸社区猫狗图像数据集。https://www.heywhale.com/mw/project/631aedb893f47b16cb062b2a
1.train_and_test.py
# 导入 PyTorch 库和相关模块
import torch # PyTorch 的核心库提供张量计算和自动求导功能
import torchvision.transforms as transforms # 提供图像数据增强和预处理的功能
from torch.utils.data import Dataset # 用于自定义数据集
from torch import nn, optim # nn 用于构建神经网络optim 用于优化算法
from PIL import Image # 用于加载和处理图像文件
import time # 用于记录训练时长和其他时间相关操作
import torchvision.models as models # 包含一些预训练模型如 AlexNet、ResNet 等
import os # 用于与操作系统交互如文件路径处理、创建目录等
import matplotlib.pyplot as plt # 用于绘制图表如准确率曲线、损失曲线等
from tqdm import tqdm # 用于显示训练过程中的进度条
from sklearn.metrics import confusion_matrix # 用于计算混淆矩阵评估分类性能
import seaborn as sns # 用于绘制混淆矩阵的热图提供美观的图表风格device torch.device(cpu)# 数据预处理缩放到224x224大小并转换为Tensor
transformer transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])# 加载训练数据集
DogTrainImageList os.listdir(r./catsdogs/train/Dog) # 加载训练集中的狗图片列表
CatTrainImageList os.listdir(r./catsdogs/train/Cat) # 加载训练集中的猫图片列表
train_label [] # 存储训练数据的标签
train_data [] # 存储训练数据的图像数据
dog_train_data_dir r./catsdogs/train/Dog/ # 狗的图片目录路径
cat_train_data_dir r./catsdogs/train/Cat/ # 猫的图片目录路径# 将狗的图片加载进训练数据集
for i in range(len(DogTrainImageList)):train_label.append(1) # 狗的标签为1dog_img Image.open(dog_train_data_dir DogTrainImageList[i]).convert(RGB) # 打开图片并转换为RGBdog_img transformer(dog_img) # 进行预处理train_data.append(dog_img) # 添加到训练数据# 将猫的图片加载进训练数据集
for i in range(len(CatTrainImageList)):train_label.append(0) # 猫的标签为0cat_img Image.open(cat_train_data_dir CatTrainImageList[i]).convert(RGB) # 打开图片并转换为RGBcat_img transformer(cat_img) # 进行预处理train_data.append(cat_img) # 添加到训练数据# 加载测试数据集与训练集类似
DogTestImageList os.listdir(r./catsdogs/train/Dog)
CatTestImageList os.listdir(r./catsdogs/train/Cat)
test_label [] # 存储测试数据的标签
test_data [] # 存储测试数据的图像数据
dog_test_data_dir r./catsdogs/train/Dog/ # 狗的测试图片目录路径
cat_test_data_dir r./catsdogs/train/Cat/ # 猫的测试图片目录路径# 将狗的测试图片加载进测试数据集
for i in range(len(DogTestImageList)):test_label.append(1) # 狗的标签为1dog_img Image.open(dog_test_data_dir DogTestImageList[i]).convert(RGB)dog_img transformer(dog_img)test_data.append(dog_img)# 将猫的测试图片加载进测试数据集
for i in range(len(CatTestImageList)):test_label.append(0) # 猫的标签为0cat_img Image.open(cat_test_data_dir CatTestImageList[i]).convert(RGB)cat_img transformer(cat_img)test_data.append(cat_img)# 自定义的数据集类用于加载图像数据
class DealDataset(Dataset):def __init__(self, data, label, transformNone):self.data data # 图像数据self.label label # 图像标签self.transform transform # 图像预处理def __getitem__(self, index):data, label self.data[index], int(self.label[index]) # 获取指定索引的数据和标签return data, label # 返回数据和标签def __len__(self):return len(self.data) # 返回数据集的大小# 将训练数据集和测试数据集包装为DealDataset对象
TrainDataSet DealDataset(train_data, train_label, transformtransformer)
TestDataSet DealDataset(test_data, test_label, transformtransformer)# 定义AlexNet模型
class AlexNet(nn.Module):def __init__(self):super(AlexNet, self).__init__()# 定义卷积层部分self.conv nn.Sequential(nn.Conv2d(3, 64, kernel_size11, stride4),nn.ReLU(),nn.MaxPool2d(kernel_size3, stride2),nn.BatchNorm2d(64),nn.Conv2d(64, 192, kernel_size5, padding2),nn.ReLU(),nn.MaxPool2d(kernel_size3, stride2),nn.BatchNorm2d(192),nn.Conv2d(192, 384, kernel_size3, padding1),nn.ReLU(),nn.Conv2d(384, 256, kernel_size3, padding1),nn.ReLU(),nn.Conv2d(256, 256, kernel_size3, padding1),nn.ReLU(),nn.MaxPool2d(kernel_size3, stride2),nn.BatchNorm2d(256))# 定义全连接层部分self.fc nn.Sequential(nn.Linear(256 * 5 * 5, 4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096, 4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096, 2) # 输出2个类别猫或狗)def forward(self, img):feature self.conv(img) # 通过卷积层提取特征output self.fc(feature.view(img.shape[0], -1)) # 展开特征并通过全连接层进行分类return output# 使用预训练的VGG16模型并修改最后的全连接层以适应2个输出类别
class VGG16(nn.Module):def __init__(self, num_classes2):super(VGG16, self).__init__()self.model models.vgg16(pretrainedTrue) # 加载预训练的VGG16模型self.model.classifier[-1] nn.Linear(self.model.classifier[-1].in_features, num_classes) # 修改输出层def forward(self, x):return self.model(x) # 返回模型的输出# 使用ResNet18模型并修改最后的全连接层以适应2个输出类别
class ResNet18(nn.Module):def __init__(self):super(ResNet18, self).__init__()self.model models.resnet18(pretrainedFalse) # 加载ResNet18模型self.model.fc nn.Linear(self.model.fc.in_features, 2) # 修改输出层为2个类别def forward(self, x):return self.model(x) # 返回模型的输出# 绘制混淆矩阵的函数
def plot_combined_confusion_matrix(true_labels_dict, predicted_labels_dict, classes,save_pathcombined_confusion_matrix.png):# 创建一个子图用来显示多个模型的混淆矩阵fig, axes plt.subplots(1, len(true_labels_dict), figsize(15, 5))# 遍历每个模型并绘制其混淆矩阵for i, (model_name, true_labels) in enumerate(true_labels_dict.items()):predicted_labels predicted_labels_dict[model_name]cm confusion_matrix(true_labels, predicted_labels) # 计算混淆矩阵# 使用Seaborn绘制热图sns.heatmap(cm, annotTrue, fmtd, cmapBlues, xticklabelsclasses, yticklabelsclasses,axaxes[i], cbarFalse, annot_kws{size: 14})axes[i].set_xlabel(Predicted labels, fontsize12)axes[i].set_ylabel(True labels, fontsize12)axes[i].set_title(f{model_name} Confusion Matrix, fontsize14)# 调整布局并保存图像plt.tight_layout()plt.savefig(save_path)plt.show()# 计算模型在测试集上的准确率
def evaluate_accuracy(data_iter, net, deviceNone):if device is None and isinstance(net, torch.nn.Module):device list(net.parameters())[0].device # 获取模型的设备acc_sum, n 0.0, 0predicted_labels []true_labels []with torch.no_grad(): # 在测试时不需要计算梯度for X, y in tqdm(data_iter, desc加载中, leaveTrue):net.eval() # 将模型设置为评估模式outputs net(X.to(device)) # 获取模型输出predicted outputs.argmax(dim1) # 获取预测的标签true_labels.extend(y.cpu().numpy()) # 存储真实标签predicted_labels.extend(predicted.cpu().numpy()) # 存储预测标签acc_sum (predicted y.to(device)).float().sum().cpu().item() # 累加准确的样本数n y.shape[0] # 累加样本总数return acc_sum / n, true_labels, predicted_labels # 返回准确率真实标签和预测标签# 训练和评估模型
def train_and_evaluate_models(models, model_names, train_iter, test_iter, batch_size, optimizer_dict, device,num_epochs, save_model_paths, plot_path):train_acc_history {name: [] for name in model_names} # 存储训练过程中每个模型的训练准确率test_acc_history {name: [] for name in model_names} # 存储测试过程中每个模型的测试准确率train_loss_history {name: [] for name in model_names} # 存储每个模型的训练损失# 存储每个模型的混淆矩阵数据true_labels_dict {name: [] for name in model_names}predicted_labels_dict {name: [] for name in model_names}# 迭代训练周期for epoch in range(num_epochs):for model, model_name in zip(models, model_names): # 遍历每个模型model.train()optimizer optimizer_dict[model_name] # 获取当前模型的优化器loss_fn torch.nn.CrossEntropyLoss() # 定义损失函数scheduler torch.optim.lr_scheduler.StepLR(optimizer, step_size3, gamma0.7) # 学习率衰减策略train_l_sum, train_acc_sum, n, batch_count, start 0.0, 0.0, 0, 0, time.time()# 训练每个模型for X, y in train_iter:X, y X.to(device), y.to(device)y_hat model(X) # 获取模型预测loss loss_fn(y_hat, y) # 计算损失optimizer.zero_grad() # 清空梯度loss.backward() # 反向传播optimizer.step() # 更新参数train_l_sum loss.item() # 累加损失train_acc_sum (y_hat.argmax(dim1) y).sum().cpu().item() # 累加准确的样本数n y.shape[0]batch_count 1scheduler.step() # 学习率衰减# 计算训练集和测试集的准确率train_acc train_acc_sum / ntest_acc, true_labels, predicted_labels evaluate_accuracy(test_iter, model, device)# 存储每个模型的混淆矩阵数据true_labels_dict[model_name].extend(true_labels)predicted_labels_dict[model_name].extend(predicted_labels)train_acc_history[model_name].append(train_acc)test_acc_history[model_name].append(test_acc)train_loss_history[model_name].append(train_l_sum / batch_count)print(f{model_name} epoch {epoch 1}, loss {train_l_sum / batch_count:.4f}, ftrain acc {train_acc:.3f}, test acc {test_acc:.3f}, time {time.time() - start:.1f} sec)# 保存模型torch.save(model.state_dict(), save_model_paths[model_name]) # 保存模型的权重print(f{model_name} Model saved to {save_model_paths[model_name]} after epoch {epoch 1})# 在所有训练完成后生成混淆矩阵的综合图plot_combined_confusion_matrix(true_labels_dict, predicted_labels_dict, [Cat, Dog],save_pathos.path.join(plot_path, combined_confusion_matrix.png))return train_acc_history, test_acc_history, train_loss_history# 可视化训练结果并保存
def plot_and_save_results(train_acc_history, test_acc_history, train_loss_history, num_epochs, save_plots_path):plt.figure(figsize(10, 5))# 绘制每个模型的训练与测试准确率曲线for model_name in [AlexNet, ResNet18, VGG16]:if model_name in train_acc_history and model_name in test_acc_history:plt.plot(range(num_epochs), train_acc_history[model_name], labelf{model_name} Train Accuracy)plt.plot(range(num_epochs), test_acc_history[model_name], labelf{model_name} Test Accuracy)plt.xlabel(Epochs)plt.ylabel(Accuracy)plt.title(AlexNet, ResNet18, VGG16 - Training and Test Accuracy Comparison)plt.legend()plt.grid(True)plt.savefig(os.path.join(save_plots_path, accuracy_plot.png)) # 保存准确率图像plt.show()plt.figure(figsize(10, 5))# 绘制每个模型的训练损失曲线for model_name in [AlexNet, ResNet18, VGG16]:if model_name in train_loss_history:plt.plot(range(num_epochs), train_loss_history[model_name], labelf{model_name} Train Loss)plt.xlabel(Epochs)plt.ylabel(Loss)plt.title(Training Loss Comparison)plt.legend()plt.grid(True)plt.savefig(os.path.join(save_plots_path, loss_plot.png)) # 保存损失图像plt.show()if __name__ __main__:# 设置训练参数num_epochs 25 # 设置为可配置参数batch_size 16 # 设置为可配置参数learning_rate 0.009 # 设置为可配置参数save_model_paths {AlexNet: AlexNet.pth,ResNet18: ResNet18.pth,VGG16: VGG16.pth}save_plots_path ./pythonos.makedirs(save_plots_path, exist_okTrue) # 创建保存模型和图像的文件夹# 创建模型实例alexnet_model AlexNet().to(device)resnet_model ResNet18().to(device)vgg_model VGG16().to(device)# 创建数据加载器train_iter torch.utils.data.DataLoader(TrainDataSet, batch_sizebatch_size, shuffleTrue, num_workers2)test_iter torch.utils.data.DataLoader(TestDataSet, batch_sizebatch_size, shuffleFalse, num_workers2)# 优化器字典optimizer_dict {AlexNet: torch.optim.SGD(alexnet_model.parameters(), lrlearning_rate),ResNet18: torch.optim.SGD(resnet_model.parameters(), lrlearning_rate),VGG16: torch.optim.SGD(vgg_model.parameters(), lrlearning_rate)}# 训练并评估models [alexnet_model, resnet_model, vgg_model]model_names [AlexNet, ResNet18, VGG16]train_acc_history, test_acc_history, train_loss_history train_and_evaluate_models(models, model_names, train_iter, test_iter, batch_size, optimizer_dict, device, num_epochs, save_model_paths, save_plots_path)# 绘制并保存准确率和损失曲线plot_and_save_results(train_acc_history, test_acc_history, train_loss_history, num_epochs, save_plots_path)
2、view.py可视化界面
import sys
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QPushButton, QFileDialog, QVBoxLayout, QGridLayout, \QTextEdit, QComboBox, QSpacerItem, QSizePolicy
from PyQt5.QtGui import QPixmap, QFont, QTextCursor
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import torchvision.models as modelsclass AnimalClassifierApp(QWidget):def __init__(self):super().__init__()self.initUI()def initUI(self):self.setWindowTitle(猫狗识别系统)self.resize(600, 400) # 更小的窗口尺寸# 创建布局grid QGridLayout()grid.setContentsMargins(10, 10, 10, 10) # 设置间距grid.setSpacing(5) # 设置控件间距# 显示图像的标签self.image_label QLabel(self)self.image_label.setFixedSize(250, 250) # 调整图像显示尺寸self.image_label.setAlignment(Qt.AlignCenter)grid.addWidget(self.image_label, 1, 0, 2, 1)# 识别结果的标签self.result_label QTextEdit(self)self.result_label.setFixedSize(250, 80)self.result_label.setReadOnly(True)self.result_label.setStyleSheet(color: red; font-size: 14px;)self.result_label.setAlignment(Qt.AlignCenter)grid.addWidget(self.result_label, 1, 1, 1, 2)# 模型选择下拉框self.model_selector QComboBox(self)self.model_selector.addItem(AlexNet)self.model_selector.addItem(VGG16)self.model_selector.addItem(ResNet18)grid.addWidget(self.model_selector, 2, 0, 1, 2)# 按钮布局button_layout QVBoxLayout()button_layout.setSpacing(5) # 设置按钮间距# 上传图像按钮upload_btn QPushButton(上传, self)upload_btn.clicked.connect(self.load_image)button_layout.addWidget(upload_btn)# 识别按钮recognize_btn QPushButton(识别, self)recognize_btn.clicked.connect(self.classify_image)button_layout.addWidget(recognize_btn)# 添加按钮布局button_layout.addSpacerItem(QSpacerItem(10, 10, QSizePolicy.Minimum, QSizePolicy.Expanding))grid.addLayout(button_layout, 3, 1, 1, 2)self.setLayout(grid)# 加载模型self.device torch.device(cpu)# 定义数据转换self.transform transforms.Compose([transforms.Resize((148, 148)),transforms.ToTensor(),transforms.Normalize(mean[0.4, 0.4, 0.4], std[0.2, 0.2, 0.2])])self.image_path self.model None # 模型初始化为空def load_image(self):options QFileDialog.Options()options | QFileDialog.ReadOnlyfile_name, _ QFileDialog.getOpenFileName(self, 上传图片, , 图片文件 (*.jpg *.jpeg *.png),optionsoptions)if file_name:self.image_path file_namepixmap QPixmap(file_name)pixmap pixmap.scaled(self.image_label.width(), self.image_label.height(), Qt.KeepAspectRatio)self.image_label.setPixmap(pixmap)self.result_label.setText(识别结果: )def classify_image(self):if self.image_path:# 根据选择的模型加载相应的模型selected_model self.model_selector.currentText()if selected_model AlexNet:self.model self.load_alexnet_model()elif selected_model VGG16:self.model self.load_vgg16_model()elif selected_model ResNet18:self.model self.load_resnet18_model()image Image.open(self.image_path).convert(RGB)image_tensor self.transform(image).unsqueeze(0).to(self.device)with torch.no_grad():output self.model(image_tensor)probabilities torch.nn.functional.softmax(output, dim1)confidence, predicted torch.max(probabilities, 1)label cat if predicted.item() 0 else dogconfidence confidence.item()# 将图像转换为QPixmappixmap QPixmap(self.image_path)pixmap pixmap.scaled(self.image_label.width(), self.image_label.height(), Qt.KeepAspectRatio)self.image_label.setPixmap(pixmap)# 设置识别结果字体颜色和对齐方式self.result_label.setText(f识别结果: {label} \n\n置信度: {confidence:.2f})self.result_label.setAlignment(Qt.AlignCenter)cursor self.result_label.textCursor()cursor.select(QTextCursor.Document)self.result_label.setTextCursor(cursor)def load_alexnet_model(self):model models.alexnet(pretrainedTrue)model.classifier[6] nn.Linear(model.classifier[6].in_features, 2) # 修改最后一层model model.to(self.device)model.eval()return modeldef load_vgg16_model(self):model models.vgg16(pretrainedTrue)model.classifier[6] nn.Linear(model.classifier[6].in_features, 2) # 修改最后一层model model.to(self.device)model.eval()return modeldef load_resnet18_model(self):model models.resnet18(pretrainedTrue)model.fc nn.Linear(model.fc.in_features, 2) # 修改最后一层model model.to(self.device)model.eval()return modelif __name__ __main__:app QApplication(sys.argv)ex AnimalClassifierApp()ex.show()sys.exit(app.exec_())
