20.微调ResNet-18网络分类热狗数据集(失败版本)

import os
import torch
import torchvision
from torch import nn
import torchvision.models as models
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.metrics import accuracy_score
##########################################################################################################################
def plot_metrics(train_loss_list, train_acc_list, test_acc_list, title='Training Curve'):
    epochs = range(1, len(train_loss_list) + 1)
    plt.figure(figsize=(4, 3))
    plt.plot(epochs, train_loss_list, label='Train Loss')
    plt.plot(epochs, train_acc_list, label='Train Acc',linestyle='--')
    plt.plot(epochs, test_acc_list, label='Test Acc', linestyle='--')
    plt.xlabel('Epoch')
    plt.ylabel('Value')
    plt.title(title)
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()
def train_model(model,train_data,test_data,num_epochs):
    train_loss_list = []
    train_acc_list = []
    test_acc_list = []
    for epoch in range(num_epochs):
        total_loss=0
        total_acc_sample=0
        total_samples=0
        loop1=tqdm(train_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
        for X,y in loop1:
            #X=X.reshape(X.shape[0],-1)
            #print(X.shape)
            X=X.to(device)
            y=y.to(device)
            y_hat=model(X)
            loss=CEloss(y_hat,y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            #loss累加
            total_loss+=loss.item()*X.shape[0]
            y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
            y_true=y.detach().cpu().numpy()
            total_acc_sample+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
            total_samples+=X.shape[0]
        test_acc_samples=0
        test_samples=0
        loop2=tqdm(test_data,desc=f"EPOCHS[{epoch+1}/{num_epochs}]")
        for X,y in loop2:
            X=X.to(device)
            y=y.to(device)
            #X=X.reshape(X.shape[0],-1)
            y_hat=model(X)
            y_pred=y_hat.argmax(dim=1).detach().cpu().numpy()
            y_true=y.detach().cpu().numpy()
            test_acc_samples+=accuracy_score(y_pred,y_true)*X.shape[0]#保存样本数
            test_samples+=X.shape[0]
        avg_train_loss=total_loss/total_samples
        avg_train_acc=total_acc_sample/total_samples
        avg_test_acc=test_acc_samples/test_samples
        train_loss_list.append(avg_train_loss)
        train_acc_list.append(avg_train_acc)
        test_acc_list.append(avg_test_acc)
        print(f"Epoch {epoch+1}: Loss: {avg_train_loss:.4f},Trian Accuracy: {avg_train_acc:.4f},test Accuracy: {avg_test_acc:.4f}")
    plot_metrics(train_loss_list, train_acc_list, test_acc_list)
    return model
##########################################################################################################################
data_dir=r'./hotdog_dataset/hotdog'
# 使用RGB通道的均值和标准差，以标准化每个通道
train_augs = torchvision.transforms.Compose([
    torchvision.transforms.RandomResizedCrop(224),
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

test_augs = torchvision.transforms.Compose([
    torchvision.transforms.Resize([256, 256]),
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
train_imgs = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'train'),transform=train_augs)
test_imgs = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'test'),transform=test_augs)
train_data=DataLoader(train_imgs,batch_size=16,num_workers=4,shuffle=True)
test_data=DataLoader(test_imgs,batch_size=16,num_workers=4,shuffle=False)
##########################################################################################################################
pretrained_net = models.resnet18(pretrained=True)
#加载预训练模型并且更改最后层
finetune_net=models.resnet50(pretrained=True)
finetune_net.fc=nn.Linear(finetune_net.fc.in_features,2)
nn.init.xavier_normal(finetune_net.fc.weight)
##########################################################################################################################
#开始训练
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
finetune_net.to(device)
CEloss=nn.CrossEntropyLoss()
params_1x = [param for name, param in finetune_net.named_parameters() if name not in ["fc.weight", "fc.bias"]]
optimizer = torch.optim.SGD([
        {'params': params_1x},
        {'params': finetune_net.fc.parameters(), 'lr':0.001 * 10}
    ], lr=0.001, weight_decay=1e-4)
model=train_model(finetune_net,train_data,test_data,num_epochs=10)