python
复制代码
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)
