model_train.py

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torchvision.models import efficientnet_b0
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
#use efficient net to classcify the pictures
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder(root=r'C:\Users\GuStardust\PycharmProjects\ntu_place_recognize\train', transform=transform)
test_dataset = datasets.ImageFolder(root=r'C:\Users\GuStardust\PycharmProjects\ntu_place_recognize\vaidation', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

model = efficientnet_b0(pretrained=True)
num_ftrs = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_ftrs, 8)#分类的数量

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
num_epochs = 10
# 储存loss，绘制loss曲线
losses = []
for epoch in range(num_epochs):
    running_loss = 0.0
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    epoch_loss = running_loss / len(train_loader)
    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}')
    #储存loss
    losses.append(epoch_loss)

plt.plot(losses)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

model.eval()
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f'Test Accuracy: {accuracy:.2f}%')
# save the model
torch.save(model.state_dict(), 'model.pth')