- 🍨 本文为🔗365天深度学习训练营中的学习记录博客
- 🍖 原作者：K同学啊
1.检查GPU
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvisiondevice=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
2.查看数据
import os,PIL,random,pathlibdata_dir='data/45-data/'
data_dir=pathlib.Path(data_dir)
data_paths=list(data_dir.glob('*'))
classNames=[str(path).split('\\')[2] for path in data_paths]
classNames
3.划分数据集
total_datadir='data/45-data/'
train_trainsforms=transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),
])
total_data=datasets.ImageFolder(total_datadir,train_trainsforms)
total_dataimport torch.utilstrain_size=int(0.8*len(total_data))
test_size=len(total_data)-train_size
train_dataset,test_dataset=torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_datasetimport torch.utils.data
import torch.utils.data.dataloaderbatch_size=32
train_dl=torch.utils.data.DataLoader(train_dataset,batch_size,shuffle=True,num_workers=1)
test_dl=torch.utils.data.DataLoader(test_dataset,batch_size,shuffle=True,num_workers=1)for X,y in test_dl:print('shape of X [N C H W]',X.shape)print('shape of y:',y.shape)break
4.构建模型
import torch.nn.functional as Fclass Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(12)self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0)self.bn2 = nn.BatchNorm2d(12)self.pool1 = nn.MaxPool2d(2,2)self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)self.bn4 = nn.BatchNorm2d(24)self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)self.bn5 = nn.BatchNorm2d(24)self.pool2 = nn.MaxPool2d(2,2)self.fc1 = nn.Linear(24*50*50, len(classNames))def forward(self, x):x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = self.pool1(x) x = F.relu(self.bn4(self.conv4(x))) x = F.relu(self.bn5(self.conv5(x))) x = self.pool2(x) x = x.view(-1, 24*50*50)x = self.fc1(x)return xdevice = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))model = Network_bn().to(device)
model
5.编译及训练模型
loss_fn=nn.CrossEntropyLoss()
learn_rate=1e-3
opt=torch.optim.SGD(model.parameters(),lr=learn_rate)def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)num_batches = len(dataloader)train_loss, correct = 0, 0model.train()for X, y in dataloader:X, y = X.to(device), y.to(device)pred = model(X)loss = loss_fn(pred, y)optimizer.zero_grad()loss.backward()optimizer.step()train_loss += loss.item()correct += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss /= num_batchestrain_acc = correct / sizereturn train_acc, train_lossdef test(dataloader, model, loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)target_pred = model(imgs)loss = loss_fn(target_pred, target)test_loss += loss.item()correct += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_loss /= num_batchestest_acc = correct / sizereturn test_acc, test_lossdef save_best_model(model, best_acc, current_acc, path='best_model.pth'):if current_acc > best_acc:best_acc = current_acctorch.save(model.state_dict(), path)print(f"Best model saved with accuracy: {best_acc*100:.2f}%")return best_accepochs = 20
best_test_acc = 0.0
train_losses = []
train_accuracies = []
test_losses = []
test_accuracies = []for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型best_test_acc = save_best_model(model, best_test_acc, epoch_test_acc)# 存储结果用于绘图train_losses.append(epoch_train_loss)train_accuracies.append(epoch_train_acc)test_losses.append(epoch_test_loss)test_accuracies.append(epoch_test_acc)print(f'Epoch:{epoch+1:2d}, Train_acc:{epoch_train_acc*100:.1f}%, Train_loss:{epoch_train_loss:.3f}, 'f'Test_acc:{epoch_test_acc*100:.1f}%, Test_loss:{epoch_test_loss:.3f}')print('Finished Training')
6.结果可视化
import matplotlib.pyplot as plt
# 绘制训练和测试的损失与准确率变化趋势
plt.figure(figsize=(12, 5))# 绘制损失变化趋势
plt.subplot(1, 2, 1)
plt.plot(range(1, epochs + 1), train_losses, label='Train Loss')
plt.plot(range(1, epochs + 1), test_losses, label='Test Loss', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Loss over Epochs')
plt.legend()# 绘制准确率变化趋势
plt.subplot(1, 2, 2)
plt.plot(range(1, epochs + 1), [acc * 100 for acc in train_accuracies], label='Train Accuracy')
plt.plot(range(1, epochs + 1), [acc * 100 for acc in test_accuracies], label='Test Accuracy', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')
plt.title('Accuracy over Epochs')
plt.legend()plt.tight_layout()
plt.show()
7.加载本地模型并预测本地图片
from torch.utils.data import DataLoader
def load_best_model_and_predict(image_path, model, transform=None):# 加载最佳模型model.load_state_dict(torch.load('best_model.pth'))model.eval()# 对单张图片进行预测if transform is None:transform = transforms.Compose([transforms.Resize((224, 224)), # 根据模型需求调整尺寸transforms.ToTensor(),])image = datasets.ImageFolder(image_path, transform=transform)image_loader = DataLoader(image, batch_size=1, shuffle=False)with torch.no_grad():for img, _ in image_loader:img = img.to(device)output = model(img)_, predicted = torch.max(output, 1)print(f'Predicted class: {predicted.item()}')break # 我们只预测一张图片return output,predicted# 加载最佳模型并预测本地图片
image_path = 'data/猴痘预测'
output,predicted=load_best_model_and_predict(image_path, model)
print(output)
print(predicted)
总结：
1.保存最优模型参数到本地
def save_best_model(model, best_acc, current_acc, path='best_model.pth'):if current_acc > best_acc:best_acc = current_acctorch.save(model.state_dict(), path)print(f"Best model saved with accuracy: {best_acc*100:.2f}%")return best_acc2.使用本地模型参数预测本地图片
from torch.utils.data import DataLoader
def load_best_model_and_predict(image_path, model, transform=None):# 加载最佳模型model.load_state_dict(torch.load('best_model.pth'))model.eval()# 对单张图片进行预测if transform is None:transform = transforms.Compose([transforms.Resize((224, 224)), # 根据模型需求调整尺寸transforms.ToTensor(),])image = datasets.ImageFolder(image_path, transform=transform)image_loader = DataLoader(image, batch_size=1, shuffle=False)with torch.no_grad():for img, _ in image_loader:img = img.to(device)output = model(img)_, predicted = torch.max(output, 1)print(f'Predicted class: {predicted.item()}')break # 我们只预测一张图片return output,predicted# 加载最佳模型并预测本地图片
image_path = 'data/猴痘预测'
output,predicted=load_best_model_and_predict(image_path, model)
print(output)
print(predicted)
