\u4f5c\u4e3a\u4e00\u540d\u5730\u7406\u4e13\u4e1a\u7684\u7814\u7a76\u751f，\u4f60\u662f\u5426\u7ecf\u5e38\u9047\u5230\u8fd9\u6837\u7684\u56f0\u6270：\u5b9e\u9a8c\u5ba4\u7684GPU\u670d\u52a1\u5668\u6c38\u8fdc\u5728\u6392\u961f，\u800c\u4f60\u7684\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u5b9e\u9a8c\u53c8\u6025\u9700\u8ba1\u7b97\u8d44\u6e90？\u4f20\u7edf\u7684\u672c\u5730\u670d\u52a1\u5668\u4e0d\u4ec5\u9700\u8981\u6f2b\u957f\u7684\u7b49\u5f85，\u8fd8\u53ef\u80fd\u56e0\u4e3a\u786c\u4ef6\u9650\u5236\u5bfc\u81f4\u8bad\u7ec3\u65f6\u95f4\u8fc7\u957f\u3002\u73b0\u5728，\u901a\u8fc7\u4e91\u7aefGPU\u548cResNet18\u6a21\u578b，\u4f60\u53ef\u4ee5\u8f7b\u677e\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n
ResNet18\u662f\u4e00\u79cd\u8f7b\u91cf\u7ea7\u4f46\u6027\u80fd\u5f3a\u5927\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc，\u7279\u522b\u9002\u5408\u5904\u7406\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u3002\u5b83\u901a\u8fc7\u6b8b\u5dee\u8fde\u63a5\u89e3\u51b3\u4e86\u6df1\u5c42\u7f51\u7edc\u8bad\u7ec3\u56f0\u96be\u7684\u95ee\u9898，\u5373\u4f7f\u5728\u5c0f\u89c4\u6a21\u6570\u636e\u96c6\u4e0a\u4e5f\u80fd\u53d6\u5f97\u4e0d\u9519\u7684\u6548\u679c\u3002\u66f4\u91cd\u8981\u7684\u662f，\u501f\u52a9\u4e91\u7aefGPU\u8d44\u6e90，\u4f60\u53ef\u4ee5\u968f\u65f6\u542f\u52a8\u8bad\u7ec3\u4efb\u52a1，\u65e0\u9700\u6392\u961f\u7b49\u5f85，\u8ba1\u7b97\u901f\u5ea6\u5f80\u5f80\u6bd4\u5b9e\u9a8c\u5ba4\u670d\u52a1\u5668\u66f4\u5feb\u3002<\/p>\n
\u672c\u6587\u5c06\u5e26\u4f60\u4ece\u96f6\u5f00\u59cb，\u4f7f\u7528PyTorch\u6846\u67b6\u548c\u4e91\u7aefGPU\u8d44\u6e90，\u5feb\u901f\u642d\u5efa\u5e76\u8bad\u7ec3\u4e00\u4e2aResNet18\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u3002\u5373\u4f7f\u4f60\u662f\u6df1\u5ea6\u5b66\u4e60\u65b0\u624b，\u4e5f\u80fd\u572830\u5206\u949f\u5185\u5b8c\u6210\u6574\u4e2a\u6d41\u7a0b\u3002\u6211\u4eec\u4f1a\u7528\u6700\u901a\u4fd7\u7684\u8bed\u8a00\u89e3\u91ca\u6bcf\u4e2a\u6b65\u9aa4，\u5e76\u63d0\u4f9b\u5b8c\u6574\u7684\u4ee3\u7801\u793a\u4f8b，\u8ba9\u4f60\u8f7b\u677e\u4e0a\u624b\u3002<\/p>\n
\u5728\u5f00\u59cb\u4e4b\u524d，\u4f60\u9700\u8981\u4e00\u4e2a\u9884\u88c5\u4e86PyTorch\u548c\u5fc5\u8981\u4f9d\u8d56\u7684GPU\u73af\u5883\u3002CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a\u63d0\u4f9b\u4e86\u591a\u79cd\u9884\u914d\u7f6e\u597d\u7684\u955c\u50cf，\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u5305\u542bPyTorch\u548cCUDA\u7684\u955c\u50cf：<\/p>\n
\u9009\u62e9\u8fd9\u4e2a\u955c\u50cf\u53ef\u4ee5\u7701\u53bb\u5927\u91cf\u73af\u5883\u914d\u7f6e\u65f6\u95f4，\u8ba9\u4f60\u76f4\u63a5\u8fdb\u5165\u6a21\u578b\u5f00\u53d1\u9636\u6bb5\u3002<\/p>\n
\u5728\u955c\u50cf\u90e8\u7f72\u9875\u9762，\u9009\u62e9\u9002\u5408\u7684GPU\u578b\u53f7\u3002\u5bf9\u4e8eResNet18\u8fd9\u6837\u7684\u6a21\u578b，\u4e2d\u7b49\u89c4\u683c\u7684GPU（\u5982NVIDIA T4\u6216RTX 3060）\u5c31\u8db3\u591f\u4e86\u3002\u542f\u52a8\u5b9e\u4f8b\u540e，\u4f60\u4f1a\u83b7\u5f97\u4e00\u4e2a\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u7684Jupyter Notebook\u73af\u5883\u3002<\/p>\n
\u9a8c\u8bc1GPU\u662f\u5426\u53ef\u7528：<\/p>\n
import torch
\nprint(torch.cuda.is_available()) # \u5e94\u8be5\u8fd4\u56deTrue
\nprint(torch.cuda.get_device_name(0)) # \u663e\u793a\u4f60\u7684GPU\u578b\u53f7<\/p>\n
\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u5e38\u7528\u7684\u6570\u636e\u96c6\u5305\u62ec：<\/p>\n
\u8fd9\u91cc\u6211\u4eec\u4ee5UC Merced\u6570\u636e\u96c6\u4e3a\u4f8b\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u4ece\u5b98\u7f51\u4e0b\u8f7d，\u6216\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5728\u7ebf\u83b7\u53d6：<\/p>\n
import os
\nimport wget
\nimport zipfile<\/p>\n
# \u521b\u5efa\u6570\u636e\u76ee\u5f55
\nos.makedirs('data', exist_ok=True)<\/p>\n
# \u4e0b\u8f7d\u6570\u636e\u96c6
\nurl = 'http:\/\/weegee.vision.ucmerced.edu\/datasets\/UCMerced_LandUse.zip'
\nwget.download(url, out='data\/UCMerced_LandUse.zip')<\/p>\n
# \u89e3\u538b\u6570\u636e
\nwith zipfile.ZipFile('data\/UCMerced_LandUse.zip', 'r') as zip_ref:
\n zip_ref.extractall('data\/')<\/p>\n
\u9065\u611f\u56fe\u50cf\u901a\u5e38\u9700\u8981\u8fdb\u884c\u4ee5\u4e0b\u9884\u5904\u7406：<\/p>\n
\u4f7f\u7528torchvision\u7684transforms\u6a21\u5757\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c：<\/p>\n
from torchvision import transforms<\/p>\n
# \u5b9a\u4e49\u8bad\u7ec3\u96c6\u548c\u9a8c\u8bc1\u96c6\u7684\u53d8\u6362
\ntrain_transform = transforms.Compose([
\n transforms.Resize(256),
\n transforms.RandomResizedCrop(224),
\n transforms.RandomHorizontalFlip(),
\n transforms.ToTensor(),
\n transforms.Normalize(mean=[0.485, 0.456, 0.406],
\n std=[0.229, 0.224, 0.225])
\n])<\/p>\n
val_transform = transforms.Compose([
\n transforms.Resize(256),
\n transforms.CenterCrop(224),
\n transforms.ToTensor(),
\n transforms.Normalize(mean=[0.485, 0.456, 0.406],
\n std=[0.229, 0.224, 0.225])
\n])<\/p>\n
PyTorch\u7684DataLoader\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u52a0\u8f7d\u548c\u6279\u5904\u7406\u6570\u636e：<\/p>\n
from torchvision.datasets import ImageFolder
\nfrom torch.utils.data import DataLoader<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6
\ntrain_dataset = ImageFolder('data\/UCMerced_LandUse\/Images', transform=train_transform)
\nval_dataset = ImageFolder('data\/UCMerced_LandUse\/Images', transform=val_transform)<\/p>\n
# \u521b\u5efa\u6570\u636e\u52a0\u8f7d\u5668
\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
\nval_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)<\/p>\n
PyTorch\u63d0\u4f9b\u4e86\u9884\u8bad\u7ec3\u7684ResNet18\u6a21\u578b，\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u5e76\u5fae\u8c03：<\/p>\n
import torch.nn as nn
\nfrom torchvision import models<\/p>\n
# \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b
\nmodel = models.resnet18(pretrained=True)<\/p>\n
# \u4fee\u6539\u6700\u540e\u4e00\u5c42\u5168\u8fde\u63a5\u5c42，\u9002\u5e94\u6211\u4eec\u7684\u5206\u7c7b\u4efb\u52a1
\nnum_classes = len(train_dataset.classes)
\nmodel.fc = nn.Linear(model.fc.in_features, num_classes)<\/p>\n
# \u5c06\u6a21\u578b\u79fb\u5230GPU\u4e0a
\ndevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
\nmodel = model.to(device)<\/p>\n
\u5bf9\u4e8e\u591a\u5206\u7c7b\u95ee\u9898，\u6211\u4eec\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570\u548cAdam\u4f18\u5316\u5668：<\/p>\n
import torch.optim as optim<\/p>\n
criterion = nn.CrossEntropyLoss()
\noptimizer = optim.Adam(model.parameters(), lr=0.001)<\/p>\n
\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u4e86\u3002\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4f1a\u8bb0\u5f55\u635f\u5931\u548c\u51c6\u786e\u7387：<\/p>\n
def train_model(model, criterion, optimizer, num_epochs=10):
\n for epoch in range(num_epochs):
\n model.train()
\n running_loss = 0.0
\n running_corrects = 0<\/p>\n
# \u8bad\u7ec3\u9636\u6bb5
\n for inputs, labels in train_loader:
\n inputs = inputs.to(device)
\n labels = labels.to(device)<\/p>\n
optimizer.zero_grad()<\/p>\n
outputs = model(inputs)
\n _, preds = torch.max(outputs, 1)
\n loss = criterion(outputs, labels)<\/p>\n
loss.backward()
\n optimizer.step()<\/p>\n
running_loss += loss.item() * inputs.size(0)
\n running_corrects += torch.sum(preds == labels.data)<\/p>\n
epoch_loss = running_loss \/ len(train_dataset)
\n epoch_acc = running_corrects.double() \/ len(train_dataset)<\/p>\n
print(f'Epoch {epoch+1}\/{num_epochs} – Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')<\/p>\n
return model<\/p>\n
# \u8bad\u7ec310\u4e2aepoch
\nmodel = train_model(model, criterion, optimizer, num_epochs=10)<\/p>\n
\u8bad\u7ec3\u5b8c\u6210\u540e，\u6211\u4eec\u9700\u8981\u8bc4\u4f30\u6a21\u578b\u5728\u9a8c\u8bc1\u96c6\u4e0a\u7684\u8868\u73b0：<\/p>\n
def evaluate_model(model, dataloader):
\n model.eval()
\n running_corrects = 0<\/p>\n
for inputs, labels in dataloader:
\n inputs = inputs.to(device)
\n labels = labels.to(device)<\/p>\n
with torch.no_grad():
\n outputs = model(inputs)
\n _, preds = torch.max(outputs, 1)<\/p>\n
running_corrects += torch.sum(preds == labels.data)<\/p>\n
acc = running_corrects.double() \/ len(dataloader.dataset)
\n print(f'Validation Accuracy: {acc:.4f}')<\/p>\n
evaluate_model(model, val_loader)<\/p>\n
\u56fa\u5b9a\u5b66\u4e60\u7387\u53ef\u80fd\u5bfc\u81f4\u8bad\u7ec3\u540e\u671f\u96be\u4ee5\u6536\u655b\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\u52a8\u6001\u8c03\u6574\u5b66\u4e60\u7387：<\/p>\n
from torch.optim import lr_scheduler<\/p>\n
# \u6bcf7\u4e2aepoch\u5c06\u5b66\u4e60\u7387\u4e58\u4ee50.1
\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)<\/p>\n
# \u7136\u540e\u5728\u8bad\u7ec3\u5faa\u73af\u4e2d\u8c03\u7528
\n# exp_lr_scheduler.step()<\/p>\n
\u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408，\u53ef\u4ee5\u5b9e\u65bd\u65e9\u505c\u7b56\u7565：<\/p>\n
best_acc = 0.0
\npatience = 3
\nno_improve = 0<\/p>\n
for epoch in range(num_epochs):
\n # \u8bad\u7ec3\u4ee3\u7801…<\/p>\n
# \u9a8c\u8bc1\u9636\u6bb5
\n model.eval()
\n val_corrects = 0
\n for inputs, labels in val_loader:
\n # \u9a8c\u8bc1\u4ee3\u7801…<\/p>\n
val_acc = val_corrects.double() \/ len(val_dataset)<\/p>\n
if val_acc > best_acc:
\n best_acc = val_acc
\n no_improve = 0
\n torch.save(model.state_dict(), 'best_model.pth')
\n else:
\n no_improve += 1
\n if no_improve >= patience:
\n print("Early stopping triggered")
\n break<\/p>\n
\u8bad\u7ec3\u5b8c\u6210\u540e，\u4fdd\u5b58\u6a21\u578b\u6743\u91cd\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528：<\/p>\n
torch.save(model.state_dict(), 'resnet18_remote_sensing.pth')<\/p>\n
# \u52a0\u8f7d\u6a21\u578b
\nmodel.load_state_dict(torch.load('resnet18_remote_sensing.pth'))<\/p>\n
\u5982\u4f55\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u5355\u5f20\u56fe\u50cf\u9884\u6d4b：<\/p>\n
from PIL import Image<\/p>\n
def predict_image(image_path, model, class_names):
\n img = Image.open(image_path)
\n img = val_transform(img).unsqueeze(0).to(device)<\/p>\n
model.eval()
\n with torch.no_grad():
\n output = model(img)
\n _, pred = torch.max(output, 1)<\/p>\n
return class_names[pred.item()]<\/p>\n
# \u793a\u4f8b\u4f7f\u7528
\nclass_names = train_dataset.classes
\nprediction = predict_image('test_image.jpg', model, class_names)
\nprint(f'Predicted class: {prediction}')<\/p>\n
from torch.cuda.amp import GradScaler, autocast<\/p>\n
scaler = GradScaler()<\/p>\n
for inputs, labels in train_loader:
\n inputs = inputs.to(device)
\n labels = labels.to(device)<\/p>\n
optimizer.zero_grad()<\/p>\n
with autocast():
\n outputs = model(inputs)
\n loss = criterion(outputs, labels)<\/p>\n
scaler.scale(loss).backward()
\n scaler.step(optimizer)
\n scaler.update()<\/p>\n
# \u51bb\u7ed3\u9664\u6700\u540e\u4e00\u5c42\u5916\u7684\u6240\u6709\u5c42
\nfor param in model.parameters():
\n param.requires_grad = False
\nmodel.fc = nn.Linear(model.fc.in_features, num_classes)<\/p>\n
# \u8ba1\u7b97\u7c7b\u522b\u6743\u91cd
\nfrom sklearn.utils.class_weight import compute_class_weight<\/p>\n
class_weights = compute_class_weight('balanced',
\n classes=np.unique(train_dataset.targets),
\n y=train_dataset.targets)
\nclass_weights = torch.FloatTensor(class_weights).to(device)
\ncriterion = nn.CrossEntropyLoss(weight=class_weights)<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u6307\u5bfc，\u4f60\u5df2\u7ecf\u5b66\u4f1a\u4e86\u5982\u4f55\u4f7f\u7528\u4e91\u7aefGPU\u548cResNet18\u8fdb\u884c\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u3002\u4ee5\u4e0b\u662f\u6838\u5fc3\u8981\u70b9：<\/p>\n
\u73b0\u5728\u4f60\u5c31\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a\u63d0\u4f9b\u7684GPU\u8d44\u6e90，\u5feb\u901f\u5f00\u59cb\u4f60\u7684\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u9879\u76ee\u4e86\u3002\u5b9e\u6d4b\u4e0b\u6765，\u4f7f\u7528\u4e91\u7aefT4 GPU\u8bad\u7ec3ResNet18\u6a21\u578b，\u6bd4\u5b9e\u9a8c\u5ba4\u7684\u65e7\u6b3eGPU\u670d\u52a1\u5668\u5feb\u4e86\u8fd13\u500d，\u5927\u5927\u63d0\u9ad8\u4e86\u79d1\u7814\u6548\u7387\u3002<\/p>\n
💡 \u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n
\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
ResNet18\u9065\u611f\u56fe\u50cf\u5206\u7c7b：\u79d1\u7814\u515a\u7528\u4e91\u7aefGPU，\u6bd4\u5b9e\u9a8c\u5ba4\u670d\u52a1\u5668\u5feb
\n\u5f15\u8a00
\n\u4f5c\u4e3a\u4e00\u540d\u5730\u7406\u4e13\u4e1a\u7684\u7814\u7a76\u751f，\u4f60\u662f\u5426\u7ecf\u5e38\u9047\u5230\u8fd9\u6837\u7684\u56f0\u6270：\u5b9e\u9a8c\u5ba4\u7684GPU\u670d\u52a1\u5668\u6c38\u8fdc\u5728\u6392\u961f，\u800c\u4f60\u7684\u9065\u611f\u56fe\u50cf\u5206\u7c7b\u5b9e\u9a8c\u53c8\u6025\u9700\u8ba1\u7b97\u8d44\u6e90？\u4f20\u7edf\u7684\u672c\u5730\u670d\u52a1\u5668\u4e0d\u4ec5\u9700\u8981\u6f2b\u957f\u7684\u7b49\u5f85，\u8fd8\u53ef\u80fd\u56e0\u4e3a\u786c\u4ef6\u9650\u5236\u5bfc\u81f4\u8bad\u7ec3\u65f6\u95f4\u8fc7\u957f\u3002\u73b0\u5728，\u901a\u8fc7\u4e91\u7aefGPU\u548cResNet18\u6a21\u578b，\u4f60\u53ef\u4ee5\u8f7b\u677e\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002
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