pytorch训练MobileNetV2十分类任务的代码
时间: 2023-12-12 19:04:34 浏览: 25
以下是使用PyTorch训练MobileNetV2进行十分类任务的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torchvision.models import mobilenet_v2
# 定义数据预处理
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 加载数据集
train_dataset = datasets.ImageFolder('/path/to/train', transform=transform_train)
test_dataset = datasets.ImageFolder('/path/to/test', transform=transform_test)
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False, num_workers=4)
# 定义模型
model = mobilenet_v2(pretrained=True)
model.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(1280, 10)
)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy: %d %%' % (100 * correct / total))
```
在上述代码中,我们首先定义了数据预处理方式,包括随机裁剪、随机水平翻转和归一化等操作。然后加载了训练集和测试集,并定义了数据加载器。接着,我们使用`mobilenet_v2()`方法加载了预训练的MobileNetV2模型,并修改最后一层的输出类别数为10。我们使用交叉熵损失函数和Adam优化器来进行训练,最后对模型进行测试并统计准确率。