基于pytorch框架下写一份根据图像训练车位识别模型的代码
时间: 2023-04-03 19:02:35 浏览: 76
你好,我可以回答这个问题。以下是基于 PyTorch 框架下训练车位识别模型的代码示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
# 定义数据预处理
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])
# 加载数据集
train_dataset = datasets.ImageFolder(root='train', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
# 定义模型
class CarSpotModel(nn.Module):
def __init__(self):
super(CarSpotModel, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.fc1 = nn.Linear(32 * 56 * 56, 128)
self.fc2 = nn.Linear(128, 2)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 32 * 56 * 56)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
model = CarSpotModel()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=.001, momentum=.9)
# 训练模型
for epoch in range(10):
running_loss = .
for i, data in enumerate(train_loader, ):
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 = .
print('Finished Training')
```
希望这个代码示例对你有所帮助。