googlenet图像分类代码
时间: 2023-08-25 13:06:20 浏览: 53
以下是使用PyTorch实现GoogleNet图像分类的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
class Inception(nn.Module):
def __init__(self, in_channels, n1x1, n3x3_reduce, n3x3, n5x5_reduce, n5x5, pool_proj):
super(Inception, self).__init__()
# 1x1 convolution branch
self.conv1x1 = nn.Sequential(
nn.Conv2d(in_channels, n1x1, kernel_size=1),
nn.ReLU(inplace=True)
)
# 3x3 convolution branch
self.conv3x3 = nn.Sequential(
nn.Conv2d(in_channels, n3x3_reduce, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(n3x3_reduce, n3x3, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
# 5x5 convolution branch
self.conv5x5 = nn.Sequential(
nn.Conv2d(in_channels, n5x5_reduce, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(n5x5_reduce, n5x5, kernel_size=5, padding=2),
nn.ReLU(inplace=True)
)
# Max pooling branch
self.max_pool = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, pool_proj, kernel_size=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
x1 = self.conv1x1(x)
x2 = self.conv3x3(x)
x3 = self.conv5x5(x)
x4 = self.max_pool(x)
return torch.cat([x1, x2, x3, x4], 1)
class GoogleNet(nn.Module):
def __init__(self, num_classes=1000):
super(GoogleNet, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
self.conv2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 192, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.max_pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.max_pool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
self.avg_pool = nn.AdaptiveAvgPool2d((1,1))
self.dropout = nn.Dropout(p=0.4)
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.max_pool1(x)
x = self.inception4a(x)
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
x = self.inception4e(x)
x = self.max_pool2(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.avg_pool(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x)
return x
# Load the data
data_path = 'path/to/data/'
train_dataset = datasets.ImageFolder(
root=data_path + 'train/',
transform=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])
])
)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = datasets.ImageFolder(
root=data_path + 'test/',
transform=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])
])
)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# Define the model, loss function, and optimizer
model = GoogleNet(num_classes=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Train the model
for epoch in range(10):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_loader), loss.item()))
# Test the model
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the test images: {} %'.format(100 * correct / total))
```
在这个示例中,我们使用了ImageNet数据集的一部分来进行训练和测试。您需要将`data_path`变量设置为包含`train`和`test`文件夹的根目录。此外,我们使用了PyTorch的内置数据增强方法来对图像进行预处理,并使用了交叉熵损失和随机梯度下降(SGD)优化器来训练模型。最后,我们使用测试集来评估模型的准确性。
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