Compose图像增强算法
时间: 2023-11-25 09:50:45 浏览: 36
根据提供的引用内容,Compose是一种图像增强算法,它可以通过组合多种图像增强方法来提高图像质量。下面是一个使用Compose进行图像增强的例子:
```python
from PIL import Image
from torchvision.transforms import Compose, ColorJitter, RandomRotation, RandomHorizontalFlip
# 定义增强方法
transform = Compose([
ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
RandomRotation(degrees=30),
RandomHorizontalFlip(p=0.5)
])
# 加载图像
img = Image.open('example.jpg')
# 对图像进行增强
img_transformed = transform(img)
# 显示增强后的图像
img_transformed.show()
```
上述代码中,我们使用了PIL库和torchvision.transforms库中的Compose方法,定义了三种图像增强方法:颜色抖动、随机旋转和随机水平翻转。然后,我们加载了一张名为example.jpg的图像,并使用定义的增强方法对其进行增强。最后,我们显示了增强后的图像。
相关问题
用Pytorch+LeNet生成图像分类算法”
本教程将介绍如何使用PyTorch实现LeNet图像分类算法。LeNet是一个经典的卷积神经网络,最初用于手写数字识别。它是深度学习领域的重要里程碑之一,为后来的卷积神经网络打下了基础。
在本教程中,我们将使用PyTorch实现LeNet来对Fashion-MNIST数据集进行图像分类。Fashion-MNIST是一个包含10个类别的衣服图像数据集,每个类别有6000个训练样本和1000个测试样本。我们将使用LeNet对每个图像进行分类。
首先,我们需要导入所需的库。
``` python
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
```
接下来,我们将定义一些超参数,如批量大小、学习率和训练周期数。
``` python
# Hyper-parameters
batch_size = 100
learning_rate = 0.001
num_epochs = 10
```
然后,我们需要加载Fashion-MNIST数据集。我们将对数据进行归一化和数据增强,以提高模型的性能。
``` python
# Fashion-MNIST dataset
train_dataset = torchvision.datasets.FashionMNIST(root='./data',
train=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(28, padding=4),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]),
download=True)
test_dataset = torchvision.datasets.FashionMNIST(root='./data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
```
接下来,我们将定义LeNet模型。LeNet包含两个卷积层和三个全连接层。我们将使用ReLU作为激活函数,并在每个卷积层之间添加最大池化层。
``` python
# LeNet-5
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.relu = nn.ReLU()
def forward(self, x):
out = self.conv1(x)
out = self.relu(out)
out = self.pool1(out)
out = self.conv2(out)
out = self.relu(out)
out = self.pool2(out)
out = out.view(out.size(0), -1)
out = self.fc1(out)
out = self.relu(out)
out = self.fc2(out)
out = self.relu(out)
out = self.fc3(out)
return out
model = LeNet()
```
接下来,我们将定义损失函数和优化器。
``` python
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
```
最后,我们将训练和测试模型。
``` python
# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# Test the model
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('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
```
这就是如何使用PyTorch实现LeNet图像分类算法。在本教程中,我们使用LeNet对Fashion-MNIST数据集进行了分类。您可以使用相同的方法来实现其他图像分类任务。
cifar10图像分类pytorch 用vgg算法的代码
以下是使用 VGG 算法进行 CIFAR-10 图像分类的 PyTorch 代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义 VGG 网络
class VGG(nn.Module):
def __init__(self):
super(VGG, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv5 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 4 * 4, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fc3 = nn.Linear(4096, 10)
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout()
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.pool(x)
x = self.conv2(x)
x = self.relu(x)
x = self.pool(x)
x = self.conv3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.relu(x)
x = self.pool(x)
x = self.conv5(x)
x = self.relu(x)
x = self.pool(x)
x = x.view(-1, 512 * 4 * 4)
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc3(x)
return x
# 数据预处理
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
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.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# 加载数据集
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
# 定义模型、损失函数和优化器
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = VGG().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
# 训练模型
for epoch in range(100):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(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 testloader:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))
```
在上面的代码中,我们定义了一个 VGG 网络,然后使用 CIFAR-10 数据集进行训练。我们使用了数据增强技术,包括随机裁剪和随机水平翻转,以提高模型的泛化能力。我们将模型的参数保存在 GPU 上,如果 GPU 可用的话。我们使用随机梯度下降(SGD)作为优化器,并使用交叉熵损失作为损失函数。在训练过程中,我们每训练 100 个批次就输出一次损失值。最后,我们使用测试集评估模型的准确率。