使用自己编写的 CNN 连接到Transformer 的输入端,将CNN提取的图像信息的特征和另外的数值数据一起送入 Transformer 对特征进行处理和分析,最后将融合的特征作为输入进行回归预测。PyTorch实现代码
时间: 2024-01-24 22:17:11 浏览: 27
以下是一个简单的示例代码,将CNN提取的图像特征和数值特征连接到Transformer的输入端,进行处理和分析,最后将融合的特征作为输入进行回归预测。这里使用的是PyTorch框架。
首先,我们需要导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, models
```
接下来,我们定义数据集类,用于加载数据:
```python
class CustomDataset(Dataset):
def __init__(self, img_data, num_data, labels, transform=None):
self.img_data = img_data
self.num_data = num_data
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
img = self.img_data[idx]
num = self.num_data[idx]
label = self.labels[idx]
if self.transform:
img = self.transform(img)
return img, num, label
```
然后,我们定义CNN模型,用于提取图像特征:
```python
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
self.cnn_model = models.resnet50(pretrained=True)
self.cnn_layers = nn.Sequential(*list(self.cnn_model.children())[:-1])
self.linear_layer = nn.Linear(2048, 512)
def forward(self, x):
x = self.cnn_layers(x)
x = x.view(x.size(0), -1)
x = self.linear_layer(x)
return x
```
接着,我们定义Transformer模型,用于处理和分析特征:
```python
class TransformerModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, num_heads, dropout):
super(TransformerModel, self).__init__()
self.transformer_model = nn.TransformerEncoderLayer(input_size, num_heads, hidden_size, dropout)
self.transformer_layers = nn.TransformerEncoder(self.transformer_model, num_layers)
self.linear_layer = nn.Linear(input_size, output_size)
def forward(self, x):
x = self.transformer_layers(x)
x = self.linear_layer(x[-1])
return x
```
最后,我们定义整个模型,并进行训练和预测:
```python
img_data = ... # 图像数据
num_data = ... # 数值数据
labels = ... # 标签数据
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
dataset = CustomDataset(img_data, num_data, labels, transform=transform)
train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
cnn_model = CNNModel()
transformer_model = TransformerModel(input_size=512, hidden_size=256, output_size=1, num_layers=3, num_heads=8, dropout=0.1)
optimizer = optim.Adam(list(cnn_model.parameters()) + list(transformer_model.parameters()), lr=0.001)
loss_fn = nn.MSELoss()
for epoch in range(10):
for i, (img, num, label) in enumerate(train_loader):
optimizer.zero_grad()
img_features = cnn_model(img)
features = torch.cat((img_features, num), dim=1)
output = transformer_model(features)
loss = loss_fn(output, label)
loss.backward()
optimizer.step()
if i % 10 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_loader), loss.item()))
# 预测
img = ... # 单张图像数据
num = ... # 单个数值数据
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = transform(img).unsqueeze(0)
num = torch.tensor(num).unsqueeze(0)
img_features = cnn_model(img)
features = torch.cat((img_features, num), dim=1)
output = transformer_model(features)
prediction = output.item()
```