Multiple Feature Space Adaptation Network(MFSAN )Python代码
时间: 2024-04-06 14:32:33 浏览: 27
非常抱歉,我之前的回答仍然有误。目前还没有公开的官方代码实现。如果您想使用 MFSAN 模型进行实验,您需要自己实现该模型。以下是一个基于 PyTorch 的 MFSAN 实现示例,供您参考:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.utils.data import DataLoader
class FeatureExtractor(nn.Module):
def __init__(self):
super(FeatureExtractor, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=5)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(64, 64, kernel_size=5)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=5)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = F.relu(self.conv3(x))
x = self.pool3(x)
x = x.view(x.size(0), -1)
return x
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.fc1 = nn.Linear(256, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
class MFSAN(nn.Module):
def __init__(self):
super(MFSAN, self).__init__()
self.feature_extractor = FeatureExtractor()
self.classifier = Classifier()
def forward(self, x1, x2, x3):
x1 = self.feature_extractor(x1)
x2 = self.feature_extractor(x2)
x3 = self.feature_extractor(x3)
x = torch.cat([x1, x2, x3], dim=1)
x = self.classifier(x)
return x
class GradientReversalFunction(Function):
@staticmethod
def forward(ctx, x, lambda_):
ctx.lambda_ = lambda_
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.neg() * ctx.lambda_, None
class GradientReversalLayer(nn.Module):
def __init__(self, lambda_=1.0):
super(GradientReversalLayer, self).__init__()
self.lambda_ = lambda_
def forward(self, x):
return GradientReversalFunction.apply(x, self.lambda_)
def train(model, dataloader, optimizer, criterion, grl_lambda):
model.train()
running_loss = 0.0
running_corrects = 0
for inputs, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
# forward pass
features1 = inputs[:, :3, :, :]
features2 = inputs[:, 3:6, :, :]
features3 = inputs[:, 6:, :, :]
outputs = model(features1, features2, features3)
# compute loss
loss = criterion(outputs, labels)
# backward pass
loss.backward()
# gradient reversal layer
grl_layer = GradientReversalLayer(grl_lambda)
grl_layer.to(device)
grl_outputs = grl_layer(features1)
grl_outputs = grl_layer(features2)
grl_outputs = grl_layer(features3)
# backward pass on the gradient reversal layer
grl_outputs.backward()
optimizer.step()
# compute statistics
_, preds = torch.max(outputs, 1)
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = running_corrects.double() / len(dataloader.dataset)
return epoch_loss, epoch_acc
def test(model, dataloader, criterion):
model.eval()
running_loss = 0.0
running_corrects = 0
with torch.no_grad():
for inputs, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
# forward pass
features1 = inputs[:, :3, :, :]
features2 = inputs[:, 3:6, :, :]
features3 = inputs[:, 6:, :, :]
outputs = model(features1, features2, features3)
# compute loss
loss = criterion(outputs, labels)
# compute statistics
_, preds = torch.max(outputs, 1)
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = running_corrects.double() / len(dataloader.dataset)
return epoch_loss, epoch_acc
if __name__ == '__main__':
# parameters
batch_size = 64
num_epochs = 10
learning_rate = 0.001
grl_lambda = 0.1
# data
train_dataset = ...
test_dataset = ...
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# model
model = MFSAN()
model.to(device)
# optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# criterion
criterion = nn.CrossEntropyLoss()
# training and testing
for epoch in range(num_epochs):
train_loss, train_acc = train(model, train_dataloader, optimizer, criterion, grl_lambda)
test_loss, test_acc = test(model, test_dataloader, criterion)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))
```
需要注意的是,以上示例代码中的 MFSAN 模型结构和超参数设置可能与原论文略有不同,您可以根据自己的需求进行调整。同时,代码中的数据读取和预处理部分需要您自行实现,以适应您的数据集。