def __init__(self, pretrained=False): super(Resnet18Triplet, self).__init__() self.model = resnet18(pretrained=pretrained) # Output self.input_features_fc_layer = self.model.fc.in_features self.model.fc = common_functions.Identity() def forward(self, images): """Forward pass to output the embedding vector (feature vector) after l2-normalization.""" embedding = self.model(images) return embedding
时间: 2024-04-06 20:29:46 浏览: 49
这段代码是一个PyTorch模型的定义,使用ResNet-18作为backbone,在此基础上构建一个面向triplet loss的模型。其中,`__init__`方法中使用`resnet18`函数加载预训练的ResNet-18模型,并将最后的全连接层替换成一个空白的Identity层。`forward`方法中,输入一张图片,通过模型的计算,输出该图片的特征向量,并经过L2标准化后返回。这个特征向量可以用于计算triplet loss。
相关问题
class MyNet(nn.Module): def __init__(self): super(MyNet, self).__init__() self.vgg16 = vgg16(pretrained=True) self.resnet18 = resnet18(pretrained=True) self.vgg16.classifier = nn.Identity() self.resnet18.fc = nn.Identity() self.fc = nn.Linear(25600, 2) def forward(self, x): x1 = self.vgg16(x) x2 = self.resnet18(x) x1 = x1.view(x1.size(0), -1) x2 = x2.view(x2.size(0), -1) x = torch.cat((x1, x2), dim=1) x = self.fc(x) return x 将以上代码加入DANet注意力机制
可以将 DAnet 注意力机制加入到 MyNet 的 forward 函数中,如下所示:
```python
import torch
import torch.nn as nn
class DAnet(nn.Module):
def __init__(self, in_channels, out_channels, reduction=16):
super(DAnet, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, kernel_size=1, stride=1, padding=0)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels // reduction, out_channels, kernel_size=1, stride=1, padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x)
y = self.conv1(y)
y = self.relu(y)
y = self.conv2(y)
y = self.sigmoid(y)
return x * y.expand_as(x)
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.vgg16 = vgg16(pretrained=True)
self.resnet18 = resnet18(pretrained=True)
self.vgg16.classifier = nn.Identity()
self.resnet18.fc = nn.Identity()
self.danet1 = DAnet(512, 512)
self.danet2 = DAnet(512, 512)
self.fc = nn.Linear(25600, 2)
def forward(self, x):
x1 = self.vgg16.features(x)
x1 = self.danet1(x1)
x1 = self.vgg16.avgpool(x1)
x1 = torch.flatten(x1, 1)
x2 = self.resnet18.conv1(x)
x2 = self.resnet18.bn1(x2)
x2 = self.resnet18.relu(x2)
x2 = self.resnet18.maxpool(x2)
x2 = self.resnet18.layer1(x2)
x2 = self.resnet18.layer2(x2)
x2 = self.resnet18.layer3(x2)
x2 = self.resnet18.layer4(x2)
x2 = self.danet2(x2)
x2 = self.resnet18.avgpool(x2)
x2 = torch.flatten(x2, 1)
x = torch.cat((x1, x2), dim=1)
x = self.fc(x)
return x
```
在 forward 函数中,我们首先对 VGG16 和 ResNet18 的特征提取部分进行计算,然后将 VGG16 的特征图 x1 和 ResNet18 的特征图 x2 分别输入到两个 DAnet 注意力机制中进行加权,最后将加权后的两个特征图拼接在一起,并通过全连接层进行分类。
class ResNet_101(nn.Module): def __init__(self, num_classes): super(ResNet_101, self).__init__() model = torchvision.models.resnet50(pretrained=True) model.fc = nn.Sequential( nn.Linear(model.fc.in_features, num_classes, bias=True), ) self.net = model def forward(self, img): output = self.net(img) return output如何做def state_dict(self):
你可以使用`state_dict()`方法来返回当前模型的状态字典。状态字典是一个Python字典对象,其中包含模型的所有参数。在这个示例中,你可以通过以下方式实现`state_dict()`方法:
```python
def state_dict(self):
state_dict = {
'model': self.net.state_dict(),
}
return state_dict
```
在这个方法中,你创建了一个名为`state_dict`的字典对象,并将整个模型的状态字典存储在其中的一个键值对中,键为`'model'`。你可以使用`self.net.state_dict()`方法来获取`ResNet_101`模型内部的所有参数的状态字典。最后,你返回整个状态字典对象。
阅读全文