from collections import OrderedDict import torch import torch.nn.functional as F import torchvision from torch import nn import models.vgg_ as models class BackboneBase_VGG(nn.Module): def __init__(self, backbone: nn.Module, num_channels: int, name: str, return_interm_layers: bool): super().__init__() features = list(backbone.features.children()) if return_interm_layers: if name == 'vgg16_bn': self.body1 = nn.Sequential(*features[:13]) self.body2 = nn.Sequential(*features[13:23]) self.body3 = nn.Sequential(*features[23:33]) self.body4 = nn.Sequential(*features[33:43]) else: self.body1 = nn.Sequential(*features[:9]) self.body2 = nn.Sequential(*features[9:16]) self.body3 = nn.Sequential(*features[16:23]) self.body4 = nn.Sequential(*features[23:30]) else: if name == 'vgg16_bn': self.body = nn.Sequential(*features[:44]) # 16x down-sample elif name == 'vgg16': self.body = nn.Sequential(*features[:30]) # 16x down-sample self.num_channels = num_channels self.return_interm_layers = return_interm_layers def forward(self, tensor_list): out = [] if self.return_interm_layers: xs = tensor_list for _, layer in enumerate([self.body1, self.body2, self.body3, self.body4]): xs = layer(xs) out.append(xs) else: xs = self.body(tensor_list) out.append(xs) return out class Backbone_VGG(BackboneBase_VGG): """ResNet backbone with frozen BatchNorm.""" def __init__(self, name: str, return_interm_layers: bool): if name == 'vgg16_bn': backbone = models.vgg16_bn(pretrained=True) elif name == 'vgg16': backbone = models.vgg16(pretrained=True) num_channels = 256 super().__init__(backbone, num_channels, name, return_interm_layers) def build_backbone(args): backbone = Backbone_VGG(args.backbone, True) return backbone if __name__ == '__main__': Backbone_VGG('vgg16', True)

这段代码是一个基于VGG的神经网络模型的定义，主要用于特征提取。其中包括两个类：`BackboneBase_VGG`和`Backbone_VGG`以及一个函数`build_backbone`。

`BackboneBase_VGG`类是VGG的基础类，用于构建VGG神经网络模型。它的初始化函数接受四个参数：`backbone`，`num_channels`，`name`和`return_interm_layers`，其中`backbone`表示VGG的主干网络，`num_channels`表示输出的通道数，`name`表示VGG的名称，`return_interm_layers`表示是否返回中间层的输出。

`Backbone_VGG`类继承自`BackboneBase_VGG`类，主要用于构建一个冻结BatchNorm的VGG神经网络模型。它的初始化函数接受两个参数：`name`和`return_interm_layers`，其中`name`表示VGG的名称，`return_interm_layers`表示是否返回中间层的输出。这个类的主要作用是将`BackboneBase_VGG`类中的属性和方法进行实例化，然后通过调用父类的`__init__`方法进行初始化。

`build_backbone`函数是用于构建VGG神经网络模型的函数，它接受一个参数`args`，其中包含了VGG神经网络模型的一些参数。在这个函数中，会调用`Backbone_VGG`类来构建一个VGG神经网络模型，并将其返回。

最后，在`if __name__ == '__main__':`中，会创建一个`Backbone_VGG`类的实例对象，并传入参数`'vgg16'`和`True`进行初始化。