self.vgg = models.vgg19_bn(pretrained=True)
时间: 2024-01-06 08:07:08 浏览: 41
This line of code initializes a VGG19 model with batch normalization layers and loads pre-trained weights from the ImageNet dataset. The `models` module is from the PyTorch library, which contains pre-trained models for various tasks. The VGG19 model is a deep convolutional neural network architecture that is commonly used for image classification tasks. The 'bn' suffix indicates that batch normalization layers have been added to the original VGG19 architecture. The `pretrained=True` argument loads the pre-trained weights for the VGG19 model, which have been trained on the large-scale ImageNet dataset.
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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。
Vgg加FPN和ResNet加FPN后 在分类前融合 代码
以下是使用PyTorch实现VGG和ResNet与FPN的融合的代码:
``` python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import vgg16, resnet50
class VGG_FPN(nn.Module):
def __init__(self, num_classes=1000):
super(VGG_FPN, self).__init__()
vgg = vgg16(pretrained=True)
self.features = vgg.features
self.fpn_layers = nn.Sequential(
nn.Conv2d(512, 256, kernel_size=1, stride=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 512, kernel_size=1, stride=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 1024, kernel_size=1, stride=1),
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True),
nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True)
)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(1024 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes)
)
def forward(self, x):
x = self.features(x)
c3, c4, c5 = x.size()
p5 = self.fpn_layers(x)
p4 = F.upsample(p5, size=(c4), mode="nearest") + self.fpn_layers(x)
p3 = F.upsample(p4, size=(c3), mode="nearest") + self.fpn_layers(x)
x = F.adaptive_avg_pool2d(p3, output_size=(1, 1))
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
class ResNet_FPN(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet_FPN, self).__init__()
resnet = resnet50(pretrained=True)
self.conv1 = resnet.conv1
self.bn1 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
self.fpn_layers = nn.Sequential(
nn.Conv2d(2048, 256, kernel_size=1, stride=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 512, kernel_size=1, stride=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 1024, kernel_size=1, stride=1),
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True),
nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True)
)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.fc = nn.Linear(1024 * 7 * 7, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
c2 = self.layer1(x)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
p5 = self.fpn_layers(c5)
p4 = F.upsample(p5, size=(c4.size()[2], c4.size()[3]), mode="nearest") + self.fpn_layers(c4)
p3 = F.upsample(p4, size=(c3.size()[2], c3.size()[3]), mode="nearest") + self.fpn_layers(c3)
x = F.adaptive_avg_pool2d(p3, output_size=(1, 1))
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
以上代码实现了VGG和ResNet与FPN的融合,其中VGG_FPN和ResNet_FPN分别代表融合后的模型。模型分为特征提取层和分类层两部分,特征提取层通过VGG或ResNet提取特征,然后再使用FPN将多层特征融合。分类层则将融合后的特征作为输入,输出分类结果。注意,这里使用了nn.Sequential()将多个卷积层组成了一个整体。
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