卷积神经网络经典backbone

特征提取是数据分析和机器学习中的基本概念，是将原始数据转换为更适合分析或建模的格式过程中的关键步骤。特征，也称为变量或属性，是我们用来进行预测、对对象进行分类或从数据中获取见解的数据点的特定特征或属性。

1.AlexNet

paper：https://dl.acm.org/doi/pdf/10.1145/3065386

作者： Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton

显然该网络是按照作者名字命名的,但是现在这个bacbone比较老了，性能欠佳

框架：

整体结构主要由五个卷积层、三个全连接层构成，中间穿插着最大池化、ReLU、Dropout

使用ReLu非线性激活函数

code_Pytorch

class AlexNet(nn.Module):"""Neural network model consisting of layers propsed by AlexNet paper."""def __init__(self, num_classes=1000):"""Define and allocate layers for this neural net.Args:num_classes (int): number of classes to predict with this model"""super().__init__()# input size should be : (b x 3 x 227 x 227)# The image in the original paper states that width and height are 224 pixels, but# the dimensions after first convolution layer do not lead to 55 x 55.self.net = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4),  # (b x 96 x 55 x 55)nn.ReLU(),nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),  # section 3.3nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 96 x 27 x 27)nn.Conv2d(96, 256, 5, padding=2),  # (b x 256 x 27 x 27)nn.ReLU(),nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 256 x 13 x 13)nn.Conv2d(256, 384, 3, padding=1),  # (b x 384 x 13 x 13)nn.ReLU(),nn.Conv2d(384, 384, 3, padding=1),  # (b x 384 x 13 x 13)nn.ReLU(),nn.Conv2d(384, 256, 3, padding=1),  # (b x 256 x 13 x 13)nn.ReLU(),nn.MaxPool2d(kernel_size=3, stride=2),  # (b x 256 x 6 x 6))# classifier is just a name for linear layersself.classifier = nn.Sequential(nn.Dropout(p=0.5, inplace=True),nn.Linear(in_features=(256 * 6 * 6), out_features=4096),nn.ReLU(),nn.Dropout(p=0.5, inplace=True),nn.Linear(in_features=4096, out_features=4096),nn.ReLU(),nn.Linear(in_features=4096, out_features=num_classes),)self.init_bias()  # initialize biasdef init_bias(self):for layer in self.net:if isinstance(layer, nn.Conv2d):nn.init.normal_(layer.weight, mean=0, std=0.01)nn.init.constant_(layer.bias, 0)# original paper = 1 for Conv2d layers 2nd, 4th, and 5th conv layersnn.init.constant_(self.net[4].bias, 1)nn.init.constant_(self.net[10].bias, 1)nn.init.constant_(self.net[12].bias, 1)def forward(self, x):"""Pass the input through the net.Args:x (Tensor): input tensorReturns:output (Tensor): output tensor"""x = self.net(x)x = x.view(-1, 256 * 6 * 6)  # reduce the dimensions for linear layer inputreturn self.classifier(x)

2.VGG

paper:https://arxiv.org/abs/1409.1556

作者：Karen Simonyan, Andrew Zisserman

超级超级经典的网络，从14年到现在还是广泛使用

框架：

相比AlexNet而言加深了网络的深度，VGG16（13层conv+3层FC）和VGG19（16层conv+3层FC）是指表中的D、E两个模型。

code_vgg_Pytorch

'''
Modified from https://github.com/pytorch/vision.git
'''
import mathimport torch.nn as nn
import torch.nn.init as init__all__ = ['VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn','vgg19_bn', 'vgg19',
]class VGG(nn.Module):'''VGG model '''def __init__(self, features):super(VGG, self).__init__()self.features = featuresself.classifier = nn.Sequential(nn.Dropout(),nn.Linear(512, 512),nn.ReLU(True),nn.Dropout(),nn.Linear(512, 512),nn.ReLU(True),nn.Linear(512, 10),)# Initialize weightsfor m in self.modules():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.weight.data.normal_(0, math.sqrt(2. / n))m.bias.data.zero_()def forward(self, x):x = self.features(x)x = x.view(x.size(0), -1)x = self.classifier(x)return xdef make_layers(cfg, batch_norm=False):layers = []in_channels = 3for v in cfg:if v == 'M':layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)if batch_norm:layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]else:layers += [conv2d, nn.ReLU(inplace=True)]in_channels = vreturn nn.Sequential(*layers)cfg = {'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}def vgg11():"""VGG 11-layer model (configuration "A")"""return VGG(make_layers(cfg['A']))def vgg11_bn():"""VGG 11-layer model (configuration "A") with batch normalization"""return VGG(make_layers(cfg['A'], batch_norm=True))def vgg13():"""VGG 13-layer model (configuration "B")"""return VGG(make_layers(cfg['B']))def vgg13_bn():"""VGG 13-layer model (configuration "B") with batch normalization"""return VGG(make_layers(cfg['B'], batch_norm=True))def vgg16():"""VGG 16-layer model (configuration "D")"""return VGG(make_layers(cfg['D']))def vgg16_bn():"""VGG 16-layer model (configuration "D") with batch normalization"""return VGG(make_layers(cfg['D'], batch_norm=True))def vgg19():"""VGG 19-layer model (configuration "E")"""return VGG(make_layers(cfg['E']))def vgg19_bn():"""VGG 19-layer model (configuration 'E') with batch normalization"""return VGG(make_layers(cfg['E'], batch_norm=True))

3.ResNet

paper:https://arxiv.org/abs/1512.03385

作者：Kaiming He、Xiangyu Zhang、Shaoqing Ren；Microsoft Research；

使用残差网络避免模型变深带来的梯度爆炸和梯度消失的问题，使得网络层数可以达到很深。

框架：

残差连接：

（1）完成恒等映射：浅层特征可以直接的传递到深层特征中。

（2）梯度回传：深层的梯度可以通过残差的结构直接传递到浅层的网络中。

基于上面的分析提出残差连接结构，构建了不同的网络，有18、34、50、101、152等。

code_ResNet_Pytorch

import torch
import torch.nn as nn
import torchvision.models.resnet
from torchvision.models.resnet import BasicBlock, Bottleneckclass ResNet(torchvision.models.resnet.ResNet):def __init__(self, block, layers, num_classes=1000, group_norm=False):if group_norm:norm_layer = lambda x: nn.GroupNorm(32, x)else:norm_layer = Nonesuper(ResNet, self).__init__(block, layers, num_classes, norm_layer=norm_layer)if not group_norm:self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True) # changefor i in range(2, 5):getattr(self, 'layer%d'%i)[0].conv1.stride = (2,2)getattr(self, 'layer%d'%i)[0].conv2.stride = (1,1)def resnet18(pretrained=False):"""Constructs a ResNet-18 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [2, 2, 2, 2])if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))return modeldef resnet34(pretrained=False):"""Constructs a ResNet-34 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(BasicBlock, [3, 4, 6, 3])if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))return modeldef resnet50(pretrained=False):"""Constructs a ResNet-50 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 6, 3])if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))return modeldef resnet50_gn(pretrained=False):"""Constructs a ResNet-50 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 6, 3], group_norm=True)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))return modeldef resnet101(pretrained=False):"""Constructs a ResNet-101 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 23, 3])if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))return modeldef resnet101_gn(pretrained=False):"""Constructs a ResNet-101 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 4, 23, 3], group_norm=True)return modeldef resnet152(pretrained=False):"""Constructs a ResNet-152 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNet"""model = ResNet(Bottleneck, [3, 8, 36, 3])if pretrained:model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))return model