【图像分类】手撕ResNet——复现ResNet（Pytorch）
【摘要】
目录
摘要
实现残差模块
ResNet18， ResNet34
RseNet50、 RseNet101、 RseNet152、
摘要
ResNet（Residual Neural Network）由微软研究院的Kaiming He等四名华人提出，通过使用ResNet Unit成功...
目录
RseNet50、 RseNet101、 RseNet152、
摘要
ResNet（Residual Neural Network）由微软研究院的Kaiming He等四名华人提出，通过使用ResNet Unit成功训练出了152层的神经网络，并在ILSVRC2015比赛中取得冠军，在top5上的错误率为3.57%，同时参数量比VGGNet低，效果非常明显。
模型的创新点在于提出残差学习的思想，在网络中增加了直连通道，将原始输入信息直接传到后面的层中，如下图所示：
传统的卷积网络或者全连接网络在信息传递的时候或多或少会存在信息丢失，损耗等问题，同时还有导致梯度消失或者梯度爆炸，导致很深的网络无法训练。ResNet在一定程度上解决了这个问题，通过直接将输入信息绕道传到输出，保护信息的完整性，整个网络只需要学习输入、输出差别的那一部分，简化学习目标和难度。VGGNet和ResNet的对比如下图所示。ResNet最大的区别在于有很多的旁路将输入直接连接到后面的层，这种结构也被称为shortcut或者skip connections。
在ResNet网络结构中会用到两种残差模块，一种是以两个3*3的卷积网络串接在一起作为一个残差模块，另外一种是1*1、3*3、1*1的3个卷积网络串接在一起作为一个残差模块。如下图所示：
ResNet有不同的网络层数，比较常用的是18-layer，34-layer，50-layer，101-layer，152-layer。他们都是由上述的残差模块堆叠在一起实现的。 下图展示了不同的ResNet模型。
实现残差模块
第一个残差模块
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class ResidualBlock(nn.Module):
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"""
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实现子module: Residual Block
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"""
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def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
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super(ResidualBlock, self).__init__()
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self.left = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
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nn.BatchNorm2d(outchannel),
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nn.ReLU(inplace=True),
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nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
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nn.BatchNorm2d(outchannel))
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self.right = shortcut
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def forward(self, x):
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out = self.left(x)
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residual = x if self.right is None else self.right(x)
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out += residual
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return F.relu(out)
第二个残差模块
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class Bottleneck(nn.Module):
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def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):
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super(Bottleneck,self).__init__()
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self.expansion = expansion
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self.downsampling = downsampling
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self.bottleneck = nn.Sequential(
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nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(places*self.expansion),
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)
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if self.downsampling:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(places*self.expansion)
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)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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residual = x
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out = self.bottleneck(x)
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if self.downsampling:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
ResNet18， ResNet34
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import torch
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import torchvision
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from torch import nn
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from torch.nn import functional as F
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from torchsummary import summary
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-
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class ResidualBlock(nn.Module):
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"""
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实现子module: Residual Block
-
"""
-
-
def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
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super(ResidualBlock, self).__init__()
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self.left = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
-
nn.BatchNorm2d(outchannel),
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nn.ReLU(inplace=True),
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nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
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nn.BatchNorm2d(outchannel)
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)
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self.right = shortcut
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def forward(self, x):
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out = self.left(x)
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residual = x if self.right is None else self.right(x)
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out += residual
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return F.relu(out)
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-
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class ResNet(nn.Module):
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"""
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实现主module：ResNet34
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ResNet34包含多个layer，每个layer又包含多个Residual block
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用子module来实现Residual block，用_make_layer函数来实现layer
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"""
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def __init__(self, blocks, num_classes=1000):
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super(ResNet, self).__init__()
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self.model_name = 'resnet34'
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# 前几层: 图像转换
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self.pre = nn.Sequential(
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nn.Conv2d(3, 64, 7, 2, 3, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(3, 2, 1))
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# 重复的layer，分别有3，4，6，3个residual block
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self.layer1 = self._make_layer(64, 64, blocks[0])
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self.layer2 = self._make_layer(64, 128, blocks[1], stride=2)
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self.layer3 = self._make_layer(128, 256, blocks[2], stride=2)
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self.layer4 = self._make_layer(256, 512, blocks[3], stride=2)
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# 分类用的全连接
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self.fc = nn.Linear(512, num_classes)
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def _make_layer(self, inchannel, outchannel, block_num, stride=1):
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"""
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构建layer,包含多个residual block
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"""
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shortcut = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, 1, stride, bias=False),
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nn.BatchNorm2d(outchannel),
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nn.ReLU()
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)
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layers = []
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layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
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for i in range(1, block_num):
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layers.append(ResidualBlock(outchannel, outchannel))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.pre(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = F.avg_pool2d(x, 7)
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x = x.view(x.size(0), -1)
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return self.fc(x)
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-
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def ResNet18():
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return ResNet([2, 2, 2, 2])
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-
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def ResNet34():
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return ResNet([3, 4, 6, 3])
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-
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if __name__ == '__main__':
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = ResNet34()
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model.to(device)
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summary(model, (3, 224, 224))
RseNet50、 RseNet101、 RseNet152、
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import torch
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import torch.nn as nn
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import torchvision
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import numpy as np
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print("PyTorch Version: ",torch.__version__)
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print("Torchvision Version: ",torchvision.__version__)
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__all__ = ['ResNet50', 'ResNet101','ResNet152']
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def Conv1(in_planes, places, stride=2):
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return nn.Sequential(
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nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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)
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class Bottleneck(nn.Module):
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def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):
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super(Bottleneck,self).__init__()
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self.expansion = expansion
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self.downsampling = downsampling
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self.bottleneck = nn.Sequential(
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nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(places*self.expansion),
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)
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if self.downsampling:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(places*self.expansion)
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)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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residual = x
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out = self.bottleneck(x)
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if self.downsampling:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self,blocks, num_classes=1000, expansion = 4):
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super(ResNet,self).__init__()
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self.expansion = expansion
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self.conv1 = Conv1(in_planes = 3, places= 64)
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self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)
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self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)
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self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)
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self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)
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self.avgpool = nn.AvgPool2d(7, stride=1)
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self.fc = nn.Linear(2048,num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def make_layer(self, in_places, places, block, stride):
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layers = []
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layers.append(Bottleneck(in_places, places,stride, downsampling =True))
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for i in range(1, block):
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layers.append(Bottleneck(places*self.expansion, places))
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return nn.Sequential(*layers)
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-
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def forward(self, x):
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x = self.conv1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def ResNet50():
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return ResNet([3, 4, 6, 3])
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def ResNet101():
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return ResNet([3, 4, 23, 3])
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def ResNet152():
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return ResNet([3, 8, 36, 3])
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if __name__=='__main__':
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#model = torchvision.models.resnet50()
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model = ResNet50()
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print(model)
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input = torch.randn(1, 3, 224, 224)
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out = model(input)
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print(out.shape)
文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/117383956
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