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基于pytorch搭建ResNet神经网络用于花类识别

花匠小妙招
2024-11-08 03:18

作者简介：秃头小苏，致力于用最通俗的语言描述问题

往期回顾：基于pytorch搭建VGGNet神经网络用于花类识别基于pytorch搭建AlexNet神经网络用于花类识别

近期目标：拥有5000粉丝
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文章目录基于pytorch搭建ResNet神经网络用于花类识别写在前面ResNet网络模型搭建✨✨✨训练结果展示小结

基于pytorch搭建ResNet神经网络用于花类识别

写在前面

这一系列已经写了好几篇了，这篇结束后可能就会停更一系列了，因为一方面，看懂了已经更新的这些我认为其他的网络大概就是照葫芦画瓢，自己多多少少是能看明白个大概的。【当然这是要在你对这部分网络结构的理论有充分的了解之后】另一方面，我觉得这部分真的得你自己切切实实的钻研，自己一步步的调试，看别人的文章、甚至是视频，你可能会得到短暂的满足，但是许多细节你是体验不到的。所以这里给出基于pytorch搭建ResNet神经网络用于花类识别的完整代码，希望大家下去后仔细阅读

至于这一系列再次更新的话不出意外会讲讲一些轻量级网络像MobileNet、shuffleNet等，当然了这部分都已经做过理论部分的概述了

还是回归到本文上来，首先你需要具备以下知识：

ResNet的理论pytorch搭建网络基础

当然，这些我在前文都已经介绍过，大家抱着缺啥补啥的态度去看看呗

深度学习经典网络模型汇总使用pytorch自己构建网络模型实战基于pytorch搭建AlexNet神经网络用于花类识别基于pytorch搭建VGGNet神经网络用于花类识别

ResNet网络模型搭建✨✨✨

自己写文章的宗旨是致力于用最通俗的语言描述问题嘛但是对于一些关乎于代码的文章，有的时候单纯的文字确实很难将问题表述清楚，因此我建议你先观看此视频，对ResNet网络模型搭建的整理流程有了一个大致的了解后再来阅读此文，然后再以这篇文章为辅进行学习，这样我觉得是高效的学习方式【当然这样还是不够的，你一定要自己去独立的阅读代码，一步步的调试运行，这一点我想我再强调也不为过】

ResNeta网络是有大量重复的结构堆叠而成的，它的网络层数主要有18层、34层、50层、101层和152层。对于18层和34层的网络它的基础模块为basic block，而对于50层、101层和152层的网络其基础模块为bottleneck block。我们可以分别来定义这两个基础模块，如下：

# 定义BasicBlock class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=stride, padding=1, bias=False) # 特征图尺寸不变 self.bn1 = nn.BatchNorm2d(out_channel) # BN层建议放在卷积和激活层之间 self.relu = nn.ReLU() self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channel) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += identity out = self.relu(out) return out 12345678910111213141516171819202122232425262728293031

# 定义Bottleneck class Bottleneck(nn.Module): """ 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，这么做的好处是能够在top1上提升大概0.5%的准确率。可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch """ expansion = 4 def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64): super(Bottleneck, self).__init__() width = int(out_channel * (width_per_group / 64.)) * groups self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += identity out = self.relu(out) return out 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

接着我们就可以来定义我们的ResNet网络了：

class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=1000, include_top=True, groups=1, width_per_group=64): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.groups = groups self.width_per_group = width_per_group self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152

我们可以看出再ResNet 的定义中有这样的函数：

该函数表示对ResNet的每个基础模块一个整合，即layer1对应conv2_x、layer2对应conv3_x、layer3对应conv4_x、layer4对应conv5_x

_make_layer函数的定义如下:

def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride, groups=self.groups, width_per_group=self.width_per_group)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel, groups=self.groups, width_per_group=self.width_per_group)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x 1234567891011121314151617181920212223242526272829303132333435363738394041

最后我们来看看如何定义一个具体的网络：

def resnet34(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet34-333f7ec4.pth return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet50(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet50-19c8e357.pth return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet101(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) 12345678910111213