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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:rose::rose::rose:
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:lemon::lemon::lemon:
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:ear_of_rice::ear_of_rice::ear_of_rice:
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:sparkles::sparkles::sparkles:
自己写文章的宗旨是致力于用最通俗的语言描述问题嘛:dart::dart::dart: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:four_leaf_clover::four_leaf_clover::four_leaf_clover:【 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
# 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，
这幺做的好处是能够在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
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
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:rice::rice::rice:
_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
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)
训练结构展示
ResNet34训练结果：
ResNet50训练结果：
ResNet101训练结果：
迁移学习使用ResNet34预加载模型：
下面给出各种模型生成的权重文件，如下：
小结
这一部分我感到有一些的奇怪，即上文用resnet训时，resnet101和resnet50的效果要比resnet34效果差，但是理论部分不是说resnet层数深效果越好嘛，这是什幺原因呢？希望知道的可以告知。:herb::herb::herb:
问了一些大佬，对于上述问题他们的解答是：这个和自己任务也有关系，简单的任务可能用小网络效果更好。
参考视频： www.bilibili.com/video/BV14E…
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