deeplabv3+源码之慢慢解析18 第四章network文件夹(1)backbone文件夹(c2)resnet.py--ResNet类和10个不同结构的调用函数
系列文章目录（更新中）
第一章deeplabv3+源码之慢慢解析 根目录(1)main.py–get_argparser函数
第一章deeplabv3+源码之慢慢解析 根目录(2)main.py–get_dataset函数
第一章deeplabv3+源码之慢慢解析 根目录(3)main.py–validate函数
第一章deeplabv3+源码之慢慢解析 根目录(4)main.py–main函数
第一章deeplabv3+源码之慢慢解析 根目录(5)predict.py–get_argparser函数和main函数
第二章deeplabv3+源码之慢慢解析 datasets文件夹(1)voc.py–voc_cmap函数和download_extract函数
第二章deeplabv3+源码之慢慢解析 datasets文件夹(2)voc.py–VOCSegmentation类
第二章deeplabv3+源码之慢慢解析 datasets文件夹(3)cityscapes.py–Cityscapes类
第二章deeplabv3+源码之慢慢解析 datasets文件夹(4)utils.py–6个小函数
第三章deeplabv3+源码之慢慢解析 metrics文件夹stream_metrics.py–StreamSegMetrics类和AverageMeter类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(a1)hrnetv2.py–4个函数和可执行代码
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(a2)hrnetv2.py–Bottleneck类和BasicBlock类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(a3)hrnetv2.py–StageModule类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(a4)hrnetv2.py–HRNet类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(b1)mobilenetv2.py–2个类和2个函数
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(b2)mobilenetv2.py–MobileNetV2类和mobilenet_v2函数
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(c1)resnet.py–2个基础函数，BasicBlock类和Bottleneck类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(c2)resnet.py–ResNet类和10个不同结构的调用函数
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(d1)xception.py–SeparableConv2d类和Block类
第四章deeplabv3+源码之慢慢解析 network文件夹(1)backbone文件夹(d2)xception.py–Xception类和xception函数
第四章deeplabv3+源码之慢慢解析 network文件夹(2)_deeplab.py–ASPP相关的4个类和1个函数
第四章deeplabv3+源码之慢慢解析 network文件夹(3)_deeplab.py–DeepLabV3类，DeepLabHeadV3Plus类和DeepLabHead类
第四章deeplabv3+源码之慢慢解析 network文件夹(4)modeling.py–5个私有函数（4个骨干网，1个模型载入）
第四章deeplabv3+源码之慢慢解析 network文件夹(5)modeling.py–12个调用函数
第四章deeplabv3+源码之慢慢解析 network文件夹(6)utils.py–_SimpleSegmentationModel类和IntermediateLayerGetter类
第五章deeplabv3+源码之慢慢解析 utils文件夹(1)ext_transforms.py.py–[17个类]
第五章deeplabv3+源码之慢慢解析 utils文件夹(2)loss.py–[1个类]
第五章deeplabv3+源码之慢慢解析 utils文件夹(3)scheduler.py–[1个类]
第五章deeplabv3+源码之慢慢解析 utils文件夹(4)utils.py–[1个类,4个函数]
第五章deeplabv3+源码之慢慢解析 utils文件夹(5)visualizer.py–[1个类]
总结
文章目录
- 系列文章目录（更新中）
-
- ResNet类
- 10个关于不同结构的小函数
ResNet类
提示：ResNet类是resnet.py中的核心，整合各个功能。
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False] #有三层需要判断是否使用空洞卷积。
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)#数据预处理，读入rgb通道的图像。按论文kernel_size=7, padding=3。stride=2长宽减半。
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)#按论文kernel_size=3, padding=1。stride=2长宽减半。
self.layer1 = self._make_layer(block, 64, layers[0])#详见本类内_make_layer函数。此时默认输入通道数self.inplanes==64，输出通道数传入参数64，此部分包含layers[0]个具体的块。
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])#此时默认输入通道数64*block.expansion，输出通道数传入参数128，此部分包含layers[1]个具体的块。stride=2长宽减半。
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])#此时默认输入通道数128*block.expansion，输出通道数传入参数256，此部分包含layers[2]个具体的块。stride=2长宽减半。
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])#此时默认输入通道数256*block.expansion，输出通道数传入参数512，此部分包含layers[3]个具体的块。stride=2长宽减半。
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))#按论文AvgPooling
self.fc = nn.Linear(512 * block.expansion, num_classes)#全连接层。
for m in self.modules():#权重初始化
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
#根据链接的论文，残差块的0初始化有助于提升模型0.2~0.3%的效果。
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):#建层，包含block（即BasicBlock块和Bottleneck块）。
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation #self.dilation初值是1.
if dilate:
self.dilation *= stride #若replace_stride_with_dilation列表中对应的值为True，则使用空洞卷积，且dilation=dilation*stride。
stride = 1 #在使用空洞代替stride之后，将stride重置为1.
if stride != 1 or self.inplanes != planes * block.expansion:#即输入和输出的维度不同，则不能使用输入作为shortcut，而是要下采样。
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),#此处其实就是调整一下（按self.inplanes和 planes * block.expansion）输出的维度和（按stride）长宽。
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))#新建第一个块：即BasicBlock块或者Bottleneck块。
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))#循环增加第二(索引为1)到第blocks个（索引为blocks-1）块。（即此循环增加blocks-1个块）。
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x) #数据预处理部分
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x) #四个部分，每个部分可以有N块。
x = self.avgpool(x)
x = torch.flatten(x, 1)#扁平化
x = self.fc(x) #全连接线性层输出
return x
10个关于不同结构的小函数
提示：hrnetv2.py包含4个函数，相对简单。
1，文件开头的函数
def _resnet(arch, block, layers, pretrained, progress, **kwargs):
model = ResNet(block, layers, **kwargs)#新建一个resnet，传入所用的块（即BasicBlock或者Bottleneck），每个块的在四个部分出现的次数（长度为4的整数列表），和其他参数。
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],#arch对应程序最前面model_urls字典中的键。
progress=progress)
model.load_state_dict(state_dict)#load_state_dict_from_url是导入函数。若选择预训练，则载入下载的模型。
return model
def resnet18(pretrained=False, progress=True, **kwargs):
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
**kwargs) #即'resnet18'使用BasicBlock块（含有2个3*3conv层）,四个部分每个块的次数为[2, 2, 2, 2]。即16层，加上前面的数据预处理（含池化层）和后面的分类层（含池化层），共16+2=18层。
def resnet34(pretrained=False, progress=True, **kwargs):
r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
**kwargs)#即'resnet34'使用BasicBlock块,四个部分每个块的次数为[3, 4, 6, 3]。即32层，共32+2=34层。
def resnet50(pretrained=False, progress=True, **kwargs):
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
**kwargs)#即'resnet50'使用Bottleneck块（含3个层：1*1conv+3*3conv+1*1conv）,四个部分每个块的次数为[3, 4, 6, 3]。即48层，共48+2=50层。
def resnet101(pretrained=False, progress=True, **kwargs):
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs)#即'resnet101'使用Bottleneck块,四个部分每个块的次数为[3, 4, 23, 3]。即99层，共99+2=101层。
def resnet152(pretrained=False, progress=True, **kwargs):
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
**kwargs) #即'resnet152'使用Bottleneck块,四个部分每个块的次数为[3, 8, 36, 3]。即150层，共150+2=152层。
def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 4
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)#即'resnet50'使用Bottleneck块,50层。 width_per_group，即self.base_width=4，每个分组的输出通道数。 width = int(planes * (base_width / 64.)) * groups 即通道数扩张了(base_width* groups / 64.) 倍。
def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)#同理，如resnext50_32x4d，只是每组通道数升为8。
def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" `_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)#wide_resnet50_2，即'resnet50'，中间的 width = int(planes * (base_width / 64.)) * groups，即宽度width=2*planes。
def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" `_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs) #同理，wide_resnet101_2，即宽度width=2*planes。
Tips
- 补充：ResNeXt。
- 补充：分组卷积。
- resnet.py实际上呈现了ResNet，ResNeXt和Wide-ResNet，注意关键位置的区别。主要是引入了groups，width_per_group即self.base_width。
- 下一节是xception.py，本套源码的最后一个主干网络。