这次是对Densenet模型的讲解，这个模型也是需要大家精通掌握的重要模型之一，首先要知道它的网络是怎么传递的，它的出现打破了以往网络设计的思路，是一种非常新颖的设计。
如图所示,第l层的输入不仅与l-1层的输出相关，还有所有之前层的输出有关。
大家看这个可能不是很明白，我就仔细的讲解下这里，图中的照片里的板块（红，绿，紫，黄，橙）分别代表不同的特征图，当然红的是输入（input）图，这里为了方便讲解就是X0，X1，X2，X3，X4讲解，当照片输入的时候经过卷积到x1是的时候加上之前x0是完全传递过来了，x1经过卷积到x2的过程中，x1的特征加上了x0的特征，注意看图中的连线一起形成了x2，x2到x3的时候x1，x2又一次相加一起到了x3。图中的连线是重点，可以理解为传递的过程相加了那几条线。
比如growth_rate是增长率16，举个简单计算，（3,16），（19,16），（16+3+19,16）,这样densenet的核心就是输入层一直在变化，输出层没有变化
然后我们需要在大致了解Densenet的设计结构，图中又在次出现重复额板块，所以我们可以将其封装成一个类来写，
def conv_block(in_channel, out_channel):
layer = nn.Sequential(
nn.BatchNorm2d(in_channel),
nn.ReLU(),
nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False)
)
return layer
这个函数就是方便多次使用
class dense_block(nn.Module):
def __init__(self, in_channel, growth_rate, num_layers):
super(dense_block, self).__init__()
block = []
channel = in_channel
for i in range(num_layers):
block.append(conv_block(channel, growth_rate))
channel += growth_rate
self.net = nn.Sequential(*block)
def forward(self, x):
for layer in self.net:
out = layer(x)
x = torch.cat((out, x), dim=1)
return x
定义这个类的目的是通过for循环多次构造卷积，in_channel是输入通道数，growth_rate是通道数变化的速度，为什么不是固定？block.append(conv_block(channel, growth_rate))
channel += growth_rate
通道数一直是在变化的，nn.Sequential(*block)这里是将block列表里的类型转换，
def transition(in_channel, out_channel):
trans_layer = nn.Sequential(
nn.BatchNorm2d(in_channel),
nn.ReLU(),
nn.Conv2d(in_channel, out_channel, 1),
nn.AvgPool2d(2, 2)
)
return trans_layer
这里是对应这个板块写的函数，它里面的参数都是固定的，很好理解，这个函数的目的是通过1X1的卷积层减少通道数，并使用步幅为2的平均池化层减半高和宽，降低模型的复杂度。
def _make_dense_block(self,channels, growth_rate, num):
block = []
block.append(dense_block(channels, growth_rate, num))
channels += num * growth_rate
return nn.Sequential(*block)
def _make_transition_layer(self,channels):
block = []
block.append(transition(channels, channels // 2))
return nn.Sequential(*block)
由于图中有很多的dense_block块，transition_layer块，所以我们写成函数的， channels // 2就是为了降低复杂度。
class densenet(nn.Module):
def __init__(self, in_channel, num_classes, growth_rate=32, block_layers=[6, 12, 24, 16]):
super(densenet, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channel, 64, 7, 2, 3),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(3, 2, padding=1)
)
self.DB1 = self._make_dense_block(64, growth_rate,num=block_layers[0])
self.TL1 = self._make_transition_layer(256)
self.DB2 = self._make_dense_block(128, growth_rate, num=block_layers[1])
self.TL2 = self._make_transition_layer(512)
self.DB3 = self._make_dense_block(256, growth_rate, num=block_layers[2])
self.TL3 = self._make_transition_layer(1024)
self.DB4 = self._make_dense_block(512, growth_rate, num=block_layers[3])
self.global_average = nn.Sequential(
nn.BatchNorm2d(1024),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1,1)),
)
self.classifier = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.block1(x)
x = self.DB1(x)
x = self.TL1(x)
x = self.DB2(x)
x = self.TL2(x)
x = self.DB3(x)
x = self.TL3(x)
x = self.DB4(x)
x = self.global_average(x)
x = x.view(x.shape[0], -1)
x = self.classifier(x)
return x
这是densenet网络的全部流程，对应图片中的结构，大家可能会对self.DB1, self.TL1,里面的参数设置不太了解，这个是我计算出来的，这里的卷积过程可以换成简单写法，我主要是让大家看清楚Densenet网络结构和练习自己的计算能力。
for i, layers in enumerate(block_layers):
block.append(dense_block(channels, growth_rate, layers))
channels += layers * growth_rate
if i != len(block_layers) - 1:
# 每经过一个dense_block模块，则在后面添加一个过渡模块，通道数减半channels//2
block.append(transition(channels, channels // 2))
channels = channels // 2
原本的写法这样更简单，一样是for循环，这里的block_layers=【6,12,24,16】对应每个版块的重复次数，if i != len(block_layers) - 1通过if 判断一下是不是这个卷积最后一次使用，是的话就直接接上transition Layer然后在进行Dense Block 的构造
全部源码
import torch
from torch import nn
def conv_block(in_channel, out_channel):
layer = nn.Sequential(
nn.BatchNorm2d(in_channel),
nn.ReLU(),
nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False)
)
return layer
class dense_block(nn.Module):
def __init__(self, in_channel, growth_rate, num_layers):
super(dense_block, self).__init__()
block = []
channel = in_channel
for i in range(num_layers):
block.append(conv_block(channel, growth_rate))
channel += growth_rate
self.net = nn.Sequential(*block)
def forward(self, x):
for layer in self.net:
out = layer(x)
x = torch.cat((out, x), dim=1)
return x
def transition(in_channel, out_channel):
trans_layer = nn.Sequential(
nn.BatchNorm2d(in_channel),
nn.ReLU(),
nn.Conv2d(in_channel, out_channel, 1),
nn.AvgPool2d(2, 2)
)
return trans_layer
class densenet(nn.Module):
def __init__(self, in_channel, num_classes, growth_rate=32, block_layers=[6, 12, 24, 16]):
super(densenet, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channel, 64, 7, 2, 3),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(3, 2, padding=1)
)
self.DB1 = self._make_dense_block(64, growth_rate,num=block_layers[0])
self.TL1 = self._make_transition_layer(256)
self.DB2 = self._make_dense_block(128, growth_rate, num=block_layers[1])
self.TL2 = self._make_transition_layer(512)
self.DB3 = self._make_dense_block(256, growth_rate, num=block_layers[2])
self.TL3 = self._make_transition_layer(1024)
self.DB4 = self._make_dense_block(512, growth_rate, num=block_layers[3])
self.global_average = nn.Sequential(
nn.BatchNorm2d(1024),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1,1)),
)
self.classifier = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.block1(x)
x = self.DB1(x)
x = self.TL1(x)
x = self.DB2(x)
x = self.TL2(x)
x = self.DB3(x)
x = self.TL3(x)
x = self.DB4(x)
x = self.global_average(x)
x = x.view(x.shape[0], -1)
x = self.classifier(x)
return x
def _make_dense_block(self,channels, growth_rate, num):
block = []
block.append(dense_block(channels, growth_rate, num))
channels += num * growth_rate
return nn.Sequential(*block)
def _make_transition_layer(self,channels):
block = []
block.append(transition(channels, channels // 2))
return nn.Sequential(*block)
net = densenet(3,10)
x = torch.rand(1,3,224,224)
for name,layer in net.named_children():
if name != "classifier":
x = layer(x)
print(name, 'output shape:', x.shape)
else:
x = x.view(x.size(0), -1)
x = layer(x)
print(name, 'output shape:', x.shape)
后续持续更新的新的模型讲解，
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