class FCDenseNet(nn.Module):
def __init__(self, in_channels=3, down_blocks=(5,5,5,5,5), #其中down_blocks是指下采样的denseblock（即论文中的db块+C+TD）,这里一共有5个denseblock块且每个denseblock块中默认有5个卷积层。
up_blocks=(5,5,5,5,5), bottleneck_layers=5, # up_blocks代表上采样的denseblock，同理共有五个denseblock块，并且每个参数默认为5代表每个块有5个卷积层
growth_rate=16, out_chans_first_conv=48, n_classes=12): #增长率为16，代表每层提取的特征数16的比率增加；
super().__init__()
self.down_blocks = down_blocks
self.up_blocks = up_blocks
cur_channels_count = 0
skip_connection_channel_counts = []
## First Convolution ##
self.add_module('firstconv', nn.Conv2d(in_channels=in_channels, #刚开始为一个卷积层；
out_channels=out_chans_first_conv, kernel_size=3,
stride=1, padding=1, bias=True))
cur_channels_count = out_chans_first_conv
#####################
# Downsampling path #
#####################
self.denseBlocksDown = nn.ModuleList([])
self.transDownBlocks = nn.ModuleList([])
for i in range(len(down_blocks)): #5个denseblock,每一个denseblock分别由论文中的db块+C+TD组成：即dense块、链接、下采样组成；
self.denseBlocksDown.append(
DenseBlock(cur_channels_count, growth_rate, down_blocks[i]))
cur_channels_count += (growth_rate*down_blocks[i])
skip_connection_channel_counts.insert(0,cur_channels_count)
self.transDownBlocks.append(TransitionDown(cur_channels_count))
#####################
# Bottleneck #
#####################
self.add_module('bottleneck',Bottleneck(cur_channels_count,
growth_rate, bottleneck_layers))
prev_block_channels = growth_rate*bottleneck_layers
cur_channels_count += prev_block_channels
#######################
# Upsampling path #
#######################
self.transUpBlocks = nn.ModuleList([])
self.denseBlocksUp = nn.ModuleList([])
for i in range(len(up_blocks)-1):
self.transUpBlocks.append(TransitionUp(prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + skip_connection_channel_counts[i]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[i],
upsample=True))
prev_block_channels = growth_rate*up_blocks[i]
cur_channels_count += prev_block_channels
## Final DenseBlock ##
self.transUpBlocks.append(TransitionUp(
prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + skip_connection_channel_counts[-1]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[-1],
upsample=False))
cur_channels_count += growth_rate*up_blocks[-1]
## Softmax ##
self.finalConv = nn.Conv2d(in_channels=cur_channels_count,
out_channels=n_classes, kernel_size=1, stride=1,
padding=0, bias=True)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x):
out = self.firstconv(x)
skip_connections = []
for i in range(len(self.down_blocks)):
out = self.denseBlocksDown[i](out)
skip_connections.append(out)
out = self.transDownBlocks[i](out)
out = self.bottleneck(out)
for i in range(len(self.up_blocks)):
skip = skip_connections.pop()
out = self.transUpBlocks[i](out, skip)
out = self.denseBlocksUp[i](out)
out = self.finalConv(out)
out = self.softmax(out)
return out
I want to ask what the meaning of this sentense skip_connection_channel_counts.insert(0,cur_channels_count)???