目录
Mobilenetv2的改进
浅层特征和深层特征的融合
完整代码
参考资料
Mobilenetv2的改进
在DeeplabV3当中，一般不会5次下采样，可选的有3次下采样和4次下采样。因为要进行五次下采样的话会损失较多的信息。
在这里mobilenetv2会从之前写好的模块中得到，但注意的是，我们在这里获得的特征是[-1]，也就是最后的1x1卷积不取，只取循环完后的模型。
down_idx是InvertedResidual进行的次数。
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2], 2
[6, 32, 3, 2], 4
[6, 64, 4, 2], 7
[6, 96, 3, 1],
[6, 160, 3, 2], 14
[6, 320, 1, 1], 根据下采样的不同，当downsample_factor=8时，进行3次下采样，对倒数两次，步长为2的InvertedResidual进行参数的修改，让步长变为1，膨胀系数为2。
当downsample_factor=16时，进行4次下采样，只需对最后一次进行参数的修改。
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from net.mobilenetv2 import mobilenetv2
from net.ASPP import ASPP
class MobileNetV2(nn.Module):
def __init__(self, downsample_factor=8, pretrained=True):
super(MobileNetV2, self).__init__()
model = mobilenetv2(pretrained)
self.features = model.features[:-1]
self.total_idx = len(self.features)
self.down_idx = [2, 4, 7, 14]
if downsample_factor == 8:
for i in range(self.down_idx[-2], self.down_idx[-1]):
self.features[i].apply(
partial(self._nostride_dilate, dilate=2)
)
for i in range(self.down_idx[-1], self.total_idx):
self.features[i].apply(
partial(self._nostride_dilate, dilate=4)
)
elif downsample_factor == 16:
for i in range(self.down_idx[-1], self.total_idx):
self.features[i].apply(
partial(self._nostride_dilate, dilate=2)
)
def _nostride_dilate(self, m, dilate):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate//2, dilate//2)
m.padding = (dilate//2, dilate//2)
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
def forward(self, x):
low_level_features = self.features[:4](x)
x = self.features[4:](low_level_features)
return low_level_features, xforward当中，会输出两个特征层，一个是浅层特征层，具有浅层的语义信息；另一个是深层特征层，具有深层的语义信息。
浅层特征和深层特征的融合
具有高语义信息的部分先进行上采样，低语义信息的特征层进行1x1卷积，二者进行特征融合，再进行3x3卷积进行特征提取
self.aspp = ASPP(dim_in=in_channels, dim_out=256, rate=16//downsample_factor)这一步就是获得那个绿色的特征层；
low_level_features = self.shortcut_conv(low_level_features)从这里将是对浅层特征的初步处理（1x1卷积）；
x = F.interpolate(x, size=(low_level_features.size(2), low_level_features.size(3)), mode='bilinear', align_corners=True)
x = self.cat_conv(torch.cat((x, low_level_features), dim=1))上采样后进行特征融合，这样我们输入和输出的大小才相同，每一个像素点才能进行预测；
完整代码
# deeplabv3plus.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from net.xception import xception
from net.mobilenetv2 import mobilenetv2
from net.ASPP import ASPP
class MobileNetV2(nn.Module):
def __init__(self, downsample_factor=8, pretrained=True):
super(MobileNetV2, self).__init__()
model = mobilenetv2(pretrained)
self.features = model.features[:-1]
self.total_idx = len(self.features)
self.down_idx = [2, 4, 7, 14]
if downsample_factor == 8:
for i in range(self.down_idx[-2], self.down_idx[-1]):
self.features[i].apply(
partial(self._nostride_dilate, dilate=2)
)
for i in range(self.down_idx[-1], self.total_idx):
self.features[i].apply(
partial(self._nostride_dilate, dilate=4)
)
elif downsample_factor == 16:
for i in range(self.down_idx[-1], self.total_idx):
self.features[i].apply(
partial(self._nostride_dilate, dilate=2)
)
def _nostride_dilate(self, m, dilate):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate//2, dilate//2)
m.padding = (dilate//2, dilate//2)
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
def forward(self, x):
low_level_features = self.features[:4](x)
x = self.features[4:](low_level_features)
return low_level_features, x
class DeepLab(nn.Module):
def __init__(self, num_classes, backbone="mobilenet", pretrained=True, downsample_factor=16):
super(DeepLab, self).__init__()
if backbone=="xception":
# 获得两个特征层：浅层特征 主干部分
self.backbone = xception(downsample_factor=downsample_factor, pretrained=pretrained)
in_channels = 2048
low_level_channels = 256
elif backbone=="mobilenet":
# 获得两个特征层：浅层特征 主干部分
self.backbone = MobileNetV2(downsample_factor=downsample_factor, pretrained=pretrained)
in_channels = 320
low_level_channels = 24
else:
raise ValueError('Unsupported backbone - `{}`, Use mobilenet, xception.'.format(backbone))
# ASPP特征提取模块
# 利用不同膨胀率的膨胀卷积进行特征提取
self.aspp = ASPP(dim_in=in_channels, dim_out=256, rate=16//downsample_factor)
# 浅层特征边
self.shortcut_conv = nn.Sequential(
nn.Conv2d(low_level_channels, 48, 1),
nn.BatchNorm2d(48),
nn.ReLU(inplace=True)
)
self.cat_conv = nn.Sequential(
nn.Conv2d(48+256, 256, kernel_size=(3,3), stride=(1,1), padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Conv2d(256, 256, kernel_size=(3,3), stride=(1,1), padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Dropout(0.1),
)
self.cls_conv = nn.Conv2d(256, num_classes, kernel_size=(1,1), stride=(1,1))
def forward(self, x):
H, W = x.size(2), x.size(3)
# 获得两个特征层，low_level_features: 浅层特征-进行卷积处理
# x : 主干部分-利用ASPP结构进行加强特征提取
low_level_features, x = self.backbone(x)
x = self.aspp(x)
low_level_features = self.shortcut_conv(low_level_features)
# 将加强特征边上采样，与浅层特征堆叠后利用卷积进行特征提取
x = F.interpolate(x, size=(low_level_features.size(2), low_level_features.size(3)), mode='bilinear', align_corners=True)
x = self.cat_conv(torch.cat((x, low_level_features), dim=1))
x = self.cls_conv(x)
x = F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True)
return x
参考资料
DeepLabV3-/论文精选 at main · Auorui/DeepLabV3- (github.com)
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