import torch
import torch.nn as nn
import torch.nn.functional as F
from .segbase import SegBaseModel
from .model_zoo import MODEL_REGISTRY
from ..modules import _ConvBNReLU
__all__ = ['LEDNet']
@MODEL_REGISTRY.register()
class LEDNet(SegBaseModel):
r"""LEDNet
Reference:
Yu Wang, et al. "LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation."
arXiv preprint arXiv:1905.02423 (2019).
"""
def __init__(self):
super(LEDNet, self).__init__(need_backbone=False)
self.encoder = nn.Sequential(
Downsampling(3, 32),
SSnbt(32, norm_layer=self.norm_layer),
SSnbt(32, norm_layer=self.norm_layer),
SSnbt(32, norm_layer=self.norm_layer),
Downsampling(32, 64),
SSnbt(64, norm_layer=self.norm_layer),
SSnbt(64, norm_layer=self.norm_layer),
Downsampling(64, 128),
SSnbt(128, norm_layer=self.norm_layer),
SSnbt(128, 2, norm_layer=self.norm_layer),
SSnbt(128, 5, norm_layer=self.norm_layer),
SSnbt(128, 9, norm_layer=self.norm_layer),
SSnbt(128, 2, norm_layer=self.norm_layer),
SSnbt(128, 5, norm_layer=self.norm_layer),
SSnbt(128, 9, norm_layer=self.norm_layer),
SSnbt(128, 17, norm_layer=self.norm_layer),
)
self.head = APNModule(128, self.nclass, norm_layer=self.norm_layer)
self.__setattr__('decoder', ['head'])
def forward(self, x):
size = x.size()[2:]
x = self.encoder(x)
x = self.head(x)
outputs = list()
x = F.interpolate(x, size, mode='bilinear', align_corners=True)
outputs.append(x)
return tuple(outputs)
class Downsampling(nn.Module):
def __init__(self, in_channels, out_channels):
super(Downsampling, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels // 2, 3, 2, 2, bias=False)
self.conv2 = nn.Conv2d(in_channels, out_channels // 2, 3, 2, 2, bias=False)
self.pool = nn.MaxPool2d(kernel_size=2, stride=1)
def forward(self, x):
x1 = self.conv1(x)
x1 = self.pool(x1)
x2 = self.conv2(x)
x2 = self.pool(x2)
return torch.cat([x1, x2], dim=1)
class SSnbt(nn.Module):
def __init__(self, in_channels, dilation=1, norm_layer=nn.BatchNorm2d):
super(SSnbt, self).__init__()
inter_channels = in_channels // 2
self.branch1 = nn.Sequential(
nn.Conv2d(inter_channels, inter_channels, (3, 1), padding=(1, 0), bias=False),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (1, 3), padding=(0, 1), bias=False),
norm_layer(inter_channels),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (3, 1), padding=(dilation, 0), dilation=(dilation, 1),
bias=False),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (1, 3), padding=(0, dilation), dilation=(1, dilation),
bias=False),
norm_layer(inter_channels),
nn.ReLU(True))
self.branch2 = nn.Sequential(
nn.Conv2d(inter_channels, inter_channels, (1, 3), padding=(0, 1), bias=False),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (3, 1), padding=(1, 0), bias=False),
norm_layer(inter_channels),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (1, 3), padding=(0, dilation), dilation=(1, dilation),
bias=False),
nn.ReLU(True),
nn.Conv2d(inter_channels, inter_channels, (3, 1), padding=(dilation, 0), dilation=(dilation, 1),
bias=False),
norm_layer(inter_channels),
nn.ReLU(True))
self.relu = nn.ReLU(True)
@staticmethod
def channel_shuffle(x, groups):
n, c, h, w = x.size()
channels_per_group = c // groups
x = x.view(n, groups, channels_per_group, h, w)
x = torch.transpose(x, 1, 2).contiguous()
x = x.view(n, -1, h, w)
return x
def forward(self, x):
# channels split
x1, x2 = x.split(x.size(1) // 2, 1)
x1 = self.branch1(x1)
x2 = self.branch2(x2)
out = torch.cat([x1, x2], dim=1)
out = self.relu(out + x)
out = self.channel_shuffle(out, groups=2)
return out
class APNModule(nn.Module):
def __init__(self, in_channels, nclass, norm_layer=nn.BatchNorm2d):
super(APNModule, self).__init__()
self.conv1 = _ConvBNReLU(in_channels, in_channels, 3, 2, 1, norm_layer=norm_layer)
self.conv2 = _ConvBNReLU(in_channels, in_channels, 5, 2, 2, norm_layer=norm_layer)
self.conv3 = _ConvBNReLU(in_channels, in_channels, 7, 2, 3, norm_layer=norm_layer)
self.level1 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level2 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level3 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level4 = _ConvBNReLU(in_channels, nclass, 1, norm_layer=norm_layer)
self.level5 = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
_ConvBNReLU(in_channels, nclass, 1))
def forward(self, x):
w, h = x.size()[2:]
branch3 = self.conv1(x)
branch2 = self.conv2(branch3)
branch1 = self.conv3(branch2)
out = self.level1(branch1)
out = F.interpolate(out, ((w + 3) // 4, (h + 3) // 4), mode='bilinear', align_corners=True)
out = self.level2(branch2) + out
out = F.interpolate(out, ((w + 1) // 2, (h + 1) // 2), mode='bilinear', align_corners=True)
out = self.level3(branch3) + out
out = F.interpolate(out, (w, h), mode='bilinear', align_corners=True)
out = self.level4(x) * out
out = self.level5(x) + out
return out
