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import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearBottleNeck(nn.Module):
def __init__(self, in_channels, out_channels, stride, t=6, class_num=1):
super().__init__()
self.residual = nn.Sequential(
nn.Conv2d(in_channels, in_channels * t, 1),
nn.BatchNorm2d(in_channels * t),
nn.ReLU6(inplace=True),
nn.Conv2d(in_channels * t, in_channels * t, 3, stride=stride, padding=1, groups=in_channels * t),
nn.BatchNorm2d(in_channels * t),
nn.ReLU6(inplace=True),
nn.Conv2d(in_channels * t, out_channels, 1),
nn.BatchNorm2d(out_channels)
)
self.stride = stride
self.in_channels = in_channels
self.out_channels = out_channels
def forward(self, x):
residual = self.residual(x)
if self.stride == 1 and self.in_channels == self.out_channels:
residual += x
return residual
class ImplicitNet(nn.Module):
def __init__(self, class_num=1):
super().__init__()
self.pre = nn.Sequential(
nn.Conv2d(5, 32, 1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU6(inplace=True)
)
self.stage1 = LinearBottleNeck(32, 16, 1, 1)
self.stage2 = self._make_stage(2, 16, 24, 2, 6)
self.stage3 = self._make_stage(3, 24, 32, 2, 6)
self.stage4 = self._make_stage(4, 32, 64, 2, 6)
self.stage5 = self._make_stage(3, 64, 96, 1, 6)
self.stage6 = self._make_stage(3, 96, 160, 1, 6)
self.stage7 = LinearBottleNeck(160, 320, 1, 6)
self.conv1 = nn.Sequential(
nn.Conv2d(320, 1280, 1),
nn.BatchNorm2d(1280),
nn.ReLU6(inplace=True)
)
self.conv2 = nn.Conv2d(1280, class_num, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, seg, label, natural):
label = label.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(seg.size())
x = torch.cat((label,natural,seg),1) # concated input
x = self.pre(x)
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.stage5(x)
x = self.stage6(x)
x = self.stage7(x)
x = self.conv1(x)
#x = F.adaptive_avg_pool2d(x, 1)
x = self.conv2(x) # (b,h/s,w/s,1)
x = self.sigmoid(x)
return x
def _make_stage(self, repeat, in_channels, out_channels, stride, t):
layers = []
layers.append(LinearBottleNeck(in_channels, out_channels, stride, t))
while repeat - 1:
layers.append(LinearBottleNeck(out_channels, out_channels, 1, t))
repeat -= 1
return nn.Sequential(*layers)
def implicitnet():
return ImplicitNet() |