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