| 35 | ) |
| 36 | |
| 37 | class ResBlock(nn.Module): |
| 38 | def __init__(self, in_planes, out_planes, stride=2): |
| 39 | super(ResBlock, self).__init__() |
| 40 | if in_planes == out_planes and stride == 1: |
| 41 | self.conv0 = nn.Identity() |
| 42 | else: |
| 43 | self.conv0 = nn.Conv2d(in_planes, out_planes, |
| 44 | 3, stride, 1, bias=False) |
| 45 | self.conv1 = conv(in_planes, out_planes, 3, stride, 1) |
| 46 | self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1) |
| 47 | self.relu1 = nn.PReLU(1) |
| 48 | self.relu2 = nn.PReLU(out_planes) |
| 49 | self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False) |
| 50 | self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False) |
| 51 | |
| 52 | def forward(self, x): |
| 53 | y = self.conv0(x) |
| 54 | x = self.conv1(x) |
| 55 | x = self.conv2(x) |
| 56 | w = x.mean(3, True).mean(2, True) |
| 57 | w = self.relu1(self.fc1(w)) |
| 58 | w = torch.sigmoid(self.fc2(w)) |
| 59 | x = self.relu2(x * w + y) |
| 60 | return x |
| 61 | |
| 62 | c = 32 |
| 63 | |