(self, x)
| 162 | self.classifier=nn.Conv2d(128, num_classes, 1) |
| 163 | |
| 164 | def forward(self, x): |
| 165 | x8,x16= x["8"], x["16"] |
| 166 | |
| 167 | avg=self.avg_pool(x16) |
| 168 | avg = self.conv_avg(avg) |
| 169 | avg_up = F.interpolate(avg, size=x16.shape[-2:], mode='nearest') |
| 170 | |
| 171 | x16 = self.arm16(x16) |
| 172 | x16 = x16 + avg_up |
| 173 | x16 = F.interpolate(x16, size=x8.shape[-2:], mode='nearest') |
| 174 | x16 = self.conv_head16(x16) |
| 175 | |
| 176 | x=self.ffm(x8,x16) |
| 177 | x=self.conv(x) |
| 178 | x=self.classifier(x) |
| 179 | return x |
| 180 | class SFNetDecoder(nn.Module): |
| 181 | def __init__(self, num_classes, channels, fpn_dim=64, fpn_dsn=False): |
| 182 | super().__init__() |
nothing calls this directly
no outgoing calls
no test coverage detected