(self, x)
| 282 | self.classifier=nn.Conv2d(64, num_classes, 1) |
| 283 | |
| 284 | def forward(self, x): |
| 285 | x4, x8, x16=x["4"], x["8"],x["16"] |
| 286 | x16=self.head16(x16) |
| 287 | x8=self.head8(x8) |
| 288 | x4=self.head4(x4) |
| 289 | x16 = F.interpolate(x16, size=x8.shape[-2:], mode='bilinear', align_corners=False) |
| 290 | x8= x8 + x16 |
| 291 | x8=self.conv8(x8) |
| 292 | x8 = F.interpolate(x8, size=x4.shape[-2:], mode='bilinear', align_corners=False) |
| 293 | x4=torch.cat((x8,x4),dim=1) |
| 294 | x4=self.conv4(x4) |
| 295 | x4=self.classifier(x4) |
| 296 | return x4 |
| 297 | |
| 298 | class Exp2_Decoder29(nn.Module): |
| 299 | def __init__(self, num_classes, channels): |
nothing calls this directly
no outgoing calls
no test coverage detected