def forward(self, x, device):
LL, x1 = pre.multiscale_wd(x, device, int(x.size()[2]/2))
LL, x2 = pre.multiscale_wd(LL, device, int(LL.size()[2]/2))
x = utils.tensor_cat(x1,x2, padding = False)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def forward(self, x, device):
x2 = self.block2x2(x)
x3 = self.block3x3(x)
x5 = self.block5x5(x)
x = utils.tensor_cat(x2,x3,x5, padding = False)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x