Hey,
So I’ve just finished re-configuring a network. I replaced nn.Upsample with the upConv sequential container shown in the code below. I’ve verified that everything is lined up by running summary(UNetPP, (3, 128, 128)) which runs with no issue.
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)
        
        
class blockUNetPP(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels):
        super().__init__()
        self.relu = nn.LeakyReLU(0.2, inplace=True)
        self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(middle_channels)
        self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
    

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        return out
    
class upConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.upc = nn.Sequential(
                                 nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
                                 nn.Conv2d(in_ch, out_ch*2, 3, stride=1, padding=1),
                                 nn.BatchNorm2d(out_ch*2),
                                 nn.ReLU(inplace=True)
                                 )
    def forward(self, x):
        out = self.upc(x)
        return out

My issue is that when I try to start training the model I get the following issue:
Traceback (most recent call last):
  File "runTrain.py", line 90, in <module>
    netG.apply(weights_init)
  File "C:\Users\Anaconda3\envs\CFD\lib\site-packages\torch\nn\modules\module.py", line 289, in apply
    module.apply(fn)
  File "C:\Users\Anaconda3\envs\CFD\lib\site-packages\torch\nn\modules\module.py", line 290, in apply
    fn(self)
  File "D:\Thesis Models\Deep_learning_models\UNet\train\NetC.py", line 8, in weights_init
    m.weight.data.normal_(0.0, 0.02)
  File "C:\Users\Anaconda3\envs\CFD\lib\site-packages\torch\nn\modules\module.py", line 594, in __getattr__
    type(self).__name__, name))
AttributeError: 'upConv' object has no attribute 'weight'

I’ve looked up  solutions 1 which suggest looping over container modules, but I’m already doing this with weights_init(m). Could someone explain whats wrong with my current setup?