Error loading state_dict()

username: Asad_Imtiaz
cooked: <pre><code class="lang-auto">model = torch.jit.load('fod.pth')
torch.save(model.state_dict(), 'weights.pt')
u2net = U2NETP()
u2net.eval()
u2net.load_state_dict(torch.load('/content/weights.pt'), strict=False)
</code></pre>
<p>I am using this code to load my model weights to the U2NETP() network. But it keeps on giving me Incompatible keys. I tried visualizing the model using netron and the architecture is similar to that of the one used in U2NETP().</p>
<p>Please help me what should I do ?</p>
<p>P.S. Here are the keys that seem to be missing in the fod.pth file</p>
<pre><code class="lang-auto">_IncompatibleKeys(missing_keys=['stage1.rebnconvin.bn_s1.running_mean', 'stage1.rebnconvin.bn_s1.running_var', 'stage1.rebnconv1.bn_s1.running_mean', 'stage1.rebnconv1.bn_s1.running_var', 'stage1.rebnconv2.bn_s1.running_mean', 'stage1.rebnconv2.bn_s1.running_var', 'stage1.rebnconv3.bn_s1.running_mean', 'stage1.rebnconv3.bn_s1.running_var', 'stage1.rebnconv4.bn_s1.running_mean', 'stage1.rebnconv4.bn_s1.running_var', 'stage1.rebnconv5.bn_s1.running_mean', 'stage1.rebnconv5.bn_s1.running_var', 'stage1.rebnconv6.bn_s1.running_mean', 'stage1.rebnconv6.bn_s1.running_var', 'stage1.rebnconv7.bn_s1.running_mean', 'stage1.rebnconv7.bn_s1.running_var', 'stage1.rebnconv6d.bn_s1.running_mean', 'stage1.rebnconv6d.bn_s1.running_var', 'stage1.rebnconv5d.bn_s1.running_mean', 'stage1.rebnconv5d.bn_s1.running_var', 'stage1.rebnconv4d.bn_s1.running_mean', 'stage1.rebnconv4d.bn_s1.running_var', 'stage1.rebnconv3d.bn_s1.running_mean', 'stage1.rebnconv3d.bn_s1.running_var', 'stage1.rebnconv2d.bn_s1.running_mean', 'stage1.rebnconv2d.bn_s1.running_var', 'stage1.rebnconv1d.bn_s1.running_mean', 'stage1.rebnconv1d.bn_s1.running_var', 'stage2.rebnconvin.bn_s1.running_mean', 'stage2.rebnconvin.bn_s1.running_var', 'stage2.rebnconv1.bn_s1.running_mean', 'stage2.rebnconv1.bn_s1.running_var', 'stage2.rebnconv2.bn_s1.running_mean', 'stage2.rebnconv2.bn_s1.running_var', 'stage2.rebnconv3.bn_s1.running_mean', 'stage2.rebnconv3.bn_s1.running_var', 'stage2.rebnconv4.bn_s1.running_mean', 'stage2.rebnconv4.bn_s1.running_var', 'stage2.rebnconv5.bn_s1.running_mean', 'stage2.rebnconv5.bn_s1.running_var', 'stage2.rebnconv6.bn_s1.running_mean', 'stage2.rebnconv6.bn_s1.running_var', 'stage2.rebnconv5d.bn_s1.running_mean', 'stage2.rebnconv5d.bn_s1.running_var', 'stage2.rebnconv4d.bn_s1.running_mean', 'stage2.rebnconv4d.bn_s1.running_var', 'stage2.rebnconv3d.bn_s1.running_mean', 'stage2.rebnconv3d.bn_s1.running_var', 'stage2.rebnconv2d.bn_s1.running_mean', 'stage2.rebnconv2d.bn_s1.running_var', 'stage2.rebnconv1d.bn_s1.running_mean', 'stage2.rebnconv1d.bn_s1.running_var', 'stage3.rebnconvin.bn_s1.running_mean', 'stage3.rebnconvin.bn_s1.running_var', 'stage3.rebnconv1.bn_s1.running_mean', 'stage3.rebnconv1.bn_s1.running_var', 'stage3.rebnconv2.bn_s1.running_mean', 'stage3.rebnconv2.bn_s1.running_var', 'stage3.rebnconv3.bn_s1.running_mean', 'stage3.rebnconv3.bn_s1.running_var', 'stage3.rebnconv4.bn_s1.running_mean', 'stage3.rebnconv4.bn_s1.running_var', 'stage3.rebnconv5.bn_s1.running_mean', 'stage3.rebnconv5.bn_s1.running_var', 'stage3.rebnconv4d.bn_s1.running_mean', 'stage3.rebnconv4d.bn_s1.running_var', 'stage3.rebnconv3d.bn_s1.running_mean', 'stage3.rebnconv3d.bn_s1.running_var', 'stage3.rebnconv2d.bn_s1.running_mean', 'stage3.rebnconv2d.bn_s1.running_var', 'stage3.rebnconv1d.bn_s1.running_mean', 'stage3.rebnconv1d.bn_s1.running_var', 'stage4.rebnconvin.bn_s1.running_mean', 'stage4.rebnconvin.bn_s1.running_var', 'stage4.rebnconv1.bn_s1.running_mean', 'stage4.rebnconv1.bn_s1.running_var', 'stage4.rebnconv2.bn_s1.running_mean', 'stage4.rebnconv2.bn_s1.running_var', 'stage4.rebnconv3.bn_s1.running_mean', 'stage4.rebnconv3.bn_s1.running_var', 'stage4.rebnconv4.bn_s1.running_mean', 'stage4.rebnconv4.bn_s1.running_var', 'stage4.rebnconv3d.bn_s1.running_mean', 'stage4.rebnconv3d.bn_s1.running_var', 'stage4.rebnconv2d.bn_s1.running_mean', 'stage4.rebnconv2d.bn_s1.running_var', 'stage4.rebnconv1d.bn_s1.running_mean', 'stage4.rebnconv1d.bn_s1.running_var', 'stage5.rebnconvin.bn_s1.running_mean', 'stage5.rebnconvin.bn_s1.running_var', 'stage5.rebnconv1.bn_s1.running_mean', 'stage5.rebnconv1.bn_s1.running_var', 'stage5.rebnconv2.bn_s1.running_mean', 'stage5.rebnconv2.bn_s1.running_var', 'stage5.rebnconv3.bn_s1.running_mean', 'stage5.rebnconv3.bn_s1.running_var', 'stage5.rebnconv4.bn_s1.running_mean', 'stage5.rebnconv4.bn_s1.running_var', 'stage5.rebnconv3d.bn_s1.running_mean', 'stage5.rebnconv3d.bn_s1.running_var', 'stage5.rebnconv2d.bn_s1.running_mean', 'stage5.rebnconv2d.bn_s1.running_var', 'stage5.rebnconv1d.bn_s1.running_mean', 'stage5.rebnconv1d.bn_s1.running_var', 'stage6.rebnconvin.bn_s1.running_mean', 'stage6.rebnconvin.bn_s1.running_var', 'stage6.rebnconv1.bn_s1.running_mean', 'stage6.rebnconv1.bn_s1.running_var', 'stage6.rebnconv2.bn_s1.running_mean', 'stage6.rebnconv2.bn_s1.running_var', 'stage6.rebnconv3.bn_s1.running_mean', 'stage6.rebnconv3.bn_s1.running_var', 'stage6.rebnconv4.bn_s1.running_mean', 'stage6.rebnconv4.bn_s1.running_var', 'stage6.rebnconv3d.bn_s1.running_mean', 'stage6.rebnconv3d.bn_s1.running_var', 'stage6.rebnconv2d.bn_s1.running_mean', 'stage6.rebnconv2d.bn_s1.running_var', 'stage6.rebnconv1d.bn_s1.running_mean', 'stage6.rebnconv1d.bn_s1.running_var', 'stage5d.rebnconvin.bn_s1.running_mean', 'stage5d.rebnconvin.bn_s1.running_var', 'stage5d.rebnconv1.bn_s1.running_mean', 'stage5d.rebnconv1.bn_s1.running_var', 'stage5d.rebnconv2.bn_s1.running_mean', 'stage5d.rebnconv2.bn_s1.running_var', 'stage5d.rebnconv3.bn_s1.running_mean', 'stage5d.rebnconv3.bn_s1.running_var', 'stage5d.rebnconv4.bn_s1.running_mean', 'stage5d.rebnconv4.bn_s1.running_var', 'stage5d.rebnconv3d.bn_s1.running_mean', 'stage5d.rebnconv3d.bn_s1.running_var', 'stage5d.rebnconv2d.bn_s1.running_mean', 'stage5d.rebnconv2d.bn_s1.running_var', 'stage5d.rebnconv1d.bn_s1.running_mean', 'stage5d.rebnconv1d.bn_s1.running_var', 'stage4d.rebnconvin.bn_s1.running_mean', 'stage4d.rebnconvin.bn_s1.running_var', 'stage4d.rebnconv1.bn_s1.running_mean', 'stage4d.rebnconv1.bn_s1.running_var', 'stage4d.rebnconv2.bn_s1.running_mean', 'stage4d.rebnconv2.bn_s1.running_var', 'stage4d.rebnconv3.bn_s1.running_mean', 'stage4d.rebnconv3.bn_s1.running_var', 'stage4d.rebnconv4.bn_s1.running_mean', 'stage4d.rebnconv4.bn_s1.running_var', 'stage4d.rebnconv3d.bn_s1.running_mean', 'stage4d.rebnconv3d.bn_s1.running_var', 'stage4d.rebnconv2d.bn_s1.running_mean', 'stage4d.rebnconv2d.bn_s1.running_var', 'stage4d.rebnconv1d.bn_s1.running_mean', 'stage4d.rebnconv1d.bn_s1.running_var', 'stage3d.rebnconvin.bn_s1.running_mean', 'stage3d.rebnconvin.bn_s1.running_var', 'stage3d.rebnconv1.bn_s1.running_mean', 'stage3d.rebnconv1.bn_s1.running_var', 'stage3d.rebnconv2.bn_s1.running_mean', 'stage3d.rebnconv2.bn_s1.running_var', 'stage3d.rebnconv3.bn_s1.running_mean', 'stage3d.rebnconv3.bn_s1.running_var', 'stage3d.rebnconv4.bn_s1.running_mean', 'stage3d.rebnconv4.bn_s1.running_var', 'stage3d.rebnconv5.bn_s1.running_mean', 'stage3d.rebnconv5.bn_s1.running_var', 'stage3d.rebnconv4d.bn_s1.running_mean', 'stage3d.rebnconv4d.bn_s1.running_var', 'stage3d.rebnconv3d.bn_s1.running_mean', 'stage3d.rebnconv3d.bn_s1.running_var', 'stage3d.rebnconv2d.bn_s1.running_mean', 'stage3d.rebnconv2d.bn_s1.running_var', 'stage3d.rebnconv1d.bn_s1.running_mean', 'stage3d.rebnconv1d.bn_s1.running_var', 'stage2d.rebnconvin.bn_s1.running_mean', 'stage2d.rebnconvin.bn_s1.running_var', 'stage2d.rebnconv1.bn_s1.running_mean', 'stage2d.rebnconv1.bn_s1.running_var', 'stage2d.rebnconv2.bn_s1.running_mean', 'stage2d.rebnconv2.bn_s1.running_var', 'stage2d.rebnconv3.bn_s1.running_mean', 'stage2d.rebnconv3.bn_s1.running_var', 'stage2d.rebnconv4.bn_s1.running_mean', 'stage2d.rebnconv4.bn_s1.running_var', 'stage2d.rebnconv5.bn_s1.running_mean', 'stage2d.rebnconv5.bn_s1.running_var', 'stage2d.rebnconv6.bn_s1.running_mean', 'stage2d.rebnconv6.bn_s1.running_var', 'stage2d.rebnconv5d.bn_s1.running_mean', 'stage2d.rebnconv5d.bn_s1.running_var', 'stage2d.rebnconv4d.bn_s1.running_mean', 'stage2d.rebnconv4d.bn_s1.running_var', 'stage2d.rebnconv3d.bn_s1.running_mean', 'stage2d.rebnconv3d.bn_s1.running_var', 'stage2d.rebnconv2d.bn_s1.running_mean', 'stage2d.rebnconv2d.bn_s1.running_var', 'stage2d.rebnconv1d.bn_s1.running_mean', 'stage2d.rebnconv1d.bn_s1.running_var', 'stage1d.rebnconvin.bn_s1.running_mean', 'stage1d.rebnconvin.bn_s1.running_var', 'stage1d.rebnconv1.bn_s1.running_mean', 'stage1d.rebnconv1.bn_s1.running_var', 'stage1d.rebnconv2.bn_s1.running_mean', 'stage1d.rebnconv2.bn_s1.running_var', 'stage1d.rebnconv3.bn_s1.running_mean', 'stage1d.rebnconv3.bn_s1.running_var', 'stage1d.rebnconv4.bn_s1.running_mean', 'stage1d.rebnconv4.bn_s1.running_var', 'stage1d.rebnconv5.bn_s1.running_mean', 'stage1d.rebnconv5.bn_s1.running_var', 'stage1d.rebnconv6.bn_s1.running_mean', 'stage1d.rebnconv6.bn_s1.running_var', 'stage1d.rebnconv7.bn_s1.running_mean', 'stage1d.rebnconv7.bn_s1.running_var', 'stage1d.rebnconv6d.bn_s1.running_mean', 'stage1d.rebnconv6d.bn_s1.running_var', 'stage1d.rebnconv5d.bn_s1.running_mean', 'stage1d.rebnconv5d.bn_s1.running_var', 'stage1d.rebnconv4d.bn_s1.running_mean', 'stage1d.rebnconv4d.bn_s1.running_var', 'stage1d.rebnconv3d.bn_s1.running_mean', 'stage1d.rebnconv3d.bn_s1.running_var', 'stage1d.rebnconv2d.bn_s1.running_mean', 'stage1d.rebnconv2d.bn_s1.running_var', 'stage1d.rebnconv1d.bn_s1.running_mean', 'stage1d.rebnconv1d.bn_s1.running_var'], unexpected_keys=[])
</code></pre>
<p>My model architecture is like this</p>
<pre><code class="lang-auto">class REBNCONV(nn.Module):
    def __init__(self,in_ch=3,out_ch=3,dirate=1):
        super(REBNCONV,self).__init__()

        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
        self.bn_s1 = nn.BatchNorm2d(out_ch)
        self.relu_s1 = nn.ReLU(inplace=True)

    def forward(self,x):

        hx = x
        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))

        return xout

## upsample tensor 'src' to have the same spatial size with tensor 'tar'
def _upsample_like(src,tar):

    src = F.upsample(src,size=tar.shape[2:],mode='bilinear', align_corners=True)

    return src


### RSU-7 ###
class RSU7(nn.Module):#UNet07DRES(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU7,self).__init__()

        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)

        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)

        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)

        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)

    def forward(self,x):

        hx = x
        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)

        hx5 = self.rebnconv5(hx)
        hx = self.pool5(hx5)

        hx6 = self.rebnconv6(hx)

        hx7 = self.rebnconv7(hx6)

        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
        hx6dup = _upsample_like(hx6d,hx5)

        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
        hx5dup = _upsample_like(hx5d,hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
        hx4dup = _upsample_like(hx4d,hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))

        return hx1d + hxin

### RSU-6 ###
class RSU6(nn.Module):#UNet06DRES(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU6,self).__init__()

        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)

        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)

        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)

        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)

    def forward(self,x):

        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)

        hx5 = self.rebnconv5(hx)

        hx6 = self.rebnconv6(hx5)


        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
        hx5dup = _upsample_like(hx5d,hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
        hx4dup = _upsample_like(hx4d,hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))

        return hx1d + hxin

### RSU-5 ###
class RSU5(nn.Module):#UNet05DRES(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU5,self).__init__()

        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)

        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)

        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)

        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)

    def forward(self,x):

        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)

        hx5 = self.rebnconv5(hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
        hx4dup = _upsample_like(hx4d,hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))

        return hx1d + hxin

### RSU-4 ###
class RSU4(nn.Module):#UNet04DRES(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4,self).__init__()

        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)

        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)

        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)

        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)

    def forward(self,x):

        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)

        hx4 = self.rebnconv4(hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))

        return hx1d + hxin

### RSU-4F ###
class RSU4F(nn.Module):#UNet04FRES(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4F,self).__init__()

        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)

        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)

        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)

        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)

    def forward(self,x):

        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx2 = self.rebnconv2(hx1)
        hx3 = self.rebnconv3(hx2)

        hx4 = self.rebnconv4(hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))

        return hx1d + hxin


##### U^2-Net ####
class U2NET(nn.Module):

    def __init__(self,in_ch=3,out_ch=1):
        super(U2NET,self).__init__()

        self.stage1 = RSU7(in_ch,32,64)
        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage2 = RSU6(64,32,128)
        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage3 = RSU5(128,64,256)
        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage4 = RSU4(256,128,512)
        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage5 = RSU4F(512,256,512)
        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage6 = RSU4F(512,256,512)

        # decoder
        self.stage5d = RSU4F(1024,256,512)
        self.stage4d = RSU4(1024,128,256)
        self.stage3d = RSU5(512,64,128)
        self.stage2d = RSU6(256,32,64)
        self.stage1d = RSU7(128,16,64)

        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
        self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
        self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
        self.side6 = nn.Conv2d(512,out_ch,3,padding=1)

        self.outconv = nn.Conv2d(6*out_ch,out_ch,1)

    def forward(self,x):

        hx = x

        #stage 1
        hx1 = self.stage1(hx)
        hx = self.pool12(hx1)

        #stage 2
        hx2 = self.stage2(hx)
        hx = self.pool23(hx2)

        #stage 3
        hx3 = self.stage3(hx)
        hx = self.pool34(hx3)

        #stage 4
        hx4 = self.stage4(hx)
        hx = self.pool45(hx4)

        #stage 5
        hx5 = self.stage5(hx)
        hx = self.pool56(hx5)

        #stage 6
        hx6 = self.stage6(hx)
        hx6up = _upsample_like(hx6,hx5)

        #-------------------- decoder --------------------
        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
        hx5dup = _upsample_like(hx5d,hx4)

        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
        hx4dup = _upsample_like(hx4d,hx3)

        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))


        #side output
        d1 = self.side1(hx1d)

        d2 = self.side2(hx2d)
        d2 = _upsample_like(d2,d1)

        d3 = self.side3(hx3d)
        d3 = _upsample_like(d3,d1)

        d4 = self.side4(hx4d)
        d4 = _upsample_like(d4,d1)

        d5 = self.side5(hx5d)
        d5 = _upsample_like(d5,d1)

        d6 = self.side6(hx6)
        d6 = _upsample_like(d6,d1)

        d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))

        return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)

### U^2-Net small ###
class U2NETP(nn.Module):

    def __init__(self,in_ch=3,out_ch=1):
        super(U2NETP,self).__init__()

        self.stage1 = RSU7(in_ch,16,64)
        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage2 = RSU6(64,16,64)
        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage3 = RSU5(64,16,64)
        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage4 = RSU4(64,16,64)
        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage5 = RSU4F(64,16,64)
        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

        self.stage6 = RSU4F(64,16,64)

        # decoder
        self.stage5d = RSU4F(128,16,64)
        self.stage4d = RSU4(128,16,64)
        self.stage3d = RSU5(128,16,64)
        self.stage2d = RSU6(128,16,64)
        self.stage1d = RSU7(128,16,64)

        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side3 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side4 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side5 = nn.Conv2d(64,out_ch,3,padding=1)
        self.side6 = nn.Conv2d(64,out_ch,3,padding=1)

        self.outconv = nn.Conv2d(6*out_ch,out_ch,1)

    def forward(self,x):

        hx = x

        #stage 1
        hx1 = self.stage1(hx)
        hx = self.pool12(hx1)

        #stage 2
        hx2 = self.stage2(hx)
        hx = self.pool23(hx2)

        #stage 3
        hx3 = self.stage3(hx)
        hx = self.pool34(hx3)

        #stage 4
        hx4 = self.stage4(hx)
        hx = self.pool45(hx4)

        #stage 5
        hx5 = self.stage5(hx)
        hx = self.pool56(hx5)

        #stage 6
        hx6 = self.stage6(hx)
        hx6up = _upsample_like(hx6,hx5)

        #decoder
        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
        hx5dup = _upsample_like(hx5d,hx4)

        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
        hx4dup = _upsample_like(hx4d,hx3)

        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
        hx3dup = _upsample_like(hx3d,hx2)

        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
        hx2dup = _upsample_like(hx2d,hx1)

        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))


        #side output
        d1 = self.side1(hx1d)

        d2 = self.side2(hx2d)
        d2 = _upsample_like(d2,d1)

        d3 = self.side3(hx3d)
        d3 = _upsample_like(d3,d1)

        d4 = self.side4(hx4d)
        d4 = _upsample_like(d4,d1)

        d5 = self.side5(hx5d)
        d5 = _upsample_like(d5,d1)

        d6 = self.side6(hx6)
        d6 = _upsample_like(d6,d1)

        d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))

        return F.sigmoid(d0)
</code></pre>

username: ptrblck
cooked: <p>Based on the error message the <code>stage</code> module parameters are missing, so you would have to check the original implementation (which created the <code>state_dict</code>) and check, if these modules were also used there (doesn’t seem to be the case).<br>
In case you are expecting this error and explicitly defines these <code>stage</code> modules additionally in your new architecture, you can ignore the error using <code>strict=False</code> while loading the <code>state_dict</code>, but please make sure you are indeed expecting the error.</p>

username: Asad_Imtiaz
cooked: <p>Thank you so much for your response. I solved this issue by changing the pytorch version to v1.8.0 apparently that was not allowing the model to load weights properly.</p>