Problem in U-NET

username: randinoo
cooked: <p>Hi,I made this U-net code</p>
<pre><code class="lang-auto">class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 1, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True))
    def forward(self, x):
        x = self.conv(x)
        return x
class InConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(InConv, self).__init__()
        self.conv = DoubleConv(in_ch, out_ch)
    def forward(self, x):
        x = self.conv(x)
        return x
class Down(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(Down, self).__init__()
        self.mpconv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_ch, out_ch)
        )
    def forward(self, x):
        x = self.mpconv(x)
        return x
class Up(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(Up, self).__init__()
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2)
        self.conv = DoubleConv(in_ch, out_ch)
    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2))
        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x
class OutConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_ch, out_ch, 1)
    def forward(self, x):
        x = self.conv(x)
        return x
class Unet(nn.Module):
    def __init__(self, in_channels, classes):
        super(Unet, self).__init__()
        self.n_channels = 3
        self.n_classes = 2
        self.inc = InConv(in_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.down4 = Down(512, 512)
        self.up1 = Up(1024, 256)
        self.up2 = Up(512, 128)
        self.up3 = Up(256, 64)
        self.up4 = Up(128, 64)
        self.outc = OutConv(64, classes)
    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        x = self.outc(x)
        return x

</code></pre>
<p>problem : RuntimeError: Given groups=1, weight of size [64, 4, 1, 1], expected input[6451, 1, 5, 5] to have 4 channels, but got 1 channels instead</p>
<p>help me plzz</p>

username: eqy
cooked: <p>It looks like there is an n_channels member of the Unet class that is defined but never used, and in_channels is defined when the Unet is created (this value seems like it is set to 4). However, the input has only a single channel? I would check how these parameters are used and whether the input data is correct because the shape seems very strange.</p>

username: randinoo
cooked: <p>Hi <a class="mention" href="/u/eqy">@eqy</a>  yes a single channel.   I prefer to get this architecture</p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png" data-download-href="https://discuss.pytorch.org/uploads/default/0c03867186535a0057fe0c190b6505a53d839f11" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11_2_534x500.png" alt="image" data-base62-sha1="1Ihhv6PMSTf2xM5thprVCE22Xp7" width="534" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11_2_534x500.png, https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png 2x" data-dominant-color="F5F7F6"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">569×532 28.5 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
<p>help me plzz eqy</p>

username: randinoo
cooked: <p>help me plzz <img src="https://discuss.pytorch.org/images/emoji/apple/pensive.png?v=9" title=":pensive:" class="emoji" alt=":pensive:"> <img src="https://discuss.pytorch.org/images/emoji/apple/pensive.png?v=9" title=":pensive:" class="emoji" alt=":pensive:"> <img src="https://discuss.pytorch.org/images/emoji/apple/pensive.png?v=9" title=":pensive:" class="emoji" alt=":pensive:"> <img src="https://discuss.pytorch.org/images/emoji/apple/pensive.png?v=9" title=":pensive:" class="emoji" alt=":pensive:"></p>

username: Hmrishav_Bandyopadhy
cooked: <p>There is something wrong with your input. What is the input shape?</p>

username: randinoo
cooked: <p>Hi <a class="mention" href="/u/hmrishav_bandyopadhy">@Hmrishav_Bandyopadhy</a>  I solved my problem, but I couldn’t do this architecture</p>
<p>can you help me plzz</p>
<p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png" data-download-href="https://discuss.pytorch.org/uploads/default/0c03867186535a0057fe0c190b6505a53d839f11" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11_2_534x500.png" alt="image" data-base62-sha1="1Ihhv6PMSTf2xM5thprVCE22Xp7" width="534" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11_2_534x500.png, https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/0/c/0c03867186535a0057fe0c190b6505a53d839f11.png 2x" data-dominant-color="F5F7F6"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">569×532 28.5 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

username: Hmrishav_Bandyopadhy
cooked: <p>This looks like the Unet architecture with some blocks skipped. You can see an example implementation in this tutorial video <a href="https://www.youtube.com/watch?v=u1loyDCoGbE" rel="noopener nofollow ugc">here</a></p>

username: randinoo
cooked: <p>Hi <a class="mention" href="/u/hmrishav_bandyopadhy">@Hmrishav_Bandyopadhy</a>  I tested this code but</p>
<p>problem : RuntimeError: only batches of spatial targets supported (3D tensors) but got targets of dimension: 1</p>
<p>I prefer to detect two classes</p>

username: Hmrishav_Bandyopadhy
cooked: <p>Once again if you have targets of dimension 1 then there is an issue with the data that you are using. UNets are segmentation models which means that the input and output would be 2D data…Add a dimension for batch size and you have 3D tensors</p>

username: randinoo
cooked: <p>just segmentation unet? do not make a classification</p>

username: randinoo
cooked: <p>When I tested this code</p>
<pre><code class="lang-auto">class UNet(nn.Module):
  def __init__(self): 
   super(UNet, self).__init__()
   self.max_pool = nn.MaxPool2d(2,2)
   self.down_conv1 = doubleconv(1,64)
   self.down_conv2= doubleconv(64,128)
   self.down_conv3= doubleconv(128,256)
   self.down_conv4= doubleconv(256,512)
   self.down_conv5= doubleconv(512,1024)

  def forward (self,x) : 

     x1= self.down_conv1(x)
     #x2= self.max_pool(x1)
     x3= self.down_conv2(x1)
     #x4= self.max_pool(x3)
     x5= self.down_conv3(x3)
     #x6= self.max_pool(x5)
     x7= self.down_conv4(x5)
     #x8= self.max_pool(x7)
     x9= self.down_conv5(x7)
</code></pre>
<p>RuntimeError: Given groups=1, weight of size [64, 1, 3, 3], expected input[1481, 128, 1, 1] to have 1 channels, but got 128 channels instead</p>
<p>help me plzz</p>

username: randinoo
cooked: <p>I solved my problem <img src="https://discuss.pytorch.org/images/emoji/apple/blush.png?v=9" title=":blush:" class="emoji" alt=":blush:"> <img src="https://discuss.pytorch.org/images/emoji/apple/blush.png?v=9" title=":blush:" class="emoji" alt=":blush:"> <img src="https://discuss.pytorch.org/images/emoji/apple/blush.png?v=9" title=":blush:" class="emoji" alt=":blush:"></p>