RuntimeError: Given groups=1, weight of size [64, 4, 3, 3], expected input[2, 3, 240, 240] to have 4 channels, but got 3 channels instead

ATU_ATU: <p>Hello everyone, I need your support. I made some modifications to increase group size from 3 to 4 by adding   <code>self.g4= Group(conv, self.dim, kernel_size,blocks=blocks)</code>   to the <code>class FFA(nn.Module)</code> and also adding <code>res4 </code>on the <code>def forward(self, x1):</code> block. I am getting error that says <code>RuntimeError: Given groups = 1, weight of size [64, 4, 3, 3], expected input[2, 3, 240, 240] to have 4 channels, but got 3 channels instead</code>  . I will appreciate your assistance. I have attached the <code>.py file</code> content of 1. <code>Original Code</code>, 2. <code>Modified code</code>. I’m seeing a couple of these posts in the forum, but I’m having trouble connecting them to my own problem below. Thank you</p>
<p>Original code:</p>
<pre><code class="lang-auto">import torch.nn as nn
import torch

def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(in_channels, out_channels, kernel_size,padding=(kernel_size//2), bias=bias)
    
class PALayer(nn.Module):
    def __init__(self, channel):
        super(PALayer, self).__init__()
        self.pa = nn.Sequential(
                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // 8, 1, 1, padding=0, bias=True),
                nn.Sigmoid()
        )
    def forward(self, x):
        y = self.pa(x)
        return x * y

class CALayer(nn.Module):
    def __init__(self, channel):
        super(CALayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.ca = nn.Sequential(
                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // 8, channel, 1, padding=0, bias=True),
                nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.ca(y)
        return x * y

class Block(nn.Module):
    def __init__(self, conv, dim, kernel_size,):
        super(Block, self).__init__()
        self.conv1=conv(dim, dim, kernel_size, bias=True)
        self.act1=nn.ReLU(inplace=True)
        self.conv2=conv(dim,dim,kernel_size,bias=True)
        self.calayer=CALayer(dim)
        self.palayer=PALayer(dim)
    def forward(self, x):
        res=self.act1(self.conv1(x))
        res=res+x 
        res=self.conv2(res)
        res=self.calayer(res)
        res=self.palayer(res)
        res += x 
        return res
class Group(nn.Module):
    def __init__(self, conv, dim, kernel_size, blocks):
        super(Group, self).__init__()
        modules = [ Block(conv, dim, kernel_size)  for _ in range(blocks)]
        modules.append(conv(dim, dim, kernel_size))
        self.gp = nn.Sequential(*modules)
    def forward(self, x):
        res = self.gp(x)
        res += x
        return res

class FFA(nn.Module):
    def __init__(self,gps,blocks,conv=default_conv):
        super(FFA, self).__init__()
        self.gps=gps
        self.dim=64
        kernel_size=3
        pre_process = [conv(3, self.dim, kernel_size)]
        assert self.gps==3
        self.g1= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.g2= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.g3= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.ca=nn.Sequential(*[
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(self.dim*self.gps,self.dim//16,1,padding=0),
            nn.ReLU(inplace=True),
            nn.Conv2d(self.dim//16, self.dim*self.gps, 1, padding=0, bias=True),
            nn.Sigmoid()
            ])
        self.palayer=PALayer(self.dim)

        post_precess = [
            conv(self.dim, self.dim, kernel_size),
            conv(self.dim, 3, kernel_size)]

        self.pre = nn.Sequential(*pre_process)
        self.post = nn.Sequential(*post_precess)

    def forward(self, x1):
        x = self.pre(x1)
        res1=self.g1(x)
        res2=self.g2(res1)
        res3=self.g3(res2)
        w=self.ca(torch.cat([res1,res2,res3],dim=1))
        w=w.view(-1,self.gps,self.dim)[:,:,:,None,None]
        out=w[:,0,::]*res1+w[:,1,::]*res2+w[:,2,::]*res3
        out=self.palayer(out)
        x=self.post(out)
        return x + x1
if __name__ == "__main__":
    net=FFA(gps=3,blocks=19)
    print(net)
</code></pre>
<ol start="2">
<li>
<code>Modified code</code> that generates the error.</li>
</ol>
<pre><code class="lang-auto">import torch.nn as nn
import torch

def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(in_channels, out_channels, kernel_size,padding=(kernel_size//2), bias=bias)
    
class PALayer(nn.Module):
    def __init__(self, channel):
        super(PALayer, self).__init__()
        self.pa = nn.Sequential(
                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // 8, 1, 1, padding=0, bias=True),
                nn.Sigmoid()
        )
    def forward(self, x):
        y = self.pa(x)
        return x * y

class CALayer(nn.Module):
    def __init__(self, channel):
        super(CALayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.ca = nn.Sequential(
                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // 8, channel, 1, padding=0, bias=True),
                nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.ca(y)
        return x * y

class Block(nn.Module):
    def __init__(self, conv, dim, kernel_size,):
        super(Block, self).__init__()
        self.conv1=conv(dim, dim, kernel_size, bias=True)
        self.act1=nn.ReLU(inplace=True)
        self.conv2=conv(dim,dim,kernel_size,bias=True)
        self.calayer=CALayer(dim)
        self.palayer=PALayer(dim)
    def forward(self, x):
        res=self.act1(self.conv1(x))
        res=res+x 
        res=self.conv2(res)
        res=self.calayer(res)
        res=self.palayer(res)
        res += x 
        return res
class Group(nn.Module):
    def __init__(self, conv, dim, kernel_size, blocks):
        super(Group, self).__init__()
        modules = [ Block(conv, dim, kernel_size)  for _ in range(blocks)]
        modules.append(conv(dim, dim, kernel_size))
        self.gp = nn.Sequential(*modules)
    def forward(self, x):
        res = self.gp(x)
        res += x
        return res

class FFA(nn.Module):
    def __init__(self,gps,blocks,conv=default_conv):
        super(FFA, self).__init__()
        self.gps=gps
        self.dim=64
        kernel_size=3
        pre_process = [conv(3, self.dim, kernel_size)]
        assert self.gps==4
        self.g1= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.g2= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.g3= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.g4= Group(conv, self.dim, kernel_size,blocks=blocks)
        self.ca=nn.Sequential(*[
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(self.dim*self.gps,self.dim//16,1,padding=0),
            nn.ReLU(inplace=True),
            nn.Conv2d(self.dim//16, self.dim*self.gps, 1, padding=0, bias=True),
            nn.Sigmoid()
            ])
        self.palayer=PALayer(self.dim)

        post_precess = [
            conv(self.dim, self.dim, kernel_size),
            conv(self.dim, 3, kernel_size)]

        self.pre = nn.Sequential(*pre_process)
        self.post = nn.Sequential(*post_precess)

    def forward(self, x1):
        x = self.pre(x1)
        res1=self.g1(x)
        res2=self.g2(res1)
        res3=self.g3(res2)
        res4=self.g4(res3)
        w=self.ca(torch.cat([res1,res2,res3,res4],dim=1))
        w=w.view(-1,self.gps,self.dim)[:,:,:,None,None]
        out=w[:,0,::]*res1+w[:,1,::]*res2+w[:,2,::]*res3+w[:,3,::]*res4
        out=self.palayer(out)
        x=self.post(out)
        return x + x1
if __name__ == "__main__":
    net=FFA(gps=4,blocks=19)
    print(net)
</code></pre>