RuntimeError: Given groups=1, weight of size [3, 6, 1, 1], expected input[64, 9, 512, 512] to have 6 channels, but got 9 channels instead

i am trying to create a GAN this is my code architecture

generator = Generator(in_channels=3, out_channels=1).to(device)

The following is the Generator architecture

# Pass the in and out (generator in_channels=3, out_channels=1)channels as argument to the Generator class constructor's parameter
generator = Generator(in_channels=3, out_channels=1).to(device)
generator

output
Generator(
  (dilated_conv_block): Sequential(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), groups=3)
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (concat_noise): Sequential(
    (0): Conv2d(6, 3, kernel_size=(1, 1), stride=(1, 1))
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (transpose_conv_block): Sequential(
    (0): ConvTranspose2d(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (residual_block): FeatureReuseResidualBlock(
    (conv1): Sequential(
      (0): Conv2d(3, 32, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv2): Sequential(
      (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv3): Sequential(
      (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (concat_block): Conv2d(6, 256, kernel_size=(1, 1), stride=(1, 1))
    (relu): ReLU(inplace=True)
  )
  (conv_block): Sequential(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (inception_block): Sequential(
    (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace=True)
  )
  (output_conv_block): Sequential(
    (0): Conv2d(3, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): Tanh()
  )
)

this is the error

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-39-6c735aad3ee7> in <cell line: 3>()
     15         print(noise.shape)
     16         # Forward pass
---> 17         generated_images = generator(real_images, noise)
     18 
     19         # Compute loss

9 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    454                             weight, bias, self.stride,
    455                             _pair(0), self.dilation, self.groups)
--> 456         return F.conv2d(input, weight, bias, self.stride,
    457                         self.padding, self.dilation, self.groups)
    458 

Could you post a minimal and executable code snippet reproducing the issue, please?

thanks the code is much but this is the part that am thinking is given the error its might be the concatination as the value is for input and output channel is 6,3

class Generator(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Generator, self).__init__()

        # Depthwise Dilated Convolution Block (C)
        self.dilated_conv_block = nn.Sequential(
            nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=2, dilation=2, groups=in_channels),
            nn.BatchNorm2d(in_channels),
            nn.ReLU(inplace=True)
        )

        # Concatenate noise (C)
        self.concat_noise = nn.Sequential(
            nn.Conv2d(in_channels * 2, in_channels, kernel_size=1),
            nn.Conv2d(in_channels, in_channels, kernel_size=1),
            nn.BatchNorm2d(in_channels),
            nn.ReLU(inplace=True)
        )

        # Feature Reuse Residual Block (C)
        self.residual_block = FeatureReuseResidualBlock(in_channels, in_channels)  # Adjust channels here

    def forward(self, image, noise):
        # Depthwise Dilated Convolution
        x = self.dilated_conv_block(image)

        # Concatenate noise
        x = self.concat_noise(torch.cat((x, noise), dim=1))

        # Feature Reuse Residual Block
        x = self.residual_block(x)

        # 3x3 Convolution - Output
        output = self.output_conv_block(x)
        print(output.shape)

        return output

# Pass the in and out (generator in_channels=3, out_channels=1)channels as argument to the Generator class constructor's parameter
generator = Generator(in_channels=3, out_channels=1).to(device)
generator

output
Generator(
  (dilated_conv_block): Sequential(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), groups=3)
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (concat_noise): Sequential(
    (0): Conv2d(6, 3, kernel_size=(1, 1), stride=(1, 1))
    (1): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
    (2): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (3): ReLU(inplace=True)
  )
  (residual_block): FeatureReuseResidualBlock(
    (conv1): Sequential(
      (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv2): Sequential(
      (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (conv3): Sequential(
      (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (concat_block): Conv2d(6, 3, kernel_size=(1, 1), stride=(1, 1))
    (relu): ReLU(inplace=True)
  )
)

this is my Feature reused block

class FeatureReuseResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(FeatureReuseResidualBlock, self).__init__()

        # Convolutional Block 1
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

        # Convolutional Block 2
        self.conv2 = nn.Sequential(
            #nn.Conv2d(out_channels=32, out_channels=64, kernel_size=3, padding=1),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

        # Convolutional Block 3
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

        # Concatenation Block
        self.concat_block = nn.Conv2d(in_channels * 2, out_channels, kernel_size=1)
        #self.concat_block = nn.Conv2d(out_channels, out_channels, kernel_size=1)

        # Final ReLU
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        # Convolutional Block 1
        out1 = self.conv1(x)

        # Convolutional Block 2
        out2 = self.conv2(out1)

        # Convolutional Block 3
        out3 = self.conv3(out2)

        # Concatenate Block
        concatenated = torch.cat((out1, out2, out3), dim=1)
        concatenated = self.concat_block(concatenated)

        # Residual Connection
        output = x + concatenated

        # Final ReLU
        output = self.relu(output)


        return output

Yes, indeed:

concatenated = torch.cat((out1, out2, out3), dim=1)

creates the activation with 9 channels, as all outX tensors have 3 channels based on the out_channel setting in the corresponding layers creating them.
You might thus want to use self.concat_block = nn.Conv2d(out_channels * 3, out_channels, kernel_size=1) to fix this error. Your model will then fail with:

AttributeError: 'Generator' object has no attribute 'output_conv_block'

as it’s undefined.

Thank you very much for your help everything is solved.

我遇到了一个类似的问题:RuntimeError: Given groups=1, weight of size [256, 1, 1, 7], expected input[220, 1000, 2, 128] to have 1 channels, but got 1000 channels instead。
我的代码为:

def build_models():
    # Dropout rate  Dropout 会在训练过程中随机丢弃一部分神经元的输出,以减少模型对某些输入特征的依赖,从而提高模型泛化性能。
    # 一般来说,丢弃率越高,模型的泛化能力就越强,但同时也可能会影响模型的训练效果和收敛速度,因此需要根据具体问题进行调整。
    dr = 0.6

    # Build CNN model using Torch

    # 定义了三个卷积层,每个卷积层后面分别添加了一个归一化和dropout层
    # 定义了三个全连接层,并在每个全连接层后面添加了 ReLU 激活函数。
    # 最后一个全连接层 self.fc4 输出最终的预测结果,长度为 len(mods),并使用了 sigmoid 激活函数。
    class CovNet(nn.Module):
        def __init__(self):
            super(CovNet, self).__init__()
            self.conv1 = nn.Conv2d(1, 256, (1, 7), padding=(0, 3))  # 添加第一个卷积层,调用了nn里面的Conv2d()
            # self.conv1 = nn.Conv2d(1, 128, (1, 7), padding=(0, 3))  # 添加第一个卷积层,调用了nn里面的Conv2d()
            self.b1 = nn.BatchNorm2d(256)  # 归一化层
            # Xavier 初始化是一种常用的权重初始化方法,旨在有效地解决梯度消失和梯度爆炸的问题,以及加速收敛过程。
            nn.init.xavier_uniform_(self.conv1.weight, gain=nn.init.calculate_gain('relu'))
            self.dropout1 = nn.Dropout(dr)  # dropout层
            self.zeropad2 = nn.ZeroPad2d((0, 2))  # 将输入在宽度方向上扩充 2 个单位,以便在卷积过程中保持特征图的大小不变。
            self.conv2 = nn.Conv2d(256, 128, (1, 7), padding=(0, 3))
            nn.init.xavier_uniform_(self.conv2.weight, gain=nn.init.calculate_gain('relu'))
            self.b2 = nn.BatchNorm2d(128)
            self.dropout2 = nn.Dropout(dr)
            self.conv3 = nn.Conv2d(128, 80, (2, 7), padding=(0, 3))
            nn.init.xavier_uniform_(self.conv3.weight, gain=nn.init.calculate_gain('relu'))
            self.b3 = nn.BatchNorm2d(80)
            self.dropout3 = nn.Dropout(dr)
            self.fc1 = nn.Linear(80 * 128, 256)  # 表示输入特征向量的长度为 80*128,输出特征向量的长度为 256
            nn.init.kaiming_normal_(self.fc1.weight, nonlinearity='relu')
            self.fc2 = nn.Linear(256, 128)
            nn.init.kaiming_normal_(self.fc2.weight, nonlinearity='relu')
            self.dropout4 = nn.Dropout(dr)
            self.fc3 = nn.Linear(128, 64)
            nn.init.kaiming_normal_(self.fc3.weight, nonlinearity='relu')
            self.fc4 = nn.Linear(64, len(mods))  # 全连接层 self.fc4 的输出维度为分类数目
            nn.init.kaiming_normal_(self.fc4.weight, nonlinearity='sigmoid')

        def forward(self, x):
            x = F.relu(self.conv1(x))
            x = self.b1(x)
            x = self.dropout1(x)
            x = F.relu(self.conv2(x))
            x = self.dropout2(x)
            x = self.b2(x)
            x = F.relu(self.conv3(x))
            x = self.dropout3(x)
            x = self.b3(x)
            x = x.view(-1, 80 * 128)  # 将卷积层的输出结果变形为合适的形状,以便传递给全连接层进行进一步的处理。
            x = F.relu(self.fc1(x))
            x = F.relu(self.fc2(x))
            x = self.dropout4(x)
            x = F.relu(self.fc3(x))
            out = self.fc4(x)
            return out

    cnn = CovNet().to(device=device)  # 定义的 CovNet 类的实例化对象 将CovNet 模型移动到指定的设备上;
    # summary 函数将打印出模型的概要信息,包括每个层的输出形状、参数数量等。
    # cnn 是定义的 CovNet 类的实例化对象,(1, 2, 128) 表示输入张量的形状。
    summary(cnn, (1, 2, 128))
    return cnn

我似乎不明白错误在哪里,请问如何解决呢?

From Google translate:

I don’t seem to understand where the error is. How can I solve it?

The error is raised from:

self.conv1 = nn.Conv2d(1, 256, (1, 7), padding=(0, 3)) 

which expects an input activation with a single channel, while you are passing an input with 1000 channels in the shape [220, 1000, 2, 128] to the model.