Problem Resnet in pytorch

1 
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn .Conv2d(1, 10, 1)
        self.bn1 = nn.BatchNorm2d(10)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(10, 10, 1)
        self.bn2 = nn.BatchNorm2d(10)
        self.downsample = downsample

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out
class ResNet34(nn.Module):
    def __init__(self):
        super(ResNet34,self).__init__()
        
        self.block1 = nn.Sequential(
            nn.Conv2d(1,10,kernel_size=2,stride=2,padding=3,bias=False),
            nn.BatchNorm2d(10),
            nn.ReLU(True)
        )
        
        self.block2 = nn.Sequential(
            nn.MaxPool2d(1,1),
            ResidualBlock(64,64),
            ResidualBlock(64,64,2)
        )
        
        self.block3 = nn.Sequential(
            ResidualBlock(64,128),
            ResidualBlock(128,128,2)
        )
        
        self.block4 = nn.Sequential(
            ResidualBlock(128,256),
            ResidualBlock(256,256,2)
        )
        self.block5 = nn.Sequential(
            ResidualBlock(256,512),
            ResidualBlock(512,512,2)
        )
        
        self.avgpool = nn.AvgPool2d(2)
        # vowel_diacritic
        self.fc1 = nn.Linear(512,11)
        # grapheme_root
        self.fc2 = nn.Linear(512,168)
        # consonant_diacritic
        self.fc3 = nn.Linear(512,7)
        
    def forward(self,x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)
        x = self.avgpool(x)
        x = x.view(x.size(0),-1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

problem : RuntimeError: Given groups=1, weight of size [10, 1, 1, 1], expected input[26891, 10, 5, 5] to have 1 channels, but got 10 channels instead

2

In your custom ResidualBlock implementation you are passing in_channels as well as out_channels as arguments, but are never using them and are hard-coding the channels instead as 1 input channel and 10 output channels.
Based on this, I would guess that self.block2 will raise the shape mismatch as it would expect to get an input with 1 input channel while the incoming activation (coming from self.block1) would have 10 channels.
Also, you are trying to set the in_/out_channels in block2 to 64, 64, which will also not work (as I previously described these values are not used currently but if they were they would also raise a shape mismatch).