Hi everyone,
I have a question about how to change the Normlization methond in resnet. When I first look at the code of resnet, I found that there is a attribute named norm_layer, where we could create BN layer. So, I try to initializing the norm_layer with nn.GroupNorm. However, I notice in the code of resnet, we just deliver the name nn.BatchNorm to norm_layer and use it to create our network. Sadly, the GN has two parameters, which means I can’t deliver the parameters of GN to ResNet class. So, my question is if I wanna change the Normlization from BN to GN, should I rewrite the Bottleneck and ResNet.
And I already try it but get some error with the weight shape and network input shape.
def norm2d(num_channels_per_group, planes):
    print("num_channels_per_group:{}".format(num_channels_per_group))
    if num_channels_per_group > 0:
        return GroupNorm(num_channels_per_group, planes, affine=True)
    else:
        return nn.BatchNorm2d(planes)

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 group_norm=0):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = norm2d(group_norm, planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = norm2d(group_norm, planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = norm2d(group_norm, planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
    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)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, group_norm=0):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm2d(64, group_norm)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0],
                                       group_norm=group_norm)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       group_norm=group_norm)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       group_norm=group_norm)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       group_norm=group_norm)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, GroupNorm):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

        for m in self.modules():
            if isinstance(m, Bottleneck):
                m.bn3.weight.data.fill_(0)
            if isinstance(m, BasicBlock):
                m.bn2.weight.data.fill_(0)

    def _make_layer(self, block, planes, blocks, stride=1, group_norm=0):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                norm2d(planes * block.expansion, group_norm),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample,
                            group_norm))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, group_norm=group_norm))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def _resnet_gn(arch, block, layers, pretrained, num_classes, group_norm, **kwargs):
    model = ResNet(block, layers, num_classes, group_norm, **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(MODEL_DIR))
    return model

class PD2SEModel(nn.Module):
    def __init__(self):
        super(PD2SEModel, self).__init__()
        # res_net_50_base = models.resnet50(pretrained = True)
        res_net_50_gn = _resnet_gn('resnet50', Bottleneck, [3, 4, 6, 3], pretrained=False, num_classes=45, group_norm=16)

        # res_net_50_base = models.resnet50()
        children_list = list(res_net_50_gn.children())
...

And the error is as follow:

Traceback (most recent call last):
File “/home/wzq/Work4money/AI-challenge-plant/code/train1.py”, line 305, in 
train_model()
File “/home/wzq/Work4money/AI-challenge-plant/code/train1.py”, line 140, in train_model
out1, out2, out3 = PD2SE(img)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 547, in call
result = self.forward(*input, **kwargs)
File “/home/wzq/Work4money/AI-challenge-plant/code/network.py”, line 55, in forward
severity_class = self.Layer0(x)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 547, in call
result = self.forward(*input, **kwargs)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/container.py”, line 92, in forward
input = module(input)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 547, in call
result = self.forward(*input, **kwargs)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/normalization.py”, line 225, in forward
input, self.num_groups, self.weight, self.bias, self.eps)
File “/usr/local/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/functional.py”, line 1692, in group_norm
torch.backends.cudnn.enabled)
RuntimeError: Expected weight to be a vector of size equal to the number of channels in input, but got weight of shape [16] and input of shape [32, 64, 112, 112]

Hope for your reply. Thanks a lot.