using this link : https://github.com/joe-siyuan-qiao/WeightStandardization 9 from  pytorch-classification  folder i was using resnet50 for a classification task,
here  is what i did - copied code from here : https://github.com/joe-siyuan-qiao/pytorch-classification/blob/e6355f829e85ac05a71b8889f4fff77b9ab95d0b/models/imagenet/resnet.py 2
and did - model = l_resnet50()
now we have resnet50 model that uses gn and ws together but then i was trying to load resnet50 weight file provided by author of that repo
but i get this  error :
RuntimeError: Error(s) in loading state_dict for ResNet:
	Missing key(s) in state_dict: "layer3.6.conv1.weight", "layer3.6.bn1.weight", "layer3.6.bn1.bias", "layer3.6.conv2.weight", "layer3.6.bn2.weight", "layer3.6.bn2.bias", "layer3.6.conv3.weight", "layer3.6.bn3.weight", "layer3.6.bn3.bias", "layer3.7.conv1.weight", "layer3.7.bn1.weight", "layer3.7.bn1.bias", "layer3.7.conv2.weight", "layer3.7.bn2.weight", "layer3.7.bn2.bias", "layer3.7.conv3.weight", "layer3.7.bn3.weight", "layer3.7.bn3.bias", "layer3.8.conv1.weight", "layer3.8.bn1.weight", "layer3.8.bn1.bias", "layer3.8.conv2.weight", "layer3.8.bn2.weight", "layer3.8.bn2.bias", "layer3.8.conv3.weight", "layer3.8.bn3.weight", "layer3.8.bn3.bias", "layer3.9.conv1.weight", "layer3.9.bn1.weight", "layer3.9.bn1.bias", "layer3.9.conv2.weight", "layer3.9.bn2.weight", "layer3.9.bn2.bias", "layer3.9.conv3.weight", "layer3.9.bn3.weight", "layer3.9.bn3.bias", "layer3.10.conv1.weight", "layer3.10.bn1.weight", "layer3.10.bn1.bias", "layer3.10.conv2.weight", "layer3.10.bn2.weight", "layer3.10.bn2.bias", "layer3.10.conv3.weight", "layer3.10.bn3.weight", "layer3.10.bn3.bias", "layer3.11.conv1.weight", "layer3.11.bn1.weight", "layer3.11.bn1.bias", "layer3.11.conv2.weight", "layer3.11.bn2.weight", "layer3.11.bn2.bias", "layer3.11.conv3.weight", "layer3.11.bn3.weight", "layer3.11.bn3.bias", "layer3.12.conv1.weight", "layer3.12.bn1.weight", "layer3.12.bn1.bias", "layer3.12.conv2.weight", "layer3.12.bn2.weight", "layer3.12.bn2.bias", "layer3.12.conv3.weight", "layer3.12.bn3.weight", "layer3.12.bn3.bias", "layer3.13.conv1.weight", "layer3.13.bn1.weight", "layer3.13.bn1.bias", "layer3.13.conv2.weight", "layer3.13.bn2.weight", "layer3.13.bn2.bias", "layer3.13.conv3.weight", "layer3.13.bn3.weight", "layer3.13.bn3.bias", "layer3.14.conv1.weight", "layer3.14.bn1.weight", "layer3.14.bn1.bias", "layer3.14.conv2.weight", "layer3.14.bn2.weight", "layer3.14.bn2.bias", "layer3.14.conv3.weight", "layer3.14.bn3.weight", "layer3.14.bn3.bias", "layer3.15.conv1.weight", "layer3.15.bn1.weight", "layer3.15.bn1.bias", "layer3.15.conv2.weight", "layer3.15.bn2.weight", "layer3.15.bn2.bias", "layer3.15.conv3.weight", "layer3.15.bn3.weight", "layer3.15.bn3.bias", "layer3.16.conv1.weight", "layer3.16.bn1.weight", "layer3.16.bn1.bias", "layer3.16.conv2.weight", "layer3.16.bn2.weight", "layer3.16.bn2.bias", "layer3.16.conv3.weight", "layer3.16.bn3.weight", "layer3.16.bn3.bias", "layer3.17.conv1.weight", "layer3.17.bn1.weight", "layer3.17.bn1.bias", "layer3.17.conv2.weight", "layer3.17.bn2.weight", "layer3.17.bn2.bias", "layer3.17.conv3.weight", "layer3.17.bn3.weight", "layer3.17.bn3.bias", "layer3.18.conv1.weight", "layer3.18.bn1.weight", "layer3.18.bn1.bias", "layer3.18.conv2.weight", "layer3.18.bn2.weight", "layer3.18.bn2.bias", "layer3.18.conv3.weight", "layer3.18.bn3.weight", "layer3.18.bn3.bias", "layer3.19.conv1.weight", "layer3.19.bn1.weight", "layer3.19.bn1.bias", "layer3.19.conv2.weight", "layer3.19.bn2.weight", "layer3.19.bn2.bias", "layer3.19.conv3.weight", "layer3.19.bn3.weight", "layer3.19.bn3.bias", "layer3.20.conv1.weight", "layer3.20.bn1.weight", "layer3.20.bn1.bias", "layer3.20.conv2.weight", "layer3.20.bn2.weight", "layer3.20.bn2.bias", "layer3.20.conv3.weight", "layer3.20.bn3.weight", "layer3.20.bn3.bias", "layer3.21.conv1.weight", "layer3.21.bn1.weight", "layer3.21.bn1.bias", "layer3.21.conv2.weight", "layer3.21.bn2.weight", "layer3.21.bn2.bias", "layer3.21.conv3.weight", "layer3.21.bn3.weight", "layer3.21.bn3.bias", "layer3.22.conv1.weight", "layer3.22.bn1.weight", "layer3.22.bn1.bias", "layer3.22.conv2.weight", "layer3.22.bn2.weight", "layer3.22.bn2.bias", "layer3.22.conv3.weight", "layer3.22.bn3.weight", "layer3.22.bn3.bias". 
	Unexpected key(s) in state_dict: "bn1.running_mean", "bn1.running_var", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.0.bn3.running_mean", "layer1.0.bn3.running_var", "layer1.0.downsample.1.running_mean", "layer1.0.downsample.1.running_var", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer1.1.bn3.running_mean", "layer1.1.bn3.running_var", "layer1.2.bn1.running_mean", "layer1.2.bn1.running_var", "layer1.2.bn2.running_mean", "layer1.2.bn2.running_var", "layer1.2.bn3.running_mean", "layer1.2.bn3.running_var", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.bn3.running_mean", "layer2.0.bn3.running_var", "layer2.0.downsample.1.running_mean", "layer2.0.downsample.1.running_var", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer2.1.bn3.running_mean", "layer2.1.bn3.running_var", "layer2.2.bn1.running_mean", "layer2.2.bn1.running_var", "layer2.2.bn2.running_mean", "layer2.2.bn2.running_var", "layer2.2.bn3.running_mean", "layer2.2.bn3.running_var", "layer2.3.bn1.running_mean", "layer2.3.bn1.running_var", "layer2.3.bn2.running_mean", "layer2.3.bn2.running_var", "layer2.3.bn3.running_mean", "layer2.3.bn3.running_var", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.bn3.running_mean", "layer3.0.bn3.running_var", "layer3.0.downsample.1.running_mean", "layer3.0.downsample.1.running_var", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer3.1.bn3.running_mean", "layer3.1.bn3.running_var", "layer3.2.bn1.running_mean", "layer3.2.bn1.running_var", "layer3.2.bn2.running_mean", "layer3.2.bn2.running_var", "layer3.2.bn3.running_mean", "layer3.2.bn3.running_var", "layer3.3.bn1.running_mean", "layer3.3.bn1.running_var", "layer3.3.bn2.running_mean", "layer3.3.bn2.running_var", "layer3.3.bn3.running_mean", "layer3.3.bn3.running_var", "layer3.4.bn1.running_mean", "layer3.4.bn1.running_var", "layer3.4.bn2.running_mean", "layer3.4.bn2.running_var", "layer3.4.bn3.running_mean", "layer3.4.bn3.running_var", "layer3.5.bn1.running_mean", "layer3.5.bn1.running_var", "layer3.5.bn2.running_mean", "layer3.5.bn2.running_var", "layer3.5.bn3.running_mean", "layer3.5.bn3.running_var", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.bn3.running_mean", "layer4.0.bn3.running_var", "layer4.0.downsample.1.running_mean", "layer4.0.downsample.1.running_var", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "layer4.1.bn3.running_mean", "layer4.1.bn3.running_var", "layer4.2.bn1.running_mean", "layer4.2.bn1.running_var", "layer4.2.bn2.running_mean", "layer4.2.bn2.running_var", "layer4.2.bn3.running_mean", "layer4.2.bn3.running_var". 


then i tried loading that weight  file into resnet50 model of torchvision and  it works fine there, so it is clear that the error coming from modified resnet50 with gn and  ws but how do i use pretrained weight of author then? the author shared pretrained weight link in his repo and i can’t load that in gn,ws resnet50  model that he designed,i can only use his resnet50 with gn,ws and without the resnet50 weight file he provided,that means i am not able to load weights he shared,where  am i making mistakes?
resnet50 weight was collected from here : https://github.com/joe-siyuan-qiao/pytorch-classification/tree/e6355f829e85ac05a71b8889f4fff77b9ab95d0b 1