Hi, I replace the model in the tutorials with deep learning models like ResNet and DenseNet, errors occur during the simulation: in _load_from_state_dict input_param = input_param[0]

IndexError: index 0 is out of bounds for dimension 0 with size 0.

I have no idea how to solve it since there’s no error when I train and test the deep learning model without federated learning.

Hello @thefool. It looks maybe like an issue with converting the model weights to `NDArrays`

. Can you share more details of the error and/or your code?

Thanks for the reply.

I just replace the model in the tutorial with the models pytorch have and modify them to satisfy the input and output size of CIFAR-10. For example:

net=densenet121() net.classifier = nn.Linear(1024, 10) OR

class CIFAR10ResNet(nn.Module):

def **init**(self):

super(CIFAR10ResNet, self).**init**()

self.resnet = models.resnet18(weights=None)

self.resnet.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)

self.resnet.maxpool = nn.Identity()

self.resnet.fc = nn.Linear(512, 10)

def forward(self, x):

return self.resnet(x)

I would appreciate it if anyone could replace the model in the tutorials with models like ResNet and DenseNet to see if they can work.

That’s a good suggestion. In our old examples, we used `MobileNetV2`

(see this for example) but no longer include that in our examples. Perhaps it’s worth doing so now.

Would you be willing to contribute an example @thefool?

But as I state in this post when I want to use deep learning models to replace the models in the flower tutorials I meet errors. Sorry I can’t contribute an example.

I added a more complete minimum reproducible example in the corresponding Github issue: State dict creation bug for e.g. resnet18 · Issue #4344 · adap/flower · GitHub

Thank you for the solution!

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