Malik Hashmat
Malik Hashmat
model = LightMUNet( spatial_dims=3, init_filters=32, in_channels=1, out_channels=14, blocks_down=[1, 2, 2, 2], blocks_up=[1, 1, 1], ) are the above arguments correct for loading the 3d model discussed in the paper?
> So the current model does not reflect the one mentioned in the paper? Even the initial and final layers don't have DWConv. Even with these changes, the model size...
model = LightMUNet( spatial_dims=3, init_filters=32, in_channels=input_channels, out_channels=num_classes, blocks_down=[1, 2, 2, 2], blocks_up=[1, 1, 1], ) Using the the model class from your updated code i am getting 2.9 M parameters.
Input channels: 1 Output channels: 14
Can you please provide a short script for this then? How many total parameters are you getting?
` from thop import profile model = LightMUNet( spatial_dims = 3, init_filters = 32, in_channels=1, out_channels=14, blocks_down=[1, 2, 2, 2], blocks_up=[1, 1, 1], ).cuda() data = torch.rand(1, 1, 256, 256,...
Haven't used thop package before, so I am also a bit confused. Using `from torchinfo import summary` to calculate parameters also gives 2.9M parameters
I think, ensemble does an apgd attack at different precisions as well as some other changes, as mentioned in the experiment section. This is a much stronger attack, therefore the...
Any update on this?