Exporting weights to Pytorch StyleGAN2 implementation

[adymaharana]

Hi,

Thanks for the wonderful work! I am trying to export the pretrained weights released in this repository to rosinality's implementation. I am using the export script from the stylegan2-ada-pytorch repository which is known to work for checkpoints using that repo (see here). However, when I used the converted Lafite checkpoints for the pretrained model COCO2014_CLIP_ViTB32_all_text.pkl, I am facing the following error:

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Generator:
size mismatch for conv1.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for to_rgb1.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.0.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.1.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.2.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.3.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.4.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.5.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.6.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.7.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.8.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for convs.9.conv.modulation.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for convs.10.conv.modulation.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for convs.11.conv.modulation.weight: copying a param with shape torch.Size([128, 1024]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for to_rgbs.0.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for to_rgbs.1.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for to_rgbs.2.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for to_rgbs.3.conv.modulation.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for to_rgbs.4.conv.modulation.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for to_rgbs.5.conv.modulation.weight: copying a param with shape torch.Size([128, 1024]) from checkpoint, the shape in current model is torch.Size([128, 512]).

Any idea, why this mismatch might be happening?

[drboog]

Hi,
Our architecture is different from original StyleGAN2. Also, we use fmap=1 by default, which influences the channel number, see

Lafite/train.py

Line 93 in a79c66a

fmap = 1.

Lafite/train.py

Line 243 in a79c66a

spec.fmaps = 1 if res >= 512 else fmap

Lafite/train.py

Line 251 in a79c66a

args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(spec.fmaps * 32768)