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alterzero / DBPN-Pytorch

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)
https://alterzero.github.io/projects/DBPN.html
MIT License
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Error when using eval.py on pretrained models: size mismatch for module.output_conv.conv.weight: copying a param with shape torch.Size([3, 448, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 640, 3, 3]). #38

Closed titsitits closed 5 years ago

titsitits commented 5 years ago

Hi,

I am just trying your pre-trained models. This commands works fine for me: python eval.py --model './DBPNLL_x8.pth'

But using any other model, with or without changing the parameter "upscale_factor" leads to an error: python eval.py --upscale_factor 4 --model './DBPN_x4.pth' or python eval.py --model './DBPN_x4.pth' or python eval.py --model './DBPN_x8.pth'

==> RuntimeError: Error(s) in loading state_dict for DataParallel: Missing key(s) in state_dict: "module.down7.conv.conv.weight", "module.down7.conv.conv.bias", "module.down7.conv.act.weight", "module.down7.down_conv1.conv.weight", "module.down7.down_conv1.conv.bias", "module.down7.down_conv1.act.weight", "module.down7.down_conv2.deconv.weight", "module.down7.down_conv2.deconv.bias", "module.down7.down_conv2.act.weight", "module.down7.down_conv3.conv.weight", "module.down7.down_conv3.conv.bias", "module.down7.down_conv3.act.weight", "module.up8.conv.conv.weight", "module.up8.conv.conv.bias", "module.up8.conv.act.weight", "module.up8.up_conv1.deconv.weight", "module.up8.up_conv1.deconv.bias", "module.up8.up_conv1.act.weight", "module.up8.up_conv2.conv.weight", "module.up8.up_conv2.conv.bias", "module.up8.up_conv2.act.weight", "module.up8.up_conv3.deconv.weight", "module.up8.up_conv3.deconv.bias", "module.up8.up_conv3.act.weight", "module.down8.conv.conv.weight", "module.down8.conv.conv.bias", "module.down8.conv.act.weight", "module.down8.down_conv1.conv.weight", "module.down8.down_conv1.conv.bias", "module.down8.down_conv1.act.weight", "module.down8.down_conv2.deconv.weight", "module.down8.down_conv2.deconv.bias", "module.down8.down_conv2.act.weight", "module.down8.down_conv3.conv.weight", "module.down8.down_conv3.conv.bias", "module.down8.down_conv3.act.weight", "module.up9.conv.conv.weight", "module.up9.conv.conv.bias", "module.up9.conv.act.weight", "module.up9.up_conv1.deconv.weight", "module.up9.up_conv1.deconv.bias", "module.up9.up_conv1.act.weight", "module.up9.up_conv2.conv.weight", "module.up9.up_conv2.conv.bias", "module.up9.up_conv2.act.weight", "module.up9.up_conv3.deconv.weight", "module.up9.up_conv3.deconv.bias", "module.up9.up_conv3.act.weight", "module.down9.conv.conv.weight", "module.down9.conv.conv.bias", "module.down9.conv.act.weight", "module.down9.down_conv1.conv.weight", "module.down9.down_conv1.conv.bias", "module.down9.down_conv1.act.weight", "module.down9.down_conv2.deconv.weight", "module.down9.down_conv2.deconv.bias", "module.down9.down_conv2.act.weight", "module.down9.down_conv3.conv.weight", "module.down9.down_conv3.conv.bias", "module.down9.down_conv3.act.weight", "module.up10.conv.conv.weight", "module.up10.conv.conv.bias", "module.up10.conv.act.weight", "module.up10.up_conv1.deconv.weight", "module.up10.up_conv1.deconv.bias", "module.up10.up_conv1.act.weight", "module.up10.up_conv2.conv.weight", "module.up10.up_conv2.conv.bias", "module.up10.up_conv2.act.weight", "module.up10.up_conv3.deconv.weight", "module.up10.up_conv3.deconv.bias", "module.up10.up_conv3.act.weight". size mismatch for module.output_conv.conv.weight: copying a param with shape torch.Size([3, 448, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 640, 3, 3]).

Thank you in advance for your help.

titsitits commented 5 years ago

OK there is a need to define the model type correctly for it to work: python eval.py --upscale_factor 4 --model './DBPN_x4.pth' --model_type 'DBPN'

Hope it can help other users!