ytzhang99 / cf-net Goto Github PK

Official repository of "Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution"

MATLAB 7.50% Python 92.50%

deep-learning exposure-fusion pytorch super-resolution

cf-net's Introduction

Hi, I'm Zhang Yutong, nice to see you !👋

I'm a Postgradute Student in MC^2 Lab at Beihang University, working on High Dynamic Range Imaging (HDR), Multi-modal Image Restoration (MIR) and other computer vision tasks.

Official repositories:

CF-Net : Achieving multi-exposure fusion and image super-resolution simultaniously by exploring the latent relationship between the two tasks. (TIP 2021)

Paper implementations:

U2Fusion: A unified network for multi-modal image fusion. (TPAMI 2021)
CU-Net : An interpretable network for multi-modal image restoration and fusion. (TPAMI 2020)
NHDRRNet : A non-local network for ghost-free HDR imaging. (TIP 2020)
AHDRNet : An attention network for ghost-free HDR imaging. (CVPR 2019)

Academic Collections:

Awesome-HDR: A collection of HDR imaging papers and codes read and selected by myself.

😄 Nice and Passionate !

cf-net's People

Contributors

Stargazers

Watchers

Forkers

zebrajack zhwzhong nankeermeng xrosliang yimilirui xiaofeiwang2018 tjussh jjjjjjamesharden yilifan mc-2-lab mayanlee forkprojectinxmu cuiyixin555 sweeet12138 yycocl

cf-net's Issues

Models

Is there a trained model?

Supplied model is not match the code

Please check the model.py and option.py version is same with .pth model
Numerous state_dict names and dimensions is not match!

-------------------------------------------- Wrong message ------------------------------------------------
RuntimeError: Error(s) in loading state_dict for CFNet:
Missing key(s) in state_dict: "srb_1.compress_in.0.weight", "srb_1.compress_in.0.bias", "srb_1.compress_in.1.weight", "srb_1.upBlocks.0.0.weight", "srb_1.upBlocks.0.0.bias", "srb_1.upBlocks.0.1.weight", "srb_1.upBlocks.1.0.weight", "srb_1.upBlocks.1.0.bias", "srb_1.upBlocks.1.1.weight", "srb_1.upBlocks.2.0.weight", "srb_1.upBlocks.2.0.bias", "srb_1.upBlocks.2.1.weight", "srb_1.upBlocks.3.0.weight", "srb_1.upBlocks.3.0.bias", "srb_1.upBlocks.3.1.weight", "srb_1.upBlocks.4.0.weight", "srb_1.upBlocks.4.0.bias", "srb_1.upBlocks.4.1.weight", "srb_1.upBlocks.5.0.weight", "srb_1.upBlocks.5.0.bias", "srb_1.upBlocks.5.1.weight", "srb_1.downBlocks.0.0.weight", "srb_1.downBlocks.0.0.bias", "srb_1.downBlocks.0.1.weight", "srb_1.downBlocks.1.0.weight", "srb_1.downBlocks.1.0.bias", "srb_1.downBlocks.1.1.weight", "srb_1.downBlocks.2.0.weight", "srb_1.downBlocks.2.0.bias", "srb_1.downBlocks.2.1.weight", "srb_1.downBlocks.3.0.weight", "srb_1.downBlocks.3.0.bias", "srb_1.downBlocks.3.1.weight", "srb_1.downBlocks.4.0.weight", "srb_1.downBlocks.4.0.bias", "srb_1.downBlocks.4.1.weight", "srb_1.downBlocks.5.0.weight", "srb_1.downBlocks.5.0.bias", "srb_1.downBlocks.5.1.weight", "srb_1.uptranBlocks.0.0.weight", "srb_1.uptranBlocks.0.0.bias", "srb_1.uptranBlocks.0.1.weight", "srb_1.uptranBlocks.1.0.weight", "srb_1.uptranBlocks.1.0.bias", "srb_1.uptranBlocks.1.1.weight", "srb_1.uptranBlocks.2.0.weight", "srb_1.uptranBlocks.2.0.bias", "srb_1.uptranBlocks.2.1.weight", "srb_1.uptranBlocks.3.0.weight", "srb_1.uptranBlocks.3.0.bias", "srb_1.uptranBlocks.3.1.weight", "srb_1.uptranBlocks.4.0.weight", "srb_1.uptranBlocks.4.0.bias", "srb_1.uptranBlocks.4.1.weight", "srb_1.downtranBlocks.0.0.weight", "srb_1.downtranBlocks.0.0.bias", "srb_1.downtranBlocks.0.1.weight", "srb_1.downtranBlocks.1.0.weight", "srb_1.downtranBlocks.1.0.bias", "srb_1.downtranBlocks.1.1.weight", "srb_1.downtranBlocks.2.0.weight", "srb_1.downtranBlocks.2.0.bias", "srb_1.downtranBlocks.2.1.weight", "srb_1.downtranBlocks.3.0.weight", "srb_1.downtranBlocks.3.0.bias", "srb_1.downtranBlocks.3.1.weight", "srb_1.downtranBlocks.4.0.weight", "srb_1.downtranBlocks.4.0.bias", "srb_1.downtranBlocks.4.1.weight", "srb_1.compress_out.0.weight", "srb_1.compress_out.0.bias", "srb_1.compress_out.1.weight", "srb_2.compress_in.0.weight", "srb_2.compress_in.0.bias", "srb_2.compress_in.1.weight", "srb_2.upBlocks.0.0.weight", "srb_2.upBlocks.0.0.bias", "srb_2.upBlocks.0.1.weight", "srb_2.upBlocks.1.0.weight", "srb_2.upBlocks.1.0.bias", "srb_2.upBlocks.1.1.weight", "srb_2.upBlocks.2.0.weight", "srb_2.upBlocks.2.0.bias", "srb_2.upBlocks.2.1.weight", "srb_2.upBlocks.3.0.weight", "srb_2.upBlocks.3.0.bias", "srb_2.upBlocks.3.1.weight", "srb_2.upBlocks.4.0.weight", "srb_2.upBlocks.4.0.bias", "srb_2.upBlocks.4.1.weight", "srb_2.upBlocks.5.0.weight", "srb_2.upBlocks.5.0.bias", "srb_2.upBlocks.5.1.weight", "srb_2.downBlocks.0.0.weight", "srb_2.downBlocks.0.0.bias", "srb_2.downBlocks.0.1.weight", "srb_2.downBlocks.1.0.weight", "srb_2.downBlocks.1.0.bias", "srb_2.downBlocks.1.1.weight", "srb_2.downBlocks.2.0.weight", "srb_2.downBlocks.2.0.bias", "srb_2.downBlocks.2.1.weight", "srb_2.downBlocks.3.0.weight", "srb_2.downBlocks.3.0.bias", "srb_2.downBlocks.3.1.weight", "srb_2.downBlocks.4.0.weight", "srb_2.downBlocks.4.0.bias", "srb_2.downBlocks.4.1.weight", "srb_2.downBlocks.5.0.weight", "srb_2.downBlocks.5.0.bias", "srb_2.downBlocks.5.1.weight", "srb_2.uptranBlocks.0.0.weight", "srb_2.uptranBlocks.0.0.bias", "srb_2.uptranBlocks.0.1.weight", "srb_2.uptranBlocks.1.0.weight", "srb_2.uptranBlocks.1.0.bias", "srb_2.uptranBlocks.1.1.weight", "srb_2.uptranBlocks.2.0.weight", "srb_2.uptranBlocks.2.0.bias", "srb_2.uptranBlocks.2.1.weight", "srb_2.uptranBlocks.3.0.weight", "srb_2.uptranBlocks.3.0.bias", "srb_2.uptranBlocks.3.1.weight", "srb_2.uptranBlocks.4.0.weight", "srb_2.uptranBlocks.4.0.bias", "srb_2.uptranBlocks.4.1.weight", "srb_2.downtranBlocks.0.0.weight", "srb_2.downtranBlocks.0.0.bias", "srb_2.downtranBlocks.0.1.weight", "srb_2.downtranBlocks.1.0.weight", "srb_2.downtranBlocks.1.0.bias", "srb_2.downtranBlocks.1.1.weight", "srb_2.downtranBlocks.2.0.weight", "srb_2.downtranBlocks.2.0.bias", "srb_2.downtranBlocks.2.1.weight", "srb_2.downtranBlocks.3.0.weight", "srb_2.downtranBlocks.3.0.bias", "srb_2.downtranBlocks.3.1.weight", "srb_2.downtranBlocks.4.0.weight", "srb_2.downtranBlocks.4.0.bias", "srb_2.downtranBlocks.4.1.weight", "srb_2.compress_out.0.weight", "srb_2.compress_out.0.bias", "srb_2.compress_out.1.weight", "out_1.0.0.weight", "out_1.0.0.bias", "out_1.0.1.weight", "out_1.1.0.weight", "out_1.1.0.bias", "out_1.1.1.weight", "out_1.2.0.weight", "out_1.2.0.bias", "out_1.2.1.weight", "conv_out_1.0.0.weight", "conv_out_1.0.0.bias", "conv_out_1.1.0.weight", "conv_out_1.1.0.bias", "conv_out_1.2.0.weight", "conv_out_1.2.0.bias", "out_2.0.0.weight", "out_2.0.0.bias", "out_2.0.1.weight", "out_2.1.0.weight", "out_2.1.0.bias", "out_2.1.1.weight", "out_2.2.0.weight", "out_2.2.0.bias", "out_2.2.1.weight", "conv_out_2.0.0.weight", "conv_out_2.0.0.bias", "conv_out_2.1.0.weight", "conv_out_2.1.0.bias", "conv_out_2.2.0.weight", "conv_out_2.2.0.bias", "block_over_0.re_guide.0.weight", "block_over_0.re_guide.0.bias", "block_over_0.re_guide.1.weight", "block_under_0.re_guide.0.weight", "block_under_0.re_guide.0.bias", "block_under_0.re_guide.1.weight", "block_over_1.re_guide.0.weight", "block_over_1.re_guide.0.bias", "block_over_1.re_guide.1.weight", "block_under_1.re_guide.0.weight", "block_under_1.re_guide.0.bias", "block_under_1.re_guide.1.weight", "block_over_2.re_guide.0.weight", "block_over_2.re_guide.0.bias", "block_over_2.re_guide.1.weight", "block_under_2.re_guide.0.weight", "block_under_2.re_guide.0.bias", "block_under_2.re_guide.1.weight".
Unexpected key(s) in state_dict: "conv_in_over_1.0.weight", "conv_in_over_1.0.bias", "conv_in_over_1.1.weight", "feat_in_over_1.0.weight", "feat_in_over_1.0.bias", "feat_in_over_1.1.weight", "out_over_1.0.weight", "out_over_1.0.bias", "out_over_1.1.weight", "conv_out_over_1.0.weight", "conv_out_over_1.0.bias", "conv_in_under_1.0.weight", "conv_in_under_1.0.bias", "conv_in_under_1.1.weight", "feat_in_under_1.0.weight", "feat_in_under_1.0.bias", "feat_in_under_1.1.weight", "out_under_1.0.weight", "out_under_1.0.bias", "out_under_1.1.weight", "conv_out_under_1.0.weight", "conv_out_under_1.0.bias", "conv_in_over_2.0.weight", "conv_in_over_2.0.bias", "conv_in_over_2.1.weight", "feat_in_over_2.0.weight", "feat_in_over_2.0.bias", "feat_in_over_2.1.weight", "out_over_2.0.weight", "out_over_2.0.bias", "out_over_2.1.weight", "conv_out_over_2.0.weight", "conv_out_over_2.0.bias", "conv_in_under_2.0.weight", "conv_in_under_2.0.bias", "conv_in_under_2.1.weight", "feat_in_under_2.0.weight", "feat_in_under_2.0.bias", "feat_in_under_2.1.weight", "out_under_2.0.weight", "out_under_2.0.bias", "out_under_2.1.weight", "conv_out_under_2.0.weight", "conv_out_under_2.0.bias", "conv_in_over_3.0.weight", "conv_in_over_3.0.bias", "conv_in_over_3.1.weight", "feat_in_over_3.0.weight", "feat_in_over_3.0.bias", "feat_in_over_3.1.weight", "block_over_3.compress_in.0.weight", "block_over_3.compress_in.0.bias", "block_over_3.compress_in.1.weight", "block_over_3.upBlocks.0.0.weight", "block_over_3.upBlocks.0.0.bias", "block_over_3.upBlocks.0.1.weight", "block_over_3.upBlocks.1.0.weight", "block_over_3.upBlocks.1.0.bias", "block_over_3.upBlocks.1.1.weight", "block_over_3.upBlocks.2.0.weight", "block_over_3.upBlocks.2.0.bias", "block_over_3.upBlocks.2.1.weight", "block_over_3.upBlocks.3.0.weight", "block_over_3.upBlocks.3.0.bias", "block_over_3.upBlocks.3.1.weight", "block_over_3.upBlocks.4.0.weight", "block_over_3.upBlocks.4.0.bias", "block_over_3.upBlocks.4.1.weight", "block_over_3.upBlocks.5.0.weight", "block_over_3.upBlocks.5.0.bias", "block_over_3.upBlocks.5.1.weight", "block_over_3.downBlocks.0.0.weight", "block_over_3.downBlocks.0.0.bias", "block_over_3.downBlocks.0.1.weight", "block_over_3.downBlocks.1.0.weight", "block_over_3.downBlocks.1.0.bias", "block_over_3.downBlocks.1.1.weight", "block_over_3.downBlocks.2.0.weight", "block_over_3.downBlocks.2.0.bias", "block_over_3.downBlocks.2.1.weight", "block_over_3.downBlocks.3.0.weight", "block_over_3.downBlocks.3.0.bias", "block_over_3.downBlocks.3.1.weight", "block_over_3.downBlocks.4.0.weight", "block_over_3.downBlocks.4.0.bias", "block_over_3.downBlocks.4.1.weight", "block_over_3.downBlocks.5.0.weight", "block_over_3.downBlocks.5.0.bias", "block_over_3.downBlocks.5.1.weight", "block_over_3.uptranBlocks.0.0.weight", "block_over_3.uptranBlocks.0.0.bias", "block_over_3.uptranBlocks.0.1.weight", "block_over_3.uptranBlocks.1.0.weight", "block_over_3.uptranBlocks.1.0.bias", "block_over_3.uptranBlocks.1.1.weight", "block_over_3.uptranBlocks.2.0.weight", "block_over_3.uptranBlocks.2.0.bias", "block_over_3.uptranBlocks.2.1.weight", "block_over_3.uptranBlocks.3.0.weight", "block_over_3.uptranBlocks.3.0.bias", "block_over_3.uptranBlocks.3.1.weight", "block_over_3.uptranBlocks.4.0.weight", "block_over_3.uptranBlocks.4.0.bias", "block_over_3.uptranBlocks.4.1.weight", "block_over_3.downtranBlocks.0.0.weight", "block_over_3.downtranBlocks.0.0.bias", "block_over_3.downtranBlocks.0.1.weight", "block_over_3.downtranBlocks.1.0.weight", "block_over_3.downtranBlocks.1.0.bias", "block_over_3.downtranBlocks.1.1.weight", "block_over_3.downtranBlocks.2.0.weight", "block_over_3.downtranBlocks.2.0.bias", "block_over_3.downtranBlocks.2.1.weight", "block_over_3.downtranBlocks.3.0.weight", "block_over_3.downtranBlocks.3.0.bias", "block_over_3.downtranBlocks.3.1.weight", "block_over_3.downtranBlocks.4.0.weight", "block_over_3.downtranBlocks.4.0.bias", "block_over_3.downtranBlocks.4.1.weight", "block_over_3.compress_out.0.weight", "block_over_3.compress_out.0.bias", "block_over_3.compress_out.1.weight", "out_over_3.0.weight", "out_over_3.0.bias", "out_over_3.1.weight", "conv_out_over_3.0.weight", "conv_out_over_3.0.bias", "conv_in_under_3.0.weight", "conv_in_under_3.0.bias", "conv_in_under_3.1.weight", "feat_in_under_3.0.weight", "feat_in_under_3.0.bias", "feat_in_under_3.1.weight", "block_under_3.compress_in.0.weight", "block_under_3.compress_in.0.bias", "block_under_3.compress_in.1.weight", "block_under_3.upBlocks.0.0.weight", "block_under_3.upBlocks.0.0.bias", "block_under_3.upBlocks.0.1.weight", "block_under_3.upBlocks.1.0.weight", "block_under_3.upBlocks.1.0.bias", "block_under_3.upBlocks.1.1.weight", "block_under_3.upBlocks.2.0.weight", "block_under_3.upBlocks.2.0.bias", "block_under_3.upBlocks.2.1.weight", "block_under_3.upBlocks.3.0.weight", "block_under_3.upBlocks.3.0.bias", "block_under_3.upBlocks.3.1.weight", "block_under_3.upBlocks.4.0.weight", "block_under_3.upBlocks.4.0.bias", "block_under_3.upBlocks.4.1.weight", "block_under_3.upBlocks.5.0.weight", "block_under_3.upBlocks.5.0.bias", "block_under_3.upBlocks.5.1.weight", "block_under_3.downBlocks.0.0.weight", "block_under_3.downBlocks.0.0.bias", "block_under_3.downBlocks.0.1.weight", "block_under_3.downBlocks.1.0.weight", "block_under_3.downBlocks.1.0.bias", "block_under_3.downBlocks.1.1.weight", "block_under_3.downBlocks.2.0.weight", "block_under_3.downBlocks.2.0.bias", "block_under_3.downBlocks.2.1.weight", "block_under_3.downBlocks.3.0.weight", "block_under_3.downBlocks.3.0.bias", "block_under_3.downBlocks.3.1.weight", "block_under_3.downBlocks.4.0.weight", "block_under_3.downBlocks.4.0.bias", "block_under_3.downBlocks.4.1.weight", "block_under_3.downBlocks.5.0.weight", "block_under_3.downBlocks.5.0.bias", "block_under_3.downBlocks.5.1.weight", "block_under_3.uptranBlocks.0.0.weight", "block_under_3.uptranBlocks.0.0.bias", "block_under_3.uptranBlocks.0.1.weight", "block_under_3.uptranBlocks.1.0.weight", "block_under_3.uptranBlocks.1.0.bias", "block_under_3.uptranBlocks.1.1.weight", "block_under_3.uptranBlocks.2.0.weight", "block_under_3.uptranBlocks.2.0.bias", "block_under_3.uptranBlocks.2.1.weight", "block_under_3.uptranBlocks.3.0.weight", "block_under_3.uptranBlocks.3.0.bias", "block_under_3.uptranBlocks.3.1.weight", "block_under_3.uptranBlocks.4.0.weight", "block_under_3.uptranBlocks.4.0.bias", "block_under_3.uptranBlocks.4.1.weight", "block_under_3.downtranBlocks.0.0.weight", "block_under_3.downtranBlocks.0.0.bias", "block_under_3.downtranBlocks.0.1.weight", "block_under_3.downtranBlocks.1.0.weight", "block_under_3.downtranBlocks.1.0.bias", "block_under_3.downtranBlocks.1.1.weight", "block_under_3.downtranBlocks.2.0.weight", "block_under_3.downtranBlocks.2.0.bias", "block_under_3.downtranBlocks.2.1.weight", "block_under_3.downtranBlocks.3.0.weight", "block_under_3.downtranBlocks.3.0.bias", "block_under_3.downtranBlocks.3.1.weight", "block_under_3.downtranBlocks.4.0.weight", "block_under_3.downtranBlocks.4.0.bias", "block_under_3.downtranBlocks.4.1.weight", "block_under_3.compress_out.0.weight", "block_under_3.compress_out.0.bias", "block_under_3.compress_out.1.weight", "out_under_3.0.weight", "out_under_3.0.bias", "out_under_3.1.weight", "conv_out_under_3.0.weight", "conv_out_under_3.0.bias", "conv_in_under_0.1.weight".
size mismatch for block_over_0.compress_in.0.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 192, 1, 1]).
size mismatch for block_under_0.compress_in.0.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 192, 1, 1]).

mistakes

Please check the model.py and option.py version is same with .pth model
Numerous state_dict names and dimensions is not match!

-------------------------------------------- Wrong message ------------------------------------------------
RuntimeError: Error(s) in loading state_dict for CFNet:
size mismatch for srb_1.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_1.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_over.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for srb_2.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_under.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_1.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_1.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_1.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_2.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_2.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for out_2.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over0.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under0.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over1.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under1.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_over2.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.upBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.0.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.1.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.2.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.3.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.4.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).
size mismatch for cfb_under2.downBlocks.5.0.weight: copying a param with shape torch.Size([64, 64, 8, 8]) from checkpoint, the shape in current model is torch.Size([64, 64, 6, 6]).

quantitative performance

Hi, Zhang!
Excellent work. But I wonder what the function to calculate PSNR and SSIM. The result obtained by the function in test.py seems not to match the score in Table V (e.g. 4x, the re-evaluated result is about 20.5db)

RuntimeError

RuntimeError: stack expects each tensor to be equal size, but got [3, 128, 128] at entry 0 and [3, 28, 128] at entry 1
I have processed the data as requested. So is there any other needed process?

File "D:\CF-Net\CF-Net-master\CF-Net-master\train.py", line 57, in train
for l_over, l_under, h_over, h_under, h in self.train_loader:
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next
data = self._next_data()
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data\dataloader.py", line 561, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data_utils\fetch.py", line 47, in fetch
return self.collate_fn(data)
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data_utils\collate.py", line 84, in default_collate
return [default_collate(samples) for samples in transposed]
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data_utils\collate.py", line 84, in
return [default_collate(samples) for samples in transposed]
File "D:\Anaconda\envs\torch\lib\site-packages\torch\utils\data_utils\collate.py", line 56, in default_collate
return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [3, 128, 128] at entry 0 and [3, 28, 128] at entry 1

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