Checkpoints for Synthetic Burst SR does not match with the model

[Magauiya]

Dear authors!

Thank you for sharing your work with the community. Recently, I tried to inference Burstormer on Synthetic Burst SR validation set and found that provided checkpoint does not match with the model:

Missing key(s) in state_dict: "back_projection1.feat_fusion.0.weight", "back_projection1.feat_fusion.0.bias", "back_projection1.feat_expand.0.weight", "back_projection1.feat_expand.0.bias", "back_projection2.feat_fusion.0.weight", "back_projection2.feat_fusion.0.bias", "back_projection2.feat_expand.0.weight", "back_projection2.feat_expand.0.bias".
Unexpected key(s) in state_dict: "back_projection1.diff_fusion.weight", "back_projection1.feat_fusion.weight", "back_projection1.feat_expand.weight", "back_projection2.diff_fusion.weight", "back_projection2.feat_fusion.weight", "back_projection2.feat_expand.weight".
size mismatch for align.alignment0.back_projection.encoder1.0.norm1.body.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for align.alignment0.back_projection.encoder1.0.norm1.body.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]).
size mismatch for align.alignment0.back_projection.encoder1.0.attn.qk.weight: copying a param with shape torch.Size([192, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 48, 1, 1]).

I increased the num. of channels twice in ref_back_projection.encoder1, but still there is a mismatch in no_ref_back_projection. Checkpoint contains diff_fusion block weights, but the Burstormer model does not have it.

Can you please help to resolve this issue?

[DADAstrong]

Dear authors!

Thank you for sharing your work with the community. Recently, I tried to inference Burstormer on Synthetic Burst SR validation set and found that provided checkpoint does not match with the model:

Missing key(s) in state_dict: "back_projection1.feat_fusion.0.weight", "back_projection1.feat_fusion.0.bias", "back_projection1.feat_expand.0.weight", "back_projection1.feat_expand.0.bias", "back_projection2.feat_fusion.0.weight", "back_projection2.feat_fusion.0.bias", "back_projection2.feat_expand.0.weight", "back_projection2.feat_expand.0.bias". Unexpected key(s) in state_dict: "back_projection1.diff_fusion.weight", "back_projection1.feat_fusion.weight", "back_projection1.feat_expand.weight", "back_projection2.diff_fusion.weight", "back_projection2.feat_fusion.weight", "back_projection2.feat_expand.weight". size mismatch for align.alignment0.back_projection.encoder1.0.norm1.body.weight: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]). size mismatch for align.alignment0.back_projection.encoder1.0.norm1.body.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([48]). size mismatch for align.alignment0.back_projection.encoder1.0.attn.qk.weight: copying a param with shape torch.Size([192, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 48, 1, 1]).

I increased the num. of channels twice in ref_back_projection.encoder1, but still there is a mismatch in no_ref_back_projection. Checkpoint contains diff_fusion block weights, but the Burstormer model does not have it.

Can you please help to resolve this issue?

Hi, did you solve this issue?