Question about your paper reproduction

Question

Question about your paper reproduction

sooyalim opened this issue 3 years ago · comments

Hi, I am a beginner in machine learning and I enjoyed reading your paper and I tried to reproduce some of your results.

However, after implementing ilvr_sample code, I keep get stuck when loading pre-trained model to the code. (there are some size mismatchs for U-net model)

I have not modified any of your codes on github.

I tried with model_path of models/ffhq_10m.pt and base_samples of ref_imgs/face.

Did this problem happen due to my poor skill?

I am sorry for asking this, but could you please check if the uploaded codes work well without any modification?

Thank you for reading.

These are the error messages I got.

Logging to ./output
creating model...
Traceback (most recent call last):
File "ilvr_sample.py", line 126, in
main()
File "ilvr_sample.py", line 50, in main
dist_util.load_state_dict(args.model_path, map_location="cpu")
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1483, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for UNetModel:
Missing key(s) in state_dict: "input_blocks.3.0.op.weight", "input_blocks.3.0.op.bias", "input_blocks.4.0.skip_connection.weight", "input_blocks.4.0.skip_connection.bias", "input_blocks.6.0.op.weight", "input_blocks.6.0.op.bias", "input_blocks.7.0.skip_connection.weight", "input_blocks.7.0.skip_connection.bias", "input_blocks.7.1.norm.weight", "input_blocks.7.1.norm.bias", "input_blocks.7.1.qkv.weight", "input_blocks.7.1.qkv.bias", "input_blocks.7.1.proj_out.weight", "input_blocks.7.1.proj_out.bias", "input_blocks.8.1.norm.weight", "input_blocks.8.1.norm.bias", "input_blocks.8.1.qkv.weight", "input_blocks.8.1.qkv.bias", "input_blocks.8.1.proj_out.weight", "input_blocks.8.1.proj_out.bias", "input_blocks.9.0.op.weight", "input_blocks.9.0.op.bias", "input_blocks.10.0.skip_connection.weight", "input_blocks.10.0.skip_connection.bias", "input_blocks.10.1.norm.weight", "input_blocks.10.1.norm.bias", "input_blocks.10.1.qkv.weight", "input_blocks.10.1.qkv.bias", "input_blocks.10.1.proj_out.weight", "input_blocks.10.1.proj_out.bias", "input_blocks.11.1.norm.weight", "input_blocks.11.1.norm.bias", "input_blocks.11.1.qkv.weight", "input_blocks.11.1.qkv.bias", "input_blocks.11.1.proj_out.weight", "input_blocks.11.1.proj_out.bias", "output_blocks.0.1.norm.weight", "output_blocks.0.1.norm.bias", "output_blocks.0.1.qkv.weight", "output_blocks.0.1.qkv.bias", "output_blocks.0.1.proj_out.weight", "output_blocks.0.1.proj_out.bias", "output_blocks.1.1.norm.weight", "output_blocks.1.1.norm.bias", "output_blocks.1.1.qkv.weight", "output_blocks.1.1.qkv.bias", "output_blocks.1.1.proj_out.weight", "output_blocks.1.1.proj_out.bias", "output_blocks.2.2.conv.weight", "output_blocks.2.2.conv.bias", "output_blocks.4.1.norm.weight", "output_blocks.4.1.norm.bias", "output_blocks.4.1.qkv.weight", "output_blocks.4.1.qkv.bias", "output_blocks.4.1.proj_out.weight", "output_blocks.4.1.proj_out.bias", "output_blocks.5.1.norm.weight", "output_blocks.5.1.norm.bias", "output_blocks.5.1.qkv.weight", "output_blocks.5.1.qkv.bias", "output_blocks.5.1.proj_out.weight", "output_blocks.5.1.proj_out.bias", "output_blocks.5.2.conv.weight", "output_blocks.5.2.conv.bias", "output_blocks.8.1.conv.weight", "output_blocks.8.1.conv.bias".
Unexpected key(s) in state_dict: "input_blocks.3.0.in_layers.0.weight", "input_blocks.3.0.in_layers.0.bias", "input_blocks.3.0.in_layers.2.weight", "input_blocks.3.0.in_layers.2.bias", "input_blocks.3.0.emb_layers.1.weight", "input_blocks.3.0.emb_layers.1.bias", "input_blocks.3.0.out_layers.0.weight", "input_blocks.3.0.out_layers.0.bias", "input_blocks.3.0.out_layers.3.weight", "input_blocks.3.0.out_layers.3.bias", "input_blocks.5.0.skip_connection.weight", "input_blocks.5.0.skip_connection.bias", "input_blocks.6.0.in_layers.0.weight", "input_blocks.6.0.in_layers.0.bias", "input_blocks.6.0.in_layers.2.weight", "input_blocks.6.0.in_layers.2.bias", "input_blocks.6.0.emb_layers.1.weight", "input_blocks.6.0.emb_layers.1.bias", "input_blocks.6.0.out_layers.0.weight", "input_blocks.6.0.out_layers.0.bias", "input_blocks.6.0.out_layers.3.weight", "input_blocks.6.0.out_layers.3.bias", "input_blocks.9.1.norm.weight", "input_blocks.9.1.norm.bias", "input_blocks.9.1.qkv.weight", "input_blocks.9.1.qkv.bias", "input_blocks.9.1.proj_out.weight", "input_blocks.9.1.proj_out.bias", "input_blocks.9.0.in_layers.0.weight", "input_blocks.9.0.in_layers.0.bias", "input_blocks.9.0.in_layers.2.weight", "input_blocks.9.0.in_layers.2.bias", "input_blocks.9.0.emb_layers.1.weight", "input_blocks.9.0.emb_layers.1.bias", "input_blocks.9.0.out_layers.0.weight", "input_blocks.9.0.out_layers.0.bias", "input_blocks.9.0.out_layers.3.weight", "input_blocks.9.0.out_layers.3.bias", "input_blocks.9.0.skip_connection.weight", "input_blocks.9.0.skip_connection.bias", "output_blocks.1.1.in_layers.0.weight", "output_blocks.1.1.in_layers.0.bias", "output_blocks.1.1.in_layers.2.weight", "output_blocks.1.1.in_layers.2.bias", "output_blocks.1.1.emb_layers.1.weight", "output_blocks.1.1.emb_layers.1.bias", "output_blocks.1.1.out_layers.0.weight", "output_blocks.1.1.out_layers.0.bias", "output_blocks.1.1.out_layers.3.weight", "output_blocks.1.1.out_layers.3.bias", "output_blocks.3.2.in_layers.0.weight", "output_blocks.3.2.in_layers.0.bias", "output_blocks.3.2.in_layers.2.weight", "output_blocks.3.2.in_layers.2.bias", "output_blocks.3.2.emb_layers.1.weight", "output_blocks.3.2.emb_layers.1.bias", "output_blocks.3.2.out_layers.0.weight", "output_blocks.3.2.out_layers.0.bias", "output_blocks.3.2.out_layers.3.weight", "output_blocks.3.2.out_layers.3.bias", "output_blocks.5.1.in_layers.0.weight", "output_blocks.5.1.in_layers.0.bias", "output_blocks.5.1.in_layers.2.weight", "output_blocks.5.1.in_layers.2.bias", "output_blocks.5.1.emb_layers.1.weight", "output_blocks.5.1.emb_layers.1.bias", "output_blocks.5.1.out_layers.0.weight", "output_blocks.5.1.out_layers.0.bias", "output_blocks.5.1.out_layers.3.weight", "output_blocks.5.1.out_layers.3.bias", "output_blocks.7.1.in_layers.0.weight", "output_blocks.7.1.in_layers.0.bias", "output_blocks.7.1.in_layers.2.weight", "output_blocks.7.1.in_layers.2.bias", "output_blocks.7.1.emb_layers.1.weight", "output_blocks.7.1.emb_layers.1.bias", "output_blocks.7.1.out_layers.0.weight", "output_blocks.7.1.out_layers.0.bias", "output_blocks.7.1.out_layers.3.weight", "output_blocks.7.1.out_layers.3.bias", "output_blocks.9.1.in_layers.0.weight", "output_blocks.9.1.in_layers.0.bias", "output_blocks.9.1.in_layers.2.weight", "output_blocks.9.1.in_layers.2.bias", "output_blocks.9.1.emb_layers.1.weight", "output_blocks.9.1.emb_layers.1.bias", "output_blocks.9.1.out_layers.0.weight", "output_blocks.9.1.out_layers.0.bias", "output_blocks.9.1.out_layers.3.weight", "output_blocks.9.1.out_layers.3.bias".
size mismatch for input_blocks.4.0.in_layers.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 128, 3, 3]).
size mismatch for input_blocks.4.0.in_layers.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.4.0.emb_layers.1.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for input_blocks.4.0.emb_layers.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for input_blocks.4.0.out_layers.0.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.4.0.out_layers.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.4.0.out_layers.3.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for input_blocks.4.0.out_layers.3.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.5.0.in_layers.0.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.5.0.in_layers.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for input_blocks.5.0.in_layers.2.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for input_blocks.7.0.in_layers.2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 256, 3, 3]).
size mismatch for input_blocks.7.0.in_layers.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.7.0.emb_layers.1.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([768, 512]).
size mismatch for input_blocks.7.0.emb_layers.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for input_blocks.7.0.out_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.7.0.out_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.7.0.out_layers.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for input_blocks.7.0.out_layers.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.in_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.in_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.in_layers.2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for input_blocks.8.0.in_layers.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.emb_layers.1.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([768, 512]).
size mismatch for input_blocks.8.0.emb_layers.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for input_blocks.8.0.out_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.out_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.8.0.out_layers.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for input_blocks.8.0.out_layers.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.10.0.in_layers.0.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.10.0.in_layers.0.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for input_blocks.10.0.in_layers.2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 384, 3, 3]).
size mismatch for output_blocks.2.0.in_layers.0.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([896]).
size mismatch for output_blocks.2.0.in_layers.0.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([896]).
size mismatch for output_blocks.2.0.in_layers.2.weight: copying a param with shape torch.Size([512, 1024, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 896, 3, 3]).
size mismatch for output_blocks.2.0.skip_connection.weight: copying a param with shape torch.Size([512, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 896, 1, 1]).
size mismatch for output_blocks.3.0.in_layers.0.weight: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([896]).
size mismatch for output_blocks.3.0.in_layers.0.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([896]).
size mismatch for output_blocks.3.0.in_layers.2.weight: copying a param with shape torch.Size([512, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 896, 3, 3]).
size mismatch for output_blocks.3.0.in_layers.2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.0.emb_layers.1.weight: copying a param with shape torch.Size([1024, 512]) from checkpoint, the shape in current model is torch.Size([768, 512]).
size mismatch for output_blocks.3.0.emb_layers.1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for output_blocks.3.0.out_layers.0.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.0.out_layers.0.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.0.out_layers.3.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for output_blocks.3.0.out_layers.3.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.0.skip_connection.weight: copying a param with shape torch.Size([512, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 896, 1, 1]).
size mismatch for output_blocks.3.0.skip_connection.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.1.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.1.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.3.1.qkv.weight: copying a param with shape torch.Size([1536, 512, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
size mismatch for output_blocks.3.1.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
size mismatch for output_blocks.3.1.proj_out.weight: copying a param with shape torch.Size([512, 512, 1]) from checkpoint, the shape in current model is torch.Size([384, 384, 1]).
size mismatch for output_blocks.3.1.proj_out.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.4.0.in_layers.2.weight: copying a param with shape torch.Size([256, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 768, 3, 3]).
size mismatch for output_blocks.4.0.in_layers.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.4.0.emb_layers.1.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([768, 512]).
size mismatch for output_blocks.4.0.emb_layers.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for output_blocks.4.0.out_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.4.0.out_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.4.0.out_layers.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for output_blocks.4.0.out_layers.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.4.0.skip_connection.weight: copying a param with shape torch.Size([256, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 768, 1, 1]).
size mismatch for output_blocks.4.0.skip_connection.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.5.0.in_layers.0.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for output_blocks.5.0.in_layers.0.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for output_blocks.5.0.in_layers.2.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 640, 3, 3]).
size mismatch for output_blocks.5.0.in_layers.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.5.0.emb_layers.1.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([768, 512]).
size mismatch for output_blocks.5.0.emb_layers.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for output_blocks.5.0.out_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.5.0.out_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.5.0.out_layers.3.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 384, 3, 3]).
size mismatch for output_blocks.5.0.out_layers.3.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.5.0.skip_connection.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([384, 640, 1, 1]).
size mismatch for output_blocks.5.0.skip_connection.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.6.0.in_layers.0.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for output_blocks.6.0.in_layers.0.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([640]).
size mismatch for output_blocks.6.0.in_layers.2.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 640, 3, 3]).
size mismatch for output_blocks.6.0.skip_connection.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 640, 1, 1]).
size mismatch for output_blocks.7.0.in_layers.0.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for output_blocks.7.0.in_layers.0.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for output_blocks.7.0.in_layers.2.weight: copying a param with shape torch.Size([256, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3]).
size mismatch for output_blocks.7.0.skip_connection.weight: copying a param with shape torch.Size([256, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).
size mismatch for output_blocks.8.0.in_layers.2.weight: copying a param with shape torch.Size([128, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 384, 3, 3]).
size mismatch for output_blocks.8.0.in_layers.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for output_blocks.8.0.emb_layers.1.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for output_blocks.8.0.emb_layers.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for output_blocks.8.0.out_layers.0.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for output_blocks.8.0.out_layers.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for output_blocks.8.0.out_layers.3.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for output_blocks.8.0.out_layers.3.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for output_blocks.8.0.skip_connection.weight: copying a param with shape torch.Size([128, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 384, 1, 1]).
size mismatch for output_blocks.8.0.skip_connection.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for output_blocks.9.0.in_layers.0.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.9.0.in_layers.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
size mismatch for output_blocks.9.0.in_layers.2.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 384, 3, 3]).
size mismatch for output_blocks.9.0.skip_connection.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 384, 1, 1]).
size mismatch for out.2.weight: copying a param with shape torch.Size([6, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 128, 3, 3]).
size mismatch for out.2.bias: copying a param with shape torch.Size([6]) from checkpoint, the shape in current model is torch.Size([3]).
[565458df4300:00904] *** Process received signal ***
[565458df4300:00904] Signal: Segmentation fault (11)
[565458df4300:00904] Signal code: Address not mapped (1)
[565458df4300:00904] Failing at address: 0x7ff91ee2320d
[565458df4300:00904] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7ff921acd980]
[565458df4300:00904] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7ff92170c8a5]
[565458df4300:00904] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7ff921f77e44]
[565458df4300:00904] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7ff92170d735]
[565458df4300:00904] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7ff921f75cb3]
[565458df4300:00904] *** End of error message ***

Jooyoung Choi · Answer 1 · Wed Dec 08 2021 13:55:27 GMT+0800 (China Standard Time)

Did you check the model flag when you run the script? For example:
--learn_sigma True --num_channels 128 --num_head_channels 64 --num_res_blocks 1 --resblock_updown True --use_scale_shift_norm True

sooyalim · Answer 2 · Wed Dec 08 2021 15:01:40 GMT+0800 (China Standard Time)

Thanks for the reply!!!! I could generate some samples with your help.

I will enjoy changing the number of N and range t to produce various results.

Thanks again for helping me out!

Fei Gao · Answer 3 · Tue Jun 27 2023 14:41:54 GMT+0800 (China Standard Time)

Did you check the model flag when you run the script? For example: --learn_sigma True --num_channels 128 --num_head_channels 64 --num_res_blocks 1 --resblock_updown True --use_scale_shift_norm True

Hi, I also met this RuntimeError: Error(s) in loading state_dict for UNetModel: when I want to run the code on my own dataset.

There is my code to run the script:
python scripts/ilvr_sample.py --attention_resolutions 16 --class_cond False --diffusion_steps 1000 --dropout 0.0 --image_size 512 --learn_sigma True --noise_schedule linear --num_channels 128 --num_head_channels 64 --num_res_blocks 3 --resblock_updown True --use_fp16 False --use_scale_shift_norm True --timestep_respacing 100 --model_path models/512x512_diffusion.pt --base_samples ref_imgs/magnetogram/500G --down_N 32 --range_t 20 --save_dir output/magnetogram_N32

Could you please tell me how to solve this problem? @jychoi118
Thanks a lot!!!

LZX-ZNRA · Answer 4 · Tue Jul 04 2023 23:41:37 GMT+0800 (China Standard Time)

My problem is also #2 (comment).

Could you please provide a way to solve it. Thanks. @jychoi118