I couldn't run the test with the pretrained weights.

[weizhang920]

The weight runs I trained myself performed poorly, and the pretrained weights gave me errors. Can the teacher help me?
Errors are as follows:
File "F:\BlumNet-main\detection\gcd\test.py", line 42, in main
missing_keys, unexpected_keys = model.load_state_dict(checkpoint['model'], strict=False)
File "D:\anaconda3\anaconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1406, in load_state_dict
raise RuntimeError(r'Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DeformableDETR:\n\tsize mismatch for transformer.level_embed: copying a param with shape torch.Size([3, 256]) from checkpoint, the shape in current model is torch.Size([4, 256]).
size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.encoder.layers.5.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.encoder.layers.5.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.encoder.layers.5.self_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.encoder.layers.5.self_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for transformer.decoder.layers.5.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([384, 256]) from checkpoint, the shape in current model is torch.Size([512, 256]).
size mismatch for transformer.decoder.layers.5.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for transformer.decoder.layers.5.cross_attn.attention_weights.weight: copying a param with shape torch.Size([192, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
size mismatch for transformer.decoder.layers.5.cross_attn.attention_weights.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for query_embed.weight: copying a param with shape torch.Size([1152, 512]) from checkpoint, the shape in current model is torch.Size([1024, 512]).

[Tcorpion]

The mismatch shape seems like your setting of out_pts is 0. If the parameter out_pts is 0, the model only outputs curves of 1024 query, the default setting is 128 for out_pts ( 128 query for junction point and endpoint, 128 + 1024 = 1152) in detection/train_gcd.sh.