size mismatch for transformer_decoder.layers.0.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]).

[stiwfmjX]

我用LEVIR数据集训练的模型，在测试的时候报错： Traceback (most recent call last): File "eval_cd.py", line 58, in main() File "eval_cd.py", line 54, in main model.eval_models(checkpoint_name=args.checkpoint_name) File "/tmp/pycharm_project_668/models/evaluator.py", line 158, in eval_models self._load_checkpoint(checkpoint_name) File "/tmp/pycharm_project_668/models/evaluator.py", line 70, in _load_checkpoint self.net_G.load_state_dict(checkpoint['model_G_state_dict']) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for BASE_Transformer: size mismatch for transformer_decoder.layers.0.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.0.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.0.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.0.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.1.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.1.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.1.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.1.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.2.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.2.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.2.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.2.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.3.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.3.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.3.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.3.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.4.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.4.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.4.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.4.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.5.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.5.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.5.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.5.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.6.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.6.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.6.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.6.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). size mismatch for transformer_decoder.layers.7.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.7.0.fn.fn.to_k.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.7.0.fn.fn.to_v.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). size mismatch for transformer_decoder.layers.7.0.fn.fn.to_out.0.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([32, 64]). 数据集的size是256*256的，训练和测试都是按照readme里写的方法，有人知道这个问题怎么解决吗？

[autumoon]

经过不断调试和测试，花了一天的时间才找到问题的所在。。。因为可能是作者故意留下的问题，所以这里就先不写出解决办法了，需要解决办法的可以私信我取得联系，9506#163.com

[stiwfmjX]

经过不断调试和测试，花了一天的时间才找到问题的所在。。。因为可能是作者故意留下的问题，所以这里就先不写出解决办法了，需要解决办法的可以私信我取得联系，9506#163.com

联系您啦！

[autumoon]

已经回复，请查收邮件

[ChengxiHAN]

已经回复，请查收邮件

你好，我的QQ是1121399040，想请教一下可视化的问题。

[wuwuevan]

已经回复，请查收邮件 请问您可以加一下我吗？我的qq是1261311737

[autumoon]

加你了 可视化的问题我没注意测试 可能我也帮不了你什么  

秋月的私语 @.***

 

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2022年9月12日(星期一) 中午11:16 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [justchenhao/BIT_CD] size mismatch for transformer_decoder.layers.0.0.fn.fn.to_q.weight: copying a param with shape torch.Size([512, 32]) from checkpoint, the shape in current model is torch.Size([64, 32]). (Issue #27)

已经回复，请查收邮件

你好，我的QQ是1121399040，想请教一下可视化的问题。

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.***>

[sun321123]

他把那个测试模型改了，测试模型换成和训练模型一样就行，改了半天，我以为是模型出错了呢，打印保存模型和现有模型才发现这个问题

[stiwfmjX]

他把那个测试模型改了，测试模型换成和训练模型一样就行，改了半天，我以为是模型出错了呢，打印保存模型和现有模型才发现这个问题

十分感谢！这个问题已经解决啦

[autumoon]

他把那个测试模型改了，测试模型换成和训练模型一样就行，改了半天，我以为是模型出错了呢，打印保存模型和现有模型才发现这个问题

是的 就是这个问题 我当时也是搞了半天。。

[zdw9915]

他把那个测试模型改了，测试模型换成和训练模型一样就行，改了半天，我以为是模型出错了呢，打印保存模型和现有模型才发现这个问题 非常感谢！问题已经解决了