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BatchNorm dimension mismatch
Dear authors,
I'm trying to reproduce the office-home experiments in the cls
directory with python run_experiments.py --exp 1
. However, I commented out Line 117 ('VisDA2017', 'Synthetic', 'Real')
. When running the experiment, I got the following error:
[INFORMATION] The bottleneck dim is 256
[Masking] Use color augmentation.
lr_bbone: 0.0002
lr_btlnck: 0.002
Traceback (most recent call last):
File "cdan_mcc_sdat_masking.py", line 393, in <module>
main(args)
File "cdan_mcc_sdat_masking.py", line 171, in main
train(train_source_iter, train_target_iter, classifier, teacher,
File "cdan_mcc_sdat_masking.py", line 235, in train
pseudo_label_t, pseudo_prob_t = teacher(x_t)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "../dalib/modules/teacher.py", line 58, in forward
logits, _ = self.ema_model(target_img)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "../common/modules/classifier.py", line 80, in forward
f = self.bottleneck(f)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py", line 168, in forward
return F.batch_norm(
File "/hpc/home/zj63/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2282, in batch_norm
return torch.batch_norm(
RuntimeError: running_mean should contain 197 elements not 256
Could you please give me some instructions on how to specify the hyperparameters correctly to reproduce only the office-home experiments?
Thanks and best regards
Hi, have you solved this problem? I also encountered the same problem.
Hi, have you solved this problem? I also encountered the same problem.
Hi, have you solved this problem? I also encountered the same problem.
No, I ended up using another method for benchmarking lol...