jessicachen01
commented
1 year ago I just install all the pkgs needed and downloaded the dataset, and then I got
$ python run.py train -c myconfigs/config.json --cfg crnn.cfg Compose( ProcessChannels(mode=avg) AdditiveNoise(prob=0.3, sig=0.001, dist_type=normal) RandomCropLength(prob=0.4, sig=0.25, dist_type=half) ToTensorAudio() ) /home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torchaudio/transforms.py:917: UserWarning: torchaudio.transforms.ComplexNorm has been deprecated and will be removed from future release.Please convert the input Tensor to complex type with `torch.view_as_complex` then use `torch.abs` and `torch.angle`. Please refer to https://github.com/pytorch/audio/issues/1337 for more details about torchaudio's plan to migrate to native complex type. warnings.warn( /home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torchparse-0.1-py3.9.egg/torchparse/utils.py:54: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). return (spatial + p2 - k)//s + 1 0%| | 0/311 [00:00<?, ?it/s]/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/net/audio.py:10: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). return (lengths + 2 * pad - fft_length + hop_length) // hop_length /home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torchaudio/transforms.py:936: UserWarning: torchaudio.functional.functional.complex_norm has been deprecated and will be removed from 0.11 release. Please convert the input Tensor to complex type with `torch.view_as_complex` then use `torch.abs`. Please refer to https://github.com/pytorch/audio/issues/1337 for more details about torchaudio's plan to migrate to native complex type. return F.complex_norm(complex_tensor, self.power) 0%| | 0/311 [00:01<?, ?it/s] Traceback (most recent call last): File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/run.py", line 175, in <module> train_main(config, args.resume) File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/run.py", line 115, in train_main trainer.train() File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/train/base_trainer.py", line 88, in train result = self._train_epoch(epoch) File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/train/trainer.py", line 68, in _train_epoch output = self.model(data) File "/home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/net/model.py", line 52, in forward xt, lengths = self.spec(xt, lengths) File "/home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/gpu-server/disk/disk1/xuxin_workspace/projects/crnn-audio-classification-master/net/audio.py", line 55, in forward x = self.mel_scale(x) File "/home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/gpu-server/anaconda3/envs/audio_cls/lib/python3.9/site-packages/torchaudio/transforms.py", line 386, in forward mel_specgram = torch.matmul(specgram.transpose(-1, -2), self.fb).transpose(-1, -2) RuntimeError: mat1 and mat2 shapes cannot be multiplied (24600x1 and 1025x128)which is sad
Hi, just come across with the same error, have you fixed it? Look forward to your reply.
cpperrpr
crescentxxx
I just install all the pkgs needed and downloaded the dataset, and then I got
which is sad