Error while training using notebook

[dhaniram-kshirsagar]

!python run.py train -c my-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()
)
Traceback (most recent call last):
File "run.py", line 176, in
train_main(config, args.resume)
File "run.py", line 85, in train_main
model = getattr(net_module, m_name)(classes, config=config)
File "/Users/dk/projects/ns/misc_git_projects/crnn-audio-classification/net/model.py", line 29, in init
self.net = parse_cfg(config['cfg'], in_shape=[in_chan, self.spec.num_mels, 400])
File "/Users/dk/py_env/ts/lib/python3.7/site-packages/torchparse/parser.py", line 139, in parse_cfg
return CFGParser(fname).get_modules(in_shape)
File "/Users/dk/py_env/ts/lib/python3.7/site-packages/torchparse/parser.py", line 120, in get_modules
model = self._flow(in_shape)
File "/Users/dk/py_env/ts/lib/python3.7/site-packages/torchparse/parser.py", line 108, in _flow
in_shape = layer.get_out_shape()
File "/Users/dk/py_env/ts/lib/python3.7/site-packages/torchparse/base_layers.py", line 40, in get_out_shape
return torch.cat([channel, spatial])
RuntimeError: Expected object of scalar type Long but got scalar type Float for sequence element 1 in sequence argument at position #1 'tensors

[DonRichards]

Same

[giakoumidis]

same

[physiii]

Same.

Converted tensor to Long and was welcomed with new errors

[wayne391]

Hi, I got the same error before and successfully run the program by changing the Pytorch version.

conda install pytorch==1.2.0 torchvision==0.4.0 -c pytorch
pip install pillow=6.2.2

[undefinedXD]

Hi, I got the same error before and successfully run the program by changing the Pytorch version.

conda install pytorch==1.2.0 torchvision==0.4.0 -c pytorch
pip install pillow=6.2.2

it solves my problem ,thx

[ZishunYu]

Changing the 40th line in base_layers.py file in the torchparse package works for me.
- return torch.cat([channel, spatial])
+ return torch.cat([channel, spatial.long()])