Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\core.py:77 in init
kwargs[nm].setup(self)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:202 in setup
for t in tfms: self.add(t,items, train_setup)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:206 in add
for t in ts: t.setup(items, train_setup)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:89 in setup
return self.setups(getattr(items, 'train', items) if train_setup else items)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\dispatch.py:122 in call
return f(*args, **kwargs)
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\preprocessing.py:159 in setups
o, *_ = dl.one_batch()
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:189 in one_batch
with self.fake_l.no_multiproc(): res = first(self)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\basics.py:709 in first
return next(x, None)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:129 in iter
for b in _loadersself.fake_l.num_workers==0:
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data\dataloader.py:631 in next
data = self._next_data()
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data\dataloader.py:675 in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data_utils\fetch.py:41 in fetch
data = next(self.dataset_iter)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:140 in create_batches
yield from map(self.do_batch, self.chunkify(res))
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:185 in do_batch
def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:656 in create_batch
return self.dataset[b]
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:515 in getitem
return tuple([ptl[it] for ptl in self.ptls])
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:515 in
return tuple([ptl[it] for ptl in self.ptls])
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\torch_core.py:384 in torch_function
res = super().torch_function(func, types, args, ifnone(kwargs, {}))
File D:\Anaconda\envs\TSAI\lib\site-packages\torch_tensor.py:1418 in __torch_function__
ret = func(*args, **kwargs)
RuntimeError: Could not infer dtype of numpy.int16
mPowc
commented
2 hours ago
When I attempted to use the TSC function, I encountered some errors. Could you assist me in resolving these issues?
dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=[64, 128], batch_tfms=[TSStandardize()], num_workers=0) Traceback (most recent call last):
Cell In[20], line 1 dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=[64, 128], batch_tfms=[TSStandardize()], num_workers=0)
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:916 in from_dsets loaders = [cls._dl_type(d, bs=b, num_workers=num_workers, batch_tfms=batch_tfms, weights=w, partial_n=n, sampler=s, sort=sort, vocab=vocab, **k)\
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:916 in
loaders = [cls._dl_type(d, bs=b, num_workers=num_workers, batch_tfms=batch_tfms, weights=w, partial_n=n, sampler=s, sort=sort, vocab=vocab, **k)\
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:630 in init super().init(dataset, bs=bs, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers, verbose=verbose, do_setup=do_setup, **kwargs)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\core.py:77 in init kwargs[nm].setup(self)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:202 in setup for t in tfms: self.add(t,items, train_setup)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:206 in add for t in ts: t.setup(items, train_setup)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\transform.py:89 in setup return self.setups(getattr(items, 'train', items) if train_setup else items)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\dispatch.py:122 in call return f(*args, **kwargs)
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\preprocessing.py:159 in setups o, *_ = dl.one_batch()
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:189 in one_batch with self.fake_l.no_multiproc(): res = first(self)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastcore\basics.py:709 in first return next(x, None)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:129 in iter for b in _loadersself.fake_l.num_workers==0:
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data\dataloader.py:631 in next data = self._next_data()
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data\dataloader.py:675 in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File D:\Anaconda\envs\TSAI\lib\site-packages\torch\utils\data_utils\fetch.py:41 in fetch data = next(self.dataset_iter)
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:140 in create_batches yield from map(self.do_batch, self.chunkify(res))
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\data\load.py:185 in do_batch def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:656 in create_batch return self.dataset[b]
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:515 in getitem return tuple([ptl[it] for ptl in self.ptls])
File D:\Anaconda\envs\TSAI\lib\site-packages\tsai\data\core.py:515 in
return tuple([ptl[it] for ptl in self.ptls])
File D:\Anaconda\envs\TSAI\lib\site-packages\fastai\torch_core.py:384 in torch_function res = super().torch_function(func, types, args, ifnone(kwargs, {}))
File D:\Anaconda\envs\TSAI\lib\site-packages\torch_tensor.py:1418 in __torch_function__ ret = func(*args, **kwargs)
RuntimeError: Could not infer dtype of numpy.int16