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# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_data.load.ipynb (unless otherwise specified).
__all__ = ['fa_collate', 'fa_convert', 'SkipItemException', 'DataLoader']
# Cell
from ..torch_basics import *
from import _MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter,_DatasetKind
_loaders = (_MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter)
# Cell
def _wif(worker_id):
info = get_worker_info()
ds = info.dataset.d
ds.nw,ds.offs = info.num_workers,
class _FakeLoader:
_IterableDataset_len_called,_auto_collation,collate_fn,drop_last,dataset_kind,_dataset_kind,_index_sampler = (
def __init__(self, d, pin_memory, num_workers, timeout):
self.dataset,self.default,self.worker_init_fn = self,d,_wif
store_attr(self, 'd,pin_memory,num_workers,timeout')
def __iter__(self): return iter(self.d.create_batches(self.d.sample()))
def multiprocessing_context(self): return (None,multiprocessing)[self.num_workers>0]
def no_multiproc(self):
old_nw = self.num_workers
self.num_workers = 0
yield self.d
finally: self.num_workers = old_nw
_collate_types = (ndarray, Tensor, typing.Mapping, str)
# Cell
def fa_collate(t):
b = t[0]
return (default_collate(t) if isinstance(b, _collate_types)
else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
else default_collate(t))
# Cell
def fa_convert(t):
return (default_convert(t) if isinstance(t, _collate_types)
else type(t)([fa_convert(s) for s in t]) if isinstance(t, Sequence)
else default_convert(t))
# Cell
class SkipItemException(Exception): pass
# Cell
class DataLoader(GetAttr):
_noop_methods = 'wif before_iter after_item before_batch after_batch after_iter'.split()
for o in _noop_methods:
exec(f"def {o}(self, x=None, *args, **kwargs): return x")
_methods = _noop_methods + 'create_batches create_item create_batch retain \
get_idxs sample shuffle_fn do_batch create_batch'.split()
_default = 'dataset'
def __init__(self, dataset=None, bs=None, num_workers=0, pin_memory=False, timeout=0, batch_size=None,
shuffle=False, drop_last=False, indexed=None, n=None, device=None, **kwargs):
if batch_size is not None: bs = batch_size # PyTorch compatibility
assert not (bs is None and drop_last)
if indexed is None: indexed = dataset is not None and hasattr(dataset,'__getitem__')
if n is None:
try: n = len(dataset)
except TypeError: pass
store_attr(self, 'dataset,bs,shuffle,drop_last,indexed,n,pin_memory,timeout,device')
self.rng,self.nw,self.offs = random.Random(),1,0
self.fake_l = _FakeLoader(self, pin_memory, num_workers, timeout)
def __len__(self):
if self.n is None: raise TypeError
if is None: return self.n
return self.n// + (0 if self.drop_last or else 1)
def get_idxs(self):
idxs = Inf.count if self.indexed else Inf.nones
if self.n is not None: idxs = list(itertools.islice(idxs, self.n))
if self.shuffle: idxs = self.shuffle_fn(idxs)
return idxs
def sample(self):
idxs = self.get_idxs()
return (b for i,b in enumerate(idxs) if i//( or 1)%self.nw==self.offs)
def __iter__(self):
for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
if self.device is not None: b = to_device(b, self.device)
yield self.after_batch(b)
if hasattr(self, 'it'): delattr(self, 'it')
def create_batches(self, samps): = iter(self.dataset) if self.dataset is not None else None
res = filter(lambda o:o is not None, map(self.do_item, samps))
yield from map(self.do_batch, self.chunkify(res))
def new(self, dataset=None, cls=None, **kwargs):
if dataset is None: dataset = self.dataset
if cls is None: cls = type(self)
cur_kwargs = dict(dataset=dataset, num_workers=self.fake_l.num_workers, pin_memory=self.pin_memory, timeout=self.timeout,, shuffle=self.shuffle, drop_last=self.drop_last, indexed=self.indexed, device=self.device)
for n in self._methods: cur_kwargs[n] = getattr(self, n)
return cls(**merge(cur_kwargs, kwargs))
def prebatched(self): return is None
def do_item(self, s):
try: return self.after_item(self.create_item(s))
except SkipItemException: return None
def chunkify(self, b): return b if self.prebatched else chunked(b,, self.drop_last)
def shuffle_fn(self, idxs): return self.rng.sample(idxs, len(idxs))
def randomize(self): self.rng = random.Random(self.rng.randint(0,2**32-1))
def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)
def create_item(self, s): return next( if s is None else self.dataset[s]
def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
def one_batch(self):
if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
with self.fake_l.no_multiproc(): res = first(self)
if hasattr(self, 'it'): delattr(self, 'it')
return res
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