API compatible with PyTorch DataLoader, with a lot more callbacks and flexibility
bs = 4
letters = list(string.ascii_lowercase)

DataLoader

fa_collate[source]

fa_collate(t)

t = [(1,(2,3)),(1,(2,3))]
test_eq(fa_collate(t), default_collate(t))
test_eq(L(fa_collate(t)).map(type), [Tensor,tuple])

t = [(1,(2,(3,4))),(1,(2,(3,4)))]
test_eq(fa_collate(t), default_collate(t))
test_eq(L(fa_collate(t)).map(type), [Tensor,tuple])
test_eq(L(fa_collate(t)[1]).map(type), [Tensor,tuple])

fa_convert[source]

fa_convert(t)

t0 = array([1,2])
t = [t0,(t0,t0)]

test_eq(fa_convert(t), default_convert(t))
test_eq(L(fa_convert(t)).map(type), [Tensor,tuple])

class SkipItemException[source]

SkipItemException() :: Exception

Common base class for all non-exit exceptions.

class DataLoader[source]

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, multiprocessing_context=None, generator=None)

Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset.

The :class:~torch.utils.data.DataLoader supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching (collation) and memory pinning.

See :py:mod:torch.utils.data documentation page for more details.

Arguments: dataset (Dataset): dataset from which to load the data. batch_size (int, optional): how many samples per batch to load (default: 1). shuffle (bool, optional): set to True to have the data reshuffled at every epoch (default: False). sampler (Sampler or Iterable, optional): defines the strategy to draw samples from the dataset. Can be any Iterable with __len__ implemented. If specified, :attr:shuffle must not be specified. batch_sampler (Sampler or Iterable, optional): like :attr:sampler, but returns a batch of indices at a time. Mutually exclusive with :attr:batch_size, :attr:shuffle, :attr:sampler, and :attr:drop_last. num_workers (int, optional): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) collate_fn (callable, optional): merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset. pin_memory (bool, optional): If True, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your :attr:collate_fn returns a batch that is a custom type, see the example below. drop_last (bool, optional): set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False) timeout (numeric, optional): if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0) worker_init_fn (callable, optional): If not None, this will be called on each worker subprocess with the worker id (an int in [0, num_workers - 1]) as input, after seeding and before data loading. (default: None)

.. warning:: If the spawn start method is used, :attr:worker_init_fn cannot be an unpicklable object, e.g., a lambda function. See :ref:multiprocessing-best-practices on more details related to multiprocessing in PyTorch.

.. warning:: len(dataloader) heuristic is based on the length of the sampler used. When :attr:dataset is an :class:~torch.utils.data.IterableDataset, it instead returns an estimate based on len(dataset) / batch_size, with proper rounding depending on :attr:drop_last, regardless of multi-process loading configurations. This represents the best guess PyTorch can make because PyTorch trusts user :attr:dataset code in correctly handling multi-process loading to avoid duplicate data.

         However, if sharding results in multiple workers having incomplete last batches,
         this estimate can still be inaccurate, because (1) an otherwise complete batch can
         be broken into multiple ones and (2) more than one batch worth of samples can be
         dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
         cases in general.

         See `Dataset Types`_ for more details on these two types of datasets and how
         :class:`~torch.utils.data.IterableDataset` interacts with
         `Multi-process data loading`_.

Override item and use the default infinite sampler to get a stream of unknown length (stop() when you want to stop the stream).

class RandDL(DataLoader):
    def create_item(self, s):
        r = random.random()
        return r if r<0.95 else stop()

L(RandDL())

(#39) [0.24813649034165253,0.7183731274355801,0.46707243201681625,0.3819386594236871,0.37001598891686693,0.00048487460559387685,0.37430607545258365,0.28122066066872486,0.7108328343496174,0.4052328635806347...]
L(RandDL(bs=4, drop_last=True)).map(len)

(#1) [4]
dl = RandDL(bs=4, num_workers=4, drop_last=True)
L(dl).map(len)

(#15) [4,4,4,4,4,4,4,4,4,4...]
test_eq(dl.fake_l.num_workers, 4)
with dl.fake_l.no_multiproc(): 
    test_eq(dl.fake_l.num_workers, 0)
    L(dl).map(len)
test_eq(dl.fake_l.num_workers, 4)

def _rand_item(s):
    r = random.random()
    return r if r<0.95 else stop()

L(DataLoader(create_item=_rand_item))

(#18) [0.09369657046580104,0.022311107860009227,0.12902272918569346,0.8060082768103013,0.2512204187078644,0.40772772960651604,0.2115850693953002,0.23026583510965482,0.7840788021237788,0.18360739628018286...]

If you don't set bs, then dataset is assumed to provide an iterator or a __getitem__ that returns a batch.

ds1 = DataLoader(letters)
test_eq(L(ds1), letters)
test_eq(len(ds1), 26)

test_shuffled(L(DataLoader(letters, shuffle=True)), letters)

ds1 = DataLoader(letters, indexed=False)
test_eq(L(ds1), letters)
test_eq(len(ds1), 26)

t2 = L(tensor([0,1,2]),tensor([3,4,5]))
ds2 = DataLoader(t2)
test_eq_type(L(ds2), t2)

t3 = L(array([0,1,2]),array([3,4,5]))
ds3 = DataLoader(t3)
test_eq_type(L(ds3), t3.map(tensor))

ds4 = DataLoader(t3, create_batch=noop, after_iter=lambda: setattr(t3, 'f', 1))
test_eq_type(L(ds4), t3)
test_eq(t3.f, 1)

If you do set bs, then dataset is assumed to provide an iterator or a __getitem__ that returns a single item of a batch.

def twoepochs(d): return ' '.join(''.join(list(o)) for _ in range(2) for o in d)

ds1 = DataLoader(letters, bs=4, drop_last=True, num_workers=0)
test_eq(twoepochs(ds1), 'abcd efgh ijkl mnop qrst uvwx abcd efgh ijkl mnop qrst uvwx')

ds1 = DataLoader(letters,4,num_workers=2)
test_eq(twoepochs(ds1), 'abcd efgh ijkl mnop qrst uvwx yz abcd efgh ijkl mnop qrst uvwx yz')

ds1 = DataLoader(range(12), bs=4, num_workers=3)
test_eq_type(L(ds1), L(tensor([0,1,2,3]),tensor([4,5,6,7]),tensor([8,9,10,11])))

ds1 = DataLoader([str(i) for i in range(11)], bs=4, after_iter=lambda: setattr(t3, 'f', 2))
test_eq_type(L(ds1), L(['0','1','2','3'],['4','5','6','7'],['8','9','10']))
test_eq(t3.f, 2)

it = iter(DataLoader(map(noop,range(20)), bs=4, num_workers=1))
test_eq_type([next(it) for _ in range(3)], [tensor([0,1,2,3]),tensor([4,5,6,7]),tensor([8,9,10,11])])

class SleepyDL(list):
    def __getitem__(self,i):
        time.sleep(random.random()/50)
        return super().__getitem__(i)

t = SleepyDL(letters)

%time test_eq(DataLoader(t, num_workers=0), letters)
%time test_eq(DataLoader(t, num_workers=2), letters)
%time test_eq(DataLoader(t, num_workers=4), letters)

dl = DataLoader(t, shuffle=True, num_workers=1)
test_shuffled(L(dl), letters)
test_shuffled(L(dl), L(dl))

CPU times: user 8 ms, sys: 0 ns, total: 8 ms
Wall time: 249 ms
CPU times: user 12 ms, sys: 16 ms, total: 28 ms
Wall time: 160 ms
CPU times: user 20 ms, sys: 28 ms, total: 48 ms
Wall time: 116 ms

class SleepyQueue():
    "Simulate a queue with varying latency"
    def __init__(self, q): self.q=q
    def __iter__(self):
        while True:
            time.sleep(random.random()/100)
            try: yield self.q.get_nowait()
            except queues.Empty: return

q = Queue()
for o in range(30): q.put(o)
it = SleepyQueue(q)

%time test_shuffled(L(DataLoader(it, num_workers=4)), range(30))

CPU times: user 8 ms, sys: 36 ms, total: 44 ms
Wall time: 104 ms

class A(TensorBase): pass

for nw in (0,2):
    t = A(tensor([1,2]))
    dl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=nw)
    b = first(dl)
    test_eq(type(b), A)

    t = (A(tensor([1,2])),)
    dl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=nw)
    b = first(dl)
    test_eq(type(b[0]), A)

class A(TensorBase): pass
t = A(tensor(1,2))

tdl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=2, after_batch=to_device)
b = first(tdl)
test_eq(type(b), A)

# Unknown attributes are delegated to `dataset`
test_eq(tdl.pop(), tensor(1,2))