def Value(typecode_or_type, *args, lock=None):
'''
Return a synchronization wrapper for a Value
'''
obj = RawValue(typecode_or_type, *args)
if lock is False:
return obj
if lock in (True, None):
lock = RLock()
if not hasattr(lock, 'acquire'):
raise AttributeError("'%r' has no method 'acquire'" % lock)
return synchronized(obj, lock)
def map(self, fn, lazy=True, batched=False, num_workers=0):
"""
Performs specific function on the dataset to transform and update every sample.
Args:
fn (callable): Transformations to be performed. It receives single
sample as argument if batched is False. Else it receives all examples.
lazy (bool, optional): If True, transformations would be delayed and
performed on demand. Otherwise, transforms all samples at once. Note that
if `fn` is stochastic, `lazy` should be True or you will get the same
result on all epochs. Defalt: False.
batched(bool, optional): If True, transformations would take all examples as
input and return a collection of transformed examples. Note that if set
True, `lazy` option would be ignored. Defalt: False.
num_workers(int, optional): Number of processes for multiprocessing. If
set to 0, it doesn't use multiprocessing. Note that if set to positive
value, `lazy` option would be ignored. Defalt: 0.
"""
assert num_workers >= 0, "num_workers should be a non-negative value"
if num_workers > 0:
with Pool(num_workers, initargs=(RLock(), )) as pool:
def map_shard(num_workers, index, fn, batched):
self.shard(num_shards=num_workers,
index=index,
contiguous=True)
self._map(fn=fn, lazy=False, batched=batched)
return self
kwds_per_shard = [
dict(num_workers=num_workers,
index=rank,
fn=fn,
batched=batched) for rank in range(num_workers)
]
results = [
pool.apply_async(map_shard, kwds=kwds)
for kwds in kwds_per_shard
]
transformed_shards = [r.get() for r in results]
self.new_data = []
for i in range(num_workers):
self.new_data += transformed_shards[i].new_data
return self
else:
return self._map(fn, lazy=lazy, batched=batched)
def Array(typecode_or_type, size_or_initializer, **kwds):
'''
Return a synchronization wrapper for a RawArray
'''
lock = kwds.pop('lock', None)
if kwds:
raise ValueError('unrecognized keyword argument(s): %s' % kwds.keys())
obj = RawArray(typecode_or_type, size_or_initializer)
if lock is False:
return obj
if lock in (True, None):
lock = RLock()
if not hasattr(lock, 'acquire'):
raise AttributeError("'%r' has no method 'acquire'" % lock)
return synchronized(obj, lock)
def filter(self, fn, num_workers=0):
"""
Filters samples by the filter function and uses the filtered data to
update this dataset.
Args:
fn (callable): A filter function that takes a sample as input and
returns a boolean. Samples that return False would be discarded.
num_workers(int, optional): Number of processes for multiprocessing. If
set to 0, it doesn't use multiprocessing. Defaults to `0`.
"""
assert num_workers >= 0, "num_workers should be a non-negative value"
if num_workers > 1:
shards = [
self._shard(
num_shards=num_workers, index=index, contiguous=True)
for index in range(num_workers)
]
kwds_per_shard = [
dict(
self=shards[rank], fn=fn) for rank in range(num_workers)
]
pool = Pool(num_workers, initargs=(RLock(), ))
results = [
pool.apply_async(
self.__class__._filter, kwds=kwds)
for kwds in kwds_per_shard
]
transformed_shards = [r.get() for r in results]
pool.close()
pool.join()
self.new_data = []
for i in range(num_workers):
self.new_data += transformed_shards[i].new_data
return self
else:
return self._filter(fn)
def __init__(self, obj, lock=None):
self._obj = obj
self._lock = lock or RLock()
self.acquire = self._lock.acquire
self.release = self._lock.release
