def __init__(self,
pipelines,
output_map,
size=-1,
reader_name=None,
auto_reset=False,
fill_last_batch=None,
dynamic_shape=False,
last_batch_padded=False,
last_batch_policy=LastBatchPolicy.FILL):
# check the assert first as _DaliBaseIterator would run the prefetch
assert len(set(output_map)) == len(
output_map), "output_map names should be distinct"
self._output_categories = set(output_map)
self.output_map = output_map
_DaliBaseIterator.__init__(self, pipelines, size, reader_name,
auto_reset, fill_last_batch,
last_batch_padded, last_batch_policy)
self._dynamic_shape = dynamic_shape
# Use double-buffering of data batches
self._data_batches = [None for i in range(self._num_gpus)]
self._first_batch = None
try:
self._first_batch = DALIGenericIterator.__next__(self)
except StopIteration:
assert False, "It seems that there is no data in the pipeline. This may happen if `last_batch_policy` is set to PARTIAL and the requested batch size is greater than the shard size."
def __init__(self,
pipelines,
output_map,
size=-1,
reader_name=None,
auto_reset=False,
fill_last_batch=True,
dynamic_shape=False,
last_batch_padded=False):
_DaliBaseIterator.__init__(self, pipelines, size, reader_name,
auto_reset, fill_last_batch,
last_batch_padded)
self._dynamic_shape = dynamic_shape
# Use double-buffering of data batches
self._data_batches = [None for i in range(self._num_gpus)]
assert len(set(output_map)) == len(
output_map), "output_map names should be distinct"
self._output_categories = set(output_map)
self.output_map = output_map
# We need data about the batches (like shape information),
# so we need to run a single batch as part of setup to get that info
for p in self._pipes:
with p._check_api_type_scope(types.PipelineAPIType.ITERATOR):
p.schedule_run()
self._first_batch = None
self._first_batch = self.next()
def __init__(self,
pipelines,
size=-1,
reader_name=None,
fill_last_batch=False,
last_batch_padded=False,
auto_reset=False):
_DaliBaseIterator.__init__(self, pipelines, size, reader_name, auto_reset, fill_last_batch, last_batch_padded)
def __init__(self,
pipelines,
output_map,
size=-1,
reader_name=None,
auto_reset=False,
fill_last_batch=None,
dynamic_shape=False,
last_batch_padded=False,
last_batch_policy=LastBatchPolicy.FILL,
prepare_first_batch=True):
normalized_map = {}
for v in output_map:
if isinstance(v, str):
normalized_map[v] = 0
else:
normalized_map[v[0]] = v[1]
self.normalized_map = normalized_map
# check the assert first as _DaliBaseIterator would run the prefetch
output_map = [isinstance(v, str) and v or v[0] for v in output_map]
assert len(set(output_map)) == len(output_map), \
"output_map names should be distinct"
self.output_map = output_map
_DaliBaseIterator.__init__(self,
pipelines,
size,
reader_name,
auto_reset,
fill_last_batch,
last_batch_padded,
last_batch_policy,
prepare_first_batch=prepare_first_batch)
self._counter = 0
self._first_batch = None
if self._prepare_first_batch:
try:
self._first_batch = DALIGenericIterator.__next__(self)
# call to `next` sets _ever_consumed to True but if we are just calling it from
# here we should set if to False again
self._ever_consumed = False
except StopIteration:
assert False, "It seems that there is no data in the pipeline. This may happen " \
"if `last_batch_policy` is set to PARTIAL and the requested batch size is " \
"greater than the shard size."
def __init__(self,
pipelines,
output_map,
size=-1,
reader_name=None,
auto_reset=False,
fill_last_batch=None,
dynamic_shape=False,
last_batch_padded=False,
last_batch_policy=LastBatchPolicy.FILL,
prepare_first_batch=True):
normalized_map = {}
for v in output_map:
if isinstance(v, str):
normalized_map[v] = 0
else:
normalized_map[v[0]] = v[1]
self.normalized_map = normalized_map
# check the assert first as _DaliBaseIterator would run the prefetch
output_map = [isinstance(v, str) and v or v[0] for v in output_map]
assert len(set(output_map)) == len(output_map), \
"output_map names should be distinct"
self.output_map = output_map
_DaliBaseIterator.__init__(self,
pipelines,
size,
reader_name,
auto_reset,
fill_last_batch,
last_batch_padded,
last_batch_policy,
prepare_first_batch=prepare_first_batch)
self._dynamic_shape = dynamic_shape
# Use double-buffering of data batches
self._data_batches = [None for i in range(self._num_gpus)]
self._counter = 0
self._first_batch = None
if self._prepare_first_batch:
try:
self._first_batch = DALIGenericIterator.__next__(self)
except StopIteration:
assert False, "It seems that there is no data in the pipeline. This may happen if `last_batch_policy` is set to PARTIAL and the requested batch size is greater than the shard size."
def __init__(self,
pipelines,
size=-1,
reader_name=None,
fill_last_batch=None,
last_batch_padded=False,
auto_reset=False,
last_batch_policy=LastBatchPolicy.FILL,
prepare_first_batch=True):
_DaliBaseIterator.__init__(self,
pipelines,
size,
reader_name,
auto_reset,
fill_last_batch,
last_batch_padded,
last_batch_policy,
prepare_first_batch=prepare_first_batch)
def __init__(self,
pipelines,
output_map,
size=-1,
reader_name=None,
auto_reset=False,
fill_last_batch=True,
dynamic_shape=False,
last_batch_padded=False):
_DaliBaseIterator.__init__(self, pipelines, size, reader_name,
auto_reset, fill_last_batch,
last_batch_padded)
self._dynamic_shape = dynamic_shape
# Use double-buffering of data batches
self._data_batches = [None for i in range(self._num_gpus)]
self._counter = 0
normalized_map = {}
for v in output_map:
if isinstance(v, str):
normalized_map[v] = 0
else:
normalized_map[v[0]] = v[1]
self.normalized_map = normalized_map
output_map = [isinstance(v, str) and v or v[0] for v in output_map]
assert len(set(output_map)) == len(output_map), \
"output_map names should be distinct"
self.output_map = output_map
# We need data about the batches (like shape information),
# so we need to run a single batch as part of setup to get that info
for p in self._pipes:
with p._check_api_type_scope(types.PipelineAPIType.ITERATOR):
p.schedule_run()
self._first_batch = None
self._first_batch = DALIGenericIterator.__next__(self)
