def __init__(self,
source=None,
num_outputs=None,
batch_size=-1,
cycle=None,
name=None,
layout=None,
batch=None,
batch_info=None):
if name is not None and num_outputs is not None:
raise ValueError(
"`num_outputs` is not compatible with named `ExternalSource`")
callback, source_desc = _get_callback_from_source(
source, cycle, batch_info or False)
self._name = name
self._layout = layout
self._num_outputs = num_outputs
self._batch = batch
self._batch_size = batch_size
self._callback = callback
self._source_desc = source_desc
self._batch_info = batch_info
self._current_iter = 0
self._current_sample = 0
self._feed_inputs = Queue()
if callback is not None:
arg_count = _accepted_arg_count(callback)
if arg_count not in [0, 1]:
raise TypeError(
"External source callback must be a callable with 0 or 1 argument"
)
self.accepts_arg = arg_count > 0
def __init__(
self, source=None, num_outputs=None, *, cycle=None, layout=None, dtype=None, name=None,
device="cpu", cuda_stream=None, use_copy_kernel=None, batch=None, parallel=None,
no_copy=None, prefetch_queue_depth=None, batch_info=None, **kwargs):
self._schema = _b.GetSchema("ExternalSource")
self._spec = _b.OpSpec("ExternalSource")
self._device = device
self._layout = layout
self._dtype = dtype
self._cuda_stream = cuda_stream
self._use_copy_kernel = use_copy_kernel
import nvidia.dali.ops
kwargs, self._call_args = nvidia.dali.ops._separate_kwargs(kwargs)
callback, source_desc = _get_callback_from_source(source, cycle, batch_info or False)
if name is not None and num_outputs is not None:
raise ValueError("`num_outputs` is not compatible with named `ExternalSource`")
self._name = name
self._num_outputs = num_outputs
self._batch = batch
self._callback = callback
self._source_desc = source_desc
self._parallel = parallel
self._no_copy = no_copy
self._prefetch_queue_depth = prefetch_queue_depth
self._batch_info = batch_info
self._spec.AddArg("device", device)
for key, value in kwargs.items():
self._spec.AddArg(key, value)
def __call__(
self, *, source=None, cycle=None, name=None, layout=None, dtype=None, cuda_stream=None,
use_copy_kernel=None, batch=None, parallel=None, no_copy=None,
prefetch_queue_depth=None, batch_info=None, **kwargs):
""
from nvidia.dali.ops import _OperatorInstance
if batch_info is None:
batch_info = self._batch_info or False
elif self._batch_info is not None:
raise ValueError(
"The argument ``batch_info`` already specified in constructor.")
if source is None:
if cycle is not None:
if self._callback:
raise ValueError("The argument ``cycle`` can only be specified if ``source`` is an iterable object "
"or a generator function specified in this call. To cycle through an iterable specified in "
"``__init__``, set ``cycle`` there.")
else:
raise ValueError("The argument ``cycle`` can only be specified if ``source`` is a "
"reusable iterable or a generator function.")
callback = self._callback
source_desc = self._source_desc
else:
if self._callback is not None:
raise RuntimeError("``source`` already specified in constructor.")
callback, source_desc = _get_callback_from_source(source, cycle, self._batch_info)
# Keep the metadata for Pipeline inspection
self._source_desc = source_desc
if parallel is None:
parallel = self._parallel or False
elif self._parallel is not None:
raise ValueError("The argument ``parallel`` already specified in constructor.")
if batch is None:
batch = self._batch
elif self._batch is not None:
raise ValueError("The argument ``batch`` already specified in constructor.")
# By default parallel is False, so batch will be True
if batch is None:
batch = not parallel
if prefetch_queue_depth is None:
prefetch_queue_depth = self._prefetch_queue_depth
elif self._prefetch_queue_depth is not None:
raise ValueError(
"The argument ``prefetch_queue_depth`` already specified in constructor.")
if no_copy is None:
no_copy = self._no_copy
elif self._no_copy is not None:
raise ValueError("The argument ``no_copy`` already specified in constructor.")
if parallel:
if prefetch_queue_depth is None:
prefetch_queue_depth = 1
if no_copy is None:
no_copy = True
if not no_copy:
raise ValueError("The argument ``no_copy`` cannot be specified to False " +
" when used with ``parallel=True``.")
if prefetch_queue_depth < 1:
raise ValueError(
"``prefetch_queue_depth`` must be a positive integer, got {}.".format(
prefetch_queue_depth))
if source_desc.kind == _SourceKind.CALLABLE:
if not source_desc.has_inputs:
raise TypeError(("Callable passed to External Source in parallel mode (when `parallel=True`) "
"must accept exactly one argument: `nvidia.dali.types.SampleInfo` "
"if run with `batch=False` or either `nvidia.dali.types.BatchInfo` or integer that "
"represents the index of the batch within the epoch if `batch=True`. "
"Got a callable that does not accept arguments instead."))
elif not batch:
what = "an iterable" if source_desc.kind == _SourceKind.ITERABLE else "a generator function"
raise TypeError("Parallel external source with {} must be run in a batch mode "
"(specify `batch=True` in the external source definition and make sure "
"your source returns batches)".format(what))
else:
if prefetch_queue_depth is not None:
raise ValueError("The argument `prefetch_queue_depth` is valid only for " +
"parallel external sources (when ``parallel`` is True).")
if self._layout is not None:
if layout is not None:
raise RuntimeError("``layout`` already specified in constructor.")
else:
layout = self._layout
if self._dtype is not None:
if dtype is not None:
raise RuntimeError("``dtype`` already specified in constructor.")
else:
dtype = self._dtype
if self._cuda_stream is not None:
if cuda_stream is not None:
raise RuntimeError("``cuda_stream`` already specified in constructor.")
else:
cuda_stream = self._cuda_stream
if self._use_copy_kernel is not None:
if use_copy_kernel is not None:
raise RuntimeError("``use_copy_kernel`` already specified in constructor.")
else:
use_copy_kernel = self._use_copy_kernel
if name is None:
name = self._name
else:
self._name = name
if name is not None and self._num_outputs is not None:
raise RuntimeError("``num_outputs`` is not compatible with named ``ExternalSource``.")
group_common_kwargs = {
'cuda_stream': cuda_stream,
'use_copy_kernel': use_copy_kernel,
'batch': batch,
'batch_info': batch_info,
'parallel': parallel,
'prefetch_queue_depth': prefetch_queue_depth,
}
if self._num_outputs is not None:
outputs = []
kwargs = {"no_copy": no_copy}
group = _ExternalSourceGroup(callback, source_desc, True, **group_common_kwargs)
for i in range(self._num_outputs):
if dtype is not None:
if isinstance(dtype, (list, tuple)):
kwargs['dtype'] = dtype[i] if i < len(dtype) else nvidia.dali.types.DALIDataType.NO_TYPE
else:
kwargs['dtype'] = dtype
op_instance = _OperatorInstance([], self, **kwargs)
op_instance._callback = callback
op_instance._output_index = i
op_instance._group = group
if layout is not None:
if isinstance(layout, (list, tuple)):
op_instance._layout = layout[i] if i < len(layout) else ""
else:
op_instance._layout = layout
else:
op_instance._layout = None
op_instance._batch = batch
group.append(op_instance)
op_instance.generate_outputs()
outputs.append(op_instance.unwrapped_outputs)
return outputs
else:
if name is not None:
kwargs["name"] = name
if no_copy is not None:
kwargs["no_copy"] = no_copy
if dtype is not None:
kwargs['dtype'] = dtype
op_instance = _OperatorInstance([], self, **kwargs)
op_instance._callback = callback
op_instance._output_index = None
op_instance._group = _ExternalSourceGroup(
callback, source_desc, False, [op_instance], **group_common_kwargs)
op_instance._layout = layout
op_instance._batch = batch
op_instance.generate_outputs()
return op_instance.unwrapped_outputs
