def fn_wrapper(*inputs, **kwargs):
from nvidia.dali._debug_mode import _PipelineDebug
current_pipeline = _PipelineDebug.current()
if getattr(current_pipeline, '_debug_on', False):
return current_pipeline._wrap_op_call(op_wrapper, inputs, kwargs)
else:
return op_wrapper(*inputs, **kwargs)
def external_source(source=None, num_outputs=None, *, cycle=None, name=None, device="cpu",
layout=None, dtype=None, ndim=None, cuda_stream=None, use_copy_kernel=None,
batch=True, **kwargs):
"""Creates a data node which is populated with data from a Python source.
The data can be provided by the ``source`` function or iterable, or it can be provided by
``pipeline.feed_input(name, data, layout, cuda_stream)`` inside ``pipeline.iter_setup``.
In the case of the GPU input, it is the user responsibility to modify the
provided GPU memory content only using provided stream (DALI schedules a copy on it
and all work is properly queued). If no stream is provided feeding input blocks until the
provided memory is copied to the internal buffer.
.. note::
:meth:`nvidia.dali.fn.external_source` operator is partially compatible with TensorFlow
integration via :meth:`nvidia.dali.plugin.tf.experimental.DALIDatasetWithInputs`.
Please refer to its documentation for details.
.. note::
To return a batch of copies of the same tensor, use :func:`nvidia.dali.types.Constant`,
which is more performant.
"""
from nvidia.dali._debug_mode import _PipelineDebug
def _external_source(source=None, num_outputs=None, *, cycle=None, name=None, device="cpu",
layout=None, dtype=None, ndim=None, cuda_stream=None, use_copy_kernel=None,
batch=True, **kwargs):
if batch is None:
batch = True
if num_outputs is not None:
if source is None:
raise ValueError(
"The parameter ``num_outputs`` is only valid when using ``source`` to "
"provide data. To feed multiple external sources in ``feed_input``, "
"use multiple ``external_source`` nodes.")
op = ExternalSource(device=device, num_outputs=num_outputs, source=source, cycle=cycle,
layout=layout, dtype=dtype, ndim=ndim, cuda_stream=cuda_stream,
use_copy_kernel=use_copy_kernel, batch=batch, **kwargs)
return op(name=name)
# Wrapper around external_source to switch between standard and debug mode.
current_pipeline = _PipelineDebug.current()
if getattr(current_pipeline, '_debug_on', False):
return current_pipeline._external_source(
source=source, num_outputs=num_outputs, cycle=cycle, name=name, device=device,
layout=layout, batch=batch, **kwargs)
else:
return _external_source(source, num_outputs, cycle=cycle, name=name, device=device,
layout=layout, dtype=dtype, ndim=ndim, cuda_stream=cuda_stream,
use_copy_kernel=use_copy_kernel, batch=batch, **kwargs)