def feed_input(self, data_node, data, layout=""):
"""Bind a NumPy array (or a list thereof) to an output of ExternalSource.
Parameters
----------
data_node : :class:`DataNode` or str
The :class:`DataNode` returned by a call to ExternalSource or a name of the
:class:`nvidia.dali.ops.ExternalSource`
data : numpy.ndarray or a list thereof
The data to be used as the output of the ExternalSource referred to by `data_node`.
In case of GPU external sources, this must be a ``numpy.ndarray``.
layout : str
The description of the data layout (or empty string, if not specified).
It should be a string of the length that matches the dimensionality of the data, batch
dimension excluded. For a batch of channel-first images, this should be "CHW", for
channel-last video it's "FHWC" and so on.
"""
if not self._built:
raise RuntimeError("Pipeline must be built first.")
if isinstance(data_node, str):
name = data_node
else:
_data_node._check(data_node)
name = data_node.name
from nvidia.dali.external_source import _check_data_batch
_check_data_batch(data, self._batch_size, layout)
if isinstance(data, list):
inputs = []
for datum in data:
inputs.append(Tensors.TensorCPU(datum, layout))
self._pipe.SetExternalTensorInput(name, inputs)
else:
inp = Tensors.TensorListCPU(data, layout)
self._pipe.SetExternalTLInput(name, inp)
def feed_input(self,
data_node,
data,
layout="",
cuda_stream=None,
use_copy_kernel=False):
"""Pass a mutlidimensional array or DLPack (or a list thereof) to an output of ExternalSource.
In the case of the GPU input, the data must be modified on the same stream as the one
used by feed_input. See ``cuda_stream`` parameter for details.
Parameters
----------
data_node : :class:`DataNode` or str
The name of the :class:`nvidia.dali.ops.ExternalSource` node or a :class:`DataNode`
object returned by a call to that ExternalSource.
data : an ndarray or DLPack or a list thereof
The array(s) may be one of:
* NumPy ndarray (CPU)
* MXNet ndarray (CPU)
* PyTorch tensor (CPU or GPU)
* CuPy array (GPU)
* objects implementing ``__cuda_array_interface__``
The data to be used as the output of the ExternalSource referred to by `data_node`.
layout : str
The description of the data layout (or empty string, if not specified).
It should be a string of the length that matches the dimensionality of the data, batch
dimension excluded. For a batch of channel-first images, this should be "CHW", for
channel-last video it's "FHWC" and so on.
cuda_stream : optional, `cudaStream_t` or an object convertible to `cudaStream_t`, e.g. `cupy.cuda.Stream`, `torch.cuda.Stream`
The CUDA stream, which is going to be used for copying data to GPU or from a GPU
source. If not set, best effort will be taken to maintain correctness - i.e. if the data
is provided as a tensor/array from a recognized library (CuPy, PyTorch), the library's
current stream is used. This should work in typical scenarios, but advanced use cases
(and code using unsupported libraries) may still need to supply the stream handle
explicitly.
Special values:
* 0 - use default CUDA stream
* -1 - use DALI's internal stream
If internal stream is used, the call to ``feed_input`` will block until the copy to
internal buffer is complete, since there's no way to synchronize with this stream to
prevent overwriting the array with new data in another stream.
use_copy_kernel : optional, `bool`
If set to True, DALI will use a CUDA kernel to feed the data (only applicable when copying
data to/from GPU memory) instead of cudaMemcpyAsync (default).
"""
if not self._built:
raise RuntimeError("Pipeline must be built first.")
if isinstance(data_node, str):
name = data_node
else:
_data_node._check(data_node)
name = data_node.name
from nvidia.dali.external_source import _check_data_batch
infer_stream = False
if cuda_stream is None:
infer_stream = True
if cuda_stream == -1:
cuda_stream = None
else:
cuda_stream = types._raw_cuda_stream(cuda_stream)
def to_numpy(x):
if types._is_mxnet_array(x):
return x.asnumpy()
elif types._is_torch_tensor(x):
return x.numpy()
else:
return x
# __cuda_array_interface__ doesn't provide any way to pass the information about the device
# where the memory is located. It is assumed that the current device is the one that the memory belongs to,
# unless the user sets the device explicitly creating TensorGPU/TensorListGPU
if isinstance(data, list):
inputs = []
checked = False
for datum in data:
info = CheckDLPackCapsule(datum)
if not info[0] and not checked:
_check_data_batch(data, self._batch_size, layout)
checked = True
if hasattr(datum, "__cuda_array_interface__") or (info[0]
and info[1]):
if infer_stream:
cuda_stream = _get_default_stream_for_array(datum)
inp = Tensors.TensorGPU(datum, layout)
else:
datum = to_numpy(datum)
inp = Tensors.TensorCPU(datum, layout)
inputs.append(inp)
assert all(isinstance(inp, type(inputs[0])) for inp in inputs), \
"Mixed input types are not support, all need to reside on the CPU or GPU"
self._pipe.SetExternalTensorInput(name, inputs,
ctypes.c_void_p(cuda_stream),
use_copy_kernel)
else:
info = CheckDLPackCapsule(data)
if not info[0]:
_check_data_batch(data, self._batch_size, layout)
if hasattr(data, "__cuda_array_interface__") or (info[0]
and info[1]):
if infer_stream:
cuda_stream = _get_default_stream_for_array(data)
inp = Tensors.TensorListGPU(data, layout)
else:
data = to_numpy(data)
inp = Tensors.TensorListCPU(data, layout)
self._pipe.SetExternalTLInput(name, inp,
ctypes.c_void_p(cuda_stream),
use_copy_kernel)