nvidia.dali.plugin.pytorch.fn.torch_python_function — NVIDIA DALI (original) (raw)

nvidia.dali.plugin.pytorch.fn.torch_python_function(*input, batch_processing=True, bytes_per_sample_hint=[0], function, num_outputs=1, output_layouts=None, preserve=False, device=None, name=None)#

Executes a function that is operating on Torch tensors.

This class is analogous to nvidia.dali.fn.python_function() but the tensor data is handled as PyTorch tensors.

This operator allows sequence inputs and supports volumetric data.

This operator will not be optimized out of the graph.

Supported backends

‘cpu’
‘gpu’

Parameters:

__input_¶[ 0..255 ] (TensorList , optional) – This function accepts up to 256 optional positional inputs

Keyword Arguments:

batch_processing¶ (bool, optional, default = True) – Determines whether the function gets an entire batch as an input.
bytes_per_sample_hint¶ (int or list of int, optional, default = [0]) –
Output size hint, in bytes per sample.
If specified, the operator’s outputs residing in GPU or page-locked host memory will be preallocated to accommodate a batch of samples of this size.
function¶ (object) –
A callable object that defines the function of the operator.
Warning
The function must not hold a reference to the pipeline in which it is used. If it does, a circular reference to the pipeline will form and the pipeline will never be freed.
num_outputs¶ (int, optional, default = 1) – Number of outputs.
output_layouts¶ (layout str or list of layout str, optional) –
Tensor data layouts for the outputs.
This argument can be a list that contains a distinct layout for each output. If the list has fewer than num_outputs elements, only the first outputs have the layout set and the rest of the outputs have no layout assigned.
preserve¶ (bool, optional, default = False) – Prevents the operator from being removed from the graph even if its outputs are not used.