nvidia.dali.fn.torch_python_function

nvidia.dali.fn.torch_python_function(*input, function, batch_processing=True, bytes_per_sample_hint=[0], num_outputs=1, output_layouts=None, preserve=False, seed=-1, 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:

• 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.

• 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.

• 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.

• seed (int, optional, default = -1) –

  Random seed.

  If not provided, it will be populated based on the global seed of the pipeline.