MXNet Plugin API reference

class nvidia.dali.plugin.mxnet.DALIClassificationIterator(pipelines, size, data_name='data', label_name='softmax_label', data_layout='NCHW', fill_last_batch=True, auto_reset=False, squeeze_labels=True, dynamic_shape=False, last_batch_padded=False)

DALI iterator for classification tasks for MXNet. It returns 2 outputs (data and label) in the form of MXNet’s DataBatch of NDArrays.

Calling

DALIClassificationIterator(pipelines, size, data_name,
                           label_name, data_layout)

is equivalent to calling

DALIGenericIterator(pipelines,
                    [data_name, DALIClassificationIterator.DATA_TAG,
                     label_name, DALIClassificationIterator.LABEL_TAG],
                    size, data_name, label_name,
                    data_layout)
Parameters

  • pipelines (list of nvidia.dali.pipeline.Pipeline) – List of pipelines to use

  • size (int) – Number of samples in the epoch (Usually the size of the dataset).

  • data_name (str, optional, default = 'data') – Data name for provided symbols.

  • label_name (str, optional, default = 'softmax_label') – Label name for provided symbols.

  • data_layout (str, optional, default = 'NCHW') – Either ‘NHWC’ or ‘NCHW’ - layout of the pipeline outputs.

  • fill_last_batch (bool, optional, default = True) – Whether to return a fraction of a full batch of data such that the total entries returned by the iterator == ‘size’. Setting this flag to False will cause the iterator to return the first integer multiple of self._num_gpus * self.batch_size which exceeds ‘size’.

  • auto_reset (bool, optional, default = False) – Whether the iterator resets itself for the next epoch or it requires reset() to be called separately.

  • squeeze_labels (bool, optional, default = True) – Whether the iterator should squeeze the labels before copying them to the ndarray.

  • dynamic_shape (bool, optional, default = False) – Whether the shape of the output of the DALI pipeline can change during execution. If True, the mxnet.ndarray will be resized accordingly if the shape of DALI returned tensors changes during execution. If False, the iterator will fail in case of change.

  • last_batch_padded (bool, optional, default = False) – Whether the last batch provided by DALI is padded with the last sample or it just wraps up. In the conjunction with fill_last_batch it tells if the iterator returning last batch with data only partially filled with data from the current epoch is dropping padding samples or samples from the next epoch. If set to True next epoch will end sooner as data from it was consumed but dropped. If set to false next epoch would be the same length as the first one.

DATA_TAG = 'data'
LABEL_TAG = 'label'
next()

Returns the next batch of data.

reset()

Resets the iterator after the full epoch. DALI iterators do not support resetting before the end of the epoch and will ignore such request.

class nvidia.dali.plugin.mxnet.DALIGenericIterator(pipelines, output_map, size, data_layout='NCHW', fill_last_batch=True, auto_reset=False, squeeze_labels=True, dynamic_shape=False, last_batch_padded=False)

General DALI iterator for MXNet. It can return any number of outputs from the DALI pipeline in the form of MXNet’s DataBatch of NDArrays.

Parameters

  • pipelines (list of nvidia.dali.pipeline.Pipeline) – List of pipelines to use

  • output_map (list of (str, str)) – List of pairs (output_name, tag) which maps consecutive outputs of DALI pipelines to proper field in MXNet’s DataBatch. tag is one of DALIGenericIterator.DATA_TAG and DALIGenericIterator.LABEL_TAG mapping given output for data or label correspondingly. output_names should be distinct.

  • size (int) – Number of samples in the epoch (Usually the size of the dataset).

  • data_layout (str, optional, default = 'NCHW') – Either ‘NHWC’ or ‘NCHW’ - layout of the pipeline outputs.

  • fill_last_batch (bool, optional, default = True) – Whether to return a fraction of a full batch of data such that the total entries returned by the iterator == ‘size’. Setting this flag to False will cause the iterator to return the first integer multiple of self._num_gpus * self.batch_size which exceeds ‘size’.

  • auto_reset (bool, optional, default = False) – Whether the iterator resets itself for the next epoch or it requires reset() to be called separately.

  • squeeze_labels (bool, optional, default = True) – Whether the iterator should squeeze the labels before copying them to the ndarray.

  • dynamic_shape (bool, optional, default = False) – Whether the shape of the output of the DALI pipeline can change during execution. If True, the mxnet.ndarray will be resized accordingly if the shape of DALI returned tensors changes during execution. If False, the iterator will fail in case of change.

  • last_batch_padded (bool, optional, default = False) – Whether the last batch provided by DALI is padded with the last sample or it just wraps up. In the conjunction with fill_last_batch it tells if the iterator returning last batch with data only partially filled with data from the current epoch is dropping padding samples or samples from the next epoch. If set to True next epoch will end sooner as data from it was consumed but dropped. If set to false next epoch would be the same length as the first one.

DATA_TAG = 'data'
LABEL_TAG = 'label'
next()

Returns the next batch of data.

reset()

Resets the iterator after the full epoch. DALI iterators do not support resetting before the end of the epoch and will ignore such request.

nvidia.dali.plugin.mxnet.feed_ndarray(dali_tensor, arr)

Copy contents of DALI tensor to MXNet’s NDArray.

Parameters