trw.train.sequence_array

Module Contents

Classes

SequenceArray

Create a sequence of batches from numpy arrays, lists and torch.Tensor

Attributes

sample_uid_name

trw.train.sequence_array.sample_uid_name = sample_uid
class trw.train.sequence_array.SequenceArray(split, sampler=sampler.SamplerRandom(), transforms=None, use_advanced_indexing=True, sample_uid_name=sample_uid_name)

Bases: trw.train.sequence.Sequence

Create a sequence of batches from numpy arrays, lists and torch.Tensor

subsample(self, nb_samples)

Sub-sample a sequence to a fixed number of samples.

The purpose is to obtain a smaller sequence, this is particularly useful for the export of augmentations, samples.

Parameters

nb_samples – the number of samples desired in the original sequence

Returns

a subsampled Sequence

subsample_uids(self, uids, uids_name, new_sampler=None)

Sub-sample a sequence to samples with specified UIDs.

Parameters

  • uids (list) – the uids. If new_sampler keeps the ordering, then the samples of the resampled sequence should follow uids ordering

  • uids_name (str) – the name of the UIDs

  • new_sampler (Sampler) – the sampler to be used for the subsampler sequence. If None, re-use the existing

Returns

a subsampled Sequence

initializer(self)
static get(split, nb_samples, indices, transforms, use_advanced_indexing)

Collect the split indices given and apply a series of transformations

Parameters

  • nb_samples – the total number of samples of split

  • split – a mapping of np.ndarray or torch.Tensor

  • indices – a list of indices as numpy array

  • transforms – a transformation or list of transformations or None

  • use_advanced_indexing – if True, use the advanced indexing mechanism else use a simple list (original data is referenced) advanced indexing is typically faster for small objects, however for large objects (e.g., 3D data) the advanced indexing makes a copy of the data making it very slow.

Returns

a split with the indices provided

get_next(self)
__next__(self)
Returns

The next batch of data

__iter__(self)
Returns

An iterator of batches