sup3r.preprocessing.batch_queues.dual.DualBatchQueue

class DualBatchQueue(samplers, *, batch_size=16, n_batches=64, s_enhance=1, t_enhance=1, queue_cap=None, transform_kwargs=None, thread_name='training', mode='lazy')

Bases: AbstractBatchQueue

Base BatchQueue for use with DualSampler objects.

Parameters:

• samplers (List[Sampler]) – List of Sampler instances

• batch_size (int) – Number of observations / samples in a batch

• n_batches (int) – Number of batches in an epoch, this sets the iteration limit for this object.

• s_enhance (int) – Integer factor by which the spatial axes is to be enhanced.

• t_enhance (int) – Integer factor by which the temporal axes is to be enhanced.

• queue_cap (int) – Maximum number of batches the batch queue can store.

• transform_kwargs (Union[Dict, None]) – Dictionary of kwargs to be passed to self.transform. This method performs smoothing / coarsening.

• thread_name (str) – Name of the queue thread. Default is ‘training’. Used to set name to ‘validation’ for BatchHandler, which has a training and validation queue.

• mode (str) – Loading mode. Default is ‘lazy’, which only loads data into memory as batches are queued. ‘eager’ will load all data into memory right away.

Methods

check_enhancement_factors()	Make sure each DualSampler has the same enhancment factors and they match those provided to the BatchQueue.
check_features()	Make sure all samplers have the same sets of features.
check_shared_attr(attr)	Check if all containers have the same value for attr.
enqueue_batch()	Build batch and send to queue.
enqueue_batches()	Callback function for queue thread.
get_batch()	Get batch from queue or directly from a Sampler through sample_batch.
get_container_index()	Get random container index based on weights
get_queue()	Return FIFO queue for storing batches.
get_random_container()	Get random container based on container weights
log_queue_info()	Log info about queue size.
post_init_log([args_dict])	Log additional arguments after initialization.
post_proc(samples)	Performs some post proc on dequeued samples before sending out for training.
preflight()	Run checks before kicking off the queue.
sample_batch()	Get random sampler from collection and return a batch of samples from that sampler.
start()	Start thread to keep sample queue full for batches.
stop()	Stop loading batches.
transform(samples[, smoothing, smoothing_ignore])	Perform smoothing if requested.
wrap(data)	Return a Sup3rDataset object or tuple of such.

Attributes

container_weights	Get weights used to sample from different containers based on relative sizes
data	Return underlying data.
features	Get all features contained in data.
hr_shape	Shape of high resolution sample in a low-res / high-res pair.
lr_shape	Shape of low resolution sample in a low-res / high-res pair.
queue_shape	Shape of objects stored in the queue.
queue_thread	Get new queue thread.
running	Boolean to check whether to keep enqueueing batches.
shape	Get shape of underlying data.

property queue_shape

Shape of objects stored in the queue.

check_enhancement_factors()

Make sure each DualSampler has the same enhancment factors and they match those provided to the BatchQueue.

transform(samples, smoothing=None, smoothing_ignore=None)

Perform smoothing if requested.

Note

This does not include temporal or spatial coarsening like SingleBatchQueue

class Batch(low_res, high_res)

Bases: tuple

Create new instance of Batch(low_res, high_res)

__add__(value, /)

Return self+value.

__mul__(value, /)

Return self*value.

count(value, /)

Return number of occurrences of value.

high_res

Alias for field number 1

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

low_res

Alias for field number 0

check_features()

Make sure all samplers have the same sets of features.

check_shared_attr(attr)

Check if all containers have the same value for attr. If they do the collection effectively inherits those attributes.

property container_weights

Get weights used to sample from different containers based on relative sizes

property data

Return underlying data.

Returns:

Sup3rDataset

See also

wrap()

enqueue_batch()

Build batch and send to queue.

enqueue_batches() → None

Callback function for queue thread. While training, the queue is checked for empty spots and filled. In the training thread, batches are removed from the queue.

property features

Get all features contained in data.

get_batch() → Batch

Get batch from queue or directly from a Sampler through sample_batch.

get_container_index()

Get random container index based on weights

get_queue()

Return FIFO queue for storing batches.

get_random_container()

Get random container based on container weights

property hr_shape

Shape of high resolution sample in a low-res / high-res pair. (e.g. (spatial_1, spatial_2, temporal, features))

log_queue_info()

Log info about queue size.

property lr_shape

Shape of low resolution sample in a low-res / high-res pair. (e.g. (spatial_1, spatial_2, temporal, features))

post_init_log(args_dict=None)

Log additional arguments after initialization.

post_proc(samples) → Batch

Performs some post proc on dequeued samples before sending out for training. Post processing can include coarsening on high-res data (if Collection consists of Sampler objects and not DualSampler objects), smoothing, etc

Returns:

Batch (namedtuple) – namedtuple with low_res and high_res attributes

preflight()

Run checks before kicking off the queue.

property queue_thread

Get new queue thread.

property running

Boolean to check whether to keep enqueueing batches.

sample_batch()

Get random sampler from collection and return a batch of samples from that sampler.

Notes

These samples are wrapped in an np.asarray call, so they have been loaded into memory.

property shape

Get shape of underlying data.

start() → None

Start thread to keep sample queue full for batches.

stop() → None

Stop loading batches.

wrap(data)

Return a Sup3rDataset object or tuple of such. This is a tuple when the .data attribute belongs to a Collection object like BatchHandler. Otherwise this is Sup3rDataset object, which is either a wrapped 2-tuple or 1-tuple (e.g. len(data) == 2 or len(data) == 1). This is a 2-tuple when .data belongs to a dual container object like DualSampler and a 1-tuple otherwise.