sup3r.preprocessing.batch_queues.dc.ValBatchQueueDC

sup3r.preprocessing.batch_queues.dc.ValBatchQueueDC#

class ValBatchQueueDC(samplers, *, n_space_bins=1, n_time_bins=1, batch_size=16, n_batches=64, s_enhance=1, t_enhance=1, queue_cap=None, transform_kwargs=None, thread_name='training', mode='lazy', **kwargs)[source]#

Bases: BatchQueueDC

Queue to construct a single batch for each spatiotemporal validation bin. e.g. If we have 4 time bins and 1 space bin this will get batch_size samples for 4 batches, with batch_size samples from each bin. The model performance across these batches will determine the weights for how the training batch queue is sampled.

Parameters:

samplers (List[Sampler]) – List of Sampler instances
n_space_bins (int) – Number of spatial bins to use for weighted sampling. e.g. if this is 4 the spatial domain will be divided into 4 equal regions and losses will be calculated across these regions during traning in order to adaptively sample from lower performing regions.
n_time_bins (int) – Number of time bins to use for weighted sampling. e.g. if this is 4 the temporal domain will be divided into 4 equal periods and losses will be calculated across these periods during traning in order to adaptively sample from lower performing time periods.
kwargs (dict) – Keyword arguments for parent class.
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 the enhancement factors evenly divide the sample_shape.
`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`()	Update weights and get batch of samples from sampled container.
`start`()	Start thread to keep sample queue full for batches.
`stop`()	Stop loading batches.
`transform`(samples[, smoothing, ...])	Coarsen high res data to get corresponding low res batch.
`update_weights`(spatial_weights, temporal_weights)	Set weights used to sample spatial and temporal bins.
`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.
`spatial_weights`	Sample entirely from this spatial bin determined by the batch number.
`temporal_weights`	Sample entirely from this temporal bin determined by the batch number.

property spatial_weights#: Sample entirely from this spatial bin determined by the batch number.

property temporal_weights#: Sample entirely from this temporal bin determined by the batch number.

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_enhancement_factors()#: Make sure the enhancement factors evenly divide the sample_shape.

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

sup3r.preprocessing.batch_queues.dc.ValBatchQueueDC

Contents

sup3r.preprocessing.batch_queues.dc.ValBatchQueueDC#