# SlicedWassersteinDistance¶

Inheritance Diagram

class ashpy.metrics.sliced_wasserstein_metric.SlicedWassersteinDistance(name='SWD', model_selection_operator=<built-in function lt>, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), resolution=128, resolution_min=16, patches_per_image=64, patch_size=7, random_sampling_count=1, random_projection_dim=147, use_svd=False)[source]

Sliced Wasserstein Distance.

Used as metric in Progressive Growing of GANs [1].

 [1] Progressive Growing of GANs https://arxiv.org/abs/1710.10196

Methods

 __init__([name, model_selection_operator, …]) Initialize the Metric. log(step) Log the SWD mean and each sub-metric. model_selection(checkpoint, global_step) Perform model selection for each sub-metric. reset_states() Reset the state of the metric and the state of each child metric. update_state(context) Update the internal state of the metric, using the information from the context object.

Attributes

 best_folder Retrieve the folder used to save the best model when doing model selection. best_model_sel_file Retrieve the path to JSON file containing the measured performance of the best model. logdir Retrieve the log directory. metric Retrieve the tf.keras.metrics.Metric object. model_selection_operator Retrieve the operator used for model selection. name Retrieve the metric name. sanitized_name all / are _.
__init__(name='SWD', model_selection_operator=<built-in function lt>, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), resolution=128, resolution_min=16, patches_per_image=64, patch_size=7, random_sampling_count=1, random_projection_dim=147, use_svd=False)[source]

Initialize the Metric.

Parameters: name (str) – Name of the metric. model_selection_operator (typing.Callable) – The operation that will be used when model_selection is triggered to compare the metrics, used by the update_state. Any typing.Callable behaving like an operator is accepted. Note Model selection is done ONLY if an operator is specified here. logdir (str) – Path to the log dir, defaults to a log folder in the current directory. resolution (int) – Image Resolution, defaults to 128 resolution_min (int) – Min Resolution achieved by the metric patches_per_image (int) – Number of patches to extract per image per Laplacian level. patch_size (int) – Width of a square patch. random_sampling_count (int) – Number of random projections to average. random_projection_dim (int) – Dimension of the random projection space. use_svd (bool) – experimental method to compute a more accurate distance. None
log(step)[source]

Log the SWD mean and each sub-metric.

logdir

Retrieve the log directory.

Return type: str
model_selection(checkpoint, global_step)[source]

Perform model selection for each sub-metric.

Return type: None
reset_states()[source]

Reset the state of the metric and the state of each child metric.

Return type: None
update_state(context)[source]

Update the internal state of the metric, using the information from the context object.

Parameters: context (ashpy.contexts.gan.GANContext) – An AshPy Context Object that carries all the information the Metric needs. None