:py:mod:`ggt.utils.tensor_utils`
================================

.. py:module:: ggt.utils.tensor_utils


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   ggt.utils.tensor_utils.tensor_to_numpy
   ggt.utils.tensor_utils.arsinh_normalize
   ggt.utils.tensor_utils.load_tensor
   ggt.utils.tensor_utils.standardize_labels
   ggt.utils.tensor_utils.metric_output_transform_al_loss
   ggt.utils.tensor_utils.metric_output_transform_al_cov_loss



.. py:function:: tensor_to_numpy(x)

   Convert a torch tensor to NumPy for plotting.


.. py:function:: arsinh_normalize(X)

   Normalize a Torch tensor with arsinh.


.. py:function:: load_tensor(filename, tensors_path, as_numpy=True)

   Load a Torch tensor from disk.


.. py:function:: standardize_labels(input, data_dir, split, slug, label_col, scaling, invert=False)

   Standardizes data. During training, input should
   be the labels, and during inference, input should be the
   predictions.


.. py:function:: metric_output_transform_al_loss(output)

   Transforms the output of the model, when using
   aleatoric loss, to a form which can be used by the
   ignote metric calculators


.. py:function:: metric_output_transform_al_cov_loss(output)

   Transforms the output of the model, when using
   aleatoric covariance loss, to a form which can be used by the
   ignote metric calculators