:py:mod:`mipcandy.metrics`
==========================

.. py:module:: mipcandy.metrics

.. autodoc2-docstring:: mipcandy.metrics
   :allowtitles:

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

Functions
~~~~~~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`_args_check <mipcandy.metrics._args_check>`
     - .. autodoc2-docstring:: mipcandy.metrics._args_check
          :summary:
   * - :py:obj:`do_reduction <mipcandy.metrics.do_reduction>`
     - .. autodoc2-docstring:: mipcandy.metrics.do_reduction
          :summary:
   * - :py:obj:`binary_dice <mipcandy.metrics.binary_dice>`
     - .. autodoc2-docstring:: mipcandy.metrics.binary_dice
          :summary:
   * - :py:obj:`dice_similarity_coefficient <mipcandy.metrics.dice_similarity_coefficient>`
     - .. autodoc2-docstring:: mipcandy.metrics.dice_similarity_coefficient
          :summary:
   * - :py:obj:`soft_dice <mipcandy.metrics.soft_dice>`
     - .. autodoc2-docstring:: mipcandy.metrics.soft_dice
          :summary:

API
~~~

.. py:function:: _args_check(outputs: torch.Tensor, labels: torch.Tensor, *, dtype: torch.dtype | None = None, device: mipcandy.types.Device | None = None) -> tuple[torch.dtype, mipcandy.types.Device]
   :canonical: mipcandy.metrics._args_check

   .. autodoc2-docstring:: mipcandy.metrics._args_check

.. py:function:: do_reduction(x: torch.Tensor, method: mipcandy.types.Reduction) -> torch.Tensor
   :canonical: mipcandy.metrics.do_reduction

   .. autodoc2-docstring:: mipcandy.metrics.do_reduction

.. py:function:: binary_dice(outputs: torch.Tensor, labels: torch.Tensor, *, if_empty: float = 1, reduction: mipcandy.types.Reduction = 'mean') -> torch.Tensor
   :canonical: mipcandy.metrics.binary_dice

   .. autodoc2-docstring:: mipcandy.metrics.binary_dice

.. py:function:: dice_similarity_coefficient(outputs: torch.Tensor, labels: torch.Tensor, *, if_empty: float = 1, reduction: mipcandy.types.Reduction = 'mean') -> torch.Tensor
   :canonical: mipcandy.metrics.dice_similarity_coefficient

   .. autodoc2-docstring:: mipcandy.metrics.dice_similarity_coefficient

.. py:function:: soft_dice(outputs: torch.Tensor, labels: torch.Tensor, *, smooth: float = 1, batch_dice: bool = True, reduction: mipcandy.types.Reduction = 'mean') -> torch.Tensor
   :canonical: mipcandy.metrics.soft_dice

   .. autodoc2-docstring:: mipcandy.metrics.soft_dice