:py:mod:`kwimage.algo._nms_backend.torch_nms`
=============================================

.. py:module:: kwimage.algo._nms_backend.torch_nms


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


Functions
~~~~~~~~~

.. autoapisummary::

   kwimage.algo._nms_backend.torch_nms.torch_nms
   kwimage.algo._nms_backend.torch_nms.test_class_torch



Attributes
~~~~~~~~~~

.. autoapisummary::

   kwimage.algo._nms_backend.torch_nms.torch


.. py:data:: torch
   

   

.. py:function:: torch_nms(ltrb, scores, classes=None, thresh=0.5, bias=0, fast=False)

   Non maximum suppression implemented with pytorch tensors

   CURRENTLY NOT WORKING

   :Parameters: * **ltrb** (*Tensor*) -- Bounding boxes of one image in the format (ltrb)
                * **scores** (*Tensor*) -- Scores of each box
                * **classes** (*Tensor, optional*) -- the classes of each box. If specified nms is applied to each class separately.
                * **thresh** (*float*) -- iou threshold

   :returns: keep: boolean array indicating which boxes were not pruned.
   :rtype: ByteTensor

   .. rubric:: Example

   >>> # DISABLE_DOCTEST
   >>> # xdoctest: +REQUIRES(module:torch)
   >>> import torch
   >>> import numpy as np
   >>> ltrb = torch.FloatTensor(np.array([
   >>>     [0, 0, 100, 100],
   >>>     [100, 100, 10, 10],
   >>>     [10, 10, 100, 100],
   >>>     [50, 50, 100, 100],
   >>>     [100, 100, 130, 130],
   >>>     [100, 100, 130, 130],
   >>>     [100, 100, 130, 130],
   >>> ], dtype=np.float32))
   >>> scores = torch.FloatTensor(np.array([.1, .5, .9, .1, .3, .5, .4]))
   >>> classes = torch.LongTensor(np.array([0, 0, 0, 0, 0, 0, 0]))
   >>> thresh = .5
   >>> flags = torch_nms(ltrb, scores, classes, thresh)
   >>> keep = np.nonzero(flags).view(-1)
   >>> ltrb[flags]
   >>> ltrb[keep]

   .. rubric:: Example

   >>> # DISABLE_DOCTEST
   >>> # xdoctest: +REQUIRES(module:torch)
   >>> import torch
   >>> import numpy as np
   >>> # Test to check that conflicts are correctly resolved
   >>> ltrb = torch.FloatTensor(np.array([
   >>>     [100, 100, 150, 101],
   >>>     [120, 100, 180, 101],
   >>>     [150, 100, 200, 101],
   >>> ], dtype=np.float32))
   >>> scores = torch.FloatTensor(np.linspace(.8, .9, len(ltrb)))
   >>> classes = None
   >>> thresh = .3
   >>> keep = torch_nms(ltrb, scores, classes, thresh, fast=False)
   >>> bboxes[keep]


.. py:function:: test_class_torch()