pyiqa.archs.dmm_arch
====================

.. py:module:: pyiqa.archs.dmm_arch

.. autoapi-nested-parse::

   Debiased Mapping for Full-Reference Image Quality Assessment

   @article{chen2025debiased,
     title={Debiased mapping for full-reference image quality assessment},
     author={Chen, Baoliang and Zhu, Hanwei and Zhu, Lingyu and Wang, Shanshe and Pan, Jingshan and Wang, Shiqi},
     journal={IEEE Transactions on Multimedia},
     year={2025},
     publisher={IEEE}
   }

   Reference:
       - Arxiv link: https://ieeexplore.ieee.org/abstract/document/10886996
       - Official Github: https://github.com/Baoliang93/DMM







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

.. py:data:: names

.. py:class:: FeaturesExtractor(target_features=('relu3_3', 'relu4_3'), use_input_norm=False, requires_grad=False, replace_pooling=True)

   Bases: :py:obj:`torch.nn.Module`


   .. py:method:: forward(x)


   .. py:method:: replace_pooling(module: torch.nn.Module) -> torch.nn.Module


.. py:class:: L2Pool2d(kernel_size: int = 3, stride: int = 2, padding=1)

   Bases: :py:obj:`torch.nn.Module`


   Applies L2 pooling with Hann window of size 3x3
   :param x: Tensor with shape (N, C, H, W)


   .. py:method:: forward(x: torch.Tensor) -> torch.Tensor


.. py:class:: DMM(reduce_dim=256, kernel_size=5, features_to_compute=('relu3_3', 'relu4_3'), criterion=torch.nn.CosineSimilarity(), use_dropout=True, **kwargs)

   Bases: :py:obj:`torch.nn.Module`


   .. py:method:: forward(Dist, Ref, as_loss=False)


   .. py:method:: prepare_image_adt(tensor_image)