pyiqa.archs.maclip_arch
=======================

.. py:module:: pyiqa.archs.maclip_arch

.. autoapi-nested-parse::

   Beyond Cosine Similarity: Magnitude-Aware CLIP for No-Reference Image Quality Assessment

   @article{liao2025beyond,
     title={Beyond Cosine Similarity Magnitude-Aware CLIP for No-Reference Image Quality Assessment},
     author={Liao, Zhicheng and Wu, Dongxu and Shi, Zhenshan and Mai, Sijie and Zhu, Hanwei and Zhu, Lingyu and Jiang, Yuncheng and Chen, Baoliang},
     journal={arXiv preprint arXiv:2511.09948},
     year={2025}
   }

   Accepted by AAAI 2026.

   Reference:
       - Arxiv link: https://arxiv.org/abs/2511.09948
       - Official Github: https://github.com/zhix000/MA-CLIP





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

.. py:class:: CustomCLIP(backbone: str, device='cpu')

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


   .. py:method:: forward(image, text, pos_embedding=False, text_features=None)


.. py:class:: MACLIP(model_type='clipiqa', backbone='RN50', pos_embedding=False)

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


   .. py:method:: preprocess(img)


   .. py:method:: box_cox(x, lam=0.5, epsilon=1e-06)


   .. py:method:: fusion(cos, norm, base_cos=1.0, base_norm=0.6, alpha=1.0)

      :param box_lam: Lambda parameter for Box-Cox transformation (default: 0.5)
      :param base_cos/base_norm: Base weights for fusion of cosine similarity and magnitude cues (default: 1.0/0.6).
      :param alpha: Fusion coefficient (default: 1.0)



   .. py:method:: forward(x, box_lam=0.5, base_cos=1.0, base_norm=0.6, alpha=1.0)