pyiqa.archs.cnniqa_arch
=======================

.. py:module:: pyiqa.archs.cnniqa_arch

.. autoapi-nested-parse::

   CNNIQA Model.

   Zheng, Heliang, Huan Yang, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo.
   "Learning conditional knowledge distillation for degraded-reference image
   quality assessment." In Proceedings of the IEEE/CVF International Conference
   on Computer Vision (ICCV), pp. 10242-10251. 2021.

   Ref url: https://github.com/lidq92/CNNIQA
   Re-implemented by: Chaofeng Chen (https://github.com/chaofengc) with modification:
       - We use 3 channel RGB input.







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

.. py:data:: default_model_urls

.. py:class:: CNNIQA(ker_size=7, n_kers=50, n1_nodes=800, n2_nodes=800, pretrained='koniq10k', pretrained_model_path=None)

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


   CNNIQA no-reference image quality model.

   :param ker_size: Kernel size.
   :type ker_size: int
   :param n_kers: Number of kernels.
   :type n_kers: int
   :param n1_nodes: Number of n1 nodes.
   :type n1_nodes: int
   :param n2_nodes: Number of n2 nodes.
   :type n2_nodes: int
   :param pretrained: Pretrained model key.
   :type pretrained: str | None
   :param pretrained_model_path: Optional local checkpoint path.
   :type pretrained_model_path: str | None


   .. py:method:: forward(x)

      Predict quality score with CNNIQA.

      :param x: Input tensor with shape ``(N, 3, H, W)``.
      :type x: torch.Tensor

      :returns: Predicted score tensor with shape ``(N, 1)``.
      :rtype: torch.Tensor