pyiqa.archs.iqt_arch
====================

.. py:module:: pyiqa.archs.iqt_arch

.. autoapi-nested-parse::

   IQA metric introduced by

   @inproceedings{cheon2021iqt,
     title={Perceptual image quality assessment with transformers},
     author={Cheon, Manri and Yoon, Sung-Jun and Kang, Byungyeon and Lee, Junwoo},
     booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     pages={433--442},
     year={2021}
   }

   Ref url: https://github.com/anse3832/IQT
   Re-implemented by: Chaofeng Chen (https://github.com/chaofengc)







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

.. py:class:: IQARegression(config)

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


   .. py:method:: forward(enc_inputs, enc_inputs_embed, dec_inputs, dec_inputs_embed)


.. py:class:: Transformer(config)

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


   .. py:method:: forward(enc_inputs, enc_inputs_embed, dec_inputs, dec_inputs_embed)


.. py:class:: Encoder(config)

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


   .. py:method:: forward(inputs, inputs_embed)


.. py:class:: EncoderLayer(config)

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


   .. py:method:: forward(inputs, attn_mask)


.. py:function:: get_sinusoid_encoding_table(n_seq, d_hidn)

.. py:function:: get_attn_pad_mask(seq_q, seq_k, i_pad)

.. py:class:: MultiHeadAttention(config)

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


   .. py:method:: forward(Q, K, V, attn_mask)


.. py:class:: ScaledDotProductAttention(config)

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


   .. py:method:: forward(Q, K, V, attn_mask)


.. py:class:: PoswiseFeedForwardNet(config)

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


   .. py:method:: forward(inputs)


.. py:class:: Decoder(config)

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


   .. py:method:: forward(dec_inputs, dec_inputs_embed, enc_inputs, enc_outputs)


.. py:class:: DecoderLayer(config)

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


   .. py:method:: forward(dec_inputs, enc_outputs, self_attn_mask, dec_enc_attn_mask)


.. py:function:: get_attn_decoder_mask(seq)

.. py:class:: SaveOutput

   .. py:method:: clear(device)


.. py:class:: DeformFusion(patch_size=8, in_channels=768 * 5, cnn_channels=256 * 3, out_channels=256 * 3)

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


   .. py:method:: forward(cnn_feat, vit_feat)


.. py:class:: Pixel_Prediction(inchannels=768 * 5 + 256 * 3, outchannels=256, d_hidn=1024)

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


   .. py:method:: forward(f_dis, f_ref, cnn_dis, cnn_ref)


.. py:class:: IQT(num_crop=20, config_dataset='live', default_mean=timm.data.IMAGENET_INCEPTION_MEAN, default_std=timm.data.IMAGENET_INCEPTION_STD, pretrained=False, pretrained_model_path=None)

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


   Image Quality Transformer (IQT) model for image quality assessment.

   :param - num_crop: Number of crops to take from the input image.
   :type - num_crop: int
   :param - config_dataset: Name of the dataset to use for configuration.
   :type - config_dataset: str
   :param - default_mean: Default mean values for input normalization.
   :type - default_mean: list
   :param - default_std: Default standard deviation values for input normalization.
   :type - default_std: list
   :param - pretrained: Whether to use a pretrained model.
   :type - pretrained: bool
   :param - pretrained_model_path: Path to the pretrained model.
   :type - pretrained_model_path: str

   .. attribute:: - backbone

      Inception ResNet V2 backbone model.

      :type: nn.Module

   .. attribute:: - config

      Configuration object for the IQT model.

      :type: Config

   .. attribute:: - enc_inputs

      Encoded input tensor.

      :type: torch.Tensor

   .. attribute:: - dec_inputs

      Decoded input tensor.

      :type: torch.Tensor

   .. attribute:: - regressor

      Regression model for IQT.

      :type: IQARegression

   .. attribute:: - default_mean

      Default mean values for input normalization.

      :type: torch.Tensor

   .. attribute:: - default_std

      Default standard deviation values for input normalization.

      :type: torch.Tensor

   .. attribute:: - eps

      Epsilon value for numerical stability.

      :type: float

   .. attribute:: - crops

      Number of crops to take from the input image.

      :type: int

   .. attribute:: - crop_size

      Size of the input image crop.

      :type: int


   .. py:method:: init_saveoutput()

      Initialize the SaveOutput object and register hook handles for the backbone model.



   .. py:method:: fix_network(model)

      Fix the network by setting all parameters to not require gradients.

      :param model: The model to fix.
      :type model: nn.Module



   .. py:method:: preprocess(x)

      Preprocess the input tensor by normalizing it.

      :param x: The input tensor.
      :type x: torch.Tensor

      :returns: The normalized input tensor.
      :rtype: torch.Tensor



   .. py:method:: get_backbone_feature(x)

      Get the backbone features for the input tensor.

      :param x: The input tensor.
      :type x: torch.Tensor

      :returns: The backbone features for the input tensor.
      :rtype: torch.Tensor



   .. py:method:: regress_score(dis, ref)

      Regress the score for the input image.

      :param dis: The distorted image.
      :type dis: torch.Tensor
      :param ref: The reference image.
      :type ref: torch.Tensor

      :returns: The predicted score for the input image.
      :rtype: torch.Tensor



   .. py:method:: forward(x, y)