pyiqa.archs.tres_arch
=====================

.. py:module:: pyiqa.archs.tres_arch

.. autoapi-nested-parse::

   TReS model.

   Reference:
       No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency.
       S. Alireza Golestaneh, Saba Dadsetan, Kris M. Kitani
       WACV2022

   Official code: https://github.com/isalirezag/TReS







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

.. py:data:: default_model_urls

.. py:class:: Transformer(d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False, return_intermediate_dec=False)

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


   .. py:method:: forward(src, pos_embed)


.. py:class:: TransformerEncoder(encoder_layer, num_layers, norm=None)

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


   .. py:method:: forward(src, mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, pos: Optional[torch.Tensor] = None)


.. py:class:: TransformerEncoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False)

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


   .. py:method:: with_pos_embed(tensor, pos: Optional[torch.Tensor])


   .. py:method:: forward_post(src, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, pos: Optional[torch.Tensor] = None)


   .. py:method:: forward_pre(src, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, pos: Optional[torch.Tensor] = None)


   .. py:method:: forward(src, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, pos: Optional[torch.Tensor] = None)


.. py:class:: PositionEmbeddingSine(num_pos_feats=64, temperature=10000, normalize=False, scale=None)

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


   This is a more standard version of the position embedding, very similar to the one
   used by the Attention is all you need paper, generalized to work on images.


   .. py:method:: forward(tensor_val)


.. py:class:: L2pooling(filter_size=5, stride=1, channels=None, pad_off=0)

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


   .. py:method:: forward(input)


.. py:class:: TReS(network='resnet50', train_dataset='koniq', nheadt=16, num_encoder_layerst=2, dim_feedforwardt=64, test_sample=50, default_mean=[0.485, 0.456, 0.406], default_std=[0.229, 0.224, 0.225], pretrained=True, pretrained_model_path=None)

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


   .. py:method:: forward_backbone(model, x)


   .. py:method:: forward(x)