pyiqa.archs.maniqa_swin
=======================

.. py:module:: pyiqa.archs.maniqa_swin






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

.. py:class:: Mlp(in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0)

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


   .. py:method:: forward(x)


.. py:function:: window_partition(x, window_size)

   :param x: (B, H, W, C)
   :param window_size: window size
   :type window_size: int

   :returns: (num_windows*B, window_size, window_size, C)
   :rtype: windows


.. py:function:: window_reverse(windows, window_size, H, W)

   :param windows: (num_windows*B, window_size, window_size, C)
   :param window_size: Window size
   :type window_size: int
   :param H: Height of image
   :type H: int
   :param W: Width of image
   :type W: int

   :returns: (B, H, W, C)
   :rtype: x


.. py:class:: WindowAttention(dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0)

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


   Window based multi-head self attention (W-MSA) module with relative position bias.
   It supports both of shifted and non-shifted window.

   :param dim: Number of input channels.
   :type dim: int
   :param window_size: The height and width of the window.
   :type window_size: tuple[int]
   :param num_heads: Number of attention heads.
   :type num_heads: int
   :param qkv_bias: If True, add a learnable bias to query, key, value. Default: True
   :type qkv_bias: bool, optional
   :param qk_scale: Override default qk scale of head_dim ** -0.5 if set
   :type qk_scale: float | None, optional
   :param attn_drop: Dropout ratio of attention weight. Default: 0.0
   :type attn_drop: float, optional
   :param proj_drop: Dropout ratio of output. Default: 0.0
   :type proj_drop: float, optional


   .. py:method:: forward(x, mask=None)

      :param x: input features with shape of (num_windows*B, N, C)
      :param mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None



   .. py:method:: extra_repr() -> str


   .. py:method:: flops(N)


.. py:class:: SwinBlock(dim, input_resolution, num_heads, window_size=7, shift_size=0, dim_mlp=1024.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm)

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


   Swin Transformer Block.

   :param dim: Number of input channels.
   :type dim: int
   :param input_resolution: Input resolution.
   :type input_resolution: tuple[int]
   :param num_heads: Number of attention heads.
   :type num_heads: int
   :param window_size: Window size.
   :type window_size: int
   :param shift_size: Shift size for SW-MSA.
   :type shift_size: int
   :param mlp_ratio: Ratio of mlp hidden dim to embedding dim.
   :type mlp_ratio: float
   :param qkv_bias: If True, add a learnable bias to query, key, value. Default: True
   :type qkv_bias: bool, optional
   :param qk_scale: Override default qk scale of head_dim ** -0.5 if set.
   :type qk_scale: float | None, optional
   :param drop: Dropout rate. Default: 0.0
   :type drop: float, optional
   :param attn_drop: Attention dropout rate. Default: 0.0
   :type attn_drop: float, optional
   :param drop_path: Stochastic depth rate. Default: 0.0
   :type drop_path: float, optional
   :param act_layer: Activation layer. Default: nn.GELU
   :type act_layer: nn.Module, optional
   :param norm_layer: Normalization layer.  Default: nn.LayerNorm
   :type norm_layer: nn.Module, optional


   .. py:method:: forward(x)


.. py:class:: BasicLayer(dim, input_resolution, depth, num_heads, window_size=7, dim_mlp=1024, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False)

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


   A basic Swin Transformer layer for one stage.

   :param dim: Number of input channels.
   :type dim: int
   :param input_resolution: Input resolution.
   :type input_resolution: tuple[int]
   :param depth: Number of blocks.
   :type depth: int
   :param num_heads: Number of attention heads.
   :type num_heads: int
   :param window_size: Local window size.
   :type window_size: int
   :param mlp_ratio: Ratio of mlp hidden dim to embedding dim.
   :type mlp_ratio: float
   :param qkv_bias: If True, add a learnable bias to query, key, value. Default: True
   :type qkv_bias: bool, optional
   :param qk_scale: Override default qk scale of head_dim ** -0.5 if set.
   :type qk_scale: float | None, optional
   :param drop: Dropout rate. Default: 0.0
   :type drop: float, optional
   :param attn_drop: Attention dropout rate. Default: 0.0
   :type attn_drop: float, optional
   :param drop_path: Stochastic depth rate. Default: 0.0
   :type drop_path: float | tuple[float], optional
   :param norm_layer: Normalization layer. Default: nn.LayerNorm
   :type norm_layer: nn.Module, optional
   :param downsample: Downsample layer at the end of the layer. Default: None
   :type downsample: nn.Module | None, optional
   :param use_checkpoint: Whether to use checkpointing to save memory. Default: False.
   :type use_checkpoint: bool


   .. py:method:: forward(x)


   .. py:method:: extra_repr() -> str


   .. py:method:: flops()


.. py:class:: SwinTransformer(patches_resolution, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], embed_dim=256, drop=0.1, drop_rate=0.0, drop_path_rate=0.1, dropout=0.0, window_size=7, dim_mlp=1024, qkv_bias=True, qk_scale=None, attn_drop_rate=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, scale=0.8, **kwargs)

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


   .. py:method:: forward(x)