timesead.models.layers.anom_attention
=====================================

.. py:module:: timesead.models.layers.anom_attention


Classes
-------

.. autoapisummary::

   timesead.models.layers.anom_attention.TriangularCausalMask
   timesead.models.layers.anom_attention.AnomalyAttention
   timesead.models.layers.anom_attention.AttentionLayer


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

.. py:class:: TriangularCausalMask(B, L, device: str = 'cpu')

   .. py:property:: mask
      :type: torch.Tensor



.. py:class:: AnomalyAttention(win_size: int, mask_flag: bool = True, scale: Optional[float] = None, attention_dropout: float = 0.0, output_attention: bool = False)

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


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: scale
      :value: None



   .. py:attribute:: mask_flag
      :value: True



   .. py:attribute:: output_attention
      :value: False



   .. py:attribute:: dropout


   .. py:method:: forward(queries, keys, values, sigma, attn_mask)


.. py:class:: AttentionLayer(attention: torch.nn.Module, d_model: int, n_heads: int, d_keys: Optional[int] = None, d_values: Optional[int] = None)

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


   Base class for all neural network modules.

   Your models should also subclass this class.

   Modules can also contain other Modules, allowing them to be nested in
   a tree structure. You can assign the submodules as regular attributes::

       import torch.nn as nn
       import torch.nn.functional as F

       class Model(nn.Module):
           def __init__(self) -> None:
               super().__init__()
               self.conv1 = nn.Conv2d(1, 20, 5)
               self.conv2 = nn.Conv2d(20, 20, 5)

           def forward(self, x):
               x = F.relu(self.conv1(x))
               return F.relu(self.conv2(x))

   Submodules assigned in this way will be registered, and will also have their
   parameters converted when you call :meth:`to`, etc.

   .. note::
       As per the example above, an ``__init__()`` call to the parent class
       must be made before assignment on the child.

   :ivar training: Boolean represents whether this module is in training or
                   evaluation mode.
   :vartype training: bool

   Initialize internal Module state, shared by both nn.Module and ScriptModule.


   .. py:attribute:: norm


   .. py:attribute:: inner_attention


   .. py:attribute:: query_projection


   .. py:attribute:: key_projection


   .. py:attribute:: value_projection


   .. py:attribute:: sigma_projection


   .. py:attribute:: out_projection


   .. py:attribute:: n_heads


   .. py:method:: forward(queries, keys, values, attn_mask)