ISubGVQA.models.masking
=======================

.. py:module:: ISubGVQA.models.masking


Classes
-------

.. autoapisummary::

   ISubGVQA.models.masking.MaskingModel


Functions
---------

.. autoapisummary::

   ISubGVQA.models.masking.topk_sampling
   ISubGVQA.models.masking.get_imle_samplers
   ISubGVQA.models.masking.get_aimle_samplers


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

.. py:class:: MaskingModel(dim_nodes, dim_questions, masking_threshold=0.3, use_topk=False, sample_k=None, sampler_type=None, nb_samples=1, alpha=1.0, beta=10.0, tau=1.0)

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


   A PyTorch module for masking nodes in a graph based on various sampling methods.
   Args:
       dim_nodes (int): Dimension of node features.
       dim_questions (int): Dimension of question features.
       masking_threshold (float, optional): Threshold for masking. Defaults to 0.3.
       use_topk (bool, optional): Whether to use top-k pooling. Defaults to False.
       sample_k (int, optional): Number of samples for the sampler. Defaults to None.
       sampler_type (str, optional): Type of sampler to use ('imle', 'aimle', 'simple', 'gumbel'). Defaults to None.
       nb_samples (int, optional): Number of samples for I-MLE samplers. Defaults to 1.
       alpha (float, optional): Alpha parameter for I-MLE samplers. Defaults to 1.0.
       beta (float, optional): Beta parameter for I-MLE samplers. Defaults to 10.0.
       tau (float, optional): Tau parameter for I-MLE samplers. Defaults to 1.0.
   Methods:
       reset_parameters():
           Resets the parameters of the neural networks.
       forward(x, u, batch, edge_index, size=None, use_all_instrs=True):
           Forward pass of the model.
           Args:
               x (Tensor): Node features.
               u (Tensor): Question features.
               batch (Tensor): Batch indices.
               edge_index (Tensor): Edge indices.
               size (int, optional): Size of the batch. Defaults to None.
               use_all_instrs (bool, optional): Whether to use all instructions. Defaults to True.
           Returns:
               Tensor: Masked node features.


   .. py:attribute:: use_topk
      :value: False



   .. py:attribute:: sample_k
      :value: None



   .. py:attribute:: sampler_type
      :value: None



   .. py:attribute:: masking_threshold
      :value: 0



   .. py:attribute:: dim_nodes


   .. py:attribute:: dim_questions


   .. py:attribute:: gate_nn


   .. py:attribute:: node_nn


   .. py:attribute:: ques_nn


   .. py:method:: reset_parameters()


   .. py:method:: forward(x, u, batch, edge_index, size=None, use_all_instrs=True)


.. py:function:: topk_sampling(gate, masking_threshold, batch)

.. py:function:: get_imle_samplers(sample_k, beta=10, alpha=1.0, tau=1.0, noise_scale=0.3, nb_samples=1, device=None)

.. py:function:: get_aimle_samplers(sample_k, alpha=1.0, tau=1.0, noise_scale=0.3, nb_samples=1, device=None)