ISubGVQA.models.isubgvqa
========================

.. py:module:: ISubGVQA.models.isubgvqa


Classes
-------

.. autoapisummary::

   ISubGVQA.models.isubgvqa.ISubGVQA


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

.. py:class:: ISubGVQA(args, use_imle=False, use_masking=True, use_instruction=True, use_mgat=False, mgat_masks=None, use_topk=False, interpretable_mode=True, concat_instr=False, embed_cat=True)

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


   ISubGVQA is a PyTorch neural network module designed for Visual Question Answering (VQA) tasks.
   It integrates various components such as scene graph encoding, question encoding, and multi-head
   graph attention networks to process and interpret visual and textual data.
   Args:
       args (Namespace): Configuration arguments.
       use_imle (bool, optional): Flag to use IMLE. Defaults to False.
       use_masking (bool, optional): Flag to use masking. Defaults to True.
       use_instruction (bool, optional): Flag to use instruction. Defaults to True.
       use_mgat (bool, optional): Flag to use multi-head graph attention. Defaults to False.
       mgat_masks (optional): Masks for MGAT. Defaults to None.
       use_topk (bool, optional): Flag to use top-k sampling. Defaults to False.
       interpretable_mode (bool, optional): Flag for interpretable mode. Defaults to True.
       concat_instr (bool, optional): Flag to concatenate instructions. Defaults to False.
       embed_cat (bool, optional): Flag to embed categories. Defaults to True.
   Attributes:
       args (Namespace): Configuration arguments.
       n_train_steps (int): Number of training steps.
       n_valid_steps (int): Number of validation steps.
       use_imle (bool): Flag to use IMLE.
       use_instruction (bool): Flag to use instruction.
       use_masking (bool): Flag to use masking.
       use_mgat (bool): Flag to use multi-head graph attention.
       interpretable_mode (bool): Flag for interpretable mode.
       concat_instr (bool): Flag to concatenate instructions.
       embed_cat (bool): Flag to embed categories.
       text_sampling (bool): Flag for text sampling.
       general_hidden_dim (int): General hidden dimension size.
       scene_graph_encoder (SceneGraphEncoder): Scene graph encoder module.
       text_emb_dim (int): Text embedding dimension size.
       text_vocab_embedding (torch.nn.Embedding): Text vocabulary embedding.
       question_hidden_dim (int): Question hidden dimension size.
       question_encoder (QuestionEncoder): Question encoder module.
       text_sampler (EdgeSIMPLEBatched, optional): Text sampler module.
       qsts_att_keys (torch.nn.Sequential, optional): Attention keys for questions.
       qsts_att_query (torch.nn.Sequential, optional): Attention query for questions.
       program_decoder (QuestionDecoder): Program decoder module.
       gat_seq (MGAT): Multi-head graph attention module.
       graph_global_attention_pooling (GlobalAttention): Global attention pooling layer.
       qsts_reduction (torch.nn.Sequential): Question reduction layer.
       instr_reduction (torch.nn.Sequential): Instruction reduction layer.
       embedding (torch.nn.Sequential): Embedding layer.
       logit_fc (torch.nn.Linear): Final classification layer.
   Methods:
       forward(node_embeddings, edge_index, edge_embeddings, batch, questions, qsts_att_mask,
               return_masks=False, explainer=False, explainer_stage=False, expl_bypass_x=False,
               scene_graphs=None):
           Forward pass of the model.
           Args:
               node_embeddings (torch.Tensor): Node embeddings.
               edge_index (torch.Tensor): Edge indices.
               edge_embeddings (torch.Tensor): Edge embeddings.
               batch (torch.Tensor): Batch indices.
               questions (torch.Tensor): Encoded questions.
               qsts_att_mask (torch.Tensor): Attention mask for questions.
               return_masks (bool, optional): Flag to return masks. Defaults to False.
               explainer (bool, optional): Flag for explainer mode. Defaults to False.
               explainer_stage (bool, optional): Stage for explainer. Defaults to False.
               expl_bypass_x (bool, optional): Bypass for explainer. Defaults to False.
               scene_graphs (optional): Scene graphs. Defaults to None.
           Returns:
               torch.Tensor: Model logits.
               torch.Tensor: IMLE mask.
               torch.Tensor: MGAT gate.
               torch.Tensor: Node logits layers.
               torch.Tensor: Mask text.


   .. py:attribute:: args


   .. py:attribute:: n_train_steps
      :value: 0



   .. py:attribute:: n_valid_steps
      :value: 0



   .. py:attribute:: use_imle
      :value: False



   .. py:attribute:: use_instruction
      :value: True



   .. py:attribute:: use_masking
      :value: True



   .. py:attribute:: use_mgat
      :value: False



   .. py:attribute:: interpretable_mode
      :value: True



   .. py:attribute:: concat_instr
      :value: False



   .. py:attribute:: embed_cat
      :value: True



   .. py:attribute:: text_sampling


   .. py:attribute:: general_hidden_dim


   .. py:attribute:: scene_graph_encoder


   .. py:attribute:: text_emb_dim
      :value: 512



   .. py:attribute:: text_vocab_embedding


   .. py:attribute:: question_hidden_dim


   .. py:attribute:: question_encoder


   .. py:attribute:: program_decoder


   .. py:attribute:: gat_seq


   .. py:attribute:: graph_global_attention_pooling


   .. py:attribute:: qsts_reduction


   .. py:attribute:: instr_reduction


   .. py:attribute:: embedding


   .. py:attribute:: logit_fc


   .. py:method:: forward(node_embeddings, edge_index, edge_embeddings, batch, questions, qsts_att_mask, return_masks=False, explainer=False, explainer_stage=False, expl_bypass_x=False, scene_graphs=None)