Module pysimt.layers.transformers.cross_attention_sublayer_mm_parallel

Classes

class ParallelMMCrossAttentionSublayer (model_dim, n_heads, dropout=0.1, attn_dropout=0.0, is_pre_norm=False, fusion='sum')

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them 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):
        super(Model, self).__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 have their parameters converted too when you call :meth:to, etc.

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

Creates a ParallelCrossAttentionSublayer. :param model_dim: The model dimensions. :param n_heads: The number of attention heads. :param dropout: The dropout rate for the residual connection. :param is_pre_norm: Whether the layer type is pre_norm. Default: True.

Expand source code

class ParallelMMCrossAttentionSublayer(BaseSublayer):
    def __init__(self, model_dim, n_heads, dropout=0.1, attn_dropout=0.0, is_pre_norm=False, fusion='sum'):
        """
        Creates a ParallelCrossAttentionSublayer.
        :param model_dim: The model dimensions.
        :param n_heads: The number of attention heads.
        :param dropout: The dropout rate for the residual connection.
        :param is_pre_norm: Whether the layer type is pre_norm. Default: True.
        """
        super().__init__(model_dim, dropout, is_pre_norm)
        self.attn_txt = ScaledDotAttention(model_dim, n_heads, attn_dropout)
        self.attn_img = ScaledDotAttention(model_dim, n_heads, attn_dropout)
        self.fusion = fusion

    def forward(self, query, key_txt, value_txt, mask_txt, key_img, value_img, mask_img=None):
        """
        Performs a forward pass over the CrossAttentionSublayer.
        :param query: The query. For encoder-decoder attention, it is the output from the previous decoder layer.
        :param key_txt: The key. For encoder-decoder attention, it is the output from the encoder.
        :param value_txt: The mask. For encoder-decoder attention, it is the output from the encoder.
        :param value_img:
        :param key_img:
        :param mask_txt: The textual encoder mask.
        :param mask_img: The visual features mask.
        :return: The output of the CrossAttentionSublayer.
        """
        residual = query
        query = self.apply_pre_norm_if_needed(query)

        attn_txt, attn_weights_txt = self.attn_txt((query, key_txt, value_txt, mask_txt))
        attn_img, attn_weights_img = self.attn_img((query, key_img, value_img, mask_img))

        attn_combined = torch.add(attn_txt, attn_img)
        out = self.apply_residual(residual, attn_combined)
        out = self.apply_post_norm_if_needed(out)
        return out, {'txt': attn_weights_txt, 'img': attn_weights_img}

Ancestors

• BaseSublayer
• torch.nn.modules.module.Module

Class variables

var dump_patches : bool

var training : bool

Methods

def forward(self, query, key_txt, value_txt, mask_txt, key_img, value_img, mask_img=None) ‑> Callable[..., Any]

Performs a forward pass over the CrossAttentionSublayer. :param query: The query. For encoder-decoder attention, it is the output from the previous decoder layer. :param key_txt: The key. For encoder-decoder attention, it is the output from the encoder. :param value_txt: The mask. For encoder-decoder attention, it is the output from the encoder. :param value_img: :param key_img: :param mask_txt: The textual encoder mask. :param mask_img: The visual features mask. :return: The output of the CrossAttentionSublayer.

Expand source code

def forward(self, query, key_txt, value_txt, mask_txt, key_img, value_img, mask_img=None):
    """
    Performs a forward pass over the CrossAttentionSublayer.
    :param query: The query. For encoder-decoder attention, it is the output from the previous decoder layer.
    :param key_txt: The key. For encoder-decoder attention, it is the output from the encoder.
    :param value_txt: The mask. For encoder-decoder attention, it is the output from the encoder.
    :param value_img:
    :param key_img:
    :param mask_txt: The textual encoder mask.
    :param mask_img: The visual features mask.
    :return: The output of the CrossAttentionSublayer.
    """
    residual = query
    query = self.apply_pre_norm_if_needed(query)

    attn_txt, attn_weights_txt = self.attn_txt((query, key_txt, value_txt, mask_txt))
    attn_img, attn_weights_img = self.attn_img((query, key_img, value_img, mask_img))

    attn_combined = torch.add(attn_txt, attn_img)
    out = self.apply_residual(residual, attn_combined)
    out = self.apply_post_norm_if_needed(out)
    return out, {'txt': attn_weights_txt, 'img': attn_weights_img}

Inherited members

• BaseSublayer:
  • apply_post_norm_if_needed
  • apply_pre_norm_if_needed
  • apply_residual