Conventional Transformer

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torchmil.nn.transformers.TransformerEncoder

Bases: Encoder

A Transformer encoder with skip connections and layer normalization.

Given an input bag input bag \(\mathbf{X} = \left[ \mathbf{x}_1, \ldots, \mathbf{x}_N \right]^\top \in \mathbb{R}^{N \times D}\), it computes:

\[\begin{align*} \mathbf{X}^{0} & = \mathbf{X} \\ \mathbf{Z}^{l} & = \mathbf{X}^{l-1} + \operatorname{SelfAttention}( \operatorname{LayerNorm}(\mathbf{X}^{l-1}) ), \quad l = 1, \ldots, L \\ \mathbf{X}^{l} & = \mathbf{Z}^{l} + \operatorname{MLP}(\operatorname{LayerNorm}(\mathbf{Z}^{l})), \quad l = 1, \ldots, L. \\ \end{align*}\]

This module outputs \(\operatorname{TransformerEncoder}(\mathbf{X}) = \mathbf{X}^{L}\) if add_self=False, and \(\operatorname{TransformerEncoder}(\mathbf{X}) = \mathbf{X}^{L} + \mathbf{X}\) if add_self=True.

__init__(in_dim, out_dim=None, att_dim=512, n_heads=4, n_layers=4, use_mlp=True, add_self=False, dropout=0.0)

Class constructor

Parameters:

• in_dim (int) –

  Input dimension.

• out_dim (int, default: None ) –

  Output dimension. If None, out_dim = in_dim.

• att_dim (int, default: 512 ) –

  Attention dimension.

• n_heads (int, default: 4 ) –

  Number of heads.

• n_layers (int, default: 4 ) –

  Number of layers.

• use_mlp (bool, default: True ) –

  Whether to use feedforward layer.

• add_self (bool, default: False ) –

  Whether to add input to output. If True, att_dim must be equal to in_dim.

• dropout (float, default: 0.0 ) –

  Dropout rate.

forward(X, mask=None, return_att=False)

Forward method.

Parameters:

• X (Tensor) –

  Input tensor of shape (batch_size, bag_size, in_dim).

• mask (Tensor, default: None ) –

  Mask tensor of shape (batch_size, bag_size).

• return_att (bool, default: False ) –

  If True, returns attention weights, of shape (n_layers, batch_size, n_heads, bag_size, bag_size).

Returns:

• Y ( Tensor ) –

  Output tensor of shape (batch_size, bag_size, in_dim).

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torchmil.nn.transformers.TransformerLayer

Bases: Layer

One layer of the Transformer encoder.

Given an input bag \(\mathbf{X} = \left[ \mathbf{x}_1, \ldots, \mathbf{x}_N \right]^\top \in \mathbb{R}^{N \times D}\), this module computes:

\[\begin{align*} \mathbf{Z} & = \mathbf{X} + \operatorname{SelfAttention}( \operatorname{LayerNorm}(\mathbf{X}) ) \\ \mathbf{Y} & = \mathbf{Z} + \operatorname{MLP}(\operatorname{LayerNorm}(\mathbf{Z})), \\ \end{align*}\]

and outputs \(\mathbf{Y}\).

__init__(in_dim, out_dim=None, att_dim=512, n_heads=4, use_mlp=True, dropout=0.0)

Class constructor.

Parameters:

• in_dim (int) –

  Input dimension.

• out_dim –

  Output dimension. If None, out_dim = in_dim.

• att_dim (int, default: 512 ) –

  Attention dimension.

• n_heads (int, default: 4 ) –

  Number of heads.

• use_mlp (bool, default: True ) –

  Whether to use feedforward layer.

• dropout (float, default: 0.0 ) –

  Dropout rate

forward(X, mask=None, return_att=False)

Forward method.

Parameters:

• X (Tensor) –

  Input tensor of shape (batch_size, bag_size, in_dim).

• mask (Tensor, default: None ) –

  Mask tensor of shape (batch_size, bag_size).

• return_att (bool, default: False ) –

  If True, returns attention weights, of shape (batch_size, n_heads, bag_size, bag_size).

Returns:

• Y ( Tensor ) –

  Output tensor of shape (batch_size, bag_size, out_dim).