Source code for torchdrug.models.lstm

from collections.abc import Sequence

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

from torchdrug import core
from torchdrug.layers import functional
from torchdrug.core import Registry as R


[docs]@R.register("models.ProteinLSTM") class ProteinLSTM(nn.Module, core.Configurable): """ Protein LSTM proposed in `Evaluating Protein Transfer Learning with TAPE`_. .. _Evaluating Protein Transfer Learning with TAPE: https://arxiv.org/pdf/1906.08230.pdf Parameters: input_dim (int): input dimension hidden_dim (int, optional): hidden dimension num_layers (int, optional): number of LSTM layers activation (str or function, optional): activation function layer_norm (bool, optional): apply layer normalization or not dropout (float, optional): dropout ratio of input features """ def __init__(self, input_dim, hidden_dim, num_layers, activation='tanh', layer_norm=False, dropout=0): super(ProteinLSTM, self).__init__() self.input_dim = input_dim self.output_dim = hidden_dim # output_dim for node feature is 2 * hidden_dim self.node_output_dim = 2 * hidden_dim self.num_layers = num_layers self.padding_id = input_dim - 1 self.embedding = nn.Linear(input_dim, hidden_dim) if layer_norm: self.layer_norm = nn.LayerNorm(hidden_dim) else: self.layer_norm = None if dropout: self.dropout = nn.Dropout(dropout) else: self.dropout = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.lstm = nn.LSTM(hidden_dim, hidden_dim, num_layers, batch_first=True, dropout=dropout, bidirectional=True) self.reweight = nn.Linear(2 * num_layers, 1) self.linear = nn.Linear(hidden_dim, hidden_dim)
[docs] def forward(self, graph, input, all_loss=None, metric=None): """ Compute the residue representations and the graph representation(s). Parameters: graph (Protein): :math:`n` protein(s) input (Tensor): input node representations all_loss (Tensor, optional): if specified, add loss to this tensor metric (dict, optional): if specified, output metrics to this dict Returns: dict with ``residue_feature`` and ``graph_feature`` fields: residue representations of shape :math:`(|V_{res}|, d)`, graph representations of shape :math:`(n, d)` """ input = graph.residue_feature.float() input = functional.variadic_to_padded(input, graph.num_residues, value=self.padding_id)[0] input = self.embedding(input) if self.layer_norm: input = self.layer_norm(input) if self.dropout: input = self.dropout(input) output, hidden = self.lstm(input) residue_feature = functional.padded_to_variadic(output, graph.num_residues) # (2 * num_layer, B, d) graph_feature = self.reweight(hidden[0].permute(1, 2, 0)).squeeze(-1) graph_feature = self.linear(graph_feature) graph_feature = self.activation(graph_feature) return { "graph_feature": graph_feature, "residue_feature": residue_feature }