Source code for onmt.encoders.cnn_encoder

"""
Implementation of "Convolutional Sequence to Sequence Learning"
"""
import torch.nn as nn

from onmt.encoders.encoder import EncoderBase
from onmt.utils.cnn_factory import shape_transform, StackedCNN

SCALE_WEIGHT = 0.5 ** 0.5


[docs]class CNNEncoder(EncoderBase): """Encoder based on "Convolutional Sequence to Sequence Learning" :cite:`DBLP:journals/corr/GehringAGYD17`. """ def __init__(self, num_layers, hidden_size, cnn_kernel_width, dropout, embeddings): super(CNNEncoder, self).__init__() self.embeddings = embeddings input_size = embeddings.embedding_size self.linear = nn.Linear(input_size, hidden_size) self.cnn = StackedCNN(num_layers, hidden_size, cnn_kernel_width, dropout)
[docs] @classmethod def from_opt(cls, opt, embeddings): """Alternate constructor.""" return cls( opt.enc_layers, opt.enc_rnn_size, opt.cnn_kernel_width, opt.dropout[0] if type(opt.dropout) is list else opt.dropout, embeddings)
[docs] def forward(self, input, lengths=None, hidden=None): """See :class:`onmt.modules.EncoderBase.forward()`""" self._check_args(input, lengths, hidden) emb = self.embeddings(input) # s_len, batch, emb_dim = emb.size() emb = emb.transpose(0, 1).contiguous() emb_reshape = emb.view(emb.size(0) * emb.size(1), -1) emb_remap = self.linear(emb_reshape) emb_remap = emb_remap.view(emb.size(0), emb.size(1), -1) emb_remap = shape_transform(emb_remap) out = self.cnn(emb_remap) return emb_remap.squeeze(3).transpose(0, 1).contiguous(), \ out.squeeze(3).transpose(0, 1).contiguous(), lengths
def update_dropout(self, dropout): self.cnn.dropout.p = dropout