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"""
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
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)
@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)
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, attention_dropout=None):
self.cnn.dropout.p = dropout
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