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""" |
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Implementation of "Convolutional Sequence to Sequence Learning" |
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""" |
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import torch.nn as nn |
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from onmt.encoders.encoder import EncoderBase |
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from onmt.utils.cnn_factory import shape_transform, StackedCNN |
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SCALE_WEIGHT = 0.5**0.5 |
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class CNNEncoder(EncoderBase): |
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"""Encoder based on "Convolutional Sequence to Sequence Learning" |
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:cite:`DBLP:journals/corr/GehringAGYD17`. |
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""" |
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def __init__(self, num_layers, hidden_size, cnn_kernel_width, dropout, embeddings): |
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super(CNNEncoder, self).__init__() |
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self.embeddings = embeddings |
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input_size = embeddings.embedding_size |
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self.linear = nn.Linear(input_size, hidden_size) |
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self.cnn = StackedCNN(num_layers, hidden_size, cnn_kernel_width, dropout) |
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@classmethod |
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def from_opt(cls, opt, embeddings): |
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"""Alternate constructor.""" |
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return cls( |
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opt.enc_layers, |
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opt.enc_hid_size, |
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opt.cnn_kernel_width, |
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opt.dropout[0] if type(opt.dropout) is list else opt.dropout, |
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embeddings, |
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) |
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def forward(self, input, src_len=None, hidden=None): |
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"""See :func:`EncoderBase.forward()`""" |
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emb = self.embeddings(input) |
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emb_reshape = emb.view(emb.size(0) * emb.size(1), -1) |
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emb_remap = self.linear(emb_reshape) |
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emb_remap = emb_remap.view(emb.size(0), emb.size(1), -1) |
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emb_remap = shape_transform(emb_remap) |
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out = self.cnn(emb_remap) |
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return out.squeeze(3), emb_remap.squeeze(3), src_len |
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def update_dropout(self, dropout, attention_dropout=None): |
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self.cnn.dropout.p = dropout |
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