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"""Base class for encoders and generic multi encoders.""" |
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import torch.nn as nn |
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from onmt.utils.misc import aeq |
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class EncoderBase(nn.Module): |
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""" |
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Base encoder class. Specifies the interface used by different encoder types |
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and required by :class:`onmt.Models.NMTModel`. |
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.. mermaid:: |
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graph BT |
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A[Input] |
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subgraph RNN |
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C[Pos 1] |
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D[Pos 2] |
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E[Pos N] |
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end |
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F[Memory_Bank] |
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G[Final] |
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A-->C |
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A-->D |
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A-->E |
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C-->F |
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D-->F |
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E-->F |
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E-->G |
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""" |
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@classmethod |
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def from_opt(cls, opt, embeddings=None): |
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raise NotImplementedError |
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def _check_args(self, src, lengths=None, hidden=None): |
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n_batch = src.size(1) |
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if lengths is not None: |
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n_batch_, = lengths.size() |
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aeq(n_batch, n_batch_) |
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def forward(self, src, lengths=None): |
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""" |
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Args: |
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src (LongTensor): |
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padded sequences of sparse indices ``(src_len, batch, nfeat)`` |
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lengths (LongTensor): length of each sequence ``(batch,)`` |
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Returns: |
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(FloatTensor, FloatTensor, FloatTensor): |
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* final encoder state, used to initialize decoder |
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* memory bank for attention, ``(src_len, batch, hidden)`` |
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* lengths |
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""" |
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raise NotImplementedError |
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