ないコードの最も美しい作品が、これは私がPyTorchフォーラムやドキュメントを経て、私の個人的な使用のために集められたものです。確かに、ソートを処理するより良い方法があるかもしれません - 復元部分ですが、ネットワーク自体に入れるように選択しました
class Encoder(nn.Module):
def __init__(self, vocab_size, embedding_size, embedding_vectors=None, tune_embeddings=True, use_gru=True,
hidden_size=128, num_layers=1, bidrectional=True, dropout=0.6):
super(Encoder, self).__init__()
self.embed = nn.Embedding(vocab_size, embedding_size, padding_idx=0)
self.embed.weight.requires_grad = tune_embeddings
if embedding_vectors is not None:
assert embedding_vectors.shape[0] == vocab_size and embedding_vectors.shape[1] == embedding_size
self.embed.weight = nn.Parameter(torch.FloatTensor(embedding_vectors))
cell = nn.GRU if use_gru else nn.LSTM
self.rnn = cell(input_size=embedding_size, hidden_size=hidden_size, num_layers=num_layers,
batch_first=True, bidirectional=True, dropout=dropout)
def forward(self, x, x_lengths):
sorted_seq_lens, original_ordering = torch.sort(torch.LongTensor(x_lengths), dim=0, descending=True)
ex = self.embed(x[original_ordering])
pack = torch.nn.utils.rnn.pack_padded_sequence(ex, sorted_seq_lens.tolist(), batch_first=True)
out, _ = self.rnn(pack)
unpacked, unpacked_len = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
indices = Variable(torch.LongTensor(np.array(unpacked_len) - 1).view(-1, 1)
.expand(unpacked.size(0), unpacked.size(2))
.unsqueeze(1))
last_encoded_states = unpacked.gather(dim=1, index=indices).squeeze(dim=1)
scatter_indices = Variable(original_ordering.view(-1, 1).expand_as(last_encoded_states))
encoded_reordered = last_encoded_states.clone().scatter_(dim=0, index=scatter_indices, src=last_encoded_states)
return encoded_reordered
私は6ヶ月前の質問= – chiragjn