Python rnn.pack_padded_sequence函数代码示例

    def encode_table_header(self, tables):
        # input, ids of table word: (batch_size, max_column_num)
        # encode_output: (max_head_word_num, batch_size, max_column_num, hidden_size)

        # (batch_size, max_column_num, max_head_word_num)
        # table_head_mask: (batch_size, max_column_num)
        # table_col_lens: (batch_size, max_column_num)
        table_head_wids, table_col_lens = WikiSqlBatch.get_table_header_input_tensor(tables,
                                                                                     self.vocab.source,
                                                                                     cuda=self.args.cuda)

        # hack: pack_padded_sequence requires seq length to be greater than 1
        for tbl in table_col_lens:
            for i in range(len(tbl)):
                if tbl[i] == 0: tbl[i] = 1

        table_header_mask = WikiSqlBatch.get_table_header_mask(tables, cuda=self.args.cuda)

        # (batch_size, max_column_num, max_head_word_num, word_embed_size)
        table_head_word_embeds = self.src_embed(table_head_wids.view(-1)).view(list(table_head_wids.size()) + [self.src_embed.embedding_dim])

        batch_size = table_head_word_embeds.size(0)
        max_col_num = table_head_word_embeds.size(1)
        max_col_word_num = table_head_word_embeds.size(2)

        # (batch_size * max_column_num, max_head_word_num, word_embed_size)
        table_head_word_embeds_flatten = table_head_word_embeds.view(batch_size * max_col_num,
                                                                     max_col_word_num, -1)
        table_col_lens_flatten = list(chain.from_iterable(table_col_lens))
        sorted_col_ids = sorted(list(range(len(table_col_lens_flatten))), key=lambda x: -table_col_lens_flatten[x])
        sorted_table_col_lens_flatten = [table_col_lens_flatten[i] for i in sorted_col_ids]

        col_old_pos_map = [-1] * len(sorted_col_ids)
        for new_pos, old_pos in enumerate(sorted_col_ids):
            col_old_pos_map[old_pos] = new_pos

        # (batch_size * max_column_num, max_head_word_num, word_embed_size)
        sorted_table_head_word_embeds = table_head_word_embeds_flatten[sorted_col_ids, :, :]

        packed_table_head_word_embeds = pack_padded_sequence(sorted_table_head_word_embeds, sorted_table_col_lens_flatten, batch_first=True)

        # column_word_encodings: (batch_size * max_column_num, max_head_word_num, hidden_size)
        column_word_encodings, (table_header_encoding, table_head_last_cell) = self.table_header_lstm(packed_table_head_word_embeds)
        column_word_encodings, _ = pad_packed_sequence(column_word_encodings, batch_first=True)

        # (batch_size * max_column_num, max_head_word_num, hidden_size)
        column_word_encodings = column_word_encodings[col_old_pos_map]
        # (batch_size, max_column_num, max_head_word_num, hidden_size)
        column_word_encodings = column_word_encodings.view(batch_size, max_col_num, max_col_word_num, -1)

        # (batch_size, hidden_size * 2)
        table_header_encoding = torch.cat([table_header_encoding[0], table_header_encoding[1]], -1)
        # table_head_last_cell = torch.cat([table_head_last_cell[0], table_head_last_cell[1]], -1)

        # same
        table_header_encoding = table_header_encoding[col_old_pos_map]
        # (batch_size, max_column_num, hidden_size)
        table_header_encoding = table_header_encoding.view(batch_size, max_col_num, -1)

        return column_word_encodings, table_header_encoding, table_header_mask

 def forward(self, word_inputs, feature_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover):
     """
         input:
             word_inputs: (batch_size, sent_len)
             word_seq_lengths: list of batch_size, (batch_size,1)
             char_inputs: (batch_size*sent_len, word_length)
             char_seq_lengths: list of whole batch_size for char, (batch_size*sent_len, 1)
             char_seq_recover: variable which records the char order information, used to recover char order
         output:
             Variable(batch_size, sent_len, hidden_dim)
     """
     word_represent = self.wordrep(word_inputs,feature_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
     ## word_embs (batch_size, seq_len, embed_size)
     if self.word_feature_extractor == "CNN":
         word_in = F.tanh(self.word2cnn(word_represent)).transpose(2,1).contiguous()
         for idx in range(self.cnn_layer):
             if idx == 0:
                 cnn_feature = F.relu(self.cnn_list[idx](word_in))
             else:
                 cnn_feature = F.relu(self.cnn_list[idx](cnn_feature))
             cnn_feature = self.cnn_drop_list[idx](cnn_feature)
             cnn_feature = self.cnn_batchnorm_list[idx](cnn_feature)
         feature_out = cnn_feature.transpose(2,1).contiguous()
     else:
         packed_words = pack_padded_sequence(word_represent, word_seq_lengths.cpu().numpy(), True)
         hidden = None
         lstm_out, hidden = self.lstm(packed_words, hidden)
         lstm_out, _ = pad_packed_sequence(lstm_out)
         ## lstm_out (seq_len, seq_len, hidden_size)
         feature_out = self.droplstm(lstm_out.transpose(1,0))
     ## feature_out (batch_size, seq_len, hidden_size)
     outputs = self.hidden2tag(feature_out)
     return outputs

    def forward(self, embs, lengths):
        """
        This is the heart of the model. This function, defines how the data
        passes through the network.
        Args:
            embs (): word embeddings
            lengths (): the lengths of each sentence

        Returns: the logits for each class

        """
        # pack the batch
        packed = pack_padded_sequence(embs, list(lengths.data),
                                      batch_first=True)

        out_packed, _ = self.rnn(packed)

        # unpack output - no need if we are going to use only the last outputs
        outputs, _ = pad_packed_sequence(out_packed, batch_first=True)

        # get the outputs from the last *non-masked* timestep for each sentence
        last_outputs = self.last_timestep(outputs, lengths,
                                          self.rnn.bidirectional)

        # apply dropout to the outputs of the RNN
        last_outputs = self.drop_rnn(last_outputs)

        return outputs, last_outputs

    def test_forward_pulls_out_correct_tensor_with_unsorted_batches(self):
        lstm = LSTM(bidirectional=True, num_layers=3, input_size=3, hidden_size=7, batch_first=True)
        encoder = PytorchSeq2VecWrapper(lstm)

        input_tensor = torch.rand([5, 7, 3])
        input_tensor[0, 3:, :] = 0
        input_tensor[1, 4:, :] = 0
        input_tensor[2, 2:, :] = 0
        input_tensor[3, 6:, :] = 0
        mask = torch.ones(5, 7)
        mask[0, 3:] = 0
        mask[1, 4:] = 0
        mask[2, 2:] = 0
        mask[3, 6:] = 0

        sequence_lengths = get_lengths_from_binary_sequence_mask(mask)
        sorted_inputs, sorted_sequence_lengths, restoration_indices, _ = sort_batch_by_length(input_tensor,
                                                                                              sequence_lengths)
        packed_sequence = pack_padded_sequence(sorted_inputs,
                                               sorted_sequence_lengths.tolist(),
                                               batch_first=True)
        _, state = lstm(packed_sequence)
        # Transpose output state, extract the last forward and backward states and
        # reshape to be of dimension (batch_size, 2 * hidden_size).
        sorted_transposed_state = state[0].transpose(0, 1).index_select(0, restoration_indices)
        reshaped_state = sorted_transposed_state[:, -2:, :].contiguous()
        explicitly_concatenated_state = torch.cat([reshaped_state[:, 0, :].squeeze(1),
                                                   reshaped_state[:, 1, :].squeeze(1)], -1)
        encoder_output = encoder(input_tensor, mask)
        assert_almost_equal(encoder_output.data.numpy(), explicitly_concatenated_state.data.numpy())

    def encode(self, indices, lengths, noise):
        embeddings = self.embedding(indices)
        packed_embeddings = pack_padded_sequence(input=embeddings,
                                                 lengths=lengths,
                                                 batch_first=True)

        # Encode
        packed_output, state = self.encoder(packed_embeddings)

        hidden, cell = state
        # batch_size x nhidden
        hidden = hidden[-1]  # get hidden state of last layer of encoder

        # normalize to unit ball (l2 norm of 1) - p=2, dim=1
        norms = torch.norm(hidden, 2, 1)
        
        # For older versions of PyTorch use:
        hidden = torch.div(hidden, norms.expand_as(hidden))
        # For newest version of PyTorch (as of 8/25) use this:
        # hidden = torch.div(hidden, norms.unsqueeze(1).expand_as(hidden))

        if noise and self.noise_radius > 0:
            gauss_noise = torch.normal(means=torch.zeros(hidden.size()),
                                       std=self.noise_radius)
            hidden = hidden + to_gpu(self.gpu, Variable(gauss_noise))

        return hidden

    def forward(self, vocab):
        with torch.no_grad():
            batch_shape = vocab['sentence'].shape
            s_embedding = self.embedding(vocab['sentence'].cuda())
            a_embedding = self.embedding(vocab['aspect'].cuda())

            packed_s = pack_padded_sequence(s_embedding, vocab['sent_len'], batch_first=True)

        out_s, (h_s, c1) = self.lstm_s(packed_s) # packed output
        out_a, (h_a, c2) = self.lstm_a(a_embedding)

        with torch.no_grad():
            unpacked_out_s, _ = pad_packed_sequence(out_s, batch_first=True)

        # Pair-wise interaction matrix
        I_matrix = torch.bmm(unpacked_out_s, out_a.permute(0,2,1))

        # Column-wise softmax
        a2s_attn = F.softmax(I_matrix, dim=1)

        # Row-wise softmax => Column-wise average => aspect attention
        s2a_attn = F.softmax(I_matrix, dim=2)
        a_attn = torch.mean(s2a_attn, dim=1)

        # Final sentence attn => weighted sum of each individual a2s_attn
        s_attn = torch.bmm(a2s_attn, a_attn.unsqueeze(-1))

        final_rep = torch.bmm(unpacked_out_s.permute(0,2,1), s_attn).squeeze(-1)
        pred = self.fc(final_rep)
        return pred

    def postprocess_sequence(self, X):
        """Embed (variable-length) sequences

        Parameters
        ----------
        X : list
            List of input sequences

        Returns
        -------
        fX : numpy array
            Batch of sequence embeddings.
        """

        lengths = torch.tensor([len(x) for x in X])
        sorted_lengths, sort = torch.sort(lengths, descending=True)
        _, unsort = torch.sort(sort)

        sequences = [torch.tensor(X[i],
                                  dtype=torch.float32,
                                  device=self.device) for i in sort]
        padded = pad_sequence(sequences, batch_first=True, padding_value=0)
        packed = pack_padded_sequence(padded, sorted_lengths,
                                      batch_first=True)

        cpu = torch.device('cpu')
        fX = self.model(packed).detach().to(cpu).numpy()
        return fX[unsort]

    def forward(self, xs):
        bsz = len(xs)

        # embed input tokens
        xes = F.dropout(self.lt(xs), p=self.dropout, training=self.training)
        x_lens = [x for x in torch.sum((xs > 0).int(), dim=1).data]
        xes_packed = pack_padded_sequence(xes, x_lens, batch_first=True)

        zeros = self.zeros(xs)
        if zeros.size(1) != bsz:
            zeros.resize_(self.layers * self.dirs, bsz, self.hsz).fill_(0)
        h0 = Variable(zeros, requires_grad=False)

        if type(self.rnn) == nn.LSTM:
            encoder_output_packed, hidden = self.rnn(xes_packed, (h0, h0))
            # take elementwise max between forward and backward hidden states
            hidden = (hidden[0].view(-1, self.dirs, bsz, self.hsz).max(1)[0],
                      hidden[1].view(-1, self.dirs, bsz, self.hsz).max(1)[0])
        else:
            encoder_output_packed, hidden = self.rnn(xes_packed, h0)

            # take elementwise max between forward and backward hidden states
            hidden = hidden.view(-1, self.dirs, bsz, self.hsz).max(1)[0]
        encoder_output, _ = pad_packed_sequence(encoder_output_packed,
                                                batch_first=True)
        return encoder_output, hidden

    def encode(self, src_sents_var, src_sents_len):
        """Encode the input natural language utterance

        Args:
            src_sents_var: a variable of shape (src_sent_len, batch_size), representing word ids of the input
            src_sents_len: a list of lengths of input source sentences, sorted by descending order

        Returns:
            src_encodings: source encodings of shape (batch_size, src_sent_len, hidden_size * 2)
            last_state, last_cell: the last hidden state and cell state of the encoder,
                                   of shape (batch_size, hidden_size)
        """

        # (tgt_query_len, batch_size, embed_size)
        # apply word dropout
        if self.training and self.args.word_dropout:
            mask = Variable(self.new_tensor(src_sents_var.size()).fill_(1. - self.args.word_dropout).bernoulli().long())
            src_sents_var = src_sents_var * mask + (1 - mask) * self.vocab.source.unk_id

        src_token_embed = self.src_embed(src_sents_var)
        packed_src_token_embed = pack_padded_sequence(src_token_embed, src_sents_len)

        # src_encodings: (tgt_query_len, batch_size, hidden_size)
        src_encodings, (last_state, last_cell) = self.encoder_lstm(packed_src_token_embed)
        src_encodings, _ = pad_packed_sequence(src_encodings)
        # src_encodings: (batch_size, tgt_query_len, hidden_size)
        src_encodings = src_encodings.permute(1, 0, 2)

        # (batch_size, hidden_size * 2)
        last_state = torch.cat([last_state[0], last_state[1]], 1)
        last_cell = torch.cat([last_cell[0], last_cell[1]], 1)

        return src_encodings, (last_state, last_cell)

    def forward(self, x):
        """Receives a Variable of indices (n_timesteps, n_samples) and
        returns their recurrent representations."""
        # sort the batch by decreasing length of sequences
        # oidxs: to recover original order
        # sidxs: idxs to sort the batch
        # slens: lengths in sorted order for pack_padded_sequence()
        oidxs, sidxs, slens, mask = sort_batch(x)

        # Fetch embeddings for the sorted batch
        embs = self.emb(x[:, sidxs])

        if self.dropout_emb > 0:
            embs = self.do_emb(embs)

        # Pack and encode
        packed_emb = pack_padded_sequence(embs, slens)
        packed_hs, h_t = self.enc(packed_emb)

        # Get hidden states and revert the order
        hs = pad_packed_sequence(packed_hs)[0][:, oidxs]

        if self.dropout_ctx > 0:
            hs = self.do_ctx(hs)

        return hs, mask

    def test_forward_pulls_out_correct_tensor_with_sequence_lengths(self):
        lstm = LSTM(bidirectional=True, num_layers=3, input_size=3, hidden_size=7, batch_first=True)
        encoder = PytorchSeq2VecWrapper(lstm)

        tensor = torch.rand([5, 7, 3])
        tensor[1, 6:, :] = 0
        tensor[2, 4:, :] = 0
        tensor[3, 2:, :] = 0
        tensor[4, 1:, :] = 0
        mask = torch.ones(5, 7)
        mask[1, 6:] = 0
        mask[2, 4:] = 0
        mask[3, 2:] = 0
        mask[4, 1:] = 0

        input_tensor = Variable(tensor)
        mask = Variable(mask)
        sequence_lengths = get_lengths_from_binary_sequence_mask(mask)
        packed_sequence = pack_padded_sequence(input_tensor, list(sequence_lengths.data), batch_first=True)
        _, state = lstm(packed_sequence)
        # Transpose output state, extract the last forward and backward states and
        # reshape to be of dimension (batch_size, 2 * hidden_size).
        reshaped_state = state[0].transpose(0, 1)[:, -2:, :].contiguous()
        explicitly_concatenated_state = torch.cat([reshaped_state[:, 0, :].squeeze(1),
                                                   reshaped_state[:, 1, :].squeeze(1)], -1)
        encoder_output = encoder(input_tensor, mask)
        assert_almost_equal(encoder_output.data.numpy(), explicitly_concatenated_state.data.numpy())

    def encode(self, src_sents_var: torch.Tensor, src_sent_lens: List[int]) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """
        Use a GRU/LSTM to encode source sentences into hidden states

        Args:
            src_sents: list of source sentence tokens

        Returns:
            src_encodings: hidden states of tokens in source sentences, this could be a variable
                with shape (batch_size, source_sentence_length, encoding_dim), or in orther formats
            decoder_init_state: decoder GRU/LSTM's initial state, computed from source encodings
        """

        # (src_sent_len, batch_size, embed_size)
        src_word_embeds = self.src_embed(src_sents_var)
        packed_src_embed = pack_padded_sequence(src_word_embeds, src_sent_lens)

        # src_encodings: (src_sent_len, batch_size, hidden_size * 2)
        src_encodings, (last_state, last_cell) = self.encoder_lstm(packed_src_embed)
        src_encodings, _ = pad_packed_sequence(src_encodings)

        # (batch_size, src_sent_len, hidden_size * 2)
        src_encodings = src_encodings.permute(1, 0, 2)

        dec_init_cell = self.decoder_cell_init(torch.cat([last_cell[0], last_cell[1]], dim=1))
        dec_init_state = torch.tanh(dec_init_cell)

        return src_encodings, (dec_init_state, dec_init_cell)

    def forward(self, question,length):
        length = list(length.data.cpu().numpy())
        
        
        emb = self.drop(self.encoder(question))
        emb = self.tanh(emb)

        hidden = self.init_hidden(len(length))
        seqs = trnn.pack_padded_sequence(emb, length, batch_first=True)

        seqs, hidden = self.rnn(seqs, hidden)
        h,_ = trnn.pad_packed_sequence(seqs, batch_first=True)

        #attention
        weights = self.softmax(self.att2(torch.transpose(h, 1, 2)).squeeze(1)).unsqueeze(-1)
        weights = weights.expand_as(h)
        
        bilstmout = torch.sum(h*weights, 1).squeeze(1)


        #bilstmout = torch.cat([hidden[0][0],hidden[0][1]],-1)


        fc1fea = self.fc1(bilstmout)

        return fc1fea

    def test_augmented_lstm_computes_same_function_as_pytorch_lstm(self):
        augmented_lstm = AugmentedLstm(10, 11)
        pytorch_lstm = LSTM(10, 11, num_layers=1, batch_first=True)
        # Initialize all weights to be == 1.
        initializer = InitializerApplicator([(".*", lambda tensor: torch.nn.init.constant_(tensor, 1.))])
        initializer(augmented_lstm)
        initializer(pytorch_lstm)

        initial_state = torch.zeros([1, 5, 11])
        initial_memory = torch.zeros([1, 5, 11])

        # Use bigger numbers to avoid floating point instability.
        sorted_tensor, sorted_sequence, _, _ = sort_batch_by_length(self.random_tensor * 5., self.sequence_lengths)
        lstm_input = pack_padded_sequence(sorted_tensor, sorted_sequence.data.tolist(), batch_first=True)

        augmented_output, augmented_state = augmented_lstm(lstm_input, (initial_state, initial_memory))
        pytorch_output, pytorch_state = pytorch_lstm(lstm_input, (initial_state, initial_memory))
        pytorch_output_sequence, _ = pad_packed_sequence(pytorch_output, batch_first=True)
        augmented_output_sequence, _ = pad_packed_sequence(augmented_output, batch_first=True)

        numpy.testing.assert_array_almost_equal(pytorch_output_sequence.data.numpy(),
                                                augmented_output_sequence.data.numpy(), decimal=4)
        numpy.testing.assert_array_almost_equal(pytorch_state[0].data.numpy(),
                                                augmented_state[0].data.numpy(), decimal=4)
        numpy.testing.assert_array_almost_equal(pytorch_state[1].data.numpy(),
                                                augmented_state[1].data.numpy(), decimal=4)

 def forward(self, input, *args):
     args, seq_lengths = args[:-1], args[-1]
     input = rnn_utils.pack_padded_sequence(input, seq_lengths, self.batch_first)
     rets = self.model(input, *args)
     ret, rets = rets[0], rets[1:]
     ret, _ = rnn_utils.pad_packed_sequence(ret, self.batch_first)
     return tuple([ret] + list(rets))