From 842d8aa14902c75d26f307b72a320e0a6df4c28e Mon Sep 17 00:00:00 2001 From: Kyle Gao Date: Tue, 24 Oct 2017 09:46:35 -0400 Subject: [PATCH 01/70] Updated TopKDecoder (#86) * Fixed topk decoder. --- scripts/integration_test.py | 6 +- seq2seq/models/DecoderRNN.py | 56 +++++---- seq2seq/models/TopKDecoder.py | 213 +++++++++++++++++++--------------- tests/test_topkdecoder.py | 143 +++++++++++++++++++++++ 4 files changed, 297 insertions(+), 121 deletions(-) create mode 100644 tests/test_topkdecoder.py diff --git a/scripts/integration_test.py b/scripts/integration_test.py index d5843a9..68e8787 100644 --- a/scripts/integration_test.py +++ b/scripts/integration_test.py @@ -7,7 +7,7 @@ import seq2seq from seq2seq.trainer import SupervisedTrainer -from seq2seq.models import EncoderRNN, DecoderRNN, Seq2seq +from seq2seq.models import EncoderRNN, DecoderRNN, TopKDecoder, Seq2seq from seq2seq.loss import Perplexity from seq2seq.dataset import SourceField, TargetField from seq2seq.evaluator import Predictor, Evaluator @@ -108,7 +108,9 @@ def len_filter(example): dev_loss, accuracy = evaluator.evaluate(seq2seq, dev) assert dev_loss < 1.5 -predictor = Predictor(seq2seq, input_vocab, output_vocab) +beam_search = Seq2seq(seq2seq.encoder, TopKDecoder(seq2seq.decoder, 3)) + +predictor = Predictor(beam_search, input_vocab, output_vocab) inp_seq = "1 3 5 7 9" seq = predictor.predict(inp_seq.split()) assert " ".join(seq[:-1]) == inp_seq[::-1] diff --git a/seq2seq/models/DecoderRNN.py b/seq2seq/models/DecoderRNN.py index bb96a8f..ff63866 100644 --- a/seq2seq/models/DecoderRNN.py +++ b/seq2seq/models/DecoderRNN.py @@ -109,32 +109,10 @@ def forward(self, inputs=None, encoder_hidden=None, encoder_outputs=None, function=F.log_softmax, teacher_forcing_ratio=0): ret_dict = dict() if self.use_attention: - if encoder_outputs is None: - raise ValueError("Argument encoder_outputs cannot be None when attention is used.") ret_dict[DecoderRNN.KEY_ATTN_SCORE] = list() - if inputs is None: - if teacher_forcing_ratio > 0: - raise ValueError("Teacher forcing has to be disabled (set 0) when no inputs is provided.") - if inputs is None and encoder_hidden is None: - batch_size = 1 - else: - if inputs is not None: - batch_size = inputs.size(0) - else: - if self.rnn_cell is nn.LSTM: - batch_size = encoder_hidden[0].size(1) - elif self.rnn_cell is nn.GRU: - batch_size = encoder_hidden.size(1) - - if inputs is None: - inputs = Variable(torch.LongTensor([self.sos_id]), - volatile=True).view(batch_size, -1) - if torch.cuda.is_available(): - inputs = inputs.cuda() - max_length = self.max_length - else: - max_length = inputs.size(1) - 1 # minus the start of sequence symbol + inputs, batch_size, max_length = self._validate_args(inputs, encoder_hidden, encoder_outputs, + function, teacher_forcing_ratio) decoder_hidden = self._init_state(encoder_hidden) use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False @@ -200,3 +178,33 @@ def _cat_directions(self, h): h = torch.cat([h[0:h.size(0):2], h[1:h.size(0):2]], 2) return h + def _validate_args(self, inputs, encoder_hidden, encoder_outputs, function, teacher_forcing_ratio): + if self.use_attention: + if encoder_outputs is None: + raise ValueError("Argument encoder_outputs cannot be None when attention is used.") + + # inference batch size + if inputs is None and encoder_hidden is None: + batch_size = 1 + else: + if inputs is not None: + batch_size = inputs.size(0) + else: + if self.rnn_cell is nn.LSTM: + batch_size = encoder_hidden[0].size(1) + elif self.rnn_cell is nn.GRU: + batch_size = encoder_hidden.size(1) + + # set default input and max decoding length + if inputs is None: + if teacher_forcing_ratio > 0: + raise ValueError("Teacher forcing has to be disabled (set 0) when no inputs is provided.") + inputs = Variable(torch.LongTensor([self.sos_id] * batch_size), + volatile=True).view(batch_size, 1) + if torch.cuda.is_available(): + inputs = inputs.cuda() + max_length = self.max_length + else: + max_length = inputs.size(1) - 1 # minus the start of sequence symbol + + return inputs, batch_size, max_length diff --git a/seq2seq/models/TopKDecoder.py b/seq2seq/models/TopKDecoder.py index efd3623..42b1888 100644 --- a/seq2seq/models/TopKDecoder.py +++ b/seq2seq/models/TopKDecoder.py @@ -3,8 +3,44 @@ from torch.autograd import Variable from .baseRNN import BaseRNN +def _inflate(tensor, times, dim): + """ + Given a tensor, 'inflates' it along the given dimension by replicating each slice specified number of times (in-place) + + Args: + tensor: A :class:`Tensor` to inflate + times: number of repetitions + dimension: axis for inflation (default=0) + + Returns: + A :class:`Tensor` -class TopKDecoder(BaseRNN): + Examples:: + >> a = torch.LongTensor([[1, 2], [3, 4]]) + >> a + 1 2 + 3 4 + [torch.LongTensor of size 2x2] + >> decoder = TopKDecoder(nn.RNN(10, 20, 2), 3) + >> b = decoder._inflate(a, 1, dimension=1) + >> b + 1 1 2 2 + 3 3 4 4 + [torch.LongTensor of size 2x4] + >> c = decoder._inflate(a, 1, dimension=0) + >> c + 1 2 + 1 2 + 3 4 + 3 4 + [torch.LongTensor of size 4x2] + + """ + repeat_dims = [1] * tensor.dim() + repeat_dims[dim] = times + return tensor.repeat(*repeat_dims) + +class TopKDecoder(torch.nn.Module): r""" Top-K decoding with beam search. @@ -31,53 +67,57 @@ class TopKDecoder(BaseRNN): - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden state of the decoder. - **ret_dict**: dictionary containing additional information as follows {*length* : list of integers - representing lengths of output sequences, *sequence* : list of sequences, where each sequence is a list of - predicted token IDs, *inputs* : target outputs if provided for decoding}. - + representing lengths of output sequences, *topk_length*: list of integers representing lengths of beam search + sequences, *sequence* : list of sequences, where each sequence is a list of predicted token IDs, + *topk_sequence* : list of beam search sequences, each beam is a list of token IDs, *inputs* : target + outputs if provided for decoding}. """ def __init__(self, decoder_rnn, k): - super(TopKDecoder, self).__init__(decoder_rnn.lang, decoder_rnn.max_length) + super(TopKDecoder, self).__init__() self.rnn = decoder_rnn self.k = k - self.V = self.rnn.lang.get_vocab_size() - self.SOS = self.rnn.lang.SOS_token_id - self.EOS = self.rnn.lang.EOS_token_id + self.hidden_size = self.rnn.hidden_size + self.V = self.rnn.output_size + self.SOS = self.rnn.sos_id + self.EOS = self.rnn.eos_id - def forward_rnn(self, inputs=None, encoder_hidden=None, encoder_outputs=None, function=F.log_softmax, - retain_output_probs=True): + def forward(self, inputs=None, encoder_hidden=None, encoder_outputs=None, function=F.log_softmax, + teacher_forcing_ratio=0, retain_output_probs=True): """ Forward rnn for MAX_LENGTH steps. Look at :func:`seq2seq.models.DecoderRNN.DecoderRNN.forward_rnn` for details. """ - # TODO: Looks like encoder_hidden is not optional, we need unit tests - # for this class - # Get batch size, assuming h_0 is num_layers*directions x b x hidden_dim - b = encoder_hidden.size(1) - h = encoder_hidden.size(2) + inputs, batch_size, max_length = self.rnn._validate_args(inputs, encoder_hidden, encoder_outputs, + function, teacher_forcing_ratio) - self.pos_index = Variable(torch.LongTensor(range(b)) * self.k).view(-1, 1) + self.pos_index = Variable(torch.LongTensor(range(batch_size)) * self.k).view(-1, 1) # Inflate the initial hidden states to be of size: b*k x h - hidden = self._inflate(encoder_hidden, self.k) + encoder_hidden = self.rnn._init_state(encoder_hidden) + if encoder_hidden is None: + hidden = None + else: + if isinstance(encoder_hidden, tuple): + hidden = tuple([_inflate(h, self.k, 1) for h in encoder_hidden]) + else: + hidden = _inflate(encoder_hidden, self.k, 1) + # ... same idea for encoder_outputs and decoder_outputs if self.rnn.use_attention: - if encoder_outputs is None: - raise ValueError("Argument encoder_outputs cannot be None when attention is used.") - else: - inflated_encoder_outputs = self._inflate(encoder_outputs, self.k) + inflated_encoder_outputs = _inflate(encoder_outputs, self.k, 0) else: inflated_encoder_outputs = None # Initialize the scores; for the first step, # ignore the inflated copies to avoid duplicate entries in the top k - sequence_scores = torch.Tensor(b*self.k, 1) + sequence_scores = torch.Tensor(batch_size * self.k, 1) sequence_scores.fill_(-float('Inf')) - sequence_scores.index_fill_(0, torch.LongTensor([i*self.k for i in range(0, b)]), 0.0) + sequence_scores.index_fill_(0, torch.LongTensor([i * self.k for i in range(0, batch_size)]), 0.0) sequence_scores = Variable(sequence_scores) # Initialize the input vector - input_var = Variable(torch.transpose(torch.LongTensor([[self.SOS]*b*self.k]), 0, 1)) + input_var = Variable(torch.transpose(torch.LongTensor([[self.SOS] * batch_size * self.k]), 0, 1)) # Store decisions for backtracking stored_outputs = list() @@ -86,27 +126,31 @@ def forward_rnn(self, inputs=None, encoder_hidden=None, encoder_outputs=None, fu stored_emitted_symbols = list() stored_hidden = list() - for _ in range(0, self.rnn.max_length): + for _ in range(0, max_length): # Run the RNN one step forward - log_softmax_output, hidden, _ = self.rnn.forward_step(input_var, hidden, inflated_encoder_outputs, function=function) + log_softmax_output, hidden, _ = self.rnn.forward_step(input_var, hidden, + inflated_encoder_outputs, function=function) # If doing local backprop (e.g. supervised training), retain the output layer if retain_output_probs: stored_outputs.append(log_softmax_output) # To get the full sequence scores for the new candidates, add the local scores for t_i to the predecessor scores for t_(i-1) - sequence_scores = self._inflate(sequence_scores, self.V) - sequence_scores += log_softmax_output - scores, candidates = sequence_scores.view(b, -1).topk(self.k, dim=1) + sequence_scores = _inflate(sequence_scores, self.V, 1) + sequence_scores += log_softmax_output.squeeze(1) + scores, candidates = sequence_scores.view(batch_size, -1).topk(self.k, dim=1) # Reshape input = (bk, 1) and sequence_scores = (bk, 1) - input_var = (candidates % self.V).view(b * self.k, 1) - sequence_scores = scores.view(b * self.k, 1) + input_var = (candidates % self.V).view(batch_size * self.k, 1) + sequence_scores = scores.view(batch_size * self.k, 1) # Update fields for next timestep - predecessors = (candidates / self.V + self.pos_index.expand_as(candidates)).view(b*self.k, 1) - hidden = hidden.index_select(1, predecessors.squeeze()) + predecessors = (candidates / self.V + self.pos_index.expand_as(candidates)).view(batch_size * self.k, 1) + if isinstance(hidden, tuple): + hidden = tuple([h.index_select(1, predecessors.squeeze()) for h in hidden]) + else: + hidden = hidden.index_select(1, predecessors.squeeze()) # Update sequence scores and erase scores for end-of-sentence symbol so that they aren't expanded stored_scores.append(sequence_scores.clone()) @@ -121,18 +165,24 @@ def forward_rnn(self, inputs=None, encoder_hidden=None, encoder_outputs=None, fu # Do backtracking to return the optimal values output, h_t, h_n, s, l, p = self._backtrack(stored_outputs, stored_hidden, - stored_predecessors, stored_emitted_symbols, stored_scores, b, h) + stored_predecessors, stored_emitted_symbols, + stored_scores, batch_size, self.hidden_size) # Build return objects decoder_outputs = [step[:, 0, :] for step in output] - decoder_hidden = h_n[:, :, 0, :] + if isinstance(h_n, tuple): + decoder_hidden = tuple([h[:, :, 0, :] for h in h_n]) + else: + decoder_hidden = h_n[:, :, 0, :] metadata = {} metadata['inputs'] = inputs metadata['output'] = output metadata['h_t'] = h_t metadata['score'] = s - metadata['length'] = l - metadata['sequence'] = p + metadata['topk_length'] = l + metadata['topk_sequence'] = p + metadata['length'] = [seq_len[0] for seq_len in l] + metadata['sequence'] = [seq[0] for seq in p] return decoder_outputs, decoder_hidden, metadata def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hidden_size): @@ -163,14 +213,21 @@ def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hid p (batch, k, sequence_len): A Tensor containing predicted sequence """ + lstm = isinstance(nw_hidden[0], tuple) + # initialize return variables given different types output = list() h_t = list() p = list() - h_n = torch.zeros(nw_hidden[0].size()) # Placeholder for last hidden state of top-k sequences. - # If a (top-k) sequence ends early in decoding, `h_n` contains - # its hidden state when it sees EOS. Otherwise, `h_n` contains - # the last hidden state of decoding. + # Placeholder for last hidden state of top-k sequences. + # If a (top-k) sequence ends early in decoding, `h_n` contains + # its hidden state when it sees EOS. Otherwise, `h_n` contains + # the last hidden state of decoding. + if lstm: + state_size = nw_hidden[0][0].size() + h_n = tuple([torch.zeros(state_size), torch.zeros(state_size)]) + else: + h_n = torch.zeros(nw_hidden[0].size()) l = [[self.rnn.max_length] * self.k for _ in range(b)] # Placeholder for lengths of top-k sequences # Similar to `h_n` @@ -190,7 +247,10 @@ def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hid while t >= 0: # Re-order the variables with the back pointer current_output = nw_output[t].index_select(0, t_predecessors) - current_hidden = nw_hidden[t].index_select(1, t_predecessors) + if lstm: + current_hidden = tuple([h.index_select(1, t_predecessors) for h in nw_hidden[t]]) + else: + current_hidden = nw_hidden[t].index_select(1, t_predecessors) current_symbol = symbols[t].index_select(0, t_predecessors) # Re-order the back pointer of the previous step with the back pointer of # the current step @@ -220,7 +280,7 @@ def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hid # with b*k as the first dimension, and b, k for # the first two dimensions idx = eos_indices[i] - b_idx = idx[0] / self.k + b_idx = int(idx[0] / self.k) # The indices of the replacing position # according to the replacement strategy noted above res_k_idx = self.k - (batch_eos_found[b_idx] % self.k) - 1 @@ -231,8 +291,14 @@ def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hid # with the new ended sequence information t_predecessors[res_idx] = predecessors[t][idx[0]] current_output[res_idx, :] = nw_output[t][idx[0], :] - current_hidden[:, res_idx, :] = nw_hidden[t][:, idx[0], :] - h_n[:, res_idx, :] = nw_hidden[t][:, idx[0], :].data + if lstm: + current_hidden[0][:, res_idx, :] = nw_hidden[t][0][:, idx[0], :] + current_hidden[1][:, res_idx, :] = nw_hidden[t][1][:, idx[0], :] + h_n[0][:, res_idx, :] = nw_hidden[t][0][:, idx[0], :].data + h_n[1][:, res_idx, :] = nw_hidden[t][1][:, idx[0], :].data + else: + current_hidden[:, res_idx, :] = nw_hidden[t][:, idx[0], :] + h_n[:, res_idx, :] = nw_hidden[t][:, idx[0], :].data current_symbol[res_idx, :] = symbols[t][idx[0]] s[b_idx, res_k_idx] = scores[t][idx[0]].data[0] l[b_idx][res_k_idx] = t + 1 @@ -256,13 +322,14 @@ def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hid # It is reversed because the backtracking happens in reverse time order output = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(output)] p = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(p)] - h_t = [step.index_select(1, re_sorted_idx).view(-1, b, self.k, hidden_size) for step in reversed(h_t)] - h_n = h_n.index_select(1, re_sorted_idx.data).view(-1, b, self.k, hidden_size) + if lstm: + h_t = [tuple([h.index_select(1, re_sorted_idx).view(-1, b, self.k, hidden_size) for h in step]) for step in reversed(h_t)] + h_n = tuple([h.index_select(1, re_sorted_idx.data).view(-1, b, self.k, hidden_size) for h in h_n]) + else: + h_t = [step.index_select(1, re_sorted_idx).view(-1, b, self.k, hidden_size) for step in reversed(h_t)] + h_n = h_n.index_select(1, re_sorted_idx.data).view(-1, b, self.k, hidden_size) s = s.data - if self.k == 1: - l = [_l[0] for _l in l] - return output, h_t, h_n, s, l, p def _mask_symbol_scores(self, score, idx, masking_score=-float('inf')): @@ -273,48 +340,4 @@ def _mask(self, tensor, idx, dim=0, masking_score=-float('inf')): indices = idx[:, 0] tensor.index_fill_(dim, indices, masking_score) - def _inflate(self, tensor, times): - """ - Given a tensor, 'inflates' it along the given dimension by replicating each slice specified number of times (in-place) - - Args: - tensor: A :class:`Tensor` to inflate - times: number of repetitions - dimension: axis for inflation (default=0) - - Returns: - A :class:`Tensor` - - Examples:: - >> a = torch.LongTensor([[1, 2], [3, 4]]) - >> a - 1 2 - 3 4 - [torch.LongTensor of size 2x2] - >> decoder = TopKDecoder(nn.RNN(10, 20, 2), 3) - >> b = decoder._inflate(a, 1, dimension=1) - >> b - 1 1 2 2 - 3 3 4 4 - [torch.LongTensor of size 2x4] - >> c = decoder._inflate(a, 1, dimension=0) - >> c - 1 2 - 1 2 - 3 4 - 3 4 - [torch.LongTensor of size 4x2] - - """ - tensor_dim = len(tensor.size()) - if tensor_dim is 3: - b = tensor.size(1) - return tensor.repeat(1, 1, times).view(tensor.size(0), b * times, -1) - elif tensor_dim is 2: - return tensor.repeat(1, times) - elif tensor_dim is 1: - b = tensor.size(0) - return tensor.repeat(times).view(b, -1) - else: - raise ValueError("Tensor can be of 1D, 2D or 3D only. This one is {}D.".format(tensor_dim)) - + \ No newline at end of file diff --git a/tests/test_topkdecoder.py b/tests/test_topkdecoder.py new file mode 100644 index 0000000..cd797e3 --- /dev/null +++ b/tests/test_topkdecoder.py @@ -0,0 +1,143 @@ +import unittest + +import torch +import torch.nn.functional as F +import numpy as np + +from seq2seq.models import DecoderRNN, TopKDecoder + +class TestDecoderRNN(unittest.TestCase): + + @classmethod + def setUpClass(self): + self.vocab_size = 3 + + def test_init(self): + decoder = DecoderRNN(self.vocab_size, 50, 16, 0, 1, input_dropout_p=0) + TopKDecoder(decoder, 3) + + def test_k_1(self): + """ When k=1, the output of topk decoder should be the same as a normal decoder. """ + batch_size = 1 + eos = 1 + + for _ in range(10): + # Repeat the randomized test multiple times + decoder = DecoderRNN(self.vocab_size, 50, 16, 0, eos) + for param in decoder.parameters(): + param.data.uniform_(-1, 1) + topk_decoder = TopKDecoder(decoder, 1) + + output, _, other = decoder() + output_topk, _, other_topk = topk_decoder() + + self.assertEqual(len(output), len(output_topk)) + + finished = [False] * batch_size + seq_scores = [0] * batch_size + + for t_step in range(len(output)): + score, _ = output[t_step].topk(1) + symbols = other['sequence'][t_step] + for b in range(batch_size): + seq_scores[b] += score[b].data[0] + symbol = symbols[b].data[0] + if not finished[b] and symbol == eos: + finished[b] = True + self.assertEqual(other_topk['length'][b], t_step + 1) + self.assertTrue(np.isclose(seq_scores[b], other_topk['score'][b][0])) + if not finished[b]: + symbol_topk = other_topk['topk_sequence'][t_step][b].data[0][0] + self.assertEqual(symbol, symbol_topk) + self.assertTrue(torch.equal(output[t_step].data, output_topk[t_step].data)) + if sum(finished) == batch_size: + break + + def test_k_greater_then_1(self): + """ Implement beam search manually and compare results from topk decoder. """ + max_len = 50 + beam_size = 3 + batch_size = 1 + hidden_size = 8 + sos = 0 + eos = 1 + + for _ in range(10): + decoder = DecoderRNN(self.vocab_size, max_len, hidden_size, sos, eos) + for param in decoder.parameters(): + param.data.uniform_(-1, 1) + topk_decoder = TopKDecoder(decoder, beam_size) + + encoder_hidden = torch.autograd.Variable(torch.randn(1, batch_size, hidden_size)) + _, hidden_topk, other_topk = topk_decoder(encoder_hidden=encoder_hidden) + + # Queue state: + # 1. time step + # 2. symbol + # 3. hidden state + # 4. accumulated log likelihood + # 5. beam number + batch_queue = [[(-1, sos, encoder_hidden[:,b,:].unsqueeze(1), 0, None)] for b in range(batch_size)] + time_batch_queue = [batch_queue] + batch_finished_seqs = [list() for _ in range(batch_size)] + for t in range(max_len): + new_batch_queue = [] + for b in range(batch_size): + new_queue = [] + for k in range(min(len(time_batch_queue[t][b]), beam_size)): + _, inputs, hidden, seq_score, _ = time_batch_queue[t][b][k] + if inputs == eos: + batch_finished_seqs[b].append(time_batch_queue[t][b][k]) + continue + inputs = torch.autograd.Variable(torch.LongTensor([[inputs]])) + decoder_outputs, hidden, _ = decoder.forward_step(inputs, hidden, None, F.log_softmax) + topk_score, topk = decoder_outputs[0].data.topk(beam_size) + for score, sym in zip(topk_score.tolist()[0], topk.tolist()[0]): + new_queue.append((t, sym, hidden, score + seq_score, k)) + new_queue = sorted(new_queue, key=lambda x: x[3], reverse=True)[:beam_size] + new_batch_queue.append(new_queue) + time_batch_queue.append(new_batch_queue) + + # finished beams + finalist = [l[:beam_size] for l in batch_finished_seqs] + # unfinished beams + for b in range(batch_size): + if len(finalist[b]) < beam_size: + last_step = sorted(time_batch_queue[-1][b], key=lambda x: x[3], reverse=True) + finalist[b] += last_step[:beam_size - len(finalist[b])] + + # back track + topk = [] + for b in range(batch_size): + batch_topk = [] + for k in range(beam_size): + seq = [finalist[b][k]] + prev_k = seq[-1][4] + prev_t = seq[-1][0] + while prev_k is not None: + seq.append(time_batch_queue[prev_t][b][prev_k]) + prev_k = seq[-1][4] + prev_t = seq[-1][0] + batch_topk.append([s for s in reversed(seq)]) + topk.append(batch_topk) + + for b in range(batch_size): + topk[b] = sorted(topk[b], key=lambda s: s[-1][3], reverse=True) + + topk_scores = other_topk['score'] + topk_lengths = other_topk['topk_length'] + topk_pred_symbols = other_topk['topk_sequence'] + for b in range(batch_size): + precision_error = False + for k in range(beam_size - 1): + if np.isclose(topk_scores[b][k], topk_scores[b][k+1]): + precision_error = True + break + if precision_error: + break + for k in range(beam_size): + self.assertEqual(topk_lengths[b][k], len(topk[b][k]) - 1) + self.assertTrue(np.isclose(topk_scores[b][k], topk[b][k][-1][3])) + total_steps = topk_lengths[b][k] + for t in range(total_steps): + self.assertEqual(topk_pred_symbols[t][b, k].data[0], topk[b][k][t+1][1]) # topk includes SOS From a32999e4f4fd57b69c2dff0aa70db5edf75f0b1c Mon Sep 17 00:00:00 2001 From: Kyle Gao Date: Tue, 24 Oct 2017 11:29:16 -0400 Subject: [PATCH 02/70] Use torchtext from pipy (#87) * Use torchtext from pipe. * Fixed torch text sorting order. --- requirements.txt | 2 +- seq2seq/evaluator/evaluator.py | 2 +- seq2seq/trainer/supervised_trainer.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements.txt b/requirements.txt index ca721f9..d0915c1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ numpy dill tqdm --e git+https://github.com/pytorch/text.git@68a688dc9c3be89d9e49fe7c2a735cf96802247f#egg=torchtext +torchtext diff --git a/seq2seq/evaluator/evaluator.py b/seq2seq/evaluator/evaluator.py index eb872f6..dd5566d 100644 --- a/seq2seq/evaluator/evaluator.py +++ b/seq2seq/evaluator/evaluator.py @@ -38,7 +38,7 @@ def evaluate(self, model, data): device = None if torch.cuda.is_available() else -1 batch_iterator = torchtext.data.BucketIterator( dataset=data, batch_size=self.batch_size, - sort_key=lambda batch: -len(getattr(batch, seq2seq.src_field_name)), + sort=True, sort_key=lambda x: len(x.src), device=device, train=False) tgt_vocab = data.fields[seq2seq.tgt_field_name].vocab pad = tgt_vocab.stoi[data.fields[seq2seq.tgt_field_name].pad_token] diff --git a/seq2seq/trainer/supervised_trainer.py b/seq2seq/trainer/supervised_trainer.py index ed7bc06..57dae64 100644 --- a/seq2seq/trainer/supervised_trainer.py +++ b/seq2seq/trainer/supervised_trainer.py @@ -75,7 +75,7 @@ def _train_epoches(self, data, model, n_epochs, start_epoch, start_step, device = None if torch.cuda.is_available() else -1 batch_iterator = torchtext.data.BucketIterator( dataset=data, batch_size=self.batch_size, - sort_key=lambda x: -len(x.src), + sort=True, sort_key=lambda x: len(x.src), device=device, repeat=False) steps_per_epoch = len(batch_iterator) diff --git a/setup.py b/setup.py index 3e32617..106ba0a 100644 --- a/setup.py +++ b/setup.py @@ -62,7 +62,7 @@ # your project is installed. For an analysis of "install_requires" vs pip's # requirements files see: # https://packaging.python.org/en/latest/requirements.html - install_requires=['numpy', 'torch'], + install_requires=['numpy', 'torch', 'torchtext'], # List additional groups of dependencies here (e.g. development # dependencies). You can install these using the following syntax, From 3f201b886bca0165990102173931da8e6c5932ce Mon Sep 17 00:00:00 2001 From: lifengjin Date: Tue, 24 Oct 2017 11:37:45 -0400 Subject: [PATCH 03/70] attention is not required when only using teacher forcing in decoder (#90) * attention is not required when only using teacher forcing in decoder --- seq2seq/models/DecoderRNN.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/seq2seq/models/DecoderRNN.py b/seq2seq/models/DecoderRNN.py index ff63866..b46e198 100644 --- a/seq2seq/models/DecoderRNN.py +++ b/seq2seq/models/DecoderRNN.py @@ -144,7 +144,10 @@ def decode(step, step_output, step_attn): for di in range(decoder_output.size(1)): step_output = decoder_output[:, di, :] - step_attn = attn[:, di, :] + if attn is not None: + step_attn = attn[:, di, :] + else: + step_attn = None decode(di, step_output, step_attn) else: decoder_input = inputs[:, 0].unsqueeze(1) From 96af89dbdc958d8a92dc669e57f08bc4671cc7e6 Mon Sep 17 00:00:00 2001 From: kylegao91 Date: Mon, 30 Oct 2017 08:22:11 -0400 Subject: [PATCH 04/70] Updated docs and version. --- docs/public/_modules/index.html | 8 ++++---- docs/public/dataset.html | 8 ++++---- docs/public/evaluator.html | 8 ++++---- docs/public/genindex.html | 16 ++++++++-------- docs/public/index.html | 8 ++++---- docs/public/loss.html | 16 ++++++++-------- docs/public/models.html | 24 +++++++++++++----------- docs/public/notes/intro.html | 8 ++++---- docs/public/objects.inv | 6 +++--- docs/public/optim.html | 10 +++++----- docs/public/py-modindex.html | 8 ++++---- docs/public/search.html | 8 ++++---- docs/public/searchindex.js | 2 +- docs/public/trainer.html | 8 ++++---- docs/public/util.html | 8 ++++---- docs/source/conf.py | 4 ++-- setup.py | 2 +- 17 files changed, 77 insertions(+), 75 deletions(-) diff --git a/docs/public/_modules/index.html b/docs/public/_modules/index.html index 052b284..c5eae05 100644 --- a/docs/public/_modules/index.html +++ b/docs/public/_modules/index.html @@ -8,7 +8,7 @@ - Overview: module code — pytorch-seq2seq 0.1.4 documentation + Overview: module code — pytorch-seq2seq 0.1.5 documentation @@ -35,7 +35,7 @@ - + @@ -64,7 +64,7 @@
- 0.1.4 + 0.1.5
@@ -215,7 +215,7 @@

All modules for which code is available

@@ -65,7 +65,7 @@
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All modules for which code is available

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Source code for seq2seq.dataset.dataset

-import random
-from seq2seq.dataset import Vocabulary, utils
-
-
[docs]class Dataset(object):
-    """
-    A class that encapsulates a dataset.
-
-    Warning:
-        Do not use this constructor directly, use one of the class methods to initialize.
-
-    Note:
-        Source or target sequences that are longer than the respective
-        max length will be filtered.
-
-    Args:
-        src_max_len (int): maximum source sequence length
-        tgt_max_len (int): maximum target sequence length
-    """
-
-    def __init__(self, src_max_len, tgt_max_len):
-        # Prepare data
-        self.src_max_len = src_max_len
-        self.tgt_max_len = tgt_max_len
-
-        # Declare vocabulary objects
-        self.input_vocab = None
-        self.output_vocab = None
-
-        self.data = None
-
-
-
[docs]    @classmethod
-    def from_file(cls, path, src_max_len, tgt_max_len, src_vocab=None, tgt_vocab=None, src_max_vocab=50000,
-                 tgt_max_vocab=50000):
-        """
-        Initialize a dataset from the file at given path. The file
-        must contains a list of TAB-separated pairs of sequences.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            path (str): path to the dataset file
-            src_max_len (int): maximum source sequence length
-            tgt_max_len (int): maximum target sequence length
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        obj = cls(src_max_len, tgt_max_len)
-        pairs = utils.prepare_data(path, src_max_len, tgt_max_len)
-        return cls._encode(obj, pairs, src_vocab, tgt_vocab, src_max_vocab, tgt_max_vocab)

-
-
[docs]    @classmethod
-    def from_list(cls, src_data, tgt_data, src_max_len, tgt_max_len, src_vocab=None, tgt_vocab=None, src_max_vocab=50000,
-                  tgt_max_vocab=50000):
-        """
-        Initialize a dataset from the source and target lists of sequences.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            src_data (list): list of source sequences
-            tgt_data (list): list of target sequences
-            src_max_len (int): maximum source sequence length
-            tgt_max_len (int): maximum target sequence length
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        obj = cls(src_max_len, tgt_max_len)
-        pairs = utils.prepare_data_from_list(src_data, tgt_data, src_max_len, tgt_max_len)
-        return cls._encode(obj, pairs, src_vocab, tgt_vocab, src_max_vocab, tgt_max_vocab)

-
-    def _encode(self, pairs, src_vocab=None, tgt_vocab=None, src_max_vocab=50000, tgt_max_vocab=50000):
-        """
-        Encodes the source and target lists of sequences using source and target vocabularies.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            pairs (list): list of tuples (source sequences, target sequence)
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        # Read in vocabularies
-        self.input_vocab = self._init_vocab(zip(*pairs)[0], src_max_vocab, src_vocab)
-        self.output_vocab = self._init_vocab(zip(*pairs)[1], tgt_max_vocab, tgt_vocab)
-
-        # Translate input sequences to token ids
-        self.data = []
-        for pair in pairs:
-            src = self.input_vocab.indices_from_sequence(pair[0])
-            dst = self.output_vocab.indices_from_sequence(pair[1])
-            self.data.append((src, dst))
-        return self
-
-    def _init_vocab(self, sequences, max_num_vocab, vocab):
-        resp_vocab = Vocabulary(max_num_vocab)
-        if vocab is None:
-            for sequence in sequences:
-                resp_vocab.add_sequence(sequence)
-            resp_vocab.trim()
-        elif isinstance(vocab, Vocabulary):
-            resp_vocab = vocab
-        elif isinstance(vocab, str):
-            for tok in utils.read_vocabulary(vocab, max_num_vocab):
-                resp_vocab.add_token(tok)
-        else:
-            raise AttributeError('{} is not a valid instance on a vocabulary. None, instance of Vocabulary class \
-                                 and str are only supported formats for the vocabulary'.format(vocab))
-        return resp_vocab
-
-    def __len__(self):
-        return len(self.data)
-
-
[docs]    def num_batches(self, batch_size):
-        """
-        Get the number of batches given batch size.
-
-        Args:
-            batch_size(int): number of examples in a batch
-
-        Returns:
-            int: number of batches
-        """
-        return len(range(0, len(self.data), batch_size))

-
-
[docs]    def make_batches(self, batch_size):
-        """
-        Create a generator that generates batches in batch_size over data.
-
-        Args:
-            batch_size (int): number of pairs in a mini-batch
-
-        Yields:
-            (list(str), list(str)): next pair of source and target variable in a batch
-
-        """
-        if len(self.data) < batch_size:
-            raise OverflowError("batch size = {} cannot be larger than data size = {}".
-                                format(batch_size, len(self.data)))
-        for i in range(0, len(self.data), batch_size):
-            cur_batch = self.data[i:i + batch_size]
-            source_variables = [pair[0] for pair in cur_batch]
-            target_variables = [pair[1] for pair in cur_batch]
-
-            yield (source_variables, target_variables)

-
-
[docs]    def shuffle(self, seed=None):
-        """
-        Shuffle the data.
-
-        Args:
-            seed(int): provide a value for the random seed; default seed=None is truly random
-        """
-        if seed is not None:
-            random.seed(seed)
-        random.shuffle(self.data)

-
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- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
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- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/dataset/utils.html b/docs/public/_modules/seq2seq/dataset/utils.html deleted file mode 100644 index 04eb4ed..0000000 --- a/docs/public/_modules/seq2seq/dataset/utils.html +++ /dev/null @@ -1,358 +0,0 @@ - - - - - - - - - - - seq2seq.dataset.utils — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for seq2seq.dataset.utils

-import logging
-from tqdm import tqdm
-
-logger = logging.getLogger(__name__)
-
-
[docs]def filter_pair(pair, src_max_len, tgt_max_len):
-    """
-    Returns true if a sentence pair meets the length requirements, false otherwise.
-
-    Args:
-        pair ((str, str)): (source, target) sentence pair
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-    Returns:
-         bool: true if the pair is shorter than the length cutoffs, false otherwise
-    """
-    return len(pair[0]) <= src_max_len and len(pair[1]) <= tgt_max_len

-
-
-
[docs]def space_tokenize(text):
-    """
-    Tokenizes a piece of text by splitting it up based on single spaces (" ").
-
-    Args:
-     text (str): input text as a single string
-
-    Returns:
-         list(str): list of tokens obtained by splitting the text on single spaces
-    """
-    return text.split(" ")

-
-
-
[docs]def prepare_data(path, src_max_len, tgt_max_len, tokenize_func=space_tokenize):
-    """
-    Reads a tab-separated data file where each line contains a source sentence and a target sentence. Pairs containing
-    a sentence that exceeds the maximum length allowed for its language are not added.
-
-    Args:
-        path (str): path to the data file
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-        tokenize_func (func): function for splitting words in a sentence (default is single-space-delimited)
-
-    Returns:
-        list((str, str)): list of (source, target) string pairs
-    """
-
-    logger.info("Reading Lines from {}".format(path))
-    # Read the file and split into lines
-    pairs = []
-    with open(path) as fin:
-        for line in tqdm(fin):
-            try:
-                src, dst = line.strip().split("\t")
-                pair = map(tokenize_func, [src, dst])
-                if filter_pair(pair, src_max_len, tgt_max_len):
-                    pairs.append(pair)
-            except:
-                logger.error("Error when reading line: {0}".format(line))
-                raise
-
-    logger.info("Number of pairs: %s" % len(pairs))
-    return pairs

-
-
-
[docs]def prepare_data_from_list(src_list, tgt_list, src_max_len, tgt_max_len, tokenize_func=space_tokenize):
-    """
-    Reads a tab-separated data file where each line contains a source sentence and a target sentence. Pairs containing
-    a sentence that exceeds the maximum length allowed for its language are not added.
-
-    Args:
-        src_list (list): list of source sequences
-        tgt_list (list): list of target sequences
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-        tokenize_func (func): function for splitting words in a sentence (default is single-space-delimited)
-
-    Returns:
-        list((str, str)): list of (source, target) string pairs
-    """
-    if not len(src_list) == len(tgt_list):
-        raise ValueError('source sequence list and target sequence list has different number of entries.')
-
-    logger.info("Preparing pairs...")
-
-    # Read the file and split into lines
-    pairs = []
-
-    for index, _ in tqdm(enumerate(src_list)):
-        pair = map(tokenize_func, [src_list[index], tgt_list[index]])
-        if filter_pair(pair, src_max_len, tgt_max_len):
-            pairs.append(pair)
-
-    logger.info("Number of pairs: %s" % len(pairs))
-    return pairs

-
-
-
[docs]def read_vocabulary(path, max_num_vocab=50000):
-    """
-    Helper function to read a vocabulary file.
-
-    Args:
-        path (str): filepath to raw vocabulary file
-        max_num_vocab (int): maximum number of words to read from vocabulary file
-
-    Returns:
-        set: read words from vocabulary file
-    """
-    logger.info("Reading vocabulary from {}".format(path))
-    # Read the file and create list of tokens in vocabulary
-    vocab = set()
-    with open(path) as fin:
-        for line in fin:
-            if len(vocab) >= max_num_vocab:
-                break
-            try:
-                vocab.add(line.strip())
-            except:
-                logger.error("Error when reading line: {0}".format(line))
-                raise
-
-    logger.info("Size of Vocabulary: %s" % len(vocab))
-    return vocab

-
-
- -
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- -
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- - -
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- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
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- -
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- -
- -
- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/dataset/vocabulary.html b/docs/public/_modules/seq2seq/dataset/vocabulary.html deleted file mode 100644 index 9b503ab..0000000 --- a/docs/public/_modules/seq2seq/dataset/vocabulary.html +++ /dev/null @@ -1,431 +0,0 @@ - - - - - - - - - - - seq2seq.dataset.vocabulary — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for seq2seq.dataset.vocabulary

-import cPickle as pickle
-
-
-
[docs]class Vocabulary(object):
-    """
-    A Vocabulary stores a set of words belonging to a particular language. Words in the source vocabulary are mapped
-    to unique integer IDs during encoding. Words in the target vocabulary are recovered from the model's output
-    during decoding.
-
-    In addition to the words in the actual language, a Vocabulary includes three reserved tokens (and IDs) for the
-    start-of-sentence and end-of-sentence markers, and for a special 'mask' marker used to handle
-    rare/unknown words.
-
-    The Vocabulary is sorted in descending order based on frequency. If the number of words seen is greater than
-    the maximum size of the Vocabulary, the remaining least-frequent words are ignored.
-
-    Args:
-         size(int): maximum number of words allowed in this vocabulary
-    """
-    def __init__(self, size):
-        self.MASK_token_name = "MASK"
-        self.SOS_token_name = "SOS"
-        self.EOS_token_name = "EOS"
-        self.MASK_token_id = 0
-        self.SOS_token_id = 1
-        self.EOS_token_id = 2
-
-        self._reserved = set([self.MASK_token_name, self.SOS_token_name, self.EOS_token_name])
-        self._reserved_token_id = [
-                (self.MASK_token_name, self.MASK_token_id),
-                (self.SOS_token_name, self.SOS_token_id),
-                (self.EOS_token_name, self.EOS_token_id)
-        ]
-
-        self._token2index = dict([(tok, idx) for tok, idx in self._reserved_token_id])
-        self._index2token = dict([(idx, tok) for tok, idx in self._reserved_token_id])
-
-        self._token2count = {}
-
-        self._num_tokens = 0
-        self._num_reserved = 3
-
-        self.sorted = False
-        self.size = size
-
-
[docs]    def trim(self):
-        """
-        Sorts the vocabulary in descending order based on frequency
-        """
-        sorted_vocab_count = sorted(self._token2count.items(), key=lambda x: x[1], reverse=True)[:self.size]
-        self._token2index = dict([(w, self._num_reserved + idx) for idx, (w, _) in enumerate(sorted_vocab_count)])
-        self._index2token = dict([(idx, w) for w, idx in self._token2index.items()])
-        for tok, idx in self._reserved_token_id:
-            self._token2index[tok] = idx
-            self._index2token[idx] = tok
-        if self._num_tokens > self.size:
-            self._num_tokens = self.size
-        self.sorted = True

-
-
[docs]    def check_sorted(self):
-        """
-        Sorts the vocabulary (if it is not already sorted).
-        """
-        if not self.sorted:
-            self.trim()

-
-
[docs]    def get_index(self, token):
-        """
-        Returns:
-            int: ID of the given token.
-        """
-        self.check_sorted()
-        return self._token2index[token]

-
-
[docs]    def get_token(self, index):
-        """
-        Returns:
-            str: token with ID equal to the given index.
-        """
-        self.check_sorted()
-        return self._index2token[index]

-
-
[docs]    def get_vocab_size(self):
-        """
-        Returns:
-            int: maximum number of words in the vocabulary.
-        """
-        self.check_sorted()
-        return self._num_tokens + self._num_reserved

-
-
[docs]    def add_token(self, token):
-        """
-        Adds an occurrence of a token to the vocabulary, incrementing its observed frequency if the word already exists.
-
-        Args:
-             token (int): word to add
-        """
-        if token in self._reserved:
-            return
-        if token not in self._token2count:
-            self._token2count[token] = 1
-            self._num_tokens += 1
-        else:
-            self._token2count[token] += 1
-        self.sorted = False

-
-
[docs]    def add_sequence(self, sequence):
-        """
-        Adds a sequence of words to the vocabulary.
-
-        Args:
-             sequence(list(str)): list of words, e.g. representing a sentence.
-        """
-        for tok in sequence:
-            self.add_token(tok)

-
-
[docs]    def indices_from_sequence(self, sequence):
-        """
-        Maps a list of words to their token IDs, or else the 'mask' token if the word is rare/unknown.
-
-        Args:
-            sequence (list(str)): list of words to map
-        Returns:
-            list(int): list of mapped IDs
-        """
-        self.check_sorted()
-        return [self._token2index[tok]
-                if tok in self._token2index
-                else self.MASK_token_id
-                for tok in sequence]

-
-
[docs]    def sequence_from_indices(self, indices):
-        """
-        Recover a sentence from a list of token IDs.
-
-        Args:
-            indices (list(int)): list of token IDs.
-
-        Returns:
-            list(str): recovered sentence, represented as a list of words
-        """
-        seq = [self._index2token[idx] for idx in indices]
-        return seq

-
-
[docs]    def save(self, file_name):
-        """
-        Writes this Vocabulary to disk in a pickle file.
-
-        Args:
-             file_name (str): path to the target pickle file
-        """
-        self.check_sorted()
-        vocab_size = self.get_vocab_size()
-        with open(file_name, "w") as f:
-            for index, token in self._index2token.items():
-                if token in self._reserved:
-                    continue
-                if index < vocab_size -1:
-                    f.write(token + "\n")
-                else:
-                    f.write(token)

-
-
[docs]    @classmethod
-    def load(cls, file_name):
-        """
-        Loads a Vocabulary from a pickle file on disk.
-
-        Args:
-            file_name (str): path to the pickle file
-
-        Returns:
-            Vocabulary: loaded Vocabulary
-        """
-        with open(file_name, "r") as f:
-            tokens = f.readlines()
-        vocab = Vocabulary(len(tokens))
-        for token in tokens:
-            vocab.add_token(token.strip())
-        return vocab

-
-    def __eq__(self, other):
-        if not isinstance(other, self.__class__):
-            return False
-        self.check_sorted()
-        other.check_sorted()
-
-        if self._token2count == other._token2count and self._token2index == other._token2index \
-           and self._index2token == other._index2token:
-            return True
-        else:
-            return False
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash(self._token2index)

-
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Source code for seq2seq.evaluator.evaluator

-from __future__ import print_function
-
-import torch
-
-from seq2seq.loss import NLLLoss
-
-
[docs]class Evaluator(object):
-    """ Class to evaluate models with given datasets.
-    Args:
-        loss (seq2seq.loss, optional): loss for evaluator (default: seq2seq.loss.NLLLoss)
-        batch_size (int, optional): batch size for evaluator (default: 64)
-    """
-
-    def __init__(self, loss=NLLLoss(), batch_size=64):
-        self.loss = loss
-        self.batch_size = batch_size
-
-
[docs]    def evaluate(self, model, data):
-        """ Evaluate a model on given dataset and return performance.
-
-        Args:
-            model (seq2seq.models): model to evaluate
-            data (seq2seq.dataset.dataset.Dataset): dataset to evaluate against
-
-        Returns:
-            loss (float): loss of the given model on the given dataset
-        """
-        model.eval()
-        loss = self.loss
-        loss.reset()
-
-        for batch in data.make_batches(self.batch_size):
-            input_variables = batch[0]
-            target_variables = batch[1]
-
-            decoder_outputs, decoder_hidden, other = model(input_variables, target_variables, volatile=True)
-
-            # Evaluation
-            targets = other['inputs']
-            lengths = other['length']
-            for b in range(len(targets)):
-                # Batch wise loss
-                batch_target = targets[b]
-                batch_len = lengths[b]
-                # Crop output and target to batch length
-                batch_output = torch.stack([output[b] for output in decoder_outputs[:batch_len]])
-                batch_target = batch_target[:batch_len]
-                # Evaluate loss
-                loss.eval_batch(batch_output, batch_target)
-
-        return loss.get_loss()

-
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- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
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- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/evaluator/predictor.html b/docs/public/_modules/seq2seq/evaluator/predictor.html deleted file mode 100644 index 03f823d..0000000 --- a/docs/public/_modules/seq2seq/evaluator/predictor.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - - - - seq2seq.evaluator.predictor — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for seq2seq.evaluator.predictor

-
[docs]class Predictor(object):
-
-    def __init__(self, model, src_vocab, tgt_vocab):
-        """
-        Predictor class to evaluate for a given model.
-        Args:
-            model (seq2seq.models): trained model. This can be loaded from a checkpoint
-                using `seq2seq.util.checkpoint.load`
-            src_vocab (seq2seq.dataset.vocabulary.Vocabulary): source sequence vocabulary
-            tgt_vocab (seq2seq.dataset.vocabulary.Vocabulary): target sequence vocabulary
-        """
-        self.model = model
-        self.model.eval()
-        self.src_vocab = src_vocab
-        self.tgt_vocab = tgt_vocab
-
-
[docs]    def predict(self, src_seq):
-        """ Make prediction given `src_seq` as input.
-
-        Args:
-            src_seq (list): list of tokens in source language
-
-        Returns:
-            tgt_seq (list): list of tokens in target language as predicted
-            by the pre-trained model
-        """
-        src_id_seq = self.src_vocab.indices_from_sequence(src_seq)
-
-        softmax_list, _, other = self.model([src_id_seq], volatile=True)
-        length = other['length'][0]
-
-        tgt_id_seq = []
-        for i in range(length):
-            idx = softmax_list[i].max(1)[1].data[0][0]
-            tgt_id_seq.append(idx)
-
-        tgt_seq = self.tgt_vocab.sequence_from_indices(tgt_id_seq)
-        return tgt_seq

-
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Source code for seq2seq.loss.loss

-from __future__ import print_function
-import math
-import torch.nn as nn
-import numpy as np
-
-
[docs]class Loss(object):
-    """ Base class for encapsulation of the loss functions.
-
-    This class defines interfaces that are commonly used with loss functions
-    in training and inferencing.  For information regarding individual loss
-    functions, please refer to http://pytorch.org/docs/master/nn.html#loss-functions
-
-    Note:
-        Do not use this class directly, use one of the sub classes.
-
-    Args:
-        name (str): name of the loss function used by logging messages.
-        criterion (torch.nn._Loss): one of PyTorch's loss function.  Refer
-            to http://pytorch.org/docs/master/nn.html#loss-functions for
-            a list of them.
-
-    Attributes:
-        name (str): name of the loss function used by logging messages.
-        criterion (torch.nn._Loss): one of PyTorch's loss function.  Refer
-            to http://pytorch.org/docs/master/nn.html#loss-functions for
-            a list of them.  Implementation depends on individual
-            sub-classes.
-        acc_loss (int or torcn.nn.Tensor): variable that stores accumulated loss.
-        norm_term (float): normalization term that can be used to calculate
-            the loss of multiple batches.  Implementation depends on individual
-            sub-classes.
-    """
-
-    def __init__(self, name, criterion):
-        self.name = name
-        self.criterion = criterion
-        if not issubclass(type(self.criterion), nn.modules.loss._Loss):
-            raise ValueError("Criterion has to be a subclass of torch.nn._Loss")
-        # accumulated loss
-        self.acc_loss = 0
-        # normalization term
-        self.norm_term = 0
-
-
[docs]    def reset(self):
-        """ Reset the accumulated loss. """
-        self.acc_loss = 0
-        self.norm_term = 0

-
-
[docs]    def get_loss(self):
-        """ Get the loss.
-
-        This method defines how to calculate the averaged loss given the
-        accumulated loss and the normalization term.  Override to define your
-        own logic.
-
-        Returns:
-            loss (float): value of the loss.
-        """
-        raise NotImplementedError

-
-
[docs]    def eval_batch(self, outputs, target):
-        """ Evaluate and accumulate loss given outputs and expected results.
-
-        This method is called after each batch with the batch outputs and
-        the target (expected) results.  The loss and normalization term are
-        accumulated in this method.  Override it to define your own accumulation
-        method.
-
-        Args:
-            outputs (torch.Tensor): outputs of a batch.
-            target (torch.Tensor): expected output of a batch.
-        """
-        raise NotImplementedError

-
-    def cuda(self):
-        self.criterion.cuda()
-
-    def backward(self):
-        if type(self.acc_loss) is int:
-            raise ValueError("No loss to back propagate.")
-        self.acc_loss.backward()

-
-
[docs]class NLLLoss(Loss):
-    """ Batch averaged negative log-likelihood loss.
-
-    Args:
-        weight (torch.Tensor, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-        mask (int, optional): index of masked token, i.e. weight[mask] = 0.
-        size_average (bool, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-    """
-
-    _NAME = "Avg NLLLoss"
-
-    def __init__(self, weight=None, mask=None, size_average=True):
-        self.mask = mask
-        if mask is not None:
-            if weight is None:
-                raise ValueError("Must provide weight with a mask.")
-            weight[mask] = 0
-
-        super(NLLLoss, self).__init__(
-            self._NAME,
-            nn.NLLLoss(weight=weight, size_average=size_average))
-
-    def get_loss(self):
-        if type(self.acc_loss) is int:
-            return 0
-        return self.acc_loss.data[0] / self.norm_term
-
-    def eval_batch(self, outputs, target):
-        self.acc_loss += self.criterion(outputs, target)
-        self.norm_term += 1

-
-
[docs]class Perplexity(NLLLoss):
-    """ Language model perplexity loss.
-
-    Perplexity is the token averaged likelihood.  When the averaging options are the
-    same, it is the exponential of negative log-likelihood.
-
-    Args:
-        weight (torch.Tensor, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-        mask (int, optional): index of masked token, i.e. weight[mask] = 0.
-    """
-
-    _NAME = "Perplexity"
-    _MAX_EXP = 100
-
-    def __init__(self, weight=None, mask=None):
-        super(Perplexity, self).__init__(weight=weight, mask=mask, size_average=False)
-
-    def eval_batch(self, outputs, target):
-        self.acc_loss += self.criterion(outputs, target)
-        if self.mask is None:
-            self.norm_term += np.prod(target.size())
-        else:
-            self.norm_term += target.data.ne(self.mask).sum()
-
-    def get_loss(self):
-        nll = super(Perplexity, self).get_loss()
-        if nll > Perplexity._MAX_EXP:
-            print("WARNING: Loss exceeded maximum value, capping to e^100")
-            return math.exp(Perplexity._MAX_EXP)
-        return math.exp(nll)

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- © Copyright 2017, pytorch-seq2seq Contritors. - -

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Source code for seq2seq.models.DecoderRNN

-import random
-
-import numpy as np
-
-import torch
-if torch.cuda.is_available():
-    import torch.cuda as device
-else:
-    import torch as device
-import torch.nn as nn
-from torch.autograd import Variable
-import torch.nn.functional as F
-
-from attention import Attention
-from baseRNN import BaseRNN
-
-
-
[docs]class DecoderRNN(BaseRNN):
-    r"""
-    Provides functionality for decoding in a seq2seq framework, with an option for attention.
-
-    Args:
-        vocab (Vocabulary): an object of Vocabulary class
-        max_len (int): a maximum allowed length for the sequence to be processed
-        hidden_size (int): the number of features in the hidden state `h`
-        n_layers (int, optional): number of recurrent layers (default: 1)
-        rnn_cell (str, optional): type of RNN cell (default: gru)
-        input_dropout_p (float, optional): dropout probability for the input sequence (default: 0)
-        dropout_p (float, optional): dropout probability for the output sequence (default: 0)
-        use_attention(bool, optional): flag indication whether to use attention mechanism or not (default: false)
-
-    Attributes:
-        KEY_ATTN_SCORE (str): key used to indicate attention weights in `ret_dict`
-        KEY_LENGTH (str): key used to indicate a list representing lengths of output sequences in `ret_dict`
-        KEY_SEQUENCE (str): key used to indicate a list of sequences in `ret_dict`
-        KEY_INPUT (str): key used to target outputs in `ret_dict`
-
-    Inputs: inputs, encoder_hidden, encoder_outputs, function, teacher_forcing_ratio
-        - **inputs** (seq_len, batch, input_size): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs.  It is used for teacher forcing when provided. (default is `None`)
-        - **encoder_hidden** (batch, seq_len, hidden_size): tensor containing the features in the hidden state `h` of
-          encoder. Used as the initial hidden state of the decoder.
-        - **encoder_outputs** (batch, seq_len, hidden_size): tensor with containing the outputs of the encoder.
-          Used for attention mechanism (default is `None`).
-        - **function** (torch.nn.Module): A function used to generate symbols from RNN hidden state
-          (default is `torch.nn.functional.log_softmax`).
-        - **teacher_forcing_ratio** (float): The probability that teacher forcing will be used. A random number is
-          drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0).
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*KEY_LENGTH* : list of integers
-          representing lengths of output sequences, *KEY_SEQUENCE* : list of sequences, where each sequence is a list of
-          predicted token IDs, *KEY_INPUT* : target outputs if provided for decoding, *KEY_ATTN_SCORE* : list of
-          sequences, where each list is of attention weights }.
-    """
-
-    KEY_ATTN_SCORE = 'attention_score'
-    KEY_LENGTH = 'length'
-    KEY_SEQUENCE = 'sequence'
-    KEY_INPUT = 'inputs'
-
-    def __init__(self, vocab, max_len, hidden_size,
-            n_layers=1, rnn_cell='gru',
-            input_dropout_p=0, dropout_p=0, use_attention=False):
-        super(DecoderRNN, self).__init__(vocab, max_len, hidden_size,
-                input_dropout_p, dropout_p,
-                n_layers, rnn_cell)
-
-        self.output_size = self.vocab.get_vocab_size()
-        self.dropout_p = dropout_p
-        self.max_length = max_len
-        self.use_attention = use_attention
-
-        self.init_input = None
-
-        self.embedding = nn.Embedding(self.output_size, self.hidden_size)
-        self.dropout = nn.Dropout(self.dropout_p)
-        if use_attention:
-            self.attention = Attention(self.hidden_size)
-
-        self.out = nn.Linear(self.hidden_size, self.output_size)
-
-    def init_start_input(self, batch_size):
-        # GO input for decoder # Re-initialize when batch size changes
-        if self.init_input is None or self.init_input.size(0) != batch_size:
-            self.init_input = Variable(device.LongTensor([[self.vocab.SOS_token_id]*batch_size])).view(batch_size, -1)
-        return self.init_input
-
-    def forward_step(self, input_var, hidden, encoder_outputs, function):
-        batch_size = input_var.size(0)
-        output_size = input_var.size(1)
-        embedded = self.embedding(input_var)
-        embedded = self.input_dropout(embedded)
-
-        output, hidden = self.rnn(embedded, hidden)
-        output = self.dropout(output)
-
-        attn = None
-        if self.use_attention:
-            output, attn = self.attention(output, encoder_outputs)
-
-        predicted_softmax = function(self.out(output.view(-1, self.hidden_size))).view(batch_size, output_size, -1)
-        return predicted_softmax, hidden, attn
-
-    def forward_rnn(self, inputs=None, encoder_hidden=None, function=F.log_softmax,
-                    encoder_outputs=None, teacher_forcing_ratio=0):
-        ret_dict = dict()
-        if self.use_attention:
-            if encoder_outputs is None:
-                raise ValueError("Argument encoder_outputs cannot be None when attention is used.")
-            ret_dict[DecoderRNN.KEY_ATTN_SCORE] = list()
-        if inputs is None:
-            if teacher_forcing_ratio > 0:
-                raise ValueError("Teacher forcing has to be disabled (set 0) when no inputs is provided.")
-        if inputs is None and encoder_hidden is None:
-            batch_size = 1
-        else:
-            if inputs is not None:
-                batch_size = inputs.size(0)
-            else:
-                if self.rnn_cell is nn.LSTM:
-                    batch_size = encoder_hidden[0].size(1)
-                elif self.rnn_cell is nn.GRU:
-                    batch_size = encoder_hidden.size(1)
-
-        decoder_input = self.init_start_input(batch_size)
-        decoder_hidden = encoder_hidden
-
-        use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
-
-        decoder_outputs = []
-        sequence_symbols = []
-        lengths = np.array([self.max_length] * batch_size)
-
-        def decode(step, step_output, step_attn):
-            decoder_outputs.append(step_output)
-            if self.use_attention:
-                ret_dict[DecoderRNN.KEY_ATTN_SCORE].append(step_attn)
-            symbols = decoder_outputs[-1].topk(1)[1]
-            sequence_symbols.append(symbols)
-
-            eos_batches = symbols.data.eq(self.vocab.EOS_token_id)
-            if eos_batches.dim() > 0:
-                eos_batches = eos_batches.cpu().view(-1).numpy()
-                update_idx = ((lengths > di) & eos_batches) != 0
-                lengths[update_idx] = len(sequence_symbols)
-            return symbols
-
-        # Manual unrolling is used to support random teacher forcing.
-        # If teacher_forcing_ratio is True or False instead of a probability, the unrolling can be done in graph
-        if use_teacher_forcing:
-            decoder_input = torch.cat([decoder_input, inputs], dim=1)
-            decoder_output, decoder_hidden, attn = self.forward_step(decoder_input, decoder_hidden, encoder_outputs,
-                                                                     function=function)
-
-            for di in range(self.max_length):
-                step_output = decoder_output[:, di, :]
-                step_attn = attn[:, di, :]
-                decode(di, step_output, step_attn)
-        else:
-            for di in range(self.max_length):
-                decoder_output, decoder_hidden, step_attn = self.forward_step(decoder_input, decoder_hidden, encoder_outputs,
-                                                                         function=function)
-                step_output = decoder_output.squeeze(1)
-                symbols = decode(di, step_output, step_attn)
-                decoder_input = symbols
-
-        ret_dict[DecoderRNN.KEY_SEQUENCE] = sequence_symbols
-        ret_dict[DecoderRNN.KEY_LENGTH] = lengths.tolist()
-        ret_dict[DecoderRNN.KEY_INPUT] = inputs
-
-        return decoder_outputs, decoder_hidden, ret_dict

-
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Source code for seq2seq.models.EncoderRNN

-import torch.nn as nn
-from baseRNN import BaseRNN
-
-
-
[docs]class EncoderRNN(BaseRNN):
-    r"""
-    Applies a multi-layer RNN to an input sequence.
-    Args:
-        vocab (Vocabulary): an object of Vocabulary class
-        max_len (int): a maximum allowed length for the sequence to be processed
-        hidden_size (int): the number of features in the hidden state `h`
-        input_dropout_p (float, optional): dropout probability for the input sequence (default: 0)
-        dropout_p (float, optional): dropout probability for the output sequence (default: 0)
-        n_layers (int, optional): number of recurrent layers (default: 1)
-        rnn_cell (str, optional): type of RNN cell (default: gru)
-
-    Inputs: inputs, volatile
-        - **inputs**: list of sequences, whose length is the batch size and within which each sequence is a list of token IDs.
-        - **volatile** (bool, optional): boolean flag specifying whether to preserve gradients, when you are sure you
-          will not be even calling .backward().
-    Outputs: output, hidden
-        - **output** (batch, seq_len, hidden_size): tensor containing the encoded features of the input sequence
-        - **hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the features in the hidden state `h`
-
-    Examples::
-
-         >>> encoder = EncoderRNN(input_vocab, max_seq_length, hidden_size)
-         >>> output, hidden = encoder(input)
-
-    """
-    def __init__(self, vocab, max_len, hidden_size,
-            input_dropout_p=0, dropout_p=0,
-            n_layers=1, rnn_cell='gru'):
-        super(EncoderRNN, self).__init__(vocab, max_len, hidden_size,
-                input_dropout_p, dropout_p, n_layers, rnn_cell)
-
-        self.embedding = nn.Embedding(self.vocab.get_vocab_size(), hidden_size)
-        self.lengths = None
-
-    def forward(self, *args, **kwargs):
-        batch = args[0]
-        self.lengths = [min(self.max_len, len(seq)) for seq in batch]
-        return super(EncoderRNN, self).forward(batch, **kwargs)
-
-
[docs]    def forward_rnn(self, input_var):
-        """
-        Applies a multi-layer RNN to an input sequence.
-
-        Args:
-            input_var (batch, seq_len): tensor containing the features of the input sequence.
-
-       returns: output, hidden
-            - **output** (batch, seq_len, hidden_size): variable containing the encoded features of the input sequence
-            - **hidden** (num_layers * num_directions, batch, hidden_size): variable containing the features in the hidden state h
-        """
-        embedded = self.embedding(input_var)
-        embedded = self.input_dropout(embedded)
-        embedded = nn.utils.rnn.pack_padded_sequence(embedded, self.lengths, batch_first=True)
-        output, hidden = self.rnn(embedded)
-        output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
-        return output, hidden

-
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Source code for seq2seq.models.TopKDecoder

-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-from baseRNN import BaseRNN
-
-
-
[docs]class TopKDecoder(BaseRNN):
-    r"""
-    Top-K decoding with beam search.
-
-    Args:
-        decoder_rnn (DecoderRNN): An object of DecoderRNN used for decoding.
-        k (int): Size of the beam.
-
-    Inputs: inputs, encoder_hidden, encoder_outputs, function, teacher_forcing_ratio
-        - **inputs** (seq_len, batch, input_size): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs.  It is used for teacher forcing when provided. (default is `None`)
-        - **encoder_hidden** (batch, seq_len, hidden_size): tensor containing the features in the hidden state `h` of
-          encoder. Used as the initial hidden state of the decoder.
-        - **encoder_outputs** (batch, seq_len, hidden_size): tensor with containing the outputs of the encoder.
-          Used for attention mechanism (default is `None`).
-        - **function** (torch.nn.Module): A function used to generate symbols from RNN hidden state
-          (default is `torch.nn.functional.log_softmax`).
-        - **teacher_forcing_ratio** (float): The probability that teacher forcing will be used. A random number is
-          drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0).
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*length* : list of integers
-          representing lengths of output sequences, *sequence* : list of sequences, where each sequence is a list of
-          predicted token IDs, *inputs* : target outputs if provided for decoding}.
-
-    """
-
-    def __init__(self, decoder_rnn, k):
-        super(TopKDecoder, self).__init__(decoder_rnn.lang, decoder_rnn.max_length)
-        self.rnn = decoder_rnn
-        self.k = k
-        self.V = self.rnn.lang.get_vocab_size()
-        self.SOS = self.rnn.lang.SOS_token_id
-        self.EOS = self.rnn.lang.EOS_token_id
-
-
[docs]    def forward_rnn(self, inputs=None, encoder_hidden=None, encoder_outputs=None, function=F.log_softmax,
-                    retain_output_probs=True):
-        """
-        Forward rnn for MAX_LENGTH steps.  Look at :func:`seq2seq.models.DecoderRNN.DecoderRNN.forward_rnn` for details.
-        """
-
-        # TODO: Looks like encoder_hidden is not optional, we need unit tests
-        # for this class
-        # Get batch size, assuming h_0 is num_layers*directions x b x hidden_dim
-        b = encoder_hidden.size(1)
-        h = encoder_hidden.size(2)
-
-        self.pos_index = Variable(torch.LongTensor(range(b)) * self.k).view(-1, 1)
-
-        # Inflate the initial hidden states to be of size: b*k x h
-        hidden = self._inflate(encoder_hidden, self.k)
-        # ... same idea for encoder_outputs and decoder_outputs
-        if self.rnn.use_attention:
-            if encoder_outputs is None:
-                raise ValueError("Argument encoder_outputs cannot be None when attention is used.")
-            else:
-                inflated_encoder_outputs = self._inflate(encoder_outputs, self.k)
-        else:
-            inflated_encoder_outputs = None
-
-        # Initialize the scores; for the first step,
-        # ignore the inflated copies to avoid duplicate entries in the top k
-        sequence_scores = torch.Tensor(b*self.k, 1)
-        sequence_scores.fill_(-float('Inf'))
-        sequence_scores.index_fill_(0, torch.LongTensor([i*self.k for i in range(0, b)]), 0.0)
-        sequence_scores = Variable(sequence_scores)
-
-        # Initialize the input vector
-        input_var = Variable(torch.transpose(torch.LongTensor([[self.SOS]*b*self.k]), 0, 1))
-
-        # Store decisions for backtracking
-        stored_outputs = list()
-        stored_scores = list()
-        stored_predecessors = list()
-        stored_emitted_symbols = list()
-        stored_hidden = list()
-
-        for _ in range(0, self.rnn.max_length):
-
-            # Run the RNN one step forward
-            log_softmax_output, hidden, _ = self.rnn.forward_step(input_var, hidden, inflated_encoder_outputs, function=function)
-
-            # If doing local backprop (e.g. supervised training), retain the output layer
-            if retain_output_probs:
-                stored_outputs.append(log_softmax_output)
-
-            # To get the full sequence scores for the new candidates, add the local scores for t_i to the predecessor scores for t_(i-1)
-            sequence_scores = self._inflate(sequence_scores, self.V)
-            sequence_scores += log_softmax_output
-            scores, candidates = sequence_scores.view(b, -1).topk(self.k, dim=1)
-
-            # Reshape input = (bk, 1) and sequence_scores = (bk, 1)
-            input_var = (candidates % self.V).view(b * self.k, 1)
-            sequence_scores = scores.view(b * self.k, 1)
-
-            # Update fields for next timestep
-            predecessors = (candidates / self.V + self.pos_index.expand_as(candidates)).view(b*self.k, 1)
-            hidden = hidden.index_select(1, predecessors.squeeze())
-
-            # Update sequence scores and erase scores for end-of-sentence symbol so that they aren't expanded
-            stored_scores.append(sequence_scores.clone())
-            eos_indices = input_var.data.eq(self.EOS)
-            if eos_indices.nonzero().dim() > 0:
-                sequence_scores.data.masked_fill_(eos_indices, -float('inf'))
-
-            # Cache results for backtracking
-            stored_predecessors.append(predecessors)
-            stored_emitted_symbols.append(input_var)
-            stored_hidden.append(hidden)
-
-        # Do backtracking to return the optimal values
-        output, h_t, h_n, s, l, p = self._backtrack(stored_outputs, stored_hidden,
-                                                 stored_predecessors, stored_emitted_symbols, stored_scores, b, h)
-
-        # Build return objects
-        decoder_outputs = [step[:, 0, :] for step in output]
-        decoder_hidden = h_n[:, :, 0, :]
-        metadata = {}
-        metadata['inputs'] = inputs
-        metadata['output'] = output
-        metadata['h_t'] = h_t
-        metadata['score'] = s
-        metadata['length'] = l
-        metadata['sequence'] = p
-        return decoder_outputs, decoder_hidden, metadata

-
-    def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hidden_size):
-        """Backtracks over batch to generate optimal k-sequences.
-
-        Args:
-            nw_output [(batch*k, vocab_size)] * sequence_length: A Tensor of outputs from network
-            nw_hidden [(num_layers, batch*k, hidden_size)] * sequence_length: A Tensor of hidden states from network
-            predecessors [(batch*k)] * sequence_length: A Tensor of predecessors
-            symbols [(batch*k)] * sequence_length: A Tensor of predicted tokens
-            scores [(batch*k)] * sequence_length: A Tensor containing sequence scores for every token t = [0, ... , seq_len - 1]
-            b: Size of the batch
-            hidden_size: Size of the hidden state
-
-        Returns:
-            output [(batch, k, vocab_size)] * sequence_length: A list of the output probabilities (p_n)
-            from the last layer of the RNN, for every n = [0, ... , seq_len - 1]
-
-            h_t [(batch, k, hidden_size)] * sequence_length: A list containing the output features (h_n)
-            from the last layer of the RNN, for every n = [0, ... , seq_len - 1]
-
-            h_n(batch, k, hidden_size): A Tensor containing the last hidden state for all top-k sequences.
-
-            score [batch, k]: A list containing the final scores for all top-k sequences
-
-            length [batch, k]: A list specifying the length of each sequence in the top-k candidates
-
-            p (batch, k, sequence_len): A Tensor containing predicted sequence
-        """
-
-        # initialize return variables given different types
-        output = list()
-        h_t = list()
-        p = list()
-        h_n = torch.zeros(nw_hidden[0].size())  # Placeholder for last hidden state of top-k sequences.
-                                                # If a (top-k) sequence ends early in decoding, `h_n` contains
-                                                # its hidden state when it sees EOS.  Otherwise, `h_n` contains
-                                                # the last hidden state of decoding.
-        l = [[self.rnn.max_length] * self.k for _ in range(b)]  # Placeholder for lengths of top-k sequences
-                                                                # Similar to `h_n`
-
-        # the last step output of the beams are not sorted
-        # thus they are sorted here
-        sorted_score, sorted_idx = scores[-1].view(b, self.k).topk(self.k)
-        # initialize the sequence scores with the sorted last step beam scores
-        s = sorted_score.clone()
-
-        batch_eos_found = [0] * b   # the number of EOS found
-                                    # in the backward loop below for each batch
-
-        t = self.rnn.max_length - 1
-        # initialize the back pointer with the sorted order of the last step beams.
-        # add self.pos_index for indexing variable with b*k as the first dimension.
-        t_predecessors = (sorted_idx + self.pos_index.expand_as(sorted_idx)).view(b * self.k)
-        while t >= 0:
-            # Re-order the variables with the back pointer
-            current_output = nw_output[t].index_select(0, t_predecessors)
-            current_hidden = nw_hidden[t].index_select(1, t_predecessors)
-            current_symbol = symbols[t].index_select(0, t_predecessors)
-            # Re-order the back pointer of the previous step with the back pointer of
-            # the current step
-            t_predecessors = predecessors[t].index_select(0, t_predecessors).squeeze()
-
-            # This tricky block handles dropped sequences that see EOS earlier.
-            # The basic idea is summarized below:
-            #
-            #   Terms:
-            #       Ended sequences = sequences that see EOS early and dropped
-            #       Survived sequences = sequences in the last step of the beams
-            #
-            #       Although the ended sequences are dropped during decoding,
-            #   their generated symbols and complete backtracking information are still
-            #   in the backtracking variables.
-            #   For each batch, everytime we see an EOS in the backtracking process,
-            #       1. If there is survived sequences in the return variables, replace
-            #       the one with the lowest survived sequence score with the new ended
-            #       sequences
-            #       2. Otherwise, replace the ended sequence with the lowest sequence
-            #       score with the new ended sequence
-            #
-            eos_indices = symbols[t].data.squeeze(1).eq(self.EOS).nonzero()
-            if eos_indices.dim() > 0:
-                for i in range(eos_indices.size(0)-1, -1, -1):
-                    # Indices of the EOS symbol for both variables
-                    # with b*k as the first dimension, and b, k for
-                    # the first two dimensions
-                    idx = eos_indices[i]
-                    b_idx = idx[0] / self.k
-                    # The indices of the replacing position
-                    # according to the replacement strategy noted above
-                    res_k_idx = self.k - (batch_eos_found[b_idx] % self.k) - 1
-                    batch_eos_found[b_idx] += 1
-                    res_idx = b_idx * self.k + res_k_idx
-
-                    # Replace the old information in return variables
-                    # with the new ended sequence information
-                    t_predecessors[res_idx] = predecessors[t][idx[0]]
-                    current_output[res_idx, :] = nw_output[t][idx[0], :]
-                    current_hidden[:, res_idx, :] = nw_hidden[t][:, idx[0], :]
-                    h_n[:, res_idx, :] = nw_hidden[t][:, idx[0], :].data
-                    current_symbol[res_idx, :] = symbols[t][idx[0]]
-                    s[b_idx, res_k_idx] = scores[t][idx[0]].data[0]
-                    l[b_idx][res_k_idx] = t + 1
-
-            # record the back tracked results
-            output.append(current_output)
-            h_t.append(current_hidden)
-            p.append(current_symbol)
-
-            t -= 1
-
-        # Sort and re-order again as the added ended sequences may change
-        # the order (very unlikely)
-        s, re_sorted_idx = s.topk(self.k)
-        for b_idx in range(b):
-            l[b_idx] = [l[b_idx][k_idx.data[0]] for k_idx in re_sorted_idx[b_idx,:]]
-
-        re_sorted_idx = (re_sorted_idx + self.pos_index.expand_as(re_sorted_idx)).view(b * self.k)
-
-        # Reverse the sequences and re-order at the same time
-        # It is reversed because the backtracking happens in reverse time order
-        output = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(output)]
-        p = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(p)]
-        h_t = [step.index_select(1, re_sorted_idx).view(-1, b, self.k, hidden_size) for step in reversed(h_t)]
-        h_n = h_n.index_select(1, re_sorted_idx.data).view(-1, b, self.k, hidden_size)
-        s = s.data
-
-        if self.k == 1:
-            l = [_l[0] for _l in l]
-
-        return output, h_t, h_n, s, l, p
-
-    def _mask_symbol_scores(self, score, idx, masking_score=-float('inf')):
-            score[idx] = masking_score
-
-    def _mask(self, tensor, idx, dim=0, masking_score=-float('inf')):
-        if len(idx.size()) > 0:
-            indices = idx[:, 0]
-            tensor.index_fill_(dim, indices, masking_score)
-
-    def _inflate(self, tensor, times):
-        """
-        Given a tensor, 'inflates' it along the given dimension by replicating each slice specified number of times (in-place)
-
-        Args:
-            tensor: A :class:`Tensor` to inflate
-            times: number of repetitions
-            dimension: axis for inflation (default=0)
-
-        Returns:
-            A :class:`Tensor`
-
-        Examples::
-            >> a = torch.LongTensor([[1, 2], [3, 4]])
-            >> a
-            1   2
-            3   4
-            [torch.LongTensor of size 2x2]
-            >> decoder = TopKDecoder(nn.RNN(10, 20, 2), 3)
-            >> b = decoder._inflate(a, 1, dimension=1)
-            >> b
-            1   1   2   2
-            3   3   4   4
-            [torch.LongTensor of size 2x4]
-            >> c = decoder._inflate(a, 1, dimension=0)
-            >> c
-            1   2
-            1   2
-            3   4
-            3   4
-            [torch.LongTensor of size 4x2]
-
-        """
-        tensor_dim = len(tensor.size())
-        if tensor_dim is 3:
-            b = tensor.size(1)
-            return tensor.repeat(1, 1, times).view(tensor.size(0), b * times, -1)
-        elif tensor_dim is 2:
-            return tensor.repeat(1, times)
-        elif tensor_dim is 1:
-            b = tensor.size(0)
-            return tensor.repeat(times).view(b, -1)
-        else:
-            raise ValueError("Tensor can be of 1D, 2D or 3D only. This one is {}D.".format(tensor_dim))

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- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
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Source code for seq2seq.models.attention

-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-
[docs]class Attention(nn.Module):
-    r"""
-    Applies an attention mechanism on the output features from the decoder.
-
-    .. math::
-            \begin{array}{ll}
-            x = context*output \\
-            attn = exp(x_i - max_i x_i) / sum_j exp(x_j - max_i x_i) \\
-            output = \tanh(w * (attn * context) + b * output)
-            \end{array}
-
-    Args:
-        dim(int): The number of expected features in the output
-
-    Inputs: output, context
-        - **output** (batch, output_len, dimensions): tensor containing the output features from the decoder.
-        - **context** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.
-
-    Outputs: output, attn
-        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.
-        - **attn** (batch, output_len, input_len): tensor containing attention weights.
-
-    Attributes:
-        linear_out (torch.nn.Linear): applies a linear transformation to the incoming data: :math:`y = Ax + b`.
-        mask (torch.Tensor, optional): applies a :math:`-inf` to the indices specified in the `Tensor`.
-
-    Examples::
-
-         >>> attention = seq2seq.models.Attention(256)
-         >>> context = Variable(torch.randn(5, 3, 256))
-         >>> output = Variable(torch.randn(5, 5, 256))
-         >>> output, attn = attention(output, context)
-
-    """
-    def __init__(self, dim):
-        super(Attention, self).__init__()
-        self.linear_out = nn.Linear(dim*2, dim)
-        self.mask = None
-
-
[docs]    def set_mask(self, mask):
-        """
-        Sets indices to be masked
-
-        Args:
-            mask (torch.Tensor): tensor containing indices to be masked
-        """
-        self.mask = mask

-
-    def forward(self, output, context):
-        batch_size = output.size(0)
-        hidden_size = output.size(2)
-        input_size = context.size(1)
-        # (batch, out_len, dim) * (batch, in_len, dim) -> (batch, out_len, in_len)
-        attn = torch.bmm(output, context.transpose(1, 2))
-        if self.mask is not None:
-            attn.data.masked_fill_(self.mask, -float('inf'))
-        attn = F.softmax(attn.view(-1, input_size)).view(batch_size, -1, input_size)
-
-        # (batch, out_len, in_len) * (batch, in_len, dim) -> (batch, out_len, dim)
-        mix = torch.bmm(attn, context)
-
-        # concat -> (batch, out_len, 2*dim)
-        combined = torch.cat((mix, output), dim=2)
-        # output -> (batch, out_len, dim)
-        output = F.tanh(self.linear_out(combined.view(-1, 2 * hidden_size))).view(batch_size, -1, hidden_size)
-
-        return output, attn

-
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Source code for seq2seq.models.baseRNN

-""" A base class for RNN. """
-import torch
-import torch.nn as nn
-
-
-
[docs]class BaseRNN(nn.Module):
-    r"""
-    Applies a multi-layer RNN to an input sequence.
-    Note:
-        Do not use this class directly, use one of the sub classes.
-    Args:
-        vocab (Vocabulary): object of Vocabulary class
-        max_len (int): maximum allowed length for the sequence to be processed
-        hidden_size (int): number of features in the hidden state `h`
-        input_dropout_p (float): dropout probability for the input sequence
-        dropout_p (float): dropout probability for the output sequence
-        n_layers (int): number of recurrent layers
-        rnn_cell (str): type of RNN cell (Eg. 'LSTM' , 'GRU')
-
-    Inputs: ``*args``, ``**kwargs``
-        - ``*args``: variable length argument list.
-        - ``**kwargs``: arbitrary keyword arguments.
-
-    Attributes:
-        SYM_MASK: masking symbol
-        SYM_EOS: end-of-sequence symbol
-    """
-    SYM_MASK = "MASK"
-    SYM_EOS = "EOS"
-
-    def __init__(self, vocab, max_len, hidden_size, input_dropout_p, dropout_p, n_layers, rnn_cell):
-        super(BaseRNN, self).__init__()
-        self.vocab = vocab
-        self.max_len = max_len
-        self.hidden_size = hidden_size
-        self.n_layers = n_layers
-        self.input_dropout_p = input_dropout_p
-        self.input_dropout = nn.Dropout(p=input_dropout_p)
-        if rnn_cell.lower() == 'lstm':
-            self.rnn_cell = nn.LSTM
-        elif rnn_cell.lower() == 'gru':
-            self.rnn_cell = nn.GRU
-        else:
-            raise ValueError("Unsupported RNN Cell: {0}".format(rnn_cell))
-
-        self.dropout_p = dropout_p
-        self.rnn = self.rnn_cell(hidden_size, hidden_size, n_layers, batch_first=True, dropout=dropout_p)
-
-
[docs]    def balance(self, batch, volatile):
-        """
-        Add reserved symbols and balance batch input.
-        It first appends EOS symbol to each sequence and then appends multiple
-        MASK symbols to make the sequences the same length.
-        Args:
-            batch: list of sequences, each of which is a list of integers
-            volatile: boolean flag specifying whether to preserve gradients, when you are sure you will not be even calling .backward().
-
-        Returns:
-            torch.autograd.Variable: variable with balanced input data.
-        """
-        max_len = self.max_len
-        outputs = []
-        for seq in batch:
-            seq = seq[:min(len(seq), max_len - 1)]
-            outputs.append(seq + [self.vocab.EOS_token_id] + [self.vocab.MASK_token_id] * (max_len - len(seq) - 1))
-
-        outputs_var = torch.autograd.Variable(torch.LongTensor(outputs), volatile=volatile)
-        if torch.cuda.is_available():
-            outputs_var = outputs_var.cuda()
-
-        return outputs_var

-
-    def forward(self, *args, **kwargs):
-        if 'volatile' in kwargs:
-            volatile = kwargs['volatile']
-            kwargs.pop('volatile', None)
-        else:
-            volatile = False
-        if args:
-            self.balanced_batch = self.balance(args[0], volatile)
-            args = [self.balanced_batch] + list(args[1:])
-        else:
-            if 'inputs' in kwargs and kwargs['inputs'] is not None:
-                self.balanced_batch = self.balance(kwargs['inputs'], volatile)
-                kwargs['inputs'] = self.balanced_batch
-
-        return self.forward_rnn(*args, **kwargs)
-
-    def forward_rnn(self, *args, **kwargs):
-        raise NotImplementedError()

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- © Copyright 2017, pytorch-seq2seq Contritors. - -

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Source code for seq2seq.models.seq2seq

-import os
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
[docs]class Seq2seq(nn.Module):
-    """ Standard sequence-to-sequence architecture with configurable encoder
-    and decoder.
-    Args:
-        encoder (EncoderRNN): object of EncoderRNN
-        decoder (DecoderRNN): object of DecoderRNN
-        decode_function (func, optional): function to generate symbols from output hidden states (default: F.log_softmax)
-
-    Inputs: input_variable, target_variable, teacher_forcing_ratio, volatile
-        - **input_variable** (list, option): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs. This information is forwarded to the encoder.
-        - **target_variable** (list, optional): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs. This information is forwarded to the decoder.
-        - **teacher_forcing_ratio** (int, optional): The probability that teacher forcing will be used. A random number
-          is drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0)
-        - **volatile** (bool, optional): boolean flag specifying whether to preserve gradients, when you are sure you
-          will not be even calling .backward().
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*KEY_LENGTH* : list of integers
-          representing lengths of output sequences, *KEY_SEQUENCE* : list of sequences, where each sequence is a list of
-          predicted token IDs, *KEY_INPUT* : target outputs if provided for decoding, *KEY_ATTN_SCORE* : list of
-          sequences, where each list is of attention weights }.
-
-    """
-
-    def __init__(self, encoder, decoder, decode_function=F.log_softmax):
-        super(Seq2seq, self).__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.decode_function = decode_function
-
-    def forward(self, input_variable, target_variable=None,
-                teacher_forcing_ratio=0, volatile=False):
-        if target_variable is None:
-            input_variable = sorted(input_variable, len, reverse=True)
-        else:
-            sorted_input = sorted(zip(input_variable, target_variable),
-                                  key=lambda x: len(x[0]), reverse=True)
-            input_variable = [p[0] for p in sorted_input]
-            target_variable = [p[1] for p in sorted_input]
-        encoder_outputs, encoder_hidden = self.encoder(input_variable, volatile=volatile)
-        result = self.decoder(inputs=target_variable,
-                              encoder_hidden=encoder_hidden,
-                              encoder_outputs=encoder_outputs,
-                              function=self.decode_function,
-                              teacher_forcing_ratio=teacher_forcing_ratio,
-                              volatile=volatile)
-        return result

-
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- © Copyright 2017, pytorch-seq2seq Contritors. - -

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Source code for seq2seq.optim.optim

-import torch
-
-
[docs]class Optimizer(object):
-    """ The Optimizer class encapsulates torch.optim package and provides functionalities
-    for learning rate scheduling and gradient norm clipping.
-
-    Args:
-        optim_class (torch.optim.Optimizer): optimizer class, e.g. torch.optim.SGD
-        max_grad_norm (float, optional): value used for gradient norm clipping,
-            set 0 to disable (default 0)
-        lr_decay (float, optional): value for learning rate decay:
-            lr = lr_decay * lr (default 1)
-        decay_after_epoch (float, optional): learning rate starts to decay after the
-            specified epoch number, set 0 to disable (default 0)
-        **kwargs: arguments for the given optimizer class,
-            refer http://pytorch.org/docs/optim.html#algorithms for more information
-    """
-
-    _ARG_MAX_GRAD_NORM = 'max_grad_norm'
-    _ARG_DECAY_AFTER = "decay_after_epoch"
-    _ARG_LR_DECAY = "lr_decay"
-
-    def __init__(self, optim_class, **kwargs):
-        self.optim_class = optim_class
-        self.optimizer = None
-        self.parameters = None
-
-        self.max_grad_norm = self._get_remove(kwargs, Optimizer._ARG_MAX_GRAD_NORM, 0)
-        self.lr_decay = self._get_remove(kwargs, Optimizer._ARG_LR_DECAY, 1)
-        self.decay_after_epoch = self._get_remove(kwargs, Optimizer._ARG_DECAY_AFTER, 0)
-        self.optim_args = kwargs
-
-    def _get_remove(self, args, key, default):
-        value = default
-        if key in args:
-            value = args[key]
-            del args[key]
-        return value
-
-
[docs]    def set_parameters(self, parameters):
-        """ Set the parameters to optimize.
-
-        Args:
-            parameters (iterable): An iterable of torch.nn.Parameter.
-        """
-        self.parameters = parameters
-        self.optimizer = self.optim_class(parameters, **self.optim_args)

-
-
[docs]    def step(self):
-        """ Performs a single optimization step, including gradient norm clipping if necessary. """
-        if self.max_grad_norm > 0:
-            torch.nn.utils.clip_grad_norm(self.parameters, self.max_grad_norm)
-        self.optimizer.step()

-
-
[docs]    def update(self, loss, epoch):
-        """ Update the learning rate if the conditions are met. Override this method
-        to implement your own learning rate schedule.
-
-        Args:
-            loss (float): The current loss.  It could be training loss or developing loss
-                depending on the caller.  By default the supervised trainer uses developing
-                loss.
-            epoch (int): The current epoch number.
-        """
-        after_decay_epoch = self.decay_after_epoch != 0 and epoch >= self.decay_after_epoch
-        if after_decay_epoch:
-            self.optimizer.param_groups[0]['lr'] *= self.lr_decay

-
-
[docs]    def load_state_dict(self, optimizer_dict):
-        """ Wrapper for loading optimizer state_dict.
-            For further reference please refer to http://pytorch.org/docs/master/optim.html#torch.optim.Optimizer.load_state_dict
-        """
-        self.optimizer.load_state_dict(optimizer_dict)

-
-
[docs]    def state_dict(self):
-        """Wrapper for accessing optimizer state_dict.
-            For further reference please refer to http://pytorch.org/docs/master/optim.html#torch.optim.Optimizer.state_dict
-        """
-        return self.optimizer.state_dict()

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- © Copyright 2017, pytorch-seq2seq Contritors. - -

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- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/trainer/supervised_trainer.html b/docs/public/_modules/seq2seq/trainer/supervised_trainer.html deleted file mode 100644 index 82fac57..0000000 --- a/docs/public/_modules/seq2seq/trainer/supervised_trainer.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - seq2seq.trainer.supervised_trainer — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for seq2seq.trainer.supervised_trainer

-import os
-import random
-import logging
-
-import torch
-from torch import optim
-
-from seq2seq.evaluator import Evaluator
-from seq2seq.loss import NLLLoss
-from seq2seq.optim import Optimizer
-from seq2seq.util.custom_time import *
-from seq2seq.util.checkpoint import Checkpoint
-
-
[docs]class SupervisedTrainer(object):
-    """ The SupervisedTrainer class helps in setting up a training framework in a
-    supervised setting.
-
-    Args:
-        expt_dir (optional, str): experiment Directory to store details of the experiment,
-            by default it makes a folder in the current directory to store the details (default: `experiment`).
-        loss (seq2seq.loss.loss.Loss, optional): loss for training, (default: seq2seq.loss.NLLLoss)
-        batch_size (int, optional): batch size for experiment, (default: 64)
-        checkpoint_every (int, optional): number of epochs to checkpoint after, (default: 100)
-        optimizer (seq2seq.optim.Optimizer, optional): optimizer for training
-            (default: Optimizer(pytorch.optim.Adam, max_grad_norm=5))
-    """
-    def __init__(self, expt_dir='experiment', loss=NLLLoss(), batch_size=64,
-                 random_seed=None,
-                 checkpoint_every=100, print_every=100,
-                 optimizer=Optimizer(optim.Adam, max_grad_norm=5)):
-        self._trainer = "Simple Trainer"
-        self.random_seed = random_seed
-        if random_seed is not None:
-            random.seed(random_seed)
-            torch.manual_seed(random_seed)
-        self.loss = loss
-        self.evaluator = Evaluator(loss=self.loss, batch_size=batch_size)
-        self.optimizer = optimizer
-        self.checkpoint_every = checkpoint_every
-        self.print_every = print_every
-
-        if not os.path.isabs(expt_dir):
-            expt_dir = os.path.join(os.getcwd(), expt_dir)
-        self.expt_dir = expt_dir
-        if not os.path.exists(self.expt_dir):
-            os.makedirs(self.expt_dir)
-        self.batch_size = batch_size
-        self.input_vocab_file = os.path.join(self.expt_dir, 'input_vocab')
-        self.output_vocab_file = os.path.join(self.expt_dir, 'output_vocab')
-
-        self.logger = logging.getLogger(__name__)
-
-    def _train_batch(self, input_variable, target_variable, model, teacher_forcing_ratio):
-        loss = self.loss
-        # Forward propagation
-        decoder_outputs, decoder_hidden, other = model(input_variable, target_variable,
-                                                       teacher_forcing_ratio=teacher_forcing_ratio)
-        # Get loss
-        loss.reset()
-        targets = other['inputs']
-        lengths = other['length']
-        for batch in range(len(targets)):
-            # Batch wise loss
-            batch_target = targets[batch]
-            batch_len = lengths[batch]
-            # Crop output and target to batch length
-            batch_output = torch.stack([output[batch] for output in decoder_outputs[:batch_len]])
-            batch_target = batch_target[:batch_len]
-            # Evaluate loss
-            loss.eval_batch(batch_output, batch_target)
-        # Backward propagation
-        model.zero_grad()
-        loss.backward()
-        self.optimizer.step()
-
-        return loss.get_loss()
-
-    def _train_epoches(self, data, model, n_epochs, batch_size, resume, dev_data=None, teacher_forcing_ratio=0):
-        start = time.time()
-        print_loss_total = 0  # Reset every print_every
-        steps_per_epoch = data.num_batches(batch_size)
-        total_steps = steps_per_epoch * n_epochs
-
-        # If training is set to resume
-        if resume:
-            latest_checkpoint_path = Checkpoint.get_latest_checkpoint(self.expt_dir)
-            resume_checkpoint = Checkpoint.load(latest_checkpoint_path)
-            model = resume_checkpoint.model
-            self.optimizer.set_parameters(model.parameters())
-            self.optimizer.load_state_dict(resume_checkpoint.optimizer_state_dict)
-            start_epoch = resume_checkpoint.epoch
-            step = resume_checkpoint.step
-        else:
-            start_epoch = 1
-            step = 0
-            self.optimizer.set_parameters(model.parameters())
-
-        for epoch in range(start_epoch, n_epochs + 1):
-            data.shuffle(self.random_seed)
-
-            batch_generator = data.make_batches(batch_size)
-
-            # consuming seen batches from previous training
-            for _ in range((epoch - 1) * steps_per_epoch, step):
-                next(batch_generator)
-
-            model.train(True)
-            for batch in batch_generator:
-                step += 1
-
-                input_variables = batch[0]
-                target_variables = batch[1]
-
-                loss = self._train_batch(input_variables, target_variables, model, teacher_forcing_ratio)
-
-                # Record average loss
-                print_loss_total += loss
-
-                if step % self.print_every == 0:
-                    print_loss_avg = print_loss_total / (self.print_every)
-                    print_loss_total = 0
-                    log_msg = 'Time elapsed: %s, Progress: %d%%, Train %s: %.4f' % (
-                        pretty_interval(start),
-                        float(step) / total_steps * 100,
-                        self.loss.name,
-                        print_loss_avg)
-                    self.logger.info(log_msg)
-
-                # Checkpoint
-                if step % self.checkpoint_every == 0 or step == total_steps:
-                    Checkpoint(model=model,
-                               optimizer_state_dict=self.optimizer.state_dict(),
-                               epoch=epoch, step=step,
-                               input_vocab=data.input_vocab,
-                               output_vocab=data.output_vocab).save(self.expt_dir)
-
-            log_msg = "Finished epoch {0}".format(epoch)
-            if dev_data is not None:
-                dev_loss = self.evaluator.evaluate(model, dev_data)
-                self.optimizer.update(dev_loss, epoch)
-                log_msg += ", Dev %s: %.4f" % (self.loss.name, dev_loss)
-                model.train(mode=True)
-            self.logger.info(log_msg)
-
-
[docs]    def train(self, model, data, num_epochs=5, resume=False, dev_data=None, teacher_forcing_ratio=0):
-        """ Run training for a given model.
-
-         Args:
-             model (seq2seq.models): model to run training on, if `resume=True`, it would be
-                overwritten by the model loaded from the latest checkpoint.
-             data (seq2seq.dataset.dataset.Dataset): dataset object to train on
-             num_epochs (int, optional): number of epochs to run (default 5)
-             resume(bool, optional): resume training with the latest checkpoint, (default False)
-             dev_data (seq2seq.dataset.dataset.Dataset, optional): dev Dataset (default None)
-             teacher_forcing_ratio (float, optional): teaching forcing ratio (default 0)
-
-        """
-        # Make Checkpoint Directories
-        data.input_vocab.save(self.input_vocab_file)
-        data.output_vocab.save(self.output_vocab_file)
-
-        self._train_epoches(data, model, num_epochs, self.batch_size,
-                            resume=resume, dev_data=dev_data, teacher_forcing_ratio=teacher_forcing_ratio)

-
- -
-
- -
-
-
- - -
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-

- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/util/checkpoint.html b/docs/public/_modules/seq2seq/util/checkpoint.html deleted file mode 100644 index cf3bf2b..0000000 --- a/docs/public/_modules/seq2seq/util/checkpoint.html +++ /dev/null @@ -1,353 +0,0 @@ - - - - - - - - - - - seq2seq.util.checkpoint — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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  • seq2seq.util.checkpoint
    • - - -
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    • - -
- - -
-
-
-
- -

Source code for seq2seq.util.checkpoint

-import os
-import time
-import shutil
-
-import torch
-
-from seq2seq.dataset.vocabulary import Vocabulary
-
-
-
[docs]class Checkpoint(object):
-    """
-    The Checkpoint class manages the saving and loading of a model during training. It allows training to be suspended
-    and resumed at a later time (e.g. when running on a cluster using sequential jobs).
-
-    To make a checkpoint, initialize a Checkpoint object with the following args; then call that object's save() method
-    to write parameters to disk.
-
-    Args:
-        model (seq2seq): seq2seq model being trained
-        optimizer_state_dict (dict): stores the state of the optimizer
-        epoch (int): current epoch (an epoch is a loop through the full training data)
-        step (int): number of examples seen within the current epoch
-        input_vocab (Vocabulary): vocabulary for the input language
-        output_vocab (Vocabulary): vocabulary for the output language
-
-    Attributes:
-        CHECKPOINT_DIR_NAME (str): name of the checkpoint directory
-        TRAINER_STATE_NAME (str): name of the file storing trainer states
-        MODEL_NAME (str): name of the file storing model
-        INPUT_VOCAB_FILE (str): name of the input vocab file
-        OUTPUT_VOCAB_FILE (str): name of the output vocab file
-    """
-
-    CHECKPOINT_DIR_NAME = 'checkpoints'
-    TRAINER_STATE_NAME = 'trainer_states.pt'
-    MODEL_NAME = 'model.pt'
-    INPUT_VOCAB_FILE = 'input_vocab.pt'
-    OUTPUT_VOCAB_FILE = 'output_vocab.pt'
-
-    def __init__(self, model, optimizer_state_dict, epoch, step, input_vocab, output_vocab, path=None):
-        self.model = model
-        self.optimizer_state_dict = optimizer_state_dict
-        self.input_vocab = input_vocab
-        self.output_vocab = output_vocab
-        self.epoch = epoch
-        self.step = step
-        self._path = path
-
-    @property
-    def path(self):
-        if self._path is None:
-            raise LookupError("The checkpoint has not been saved.")
-        return self._path
-
-
[docs]    def save(self, experiment_dir):
-        """
-        Saves the current model and related training parameters into a subdirectory of the checkpoint directory.
-        The name of the subdirectory is the current local time in Y_M_D_H_M_S format.
-        Args:
-            experiment_dir (str): path to the experiment root directory
-        Returns:
-             str: path to the saved checkpoint subdirectory
-        """
-        date_time = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())
-
-        self._path = os.path.join(experiment_dir, self.CHECKPOINT_DIR_NAME, date_time)
-        path = self._path
-
-        if os.path.exists(path):
-            shutil.rmtree(path)
-        os.makedirs(path)
-        torch.save({'epoch': self.epoch,
-                    'step': self.step,
-                    'optimizer': self.optimizer_state_dict},
-                   os.path.join(path, self.TRAINER_STATE_NAME))
-        torch.save(self.model, os.path.join(path, self.MODEL_NAME))
-
-        if not os.path.isfile(os.path.join(path, self.INPUT_VOCAB_FILE)):
-            self.input_vocab.save(os.path.join(path, self.INPUT_VOCAB_FILE))
-        if not os.path.isfile(os.path.join(path, self.OUTPUT_VOCAB_FILE)):
-            self.output_vocab.save(os.path.join(path, self.OUTPUT_VOCAB_FILE))
-
-        return path

-
-
[docs]    @classmethod
-    def load(cls, path):
-        """
-        Loads a Checkpoint object that was previously saved to disk.
-        Args:
-            path (str): path to the checkpoint subdirectory
-        Returns:
-            checkpoint (Checkpoint): checkpoint object with fields copied from those stored on disk
-        """
-        print "Loading checkpoints from {}".format(path)
-        resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))
-        model = torch.load(os.path.join(path, cls.MODEL_NAME))
-        input_vocab = Vocabulary.load(os.path.join(path, cls.INPUT_VOCAB_FILE))
-        output_vocab = Vocabulary.load(os.path.join(path, cls.OUTPUT_VOCAB_FILE))
-        return Checkpoint(model=model, input_vocab=input_vocab,
-                          output_vocab=output_vocab,
-                          optimizer_state_dict=resume_checkpoint['optimizer'],
-                          epoch=resume_checkpoint['epoch'],
-                          step=resume_checkpoint['step'],
-                          path=path)

-
-
[docs]    @classmethod
-    def get_latest_checkpoint(cls, experiment_path):
-        """
-        Given the path to an experiment directory, returns the path to the last saved checkpoint's subdirectory.
-
-        Precondition: at least one checkpoint has been made (i.e., latest checkpoint subdirectory exists).
-        Args:
-            experiment_path (str): path to the experiment directory
-        Returns:
-             str: path to the last saved checkpoint's subdirectory
-        """
-        checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)
-        all_times = sorted(os.listdir(checkpoints_path), reverse=True)
-        return os.path.join(checkpoints_path, all_times[0])

-
- -
-
- -
-
-
- - -
- -
-

- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- -
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- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/util/custom_time.html b/docs/public/_modules/seq2seq/util/custom_time.html deleted file mode 100644 index fe0c088..0000000 --- a/docs/public/_modules/seq2seq/util/custom_time.html +++ /dev/null @@ -1,280 +0,0 @@ - - - - - - - - - - - seq2seq.util.custom_time — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • - -
  • seq2seq.util.custom_time
    • - - -
  • - - - -
    • - -
- - -
-
-
-
- -

Source code for seq2seq.util.custom_time

-import time
-
-
-
[docs]def pretty_interval(start_time):
-    """
-    Given a previous point in time (e.g. the start of process, measured by calling time.time()), calculates
-    the time elapsed since that point, divides the elapsed time days/hours/minutes/seconds, and returns that summary
-
-    Args:
-        start_time (float): the start of the interval of time, obtained by calling time.time()
-    Returns:
-        str: elapsed time in '##d ##h ##m ##s' format
-    """
-    return pretty_time(time.time() - start_time)

-
-
-
[docs]def pretty_time(timespan_in_seconds):
-    """
-    Given a length of time (measured in seconds), divides that timespan into
-    days/hours/minutes/seconds and returns that summary.
-
-    Args:
-     timespan_in_seconds (float): the number of seconds in a span of time
-
-    Returns:
-        str: timespan in '##d ##h ##m ##s' format
-
-
-    Examples::
-
-        >>> print seq2seq.util.custom_time.pretty_time(426753)
-        >>> 4d 22h 32m 33
-    """
-    seconds = abs(int(timespan_in_seconds))
-    msg = []
-    days, seconds = divmod(seconds, 86400)
-    if days > 0:
-        msg.append("%dd" % days)
-    hours, seconds = divmod(seconds, 3600)
-    if hours > 0:
-        msg.append("%dh" % hours)
-    minutes, seconds = divmod(seconds, 60)
-    if minutes > 0:
-        msg.append("%dm" % minutes)
-    msg.append("%ds" % seconds)
-    return " ".join(msg)

-
- -
-
- -
-
-
- - -
- -
-

- © Copyright 2017, pytorch-seq2seq Contritors. - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/dataset.html b/docs/public/dataset.html index d53741c..e05c869 100644 --- a/docs/public/dataset.html +++ b/docs/public/dataset.html @@ -8,7 +8,7 @@ - Dataset — pytorch-seq2seq 0.1.5 documentation + Dataset — pytorch-seq2seq 0.1.6 documentation @@ -35,7 +35,7 @@ - + @@ -66,7 +66,7 @@
- 0.1.5 + 0.1.6
@@ -260,7 +260,7 @@

Dataset var DOCUMENTATION_OPTIONS = { URL_ROOT:'./', - VERSION:'0.1.5', + VERSION:'0.1.6', COLLAPSE_INDEX:false, FILE_SUFFIX:'.html', HAS_SOURCE: true, diff --git a/docs/public/evaluator.html b/docs/public/evaluator.html index 6a97d4a..dd947a3 100644 --- a/docs/public/evaluator.html +++ b/docs/public/evaluator.html @@ -8,7 +8,7 @@ - Evaluator — pytorch-seq2seq 0.1.5 documentation + Evaluator — pytorch-seq2seq 0.1.6 documentation @@ -35,7 +35,7 @@ - + @@ -66,7 +66,7 @@
- 0.1.5 + 0.1.6
@@ -184,7 +184,7 @@

evaluator Parameters: @@ -207,7 +207,7 @@

evaluatorReturns:

loss of the given model on the given dataset

-Return type:

loss (float)

+Return type:

loss (float)

@@ -223,6 +223,11 @@

evaluator class seq2seq.evaluator.predictor.Predictor(model, src_vocab, tgt_vocab)
+
+get_decoder_features(src_seq)
+
+ +
predict(src_seq)

Make prediction given src_seq as input.

@@ -230,12 +235,42 @@

evaluator -Parameters:src_seq (list) – list of tokens in source language +Parameters:src_seq (list) – list of tokens in source language Returns:list of tokens in target language as predicted by the pre-trained model -Return type:tgt_seq (list) +Return type:tgt_seq (list) + + + +

+ +
+
+predict_n(src_seq, n=1)
+

Make ‘n’ predictions given src_seq as input.

+ +++ + + + + +
Parameters:
    +
  • src_seq (list) – list of tokens in source language
    • +
  • n (int) – number of predicted seqs to return. If None, +it will return just one seq.
    • +
+
Returns:

+
list of tokens in target language as predicted
+

by the pre-trained model

+
+
+

+
Return type:

tgt_seq (list)

+
@@ -290,7 +325,7 @@

evaluator var DOCUMENTATION_OPTIONS = { URL_ROOT:'./', - VERSION:'0.1.5', + VERSION:'0.1.6', COLLAPSE_INDEX:false, FILE_SUFFIX:'.html', HAS_SOURCE: true, diff --git a/docs/public/genindex.html b/docs/public/genindex.html index fdabc4f..6b59af9 100644 --- a/docs/public/genindex.html +++ b/docs/public/genindex.html @@ -9,7 +9,7 @@ - Index — pytorch-seq2seq 0.1.5 documentation + Index — pytorch-seq2seq 0.1.6 documentation @@ -36,7 +36,7 @@ - + @@ -65,7 +65,7 @@
- 0.1.5 + 0.1.6
@@ -257,10 +257,12 @@

F

G

@@ -324,6 +326,8 @@

P

@@ -207,7 +207,7 @@

evaluator

- @@ -223,6 +223,11 @@

evaluator class seq2seq.evaluator.predictor.Predictor(model, src_vocab, tgt_vocab)
+
+get_decoder_features(src_seq)
+
+ +
predict(src_seq)

Make prediction given src_seq as input.

@@ -230,12 +235,42 @@

evaluator

- + - + + + +
  • predict() (seq2seq.evaluator.predictor.Predictor method) +
    • +
  • predict_n() (seq2seq.evaluator.predictor.Predictor method)
  • Predictor (class in seq2seq.evaluator.predictor)
    • @@ -450,7 +454,7 @@

    U

    @@ -64,7 +64,7 @@
    - 0.1.5 + 0.1.6
    @@ -208,7 +208,7 @@ @@ -64,7 +64,7 @@
    - 0.1.5 + 0.1.6
    @@ -164,8 +164,6 @@

    All modules for which code is available

    @@ -215,7 +212,7 @@

    All modules for which code is available

    - - - - - - -
    - - - - -
    - - - - - - -
    -
    - - - - - - - - - - - - - - - - -
    - -
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    • Docs »
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      • - -
    • seq2seq.dataset.dataset
      • - - -
    • - - - -
      • - -
    - - -
    -
    -
    -
    - -

    Source code for seq2seq.dataset.dataset

    -import random
-from seq2seq.dataset import Vocabulary, utils
-
-
    [docs]class Dataset(object):
-    """
-    A class that encapsulates a dataset.
-
-    Warning:
-        Do not use this constructor directly, use one of the class methods to initialize.
-
-    Note:
-        Source or target sequences that are longer than the respective
-        max length will be filtered.
-
-    Args:
-        src_max_len (int): maximum source sequence length
-        tgt_max_len (int): maximum target sequence length
-    """
-
-    def __init__(self, src_max_len, tgt_max_len):
-        # Prepare data
-        self.src_max_len = src_max_len
-        self.tgt_max_len = tgt_max_len
-
-        # Declare vocabulary objects
-        self.input_vocab = None
-        self.output_vocab = None
-
-        self.data = None
-
-
-
    [docs]    @classmethod
-    def from_file(cls, path, src_max_len, tgt_max_len, src_vocab=None, tgt_vocab=None, src_max_vocab=50000,
-                 tgt_max_vocab=50000):
-        """
-        Initialize a dataset from the file at given path. The file
-        must contains a list of TAB-separated pairs of sequences.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            path (str): path to the dataset file
-            src_max_len (int): maximum source sequence length
-            tgt_max_len (int): maximum target sequence length
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        obj = cls(src_max_len, tgt_max_len)
-        pairs = utils.prepare_data(path, src_max_len, tgt_max_len)
-        return cls._encode(obj, pairs, src_vocab, tgt_vocab, src_max_vocab, tgt_max_vocab)
    
-
-
    [docs]    @classmethod
-    def from_list(cls, src_data, tgt_data, src_max_len, tgt_max_len, src_vocab=None, tgt_vocab=None, src_max_vocab=50000,
-                  tgt_max_vocab=50000):
-        """
-        Initialize a dataset from the source and target lists of sequences.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            src_data (list): list of source sequences
-            tgt_data (list): list of target sequences
-            src_max_len (int): maximum source sequence length
-            tgt_max_len (int): maximum target sequence length
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        obj = cls(src_max_len, tgt_max_len)
-        pairs = utils.prepare_data_from_list(src_data, tgt_data, src_max_len, tgt_max_len)
-        return cls._encode(obj, pairs, src_vocab, tgt_vocab, src_max_vocab, tgt_max_vocab)
    
-
-    def _encode(self, pairs, src_vocab=None, tgt_vocab=None, src_max_vocab=50000, tgt_max_vocab=50000):
-        """
-        Encodes the source and target lists of sequences using source and target vocabularies.
-
-        Note:
-            Source or target sequences that are longer than the respective
-            max length will be filtered.
-            As specified by maximum vocabulary size, source and target
-            vocabularies will be sorted in descending token frequency and cutoff.
-            Tokens that are in the dataset but not retained in the vocabulary
-            will be dropped in the sequences.
-
-        Args:
-            pairs (list): list of tuples (source sequences, target sequence)
-            src_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the source language,
-            default `None`. If a pre-populated Vocabulary object, `src_max_vocab` wouldn't be used.
-            tgt_vocab (Vocabulary): pre-populated Vocabulary object or a path of a file containing words for the target language,
-            default `None`. If a pre-populated Vocabulary object, `tgt_max_vocab` wouldn't be used.
-            src_max_vocab (int): maximum source vocabulary size
-            tgt_max_vocab (int): maximum target vocabulary size
-        """
-        # Read in vocabularies
-        self.input_vocab = self._init_vocab(zip(*pairs)[0], src_max_vocab, src_vocab)
-        self.output_vocab = self._init_vocab(zip(*pairs)[1], tgt_max_vocab, tgt_vocab)
-
-        # Translate input sequences to token ids
-        self.data = []
-        for pair in pairs:
-            src = self.input_vocab.indices_from_sequence(pair[0])
-            dst = self.output_vocab.indices_from_sequence(pair[1])
-            self.data.append((src, dst))
-        return self
-
-    def _init_vocab(self, sequences, max_num_vocab, vocab):
-        resp_vocab = Vocabulary(max_num_vocab)
-        if vocab is None:
-            for sequence in sequences:
-                resp_vocab.add_sequence(sequence)
-            resp_vocab.trim()
-        elif isinstance(vocab, Vocabulary):
-            resp_vocab = vocab
-        elif isinstance(vocab, str):
-            for tok in utils.read_vocabulary(vocab, max_num_vocab):
-                resp_vocab.add_token(tok)
-        else:
-            raise AttributeError('{} is not a valid instance on a vocabulary. None, instance of Vocabulary class \
-                                 and str are only supported formats for the vocabulary'.format(vocab))
-        return resp_vocab
-
-    def __len__(self):
-        return len(self.data)
-
-
    [docs]    def num_batches(self, batch_size):
-        """
-        Get the number of batches given batch size.
-
-        Args:
-            batch_size(int): number of examples in a batch
-
-        Returns:
-            int: number of batches
-        """
-        return len(range(0, len(self.data), batch_size))
    
-
-
    [docs]    def make_batches(self, batch_size):
-        """
-        Create a generator that generates batches in batch_size over data.
-
-        Args:
-            batch_size (int): number of pairs in a mini-batch
-
-        Yields:
-            (list(str), list(str)): next pair of source and target variable in a batch
-
-        """
-        if len(self.data) < batch_size:
-            raise OverflowError("batch size = {} cannot be larger than data size = {}".
-                                format(batch_size, len(self.data)))
-        for i in range(0, len(self.data), batch_size):
-            cur_batch = self.data[i:i + batch_size]
-            source_variables = [pair[0] for pair in cur_batch]
-            target_variables = [pair[1] for pair in cur_batch]
-
-            yield (source_variables, target_variables)
    
-
-
    [docs]    def shuffle(self, seed=None):
-        """
-        Shuffle the data.
-
-        Args:
-            seed(int): provide a value for the random seed; default seed=None is truly random
-        """
-        if seed is not None:
-            random.seed(seed)
-        random.shuffle(self.data)
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
    - -
    -
    - -
    - -
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    Source code for seq2seq.dataset.utils

    -import logging
-from tqdm import tqdm
-
-logger = logging.getLogger(__name__)
-
-
    [docs]def filter_pair(pair, src_max_len, tgt_max_len):
-    """
-    Returns true if a sentence pair meets the length requirements, false otherwise.
-
-    Args:
-        pair ((str, str)): (source, target) sentence pair
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-    Returns:
-         bool: true if the pair is shorter than the length cutoffs, false otherwise
-    """
-    return len(pair[0]) <= src_max_len and len(pair[1]) <= tgt_max_len
    
-
-
-
    [docs]def space_tokenize(text):
-    """
-    Tokenizes a piece of text by splitting it up based on single spaces (" ").
-
-    Args:
-     text (str): input text as a single string
-
-    Returns:
-         list(str): list of tokens obtained by splitting the text on single spaces
-    """
-    return text.split(" ")
    
-
-
-
    [docs]def prepare_data(path, src_max_len, tgt_max_len, tokenize_func=space_tokenize):
-    """
-    Reads a tab-separated data file where each line contains a source sentence and a target sentence. Pairs containing
-    a sentence that exceeds the maximum length allowed for its language are not added.
-
-    Args:
-        path (str): path to the data file
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-        tokenize_func (func): function for splitting words in a sentence (default is single-space-delimited)
-
-    Returns:
-        list((str, str)): list of (source, target) string pairs
-    """
-
-    logger.info("Reading Lines from {}".format(path))
-    # Read the file and split into lines
-    pairs = []
-    with open(path) as fin:
-        for line in tqdm(fin):
-            try:
-                src, dst = line.strip().split("\t")
-                pair = map(tokenize_func, [src, dst])
-                if filter_pair(pair, src_max_len, tgt_max_len):
-                    pairs.append(pair)
-            except:
-                logger.error("Error when reading line: {0}".format(line))
-                raise
-
-    logger.info("Number of pairs: %s" % len(pairs))
-    return pairs
    
-
-
-
    [docs]def prepare_data_from_list(src_list, tgt_list, src_max_len, tgt_max_len, tokenize_func=space_tokenize):
-    """
-    Reads a tab-separated data file where each line contains a source sentence and a target sentence. Pairs containing
-    a sentence that exceeds the maximum length allowed for its language are not added.
-
-    Args:
-        src_list (list): list of source sequences
-        tgt_list (list): list of target sequences
-        src_max_len (int): maximum length cutoff for sentences in the source language
-        tgt_max_len (int): maximum length cutoff for sentences in the target language
-        tokenize_func (func): function for splitting words in a sentence (default is single-space-delimited)
-
-    Returns:
-        list((str, str)): list of (source, target) string pairs
-    """
-    if not len(src_list) == len(tgt_list):
-        raise ValueError('source sequence list and target sequence list has different number of entries.')
-
-    logger.info("Preparing pairs...")
-
-    # Read the file and split into lines
-    pairs = []
-
-    for index, _ in tqdm(enumerate(src_list)):
-        pair = map(tokenize_func, [src_list[index], tgt_list[index]])
-        if filter_pair(pair, src_max_len, tgt_max_len):
-            pairs.append(pair)
-
-    logger.info("Number of pairs: %s" % len(pairs))
-    return pairs
    
-
-
-
    [docs]def read_vocabulary(path, max_num_vocab=50000):
-    """
-    Helper function to read a vocabulary file.
-
-    Args:
-        path (str): filepath to raw vocabulary file
-        max_num_vocab (int): maximum number of words to read from vocabulary file
-
-    Returns:
-        set: read words from vocabulary file
-    """
-    logger.info("Reading vocabulary from {}".format(path))
-    # Read the file and create list of tokens in vocabulary
-    vocab = set()
-    with open(path) as fin:
-        for line in fin:
-            if len(vocab) >= max_num_vocab:
-                break
-            try:
-                vocab.add(line.strip())
-            except:
-                logger.error("Error when reading line: {0}".format(line))
-                raise
-
-    logger.info("Size of Vocabulary: %s" % len(vocab))
-    return vocab
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/dataset/vocabulary.html b/docs/public/_modules/seq2seq/dataset/vocabulary.html deleted file mode 100644 index 9b503ab..0000000 --- a/docs/public/_modules/seq2seq/dataset/vocabulary.html +++ /dev/null @@ -1,431 +0,0 @@ - - - - - - - - - - - seq2seq.dataset.vocabulary — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for seq2seq.dataset.vocabulary

    -import cPickle as pickle
-
-
-
    [docs]class Vocabulary(object):
-    """
-    A Vocabulary stores a set of words belonging to a particular language. Words in the source vocabulary are mapped
-    to unique integer IDs during encoding. Words in the target vocabulary are recovered from the model's output
-    during decoding.
-
-    In addition to the words in the actual language, a Vocabulary includes three reserved tokens (and IDs) for the
-    start-of-sentence and end-of-sentence markers, and for a special 'mask' marker used to handle
-    rare/unknown words.
-
-    The Vocabulary is sorted in descending order based on frequency. If the number of words seen is greater than
-    the maximum size of the Vocabulary, the remaining least-frequent words are ignored.
-
-    Args:
-         size(int): maximum number of words allowed in this vocabulary
-    """
-    def __init__(self, size):
-        self.MASK_token_name = "MASK"
-        self.SOS_token_name = "SOS"
-        self.EOS_token_name = "EOS"
-        self.MASK_token_id = 0
-        self.SOS_token_id = 1
-        self.EOS_token_id = 2
-
-        self._reserved = set([self.MASK_token_name, self.SOS_token_name, self.EOS_token_name])
-        self._reserved_token_id = [
-                (self.MASK_token_name, self.MASK_token_id),
-                (self.SOS_token_name, self.SOS_token_id),
-                (self.EOS_token_name, self.EOS_token_id)
-        ]
-
-        self._token2index = dict([(tok, idx) for tok, idx in self._reserved_token_id])
-        self._index2token = dict([(idx, tok) for tok, idx in self._reserved_token_id])
-
-        self._token2count = {}
-
-        self._num_tokens = 0
-        self._num_reserved = 3
-
-        self.sorted = False
-        self.size = size
-
-
    [docs]    def trim(self):
-        """
-        Sorts the vocabulary in descending order based on frequency
-        """
-        sorted_vocab_count = sorted(self._token2count.items(), key=lambda x: x[1], reverse=True)[:self.size]
-        self._token2index = dict([(w, self._num_reserved + idx) for idx, (w, _) in enumerate(sorted_vocab_count)])
-        self._index2token = dict([(idx, w) for w, idx in self._token2index.items()])
-        for tok, idx in self._reserved_token_id:
-            self._token2index[tok] = idx
-            self._index2token[idx] = tok
-        if self._num_tokens > self.size:
-            self._num_tokens = self.size
-        self.sorted = True
    
-
-
    [docs]    def check_sorted(self):
-        """
-        Sorts the vocabulary (if it is not already sorted).
-        """
-        if not self.sorted:
-            self.trim()
    
-
-
    [docs]    def get_index(self, token):
-        """
-        Returns:
-            int: ID of the given token.
-        """
-        self.check_sorted()
-        return self._token2index[token]
    
-
-
    [docs]    def get_token(self, index):
-        """
-        Returns:
-            str: token with ID equal to the given index.
-        """
-        self.check_sorted()
-        return self._index2token[index]
    
-
-
    [docs]    def get_vocab_size(self):
-        """
-        Returns:
-            int: maximum number of words in the vocabulary.
-        """
-        self.check_sorted()
-        return self._num_tokens + self._num_reserved
    
-
-
    [docs]    def add_token(self, token):
-        """
-        Adds an occurrence of a token to the vocabulary, incrementing its observed frequency if the word already exists.
-
-        Args:
-             token (int): word to add
-        """
-        if token in self._reserved:
-            return
-        if token not in self._token2count:
-            self._token2count[token] = 1
-            self._num_tokens += 1
-        else:
-            self._token2count[token] += 1
-        self.sorted = False
    
-
-
    [docs]    def add_sequence(self, sequence):
-        """
-        Adds a sequence of words to the vocabulary.
-
-        Args:
-             sequence(list(str)): list of words, e.g. representing a sentence.
-        """
-        for tok in sequence:
-            self.add_token(tok)
    
-
-
    [docs]    def indices_from_sequence(self, sequence):
-        """
-        Maps a list of words to their token IDs, or else the 'mask' token if the word is rare/unknown.
-
-        Args:
-            sequence (list(str)): list of words to map
-        Returns:
-            list(int): list of mapped IDs
-        """
-        self.check_sorted()
-        return [self._token2index[tok]
-                if tok in self._token2index
-                else self.MASK_token_id
-                for tok in sequence]
    
-
-
    [docs]    def sequence_from_indices(self, indices):
-        """
-        Recover a sentence from a list of token IDs.
-
-        Args:
-            indices (list(int)): list of token IDs.
-
-        Returns:
-            list(str): recovered sentence, represented as a list of words
-        """
-        seq = [self._index2token[idx] for idx in indices]
-        return seq
    
-
-
    [docs]    def save(self, file_name):
-        """
-        Writes this Vocabulary to disk in a pickle file.
-
-        Args:
-             file_name (str): path to the target pickle file
-        """
-        self.check_sorted()
-        vocab_size = self.get_vocab_size()
-        with open(file_name, "w") as f:
-            for index, token in self._index2token.items():
-                if token in self._reserved:
-                    continue
-                if index < vocab_size -1:
-                    f.write(token + "\n")
-                else:
-                    f.write(token)
    
-
-
    [docs]    @classmethod
-    def load(cls, file_name):
-        """
-        Loads a Vocabulary from a pickle file on disk.
-
-        Args:
-            file_name (str): path to the pickle file
-
-        Returns:
-            Vocabulary: loaded Vocabulary
-        """
-        with open(file_name, "r") as f:
-            tokens = f.readlines()
-        vocab = Vocabulary(len(tokens))
-        for token in tokens:
-            vocab.add_token(token.strip())
-        return vocab
    
-
-    def __eq__(self, other):
-        if not isinstance(other, self.__class__):
-            return False
-        self.check_sorted()
-        other.check_sorted()
-
-        if self._token2count == other._token2count and self._token2index == other._token2index \
-           and self._index2token == other._index2token:
-            return True
-        else:
-            return False
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash(self._token2index)
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
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    • seq2seq.evaluator.evaluator
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    Source code for seq2seq.evaluator.evaluator

    -from __future__ import print_function
-
-import torch
-
-from seq2seq.loss import NLLLoss
-
-
    [docs]class Evaluator(object):
-    """ Class to evaluate models with given datasets.
-    Args:
-        loss (seq2seq.loss, optional): loss for evaluator (default: seq2seq.loss.NLLLoss)
-        batch_size (int, optional): batch size for evaluator (default: 64)
-    """
-
-    def __init__(self, loss=NLLLoss(), batch_size=64):
-        self.loss = loss
-        self.batch_size = batch_size
-
-
    [docs]    def evaluate(self, model, data):
-        """ Evaluate a model on given dataset and return performance.
-
-        Args:
-            model (seq2seq.models): model to evaluate
-            data (seq2seq.dataset.dataset.Dataset): dataset to evaluate against
-
-        Returns:
-            loss (float): loss of the given model on the given dataset
-        """
-        model.eval()
-        loss = self.loss
-        loss.reset()
-
-        for batch in data.make_batches(self.batch_size):
-            input_variables = batch[0]
-            target_variables = batch[1]
-
-            decoder_outputs, decoder_hidden, other = model(input_variables, target_variables, volatile=True)
-
-            # Evaluation
-            targets = other['inputs']
-            lengths = other['length']
-            for b in range(len(targets)):
-                # Batch wise loss
-                batch_target = targets[b]
-                batch_len = lengths[b]
-                # Crop output and target to batch length
-                batch_output = torch.stack([output[b] for output in decoder_outputs[:batch_len]])
-                batch_target = batch_target[:batch_len]
-                # Evaluate loss
-                loss.eval_batch(batch_output, batch_target)
-
-        return loss.get_loss()
    
-
    - -
    -
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/evaluator/predictor.html b/docs/public/_modules/seq2seq/evaluator/predictor.html deleted file mode 100644 index 03f823d..0000000 --- a/docs/public/_modules/seq2seq/evaluator/predictor.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - - - - seq2seq.evaluator.predictor — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for seq2seq.evaluator.predictor

    -
    [docs]class Predictor(object):
-
-    def __init__(self, model, src_vocab, tgt_vocab):
-        """
-        Predictor class to evaluate for a given model.
-        Args:
-            model (seq2seq.models): trained model. This can be loaded from a checkpoint
-                using `seq2seq.util.checkpoint.load`
-            src_vocab (seq2seq.dataset.vocabulary.Vocabulary): source sequence vocabulary
-            tgt_vocab (seq2seq.dataset.vocabulary.Vocabulary): target sequence vocabulary
-        """
-        self.model = model
-        self.model.eval()
-        self.src_vocab = src_vocab
-        self.tgt_vocab = tgt_vocab
-
-
    [docs]    def predict(self, src_seq):
-        """ Make prediction given `src_seq` as input.
-
-        Args:
-            src_seq (list): list of tokens in source language
-
-        Returns:
-            tgt_seq (list): list of tokens in target language as predicted
-            by the pre-trained model
-        """
-        src_id_seq = self.src_vocab.indices_from_sequence(src_seq)
-
-        softmax_list, _, other = self.model([src_id_seq], volatile=True)
-        length = other['length'][0]
-
-        tgt_id_seq = []
-        for i in range(length):
-            idx = softmax_list[i].max(1)[1].data[0][0]
-            tgt_id_seq.append(idx)
-
-        tgt_seq = self.tgt_vocab.sequence_from_indices(tgt_id_seq)
-        return tgt_seq
    
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    Source code for seq2seq.loss.loss

    -from __future__ import print_function
-import math
-import torch.nn as nn
-import numpy as np
-
-
    [docs]class Loss(object):
-    """ Base class for encapsulation of the loss functions.
-
-    This class defines interfaces that are commonly used with loss functions
-    in training and inferencing.  For information regarding individual loss
-    functions, please refer to http://pytorch.org/docs/master/nn.html#loss-functions
-
-    Note:
-        Do not use this class directly, use one of the sub classes.
-
-    Args:
-        name (str): name of the loss function used by logging messages.
-        criterion (torch.nn._Loss): one of PyTorch's loss function.  Refer
-            to http://pytorch.org/docs/master/nn.html#loss-functions for
-            a list of them.
-
-    Attributes:
-        name (str): name of the loss function used by logging messages.
-        criterion (torch.nn._Loss): one of PyTorch's loss function.  Refer
-            to http://pytorch.org/docs/master/nn.html#loss-functions for
-            a list of them.  Implementation depends on individual
-            sub-classes.
-        acc_loss (int or torcn.nn.Tensor): variable that stores accumulated loss.
-        norm_term (float): normalization term that can be used to calculate
-            the loss of multiple batches.  Implementation depends on individual
-            sub-classes.
-    """
-
-    def __init__(self, name, criterion):
-        self.name = name
-        self.criterion = criterion
-        if not issubclass(type(self.criterion), nn.modules.loss._Loss):
-            raise ValueError("Criterion has to be a subclass of torch.nn._Loss")
-        # accumulated loss
-        self.acc_loss = 0
-        # normalization term
-        self.norm_term = 0
-
-
    [docs]    def reset(self):
-        """ Reset the accumulated loss. """
-        self.acc_loss = 0
-        self.norm_term = 0
    
-
-
    [docs]    def get_loss(self):
-        """ Get the loss.
-
-        This method defines how to calculate the averaged loss given the
-        accumulated loss and the normalization term.  Override to define your
-        own logic.
-
-        Returns:
-            loss (float): value of the loss.
-        """
-        raise NotImplementedError
    
-
-
    [docs]    def eval_batch(self, outputs, target):
-        """ Evaluate and accumulate loss given outputs and expected results.
-
-        This method is called after each batch with the batch outputs and
-        the target (expected) results.  The loss and normalization term are
-        accumulated in this method.  Override it to define your own accumulation
-        method.
-
-        Args:
-            outputs (torch.Tensor): outputs of a batch.
-            target (torch.Tensor): expected output of a batch.
-        """
-        raise NotImplementedError
    
-
-    def cuda(self):
-        self.criterion.cuda()
-
-    def backward(self):
-        if type(self.acc_loss) is int:
-            raise ValueError("No loss to back propagate.")
-        self.acc_loss.backward()
    
-
-
    [docs]class NLLLoss(Loss):
-    """ Batch averaged negative log-likelihood loss.
-
-    Args:
-        weight (torch.Tensor, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-        mask (int, optional): index of masked token, i.e. weight[mask] = 0.
-        size_average (bool, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-    """
-
-    _NAME = "Avg NLLLoss"
-
-    def __init__(self, weight=None, mask=None, size_average=True):
-        self.mask = mask
-        if mask is not None:
-            if weight is None:
-                raise ValueError("Must provide weight with a mask.")
-            weight[mask] = 0
-
-        super(NLLLoss, self).__init__(
-            self._NAME,
-            nn.NLLLoss(weight=weight, size_average=size_average))
-
-    def get_loss(self):
-        if type(self.acc_loss) is int:
-            return 0
-        return self.acc_loss.data[0] / self.norm_term
-
-    def eval_batch(self, outputs, target):
-        self.acc_loss += self.criterion(outputs, target)
-        self.norm_term += 1
    
-
-
    [docs]class Perplexity(NLLLoss):
-    """ Language model perplexity loss.
-
-    Perplexity is the token averaged likelihood.  When the averaging options are the
-    same, it is the exponential of negative log-likelihood.
-
-    Args:
-        weight (torch.Tensor, optional): refer to http://pytorch.org/docs/master/nn.html#nllloss
-        mask (int, optional): index of masked token, i.e. weight[mask] = 0.
-    """
-
-    _NAME = "Perplexity"
-    _MAX_EXP = 100
-
-    def __init__(self, weight=None, mask=None):
-        super(Perplexity, self).__init__(weight=weight, mask=mask, size_average=False)
-
-    def eval_batch(self, outputs, target):
-        self.acc_loss += self.criterion(outputs, target)
-        if self.mask is None:
-            self.norm_term += np.prod(target.size())
-        else:
-            self.norm_term += target.data.ne(self.mask).sum()
-
-    def get_loss(self):
-        nll = super(Perplexity, self).get_loss()
-        if nll > Perplexity._MAX_EXP:
-            print("WARNING: Loss exceeded maximum value, capping to e^100")
-            return math.exp(Perplexity._MAX_EXP)
-        return math.exp(nll)
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/models/DecoderRNN.html b/docs/public/_modules/seq2seq/models/DecoderRNN.html deleted file mode 100644 index f9a1548..0000000 --- a/docs/public/_modules/seq2seq/models/DecoderRNN.html +++ /dev/null @@ -1,411 +0,0 @@ - - - - - - - - - - - seq2seq.models.DecoderRNN — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for seq2seq.models.DecoderRNN

    -import random
-
-import numpy as np
-
-import torch
-if torch.cuda.is_available():
-    import torch.cuda as device
-else:
-    import torch as device
-import torch.nn as nn
-from torch.autograd import Variable
-import torch.nn.functional as F
-
-from attention import Attention
-from baseRNN import BaseRNN
-
-
-
    [docs]class DecoderRNN(BaseRNN):
-    r"""
-    Provides functionality for decoding in a seq2seq framework, with an option for attention.
-
-    Args:
-        vocab (Vocabulary): an object of Vocabulary class
-        max_len (int): a maximum allowed length for the sequence to be processed
-        hidden_size (int): the number of features in the hidden state `h`
-        n_layers (int, optional): number of recurrent layers (default: 1)
-        rnn_cell (str, optional): type of RNN cell (default: gru)
-        input_dropout_p (float, optional): dropout probability for the input sequence (default: 0)
-        dropout_p (float, optional): dropout probability for the output sequence (default: 0)
-        use_attention(bool, optional): flag indication whether to use attention mechanism or not (default: false)
-
-    Attributes:
-        KEY_ATTN_SCORE (str): key used to indicate attention weights in `ret_dict`
-        KEY_LENGTH (str): key used to indicate a list representing lengths of output sequences in `ret_dict`
-        KEY_SEQUENCE (str): key used to indicate a list of sequences in `ret_dict`
-        KEY_INPUT (str): key used to target outputs in `ret_dict`
-
-    Inputs: inputs, encoder_hidden, encoder_outputs, function, teacher_forcing_ratio
-        - **inputs** (seq_len, batch, input_size): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs.  It is used for teacher forcing when provided. (default is `None`)
-        - **encoder_hidden** (batch, seq_len, hidden_size): tensor containing the features in the hidden state `h` of
-          encoder. Used as the initial hidden state of the decoder.
-        - **encoder_outputs** (batch, seq_len, hidden_size): tensor with containing the outputs of the encoder.
-          Used for attention mechanism (default is `None`).
-        - **function** (torch.nn.Module): A function used to generate symbols from RNN hidden state
-          (default is `torch.nn.functional.log_softmax`).
-        - **teacher_forcing_ratio** (float): The probability that teacher forcing will be used. A random number is
-          drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0).
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*KEY_LENGTH* : list of integers
-          representing lengths of output sequences, *KEY_SEQUENCE* : list of sequences, where each sequence is a list of
-          predicted token IDs, *KEY_INPUT* : target outputs if provided for decoding, *KEY_ATTN_SCORE* : list of
-          sequences, where each list is of attention weights }.
-    """
-
-    KEY_ATTN_SCORE = 'attention_score'
-    KEY_LENGTH = 'length'
-    KEY_SEQUENCE = 'sequence'
-    KEY_INPUT = 'inputs'
-
-    def __init__(self, vocab, max_len, hidden_size,
-            n_layers=1, rnn_cell='gru',
-            input_dropout_p=0, dropout_p=0, use_attention=False):
-        super(DecoderRNN, self).__init__(vocab, max_len, hidden_size,
-                input_dropout_p, dropout_p,
-                n_layers, rnn_cell)
-
-        self.output_size = self.vocab.get_vocab_size()
-        self.dropout_p = dropout_p
-        self.max_length = max_len
-        self.use_attention = use_attention
-
-        self.init_input = None
-
-        self.embedding = nn.Embedding(self.output_size, self.hidden_size)
-        self.dropout = nn.Dropout(self.dropout_p)
-        if use_attention:
-            self.attention = Attention(self.hidden_size)
-
-        self.out = nn.Linear(self.hidden_size, self.output_size)
-
-    def init_start_input(self, batch_size):
-        # GO input for decoder # Re-initialize when batch size changes
-        if self.init_input is None or self.init_input.size(0) != batch_size:
-            self.init_input = Variable(device.LongTensor([[self.vocab.SOS_token_id]*batch_size])).view(batch_size, -1)
-        return self.init_input
-
-    def forward_step(self, input_var, hidden, encoder_outputs, function):
-        batch_size = input_var.size(0)
-        output_size = input_var.size(1)
-        embedded = self.embedding(input_var)
-        embedded = self.input_dropout(embedded)
-
-        output, hidden = self.rnn(embedded, hidden)
-        output = self.dropout(output)
-
-        attn = None
-        if self.use_attention:
-            output, attn = self.attention(output, encoder_outputs)
-
-        predicted_softmax = function(self.out(output.view(-1, self.hidden_size))).view(batch_size, output_size, -1)
-        return predicted_softmax, hidden, attn
-
-    def forward_rnn(self, inputs=None, encoder_hidden=None, function=F.log_softmax,
-                    encoder_outputs=None, teacher_forcing_ratio=0):
-        ret_dict = dict()
-        if self.use_attention:
-            if encoder_outputs is None:
-                raise ValueError("Argument encoder_outputs cannot be None when attention is used.")
-            ret_dict[DecoderRNN.KEY_ATTN_SCORE] = list()
-        if inputs is None:
-            if teacher_forcing_ratio > 0:
-                raise ValueError("Teacher forcing has to be disabled (set 0) when no inputs is provided.")
-        if inputs is None and encoder_hidden is None:
-            batch_size = 1
-        else:
-            if inputs is not None:
-                batch_size = inputs.size(0)
-            else:
-                if self.rnn_cell is nn.LSTM:
-                    batch_size = encoder_hidden[0].size(1)
-                elif self.rnn_cell is nn.GRU:
-                    batch_size = encoder_hidden.size(1)
-
-        decoder_input = self.init_start_input(batch_size)
-        decoder_hidden = encoder_hidden
-
-        use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
-
-        decoder_outputs = []
-        sequence_symbols = []
-        lengths = np.array([self.max_length] * batch_size)
-
-        def decode(step, step_output, step_attn):
-            decoder_outputs.append(step_output)
-            if self.use_attention:
-                ret_dict[DecoderRNN.KEY_ATTN_SCORE].append(step_attn)
-            symbols = decoder_outputs[-1].topk(1)[1]
-            sequence_symbols.append(symbols)
-
-            eos_batches = symbols.data.eq(self.vocab.EOS_token_id)
-            if eos_batches.dim() > 0:
-                eos_batches = eos_batches.cpu().view(-1).numpy()
-                update_idx = ((lengths > di) & eos_batches) != 0
-                lengths[update_idx] = len(sequence_symbols)
-            return symbols
-
-        # Manual unrolling is used to support random teacher forcing.
-        # If teacher_forcing_ratio is True or False instead of a probability, the unrolling can be done in graph
-        if use_teacher_forcing:
-            decoder_input = torch.cat([decoder_input, inputs], dim=1)
-            decoder_output, decoder_hidden, attn = self.forward_step(decoder_input, decoder_hidden, encoder_outputs,
-                                                                     function=function)
-
-            for di in range(self.max_length):
-                step_output = decoder_output[:, di, :]
-                step_attn = attn[:, di, :]
-                decode(di, step_output, step_attn)
-        else:
-            for di in range(self.max_length):
-                decoder_output, decoder_hidden, step_attn = self.forward_step(decoder_input, decoder_hidden, encoder_outputs,
-                                                                         function=function)
-                step_output = decoder_output.squeeze(1)
-                symbols = decode(di, step_output, step_attn)
-                decoder_input = symbols
-
-        ret_dict[DecoderRNN.KEY_SEQUENCE] = sequence_symbols
-        ret_dict[DecoderRNN.KEY_LENGTH] = lengths.tolist()
-        ret_dict[DecoderRNN.KEY_INPUT] = inputs
-
-        return decoder_outputs, decoder_hidden, ret_dict
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/models/EncoderRNN.html b/docs/public/_modules/seq2seq/models/EncoderRNN.html deleted file mode 100644 index d2158ee..0000000 --- a/docs/public/_modules/seq2seq/models/EncoderRNN.html +++ /dev/null @@ -1,295 +0,0 @@ - - - - - - - - - - - seq2seq.models.EncoderRNN — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Source code for seq2seq.models.EncoderRNN

    -import torch.nn as nn
-from baseRNN import BaseRNN
-
-
-
    [docs]class EncoderRNN(BaseRNN):
-    r"""
-    Applies a multi-layer RNN to an input sequence.
-    Args:
-        vocab (Vocabulary): an object of Vocabulary class
-        max_len (int): a maximum allowed length for the sequence to be processed
-        hidden_size (int): the number of features in the hidden state `h`
-        input_dropout_p (float, optional): dropout probability for the input sequence (default: 0)
-        dropout_p (float, optional): dropout probability for the output sequence (default: 0)
-        n_layers (int, optional): number of recurrent layers (default: 1)
-        rnn_cell (str, optional): type of RNN cell (default: gru)
-
-    Inputs: inputs, volatile
-        - **inputs**: list of sequences, whose length is the batch size and within which each sequence is a list of token IDs.
-        - **volatile** (bool, optional): boolean flag specifying whether to preserve gradients, when you are sure you
-          will not be even calling .backward().
-    Outputs: output, hidden
-        - **output** (batch, seq_len, hidden_size): tensor containing the encoded features of the input sequence
-        - **hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the features in the hidden state `h`
-
-    Examples::
-
-         >>> encoder = EncoderRNN(input_vocab, max_seq_length, hidden_size)
-         >>> output, hidden = encoder(input)
-
-    """
-    def __init__(self, vocab, max_len, hidden_size,
-            input_dropout_p=0, dropout_p=0,
-            n_layers=1, rnn_cell='gru'):
-        super(EncoderRNN, self).__init__(vocab, max_len, hidden_size,
-                input_dropout_p, dropout_p, n_layers, rnn_cell)
-
-        self.embedding = nn.Embedding(self.vocab.get_vocab_size(), hidden_size)
-        self.lengths = None
-
-    def forward(self, *args, **kwargs):
-        batch = args[0]
-        self.lengths = [min(self.max_len, len(seq)) for seq in batch]
-        return super(EncoderRNN, self).forward(batch, **kwargs)
-
-
    [docs]    def forward_rnn(self, input_var):
-        """
-        Applies a multi-layer RNN to an input sequence.
-
-        Args:
-            input_var (batch, seq_len): tensor containing the features of the input sequence.
-
-       returns: output, hidden
-            - **output** (batch, seq_len, hidden_size): variable containing the encoded features of the input sequence
-            - **hidden** (num_layers * num_directions, batch, hidden_size): variable containing the features in the hidden state h
-        """
-        embedded = self.embedding(input_var)
-        embedded = self.input_dropout(embedded)
-        embedded = nn.utils.rnn.pack_padded_sequence(embedded, self.lengths, batch_first=True)
-        output, hidden = self.rnn(embedded)
-        output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
-        return output, hidden
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    - -

    Source code for seq2seq.models.TopKDecoder

    -import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-from baseRNN import BaseRNN
-
-
-
    [docs]class TopKDecoder(BaseRNN):
-    r"""
-    Top-K decoding with beam search.
-
-    Args:
-        decoder_rnn (DecoderRNN): An object of DecoderRNN used for decoding.
-        k (int): Size of the beam.
-
-    Inputs: inputs, encoder_hidden, encoder_outputs, function, teacher_forcing_ratio
-        - **inputs** (seq_len, batch, input_size): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs.  It is used for teacher forcing when provided. (default is `None`)
-        - **encoder_hidden** (batch, seq_len, hidden_size): tensor containing the features in the hidden state `h` of
-          encoder. Used as the initial hidden state of the decoder.
-        - **encoder_outputs** (batch, seq_len, hidden_size): tensor with containing the outputs of the encoder.
-          Used for attention mechanism (default is `None`).
-        - **function** (torch.nn.Module): A function used to generate symbols from RNN hidden state
-          (default is `torch.nn.functional.log_softmax`).
-        - **teacher_forcing_ratio** (float): The probability that teacher forcing will be used. A random number is
-          drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0).
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*length* : list of integers
-          representing lengths of output sequences, *sequence* : list of sequences, where each sequence is a list of
-          predicted token IDs, *inputs* : target outputs if provided for decoding}.
-
-    """
-
-    def __init__(self, decoder_rnn, k):
-        super(TopKDecoder, self).__init__(decoder_rnn.lang, decoder_rnn.max_length)
-        self.rnn = decoder_rnn
-        self.k = k
-        self.V = self.rnn.lang.get_vocab_size()
-        self.SOS = self.rnn.lang.SOS_token_id
-        self.EOS = self.rnn.lang.EOS_token_id
-
-
    [docs]    def forward_rnn(self, inputs=None, encoder_hidden=None, encoder_outputs=None, function=F.log_softmax,
-                    retain_output_probs=True):
-        """
-        Forward rnn for MAX_LENGTH steps.  Look at :func:`seq2seq.models.DecoderRNN.DecoderRNN.forward_rnn` for details.
-        """
-
-        # TODO: Looks like encoder_hidden is not optional, we need unit tests
-        # for this class
-        # Get batch size, assuming h_0 is num_layers*directions x b x hidden_dim
-        b = encoder_hidden.size(1)
-        h = encoder_hidden.size(2)
-
-        self.pos_index = Variable(torch.LongTensor(range(b)) * self.k).view(-1, 1)
-
-        # Inflate the initial hidden states to be of size: b*k x h
-        hidden = self._inflate(encoder_hidden, self.k)
-        # ... same idea for encoder_outputs and decoder_outputs
-        if self.rnn.use_attention:
-            if encoder_outputs is None:
-                raise ValueError("Argument encoder_outputs cannot be None when attention is used.")
-            else:
-                inflated_encoder_outputs = self._inflate(encoder_outputs, self.k)
-        else:
-            inflated_encoder_outputs = None
-
-        # Initialize the scores; for the first step,
-        # ignore the inflated copies to avoid duplicate entries in the top k
-        sequence_scores = torch.Tensor(b*self.k, 1)
-        sequence_scores.fill_(-float('Inf'))
-        sequence_scores.index_fill_(0, torch.LongTensor([i*self.k for i in range(0, b)]), 0.0)
-        sequence_scores = Variable(sequence_scores)
-
-        # Initialize the input vector
-        input_var = Variable(torch.transpose(torch.LongTensor([[self.SOS]*b*self.k]), 0, 1))
-
-        # Store decisions for backtracking
-        stored_outputs = list()
-        stored_scores = list()
-        stored_predecessors = list()
-        stored_emitted_symbols = list()
-        stored_hidden = list()
-
-        for _ in range(0, self.rnn.max_length):
-
-            # Run the RNN one step forward
-            log_softmax_output, hidden, _ = self.rnn.forward_step(input_var, hidden, inflated_encoder_outputs, function=function)
-
-            # If doing local backprop (e.g. supervised training), retain the output layer
-            if retain_output_probs:
-                stored_outputs.append(log_softmax_output)
-
-            # To get the full sequence scores for the new candidates, add the local scores for t_i to the predecessor scores for t_(i-1)
-            sequence_scores = self._inflate(sequence_scores, self.V)
-            sequence_scores += log_softmax_output
-            scores, candidates = sequence_scores.view(b, -1).topk(self.k, dim=1)
-
-            # Reshape input = (bk, 1) and sequence_scores = (bk, 1)
-            input_var = (candidates % self.V).view(b * self.k, 1)
-            sequence_scores = scores.view(b * self.k, 1)
-
-            # Update fields for next timestep
-            predecessors = (candidates / self.V + self.pos_index.expand_as(candidates)).view(b*self.k, 1)
-            hidden = hidden.index_select(1, predecessors.squeeze())
-
-            # Update sequence scores and erase scores for end-of-sentence symbol so that they aren't expanded
-            stored_scores.append(sequence_scores.clone())
-            eos_indices = input_var.data.eq(self.EOS)
-            if eos_indices.nonzero().dim() > 0:
-                sequence_scores.data.masked_fill_(eos_indices, -float('inf'))
-
-            # Cache results for backtracking
-            stored_predecessors.append(predecessors)
-            stored_emitted_symbols.append(input_var)
-            stored_hidden.append(hidden)
-
-        # Do backtracking to return the optimal values
-        output, h_t, h_n, s, l, p = self._backtrack(stored_outputs, stored_hidden,
-                                                 stored_predecessors, stored_emitted_symbols, stored_scores, b, h)
-
-        # Build return objects
-        decoder_outputs = [step[:, 0, :] for step in output]
-        decoder_hidden = h_n[:, :, 0, :]
-        metadata = {}
-        metadata['inputs'] = inputs
-        metadata['output'] = output
-        metadata['h_t'] = h_t
-        metadata['score'] = s
-        metadata['length'] = l
-        metadata['sequence'] = p
-        return decoder_outputs, decoder_hidden, metadata
    
-
-    def _backtrack(self, nw_output, nw_hidden, predecessors, symbols, scores, b, hidden_size):
-        """Backtracks over batch to generate optimal k-sequences.
-
-        Args:
-            nw_output [(batch*k, vocab_size)] * sequence_length: A Tensor of outputs from network
-            nw_hidden [(num_layers, batch*k, hidden_size)] * sequence_length: A Tensor of hidden states from network
-            predecessors [(batch*k)] * sequence_length: A Tensor of predecessors
-            symbols [(batch*k)] * sequence_length: A Tensor of predicted tokens
-            scores [(batch*k)] * sequence_length: A Tensor containing sequence scores for every token t = [0, ... , seq_len - 1]
-            b: Size of the batch
-            hidden_size: Size of the hidden state
-
-        Returns:
-            output [(batch, k, vocab_size)] * sequence_length: A list of the output probabilities (p_n)
-            from the last layer of the RNN, for every n = [0, ... , seq_len - 1]
-
-            h_t [(batch, k, hidden_size)] * sequence_length: A list containing the output features (h_n)
-            from the last layer of the RNN, for every n = [0, ... , seq_len - 1]
-
-            h_n(batch, k, hidden_size): A Tensor containing the last hidden state for all top-k sequences.
-
-            score [batch, k]: A list containing the final scores for all top-k sequences
-
-            length [batch, k]: A list specifying the length of each sequence in the top-k candidates
-
-            p (batch, k, sequence_len): A Tensor containing predicted sequence
-        """
-
-        # initialize return variables given different types
-        output = list()
-        h_t = list()
-        p = list()
-        h_n = torch.zeros(nw_hidden[0].size())  # Placeholder for last hidden state of top-k sequences.
-                                                # If a (top-k) sequence ends early in decoding, `h_n` contains
-                                                # its hidden state when it sees EOS.  Otherwise, `h_n` contains
-                                                # the last hidden state of decoding.
-        l = [[self.rnn.max_length] * self.k for _ in range(b)]  # Placeholder for lengths of top-k sequences
-                                                                # Similar to `h_n`
-
-        # the last step output of the beams are not sorted
-        # thus they are sorted here
-        sorted_score, sorted_idx = scores[-1].view(b, self.k).topk(self.k)
-        # initialize the sequence scores with the sorted last step beam scores
-        s = sorted_score.clone()
-
-        batch_eos_found = [0] * b   # the number of EOS found
-                                    # in the backward loop below for each batch
-
-        t = self.rnn.max_length - 1
-        # initialize the back pointer with the sorted order of the last step beams.
-        # add self.pos_index for indexing variable with b*k as the first dimension.
-        t_predecessors = (sorted_idx + self.pos_index.expand_as(sorted_idx)).view(b * self.k)
-        while t >= 0:
-            # Re-order the variables with the back pointer
-            current_output = nw_output[t].index_select(0, t_predecessors)
-            current_hidden = nw_hidden[t].index_select(1, t_predecessors)
-            current_symbol = symbols[t].index_select(0, t_predecessors)
-            # Re-order the back pointer of the previous step with the back pointer of
-            # the current step
-            t_predecessors = predecessors[t].index_select(0, t_predecessors).squeeze()
-
-            # This tricky block handles dropped sequences that see EOS earlier.
-            # The basic idea is summarized below:
-            #
-            #   Terms:
-            #       Ended sequences = sequences that see EOS early and dropped
-            #       Survived sequences = sequences in the last step of the beams
-            #
-            #       Although the ended sequences are dropped during decoding,
-            #   their generated symbols and complete backtracking information are still
-            #   in the backtracking variables.
-            #   For each batch, everytime we see an EOS in the backtracking process,
-            #       1. If there is survived sequences in the return variables, replace
-            #       the one with the lowest survived sequence score with the new ended
-            #       sequences
-            #       2. Otherwise, replace the ended sequence with the lowest sequence
-            #       score with the new ended sequence
-            #
-            eos_indices = symbols[t].data.squeeze(1).eq(self.EOS).nonzero()
-            if eos_indices.dim() > 0:
-                for i in range(eos_indices.size(0)-1, -1, -1):
-                    # Indices of the EOS symbol for both variables
-                    # with b*k as the first dimension, and b, k for
-                    # the first two dimensions
-                    idx = eos_indices[i]
-                    b_idx = idx[0] / self.k
-                    # The indices of the replacing position
-                    # according to the replacement strategy noted above
-                    res_k_idx = self.k - (batch_eos_found[b_idx] % self.k) - 1
-                    batch_eos_found[b_idx] += 1
-                    res_idx = b_idx * self.k + res_k_idx
-
-                    # Replace the old information in return variables
-                    # with the new ended sequence information
-                    t_predecessors[res_idx] = predecessors[t][idx[0]]
-                    current_output[res_idx, :] = nw_output[t][idx[0], :]
-                    current_hidden[:, res_idx, :] = nw_hidden[t][:, idx[0], :]
-                    h_n[:, res_idx, :] = nw_hidden[t][:, idx[0], :].data
-                    current_symbol[res_idx, :] = symbols[t][idx[0]]
-                    s[b_idx, res_k_idx] = scores[t][idx[0]].data[0]
-                    l[b_idx][res_k_idx] = t + 1
-
-            # record the back tracked results
-            output.append(current_output)
-            h_t.append(current_hidden)
-            p.append(current_symbol)
-
-            t -= 1
-
-        # Sort and re-order again as the added ended sequences may change
-        # the order (very unlikely)
-        s, re_sorted_idx = s.topk(self.k)
-        for b_idx in range(b):
-            l[b_idx] = [l[b_idx][k_idx.data[0]] for k_idx in re_sorted_idx[b_idx,:]]
-
-        re_sorted_idx = (re_sorted_idx + self.pos_index.expand_as(re_sorted_idx)).view(b * self.k)
-
-        # Reverse the sequences and re-order at the same time
-        # It is reversed because the backtracking happens in reverse time order
-        output = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(output)]
-        p = [step.index_select(0, re_sorted_idx).view(b, self.k, -1) for step in reversed(p)]
-        h_t = [step.index_select(1, re_sorted_idx).view(-1, b, self.k, hidden_size) for step in reversed(h_t)]
-        h_n = h_n.index_select(1, re_sorted_idx.data).view(-1, b, self.k, hidden_size)
-        s = s.data
-
-        if self.k == 1:
-            l = [_l[0] for _l in l]
-
-        return output, h_t, h_n, s, l, p
-
-    def _mask_symbol_scores(self, score, idx, masking_score=-float('inf')):
-            score[idx] = masking_score
-
-    def _mask(self, tensor, idx, dim=0, masking_score=-float('inf')):
-        if len(idx.size()) > 0:
-            indices = idx[:, 0]
-            tensor.index_fill_(dim, indices, masking_score)
-
-    def _inflate(self, tensor, times):
-        """
-        Given a tensor, 'inflates' it along the given dimension by replicating each slice specified number of times (in-place)
-
-        Args:
-            tensor: A :class:`Tensor` to inflate
-            times: number of repetitions
-            dimension: axis for inflation (default=0)
-
-        Returns:
-            A :class:`Tensor`
-
-        Examples::
-            >> a = torch.LongTensor([[1, 2], [3, 4]])
-            >> a
-            1   2
-            3   4
-            [torch.LongTensor of size 2x2]
-            >> decoder = TopKDecoder(nn.RNN(10, 20, 2), 3)
-            >> b = decoder._inflate(a, 1, dimension=1)
-            >> b
-            1   1   2   2
-            3   3   4   4
-            [torch.LongTensor of size 2x4]
-            >> c = decoder._inflate(a, 1, dimension=0)
-            >> c
-            1   2
-            1   2
-            3   4
-            3   4
-            [torch.LongTensor of size 4x2]
-
-        """
-        tensor_dim = len(tensor.size())
-        if tensor_dim is 3:
-            b = tensor.size(1)
-            return tensor.repeat(1, 1, times).view(tensor.size(0), b * times, -1)
-        elif tensor_dim is 2:
-            return tensor.repeat(1, times)
-        elif tensor_dim is 1:
-            b = tensor.size(0)
-            return tensor.repeat(times).view(b, -1)
-        else:
-            raise ValueError("Tensor can be of 1D, 2D or 3D only. This one is {}D.".format(tensor_dim))
    
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    Source code for seq2seq.models.attention

    -import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-
    [docs]class Attention(nn.Module):
-    r"""
-    Applies an attention mechanism on the output features from the decoder.
-
-    .. math::
-            \begin{array}{ll}
-            x = context*output \\
-            attn = exp(x_i - max_i x_i) / sum_j exp(x_j - max_i x_i) \\
-            output = \tanh(w * (attn * context) + b * output)
-            \end{array}
-
-    Args:
-        dim(int): The number of expected features in the output
-
-    Inputs: output, context
-        - **output** (batch, output_len, dimensions): tensor containing the output features from the decoder.
-        - **context** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.
-
-    Outputs: output, attn
-        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.
-        - **attn** (batch, output_len, input_len): tensor containing attention weights.
-
-    Attributes:
-        linear_out (torch.nn.Linear): applies a linear transformation to the incoming data: :math:`y = Ax + b`.
-        mask (torch.Tensor, optional): applies a :math:`-inf` to the indices specified in the `Tensor`.
-
-    Examples::
-
-         >>> attention = seq2seq.models.Attention(256)
-         >>> context = Variable(torch.randn(5, 3, 256))
-         >>> output = Variable(torch.randn(5, 5, 256))
-         >>> output, attn = attention(output, context)
-
-    """
-    def __init__(self, dim):
-        super(Attention, self).__init__()
-        self.linear_out = nn.Linear(dim*2, dim)
-        self.mask = None
-
-
    [docs]    def set_mask(self, mask):
-        """
-        Sets indices to be masked
-
-        Args:
-            mask (torch.Tensor): tensor containing indices to be masked
-        """
-        self.mask = mask
    
-
-    def forward(self, output, context):
-        batch_size = output.size(0)
-        hidden_size = output.size(2)
-        input_size = context.size(1)
-        # (batch, out_len, dim) * (batch, in_len, dim) -> (batch, out_len, in_len)
-        attn = torch.bmm(output, context.transpose(1, 2))
-        if self.mask is not None:
-            attn.data.masked_fill_(self.mask, -float('inf'))
-        attn = F.softmax(attn.view(-1, input_size)).view(batch_size, -1, input_size)
-
-        # (batch, out_len, in_len) * (batch, in_len, dim) -> (batch, out_len, dim)
-        mix = torch.bmm(attn, context)
-
-        # concat -> (batch, out_len, 2*dim)
-        combined = torch.cat((mix, output), dim=2)
-        # output -> (batch, out_len, dim)
-        output = F.tanh(self.linear_out(combined.view(-1, 2 * hidden_size))).view(batch_size, -1, hidden_size)
-
-        return output, attn
    
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    Source code for seq2seq.models.baseRNN

    -""" A base class for RNN. """
-import torch
-import torch.nn as nn
-
-
-
    [docs]class BaseRNN(nn.Module):
-    r"""
-    Applies a multi-layer RNN to an input sequence.
-    Note:
-        Do not use this class directly, use one of the sub classes.
-    Args:
-        vocab (Vocabulary): object of Vocabulary class
-        max_len (int): maximum allowed length for the sequence to be processed
-        hidden_size (int): number of features in the hidden state `h`
-        input_dropout_p (float): dropout probability for the input sequence
-        dropout_p (float): dropout probability for the output sequence
-        n_layers (int): number of recurrent layers
-        rnn_cell (str): type of RNN cell (Eg. 'LSTM' , 'GRU')
-
-    Inputs: ``*args``, ``**kwargs``
-        - ``*args``: variable length argument list.
-        - ``**kwargs``: arbitrary keyword arguments.
-
-    Attributes:
-        SYM_MASK: masking symbol
-        SYM_EOS: end-of-sequence symbol
-    """
-    SYM_MASK = "MASK"
-    SYM_EOS = "EOS"
-
-    def __init__(self, vocab, max_len, hidden_size, input_dropout_p, dropout_p, n_layers, rnn_cell):
-        super(BaseRNN, self).__init__()
-        self.vocab = vocab
-        self.max_len = max_len
-        self.hidden_size = hidden_size
-        self.n_layers = n_layers
-        self.input_dropout_p = input_dropout_p
-        self.input_dropout = nn.Dropout(p=input_dropout_p)
-        if rnn_cell.lower() == 'lstm':
-            self.rnn_cell = nn.LSTM
-        elif rnn_cell.lower() == 'gru':
-            self.rnn_cell = nn.GRU
-        else:
-            raise ValueError("Unsupported RNN Cell: {0}".format(rnn_cell))
-
-        self.dropout_p = dropout_p
-        self.rnn = self.rnn_cell(hidden_size, hidden_size, n_layers, batch_first=True, dropout=dropout_p)
-
-
    [docs]    def balance(self, batch, volatile):
-        """
-        Add reserved symbols and balance batch input.
-        It first appends EOS symbol to each sequence and then appends multiple
-        MASK symbols to make the sequences the same length.
-        Args:
-            batch: list of sequences, each of which is a list of integers
-            volatile: boolean flag specifying whether to preserve gradients, when you are sure you will not be even calling .backward().
-
-        Returns:
-            torch.autograd.Variable: variable with balanced input data.
-        """
-        max_len = self.max_len
-        outputs = []
-        for seq in batch:
-            seq = seq[:min(len(seq), max_len - 1)]
-            outputs.append(seq + [self.vocab.EOS_token_id] + [self.vocab.MASK_token_id] * (max_len - len(seq) - 1))
-
-        outputs_var = torch.autograd.Variable(torch.LongTensor(outputs), volatile=volatile)
-        if torch.cuda.is_available():
-            outputs_var = outputs_var.cuda()
-
-        return outputs_var
    
-
-    def forward(self, *args, **kwargs):
-        if 'volatile' in kwargs:
-            volatile = kwargs['volatile']
-            kwargs.pop('volatile', None)
-        else:
-            volatile = False
-        if args:
-            self.balanced_batch = self.balance(args[0], volatile)
-            args = [self.balanced_batch] + list(args[1:])
-        else:
-            if 'inputs' in kwargs and kwargs['inputs'] is not None:
-                self.balanced_batch = self.balance(kwargs['inputs'], volatile)
-                kwargs['inputs'] = self.balanced_batch
-
-        return self.forward_rnn(*args, **kwargs)
-
-    def forward_rnn(self, *args, **kwargs):
-        raise NotImplementedError()
    
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    Source code for seq2seq.models.seq2seq

    -import os
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
    [docs]class Seq2seq(nn.Module):
-    """ Standard sequence-to-sequence architecture with configurable encoder
-    and decoder.
-    Args:
-        encoder (EncoderRNN): object of EncoderRNN
-        decoder (DecoderRNN): object of DecoderRNN
-        decode_function (func, optional): function to generate symbols from output hidden states (default: F.log_softmax)
-
-    Inputs: input_variable, target_variable, teacher_forcing_ratio, volatile
-        - **input_variable** (list, option): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs. This information is forwarded to the encoder.
-        - **target_variable** (list, optional): list of sequences, whose length is the batch size and within which
-          each sequence is a list of token IDs. This information is forwarded to the decoder.
-        - **teacher_forcing_ratio** (int, optional): The probability that teacher forcing will be used. A random number
-          is drawn uniformly from 0-1 for every decoding token, and if the sample is smaller than the given value,
-          teacher forcing would be used (default is 0)
-        - **volatile** (bool, optional): boolean flag specifying whether to preserve gradients, when you are sure you
-          will not be even calling .backward().
-
-    Outputs: decoder_outputs, decoder_hidden, ret_dict
-        - **decoder_outputs** (batch): batch-length list of tensors with size (max_length, hidden_size) containing the
-          outputs of the decoder.
-        - **decoder_hidden** (num_layers * num_directions, batch, hidden_size): tensor containing the last hidden
-          state of the decoder.
-        - **ret_dict**: dictionary containing additional information as follows {*KEY_LENGTH* : list of integers
-          representing lengths of output sequences, *KEY_SEQUENCE* : list of sequences, where each sequence is a list of
-          predicted token IDs, *KEY_INPUT* : target outputs if provided for decoding, *KEY_ATTN_SCORE* : list of
-          sequences, where each list is of attention weights }.
-
-    """
-
-    def __init__(self, encoder, decoder, decode_function=F.log_softmax):
-        super(Seq2seq, self).__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.decode_function = decode_function
-
-    def forward(self, input_variable, target_variable=None,
-                teacher_forcing_ratio=0, volatile=False):
-        if target_variable is None:
-            input_variable = sorted(input_variable, len, reverse=True)
-        else:
-            sorted_input = sorted(zip(input_variable, target_variable),
-                                  key=lambda x: len(x[0]), reverse=True)
-            input_variable = [p[0] for p in sorted_input]
-            target_variable = [p[1] for p in sorted_input]
-        encoder_outputs, encoder_hidden = self.encoder(input_variable, volatile=volatile)
-        result = self.decoder(inputs=target_variable,
-                              encoder_hidden=encoder_hidden,
-                              encoder_outputs=encoder_outputs,
-                              function=self.decode_function,
-                              teacher_forcing_ratio=teacher_forcing_ratio,
-                              volatile=volatile)
-        return result
    
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    Source code for seq2seq.optim.optim

    -import torch
-
-
    [docs]class Optimizer(object):
-    """ The Optimizer class encapsulates torch.optim package and provides functionalities
-    for learning rate scheduling and gradient norm clipping.
-
-    Args:
-        optim_class (torch.optim.Optimizer): optimizer class, e.g. torch.optim.SGD
-        max_grad_norm (float, optional): value used for gradient norm clipping,
-            set 0 to disable (default 0)
-        lr_decay (float, optional): value for learning rate decay:
-            lr = lr_decay * lr (default 1)
-        decay_after_epoch (float, optional): learning rate starts to decay after the
-            specified epoch number, set 0 to disable (default 0)
-        **kwargs: arguments for the given optimizer class,
-            refer http://pytorch.org/docs/optim.html#algorithms for more information
-    """
-
-    _ARG_MAX_GRAD_NORM = 'max_grad_norm'
-    _ARG_DECAY_AFTER = "decay_after_epoch"
-    _ARG_LR_DECAY = "lr_decay"
-
-    def __init__(self, optim_class, **kwargs):
-        self.optim_class = optim_class
-        self.optimizer = None
-        self.parameters = None
-
-        self.max_grad_norm = self._get_remove(kwargs, Optimizer._ARG_MAX_GRAD_NORM, 0)
-        self.lr_decay = self._get_remove(kwargs, Optimizer._ARG_LR_DECAY, 1)
-        self.decay_after_epoch = self._get_remove(kwargs, Optimizer._ARG_DECAY_AFTER, 0)
-        self.optim_args = kwargs
-
-    def _get_remove(self, args, key, default):
-        value = default
-        if key in args:
-            value = args[key]
-            del args[key]
-        return value
-
-
    [docs]    def set_parameters(self, parameters):
-        """ Set the parameters to optimize.
-
-        Args:
-            parameters (iterable): An iterable of torch.nn.Parameter.
-        """
-        self.parameters = parameters
-        self.optimizer = self.optim_class(parameters, **self.optim_args)
    
-
-
    [docs]    def step(self):
-        """ Performs a single optimization step, including gradient norm clipping if necessary. """
-        if self.max_grad_norm > 0:
-            torch.nn.utils.clip_grad_norm(self.parameters, self.max_grad_norm)
-        self.optimizer.step()
    
-
-
    [docs]    def update(self, loss, epoch):
-        """ Update the learning rate if the conditions are met. Override this method
-        to implement your own learning rate schedule.
-
-        Args:
-            loss (float): The current loss.  It could be training loss or developing loss
-                depending on the caller.  By default the supervised trainer uses developing
-                loss.
-            epoch (int): The current epoch number.
-        """
-        after_decay_epoch = self.decay_after_epoch != 0 and epoch >= self.decay_after_epoch
-        if after_decay_epoch:
-            self.optimizer.param_groups[0]['lr'] *= self.lr_decay
    
-
-
    [docs]    def load_state_dict(self, optimizer_dict):
-        """ Wrapper for loading optimizer state_dict.
-            For further reference please refer to http://pytorch.org/docs/master/optim.html#torch.optim.Optimizer.load_state_dict
-        """
-        self.optimizer.load_state_dict(optimizer_dict)
    
-
-
    [docs]    def state_dict(self):
-        """Wrapper for accessing optimizer state_dict.
-            For further reference please refer to http://pytorch.org/docs/master/optim.html#torch.optim.Optimizer.state_dict
-        """
-        return self.optimizer.state_dict()
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/trainer/supervised_trainer.html b/docs/public/_modules/seq2seq/trainer/supervised_trainer.html deleted file mode 100644 index 82fac57..0000000 --- a/docs/public/_modules/seq2seq/trainer/supervised_trainer.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - seq2seq.trainer.supervised_trainer — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • seq2seq.trainer.supervised_trainer
      • - - -
    • - - - -
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    -
    -
    - -

    Source code for seq2seq.trainer.supervised_trainer

    -import os
-import random
-import logging
-
-import torch
-from torch import optim
-
-from seq2seq.evaluator import Evaluator
-from seq2seq.loss import NLLLoss
-from seq2seq.optim import Optimizer
-from seq2seq.util.custom_time import *
-from seq2seq.util.checkpoint import Checkpoint
-
-
    [docs]class SupervisedTrainer(object):
-    """ The SupervisedTrainer class helps in setting up a training framework in a
-    supervised setting.
-
-    Args:
-        expt_dir (optional, str): experiment Directory to store details of the experiment,
-            by default it makes a folder in the current directory to store the details (default: `experiment`).
-        loss (seq2seq.loss.loss.Loss, optional): loss for training, (default: seq2seq.loss.NLLLoss)
-        batch_size (int, optional): batch size for experiment, (default: 64)
-        checkpoint_every (int, optional): number of epochs to checkpoint after, (default: 100)
-        optimizer (seq2seq.optim.Optimizer, optional): optimizer for training
-            (default: Optimizer(pytorch.optim.Adam, max_grad_norm=5))
-    """
-    def __init__(self, expt_dir='experiment', loss=NLLLoss(), batch_size=64,
-                 random_seed=None,
-                 checkpoint_every=100, print_every=100,
-                 optimizer=Optimizer(optim.Adam, max_grad_norm=5)):
-        self._trainer = "Simple Trainer"
-        self.random_seed = random_seed
-        if random_seed is not None:
-            random.seed(random_seed)
-            torch.manual_seed(random_seed)
-        self.loss = loss
-        self.evaluator = Evaluator(loss=self.loss, batch_size=batch_size)
-        self.optimizer = optimizer
-        self.checkpoint_every = checkpoint_every
-        self.print_every = print_every
-
-        if not os.path.isabs(expt_dir):
-            expt_dir = os.path.join(os.getcwd(), expt_dir)
-        self.expt_dir = expt_dir
-        if not os.path.exists(self.expt_dir):
-            os.makedirs(self.expt_dir)
-        self.batch_size = batch_size
-        self.input_vocab_file = os.path.join(self.expt_dir, 'input_vocab')
-        self.output_vocab_file = os.path.join(self.expt_dir, 'output_vocab')
-
-        self.logger = logging.getLogger(__name__)
-
-    def _train_batch(self, input_variable, target_variable, model, teacher_forcing_ratio):
-        loss = self.loss
-        # Forward propagation
-        decoder_outputs, decoder_hidden, other = model(input_variable, target_variable,
-                                                       teacher_forcing_ratio=teacher_forcing_ratio)
-        # Get loss
-        loss.reset()
-        targets = other['inputs']
-        lengths = other['length']
-        for batch in range(len(targets)):
-            # Batch wise loss
-            batch_target = targets[batch]
-            batch_len = lengths[batch]
-            # Crop output and target to batch length
-            batch_output = torch.stack([output[batch] for output in decoder_outputs[:batch_len]])
-            batch_target = batch_target[:batch_len]
-            # Evaluate loss
-            loss.eval_batch(batch_output, batch_target)
-        # Backward propagation
-        model.zero_grad()
-        loss.backward()
-        self.optimizer.step()
-
-        return loss.get_loss()
-
-    def _train_epoches(self, data, model, n_epochs, batch_size, resume, dev_data=None, teacher_forcing_ratio=0):
-        start = time.time()
-        print_loss_total = 0  # Reset every print_every
-        steps_per_epoch = data.num_batches(batch_size)
-        total_steps = steps_per_epoch * n_epochs
-
-        # If training is set to resume
-        if resume:
-            latest_checkpoint_path = Checkpoint.get_latest_checkpoint(self.expt_dir)
-            resume_checkpoint = Checkpoint.load(latest_checkpoint_path)
-            model = resume_checkpoint.model
-            self.optimizer.set_parameters(model.parameters())
-            self.optimizer.load_state_dict(resume_checkpoint.optimizer_state_dict)
-            start_epoch = resume_checkpoint.epoch
-            step = resume_checkpoint.step
-        else:
-            start_epoch = 1
-            step = 0
-            self.optimizer.set_parameters(model.parameters())
-
-        for epoch in range(start_epoch, n_epochs + 1):
-            data.shuffle(self.random_seed)
-
-            batch_generator = data.make_batches(batch_size)
-
-            # consuming seen batches from previous training
-            for _ in range((epoch - 1) * steps_per_epoch, step):
-                next(batch_generator)
-
-            model.train(True)
-            for batch in batch_generator:
-                step += 1
-
-                input_variables = batch[0]
-                target_variables = batch[1]
-
-                loss = self._train_batch(input_variables, target_variables, model, teacher_forcing_ratio)
-
-                # Record average loss
-                print_loss_total += loss
-
-                if step % self.print_every == 0:
-                    print_loss_avg = print_loss_total / (self.print_every)
-                    print_loss_total = 0
-                    log_msg = 'Time elapsed: %s, Progress: %d%%, Train %s: %.4f' % (
-                        pretty_interval(start),
-                        float(step) / total_steps * 100,
-                        self.loss.name,
-                        print_loss_avg)
-                    self.logger.info(log_msg)
-
-                # Checkpoint
-                if step % self.checkpoint_every == 0 or step == total_steps:
-                    Checkpoint(model=model,
-                               optimizer_state_dict=self.optimizer.state_dict(),
-                               epoch=epoch, step=step,
-                               input_vocab=data.input_vocab,
-                               output_vocab=data.output_vocab).save(self.expt_dir)
-
-            log_msg = "Finished epoch {0}".format(epoch)
-            if dev_data is not None:
-                dev_loss = self.evaluator.evaluate(model, dev_data)
-                self.optimizer.update(dev_loss, epoch)
-                log_msg += ", Dev %s: %.4f" % (self.loss.name, dev_loss)
-                model.train(mode=True)
-            self.logger.info(log_msg)
-
-
    [docs]    def train(self, model, data, num_epochs=5, resume=False, dev_data=None, teacher_forcing_ratio=0):
-        """ Run training for a given model.
-
-         Args:
-             model (seq2seq.models): model to run training on, if `resume=True`, it would be
-                overwritten by the model loaded from the latest checkpoint.
-             data (seq2seq.dataset.dataset.Dataset): dataset object to train on
-             num_epochs (int, optional): number of epochs to run (default 5)
-             resume(bool, optional): resume training with the latest checkpoint, (default False)
-             dev_data (seq2seq.dataset.dataset.Dataset, optional): dev Dataset (default None)
-             teacher_forcing_ratio (float, optional): teaching forcing ratio (default 0)
-
-        """
-        # Make Checkpoint Directories
-        data.input_vocab.save(self.input_vocab_file)
-        data.output_vocab.save(self.output_vocab_file)
-
-        self._train_epoches(data, model, num_epochs, self.batch_size,
-                            resume=resume, dev_data=dev_data, teacher_forcing_ratio=teacher_forcing_ratio)
    
-
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/util/checkpoint.html b/docs/public/_modules/seq2seq/util/checkpoint.html deleted file mode 100644 index cf3bf2b..0000000 --- a/docs/public/_modules/seq2seq/util/checkpoint.html +++ /dev/null @@ -1,353 +0,0 @@ - - - - - - - - - - - seq2seq.util.checkpoint — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • seq2seq.util.checkpoint
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    - -

    Source code for seq2seq.util.checkpoint

    -import os
-import time
-import shutil
-
-import torch
-
-from seq2seq.dataset.vocabulary import Vocabulary
-
-
-
    [docs]class Checkpoint(object):
-    """
-    The Checkpoint class manages the saving and loading of a model during training. It allows training to be suspended
-    and resumed at a later time (e.g. when running on a cluster using sequential jobs).
-
-    To make a checkpoint, initialize a Checkpoint object with the following args; then call that object's save() method
-    to write parameters to disk.
-
-    Args:
-        model (seq2seq): seq2seq model being trained
-        optimizer_state_dict (dict): stores the state of the optimizer
-        epoch (int): current epoch (an epoch is a loop through the full training data)
-        step (int): number of examples seen within the current epoch
-        input_vocab (Vocabulary): vocabulary for the input language
-        output_vocab (Vocabulary): vocabulary for the output language
-
-    Attributes:
-        CHECKPOINT_DIR_NAME (str): name of the checkpoint directory
-        TRAINER_STATE_NAME (str): name of the file storing trainer states
-        MODEL_NAME (str): name of the file storing model
-        INPUT_VOCAB_FILE (str): name of the input vocab file
-        OUTPUT_VOCAB_FILE (str): name of the output vocab file
-    """
-
-    CHECKPOINT_DIR_NAME = 'checkpoints'
-    TRAINER_STATE_NAME = 'trainer_states.pt'
-    MODEL_NAME = 'model.pt'
-    INPUT_VOCAB_FILE = 'input_vocab.pt'
-    OUTPUT_VOCAB_FILE = 'output_vocab.pt'
-
-    def __init__(self, model, optimizer_state_dict, epoch, step, input_vocab, output_vocab, path=None):
-        self.model = model
-        self.optimizer_state_dict = optimizer_state_dict
-        self.input_vocab = input_vocab
-        self.output_vocab = output_vocab
-        self.epoch = epoch
-        self.step = step
-        self._path = path
-
-    @property
-    def path(self):
-        if self._path is None:
-            raise LookupError("The checkpoint has not been saved.")
-        return self._path
-
-
    [docs]    def save(self, experiment_dir):
-        """
-        Saves the current model and related training parameters into a subdirectory of the checkpoint directory.
-        The name of the subdirectory is the current local time in Y_M_D_H_M_S format.
-        Args:
-            experiment_dir (str): path to the experiment root directory
-        Returns:
-             str: path to the saved checkpoint subdirectory
-        """
-        date_time = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())
-
-        self._path = os.path.join(experiment_dir, self.CHECKPOINT_DIR_NAME, date_time)
-        path = self._path
-
-        if os.path.exists(path):
-            shutil.rmtree(path)
-        os.makedirs(path)
-        torch.save({'epoch': self.epoch,
-                    'step': self.step,
-                    'optimizer': self.optimizer_state_dict},
-                   os.path.join(path, self.TRAINER_STATE_NAME))
-        torch.save(self.model, os.path.join(path, self.MODEL_NAME))
-
-        if not os.path.isfile(os.path.join(path, self.INPUT_VOCAB_FILE)):
-            self.input_vocab.save(os.path.join(path, self.INPUT_VOCAB_FILE))
-        if not os.path.isfile(os.path.join(path, self.OUTPUT_VOCAB_FILE)):
-            self.output_vocab.save(os.path.join(path, self.OUTPUT_VOCAB_FILE))
-
-        return path
    
-
-
    [docs]    @classmethod
-    def load(cls, path):
-        """
-        Loads a Checkpoint object that was previously saved to disk.
-        Args:
-            path (str): path to the checkpoint subdirectory
-        Returns:
-            checkpoint (Checkpoint): checkpoint object with fields copied from those stored on disk
-        """
-        print "Loading checkpoints from {}".format(path)
-        resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))
-        model = torch.load(os.path.join(path, cls.MODEL_NAME))
-        input_vocab = Vocabulary.load(os.path.join(path, cls.INPUT_VOCAB_FILE))
-        output_vocab = Vocabulary.load(os.path.join(path, cls.OUTPUT_VOCAB_FILE))
-        return Checkpoint(model=model, input_vocab=input_vocab,
-                          output_vocab=output_vocab,
-                          optimizer_state_dict=resume_checkpoint['optimizer'],
-                          epoch=resume_checkpoint['epoch'],
-                          step=resume_checkpoint['step'],
-                          path=path)
    
-
-
    [docs]    @classmethod
-    def get_latest_checkpoint(cls, experiment_path):
-        """
-        Given the path to an experiment directory, returns the path to the last saved checkpoint's subdirectory.
-
-        Precondition: at least one checkpoint has been made (i.e., latest checkpoint subdirectory exists).
-        Args:
-            experiment_path (str): path to the experiment directory
-        Returns:
-             str: path to the last saved checkpoint's subdirectory
-        """
-        checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)
-        all_times = sorted(os.listdir(checkpoints_path), reverse=True)
-        return os.path.join(checkpoints_path, all_times[0])
    
-
    - -
    -
    - -
    -
    -
    - - -
    - -
    -

    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/_modules/seq2seq/util/custom_time.html b/docs/public/_modules/seq2seq/util/custom_time.html deleted file mode 100644 index fe0c088..0000000 --- a/docs/public/_modules/seq2seq/util/custom_time.html +++ /dev/null @@ -1,280 +0,0 @@ - - - - - - - - - - - seq2seq.util.custom_time — pytorch-seq2seq 0.1.2 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • Docs »
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      • - -
    • seq2seq.util.custom_time
      • - - -
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      • - -
    - - -
    -
    -
    -
    - -

    Source code for seq2seq.util.custom_time

    -import time
-
-
-
    [docs]def pretty_interval(start_time):
-    """
-    Given a previous point in time (e.g. the start of process, measured by calling time.time()), calculates
-    the time elapsed since that point, divides the elapsed time days/hours/minutes/seconds, and returns that summary
-
-    Args:
-        start_time (float): the start of the interval of time, obtained by calling time.time()
-    Returns:
-        str: elapsed time in '##d ##h ##m ##s' format
-    """
-    return pretty_time(time.time() - start_time)
    
-
-
-
    [docs]def pretty_time(timespan_in_seconds):
-    """
-    Given a length of time (measured in seconds), divides that timespan into
-    days/hours/minutes/seconds and returns that summary.
-
-    Args:
-     timespan_in_seconds (float): the number of seconds in a span of time
-
-    Returns:
-        str: timespan in '##d ##h ##m ##s' format
-
-
-    Examples::
-
-        >>> print seq2seq.util.custom_time.pretty_time(426753)
-        >>> 4d 22h 32m 33
-    """
-    seconds = abs(int(timespan_in_seconds))
-    msg = []
-    days, seconds = divmod(seconds, 86400)
-    if days > 0:
-        msg.append("%dd" % days)
-    hours, seconds = divmod(seconds, 3600)
-    if hours > 0:
-        msg.append("%dh" % hours)
-    minutes, seconds = divmod(seconds, 60)
-    if minutes > 0:
-        msg.append("%dm" % minutes)
-    msg.append("%ds" % seconds)
-    return " ".join(msg)
    
-
    - -
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    - © Copyright 2017, pytorch-seq2seq Contritors. - -

    -
    - Built with Sphinx using a theme provided by Read the Docs. - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/public/dataset.html b/docs/public/dataset.html index d53741c..e05c869 100644 --- a/docs/public/dataset.html +++ b/docs/public/dataset.html @@ -8,7 +8,7 @@ - Dataset — pytorch-seq2seq 0.1.5 documentation + Dataset — pytorch-seq2seq 0.1.6 documentation @@ -35,7 +35,7 @@ - + @@ -66,7 +66,7 @@
    - 0.1.5 + 0.1.6
    @@ -260,7 +260,7 @@

    Dataset var DOCUMENTATION_OPTIONS = { URL_ROOT:'./', - VERSION:'0.1.5', + VERSION:'0.1.6', COLLAPSE_INDEX:false, FILE_SUFFIX:'.html', HAS_SOURCE: true, diff --git a/docs/public/evaluator.html b/docs/public/evaluator.html index 6a97d4a..dd947a3 100644 --- a/docs/public/evaluator.html +++ b/docs/public/evaluator.html @@ -8,7 +8,7 @@ - Evaluator — pytorch-seq2seq 0.1.5 documentation + Evaluator — pytorch-seq2seq 0.1.6 documentation @@ -35,7 +35,7 @@ - + @@ -66,7 +66,7 @@
    - 0.1.5 + 0.1.6
    @@ -184,7 +184,7 @@

    evaluator

Parameters:
  • loss (seq2seq.loss, optional) – loss for evaluator (default: seq2seq.loss.NLLLoss)
    • -
  • batch_size (int, optional) – batch size for evaluator (default: 64)
    • +
  • batch_size (int, optional) – batch size for evaluator (default: 64)
Returns:

loss of the given model on the given dataset
Return type:

loss (float)

+
Return type:

loss (float)
Parameters:src_seq (list) – list of tokens in source language
Parameters:src_seq (list) – list of tokens in source language
Returns:list of tokens in target language as predicted by the pre-trained model
Return type:tgt_seq (list)
Return type:tgt_seq (list)
 +

+ +
+
+predict_n(src_seq, n=1)
+

Make ‘n’ predictions given src_seq as input.

+ +++ + + + + +
Parameters:
    +
  • src_seq (list) – list of tokens in source language
    • +
  • n (int) – number of predicted seqs to return. If None, +it will return just one seq.
    • +
+
Returns:

+
list of tokens in target language as predicted
+

by the pre-trained model

+
+
+

+
Return type:

tgt_seq (list)

+
@@ -290,7 +325,7 @@

evaluator var DOCUMENTATION_OPTIONS = { URL_ROOT:'./', - VERSION:'0.1.5', + VERSION:'0.1.6', COLLAPSE_INDEX:false, FILE_SUFFIX:'.html', HAS_SOURCE: true, diff --git a/docs/public/genindex.html b/docs/public/genindex.html index fdabc4f..6b59af9 100644 --- a/docs/public/genindex.html +++ b/docs/public/genindex.html @@ -9,7 +9,7 @@ - Index — pytorch-seq2seq 0.1.5 documentation + Index — pytorch-seq2seq 0.1.6 documentation @@ -36,7 +36,7 @@ - + @@ -65,7 +65,7 @@
- 0.1.5 + 0.1.6
@@ -257,10 +257,12 @@

F

G

@@ -324,6 +326,8 @@

P

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  • predict() (seq2seq.evaluator.predictor.Predictor method) +
    • +
  • predict_n() (seq2seq.evaluator.predictor.Predictor method)
  • Predictor (class in seq2seq.evaluator.predictor)
    • @@ -450,7 +454,7 @@

    U

    @@ -64,7 +64,7 @@
    - 0.1.5 + 0.1.6
    @@ -208,7 +208,7 @@ - +
    @@ -64,7 +56,7 @@
    - 0.1.6 + 0.1.7
    @@ -91,6 +83,11 @@

    Notes

    Package Reference

      @@ -112,7 +109,7 @@
      -
    @@ -179,9 +175,7 @@

    All modules for which code is available

-
- -
+
@@ -190,11 +184,11 @@

All modules for which code is available

- © Copyright 2017, pytorch-seq2seq Contritors. + © Copyright 2017, pytorch-seq2seq Contributors.

- Built with Sphinx using a theme provided by Read the Docs. + Built with Sphinx using a theme provided by Read the Docs.
@@ -212,7 +206,8 @@

All modules for which code is available

- + - - - + \ No newline at end of file diff --git a/docs/public/_sources/dataset.rst.txt b/docs/_sources/dataset.rst.txt similarity index 100% rename from docs/public/_sources/dataset.rst.txt rename to docs/_sources/dataset.rst.txt diff --git a/docs/public/_sources/evaluator.rst.txt b/docs/_sources/evaluator.rst.txt similarity index 100% rename from docs/public/_sources/evaluator.rst.txt rename to docs/_sources/evaluator.rst.txt diff --git a/docs/public/_sources/index.rst.txt b/docs/_sources/index.rst.txt similarity index 100% rename from docs/public/_sources/index.rst.txt rename to docs/_sources/index.rst.txt diff --git a/docs/public/_sources/loss.rst.txt b/docs/_sources/loss.rst.txt similarity index 100% rename from docs/public/_sources/loss.rst.txt rename to docs/_sources/loss.rst.txt diff --git a/docs/public/_sources/models.rst.txt b/docs/_sources/models.rst.txt similarity index 100% rename from docs/public/_sources/models.rst.txt rename to docs/_sources/models.rst.txt diff --git a/docs/public/_sources/notes/intro.md.txt b/docs/_sources/notes/intro.md.txt similarity index 52% rename from docs/public/_sources/notes/intro.md.txt rename to docs/_sources/notes/intro.md.txt index 888af3f..b72feb6 100644 --- a/docs/public/_sources/notes/intro.md.txt +++ b/docs/_sources/notes/intro.md.txt @@ -1,17 +1,24 @@ # Introduction -This is a framework for sequence-to-sequence (seq2seq) models implemented in [PyTorch](http://pytorch.org). The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. +**pytorch-seq2seq** is a simple, efficient and scalable framework implemented in [PyTorch](http://pytorch.org), to get you up and running in no time on sequence-to-sequence learning tasks. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. -## Roadmap -Seq2seq is a fast evolving field with new techniques and architectures being published frequently. The goal of this library is facilitating the development of such techniques and applications. While constantly improving the quality of code and documentation, we will focus on the following items: +# What's New in 0.1.7 +* Compatible with PyTorch 0.4.1 +* Added support for pre-trained word embedding +* Minor Bug fixes + +# Roadmap +Sequence to sequence learning is a fast evolving space with new techniques and architectures being published frequently. The goal of this library is facilitating the development of such techniques and applications. While constantly improving the quality of code and documentation, we will focus on the following items: + +* Tutorials with examples on how to quickly get started with the library; * Evaluation with benchmarks such as WMT machine translation, COCO image captioning, conversational models, etc; -* Provide more flexible model options, improving the usability of the library; -* Adding latest architectures such as the CNN based model proposed by [Convolutional Sequence to Sequence Learning](https://arxiv.org/abs/1705.03122) and the transformer model proposed by [Attention Is All You Need](https://arxiv.org/abs/1706.03762); +* Provide more flexible model options and improve the usability of the library; +* Adding latest architectures such as the CNN based model proposed by [Convolutional Sequence to Sequence Learning](https://arxiv.org/abs/1705.03122), the transformer model proposed by [Attention Is All You Need](https://arxiv.org/abs/1706.03762) and the deep reinforced model proposed by [A Deep Reinforced Model for Abstractive Summarization](https://arxiv.org/abs/1705.04304); * Support features in the new versions of PyTorch. -## Installation -This package requires Python 2.7. We recommend creating a new virtual environment for this project (using virtualenv or conda). +# Installation +This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda). ### Prerequisites @@ -24,9 +31,9 @@ Currently we only support installation from source code using setuptools. Check pip install -r requirements.txt python setup.py install -If you already had a version of PyTorch installed on your system, please verify that the active torch package is at least version 0.1.11. +If you already had a version of PyTorch installed on your system, please verify that it is at least `v0.4.1` for compatibility with our latest release `v0.1.7`. -## Get Started +# Get Started ### Prepare toy dataset # Run script to generate the reverse toy dataset @@ -58,13 +65,18 @@ Checkpoints are organized by experiments and timestamps as shown in the followin The sample script by default saves checkpoints in the `experiment` folder of the root directory. Look at the usages of the sample code for more options, including resuming and loading from checkpoints. -## Troubleshoots and Contributing -If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/IBM/pytorch-seq2seq/issues/new) on Github. +# Troubleshoots and Contributing +If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/IBM/pytorch-seq2seq/issues/new). For live discussions, please go to our [Gitter lobby](https://gitter.im/pytorch-seq2seq/Lobby). -We appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues. +We appreciate any kind of feedback or contribution. Feel free to proceed with small issues like questions, bug fixes and documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues. ### Development Environment -We setup the development environment using [Vagrant](https://www.vagrantup.com/). Run `vagrant up` with our 'Vagrantfile' to get started. +We setup the development environment using [Vagrant](https://www.vagrantup.com/). To get started, make sure that you have [VirtualBox](https://www.virtualbox.org/) installed on your system, then run `vagrant up` with our 'Vagrantfile'. + +[Docker](https://www.docker.com/) support for development coming soon. + +### Test +The quality and the maintainability of the project is ensured by comprehensive tests. We encourage writing unit tests and integration tests when contributing new codes. ### Code Style We follow [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) for code style. Especially the style of docstrings is important to generate documentation. diff --git a/docs/public/_sources/optim.rst.txt b/docs/_sources/optim.rst.txt similarity index 100% rename from docs/public/_sources/optim.rst.txt rename to docs/_sources/optim.rst.txt diff --git a/docs/public/_sources/trainer.rst.txt b/docs/_sources/trainer.rst.txt similarity index 100% rename from docs/public/_sources/trainer.rst.txt rename to docs/_sources/trainer.rst.txt diff --git a/docs/public/_sources/util.rst.txt b/docs/_sources/util.rst.txt similarity index 100% rename from docs/public/_sources/util.rst.txt rename to docs/_sources/util.rst.txt diff --git a/docs/public/_static/ajax-loader.gif b/docs/_static/ajax-loader.gif similarity index 100% rename from docs/public/_static/ajax-loader.gif rename to docs/_static/ajax-loader.gif diff --git a/docs/public/_static/basic.css b/docs/_static/basic.css similarity index 95% rename from docs/public/_static/basic.css rename to docs/_static/basic.css index 6df76b0..19ced10 100644 --- a/docs/public/_static/basic.css +++ b/docs/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -82,9 +82,21 @@ div.sphinxsidebar input { } div.sphinxsidebar #searchbox input[type="text"] { - width: 170px; + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; } + img { border: 0; max-width: 100%; @@ -199,6 +211,11 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ +div.body { + min-width: 450px; + max-width: 800px; +} + div.body p, div.body dd, div.body li, div.body blockquote { -moz-hyphens: auto; -ms-hyphens: auto; @@ -332,6 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5654977..d892892 100644 --- a/docs/public/_static/doctools.js +++ b/docs/_static/doctools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for all documentation. * - * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -45,7 +45,7 @@ jQuery.urlencode = encodeURIComponent; * it will always return arrays of strings for the value parts. */ jQuery.getQueryParameters = function(s) { - if (typeof s == 'undefined') + if (typeof s === 'undefined') s = document.location.search; var parts = s.substr(s.indexOf('?') + 1).split('&'); var result = {}; @@ -66,29 +66,55 @@ jQuery.getQueryParameters = function(s) { * span elements with the given class name. */ jQuery.fn.highlightText = function(text, className) { - function highlight(node) { - if (node.nodeType == 3) { + function highlight(node, addItems) { + if (node.nodeType === 3) { var val = node.nodeValue; var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) { - var span = document.createElement("span"); - span.className = className; + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } span.appendChild(document.createTextNode(val.substr(pos, text.length))); node.parentNode.insertBefore(span, node.parentNode.insertBefore( document.createTextNode(val.substr(pos + text.length)), node.nextSibling)); node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var bbox = span.getBBox(); + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + var parentOfText = node.parentNode.parentNode; + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } } } else if (!jQuery(node).is("button, select, textarea")) { jQuery.each(node.childNodes, function() { - highlight(this); + highlight(this, addItems); }); } } - return this.each(function() { - highlight(this); + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; }; /* @@ -131,21 +157,21 @@ var Documentation = { * i18n support */ TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; }, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, LOCALE : 'unknown', // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) gettext : function(string) { var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated == 'undefined') + if (typeof translated === 'undefined') return string; - return (typeof translated == 'string') ? translated : translated[0]; + return (typeof translated === 'string') ? translated : translated[0]; }, ngettext : function(singular, plural, n) { var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated == 'undefined') + if (typeof translated === 'undefined') return (n == 1) ? singular : plural; return translated[Documentation.PLURALEXPR(n)]; }, @@ -180,7 +206,7 @@ var Documentation = { * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 */ fixFirefoxAnchorBug : function() { - if (document.location.hash) + if (document.location.hash && $.browser.mozilla) window.setTimeout(function() { document.location.href += ''; }, 10); @@ -216,7 +242,7 @@ var Documentation = { var src = $(this).attr('src'); var idnum = $(this).attr('id').substr(7); $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) == 'minus.png') + if (src.substr(-9) === 'minus.png') $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); else $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); @@ -248,7 +274,7 @@ var Documentation = { var path = document.location.pathname; var parts = path.split(/\//); $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this == '..') + if (this === '..') parts.pop(); }); var url = parts.join('/'); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js new file mode 100644 index 0000000..9c9c947 --- /dev/null +++ b/docs/_static/documentation_options.js @@ -0,0 +1,9 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '0.1.7', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + FILE_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt' +}; 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Notes

Package Reference

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-
- -
+
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- © Copyright 2017, pytorch-seq2seq Contritors. + © Copyright 2017, pytorch-seq2seq Contributors.

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Trainer var DOCUMENTATION_OPTIONS = { URL_ROOT:'./', - VERSION:'0.1.6', + VERSION:'0.1.7', + LANGUAGE:'None', COLLAPSE_INDEX:false, FILE_SUFFIX:'.html', HAS_SOURCE: true, @@ -287,19 +284,13 @@

Trainer - + - - - + \ No newline at end of file diff --git a/docs/public/util.html b/docs/util.html similarity index 85% rename from docs/public/util.html rename to docs/util.html index 7d10188..e43f6d2 100644 --- a/docs/public/util.html +++ b/docs/util.html @@ -8,7 +8,7 @@ - Util — pytorch-seq2seq 0.1.6 documentation + Util — pytorch-seq2seq 0.1.7 documentation @@ -24,27 +24,19 @@ - - - - - - - - - - - - - + + + + + + - +
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Notes

Package Reference