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Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/translate/beam.py
python
GNMTGlobalScorer.update_score
(self, beam, attn)
Function to update scores of a Beam that is not finished
Function to update scores of a Beam that is not finished
[ "Function", "to", "update", "scores", "of", "a", "Beam", "that", "is", "not", "finished" ]
def update_score(self, beam, attn): """ Function to update scores of a Beam that is not finished """ if "prev_penalty" in beam.global_state.keys(): beam.scores.add_(beam.global_state["prev_penalty"]) penalty = self.cov_penalty(beam, beam.global_state["coverage"] + attn, self.beta) beam.scores.sub_(penalty)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/translate/beam.py#L231-L240
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/translate/beam.py
python
GNMTGlobalScorer.update_global_state
(self, beam)
Keeps the coverage vector as sum of attentions
Keeps the coverage vector as sum of attentions
[ "Keeps", "the", "coverage", "vector", "as", "sum", "of", "attentions" ]
def update_global_state(self, beam): "Keeps the coverage vector as sum of attentions" if len(beam.prev_ks) == 1: beam.global_state["prev_penalty"] = beam.scores.clone().fill_(0.0) beam.global_state["coverage"] = beam.attn[-1] self.cov_total = beam.attn[-1].sum(1) else: self.cov_total += torch.min(beam.attn[-1], beam.global_state['coverage']).sum(1) beam.global_state["coverage"] = beam.global_state["coverage"] \ .index_select(0, beam.prev_ks[-1]).add(beam.attn[-1]) prev_penalty = self.cov_penalty(beam, beam.global_state["coverage"], self.beta) beam.global_state["prev_penalty"] = prev_penalty
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/translate/beam.py#L242-L257
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/translate/translation.py
python
Translation.log
(self, sent_number)
return output
Log translation.
Log translation.
[ "Log", "translation", "." ]
def log(self, sent_number): """ Log translation. """ output = '\nSENT {}: {}\n'.format(sent_number, self.src_raw) best_pred = self.pred_sents[0] best_score = self.pred_scores[0] pred_sent = ' '.join(best_pred) output += 'PRED {}: {}\n'.format(sent_number, pred_sent) output += "PRED SCORE: {:.4f}\n".format(best_score) if self.gold_sent is not None: tgt_sent = ' '.join(self.gold_sent) output += 'GOLD {}: {}\n'.format(sent_number, tgt_sent) output += ("GOLD SCORE: {:.4f}\n".format(self.gold_score)) if len(self.pred_sents) > 1: output += '\nBEST HYP:\n' for score, sent in zip(self.pred_scores, self.pred_sents): output += "[{:.4f}] {}\n".format(score, sent) return output
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/translate/translation.py#L134-L156
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
RNNDecoderBase.forward
(self, tgt, memory_bank, state, memory_lengths=None, step=None,sent_encoder=None,src_sents=None,dec=None)
return decoder_outputs, state, attns
Args: tgt (`LongTensor`): sequences of padded tokens `[tgt_len x batch x nfeats]`. memory_bank (`FloatTensor`): vectors from the encoder `[src_len x batch x hidden]`. state (:obj:`onmt.models.DecoderState`): decoder state object to initialize the decoder memory_lengths (`LongTensor`): the padded source lengths `[batch]`. Returns: (`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`): * decoder_outputs: output from the decoder (after attn) `[tgt_len x batch x hidden]`. * decoder_state: final hidden state from the decoder * attns: distribution over src at each tgt `[tgt_len x batch x src_len]`.
Args: tgt (`LongTensor`): sequences of padded tokens `[tgt_len x batch x nfeats]`. memory_bank (`FloatTensor`): vectors from the encoder `[src_len x batch x hidden]`. state (:obj:`onmt.models.DecoderState`): decoder state object to initialize the decoder memory_lengths (`LongTensor`): the padded source lengths `[batch]`. Returns: (`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`): * decoder_outputs: output from the decoder (after attn) `[tgt_len x batch x hidden]`. * decoder_state: final hidden state from the decoder * attns: distribution over src at each tgt `[tgt_len x batch x src_len]`.
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def forward(self, tgt, memory_bank, state, memory_lengths=None, step=None,sent_encoder=None,src_sents=None,dec=None): """ Args: tgt (`LongTensor`): sequences of padded tokens `[tgt_len x batch x nfeats]`. memory_bank (`FloatTensor`): vectors from the encoder `[src_len x batch x hidden]`. state (:obj:`onmt.models.DecoderState`): decoder state object to initialize the decoder memory_lengths (`LongTensor`): the padded source lengths `[batch]`. Returns: (`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`): * decoder_outputs: output from the decoder (after attn) `[tgt_len x batch x hidden]`. * decoder_state: final hidden state from the decoder * attns: distribution over src at each tgt `[tgt_len x batch x src_len]`. """ # Check assert isinstance(state, RNNDecoderState) # tgt.size() returns tgt length and batch _, tgt_batch, _ = tgt.size() _, memory_batch, _ = memory_bank.size() aeq(tgt_batch, memory_batch) # END # 23333: TODO I changed this return value 'sent_decoder' # Run the forward pass of the RNN. decoder_final, decoder_outputs, attns = self._run_forward_pass( tgt, memory_bank, state, memory_lengths=memory_lengths,sent_encoder=sent_encoder,src_sents=src_sents,dec=dec) # Update the state with the result. final_output = decoder_outputs[-1] coverage = None if "coverage" in attns: coverage = attns["coverage"][-1].unsqueeze(0) state.update_state(decoder_final, final_output.unsqueeze(0), coverage) # Concatenates sequence of tensors along a new dimension. # NOTE: v0.3 to 0.4: decoder_outputs / attns[*] may not be list # (in particular in case of SRU) it was not raising error in 0.3 # since stack(Variable) was allowed. # In 0.4, SRU returns a tensor that shouldn't be stacke if type(decoder_outputs) == list: decoder_outputs = torch.stack(decoder_outputs) for k in attns: if type(attns[k]) == list: attns[k] = torch.stack(attns[k]) return decoder_outputs, state, attns
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L115-L172
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
RNNDecoderBase.init_decoder_state
(self, src, memory_bank, encoder_final, with_cache=False)
Init decoder state with last state of the encoder
Init decoder state with last state of the encoder
[ "Init", "decoder", "state", "with", "last", "state", "of", "the", "encoder" ]
def init_decoder_state(self, src, memory_bank, encoder_final, with_cache=False): """ Init decoder state with last state of the encoder """ def _fix_enc_hidden(hidden): # The encoder hidden is (layers*directions) x batch x dim. # We need to convert it to layers x batch x (directions*dim). if self.bidirectional_encoder: hidden = torch.cat([hidden[0:hidden.size(0):2], hidden[1:hidden.size(0):2]], 2) return hidden if isinstance(encoder_final, tuple): # LSTM return RNNDecoderState(self.hidden_size, tuple([_fix_enc_hidden(enc_hid) for enc_hid in encoder_final])) else: # GRU return RNNDecoderState(self.hidden_size, _fix_enc_hidden(encoder_final))
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L174-L191
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
StdRNNDecoder._run_forward_pass
(self, tgt, memory_bank, state, memory_lengths=None, dec=False)
return decoder_final, decoder_outputs, attns
Private helper for running the specific RNN forward pass. Must be overriden by all subclasses. Args: tgt (LongTensor): a sequence of input tokens tensors [len x batch x nfeats]. memory_bank (FloatTensor): output(tensor sequence) from the encoder RNN of size (src_len x batch x hidden_size). state (FloatTensor): hidden state from the encoder RNN for initializing the decoder. memory_lengths (LongTensor): the source memory_bank lengths. Returns: decoder_final (Tensor): final hidden state from the decoder. decoder_outputs ([FloatTensor]): an array of output of every time step from the decoder. attns (dict of (str, [FloatTensor]): a dictionary of different type of attention Tensor array of every time step from the decoder.
Private helper for running the specific RNN forward pass. Must be overriden by all subclasses. Args: tgt (LongTensor): a sequence of input tokens tensors [len x batch x nfeats]. memory_bank (FloatTensor): output(tensor sequence) from the encoder RNN of size (src_len x batch x hidden_size). state (FloatTensor): hidden state from the encoder RNN for initializing the decoder. memory_lengths (LongTensor): the source memory_bank lengths. Returns: decoder_final (Tensor): final hidden state from the decoder. decoder_outputs ([FloatTensor]): an array of output of every time step from the decoder. attns (dict of (str, [FloatTensor]): a dictionary of different type of attention Tensor array of every time step from the decoder.
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def _run_forward_pass(self, tgt, memory_bank, state, memory_lengths=None, dec=False): """ Private helper for running the specific RNN forward pass. Must be overriden by all subclasses. Args: tgt (LongTensor): a sequence of input tokens tensors [len x batch x nfeats]. memory_bank (FloatTensor): output(tensor sequence) from the encoder RNN of size (src_len x batch x hidden_size). state (FloatTensor): hidden state from the encoder RNN for initializing the decoder. memory_lengths (LongTensor): the source memory_bank lengths. Returns: decoder_final (Tensor): final hidden state from the decoder. decoder_outputs ([FloatTensor]): an array of output of every time step from the decoder. attns (dict of (str, [FloatTensor]): a dictionary of different type of attention Tensor array of every time step from the decoder. """ assert not self._copy # TODO, no support yet. assert not self._coverage # TODO, no support yet. # Initialize local and return variables. attns = {} emb = self.embeddings(tgt) # Run the forward pass of the RNN. if isinstance(self.rnn, nn.GRU): rnn_output, decoder_final = self.rnn(emb, state.hidden[0]) else: rnn_output, decoder_final = self.rnn(emb, state.hidden) # Check tgt_len, tgt_batch, _ = tgt.size() output_len, output_batch, _ = rnn_output.size() aeq(tgt_len, output_len) aeq(tgt_batch, output_batch) # END # Calculate the attention. decoder_outputs, p_attn = self.attn( rnn_output.transpose(0, 1).contiguous(), memory_bank.transpose(0, 1), memory_lengths=memory_lengths ) attns["std"] = p_attn # Calculate the context gate. if self.context_gate is not None: decoder_outputs = self.context_gate( emb.view(-1, emb.size(2)), rnn_output.view(-1, rnn_output.size(2)), decoder_outputs.view(-1, decoder_outputs.size(2)) ) decoder_outputs = \ decoder_outputs.view(tgt_len, tgt_batch, self.hidden_size) decoder_outputs = self.dropout(decoder_outputs) return decoder_final, decoder_outputs, attns
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L210-L271
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
StdRNNDecoder._input_size
(self)
return self.embeddings.embedding_size
Private helper returning the number of expected features.
Private helper returning the number of expected features.
[ "Private", "helper", "returning", "the", "number", "of", "expected", "features", "." ]
def _input_size(self): """ Private helper returning the number of expected features. """ return self.embeddings.embedding_size
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L278-L282
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
InputFeedRNNDecoder._run_mmr
(self,sent_encoder,sent_decoder,src_sents, input_step)
return mmr_among_words
# sent_encoder: size (sent_len=9,batch=2,dim=512) # sent_decoder: size (sent_len=1,batch=2,dim=512) # src_sents: size (batch=2,sent_len=9) function to calculate mmr :param sent_encoder: :param sent_decoder: :param src_sents: :return:
# sent_encoder: size (sent_len=9,batch=2,dim=512) # sent_decoder: size (sent_len=1,batch=2,dim=512) # src_sents: size (batch=2,sent_len=9) function to calculate mmr :param sent_encoder: :param sent_decoder: :param src_sents: :return:
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def _run_mmr(self,sent_encoder,sent_decoder,src_sents, input_step): ''' # sent_encoder: size (sent_len=9,batch=2,dim=512) # sent_decoder: size (sent_len=1,batch=2,dim=512) # src_sents: size (batch=2,sent_len=9) function to calculate mmr :param sent_encoder: :param sent_decoder: :param src_sents: :return: ''' pdist = nn.PairwiseDistance(p=2) sent_decoder=sent_decoder.permute(1,0,2) # (2,1,512) scores =[] # define sent matrix and current vector distance as the Euclidean distance for sent in sent_encoder: # iterate over each batch sample # distance: https://pytorch.org/docs/stable/_modules/torch/nn/modules/distance.html # import pdb; # pdb.set_trace() # sim1=torch.sum(pdist(sent_encoder.permute(1,0,2),sent.unsqueeze(1)),1).unsqueeze(1) # -> this is sim2 on my equation, note this is distance! sim1 = 1 - torch.mean(pdist(sent_encoder.permute(1, 0, 2), sent.unsqueeze(1)), 1).unsqueeze(1) # this is a similarity function # sim1 shape: (batch_size,1) sim2=torch.bmm(self.mmr_W(sent_decoder),sent.unsqueeze(2)).squeeze(2) # (2,1) -> this is sim1 on my equation # scores.append(sim1-sim2) scores.append(sim2 - sim1) sent_ranking_att = torch.t(torch.cat(scores,1)) #(sent_len=9,batch_size) sent_ranking_att = torch.softmax(sent_ranking_att, dim=0).permute(1,0) #(sent_len=9,batch_size) # scores is a list of score (sent_len=9, tensor shape (batch_size, 1)) mmr_among_words = [] # should be (batch=2,input_step=200) for batch_id in range(sent_ranking_att.size()[0]): # iterate each batch, create zero weight on the input steps # mmr= torch.zeros([input_step], dtype=torch.float32).cuda() tmp = [] for id,position in enumerate(src_sents[batch_id]): for x in range(position): tmp.append(sent_ranking_att[batch_id][id]) mmr = torch.stack(tmp) # make to 1-d if len(mmr) < input_step: # pad with 0 tmp = torch.zeros(input_step - len(mmr)).float().cuda() # for x in range(input_step-len(mmr)): mmr = torch.cat((mmr, tmp), 0) else: mmr = mmr[:input_step] mmr_among_words.append(mmr.unsqueeze(0)) mmr_among_words = torch.cat(mmr_among_words,0) # shape: (batch=2, input_step=200) return mmr_among_words
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L315-L379
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
InputFeedRNNDecoder._run_forward_pass
(self, tgt, memory_bank, state, memory_lengths=None,sent_encoder=None,src_sents=None,dec=None)
return hidden, decoder_outputs, attns
See StdRNNDecoder._run_forward_pass() for description of arguments and return values. TODO: added a new param: sent_encoder, from model.py, this is the sentence matrix; add attns["mmr"] = [].
See StdRNNDecoder._run_forward_pass() for description of arguments and return values. TODO: added a new param: sent_encoder, from model.py, this is the sentence matrix; add attns["mmr"] = [].
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def _run_forward_pass(self, tgt, memory_bank, state, memory_lengths=None,sent_encoder=None,src_sents=None,dec=None): """ See StdRNNDecoder._run_forward_pass() for description of arguments and return values. TODO: added a new param: sent_encoder, from model.py, this is the sentence matrix; add attns["mmr"] = []. """ # Additional args check. input_feed = state.input_feed.squeeze(0) #print("input feed size: {}\n".format(input_feed.size())) input_feed_batch, _ = input_feed.size() _, tgt_batch, _ = tgt.size() aeq(tgt_batch, input_feed_batch) # END Additional args check. # Initialize local and return variables. decoder_outputs = [] attns = {"std": []} if self._copy: attns["copy"] = [] if self._coverage: attns["coverage"] = [] emb = self.embeddings(tgt) assert emb.dim() == 3 # len x batch x embedding_dim hidden = state.hidden coverage = state.coverage.squeeze(0) \ if state.coverage is not None else None # Input feed concatenates hidden state with # input at every time step. #print("emb size: {}\n".format(emb.size()));exit() for _, emb_t in enumerate(emb.split(1)): # for each output time step in the loop emb_t = emb_t.squeeze(0) decoder_input = torch.cat([emb_t, input_feed], 1) # TODO: the following is where we get attention! rnn_output, hidden = self.rnn(decoder_input, hidden) decoder_output, p_attn = self.attn( rnn_output, memory_bank.transpose(0, 1), memory_lengths=memory_lengths) # p_attn: size (batch=2,input_step=200) if self.context_gate is not None: # TODO: context gate should be employed (not me) # instead of second RNN transform. decoder_output = self.context_gate( decoder_input, rnn_output, decoder_output ) decoder_output = self.dropout(decoder_output) input_feed = decoder_output decoder_outputs += [decoder_output] attns["std"] += [p_attn] # Update the coverage attention. if self._coverage: coverage = coverage + p_attn \ if coverage is not None else p_attn attns["coverage"] += [coverage] # Run the forward pass of the copy attention layer. # if self._copy and not self._reuse_copy_attn: _, copy_attn = self.copy_attn(decoder_output, memory_bank.transpose(0, 1)) attns["copy"] += [copy_attn] elif self._copy: attns["copy"] = attns["std"] # attns["copy"] is a list of tensor for each output step=51, each size: [batch_size=2, input_step=200] if not dec: #if this is not dec? attns["mmr"] = [] # 2333: TODO : the sentence representation for decoder sent_decoder = decoder_outputs[-1].unsqueeze(0) # shape: (1, batch_size=2,dim=512) # Return result. # 2333: TODO: attns['std'] is a list of tensors, length is output_step, each tensor shape is (batch=2,input_step=200) # 2333: TODO: compute mmr attention here: mmr_among_words = self._run_mmr(sent_encoder, sent_decoder, src_sents,attns["std"][0].size()[-1]) # 2333: TODO: bring mmr to attention... for output_step in attns["std"]: attention_weight = output_step # pairwise multiplication attention_weight = torch.mul(mmr_among_words,attention_weight) attns["mmr"].append(attention_weight.cuda()) # pdb.set_trace() attns["std"] = attns["mmr"] # decoder_outputs is a list of tensors for each output step=51, each tensor: (batch_size=2,dim=512) return hidden, decoder_outputs, attns
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L381-L485
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
InputFeedRNNDecoder._input_size
(self)
return self.embeddings.embedding_size + self.hidden_size
Using input feed by concatenating input with attention vectors.
Using input feed by concatenating input with attention vectors.
[ "Using", "input", "feed", "by", "concatenating", "input", "with", "attention", "vectors", "." ]
def _input_size(self): """ Using input feed by concatenating input with attention vectors. """ return self.embeddings.embedding_size + self.hidden_size
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L499-L503
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
DecoderState.detach
(self)
Need to document this
Need to document this
[ "Need", "to", "document", "this" ]
def detach(self): """ Need to document this """ self.hidden = tuple([_.detach() for _ in self.hidden]) self.input_feed = self.input_feed.detach()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L514-L517
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
DecoderState.beam_update
(self, idx, positions, beam_size)
Need to document this
Need to document this
[ "Need", "to", "document", "this" ]
def beam_update(self, idx, positions, beam_size): """ Need to document this """ for e in self._all: sizes = e.size() br = sizes[1] if len(sizes) == 3: sent_states = e.view(sizes[0], beam_size, br // beam_size, sizes[2])[:, :, idx] else: sent_states = e.view(sizes[0], beam_size, br // beam_size, sizes[2], sizes[3])[:, :, idx] sent_states.data.copy_( sent_states.data.index_select(1, positions))
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L519-L534
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
RNNDecoderState.__init__
(self, hidden_size, rnnstate)
Args: hidden_size (int): the size of hidden layer of the decoder. rnnstate: final hidden state from the encoder. transformed to shape: layers x batch x (directions*dim).
Args: hidden_size (int): the size of hidden layer of the decoder. rnnstate: final hidden state from the encoder. transformed to shape: layers x batch x (directions*dim).
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def __init__(self, hidden_size, rnnstate): """ Args: hidden_size (int): the size of hidden layer of the decoder. rnnstate: final hidden state from the encoder. transformed to shape: layers x batch x (directions*dim). """ if not isinstance(rnnstate, tuple): self.hidden = (rnnstate,) else: self.hidden = rnnstate self.coverage = None # Init the input feed. batch_size = self.hidden[0].size(1) h_size = (batch_size, hidden_size) self.input_feed = self.hidden[0].data.new(*h_size).zero_() \ .unsqueeze(0)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L543-L560
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
RNNDecoderState.update_state
(self, rnnstate, input_feed, coverage)
Update decoder state
Update decoder state
[ "Update", "decoder", "state" ]
def update_state(self, rnnstate, input_feed, coverage): """ Update decoder state """ if not isinstance(rnnstate, tuple): self.hidden = (rnnstate,) else: self.hidden = rnnstate self.input_feed = input_feed self.coverage = coverage
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L566-L573
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/decoder.py
python
RNNDecoderState.repeat_beam_size_times
(self, beam_size)
Repeat beam_size times along batch dimension.
Repeat beam_size times along batch dimension.
[ "Repeat", "beam_size", "times", "along", "batch", "dimension", "." ]
def repeat_beam_size_times(self, beam_size): """ Repeat beam_size times along batch dimension. """ vars = [e.data.repeat(1, beam_size, 1) for e in self._all] self.hidden = tuple(vars[:-1]) self.input_feed = vars[-1]
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/decoder.py#L575-L580
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
load_test_model
(opt, dummy_opt)
return shared_fields, ensemble_model, shared_model_opt
Read in multiple models for ensemble
Read in multiple models for ensemble
[ "Read", "in", "multiple", "models", "for", "ensemble" ]
def load_test_model(opt, dummy_opt): """ Read in multiple models for ensemble """ shared_fields = None shared_model_opt = None models = [] for model_path in opt.models: fields, model, model_opt = \ onmt.model_builder.load_test_model(opt, dummy_opt, model_path=model_path) import pdb;pdb.set_trace() if shared_fields is None: shared_fields = fields else: for key, field in fields.items(): if field is not None and 'vocab' in field.__dict__: assert field.vocab.stoi == shared_fields[key].vocab.stoi, \ 'Ensemble models must use the same preprocessed data' models.append(model) if shared_model_opt is None: shared_model_opt = model_opt ensemble_model = EnsembleModel(models) return shared_fields, ensemble_model, shared_model_opt
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L135-L157
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
EnsembleDecoderState.repeat_beam_size_times
(self, beam_size)
Repeat beam_size times along batch dimension.
Repeat beam_size times along batch dimension.
[ "Repeat", "beam_size", "times", "along", "batch", "dimension", "." ]
def repeat_beam_size_times(self, beam_size): """ Repeat beam_size times along batch dimension. """ for model_state in self.model_decoder_states: model_state.repeat_beam_size_times(beam_size)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L27-L30
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
EnsembleDecoderOutput.squeeze
(self, dim=None)
return EnsembleDecoderOutput([ x.squeeze(dim) for x in self.model_outputs])
Delegate squeeze to avoid modifying :obj:`Translator.translate_batch()`
Delegate squeeze to avoid modifying :obj:`Translator.translate_batch()`
[ "Delegate", "squeeze", "to", "avoid", "modifying", ":", "obj", ":", "Translator", ".", "translate_batch", "()" ]
def squeeze(self, dim=None): """ Delegate squeeze to avoid modifying :obj:`Translator.translate_batch()` """ return EnsembleDecoderOutput([ x.squeeze(dim) for x in self.model_outputs])
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L41-L47
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
EnsembleDecoder.forward
(self, tgt, memory_bank, state, memory_lengths=None, step=None)
return (EnsembleDecoderOutput(outputs), EnsembleDecoderState(states), mean_attns)
See :obj:`RNNDecoderBase.forward()`
See :obj:`RNNDecoderBase.forward()`
[ "See", ":", "obj", ":", "RNNDecoderBase", ".", "forward", "()" ]
def forward(self, tgt, memory_bank, state, memory_lengths=None, step=None): """ See :obj:`RNNDecoderBase.forward()` """ # Memory_lengths is a single tensor shared between all models. # This assumption will not hold if Translator is modified # to calculate memory_lengths as something other than the length # of the input. outputs, states, attns = zip(*[ model_decoder.forward( tgt, memory_bank[i], state[i], memory_lengths, step=step) for (i, model_decoder) in enumerate(self.model_decoders)]) mean_attns = self.combine_attns(attns) return (EnsembleDecoderOutput(outputs), EnsembleDecoderState(states), mean_attns)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L72-L87
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
EnsembleDecoder.init_decoder_state
(self, src, memory_bank, enc_hidden)
return EnsembleDecoderState( [model_decoder.init_decoder_state(src, memory_bank[i], enc_hidden[i]) for (i, model_decoder) in enumerate(self.model_decoders)])
See :obj:`RNNDecoderBase.init_decoder_state()`
See :obj:`RNNDecoderBase.init_decoder_state()`
[ "See", ":", "obj", ":", "RNNDecoderBase", ".", "init_decoder_state", "()" ]
def init_decoder_state(self, src, memory_bank, enc_hidden): """ See :obj:`RNNDecoderBase.init_decoder_state()` """ return EnsembleDecoderState( [model_decoder.init_decoder_state(src, memory_bank[i], enc_hidden[i]) for (i, model_decoder) in enumerate(self.model_decoders)])
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L95-L101
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/ensemble.py
python
EnsembleGenerator.forward
(self, hidden)
return torch.stack(distributions).mean(0)
Compute a distribution over the target dictionary by averaging distributions from models in the ensemble. All models in the ensemble must share a target vocabulary.
Compute a distribution over the target dictionary by averaging distributions from models in the ensemble. All models in the ensemble must share a target vocabulary.
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def forward(self, hidden): """ Compute a distribution over the target dictionary by averaging distributions from models in the ensemble. All models in the ensemble must share a target vocabulary. """ distributions = [model_generator.forward(hidden[i]) for (i, model_generator) in enumerate(self.model_generators)] return torch.stack(distributions).mean(0)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/ensemble.py#L113-L122
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoderLayer.forward
(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask, previous_input=None, layer_cache=None, step=None)
return output, attn, all_input
Args: inputs (`FloatTensor`): `[batch_size x 1 x model_dim]` memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]` src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]` tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]` Returns: (`FloatTensor`, `FloatTensor`, `FloatTensor`): * output `[batch_size x 1 x model_dim]` * attn `[batch_size x 1 x src_len]` * all_input `[batch_size x current_step x model_dim]`
Args: inputs (`FloatTensor`): `[batch_size x 1 x model_dim]` memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]` src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]` tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]`
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def forward(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask, previous_input=None, layer_cache=None, step=None): """ Args: inputs (`FloatTensor`): `[batch_size x 1 x model_dim]` memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]` src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]` tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]` Returns: (`FloatTensor`, `FloatTensor`, `FloatTensor`): * output `[batch_size x 1 x model_dim]` * attn `[batch_size x 1 x src_len]` * all_input `[batch_size x current_step x model_dim]` """ dec_mask = torch.gt(tgt_pad_mask + self.mask[:, :tgt_pad_mask.size(1), :tgt_pad_mask.size(1)], 0) input_norm = self.layer_norm_1(inputs) all_input = input_norm if previous_input is not None: all_input = torch.cat((previous_input, input_norm), dim=1) dec_mask = None if self.self_attn_type == "scaled-dot": query, attn = self.self_attn(all_input, all_input, input_norm, mask=dec_mask, layer_cache=layer_cache, type="self") elif self.self_attn_type == "average": query, attn = self.self_attn(input_norm, mask=dec_mask, layer_cache=layer_cache, step=step) query = self.drop(query) + inputs query_norm = self.layer_norm_2(query) mid, attn = self.context_attn(memory_bank, memory_bank, query_norm, mask=src_pad_mask, layer_cache=layer_cache, type="context") output = self.feed_forward(self.drop(mid) + query) return output, attn, all_input
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L53-L97
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoderLayer._get_attn_subsequent_mask
(self, size)
return subsequent_mask
Get an attention mask to avoid using the subsequent info. Args: size: int Returns: (`LongTensor`): * subsequent_mask `[1 x size x size]`
Get an attention mask to avoid using the subsequent info.
[ "Get", "an", "attention", "mask", "to", "avoid", "using", "the", "subsequent", "info", "." ]
def _get_attn_subsequent_mask(self, size): """ Get an attention mask to avoid using the subsequent info. Args: size: int Returns: (`LongTensor`): * subsequent_mask `[1 x size x size]` """ attn_shape = (1, size, size) subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8') subsequent_mask = torch.from_numpy(subsequent_mask) return subsequent_mask
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L99-L114
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoder.forward
(self, tgt, memory_bank, state, memory_lengths=None, step=None, cache=None)
return outputs, state, attns
See :obj:`onmt.modules.RNNDecoderBase.forward()`
See :obj:`onmt.modules.RNNDecoderBase.forward()`
[ "See", ":", "obj", ":", "onmt", ".", "modules", ".", "RNNDecoderBase", ".", "forward", "()" ]
def forward(self, tgt, memory_bank, state, memory_lengths=None, step=None, cache=None): """ See :obj:`onmt.modules.RNNDecoderBase.forward()` """ src = state.src src_words = src[:, :, 0].transpose(0, 1) tgt_words = tgt[:, :, 0].transpose(0, 1) src_batch, src_len = src_words.size() tgt_batch, tgt_len = tgt_words.size() # Initialize return variables. outputs = [] attns = {"std": []} if self._copy: attns["copy"] = [] # Run the forward pass of the TransformerDecoder. emb = self.embeddings(tgt, step=step) assert emb.dim() == 3 # len x batch x embedding_dim output = emb.transpose(0, 1).contiguous() src_memory_bank = memory_bank.transpose(0, 1).contiguous() padding_idx = self.embeddings.word_padding_idx src_pad_mask = src_words.data.eq(padding_idx).unsqueeze(1) \ .expand(src_batch, tgt_len, src_len) tgt_pad_mask = tgt_words.data.eq(padding_idx).unsqueeze(1) \ .expand(tgt_batch, tgt_len, tgt_len) if state.cache is None: saved_inputs = [] for i in range(self.num_layers): prev_layer_input = None if state.cache is None: if state.previous_input is not None: prev_layer_input = state.previous_layer_inputs[i] output, attn, all_input \ = self.transformer_layers[i]( output, src_memory_bank, src_pad_mask, tgt_pad_mask, previous_input=prev_layer_input, layer_cache=state.cache["layer_{}".format(i)] if state.cache is not None else None, step=step) if state.cache is None: saved_inputs.append(all_input) if state.cache is None: saved_inputs = torch.stack(saved_inputs) output = self.layer_norm(output) # Process the result and update the attentions. outputs = output.transpose(0, 1).contiguous() attn = attn.transpose(0, 1).contiguous() attns["std"] = attn if self._copy: attns["copy"] = attn if state.cache is None: state = state.update_state(tgt, saved_inputs) return outputs, state, attns
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L172-L237
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoder.init_decoder_state
(self, src, memory_bank, enc_hidden, with_cache=False)
return state
Init decoder state
Init decoder state
[ "Init", "decoder", "state" ]
def init_decoder_state(self, src, memory_bank, enc_hidden, with_cache=False): """ Init decoder state """ state = TransformerDecoderState(src) if with_cache: state._init_cache(memory_bank, self.num_layers, self.self_attn_type) return state
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L239-L246
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoderState.__init__
(self, src)
Args: src (FloatTensor): a sequence of source words tensors with optional feature tensors, of size (len x batch).
Args: src (FloatTensor): a sequence of source words tensors with optional feature tensors, of size (len x batch).
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def __init__(self, src): """ Args: src (FloatTensor): a sequence of source words tensors with optional feature tensors, of size (len x batch). """ self.src = src self.previous_input = None self.previous_layer_inputs = None self.cache = None
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L252-L261
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoderState._all
(self)
Contains attributes that need to be updated in self.beam_update().
Contains attributes that need to be updated in self.beam_update().
[ "Contains", "attributes", "that", "need", "to", "be", "updated", "in", "self", ".", "beam_update", "()", "." ]
def _all(self): """ Contains attributes that need to be updated in self.beam_update(). """ if (self.previous_input is not None and self.previous_layer_inputs is not None): return (self.previous_input, self.previous_layer_inputs, self.src) else: return (self.src,)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L264-L274
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/transformer.py
python
TransformerDecoderState.repeat_beam_size_times
(self, beam_size)
Repeat beam_size times along batch dimension.
Repeat beam_size times along batch dimension.
[ "Repeat", "beam_size", "times", "along", "batch", "dimension", "." ]
def repeat_beam_size_times(self, beam_size): """ Repeat beam_size times along batch dimension. """ self.src = self.src.data.repeat(1, beam_size, 1)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/transformer.py#L309-L311
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/cnn_decoder.py
python
CNNDecoder.forward
(self, tgt, memory_bank, state, memory_lengths=None, step=None)
return outputs, state, attns
See :obj:`onmt.modules.RNNDecoderBase.forward()`
See :obj:`onmt.modules.RNNDecoderBase.forward()`
[ "See", ":", "obj", ":", "onmt", ".", "modules", ".", "RNNDecoderBase", ".", "forward", "()" ]
def forward(self, tgt, memory_bank, state, memory_lengths=None, step=None): """ See :obj:`onmt.modules.RNNDecoderBase.forward()`""" # NOTE: memory_lengths is only here for compatibility reasons # with onmt.modules.RNNDecoderBase.forward() # CHECKS assert isinstance(state, CNNDecoderState) _, tgt_batch, _ = tgt.size() _, contxt_batch, _ = memory_bank.size() aeq(tgt_batch, contxt_batch) # END CHECKS if state.previous_input is not None: tgt = torch.cat([state.previous_input, tgt], 0) # Initialize return variables. outputs = [] attns = {"std": []} assert not self._copy, "Copy mechanism not yet tested in conv2conv" if self._copy: attns["copy"] = [] emb = self.embeddings(tgt) assert emb.dim() == 3 # len x batch x embedding_dim tgt_emb = emb.transpose(0, 1).contiguous() # The output of CNNEncoder. src_memory_bank_t = memory_bank.transpose(0, 1).contiguous() # The combination of output of CNNEncoder and source embeddings. src_memory_bank_c = state.init_src.transpose(0, 1).contiguous() # Run the forward pass of the CNNDecoder. emb_reshape = tgt_emb.contiguous().view( tgt_emb.size(0) * tgt_emb.size(1), -1) linear_out = self.linear(emb_reshape) x = linear_out.view(tgt_emb.size(0), tgt_emb.size(1), -1) x = shape_transform(x) pad = torch.zeros(x.size(0), x.size(1), self.cnn_kernel_width - 1, 1) pad = pad.type_as(x) base_target_emb = x for conv, attention in zip(self.conv_layers, self.attn_layers): new_target_input = torch.cat([pad, x], 2) out = conv(new_target_input) c, attn = attention(base_target_emb, out, src_memory_bank_t, src_memory_bank_c) x = (x + (c + out) * SCALE_WEIGHT) * SCALE_WEIGHT output = x.squeeze(3).transpose(1, 2) # Process the result and update the attentions. outputs = output.transpose(0, 1).contiguous() if state.previous_input is not None: outputs = outputs[state.previous_input.size(0):] attn = attn[:, state.previous_input.size(0):].squeeze() attn = torch.stack([attn]) attns["std"] = attn if self._copy: attns["copy"] = attn # Update the state. state.update_state(tgt) return outputs, state, attns
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/cnn_decoder.py#L58-L122
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/cnn_decoder.py
python
CNNDecoder.init_decoder_state
(self, _, memory_bank, enc_hidden, with_cache=False)
return CNNDecoderState(memory_bank, enc_hidden)
Init decoder state.
Init decoder state.
[ "Init", "decoder", "state", "." ]
def init_decoder_state(self, _, memory_bank, enc_hidden, with_cache=False): """ Init decoder state. """ return CNNDecoderState(memory_bank, enc_hidden)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/cnn_decoder.py#L124-L128
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/cnn_decoder.py
python
CNNDecoderState._all
(self)
return (self.previous_input,)
Contains attributes that need to be updated in self.beam_update().
Contains attributes that need to be updated in self.beam_update().
[ "Contains", "attributes", "that", "need", "to", "be", "updated", "in", "self", ".", "beam_update", "()", "." ]
def _all(self): """ Contains attributes that need to be updated in self.beam_update(). """ return (self.previous_input,)
[ "def", "_all", "(", "self", ")", ":", "return", "(", "self", ".", "previous_input", ",", ")" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/cnn_decoder.py#L141-L145
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/cnn_decoder.py
python
CNNDecoderState.update_state
(self, new_input)
Called for every decoder forward pass.
Called for every decoder forward pass.
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def update_state(self, new_input): """ Called for every decoder forward pass. """ self.previous_input = new_input
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/cnn_decoder.py#L150-L152
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/Hi_MAP/onmt/decoders/cnn_decoder.py
python
CNNDecoderState.repeat_beam_size_times
(self, beam_size)
Repeat beam_size times along batch dimension.
Repeat beam_size times along batch dimension.
[ "Repeat", "beam_size", "times", "along", "batch", "dimension", "." ]
def repeat_beam_size_times(self, beam_size): """ Repeat beam_size times along batch dimension. """ self.init_src = self.init_src.data.repeat(1, beam_size, 1)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/Hi_MAP/onmt/decoders/cnn_decoder.py#L154-L156
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments._tokenize
(self, text)
return self._en(text, disable = ["tagger", "parser", "ner", "textcat"])
Tokenizes input using the fastest possible SpaCy configuration. This is optional, can be disabled in constructor.
[]
def _tokenize(self, text): """ Tokenizes input using the fastest possible SpaCy configuration. This is optional, can be disabled in constructor. """ return self._en(text, disable = ["tagger", "parser", "ner", "textcat"])
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L49-L58
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments._normalize
(self, tokens, case = False)
return [ str(t).lower() if not case else str(t) for t in tokens ]
Lowercases and turns tokens into distinct words.
[]
def _normalize(self, tokens, case = False): """ Lowercases and turns tokens into distinct words. """ return [ str(t).lower() if not case else str(t) for t in tokens ]
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L61-L74
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.overlaps
(self)
return self._matches
Return a list of Fragments.Match objects between summary and text. This is a list of named tuples of the form (summary, text, length): - summary (int): the start index of the match in the summary - text (int): the start index of the match in the reference - length (int): the length of the extractive fragment
[]
def overlaps(self): """ Return a list of Fragments.Match objects between summary and text. This is a list of named tuples of the form (summary, text, length): - summary (int): the start index of the match in the summary - text (int): the start index of the match in the reference - length (int): the length of the extractive fragment """ return self._matches
[ "def", "overlaps", "(", "self", ")", ":", "return", "self", ".", "_matches" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L77-L90
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.strings
(self, min_length = 0, raw = None, summary_base = True)
return strings
Return a list of explicit match strings between the summary and reference. Note that this will be in the same format as the strings are input. This is important to remember if tokenization is done manually. If tokenization is specified automatically on the raw strings, raw strings will automatically be returned rather than SpaCy tokenized sequences. Arguments: - min_length (int): filter out overlaps shorter than this (default = 0) - raw (bool): return raw input rather than stringified - (default = False if automatic tokenization, True otherwise) - summary_base (true): strings are based of summary text (default = True) Returns: - list of overlaps, where overlaps are strings or token sequences
[]
def strings(self, min_length = 0, raw = None, summary_base = True): """ Return a list of explicit match strings between the summary and reference. Note that this will be in the same format as the strings are input. This is important to remember if tokenization is done manually. If tokenization is specified automatically on the raw strings, raw strings will automatically be returned rather than SpaCy tokenized sequences. Arguments: - min_length (int): filter out overlaps shorter than this (default = 0) - raw (bool): return raw input rather than stringified - (default = False if automatic tokenization, True otherwise) - summary_base (true): strings are based of summary text (default = True) Returns: - list of overlaps, where overlaps are strings or token sequences """ # Compute the strings against the summary or the text? base = self.summary if summary_base else self.text # Generate strings, filtering out strings below the minimum length. strings = [ base[i : i + length] for i, j, length in self.overlaps() if length > min_length ] # By default, we just return the tokenization being used. # But if they user wants a raw string, then we convert. # Mostly, this will be used along with spacy. if self._tokens and raw: for i, s in enumerate(strings): strings[i] = str(s) # Return the list of strings. return strings
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L93-L140
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.coverage
(self, summary_base = True)
Return the COVERAGE score of the summary and text. Arguments: - summary_base (bool): use summary as numerator (default = True) Returns: - decimal COVERAGE score within [0, 1]
[]
def coverage(self, summary_base = True): """ Return the COVERAGE score of the summary and text. Arguments: - summary_base (bool): use summary as numerator (default = True) Returns: - decimal COVERAGE score within [0, 1] """ numerator = sum(o.length for o in self.overlaps()) if summary_base: denominator = len(self.summary) else: denominator = len(self.reference) if denominator == 0: return 0 else: return numerator / denominator
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L143-L165
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.density
(self, summary_base = True)
Return the DENSITY score of summary and text. Arguments: - summary_base (bool): use summary as numerator (default = True) Returns: - decimal DENSITY score within [0, ...]
[]
def density(self, summary_base = True): """ Return the DENSITY score of summary and text. Arguments: - summary_base (bool): use summary as numerator (default = True) Returns: - decimal DENSITY score within [0, ...] """ numerator = sum(o.length ** 2 for o in self.overlaps()) if summary_base: denominator = len(self.summary) else: denominator = len(self.reference) if denominator == 0: return 0 else: return numerator / denominator
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L168-L190
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.compression
(self, text_to_summary = True)
Return compression ratio between summary and text. Arguments: - text_to_summary (bool): compute text/summary ratio (default = True) Returns: - decimal compression score within [0, ...]
[]
def compression(self, text_to_summary = True): """ Return compression ratio between summary and text. Arguments: - text_to_summary (bool): compute text/summary ratio (default = True) Returns: - decimal compression score within [0, ...] """ ratio = [len(self.text), len(self.summary)] try: if text_to_summary: return ratio[0] / ratio[1] else: return ratio[1] / ratio[0] except ZeroDivisionError: return 0
[ "def", "compression", "(", "self", ",", "text_to_summary", "=", "True", ")", ":", "ratio", "=", "[", "len", "(", "self", ".", "text", ")", ",", "len", "(", "self", ".", "summary", ")", "]", "try", ":", "if", "text_to_summary", ":", "return", "ratio", "[", "0", "]", "/", "ratio", "[", "1", "]", "else", ":", "return", "ratio", "[", "1", "]", "/", "ratio", "[", "0", "]", "except", "ZeroDivisionError", ":", "return", "0" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L193-L218
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments._match
(self, a, b)
Raw procedure for matching summary in text, described in paper.
[]
def _match(self, a, b): """ Raw procedure for matching summary in text, described in paper. """ self._matches = [] a_start = b_start = 0 while a_start < len(a): best_match = None best_match_length = 0 while b_start < len(b): if a[a_start] == b[b_start]: a_end = a_start b_end = b_start while a_end < len(a) and b_end < len(b) \ and b[b_end] == a[a_end]: b_end += 1 a_end += 1 length = a_end - a_start if length > best_match_length: best_match = Fragments.Match(a_start, b_start, length) best_match_length = length b_start = b_end else: b_start += 1 b_start = 0 if best_match: if best_match_length > 0: self._matches.append(best_match) a_start += best_match_length else: a_start += 1
[ "def", "_match", "(", "self", ",", "a", ",", "b", ")", ":", "self", ".", "_matches", "=", "[", "]", "a_start", "=", "b_start", "=", "0", "while", "a_start", "<", "len", "(", "a", ")", ":", "best_match", "=", "None", "best_match_length", "=", "0", "while", "b_start", "<", "len", "(", "b", ")", ":", "if", "a", "[", "a_start", "]", "==", "b", "[", "b_start", "]", ":", "a_end", "=", "a_start", "b_end", "=", "b_start", "while", "a_end", "<", "len", "(", "a", ")", "and", "b_end", "<", "len", "(", "b", ")", "and", "b", "[", "b_end", "]", "==", "a", "[", "a_end", "]", ":", "b_end", "+=", "1", "a_end", "+=", "1", "length", "=", "a_end", "-", "a_start", "if", "length", ">", "best_match_length", ":", "best_match", "=", "Fragments", ".", "Match", "(", "a_start", ",", "b_start", ",", "length", ")", "best_match_length", "=", "length", "b_start", "=", "b_end", "else", ":", "b_start", "+=", "1", "b_start", "=", "0", "if", "best_match", ":", "if", "best_match_length", ">", "0", ":", "self", ".", "_matches", ".", "append", "(", "best_match", ")", "a_start", "+=", "best_match_length", "else", ":", "a_start", "+=", "1" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L221-L275
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments._htmltokens
(self, tokens)
return [ [ _html.escape(t.text).replace("\n", "<br/>"), _html.escape(t.whitespace_).replace("\n", "<br/>") ] for t in tokens ]
Carefully process tokens to handle whitespace and HTML characters.
[]
def _htmltokens(self, tokens): """ Carefully process tokens to handle whitespace and HTML characters. """ return [ [ _html.escape(t.text).replace("\n", "<br/>"), _html.escape(t.whitespace_).replace("\n", "<br/>") ] for t in tokens ]
[ "def", "_htmltokens", "(", "self", ",", "tokens", ")", ":", "return", "[", "[", "_html", ".", "escape", "(", "t", ".", "text", ")", ".", "replace", "(", "\"\\n\"", ",", "\"<br/>\"", ")", ",", "_html", ".", "escape", "(", "t", ".", "whitespace_", ")", ".", "replace", "(", "\"\\n\"", ",", "\"<br/>\"", ")", "]", "for", "t", "in", "tokens", "]" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L278-L293
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
data/scripts/fragments.py
python
Fragments.annotate
(self, min_length = 0, text_truncation = None, novel_italics = False)
return summary, text
Used to annotate fragments for website visualization. Arguments: - min_length (int): minimum length overlap to count (default = 0) - text_truncation (int): tuncated text length (default = None) - novel_italics (bool): italicize novel words (default = True) Returns: - a tuple of strings: (summary HTML, text HTML)
[]
def annotate(self, min_length = 0, text_truncation = None, novel_italics = False): """ Used to annotate fragments for website visualization. Arguments: - min_length (int): minimum length overlap to count (default = 0) - text_truncation (int): tuncated text length (default = None) - novel_italics (bool): italicize novel words (default = True) Returns: - a tuple of strings: (summary HTML, text HTML) """ start = """ <u style="color: {color}; border-color: {color};" data-ref="{ref}" title="Length: {length}" > """.strip() end = """ </u> """.strip() # Here we tokenize carefully to preserve sane-looking whitespace. # (This part does require text to use a SpaCy tokenization.) summary = self._htmltokens(self.summary) text = self._htmltokens(self.text) # Compute novel word set, if requested. if novel_italics: novel = set(self._norm_summary) - set(self._norm_text) for word_whitespace in summary: if word_whitespace[0].lower() in novel: word_whitespace[0] = "<em>" + word_whitespace[0] + "</em>" # Truncate text, if requested. # Must be careful later on with this. if text_truncation is not None: text = text[:text_truncation] # March through overlaps, replacing tokens with HTML-tagged strings. colors = self._itercolors() for overlap in self.overlaps(): # Skip overlaps that are too short. if overlap.length < min_length: continue # Reference ID for JavaScript highlighting. # This is random, but shared between corresponding fragments. ref = _random.randint(0, 1e10) color = next(colors) # Summary starting tag. summary[overlap.summary][0] = start.format( color = color, ref = ref, length = overlap.length, ) + summary[overlap.summary][0] # Text starting tag. text[overlap.text][0] = start.format( color = color, ref = ref, length = overlap.length, ) + text[overlap.text][0] # Summary ending tag. summary[overlap.summary + overlap.length - 1][0] += end # Text ending tag. text[overlap.text + overlap.length - 1][0] += end # Carefully join tokens and whitespace to reconstruct the string. summary = " ".join("".join("".join(tw) for tw in summary).split()) text = " ".join("".join("".join(tw) for tw in text).split()) # Return the tuple. return summary, text
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/data/scripts/fragments.py#L296-L396
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/proxy.py
python
GetIPPOOLS
(num)
return IPPOOL
#自己获取的ip IPPOOLS1=urllib.request.urlopen("http://127.0.0.1:8000/?types=0&count=20&country=%E5%9B%BD%E5%86%85").read().decode("utf-8",'ignore') IPPOOLS2=re.findall('\"(\d+\.\d+\.\d+\.\d+\"\,\s*\d+)',IPPOOLS1) IPPOOL=[i.replace('", ',':') for i in IPPOOLS2]
#自己获取的ip IPPOOLS1=urllib.request.urlopen("http://127.0.0.1:8000/?types=0&count=20&country=%E5%9B%BD%E5%86%85").read().decode("utf-8",'ignore') IPPOOLS2=re.findall('\"(\d+\.\d+\.\d+\.\d+\"\,\s*\d+)',IPPOOLS1) IPPOOL=[i.replace('", ',':') for i in IPPOOLS2]
[ "#自己获取的ip", "IPPOOLS1", "=", "urllib", ".", "request", ".", "urlopen", "(", "http", ":", "//", "127", ".", "0", ".", "0", ".", "1", ":", "8000", "/", "?types", "=", "0&count", "=", "20&country", "=", "%E5%9B%BD%E5%86%85", ")", ".", "read", "()", ".", "decode", "(", "utf", "-", "8", "ignore", ")", "IPPOOLS2", "=", "re", ".", "findall", "(", "\\", "(", "\\", "d", "+", "\\", ".", "\\", "d", "+", "\\", ".", "\\", "d", "+", "\\", ".", "\\", "d", "+", "\\", "\\", "\\", "s", "*", "\\", "d", "+", ")", "IPPOOLS1", ")", "IPPOOL", "=", "[", "i", ".", "replace", "(", ":", ")", "for", "i", "in", "IPPOOLS2", "]" ]
def GetIPPOOLS(num): #大象代理买的ip,5元20000个,每十个差不多有一个能用 IPPOOL=urllib.request.urlopen("http://tpv.daxiangdaili.com/ip/?tid=559480480576119&num="+str(num)+"&operator=1&filter=on&protocol=http&category=2&delay=1").read().decode("utf-8","ignore").split('\r\n') ''' #自己获取的ip IPPOOLS1=urllib.request.urlopen("http://127.0.0.1:8000/?types=0&count=20&country=%E5%9B%BD%E5%86%85").read().decode("utf-8",'ignore') IPPOOLS2=re.findall('\"(\d+\.\d+\.\d+\.\d+\"\,\s*\d+)',IPPOOLS1) IPPOOL=[i.replace('", ',':') for i in IPPOOLS2] ''' return IPPOOL
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/proxy.py#L17-L26
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/proxy.py
python
initIPPOOLS
(rconn)
把有效的IP存入 REDIS数据库
把有效的IP存入 REDIS数据库
[ "把有效的IP存入", "REDIS数据库" ]
def initIPPOOLS(rconn): """把有效的IP存入 REDIS数据库""" ipNum=len(rconn.keys('IP*')) if ipNum<IPPOOLNUM: IPPOOLS=GetIPPOOLS(IPPOOLNUM) for ipall in IPPOOLS: try: ip=ipall.split(':')[0] port=ipall.split(':')[1] telnetlib.Telnet(ip,port=port,timeout=2) #检验代理ip是否有效 except: logger.warning("The ip is not available !( IP:%s )" % ipall) else: logger.warning("Get ip Success!( IP:%s )" % ipall) rconn.set("IP:%s:10"%(ipall),ipall) #10 is status else: logger.warning("The number of the IP is %s!" % str(ipNum))
[ "def", "initIPPOOLS", "(", "rconn", ")", ":", "ipNum", "=", "len", "(", "rconn", ".", "keys", "(", "'IP*'", ")", ")", "if", "ipNum", "<", "IPPOOLNUM", ":", "IPPOOLS", "=", "GetIPPOOLS", "(", "IPPOOLNUM", ")", "for", "ipall", "in", "IPPOOLS", ":", "try", ":", "ip", "=", "ipall", ".", "split", "(", "':'", ")", "[", "0", "]", "port", "=", "ipall", ".", "split", "(", "':'", ")", "[", "1", "]", "telnetlib", ".", "Telnet", "(", "ip", ",", "port", "=", "port", ",", "timeout", "=", "2", ")", "#检验代理ip是否有效", "except", ":", "logger", ".", "warning", "(", "\"The ip is not available !( IP:%s )\"", "%", "ipall", ")", "else", ":", "logger", ".", "warning", "(", "\"Get ip Success!( IP:%s )\"", "%", "ipall", ")", "rconn", ".", "set", "(", "\"IP:%s:10\"", "%", "(", "ipall", ")", ",", "ipall", ")", "#10 is status", "else", ":", "logger", ".", "warning", "(", "\"The number of the IP is %s!\"", "%", "str", "(", "ipNum", ")", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/proxy.py#L28-L45
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/proxy.py
python
updateIPPOOLS
(rconn,ip,status,flag=0)
update status
update status
[ "update", "status" ]
def updateIPPOOLS(rconn,ip,status,flag=0): # 0代表对status减一,-1代表减2,1代表加1 if int(status) < 1: removeIPPOOLS(rconn,ip,status) return '''update status''' if flag == 1: #+status if int(status) < 10: rconn.delete('IP:'+ ip + ':' + status) status = int(status) + 1 rconn.set("IP:%s:%s"%(ip,str(status)),ip) elif flag == -1: rconn.delete('IP:'+ ip + ':' + status) status = int(status) - 2 rconn.set("IP:%s:%s"%(ip,str(status)),ip) else: rconn.delete('IP:'+ ip + ':' + status) status = int(status) - 1 rconn.set("IP:%s:%s"%(ip,str(status)),ip)
[ "def", "updateIPPOOLS", "(", "rconn", ",", "ip", ",", "status", ",", "flag", "=", "0", ")", ":", "# 0代表对status减一,-1代表减2,1代表加1", "if", "int", "(", "status", ")", "<", "1", ":", "removeIPPOOLS", "(", "rconn", ",", "ip", ",", "status", ")", "return", "if", "flag", "==", "1", ":", "#+status", "if", "int", "(", "status", ")", "<", "10", ":", "rconn", ".", "delete", "(", "'IP:'", "+", "ip", "+", "':'", "+", "status", ")", "status", "=", "int", "(", "status", ")", "+", "1", "rconn", ".", "set", "(", "\"IP:%s:%s\"", "%", "(", "ip", ",", "str", "(", "status", ")", ")", ",", "ip", ")", "elif", "flag", "==", "-", "1", ":", "rconn", ".", "delete", "(", "'IP:'", "+", "ip", "+", "':'", "+", "status", ")", "status", "=", "int", "(", "status", ")", "-", "2", "rconn", ".", "set", "(", "\"IP:%s:%s\"", "%", "(", "ip", ",", "str", "(", "status", ")", ")", ",", "ip", ")", "else", ":", "rconn", ".", "delete", "(", "'IP:'", "+", "ip", "+", "':'", "+", "status", ")", "status", "=", "int", "(", "status", ")", "-", "1", "rconn", ".", "set", "(", "\"IP:%s:%s\"", "%", "(", "ip", ",", "str", "(", "status", ")", ")", ",", "ip", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/proxy.py#L47-L64
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/cookie.py
python
initCookie
(rconn, spiderName)
获取所有账号的Cookies,存入Redis。如果Redis已有该账号的Cookie,则不再获取。
获取所有账号的Cookies,存入Redis。如果Redis已有该账号的Cookie,则不再获取。
[ "获取所有账号的Cookies,存入Redis。如果Redis已有该账号的Cookie,则不再获取。" ]
def initCookie(rconn, spiderName): """ 获取所有账号的Cookies,存入Redis。如果Redis已有该账号的Cookie,则不再获取。 """ for zhihu in myZhiHu: if rconn.get("%s:Cookies:%s--%s" % (spiderName, zhihu[0], zhihu[1])) is None: # 'zhihuspider:Cookies:账号--密码',为None即不存在。 cookie = getCookie(zhihu[0], zhihu[1],zhihu[2]) if len(cookie) > 0: rconn.set("%s:Cookies:%s--%s" % (spiderName, zhihu[0], zhihu[1]), cookie) cookieNum = str(rconn.keys()).count("zhihuspider:Cookies") logger.warning("The num of the cookies is %s" % cookieNum) if cookieNum == 0: logger.warning('Stopping...') os.system("pause")
[ "def", "initCookie", "(", "rconn", ",", "spiderName", ")", ":", "for", "zhihu", "in", "myZhiHu", ":", "if", "rconn", ".", "get", "(", "\"%s:Cookies:%s--%s\"", "%", "(", "spiderName", ",", "zhihu", "[", "0", "]", ",", "zhihu", "[", "1", "]", ")", ")", "is", "None", ":", "# 'zhihuspider:Cookies:账号--密码',为None即不存在。", "cookie", "=", "getCookie", "(", "zhihu", "[", "0", "]", ",", "zhihu", "[", "1", "]", ",", "zhihu", "[", "2", "]", ")", "if", "len", "(", "cookie", ")", ">", "0", ":", "rconn", ".", "set", "(", "\"%s:Cookies:%s--%s\"", "%", "(", "spiderName", ",", "zhihu", "[", "0", "]", ",", "zhihu", "[", "1", "]", ")", ",", "cookie", ")", "cookieNum", "=", "str", "(", "rconn", ".", "keys", "(", ")", ")", ".", "count", "(", "\"zhihuspider:Cookies\"", ")", "logger", ".", "warning", "(", "\"The num of the cookies is %s\"", "%", "cookieNum", ")", "if", "cookieNum", "==", "0", ":", "logger", ".", "warning", "(", "'Stopping...'", ")", "os", ".", "system", "(", "\"pause\"", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/cookie.py#L145-L156
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/cookie.py
python
updateCookie
(accountText, rconn, spiderName, cookie)
更新一个账号的Cookie
更新一个账号的Cookie
[ "更新一个账号的Cookie" ]
def updateCookie(accountText, rconn, spiderName, cookie): """ 更新一个账号的Cookie """ account = accountText.split("--")[0] #pdb.set_trace() new_cookie = UpdateCookie(account, cookie) if len(new_cookie) > 0: logger.warning("The cookie of %s has been updated successfully!" % account) rconn.set("%s:Cookies:%s" % (spiderName, accountText), new_cookie) else: logger.warning("The cookie of %s updated failed! Remove it!" % accountText) removeCookie(accountText, rconn, spiderName)
[ "def", "updateCookie", "(", "accountText", ",", "rconn", ",", "spiderName", ",", "cookie", ")", ":", "account", "=", "accountText", ".", "split", "(", "\"--\"", ")", "[", "0", "]", "#pdb.set_trace()", "new_cookie", "=", "UpdateCookie", "(", "account", ",", "cookie", ")", "if", "len", "(", "new_cookie", ")", ">", "0", ":", "logger", ".", "warning", "(", "\"The cookie of %s has been updated successfully!\"", "%", "account", ")", "rconn", ".", "set", "(", "\"%s:Cookies:%s\"", "%", "(", "spiderName", ",", "accountText", ")", ",", "new_cookie", ")", "else", ":", "logger", ".", "warning", "(", "\"The cookie of %s updated failed! Remove it!\"", "%", "accountText", ")", "removeCookie", "(", "accountText", ",", "rconn", ",", "spiderName", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/cookie.py#L158-L168
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/cookie.py
python
removeCookie
(accountText, rconn, spiderName)
删除某个账号的Cookie
删除某个账号的Cookie
[ "删除某个账号的Cookie" ]
def removeCookie(accountText, rconn, spiderName): """ 删除某个账号的Cookie """ rconn.delete("%s:Cookies:%s" % (spiderName, accountText)) cookieNum = str(rconn.keys()).count("zhihuspider:Cookies") logger.warning("The num of the cookies left is %s" % cookieNum) if cookieNum == 0: logger.warning("Stopping...") os.system("pause")
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/cookie.py#L170-L177
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/scheduler.py
python
Scheduler.__init__
(self, server, persist=False, flush_on_start=False, queue_key=defaults.SCHEDULER_QUEUE_KEY, queue_cls=defaults.SCHEDULER_QUEUE_CLASS, dupefilter_key=defaults.SCHEDULER_DUPEFILTER_KEY, dupefilter_cls=defaults.SCHEDULER_DUPEFILTER_CLASS, idle_before_close=0, serializer=None)
Initialize scheduler. Parameters ---------- server : Redis The redis server instance. persist : bool Whether to flush requests when closing. Default is False. flush_on_start : bool Whether to flush requests on start. Default is False. queue_key : str Requests queue key. queue_cls : str Importable path to the queue class. dupefilter_key : str Duplicates filter key. dupefilter_cls : str Importable path to the dupefilter class. idle_before_close : int Timeout before giving up.
Initialize scheduler.
[ "Initialize", "scheduler", "." ]
def __init__(self, server, persist=False, flush_on_start=False, queue_key=defaults.SCHEDULER_QUEUE_KEY, queue_cls=defaults.SCHEDULER_QUEUE_CLASS, dupefilter_key=defaults.SCHEDULER_DUPEFILTER_KEY, dupefilter_cls=defaults.SCHEDULER_DUPEFILTER_CLASS, idle_before_close=0, serializer=None): """Initialize scheduler. Parameters ---------- server : Redis The redis server instance. persist : bool Whether to flush requests when closing. Default is False. flush_on_start : bool Whether to flush requests on start. Default is False. queue_key : str Requests queue key. queue_cls : str Importable path to the queue class. dupefilter_key : str Duplicates filter key. dupefilter_cls : str Importable path to the dupefilter class. idle_before_close : int Timeout before giving up. """ if idle_before_close < 0: raise TypeError("idle_before_close cannot be negative") self.server = server self.persist = persist self.flush_on_start = flush_on_start self.queue_key = queue_key self.queue_cls = queue_cls self.dupefilter_cls = dupefilter_cls self.dupefilter_key = dupefilter_key self.idle_before_close = idle_before_close self.serializer = serializer self.stats = None
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/scheduler.py#L34-L77
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/pipelines.py
python
RedisPipeline.__init__
(self, server, key=defaults.PIPELINE_KEY, serialize_func=default_serialize)
Initialize pipeline. Parameters ---------- server : StrictRedis Redis client instance. key : str Redis key where to store items. serialize_func : callable Items serializer function.
Initialize pipeline.
[ "Initialize", "pipeline", "." ]
def __init__(self, server, key=defaults.PIPELINE_KEY, serialize_func=default_serialize): """Initialize pipeline. Parameters ---------- server : StrictRedis Redis client instance. key : str Redis key where to store items. serialize_func : callable Items serializer function. """ self.server = server self.key = key self.serialize = serialize_func
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/pipelines.py#L23-L40
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/pipelines.py
python
RedisPipeline.item_key
(self, item, spider)
return self.key % {'spider': spider.name}
Returns redis key based on given spider. Override this function to use a different key depending on the item and/or spider.
Returns redis key based on given spider.
[ "Returns", "redis", "key", "based", "on", "given", "spider", "." ]
def item_key(self, item, spider): """Returns redis key based on given spider. Override this function to use a different key depending on the item and/or spider. """ return self.key % {'spider': spider.name}
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/pipelines.py#L69-L76
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base.__init__
(self, server, spider, key, serializer=None)
Initialize per-spider redis queue. Parameters ---------- server : StrictRedis Redis client instance. spider : Spider Scrapy spider instance. key: str Redis key where to put and get messages. serializer : object Serializer object with ``loads`` and ``dumps`` methods.
Initialize per-spider redis queue.
[ "Initialize", "per", "-", "spider", "redis", "queue", "." ]
def __init__(self, server, spider, key, serializer=None): """Initialize per-spider redis queue. Parameters ---------- server : StrictRedis Redis client instance. spider : Spider Scrapy spider instance. key: str Redis key where to put and get messages. serializer : object Serializer object with ``loads`` and ``dumps`` methods. """ if serializer is None: # Backward compatibility. # TODO: deprecate pickle. serializer = picklecompat if not hasattr(serializer, 'loads'): raise TypeError("serializer does not implement 'loads' function: %r" % serializer) if not hasattr(serializer, 'dumps'): raise TypeError("serializer '%s' does not implement 'dumps' function: %r" % serializer) self.server = server self.spider = spider self.key = key % {'spider': spider.name} self.serializer = serializer
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L9-L38
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base._encode_request
(self, request)
return self.serializer.dumps(obj)
Encode a request object
Encode a request object
[ "Encode", "a", "request", "object" ]
def _encode_request(self, request): """Encode a request object""" obj = request_to_dict(request, self.spider) return self.serializer.dumps(obj)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L40-L43
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base._decode_request
(self, encoded_request)
return request_from_dict(obj, self.spider)
Decode an request previously encoded
Decode an request previously encoded
[ "Decode", "an", "request", "previously", "encoded" ]
def _decode_request(self, encoded_request): """Decode an request previously encoded""" obj = self.serializer.loads(encoded_request) return request_from_dict(obj, self.spider)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L45-L48
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base.__len__
(self)
Return the length of the queue
Return the length of the queue
[ "Return", "the", "length", "of", "the", "queue" ]
def __len__(self): """Return the length of the queue""" raise NotImplementedError
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L50-L52
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base.push
(self, request)
Push a request
Push a request
[ "Push", "a", "request" ]
def push(self, request): """Push a request""" raise NotImplementedError
[ "def", "push", "(", "self", ",", "request", ")", ":", "raise", "NotImplementedError" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L54-L56
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base.pop
(self, timeout=0)
Pop a request
Pop a request
[ "Pop", "a", "request" ]
def pop(self, timeout=0): """Pop a request""" raise NotImplementedError
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L58-L60
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
Base.clear
(self)
Clear queue/stack
Clear queue/stack
[ "Clear", "queue", "/", "stack" ]
def clear(self): """Clear queue/stack""" self.server.delete(self.key)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L62-L64
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
FifoQueue.__len__
(self)
return self.server.llen(self.key)
Return the length of the queue
Return the length of the queue
[ "Return", "the", "length", "of", "the", "queue" ]
def __len__(self): """Return the length of the queue""" return self.server.llen(self.key)
[ "def", "__len__", "(", "self", ")", ":", "return", "self", ".", "server", ".", "llen", "(", "self", ".", "key", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L70-L72
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
FifoQueue.push
(self, request)
Push a request
Push a request
[ "Push", "a", "request" ]
def push(self, request): """Push a request""" self.server.lpush(self.key, self._encode_request(request))
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L74-L76
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
FifoQueue.pop
(self, timeout=0)
Pop a request
Pop a request
[ "Pop", "a", "request" ]
def pop(self, timeout=0): """Pop a request""" if timeout > 0: data = self.server.brpop(self.key, timeout) if isinstance(data, tuple): data = data[1] else: data = self.server.rpop(self.key) if data: return self._decode_request(data)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L78-L87
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
PriorityQueue.__len__
(self)
return self.server.zcard(self.key)
Return the length of the queue
Return the length of the queue
[ "Return", "the", "length", "of", "the", "queue" ]
def __len__(self): """Return the length of the queue""" return self.server.zcard(self.key)
[ "def", "__len__", "(", "self", ")", ":", "return", "self", ".", "server", ".", "zcard", "(", "self", ".", "key", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L93-L95
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
PriorityQueue.push
(self, request)
Push a request
Push a request
[ "Push", "a", "request" ]
def push(self, request): """Push a request""" data = self._encode_request(request) score = -request.priority # We don't use zadd method as the order of arguments change depending on # whether the class is Redis or StrictRedis, and the option of using # kwargs only accepts strings, not bytes. self.server.execute_command('ZADD', self.key, score, data)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L97-L104
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
PriorityQueue.pop
(self, timeout=0)
Pop a request timeout not support in this queue class
Pop a request timeout not support in this queue class
[ "Pop", "a", "request", "timeout", "not", "support", "in", "this", "queue", "class" ]
def pop(self, timeout=0): """ Pop a request timeout not support in this queue class """ # use atomic range/remove using multi/exec pipe = self.server.pipeline() pipe.multi() pipe.zrange(self.key, 0, 0).zremrangebyrank(self.key, 0, 0) results, count = pipe.execute() if results: return self._decode_request(results[0])
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L106-L117
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
LifoQueue.__len__
(self)
return self.server.llen(self.key)
Return the length of the stack
Return the length of the stack
[ "Return", "the", "length", "of", "the", "stack" ]
def __len__(self): """Return the length of the stack""" return self.server.llen(self.key)
[ "def", "__len__", "(", "self", ")", ":", "return", "self", ".", "server", ".", "llen", "(", "self", ".", "key", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L123-L125
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
LifoQueue.push
(self, request)
Push a request
Push a request
[ "Push", "a", "request" ]
def push(self, request): """Push a request""" self.server.lpush(self.key, self._encode_request(request))
[ "def", "push", "(", "self", ",", "request", ")", ":", "self", ".", "server", ".", "lpush", "(", "self", ".", "key", ",", "self", ".", "_encode_request", "(", "request", ")", ")" ]
https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L127-L129
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/queue.py
python
LifoQueue.pop
(self, timeout=0)
Pop a request
Pop a request
[ "Pop", "a", "request" ]
def pop(self, timeout=0): """Pop a request""" if timeout > 0: data = self.server.blpop(self.key, timeout) if isinstance(data, tuple): data = data[1] else: data = self.server.lpop(self.key) if data: return self._decode_request(data)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/queue.py#L131-L141
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/connection.py
python
get_redis_from_settings
(settings)
return get_redis(**params)
Returns a redis client instance from given Scrapy settings object. This function uses ``get_client`` to instantiate the client and uses ``defaults.REDIS_PARAMS`` global as defaults values for the parameters. You can override them using the ``REDIS_PARAMS`` setting. Parameters ---------- settings : Settings A scrapy settings object. See the supported settings below. Returns ------- server Redis client instance. Other Parameters ---------------- REDIS_URL : str, optional Server connection URL. REDIS_HOST : str, optional Server host. REDIS_PORT : str, optional Server port. REDIS_ENCODING : str, optional Data encoding. REDIS_PARAMS : dict, optional Additional client parameters.
Returns a redis client instance from given Scrapy settings object.
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def get_redis_from_settings(settings): """Returns a redis client instance from given Scrapy settings object. This function uses ``get_client`` to instantiate the client and uses ``defaults.REDIS_PARAMS`` global as defaults values for the parameters. You can override them using the ``REDIS_PARAMS`` setting. Parameters ---------- settings : Settings A scrapy settings object. See the supported settings below. Returns ------- server Redis client instance. Other Parameters ---------------- REDIS_URL : str, optional Server connection URL. REDIS_HOST : str, optional Server host. REDIS_PORT : str, optional Server port. REDIS_ENCODING : str, optional Data encoding. REDIS_PARAMS : dict, optional Additional client parameters. """ params = defaults.REDIS_PARAMS.copy() params.update(settings.getdict('REDIS_PARAMS')) # XXX: Deprecate REDIS_* settings. for source, dest in SETTINGS_PARAMS_MAP.items(): val = settings.get(source) if val: params[dest] = val # Allow ``redis_cls`` to be a path to a class. if isinstance(params.get('redis_cls'), six.string_types): params['redis_cls'] = load_object(params['redis_cls']) return get_redis(**params)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/connection.py#L17-L60
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/connection.py
python
get_redis
(**kwargs)
Returns a redis client instance. Parameters ---------- redis_cls : class, optional Defaults to ``redis.StrictRedis``. url : str, optional If given, ``redis_cls.from_url`` is used to instantiate the class. **kwargs Extra parameters to be passed to the ``redis_cls`` class. Returns ------- server Redis client instance.
Returns a redis client instance.
[ "Returns", "a", "redis", "client", "instance", "." ]
def get_redis(**kwargs): """Returns a redis client instance. Parameters ---------- redis_cls : class, optional Defaults to ``redis.StrictRedis``. url : str, optional If given, ``redis_cls.from_url`` is used to instantiate the class. **kwargs Extra parameters to be passed to the ``redis_cls`` class. Returns ------- server Redis client instance. """ redis_cls = kwargs.pop('redis_cls', defaults.REDIS_CLS) url = kwargs.pop('url', None) if url: return redis_cls.from_url(url, **kwargs) else: return redis_cls(**kwargs)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/connection.py#L67-L90
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.start_requests
(self)
return self.next_requests()
Returns a batch of start requests from redis.
Returns a batch of start requests from redis.
[ "Returns", "a", "batch", "of", "start", "requests", "from", "redis", "." ]
def start_requests(self): """Returns a batch of start requests from redis.""" return self.next_requests()
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L18-L20
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.setup_redis
(self, crawler=None)
Setup redis connection and idle signal. This should be called after the spider has set its crawler object.
Setup redis connection and idle signal.
[ "Setup", "redis", "connection", "and", "idle", "signal", "." ]
def setup_redis(self, crawler=None): """Setup redis connection and idle signal. This should be called after the spider has set its crawler object. """ if self.server is not None: return if crawler is None: # We allow optional crawler argument to keep backwards # compatibility. # XXX: Raise a deprecation warning. crawler = getattr(self, 'crawler', None) if crawler is None: raise ValueError("crawler is required") settings = crawler.settings if self.redis_key is None: self.redis_key = settings.get( 'REDIS_START_URLS_KEY', defaults.START_URLS_KEY, ) self.redis_key = self.redis_key % {'name': self.name} if not self.redis_key.strip(): raise ValueError("redis_key must not be empty") if self.redis_batch_size is None: # TODO: Deprecate this setting (REDIS_START_URLS_BATCH_SIZE). self.redis_batch_size = settings.getint( 'REDIS_START_URLS_BATCH_SIZE', settings.getint('CONCURRENT_REQUESTS'), ) try: self.redis_batch_size = int(self.redis_batch_size) except (TypeError, ValueError): raise ValueError("redis_batch_size must be an integer") if self.redis_encoding is None: self.redis_encoding = settings.get('REDIS_ENCODING', defaults.REDIS_ENCODING) self.logger.info("Reading start URLs from redis key '%(redis_key)s' " "(batch size: %(redis_batch_size)s, encoding: %(redis_encoding)s", self.__dict__) self.server = connection.from_settings(crawler.settings) # The idle signal is called when the spider has no requests left, # that's when we will schedule new requests from redis queue crawler.signals.connect(self.spider_idle, signal=signals.spider_idle)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L22-L73
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.next_requests
(self)
Returns a request to be scheduled or none.
Returns a request to be scheduled or none.
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def next_requests(self): """Returns a request to be scheduled or none.""" use_set = self.settings.getbool('REDIS_START_URLS_AS_SET', defaults.START_URLS_AS_SET) fetch_one = self.server.spop if use_set else self.server.lpop # XXX: Do we need to use a timeout here? found = 0 # TODO: Use redis pipeline execution. while found < self.redis_batch_size: data = fetch_one(self.redis_key) if not data: # Queue empty. break req = self.make_request_from_data(data) if req: yield req found += 1 else: self.logger.debug("Request not made from data: %r", data) if found: self.logger.debug("Read %s requests from '%s'", found, self.redis_key)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L75-L95
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.make_request_from_data
(self, data)
return self.make_requests_from_url(url)
Returns a Request instance from data coming from Redis. By default, ``data`` is an encoded URL. You can override this method to provide your own message decoding. Parameters ---------- data : bytes Message from redis.
Returns a Request instance from data coming from Redis.
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def make_request_from_data(self, data): """Returns a Request instance from data coming from Redis. By default, ``data`` is an encoded URL. You can override this method to provide your own message decoding. Parameters ---------- data : bytes Message from redis. """ url = bytes_to_str(data, self.redis_encoding) return self.make_requests_from_url(url)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L97-L110
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.schedule_next_requests
(self)
Schedules a request if available
Schedules a request if available
[ "Schedules", "a", "request", "if", "available" ]
def schedule_next_requests(self): """Schedules a request if available""" # TODO: While there is capacity, schedule a batch of redis requests. for req in self.next_requests(): self.crawler.engine.crawl(req, spider=self)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L112-L116
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/spiders.py
python
RedisMixin.spider_idle
(self)
Schedules a request if available, otherwise waits.
Schedules a request if available, otherwise waits.
[ "Schedules", "a", "request", "if", "available", "otherwise", "waits", "." ]
def spider_idle(self): """Schedules a request if available, otherwise waits.""" # XXX: Handle a sentinel to close the spider. self.schedule_next_requests() raise DontCloseSpider
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/spiders.py#L118-L122
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/utils.py
python
bytes_to_str
(s, encoding='utf-8')
return s
Returns a str if a bytes object is given.
Returns a str if a bytes object is given.
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def bytes_to_str(s, encoding='utf-8'): """Returns a str if a bytes object is given.""" if six.PY3 and isinstance(s, bytes): return s.decode(encoding) return s
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/utils.py#L4-L8
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.__init__
(self, server, key, debug=False)
Initialize the duplicates filter. Parameters ---------- server : redis.StrictRedis The redis server instance. key : str Redis key Where to store fingerprints. debug : bool, optional Whether to log filtered requests.
Initialize the duplicates filter.
[ "Initialize", "the", "duplicates", "filter", "." ]
def __init__(self, server, key, debug=False): """Initialize the duplicates filter. Parameters ---------- server : redis.StrictRedis The redis server instance. key : str Redis key Where to store fingerprints. debug : bool, optional Whether to log filtered requests. """ self.server = server self.key = key self.debug = debug self.bf = BloomFilter(server, key, blockNum=1) # you can increase blockNum if your are filtering too many urls self.logdupes = True
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L25-L42
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.from_settings
(cls, settings)
return cls(server, key=key, debug=debug)
Returns an instance from given settings. This uses by default the key ``dupefilter:<timestamp>``. When using the ``scrapy_redis.scheduler.Scheduler`` class, this method is not used as it needs to pass the spider name in the key. Parameters ---------- settings : scrapy.settings.Settings Returns ------- RFPDupeFilter A RFPDupeFilter instance.
Returns an instance from given settings.
[ "Returns", "an", "instance", "from", "given", "settings", "." ]
def from_settings(cls, settings): """Returns an instance from given settings. This uses by default the key ``dupefilter:<timestamp>``. When using the ``scrapy_redis.scheduler.Scheduler`` class, this method is not used as it needs to pass the spider name in the key. Parameters ---------- settings : scrapy.settings.Settings Returns ------- RFPDupeFilter A RFPDupeFilter instance. """ server = get_redis_from_settings(settings) # XXX: This creates one-time key. needed to support to use this # class as standalone dupefilter with scrapy's default scheduler # if scrapy passes spider on open() method this wouldn't be needed # TODO: Use SCRAPY_JOB env as default and fallback to timestamp. key = defaults.DUPEFILTER_KEY % {'timestamp': int(time.time())} debug = settings.getbool('DUPEFILTER_DEBUG') return cls(server, key=key, debug=debug)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L45-L70
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.from_crawler
(cls, crawler)
return cls.from_settings(crawler.settings)
Returns instance from crawler. Parameters ---------- crawler : scrapy.crawler.Crawler Returns ------- RFPDupeFilter Instance of RFPDupeFilter.
Returns instance from crawler.
[ "Returns", "instance", "from", "crawler", "." ]
def from_crawler(cls, crawler): """Returns instance from crawler. Parameters ---------- crawler : scrapy.crawler.Crawler Returns ------- RFPDupeFilter Instance of RFPDupeFilter. """ return cls.from_settings(crawler.settings)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L73-L86
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.request_seen
(self, request)
Returns True if request was already seen. Parameters ---------- request : scrapy.http.Request Returns ------- bool
Returns True if request was already seen.
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def request_seen(self, request): """Returns True if request was already seen. Parameters ---------- request : scrapy.http.Request Returns ------- bool """ fp = request_fingerprint(request) if self.bf.isContains(fp): return True else: self.bf.insert(fp) return False
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L88-L105
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.request_fingerprint
(self, request)
return request_fingerprint(request)
Returns a fingerprint for a given request. Parameters ---------- request : scrapy.http.Request Returns ------- str
Returns a fingerprint for a given request.
[ "Returns", "a", "fingerprint", "for", "a", "given", "request", "." ]
def request_fingerprint(self, request): """Returns a fingerprint for a given request. Parameters ---------- request : scrapy.http.Request Returns ------- str """ return request_fingerprint(request)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L107-L119
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.close
(self, reason='')
Delete data on close. Called by Scrapy's scheduler. Parameters ---------- reason : str, optional
Delete data on close. Called by Scrapy's scheduler.
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def close(self, reason=''): """Delete data on close. Called by Scrapy's scheduler. Parameters ---------- reason : str, optional """ self.clear()
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L121-L129
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.clear
(self)
Clears fingerprints data.
Clears fingerprints data.
[ "Clears", "fingerprints", "data", "." ]
def clear(self): """Clears fingerprints data.""" self.server.delete(self.key)
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L131-L133
AlexTan-b-z/ZhihuSpider
7f35d157fa7f3a7ac8545b386e98286ee2764462
zhihu/zhihu/scrapy_redis/dupefilter.py
python
RFPDupeFilter.log
(self, request, spider)
Logs given request. Parameters ---------- request : scrapy.http.Request spider : scrapy.spiders.Spider
Logs given request.
[ "Logs", "given", "request", "." ]
def log(self, request, spider): """Logs given request. Parameters ---------- request : scrapy.http.Request spider : scrapy.spiders.Spider """ if self.debug: msg = "Filtered duplicate request: %(request)s" self.logger.debug(msg, {'request': request}, extra={'spider': spider}) elif self.logdupes: msg = ("Filtered duplicate request %(request)s" " - no more duplicates will be shown" " (see DUPEFILTER_DEBUG to show all duplicates)") self.logger.debug(msg, {'request': request}, extra={'spider': spider}) self.logdupes = False
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https://github.com/AlexTan-b-z/ZhihuSpider/blob/7f35d157fa7f3a7ac8545b386e98286ee2764462/zhihu/zhihu/scrapy_redis/dupefilter.py#L135-L152
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_asr.py
python
Solver.fetch_data
(self, data)
return feat, feat_len, txt, txt_len
Move data to device and compute text seq. length
Move data to device and compute text seq. length
[ "Move", "data", "to", "device", "and", "compute", "text", "seq", ".", "length" ]
def fetch_data(self, data): ''' Move data to device and compute text seq. length''' _, feat, feat_len, txt = data feat = feat.to(self.device) feat_len = feat_len.to(self.device) txt = txt.to(self.device) txt_len = torch.sum(txt != 0, dim=-1) return feat, feat_len, txt, txt_len
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_asr.py#L20-L28
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_asr.py
python
Solver.load_data
(self)
Load data for training/validation, store tokenizer and input/output shape
Load data for training/validation, store tokenizer and input/output shape
[ "Load", "data", "for", "training", "/", "validation", "store", "tokenizer", "and", "input", "/", "output", "shape" ]
def load_data(self): ''' Load data for training/validation, store tokenizer and input/output shape''' self.tr_set, self.dv_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \ load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory, self.curriculum > 0, **self.config['data']) self.verbose(msg)
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_asr.py#L30-L35
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_asr.py
python
Solver.set_model
(self)
Setup ASR model and optimizer
Setup ASR model and optimizer
[ "Setup", "ASR", "model", "and", "optimizer" ]
def set_model(self): ''' Setup ASR model and optimizer ''' # Model init_adadelta = self.config['hparas']['optimizer'] == 'Adadelta' self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta, ** self.config['model']).to(self.device) self.verbose(self.model.create_msg()) model_paras = [{'params': self.model.parameters()}] # Losses self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0) # Note: zero_infinity=False is unstable? self.ctc_loss = torch.nn.CTCLoss(blank=0, zero_infinity=False) # Plug-ins self.emb_fuse = False self.emb_reg = ('emb' in self.config) and ( self.config['emb']['enable']) if self.emb_reg: from src.plugin import EmbeddingRegularizer self.emb_decoder = EmbeddingRegularizer( self.tokenizer, self.model.dec_dim, **self.config['emb']).to(self.device) model_paras.append({'params': self.emb_decoder.parameters()}) self.emb_fuse = self.emb_decoder.apply_fuse if self.emb_fuse: self.seq_loss = torch.nn.NLLLoss(ignore_index=0) self.verbose(self.emb_decoder.create_msg()) # Optimizer self.optimizer = Optimizer(model_paras, **self.config['hparas']) self.verbose(self.optimizer.create_msg()) # Enable AMP if needed self.enable_apex() # Automatically load pre-trained model if self.paras.load is given self.load_ckpt()
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_asr.py#L37-L73
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_asr.py
python
Solver.exec
(self)
Training End-to-end ASR system
Training End-to-end ASR system
[ "Training", "End", "-", "to", "-", "end", "ASR", "system" ]
def exec(self): ''' Training End-to-end ASR system ''' self.verbose('Total training steps {}.'.format( human_format(self.max_step))) ctc_loss, att_loss, emb_loss = None, None, None n_epochs = 0 self.timer.set() while self.step < self.max_step: # Renew dataloader to enable random sampling if self.curriculum > 0 and n_epochs == self.curriculum: self.verbose( 'Curriculum learning ends after {} epochs, starting random sampling.'.format(n_epochs)) self.tr_set, _, _, _, _, _ = \ load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory, False, **self.config['data']) for data in self.tr_set: # Pre-step : update tf_rate/lr_rate and do zero_grad tf_rate = self.optimizer.pre_step(self.step) total_loss = 0 # Fetch data feat, feat_len, txt, txt_len = self.fetch_data(data) self.timer.cnt('rd') # Forward model # Note: txt should NOT start w/ <sos> ctc_output, encode_len, att_output, att_align, dec_state = \ self.model(feat, feat_len, max(txt_len), tf_rate=tf_rate, teacher=txt, get_dec_state=self.emb_reg) # Plugins if self.emb_reg: emb_loss, fuse_output = self.emb_decoder( dec_state, att_output, label=txt) total_loss += self.emb_decoder.weight*emb_loss # Compute all objectives if ctc_output is not None: if self.paras.cudnn_ctc: ctc_loss = self.ctc_loss(ctc_output.transpose(0, 1), txt.to_sparse().values().to(device='cpu', dtype=torch.int32), [ctc_output.shape[1]] * len(ctc_output), txt_len.cpu().tolist()) else: ctc_loss = self.ctc_loss(ctc_output.transpose( 0, 1), txt, encode_len, txt_len) total_loss += ctc_loss*self.model.ctc_weight if att_output is not None: b, t, _ = att_output.shape att_output = fuse_output if self.emb_fuse else att_output att_loss = self.seq_loss( att_output.view(b*t, -1), txt.view(-1)) total_loss += att_loss*(1-self.model.ctc_weight) self.timer.cnt('fw') # Backprop grad_norm = self.backward(total_loss) self.step += 1 # Logger if (self.step == 1) or (self.step % self.PROGRESS_STEP == 0): self.progress('Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}' .format(total_loss.cpu().item(), grad_norm, self.timer.show())) self.write_log( 'loss', {'tr_ctc': ctc_loss, 'tr_att': att_loss}) self.write_log('emb_loss', {'tr': emb_loss}) self.write_log('wer', {'tr_att': cal_er(self.tokenizer, att_output, txt), 'tr_ctc': cal_er(self.tokenizer, ctc_output, txt, ctc=True)}) if self.emb_fuse: if self.emb_decoder.fuse_learnable: self.write_log('fuse_lambda', { 'emb': self.emb_decoder.get_weight()}) self.write_log( 'fuse_temp', {'temp': self.emb_decoder.get_temp()}) # Validation if (self.step == 1) or (self.step % self.valid_step == 0): self.validate() # End of step # https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354 torch.cuda.empty_cache() self.timer.set() if self.step > self.max_step: break n_epochs += 1 self.log.close()
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Norm - {:.2f} | {}'", ".", "format", "(", "total_loss", ".", "cpu", "(", ")", ".", "item", "(", ")", ",", "grad_norm", ",", "self", ".", "timer", ".", "show", "(", ")", ")", ")", "self", ".", "write_log", "(", "'loss'", ",", "{", "'tr_ctc'", ":", "ctc_loss", ",", "'tr_att'", ":", "att_loss", "}", ")", "self", ".", "write_log", "(", "'emb_loss'", ",", "{", "'tr'", ":", "emb_loss", "}", ")", "self", ".", "write_log", "(", "'wer'", ",", "{", "'tr_att'", ":", "cal_er", "(", "self", ".", "tokenizer", ",", "att_output", ",", "txt", ")", ",", "'tr_ctc'", ":", "cal_er", "(", "self", ".", "tokenizer", ",", "ctc_output", ",", "txt", ",", "ctc", "=", "True", ")", "}", ")", "if", "self", ".", "emb_fuse", ":", "if", "self", ".", "emb_decoder", ".", "fuse_learnable", ":", "self", ".", "write_log", "(", "'fuse_lambda'", ",", "{", "'emb'", ":", "self", ".", "emb_decoder", ".", "get_weight", "(", ")", "}", ")", "self", ".", "write_log", "(", "'fuse_temp'", ",", "{", "'temp'", ":", "self", ".", "emb_decoder", ".", "get_temp", "(", ")", "}", ")", "# Validation", "if", "(", "self", ".", "step", "==", "1", ")", "or", "(", "self", ".", "step", "%", "self", ".", "valid_step", "==", "0", ")", ":", "self", ".", "validate", "(", ")", "# End of step", "# https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354", "torch", ".", "cuda", ".", "empty_cache", "(", ")", "self", ".", "timer", ".", "set", "(", ")", "if", "self", ".", "step", ">", "self", ".", "max_step", ":", "break", "n_epochs", "+=", "1", "self", ".", "log", ".", "close", "(", ")" ]
https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_asr.py#L77-L167
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_lm.py
python
Solver.fetch_data
(self, data)
return txt, txt_len
Move data to device, insert <sos> and compute text seq. length
Move data to device, insert <sos> and compute text seq. length
[ "Move", "data", "to", "device", "insert", "<sos", ">", "and", "compute", "text", "seq", ".", "length" ]
def fetch_data(self, data): ''' Move data to device, insert <sos> and compute text seq. length''' txt = torch.cat( (torch.zeros((data.shape[0], 1), dtype=torch.long), data), dim=1).to(self.device) txt_len = torch.sum(data != 0, dim=-1) return txt, txt_len
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_lm.py#L18-L23
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_lm.py
python
Solver.load_data
(self)
Load data for training/validation, store tokenizer and input/output shape
Load data for training/validation, store tokenizer and input/output shape
[ "Load", "data", "for", "training", "/", "validation", "store", "tokenizer", "and", "input", "/", "output", "shape" ]
def load_data(self): ''' Load data for training/validation, store tokenizer and input/output shape''' self.tr_set, self.dv_set, self.vocab_size, self.tokenizer, msg = \ load_textset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory, **self.config['data']) self.verbose(msg)
[ "def", "load_data", "(", "self", ")", ":", "self", ".", "tr_set", ",", "self", ".", "dv_set", ",", "self", ".", "vocab_size", ",", "self", ".", "tokenizer", ",", "msg", "=", "load_textset", "(", "self", ".", "paras", ".", "njobs", ",", "self", ".", "paras", ".", "gpu", ",", "self", ".", "paras", ".", "pin_memory", ",", "*", "*", "self", ".", "config", "[", "'data'", "]", ")", "self", ".", "verbose", "(", "msg", ")" ]
https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_lm.py#L25-L30
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_lm.py
python
Solver.set_model
(self)
Setup ASR model and optimizer
Setup ASR model and optimizer
[ "Setup", "ASR", "model", "and", "optimizer" ]
def set_model(self): ''' Setup ASR model and optimizer ''' # Model self.model = RNNLM(self.vocab_size, ** self.config['model']).to(self.device) self.verbose(self.model.create_msg()) # Losses self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0) # Optimizer self.optimizer = Optimizer( self.model.parameters(), **self.config['hparas']) # Enable AMP if needed self.enable_apex() # load pre-trained model if self.paras.load: self.load_ckpt() ckpt = torch.load(self.paras.load, map_location=self.device) self.model.load_state_dict(ckpt['model']) self.optimizer.load_opt_state_dict(ckpt['optimizer']) self.step = ckpt['global_step'] self.verbose('Load ckpt from {}, restarting at step {}'.format( self.paras.load, self.step))
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_lm.py#L32-L54
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
bin/train_lm.py
python
Solver.exec
(self)
Training End-to-end ASR system
Training End-to-end ASR system
[ "Training", "End", "-", "to", "-", "end", "ASR", "system" ]
def exec(self): ''' Training End-to-end ASR system ''' self.verbose('Total training steps {}.'.format( human_format(self.max_step))) self.timer.set() while self.step < self.max_step: for data in self.tr_set: # Pre-step : update tf_rate/lr_rate and do zero_grad self.optimizer.pre_step(self.step) # Fetch data txt, txt_len = self.fetch_data(data) self.timer.cnt('rd') # Forward model pred, _ = self.model(txt[:, :-1], txt_len) # Compute all objectives lm_loss = self.seq_loss( pred.view(-1, self.vocab_size), txt[:, 1:].reshape(-1)) self.timer.cnt('fw') # Backprop grad_norm = self.backward(lm_loss) self.step += 1 # Logger if self.step % self.PROGRESS_STEP == 0: self.progress('Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}' .format(lm_loss.cpu().item(), grad_norm, self.timer.show())) self.write_log('entropy', {'tr': lm_loss}) self.write_log( 'perplexity', {'tr': torch.exp(lm_loss).cpu().item()}) # Validation if (self.step == 1) or (self.step % self.valid_step == 0): self.validate() # End of step self.timer.set() if self.step > self.max_step: break self.log.close()
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/bin/train_lm.py#L56-L99
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
corpus/librispeech.py
python
read_text
(file)
Get transcription of target wave file, it's somewhat redundant for accessing each txt multiplt times, but it works fine with multi-thread
Get transcription of target wave file, it's somewhat redundant for accessing each txt multiplt times, but it works fine with multi-thread
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def read_text(file): '''Get transcription of target wave file, it's somewhat redundant for accessing each txt multiplt times, but it works fine with multi-thread''' src_file = '-'.join(file.split('-')[:-1])+'.trans.txt' idx = file.split('/')[-1].split('.')[0] with open(src_file, 'r') as fp: for line in fp: if idx == line.split(' ')[0]: return line[:-1].split(' ', 1)[1]
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/corpus/librispeech.py#L15-L25
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/bert_embedding.py
python
generate_embedding
(bert_model, labels)
return embedding
Generate bert's embedding from fine-tuned model.
Generate bert's embedding from fine-tuned model.
[ "Generate", "bert", "s", "embedding", "from", "fine", "-", "tuned", "model", "." ]
def generate_embedding(bert_model, labels): """Generate bert's embedding from fine-tuned model.""" batch_size, time = labels.shape cls_ids = torch.full( (batch_size, 1), bert_model.bert_text_encoder.cls_idx, dtype=labels.dtype, device=labels.device) bert_labels = torch.cat([cls_ids, labels], 1) # replace eos with sep eos_idx = bert_model.bert_text_encoder.eos_idx sep_idx = bert_model.bert_text_encoder.sep_idx bert_labels[bert_labels == eos_idx] = sep_idx embedding, _ = bert_model.bert(bert_labels, output_all_encoded_layers=True) # sum over all layers embedding embedding = torch.stack(embedding).sum(0) # get rid of cls embedding = embedding[:, 1:] assert labels.shape == embedding.shape[:-1] return embedding
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/bert_embedding.py#L38-L58
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/bert_embedding.py
python
load_fine_tuned_model
(bert_model, text_encoder, path)
return model
Load fine-tuned bert model given text encoder and checkpoint path.
Load fine-tuned bert model given text encoder and checkpoint path.
[ "Load", "fine", "-", "tuned", "bert", "model", "given", "text", "encoder", "and", "checkpoint", "path", "." ]
def load_fine_tuned_model(bert_model, text_encoder, path): """Load fine-tuned bert model given text encoder and checkpoint path.""" bert_text_encoder = BertLikeSentencePieceTextEncoder(text_encoder) model = BertForMaskedLM.from_pretrained(bert_model) model.bert_text_encoder = bert_text_encoder model.bert.embeddings.word_embeddings = nn.Embedding( bert_text_encoder.vocab_size, model.bert.embeddings.word_embeddings.weight.shape[1]) model.config.vocab_size = bert_text_encoder.vocab_size model.cls = BertOnlyMLMHead( model.config, model.bert.embeddings.word_embeddings.weight) model.load_state_dict(torch.load(path)) return model
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/bert_embedding.py#L61-L75
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/solver.py
python
BaseSolver.backward
(self, loss)
return grad_norm
Standard backward step with self.timer and debugger Arguments loss - the loss to perform loss.backward()
Standard backward step with self.timer and debugger Arguments loss - the loss to perform loss.backward()
[ "Standard", "backward", "step", "with", "self", ".", "timer", "and", "debugger", "Arguments", "loss", "-", "the", "loss", "to", "perform", "loss", ".", "backward", "()" ]
def backward(self, loss): ''' Standard backward step with self.timer and debugger Arguments loss - the loss to perform loss.backward() ''' self.timer.set() loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_( self.model.parameters(), self.GRAD_CLIP) if math.isnan(grad_norm): self.verbose('Error : grad norm is NaN @ step '+str(self.step)) else: self.optimizer.step() self.timer.cnt('bw') return grad_norm
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/solver.py#L76-L91
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/solver.py
python
BaseSolver.load_ckpt
(self)
Load ckpt if --load option is specified
Load ckpt if --load option is specified
[ "Load", "ckpt", "if", "--", "load", "option", "is", "specified" ]
def load_ckpt(self): ''' Load ckpt if --load option is specified ''' if self.paras.load: # Load weights ckpt = torch.load( self.paras.load, map_location=self.device if self.mode == 'train' else 'cpu') self.model.load_state_dict(ckpt['model']) if self.emb_decoder is not None: self.emb_decoder.load_state_dict(ckpt['emb_decoder']) # if self.amp: # amp.load_state_dict(ckpt['amp']) # Load task-dependent items metric = "None" score = 0.0 for k, v in ckpt.items(): if type(v) is float: metric, score = k, v if self.mode == 'train': self.step = ckpt['global_step'] self.optimizer.load_opt_state_dict(ckpt['optimizer']) self.verbose('Load ckpt from {}, restarting at step {} (recorded {} = {:.2f} %)'.format( self.paras.load, self.step, metric, score)) else: self.model.eval() if self.emb_decoder is not None: self.emb_decoder.eval() self.verbose('Evaluation target = {} (recorded {} = {:.2f} %)'.format(self.paras.load, metric, score))
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/solver.py#L93-L119
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/solver.py
python
BaseSolver.verbose
(self, msg)
Verbose function for print information to stdout
Verbose function for print information to stdout
[ "Verbose", "function", "for", "print", "information", "to", "stdout" ]
def verbose(self, msg): ''' Verbose function for print information to stdout''' if self.paras.verbose: if type(msg) == list: for m in msg: print('[INFO]', m.ljust(100)) else: print('[INFO]', msg.ljust(100))
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/solver.py#L121-L128
Alexander-H-Liu/End-to-end-ASR-Pytorch
1103d144423e8e692f1d18cd9db27a96cb49fb9d
src/solver.py
python
BaseSolver.progress
(self, msg)
Verbose function for updating progress on stdout (do not include newline)
Verbose function for updating progress on stdout (do not include newline)
[ "Verbose", "function", "for", "updating", "progress", "on", "stdout", "(", "do", "not", "include", "newline", ")" ]
def progress(self, msg): ''' Verbose function for updating progress on stdout (do not include newline) ''' if self.paras.verbose: sys.stdout.write("\033[K") # Clear line print('[{}] {}'.format(human_format(self.step), msg), end='\r')
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https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch/blob/1103d144423e8e692f1d18cd9db27a96cb49fb9d/src/solver.py#L130-L134