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summarizers.py
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from src.MemSum_Full.model import LocalSentenceEncoder as LocalSentenceEncoder_MemSum_Full
from src.MemSum_Full.model import GlobalContextEncoder as GlobalContextEncoder_MemSum_Full
from src.MemSum_Full.model import ExtractionContextDecoder as ExtractionContextDecoder_MemSum_Full
from src.MemSum_Full.model import Extractor as Extractor_MemSum_Full
from src.MemSum_Full.datautils import Vocab as Vocab_MemSum_Full
from src.MemSum_Full.datautils import SentenceTokenizer as SentenceTokenizer_MemSum_Full
import torch.nn.functional as F
from torch.distributions import Categorical
import pickle
import torch
import numpy as np
from tqdm import tqdm
import json
class MemSum:
def __init__( self, model_path, vocabulary_path, gpu = None , embed_dim=200, num_heads=8, hidden_dim = 1024, N_enc_l = 2 , N_enc_g = 2, N_dec = 3, max_seq_len =100, max_doc_len = 500 ):
with open( vocabulary_path , "rb" ) as f:
words = pickle.load(f)
self.vocab = Vocab_MemSum_Full( words )
vocab_size = len(words)
self.local_sentence_encoder = LocalSentenceEncoder_MemSum_Full( vocab_size, self.vocab.pad_index, embed_dim,num_heads,hidden_dim,N_enc_l, None )
self.global_context_encoder = GlobalContextEncoder_MemSum_Full( embed_dim, num_heads, hidden_dim, N_enc_g )
self.extraction_context_decoder = ExtractionContextDecoder_MemSum_Full( embed_dim, num_heads, hidden_dim, N_dec )
self.extractor = Extractor_MemSum_Full( embed_dim, num_heads )
ckpt = torch.load( model_path, map_location = "cpu" )
self.local_sentence_encoder.load_state_dict( ckpt["local_sentence_encoder"] )
self.global_context_encoder.load_state_dict( ckpt["global_context_encoder"] )
self.extraction_context_decoder.load_state_dict( ckpt["extraction_context_decoder"] )
self.extractor.load_state_dict(ckpt["extractor"])
self.device = torch.device( "cuda:%d"%(gpu) if gpu is not None and torch.cuda.is_available() else "cpu" )
self.local_sentence_encoder.to(self.device)
self.global_context_encoder.to(self.device)
self.extraction_context_decoder.to(self.device)
self.extractor.to(self.device)
self.sentence_tokenizer = SentenceTokenizer_MemSum_Full()
self.max_seq_len = max_seq_len
self.max_doc_len = max_doc_len
def get_ngram(self, w_list, n = 4 ):
ngram_set = set()
for pos in range(len(w_list) - n + 1 ):
ngram_set.add( "_".join( w_list[ pos:pos+n] ) )
return ngram_set
def extract( self, document_batch, p_stop_thres = 0.7, ngram_blocking = False, ngram = 3, return_sentence_position = False, return_sentence_score_history = False, max_extracted_sentences_per_document = 4 ):
"""document_batch is a batch of documents:
[ [ sen1, sen2, ... , senL1 ],
[ sen1, sen2, ... , senL2], ...
]
"""
## tokenization:
document_length_list = []
sentence_length_list = []
tokenized_document_batch = []
for document in document_batch:
tokenized_document = []
for sen in document:
tokenized_sen = self.sentence_tokenizer.tokenize( sen )
tokenized_document.append( tokenized_sen )
sentence_length_list.append( len(tokenized_sen.split()) )
tokenized_document_batch.append( tokenized_document )
document_length_list.append( len(tokenized_document) )
max_document_length = self.max_doc_len
max_sentence_length = self.max_seq_len
## convert to sequence
seqs = []
doc_mask = []
for document in tokenized_document_batch:
if len(document) > max_document_length:
# doc_mask.append( [0] * max_document_length )
document = document[:max_document_length]
else:
# doc_mask.append( [0] * len(document) +[1] * ( max_document_length - len(document) ) )
document = document + [""] * ( max_document_length - len(document) )
doc_mask.append( [ 1 if sen.strip() == "" else 0 for sen in document ] )
document_sequences = []
for sen in document:
seq = self.vocab.sent2seq( sen, max_sentence_length )
document_sequences.append(seq)
seqs.append(document_sequences)
seqs = np.asarray(seqs)
doc_mask = np.asarray(doc_mask) == 1
seqs = torch.from_numpy(seqs).to(self.device)
doc_mask = torch.from_numpy(doc_mask).to(self.device)
extracted_sentences = []
sentence_score_history = []
p_stop_history = []
with torch.no_grad():
num_sentences = seqs.size(1)
sen_embed = self.local_sentence_encoder( seqs.view(-1, seqs.size(2) ) )
sen_embed = sen_embed.view( -1, num_sentences, sen_embed.size(1) )
relevance_embed = self.global_context_encoder( sen_embed, doc_mask )
num_documents = seqs.size(0)
doc_mask = doc_mask.detach().cpu().numpy()
seqs = seqs.detach().cpu().numpy()
extracted_sentences = []
extracted_sentences_positions = []
for doc_i in range(num_documents):
current_doc_mask = doc_mask[doc_i:doc_i+1]
current_remaining_mask_np = np.ones_like(current_doc_mask ).astype(np.bool) | current_doc_mask
current_extraction_mask_np = np.zeros_like(current_doc_mask).astype(np.bool) | current_doc_mask
current_sen_embed = sen_embed[doc_i:doc_i+1]
current_relevance_embed = relevance_embed[ doc_i:doc_i+1 ]
current_redundancy_embed = None
current_hyps = []
extracted_sen_ngrams = set()
sentence_score_history_for_doc_i = []
p_stop_history_for_doc_i = []
for step in range( max_extracted_sentences_per_document+1 ) :
current_extraction_mask = torch.from_numpy( current_extraction_mask_np ).to(self.device)
current_remaining_mask = torch.from_numpy( current_remaining_mask_np ).to(self.device)
if step > 0:
current_redundancy_embed = self.extraction_context_decoder( current_sen_embed, current_remaining_mask, current_extraction_mask )
p, p_stop, _ = self.extractor( current_sen_embed, current_relevance_embed, current_redundancy_embed , current_extraction_mask )
p_stop = p_stop.unsqueeze(1)
p = p.masked_fill( current_extraction_mask, 1e-12 )
sentence_score_history_for_doc_i.append( p.detach().cpu().numpy() )
p_stop_history_for_doc_i.append( p_stop.squeeze(1).item() )
normalized_p = p / p.sum(dim=1, keepdims = True)
stop = p_stop.squeeze(1).item()> p_stop_thres #and step > 0
#sen_i = normalized_p.argmax(dim=1)[0]
_, sorted_sen_indices =normalized_p.sort(dim=1, descending= True)
sorted_sen_indices = sorted_sen_indices[0]
extracted = False
for sen_i in sorted_sen_indices:
sen_i = sen_i.item()
if sen_i< len(document_batch[doc_i]):
sen = document_batch[doc_i][sen_i]
else:
break
sen_ngrams = self.get_ngram( sen.lower().split(), ngram )
if not ngram_blocking or len( extracted_sen_ngrams & sen_ngrams ) < 1:
extracted_sen_ngrams.update( sen_ngrams )
extracted = True
break
if stop or step == max_extracted_sentences_per_document or not extracted:
extracted_sentences.append( [ document_batch[doc_i][sen_i] for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
extracted_sentences_positions.append( [ sen_i for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
break
else:
current_hyps.append(sen_i)
current_extraction_mask_np[0, sen_i] = True
current_remaining_mask_np[0, sen_i] = False
sentence_score_history.append(sentence_score_history_for_doc_i)
p_stop_history.append( p_stop_history_for_doc_i )
# if return_sentence_position:
# return extracted_sentences, extracted_sentences_positions
# else:
# return extracted_sentences
results = [extracted_sentences]
if return_sentence_position:
results.append( extracted_sentences_positions )
if return_sentence_score_history:
results+=[sentence_score_history , p_stop_history ]
if len(results) == 1:
results = results[0]
return results
class ExtractiveSummarizer_NeuSum:
def __init__( self, model_path, vocabulary_path, gpu = None , embed_dim=200,
max_seq_len =100, max_doc_len = 500 , **kwargs ):
with open( vocabulary_path , "rb" ) as f:
words = pickle.load(f)
self.vocab = Vocab_NeuSum( words )
vocab_size = len(words)
self.local_sentence_encoder = LocalSentenceEncoder_NeuSum( vocab_size, self.vocab.pad_index, embed_dim, None )
self.global_context_encoder = GlobalContextEncoder_NeuSum( embed_dim)
self.extraction_context_decoder = ExtractionContextDecoder_NeuSum( embed_dim)
self.extractor = Extractor_NeuSum( embed_dim )
ckpt = torch.load( model_path, map_location = "cpu" )
self.local_sentence_encoder.load_state_dict( ckpt["local_sentence_encoder"] )
self.global_context_encoder.load_state_dict( ckpt["global_context_encoder"] )
self.extraction_context_decoder.load_state_dict( ckpt["extraction_context_decoder"] )
self.extractor.load_state_dict(ckpt["extractor"])
self.device = torch.device( "cuda:%d"%(gpu) if gpu is not None and torch.cuda.is_available() else "cpu" )
self.local_sentence_encoder.to(self.device)
self.global_context_encoder.to(self.device)
self.extraction_context_decoder.to(self.device)
self.extractor.to(self.device)
self.sentence_tokenizer = SentenceTokenizer_NeuSum()
self.max_seq_len = max_seq_len
self.max_doc_len = max_doc_len
def extract( self, document_batch, return_sentence_position = False, max_extracted_sentences_per_document = 7, **kwargs ):
"""document_batch is a batch of documents:
[ [ sen1, sen2, ... , senL1 ],
[ sen1, sen2, ... , senL2], ...
]
"""
## tokenization:
document_length_list = []
sentence_length_list = []
tokenized_document_batch = []
for document in document_batch:
tokenized_document = []
for sen in document:
tokenized_sen = self.sentence_tokenizer.tokenize( sen )
tokenized_document.append( tokenized_sen )
sentence_length_list.append( len(tokenized_sen.split()) )
tokenized_document_batch.append( tokenized_document )
document_length_list.append( len(tokenized_document) )
max_document_length = self.max_doc_len
max_sentence_length = self.max_seq_len
## convert to sequence
seqs = []
doc_mask = []
for document in tokenized_document_batch:
if len(document) > max_document_length:
# doc_mask.append( [0] * max_document_length )
document = document[:max_document_length]
else:
# doc_mask.append( [0] * len(document) +[1] * ( max_document_length - len(document) ) )
document = document + [""] * ( max_document_length - len(document) )
doc_mask.append( [ 1 if sen.strip() == "" else 0 for sen in document ] )
document_sequences = []
for sen in document:
seq = self.vocab.sent2seq( sen, max_sentence_length )
document_sequences.append(seq)
seqs.append(document_sequences)
seqs = np.asarray(seqs)
doc_mask = np.asarray(doc_mask) == 1
seqs = torch.from_numpy(seqs).to(self.device)
doc_mask = torch.from_numpy(doc_mask).to(self.device)
with torch.no_grad():
num_sentences = seqs.size(1)
sen_embed = self.local_sentence_encoder( seqs.view(-1, seqs.size(2) ) )
sen_embed = sen_embed.view( -1, num_sentences, sen_embed.size(1) )
global_context_embed, backward_state = self.global_context_encoder( sen_embed, doc_mask, return_backward_state = True )
num_documents = seqs.size(0)
doc_mask = doc_mask.detach().cpu().numpy()
seqs = seqs.detach().cpu().numpy()
extracted_sentences = []
extracted_sentences_positions = []
for doc_i in range(num_documents):
current_doc_mask = doc_mask[doc_i:doc_i+1]
current_remaining_mask_np = np.ones_like(current_doc_mask ).astype(np.bool) | current_doc_mask
current_extraction_mask_np = np.zeros_like(current_doc_mask).astype(np.bool) | current_doc_mask
current_global_context_embed = global_context_embed[doc_i:doc_i+1]
current_hidden_state = backward_state[ doc_i:doc_i+1 ]
current_extracted_sen_embed = torch.zeros_like( current_global_context_embed[:,:1,:] )
current_hyps = []
for step in range( max_extracted_sentences_per_document ) :
current_extraction_mask = torch.from_numpy( current_extraction_mask_np ).to(self.device)
current_remaining_mask = torch.from_numpy( current_remaining_mask_np ).to(self.device)
current_hidden_state = self.extraction_context_decoder( current_extracted_sen_embed, current_hidden_state )
p = self.extractor( current_global_context_embed, current_hidden_state, current_extraction_mask )
sen_i = p.argmax(dim=1)[0]
sen_i = sen_i.item()
current_hyps.append(sen_i)
current_extraction_mask_np[0, sen_i] = True
current_remaining_mask_np[0, sen_i] = False
extracted_sentences.append( [ document_batch[doc_i][sen_i] for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
extracted_sentences_positions.append( [ sen_i for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
results = [extracted_sentences]
if return_sentence_position:
results.append( extracted_sentences_positions )
if len(results) == 1:
results = results[0]
return results