dev_labels = [] test_labels = [] train_features_tot = [] test_word = [] for i in range(file_num): dev_features0, dev_labels0 = utils.read_corpus(dev_lines[i]) test_features0, test_labels0 = utils.read_corpus(test_lines[i]) dev_features.append(dev_features0) test_features.append(test_features0) dev_labels.append(dev_labels0) test_labels.append(test_labels0) if args.output_annotation: #NEW test_word0 = utils.read_features(test_lines[i]) test_word.append(test_word0) if args.load_check_point: if os.path.isfile(args.load_check_point): print("loading checkpoint: '{}'".format(args.load_check_point)) checkpoint_file = torch.load(args.load_check_point) args.start_epoch = checkpoint_file['epoch'] f_map = checkpoint_file['f_map'] l_map = checkpoint_file['l_map'] c_map = checkpoint_file['c_map'] in_doc_words = checkpoint_file['in_doc_words'] train_features, train_labels = utils.read_corpus(lines[i]) else: print("no checkpoint found at: '{}'".format( args.load_check_point))
jd = jd['args'] checkpoint_file = torch.load(args.load_check_point, map_location=lambda storage, loc: storage) f_map = checkpoint_file['f_map'] l_map = checkpoint_file['l_map'] if args.gpu >= 0: torch.cuda.set_device(args.gpu) # loading corpus print('loading corpus') with codecs.open(args.input_file, 'r', 'utf-8') as f: lines = f.readlines() # converting format features = utils.read_features(lines) # build model print('loading model') ner_model = LSTM_CRF(len(f_map), len(l_map), jd['embedding_dim'], jd['hidden'], jd['layers'], jd['drop_out'], large_CRF=jd['small_crf']) ner_model.load_state_dict(checkpoint_file['state_dict']) if args.gpu >= 0: if_cuda = True
ner_model.cuda() packer = CRFRepack_WC(len(l_map), True) else: if_cuda = False packer = CRFRepack_WC(len(l_map), False) decode_label = (args.decode_type == 'label') predictor = predict_wc(if_cuda, f_map, c_map, l_map, f_map['<eof>'], c_map['\n'], l_map['<pad>'], l_map['<start>'], decode_label, args.batch_size, jd['caseless']) # loading corpus print('loading corpus') lines = [] features = [] with codecs.open(args.input_file, 'r', 'utf-8') as f: for line in f: if line == '\n': features.append(utils.read_features(lines)) lines = [] continue tmp = line.split() lines.append(tmp[0]) for idx in range(args.dataset_no): print('annotating the entity type', idx) fout = open(args.output_file+str(idx)+'.txt', 'w') for feature in features: predictor.output_batch(ner_model, feature, fout, idx) fout.write('\n') fout.close()
checkpoint_file = torch.load(args.load_check_point, map_location=lambda storage, loc: storage) f_map = checkpoint_file['f_map'] l_map = checkpoint_file['l_map'] c_map = checkpoint_file['c_map'] in_doc_words = checkpoint_file['in_doc_words'] if args.gpu >= 0: torch.cuda.set_device(args.gpu) # loading corpus print('loading corpus') with codecs.open(args.input_file, 'r', 'utf-8') as f: lines = f.readlines() # converting format features = utils.read_features(lines) # build model print('loading model') ner_model = LM_LSTM_CRF(len(l_map), len(c_map), jd['char_dim'], jd['char_hidden'], jd['char_layers'], jd['word_dim'], jd['word_hidden'], jd['word_layers'], len(f_map), jd['drop_out'], large_CRF=jd['small_crf'], if_highway=jd['high_way'], in_doc_words=in_doc_words, highway_layers = jd['highway_layers']) ner_model.load_state_dict(checkpoint_file['state_dict']) if args.gpu >= 0: if_cuda = True torch.cuda.set_device(args.gpu) ner_model.cuda() else: if_cuda = False decode_label = (args.decode_type == 'label')
def load_pretrain_model(file_path): print("CSCI548 model loading") parser = argparse.ArgumentParser(description='Evaluating LM-BLSTM-CRF') parser.add_argument('--load_arg', default='./checkpoint/cwlm_lstm_crf.json', help='path to arg json') parser.add_argument('--load_check_point', default='./checkpoint/cwlm_lstm_crf.model', help='path to model checkpoint file') parser.add_argument('--gpu', type=int, default=-1, help='gpu id') parser.add_argument( '--decode_type', choices=['label', 'string'], default='label', help= 'type of decode function, set `label` to couple label with text, or set `string` to insert label into test' ) parser.add_argument('--batch_size', type=int, default=50, help='size of batch') parser.add_argument('--input_file', default=file_path + "/test.tsv", help='path to input un-annotated corpus') parser.add_argument('--output_file', default='annotate/output', help='path to output file') parser.add_argument('--dataset_no', type=int, default=1, help='number of the datasets') args = parser.parse_args() print('loading dictionary') with open(args.load_arg, 'r') as f: jd = json.load(f) jd = jd['args'] checkpoint_file = torch.load(args.load_check_point, map_location=lambda storage, loc: storage) f_map = checkpoint_file['f_map'] l_map = checkpoint_file['l_map'] c_map = checkpoint_file['c_map'] in_doc_words = checkpoint_file['in_doc_words'] if args.gpu >= 0: torch.cuda.set_device(args.gpu) # build model print('loading model') ner_model = LM_LSTM_CRF(len(l_map), len(c_map), jd['char_dim'], jd['char_hidden'], jd['char_layers'], jd['word_dim'], jd['word_hidden'], jd['word_layers'], len(f_map), jd['drop_out'], args.dataset_no, large_CRF=jd['small_crf'], if_highway=jd['high_way'], in_doc_words=in_doc_words, highway_layers=jd['highway_layers']) ner_model.load_state_dict(checkpoint_file['state_dict']) if args.gpu >= 0: if_cuda = True torch.cuda.set_device(args.gpu) ner_model.cuda() packer = CRFRepack_WC(len(l_map), True) else: if_cuda = False packer = CRFRepack_WC(len(l_map), False) decode_label = (args.decode_type == 'label') predictor = predict_wc(if_cuda, f_map, c_map, l_map, f_map['<eof>'], c_map['\n'], l_map['<pad>'], l_map['<start>'], decode_label, args.batch_size, jd['caseless']) evaluator = eval_wc(packer, l_map, args.eva_matrix) # loading corpus print('loading corpus') lines = [] features = [] with codecs.open(args.input_file, 'r', 'utf-8') as f: for i, line in enumerate(f): if i == 2000: break if line == '\n': features.append(utils.read_features(lines)) lines = [] continue tmp = line.split() lines.append(tmp[0]) for idx in range(args.dataset_no): print('annotating the entity type', idx) with open(args.output_file + str(idx) + '.txt', 'w') as fout: for feature in features: predictor.output_batch(ner_model, feature, fout, idx) fout.write('\n') return args.output_file + str(idx) + '.txt'
def read_dataset(self, file_dict, dataset_name, *args, **kwargs): print('loading corpus') self.file_num = len(self.args.train_file) for i in range(self.file_num): with codecs.open(self.args.train_file[i], 'r', 'utf-8') as f: lines0 = f.readlines() lines0 = lines0[0:2000] # print (len(lines0)) self.lines.append(lines0) for i in range(self.file_num): with codecs.open(self.args.dev_file[i], 'r', 'utf-8') as f: dev_lines0 = f.readlines() dev_lines0 = dev_lines0[0:2000] self.dev_lines.append(dev_lines0) for i in range(self.file_num): with codecs.open(self.args.test_file[i], 'r', 'utf-8') as f: test_lines0 = f.readlines() test_lines0 = test_lines0[0:2000] self.test_lines.append(test_lines0) for i in range(self.file_num): dev_features0, dev_labels0 = utils.read_corpus(self.dev_lines[i]) test_features0, test_labels0 = utils.read_corpus( self.test_lines[i]) self.dev_features.append(dev_features0) self.test_features.append(test_features0) self.dev_labels.append(dev_labels0) self.test_labels.append(test_labels0) if self.args.output_annotation: # NEW test_word0, test_word_tag0 = utils.read_features( self.test_lines[i]) self.test_word.append(test_word0) self.test_word_tag.append(test_word_tag0) #print (len(self.test_word), len(self.test_labels)) if self.args.load_check_point: if os.path.isfile(self.args.load_check_point): print("loading checkpoint: '{}'".format( self.args.load_check_point)) self.checkpoint_file = torch.load( self.args.load_check_point) self.args.start_epoch = self.checkpoint_file['epoch'] self.f_map = self.checkpoint_file['f_map'] self.l_map = self.checkpoint_file['l_map'] c_map = self.checkpoint_file['c_map'] self.in_doc_words = self.checkpoint_file['in_doc_words'] self.train_features, self.train_labels = utils.read_corpus( self.lines[i]) else: print("no checkpoint found at: '{}'".format( self.args.load_check_point)) else: print('constructing coding table') train_features0, train_labels0, self.f_map, self.l_map, self.char_count = utils.generate_corpus_char( self.lines[i], self.f_map, self.l_map, self.char_count, c_thresholds=self.args.mini_count, if_shrink_w_feature=False) self.train_features.append(train_features0) self.train_labels.append(train_labels0) self.train_features_tot += train_features0 shrink_char_count = [ k for (k, v) in iter(self.char_count.items()) if v >= self.args.mini_count ] self.char_map = { shrink_char_count[ind]: ind for ind in range(0, len(shrink_char_count)) } self.char_map['<u>'] = len(self.char_map) # unk for char self.char_map[' '] = len(self.char_map) # concat for char self.char_map['\n'] = len(self.char_map) # eof for char f_set = {v for v in self.f_map} dt_f_set = f_set self.f_map = utils.shrink_features(self.f_map, self.train_features_tot, self.args.mini_count) l_set = set() for i in range(self.file_num): dt_f_set = functools.reduce( lambda x, y: x | y, map(lambda t: set(t), self.dev_features[i]), dt_f_set) dt_f_set = functools.reduce( lambda x, y: x | y, map(lambda t: set(t), self.test_features[i]), dt_f_set) l_set = functools.reduce(lambda x, y: x | y, map(lambda t: set(t), self.dev_labels[i]), l_set) l_set = functools.reduce( lambda x, y: x | y, map(lambda t: set(t), self.test_labels[i]), l_set) if not self.args.rand_embedding: print("feature size: '{}'".format(len(self.f_map))) print('loading embedding') if self.args.fine_tune: # which means does not do fine-tune self.f_map = {'<eof>': 0} self.f_map, self.embedding_tensor, self.in_doc_words = utils.load_embedding_wlm( self.args.emb_file, ' ', self.f_map, dt_f_set, self.args.caseless, self.args.unk, self.args.word_dim, shrink_to_corpus=self.args.shrink_embedding) print("embedding size: '{}'".format(len(self.f_map))) for label in l_set: if label not in self.l_map: self.l_map[label] = len(self.l_map) print('constructing dataset') for i in range(self.file_num): # construct dataset dataset, forw_corp, back_corp = utils.construct_bucket_mean_vb_wc( self.train_features[i], self.train_labels[i], self.l_map, self.char_map, self.f_map, self.args.caseless) dev_dataset, forw_dev, back_dev = utils.construct_bucket_mean_vb_wc( self.dev_features[i], self.dev_labels[i], self.l_map, self.char_map, self.f_map, self.args.caseless) test_dataset, forw_test, back_test = utils.construct_bucket_mean_vb_wc( self.test_features[i], self.test_labels[i], self.l_map, self.char_map, self.f_map, self.args.caseless) self.dataset_loader.append([ torch.utils.data.DataLoader(tup, self.args.batch_size, shuffle=True, drop_last=False) for tup in dataset ]) self.dev_dataset_loader.append([ torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in dev_dataset ]) self.test_dataset_loader.append([ torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in test_dataset ])