def load_model(self):
if not self.args.train_from:
model = CopyRNN(self.args, self.vocab2id)
else:
model_path = self.args.train_from
config_path = os.path.join(os.path.dirname(model_path),
self.get_basename(model_path) + '.json')
old_config = read_json(config_path)
old_config['train_from'] = model_path
old_config['step'] = int(model_path.rsplit('_', 1)[-1].split('.')[0])
self.args = Munch(old_config)
self.vocab2id = load_vocab(self.args.vocab_path, self.args.vocab_size)
model = CopyRNN(self.args, self.vocab2id)
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
state_dict = OrderedDict()
# avoid error when load parallel trained model
for k, v in checkpoint.items():
if k.startswith('module.'):
k = k[7:]
state_dict[k] = v
model.load_state_dict(state_dict)
return model
def __init__(self, args, model):
torch.manual_seed(0)
torch.autograd.set_detect_anomaly(True)
self.args = args
self.vocab2id = load_vocab(self.args.vocab_path, self.args.vocab_size)
self.model = model
if torch.cuda.is_available():
self.model = self.model.cuda()
if args.train_parallel:
self.model = nn.DataParallel(self.model)
self.loss_func = nn.NLLLoss(ignore_index=self.vocab2id[PAD_WORD])
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.args.learning_rate)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer,
self.args.schedule_step,
self.args.schedule_gamma)
self.logger = get_logger('train')
self.train_loader = KeyphraseDataLoader(
data_source=self.args.train_filename,
vocab2id=self.vocab2id,
mode='train',
args=args)
if self.args.train_from:
self.dest_dir = os.path.dirname(self.args.train_from) + '/'
else:
timemark = time.strftime('%Y%m%d-%H%M%S',
time.localtime(time.time()))
self.dest_dir = os.path.join(
self.args.dest_base_dir,
self.args.exp_name + '-' + timemark) + '/'
os.mkdir(self.dest_dir)
fh = logging.FileHandler(os.path.join(self.dest_dir, args.logfile))
fh.setLevel(logging.INFO)
fh.setFormatter(logging.Formatter('[%(asctime)s] %(message)s'))
self.logger.addHandler(fh)
if not self.args.tensorboard_dir:
tensorboard_dir = self.dest_dir + 'logs/'
else:
tensorboard_dir = self.args.tensorboard_dir
self.writer = SummaryWriter(tensorboard_dir)
self.eval_topn = (5, 10)
self.macro_evaluator = KeyphraseEvaluator(self.eval_topn, 'macro',
args.token_field,
args.keyphrase_field)
self.micro_evaluator = KeyphraseEvaluator(self.eval_topn, 'micro',
args.token_field,
args.keyphrase_field)
self.best_f1 = None
self.best_step = 0
self.not_update_count = 0
def __init__(self, model_info, vocab_info, beam_size, max_target_len,
max_src_length):
"""
:param model_info: input the model information.
str type: model path
dict type: must have `model` and `config` field,
indicate the model object and config object
:param vocab_info: input the vocab information.
str type: vocab path
dict type: vocab2id dict which map word to id
:param beam_size: beam size
:param max_target_len: max keyphrase token length
:param max_src_length: max source text length
"""
super().__init__(model_info)
if isinstance(vocab_info, str):
self.vocab2id = load_vocab(vocab_info)
elif isinstance(vocab_info, dict):
self.vocab2id = vocab_info
else:
raise ValueError('vocab info type error')
self.id2vocab = dict(zip(self.vocab2id.values(), self.vocab2id.keys()))
self.config = self.load_config(model_info)
self.model = self.load_model(model_info,
CopyRNN(self.config, self.vocab2id))
self.model.eval()
self.beam_size = beam_size
self.max_target_len = max_target_len
self.max_src_len = max_src_length
self.beam_searcher = BeamSearch(model=self.model,
beam_size=self.beam_size,
max_target_len=self.max_target_len,
id2vocab=self.id2vocab,
bos_idx=self.vocab2id[BOS_WORD],
unk_idx=self.vocab2id[UNK_WORD],
args=self.config)
self.pred_base_config = {
'max_oov_count': self.config.max_oov_count,
'max_src_len': self.max_src_len,
'max_target_len': self.max_target_len,
'prefetch': False,
'shuffle_in_batch': False,
'token_field': TOKENS,
'keyphrase_field': 'keyphrases'
}
def __init__(self):
self.args = self.parse_args()
self.vocab2id = load_vocab(self.args.vocab_path)
self.dest_base_dir = self.args.dest_base_dir
self.writer = tf.summary.create_file_writer(self.dest_base_dir +
'/logs')
self.exp_name = self.args.exp_name
self.pad_idx = self.vocab2id[PAD_WORD]
self.eval_topn = (5, 10)
self.macro_evaluator = KeyphraseEvaluator(self.eval_topn, 'macro',
self.args.token_field,
self.args.keyphrase_field)
self.micro_evaluator = KeyphraseEvaluator(self.eval_topn, 'micro',
self.args.token_field,
self.args.keyphrase_field)
self.best_f1 = None
self.best_step = 0
self.not_update_count = 0
self.logger = get_logger(__name__)
self.total_vocab_size = len(self.vocab2id) + self.args.max_oov_count
def __init__(self, model_info, vocab_info, beam_size, max_target_len, max_src_length):
super().__init__(model_info)
if isinstance(vocab_info, str):
self.vocab2id = load_vocab(vocab_info)
elif isinstance(vocab_info, dict):
self.vocab2id = vocab_info
else:
raise ValueError('vocab info type error')
self.id2vocab = dict(zip(self.vocab2id.values(), self.vocab2id.keys()))
self.config = self.load_config(model_info)
self.model = self.load_model(model_info, CopyTransformer(self.config, self.vocab2id))
self.model.eval()
self.beam_size = beam_size
self.max_target_len = max_target_len
self.max_src_len = max_src_length
self.beam_searcher = TransformerBeamSearch(model=self.model,
beam_size=self.beam_size,
max_target_len=self.max_target_len,
id2vocab=self.id2vocab,
bos_idx=self.vocab2id[BOS_WORD],
args=self.config)
self.pred_base_config = {'max_oov_count': self.config.max_oov_count,
'max_src_len': self.max_src_len,
'max_target_len': self.max_target_len}
def __init__(self):
self.args = self.parse_args()
self.vocab2id = load_vocab(self.args.vocab_path, self.args.vocab_size)
model = self.load_model()
super().__init__(self.args, model)
def __init__(self):
args = self.parse_args()
vocab2id = load_vocab(args.vocab_path, vocab_size=args.vocab_size)
model = CopyTransformer(args, vocab2id)
super().__init__(args, model)
