def __init__(self, opt):
self.log = create_logger(__name__, silent=False, to_disk=True,
log_file=cfg.log_filename if cfg.if_test
else [cfg.log_filename, cfg.save_root + 'log.txt'])
self.sig = Signal(cfg.signal_file)
self.opt = opt
# oracle, generator, discriminator
self.oracle = Oracle(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
cfg.padding_idx, gpu=cfg.CUDA)
self.oracle_list = [Oracle(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
cfg.padding_idx, gpu=cfg.CUDA) for _ in range(cfg.k_label)]
self.dis = None
self.clas = None
self.show_config()
self.check_oracle() # Create Oracle models if not exist
# DataLoader
self.oracle_samples = torch.load(cfg.oracle_samples_path.format(cfg.samples_num))
self.oracle_samples_list = [torch.load(cfg.multi_oracle_samples_path.format(i, cfg.samples_num))
for i in range(cfg.k_label)]
self.oracle_data = GenDataIter(self.oracle_samples)
self.oracle_data_list = [GenDataIter(self.oracle_samples_list[i]) for i in range(cfg.k_label)]
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
# Metrics
self.nll_oracle = NLL('NLL_oracle', if_use=cfg.use_nll_oracle, gpu=cfg.CUDA)
self.nll_gen = NLL('NLL_gen', if_use=cfg.use_nll_gen, gpu=cfg.CUDA)
self.nll_div = NLL('NLL_div', if_use=cfg.use_nll_div, gpu=cfg.CUDA)
self.all_metrics = [self.nll_oracle, self.nll_gen, self.nll_div]
def __init__(self, opt):
self.log = create_logger(__name__, silent=False, to_disk=True,
log_file=cfg.log_filename if cfg.if_test
else [cfg.log_filename, cfg.save_root + 'log.txt'])
self.sig = Signal(cfg.signal_file)
self.opt = opt
# oracle, generator, discriminator
self.oracle = Oracle(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
cfg.padding_idx, gpu=cfg.CUDA)
self.dis = None
self.show_config()
# DataLoader
if not os.path.exists(cfg.oracle_samples_path.format(cfg.samples_num)) or not cfg.oracle_pretrain:
create_oracle()
self.oracle.load_state_dict(torch.load(cfg.oracle_state_dict_path))
self.oracle_samples = torch.load(cfg.oracle_samples_path.format(cfg.samples_num))
self.oracle_data = GenDataIter(self.oracle_samples)
self.gen_data = None
self.dis_data = None
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = None
def __init__(self, opt):
self.log = create_logger(__name__, silent=False, to_disk=True,
log_file=cfg.log_filename if cfg.if_test
else [cfg.log_filename, cfg.save_root + 'log.txt'])
self.sig = Signal(cfg.signal_file)
self.opt = opt
self.show_config()
self.clas = None
# load dictionary
self.word2idx_dict, self.idx2word_dict = load_dict(cfg.dataset)
# Dataloader
try:
self.train_data = GenDataIter(cfg.train_data)
self.test_data = GenDataIter(cfg.test_data, if_test_data=True)
except:
pass
try:
self.train_data_list = [GenDataIter(cfg.cat_train_data.format(i)) for i in range(cfg.k_label)]
self.test_data_list = [GenDataIter(cfg.cat_test_data.format(i), if_test_data=True) for i in
range(cfg.k_label)]
self.clas_data_list = [GenDataIter(cfg.cat_test_data.format(str(i)), if_test_data=True) for i in
range(cfg.k_label)]
self.train_samples_list = [self.train_data_list[i].target for i in range(cfg.k_label)]
self.clas_samples_list = [self.clas_data_list[i].target for i in range(cfg.k_label)]
except:
pass
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
self.clas_criterion = nn.CrossEntropyLoss()
# Optimizer
self.clas_opt = None
# Metrics
self.bleu = BLEU('BLEU', gram=[2, 3, 4, 5], if_use=cfg.use_bleu)
self.nll_gen = NLL('NLL_gen', if_use=cfg.use_nll_gen, gpu=cfg.CUDA)
self.nll_div = NLL('NLL_div', if_use=cfg.use_nll_div, gpu=cfg.CUDA)
self.self_bleu = BLEU('Self-BLEU', gram=[2, 3, 4], if_use=cfg.use_self_bleu)
self.clas_acc = ACC(if_use=cfg.use_clas_acc)
self.ppl = PPL(self.train_data, self.test_data, n_gram=5, if_use=cfg.use_ppl)
self.all_metrics = [self.bleu, self.nll_gen, self.nll_div, self.self_bleu, self.ppl]
def __init__(self, opt):
self.log = create_logger(__name__,
silent=False,
to_disk=False if cfg.if_test else True,
log_file=None if cfg.if_test else
[cfg.log_filename, cfg.save_root + 'log.txt'])
self.sig = Signal(cfg.signal_file)
self.opt = opt
self.show_config()
# load dictionary
self.word_index_dict, self.index_word_dict = load_dict(cfg.dataset)
# Dataloader
self.oracle_data = GenDataIter(cfg.train_data)
self.test_data = GenDataIter(cfg.test_data)
def __init__(self, opt):
self.log = create_logger(__name__,
silent=False,
to_disk=True,
log_file=cfg.log_filename if cfg.if_test else
[cfg.log_filename, cfg.save_root + 'log.txt'])
self.sig = Signal(cfg.signal_file)
self.opt = opt
self.show_config()
# load dictionary
self.word_index_dict, self.index_word_dict = load_dict(cfg.dataset)
# Dataloader
self.train_data = GenDataIter(cfg.train_data)
self.test_data = GenDataIter(cfg.test_data, if_test_data=True)
self.gen_data = None
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = None
self.bleu = None
self.self_bleu = None
