def do_infer_sent(args):
if not os.path.exists(args.name + '.token'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.token'))
sys.exit()
if not os.path.exists(args.name + '.vocab'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.vocab'))
sys.exit()
if len(glob.glob(args.name + '.model.?????????.pth')) == 0:
logging.error('no model available: {}'.format(args.name + '.model.?????????.pth'))
sys.exit()
token = OpenNMTTokenizer(args.name + '.token')
vocab = Vocab()
vocab.read(args.name + '.vocab')
args.embedding_size, args.pooling = read_params(args)
model = Word2Vec(len(vocab), args.embedding_size, args.pooling, vocab.idx_unk)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, betas=(args.beta1,args.beta2), eps=args.eps)
n_steps, model, optimizer = load_model_optim(args.name, args.embedding_size, vocab, model, optimizer)
if args.cuda:
model.cuda()
dataset = Dataset(args, token, vocab, 'infer_sent', skip_subsampling=True)
with torch.no_grad():
model.eval()
for batch in dataset:
snts = model.SentEmbed(batch[0], batch[1], 'iEmb').cpu().detach().numpy().tolist()
for i in range(len(snts)):
sentence = ["{:.6f}".format(w) for w in snts[i]]
print('{}\t{}'.format(batch[2][i]+1, ' '.join(sentence) ))
def do_infer_word(args):
if not os.path.exists(args.name + '.token'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.token'))
sys.exit()
if not os.path.exists(args.name + '.vocab'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.vocab'))
sys.exit()
if len(glob.glob(args.name + '.model.?????????.pth')) == 0:
logging.error('no model available: {}'.format(args.name + '.model.?????????.pth'))
sys.exit()
token = OpenNMTTokenizer(args.name + '.token')
vocab = Vocab()
vocab.read(args.name + '.vocab')
args.embedding_size, args.pooling = read_params(args)
model = Word2Vec(len(vocab), args.embedding_size, args.pooling, vocab.idx_unk)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, betas=(args.beta1,args.beta2), eps=args.eps)
n_steps, model, optimizer = load_model_optim(args.name, args.embedding_size, vocab, model, optimizer)
if args.cuda:
model.cuda()
if args.sim == 'cos':
distance = nn.CosineSimilarity(dim=1, eps=1e-6)
elif args.sim == 'pairwise':
distance = nn.PairwiseDistance(eps=1e-6)
else:
logging.error('bad -sim option {}'.format(args.sim))
sys.exit()
dataset = Dataset(args, token, vocab, 'infer_word', skip_subsampling=True)
with torch.no_grad():
model.eval()
voc_i = [i for i in range(0,len(vocab))]
voc_e = model.Embed(voc_i,'iEmb')
for batch in dataset:
#batch[0] batch_wrd
#batch[1] batch_isnt
#batch[2] batch_iwrd
wrd_i = batch[0]
wrd_e = model.Embed(wrd_i, 'iEmb') #.cpu().detach().numpy().tolist()
for i in range(len(wrd_i)): ### words to find their closest
ind_snt = batch[1][i]
ind_wrd = batch[2][i]
wrd = vocab[wrd_i[i]]
out = []
out.append("{}:{}:{}".format(ind_snt,ind_wrd,wrd))
dist_wrd_voc = distance(wrd_e[i].unsqueeze(0),voc_e)
mininds = torch.argsort(dist_wrd_voc,dim=0,descending=True)
for k in range(1,len(mininds)):
ind = mininds[k].item() #cpu().detach().numpy()
if i != ind:
dis = dist_wrd_voc[ind].item()
wrd = vocab[ind]
out.append("{:.6f}:{}".format(dis,wrd))
if len(out)-1 == args.k:
break
print('\t'.join(out))
def __init__(self, **vocab_kwargs):
self.vocab = {
'rel': vocab_kwargs.get('rel', Vocab()),
'ner': vocab_kwargs.get('ner', Vocab(unk='O')),
'dep': vocab_kwargs.get('dep', Vocab()),
'pos': vocab_kwargs.get('pos', Vocab(unk='.')),
'word': vocab_kwargs.get('word', Vocab(unk='UNKNOWN')),
}
def load_data(model_type, pd):
multi_sense, n_sense = set_sense_paras(model_type, pd)
x_vocab = Vocab(pd['x_vocab_file'], multi_sense, n_sense)
y_vocab = Vocab(pd['y_vocab_file'], False, 1)
train_data = RelationData(pd['train_data_file'], multi_sense, n_sense)
test_data = RelationData(pd['test_data_file'], multi_sense, n_sense)
train_data.gen_multinomial_dist(y_vocab.size())
return train_data, test_data, x_vocab, y_vocab
def do_train(args):
if not os.path.exists(args.name + '.token'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.token'))
sys.exit()
if not os.path.exists(args.name + '.vocab'):
logging.error('missing {} file (run preprocess mode)'.format(args.name + '.vocab'))
sys.exit()
token = OpenNMTTokenizer(args.name + '.token')
vocab = Vocab()
vocab.read(args.name + '.vocab')
if os.path.exists(args.name + '.param'):
args.embedding_size, args.pooling = read_params(args)
else:
write_params(args)
model = Word2Vec(len(vocab), args.embedding_size, args.pooling, vocab.idx_unk)
if args.cuda:
model.cuda()
# optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, betas=(args.beta1,args.beta2), eps=args.eps)
# optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, betas=(args.beta1, args.beta2), eps=args.eps, weight_decay=0.01, amsgrad=False)
n_steps, model, optimizer = load_model_optim(args.name, args.embedding_size, vocab, model, optimizer)
dataset = Dataset(args, token, vocab, args.method)
n_epochs = 0
losses = []
while True:
n_epochs += 1
for batch in dataset:
model.train()
if args.method == 'skipgram':
loss = model.forward_skipgram(batch)
elif args.method == 'cbow':
loss = model.forward_cbow(batch)
elif args.method == 'sbow':
loss = model.forward_sbow(batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
n_steps += 1
losses.append(loss.data.cpu().detach().numpy())
if n_steps % args.report_every_n_steps == 0:
accum_loss = np.mean(losses)
logging.info('{} n_epoch={} n_steps={} Loss={:.6f}'.format(args.method, n_epochs,n_steps,accum_loss))
losses = []
if n_steps % args.save_every_n_steps == 0:
save_model_optim(args.name, model, optimizer, n_steps, args.keep_last_n)
if n_epochs >= args.max_epochs:
logging.info('Stop (max epochs reached)')
break
save_model_optim(args.name, model, optimizer, n_steps, args.keep_last_n)
def inference(self):
self.dropout = 0.0
self.seq_size = 0
if not self.epoch:
for e in range(999, 0, -1):
if os.path.exists(self.mdir+"/epoch{}.index".format(e)):
self.epoch = e
break
if not self.epoch:
sys.stderr.write("error: Cannot find epoch in mdir '{}'\n{}".format(self.mdir, self.usage))
sys.exit(1)
check_dataset(self.tst)
if self.output == '-':
self.output = sys.stdout
else:
self.output = open(self.output, "wb")
if not os.path.exists('{}/epoch{}.index'.format(self.mdir, self.epoch)):
sys.stderr.write('error: -epoch file {}/epoch{}.index cannot be find\n'.format(self.mdir, self.epoch))
sys.exit(1)
if not os.path.exists(self.mdir + '/topology'):
sys.stderr.write('error: topology file: {} cannot be find\n'.format(self.mdir + '/topology'))
sys.exit(1)
src_voc = 'vocab_src'
tgt_voc = 'vocab_tgt'
if os.path.exists(self.mdir + '/tokenization_src.json'):
with open(self.mdir + '/tokenization_src.json') as jsonfile:
self.tok_src = json.load(jsonfile)
src_voc = self.tok_src["vocabulary"]
else:
self.tok_src = None
if not os.path.exists(self.mdir + '/' + src_voc):
sys.stderr.write('error: vocab src file: {} cannot be find\n'.format(self.mdir + '/' + src_voc))
sys.exit(1)
if os.path.exists(self.mdir + '/tokenization_tgt.json'):
with open(self.mdir + '/tokenization_tgt.json') as jsonfile:
self.tok_tgt = json.load(jsonfile)
tgt_voc = self.tok_tgt["vocabulary"]
else:
self.tok_tgt = None
argv = []
with open(self.mdir + "/topology", 'r') as f:
for line in f:
opt, val = line.split()
argv.append('-'+opt)
argv.append(val)
# overrides options passed in command line
self.parse(argv)
# read vocabularies
self.voc_src = Vocab(self.mdir + "/" + src_voc)
self.voc_tgt = Vocab(self.mdir + "/" + tgt_voc)
return
def __init__(self, args):
# get the dir with pre-trained model
load_dir = os.path.join(args.experiment_dir, args.old_model_dir)
# initialize, and load vocab
self.vocab = Vocab()
vocab_filename = os.path.join(load_dir, "vocab.json")
self.vocab.load_from_dict(vocab_filename)
# load configuration
with open(os.path.join(load_dir, "config.json"), "r") as f:
config = json.load(f)
args.response_len = config["response_len"]
args.history_len = config["history_len"]
# initialize an empty dataset. used to get input features
self.dataset = DialogueDataset(None,
history_len=config["history_len"],
response_len=config["response_len"],
vocab=self.vocab,
update_vocab=False)
# set device
self.device = torch.device(args.device)
# initialize model
model = Transformer(config["vocab_size"],
config["vocab_size"],
config["history_len"],
config["response_len"],
d_word_vec=config["embedding_dim"],
d_model=config["model_dim"],
d_inner=config["inner_dim"],
n_layers=config["num_layers"],
n_head=config["num_heads"],
d_k=config["dim_k"],
d_v=config["dim_v"],
dropout=config["dropout"],
pretrained_embeddings=None).to(self.device)
# load checkpoint
checkpoint = torch.load(os.path.join(load_dir, args.old_model_name),
map_location=self.device)
model.load_state_dict(checkpoint['model'])
# create chatbot
self.chatbot = Chatbot(args, model)
self.args = args
def write_error_results(self, opt):
model_a_name, model_b_name = opt['cmp_model_type_list']
model_a_file = self.instance_analysis_path + model_a_name + '.txt'
model_b_file = self.instance_analysis_path + model_b_name + '.txt'
model_a_indicator = pd.read_table(model_a_file, header=None).ix[:, 0]
model_b_indicator = pd.read_table(model_b_file, header=None).ix[:, 0]
test_instances = pd.read_table(self.test_data_file,
header=None).as_matrix()
vocab = Vocab(self.x_vocab_file, n_sense=1, id_offset=0)
instances = self.select_better_instances(test_instances,
model_a_indicator,
model_b_indicator)
# where model b makes correct predictions but model a does not
output_file = self.instance_analysis_path + model_a_name + '-0-' + model_b_name + '-1-.txt'
self.write_model_instances(instances, vocab, output_file)
instances = self.select_better_instances(test_instances,
model_b_indicator,
model_a_indicator)
# where model a makes correct predictions but model b does not
output_file = self.instance_analysis_path + model_a_name + '-1-' + model_b_name + '-0-.txt'
self.write_model_instances(instances, vocab, output_file)
# where both model a and b make correct predictions
instances = self.select_equal_instances(test_instances,
model_a_indicator,
model_b_indicator, 1)
output_file = self.instance_analysis_path + model_a_name + '-1-' + model_b_name + '-1-.txt'
self.write_model_instances(instances, vocab, output_file)
# where both model a and b make wrong predictions
instances = self.select_equal_instances(test_instances,
model_a_indicator,
model_b_indicator, 0)
output_file = self.instance_analysis_path + model_a_name + '-0-' + model_b_name + '-0-.txt'
self.write_model_instances(instances, vocab, output_file)
def inference(self):
self.dropout = 0.0
self.seq_size = 0
if not self.epoch:
sys.stderr.write("error: Missing -epoch option\n{}".format(
self.usage))
sys.exit()
if not os.path.exists(self.tst):
sys.stderr.write('error: -tst file {} cannot be find\n'.format(
self.tst))
sys.exit()
if not os.path.exists(self.mdir + '/epoch' + self.epoch + '.index'):
sys.stderr.write(
'error: -epoch file {} cannot be find\n'.format(self.mdir +
'/epoch' +
self.epoch +
'.index'))
sys.exit()
if not os.path.exists(self.mdir + '/topology'):
sys.stderr.write(
'error: topology file: {} cannot be find\n'.format(
self.mdir + '/topology'))
sys.exit()
if not os.path.exists(self.mdir + '/vocab_src'):
sys.stderr.write(
'error: vocab_src file: {} cannot be find\n'.format(
self.mdir + '/vocab_src'))
sys.exit()
if not os.path.exists(self.mdir + '/vocab_tgt'):
sys.stderr.write(
'error: vocab_tgt file: {} cannot be find\n'.format(
self.mdir + '/vocab_tgt'))
sys.exit()
argv = []
with open(self.mdir + "/topology", 'r') as f:
for line in f:
opt, val = line.split()
argv.append('-' + opt)
argv.append(val)
self.parse(argv) ### this overrides options passed in command line
### read vocabularies
self.voc_src = Vocab(self.mdir + "/vocab_src")
self.voc_tgt = Vocab(self.mdir + "/vocab_tgt")
return
def __init__(self, model_file_path):
model_name = os.path.basename(model_file_path)
self._test_dir = os.path.join(config.log_root,
'decode_%s' % (model_name))
self._rouge_ref_dir = os.path.join(self._test_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._test_dir, 'rouge_dec')
for p in [self._test_dir, self._rouge_ref_dir, self._rouge_dec_dir]:
if not os.path.exists(p):
os.mkdir(p)
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.decode_data_path,
self.vocab,
mode='decode',
batch_size=config.beam_size,
single_pass=True)
time.sleep(15)
self.model = Model(model_file_path, is_eval=True)
def sampling_decode(self, vocab: Vocab, example: LMExample,
begin_symbol: int = 2, end_symbol: int = 5,
initial_hidden: Optional[HiddenState] = None, warm_up: Optional[int] = None,
max_length: int = 200, greedy: bool = False, topk: Optional[int] = None,
print_info: bool = True, color_outputs: bool = False, **_kwargs) \
-> SampledOutput:
tensor = functools.partial(sample_utils.tensor, device=self.device)
sample = functools.partial(sample_utils.sample,
greedy=greedy,
topk=topk)
self.eval()
self.init_hidden(1, None)
if warm_up is None:
inputs = [begin_symbol]
hidden = initial_hidden
total_log_prob = 0.0
else:
inputs = list(vocab.numericalize(example.sentence[:warm_up]))
total_log_prob, hidden = self.forward(tensor(inputs[:-1]),
target=tensor(inputs[1:]))
total_log_prob = -torch.sum(total_log_prob).item() * (len(inputs) -
1)
while len(inputs) < max_length and inputs[-1] != end_symbol:
# full copy of the forward pass, including dropouts. But they won't be applied due to .eval function.
# Run LSTM over the word
word_log_probs, new_hidden = self.forward(tensor(inputs[-1]),
hidden)
word_id, word_log_prob = sample(word_log_probs)
inputs.append(word_id)
hidden = new_hidden
total_log_prob += word_log_prob
sample_loss = -total_log_prob / (len(inputs) - 1)
if print_info:
print(
f"Sample loss: {sample_loss:.3f}, PPL: {math.exp(sample_loss):.3f}"
)
# Format the output
words = [vocab.i2w[token] for token in inputs]
if color_outputs and warm_up is not None:
words[:warm_up] = [
Logging.color('yellow', w) for w in words[:warm_up]
]
output = SampledOutput(sentence=words,
sample_loss=sample_loss,
complete_copies=0,
incomplete_copies=0)
return output
def __init__(self, model_path):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.eval_data_path, self.vocab, mode='eval',
batch_size=config.batch_size, single_pass=True)
time.sleep(15)
model_name = os.path.basename(model_path)
eval_dir = os.path.join(config.log_root, 'eval_%s' % (model_name))
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
self.summary_writer = tf.summary.FileWriter(eval_dir)
self.model = Model(model_path, is_eval=True)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(self.vocab,
config.train_data_path,
config.batch_size,
single_pass=False,
mode='train')
time.sleep(10)
train_dir = os.path.join(config.log_root,
'train_%d' % (int(time.time())))
if not os.path.exists(train_dir):
os.mkdir(train_dir)
self.model_dir = os.path.join(train_dir, 'models')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.summary.FileWriter(train_dir)
def main(train_data_path, test_data_path, answer_data_path, vocab_data_path,
embedding_data_path, batch_size, learning_rate, hidden_size, margin,
epoch, save_path, pretrained_path, use_cuda):
# load qa data
answer_data = AnswerData(answer_data_path)
train_data = QaData(train_data_path)
test_data = QaData(test_data_path)
# load pretrained embedding
pretrained_embedding = gensim.models.KeyedVectors.load_word2vec_format(
embedding_data_path, binary=True)
vocab = Vocab(vocab_data_path, answer_data.lexicon + train_data.lexicon)
pretrained_weights = np.zeros((len(vocab) + 1, 300)) # TODO magic number
for wid, surf in vocab.wid2surf.items():
if surf in pretrained_embedding.vocab:
pretrained_weights[wid] = pretrained_embedding.wv[surf]
# create dataset / data loader
train_dataset = InsuranceQaDataset(train_data, answer_data, vocab)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
batch_size=batch_size,
collate_fn=train_dataset.collate)
test_dataset = InsuranceQaDataset(test_data, answer_data, vocab)
# train model
if pretrained_path is not None:
model = torch.load(pretrained_path)['model']
else:
model = SentenceEncoder(pretrained_weights, hidden_size)
optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate)
criterion = QaLoss(margin=margin)
if use_cuda:
model = model.cuda()
train(model, train_data_loader, test_dataset, optimizer, criterion, epoch,
use_cuda)
# save model
torch.save({'model': model, 'vocab': vocab}, save_path)
def __init__(
self,
train_folder: str,
test_folder: str,
alpha: float = 0.01,
beta: float = 1.0,
predicted_poses: int = 20,
previous_poses: int = 10,
stride: int = None,
batch_size: int = 50,
with_context: bool = False,
embedding: str = None,
text_folder: str = None,
*args,
**kwargs
):
super().__init__()
self.save_hyperparameters()
self.encoder = Encoder(26, 150, 2, with_context)
self.decoder = Decoder(45, 150, 300, max_gen=predicted_poses)
self.predicted_poses = predicted_poses
self.previous_poses = previous_poses
self.loss = MSELoss()
self.train_folder = train_folder
self.test_folder = test_folder
self.alpha = alpha
self.beta = beta
self.stride = predicted_poses if stride is None else stride
self.batch_size = batch_size
self.with_context = with_context
if embedding is not None:
self.vocab = Vocab(embedding)
self.word_embedder = nn.Embedding(len(self.vocab.token_to_idx), len(self.vocab.weights[0]),
_weight=torch.FloatTensor(self.vocab.weights))
self.word_encoder = nn.GRU(len(self.vocab.weights[0]), 100, bidirectional=True)
else:
self.vocab = None
self.text_folder = text_folder
def do_preprocess(args):
if args.tok_conf is None:
opts = {}
opts['mode'] = 'space'
with open(args.name + '.token', 'w') as yamlfile:
_ = yaml.dump(opts, yamlfile)
else:
with open(args.tok_conf) as yamlfile:
opts = yaml.load(yamlfile, Loader=yaml.FullLoader)
#cp bpe args.name+'.bpe'
#replace in opts the bpe path
yaml.dump(opts, args.name + '.token')
logging.info('built tokenizer config file')
token = OpenNMTTokenizer(args.name + '.token')
vocab = Vocab()
vocab.build(args.data,token,min_freq=args.voc_minf,max_size=args.voc_maxs)
vocab.dump(args.name + '.vocab')
logging.info('built vocab')
mask = np.triu(np.ones(shape), k=1).astype('uint8')
mask = torch.from_numpy(mask) == 0
return mask
### for test ###
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog=sys.argv[0],
usage='python3 {}'.format(sys.argv[0]),
add_help=True)
parser.add_argument('-v', '--vocab', help='vocab file', required=True)
parser.add_argument('-i', '--input', help='input file', required=True)
parser.add_argument('-t', '--target', help='target file', required=True)
args = parser.parse_args()
vocab = Vocab(args.vocab)
dataset = AspecDataset(args.input, args.target, vocab)
loader = DataLoader(dataset,
collate_fn=MiniBatchProcess(),
shuffle=False,
batch_size=50)
for i, batch in enumerate(loader):
# test1
#if i>=2: break
#print('--- batch_idx={} ---'.format(i))
#print('id: {}'.format(batch[0]))
#print('input: {}\n{}'.format(batch[1].shape, batch[1]))
#print('input mask: {}\n{}'.format(batch[2].shape, batch[2]))
#print('target: {}\n{}'.format(batch[3].shape, batch[3]))
#print('target mask: {}\n{}'.format(batch[4].shape, batch[4]))
def __init__(self, args):
# set up output directory
self.output_dir = os.path.join(args.experiment_dir, args.run_name)
if not os.path.exists(args.experiment_dir):
os.mkdir(args.experiment_dir)
if not os.path.exists(self.output_dir):
os.mkdir(self.output_dir)
if not os.path.exists(os.path.join(args.experiment_dir,"runs/")):
os.mkdir(os.path.join(args.experiment_dir,"runs/"))
# initialize tensorboard writer
self.runs_dir = os.path.join(args.experiment_dir,"runs/",args.run_name)
self.writer = SummaryWriter(self.runs_dir)
# initialize global steps
self.train_gs = 0
self.val_gs = 0
# initialize model config
self.config = ModelConfig(args)
# check if there is a model to load
if args.old_model_dir is not None:
self.use_old_model = True
self.load_dir = args.old_model_dir
self.config.load_from_file(
os.path.join(self.load_dir, "config.json"))
# create vocab
self.vocab = Vocab()
self.vocab.load_from_dict(os.path.join(self.load_dir, "vocab.json"))
self.update_vocab = False
self.config.min_count=1
else:
self.use_old_model = False
self.vocab = None
self.update_vocab = True
# create data sets
self.dataset_filename = args.dataset_filename
# train
self.train_dataset = DialogueDataset(
os.path.join(self.dataset_filename, "train_data.json"),
self.config.sentence_len,
self.vocab,
self.update_vocab)
self.data_loader_train = torch.utils.data.DataLoader(
self.train_dataset, self.config.train_batch_size, shuffle=True)
self.config.train_len = len(self.train_dataset)
self.vocab = self.train_dataset.vocab
# eval
self.val_dataset = DialogueDataset(
os.path.join(self.dataset_filename, "val_data.json"),
self.config.sentence_len,
self.vocab,
self.update_vocab)
self.data_loader_val = torch.utils.data.DataLoader(
self.val_dataset, self.config.val_batch_size, shuffle=True)
self.config.val_len = len(self.val_dataset)
# update, and save vocab
self.vocab = self.val_dataset.vocab
self.train_dataset.vocab = self.vocab
if (self.config.min_count > 1):
self.config.old_vocab_size = len(self.vocab)
self.vocab.prune_vocab(self.config.min_count)
self.vocab.save_to_dict(os.path.join(self.output_dir, "vocab.json"))
self.vocab_size = len(self.vocab)
self.config.vocab_size = self.vocab_size
# load embeddings
if self.config.pretrained_embeddings_dir is None:
pretrained_embeddings = get_pretrained_embeddings(self.config.pretrained_embeddings_dir , self.vocab)
else:
pretrained_embeddings = None
# print and save the config file
self.config.print_config(self.writer)
self.config.save_config(os.path.join(self.output_dir, "config.json"))
# set device
self.device = torch.device('cuda')
# create model
self.model = Transformer(
self.config.vocab_size,
self.config.label_len,
self.config.sentence_len,
d_word_vec=self.config.embedding_dim,
d_model=self.config.model_dim,
d_inner=self.config.inner_dim,
n_layers=self.config.num_layers,
n_head=self.config.num_heads,
d_k=self.config.dim_k,
d_v=self.config.dim_v,
dropout=self.config.dropout,
pretrained_embeddings=pretrained_embeddings
).to(self.device)
# create optimizer
self.optimizer = torch.optim.Adam(
filter(lambda x: x.requires_grad, self.model.parameters()),
betas=(0.9, 0.98), eps=1e-09)
# load old model, optimizer if there is one
if self.use_old_model:
self.model, self.optimizer = load_checkpoint(
os.path.join(self.load_dir, "model.bin"),
self.model, self.optimizer, self.device)
# create a sceduled optimizer object
self.optimizer = ScheduledOptim(
self.optimizer, self.config.model_dim, self.config.warmup_steps)
class ModelOperator:
def __init__(self, args):
# set up output directory
self.output_dir = os.path.join(args.experiment_dir, args.run_name)
if not os.path.exists(args.experiment_dir):
os.mkdir(args.experiment_dir)
if not os.path.exists(self.output_dir):
os.mkdir(self.output_dir)
if not os.path.exists(os.path.join(args.experiment_dir,"runs/")):
os.mkdir(os.path.join(args.experiment_dir,"runs/"))
# initialize tensorboard writer
self.runs_dir = os.path.join(args.experiment_dir,"runs/",args.run_name)
self.writer = SummaryWriter(self.runs_dir)
# initialize global steps
self.train_gs = 0
self.val_gs = 0
# initialize model config
self.config = ModelConfig(args)
# check if there is a model to load
if args.old_model_dir is not None:
self.use_old_model = True
self.load_dir = args.old_model_dir
self.config.load_from_file(
os.path.join(self.load_dir, "config.json"))
# create vocab
self.vocab = Vocab()
self.vocab.load_from_dict(os.path.join(self.load_dir, "vocab.json"))
self.update_vocab = False
self.config.min_count=1
else:
self.use_old_model = False
self.vocab = None
self.update_vocab = True
# create data sets
self.dataset_filename = args.dataset_filename
# train
self.train_dataset = DialogueDataset(
os.path.join(self.dataset_filename, "train_data.json"),
self.config.sentence_len,
self.vocab,
self.update_vocab)
self.data_loader_train = torch.utils.data.DataLoader(
self.train_dataset, self.config.train_batch_size, shuffle=True)
self.config.train_len = len(self.train_dataset)
self.vocab = self.train_dataset.vocab
# eval
self.val_dataset = DialogueDataset(
os.path.join(self.dataset_filename, "val_data.json"),
self.config.sentence_len,
self.vocab,
self.update_vocab)
self.data_loader_val = torch.utils.data.DataLoader(
self.val_dataset, self.config.val_batch_size, shuffle=True)
self.config.val_len = len(self.val_dataset)
# update, and save vocab
self.vocab = self.val_dataset.vocab
self.train_dataset.vocab = self.vocab
if (self.config.min_count > 1):
self.config.old_vocab_size = len(self.vocab)
self.vocab.prune_vocab(self.config.min_count)
self.vocab.save_to_dict(os.path.join(self.output_dir, "vocab.json"))
self.vocab_size = len(self.vocab)
self.config.vocab_size = self.vocab_size
# load embeddings
if self.config.pretrained_embeddings_dir is None:
pretrained_embeddings = get_pretrained_embeddings(self.config.pretrained_embeddings_dir , self.vocab)
else:
pretrained_embeddings = None
# print and save the config file
self.config.print_config(self.writer)
self.config.save_config(os.path.join(self.output_dir, "config.json"))
# set device
self.device = torch.device('cuda')
# create model
self.model = Transformer(
self.config.vocab_size,
self.config.label_len,
self.config.sentence_len,
d_word_vec=self.config.embedding_dim,
d_model=self.config.model_dim,
d_inner=self.config.inner_dim,
n_layers=self.config.num_layers,
n_head=self.config.num_heads,
d_k=self.config.dim_k,
d_v=self.config.dim_v,
dropout=self.config.dropout,
pretrained_embeddings=pretrained_embeddings
).to(self.device)
# create optimizer
self.optimizer = torch.optim.Adam(
filter(lambda x: x.requires_grad, self.model.parameters()),
betas=(0.9, 0.98), eps=1e-09)
# load old model, optimizer if there is one
if self.use_old_model:
self.model, self.optimizer = load_checkpoint(
os.path.join(self.load_dir, "model.bin"),
self.model, self.optimizer, self.device)
# create a sceduled optimizer object
self.optimizer = ScheduledOptim(
self.optimizer, self.config.model_dim, self.config.warmup_steps)
#self.optimizer.optimizer.to(torch.device('cpu'))
def train(self, num_epochs):
metrics = {"best_epoch":0, "highest_f1":0}
# output an example
self.output_example(0)
for epoch in range(num_epochs):
#self.writer.add_graph(self.model)
#self.writer.add_embedding(
# self.model.encoder.src_word_emb.weight, global_step=epoch)
epoch_metrics = dict()
# train
epoch_metrics["train"] = self.execute_phase(epoch, "train")
# save metrics
metrics["epoch_{}".format(epoch)] = epoch_metrics
with open(os.path.join(self.output_dir, "metrics.json"), "w") as f:
json.dump(metrics, f, indent=4)
# validate
epoch_metrics["val"] = self.execute_phase(epoch, "val")
# save metrics
metrics["epoch_{}".format(epoch)] = epoch_metrics
with open(os.path.join(self.output_dir, "metrics.json"), "w") as f:
json.dump(metrics, f, indent=4)
# save checkpoint
#TODO: fix this b
if epoch_metrics["val"]["avg_results"]["F1"] > metrics["highest_f1"]:
#if epoch_metrics["train"]["loss"] < metrics["lowest_loss"]:
#if epoch % 100 == 0:
self.save_checkpoint(os.path.join(self.output_dir, "model.bin"))
metrics["lowest_f1"] = epoch_metrics["val"]["avg_results"]["F1"]
metrics["best_epoch"] = epoch
test_results = self.get_test_predictions(
os.path.join(self.dataset_filename, "test_data.json"),
os.path.join(self.output_dir, "predictions{}.json".format(epoch)))
# record metrics to tensorboard
self.writer.add_scalar("training loss total",
epoch_metrics["train"]["loss"], global_step=epoch)
self.writer.add_scalar("val loss total",
epoch_metrics["val"]["loss"], global_step=epoch)
self.writer.add_scalar("training time",
epoch_metrics["train"]["time_taken"], global_step=epoch)
self.writer.add_scalar("val time",
epoch_metrics["val"]["time_taken"], global_step=epoch)
self.writer.add_scalars("train_results", epoch_metrics["train"]["avg_results"], global_step=epoch)
self.writer.add_scalars("val_results", epoch_metrics["val"]["avg_results"],
global_step=epoch)
# output an example
self.output_example(epoch+1)
self.writer.close()
def execute_phase(self, epoch, phase):
if phase == "train":
self.model.train()
dataloader = self.data_loader_train
batch_size = self.config.train_batch_size
train = True
else:
self.model.eval()
dataloader = self.data_loader_val
batch_size = self.config.val_batch_size
train = False
start = time.clock()
phase_metrics = dict()
epoch_loss = list()
epoch_metrics = list()
results = {"accuracy": list(), "precision": list(), "recall": list(), "F1": list()}
average_epoch_loss = None
for i, batch in enumerate(tqdm(dataloader,
mininterval=2, desc=phase, leave=False)):
# prepare data
src_seq, src_pos, src_seg, tgt= map(
lambda x: x.to(self.device), batch[:4])
ids = batch[4]
start_end_idx = batch[5]
# forward
if train:
self.optimizer.zero_grad()
pred = self.model(src_seq, src_pos, src_seg, tgt)
loss = F.cross_entropy(self.prepare_pred(pred).view(-1, 2), tgt.view(-1))
average_loss = float(loss)
epoch_loss.append(average_loss)
average_epoch_loss = np.mean(epoch_loss)
if train:
self.writer.add_scalar("train_loss",
average_loss, global_step=i + epoch * self.config.train_batch_size)
# backward
loss.backward()
# update parameters
self.optimizer.step_and_update_lr()
output = torch.argmax(self.prepare_pred(pred), 3)
get_results(tgt.view(-1).cpu(), output.view(-1).cpu(), results)
phase_metrics["avg_results"] = {key: np.mean(value) for key, value in results.items()}
phase_metrics["loss"] = average_epoch_loss
phase_metrics["time_taken"] = time.clock() - start
string = ' {} loss: {:.3f} '.format(phase, average_epoch_loss)
print(string, end='\n')
return phase_metrics
def get_test_predictions(self, test_filename, save_filename):
test_dataset = DialogueDataset(
test_filename,
self.config.sentence_len,
self.vocab,
False)
test_data_loader = torch.utils.data.DataLoader(
test_dataset, self.config.val_batch_size, shuffle=True)
with open(test_filename, 'r') as f:
data = json.load(f)
start = time.clock()
phase_metrics = dict()
epoch_loss = list()
epoch_metrics = list()
results = {"accuracy": list(), "precision": list(), "recall": list(),
"F1": list()}
average_epoch_loss = None
for i, batch in enumerate(tqdm(test_data_loader,
mininterval=2, desc='test', leave=False)):
# prepare data
src_seq, src_pos, src_seg, tgt = map(
lambda x: x.to(self.device), batch[:4])
ids = batch[4]
start_end_idx = batch[5]
# forward
pred = self.model(src_seq, src_pos, src_seg, tgt)
loss = F.cross_entropy(self.prepare_pred(pred).view(-1, 2),
tgt.view(-1))
average_loss = float(loss)
epoch_loss.append(average_loss)
average_epoch_loss = np.mean(epoch_loss)
output = torch.argmax(self.prepare_pred(pred), 3)
record_predictions(output, data, ids, start_end_idx)
get_results(tgt.view(-1).cpu(), output.view(-1).cpu(), results)
phase_metrics["avg_results"] = {key: np.mean(value) for key, value in
results.items()}
phase_metrics["loss"] = average_epoch_loss
phase_metrics["time_taken"] = time.clock() - start
string = ' {} loss: {:.3f} '.format('test', average_epoch_loss)
print(string, end='\n')
data["results"] = phase_metrics
with open(save_filename, 'w') as f:
json.dump(data, f)
return phase_metrics
def save_checkpoint(self, filename):
state = {
'model': self.model.state_dict(),
'optimizer': self.optimizer.optimizer.state_dict()
}
torch.save(state, filename)
def output_example(self, epoch):
random_index = random.randint(0, len(self.val_dataset))
example = self.val_dataset[random_index]
# prepare data
src_seq, src_pos, src_seg, tgt_seq = map(
lambda x: torch.from_numpy(x).to(self.device).unsqueeze(0), example[:4])
# take out first token from target for some reason
gold = tgt_seq[:, 1:]
# forward
pred = self.model(src_seq, src_pos, src_seg, tgt_seq)
output = self.prepare_pred(pred).squeeze(0)
words = src_seq.tolist()[0]
target_strings = labels_2_mention_str(tgt_seq.squeeze(0))
output_strings = labels_2_mention_str(torch.argmax(output, dim=2))
# get history text
string = "word: output - target\n"
for word, t, o in zip(words, target_strings, output_strings):
token = self.vocab.id2token[word]
if token != "<blank>":
string += "[{}: {} - {}], \n".format(token, o, t)
# print
print("\n------------------------\n")
print(string)
print("\n------------------------\n")
# add result to tensorboard
self.writer.add_text("example_output", string, global_step=epoch)
self.writer.add_histogram("example_vocab_ranking", pred, global_step=epoch)
self.writer.add_histogram("example_vocab_choice", output,global_step=epoch)
def prepare_pred(self, pred):
temp = pred
pred = pred.view(-1)
size = pred.size()
nullclass = torch.ones(size, dtype=pred.dtype, device=self.device)
nullclass -= pred
pred = torch.stack((nullclass, pred), 1).view(-1,
self.config.sentence_len,
self.config.label_len,
2)
return pred
class BeamSearch(object):
def __init__(self, model_file_path):
model_name = os.path.basename(model_file_path)
self._test_dir = os.path.join(config.log_root,
'decode_%s' % (model_name))
self._rouge_ref_dir = os.path.join(self._test_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._test_dir, 'rouge_dec')
for p in [self._test_dir, self._rouge_ref_dir, self._rouge_dec_dir]:
if not os.path.exists(p):
os.mkdir(p)
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.decode_data_path,
self.vocab,
mode='decode',
batch_size=config.beam_size,
single_pass=True)
time.sleep(15)
self.model = Model(model_file_path, is_eval=True)
def sort_beams(self, beams):
return sorted(beams, key=lambda h: h.avg_log_prob, reverse=True)
def beam_search(self, batch):
# single example repeated across the batch
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t, coverage = \
get_input_from_batch(batch, use_cuda)
enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_lens)
s_t = self.model.reduce_state(enc_h)
dec_h, dec_c = s_t # b x hidden_dim
dec_h = dec_h.squeeze()
dec_c = dec_c.squeeze()
# decoder batch preparation, it has beam_size example initially everything is repeated
beams = [
Beam(tokens=[self.vocab.word2id(config.BOS_TOKEN)],
log_probs=[0.0],
state=(dec_h[0], dec_c[0]),
context=c_t[0],
coverage=(coverage[0] if config.is_coverage else None))
for _ in range(config.beam_size)
]
steps = 0
results = []
while steps < config.max_dec_steps and len(results) < config.beam_size:
latest_tokens = [h.latest_token for h in beams]
latest_tokens = [t if t < self.vocab.size() else self.vocab.word2id(config.UNK_TOKEN) \
for t in latest_tokens]
y_t = Variable(torch.LongTensor(latest_tokens))
if use_cuda:
y_t = y_t.cuda()
all_state_h = [h.state[0] for h in beams]
all_state_c = [h.state[1] for h in beams]
all_context = [h.context for h in beams]
s_t = (torch.stack(all_state_h,
0).unsqueeze(0), torch.stack(all_state_c,
0).unsqueeze(0))
c_t = torch.stack(all_context, 0)
coverage_t = None
if config.is_coverage:
all_coverage = [h.coverage for h in beams]
coverage_t = torch.stack(all_coverage, 0)
final_dist, s_t, c_t, attn_dist, p_gen, coverage_t = self.model.decoder(
y_t, s_t, enc_out, enc_fea, enc_padding_mask, c_t, extra_zeros,
enc_batch_extend_vocab, coverage_t, steps)
log_probs = torch.log(final_dist)
topk_log_probs, topk_ids = torch.topk(log_probs,
config.beam_size * 2)
dec_h, dec_c = s_t
dec_h = dec_h.squeeze()
dec_c = dec_c.squeeze()
all_beams = []
# On the first step, we only had one original hypothesis (the initial hypothesis). On subsequent steps, all original hypotheses are distinct.
num_orig_beams = 1 if steps == 0 else len(beams)
for i in range(num_orig_beams):
h = beams[i]
state_i = (dec_h[i], dec_c[i])
context_i = c_t[i]
coverage_i = (coverage[i] if config.is_coverage else None)
for j in range(config.beam_size *
2): # for each of the top 2*beam_size hyps:
new_beam = h.extend(token=topk_ids[i, j].item(),
log_prob=topk_log_probs[i, j].item(),
state=state_i,
context=context_i,
coverage=coverage_i)
all_beams.append(new_beam)
beams = []
for h in self.sort_beams(all_beams):
if h.latest_token == self.vocab.word2id(config.EOS_TOKEN):
if steps >= config.min_dec_steps:
results.append(h)
else:
beams.append(h)
if len(beams) == config.beam_size or len(
results) == config.beam_size:
break
steps += 1
if len(results) == 0:
results = beams
beams_sorted = self.sort_beams(results)
return beams_sorted[0]
def run(self):
counter = 0
start = time.time()
batch = self.batcher.next_batch()
while batch is not None:
# Run beam search to get best Hypothesis
best_summary = self.beam_search(batch)
# Extract the output ids from the hypothesis and convert back to words
output_ids = [int(t) for t in best_summary.tokens[1:]]
decoded_words = utils.outputids2words(
output_ids, self.vocab,
(batch.art_oovs[0] if config.pointer_gen else None))
# Remove the [STOP] token from decoded_words, if necessary
try:
fst_stop_idx = decoded_words.index(dataset.EOS_TOKEN)
decoded_words = decoded_words[:fst_stop_idx]
except ValueError:
decoded_words = decoded_words
original_abstract_sents = batch.original_abstracts_sents[0]
write_for_rouge(original_abstract_sents, decoded_words, counter,
self._rouge_ref_dir, self._rouge_dec_dir)
counter += 1
if counter % 1000 == 0:
print('%d example in %d sec' % (counter, time.time() - start))
start = time.time()
batch = self.batcher.next_batch()
print("Decoder has finished reading dataset for single_pass.")
print("Now starting ROUGE eval...")
results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir)
rouge_log(results_dict, self._test_dir)
model_dir = os.path.join(data_root_folder, 'models')
mkdir_if_missing(model_dir)
if mode == MODE_MRT:
model_name = cur_cfg.name + '_mrt'
elif mode == MODE_OBJ:
model_name = cur_cfg.name + '_obj'
else:
model_name = cur_cfg.name
model_path = os.path.join(model_dir, model_name + '.state')
print('Model path:', model_path)
jieba_base_v = Vocab(
os.path.join(data_root_folder, 'vocab', 'title_summ.vocab.pkl'),
os.path.join(data_root_folder, 'vocab', 'title_summ.emb.pkl'))
jieba_sgns_v = Vocab(
os.path.join(data_root_folder, 'vocab', 'title_summ.vocab.pkl'),
os.path.join(data_root_folder, 'vocab', 'title_summ.emb.pkl'))
jieba_flag_v = Vocab(
os.path.join(data_root_folder, 'vocab', 'title_summ.vocab.pkl'),
os.path.join(data_root_folder, 'vocab', 'title_summ.emb.pkl'))
# jieba_sgns_v = Vocab(os.path.join(data_root_folder, 'vocab', 'useless.vocab.pkl'),
# os.path.join(data_root_folder, 'vocab', 'useless.emb.pkl'))
# jieba_flag_v = Vocab(os.path.join(data_root_folder, 'vocab', 'useless.vocab.pkl'),
# os.path.join(data_root_folder, 'vocab', 'useless.emb.pkl'))
trainset_roots = [os.path.join(data_root_folder, 'val.txt')]
import os
import pickle
from models import VariationalModels
import re
def load_pickle(path):
with open(path, 'rb') as f:
return pickle.load(f)
if __name__ == '__main__':
config = get_config(mode='test')
print('Loading Vocabulary...')
vocab = Vocab()
vocab.load(config.word2id_path, config.id2word_path)
print(f'Vocabulary size: {vocab.vocab_size}')
config.vocab_size = vocab.vocab_size
data_loader = get_loader(
sentences=load_pickle(config.sentences_path),
conversation_length=load_pickle(config.conversation_length_path),
sentence_length=load_pickle(config.sentence_length_path),
vocab=vocab,
batch_size=config.batch_size,
shuffle=False)
if config.model in VariationalModels:
solver = VariationalSolver(config, None, data_loader, vocab=vocab, is_train=False)
if mode == MODE_MRT:
model_name = cur_cfg.name + '_mrt'
elif mode == MODE_OBJ:
model_name = cur_cfg.name + '_obj'
else:
model_name = cur_cfg.name
if switch:
model_name += '_switch'
if use_data1:
model_name += '_full_data'
model_path = os.path.join(model_dir, model_name + '.state')
print('Model path:', model_path)
jieba_base_v = Vocab('./data/embed/base_token_vocab_jieba.pkl',
'./data/embed/base_token_embed_jieba.pkl')
jieba_sgns_v = Vocab('./data/embed/train_sgns_vocab_jieba.pkl',
'./data/embed/train_sgns_embed_jieba.pkl')
jieba_flag_v = Vocab('./data/embed/base_flag_vocab_jieba.pkl',
'./data/embed/base_flag_embed_jieba.pkl')
if switch:
pyltp_base_v = Vocab('./data/embed/base_token_vocab_pyltp.pkl',
'./data/embed/base_token_embed_pyltp.pkl')
pyltp_sgns_v = Vocab('./data/embed/train_sgns_vocab_pyltp.pkl',
'./data/embed/train_sgns_embed_pyltp.pkl')
pyltp_flag_v = Vocab('./data/embed/base_flag_vocab_pyltp.pkl',
'./data/embed/base_flag_embed_pyltp.pkl')
transform = MaiIndexTransform(jieba_base_v, jieba_sgns_v, jieba_flag_v,
pyltp_base_v, pyltp_sgns_v, pyltp_flag_v)
val_file = os.path.join(data_root_folder, 'preprocessed', 'dev-%s.preprocessed.json' % version)
model_dir = os.path.join(data_root_folder, 'models', version)
mkdir_if_missing(model_dir)
if mode == MODE_MRT:
model_name = cur_cfg.name + '_mrt'
elif mode == MODE_OBJ:
model_name = cur_cfg.name + '_obj'
else:
model_name = cur_cfg.name
model_path = os.path.join(model_dir, model_name + '.state')
print('Model path:', model_path)
jieba_base_v = Vocab(os.path.join(data_root_folder, 'vocab', 'squad-%s.vocab.pkl' % version),
os.path.join(data_root_folder, 'vocab', 'squad-%s.emb.pkl' % version))
jieba_sgns_v = Vocab(os.path.join(data_root_folder, 'vocab', 'useless.vocab.pkl'),
os.path.join(data_root_folder, 'vocab', 'useless.emb.pkl'))
jieba_flag_v = Vocab(os.path.join(data_root_folder, 'vocab', 'useless.vocab.pkl'),
os.path.join(data_root_folder, 'vocab', 'useless.emb.pkl'))
trainset_roots = [
os.path.join(data_root_folder, 'val.txt')
]
embed_lists = {
'jieba': [jieba_base_v.embeddings, jieba_sgns_v.embeddings, jieba_flag_v.embeddings],
'pyltp': []
}
dev_src, dev_trg, dev_num, dev_src_max_len, dev_trg_max_len = process(
dev_src_path, dev_trg_path)
test_src, test_trg, test_num, test_src_max_len, test_trg_max_len = process(
test_src_path, test_trg_path)
log.write('train_num', train_num)
log.write('train_src_max_len', train_src_max_len)
log.write('train_trg_max_len', train_trg_max_len)
log.write('dev_num', dev_num)
log.write('dev_src_max_len', dev_src_max_len)
log.write('dev_trg_max_len', dev_trg_max_len)
log.write('test_num', test_num)
log.write('test_src_max_len', test_src_max_len)
log.write('test_trg_max_len', test_trg_max_len)
vocab = Vocab()
for i in range(train_num):
vocab.add_list(train_src[i])
vocab.add_list(train_trg[i])
for i in range(dev_num):
vocab.add_list(dev_src[i])
vocab.add_list(dev_trg[i])
for i in range(test_num):
vocab.add_list(test_src[i])
vocab.add_list(test_trg[i])
word2index, index2word = vocab.get_vocab(min_freq=4)
total_words = len(word2index)
def train():
parser = argparse.ArgumentParser()
parser.add_argument("-c",
"--dataset",
required=True,
type=str,
help="dataset")
# parser.add_argument("-c", "--train_dataset", required=True,
# type=str, help="train dataset for train bert")
# parser.add_argument("-t", "--test_dataset", type=str,
# default=None, help="test set for evaluate train set")
# parser.add_argument("-v", "--vocab_path", required=True,
# type=str, help="built vocab model path with bert-vocab")
parser.add_argument("-o",
"--output_path",
required=True,
type=str,
help="ex)output/bert.model")
parser.add_argument("-hs",
"--hidden",
type=int,
default=256,
help="hidden size of transformer model")
parser.add_argument("-l",
"--layers",
type=int,
default=8,
help="number of layers")
parser.add_argument("-a",
"--attn_heads",
type=int,
default=8,
help="number of attention heads")
parser.add_argument("-s",
"--seq_len",
type=int,
default=64,
help="maximum sequence len")
parser.add_argument("-b",
"--batch_size",
type=int,
default=64,
help="number of batch_size")
parser.add_argument("-e",
"--epochs",
type=int,
default=10,
help="number of epochs")
parser.add_argument("-w",
"--num_workers",
type=int,
default=5,
help="dataloader worker size")
parser.add_argument("--duplicate",
type=int,
default=5,
help="dataloader worker size")
parser.add_argument("--with_cuda",
type=bool,
default=True,
help="training with CUDA: true, or false")
parser.add_argument("--log_freq",
type=int,
default=10,
help="printing loss every n iter: setting n")
parser.add_argument("--corpus_lines",
type=int,
default=None,
help="total number of lines in corpus")
parser.add_argument("--cuda_devices",
type=int,
nargs='+',
default=None,
help="CUDA device ids")
parser.add_argument("--on_memory",
type=bool,
default=True,
help="Loading on memory: true or false")
parser.add_argument("--lr",
type=float,
default=1e-3,
help="learning rate of adam")
parser.add_argument("--adam_weight_decay",
type=float,
default=0.01,
help="weight_decay of adam")
parser.add_argument("--adam_beta1",
type=float,
default=0.9,
help="adam first beta value")
parser.add_argument("--adam_beta2",
type=float,
default=0.999,
help="adam first beta value")
parser.add_argument("--dropout",
type=float,
default=0.2,
help="dropout value")
args = parser.parse_args()
print("Load Data", args.dataset)
data_reader = DataReader(args.dataset, seq_len=args.seq_len)
neg_data_reader = DataReader(args.dataset,
graphs=data_reader.graphs,
shuffle=True,
duplicate=args.duplicate,
seq_len=args.seq_len)
# print("Loading Vocab", args.vocab_path)
print("Loading Vocab")
vocab = Vocab(data_reader.graphs)
# vocab = WordVocab.load_vocab(args.vocab_path)
print("Vocab Size: ", len(vocab))
print("Shuffle Data")
'TODO'
print("Loading Train Dataset", args.dataset)
train_dataset = CustomBERTDataset(
data_reader.graphs[:int(len(data_reader) * 0.8)],
vocab,
seq_len=args.seq_len,
on_memory=args.on_memory,
n_neg=args.duplicate)
# pdb.set_trace()
neg_train_dataset = CustomBERTDataset(
neg_data_reader.graphs[:args.duplicate * len(train_dataset)],
vocab,
seq_len=args.seq_len,
on_memory=args.on_memory,
n_neg=args.duplicate)
# pdb.set_trace()
assert len(neg_train_dataset) == args.duplicate * len(train_dataset)
# print("Loading Test Dataset", args.test_dataset)
print("Loading Dev Dataset", args.dataset)
test_dataset = CustomBERTDataset(
data_reader.graphs[int(len(data_reader) * 0.8):],
vocab,
seq_len=args.seq_len,
on_memory=args.on_memory,
n_neg=args.duplicate) # \
neg_test_dataset = CustomBERTDataset(
neg_data_reader.graphs[-args.duplicate * len(test_dataset):],
vocab,
seq_len=args.seq_len,
on_memory=args.on_memory,
n_neg=args.duplicate) # \
assert len(neg_test_dataset) == args.duplicate * len(test_dataset)
# if args.test_dataset is not None else None
# pdb.set_trace()
print("Creating Dataloader")
train_data_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=my_collate)
neg_train_data_loader = DataLoader(neg_train_dataset,
batch_size=args.batch_size *
args.duplicate,
num_workers=args.num_workers,
collate_fn=my_collate)
test_data_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=my_collate) # \
neg_test_data_loader = DataLoader(neg_test_dataset,
batch_size=args.batch_size *
args.duplicate,
num_workers=args.num_workers,
collate_fn=my_collate) # \
# if test_dataset is not None else None
# assert False
print("Building BERT model")
bert = BERT(len(vocab),
hidden=args.hidden,
n_layers=args.layers,
attn_heads=args.attn_heads,
dropout=args.dropout)
print("Creating BERT Trainer")
# trainer = BERTTrainer(bert, len(vocab), train_dataloader=train_data_loader, test_dataloader=test_data_loader,
# lr=args.lr, betas=(
# args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
# with_cuda=args.with_cuda, cuda_devices=args.cuda_devices, log_freq=args.log_freq, pad_index=vocab.pad_index)
trainer = BERTTrainer(bert,
len(vocab),
train_dataloader=train_data_loader,
test_dataloader=test_data_loader,
lr=args.lr,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
with_cuda=args.with_cuda,
cuda_devices=args.cuda_devices,
log_freq=args.log_freq,
pad_index=vocab.pad_index)
# raise NotImplementedError
print("Training Start")
best_loss = None
for epoch in range(args.epochs):
# test_loss = trainer.test(epoch)
train_loss = trainer.train(epoch)
torch.cuda.empty_cache()
# if test_data_loader is not None:
test_loss = trainer.test(epoch)
if best_loss is None or test_loss < best_loss:
best_loss = test_loss
trainer.save(epoch, args.output_path)
torch.cuda.empty_cache()
hidden_size = 128
char_embedding_dim=128 # charater-level embedding dim
word_embedding_dim=50 # word-level embedding dim
max_sent_length=35
max_word_length=16
kernel_n=3 # 卷积核长度
padding=2 # padding大小
lr = 3e-3
weight_decay = 1e-3 # 梯度衰减权值
gradient_clipping = 5 # 梯度裁剪
output_per_batchs = 1
test_per_batchs = 5
test_batchs = 1
ITORS = 100
# 加载字典
vocab = Vocab(vocab_path)
char_vocab = CharVocab(char_vocab_path)
entity_vocab = EntityVocab(entity_vocab_path)
# 创建数据集
train_data_set = DataSet(path=train_data_path,
vocab=vocab,
entity_vocab=entity_vocab,
entity_padding_len=max_sent_length)
test_data_set = DataSet(path=test_data_path,
vocab=vocab,
entity_vocab=entity_vocab,
entity_padding_len=max_sent_length)
# 创建加载器
train_data_loader = DataLoader(train_data_set, shuffle=True, batch_size=BATCH_SIZE)
test_data_loader = DataLoader(test_data_set, shuffle=True, batch_size=BATCH_SIZE)
# 是否用GPU
batch_size = opt.batch_size
# read dataset
if os.path.exists('dataset.pickle'):
with open('dataset.pickle', 'rb') as f:
train_iter, dev_iter, test_iter, vocab = pickle.load(f)
else:
root_dir = opt.data
segments = ['train', 'dev', 'test']
token_files = [
os.path.join(root_dir, seg, '%s.toks' % tok) for tok in ['a', 'b']
for seg in segments
]
vocab = Vocab(filepaths=token_files, embedpath=opt.word_embed)
train_iter, dev_iter, test_iter = [
SICKDataIter(os.path.join(root_dir, segment), vocab, num_classes)
for segment in segments
]
with open('dataset.pickle', 'wb') as f:
pickle.dump([train_iter, dev_iter, test_iter, vocab], f)
logging.info('==> SICK vocabulary size : %d ' % vocab.size)
logging.info('==> Size of train data : %d ' % len(train_iter))
logging.info('==> Size of dev data : %d ' % len(dev_iter))
logging.info('==> Size of test data : %d ' % len(test_iter))
# get network
net = SimilarityTreeLSTM(sim_hidden_size, rnn_hidden_size, vocab.size,
def main():
print("Hello!")
voca = Vocab(args.vocab_fname)
model = Model(args, voca)
batcher = Batcher(voca, args)
with tf.Session(config=GPU_config()) as sess:
model.build_graph()
if args.mode == 'train':
sess.run(tf.global_variables_initializer())
if not os.path.exists(args.train_logdir):
os.makedirs(args.train_logdir)
if not os.path.exists(args.valid_logdir):
os.makedirs(args.valid_logdir)
train_writer, valid_writer = tf.summary.FileWriter(
args.train_logdir,
sess.graph), tf.summary.FileWriter(args.valid_logdir,
sess.graph)
t = trange(args.max_step, leave=True)
for i in t:
sample, label = batcher.next_data()
_, loss, step, summaries = model.run_train_step(sample, sess)
t.set_description('Train loss: {}'.format(round(loss, 3)))
train_writer.add_summary(summaries, step)
if step % 5e3 == 0:
model.saver.save(sess, args.model_path, step)
if step % 5 == 0:
valid_sample, valid_label = batcher.next_data(
is_valid=True)
loss, step, summaries = model.run_eval_step(
valid_sample, sess)
valid_writer.add_summary(summaries, step)
t.set_description('Valid loss: {}'.format(round(loss, 3)))
if step % 100 == 0:
near_ids, near_words = model.get_nearest_words(
sess, args.near_K)
pprint(near_words)
score = coherence_score(args.test_bin_fname, voca,
near_ids)
summary = tf.Summary()
summary.value.add(tag='coherence_score_{}k'.format(
args.near_K),
simple_value=score)
valid_writer.add_summary(summary, step)
else:
load_ckpt(args.model_path, sess, model.saver)
near_words_dict = {i: [] for i in range(args.aspect_num)}
for k in range(5, 50, 5):
near_ids, near_words = model.get_nearest_words(sess, k)
score = coherence_score(args.test_bin_fname, voca, near_ids)
print(k, score)
for asp_idx in near_words:
for word in near_words[asp_idx]:
if word not in near_words_dict[asp_idx]:
near_words_dict[asp_idx].append(word)
with open(args.nearword_fname, 'w') as f:
for idx in range(len(list(near_words_dict.keys()))):
print(near_words_dict[idx])
f.write(str(idx) + ' ')
f.write(' '.join(near_words_dict[idx][:5]))
f.write('\n')
def learn(self):
check_dataset(self.trn)
if self.dev is not None:
check_dataset(self.dev)
###
# continuation
###
if os.path.exists(
self.mdir) and os.path.exists(self.mdir + '/topology'):
src_voc = 'vocab_src'
tgt_voc = 'vocab_tgt'
if os.path.exists(self.mdir + '/tokenization_src.json'):
with open(self.mdir + '/tokenization_src.json') as jsonfile:
self.tok_src = json.load(jsonfile)
src_voc = self.tok_src["vocabulary"]
else:
self.src_tok = None
if not os.path.exists(self.mdir + '/' + src_voc):
sys.stderr.write(
'error: vocab src file: {} cannot be find\n'.format(
self.mdir + '/' + src_voc))
sys.exit(1)
if os.path.exists(self.mdir + '/tokenization_tgt.json'):
with open(self.mdir + '/tokenization_tgt.json') as jsonfile:
self.tok_tgt = json.load(jsonfile)
tgt_voc = self.tok_tgt["vocabulary"]
else:
self.tgt_tok = None
if not os.path.exists(self.mdir + '/' + tgt_voc):
sys.stderr.write(
'error: vocab tgt file: {} cannot be find\n'.format(
self.mdir + '/' + tgt_voc))
sys.exit(1)
if not os.path.exists(self.mdir + '/checkpoint'):
sys.stderr.write(
'error: checkpoint file: {} cannot be find\ndelete dir {} ???\n'
.format(self.mdir + '/checkpoint', self.mdir))
sys.exit(1)
argv = []
with open(self.mdir + "/topology", 'r') as f:
for line in f:
opt, val = line.split()
argv.append('-' + opt)
argv.append(val)
# overrides options passed in command line
self.parse(argv)
# read vocabularies
self.voc_src = Vocab(self.mdir + "/" + src_voc)
self.voc_tgt = Vocab(self.mdir + "/" + tgt_voc)
# update last epoch
for e in range(999, 0, -1):
if os.path.exists(self.mdir + "/epoch{}.index".format(e)):
self.last_epoch = e
break
print("learning continuation: last epoch is {}".format(
self.last_epoch))
###
# learning from scratch
###
else:
# read file or config/vocab_src if file is not set
if self.src_tok:
if not os.path.exists(self.src_tok):
sys.stderr.write(
'error: cannot find -src_tok file: {}\n'.format(
self.src_tok))
sys.exit(1)
with open(self.src_tok) as jsonfile:
self.tok_src = json.load(jsonfile)
if not self.src_voc:
self.src_voc = self.tok_src["vocabulary"]
else:
self.tok_src = None
self.voc_src = Vocab(self.src_voc)
if self.tgt_tok:
if not os.path.exists(self.tgt_tok):
sys.stderr.write(
'error: cannot find -tgt_tok file: {}\n'.format(
self.tgt_tok))
sys.exit(1)
with open(self.tgt_tok) as jsonfile:
self.tok_tgt = json.load(jsonfile)
if not self.tgt_voc:
self.tgt_voc = self.tok_tgt["vocabulary"]
else:
self.tok_tgt = None
self.voc_tgt = Vocab(self.tgt_voc)
self.src_voc_size = self.voc_src.length
self.tgt_voc_size = self.voc_tgt.length
if not os.path.exists(self.mdir):
os.makedirs(self.mdir)
# copy vocabularies
if self.src_tok:
copyfile(self.src_voc,
self.mdir + "/" + self.tok_src["vocabulary"])
copyfile(self.src_tok, self.mdir + "/tokenization_src.json")
else:
copyfile(self.src_voc, self.mdir + "/vocab_src")
if self.tgt_tok:
copyfile(self.tgt_voc,
self.mdir + "/" + self.tok_tgt["vocabulary"])
copyfile(self.tgt_tok, self.mdir + "/tokenization_tgt.json")
else:
copyfile(self.tgt_voc, self.mdir + "/vocab_tgt")
# read embeddings
# read file or use emb_src.length if file is not set
self.emb_src = Embeddings(self.src_emb, self.voc_src,
self.src_emb_size)
self.src_emb_size = self.emb_src.dim
# read file or use emb_tgt.length if file is not set
self.emb_tgt = Embeddings(self.tgt_emb, self.voc_tgt,
self.tgt_emb_size)
self.tgt_emb_size = self.emb_tgt.dim
# write topology file
with open(self.mdir + "/topology", 'w') as f:
for opt, val in vars(self).items():
if opt.startswith("src") or opt.startswith("tgt") or \
opt == "aggr" or opt == "mode":
f.write("{} {}\n".format(opt, val))
print("learning from scratch")
return