def __init__(self, args, model_name=None):
self.args = args
vocab = args.vocab_path if args.vocab_path is not None else config.vocab_path
self.vocab = Vocab(vocab, config.vocab_size, config.embedding_file)
self.batcher = Batcher(args.train_data_path,
self.vocab,
mode='train',
batch_size=args.batch_size,
single_pass=False,
args=args)
self.eval_batcher = Batcher(args.eval_data_path,
self.vocab,
mode='eval',
batch_size=args.batch_size,
single_pass=True,
args=args)
time.sleep(15)
if model_name is None:
self.train_dir = os.path.join(config.log_root,
'train_%d' % (int(time.time())))
else:
self.train_dir = os.path.join(config.log_root, model_name)
if not os.path.exists(self.train_dir):
os.mkdir(self.train_dir)
self.model_dir = os.path.join(self.train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
def __init__(self, train_dir=None, eval_dir=None, vocab=None, vectors=None):
self.vectors = vectors
if vocab is None:
self.vocab = Vocab(config.vocab_path, config.vocab_size)
else:
self.vocab = vocab
print(self.vocab)
self.batcher_train = Batcher(config.train_data_path, self.vocab, mode='train',
batch_size=config.batch_size, single_pass=False)
time.sleep(15)
self.batcher_eval = Batcher(config.eval_data_path, self.vocab, mode='eval',
batch_size=config.batch_size, single_pass=True)
time.sleep(15)
cur_time = int(time.time())
if train_dir is None:
train_dir = os.path.join(config.log_root, 'train_%d' % (cur_time))
if not os.path.exists(train_dir):
os.mkdir(train_dir)
if eval_dir is None:
eval_dir = os.path.join(config.log_root, 'eval_%s' % (cur_time))
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
self.model_dir = os.path.join(train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer_train = writer.FileWriter(train_dir)
self.summary_writer_eval = writer.FileWriter(eval_dir)
def __init__(self):
self.vocab = Vocab(args.vocab_path, args.vocab_size)
self.batcher = Batcher(
args.decode_data_path,
self.vocab,
mode='decode',
batch_size=1,
single_pass=True) # support only 1 item at a time
time.sleep(15)
vocab_size = self.vocab.size()
self.beam_size = args.beam_size
# self.bertClient = BertClient()
self.encoder = EncoderLSTM(args.hidden_size, self.vocab.size())
self.decoder = DecoderLSTM(args.hidden_size, self.vocab.size())
if use_cuda:
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
# Prepare the output folder and files
output_dir = os.path.join(args.logs, "outputs")
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_file = os.path.join(output_dir,
"decoder_{}.txt".format(args.output_name))
self.file = open(output_file, "w+")
def __init__(self, model_file_path):
model_name = os.path.basename(model_file_path)
self._decode_dir = os.path.join(config.log_root,
'decode_%s' % (model_name))
# self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
# self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
# for p in [self._decode_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)
decode_data_path = "/Users/rowancassius/Desktop/pointer_summarizer-master/training_ptr_gen/decode_file.txt"
# decode_data_path = "/Users/rowancassius/Desktop/pointer_summarizer-master/training_ptr_gen/data_file.txt"
self.batcher = Batcher(data_path=decode_data_path,
vocab=self.vocab,
mode='decode',
batch_size=config.beam_size,
single_pass=True)
# time.sleep(15)
time.sleep(2)
self.model = Model(model_file_path, is_eval=True)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
self.val_batcher = Batcher(config.eval_data_path,
self.vocab,
mode='eval',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
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, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.compat.v1.summary.FileWriter(train_dir)
class Evaluate(object):
def __init__(self, model_file_path):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.decode_data_path, self.vocab, 'eval',
config.batch_size, single_pass=True)
time.sleep(5)
eval_dir = os.path.join(config.log_root, 'eval_%d'%(int(time.time())))
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
self.summary_writer = tf.summary.FileWriter(eval_dir)
self.model = Model(model_file_path, is_eval=True)
def eval(self, batch):
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1 = \
get_input_from_batch(batch, use_cuda)
dec_batch, dec_padding_mask, max_dec_len, dec_lens_var, target_batch = get_output_from_batch(batch, use_cuda)
encoder_outputs, encoder_hidden = self.model.encoder(enc_batch, enc_lens)
s_t_1 = self.model.reduce_state(encoder_hidden)
step_losses = []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t_1 = dec_batch[:, di] # Teacher forcing
final_dist, s_t, c_t, _, _ = self.model.decoder(y_t_1, s_t_1,
encoder_outputs, enc_padding_mask, c_t_1,
extra_zeros, enc_batch_extend_vocab)
target = target_batch[:, di]
gold_probs = torch.gather(final_dist, 1, target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_step_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_step_losses / dec_lens_var
loss = torch.mean(batch_avg_loss)
return loss.data[0]
def run_eval(self):
start = time.time()
running_avg_loss, iter = 0, 0
batch = self.batcher.next_batch()
while batch is not None:
loss = self.eval(batch)
running_avg_loss = calc_running_avg_loss(loss, running_avg_loss, self.summary_writer, iter)
iter += 1
if iter % 100 == 0:
self.summary_writer.flush()
print_interval = 1000
if iter % print_interval == 0:
print('steps %d, seconds for %d batch: %.2f , loss: %f' % (
iter, print_interval, time.time() - start, loss))
start = time.time()
batch = self.batcher.next_batch()
def __init__(self, data_path, opt, batch_size=config.batch_size):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(data_path, self.vocab, mode='eval', batch_size=batch_size,
single_pass=True)
self.opt =opt
time.sleep(5)
def __init__(self):
self.vocab = Vocab(VOCAB_PATH, VOCAB_SIZE)
self.batcher = Batcher(TRAIN_DATA_PATH, self.vocab, mode = 'train',batch_size = BATCH_SIZE, single_pass = False)
self.start_id = self.vocab.word2id(data.START_DECODING)
self.end_id = self.vocab.word2id(data.STOP_DECODING)
self.pad_id = self.vocab.word2id(data.PAD_TOKEN)
self.unk_id = self.vocab.word2id(data.UNKNOWN_TOKEN)
self.model = MyModel().to(DEVICE)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=LR)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.train_batcher = Batcher(config.train_data_path,
self.vocab,
hps=config.hps,
single_pass=False)
self.val_batcher = Batcher(config.eval_data_path,
self.vocab,
hps=config.hps,
single_pass=False)
def __init__(self, opt):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path, self.vocab, mode='train',
batch_size=config.batch_size, single_pass=False)
self.opt = opt
self.start_id = self.vocab.word2id(data.START_DECODING)
self.end_id = self.vocab.word2id(data.STOP_DECODING)
self.pad_id = self.vocab.word2id(data.PAD_TOKEN)
self.unk_id = self.vocab.word2id(data.UNKNOWN_TOKEN)
time.sleep(5)
def __init__(self, opt):
'''
opt needs to contain:
- model_file_path
- n_best
- max_token_seq_len
'''
self.opt = opt
self.device = torch.device('cuda' if use_cuda else 'cpu')
print("Max article len", config.max_article_len)
model = Model(config.vocab_size, config.vocab_size,
config.max_article_len)
checkpoint = torch.load(opt["model_file_path"],
map_location=lambda storage, location: storage)
# model saved as:
# state = {
# 'iter': iter,
# 'transformer_state_dict': self.model.state_dict(),
# 'optimizer': self.optimizer.state_dict(),
# 'current_loss': running_avg_loss
# }
model.load_state_dict(checkpoint['transformer_state_dict'])
print('[Info] Trained model state loaded.')
#model.word_prob_prj = nn.LogSoftmax(dim=1)
self.model = model.to(self.device)
self.model.eval()
self._decode_dir = os.path.join(
config.log_root,
'decode_%s' % (opt["model_file_path"].split("/")[-1]))
self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
for p in [self._decode_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.batch_size,
single_pass=True)
time.sleep(15)
print('[Info] Summarizer object created.')
def __init__(self, model_file_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_file_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)
def __init__(self, model_file_path):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.decode_data_path, self.vocab, 'eval',
config.batch_size, single_pass=True)
time.sleep(5)
eval_dir = os.path.join(config.log_root, 'eval_%d'%(int(time.time())))
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
self.summary_writer = tf.summary.FileWriter(eval_dir)
self.model = Model(model_file_path, is_eval=True)
def __init__(self, model_file_path, destination_dir):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.encode_data_path,
self.vocab,
mode='encode',
batch_size=config.batch_size,
single_pass=True)
time.sleep(5)
self.output = {}
self.destination_dir = destination_dir
self.model = Model(model_file_path, is_eval=True)
def __init__(self, model_file_path, model_type="stem", load_batcher=True):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
if load_batcher:
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)
self.model_type = model_type
def load_batches_decode():
vocab = Vocab(config.vocab_path, config.vocab_size)
batcher = Batcher(config.decode_data_path, vocab, mode='decode',
batch_size=config.beam_size, single_pass=True)
batches = [None for _ in range(TEST_DATA_SIZE)]
for i in range(TEST_DATA_SIZE):
batch = batcher.next_batch()
batches[i] = batch
with open("lib/data/batches_test.vocab{}.beam{}.pk.bin".format(vocab.size(), config.beam_size), "wb") as f:
pickle.dump(batches, f)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path, self.vocab, mode='train',
batch_size=config.batch_size, single_pass=False)
time.sleep(15)
#train_dir = os.path.join(config.log_root, 'train_%d' % (int(time.time())))
train_dir = './train_log'
if not os.path.exists(train_dir):
os.mkdir(train_dir)
self.model_dir = os.path.join(train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
def __init__(self, model_file_path):
self._decode_dir = os.path.join(config.log_root, 'decode_%d' % (int(time.time())))
self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
for p in [self._decode_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 load_batches_train():
vocab = Vocab(config.vocab_path, config.vocab_size)
batcher = Batcher(config.decode_data_path, vocab, mode='train',
batch_size=config.batch_size, single_pass=False)
TRAIN_DATA_SIZE = 287226
num_batches = int(TRAIN_DATA_SIZE / config.batch_size)
batches = [None for _ in range(num_batches)]
for i in tqdm(range(num_batches)):
batch = batcher.next_batch()
batches[i] = batch
with open("lib/data/batches_train.vocab{}.batch{}.pk.bin".format(vocab.size(), config.batch_size), "wb") as f:
pickle.dump(batches, f)
def __init__(self, args):
self.hparams = hp()
self.model = Model(self.hparams)
self.vocab = Vocab(config.vocab_path, self.hparams.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=self.hparams.batch_size,
single_pass=False)
self.args = args
self.start_id = self.vocab.word2id(data.START_DECODING)
self.end_id = self.vocab.word2id(data.STOP_DECODING)
self.pad_id = self.vocab.word2id(data.PAD_TOKEN)
self.unk_id = self.vocab.word2id(data.UNKNOWN_TOKEN)
time.sleep(3)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path, self.vocab, mode='train',
batch_size=config.batch_size, single_pass=False)
# print("MODE MUST BE train")
# time.sleep(15)
self.print_interval = config.print_interval
train_dir = config.train_dir
if not os.path.exists(train_dir):
os.mkdir(train_dir)
self.model_dir = train_dir
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
def __init__(self, use_elmo=False, finetune_glove=False):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
self.use_elmo = use_elmo
self.finetune_glove = finetune_glove
time.sleep(15)
self.model_dir = os.path.join(train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.compat.v1.summary.FileWriter(train_dir)
def __init__(self, model_file_or_model, vocab=None):
if vocab is None:
self.vocab = Vocab(config.vocab_path, config.vocab_size)
else:
assert isinstance(vocab, Vocab)
self.vocab = vocab
self.batcher = Batcher(config.eval_data_path,
self.vocab,
mode='eval',
batch_size=config.batch_size,
single_pass=True)
time.sleep(15)
if isinstance(model_file_or_model, str):
self.model = Model(device, model_file_or_model, is_eval=True)
elif isinstance(model_file_or_model, Model):
self.model = model_file_or_model
else:
raise ValueError("Cannot build model from type %s" %
type(model_file_or_model))
class Encode(object):
def __init__(self, model_file_path, destination_dir):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.encode_data_path,
self.vocab,
mode='encode',
batch_size=config.batch_size,
single_pass=True)
time.sleep(5)
self.output = {}
self.destination_dir = destination_dir
self.model = Model(model_file_path, is_eval=True)
def save_output(self, output, destination_dir):
if destination_dir is None:
torch.save(output, "output")
else:
torch.save(output, destination_dir)
def encode_one_batch(self, batch):
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1, coverage = \
get_input_from_batch(batch, use_cuda)
encoder_outputs, encoder_feature, encoder_hidden = self.model.encoder(
enc_batch, enc_lens)
h, c = self.model.reduce_state(encoder_hidden)
h, c = h.squeeze(0), c.squeeze(0)
encodes = torch.cat((h, c), 1)
for id, encode in zip(batch.original_abstracts, encodes):
print(encode)
self.output[id] = encode
def run_encode(self):
start = time.time()
batch = self.batcher.next_batch()
while batch is not None:
self.encode_one_batch(batch)
batch = self.batcher.next_batch()
self.save_output(self.output, self.destination_dir)
def __init__(self, opt, vocab, logger, writer, train_num):
self.vocab = vocab
self.train_batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
self.test_batcher = Batcher(config.test_data_path,
self.vocab,
mode='eval',
batch_size=config.batch_size,
single_pass=True)
self.opt = opt
self.start_id = self.vocab.word2id(data.START_DECODING)
self.end_id = self.vocab.word2id(data.STOP_DECODING)
self.pad_id = self.vocab.word2id(data.PAD_TOKEN)
self.unk_id = self.vocab.word2id(data.UNKNOWN_TOKEN)
self.logger = logger
self.writer = writer
self.train_num = train_num
time.sleep(5)
def __init__(self, args, model_file_path, save_path):
self.args = args
model_name = os.path.basename(model_file_path)
self._decode_dir = os.path.join(config.log_root, save_path,
'decode_%s' % (model_name))
self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
for p in [self._decode_dir, self._rouge_ref_dir, self._rouge_dec_dir]:
if not os.path.exists(p):
os.mkdir(p)
vocab = args.vocab_path if args.vocab_path is not None else config.vocab_path
self.vocab = Vocab(vocab, config.vocab_size, config.embedding_file)
self.batcher = Batcher(args.decode_data_path,
self.vocab,
mode='decode',
batch_size=args.beam_size,
single_pass=True,
args=args)
time.sleep(15)
self.model = Model(self.vocab, model_file_path, is_eval=True)
def __init__(self, model_file_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_file_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_file_path, is_eval=True)
def __init__(self, model_file_path=None):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
if not model_file_path:
train_dir = os.path.join(config.log_root,
'train_%d' % (int(time.time())))
if not os.path.exists(train_dir):
os.mkdir(train_dir)
else:
train_dir = re.sub('/model/model.*', '', model_file_path)
self.model_dir = os.path.join(train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.summary.create_file_writer(train_dir)
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
train_dir = os.path.join(config.ouput_root,
'train_%d' % (int(time.time())))
if not os.path.exists(train_dir):
os.makedirs(train_dir)
self.checkpoint_dir = os.path.join(train_dir, 'checkpoints')
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
self.train_summary_writer = tf.summary.create_file_writer(
os.path.join(train_dir, 'log', 'train'))
self.eval_summary_writer = tf.summary.create_file_writer(
os.path.join(train_dir, 'log', 'eval'))
def __init__(self):
"""
Input:
vocab_path = "xxx/finished_files/vocab",
vocab_size = 50000
Output:
class object: self.vocab --> (dicts `_word_to_id` and `_id_to_word`)
"""
self.vocab = Vocab(config.vocab_path, config.vocab_size)
"""
Input:
train_data_path = "xxx/finished_files/chunked/train_*",
self.vocab: class object,
mode = 'train', for training,
batch_size = 8,
single_pass = False
Output:
class object: self.vocab,
(dicts `_word_to_id` and `_id_to_word`)
"""
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
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, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.summary.FileWriter(train_dir)
def __init__(self):
self.vocab = Vocab(args.vocab_path, args.vocab_size)
sys.stdout.flush()
self.batcher = Batcher(args.train_data_path,
self.vocab,
mode='train',
batch_size=args.batch_size,
single_pass=False)
time.sleep(15)
vocab_size = self.vocab.size()
self.model = BertLSTMModel(args.hidden_size, self.vocab.size(),
args.max_dec_steps)
# self.model = Seq2SeqLSTM(args.hidden_size, self.vocab.size(), args.max_dec_steps)
if use_cuda:
self.model = self.model.cuda()
self.model_optimizer = torch.optim.Adam(self.model.parameters(),
lr=args.lr)
train_logs = os.path.join(args.logs, "train_logs")
eval_logs = os.path.join(args.logs, "eval_logs")
self.train_summary_writer = tf.summary.FileWriter(train_logs)
self.eval_summary_writer = tf.summary.FileWriter(eval_logs)
def __init__(self, model_file_path):
model_name = os.path.basename(model_file_path)
self._decode_dir = os.path.join(config.log_root, 'decode_%s' % (model_name))
self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
for p in [self._decode_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)
class BeamSearch(object):
def __init__(self, model_file_path):
model_name = os.path.basename(model_file_path)
self._decode_dir = os.path.join(config.log_root, 'decode_%s' % (model_name))
self._rouge_ref_dir = os.path.join(self._decode_dir, 'rouge_ref')
self._rouge_dec_dir = os.path.join(self._decode_dir, 'rouge_dec_dir')
for p in [self._decode_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 decode(self):
start = time.time()
counter = 0
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 = data.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(data.STOP_DECODING)
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._decode_dir)
def beam_search(self, batch):
#batch should have only one example
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_0, coverage_t_0 = \
get_input_from_batch(batch, use_cuda)
encoder_outputs, encoder_hidden, max_encoder_output = self.model.encoder(enc_batch, enc_lens)
s_t_0 = self.model.reduce_state(encoder_hidden)
if config.use_maxpool_init_ctx:
c_t_0 = max_encoder_output
dec_h, dec_c = s_t_0 # 1 x 2*hidden_size
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(data.START_DECODING)],
log_probs=[0.0],
state=(dec_h[0], dec_c[0]),
context = c_t_0[0],
coverage=(coverage_t_0[0] if config.is_coverage else None))
for _ in xrange(config.beam_size)]
results = []
steps = 0
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(data.UNKNOWN_TOKEN) \
for t in latest_tokens]
y_t_1 = Variable(torch.LongTensor(latest_tokens))
if use_cuda:
y_t_1 = y_t_1.cuda()
all_state_h =[]
all_state_c = []
all_context = []
for h in beams:
state_h, state_c = h.state
all_state_h.append(state_h)
all_state_c.append(state_c)
all_context.append(h.context)
s_t_1 = (torch.stack(all_state_h, 0).unsqueeze(0), torch.stack(all_state_c, 0).unsqueeze(0))
c_t_1 = torch.stack(all_context, 0)
coverage_t_1 = None
if config.is_coverage:
all_coverage = []
for h in beams:
all_coverage.append(h.coverage)
coverage_t_1 = torch.stack(all_coverage, 0)
final_dist, s_t, c_t, attn_dist, p_gen, coverage_t = self.model.decoder(y_t_1, s_t_1,
encoder_outputs, enc_padding_mask, c_t_1,
extra_zeros, enc_batch_extend_vocab, coverage_t_1)
topk_log_probs, topk_ids = torch.topk(final_dist, config.beam_size * 2)
dec_h, dec_c = s_t
dec_h = dec_h.squeeze()
dec_c = dec_c.squeeze()
all_beams = []
num_orig_beams = 1 if steps == 0 else len(beams)
for i in xrange(num_orig_beams):
h = beams[i]
state_i = (dec_h[i], dec_c[i])
context_i = c_t[i]
coverage_i = (coverage_t[i] if config.is_coverage else None)
for j in xrange(config.beam_size * 2): # for each of the top 2*beam_size hyps:
new_beam = h.extend(token=topk_ids[i, j].data[0],
log_prob=topk_log_probs[i, j].data[0],
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(data.STOP_DECODING):
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]
class Evaluate(object):
def __init__(self, model_file_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_file_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_file_path, is_eval=True)
def eval_one_batch(self, batch):
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1, coverage = \
get_input_from_batch(batch, use_cuda)
dec_batch, dec_padding_mask, max_dec_len, dec_lens_var, target_batch = \
get_output_from_batch(batch, use_cuda)
encoder_outputs, encoder_hidden, max_encoder_output = self.model.encoder(enc_batch, enc_lens)
s_t_1 = self.model.reduce_state(encoder_hidden)
if config.use_maxpool_init_ctx:
c_t_1 = max_encoder_output
step_losses = []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t_1 = dec_batch[:, di] # Teacher forcing
final_dist, s_t_1, c_t_1,attn_dist, p_gen, coverage = self.model.decoder(y_t_1, s_t_1,
encoder_outputs, enc_padding_mask, c_t_1,
extra_zeros, enc_batch_extend_vocab, coverage)
target = target_batch[:, di]
gold_probs = torch.gather(final_dist, 1, target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage), 1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_step_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_step_losses / dec_lens_var
loss = torch.mean(batch_avg_loss)
return loss.data[0]
def run_eval(self):
running_avg_loss, iter = 0, 0
start = time.time()
batch = self.batcher.next_batch()
while batch is not None:
loss = self.eval_one_batch(batch)
running_avg_loss = calc_running_avg_loss(loss, running_avg_loss, self.summary_writer, iter)
iter += 1
if iter % 100 == 0:
self.summary_writer.flush()
print_interval = 1000
if iter % print_interval == 0:
print('steps %d, seconds for %d batch: %.2f , loss: %f' % (
iter, print_interval, time.time() - start, running_avg_loss))
start = time.time()
batch = self.batcher.next_batch()
