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_feature, encoder_hidden = self.model.encoder(
enc_batch, enc_lens)
s_t_0 = self.model.reduce_state(encoder_hidden)
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:
# to do here
# ...
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 = np.zeros((len(latest_tokens), config.emb_dim))
for i, t in enumerate(latest_tokens):
if t < self.vocab.size():
w = self.vocab.id2word(t)
else:
idx = t - self.vocab.size()
if idx >= len(batch.art_oovs[i]):
w = data.UNKNOWN_TOKEN
else:
try:
w = batch.art_oovs[i][idx]
except Exception as e:
print(e.message)
if w == data.START_DECODING:
embedd = embedding.start_decoding_embedd
elif w == data.STOP_DECODING:
embedd = embedding.stop_decoding_embedd
elif w == data.PAD_TOKEN:
embedd = embedding.padding_embedd
elif w == data.UNKNOWN_TOKEN:
embedd = embedding.unknown_decoding_embedd
else:
embedd = embedding.fasttext.get_numpy_vector(w.lower())
y_t_1[i] = embedd
# y_t_1 = Variable(torch.LongTensor(latest_tokens))
y_t_1 = Variable(torch.FloatTensor(y_t_1))
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, encoder_feature,
enc_padding_mask, c_t_1, extra_zeros, enc_batch_extend_vocab,
coverage_t_1, steps)
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].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(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 Train(object):
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)
def save_model(self, running_avg_loss, iter):
state = {
'iter': iter,
'encoder_state_dict': self.model.encoder.state_dict(),
'decoder_state_dict': self.model.decoder.state_dict(),
'reduce_state_dict': self.model.reduce_state.state_dict(),
'optimizer': self.optimizer.state_dict(),
'current_loss': running_avg_loss
}
model_save_path = os.path.join(
self.model_dir, 'model_%d_%d' % (iter, int(time.time())))
torch.save(state, model_save_path)
def setup_train(self, model_file_path=None):
self.model = Model(model_file_path)
params = list(self.model.encoder.parameters()) + list(self.model.decoder.parameters()) + \
list(self.model.reduce_state.parameters())
initial_lr = config.lr_coverage if config.is_coverage else config.lr
self.optimizer = Adagrad(
params,
lr=initial_lr,
initial_accumulator_value=config.adagrad_init_acc)
self.scheduler = ExponentialLR(self.optimizer, gamma=0.99)
start_iter, start_loss = 0, 0
if model_file_path is not None: #途中から始める場合
state = torch.load(model_file_path,
map_location=lambda storage, location: storage)
start_iter = state['iter']
start_loss = state['current_loss']
if not config.is_coverage: #coverageが無しの場合
self.optimizer.load_state_dict(state['optimizer'])
if use_cuda:
for state in self.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
return start_iter, start_loss
def train_one_batch(self, batch, iter):
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)
self.optimizer.zero_grad()
encoder_outputs, encoder_feature, encoder_hidden = self.model.encoder(
enc_batch, enc_lens)
s_t_1 = self.model.reduce_state(encoder_hidden)
step_losses = []
words = []
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, next_coverage = self.model.decoder(
y_t_1, s_t_1, encoder_outputs, encoder_feature,
enc_padding_mask, c_t_1, extra_zeros, enc_batch_extend_vocab,
coverage, di)
words.append(self.vocab.id2word(final_dist[0].argmax().item()))
target = target_batch[:, di]
gold_probs = torch.gather(final_dist, 1,
target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
# print('step_loss',step_loss)
# print('step_loss.size()',step_loss.size())
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
coverage = next_coverage
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
if iter % 100 == 0:
print(words)
print([self.vocab.id2word(idx.item()) for idx in dec_batch[0]])
print([self.vocab.id2word(idx.item()) for idx in target_batch[0]])
sum_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / dec_lens_var
loss = torch.mean(batch_avg_loss)
loss.backward()
self.norm = clip_grad_norm_(self.model.encoder.parameters(),
config.max_grad_norm)
clip_grad_norm_(self.model.decoder.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.reduce_state.parameters(),
config.max_grad_norm)
self.optimizer.step()
return loss.item()
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_feature, 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_1, c_t_1, attn_dist, p_gen, next_coverage = self.model.decoder(
y_t_1, s_t_1, encoder_outputs, encoder_feature,
enc_padding_mask, c_t_1, extra_zeros, enc_batch_extend_vocab,
coverage, di)
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
coverage = next_coverage
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)
# print(loss)
# print(type(loss))
# print(loss.data)
# print(loss.data.item())
# return loss.data[0]
return loss.data.item()
def trainIters(self, n_iters, model_file_path=None):
iter, running_avg_loss = self.setup_train(model_file_path)
start = time.time()
while iter < n_iters:
batch = self.batcher.next_batch()
loss = self.train_one_batch(batch, iter)
val_loss = None
if iter % 100 == 0:
val_batch = self.val_batcher.next_batch()
val_loss = self.eval_one_batch(val_batch)
# print("val_loss",val_loss)
self.scheduler.step()
print("lr", self.scheduler.get_lr())
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 = 1
if iter % print_interval == 0:
if val_loss is not None:
print(
'steps %d, seconds for %d batch: %.2f , loss: %f , eval_loss: %f'
% (iter, print_interval, time.time() - start, loss,
val_loss))
else:
print('steps %d, seconds for %d batch: %.2f , loss: %f' %
(iter, print_interval, time.time() - start, loss))
start = time.time()
if iter % 1000 == 0:
self.save_model(running_avg_loss, iter)
