def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'decode.log')):
os.remove(os.path.join(args.recog_dir, 'decode.log'))
logger = set_logger(os.path.join(args.recog_dir, 'decode.log'),
key='decoding')
ppl_avg = 0
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
batch_size=args.recog_batch_size,
bptt=args.bptt,
serialize=args.serialize,
is_test=True)
if i == 0:
# Load the LM
if args.lm_type == 'gated_cnn':
model = GatedConvLM(args)
else:
model = RNNLM(args)
model, checkpoint = load_checkpoint(model, args.recog_model[0])
epoch = checkpoint['epoch']
model.save_path = dir_name
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
# logger.info('recog unit: %s' % args.recog_unit)
# logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('BPTT: %d' % (args.bptt))
logger.info('cache size: %d' % (args.recog_n_caches))
logger.info('cache theta: %.3f' % (args.recog_cache_theta))
logger.info('cache lambda: %.3f' % (args.recog_cache_lambda))
model.cache_theta = args.recog_cache_theta
model.cache_lambda = args.recog_cache_lambda
# GPU setting
model.cuda()
start_time = time.time()
# TODO(hirofumi): ensemble
ppl, _ = eval_ppl([model],
dataset,
batch_size=1,
bptt=args.bptt,
n_caches=args.recog_n_caches,
progressbar=True)
ppl_avg += ppl
print('PPL (%s): %.2f' % (dataset.set, ppl))
logger.info('Elasped time: %.2f [sec]:' % (time.time() - start_time))
logger.info('PPL (avg.): %.2f\n' % (ppl_avg / len(args.recog_sets)))
def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
logger = set_logger(os.path.join(args.recog_dir, 'plot.log'),
key='decoding')
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
batch_size=args.recog_batch_size,
bptt=args.bptt,
serialize=args.serialize,
is_test=True)
if i == 0:
# Load the LM
if args.lm_type == 'gated_cnn':
model = GatedConvLM(args)
else:
model = RNNLM(args)
epoch = model.load_checkpoint(args.recog_model[0])['epoch']
model.save_path = dir_name
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
# logger.info('recog unit: %s' % args.recog_unit)
# logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('BPTT: %d' % (args.bptt))
logger.info('cache size: %d' % (args.recog_n_caches))
logger.info('cache theta: %.3f' % (args.recog_cache_theta))
logger.info('cache lambda: %.3f' % (args.recog_cache_lambda))
model.cache_theta = args.recog_cache_theta
model.cache_lambda = args.recog_cache_lambda
# GPU setting
model.cuda()
assert args.recog_n_caches > 0
save_path = mkdir_join(args.recog_dir, 'cache')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
if args.unit == 'word':
idx2token = dataset.idx2word
elif args.unit == 'wp':
idx2token = dataset.idx2wp
elif args.unit == 'char':
idx2token = dataset.idx2char
elif args.unit == 'phone':
idx2token = dataset.idx2phone
else:
raise NotImplementedError(args.unit)
hidden = None
fig_count = 0
toknen_count = 0
n_tokens = args.recog_n_caches
while True:
ys, is_new_epoch = dataset.next()
for t in range(ys.shape[1] - 1):
loss, hidden = model(ys[:, t:t + 2],
hidden,
is_eval=True,
n_caches=args.recog_n_caches)[:2]
if len(model.cache_attn) > 0:
if toknen_count == n_tokens:
tokens_keys = idx2token(
model.cache_ids[:args.recog_n_caches],
return_list=True)
tokens_query = idx2token(model.cache_ids[-n_tokens:],
return_list=True)
# Slide attention matrix
n_keys = len(tokens_keys)
n_queries = len(tokens_query)
cache_probs = np.zeros(
(n_keys, n_queries)) # `[n_keys, n_queries]`
mask = np.zeros((n_keys, n_queries))
for i, aw in enumerate(model.cache_attn[-n_tokens:]):
cache_probs[:(n_keys - n_queries + i + 1),
i] = aw[0,
-(n_keys - n_queries + i + 1):]
mask[(n_keys - n_queries + i + 1):, i] = 1
plot_cache_weights(cache_probs,
keys=tokens_keys,
queries=tokens_query,
save_path=mkdir_join(
save_path,
str(fig_count) + '.png'),
figsize=(40, 16),
mask=mask)
toknen_count = 0
fig_count += 1
else:
toknen_count += 1
if is_new_epoch:
break
