def build_dict(opt, skip_if_built=False):
if isinstance(opt, ParlaiParser):
print('[ Deprecated Warning: should be passed opt not Parser ]')
opt = opt.parse_args()
if not opt.get('dict_file'):
print('Tried to build dictionary but `--dict-file` is not set. Set ' +
'this param so the dictionary can be saved.')
return
if skip_if_built and os.path.isfile(opt['dict_file']):
# Dictionary already built, skip all loading or setup
print("[ dictionary already built .]")
return None
if is_distributed():
raise ValueError(
'Dictionaries should be pre-built before distributed train.')
if opt.get('dict_class'):
# Custom dictionary class
dictionary = str2class(opt['dict_class'])(opt)
else:
# Default dictionary class
dictionary = DictionaryAgent(opt)
if os.path.isfile(opt['dict_file']):
# Dictionary already built, return loaded dictionary agent
print("[ dictionary already built .]")
return dictionary
ordered_opt = copy.deepcopy(opt)
cnt = 0
# we use train set to build dictionary
ordered_opt['numthreads'] = 1
ordered_opt['batchsize'] = 1
# Set this to none so that image features are not calculated when Teacher is
# instantiated while building the dict
ordered_opt['image_mode'] = 'no_image_model'
ordered_opt['pytorch_teacher_batch_sort'] = False
if ordered_opt['task'] == 'pytorch_teacher' or not ordered_opt['task']:
pytorch_teacher_task = ordered_opt.get('pytorch_teacher_task', '')
if pytorch_teacher_task != '':
ordered_opt['task'] = pytorch_teacher_task
datatypes = ['train:ordered:stream']
if opt.get('dict_include_valid'):
datatypes.append('valid:stream')
if opt.get('dict_include_test'):
datatypes.append('test:stream')
cnt = 0
for dt in datatypes:
ordered_opt['datatype'] = dt
world_dict = create_task(ordered_opt, dictionary)
# pass examples to dictionary
print('[ running dictionary over data.. ]')
log_time = TimeLogger()
total = world_dict.num_examples()
if opt['dict_maxexs'] >= 0:
total = min(total, opt['dict_maxexs'])
log_every_n_secs = opt.get('log_every_n_secs', None)
if log_every_n_secs:
pbar = tqdm.tqdm(total=total,
desc='Building dictionary',
unit='ex',
unit_scale=True)
else:
pbar = None
while not world_dict.epoch_done():
cnt += 1
if cnt > opt['dict_maxexs'] and opt['dict_maxexs'] >= 0:
print('Processed {} exs, moving on.'.format(
opt['dict_maxexs']))
# don't wait too long...
break
world_dict.parley()
if pbar:
pbar.update(1)
if pbar:
pbar.close()
dictionary.save(opt['dict_file'], sort=True)
print('[ dictionary built with {} tokens in {}s ]'.format(
len(dictionary), round(log_time.total_time(), 2)))
return dictionary
def build_dict(cfg):
if cfg.build_dict.skip_if_build and os.path.isfile(cfg.dict.file):
# Dictionary already built, skip all loading or setup
log.info("dictionary already built")
return None
if is_distributed():
raise ValueError(
'Dictionaries should be pre-built before distributed train.')
dictionary = hydra.utils.instantiate(cfg.dict)
if os.path.isfile(cfg.dict.file):
# Dictionary already built, return loaded dictionary agent
log.info("dictionary already built")
return dictionary
# TODO: override
# ordered_opt['numthreads'] = 1
# ordered_opt['batchsize'] = 1
# Set this to none so that image features are not calculated when Teacher is
# instantiated while building the dict
# TODO: change 'none' to 'no_image_model'
# ordered_opt['image_mode'] = 'none'
# ordered_opt['pytorch_teacher_batch_sort'] = False
# TODO: how to check if task if ??? (not set)
# if cfg.teacher.task['task'] == 'pytorch_teacher' or cfg.teacher.task is None:
# pytorch_teacher_task = ordered_opt.get('pytorch_teacher_task', '')
# if pytorch_teacher_task != '':
# ordered_opt['task'] = pytorch_teacher_task
datatypes = ['train:ordered:stream']
if cfg.build_dict.include_valid:
datatypes.append('valid:stream')
if cfg.build_dict.include_test:
datatypes.append('test:stream')
cnt = 0
for dt in datatypes:
world_dict = create_task(cfg, dictionary)
# pass examples to dictionary
log.info('running dictionary over data..')
log_time = TimeLogger()
total = world_dict.num_examples()
if opt['dict_maxexs'] >= 0:
total = min(total, opt['dict_maxexs'])
log_every_n_secs = opt.get('log_every_n_secs', None)
if log_every_n_secs:
pbar = tqdm.tqdm(total=total,
desc='Building dictionary',
unit='ex',
unit_scale=True)
else:
pbar = None
while not world_dict.epoch_done():
cnt += 1
if cnt > cfg.build_dict.maxexs and cfg.build_dict.maxexs >= 0:
log.info('Processed {} exs, moving on.'.format(
cfg.build_dict.maxexs))
# don't wait too long...
break
world_dict.parley()
if pbar:
pbar.update(1)
if pbar:
pbar.close()
dictionary.save(cfg.dict.file, sort=True)
log.info('dictionary built with {} tokens in {}s'.format(
len(dictionary), round(log_time.total_time(), 2)))
return dictionary
