def setup_task(cls, args, **kwargs):
langs, dicts, training = MultilingualDatasetManager.prepare(
cls.load_dictionary, args, **kwargs)
dict0 = None
for _, lang_dict in dicts.items():
if dict0 is None:
dict0 = lang_dict
else:
assert (
dict0 == lang_dict
), "Diffrent dictionary are specified for different languages; "
"TranslationMultiSimpleEpochTask only supports one shared dictionary across all languages"
return cls(args, langs, dicts, training)
def setup_task(cls, args, **kwargs):
langs, dicts, training = MultilingualDatasetManager.prepare(
cls.load_dictionary, args, **kwargs)
dict0 = None
for _, lang_dict in dicts.items():
if dict0 is None:
dict0 = lang_dict
else:
assert (
dict0 == lang_dict
), "Diffrent dictionary are specified for different languages; "
"TranslationMultiSimpleEpochNNITask only supports one shared dictionary across all languages"
torch.set_num_threads(int(args.intra))
torch.set_num_interop_threads(int(args.inter))
torch.backends.cudnn.benchmark = bool(args.benchmark)
torch.backends.cudnn.allow_tf32 = bool(args.allow_tf32)
# ### NNI modification ###
return cls(args, langs, dicts, training)
def setup_task(cls, args, **kwargs):
langs, dicts, training = MultilingualDatasetManager.prepare(
cls.load_dictionary, args, **kwargs)
return cls(args, langs, dicts, training)