def rouge_decoding(ids, model_params):
"""
To convert the IDs to text using the vocab and SP encoder.
:param ids: [B,T] tensor of ids in vocab
:param model_params: the defined model parameters
:return: decoded text removed from graph (stop grad)
"""
decode_text_tensor = public_parsing_ops.decode(ids, model_params.vocab_filename, model_params.encoder_type)
decode_text = tf.stop_gradient(decode_text_tensor[0])
return decode_text
def decode_host_call(tensor_dict):
for key in decode_keys:
predictions[key] = public_parsing_ops.decode(
tensor_dict[key], model_params.vocab_filename,
model_params.encoder_type)
return tensor_dict
def test_tf_decode(self, encoder_type):
string = tf.constant(["the quick brown fox.", "the quick brown\n"])
ids = parsing_ops.encode(string, 10, _SPM_VOCAB, encoder_type)
self.assertAllEqual(
parsing_ops.decode(ids, _SPM_VOCAB, encoder_type),
public_parsing_ops.decode(ids, _SPM_VOCAB, encoder_type))