def test_multicell_decode_infer(self):
r"""Tests decoding in inference mode.
"""
seq_length = np.random.randint(self._max_time, size=[self._batch_size
]) + 1
encoder_values_length = torch.tensor(seq_length)
decoder = AttentionRNNDecoder(encoder_output_size=64,
vocab_size=self._vocab_size,
input_size=self._emb_dim + 64,
hparams=self._hparams_multicell)
decoder.eval()
helper_infer = decoder.create_helper(embedding=self._embedding,
start_tokens=torch.tensor(
[1] * self._batch_size),
end_token=2)
outputs, final_state, sequence_lengths = decoder(
memory=self._encoder_output,
memory_sequence_length=encoder_values_length,
helper=helper_infer)
self.assertEqual(len(decoder.trainable_variables), 15)
self._test_outputs(decoder,
outputs,
final_state,
sequence_lengths,
test_mode=True,
is_multi=True)
def test_decode_infer(self):
r"""Tests decoding in inference mode.
"""
seq_length = np.random.randint(self._max_time, size=[self._batch_size
]) + 1
encoder_values_length = torch.tensor(seq_length)
for (cell_type, is_multi), hparams in self._test_hparams.items():
decoder = AttentionRNNDecoder(encoder_output_size=64,
token_embedder=self._embedder,
vocab_size=self._vocab_size,
input_size=self._emb_dim,
hparams=hparams)
decoder.eval()
helper_infer = decoder.create_helper(start_tokens=torch.tensor(
[1] * self._batch_size),
end_token=2)
outputs, final_state, sequence_lengths = decoder(
memory=self._encoder_output,
memory_sequence_length=encoder_values_length,
helper=helper_infer)
self._test_outputs(decoder,
outputs,
final_state,
sequence_lengths,
test_mode=True)
