def testAttentionModelWithInferIndices(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=1,
attention="scaled_luong",
attention_architecture="standard",
use_residual=False,
inference_indices=[1, 2])
# TODO(rzhao): Make infer indices support batch_size > 1.
hparams.infer_batch_size = 1
vocab_prefix = "nmt/testdata/test_infer_vocab"
hparams.src_vocab_file = vocab_prefix + "." + hparams.src
hparams.tgt_vocab_file = vocab_prefix + "." + hparams.tgt
infer_file = "nmt/testdata/test_infer_file"
out_dir = os.path.join(tf.test.get_temp_dir(),
"attention_infer_with_indices")
hparams.out_dir = out_dir
os.makedirs(out_dir)
output_infer = os.path.join(out_dir, "output_infer")
ckpt = self._createTestInferCheckpoint(hparams, out_dir)
inference.inference(ckpt, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(2, len(list(f)))
self.assertTrue(os.path.exists(output_infer + str(1) + ".png"))
self.assertTrue(os.path.exists(output_infer + str(2) + ".png"))
def testBasicModel(self):
hparams = common_test_utils.create_test_hparams(
encoder_type='uni',
num_layers=1,
attention='',
attention_architecture='',
use_residual=False)
ckpt_path = self._createTestInferenceCheckpint(hparams, 'basic_infer')
infer_file = 'nmt/testdata/test_infer_file'
output_infer = os.path.join(hparams.out_dir, 'output_infer')
inference.inference(ckpt_path, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(5, len(list(f)))
def testMultiWorkers(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=2,
attention="scaled_luong",
attention_architecture="standard",
use_residual=False,
)
vocab_prefix = "nmt/testdata/test_infer_vocab"
hparams.src_vocab_file = vocab_prefix + "." + hparams.src
hparams.tgt_vocab_file = vocab_prefix + "." + hparams.tgt
infer_file = "nmt/testdata/test_infer_file"
out_dir = os.path.join(tf.test.get_temp_dir(), "multi_worker_infer")
hparams.out_dir = out_dir
os.makedirs(out_dir)
output_infer = os.path.join(out_dir, "output_infer")
num_workers = 3
# There are 5 examples, make batch_size=3 makes job0 has 3 examples, job1
# has 2 examples, and job2 has 0 example. This helps testing some edge
# cases.
hparams.batch_size = 3
ckpt = self._createTestInferCheckpoint(hparams, out_dir)
inference.inference(ckpt,
infer_file,
output_infer,
hparams,
num_workers,
jobid=1)
inference.inference(ckpt,
infer_file,
output_infer,
hparams,
num_workers,
jobid=2)
# Note: Need to start job 0 at the end; otherwise, it will block the testing
# thread.
inference.inference(ckpt,
infer_file,
output_infer,
hparams,
num_workers,
jobid=0)
with open(output_infer) as f:
self.assertEqual(5, len(list(f)))
def testAttentionModel(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=1,
attention="scaled_luong",
attention_architecture="standard",
use_residual=False,
)
ckpt_path = self._createTestInferenceCheckpint(hparams,
"attention_infer")
infer_file = "nmt/testdata/test_infer_file"
output_infer = os.path.join(hparams.out_dir, "output_infer")
inference.inference(ckpt_path, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(5, len(list(f)))
def testBasicModelWithInferIndices(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=1,
attention="",
attention_architecture="",
use_residual=False,
inference_indices=[0])
ckpt_path = self._createTestInferenceCheckpint(
hparams, "basic_infer_with_indices")
infer_file = "nmt/testdata/test_infer_file"
output_infer = os.path.join(hparams.out_dir, "output_infer")
inference.inference(ckpt_path, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(1, len(list(f)))
def testBasicModelWithMultipleTranslations(self):
hparams = common_test_utils.create_test_hparams(
encoder_type='uni',
num_layers=1,
attention='',
attention_architecture='',
use_residual=False,
num_translations_per_input=2,
beam_width=2)
hparams.infer_mode = 'beam_search'
ckpt_path = self._createTestInferenceCheckpint(hparams,
'multi_basice_infer')
infer_file = 'nmt/testdata/test_infer_file'
output_infer = os.path.join(hparams.out_dir, 'output_infer')
inference.inference(ckpt_path, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(10, len(list(f)))
def testAttentionModelWithInferIndices(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=1,
attention="scaled_luong",
attention_architecture="standard",
use_residual=False,
inference_indices=[1, 2])
# TODO(rzhao): Make infer indices support batch_size > 1.
hparams.infer_batch_size = 1
ckpt_path = self._createTestInferenceCheckpint(
hparams, "attention_infer_with_indices")
infer_file = "nmt/testdata/test_infer_file"
output_infer = os.path.join(hparams.out_dir, "output_infer")
inference.inference(ckpt_path, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(2, len(list(f)))
self.assertTrue(os.path.exists(output_infer + str(1) + ".png"))
self.assertTrue(os.path.exists(output_infer + str(2) + ".png"))
def testBasicModel(self):
hparams = common_test_utils.create_test_hparams(
encoder_type="uni",
num_layers=1,
attention="",
attention_architecture="",
use_residual=False,
)
vocab_prefix = "nmt/testdata/test_infer_vocab"
hparams.src_vocab_file = vocab_prefix + "." + hparams.src
hparams.tgt_vocab_file = vocab_prefix + "." + hparams.tgt
infer_file = "nmt/testdata/test_infer_file"
out_dir = os.path.join(tf.test.get_temp_dir(), "basic_infer")
hparams.out_dir = out_dir
os.makedirs(out_dir)
output_infer = os.path.join(out_dir, "output_infer")
ckpt = self._createTestInferCheckpoint(hparams, out_dir)
inference.inference(ckpt, infer_file, output_infer, hparams)
with open(output_infer) as f:
self.assertEqual(5, len(list(f)))
def _testGNMTModel(self, architecture):
hparams = common_test_utils.create_test_hparams(
encoder_type='gnmt',
num_layers=4,
attention='scaled_luong',
attention_architecture=architecture)
workers, _ = tf.test.create_local_cluster(1, 0)
worker = workers[0]
# pylint: disable=line-too-long
expected_var_names = [
'dynamic_seq2seq/encoder/embedding_encoder:0',
'dynamic_seq2seq/decoder/embedding_decoder:0',
'dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0',
'dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
'dynamic_seq2seq/decoder/memory_layer/kernel:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/bias:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/luong_attention/attention_g:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0',
'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0',
'dynamic_seq2seq/decoder/output_projection/kernel:0'
]
# pylint: enable=line-too-long
test_prefix = 'GNMTModel_%s' % architecture
with tf.Graph().as_default():
with tf.Session(worker.target,
config=self._get_session_config()) as sess:
train_m = self._createTestTrainModel(gnmt_model.GNMTModel,
hparams, sess)
m_vars = tf.trainable_variables()
self._assertModelVariableNames(expected_var_names,
[v.name for v in m_vars],
test_prefix)
with tf.variable_scope('dynamic_seq2seq', reuse=True):
last_enc_weight = tf.get_variable(
'encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel'
)
last_dec_weight = tf.get_variable(
'decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel')
mem_layer_weight = tf.get_variable(
'decoder/memory_layer/kernel')
self._assertTrainStepsLoss(train_m, sess, test_prefix)
self._assertModelVariable(last_enc_weight, sess,
'%s/last_enc_weight' % test_prefix)
self._assertModelVariable(last_dec_weight, sess,
'%s/last_dec_weight' % test_prefix)
self._assertModelVariable(mem_layer_weight, sess,
'%s/mem_layer_weight' % test_prefix)
with tf.Graph().as_default():
with tf.Session(worker.target,
config=self._get_session_config()) as sess:
eval_m = self._createTestEvalModel(gnmt_model.GNMTModel,
hparams, sess)
self._assertEvalLossAndPredictCount(eval_m, sess, test_prefix)
with tf.Graph().as_default():
with tf.Session(worker.target,
config=self._get_session_config()) as sess:
infer_m = self._createTestInferModel(gnmt_model.GNMTModel,
hparams, sess)
self._assertInferLogits(infer_m, sess, test_prefix)