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 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 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)))
infer_input_file = os.path.join(trans_dir, source_filename + '.csv')
del_temp_file = True
else:
del_temp_file = False
# check model path
ckpt = args.ckpt
if not ckpt:
model_dir = hparams.out_dir
if args.model_dir:
model_dir = args.model_dir
ckpt = tf.train.latest_checkpoint(model_dir)
# decode
inference.inference(ckpt, infer_input_file, trans_file, hparams)
if args.rescore:
if not args.rescore_logdir:
rescore_dir = os.path.join(model_dir, "post_process")
post_process.rescore(trans_file, trans_file + "_prob", infer_input_file,
rescore_dir, trans_dir + source_filename + "_rescore")
print("Done")
# Create report if m_mod(21 AAs + 3 tokens) or p_mod (24 AAs + 3 tokens)
if hparams.tgt_vocab_size == 24:
report_utils.main(trans_dir, input_dir, 'm-mod')
elif hparams.tgt_vocab_size == 27:
report_utils.main(trans_dir, input_dir, 'p-mod')
if del_temp_file: