def test_get_results_for_epoch(self):
# this will take all gpu memory, but that's probably fine for tests
gpus = get_available_gpus()
length_list = []
for num_gpus in [1, 2, 3]:
if num_gpus > len(gpus):
continue
for bs in [1, 2, 3, 5, 7]:
if bs * num_gpus > 10:
continue
with tf.Graph().as_default() as g:
self.eval_config['batch_size_per_gpu'] = bs
self.eval_config['num_gpus'] = num_gpus
model = base_model(params=self.eval_config,
mode="infer",
hvd=None)
model.compile()
model.infer = lambda inputs, outputs: inputs
model.finalize_inference = lambda results: results
with self.test_session(g, use_gpu=True) as sess:
sess.run(tf.global_variables_initializer())
inputs_per_batch = get_results_for_epoch(
model, sess, False, "infer")
length = np.hstack([
inp['source_tensors'][1]
for inp in inputs_per_batch
])
ids = np.hstack(
[inp['source_ids'] for inp in inputs_per_batch])
length_list.append(length[np.argsort(ids)])
for i in range(len(length_list) - 1):
npt.assert_allclose(length_list[i], length_list[i + 1])
def test_get_batches_for_epoch(self):
# this will take all gpu memory, but that's probably fine for tests
gpus = get_available_gpus()
length_list = []
for num_gpus in [1, 2, 3]:
if num_gpus > len(gpus):
continue
for bs in [1, 2, 3, 5, 7]:
if bs * num_gpus > 10:
continue
with tf.Graph().as_default() as g:
self.eval_config['batch_size_per_gpu'] = bs
self.eval_config['num_gpus'] = num_gpus
model = base_model(params=self.eval_config, mode="eval", hvd=None)
model.compile()
model.evaluate = lambda inputs, outputs: inputs
model.finalize_evaluation = lambda results: results
with self.test_session(g, use_gpu=True) as sess:
sess.run(tf.global_variables_initializer())
inputs_per_batch = get_results_for_epoch(model, sess, False, "eval")
length_list.append(np.hstack([inp['source_tensors'][1]
for inp in inputs_per_batch]))
for i in range(len(length_list) - 1):
npt.assert_allclose(length_list[i], length_list[i + 1])
def infer_test(self):
train_config, infer_config = self.prepare_config()
train_config['num_epochs'] = 250
infer_config['batch_size_per_gpu'] = 4
with tf.Graph().as_default() as g:
with self.test_session(g, use_gpu=True) as sess:
gpus = get_available_gpus()
if len(gpus) > 1:
infer_config['num_gpus'] = 2
else:
infer_config['num_gpus'] = 1
with tf.Graph().as_default():
# pylint: disable=not-callable
train_model = self.base_model(params=train_config,
mode="train",
hvd=None)
train_model.compile()
train(train_model, None)
with tf.Graph().as_default():
# pylint: disable=not-callable
infer_model = self.base_model(params=infer_config,
mode="infer",
hvd=None)
infer_model.compile()
print(train_model.params['logdir'])
output_file = os.path.join(train_model.params['logdir'],
'infer_out.csv')
infer(
infer_model,
tf.train.latest_checkpoint(train_model.params['logdir']),
output_file,
)
pred_csv = pd.read_csv(output_file)
true_csv = pd.read_csv(
'open_seq2seq/test_utils/toy_speech_data/toy_data.csv', )
for pred_row, true_row in zip(pred_csv.as_matrix(),
true_csv.as_matrix()):
# checking file name
self.assertEqual(pred_row[0], true_row[0])
# checking prediction: no more than 5 chars difference
self.assertLess(levenshtein(pred_row[-1], true_row[-1]), 5)
def test_infer(self):
train_config, infer_config = self.prepare_config()
train_config['num_epochs'] = 200
infer_config['batch_size_per_gpu'] = 4
with tf.Graph().as_default() as g:
with self.test_session(g, use_gpu=True) as sess:
gpus = get_available_gpus()
if len(gpus) > 1:
infer_config['num_gpus'] = 2
else:
infer_config['num_gpus'] = 1
with tf.Graph().as_default():
train_model = base_model(params=train_config, mode="train", hvd=None)
train_model.compile()
train(train_model, None)
with tf.Graph().as_default():
infer_model = base_model(params=infer_config, mode="infer", hvd=None)
infer_model.compile()
print(train_model.params['logdir'])
output_file = os.path.join(train_model.params['logdir'], 'infer_out.csv')
infer(
infer_model,
tf.train.latest_checkpoint(train_model.params['logdir']),
output_file,
)
pred_csv = pd.read_csv(output_file)
true_csv = pd.read_csv(
'open_seq2seq/test_utils/toy_speech_data/toy_data.csv',
)
for pred_row, true_row in zip(pred_csv.as_matrix(), true_csv.as_matrix()):
# checking file name
self.assertEqual(pred_row[0], true_row[0])
# checking prediction
self.assertEqual(pred_row[-1], true_row[-1])
