def test_predictor(self):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=_BATCH_SIZE)
model_dir = self.create_tempdir().full_path
mock_model = mocks.MockT2RModel()
train_eval.train_eval_model(t2r_model=mock_model,
input_generator_train=input_generator,
max_train_steps=_MAX_TRAIN_STEPS,
model_dir=model_dir)
predictor = checkpoint_predictor.CheckpointPredictor(
t2r_model=mock_model, checkpoint_dir=model_dir, use_gpu=False)
with self.assertRaises(ValueError):
predictor.predict({'does_not_matter': np.zeros(1)})
self.assertEqual(predictor.model_version, -1)
self.assertEqual(predictor.global_step, -1)
self.assertTrue(predictor.restore())
self.assertGreater(predictor.model_version, 0)
self.assertEqual(predictor.global_step, 3)
ref_feature_spec = mock_model.preprocessor.get_in_feature_specification(
tf.estimator.ModeKeys.PREDICT)
tensorspec_utils.assert_equal(predictor.get_feature_specification(),
ref_feature_spec)
features = tensorspec_utils.make_random_numpy(ref_feature_spec,
batch_size=_BATCH_SIZE)
predictions = predictor.predict(features)
self.assertLen(predictions, 1)
self.assertCountEqual(sorted(predictions.keys()), ['logit'])
self.assertEqual(predictions['logit'].shape, (2, 1))
def random_train_model(self, tf_model, **module_kwargs):
"""Trains a T2R model with random inputs."""
params = self._get_params(
model_dir=self._test_case.create_tempdir().full_path,
**module_kwargs)
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=params['batch_size'])
initialize_system = tpu.initialize_system
with mock.patch.object(tpu, 'initialize_system',
autospec=True) as mock_init:
mock_init.side_effect = initialize_system
train_eval.train_eval_model(
t2r_model=tf_model,
input_generator_train=input_generator,
max_train_steps=params['max_train_steps'],
model_dir=params['model_dir'],
use_tpu_wrapper=params['use_tpu_wrapper'])
if self._use_tpu:
mock_init.assert_called()
train_eval_test_utils.assert_output_files(
test_case=self._test_case,
model_dir=params['model_dir'],
expected_output_filename_patterns=train_eval_test_utils.
DEFAULT_TRAIN_FILENAME_PATTERNS)
def test_predictor_load_final_model(self):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=_BATCH_SIZE)
model_dir = self.create_tempdir().full_path
mock_model = mocks.MockT2RModel()
train_eval.train_eval_model(
t2r_model=mock_model,
input_generator_train=input_generator,
input_generator_eval=input_generator,
max_train_steps=_MAX_TRAIN_STEPS,
eval_steps=_MAX_EVAL_STEPS,
model_dir=model_dir,
create_exporters_fn=train_eval.create_default_exporters)
export_dir = os.path.join(model_dir, 'export', 'latest_exporter_numpy')
final_export_dir = sorted(tf.io.gfile.glob(
os.path.join(export_dir, '*')),
reverse=True)[0]
predictor = exported_savedmodel_predictor.ExportedSavedModelPredictor(
export_dir=final_export_dir)
predictor.restore()
self.assertGreater(predictor.model_version, 0)
self.assertEqual(predictor.global_step, 3)
ref_feature_spec = mock_model.preprocessor.get_in_feature_specification(
tf.estimator.ModeKeys.PREDICT)
tensorspec_utils.assert_equal(predictor.get_feature_specification(),
ref_feature_spec)
def test_predictor_init_with_default_exporter(self, restore_model_option):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=_BATCH_SIZE)
model_dir = self.create_tempdir().full_path
mock_model = mocks.MockT2RModel()
train_eval.train_eval_model(
t2r_model=mock_model,
input_generator_train=input_generator,
input_generator_eval=input_generator,
max_train_steps=_MAX_TRAIN_STEPS,
eval_steps=_MAX_EVAL_STEPS,
model_dir=model_dir,
create_exporters_fn=train_eval.create_default_exporters)
predictor = exported_savedmodel_predictor.ExportedSavedModelPredictor(
export_dir=os.path.join(model_dir, 'export',
'latest_exporter_numpy'),
restore_model_option=restore_model_option)
if restore_model_option == exported_savedmodel_predictor.RestoreOptions.RESTORE_SYNCHRONOUSLY:
predictor.restore()
self.assertGreater(predictor.model_version, 0)
self.assertEqual(predictor.global_step, 3)
ref_feature_spec = mock_model.preprocessor.get_in_feature_specification(
tf.estimator.ModeKeys.PREDICT)
tensorspec_utils.assert_equal(predictor.get_feature_specification(),
ref_feature_spec)
def test_predictor(self):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=_BATCH_SIZE)
model_dir = self.create_tempdir().full_path
mock_model = mocks.MockT2RModel()
train_eval.train_eval_model(
t2r_model=mock_model,
input_generator_train=input_generator,
input_generator_eval=input_generator,
max_train_steps=_MAX_TRAIN_STEPS,
eval_steps=_MAX_EVAL_STEPS,
model_dir=model_dir,
create_exporters_fn=train_eval.create_default_exporters)
predictor = exported_savedmodel_predictor.ExportedSavedModelPredictor(
export_dir=os.path.join(model_dir, 'export',
'latest_exporter_numpy'))
with self.assertRaises(ValueError):
predictor.get_feature_specification()
with self.assertRaises(ValueError):
predictor.predict({'does_not_matter': np.zeros(1)})
with self.assertRaises(ValueError):
_ = predictor.model_version
self.assertTrue(predictor.restore())
self.assertGreater(predictor.model_version, 0)
ref_feature_spec = mock_model.preprocessor.get_in_feature_specification(
tf.estimator.ModeKeys.PREDICT)
tensorspec_utils.assert_equal(predictor.get_feature_specification(),
ref_feature_spec)
features = tensorspec_utils.make_random_numpy(ref_feature_spec,
batch_size=_BATCH_SIZE)
predictions = predictor.predict(features)
self.assertLen(predictions, 1)
self.assertEqual(predictions['logit'].shape, (2, 1))
def random_predict(self, module_name, model_name, **module_kwargs):
"""Runs predictions through a model with random inputs."""
tf_model = getattr(module_name, model_name)(**module_kwargs)
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=1)
for prediction in train_eval.predict_from_model(
t2r_model=tf_model,
input_generator_predict=input_generator,
model_dir=self._test_case.create_tempdir().full_path):
return prediction
return None
def setUp(self):
super(PoseEnvModelsTest, self).setUp()
base_dir = 'tensor2robot'
test_data = os.path.join(FLAGS.test_srcdir, base_dir,
'test_data/pose_env_test_data.tfrecord')
self._train_log_dir = FLAGS.test_tmpdir
if tf.io.gfile.exists(self._train_log_dir):
tf.io.gfile.rmtree(self._train_log_dir)
gin.bind_parameter('train_eval_model.max_train_steps', 3)
gin.bind_parameter('train_eval_model.eval_steps', 2)
self._record_input_generator = (
default_input_generator.DefaultRecordInputGenerator(
batch_size=BATCH_SIZE, file_patterns=test_data))
self._meta_record_input_generator_train = (
default_input_generator.DefaultRandomInputGenerator(
batch_size=BATCH_SIZE))
self._meta_record_input_generator_eval = (
default_input_generator.DefaultRandomInputGenerator(
batch_size=BATCH_SIZE))
def test_predictor_with_async_hook(self):
model_dir = self.create_tempdir().full_path
default_create_export_fn = functools.partial(
async_export_hook_builder.default_create_export_fn,
batch_sizes_for_export=_BATCH_SIZES_FOR_EXPORT)
export_dir = os.path.join(model_dir, _EXPORT_DIR)
hook_builder = async_export_hook_builder.AsyncExportHookBuilder(
export_dir=export_dir, create_export_fn=default_create_export_fn)
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=_BATCH_SIZE)
mock_model = mocks.MockT2RModel()
train_eval.train_eval_model(t2r_model=mock_model,
input_generator_train=input_generator,
train_hook_builders=[hook_builder],
max_train_steps=_MAX_TRAIN_STEPS,
model_dir=model_dir)
predictor = exported_savedmodel_predictor.ExportedSavedModelPredictor(
export_dir=os.path.join(model_dir, _EXPORT_DIR))
with self.assertRaises(ValueError):
predictor.get_feature_specification()
with self.assertRaises(ValueError):
predictor.predict({'does_not_matter': np.zeros(1)})
with self.assertRaises(ValueError):
_ = predictor.model_version
self.assertEqual(predictor.global_step, -1)
self.assertTrue(predictor.restore())
self.assertGreater(predictor.model_version, 0)
# NOTE: The async hook builder will export the global step.
self.assertEqual(predictor.global_step, 3)
ref_feature_spec = mock_model.preprocessor.get_in_feature_specification(
tf.estimator.ModeKeys.PREDICT)
tensorspec_utils.assert_equal(predictor.get_feature_specification(),
ref_feature_spec)
features = tensorspec_utils.make_random_numpy(ref_feature_spec,
batch_size=_BATCH_SIZE)
predictions = predictor.predict(features)
self.assertLen(predictions, 1)
self.assertCountEqual(sorted(predictions.keys()), ['logit'])
self.assertEqual(predictions['logit'].shape, (2, 1))
def test_random_dataset(self):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=2)
self._test_input_generator(input_generator)
