def _makeDataset(self,
inputter,
data_file,
data_config=None,
dataset_size=1,
shapes=None):
if data_config is not None:
inputter.initialize(data_config)
self.assertEqual(inputter.get_dataset_size(data_file), dataset_size)
dataset = inputter.make_dataset(data_file)
eager_features = inputter.make_features(iter(dataset).next(),
training=True)
eager_features = tf.nest.map_structure(lambda t: tf.expand_dims(t, 0),
eager_features)
dataset = dataset.map(lambda *arg: inputter.make_features(
item_or_tuple(arg), training=True))
dataset = dataset.apply(dataset_util.batch_dataset(1))
features = iter(dataset).next()
if shapes is not None:
self._checkFeatures(features, shapes)
self._checkFeatures(eager_features, shapes)
keep = inputter.keep_for_training(features)
self.assertIs(keep.dtype, tf.bool)
inputs = inputter(features, training=True)
if not isinstance(inputter, inputters.ExampleInputter):
self._testServing(inputter)
return self.evaluate((features, inputs))
def _makeDataset(self,
inputter,
data_file,
data_config=None,
dataset_size=1,
shapes=None):
if data_config is not None:
inputter.initialize(data_config)
dataset = inputter.make_dataset(data_file)
dataset = dataset.map(lambda *arg: inputter.make_features(
item_or_tuple(arg), training=True))
dataset = dataset.apply(dataset_util.batch_dataset(1))
features = iter(dataset).next()
if shapes is not None:
self._checkFeatures(features, shapes)
inputs = inputter(features, training=True)
return self.evaluate((features, inputs))
def _serving_fun(features):
features = inputter.make_features(features=features.copy())
inputs = inputter(features)
return inputs
