def test_build_csv_transforming_serving_input_fn_with_label(self):
feed_dict = ['15,6,1', '12,17,2']
basedir = tempfile.mkdtemp()
raw_metadata = dataset_metadata.DatasetMetadata(
schema=_make_raw_schema([]))
transform_savedmodel_dir = os.path.join(basedir,
'transform-savedmodel')
_write_transform_savedmodel(transform_savedmodel_dir)
serving_input_fn = (
input_fn_maker.build_csv_transforming_serving_input_fn(
raw_metadata=raw_metadata,
raw_keys=['raw_a', 'raw_b', 'raw_label'],
transform_savedmodel_dir=transform_savedmodel_dir))
with tf.Graph().as_default():
with tf.Session().as_default() as session:
outputs, labels, inputs = serving_input_fn()
self.assertCountEqual(
outputs.keys(),
{'transformed_a', 'transformed_b', 'transformed_label'})
self.assertIsNone(labels)
self.assertEqual(set(inputs.keys()), {'csv_example'})
feed_inputs = {inputs['csv_example']: feed_dict}
transformed_a, transformed_b, transformed_label = session.run(
[
outputs['transformed_a'], outputs['transformed_b'],
outputs['transformed_label']
],
feed_dict=feed_inputs)
batch_shape = (len(feed_dict), 1)
# transformed_b is sparse so _convert_scalars_to_vectors did not fix it
sparse_batch_shape = (len(feed_dict), )
transformed_b_dict = dict(
zip([tuple(x + [0]) for x in transformed_b.indices.tolist()],
transformed_b.values.tolist()))
self.assertEqual(batch_shape, tuple(transformed_a.shape))
self.assertEqual(sparse_batch_shape, tuple(transformed_b.dense_shape))
self.assertEqual(batch_shape, tuple(transformed_label.shape))
self.assertEqual(21, transformed_a[0][0])
self.assertEqual(9, transformed_b_dict[(0, 0)])
self.assertEqual(1000, transformed_label[0][0])
self.assertEqual(29, transformed_a[1][0])
self.assertEqual(-5, transformed_b_dict[(1, 0)])
self.assertEqual(2000, transformed_label[1][0])
def test_build_csv_transforming_serving_input_fn_with_defaults(self):
feed_dict = [',,']
basedir = tempfile.mkdtemp()
raw_metadata = dataset_metadata.DatasetMetadata(
schema=_make_raw_schema([]))
transform_savedmodel_dir = os.path.join(basedir,
'transform-savedmodel')
_write_transform_savedmodel(transform_savedmodel_dir)
serving_input_fn = (
input_fn_maker.build_csv_transforming_serving_input_fn(
raw_metadata=raw_metadata,
raw_keys=['raw_a', 'raw_b', 'raw_label'],
transform_savedmodel_dir=transform_savedmodel_dir))
with tf.Graph().as_default():
with tf.Session().as_default() as session:
outputs, labels, inputs = serving_input_fn()
self.assertCountEqual(
outputs.keys(),
{'transformed_a', 'transformed_b', 'transformed_label'})
self.assertIsNone(labels)
self.assertEqual(set(inputs.keys()), {'csv_example'})
feed_inputs = {inputs['csv_example']: feed_dict}
transformed_a, transformed_b, transformed_label = session.run(
[
outputs['transformed_a'], outputs['transformed_b'],
outputs['transformed_label']
],
feed_dict=feed_inputs)
self.assertEqual((1, 1), tuple(transformed_a.shape))
# transformed_b is sparse so _convert_scalars_to_vectors did not fix it
self.assertEqual((1, ), tuple(transformed_b.dense_shape))
self.assertEqual((1, 1), tuple(transformed_label.shape))
transformed_b_dict = dict(
zip([tuple(x) for x in transformed_b.indices.tolist()],
transformed_b.values.tolist()))
# Note the feed dict is empty. So these values come from the defaults
# in _make_raw_schema()
self.assertEqual(1, transformed_a[0][0])
# transformed_b is sparse so _convert_scalars_to_vectors did not fix it
self.assertEqual(-1, transformed_b_dict[(0, )])
self.assertEqual(-1000, transformed_label[0][0])
def test_build_csv_transforming_serving_input_fn_with_label(self):
feed_dict = ['15,6,1', '12,17,2']
basedir = tempfile.mkdtemp()
raw_metadata = dataset_metadata.DatasetMetadata(
schema=_make_raw_schema([]))
transform_savedmodel_dir = os.path.join(basedir,
'transform-savedmodel')
_write_transform_savedmodel(transform_savedmodel_dir)
serving_input_fn = (
input_fn_maker.build_csv_transforming_serving_input_fn(
raw_metadata=raw_metadata,
raw_keys=['raw_a', 'raw_b', 'raw_label'],
transform_savedmodel_dir=transform_savedmodel_dir))
with tf.Graph().as_default():
with tf.Session().as_default() as session:
outputs, labels, inputs = serving_input_fn()
feed_inputs = {inputs['csv_example']: feed_dict}
transformed_a, transformed_b, transformed_label = session.run(
[
outputs['transformed_a'], outputs['transformed_b'],
outputs['transformed_label']
],
feed_dict=feed_inputs)
self.assertEqual(21, transformed_a[0][0])
self.assertEqual(9, transformed_b[0][0])
self.assertEqual(1000, transformed_label[0][0])
self.assertEqual(29, transformed_a[1][0])
self.assertEqual(-5, transformed_b[1][0])
self.assertEqual(2000, transformed_label[1][0])
self.assertItemsEqual(
outputs, {'transformed_a', 'transformed_b', 'transformed_label'})
self.assertIsNone(labels)
self.assertEqual(set(inputs.keys()), {'csv_example'})
