def test_build_json_example_transforming_serving_input_fn(self):
example_all = {
'features': {
'feature': {
'raw_a': {
'int64List': {
'value': [42]
}
},
'raw_b': {
'int64List': {
'value': [43]
}
},
'raw_label': {
'int64List': {
'value': [44]
}
}
}
}
}
# Default values for raw_a and raw_b come from _make_raw_schema()
example_missing = {
'features': {
'feature': {
'raw_label': {
'int64List': {
'value': [3]
}
}
}
}
}
feed_dict = [json.dumps(example_all), json.dumps(example_missing)]
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_json_example_transforming_serving_input_fn(
raw_metadata=raw_metadata,
raw_label_keys=[],
raw_feature_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.assertItemsEqual(
outputs.keys(),
{'transformed_a', 'transformed_b', 'transformed_label'})
self.assertIsNone(labels)
self.assertEqual(set(inputs.keys()), {'json_example'})
feed_inputs = {inputs['json_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(85, transformed_a[0][0])
self.assertEqual(-1, transformed_b_dict[(0, 0)])
self.assertEqual(44000, transformed_label[0][0])
self.assertEqual(1, transformed_a[1][0])
self.assertEqual(-1, transformed_b_dict[(1, 0)])
self.assertEqual(3000, transformed_label[1][0])
def test_build_json_example_transforming_serving_input_fn(self):
example_all = {
'features': {
'feature': {
'raw_a': {
'int64List': {
'value': [42]
}
},
'raw_b': {
'int64List': {
'value': [43]
}
},
'raw_label': {
'int64List': {
'value': [44]
}
}
}
}
}
# Default values for raw_a and raw_b come from _make_raw_schema()
example_missing = {
'features': {
'feature': {
'raw_label': {
'int64List': {
'value': [3]
}
}
}
}
}
feed_dict = [json.dumps(example_all), json.dumps(example_missing)]
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_json_example_transforming_serving_input_fn(
raw_metadata=raw_metadata,
raw_label_keys=[],
raw_feature_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['json_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(85, transformed_a[0][0])
self.assertEqual(-1, transformed_b[0][0])
self.assertEqual(44000, transformed_label[0][0])
self.assertEqual(1, transformed_a[1][0])
self.assertEqual(-1, transformed_b[1][0])
self.assertEqual(3000, transformed_label[1][0])
self.assertItemsEqual(
outputs, {'transformed_a', 'transformed_b', 'transformed_label'})
self.assertIsNone(labels)
self.assertEqual(set(inputs.keys()), {'json_example'})
