def testMainEmptyInputs(self):
"""Test executor class import under empty inputs/outputs."""
inputs = {
'x': [
standard_artifacts.ExternalArtifact(),
standard_artifacts.ExternalArtifact()
]
}
outputs = {'y': [standard_artifacts.Examples()]}
exec_properties = {'a': 'b'}
args = [
'--executor_class_path=%s.%s' %
(FakeExecutor.__module__, FakeExecutor.__name__),
'--inputs=%s' % artifact_utils.jsonify_artifact_dict(inputs),
'--outputs=%s' % artifact_utils.jsonify_artifact_dict(outputs),
'--exec-properties=%s' % json.dumps(exec_properties),
]
with ArgsCapture() as args_capture:
run_executor.main(args)
# TODO(b/131417512): Add equal comparison to types.Artifact class so we
# can use asserters.
self.assertSetEqual(set(args_capture.input_dict.keys()),
set(inputs.keys()))
self.assertSetEqual(set(args_capture.output_dict.keys()),
set(outputs.keys()))
self.assertDictEqual(args_capture.exec_properties, exec_properties)
def setUp(self):
super(BaseComponentWithPipelineParamTest, self).setUp()
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
example_gen_buckets = data_types.RuntimeParameter(
name='example-gen-buckets', ptype=int, default=10)
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]),
output_config={
'split_config': {
'splits': [{
'name': 'examples',
'hash_buckets': example_gen_buckets
}]
}
})
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
pipeline = tfx_pipeline.Pipeline(
pipeline_name=self._test_pipeline_name,
pipeline_root='test_pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
self._tfx_ir = pipeline_pb2.Pipeline()
with dsl.Pipeline('test_pipeline'):
self.example_gen = base_component.BaseComponent(
component=example_gen,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
tfx_ir=self._tfx_ir)
self.statistics_gen = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
tfx_ir=self._tfx_ir
)
self.tfx_example_gen = example_gen
self.tfx_statistics_gen = statistics_gen
def setUp(self):
super(DriverTest, self).setUp()
# Create input splits.
test_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
self._input_base_path = os.path.join(test_dir, 'input_base')
tf.io.gfile.makedirs(self._input_base_path)
# Mock metadata.
self._mock_metadata = tf.test.mock.Mock()
self._example_gen_driver = driver.Driver(self._mock_metadata)
# Create input dict.
input_base = standard_artifacts.ExternalArtifact()
input_base.uri = self._input_base_path
self._input_channels = {
'input_base': channel_utils.as_channel([input_base])
}
# Create exec proterties.
self._exec_properties = {
'input_config':
json_format.MessageToJson(
example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(
name='s1', pattern='span{SPAN}/split1/*'),
example_gen_pb2.Input.Split(
name='s2', pattern='span{SPAN}/split2/*')
])),
}
def setUp(self):
super(BaseComponentTest, self).setUp()
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]))
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
pipeline = tfx_pipeline.Pipeline(
pipeline_name=self._test_pipeline_name,
pipeline_root='test_pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
)
self.tfx_component = statistics_gen
def setUp(self):
super(BaseComponentTest, self).setUp()
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input_base=channel_utils.as_channel([examples]))
statistics_gen = statistics_gen_component.StatisticsGen(
input_data=example_gen.outputs.examples, instance_name='foo')
pipeline = tfx_pipeline.Pipeline(
pipeline_name='test_pipeline',
pipeline_root='test_pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component=statistics_gen,
depends_on=set(),
pipeline=pipeline,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
)
self.tfx_component = statistics_gen
def testCsvExampleGenWrapper(self):
input_base = standard_artifacts.ExternalArtifact(split='')
input_base.uri = '/path/to/dataset'
with patch.object(executor, 'Executor', autospec=True) as _:
wrapper = executor_wrappers.CsvExampleGenWrapper(
argparse.Namespace(
exec_properties=json.dumps(self.exec_properties),
outputs=artifact_utils.jsonify_artifact_dict(
{'examples': self.examples}),
executor_class_path=(
'tfx.components.example_gen.csv_example_gen.executor.Executor'
),
input_base=json.dumps([input_base.json_dict()])),)
wrapper.run(output_basedir=self.output_basedir)
# TODO(b/133011207): Validate arguments for executor and Do() method.
metadata_file = os.path.join(
self.output_basedir, 'output/ml_metadata/examples')
expected_output_examples = standard_artifacts.Examples(split='dummy')
# Expect that span and path are resolved.
expected_output_examples.span = 1
expected_output_examples.uri = (
'/path/to/output/csv_example_gen/examples/mock_workflow_id/dummy/')
with tf.gfile.GFile(metadata_file) as f:
self.assertEqual(
[expected_output_examples.json_dict()], json.loads(f.read()))
def setUp(self):
input_data_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'testdata')
# Create input dict.
input_base = standard_artifacts.ExternalArtifact()
input_base.uri = os.path.join(input_data_dir, 'external')
self._input_dict = {'input_base': [input_base]}
def testEnableCache(self):
input_base = standard_artifacts.ExternalArtifact()
import_example_gen_1 = component.ImportExampleGen(
input=channel_utils.as_channel([input_base]))
self.assertEqual(None, import_example_gen_1.enable_cache)
import_example_gen_2 = component.ImportExampleGen(
input=channel_utils.as_channel([input_base]), enable_cache=True)
self.assertEqual(True, import_example_gen_2.enable_cache)
def testConstruct(self):
input_base = standard_artifacts.ExternalArtifact()
import_example_gen = component.ImportExampleGen(
input=channel_utils.as_channel([input_base]))
self.assertEqual(standard_artifacts.Examples.TYPE_NAME,
import_example_gen.outputs['examples'].type_name)
artifact_collection = import_example_gen.outputs['examples'].get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
def setUp(self):
super(ExecutorTest, self).setUp()
input_data_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'testdata')
# Create input dict.
input_base = standard_artifacts.ExternalArtifact()
input_base.uri = os.path.join(input_data_dir, 'external')
self._input_dict = {INPUT_KEY: [input_base]}
def testConstruct(self):
input_base = standard_artifacts.ExternalArtifact()
csv_example_gen = component.CsvExampleGen(
input_base=channel_utils.as_channel([input_base]))
self.assertEqual('ExamplesPath',
csv_example_gen.outputs['examples'].type_name)
artifact_collection = csv_example_gen.outputs['examples'].get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
def testConstruct(self):
input_base = standard_artifacts.ExternalArtifact()
csv_example_gen = component.CsvExampleGen(
input=channel_utils.as_channel([input_base]))
self.assertEqual(standard_artifacts.Examples.TYPE_NAME,
csv_example_gen.outputs['examples'].type_name)
artifact_collection = csv_example_gen.outputs['examples'].get()
self.assertEqual(1, len(artifact_collection))
self.assertEqual(['train', 'eval'],
artifact_utils.decode_split_names(
artifact_collection[0].split_names))
def testConstructCustomExecutor(self):
input_base = standard_artifacts.ExternalArtifact()
example_gen = component.FileBasedExampleGen(
input_base=channel_utils.as_channel([input_base]),
custom_executor_spec=executor_spec.ExecutorClassSpec(
TestExampleGenExecutor))
self.assertEqual(driver.Driver, example_gen.driver_class)
self.assertEqual('ExamplesPath', example_gen.outputs['examples'].type_name)
artifact_collection = example_gen.outputs['examples'].get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
def testConstructSubclassFileBased(self):
input_base = standard_artifacts.ExternalArtifact()
example_gen = TestFileBasedExampleGenComponent(
input_base=channel_utils.as_channel([input_base]))
self.assertIn('input_base', example_gen.inputs.get_all())
self.assertEqual(driver.Driver, example_gen.driver_class)
self.assertEqual('ExamplesPath', example_gen.outputs['examples'].type_name)
self.assertIsNone(example_gen.exec_properties.get('custom_config'))
artifact_collection = example_gen.outputs['examples'].get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
def setUp(self):
super(BaseComponentWithPipelineParamTest, self).setUp()
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
example_gen_output_name = runtime_string_parameter.RuntimeStringParameter(
name='example-gen-output-name', default='default-to-be-discarded')
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]),
output_config=example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(
name=example_gen_output_name, hash_buckets=10)
])))
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
pipeline = tfx_pipeline.Pipeline(
pipeline_name=self._test_pipeline_name,
pipeline_root='test_pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
with dsl.Pipeline('test_pipeline'):
self.example_gen = base_component.BaseComponent(
component=example_gen,
component_launcher_class=in_process_component_launcher.
InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None)
self.statistics_gen = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher.
InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
)
self.tfx_example_gen = example_gen
self.tfx_statistics_gen = statistics_gen
def external_input(uri: Text) -> types.Channel:
"""Helper function to declare external input.
Args:
uri: external path.
Returns:
input channel.
"""
instance = standard_artifacts.ExternalArtifact()
instance.uri = uri
return channel_utils.as_channel([instance])
def external_input(uri: Any) -> types.Channel:
"""Helper function to declare external input.
Args:
uri: external path, can be RuntimeParameter
Returns:
input channel.
"""
instance = standard_artifacts.ExternalArtifact()
instance.uri = str(uri)
return channel_utils.as_channel([instance])
def testConstructWithCustomConfig(self):
input_base = standard_artifacts.ExternalArtifact()
custom_config = example_gen_pb2.CustomConfig(custom_config=any_pb2.Any())
example_gen = component.FileBasedExampleGen(
input_base=channel_utils.as_channel([input_base]),
custom_config=custom_config,
custom_executor_spec=executor_spec.ExecutorClassSpec(
TestExampleGenExecutor))
stored_custom_config = example_gen_pb2.CustomConfig()
json_format.Parse(example_gen.exec_properties['custom_config'],
stored_custom_config)
self.assertEqual(custom_config, stored_custom_config)
def testConstructWithInputConfig(self):
input_base = standard_artifacts.ExternalArtifact()
example_gen = TestFileBasedExampleGenComponent(
input_base=channel_utils.as_channel([input_base]),
input_config=example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train/*'),
example_gen_pb2.Input.Split(name='eval', pattern='eval/*'),
example_gen_pb2.Input.Split(name='test', pattern='test/*')
]))
self.assertEqual('ExamplesPath', example_gen.outputs['examples'].type_name)
artifact_collection = example_gen.outputs['examples'].get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
self.assertEqual('test', artifact_collection[2].split)
def testConstructWithInputConfig(self):
input_base = standard_artifacts.ExternalArtifact()
example_gen = TestFileBasedExampleGenComponent(
input=channel_utils.as_channel([input_base]),
input_config=example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='train', pattern='train/*'),
example_gen_pb2.Input.Split(name='eval', pattern='eval/*'),
example_gen_pb2.Input.Split(name='test', pattern='test/*')
]))
self.assertEqual(standard_artifacts.Examples.TYPE_NAME,
example_gen.outputs['examples'].type_name)
artifact_collection = example_gen.outputs['examples'].get()
self.assertEqual(1, len(artifact_collection))
self.assertEqual(['train', 'eval', 'test'],
artifact_utils.decode_split_names(
artifact_collection[0].split_names))
def testConstructWithOutputConfig(self):
input_base = standard_artifacts.ExternalArtifact()
example_gen = TestFileBasedExampleGenComponent(
input_base=channel_utils.as_channel([input_base]),
output_config=example_gen_pb2.
Output(split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(name='train',
hash_buckets=2),
example_gen_pb2.SplitConfig.Split(name='eval', hash_buckets=1),
example_gen_pb2.SplitConfig.Split(name='test', hash_buckets=1)
])))
self.assertEqual('ExamplesPath',
example_gen.outputs.examples.type_name)
artifact_collection = example_gen.outputs.examples.get()
self.assertEqual('train', artifact_collection[0].split)
self.assertEqual('eval', artifact_collection[1].split)
self.assertEqual('test', artifact_collection[2].split)
def testEnableCache(self):
input_base = standard_artifacts.ExternalArtifact()
custom_config = example_gen_pb2.CustomConfig(
custom_config=any_pb2.Any())
example_gen_1 = component.FileBasedExampleGen(
input=channel_utils.as_channel([input_base]),
custom_config=custom_config,
custom_executor_spec=executor_spec.ExecutorClassSpec(
TestExampleGenExecutor))
self.assertEqual(None, example_gen_1.enable_cache)
example_gen_2 = component.FileBasedExampleGen(
input=channel_utils.as_channel([input_base]),
custom_config=custom_config,
custom_executor_spec=executor_spec.ExecutorClassSpec(
TestExampleGenExecutor),
enable_cache=True)
self.assertEqual(True, example_gen_2.enable_cache)
def __init__(self,
input_data: types.Channel = None,
output_data: Optional[types.Channel] = None,
instance_name: Optional[Text] = None):
"""Construct a HelloComponent.
Args:
input_data: A Channel of type `standard_artifacts.InferenceResult`.
output_data: A Channel of type `standard_artifacts.ExternalArtifact`.
instance_name: Optional unique name. Necessary if multiple Hello components are
declared in the same pipeline.
"""
if not output_data:
examples_artifact = standard_artifacts.ExternalArtifact()
output_data = channel_utils.as_channel([examples_artifact])
spec = HelloComponentSpec(input_data=input_data,
output_data=output_data)
super(HelloComponent, self).__init__(spec=spec,
instance_name=instance_name)
def CsvExampleGen_GCS( #
# Inputs
#input_base_path: InputPath('ExternalPath'),
input_base_path:
'ExternalPath', # A Channel of 'ExternalPath' type, which includes one artifact whose uri is an external directory with csv files inside (required).
# Outputs
#example_artifacts_path: OutputPath('ExamplesPath'),
example_artifacts_path: 'ExamplesPath',
# Execution properties
#input_config_splits: {'List' : {'item_type': 'ExampleGen.Input.Split'}},
input_config:
'ExampleGen.Input' = None, # = '{"splits": []}', # JSON-serialized example_gen_pb2.Input instance, providing input configuration. If unset, the files under input_base will be treated as a single split.
#output_config_splits: {'List' : {'item_type': 'ExampleGen.SplitConfig'}},
output_config:
'ExampleGen.Output' = None, # = '{"splitConfig": {"splits": []}}', # JSON-serialized example_gen_pb2.Output instance, providing output configuration. If unset, default splits will be 'train' and 'eval' with size 2:1.
#custom_config: 'ExampleGen.CustomConfig' = None,
) -> NamedTuple('Outputs', [
('example_artifacts', 'ExamplesPath'),
]):
"""Executes the CsvExampleGen component.
Args:
input_base: A Channel of 'ExternalPath' type, which includes one artifact
whose uri is an external directory with csv files inside (required).
input_config: An example_gen_pb2.Input instance, providing input
configuration. If unset, the files under input_base will be treated as a
single split.
output_config: An example_gen_pb2.Output instance, providing output
configuration. If unset, default splits will be 'train' and 'eval' with
size 2:1.
??? input: Forwards compatibility alias for the 'input_base' argument.
Returns:
example_artifacts: Artifact of type 'ExamplesPath' for output train and
eval examples.
"""
import json
import os
from google.protobuf import json_format
from tfx.components.example_gen.csv_example_gen.component import CsvExampleGen
from tfx.proto import example_gen_pb2
from tfx.types import standard_artifacts
from tfx.types import channel_utils
# Create input dict.
input_base = standard_artifacts.ExternalArtifact()
input_base.uri = input_base_path
input_base_channel = channel_utils.as_channel([input_base])
input_config_obj = None
if input_config:
input_config_obj = example_gen_pb2.Input()
json_format.Parse(input_config, input_config_obj)
output_config_obj = None
if output_config:
output_config_obj = example_gen_pb2.Output()
json_format.Parse(output_config, output_config_obj)
component_class_instance = CsvExampleGen(
input=input_base_channel,
input_config=input_config_obj,
output_config=output_config_obj,
)
# component_class_instance.inputs/outputs are wrappers that do not behave like real dictionaries. The underlying dict can be accessed using .get_all()
# Channel artifacts can be accessed by calling .get()
input_dict = {
name: channel.get()
for name, channel in component_class_instance.inputs.get_all().items()
}
output_dict = {
name: channel.get()
for name, channel in
component_class_instance.outputs.get_all().items()
}
exec_properties = component_class_instance.exec_properties
# Generating paths for output artifacts
for output_artifact in output_dict['examples']:
output_artifact.uri = example_artifacts_path
if output_artifact.split:
output_artifact.uri = os.path.join(output_artifact.uri,
output_artifact.split)
print('component instance: ' + str(component_class_instance))
executor = CsvExampleGen.EXECUTOR_SPEC.executor_class()
executor.Do(
input_dict=input_dict,
output_dict=output_dict,
exec_properties=exec_properties,
)
return (example_artifacts_path, )
def test_prepare_input_for_processing(self):
# Create input splits.
test_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
input_base_path = os.path.join(test_dir, 'input_base')
split1 = os.path.join(input_base_path, 'split1', 'data')
io_utils.write_string_file(split1, 'testing')
os.utime(split1, (0, 1))
split2 = os.path.join(input_base_path, 'split2', 'data')
io_utils.write_string_file(split2, 'testing2')
os.utime(split2, (0, 3))
# Mock metadata.
mock_metadata = tf.test.mock.Mock()
example_gen_driver = driver.Driver(mock_metadata)
# Mock artifact.
artifacts = []
for i in [4, 3, 2, 1]:
artifact = metadata_store_pb2.Artifact()
artifact.id = i
artifact.uri = input_base_path
# Only odd ids will be matched
if i % 2 == 1:
artifact.custom_properties[
'input_fingerprint'].string_value = 'split:s1,num_files:1,total_bytes:7,xor_checksum:1,sum_checksum:1\nsplit:s2,num_files:1,total_bytes:8,xor_checksum:3,sum_checksum:3'
else:
artifact.custom_properties[
'input_fingerprint'].string_value = 'not_match'
artifacts.append(artifact)
# Create input dict.
input_base = standard_artifacts.ExternalArtifact()
input_base.uri = input_base_path
input_dict = {'input_base': [input_base]}
# Create exec proterties.
exec_properties = {
'input_config':
json_format.MessageToJson(
example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name='s1', pattern='split1/*'),
example_gen_pb2.Input.Split(name='s2', pattern='split2/*')
])),
}
# Cache not hit.
mock_metadata.get_artifacts_by_uri.return_value = [artifacts[0]]
mock_metadata.publish_artifacts.return_value = [artifacts[3]]
updated_input_dict = example_gen_driver._prepare_input_for_processing(
copy.deepcopy(input_dict), exec_properties)
self.assertEqual(1, len(updated_input_dict))
self.assertEqual(1, len(updated_input_dict['input_base']))
updated_input_base = updated_input_dict['input_base'][0]
self.assertEqual(1, updated_input_base.id)
self.assertEqual(input_base_path, updated_input_base.uri)
# Cache hit.
mock_metadata.get_artifacts_by_uri.return_value = artifacts
mock_metadata.publish_artifacts.return_value = []
updated_input_dict = example_gen_driver._prepare_input_for_processing(
copy.deepcopy(input_dict), exec_properties)
self.assertEqual(1, len(updated_input_dict))
self.assertEqual(1, len(updated_input_dict['input_base']))
updated_input_base = updated_input_dict['input_base'][0]
self.assertEqual(3, updated_input_base.id)
self.assertEqual(input_base_path, updated_input_base.uri)
