def test_build_all_signature_defs_legacy_input_fn_not_supported(self):
"""Tests that legacy input_fn returning (features, labels) raises error.
serving_input_fn must return InputFnOps including a default input
alternative.
"""
input_features = constant_op.constant(["10"])
input_ops = ({"features": input_features}, None)
input_alternatives, _ = (
saved_model_export_utils.get_input_alternatives(input_ops))
output_1 = constant_op.constant(["1"])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
provided_output_alternatives = {
"head-1": (constants.ProblemType.LINEAR_REGRESSION, {
"some_output_1": output_1
}),
"head-2": (constants.ProblemType.CLASSIFICATION, {
"some_output_2": output_2
}),
"head-3": (constants.ProblemType.UNSPECIFIED, {
"some_output_3": output_3
}),
}
model_fn_ops = model_fn.ModelFnOps(
model_fn.ModeKeys.INFER,
predictions={"some_output": constant_op.constant(["4"])},
output_alternatives=provided_output_alternatives)
output_alternatives, _ = (saved_model_export_utils.get_output_alternatives(
model_fn_ops, "head-1"))
with self.assertRaisesRegexp(
ValueError, "A default input_alternative must be provided"):
saved_model_export_utils.build_all_signature_defs(
input_alternatives, output_alternatives, "head-1")
def test_build_all_signature_defs_legacy_input_fn_not_supported(self):
"""Tests that legacy input_fn returning (features, labels) raises error.
serving_input_fn must return InputFnOps including a default input
alternative.
"""
input_features = constant_op.constant(["10"])
input_ops = ({"features": input_features}, None)
input_alternatives, _ = (
saved_model_export_utils.get_input_alternatives(input_ops))
output_1 = constant_op.constant(["1"])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
provided_output_alternatives = {
"head-1": (constants.ProblemType.LINEAR_REGRESSION, {
"some_output_1": output_1
}),
"head-2": (constants.ProblemType.CLASSIFICATION, {
"some_output_2": output_2
}),
"head-3": (constants.ProblemType.UNSPECIFIED, {
"some_output_3": output_3
}),
}
model_fn_ops = model_fn.ModelFnOps(
model_fn.ModeKeys.INFER,
predictions={"some_output": constant_op.constant(["4"])},
output_alternatives=provided_output_alternatives)
output_alternatives, _ = (saved_model_export_utils.get_output_alternatives(
model_fn_ops, "head-1"))
with self.assertRaisesRegexp(
ValueError, "A default input_alternative must be provided"):
saved_model_export_utils.build_all_signature_defs(
input_alternatives, output_alternatives, "head-1")
def test_build_all_signature_defs(self):
input_features = constant_op.constant(["10"])
input_example = constant_op.constant(["11"])
input_ops = input_fn_utils.InputFnOps({
"features": input_features
}, None, {"default input": input_example})
input_alternatives, _ = (
saved_model_export_utils.get_input_alternatives(input_ops))
output_1 = constant_op.constant(["1"])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
provided_output_alternatives = {
"head-1": (constants.ProblemType.LINEAR_REGRESSION, {
"some_output_1": output_1
}),
"head-2": (constants.ProblemType.CLASSIFICATION, {
"some_output_2": output_2
}),
"head-3": (constants.ProblemType.UNSPECIFIED, {
"some_output_3": output_3
}),
}
model_fn_ops = model_fn.ModelFnOps(
model_fn.ModeKeys.INFER,
predictions={"some_output": constant_op.constant(["4"])},
output_alternatives=provided_output_alternatives)
output_alternatives, _ = (saved_model_export_utils.get_output_alternatives(
model_fn_ops, "head-1"))
signature_defs = saved_model_export_utils.build_all_signature_defs(
input_alternatives, output_alternatives, "head-1")
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(input_example,
output_1),
"default_input_alternative:head-1":
signature_def_utils.regression_signature_def(input_example,
output_1),
"default_input_alternative:head-2":
signature_def_utils.classification_signature_def(input_example,
output_2, None),
"default_input_alternative:head-3":
signature_def_utils.predict_signature_def({
"input": input_example
}, {"output": output_3}),
# "features_input_alternative:head-1":
# signature_def_utils.regression_signature_def(input_features,
# output_1),
# "features_input_alternative:head-2":
# signature_def_utils.classification_signature_def(input_features,
# output_2, None),
# "features_input_alternative:head-3":
# signature_def_utils.predict_signature_def({
# "input": input_features
# }, {"output": output_3}),
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs(self):
input_features = constant_op.constant(["10"])
input_example = constant_op.constant(["11"])
input_ops = input_fn_utils.InputFnOps({"features": input_features},
None,
{"default input": input_example})
input_alternatives, _ = (
saved_model_export_utils.get_input_alternatives(input_ops))
output_1 = constant_op.constant(["1"])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
provided_output_alternatives = {
"head-1": (constants.ProblemType.LINEAR_REGRESSION, {
"some_output_1": output_1
}),
"head-2": (constants.ProblemType.CLASSIFICATION, {
"some_output_2": output_2
}),
"head-3": (constants.ProblemType.UNSPECIFIED, {
"some_output_3": output_3
}),
}
model_fn_ops = model_fn.ModelFnOps(
model_fn.ModeKeys.INFER,
predictions={"some_output": constant_op.constant(["4"])},
output_alternatives=provided_output_alternatives)
output_alternatives, _ = (
saved_model_export_utils.get_output_alternatives(
model_fn_ops, "head-1"))
signature_defs = saved_model_export_utils.build_all_signature_defs(
input_alternatives, output_alternatives, "head-1")
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
input_example, output_1),
"default_input_alternative:head-1":
signature_def_utils.regression_signature_def(
input_example, output_1),
"default_input_alternative:head-2":
signature_def_utils.classification_signature_def(
input_example, output_2, None),
"default_input_alternative:head-3":
signature_def_utils.predict_signature_def({"input": input_example},
{"output": output_3}),
# "features_input_alternative:head-1":
# signature_def_utils.regression_signature_def(input_features,
# output_1),
# "features_input_alternative:head-2":
# signature_def_utils.classification_signature_def(input_features,
# output_2, None),
# "features_input_alternative:head-3":
# signature_def_utils.predict_signature_def({
# "input": input_features
# }, {"output": output_3}),
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_get_input_alternatives(self):
input_ops = input_fn_utils.InputFnOps("bogus features dict", None,
"bogus default input dict")
input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
input_ops)
self.assertEqual(input_alternatives[
saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY],
"bogus default input dict")
def test_get_input_alternatives(self):
input_ops = input_fn_utils.InputFnOps("bogus features dict", None,
"bogus default input dict")
input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
input_ops)
self.assertEqual(input_alternatives[
saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY],
"bogus default input dict")
def __init__(self,
estimator,
prediction_input_fn,
input_alternative_key=None,
output_alternative_key=None,
graph=None,
config=None):
"""Initialize a `ContribEstimatorPredictor`.
Args:
estimator: an instance of `tf.contrib.learn.Estimator`.
prediction_input_fn: a function that takes no arguments and returns an
instance of `InputFnOps`.
input_alternative_key: Optional. Specify the input alternative used for
prediction.
output_alternative_key: Specify the output alternative used for
prediction. Not needed for single-headed models but required for
multi-headed models.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
config: `ConfigProto` proto used to configure the session.
"""
self._graph = graph or ops.Graph()
with self._graph.as_default():
input_fn_ops = prediction_input_fn()
# pylint: disable=protected-access
model_fn_ops = estimator._get_predict_ops(input_fn_ops.features)
# pylint: enable=protected-access
checkpoint_path = checkpoint_management.latest_checkpoint(
estimator.model_dir)
self._session = monitored_session.MonitoredSession(
session_creator=monitored_session.ChiefSessionCreator(
config=config,
checkpoint_filename_with_path=checkpoint_path))
input_alternative_key = (
input_alternative_key
or saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY)
input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
input_fn_ops)
self._feed_tensors = input_alternatives[input_alternative_key]
(output_alternatives, output_alternative_key
) = saved_model_export_utils.get_output_alternatives(
model_fn_ops, output_alternative_key)
_, fetch_tensors = output_alternatives[output_alternative_key]
self._fetch_tensors = fetch_tensors
def __init__(self,
estimator,
prediction_input_fn,
input_alternative_key=None,
output_alternative_key=None,
graph=None,
config=None):
"""Initialize a `ContribEstimatorPredictor`.
Args:
estimator: an instance of `tf.contrib.learn.Estimator`.
prediction_input_fn: a function that takes no arguments and returns an
instance of `InputFnOps`.
input_alternative_key: Optional. Specify the input alternative used for
prediction.
output_alternative_key: Specify the output alternative used for
prediction. Not needed for single-headed models but required for
multi-headed models.
graph: Optional. The Tensorflow `graph` in which prediction should be
done.
config: `ConfigProto` proto used to configure the session.
"""
self._graph = graph or ops.Graph()
with self._graph.as_default():
input_fn_ops = prediction_input_fn()
# pylint: disable=protected-access
model_fn_ops = estimator._get_predict_ops(input_fn_ops.features)
# pylint: enable=protected-access
checkpoint_path = checkpoint_management.latest_checkpoint(
estimator.model_dir)
self._session = monitored_session.MonitoredSession(
session_creator=monitored_session.ChiefSessionCreator(
config=config,
checkpoint_filename_with_path=checkpoint_path))
input_alternative_key = (
input_alternative_key or
saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY)
input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
input_fn_ops)
self._feed_tensors = input_alternatives[input_alternative_key]
(output_alternatives,
output_alternative_key) = saved_model_export_utils.get_output_alternatives(
model_fn_ops, output_alternative_key)
_, fetch_tensors = output_alternatives[output_alternative_key]
self._fetch_tensors = fetch_tensors
