def test_build_all_signature_defs_serving_only(self):
receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
output_1 = constant_op.constant([1.])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(outputs=output_1),
"train": export_output.TrainOutput(loss=output_1),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default": signature_def_utils.predict_signature_def(
receiver_tensor, {"output": output_1})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, serving_only=False)
expected_signature_defs.update({
"train": signature_def_utils.supervised_train_signature_def(
receiver_tensor, loss={"loss": output_1})
})
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_serving_only(self):
receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
output_1 = constant_op.constant([1.])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(outputs=output_1),
"train": export_output.TrainOutput(loss=output_1),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default": signature_def_utils.predict_signature_def(
receiver_tensor, {"output": output_1})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, serving_only=False)
expected_signature_defs.update({
"train": signature_def_utils.supervised_train_signature_def(
receiver_tensor, loss={"loss": output_1})
})
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_serving_only(self):
# Force the test to run in graph mode.
# This tests a deprecated v1 API that depends on graph-only functions such
# as build_tensor_info.
with ops.Graph().as_default():
receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
output_1 = constant_op.constant([1.])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(outputs=output_1),
"train":
export_output.TrainOutput(loss=output_1),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default":
signature_def_utils.predict_signature_def(
receiver_tensor, {"output": output_1})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, serving_only=False)
expected_signature_defs.update({
"train":
signature_def_utils.supervised_train_signature_def(
receiver_tensor, loss={"loss": output_1})
})
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_export_outputs_required(self):
receiver_tensor = constant_op.constant(["11"])
with self.assertRaises(ValueError) as e:
export_utils.build_all_signature_defs(receiver_tensor, None)
self.assertTrue(str(e.exception).startswith(
"export_outputs must be a dict"))
def test_build_all_signature_defs_export_outputs_required(self):
receiver_tensor = constant_op.constant(["11"])
with self.assertRaises(ValueError) as e:
export_utils.build_all_signature_defs(receiver_tensor, None)
self.assertTrue(str(e.exception).startswith(
"export_outputs must be a dict"))
def test_build_all_signature_defs_without_receiver_alternatives(self):
receiver_tensor = array_ops.placeholder(dtypes.string)
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2": export_output.ClassificationOutput(classes=output_2),
"head-3": export_output.PredictOutput(outputs={
"some_output_3": output_3
}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(receiver_tensor,
output_1),
"head-2":
signature_def_utils.classification_signature_def(receiver_tensor,
output_2, None),
"head-3":
signature_def_utils.predict_signature_def({
"input": receiver_tensor
}, {"some_output_3": output_3})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_without_receiver_alternatives(self):
receiver_tensor = array_ops.placeholder(dtypes.string)
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2": export_output.ClassificationOutput(classes=output_2),
"head-3": export_output.PredictOutput(outputs={
"some_output_3": output_3
}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(receiver_tensor,
output_1),
"head-2":
signature_def_utils.classification_signature_def(receiver_tensor,
output_2, None),
"head-3":
signature_def_utils.predict_signature_def({
"input": receiver_tensor
}, {"some_output_3": output_3})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_without_receiver_alternatives(self):
# Force the test to run in graph mode.
# This tests a deprecated v1 API that depends on graph-only functions such
# as build_tensor_info.
with ops.Graph().as_default():
receiver_tensor = array_ops.placeholder(dtypes.string)
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2":
export_output.ClassificationOutput(classes=output_2),
"head-3":
export_output.PredictOutput(
outputs={"some_output_3": output_3}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor, output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor, output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor}, {"some_output_3": output_3})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_with_single_alternatives(self):
# Force the test to run in graph mode.
# This tests a deprecated v1 API that depends on graph-only functions such
# as build_tensor_info.
with ops.Graph().as_default():
receiver_tensor = array_ops.placeholder(dtypes.string)
receiver_tensors_alternative_1 = array_ops.placeholder(
dtypes.int64)
receiver_tensors_alternative_2 = array_ops.sparse_placeholder(
dtypes.float32)
# Note we are passing single Tensors as values of
# receiver_tensors_alternatives, where normally that is a dict.
# In this case a dict will be created using the default receiver tensor
# name "input".
receiver_tensors_alternatives = {
"other1": receiver_tensors_alternative_1,
"other2": receiver_tensors_alternative_2
}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2":
export_output.ClassificationOutput(classes=output_2),
"head-3":
export_output.PredictOutput(
outputs={"some_output_3": output_3}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, receiver_tensors_alternatives)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor, output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor, output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor}, {"some_output_3": output_3}),
"other1:head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensors_alternative_1},
{"some_output_3": output_3}),
"other2:head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensors_alternative_2},
{"some_output_3": output_3})
# Note that the alternatives 'other:serving_default' and
# 'other:head-2' are invalid, because regression and classification
# signatures must take a single string input. Here we verify that
# these invalid signatures are not included in the export_utils.
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_with_single_alternatives(self):
receiver_tensor = array_ops.placeholder(dtypes.string)
receiver_tensors_alternative_1 = array_ops.placeholder(dtypes.int64)
receiver_tensors_alternative_2 = array_ops.sparse_placeholder(
dtypes.float32)
# Note we are passing single Tensors as values of
# receiver_tensors_alternatives, where normally that is a dict.
# In this case a dict will be created using the default receiver tensor
# name "input".
receiver_tensors_alternatives = {"other1": receiver_tensors_alternative_1,
"other2": receiver_tensors_alternative_2}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2": export_output.ClassificationOutput(classes=output_2),
"head-3": export_output.PredictOutput(outputs={
"some_output_3": output_3
}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, receiver_tensors_alternatives)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor,
output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor,
output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor},
{"some_output_3": output_3}),
"other1:head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensors_alternative_1},
{"some_output_3": output_3}),
"other2:head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensors_alternative_2},
{"some_output_3": output_3})
# Note that the alternatives 'other:serving_default' and 'other:head-2'
# are invalid, because regession and classification signatures must take
# a single string input. Here we verify that these invalid signatures
# are not included in the export_utils.
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_with_dict_alternatives(self):
with context.graph_mode():
receiver_tensor = array_ops.placeholder(dtypes.string)
receiver_tensors_alternative_1 = {
"foo": array_ops.placeholder(dtypes.int64),
"bar": array_ops.sparse_placeholder(dtypes.float32)}
receiver_tensors_alternatives = {"other": receiver_tensors_alternative_1}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2": export_output.ClassificationOutput(classes=output_2),
"head-3": export_output.PredictOutput(outputs={
"some_output_3": output_3
}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, receiver_tensors_alternatives)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor,
output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor,
output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor},
{"some_output_3": output_3}),
"other:head-3":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_1,
{"some_output_3": output_3})
# Note that the alternatives 'other:serving_default' and
# 'other:head-2' are invalid, because regession and classification
# signatures must take a single string input. Here we verify that
# these invalid signatures are not included in the export_utils.
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_with_dict_alternatives(self):
receiver_tensor = array_ops.placeholder(dtypes.string)
receiver_tensors_alternative_1 = {
"foo": array_ops.placeholder(dtypes.int64),
"bar": array_ops.sparse_placeholder(dtypes.float32)}
receiver_tensors_alternatives = {"other": receiver_tensors_alternative_1}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2": export_output.ClassificationOutput(classes=output_2),
"head-3": export_output.PredictOutput(outputs={
"some_output_3": output_3
}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, receiver_tensors_alternatives)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor,
output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor,
output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor},
{"some_output_3": output_3}),
"other:head-3":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_1,
{"some_output_3": output_3})
# Note that the alternatives 'other:serving_default' and
# 'other:head-2' are invalid, because regession and classification
# signatures must take a single string input. Here we verify that
# these invalid signatures are not included in the export_utils.
}
self.assertDictEqual(expected_signature_defs, signature_defs)
def test_build_all_signature_defs_with_dict_alternatives(self):
# Force the test to run in graph mode.
# This tests a deprecated v1 API that depends on graph-only functions such
# as build_tensor_info.
with ops.Graph().as_default():
receiver_tensor = array_ops.placeholder(dtypes.string)
receiver_tensors_alternative_1 = {
"foo": array_ops.placeholder(dtypes.int64),
"bar": array_ops.sparse_placeholder(dtypes.float32)
}
unfed_input = array_ops.placeholder(dtypes.bool)
receiver_tensors_alternative_2 = {"unfed": unfed_input}
receiver_tensors_alternatives = {
"other": receiver_tensors_alternative_1,
"with_unfed_input": receiver_tensors_alternative_2
}
output_1 = constant_op.constant([1.])
output_2 = constant_op.constant(["2"])
output_3 = constant_op.constant(["3"])
output_4 = unfed_input
output_5 = math_ops.logical_not(unfed_input)
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.RegressionOutput(value=output_1),
"head-2":
export_output.ClassificationOutput(classes=output_2),
"head-3":
export_output.PredictOutput(outputs={"some_output_3": output_3}),
"head-4":
export_output.PredictOutput(outputs={"some_output_4": output_4}),
"head-5":
export_output.PredictOutput(outputs={"some_output_5": output_5}),
}
signature_defs = export_utils.build_all_signature_defs(
receiver_tensor, export_outputs, receiver_tensors_alternatives)
expected_signature_defs = {
"serving_default":
signature_def_utils.regression_signature_def(
receiver_tensor, output_1),
"head-2":
signature_def_utils.classification_signature_def(
receiver_tensor, output_2, None),
"head-3":
signature_def_utils.predict_signature_def(
{"input": receiver_tensor}, {"some_output_3": output_3}),
"other:head-3":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_1, {"some_output_3": output_3}),
# Note that the alternatives 'other:serving_default' and
# 'other:head-2' are invalid, because regression and classification
# signatures must take a single string input. Here we verify that
# these invalid signatures are not included in the export_utils.
# Similarly, we verify that 'head-4' and 'head-5', which depend on an
# input that is not being fed as a receiver tensor, are also omitted.
# All the three heads are present when that input is fed, however:
"with_unfed_input:head-3":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_2, {"some_output_3": output_3}),
"with_unfed_input:head-4":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_2, {"some_output_4": output_4}),
"with_unfed_input:head-5":
signature_def_utils.predict_signature_def(
receiver_tensors_alternative_2, {"some_output_5": output_5})
}
self.assertDictEqual(expected_signature_defs, signature_defs)
