Remove deprecated_graph_mode_only annotations from model_utils:export_test.
One of the tests actually did not need the annotation. The others all used APIs that found their way to build_tensor_info, which is a graph-mode only function, and were forced to run in graph mode. PiperOrigin-RevId: 323835871 Change-Id: Ie97b638db3f3205f55be268bf96af32009d0103a
This commit is contained in:
Cesar Crusius
TensorFlower Gardener
parent
532e00e6bc
commit
d85aceb862
@ -103,6 +103,7 @@ py_strict_test(
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:constant_op",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python/saved_model:signature_constants",
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"//tensorflow/python/saved_model:signature_def_utils",
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@ -24,6 +24,7 @@ import time
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.platform import test
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@ -36,136 +37,147 @@ from tensorflow.python.saved_model.model_utils.mode_keys import KerasModeKeys
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class ExportTest(test_util.TensorFlowTestCase):
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@test_util.deprecated_graph_mode_only
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def test_build_all_signature_defs_without_receiver_alternatives(self):
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receiver_tensor = array_ops.placeholder(dtypes.string)
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2": export_output.ClassificationOutput(classes=output_2),
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"head-3": export_output.PredictOutput(outputs={
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"some_output_3": output_3
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}),
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}
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# Force the test to run in graph mode.
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# This tests a deprecated v1 API that depends on graph-only functions such
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# as build_tensor_info.
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with ops.Graph().as_default():
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receiver_tensor = array_ops.placeholder(dtypes.string)
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2":
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export_output.ClassificationOutput(classes=output_2),
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"head-3":
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export_output.PredictOutput(outputs={"some_output_3": output_3}),
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}
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs)
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(receiver_tensor,
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output_1),
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"head-2":
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signature_def_utils.classification_signature_def(receiver_tensor,
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output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def({
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"input": receiver_tensor
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}, {"some_output_3": output_3})
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}
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(
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receiver_tensor, output_1),
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"head-2":
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signature_def_utils.classification_signature_def(
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receiver_tensor, output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensor}, {"some_output_3": output_3})
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}
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self.assertDictEqual(expected_signature_defs, signature_defs)
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self.assertDictEqual(expected_signature_defs, signature_defs)
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@test_util.deprecated_graph_mode_only
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def test_build_all_signature_defs_with_dict_alternatives(self):
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receiver_tensor = array_ops.placeholder(dtypes.string)
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receiver_tensors_alternative_1 = {
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"foo": array_ops.placeholder(dtypes.int64),
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"bar": array_ops.sparse_placeholder(dtypes.float32)}
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receiver_tensors_alternatives = {"other": receiver_tensors_alternative_1}
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2": export_output.ClassificationOutput(classes=output_2),
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"head-3": export_output.PredictOutput(outputs={
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"some_output_3": output_3
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}),
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}
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# Force the test to run in graph mode.
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# This tests a deprecated v1 API that depends on graph-only functions such
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# as build_tensor_info.
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with ops.Graph().as_default():
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receiver_tensor = array_ops.placeholder(dtypes.string)
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receiver_tensors_alternative_1 = {
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"foo": array_ops.placeholder(dtypes.int64),
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"bar": array_ops.sparse_placeholder(dtypes.float32)
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}
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receiver_tensors_alternatives = {"other": receiver_tensors_alternative_1}
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2":
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export_output.ClassificationOutput(classes=output_2),
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"head-3":
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export_output.PredictOutput(outputs={"some_output_3": output_3}),
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}
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, receiver_tensors_alternatives)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, receiver_tensors_alternatives)
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(
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receiver_tensor, output_1),
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"head-2":
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signature_def_utils.classification_signature_def(
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receiver_tensor, output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensor}, {"some_output_3": output_3}),
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"other:head-3":
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signature_def_utils.predict_signature_def(
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receiver_tensors_alternative_1, {"some_output_3": output_3})
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(
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receiver_tensor, output_1),
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"head-2":
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signature_def_utils.classification_signature_def(
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receiver_tensor, output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensor}, {"some_output_3": output_3}),
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"other:head-3":
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signature_def_utils.predict_signature_def(
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receiver_tensors_alternative_1, {"some_output_3": output_3})
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# Note that the alternatives 'other:serving_default' and
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# 'other:head-2' are invalid, because regression and classification
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# signatures must take a single string input. Here we verify that
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# these invalid signatures are not included in the export_utils.
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}
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# Note that the alternatives 'other:serving_default' and
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# 'other:head-2' are invalid, because regression and classification
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# signatures must take a single string input. Here we verify that
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# these invalid signatures are not included in the export_utils.
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}
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self.assertDictEqual(expected_signature_defs, signature_defs)
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self.assertDictEqual(expected_signature_defs, signature_defs)
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@test_util.deprecated_graph_mode_only
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def test_build_all_signature_defs_with_single_alternatives(self):
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receiver_tensor = array_ops.placeholder(dtypes.string)
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receiver_tensors_alternative_1 = array_ops.placeholder(dtypes.int64)
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receiver_tensors_alternative_2 = array_ops.sparse_placeholder(
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dtypes.float32)
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# Note we are passing single Tensors as values of
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# receiver_tensors_alternatives, where normally that is a dict.
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# In this case a dict will be created using the default receiver tensor
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# name "input".
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receiver_tensors_alternatives = {"other1": receiver_tensors_alternative_1,
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"other2": receiver_tensors_alternative_2}
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2": export_output.ClassificationOutput(classes=output_2),
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"head-3": export_output.PredictOutput(outputs={
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"some_output_3": output_3
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}),
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}
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# Force the test to run in graph mode.
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# This tests a deprecated v1 API that depends on graph-only functions such
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# as build_tensor_info.
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with ops.Graph().as_default():
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receiver_tensor = array_ops.placeholder(dtypes.string)
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receiver_tensors_alternative_1 = array_ops.placeholder(dtypes.int64)
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receiver_tensors_alternative_2 = array_ops.sparse_placeholder(
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dtypes.float32)
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# Note we are passing single Tensors as values of
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# receiver_tensors_alternatives, where normally that is a dict.
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# In this case a dict will be created using the default receiver tensor
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# name "input".
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receiver_tensors_alternatives = {
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"other1": receiver_tensors_alternative_1,
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"other2": receiver_tensors_alternative_2
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}
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output_1 = constant_op.constant([1.])
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output_2 = constant_op.constant(["2"])
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output_3 = constant_op.constant(["3"])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.RegressionOutput(value=output_1),
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"head-2":
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export_output.ClassificationOutput(classes=output_2),
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"head-3":
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export_output.PredictOutput(outputs={"some_output_3": output_3}),
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}
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, receiver_tensors_alternatives)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, receiver_tensors_alternatives)
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(
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receiver_tensor, output_1),
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"head-2":
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signature_def_utils.classification_signature_def(
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receiver_tensor, output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensor}, {"some_output_3": output_3}),
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"other1:head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensors_alternative_1},
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{"some_output_3": output_3}),
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"other2:head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensors_alternative_2},
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{"some_output_3": output_3})
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.regression_signature_def(
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receiver_tensor, output_1),
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"head-2":
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signature_def_utils.classification_signature_def(
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receiver_tensor, output_2, None),
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"head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensor}, {"some_output_3": output_3}),
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"other1:head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensors_alternative_1},
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{"some_output_3": output_3}),
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"other2:head-3":
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signature_def_utils.predict_signature_def(
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{"input": receiver_tensors_alternative_2},
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{"some_output_3": output_3})
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# Note that the alternatives 'other:serving_default' and 'other:head-2'
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# are invalid, because regression and classification signatures must take
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# a single string input. Here we verify that these invalid signatures
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# are not included in the export_utils.
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}
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# Note that the alternatives 'other:serving_default' and
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# 'other:head-2' are invalid, because regression and classification
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# signatures must take a single string input. Here we verify that
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# these invalid signatures are not included in the export_utils.
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}
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self.assertDictEqual(expected_signature_defs, signature_defs)
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self.assertDictEqual(expected_signature_defs, signature_defs)
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def test_build_all_signature_defs_export_outputs_required(self):
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receiver_tensor = constant_op.constant(["11"])
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@ -210,37 +222,42 @@ class ExportTest(test_util.TensorFlowTestCase):
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self.assertEqual(tmp_export_dir,
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os.path.join(b"tmp", b"export", b"temp-1576013284"))
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@test_util.deprecated_graph_mode_only
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def test_build_all_signature_defs_serving_only(self):
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receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
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output_1 = constant_op.constant([1.])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.PredictOutput(outputs=output_1),
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"train": export_output.TrainOutput(loss=output_1),
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}
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# Force the test to run in graph mode.
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# This tests a deprecated v1 API that depends on graph-only functions such
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# as build_tensor_info.
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with ops.Graph().as_default():
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receiver_tensor = {"input": array_ops.placeholder(dtypes.string)}
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output_1 = constant_op.constant([1.])
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export_outputs = {
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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export_output.PredictOutput(outputs=output_1),
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"train":
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export_output.TrainOutput(loss=output_1),
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}
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs)
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expected_signature_defs = {
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"serving_default": signature_def_utils.predict_signature_def(
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receiver_tensor, {"output": output_1})
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}
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expected_signature_defs = {
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"serving_default":
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signature_def_utils.predict_signature_def(receiver_tensor,
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{"output": output_1})
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}
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self.assertDictEqual(expected_signature_defs, signature_defs)
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self.assertDictEqual(expected_signature_defs, signature_defs)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, serving_only=False)
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signature_defs = export_utils.build_all_signature_defs(
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receiver_tensor, export_outputs, serving_only=False)
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expected_signature_defs.update({
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"train": signature_def_utils.supervised_train_signature_def(
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receiver_tensor, loss={"loss": output_1})
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})
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expected_signature_defs.update({
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"train":
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signature_def_utils.supervised_train_signature_def(
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receiver_tensor, loss={"loss": output_1})
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})
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self.assertDictEqual(expected_signature_defs, signature_defs)
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self.assertDictEqual(expected_signature_defs, signature_defs)
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@test_util.deprecated_graph_mode_only
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def test_export_outputs_for_mode(self):
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predictions = {"predictions": constant_op.constant([1.])}
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loss = {"loss": constant_op.constant([2.])}
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