def test_build_standardized_signature_def(self):
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.float32, 1, name="input-tensor-1")
}
output_tensors = {
"output-1":
array_ops.placeholder(dtypes.float32, 1, name="output-tensor-1")
}
problem_type = constants.ProblemType.LINEAR_REGRESSION
regression_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_regression_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype = types_pb2.DataType.Value("DT_FLOAT")
expected_regression_signature_def.inputs[
signature_constants.REGRESS_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(name="input-tensor-1:0",
dtype=dtype,
tensor_shape=shape))
expected_regression_signature_def.outputs[
signature_constants.REGRESS_OUTPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-1:0",
dtype=dtype,
tensor_shape=shape))
expected_regression_signature_def.method_name = (
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(regression_signature_def,
expected_regression_signature_def)
def test_build_standardized_signature_def(self):
input_tensors = {
"input-1":
array_ops.placeholder(
dtypes.float32, 1, name="input-tensor-1")
}
output_tensors = {
"output-1":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-1")
}
problem_type = constants.ProblemType.LINEAR_REGRESSION
regression_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_regression_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype = types_pb2.DataType.Value("DT_FLOAT")
expected_regression_signature_def.inputs[
signature_constants.REGRESS_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(
name="input-tensor-1:0", dtype=dtype, tensor_shape=shape))
expected_regression_signature_def.outputs[
signature_constants.REGRESS_OUTPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-1:0", dtype=dtype, tensor_shape=shape))
expected_regression_signature_def.method_name = (
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(regression_signature_def,
expected_regression_signature_def)
def test_build_standardized_signature_def_classification(self):
"""Tests classification with one output tensor."""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"output-1":
array_ops.placeholder(dtypes.string, 1, name="output-tensor-1")
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs[signature_constants.CLASSIFY_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(
name="input-tensor-1:0", dtype=dtype_string, tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_CLASSES].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-1:0",
dtype=dtype_string,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification4(self):
"""Tests classification without classes tensor."""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.float32, 1, name="input-tensor-1")
}
output_tensors = {
"probabilities":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-proba"),
"logits":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-logits-unused"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
expected_signature_def.inputs[signature_constants.CLASSIFY_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(
name="input-tensor-1:0", dtype=dtype_float, tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_SCORES].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-proba:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification2(self):
"""Tests multiple output tensors that include classes and probabilities."""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"classes":
array_ops.placeholder(dtypes.string,
1,
name="output-tensor-classes"),
# Will be used for CLASSIFY_OUTPUT_SCORES.
"probabilities":
array_ops.placeholder(dtypes.float32,
1,
name="output-tensor-proba"),
"logits":
array_ops.placeholder(dtypes.float32,
1,
name="output-tensor-logits-unused"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs[
signature_constants.CLASSIFY_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(name="input-tensor-1:0",
dtype=dtype_string,
tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_CLASSES].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-classes:0",
dtype=dtype_string,
tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_SCORES].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-proba:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification5(self):
"""Tests multiple output tensors that include integer classes and scores.
Integer classes are dropped out, because Servo classification can only serve
string classes. So, only scores are present in the signature.
"""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"classes":
array_ops.placeholder(dtypes.int64,
1,
name="output-tensor-classes"),
"scores":
array_ops.placeholder(dtypes.float32,
1,
name="output-tensor-scores"),
"logits":
array_ops.placeholder(dtypes.float32,
1,
name="output-tensor-logits-unused"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs[
signature_constants.CLASSIFY_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(name="input-tensor-1:0",
dtype=dtype_string,
tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_SCORES].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-scores:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification6(self):
"""Tests multiple output tensors that with integer classes and no scores.
Servo classification cannot serve integer classes, but no scores are
available. So, we fall back to predict signature.
"""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"classes":
array_ops.placeholder(dtypes.int64,
1,
name="output-tensor-classes"),
"logits":
array_ops.placeholder(dtypes.float32,
1,
name="output-tensor-logits"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_int64 = types_pb2.DataType.Value("DT_INT64")
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs["input-1"].CopyFrom(
meta_graph_pb2.TensorInfo(name="input-tensor-1:0",
dtype=dtype_string,
tensor_shape=shape))
expected_signature_def.outputs["classes"].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-classes:0",
dtype=dtype_int64,
tensor_shape=shape))
expected_signature_def.outputs["logits"].CopyFrom(
meta_graph_pb2.TensorInfo(name="output-tensor-logits:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification6(self):
"""Tests multiple output tensors that with integer classes and no scores.
Servo classification cannot serve integer classes, but no scores are
available. So, we fall back to predict signature.
"""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"classes":
array_ops.placeholder(
dtypes.int64, 1, name="output-tensor-classes"),
"logits":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-logits"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_int64 = types_pb2.DataType.Value("DT_INT64")
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs["input-1"].CopyFrom(
meta_graph_pb2.TensorInfo(
name="input-tensor-1:0", dtype=dtype_string, tensor_shape=shape))
expected_signature_def.outputs["classes"].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-classes:0",
dtype=dtype_int64,
tensor_shape=shape))
expected_signature_def.outputs["logits"].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-logits:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
def test_build_standardized_signature_def_classification5(self):
"""Tests multiple output tensors that include integer classes and scores.
Integer classes are dropped out, because Servo classification can only serve
string classes. So, only scores are present in the signature.
"""
input_tensors = {
"input-1":
array_ops.placeholder(dtypes.string, 1, name="input-tensor-1")
}
output_tensors = {
"classes":
array_ops.placeholder(
dtypes.int64, 1, name="output-tensor-classes"),
"scores":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-scores"),
"logits":
array_ops.placeholder(
dtypes.float32, 1, name="output-tensor-logits-unused"),
}
problem_type = constants.ProblemType.CLASSIFICATION
actual_signature_def = (
saved_model_export_utils.build_standardized_signature_def(
input_tensors, output_tensors, problem_type))
expected_signature_def = meta_graph_pb2.SignatureDef()
shape = tensor_shape_pb2.TensorShapeProto(
dim=[tensor_shape_pb2.TensorShapeProto.Dim(size=1)])
dtype_float = types_pb2.DataType.Value("DT_FLOAT")
dtype_string = types_pb2.DataType.Value("DT_STRING")
expected_signature_def.inputs[signature_constants.CLASSIFY_INPUTS].CopyFrom(
meta_graph_pb2.TensorInfo(
name="input-tensor-1:0", dtype=dtype_string, tensor_shape=shape))
expected_signature_def.outputs[
signature_constants.CLASSIFY_OUTPUT_SCORES].CopyFrom(
meta_graph_pb2.TensorInfo(
name="output-tensor-scores:0",
dtype=dtype_float,
tensor_shape=shape))
expected_signature_def.method_name = (
signature_constants.CLASSIFY_METHOD_NAME)
self.assertEqual(actual_signature_def, expected_signature_def)
