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Merge 5daa772 into a8e4648 
2 parents a8e4648 + 5daa772 commit 627c8f2

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Lines changed: 3150 additions & 893 deletions

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‎docs/Changelog.md‎

Lines changed: 1365 additions & 0 deletions
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‎docs/Operators.md‎

Lines changed: 112 additions & 56 deletions
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‎onnx/cpp2py_export.cc‎

Lines changed: 1 addition & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -41,10 +41,7 @@ PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
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"type_constraints", &OpSchema::typeConstraintParams)
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.def_static(
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"is_infinite",
44-
[](int v) { return v == std::numeric_limits<int>::max(); })
45-
.def("consumed", [](const OpSchema& schema, int i) {
46-
return schema.consumed(i);
47-
});
44+
[](int v) { return v == std::numeric_limits<int>::max(); });
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4946
py::class_<OpSchema::Attribute>(op_schema, "Attribute")
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.def_readonly("name", &OpSchema::Attribute::name)
@@ -90,11 +87,6 @@ PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
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.value("COMMON", OpSchema::SupportType::COMMON)
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.value("EXPERIMENTAL", OpSchema::SupportType::EXPERIMENTAL);
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93-
py::enum_<OpSchema::UseType>(op_schema, "UseType")
94-
.value("DEFAULT", OpSchema::UseType::DEFAULT)
95-
.value("CONSUME_ALLOWED", OpSchema::UseType::CONSUME_ALLOWED)
96-
.value("CONSUME_ENFORCED", OpSchema::UseType::CONSUME_ENFORCED);
97-
9890
defs.def("has_schema", [](const std::string& op_type) -> bool {
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return OpSchemaRegistry::Schema(op_type) != nullptr;
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});

‎onnx/defs/experiments/defs.cc‎

Lines changed: 8 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -35,7 +35,6 @@ is applied to the tensor elementwise.
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ONNX_OPERATOR_SCHEMA(ThresholdedRelu)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
38-
.AllowConsumed({{0, 0}})
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.SetDoc(R"DOC(
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ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
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(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
@@ -52,7 +51,6 @@ is applied to the tensor elementwise.
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ONNX_OPERATOR_SCHEMA(ScaledTanh)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
55-
.AllowConsumed({{0, 0}})
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.SetDoc(R"DOC(
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Calculates the scaled hyperbolic tangent of the given input tensor element-wise,
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alpha * tanh(beta * x). This operation can be done in an in-place fashion too,
@@ -68,7 +66,6 @@ by providing the same input and output blobs.
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ONNX_OPERATOR_SCHEMA(ParametricSoftplus)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
71-
.AllowConsumed({{0, 0}})
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.SetDoc(R"DOC(
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ParametricSoftplus takes one input data (Tensor<T>) and produces one output data
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(Tensor<T>) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to
@@ -83,7 +80,6 @@ the tensor elementwise.
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ONNX_OPERATOR_SCHEMA(ConstantFill)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
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.AllowConsumed({{0, 0}})
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.SetDoc(R"DOC(
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The operator fills the elements of the output tensor with a constant value
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specified by the 'value' attribute.
@@ -156,18 +152,17 @@ NOTE: Currently, it supports data type of float, int32, int64, and bool.
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"Constrain output types to float, int32, int64, bool tensors.");
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ONNX_OPERATOR_SCHEMA(GivenTensorFill)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
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.Input(0, "shape", "The shape of filled tensor", "T", OpSchema::Optional)
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.Output(0, "X", "The filled tensor", "T")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
155+
.SetSupportLevel(SupportType::EXPERIMENTAL)
156+
.Input(0, "shape", "The shape of filled tensor", "T", OpSchema::Optional)
157+
.Output(0, "X", "The filled tensor", "T")
158+
.TypeConstraint(
159+
"T",
160+
{ "tensor(float16)", "tensor(float)", "tensor(double)" },
161+
"Constrain input and output types to float tensors.")
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.Attr("values", "", AttributeProto::FLOATS, OPTIONAL)
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.Attr("shape", "", AttributeProto::INTS, OPTIONAL)
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.Attr("input_as_shape", "", AttributeProto::INT, OPTIONAL)
169-
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL)
170-
.AllowConsumed({{0, 0}});
165+
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL);
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172167
ONNX_OPERATOR_SCHEMA(FC)
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.SetSupportLevel(SupportType::EXPERIMENTAL)
@@ -231,7 +226,6 @@ ONNX_OPERATOR_SCHEMA(Scale)
231226
"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
233228
"Constrain input and output types to float tensors.")
234-
.AllowConsumed({{0, 0}})
235229
.SetDoc(R"DOC(
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Scale takes one input data (Tensor<float>) and produces one output data
237231
(Tensor<float>) whose value is the input data tensor scaled element-wise.
@@ -290,7 +284,6 @@ and op)DOC")
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ONNX_OPERATOR_SCHEMA(ImageScaler)
292286
.SetSupportLevel(SupportType::EXPERIMENTAL)
293-
.AllowConsumed({{0, 0}})
294287
.SetDoc(R"DOC(Scale and bias the input image. Bias values are stored in
295288
the same ordering as the image pixel format.)DOC")
296289
.Attr("bias", "Bias applied to each channel, same size as C.", AttributeProto::FLOATS, OPTIONAL)
@@ -304,7 +297,6 @@ the same ordering as the image pixel format.)DOC")
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305298
ONNX_OPERATOR_SCHEMA(MeanVarianceNormalization)
306299
.SetSupportLevel(SupportType::EXPERIMENTAL)
307-
.AllowConsumed({{0, 0}})
308300
.SetDoc(R"DOC(Perform mean variance normalization.)DOC")
309301
.Attr("across_channels", "If 1, mean and variance are computed across channels. Default is 0.", AttributeProto::INT, static_cast<int64_t>(0))
310302
.Attr("normalize_variance", "If 0, normalize the mean only. Default is 1.", AttributeProto::INT, static_cast<int64_t>(1))
@@ -317,7 +309,6 @@ ONNX_OPERATOR_SCHEMA(MeanVarianceNormalization)
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318310
ONNX_OPERATOR_SCHEMA(Crop)
319311
.SetSupportLevel(SupportType::EXPERIMENTAL)
320-
.AllowConsumed({{0, 0}})
321312
.SetDoc(R"DOC(Crop and image to the specified spatial dimensions. If scale is given,
322313
then optionally start the crop offset by the left/top border amounts.
323314
If scale is not provided, crop the borders as provided.)DOC")

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