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#include "caffe2/operators/quantized/int8_relu_op.h"
namespace caffe2 {
namespace {
OpSchema::Cost CostInferenceForRelu(
const OperatorDef& def,
const vector<TensorShape>& in) {
struct OpSchema::Cost cost = PointwiseCostInference<0>(def, in);
cost.params_bytes = 0;
return cost;
}
} // namespace
REGISTER_CPU_OPERATOR(Int8Relu, int8::Int8ReluOp);
// Input: X, output: Y
OPERATOR_SCHEMA(Int8Relu)
.NumInputs(1)
.NumOutputs(1)
.Arg("Y_scale", "Output tensor quantization scale")
.Arg("Y_zero_point", "Output tensor quantization offset")
.AllowInplace({{0, 0}})
.CostInferenceFunction(CostInferenceForRelu)
.IdenticalTypeAndShape()
.SetDoc(R"DOC(
Relu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
the tensor elementwise.
)DOC")
.Input(0, "X", "1D input tensor")
.Output(0, "Y", "1D input tensor")
.InheritOnnxSchema("Relu");
} // namespace caffe2
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