saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-07-06 04:28:32 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Type and Shape inference for QuantizeLinear and DeQuantizeLinear Ops (#408)

Browse source 
* Type and Shape inference for QuantizeeLinear and DeQuantizeLinear Ops

* removing redundant type checking for some inputs and outputs

* remove unnecessary type check deom type inference
This commit is contained in:
Ashwini Khade 2019-02-01 07:59:45 -08:00 committed by Ke Zhang
parent 60cdb79204
commit 93bcd9beb6
1 changed files with 22 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

24
onnxruntime/core/graph/contrib_ops/contrib_defs.cc
View file

@ -522,7 +522,16 @@ activation and leaky_relu_alpha.)DOC")
.SetDoc(R"DOC(
The linear quantization operator. It consumes a full precision data, a scale, a zero point and computes the quantized data.
The quantization formula is y = (x / y_scale) + y_zero_point. For (x / y_scale), it computes the nearest integer value to arg (in floating-point format),
rounding halfway cases away from zero. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').)DOC");
rounding halfway cases away from zero. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').)DOC")
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 2, 0);
if (!hasInputShape(ctx, 0))
return;
auto& input_shape = getInputShape(ctx, 0);
updateOutputShape(ctx, 0, input_shape);
});
ONNX_CONTRIB_OPERATOR_SCHEMA(DequantizeLinear)
.SetDomain(kMSDomain)
@ -543,7 +552,18 @@ The quantization formula is y = (x / y_scale) + y_zero_point. For (x / y_scale),
.SetDoc(R"DOC(
The linear de-quantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data.
The dequantization formula is y = (x - x_zero_point) * x_scale.
Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').)DOC");
Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').)DOC")
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
auto y_type = ctx.getOutputType(0);
// only float is supported
y_type->mutable_tensor_type()->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
if (!hasInputShape(ctx, 0))
return;
auto& input_shape = getInputShape(ctx, 0);
updateOutputShape(ctx, 0, input_shape);
});
ONNX_CONTRIB_OPERATOR_SCHEMA(QLinearMatMul)
.SetDomain(kMSDomain)