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vatsalashanubhagvatsalashanubhag
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test qlinear concat
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‎src/frontends/onnx/tests/models/com.microsoft/qlinear_concat_i8.prototxt

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ir_version: 3
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producer_name: "OpenVINO ONNX Frontend"
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producer_version: ""
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model_version: 0
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graph {
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name: "test_qlinear_concat_i8"
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node {
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input: "Y_scale"
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input: "Y_zero_point"
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input: "X1"
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input: "X1_scale"
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input: "X1_zero_point"
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input: "X2"
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input: "X2_scale"
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input: "X2_zero_point"
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output: "Y"
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op_type: "QLinearConcat"
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attribute {
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name: "axis"
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i: 1
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type: INT
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}
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domain: "com.microsoft"
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}
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input {
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name: "Y_scale"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "Y_zero_point"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "X1"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 2 }
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dim { dim_value: 2 }
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}
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}
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}
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}
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input {
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name: "X1_scale"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "X1_zero_point"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "X2"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 2 }
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dim { dim_value: 2 }
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}
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}
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}
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}
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input {
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name: "X2_scale"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "X2_zero_point"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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output {
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name: "Y"
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type {
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tensor_type {
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elem_type: 3
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shape {
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dim { dim_value: 2 }
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dim { dim_value: 4 }
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}
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}
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}
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}
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}
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opset_import {
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version: 1
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}

‎src/frontends/onnx/tests/models/com.microsoft/qlinear_concat_u8.prototxt

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ir_version: 3
2+
producer_name: "OpenVINO ONNX Frontend"
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producer_version: ""
4+
model_version: 0
5+
graph {
6+
name: "test_qlinear_concat_u8"
7+
8+
node {
9+
input: "Y_scale"
10+
input: "Y_zero_point"
11+
input: "X1"
12+
input: "X1_scale"
13+
input: "X1_zero_point"
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input: "X2"
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input: "X2_scale"
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input: "X2_zero_point"
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output: "Y"
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op_type: "QLinearConcat"
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attribute {
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name: "axis"
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i: 1
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type: INT
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}
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domain: "com.microsoft"
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}
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input {
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name: "Y_scale"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "Y_zero_point"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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input {
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name: "X1"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim { dim_value: 4 }
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dim { dim_value: 6 }
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}
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}
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}
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}
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input {
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name: "X1_scale"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim { dim_value: 1 }
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}
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input {
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name: "X1_zero_point"
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type {
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tensor_type {
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dim { dim_value: 1 }
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}
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}
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input {
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name: "X2"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim { dim_value: 4 }
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dim { dim_value: 6 }
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}
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input {
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name: "X2_scale"
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dim { dim_value: 1 }
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}
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input {
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name: "X2_zero_point"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim { dim_value: 1 }
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}
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}
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}
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}
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output {
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name: "Y"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim { dim_value: 4 }
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dim { dim_value: 12 }
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}
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}
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}
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}
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}
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opset_import {
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version: 1
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}

‎src/frontends/onnx/tests/onnx_import_com_microsoft.in.cpp

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@@ -1740,3 +1740,71 @@ OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_bias_add) {
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test_case.run();
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}
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OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_qlinear_concat_i8) {
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const auto model = convert_model("com.microsoft/qlinear_concat_i8.onnx");
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auto test_case = ov::test::TestCase(model, s_device);
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const std::vector<float> y_scale{0.1f};
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const std::vector<int8_t> y_zero_point{5};
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const std::vector<int8_t> X1{1, 2, 3, 4};
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const std::vector<float> X1_scale{0.1f};
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const std::vector<int8_t> X1_zero_point{0};
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const std::vector<int8_t> X2{5, 6, 7, 8};
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const std::vector<float> X2_scale{0.1f};
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const std::vector<int8_t> X2_zero_point{0};
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const std::vector<int8_t> expected_output{6, 7, 10, 11, 8, 9, 12, 13};
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test_case.add_input<float>(Shape{1}, y_scale);
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test_case.add_input<int8_t>(Shape{1}, y_zero_point);
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test_case.add_input<int8_t>(Shape{2, 2}, X1);
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test_case.add_input<float>(Shape{1}, X1_scale);
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test_case.add_input<int8_t>(Shape{1}, X1_zero_point);
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test_case.add_input<int8_t>(Shape{2, 2}, X2);
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test_case.add_input<float>(Shape{1}, X2_scale);
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test_case.add_input<int8_t>(Shape{1}, X2_zero_point);
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test_case.add_expected_output<int8_t>(Shape{2, 4}, expected_output);
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test_case.run();
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}
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OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_qlinear_concat_u8) {
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const auto model = convert_model("com.microsoft/qlinear_concat_u8.onnx");
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auto test_case = ov::test::TestCase(model, s_device);
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const std::vector<float> y_scale{0.1f};
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const std::vector<uint8_t> y_zero_point{5};
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const std::vector<uint8_t> X1{10, 20, 30, 40, 50, 60, 15, 25, 35, 45, 55, 65,
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12, 22, 32, 42, 52, 62, 18, 28, 38, 48, 58, 68};
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const std::vector<float> X1_scale{0.1f};
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const std::vector<uint8_t> X1_zero_point{0};
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const std::vector<uint8_t> X2{70, 80, 90, 100, 110, 120, 75, 85, 95, 105, 115, 125,
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72, 82, 92, 102, 112, 122, 78, 88, 98, 108, 118, 128};
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const std::vector<float> X2_scale{0.1f};
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const std::vector<uint8_t> X2_zero_point{0};
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const std::vector<uint8_t> expected_output{
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15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,
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17, 27, 37, 47, 57, 67, 77, 87, 97, 107, 117, 127, 23, 33, 43, 53, 63, 73, 83, 93, 103, 113, 123, 133};
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test_case.add_input<float>(Shape{1}, y_scale);
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test_case.add_input<uint8_t>(Shape{1}, y_zero_point);
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test_case.add_input<uint8_t>(Shape{4, 6}, X1);
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test_case.add_input<float>(Shape{1}, X1_scale);
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test_case.add_input<uint8_t>(Shape{1}, X1_zero_point);
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test_case.add_input<uint8_t>(Shape{4, 6}, X2);
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test_case.add_input<float>(Shape{1}, X2_scale);
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test_case.add_input<uint8_t>(Shape{1}, X2_zero_point);
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test_case.add_expected_output<uint8_t>(Shape{4, 12}, expected_output);
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test_case.run();
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}

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