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Merged
merged 7 commits into from
Feb 19, 2024
Merged

(5.x) Merge 4.x #1149

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3bcad5f
Add QR code for regression test
akretz Dec 24, 2023
ae9e8bb
Adding test data for G-API OV backend
AsyaPronina Jan 8, 2024
3c642c0
Merge pull request #1139 from AsyaPronina:mean_scale_for_gapi_ov
asmorkalov Feb 12, 2024
007fb7d
Merge pull request #1133 from akretz:fix_issue_24679
asmorkalov Feb 13, 2024
fe77f0b
Add QR codes for regression tests
akretz Feb 14, 2024
798ea12
Merge pull request #1148 from akretz:fix_qrcode_bugs
asmorkalov Feb 16, 2024
680fcd9
Merge branch 4.x
asmorkalov Feb 16, 2024
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185 changes: 185 additions & 0 deletions testdata/gapi/ov/identity_net_100x100.xml
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Original file line number Diff line number Diff line change
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<?xml version="1.0"?>
<net name="main_graph" version="11">
<layers>
<layer id="0" name="onnx::Reshape_0" type="Parameter" version="opset1">
<data shape="1,100,100,3" element_type="f32" />
<rt_info>
<attribute name="old_api_map_element_type" version="0" value="f16" />
</rt_info>
<output>
<port id="0" precision="FP32" names="onnx::Reshape_0">
<dim>1</dim>
<dim>100</dim>
<dim>100</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="1" name="/Constant" type="Const" version="opset1">
<data element_type="i64" shape="2" offset="0" size="16" />
<rt_info>
<attribute name="precise" version="0" />
</rt_info>
<output>
<port id="0" precision="I64" names="/Constant_output_0">
<dim>2</dim>
</port>
</output>
</layer>
<layer id="2" name="/Reshape" type="Reshape" version="opset1">
<data special_zero="true" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>100</dim>
<dim>100</dim>
<dim>3</dim>
</port>
<port id="1" precision="I64">
<dim>2</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="/Reshape_output_0">
<dim>30000</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="3" name="/conv1d/Constant" type="Const" version="opset1">
<data element_type="i64" shape="1" offset="16" size="8" />
<output>
<port id="0" precision="I64" names="/conv1d/Constant_output_0">
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="/conv1d/Unsqueeze" type="Unsqueeze" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>30000</dim>
<dim>1</dim>
</port>
<port id="1" precision="I64">
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="/conv1d/Unsqueeze_output_0">
<dim>1</dim>
<dim>30000</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="5" name="Reshape_15_compressed" type="Const" version="opset1">
<data element_type="f16" shape="30000, 1, 1, 1" offset="24" size="60000" />
<output>
<port id="0" precision="FP16">
<dim>30000</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="6" name="Reshape_15" type="Convert" version="opset1">
<data destination_type="f32" />
<rt_info>
<attribute name="decompression" version="0" />
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>30000</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>30000</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="7" name="/conv1d/Conv/WithoutBiases" type="GroupConvolution" version="opset1">
<data strides="1" pads_begin="0" pads_end="0" dilations="1" auto_pad="explicit" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>30000</dim>
<dim>1</dim>
</port>
<port id="1" precision="FP32">
<dim>30000</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="/conv1d/Conv_output_0">
<dim>1</dim>
<dim>30000</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="8" name="/conv1d/Constant_1" type="Const" version="opset1">
<data element_type="i64" shape="1" offset="16" size="8" />
<output>
<port id="0" precision="I64" names="/conv1d/Constant_1_output_0">
<dim>1</dim>
</port>
</output>
</layer>
<layer id="9" name="9" type="Squeeze" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>30000</dim>
<dim>1</dim>
</port>
<port id="1" precision="I64">
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="output">
<dim>30000</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="10" name="9/sink_port_0" type="Result" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>30000</dim>
<dim>1</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="2" to-port="0" />
<edge from-layer="1" from-port="0" to-layer="2" to-port="1" />
<edge from-layer="2" from-port="2" to-layer="4" to-port="0" />
<edge from-layer="3" from-port="0" to-layer="4" to-port="1" />
<edge from-layer="4" from-port="2" to-layer="7" to-port="0" />
<edge from-layer="5" from-port="0" to-layer="6" to-port="0" />
<edge from-layer="6" from-port="1" to-layer="7" to-port="1" />
<edge from-layer="7" from-port="2" to-layer="9" to-port="0" />
<edge from-layer="8" from-port="0" to-layer="9" to-port="1" />
<edge from-layer="9" from-port="2" to-layer="10" to-port="0" />
</edges>
<rt_info>
<Runtime_version value="2023.2.0-13089-cfd42bd2cb0-HEAD" />
<conversion_parameters>
<is_python_object value="False" />
<output_model value="test_net.xml" />
</conversion_parameters>
</rt_info>
</net>