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Add functional tests and remove leayer creator for Add opp. (#2759)

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* Add functional tests and remove leayer creator for Add opp.

* Remove batchNorm template.
This commit is contained in:
Szymon Durawa 2020-11-20 12:03:47 +01:00 committed by GitHub
parent dee00cdc92
commit 1e1eaa582a
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 298 additions and 9 deletions
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9
inference-engine/src/legacy_api/src/ie_cnn_layer_builder_ngraph.cpp
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@ -630,15 +630,6 @@ CNNLayer::Ptr NodeConverter<ngraph::op::v1::Multiply>::createLayer(const std::sh
return res;
}
template <>
CNNLayer::Ptr NodeConverter<ngraph::op::v1::Add>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
LayerParams params = {layer->get_friendly_name(), "Eltwise",
details::convertPrecision(layer->get_output_element_type(0))};
auto res = std::make_shared<InferenceEngine::EltwiseLayer>(params);
res->params["operation"] = "sum";
return res;
}
template <>
CNNLayer::Ptr NodeConverter<ngraph::op::Squeeze>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
LayerParams params = {layer->get_friendly_name(), "Squeeze",

298
inference-engine/tests/functional/inference_engine/ngraph_reader/add_tests.cpp Normal file
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@ -0,0 +1,298 @@
// Copyright (C) 2018-2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <string>
#include "ngraph_reader_tests.hpp"
TEST_F(NGraphReaderTests, ReadAddBcastNetwork) {
std::string model = R"V0G0N(
<net name="add_bcast_model" version="10">
<layers>
<layer id="0" name="x" type="Parameter" version="opset1">
<data element_type="f32" shape="3,4,5"/>
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="1" name="y" type="Parameter" version="opset1">
<data element_type="f32" shape="5"/>
<output>
<port id="0" precision="FP32">
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="EltwiseReshapeNormalization/Cast_151_const" type="Const" version="opset1">
<data element_type="i64" offset="0" shape="3" size="24"/>
<output>
<port id="1" precision="I64">
<dim>3</dim>
</port>
</output>
</layer>
<layer id="3" name="EltwiseReshapeNormalization" type="Reshape" version="opset1">
<data special_zero="True"/>
<input>
<port id="0">
<dim>5</dim>
</port>
<port id="1">
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="4" name="sum" type="Add" version="opset1">
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="5" name="sum/sink_port_0" type="Result" version="opset1">
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="1" from-port="0" to-layer="3" to-port="0"/>
<edge from-layer="2" from-port="1" to-layer="3" to-port="1"/>
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
<edge from-layer="3" from-port="2" to-layer="4" to-port="1"/>
<edge from-layer="4" from-port="2" to-layer="5" to-port="0"/>
</edges>
</net>
)V0G0N";
std::string modelV7 = R"V0G0N(
<net name="add_bcast_model" version="7">
<layers>
<layer id="0" name="x" type="Input">
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="1" name="y" type="Input">
<output>
<port id="0" precision="FP32">
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="EltwiseReshapeNormalization/Cast_161_const" type="Const">
<output>
<port id="1" precision="I64">
<dim>3</dim>
</port>
</output>
<blobs>
<custom offset="0" precision="I64" size="12"/>
</blobs>
</layer>
<layer id="3" name="EltwiseReshapeNormalization" type="Reshape">
<data special_zero="True"/>
<input>
<port id="0">
<dim>5</dim>
</port>
<port id="1">
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="4" name="sum" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
</layers>
<edges>
<edge from-layer="1" from-port="0" to-layer="3" to-port="0"/>
<edge from-layer="2" from-port="1" to-layer="3" to-port="1"/>
<edge from-layer="0" from-port="0" to-layer="4" to-port="0"/>
<edge from-layer="3" from-port="2" to-layer="4" to-port="1"/>
</edges>
</net>
)V0G0N";
compareIRs(model, modelV7, 24, [](Blob::Ptr& weights) {
auto *data = weights->buffer().as<int64_t *>();
data[0] = 1;
data[1] = 1;
data[2] = 5;
});
}
TEST_F(NGraphReaderTests, ReadAddNetwork) {
std::string model = R"V0G0N(
<net name="add_model" version="10">
<layers>
<layer id="0" name="x" type="Parameter" version="opset1">
<data element_type="f32" shape="3,4,5"/>
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="1" name="y" type="Parameter" version="opset1">
<data element_type="f32" shape="3,4,5"/>
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="sum" type="Add" version="opset1">
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
<port id="1">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="3" name="sum/sink_port_0" type="Result" version="opset1">
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</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="3" to-port="0"/>
</edges>
</net>
)V0G0N";
std::string modelV7 = R"V0G0N(
<net name="add_model" version="7">
<layers>
<layer id="0" name="x" type="Input">
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="1" name="y" type="Input">
<output>
<port id="0" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</layer>
<layer id="2" name="sum" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
<port id="1">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>3</dim>
<dim>4</dim>
<dim>5</dim>
</port>
</output>
</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"/>
</edges>
</net>
)V0G0N";
compareIRs(model, modelV7, 0);
}