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Migrate normalization to template plugin test (openvinotoolkit#8240)
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* Migrate normalization to template plugin test

* Migrate result, parameter to template plugin test
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@davidsnam-intel
davidsnam-intel authored Oct 30, 2021
1 parent f453051 commit cc457e0
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261 changes: 261 additions & 0 deletions docs/template_plugin/tests/functional/op_reference/batch_norm.cpp
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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include <gtest/gtest.h>

#include "base_reference_test.hpp"
#include "openvino/op/constant.hpp"
#include "openvino/op/batch_norm.hpp"

using namespace reference_tests;
using namespace ov;

namespace {

struct BatchNormParams {
template <class T>
BatchNormParams(const Shape& input_shape,
const Shape& expected_shape,
const element::Type& input_type,
const element::Type& expected_type,
const std::vector<T>& input_value,
const std::vector<T>& expected_value,
const std::vector<T>& gamma,
const std::vector<T>& beta,
const std::vector<T>& mean,
const std::vector<T>& variance,
const float epsilon)
: m_input_shape(input_shape),
m_expected_shape(expected_shape),
m_input_type(input_type),
m_expected_type(expected_type),
m_input_value(CreateTensor(input_type, input_value)),
m_expected_value(CreateTensor(expected_type, expected_value)),
m_gamma(CreateTensor(Shape{input_shape.at(1)}, input_type, gamma)),
m_beta(CreateTensor(Shape{input_shape.at(1)}, input_type, beta)),
m_mean(CreateTensor(Shape{input_shape.at(1)}, input_type, mean)),
m_variance(CreateTensor(Shape{input_shape.at(1)}, input_type, variance)),
m_epsilon(epsilon) {}

Shape m_input_shape;
Shape m_expected_shape;
element::Type m_input_type;
element::Type m_expected_type;
runtime::Tensor m_input_value;
runtime::Tensor m_expected_value;
runtime::Tensor m_gamma;
runtime::Tensor m_beta;
runtime::Tensor m_mean;
runtime::Tensor m_variance;
float m_epsilon;
};

class ReferenceBatchNormV0LayerTest : public testing::TestWithParam<BatchNormParams>, public CommonReferenceTest {
public:
void SetUp() override {
const auto params = GetParam();
function = CreateFunction(params.m_input_shape, params.m_input_type, params.m_epsilon);
inputData = {params.m_input_value, params.m_gamma, params.m_beta, params.m_mean, params.m_variance};
refOutData = {params.m_expected_value};
}

static std::string getTestCaseName(const testing::TestParamInfo<BatchNormParams>& obj) {
const auto param = obj.param;
std::ostringstream result;

result << "input_shape=" << param.m_input_shape << "; ";
result << "output_shape=" << param.m_expected_shape << "; ";
result << "input_type=" << param.m_input_type << "; ";
result << "output_type=" << param.m_expected_type << "; ";
result << "gamma=" << param.m_gamma.data() << "; ";
result << "beta=" << param.m_beta.data() << "; ";
result << "mean=" << param.m_mean.data() << "; ";
result << "variance=" << param.m_variance.data();

return result.str();
}

private:
static std::shared_ptr<Function> CreateFunction(const Shape& input_shape,
const element::Type_t& input_type,
const float epsilon) {
Shape channel_shape{input_shape.at(1)};
auto in = std::make_shared<op::v0::Parameter>(input_type, input_shape);
auto gamma = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto beta = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto mean = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto variance = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto batch_norm = std::make_shared<op::v0::BatchNormInference>(in, gamma, beta, mean, variance, epsilon);

return std::make_shared<ov::Function>(batch_norm, ParameterVector{in, gamma, beta, mean, variance});
}
};

class ReferenceBatchNormV5LayerTest : public ReferenceBatchNormV0LayerTest {
public:
void SetUp() override {
const auto params = GetParam();
function = CreateFunction(params.m_input_shape, params.m_input_type, params.m_epsilon);
inputData = {params.m_input_value, params.m_gamma, params.m_beta, params.m_mean, params.m_variance};
refOutData = {params.m_expected_value};
}

private:
static std::shared_ptr<Function> CreateFunction(const Shape& input_shape,
const element::Type_t& input_type,
const float epsilon) {
Shape channel_shape{input_shape.at(1)};
auto in = std::make_shared<op::v0::Parameter>(input_type, input_shape);
auto gamma = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto beta = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto mean = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto variance = std::make_shared<op::v0::Parameter>(input_type, channel_shape);
auto batch_norm = std::make_shared<op::v5::BatchNormInference>(in, gamma, beta, mean, variance, epsilon);

return std::make_shared<ov::Function>(batch_norm, ParameterVector{in, gamma, beta, mean, variance});
}
};

TEST_P(ReferenceBatchNormV0LayerTest, CompareWithHardcodedRefs) {
Exec();
}

TEST_P(ReferenceBatchNormV5LayerTest, CompareWithHardcodedRefs) {
Exec();
}

template <element::Type_t ET>
std::vector<BatchNormParams> generateParamsForBatchNorm() {
using T = typename element_type_traits<ET>::value_type;

std::vector<BatchNormParams> params{
/*------------- 2d --------------*/
BatchNormParams(Shape{2, 3},
Shape{2, 3},
ET,
ET,
std::vector<T>{1, 2, 3, -1, -2, -3},
std::vector<T>{2, 6, 12, -2, -6, -12},
std::vector<T>{2.0, 3.0, 4.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{0.75, 0.75, 0.75},
0.25),
BatchNormParams(Shape{2, 3},
Shape{2, 3},
ET,
ET,
std::vector<T>{1, 2, 3, -1, -2, -3},
std::vector<T>{3, 0, 6, 1, -4, 0},
std::vector<T>{1.0, 1.0, 1.0},
std::vector<T>{2.0, -2.0, 3.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{0.75, 0.75, 0.75},
0.25),
BatchNormParams(Shape{2, 3},
Shape{2, 3},
ET,
ET,
std::vector<T>{1, 2, 3, -1, -2, -3},
std::vector<T>{3, 0, 6, 1, -4, 0},
std::vector<T>{1.0, 1.0, 1.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{-2.0, 2.0, -3.0},
std::vector<T>{0.75, 0.75, 0.75},
0.25),
BatchNormParams(Shape{2, 3},
Shape{2, 3},
ET,
ET,
std::vector<T>{3, 5, 1, -3, -5, -1},
std::vector<T>{2, 2, 2, -2, -2, -2},
std::vector<T>{1.0, 1.0, 1.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{0.0, 0.0, 0.0},
std::vector<T>{2.0, 6.0, 0.0},
0.25),
/*------------- 4d --------------*/
BatchNormParams(Shape{2, 2, 2, 1},
Shape{2, 2, 2, 1},
ET,
ET,
std::vector<T>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f},
std::vector<T>{0.54903894f,
0.71533161f,
0.60296183f,
0.54511058f,
0.42394274f,
0.64607101f,
0.43786817f,
0.89182704f},
std::vector<T>{1.0, 1.0},
std::vector<T>{1.0, 1.0},
std::vector<T>{1.0, 1.0},
std::vector<T>{1.0, 1.0},
0.001),
BatchNormParams(Shape{2, 2, 2, 1},
Shape{2, 2, 2, 1},
ET,
ET,
std::vector<T>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f},
std::vector<T>{-0.30327f,
1.1561f,
-0.096382f,
-0.434702f,
-1.4011f,
0.548275f,
-1.06187f,
1.59295f},
std::vector<T>{1.0, 1.0},
std::vector<T>{0.0f, 0.0f},
std::vector<T>{0.583388f, 0.619252f},
std::vector<T>{0.0119972f, 0.0282681f},
0.001),
};

return params;
}

std::vector<BatchNormParams> generateCombinedParamsForBatchNorm() {
const std::vector<std::vector<BatchNormParams>> allTypeParams{
generateParamsForBatchNorm<element::Type_t::f32>(),
generateParamsForBatchNorm<element::Type_t::f16>()
};

std::vector<BatchNormParams> combinedParams;

for (const auto& params : allTypeParams) {
combinedParams.insert(combinedParams.end(), params.begin(), params.end());
}

return combinedParams;
}

INSTANTIATE_TEST_SUITE_P(
smoke_BatchNorm_With_Hardcoded_Refs,
ReferenceBatchNormV0LayerTest,
::testing::ValuesIn(generateCombinedParamsForBatchNorm()),
ReferenceBatchNormV0LayerTest::getTestCaseName);

INSTANTIATE_TEST_SUITE_P(
smoke_BatchNorm_With_Hardcoded_Refs,
ReferenceBatchNormV5LayerTest,
::testing::ValuesIn(generateCombinedParamsForBatchNorm()),
ReferenceBatchNormV5LayerTest::getTestCaseName);

} // namespace
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