147 lines
6.5 KiB
C++
147 lines
6.5 KiB
C++
// Copyright (C) 2020 Intel Corporation
|
|
// SPDX-License-Identifier: Apache-2.0
|
|
//
|
|
|
|
#include "bfloat16_helpers.hpp"
|
|
|
|
#include <memory>
|
|
#include <tuple>
|
|
#include <vector>
|
|
#include <string>
|
|
#include <map>
|
|
#include <functional>
|
|
#include <utility>
|
|
|
|
#include <ie_core.hpp>
|
|
#include <ie_plugin_config.hpp>
|
|
|
|
#include "common_test_utils/common_utils.hpp"
|
|
|
|
#include "ngraph/opsets/opset1.hpp"
|
|
|
|
using namespace std;
|
|
using namespace ngraph;
|
|
using namespace InferenceEngine;
|
|
|
|
namespace LayerTestsDefinitions {
|
|
|
|
class Interpolation : public BasicBF16Test {
|
|
protected:
|
|
std::shared_ptr<ngraph::Function> createGraph(InferenceEngine::Precision netPrecision) override {
|
|
// Convolution (BF16)
|
|
// |
|
|
// Interpolation (In the case of mode = "linear") (FP32)
|
|
// |
|
|
// Convolution (BF16)
|
|
|
|
// STAGE1: construction of the GRAPH
|
|
ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16;
|
|
auto channelsCount = inputShapes[1];
|
|
|
|
// add
|
|
auto input1 = std::make_shared<opset1::Parameter>(ntype, ngraph::Shape{inputShapes});
|
|
input1->set_friendly_name("Input_1");
|
|
std::shared_ptr<ngraph::opset1::Constant> addConst = nullptr;
|
|
if (netPrecision == Precision::FP32) {
|
|
addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f });
|
|
} else {
|
|
addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(FuncTestUtils::Bf16TestUtils::reducePrecisionBitwiseS(2.0f)) });
|
|
}
|
|
auto addNode = std::make_shared<opset1::Multiply>(input1, addConst);
|
|
addNode->set_friendly_name("Add_1");
|
|
|
|
// convolution
|
|
std::shared_ptr<ngraph::opset1::Constant> weightsNode1 = nullptr, weightsNode2 = nullptr;
|
|
ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w
|
|
if (netPrecision == Precision::FP32) {
|
|
std::vector<float> weightValuesFP32;
|
|
weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3);
|
|
FuncTestUtils::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size());
|
|
weightsNode1 = std::make_shared<ngraph::opset1::Constant>(ntype, convFilterShape, weightValuesFP32);
|
|
weightsNode2 = std::make_shared<ngraph::opset1::Constant>(ntype, convFilterShape, weightValuesFP32);
|
|
} else {
|
|
std::vector<short> weightValuesBF16;
|
|
weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3);
|
|
FuncTestUtils::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size());
|
|
weightsNode1 = std::make_shared<ngraph::opset1::Constant>(ntype, convFilterShape, weightValuesBF16.data());
|
|
weightsNode2 = std::make_shared<ngraph::opset1::Constant>(ntype, convFilterShape, weightValuesBF16.data());
|
|
}
|
|
|
|
std::shared_ptr<ngraph::Node> convNode1 = std::make_shared<ngraph::opset1::Convolution>(
|
|
addNode, weightsNode1,
|
|
ngraph::Strides({ 1, 1 }), // strides
|
|
ngraph::CoordinateDiff({ 1, 1 }), // pad begin
|
|
ngraph::CoordinateDiff({ 1, 1 }), // pad end
|
|
ngraph::Strides({ 1, 1 }), // dilation
|
|
ngraph::op::PadType::EXPLICIT); // pad type
|
|
convNode1->set_friendly_name("Convolution_1");
|
|
|
|
// interpolation
|
|
auto heightSize = static_cast<long>(inputShapes[2]);
|
|
auto weigthSize = static_cast<long>(inputShapes[3]);
|
|
std::vector<int64_t> outShape = {2 * heightSize, 2 * weigthSize};
|
|
|
|
auto interpolShape = std::make_shared<ngraph::op::v0::Constant>(ngraph::element::i64, ngraph::Shape{2}, outShape);
|
|
ngraph::op::v0::InterpolateAttrs attrs;
|
|
attrs.pads_begin.push_back(0);
|
|
attrs.pads_end.push_back(0);
|
|
attrs.axes = ngraph::AxisSet{2, 3};
|
|
attrs.align_corners = false;
|
|
attrs.mode = "linear";
|
|
attrs.antialias = false;
|
|
auto interpolNode = std::make_shared<opset1::Interpolate>(
|
|
convNode1,
|
|
interpolShape, attrs);
|
|
interpolNode->set_friendly_name("Interp");
|
|
|
|
std::shared_ptr<ngraph::Node> convNode2 = std::make_shared<ngraph::opset1::Convolution>(
|
|
interpolNode, weightsNode2,
|
|
ngraph::Strides({ 1, 1 }), // strides
|
|
ngraph::CoordinateDiff({ 1, 1 }), // pad begin
|
|
ngraph::CoordinateDiff({ 1, 1 }), // pad end
|
|
ngraph::Strides({ 1, 1 }), // dilation
|
|
ngraph::op::PadType::EXPLICIT); // pad type
|
|
convNode2->set_friendly_name("Convolution_2");
|
|
return std::make_shared<ngraph::Function>(convNode2, ngraph::ParameterVector{input1});
|
|
}
|
|
void SetUp() override {
|
|
std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam();
|
|
fnPtr = createGraph(netPrecision);
|
|
|
|
// STAGE2: set up safe threshold <= 5% from maximum value of output tensor
|
|
threshold = 0.02f; // Max in fp32 network by output: 2.531
|
|
|
|
// STAGE3:
|
|
// filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in
|
|
// performance counters
|
|
expectedPrecisions["Convolution_1"] = "BF16";
|
|
expectedPrecisions["Interp"] = "FP32";
|
|
expectedPrecisions["Convolution_2"] = "BF16";
|
|
}
|
|
};
|
|
|
|
TEST_P(Interpolation, CompareWithRefImpl) {
|
|
test();
|
|
};
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(smoke_FP32_bfloat16_NoReshape, Interpolation,
|
|
::testing::Combine(
|
|
::testing::Values(Precision::FP32),
|
|
::testing::Values(Precision::FP32),
|
|
::testing::Values(SizeVector({ 1, 1, 2, 2 })),
|
|
::testing::Values(SizeVector()),
|
|
::testing::Values(CommonTestUtils::DEVICE_CPU)),
|
|
Interpolation::getTestCaseName);
|
|
|
|
INSTANTIATE_TEST_CASE_P(smoke_BF16_bfloat16_NoReshape, Interpolation,
|
|
::testing::Combine(
|
|
::testing::Values(Precision::FP32),
|
|
::testing::Values(Precision::BF16),
|
|
::testing::Values(SizeVector({ 1, 1, 2, 2 })),
|
|
::testing::Values(SizeVector()),
|
|
::testing::Values(CommonTestUtils::DEVICE_CPU)),
|
|
Interpolation::getTestCaseName);
|
|
|
|
} // namespace LayerTestsDefinitions
|