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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn/ArmNN.hpp>
#include <Graph.hpp>
#include <layers/BatchToSpaceNdLayer.hpp>
#include <layers/SpaceToDepthLayer.hpp>
#include <layers/PreluLayer.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/test/unit_test.hpp>
void BatchToSpaceInferOutputShapeTest()
{
armnn::Graph graph;
armnn::BatchToSpaceNdDescriptor descriptor;
descriptor.m_BlockShape = {2, 2};
descriptor.m_Crops = {{0, 0}, {2, 0}};
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
armnn::BatchToSpaceNdLayer* const batchToSpaceLayer =
graph.AddLayer<armnn::BatchToSpaceNdLayer>(descriptor, "batchToSpace");
std::vector<armnn::TensorShape> shapes;
const std::vector<unsigned int> theDimSizes = {8, 1, 3, 1};
armnn::TensorShape shape(4, theDimSizes.data());
shapes.push_back(shape);
const std::vector<unsigned int> expectedDimSizes = {2, 2, 4, 1};
armnn::TensorShape expectedShape(4, expectedDimSizes.data());
BOOST_CHECK(expectedShape == batchToSpaceLayer->InferOutputShapes(shapes).at(0));
}
void SpaceToDepthInferOutputShapeTest()
{
armnn::Graph graph;
armnn::SpaceToDepthDescriptor descriptor;
descriptor.m_BlockSize = 2;
descriptor.m_DataLayout = armnn::DataLayout::NHWC;
armnn::SpaceToDepthLayer* const spaceToDepthLayer =
graph.AddLayer<armnn::SpaceToDepthLayer>(descriptor, "spaceToDepth");
std::vector<armnn::TensorShape> shapes;
const std::vector<unsigned int> dimSizes{ 1, 16, 8, 3 };
armnn::TensorShape shape(4, dimSizes.data());
shapes.push_back(shape);
const std::vector<unsigned int> expectedDimSizes{ 1, 8, 4, 12 };
armnn::TensorShape expectedShape(4, expectedDimSizes.data());
BOOST_CHECK(expectedShape == spaceToDepthLayer->InferOutputShapes(shapes).at(0));
}
void PreluInferOutputShapeImpl(const std::vector<armnn::TensorShape>& inputShapes,
std::vector<armnn::TensorShape>& outputShapes)
{
armnn::Graph graph;
armnn::PreluLayer* const preluLayer = graph.AddLayer<armnn::PreluLayer>("prelu");
outputShapes = preluLayer->InferOutputShapes(inputShapes);
}
void PreluInferOutputShapeSameDimsTest()
{
const std::vector<armnn::TensorShape> inputShapes
{
{ 5, 1, 1, 7 }, // Input shape
{ 5, 4, 3, 1 } // Alpha shape
};
const std::vector<armnn::TensorShape> expectedOutputShapes
{
{ 5, 4, 3, 7 } // Output shape
};
std::vector<armnn::TensorShape> outputShapes;
BOOST_CHECK_NO_THROW(PreluInferOutputShapeImpl(inputShapes, outputShapes));
BOOST_CHECK(outputShapes.size() == 1);
BOOST_CHECK(outputShapes[0] == expectedOutputShapes[0]);
}
void PreluInferOutputShapeInputBiggerTest()
{
const std::vector<armnn::TensorShape> inputShapes
{
{ 4, 1, 4, 8 }, // Input shape
{ 5, 4, 1 } // Alpha shape
};
const std::vector<armnn::TensorShape> expectedOutputShapes
{
{ 4, 5, 4, 8 } // Output shape
};
std::vector<armnn::TensorShape> outputShapes;
BOOST_CHECK_NO_THROW(PreluInferOutputShapeImpl(inputShapes, outputShapes));
BOOST_CHECK(outputShapes.size() == 1);
BOOST_CHECK(outputShapes[0] == expectedOutputShapes[0]);
}
void PreluInferOutputShapeAlphaBiggerTest()
{
const std::vector<armnn::TensorShape> inputShapes
{
{ 4, 1, 2 }, // Input shape
{ 5, 4, 3, 1 } // Alpha shape
};
const std::vector<armnn::TensorShape> expectedOutputShapes
{
{ 5, 4, 3, 2 } // Output shape
};
std::vector<armnn::TensorShape> outputShapes;
BOOST_CHECK_NO_THROW(PreluInferOutputShapeImpl(inputShapes, outputShapes));
BOOST_CHECK(outputShapes.size() == 1);
BOOST_CHECK(outputShapes[0] == expectedOutputShapes[0]);
}
void PreluInferOutputShapeNoMatchTest()
{
const std::vector<armnn::TensorShape> inputShapes
{
{ 4, 1, 2 }, // Input shape
{ 5, 4, 3, 1 } // Alpha shape
};
const std::vector<armnn::TensorShape> expectedOutputShapes
{
{ 5, 7, 3, 2 } // Output shape
};
std::vector<armnn::TensorShape> outputShapes;
BOOST_CHECK_NO_THROW(PreluInferOutputShapeImpl(inputShapes, outputShapes));
BOOST_CHECK(outputShapes.size() == 1);
BOOST_CHECK(outputShapes[0] != expectedOutputShapes[0]);
}
void CreatePreluLayerHelper(armnn::Graph& graph,
const armnn::TensorShape& inputShape,
const armnn::TensorShape& alphaShape,
const armnn::TensorShape& outputShape)
{
// Creates the PReLU layer
armnn::Layer* const preluLayer = graph.AddLayer<armnn::PreluLayer>("prelu");
// Creates extra layers
armnn::Layer* const input = graph.AddLayer<armnn::InputLayer> (0, "input");
armnn::Layer* const alpha = graph.AddLayer<armnn::InputLayer> (1, "alpha");
armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output");
// Connects up
armnn::TensorInfo inputTensorInfo (inputShape, armnn::DataType::Float32);
armnn::TensorInfo alphaTensorInfo (alphaShape, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32);
Connect(input, preluLayer, inputTensorInfo, 0, 0);
Connect(alpha, preluLayer, alphaTensorInfo, 0, 1);
Connect(preluLayer, output, outputTensorInfo, 0, 0);
}
void PreluValidateTensorShapesFromInputsMatchTest()
{
armnn::Graph graph;
// Creates the PReLU layer
CreatePreluLayerHelper(graph, { 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 });
// Graph::InferTensorInfos calls Layer::ValidateTensorShapesFromInputs
BOOST_CHECK_NO_THROW(graph.InferTensorInfos());
}
void PreluValidateTensorShapesFromInputsNoMatchTest()
{
armnn::Graph graph;
// Creates the PReLU layer
CreatePreluLayerHelper(graph, { 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 7, 3, 2 });
// Graph::InferTensorInfos calls Layer::ValidateTensorShapesFromInputs
BOOST_CHECK_THROW(graph.InferTensorInfos(), armnn::LayerValidationException);
}
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