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//
// Copyright © 2023-2024 Arm Ltd and contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <ResolveType.hpp>
#include <armnn/INetwork.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <doctest/doctest.h>
#include <vector>
namespace
{
template<armnn::DataType ArmnnTypeInput>
INetworkPtr CreateElementwiseBinaryNetwork(const TensorShape& input1Shape,
const TensorShape& input2Shape,
const TensorShape& outputShape,
BinaryOperation operation,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
INetworkPtr net(INetwork::Create());
TensorInfo input1TensorInfo(input1Shape, ArmnnTypeInput, qScale, qOffset, true);
TensorInfo input2TensorInfo(input2Shape, ArmnnTypeInput, qScale, qOffset, true);
TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
IConnectableLayer* input1 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(0));
IConnectableLayer* input2 = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(1));
IConnectableLayer* elementwiseBinaryLayer = net->AddElementwiseBinaryLayer(operation, "elementwiseUnary");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
Connect(input1, elementwiseBinaryLayer, input1TensorInfo, 0, 0);
Connect(input2, elementwiseBinaryLayer, input2TensorInfo, 0, 1);
Connect(elementwiseBinaryLayer, output, outputTensorInfo, 0, 0);
return net;
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ElementwiseBinarySimpleEndToEnd(const std::vector<BackendId>& backends,
BinaryOperation operation)
{
using namespace armnn;
const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
const TensorShape& input1Shape = { 2, 2, 2, 2 };
const TensorShape& input2Shape = { 1 };
const TensorShape& outputShape = { 2, 2, 2, 2 };
// Builds up the structure of the network
INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
operation, qScale, qOffset);
CHECK(net);
const std::vector<float> input1({ 1, -1, 1, 1, 5, -5, 5, 5, -3, 3, 3, 3, 4, 4, -4, 4 });
const std::vector<float> input2({ 2 });
std::vector<float> expectedOutput;
switch (operation) {
case armnn::BinaryOperation::Add:
expectedOutput = { 3, 1, 3, 3, 7, -3, 7, 7, -1, 5, 5, 5, 6, 6, -2, 6 };
break;
case armnn::BinaryOperation::Div:
expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f, 2.5f, -2.5f, 2.5f, 2.5f, -1.5f, 1.5f, 1.5f, 1.5f, 2, 2, -2, 2};
break;
case armnn::BinaryOperation::Maximum:
expectedOutput = { 2, 2, 2, 2, 5, 2, 5, 5, 2, 3, 3, 3, 4, 4, 2, 4 };
break;
case armnn::BinaryOperation::Minimum:
expectedOutput = { 1, -1, 1, 1, 2, -5, 2, 2, -3, 2, 2, 2, 2, 2, -4, 2 };
break;
case armnn::BinaryOperation::Mul:
expectedOutput = { 2, -2, 2, 2, 10, -10, 10, 10, -6, 6, 6, 6, 8, 8, -8, 8 };
break;
case armnn::BinaryOperation::Sub:
expectedOutput = { -1, -3, -1, -1, 3, -7, 3, 3, -5, 1, 1, 1, 2, 2, -6, 2 };
break;
case armnn::BinaryOperation::SqDiff:
expectedOutput = { 1, 9, 1, 1, 9, 49, 9, 9, 25, 1, 1, 1, 4, 4, 36, 4 };
break;
case armnn::BinaryOperation::Power:
expectedOutput = { 1, 1, 1, 1, 25, 25, 25, 25, 9, 9, 9, 9, 16, 16, 16, 16 };
break;
default:
throw("Invalid Elementwise Binary operation");
}
const std::vector<float> expectedOutput_const = expectedOutput;
// quantize data
std::vector<TInput> qInput1Data = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
std::vector<TInput> qInput2Data = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInput1Data }, { 1, qInput2Data }};
std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ElementwiseBinarySimple3DEndToEnd(const std::vector<BackendId>& backends,
BinaryOperation operation)
{
using namespace armnn;
const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
const TensorShape& input1Shape = { 2, 2 };
const TensorShape& input2Shape = { 2, 2 };
const TensorShape& outputShape = { 2, 2 };
// Builds up the structure of the network
INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
operation, qScale, qOffset);
CHECK(net);
const std::vector<float> input1({ 1, -1, 1, 1 });
const std::vector<float> input2({ 2, 2, 2, 2 });
std::vector<float> expectedOutput;
switch (operation) {
case armnn::BinaryOperation::Add:
expectedOutput = { 3, 1, 3, 3 };
break;
case armnn::BinaryOperation::Div:
expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f };
break;
case armnn::BinaryOperation::Maximum:
expectedOutput = { 2, 2, 2, 2 };
break;
case armnn::BinaryOperation::Minimum:
expectedOutput = { 1, -1, 1, 1 };
break;
case armnn::BinaryOperation::Mul:
expectedOutput = { 2, -2, 2, 2 };
break;
case armnn::BinaryOperation::Sub:
expectedOutput = { -1, -3, -1, -1 };
break;
case armnn::BinaryOperation::SqDiff:
expectedOutput = { 1, 9, 1, 1 };
break;
case armnn::BinaryOperation::Power:
expectedOutput = { 1, 1, 1, 1 };
break;
default:
throw("Invalid Elementwise Binary operation");
}
const std::vector<float> expectedOutput_const = expectedOutput;
// quantize data
std::vector<TInput> qInput1Data = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
std::vector<TInput> qInput2Data = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInput1Data }, { 1, qInput2Data }};
std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ElementwiseBinarySimpleNoReshapeEndToEnd(const std::vector<BackendId>& backends,
BinaryOperation operation)
{
using namespace armnn;
const float qScale = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<TInput>() ? 50 : 0;
const TensorShape& input1Shape = { 2, 2, 2, 2 };
const TensorShape& input2Shape = { 2, 2, 2, 2 };
const TensorShape& outputShape = { 2, 2, 2, 2 };
// Builds up the structure of the network
INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
operation, qScale, qOffset);
CHECK(net);
const std::vector<float> input1({ 1, -1, 1, 1, 5, -5, 5, 5, -3, 3, 3, 3, 4, 4, -4, 4 });
const std::vector<float> input2({ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 });
std::vector<float> expectedOutput;
switch (operation) {
case armnn::BinaryOperation::Add:
expectedOutput = { 3, 1, 3, 3, 7, -3, 7, 7, -1, 5, 5, 5, 6, 6, -2, 6 };
break;
case armnn::BinaryOperation::Div:
expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f, 2.5f, -2.5f, 2.5f, 2.5f, -1.5f, 1.5f, 1.5f, 1.5f, 2, 2, -2, 2};
break;
case armnn::BinaryOperation::Maximum:
expectedOutput = { 2, 2, 2, 2, 5, 2, 5, 5, 2, 3, 3, 3, 4, 4, 2, 4 };
break;
case armnn::BinaryOperation::Minimum:
expectedOutput = { 1, -1, 1, 1, 2, -5, 2, 2, -3, 2, 2, 2, 2, 2, -4, 2 };
break;
case armnn::BinaryOperation::Mul:
expectedOutput = { 2, -2, 2, 2, 10, -10, 10, 10, -6, 6, 6, 6, 8, 8, -8, 8 };
break;
case armnn::BinaryOperation::Sub:
expectedOutput = { -1, -3, -1, -1, 3, -7, 3, 3, -5, 1, 1, 1, 2, 2, -6, 2 };
break;
case armnn::BinaryOperation::SqDiff:
expectedOutput = { 1, 9, 1, 1, 9, 49, 9, 9, 25, 1, 1, 1, 4, 4, 36, 4 };
break;
case armnn::BinaryOperation::Power:
expectedOutput = { 1, 1, 1, 1, 25, 25, 25, 25, 9, 9, 9, 9, 16, 16, 16, 16 };
break;
default:
throw("Invalid Elementwise Binary operation");
}
const std::vector<float> expectedOutput_const = expectedOutput;
// quantize data
std::vector<TInput> qInput1Data = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
std::vector<TInput> qInput2Data = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, qInput1Data }, { 1, qInput2Data }};
std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace
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