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//
// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <TestUtils.hpp>
#include <ResolveType.hpp>
#include <armnnUtils/QuantizeHelper.hpp>
#include <armnn/INetwork.hpp>
#include <doctest/doctest.h>
using namespace armnn;
namespace
{
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void MulMaxMinTest(Compute backendId, size_t numLayers)
{
const TensorInfo input0TensorInfo({ 1, 2, 2, 3 },
ArmnnType,
IsQuantizedType<T>() ? 0.25f : 1,
IsQuantizedType<T>() ? 10 : 0,
true);
const TensorInfo input1TensorInfo({ 1, 1, 1, 1 },
ArmnnType,
IsQuantizedType<T>() ? 0.25f : 1,
IsQuantizedType<T>() ? 11 : 0,
true);
const TensorInfo maxInput1TensorInfo({ 1, 1, 1, 1 },
ArmnnType,
IsQuantizedType<T>() ? 0.25f : 1,
IsQuantizedType<T>() ? 12 : 0,
true);
const TensorInfo minInput1TensorInfo({ 1, 1, 1, 1 },
ArmnnType,
IsQuantizedType<T>() ? 0.25f : 1,
IsQuantizedType<T>() ? 13 : 0,
true);
const TensorInfo outputTensorInfo({ 1, 2, 2, 3 },
ArmnnType,
IsQuantizedType<T>() ? 0.5f : 1,
IsQuantizedType<T>() ? 14 : 0);
std::vector<float> input0Data
{
0.0f, 0.0f, 0.0f,
1.0f, 1.0f, 1.0f,
-1.0f, -1.0f, -1.0f,
-2.0f, -2.0f, -2.0f
};
std::vector<float> input1Data
{
1.0f
};
std::vector<float> maxInput1Data
{
-100.0f
};
std::vector<float> minInput1Data
{
100.0f
};
std::vector<float> outputExpectedData =
{
0.0f, 0.0f, 0.0f,
1.0f, 1.0f, 1.0f,
-1.0f, -1.0f, -1.0f,
-2.0f, -2.0f, -2.0f
};
std::vector<T> input0 = armnnUtils::QuantizedVector<T>(input0Data,
input0TensorInfo.GetQuantizationScale(),
input0TensorInfo.GetQuantizationOffset());
std::vector<T> input1 = armnnUtils::QuantizedVector<T>(input1Data,
input1TensorInfo.GetQuantizationScale(),
input1TensorInfo.GetQuantizationOffset());
std::vector<T> maxInput1 = armnnUtils::QuantizedVector<T>(maxInput1Data,
maxInput1TensorInfo.GetQuantizationScale(),
maxInput1TensorInfo.GetQuantizationOffset());
std::vector<T> minInput1 = armnnUtils::QuantizedVector<T>(minInput1Data,
minInput1TensorInfo.GetQuantizationScale(),
minInput1TensorInfo.GetQuantizationOffset());
std::vector<T> outputExpected = armnnUtils::QuantizedVector<T>(outputExpectedData,
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset());
std::vector<T> outputActual(outputTensorInfo.GetNumElements());
// Create a network
INetworkPtr network = INetwork::Create();
// add layers to network
IConnectableLayer* const input0Layer = network->AddInputLayer(0);
IConnectableLayer* const input1Layer = network->AddInputLayer(1);
IConnectableLayer* const mulLayer = network->AddElementwiseBinaryLayer(BinaryOperation::Mul, "mul");
auto constMaxInput1Tensor = ConstTensor(maxInput1TensorInfo, maxInput1);
IConnectableLayer* const maxInput1Layer = network->AddConstantLayer(constMaxInput1Tensor, "maxInput1");
IConnectableLayer* const maxLayer = network->AddElementwiseBinaryLayer(BinaryOperation::Maximum, "max");
auto constMinInput1Tensor = ConstTensor(minInput1TensorInfo, minInput1);
IConnectableLayer* const minInput1Layer = network->AddConstantLayer(constMinInput1Tensor, "minInput1");
IConnectableLayer* const minLayer = network->AddElementwiseBinaryLayer(BinaryOperation::Minimum, "min");
IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
// set tensor info to output slots
input0Layer->GetOutputSlot(0).SetTensorInfo(input0TensorInfo);
input1Layer->GetOutputSlot(0).SetTensorInfo(input1TensorInfo);
mulLayer->GetOutputSlot(0).SetTensorInfo(input0TensorInfo);
maxInput1Layer->GetOutputSlot(0).SetTensorInfo(maxInput1TensorInfo);
maxLayer->GetOutputSlot(0).SetTensorInfo(input0TensorInfo);
minInput1Layer->GetOutputSlot(0).SetTensorInfo(minInput1TensorInfo);
minLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// connect layers.
// In0 In1
// \ /
// Mul maxIn1
// | /
// Max minIn1
// | /
// Min
// |
// Out
input0Layer ->GetOutputSlot(0).Connect(mulLayer->GetInputSlot(0));
input1Layer ->GetOutputSlot(0).Connect(mulLayer->GetInputSlot(1));
mulLayer ->GetOutputSlot(0).Connect(maxLayer->GetInputSlot(0));
maxInput1Layer->GetOutputSlot(0).Connect(maxLayer->GetInputSlot(1));
maxLayer ->GetOutputSlot(0).Connect(minLayer->GetInputSlot(0));
minInput1Layer->GetOutputSlot(0).Connect(minLayer->GetInputSlot(1));
minLayer ->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
// Create ArmNN runtime
IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions());
// Optimise ArmNN network
IOptimizedNetworkPtr optNet = Optimize(*network, {backendId}, run->GetDeviceSpec());
Graph& graph = GetGraphForTesting(optNet.get());
auto checkMul = [ ](const armnn::Layer* const layer) -> bool
{
auto* mulLayer = PolymorphicDowncast<const ElementwiseBinaryLayer*>(layer);
return IsLayerOfType<ElementwiseBinaryLayer>(layer) &&
(mulLayer->GetParameters().m_Operation == BinaryOperation::Mul);
};
auto checkBoundedRelu = [ ](const armnn::Layer* const layer) -> bool
{
auto* activationLayer = PolymorphicDowncast<const ActivationLayer*>(layer);
return IsLayerOfType<ActivationLayer>(layer) &&
(activationLayer->GetParameters().m_Function == ActivationFunction::BoundedReLu);
};
// 2 inputs, mul, activation(in CpuRef and CpuAcc), output
CHECK((graph.GetNumLayers() == numLayers));
if (numLayers == 4)
{
CHECK(CheckSequence(graph.cbegin(),
graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
checkMul,
&IsLayerOfType<OutputLayer>));
}
else if (numLayers == 5)
{
CHECK(CheckSequence(graph.cbegin(),
graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
checkMul,
checkBoundedRelu,
&IsLayerOfType<OutputLayer>));
}
// Load network into runtime
NetworkId networkIdentifier;
run->LoadNetwork(networkIdentifier, std::move(optNet));
// Create input and output tensors
InputTensors inputTensors
{
{0, ConstTensor(input0TensorInfo, input0.data())},
{1, ConstTensor(input1TensorInfo, input1.data())}
};
OutputTensors outputTensors
{
{0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputActual.data())}
};
// Run inference
run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
// Checks the results
CHECK(outputActual == outputExpected);
}
}
TEST_SUITE("Optimizer")
{
#if defined(ARMNNREF_ENABLED)
TEST_CASE("FuseMulMaxMinTest_Float_CpuRef")
{
MulMaxMinTest<DataType::Float32>(Compute::CpuRef, 5);
}
#endif
#if defined(ARMCOMPUTENEON_ENABLED)
TEST_CASE("FuseMulMaxMinTest_Float_CpuAcc")
{
MulMaxMinTest<DataType::Float32>(Compute::CpuAcc, 5);
}
#endif
#if defined(ARMCOMPUTECL_ENABLED)
TEST_CASE("FuseMulMaxMinTest_Float_GpuAcc")
{
MulMaxMinTest<DataType::Float32>(Compute::GpuAcc, 4);
}
#endif
}
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