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//
// Copyright © 2020-2021,2023-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "EndToEndTestImpl.hpp"
#include <armnn/INetwork.hpp>
#include <armnn/TypesUtils.hpp>
#include <CommonTestUtils.hpp>
#include <ResolveType.hpp>
namespace
{
/** Defines the acceptable tolerance of ActivationFunction-DataType combinations.
*
* @param activationFunction The activation function used
* @param dataType Data type used
*
* @return Tolerance depending on the activation function and data type
*/
float GetActivationTolerance(const armnn::ActivationFunction& activationFunction, DataType dataType)
{
constexpr float defaultTolerance = 1e-6f;
switch (activationFunction)
{
// The following values are taken from ArmComputeLibrary/tests/validation/CL/ActivationLayer.cpp
case ActivationFunction::Elu:
return (dataType == DataType::Float16 ? 0.01f : 0.00001f);
case ActivationFunction::HardSwish:
return (dataType == DataType::Float16 ? 0.01f : defaultTolerance);
default:
return defaultTolerance;
}
}
/** Creates a network with one layer of the activation function specified in the activation descriptor.
*
* @param inputInfo Tensor info of inputs
* @param outputInfo Tensor info of outputs
* @param descriptor Activation descriptor
*
* @return INetworkPtr A pointer to the created network
*/
armnn::INetworkPtr CreateActivationNetwork(const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ActivationDescriptor& descriptor)
{
using namespace armnn;
char const* ActivationName = GetActivationFunctionAsCString(descriptor.m_Function);
INetworkPtr net(INetwork::Create());
IConnectableLayer* inputLayer = net->AddInputLayer(0, "input");
IConnectableLayer* activationLayer = net->AddActivationLayer(descriptor, ActivationName);
IConnectableLayer* outputLayer = net->AddOutputLayer(0, "output");
Connect(inputLayer, activationLayer, inputInfo, 0, 0);
Connect(activationLayer, outputLayer, outputInfo, 0, 0);
return net;
}
/** Specifies the implementation of end to end tests for activation functions.
*
* - Converts input data and expected-output data to the data type that is desired for the test (ArmnnType)
* - Creates a network with one layer of the activation function specified in the activation descriptor.
* - Executes the network on specified backends and compares results to expected output values
*
* @tparam ArmnnType The armnn data type for the input and expected-output data
* @param backends Backends to run test on
* @param floatInputData Input data given as vector of float
* @param floatExpectedOutputData Expected output data given as vector of float
* @param inputInfo Tensor info of inputs
* @param outputInfo Tensor info of outputs
* @param descriptor Activation descriptor
*/
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void ActivationEndToEndImpl(const std::vector<armnn::BackendId>& backends,
const std::vector<float>& floatInputData,
const std::vector<float>& floatExpectedOutputData,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ActivationDescriptor& descriptor)
{
using namespace armnn;
// Selectively quantizes/transforms float values to the needed data type
std::vector<T> inputData = armnnUtils::QuantizedVector<T>( floatInputData,
inputInfo.GetQuantizationScale(),
inputInfo.GetQuantizationOffset());
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>( floatExpectedOutputData,
outputInfo.GetQuantizationScale(),
outputInfo.GetQuantizationOffset());
INetworkPtr net = CreateActivationNetwork(inputInfo, outputInfo, descriptor);
std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
std::map<int, std::vector<T>> expectedOutputTensorData = { { 0, expectedOutputData } };
float tolerance = GetActivationTolerance(descriptor.m_Function, ArmnnType);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net),
inputTensorData,
expectedOutputTensorData,
backends,
tolerance);
}
std::vector<float> Activation(const std::vector<float>& input,
const ActivationDescriptor& descriptor)
{
float a = descriptor.m_A;
float b = descriptor.m_B;
std::vector<float> output;
output.reserve(input.size());
// Compute the result of the activation function.
switch (descriptor.m_Function)
{
case ActivationFunction::Linear:
{
for (auto in :input)
{
auto out = a * in + b;
output.push_back(out);
}
break;
}
case ActivationFunction::Sigmoid:
{
for (auto in :input)
{
auto out = 1.f / (1.f + expf(-in));
output.push_back(out);
}
break;
}
case ActivationFunction::ReLu:
{
for (auto in :input)
{
auto out = std::max(0.f, in);
output.push_back(out);
}
break;
}
case ActivationFunction::BoundedReLu:
{
for (auto in :input)
{
auto out = std::min(a, std::max(b, in));
output.push_back(out);
}
break;
}
case ActivationFunction::SoftReLu:
{
for (auto in :input)
{
auto out = logf(1.0f + expf(in));
output.push_back(out);
}
break;
}
case ActivationFunction::LeakyReLu:
{
for (auto in :input)
{
auto out = in > 0.0f ? in : (in * a);
output.push_back(out);
}
break;
}
case ActivationFunction::Abs:
{
for (auto in :input)
{
auto out = in < 0 ? -in : in;
output.push_back(out);
}
break;
}
case ActivationFunction::Sqrt:
{
for (auto in :input)
{
auto out = sqrtf(in);
output.push_back(out);
}
break;
}
case ActivationFunction::Square:
{
for (auto in :input)
{
auto out = in * in;
output.push_back(out);
}
break;
}
case ActivationFunction::TanH:
{
for (auto in :input)
{
auto out = a * tanhf(b * in);
output.push_back(out);
}
break;
}
case ActivationFunction::Elu:
{
for (auto in: input) {
auto out = (in >= 0) ? in : a * (expf(in) - 1);
output.push_back(out);
}
break;
}
case ActivationFunction::HardSwish:
{
for (auto in :input)
{
// hard_swish(x) = x * relu6(x+3) / 6
// relu6(x) = min(max(x,0),6)
auto out = in * (std::min(std::max((in + 3), 0.0f), 6.0f)) / 6;
output.push_back(out);
}
break;
}
case ActivationFunction::Gelu:
{
for (auto in :input)
{
// gelu(x) = x * 1/2 * (1 + erf(x / sqrt(2))),
// where erf is Gaussian error function
auto out = in * (0.5f * (1.0f + erff(static_cast<float>(in / std::sqrt(2)))));
output.push_back(out);
}
break;
}
default:
{
throw InvalidArgumentException("Unsupported activation function");
}
}
return output;
}
/** Executes an end to end test for activation layers with specific input and expected-output data
*
* @tparam ArmnnType The armnn data type for the input and expected-output data
* @param backends The backends on which to run the test
*/
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void ActivationEndToEndTest(const std::vector<BackendId>& backends,
const ActivationFunction activationFunction,
const float qScale=1.0f,
const int32_t qOffset=0,
const float a = 1,
const float b = 0)
{
std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f,
1.0f, 2.0f, 3.0f, 4.0f };
ActivationDescriptor descriptor(activationFunction, a, b);
std::vector<float> floatExpectedOutputData = Activation(floatInputData, descriptor);
armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset, true);
armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
ActivationEndToEndImpl<ArmnnType>(backends,
floatInputData,
floatExpectedOutputData,
inputInfo,
outputInfo,
descriptor);
}
} // anonymous namespace
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