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//
// Copyright © 2019 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "EndToEndTestImpl.hpp"
#include "LogSoftmaxEndToEndTestImpl.hpp"
#include <armnn/INetwork.hpp>
#include <test/TestUtils.hpp>
#include <doctest/doctest.h>
namespace {
template <typename armnn::DataType DataType>
armnn::INetworkPtr CreateLogSoftmaxNetwork(const armnn::TensorShape& inputShape,
const armnn::TensorShape& outputShape,
const float beta,
const int axis,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
// Builds up the structure of the network.
INetworkPtr net(INetwork::Create());
TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true);
LogSoftmaxDescriptor logSoftmaxDesc;
logSoftmaxDesc.m_Beta = beta;
logSoftmaxDesc.m_Axis = axis;
IConnectableLayer* logSoftmax = net->AddLogSoftmaxLayer(logSoftmaxDesc, "Log_Softmax");
IConnectableLayer* input = net->AddInputLayer(0, "input");
Connect(input, logSoftmax, inputTensorInfo, 0, 0);
TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
IConnectableLayer* output = net->AddOutputLayer(0, "output");
Connect(logSoftmax, output, outputTensorInfo, 0, 0);
return net;
}
void LogSoftmaxEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::TensorInfo& inputTensorInfo,
armnn::TensorInfo& outputTensorInfo,
std::vector<float>& inputData,
std::vector<float>& expectedOutputData,
const float beta,
const int axis)
{
using namespace armnn;
// Builds up the structure of the network
INetworkPtr net = CreateLogSoftmaxNetwork<DataType::Float32>(inputTensorInfo.GetShape(),
outputTensorInfo.GetShape(),
beta,
axis);
CHECK(net);
std::map<int, std::vector<float>> inputTensorData = { {0, inputData} };
std::map<int, std::vector<float>> expectedOutputTensorData = { {0, expectedOutputData} };
EndToEndLayerTestImpl<DataType::Float32, DataType::Float32>(move(net),
inputTensorData,
expectedOutputTensorData,
backends);
}
} // anonymous namespace
void LogSoftmaxEndToEndTest(const std::vector<armnn::BackendId>& defaultBackends)
{
using namespace armnn;
const float beta = 10.0f; // non-default beta
const int axis = 3; // positive axis
const TensorShape inputShape{1, 1, 2, 4};
TensorInfo inputTensorInfo(inputShape, DataType::Float32);
const TensorShape outputShape{1, 1, 2, 4};
TensorInfo outputTensorInfo(outputShape, DataType::Float32);
std::vector<float> inputData = std::vector<float>({
0.0f, -0.6f, 0.2f, 0.4f,
0.3f, -0.2f, 1.0f, 0.1f
});
std::vector<float> expectedOutputData = std::vector<float>({
-4.14297f, -10.14297f, -2.14297f, -0.14297f,
-7.00104f, -12.00104f, -0.00104087f, -9.00104f
});
LogSoftmaxEndToEnd(defaultBackends,
inputTensorInfo,
outputTensorInfo,
inputData,
expectedOutputData,
beta,
axis);
}
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