IVGCVSW-3895 Add EndToEndLayerTest for the Reference Implementation of LOG_SOFTMAX
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I500f0b819be543dc5829990d363e5d911197c9ec
diff --git a/src/backends/backendsCommon/test/LogSoftmaxEndToEndTestImpl.cpp b/src/backends/backendsCommon/test/LogSoftmaxEndToEndTestImpl.cpp
new file mode 100644
index 0000000..f1e6242
--- /dev/null
+++ b/src/backends/backendsCommon/test/LogSoftmaxEndToEndTestImpl.cpp
@@ -0,0 +1,106 @@
+//
+// 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 <boost/test/unit_test.hpp>
+
+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);
+
+ 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);
+
+ BOOST_TEST_CHECKPOINT("Create a network");
+
+ 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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