IVGCVSW-2051 Added unit tests in the Android NN Driver for the new
Mean layer
* Added unit tests (in a new file test/1.1/Mean.cpp)
* Refactored ArmnnDriverImpl to remove redundant tags in the log
messages
* Modified AddInputOperand to accept a quantized input tensor
Change-Id: Ie037ce426777daab28b0501124e1cc8686ad6184
diff --git a/test/1.1/Mean.cpp b/test/1.1/Mean.cpp
new file mode 100644
index 0000000..4ebb8cf
--- /dev/null
+++ b/test/1.1/Mean.cpp
@@ -0,0 +1,137 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../DriverTestHelpers.hpp"
+#include "../TestTensor.hpp"
+
+#include <boost/array.hpp>
+#include <boost/test/data/test_case.hpp>
+
+BOOST_AUTO_TEST_SUITE(MeanTests)
+
+using namespace android::hardware;
+using namespace driverTestHelpers;
+using namespace armnn_driver;
+
+namespace
+{
+
+static const boost::array<armnn::Compute, 2> COMPUTE_DEVICES = {{ armnn::Compute::CpuRef, armnn::Compute::GpuAcc }};
+
+void MeanTestImpl(const TestTensor& input,
+ const hidl_vec<uint32_t>& axisDimensions,
+ const int32_t* axisValues,
+ int32_t keepDims,
+ const TestTensor& expectedOutput,
+ bool fp16Enabled,
+ armnn::Compute computeDevice)
+{
+ auto driver = std::make_unique<ArmnnDriver>(DriverOptions(computeDevice, fp16Enabled));
+
+ V1_1::Model model = {};
+ AddInputOperand (model, input.GetDimensions());
+ AddTensorOperand(model, axisDimensions, const_cast<int32_t*>(axisValues), OperandType::TENSOR_INT32);
+ AddIntOperand (model, keepDims);
+ AddOutputOperand(model, expectedOutput.GetDimensions());
+
+ model.operations.resize(1);
+ model.operations[0].type = V1_1::OperationType::MEAN;
+ model.operations[0].inputs = hidl_vec<uint32_t>{ 0, 1, 2 };
+ model.operations[0].outputs = hidl_vec<uint32_t>{ 3 };
+ model.relaxComputationFloat32toFloat16 = fp16Enabled;
+
+ android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
+
+ // The request's memory pools will follow the same order as the inputs
+ DataLocation inLoc = {};
+ inLoc.poolIndex = 0;
+ inLoc.offset = 0;
+ inLoc.length = input.GetNumElements() * sizeof(float);
+ RequestArgument inArg = {};
+ inArg.location = inLoc;
+ inArg.dimensions = input.GetDimensions();
+
+ // An additional memory pool is needed for the output
+ DataLocation outLoc = {};
+ outLoc.poolIndex = 1;
+ outLoc.offset = 0;
+ outLoc.length = expectedOutput.GetNumElements() * sizeof(float);
+ RequestArgument outArg = {};
+ outArg.location = outLoc;
+ outArg.dimensions = expectedOutput.GetDimensions();
+
+ // Make the request based on the arguments
+ Request request = {};
+ request.inputs = hidl_vec<RequestArgument>{ inArg };
+ request.outputs = hidl_vec<RequestArgument>{ outArg };
+
+ // Set the input data
+ AddPoolAndSetData(input.GetNumElements(), request, input.GetData());
+
+ // Add memory for the output
+ android::sp<IMemory> outMemory = AddPoolAndGetData(expectedOutput.GetNumElements(), request);
+ const float* outputData = static_cast<const float*>(static_cast<void*>(outMemory->getPointer()));
+
+ ErrorStatus execStatus = Execute(preparedModel, request);
+ BOOST_TEST(execStatus == ErrorStatus::NONE);
+
+ const float* expectedOutputData = expectedOutput.GetData();
+ for (unsigned int i = 0; i < expectedOutput.GetNumElements(); i++)
+ {
+ BOOST_TEST(outputData[i] == expectedOutputData[i]);
+ }
+}
+
+} // anonymous namespace
+
+BOOST_DATA_TEST_CASE(MeanNoKeepDimsTest, COMPUTE_DEVICES)
+{
+ TestTensor input{ armnn::TensorShape{ 4, 3, 2 }, { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
+ 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
+ 20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
+ hidl_vec<uint32_t> axisDimensions = { 2 };
+ int32_t axisValues[] = { 0, 1 };
+ int32_t keepDims = 0;
+ TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
+
+ MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, sample);
+}
+
+BOOST_DATA_TEST_CASE(MeanKeepDimsTest, COMPUTE_DEVICES)
+{
+ TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
+ hidl_vec<uint32_t> axisDimensions = { 1 };
+ int32_t axisValues[] = { 2 };
+ int32_t keepDims = 1;
+ TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
+
+ MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, sample);
+}
+
+BOOST_DATA_TEST_CASE(MeanFp16NoKeepDimsTest, COMPUTE_DEVICES)
+{
+ TestTensor input{ armnn::TensorShape{ 4, 3, 2 }, { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
+ 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
+ 20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
+ hidl_vec<uint32_t> axisDimensions = { 2 };
+ int32_t axisValues[] = { 0, 1 };
+ int32_t keepDims = 0;
+ TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
+
+ MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, sample);
+}
+
+BOOST_DATA_TEST_CASE(MeanFp16KeepDimsTest, COMPUTE_DEVICES)
+{
+ TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
+ hidl_vec<uint32_t> axisDimensions = { 1 };
+ int32_t axisValues[] = { 2 };
+ int32_t keepDims = 1;
+ TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
+
+ MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, sample);
+}
+
+BOOST_AUTO_TEST_SUITE_END()