IVGCVSW-1713 Create a minimum unit test to compare the results
before and after passing the FP16 flag in the Android-nn-driver
Change-Id: If8d4ca12421c3bee2526eec98f11d393af822373
diff --git a/test/1.0/Convolution2D.cpp b/test/1.0/Convolution2D.cpp
new file mode 100644
index 0000000..9a5d239
--- /dev/null
+++ b/test/1.0/Convolution2D.cpp
@@ -0,0 +1,42 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../DriverTestHelpers.hpp"
+#include "../Convolution2D.hpp"
+#include "../../1.0/HalPolicy.hpp"
+
+#include <boost/test/unit_test.hpp>
+#include <log/log.h>
+
+#include <OperationsUtils.h>
+
+BOOST_AUTO_TEST_SUITE(Convolution2DTests)
+
+using namespace android::hardware;
+using namespace driverTestHelpers;
+using namespace armnn_driver;
+
+namespace driverTestHelpers
+{
+
+void SetModelFp16Flag(V1_0::Model&, bool)
+{
+ // Nothing to do, the V1_0::Model does not support fp16 precision relaxation.
+ // This function is used for compatibility only.
+}
+
+} // namespace driverTestHelpers
+
+BOOST_AUTO_TEST_CASE(ConvValidPadding_Hal_1_0)
+{
+ PaddingTestImpl<hal_1_0::HalPolicy>(android::nn::kPaddingValid);
+}
+
+BOOST_AUTO_TEST_CASE(ConvSamePadding_Hal_1_0)
+{
+ PaddingTestImpl<hal_1_0::HalPolicy>(android::nn::kPaddingSame);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/test/1.1/Convolution2D.cpp b/test/1.1/Convolution2D.cpp
new file mode 100644
index 0000000..32d5018
--- /dev/null
+++ b/test/1.1/Convolution2D.cpp
@@ -0,0 +1,52 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../DriverTestHelpers.hpp"
+#include "../Convolution2D.hpp"
+#include "../../1.1/HalPolicy.hpp"
+
+#include <boost/test/unit_test.hpp>
+#include <log/log.h>
+
+#include <OperationsUtils.h>
+
+BOOST_AUTO_TEST_SUITE(Convolution2DTests)
+
+using namespace android::hardware;
+using namespace driverTestHelpers;
+using namespace armnn_driver;
+
+namespace driverTestHelpers
+{
+
+void SetModelFp16Flag(V1_1::Model& model, bool fp16Enabled)
+{
+ // Set the fp16 flag in the given model
+ model.relaxComputationFloat32toFloat16 = fp16Enabled;
+}
+
+} // namespace driverTestHelpers
+
+BOOST_AUTO_TEST_CASE(ConvValidPadding_Hal_1_1)
+{
+ PaddingTestImpl<hal_1_1::HalPolicy>(android::nn::kPaddingValid);
+}
+
+BOOST_AUTO_TEST_CASE(ConvSamePadding_Hal_1_1)
+{
+ PaddingTestImpl<hal_1_1::HalPolicy>(android::nn::kPaddingSame);
+}
+
+BOOST_AUTO_TEST_CASE(ConvValidPaddingFp16Flag_Hal_1_1)
+{
+ PaddingTestImpl<hal_1_1::HalPolicy>(android::nn::kPaddingValid, true);
+}
+
+BOOST_AUTO_TEST_CASE(ConvSamePaddingFp16Flag_Hal_1_1)
+{
+ PaddingTestImpl<hal_1_1::HalPolicy>(android::nn::kPaddingSame, true);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/test/Android.mk b/test/Android.mk
index cab874c..c38b92e 100644
--- a/test/Android.mk
+++ b/test/Android.mk
@@ -44,10 +44,10 @@
endif # PLATFORM_VERSION == 9
LOCAL_SRC_FILES := \
+ 1.0/Convolution2D.cpp \
Tests.cpp \
UtilsTests.cpp \
Concurrent.cpp \
- Convolution2D.cpp \
FullyConnected.cpp \
GenericLayerTests.cpp \
DriverTestHelpers.cpp \
@@ -118,10 +118,11 @@
-DARMNN_ANDROID_NN_V1_1
LOCAL_SRC_FILES := \
+ 1.0/Convolution2D.cpp \
+ 1.1/Convolution2D.cpp \
Tests.cpp \
UtilsTests.cpp \
Concurrent.cpp \
- Convolution2D.cpp \
FullyConnected.cpp \
GenericLayerTests.cpp \
DriverTestHelpers.cpp \
diff --git a/test/Convolution2D.cpp b/test/Convolution2D.hpp
similarity index 61%
rename from test/Convolution2D.cpp
rename to test/Convolution2D.hpp
index 3f097b8..ff417d9 100644
--- a/test/Convolution2D.cpp
+++ b/test/Convolution2D.hpp
@@ -2,7 +2,11 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
+
+#pragma once
+
#include "DriverTestHelpers.hpp"
+
#include <boost/test/unit_test.hpp>
#include <log/log.h>
@@ -14,19 +18,29 @@
using namespace driverTestHelpers;
using namespace armnn_driver;
-namespace
+namespace driverTestHelpers
{
-void PaddingTestImpl(android::nn::PaddingScheme paddingScheme)
+void SetModelFp16Flag(V1_0::Model& model, bool fp16Enabled);
+
+#ifdef ARMNN_ANDROID_NN_V1_1
+void SetModelFp16Flag(V1_1::Model& model, bool fp16Enabled);
+#endif
+
+template<typename HalPolicy>
+void PaddingTestImpl(android::nn::PaddingScheme paddingScheme, bool fp16Enabled = false)
{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
- neuralnetworks::V1_0::Model model = {};
+ using HalModel = typename HalPolicy::Model;
+ using HalOperationType = typename HalPolicy::OperationType;
+
+ auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::GpuAcc, fp16Enabled));
+ HalModel model = {};
uint32_t outSize = paddingScheme == android::nn::kPaddingSame ? 2 : 1;
// add operands
- float weightValue[] = {1, -1, 0, 1};
- float biasValue[] = {0};
+ float weightValue[] = {1.f, -1.f, 0.f, 1.f};
+ float biasValue[] = {0.f};
AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 3, 1});
AddTensorOperand(model, hidl_vec<uint32_t>{1, 2, 2, 1}, weightValue);
@@ -39,11 +53,12 @@
// make the convolution operation
model.operations.resize(1);
- model.operations[0].type = neuralnetworks::V1_0::OperationType::CONV_2D;
+ model.operations[0].type = HalOperationType::CONV_2D;
model.operations[0].inputs = hidl_vec<uint32_t>{0, 1, 2, 3, 4, 5, 6};
model.operations[0].outputs = hidl_vec<uint32_t>{7};
// make the prepared model
+ SetModelFp16Flag(model, fp16Enabled);
android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
// construct the request
@@ -67,44 +82,48 @@
request.inputs = hidl_vec<RequestArgument>{input};
request.outputs = hidl_vec<RequestArgument>{output};
-
// set the input data (matching source test)
- float indata[] = {4, 1, 0, 3, -1, 2};
+ float indata[] = {1024.25f, 1.f, 0.f, 3.f, -1, -1024.25f};
AddPoolAndSetData(6, request, indata);
// add memory for the output
android::sp<IMemory> outMemory = AddPoolAndGetData(outSize, request);
- float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
+ float* outdata = reinterpret_cast<float*>(static_cast<void*>(outMemory->getPointer()));
// run the execution
Execute(preparedModel, request);
// check the result
- if (paddingScheme == android::nn::kPaddingValid)
+ switch (paddingScheme)
{
- BOOST_TEST(outdata[0] == 2);
- }
- else if (paddingScheme == android::nn::kPaddingSame)
- {
- BOOST_TEST(outdata[0] == 2);
- BOOST_TEST(outdata[1] == 0);
- }
- else
- {
+ case android::nn::kPaddingValid:
+ if (fp16Enabled)
+ {
+ BOOST_TEST(outdata[0] == 1022.f);
+ }
+ else
+ {
+ BOOST_TEST(outdata[0] == 1022.25f);
+ }
+ break;
+ case android::nn::kPaddingSame:
+ if (fp16Enabled)
+ {
+ BOOST_TEST(outdata[0] == 1022.f);
+ BOOST_TEST(outdata[1] == 0.f);
+ }
+ else
+ {
+ BOOST_TEST(outdata[0] == 1022.25f);
+ BOOST_TEST(outdata[1] == 0.f);
+ }
+ break;
+ default:
BOOST_TEST(false);
+ break;
}
}
-} // namespace <anonymous>
-
-BOOST_AUTO_TEST_CASE(ConvValidPadding)
-{
- PaddingTestImpl(android::nn::kPaddingValid);
-}
-
-BOOST_AUTO_TEST_CASE(ConvSamePadding)
-{
- PaddingTestImpl(android::nn::kPaddingSame);
-}
+} // namespace driverTestHelpers
BOOST_AUTO_TEST_SUITE_END()
diff --git a/test/DriverTestHelpers.cpp b/test/DriverTestHelpers.cpp
index 1115491..ded2459 100644
--- a/test/DriverTestHelpers.cpp
+++ b/test/DriverTestHelpers.cpp
@@ -109,68 +109,11 @@
memcpy(dst, data, size * sizeof(float));
}
-void AddOperand(neuralnetworks::V1_0::Model& model, const Operand& op)
-{
- model.operands.resize(model.operands.size() + 1);
- model.operands[model.operands.size() - 1] = op;
-}
-
-void AddIntOperand(neuralnetworks::V1_0::Model& model, int32_t value)
-{
- DataLocation location = {};
- location.offset = model.operandValues.size();
- location.length = sizeof(int32_t);
-
- Operand op = {};
- op.type = OperandType::INT32;
- op.dimensions = hidl_vec<uint32_t>{};
- op.lifetime = OperandLifeTime::CONSTANT_COPY;
- op.location = location;
-
- model.operandValues.resize(model.operandValues.size() + location.length);
- *reinterpret_cast<int32_t*>(&model.operandValues[location.offset]) = value;
-
- AddOperand(model, op);
-}
-
-void AddInputOperand(neuralnetworks::V1_0::Model& model,
- hidl_vec<uint32_t> dimensions,
- neuralnetworks::V1_0::OperandType operandType)
-{
- Operand op = {};
- op.type = operandType;
- op.dimensions = dimensions;
- op.lifetime = OperandLifeTime::MODEL_INPUT;
-
- AddOperand(model, op);
-
- model.inputIndexes.resize(model.inputIndexes.size() + 1);
- model.inputIndexes[model.inputIndexes.size() - 1] = model.operands.size() - 1;
-}
-
-void AddOutputOperand(neuralnetworks::V1_0::Model& model,
- hidl_vec<uint32_t> dimensions,
- neuralnetworks::V1_0::OperandType operandType)
-{
- Operand op = {};
- op.type = operandType;
- op.scale = operandType == neuralnetworks::V1_0::OperandType::TENSOR_QUANT8_ASYMM ? 1.f / 255.f : 0.f;
- op.dimensions = dimensions;
- op.lifetime = OperandLifeTime::MODEL_OUTPUT;
-
- AddOperand(model, op);
-
- model.outputIndexes.resize(model.outputIndexes.size() + 1);
- model.outputIndexes[model.outputIndexes.size() - 1] = model.operands.size() - 1;
-}
-
-
android::sp<IPreparedModel> PrepareModelWithStatus(const neuralnetworks::V1_0::Model& model,
armnn_driver::ArmnnDriver& driver,
- ErrorStatus & prepareStatus,
+ ErrorStatus& prepareStatus,
ErrorStatus expectedStatus)
{
-
android::sp<PreparedModelCallback> cb(new PreparedModelCallback());
driver.prepareModel(model, cb);
@@ -183,13 +126,27 @@
return cb->GetPreparedModel();
}
-android::sp<IPreparedModel> PrepareModel(const neuralnetworks::V1_0::Model& model,
- armnn_driver::ArmnnDriver& driver)
+#if defined(ARMNN_ANDROID_NN_V1_1) // Using ::android::hardware::neuralnetworks::V1_1.
+
+android::sp<IPreparedModel> PrepareModelWithStatus(const neuralnetworks::V1_1::Model& model,
+ armnn_driver::ArmnnDriver& driver,
+ ErrorStatus& prepareStatus,
+ ErrorStatus expectedStatus)
{
- ErrorStatus prepareStatus = ErrorStatus::NONE;
- return PrepareModelWithStatus(model, driver, prepareStatus);
+ android::sp<PreparedModelCallback> cb(new PreparedModelCallback());
+ driver.prepareModel_1_1(model, neuralnetworks::V1_1::ExecutionPreference::LOW_POWER, cb);
+
+ prepareStatus = cb->GetErrorStatus();
+ BOOST_TEST(prepareStatus == expectedStatus);
+ if (expectedStatus == ErrorStatus::NONE)
+ {
+ BOOST_TEST((cb->GetPreparedModel() != nullptr));
+ }
+ return cb->GetPreparedModel();
}
+#endif
+
ErrorStatus Execute(android::sp<IPreparedModel> preparedModel,
const Request& request,
ErrorStatus expectedStatus)
diff --git a/test/DriverTestHelpers.hpp b/test/DriverTestHelpers.hpp
index 03dbeb9..ce09ee6 100644
--- a/test/DriverTestHelpers.hpp
+++ b/test/DriverTestHelpers.hpp
@@ -10,6 +10,7 @@
#include "../ArmnnDriver.hpp"
#include <iosfwd>
+#include <boost/test/unit_test.hpp>
namespace android
{
@@ -72,9 +73,31 @@
void AddPoolAndSetData(uint32_t size, Request& request, const float* data);
-void AddOperand(::android::hardware::neuralnetworks::V1_0::Model& model, const Operand& op);
+template<typename HalModel>
+void AddOperand(HalModel& model, const Operand& op)
+{
+ model.operands.resize(model.operands.size() + 1);
+ model.operands[model.operands.size() - 1] = op;
+}
-void AddIntOperand(::android::hardware::neuralnetworks::V1_0::Model& model, int32_t value);
+template<typename HalModel>
+void AddIntOperand(HalModel& model, int32_t value)
+{
+ DataLocation location = {};
+ location.offset = model.operandValues.size();
+ location.length = sizeof(int32_t);
+
+ Operand op = {};
+ op.type = OperandType::INT32;
+ op.dimensions = hidl_vec<uint32_t>{};
+ op.lifetime = OperandLifeTime::CONSTANT_COPY;
+ op.location = location;
+
+ model.operandValues.resize(model.operandValues.size() + location.length);
+ *reinterpret_cast<int32_t*>(&model.operandValues[location.offset]) = value;
+
+ AddOperand<HalModel>(model, op);
+}
template<typename T>
OperandType TypeToOperandType();
@@ -85,8 +108,8 @@
template<>
OperandType TypeToOperandType<int32_t>();
-template<typename T>
-void AddTensorOperand(::android::hardware::neuralnetworks::V1_0::Model& model,
+template<typename HalModel, typename T>
+void AddTensorOperand(HalModel& model,
hidl_vec<uint32_t> dimensions,
T* values,
OperandType operandType = OperandType::TENSOR_FLOAT32)
@@ -113,28 +136,67 @@
*(reinterpret_cast<T*>(&model.operandValues[location.offset]) + i) = values[i];
}
- AddOperand(model, op);
+ AddOperand<HalModel>(model, op);
}
-void AddInputOperand(::android::hardware::neuralnetworks::V1_0::Model& model,
+template<typename HalModel>
+void AddInputOperand(HalModel& model,
hidl_vec<uint32_t> dimensions,
- ::android::hardware::neuralnetworks::V1_0::OperandType operandType = OperandType::TENSOR_FLOAT32);
+ OperandType operandType = OperandType::TENSOR_FLOAT32)
+{
+ Operand op = {};
+ op.type = operandType;
+ op.dimensions = dimensions;
+ op.lifetime = OperandLifeTime::MODEL_INPUT;
-void AddOutputOperand(::android::hardware::neuralnetworks::V1_0::Model& model,
+ AddOperand<HalModel>(model, op);
+
+ model.inputIndexes.resize(model.inputIndexes.size() + 1);
+ model.inputIndexes[model.inputIndexes.size() - 1] = model.operands.size() - 1;
+}
+
+template<typename HalModel>
+void AddOutputOperand(HalModel& model,
hidl_vec<uint32_t> dimensions,
- ::android::hardware::neuralnetworks::V1_0::OperandType operandType = OperandType::TENSOR_FLOAT32);
+ OperandType operandType = OperandType::TENSOR_FLOAT32)
+{
+ Operand op = {};
+ op.type = operandType;
+ op.scale = operandType == OperandType::TENSOR_QUANT8_ASYMM ? 1.f / 255.f : 0.f;
+ op.dimensions = dimensions;
+ op.lifetime = OperandLifeTime::MODEL_OUTPUT;
-android::sp<IPreparedModel> PrepareModel(const ::android::hardware::neuralnetworks::V1_0::Model& model,
- armnn_driver::ArmnnDriver& driver);
+ AddOperand<HalModel>(model, op);
+
+ model.outputIndexes.resize(model.outputIndexes.size() + 1);
+ model.outputIndexes[model.outputIndexes.size() - 1] = model.operands.size() - 1;
+}
android::sp<IPreparedModel> PrepareModelWithStatus(const ::android::hardware::neuralnetworks::V1_0::Model& model,
armnn_driver::ArmnnDriver& driver,
- ErrorStatus & prepareStatus,
- ErrorStatus expectedStatus=ErrorStatus::NONE);
+ ErrorStatus& prepareStatus,
+ ErrorStatus expectedStatus = ErrorStatus::NONE);
+
+#if defined(ARMNN_ANDROID_NN_V1_1) // Using ::android::hardware::neuralnetworks::V1_1.
+
+android::sp<IPreparedModel> PrepareModelWithStatus(const ::android::hardware::neuralnetworks::V1_1::Model& model,
+ armnn_driver::ArmnnDriver& driver,
+ ErrorStatus& prepareStatus,
+ ErrorStatus expectedStatus = ErrorStatus::NONE);
+
+#endif
+
+template<typename HalModel>
+android::sp<IPreparedModel> PrepareModel(const HalModel& model,
+ armnn_driver::ArmnnDriver& driver)
+{
+ ErrorStatus prepareStatus = ErrorStatus::NONE;
+ return PrepareModelWithStatus(model, driver, prepareStatus);
+}
ErrorStatus Execute(android::sp<IPreparedModel> preparedModel,
const Request& request,
- ErrorStatus expectedStatus=ErrorStatus::NONE);
+ ErrorStatus expectedStatus = ErrorStatus::NONE);
android::sp<ExecutionCallback> ExecuteNoWait(android::sp<IPreparedModel> preparedModel,
const Request& request);
diff --git a/test/FullyConnected.cpp b/test/FullyConnected.cpp
index 85e58d0..5c204ca 100644
--- a/test/FullyConnected.cpp
+++ b/test/FullyConnected.cpp
@@ -19,7 +19,7 @@
// but that uses slightly weird dimensions which I don't think we need to support for now
auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
- neuralnetworks::V1_0::Model model = {};
+ V1_0::Model model = {};
// add operands
int32_t actValue = 0;
@@ -34,7 +34,7 @@
// make the fully connected operation
model.operations.resize(1);
- model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED;
+ model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED;
model.operations[0].inputs = hidl_vec<uint32_t>{0, 1, 2, 3};
model.operations[0].outputs = hidl_vec<uint32_t>{4};
@@ -90,7 +90,7 @@
sup = supported;
};
- neuralnetworks::V1_0::Model model = {};
+ V1_0::Model model = {};
// operands
int32_t actValue = 0;
@@ -113,7 +113,7 @@
model.operations.resize(1);
- model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED;
+ model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED;
model.operations[0].inputs = hidl_vec<uint32_t>{0,1,2,3};
model.operations[0].outputs = hidl_vec<uint32_t>{4};
@@ -177,7 +177,7 @@
sup = supported;
};
- neuralnetworks::V1_0::Model model = {};
+ V1_0::Model model = {};
// operands
int32_t actValue = 0;
@@ -200,7 +200,7 @@
model.operations.resize(1);
- model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED;
+ model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED;
model.operations[0].inputs = hidl_vec<uint32_t>{0,1,2,3};
model.operations[0].outputs = hidl_vec<uint32_t>{4};
diff --git a/test/GenericLayerTests.cpp b/test/GenericLayerTests.cpp
index fd58a58..c66854f 100644
--- a/test/GenericLayerTests.cpp
+++ b/test/GenericLayerTests.cpp
@@ -216,7 +216,7 @@
// during mem pool mapping we properly report an error to the framework via a callback
BOOST_AUTO_TEST_CASE(ModelToINetworkConverterMemPoolFail)
{
- auto driver = std::make_unique<ArmnnDriver>(armnn::Compute::CpuRef);
+ auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
ErrorStatus errorStatus;
std::vector<bool> supported;