Release 18.05
diff --git a/test/Tests.cpp b/test/Tests.cpp
index 0ab2908..37aece7 100644
--- a/test/Tests.cpp
+++ b/test/Tests.cpp
@@ -2,43 +2,18 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
-
#define LOG_TAG "ArmnnDriverTests"
#define BOOST_TEST_MODULE armnn_driver_tests
#include <boost/test/unit_test.hpp>
#include <log/log.h>
-#include "../ArmnnDriver.hpp"
-#include "../SystemPropertiesUtils.hpp"
-
-#include "OperationsUtils.h"
-
-#include <condition_variable>
-
-namespace android
-{
-namespace hardware
-{
-namespace neuralnetworks
-{
-namespace V1_0
-{
-
-std::ostream& operator<<(std::ostream& os, ErrorStatus stat)
-{
- return os << static_cast<int>(stat);
-}
-
-}
-}
-}
-}
+#include "DriverTestHelpers.hpp"
BOOST_AUTO_TEST_SUITE(DriverTests)
-using namespace armnn_driver;
-using namespace android::nn;
-using namespace android;
+using ArmnnDriver = armnn_driver::ArmnnDriver;
+using DriverOptions = armnn_driver::DriverOptions;
+using namespace driverTestHelpers;
BOOST_AUTO_TEST_CASE(Init)
{
@@ -73,904 +48,4 @@
BOOST_TEST(cap.quantized8Performance.powerUsage > 0.f);
}
-BOOST_AUTO_TEST_CASE(SystemProperties)
-{
- // Test default value
- {
- auto p = __system_property_find("thisDoesNotExist");
- BOOST_TEST((p == nullptr));
-
- int defaultValue = ParseSystemProperty("thisDoesNotExist", -4);
- BOOST_TEST((defaultValue == -4));
- }
-
- // Test default value from bad data type
- {
- __system_property_set("thisIsNotFloat", "notfloat");
- float defaultValue = ParseSystemProperty("thisIsNotFloat", 0.1f);
- BOOST_TEST((defaultValue == 0.1f));
- }
-
- // Test fetching bool values
- {
- __system_property_set("myTestBool", "1");
- bool b = ParseSystemProperty("myTestBool", false);
- BOOST_TEST((b == true));
- }
- {
- __system_property_set("myTestBool", "0");
- bool b = ParseSystemProperty("myTestBool", true);
- BOOST_TEST((b == false));
- }
-
- // Test fetching int
- {
- __system_property_set("myTestInt", "567");
- int i = ParseSystemProperty("myTestInt", 890);
- BOOST_TEST((i==567));
- }
-
- // Test fetching float
- {
- __system_property_set("myTestFloat", "1.2f");
- float f = ParseSystemProperty("myTestFloat", 3.4f);
- BOOST_TEST((f==1.2f));
- }
-}
-
-// The following are helpers for writing unit tests for the driver
-namespace
-{
-
-struct ExecutionCallback : public IExecutionCallback
-{
- ExecutionCallback()
- : mNotified(false)
- {
- }
-
- Return<void> notify(ErrorStatus status) override
- {
- (void)status;
- ALOGI("ExecutionCallback::notify invoked");
- std::lock_guard<std::mutex> executionLock(mMutex);
- mNotified = true;
- mCondition.notify_one();
- return Void();
- }
-
- /// wait until the callback has notified us that it is done
- Return<void> wait()
- {
- ALOGI("ExecutionCallback::wait invoked");
- std::unique_lock<std::mutex> executionLock(mMutex);
- while (!mNotified)
- {
- mCondition.wait(executionLock);
- }
- mNotified = false;
- return Void();
- }
-
-private:
- // use a mutex and a condition variable to wait for asynchronous callbacks
- std::mutex mMutex;
- std::condition_variable mCondition;
- // and a flag, in case we are notified before the wait call
- bool mNotified;
-};
-
-class PreparedModelCallback : public IPreparedModelCallback
-{
-public:
- PreparedModelCallback()
- {
- }
-
- ~PreparedModelCallback() override
- {
- }
-
- Return<void> notify(ErrorStatus status, const sp<IPreparedModel>& preparedModel) override
- {
- m_ErrorStatus = status;
- m_PreparedModel = preparedModel;
- return Void();
- }
-
- ErrorStatus GetErrorStatus()
- {
- return m_ErrorStatus;
- }
-
- sp<IPreparedModel> GetPreparedModel()
- {
- return m_PreparedModel;
- }
-
-
-private:
- ErrorStatus m_ErrorStatus;
- sp<IPreparedModel> m_PreparedModel;
-};
-
-// lifted from common/Utils.cpp
-hidl_memory allocateSharedMemory(int64_t size)
-{
- hidl_memory memory;
-
- const std::string& type = "ashmem";
- android::sp<IAllocator> allocator = IAllocator::getService(type);
- allocator->allocate(size, [&](bool success, const hidl_memory& mem) {
- if (!success)
- {
- ALOGE("unable to allocate %li bytes of %s", size, type.c_str());
- }
- else
- {
- memory = mem;
- }
- });
-
- return memory;
-}
-
-
-android::sp<IMemory> AddPoolAndGetData(uint32_t size, Request& request)
-{
- hidl_memory pool;
-
- android::sp<IAllocator> allocator = IAllocator::getService("ashmem");
- allocator->allocate(sizeof(float) * size, [&](bool success, const hidl_memory& mem) {
- BOOST_TEST(success);
- pool = mem;
- });
-
- request.pools.resize(request.pools.size() + 1);
- request.pools[request.pools.size() - 1] = pool;
-
- android::sp<IMemory> mapped = mapMemory(pool);
- mapped->update();
- return mapped;
-}
-
-void AddPoolAndSetData(uint32_t size, Request& request, float* data)
-{
- android::sp<IMemory> memory = AddPoolAndGetData(size, request);
-
- float* dst = static_cast<float*>(static_cast<void*>(memory->getPointer()));
-
- memcpy(dst, data, size * sizeof(float));
-}
-
-void AddOperand(Model& model, const Operand& op)
-{
- model.operands.resize(model.operands.size() + 1);
- model.operands[model.operands.size() - 1] = op;
-}
-
-void AddIntOperand(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);
-}
-
-template<typename T>
-OperandType TypeToOperandType();
-
-template<>
-OperandType TypeToOperandType<float>()
-{
- return OperandType::TENSOR_FLOAT32;
-};
-
-template<>
-OperandType TypeToOperandType<int32_t>()
-{
- return OperandType::TENSOR_INT32;
-};
-
-
-
-template<typename T>
-void AddTensorOperand(Model& model, hidl_vec<uint32_t> dimensions, T* values)
-{
- uint32_t totalElements = 1;
- for (uint32_t dim : dimensions)
- {
- totalElements *= dim;
- }
-
- DataLocation location = {};
- location.offset = model.operandValues.size();
- location.length = totalElements * sizeof(T);
-
- Operand op = {};
- op.type = TypeToOperandType<T>();
- op.dimensions = dimensions;
- op.lifetime = OperandLifeTime::CONSTANT_COPY;
- op.location = location;
-
- model.operandValues.resize(model.operandValues.size() + location.length);
- for (uint32_t i = 0; i < totalElements; i++)
- {
- *(reinterpret_cast<T*>(&model.operandValues[location.offset]) + i) = values[i];
- }
-
- AddOperand(model, op);
-}
-
-void AddInputOperand(Model& model, hidl_vec<uint32_t> dimensions)
-{
- Operand op = {};
- op.type = OperandType::TENSOR_FLOAT32;
- 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(Model& model, hidl_vec<uint32_t> dimensions)
-{
- Operand op = {};
- op.type = OperandType::TENSOR_FLOAT32;
- 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> PrepareModel(const Model& model, ArmnnDriver& driver)
-{
-
- sp<PreparedModelCallback> cb(new PreparedModelCallback());
- driver.prepareModel(model, cb);
-
- BOOST_TEST((cb->GetErrorStatus() == ErrorStatus::NONE));
- BOOST_TEST((cb->GetPreparedModel() != nullptr));
-
- return cb->GetPreparedModel();
-}
-
-void Execute(android::sp<IPreparedModel> preparedModel, const Request& request)
-{
- sp<ExecutionCallback> cb(new ExecutionCallback());
- BOOST_TEST(preparedModel->execute(request, cb) == ErrorStatus::NONE);
- ALOGI("Execute: waiting for callback to be invoked");
- cb->wait();
-}
-
-sp<ExecutionCallback> ExecuteNoWait(android::sp<IPreparedModel> preparedModel, const Request& request)
-{
- sp<ExecutionCallback> cb(new ExecutionCallback());
- BOOST_TEST(preparedModel->execute(request, cb) == ErrorStatus::NONE);
- ALOGI("ExecuteNoWait: returning callback object");
- return cb;
-}
-}
-
-// Add our own test here since we fail the fc tests which Google supplies (because of non-const weights)
-BOOST_AUTO_TEST_CASE(FullyConnected)
-{
- // this should ideally replicate fully_connected_float.model.cpp
- // 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));
- Model model = {};
-
- // add operands
- int32_t actValue = 0;
- float weightValue[] = {2, 4, 1};
- float biasValue[] = {4};
-
- AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
- AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
- AddIntOperand(model, actValue);
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
- // make the fully connected operation
- model.operations.resize(1);
- model.operations[0].type = OperationType::FULLY_CONNECTED;
- model.operations[0].inputs = hidl_vec<uint32_t>{0, 1, 2, 3};
- model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
- // make the prepared model
- android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
- // construct the request
- DataLocation inloc = {};
- inloc.poolIndex = 0;
- inloc.offset = 0;
- inloc.length = 3 * sizeof(float);
- RequestArgument input = {};
- input.location = inloc;
- input.dimensions = hidl_vec<uint32_t>{};
-
- DataLocation outloc = {};
- outloc.poolIndex = 1;
- outloc.offset = 0;
- outloc.length = 1 * sizeof(float);
- RequestArgument output = {};
- output.location = outloc;
- output.dimensions = hidl_vec<uint32_t>{};
-
- Request request = {};
- request.inputs = hidl_vec<RequestArgument>{input};
- request.outputs = hidl_vec<RequestArgument>{output};
-
- // set the input data (matching source test)
- float indata[] = {2, 32, 16};
- AddPoolAndSetData(3, request, indata);
-
- // add memory for the output
- android::sp<IMemory> outMemory = AddPoolAndGetData(1, request);
- float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
- // run the execution
- Execute(preparedModel, request);
-
- // check the result
- BOOST_TEST(outdata[0] == 152);
-}
-
-// Add our own test for concurrent execution
-// The main point of this test is to check that multiple requests can be
-// executed without waiting for the callback from previous execution.
-// The operations performed are not significant.
-BOOST_AUTO_TEST_CASE(ConcurrentExecute)
-{
- ALOGI("ConcurrentExecute: entry");
-
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
- Model model = {};
-
- // add operands
- int32_t actValue = 0;
- float weightValue[] = {2, 4, 1};
- float biasValue[] = {4};
-
- AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
- AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
- AddIntOperand(model, actValue);
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
- // make the fully connected operation
- model.operations.resize(1);
- model.operations[0].type = OperationType::FULLY_CONNECTED;
- model.operations[0].inputs = hidl_vec<uint32_t>{0, 1, 2, 3};
- model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
- // make the prepared models
- const size_t maxRequests = 5;
- android::sp<IPreparedModel> preparedModels[maxRequests];
- for (size_t i = 0; i < maxRequests; ++i)
- {
- preparedModels[i] = PrepareModel(model, *driver);
- }
-
- // construct the request data
- DataLocation inloc = {};
- inloc.poolIndex = 0;
- inloc.offset = 0;
- inloc.length = 3 * sizeof(float);
- RequestArgument input = {};
- input.location = inloc;
- input.dimensions = hidl_vec<uint32_t>{};
-
- DataLocation outloc = {};
- outloc.poolIndex = 1;
- outloc.offset = 0;
- outloc.length = 1 * sizeof(float);
- RequestArgument output = {};
- output.location = outloc;
- output.dimensions = hidl_vec<uint32_t>{};
-
- // build the requests
- Request requests[maxRequests];
- android::sp<IMemory> outMemory[maxRequests];
- float* outdata[maxRequests];
- for (size_t i = 0; i < maxRequests; ++i)
- {
- requests[i].inputs = hidl_vec<RequestArgument>{input};
- requests[i].outputs = hidl_vec<RequestArgument>{output};
- // set the input data (matching source test)
- float indata[] = {2, 32, 16};
- AddPoolAndSetData(3, requests[i], indata);
- // add memory for the output
- outMemory[i] = AddPoolAndGetData(1, requests[i]);
- outdata[i] = static_cast<float*>(static_cast<void*>(outMemory[i]->getPointer()));
- }
-
- // invoke the execution of the requests
- ALOGI("ConcurrentExecute: executing requests");
- sp<ExecutionCallback> cb[maxRequests];
- for (size_t i = 0; i < maxRequests; ++i)
- {
- cb[i] = ExecuteNoWait(preparedModels[i], requests[i]);
- }
-
- // wait for the requests to complete
- ALOGI("ConcurrentExecute: waiting for callbacks");
- for (size_t i = 0; i < maxRequests; ++i)
- {
- cb[i]->wait();
- }
-
- // check the results
- ALOGI("ConcurrentExecute: validating results");
- for (size_t i = 0; i < maxRequests; ++i)
- {
- BOOST_TEST(outdata[i][0] == 152);
- }
- ALOGI("ConcurrentExecute: exit");
-}
-
-BOOST_AUTO_TEST_CASE(GetSupportedOperations)
-{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
- ErrorStatus error;
- std::vector<bool> sup;
-
- ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
- {
- error = status;
- sup = supported;
- };
-
- Model model1 = {};
-
- // add operands
- int32_t actValue = 0;
- float weightValue[] = {2, 4, 1};
- float biasValue[] = {4};
-
- AddInputOperand(model1, hidl_vec<uint32_t>{1, 3});
- AddTensorOperand(model1, hidl_vec<uint32_t>{1, 3}, weightValue);
- AddTensorOperand(model1, hidl_vec<uint32_t>{1}, biasValue);
- AddIntOperand(model1, actValue);
- AddOutputOperand(model1, hidl_vec<uint32_t>{1, 1});
-
- // make a correct fully connected operation
- model1.operations.resize(2);
- model1.operations[0].type = OperationType::FULLY_CONNECTED;
- model1.operations[0].inputs = hidl_vec<uint32_t>{0, 1, 2, 3};
- model1.operations[0].outputs = hidl_vec<uint32_t>{4};
-
- // make an incorrect fully connected operation
- AddIntOperand(model1, actValue);
- AddOutputOperand(model1, hidl_vec<uint32_t>{1, 1});
- model1.operations[1].type = OperationType::FULLY_CONNECTED;
- model1.operations[1].inputs = hidl_vec<uint32_t>{4};
- model1.operations[1].outputs = hidl_vec<uint32_t>{5};
-
- driver->getSupportedOperations(model1, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::NONE);
- BOOST_TEST(sup[0] == true);
- BOOST_TEST(sup[1] == false);
-
- // Broadcast add/mul are not supported
- Model model2 = {};
-
- AddInputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
- AddInputOperand(model2, hidl_vec<uint32_t>{4});
- AddOutputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
- AddOutputOperand(model2, hidl_vec<uint32_t>{1, 1, 3, 4});
-
- model2.operations.resize(2);
-
- model2.operations[0].type = OperationType::ADD;
- model2.operations[0].inputs = hidl_vec<uint32_t>{0,1};
- model2.operations[0].outputs = hidl_vec<uint32_t>{2};
-
- model2.operations[1].type = OperationType::MUL;
- model2.operations[1].inputs = hidl_vec<uint32_t>{0,1};
- model2.operations[1].outputs = hidl_vec<uint32_t>{3};
-
- driver->getSupportedOperations(model2, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::NONE);
- BOOST_TEST(sup[0] == false);
- BOOST_TEST(sup[1] == false);
-
- Model model3 = {};
-
- // Add unsupported operation, should return no error but we don't support it
- AddInputOperand(model3, hidl_vec<uint32_t>{1, 1, 1, 8});
- AddIntOperand(model3, 2);
- AddOutputOperand(model3, hidl_vec<uint32_t>{1, 2, 2, 2});
- model3.operations.resize(1);
- model3.operations[0].type = OperationType::DEPTH_TO_SPACE;
- model1.operations[0].inputs = hidl_vec<uint32_t>{0, 1};
- model3.operations[0].outputs = hidl_vec<uint32_t>{2};
-
- driver->getSupportedOperations(model3, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::NONE);
- BOOST_TEST(sup[0] == false);
-
- // Add invalid operation
- Model model4 = {};
- AddIntOperand(model4, 0);
- model4.operations.resize(1);
- model4.operations[0].type = static_cast<OperationType>(100);
- model4.operations[0].outputs = hidl_vec<uint32_t>{0};
-
- driver->getSupportedOperations(model4, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::INVALID_ARGUMENT);
-}
-
-// The purpose of this test is to ensure that when encountering an unsupported operation
-// it is skipped and getSupportedOperations() continues (rather than failing and stopping).
-// As per IVGCVSW-710.
-BOOST_AUTO_TEST_CASE(UnsupportedLayerContinueOnFailure)
-{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
- ErrorStatus error;
- std::vector<bool> sup;
-
- ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
- {
- error = status;
- sup = supported;
- };
-
- Model model = {};
-
- // operands
- int32_t actValue = 0;
- float weightValue[] = {2, 4, 1};
- float biasValue[] = {4};
-
- // broadcast add is unsupported at the time of writing this test, but any unsupported layer will do
- AddInputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
- AddInputOperand(model, hidl_vec<uint32_t>{4});
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
-
- // fully connected
- AddInputOperand(model, hidl_vec<uint32_t>{1, 3});
- AddTensorOperand(model, hidl_vec<uint32_t>{1, 3}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
- AddIntOperand(model, actValue);
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1});
-
- // broadcast mul is unsupported
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, 3, 4});
-
- model.operations.resize(3);
-
- // unsupported
- model.operations[0].type = OperationType::ADD;
- model.operations[0].inputs = hidl_vec<uint32_t>{0,1};
- model.operations[0].outputs = hidl_vec<uint32_t>{2};
-
- // supported
- model.operations[1].type = OperationType::FULLY_CONNECTED;
- model.operations[1].inputs = hidl_vec<uint32_t>{3, 4, 5, 6};
- model.operations[1].outputs = hidl_vec<uint32_t>{7};
-
- // unsupported
- model.operations[2].type = OperationType::MUL;
- model.operations[2].inputs = hidl_vec<uint32_t>{0,1};
- model.operations[2].outputs = hidl_vec<uint32_t>{8};
-
- // we are testing that the unsupported layers return false and the test continues
- // rather than failing and stopping.
- driver->getSupportedOperations(model, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::NONE);
- BOOST_TEST(sup[0] == false);
- BOOST_TEST(sup[1] == true);
- BOOST_TEST(sup[2] == false);
-}
-
-// The purpose of this test is to ensure that when encountering an failure
-// 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);
-
- ErrorStatus error;
- std::vector<bool> sup;
-
- ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
- {
- error = status;
- sup = supported;
- };
-
- Model model = {};
-
- model.pools = hidl_vec<hidl_memory>{hidl_memory("Unsuported hidl memory type", nullptr, 0)};
-
- //memory pool mapping should fail, we should report an error
- driver->getSupportedOperations(model, cb);
- BOOST_TEST((int)error == (int)ErrorStatus::GENERAL_FAILURE);
-}
-
-namespace
-{
-
-void PaddingTestImpl(android::nn::PaddingScheme paddingScheme)
-{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
- Model model = {};
-
- uint32_t outSize = paddingScheme == kPaddingSame ? 2 : 1;
-
- // add operands
- float weightValue[] = {1, -1, 0, 1};
- float biasValue[] = {0};
-
- AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 3, 1});
- AddTensorOperand(model, hidl_vec<uint32_t>{1, 2, 2, 1}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
- AddIntOperand(model, (int32_t)paddingScheme); // padding
- AddIntOperand(model, 2); // stride x
- AddIntOperand(model, 2); // stride y
- AddIntOperand(model, 0); // no activation
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, outSize, 1});
-
- // make the convolution operation
- model.operations.resize(1);
- model.operations[0].type = OperationType::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
- android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
- // construct the request
- DataLocation inloc = {};
- inloc.poolIndex = 0;
- inloc.offset = 0;
- inloc.length = 6 * sizeof(float);
- RequestArgument input = {};
- input.location = inloc;
- input.dimensions = hidl_vec<uint32_t>{};
-
- DataLocation outloc = {};
- outloc.poolIndex = 1;
- outloc.offset = 0;
- outloc.length = outSize * sizeof(float);
- RequestArgument output = {};
- output.location = outloc;
- output.dimensions = hidl_vec<uint32_t>{};
-
- Request request = {};
- 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};
- 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()));
-
- // run the execution
- Execute(preparedModel, request);
-
- // check the result
- if (paddingScheme == kPaddingValid)
- {
- BOOST_TEST(outdata[0] == 2);
- }
- else if (paddingScheme == kPaddingSame)
- {
- BOOST_TEST(outdata[0] == 2);
- BOOST_TEST(outdata[1] == 0);
- }
- else
- {
- BOOST_TEST(false);
- }
-}
-
-}
-
-BOOST_AUTO_TEST_CASE(ConvValidPadding)
-{
- PaddingTestImpl(kPaddingValid);
-}
-
-BOOST_AUTO_TEST_CASE(ConvSamePadding)
-{
- PaddingTestImpl(kPaddingSame);
-}
-
-BOOST_AUTO_TEST_CASE(TestFullyConnected4dInput)
-{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
- ErrorStatus error;
- std::vector<bool> sup;
-
- ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
- {
- error = status;
- sup = supported;
- };
-
- Model model = {};
-
- // operands
- int32_t actValue = 0;
- float weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
- 0, 1, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0,
- 0, 0, 0, 1, 0, 0, 0, 0,
- 0, 0, 0, 0, 1, 0, 0, 0,
- 0, 0, 0, 0, 0, 1, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 0,
- 0, 0, 0, 0, 0, 0, 0, 1}; //identity
- float biasValue[] = {0, 0, 0, 0, 0, 0, 0, 0};
-
- // fully connected operation
- AddInputOperand(model, hidl_vec<uint32_t>{1, 1, 1, 8});
- AddTensorOperand(model, hidl_vec<uint32_t>{8, 8}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{8}, biasValue);
- AddIntOperand(model, actValue);
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 8});
-
- model.operations.resize(1);
-
- model.operations[0].type = OperationType::FULLY_CONNECTED;
- model.operations[0].inputs = hidl_vec<uint32_t>{0,1,2,3};
- model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
- // make the prepared model
- android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-
- // construct the request
- DataLocation inloc = {};
- inloc.poolIndex = 0;
- inloc.offset = 0;
- inloc.length = 8 * sizeof(float);
- RequestArgument input = {};
- input.location = inloc;
- input.dimensions = hidl_vec<uint32_t>{};
-
- DataLocation outloc = {};
- outloc.poolIndex = 1;
- outloc.offset = 0;
- outloc.length = 8 * sizeof(float);
- RequestArgument output = {};
- output.location = outloc;
- output.dimensions = hidl_vec<uint32_t>{};
-
- Request request = {};
- request.inputs = hidl_vec<RequestArgument>{input};
- request.outputs = hidl_vec<RequestArgument>{output};
-
- // set the input data
- float indata[] = {1,2,3,4,5,6,7,8};
- AddPoolAndSetData(8, request, indata);
-
- // add memory for the output
- android::sp<IMemory> outMemory = AddPoolAndGetData(8, request);
- float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
- // run the execution
- Execute(preparedModel, request);
-
- // check the result
- BOOST_TEST(outdata[0] == 1);
- BOOST_TEST(outdata[1] == 2);
- BOOST_TEST(outdata[2] == 3);
- BOOST_TEST(outdata[3] == 4);
- BOOST_TEST(outdata[4] == 5);
- BOOST_TEST(outdata[5] == 6);
- BOOST_TEST(outdata[6] == 7);
- BOOST_TEST(outdata[7] == 8);
-}
-
-BOOST_AUTO_TEST_CASE(TestFullyConnected4dInputReshape)
-{
- auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
-
- ErrorStatus error;
- std::vector<bool> sup;
-
- ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
- {
- error = status;
- sup = supported;
- };
-
- Model model = {};
-
- // operands
- int32_t actValue = 0;
- float weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
- 0, 1, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0,
- 0, 0, 0, 1, 0, 0, 0, 0,
- 0, 0, 0, 0, 1, 0, 0, 0,
- 0, 0, 0, 0, 0, 1, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 0,
- 0, 0, 0, 0, 0, 0, 0, 1}; //identity
- float biasValue[] = {0, 0, 0, 0, 0, 0, 0, 0};
-
- // fully connected operation
- AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 2, 2});
- AddTensorOperand(model, hidl_vec<uint32_t>{8, 8}, weightValue);
- AddTensorOperand(model, hidl_vec<uint32_t>{8}, biasValue);
- AddIntOperand(model, actValue);
- AddOutputOperand(model, hidl_vec<uint32_t>{1, 8});
-
- model.operations.resize(1);
-
- model.operations[0].type = OperationType::FULLY_CONNECTED;
- model.operations[0].inputs = hidl_vec<uint32_t>{0,1,2,3};
- model.operations[0].outputs = hidl_vec<uint32_t>{4};
-
- // make the prepared model
- android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);
-
-
- // construct the request
- DataLocation inloc = {};
- inloc.poolIndex = 0;
- inloc.offset = 0;
- inloc.length = 8 * sizeof(float);
- RequestArgument input = {};
- input.location = inloc;
- input.dimensions = hidl_vec<uint32_t>{};
-
- DataLocation outloc = {};
- outloc.poolIndex = 1;
- outloc.offset = 0;
- outloc.length = 8 * sizeof(float);
- RequestArgument output = {};
- output.location = outloc;
- output.dimensions = hidl_vec<uint32_t>{};
-
- Request request = {};
- request.inputs = hidl_vec<RequestArgument>{input};
- request.outputs = hidl_vec<RequestArgument>{output};
-
- // set the input data
- float indata[] = {1,2,3,4,5,6,7,8};
- AddPoolAndSetData(8, request, indata);
-
- // add memory for the output
- android::sp<IMemory> outMemory = AddPoolAndGetData(8, request);
- float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));
-
- // run the execution
- Execute(preparedModel, request);
-
- // check the result
- BOOST_TEST(outdata[0] == 1);
- BOOST_TEST(outdata[1] == 2);
- BOOST_TEST(outdata[2] == 3);
- BOOST_TEST(outdata[3] == 4);
- BOOST_TEST(outdata[4] == 5);
- BOOST_TEST(outdata[5] == 6);
- BOOST_TEST(outdata[6] == 7);
- BOOST_TEST(outdata[7] == 8);
-}
-
BOOST_AUTO_TEST_SUITE_END()