Release 18.02
Change-Id: I41a89c149534a7c354a58e2c66a32cba572fc0c1
diff --git a/test/Tests.cpp b/test/Tests.cpp
new file mode 100755
index 0000000..5f3dd6f
--- /dev/null
+++ b/test/Tests.cpp
@@ -0,0 +1,978 @@
+//
+// 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);
+}
+
+}
+}
+}
+}
+
+BOOST_AUTO_TEST_SUITE(DriverTests)
+
+using namespace armnn_driver;
+using namespace android::nn;
+using namespace android;
+
+BOOST_AUTO_TEST_CASE(Init)
+{
+ // Making the driver object on the stack causes a weird libc error, so make it on the heap instead
+ auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
+
+ DeviceStatus status = driver->getStatus();
+ // Note double-parentheses to avoid compile error from Boost trying to printf the DeviceStatus
+ BOOST_TEST((status == DeviceStatus::AVAILABLE));
+}
+
+BOOST_AUTO_TEST_CASE(TestCapabilities)
+{
+ // Making the driver object on the stack causes a weird libc error, so make it on the heap instead
+ auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
+
+ ErrorStatus error;
+ Capabilities cap;
+
+ ArmnnDriver::getCapabilities_cb cb = [&](ErrorStatus status, const Capabilities& capabilities)
+ {
+ error = status;
+ cap = capabilities;
+ };
+
+ driver->getCapabilities(cb);
+
+ BOOST_TEST((int)error == (int)ErrorStatus::NONE);
+ BOOST_TEST(cap.float32Performance.execTime > 0.f);
+ BOOST_TEST(cap.float32Performance.powerUsage > 0.f);
+ BOOST_TEST(cap.quantized8Performance.execTime > 0.f);
+ 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()