#include "VtsHalNeuralnetworksV1_0TargetTest.h"
#include "Event.h"
+#include "Models.h"
#include "TestHarness.h"
#include <android-base/logging.h>
void NeuralnetworksHidlTest::TearDown() {}
+sp<IPreparedModel> NeuralnetworksHidlTest::doPrepareModelShortcut(const Model& model) {
+ sp<IPreparedModel> preparedModel;
+ ErrorStatus prepareStatus;
+ sp<Event> preparationEvent = new Event();
+ if (preparationEvent.get() == nullptr) {
+ return nullptr;
+ }
+
+ Return<void> prepareRet = device->prepareModel(
+ model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+ prepareStatus = status;
+ preparedModel = prepared;
+ });
+
+ if (!prepareRet.isOk() || prepareStatus != ErrorStatus::NONE ||
+ preparedModel.get() == nullptr) {
+ return nullptr;
+ }
+ Event::Status eventStatus = preparationEvent->wait();
+ if (eventStatus != Event::Status::SUCCESS) {
+ return nullptr;
+ }
+
+ return preparedModel;
+}
+
// create device test
TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
EXPECT_TRUE(ret.isOk());
}
-namespace {
-// create the model
-Model createTestModel() {
- const std::vector<float> operand2Data = {5.0f, 6.0f, 7.0f, 8.0f};
- const uint32_t size = operand2Data.size() * sizeof(float);
-
- const uint32_t operand1 = 0;
- const uint32_t operand2 = 1;
- const uint32_t operand3 = 2;
- const uint32_t operand4 = 3;
-
- const std::vector<Operand> operands = {
- {
- .type = OperandType::TENSOR_FLOAT32,
- .dimensions = {1, 2, 2, 1},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::MODEL_INPUT,
- .location = {.poolIndex = 0, .offset = 0, .length = 0},
- },
- {
- .type = OperandType::TENSOR_FLOAT32,
- .dimensions = {1, 2, 2, 1},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = 0, .length = size},
- },
- {
- .type = OperandType::INT32,
- .dimensions = {},
- .numberOfConsumers = 1,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::CONSTANT_COPY,
- .location = {.poolIndex = 0, .offset = size, .length = sizeof(int32_t)},
- },
- {
- .type = OperandType::TENSOR_FLOAT32,
- .dimensions = {1, 2, 2, 1},
- .numberOfConsumers = 0,
- .scale = 0.0f,
- .zeroPoint = 0,
- .lifetime = OperandLifeTime::MODEL_OUTPUT,
- .location = {.poolIndex = 0, .offset = 0, .length = 0},
- },
- };
-
- const std::vector<Operation> operations = {{
- .opTuple = {OperationType::ADD, OperandType::TENSOR_FLOAT32},
- .inputs = {operand1, operand2, operand3},
- .outputs = {operand4},
- }};
-
- const std::vector<uint32_t> inputIndexes = {operand1};
- const std::vector<uint32_t> outputIndexes = {operand4};
- std::vector<uint8_t> operandValues(
- reinterpret_cast<const uint8_t*>(operand2Data.data()),
- reinterpret_cast<const uint8_t*>(operand2Data.data()) + size);
- int32_t activation[1] = {static_cast<int32_t>(FusedActivationFunc::NONE)};
- operandValues.insert(operandValues.end(), reinterpret_cast<const uint8_t*>(&activation[0]),
- reinterpret_cast<const uint8_t*>(&activation[1]));
-
- const std::vector<hidl_memory> pools = {};
-
- return {
- .operands = operands,
- .operations = operations,
- .inputIndexes = inputIndexes,
- .outputIndexes = outputIndexes,
- .operandValues = operandValues,
- .pools = pools,
- };
+// supported operations positive test
+TEST_F(NeuralnetworksHidlTest, SupportedOperationsPositiveTest) {
+ Model model = createValidTestModel();
+ Return<void> ret = device->getSupportedOperations(
+ model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
+ EXPECT_EQ(ErrorStatus::NONE, status);
+ EXPECT_EQ(model.operations.size(), supported.size());
+ });
+ EXPECT_TRUE(ret.isOk());
}
-} // anonymous namespace
-// allocator helper
-hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem") {
- hidl_memory memory;
-
- sp<IAllocator> allocator = IAllocator::getService(type);
- if (!allocator.get()) {
- return {};
- }
-
- Return<void> ret = allocator->allocate(size, [&](bool success, const hidl_memory& mem) {
- ASSERT_TRUE(success);
- memory = mem;
- });
- if (!ret.isOk()) {
- return {};
- }
-
- return memory;
+// supported operations negative test 1
+TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest1) {
+ Model model = createInvalidTestModel1();
+ Return<void> ret = device->getSupportedOperations(
+ model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ (void)supported;
+ });
+ EXPECT_TRUE(ret.isOk());
}
-// supported subgraph test
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsTest) {
- Model model = createTestModel();
+// supported operations negative test 2
+TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest2) {
+ Model model = createInvalidTestModel2();
Return<void> ret = device->getSupportedOperations(
model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
- EXPECT_EQ(ErrorStatus::NONE, status);
- EXPECT_EQ(model.operations.size(), supported.size());
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ (void)supported;
});
EXPECT_TRUE(ret.isOk());
}
-// execute simple graph
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphTest) {
- std::vector<float> inputData = {1.0f, 2.0f, 3.0f, 4.0f};
- std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
- std::vector<float> expectedData = {6.0f, 8.0f, 10.0f, 12.0f};
- const uint32_t INPUT = 0;
- const uint32_t OUTPUT = 1;
-
- // prepare request
- Model model = createTestModel();
- sp<IPreparedModel> preparedModel;
+// prepare simple model positive test
+TEST_F(NeuralnetworksHidlTest, SimplePrepareModelPositiveTest) {
+ Model model = createValidTestModel();
sp<Event> preparationEvent = new Event();
ASSERT_NE(nullptr, preparationEvent.get());
Return<void> prepareRet = device->prepareModel(
model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
EXPECT_EQ(ErrorStatus::NONE, status);
- preparedModel = prepared;
+ (void)prepared;
+ });
+ ASSERT_TRUE(prepareRet.isOk());
+}
+
+// prepare simple model negative test 1
+TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest1) {
+ Model model = createInvalidTestModel1();
+ sp<Event> preparationEvent = new Event();
+ ASSERT_NE(nullptr, preparationEvent.get());
+ Return<void> prepareRet = device->prepareModel(
+ model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ (void)prepared;
+ });
+ ASSERT_TRUE(prepareRet.isOk());
+}
+
+// prepare simple model negative test 2
+TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest2) {
+ Model model = createInvalidTestModel2();
+ sp<Event> preparationEvent = new Event();
+ ASSERT_NE(nullptr, preparationEvent.get());
+ Return<void> prepareRet = device->prepareModel(
+ model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+ (void)prepared;
});
ASSERT_TRUE(prepareRet.isOk());
+}
+
+// execute simple graph positive test
+TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphPositiveTest) {
+ Model model = createValidTestModel();
+ sp<IPreparedModel> preparedModel = doPrepareModelShortcut(model);
ASSERT_NE(nullptr, preparedModel.get());
- Event::Status preparationStatus = preparationEvent->wait();
- EXPECT_EQ(Event::Status::SUCCESS, preparationStatus);
-
- // prepare inputs
- uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
- uint32_t outputSize = static_cast<uint32_t>(outputData.size() * sizeof(float));
- std::vector<RequestArgument> inputs = {{
- .location = {.poolIndex = INPUT, .offset = 0, .length = inputSize}, .dimensions = {},
- }};
- std::vector<RequestArgument> outputs = {{
- .location = {.poolIndex = OUTPUT, .offset = 0, .length = outputSize}, .dimensions = {},
- }};
- std::vector<hidl_memory> pools = {allocateSharedMemory(inputSize),
- allocateSharedMemory(outputSize)};
- ASSERT_NE(0ull, pools[INPUT].size());
- ASSERT_NE(0ull, pools[OUTPUT].size());
-
- // load data
- sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
- sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
- ASSERT_NE(nullptr, inputMemory.get());
- ASSERT_NE(nullptr, outputMemory.get());
- float* inputPtr = reinterpret_cast<float*>(static_cast<void*>(inputMemory->getPointer()));
- float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
- ASSERT_NE(nullptr, inputPtr);
- ASSERT_NE(nullptr, outputPtr);
- inputMemory->update();
- outputMemory->update();
- std::copy(inputData.begin(), inputData.end(), inputPtr);
- std::copy(outputData.begin(), outputData.end(), outputPtr);
- inputMemory->commit();
- outputMemory->commit();
+ Request request = createValidTestRequest();
- // execute request
sp<Event> executionEvent = new Event();
ASSERT_NE(nullptr, executionEvent.get());
- Return<ErrorStatus> executeStatus = preparedModel->execute(
- {.inputs = inputs, .outputs = outputs, .pools = pools}, executionEvent);
+ Return<ErrorStatus> executeStatus = preparedModel->execute(request, executionEvent);
ASSERT_TRUE(executeStatus.isOk());
EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeStatus));
Event::Status eventStatus = executionEvent->wait();
EXPECT_EQ(Event::Status::SUCCESS, eventStatus);
- // validate results { 1+5, 2+6, 3+7, 4+8 }
+ std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
+ std::vector<float> expectedData = {6.0f, 8.0f, 10.0f, 12.0f};
+ const uint32_t OUTPUT = 1;
+
+ sp<IMemory> outputMemory = mapMemory(request.pools[OUTPUT]);
+ ASSERT_NE(nullptr, outputMemory.get());
+ float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
+ ASSERT_NE(nullptr, outputPtr);
outputMemory->read();
std::copy(outputPtr, outputPtr + outputData.size(), outputData.begin());
outputMemory->commit();
EXPECT_EQ(expectedData, outputData);
}
+// execute simple graph negative test 1
+TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest1) {
+ Model model = createValidTestModel();
+ sp<IPreparedModel> preparedModel = doPrepareModelShortcut(model);
+ ASSERT_NE(nullptr, preparedModel.get());
+ Request request = createInvalidTestRequest1();
+
+ sp<Event> executionEvent = new Event();
+ ASSERT_NE(nullptr, executionEvent.get());
+ Return<ErrorStatus> executeStatus = preparedModel->execute(request, executionEvent);
+ ASSERT_TRUE(executeStatus.isOk());
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeStatus));
+ executionEvent->wait();
+}
+
+// execute simple graph negative test 2
+TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest2) {
+ Model model = createValidTestModel();
+ sp<IPreparedModel> preparedModel = doPrepareModelShortcut(model);
+ ASSERT_NE(nullptr, preparedModel.get());
+ Request request = createInvalidTestRequest2();
+
+ sp<Event> executionEvent = new Event();
+ ASSERT_NE(nullptr, executionEvent.get());
+ Return<ErrorStatus> executeStatus = preparedModel->execute(request, executionEvent);
+ ASSERT_TRUE(executeStatus.isOk());
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeStatus));
+ executionEvent->wait();
+}
+
// Mixed-typed examples
typedef MixedTypedExampleType MixedTypedExample;
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