/* * Copyright (C) 2019 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ // This file tests that various abnormal uses of ANeuralNetworks*_free() don't crash. // // Limitation: It doesn't set various combinations of properties on objects before // freeing those objects. #include #include #include "NeuralNetworks.h" namespace { ANeuralNetworksModel* createUnfinishedModel() { ANeuralNetworksModel* model = nullptr; EXPECT_EQ(ANeuralNetworksModel_create(&model), ANEURALNETWORKS_NO_ERROR); uint32_t dimensions[] = {1}; ANeuralNetworksOperandType type = { .type = ANEURALNETWORKS_TENSOR_FLOAT32, .dimensionCount = 1, .dimensions = dimensions}; EXPECT_EQ(ANeuralNetworksModel_addOperand(model, &type), ANEURALNETWORKS_NO_ERROR); EXPECT_EQ(ANeuralNetworksModel_addOperand(model, &type), ANEURALNETWORKS_NO_ERROR); const uint32_t inList[]{0}; const uint32_t outList[]{1}; EXPECT_EQ( ANeuralNetworksModel_addOperation(model, ANEURALNETWORKS_FLOOR, 1, inList, 1, outList), ANEURALNETWORKS_NO_ERROR); EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(model, 1, inList, 1, outList), ANEURALNETWORKS_NO_ERROR); return model; } ANeuralNetworksModel* createFinishedModel() { ANeuralNetworksModel* const model = createUnfinishedModel(); EXPECT_EQ(ANeuralNetworksModel_finish(model), ANEURALNETWORKS_NO_ERROR); return model; } std::vector createDeviceList() { std::vector devices; uint32_t numDevices = 0; EXPECT_EQ(ANeuralNetworks_getDeviceCount(&numDevices), ANEURALNETWORKS_NO_ERROR); for (uint32_t devIndex = 0; devIndex < numDevices; ++devIndex) { ANeuralNetworksDevice* device = nullptr; const int getDeviceStatus = ANeuralNetworks_getDevice(devIndex, &device); EXPECT_EQ(getDeviceStatus, ANEURALNETWORKS_NO_ERROR); if (getDeviceStatus == ANEURALNETWORKS_NO_ERROR) { devices.push_back(device); } } return devices; } TEST(Free, Null) { ANeuralNetworksBurst_free(nullptr); ANeuralNetworksCompilation_free(nullptr); ANeuralNetworksEvent_free(nullptr); ANeuralNetworksExecution_free(nullptr); ANeuralNetworksMemory_free(nullptr); ANeuralNetworksModel_free(nullptr); } TEST(Free, UnfinishedModel) { ANeuralNetworksModel* const model = createUnfinishedModel(); ANeuralNetworksModel_free(model); } TEST(Free, UnfinishedCompilation) { ANeuralNetworksModel* const model = createFinishedModel(); ANeuralNetworksCompilation* compilation = nullptr; ASSERT_EQ(ANeuralNetworksCompilation_create(model, &compilation), ANEURALNETWORKS_NO_ERROR); ANeuralNetworksCompilation_free(compilation); ANeuralNetworksModel_free(model); } TEST(Free, UnfinishedCompilationForDevices) { ANeuralNetworksModel* const model = createFinishedModel(); const auto devices = createDeviceList(); ANeuralNetworksCompilation* compilation = nullptr; ASSERT_EQ(ANeuralNetworksCompilation_createForDevices(model, devices.data(), devices.size(), &compilation), ANEURALNETWORKS_NO_ERROR); ANeuralNetworksCompilation_free(compilation); ANeuralNetworksModel_free(model); } TEST(Free, UnscheduledExecution) { ANeuralNetworksModel* const model = createFinishedModel(); ANeuralNetworksCompilation* compilation = nullptr; ASSERT_EQ(ANeuralNetworksCompilation_create(model, &compilation), ANEURALNETWORKS_NO_ERROR); ASSERT_EQ(ANeuralNetworksCompilation_finish(compilation), ANEURALNETWORKS_NO_ERROR); ANeuralNetworksExecution* execution = nullptr; ASSERT_EQ(ANeuralNetworksExecution_create(compilation, &execution), ANEURALNETWORKS_NO_ERROR); ANeuralNetworksExecution_free(execution); ANeuralNetworksCompilation_free(compilation); ANeuralNetworksModel_free(model); } } // namespace