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