You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
4.2 KiB
114 lines
4.2 KiB
/*
|
|
* 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.
|
|
*/
|
|
|
|
#define LOG_TAG "TestControlFlow"
|
|
|
|
#include <ControlFlow.h>
|
|
#include <android-base/logging.h>
|
|
#include <gtest/gtest.h>
|
|
|
|
#include "TestNeuralNetworksWrapper.h"
|
|
|
|
namespace android::nn {
|
|
namespace {
|
|
|
|
using test_wrapper::Compilation;
|
|
using test_wrapper::Execution;
|
|
using test_wrapper::Model;
|
|
using test_wrapper::OperandType;
|
|
using test_wrapper::Result;
|
|
using test_wrapper::Type;
|
|
|
|
constexpr uint64_t kMillisecondsInNanosecond = 1'000'000;
|
|
constexpr int32_t kNoActivation = ANEURALNETWORKS_FUSED_NONE;
|
|
|
|
class ControlFlowTest : public ::testing::Test {};
|
|
|
|
TEST_F(ControlFlowTest, InfiniteLoop) {
|
|
// Expected result: execution aborted after the specified timeout.
|
|
// Model: given n <= 1.0, never returns.
|
|
//
|
|
// i = 1.0
|
|
// while i >= n:
|
|
// i = i + 1.0
|
|
|
|
OperandType boolType(Type::TENSOR_BOOL8, {1});
|
|
OperandType activationType(Type::INT32, {});
|
|
OperandType counterType(Type::TENSOR_FLOAT32, {1});
|
|
|
|
Model conditionModel;
|
|
{
|
|
uint32_t i = conditionModel.addOperand(&counterType);
|
|
uint32_t n = conditionModel.addOperand(&counterType);
|
|
uint32_t out = conditionModel.addOperand(&boolType);
|
|
conditionModel.addOperation(ANEURALNETWORKS_GREATER_EQUAL, {i, n}, {out});
|
|
conditionModel.identifyInputsAndOutputs({i, n}, {out});
|
|
ASSERT_EQ(conditionModel.finish(), Result::NO_ERROR);
|
|
ASSERT_TRUE(conditionModel.isValid());
|
|
}
|
|
|
|
Model bodyModel;
|
|
{
|
|
uint32_t i = bodyModel.addOperand(&counterType);
|
|
uint32_t n = bodyModel.addOperand(&counterType);
|
|
uint32_t one = bodyModel.addConstantOperand(&counterType, 1.0f);
|
|
uint32_t noActivation = bodyModel.addConstantOperand(&activationType, kNoActivation);
|
|
uint32_t iOut = bodyModel.addOperand(&counterType);
|
|
bodyModel.addOperation(ANEURALNETWORKS_ADD, {i, one, noActivation}, {iOut});
|
|
bodyModel.identifyInputsAndOutputs({i, n}, {iOut});
|
|
ASSERT_EQ(bodyModel.finish(), Result::NO_ERROR);
|
|
ASSERT_TRUE(bodyModel.isValid());
|
|
}
|
|
|
|
Model model;
|
|
{
|
|
uint32_t iInit = model.addConstantOperand(&counterType, 1.0f);
|
|
uint32_t n = model.addOperand(&counterType);
|
|
uint32_t conditionOperand = model.addModelOperand(&conditionModel);
|
|
uint32_t bodyOperand = model.addModelOperand(&bodyModel);
|
|
uint32_t iOut = model.addOperand(&counterType);
|
|
model.addOperation(ANEURALNETWORKS_WHILE, {conditionOperand, bodyOperand, iInit, n},
|
|
{iOut});
|
|
model.identifyInputsAndOutputs({n}, {iOut});
|
|
ASSERT_EQ(model.finish(), Result::NO_ERROR);
|
|
ASSERT_TRUE(model.isValid());
|
|
}
|
|
|
|
Compilation compilation(&model);
|
|
ASSERT_EQ(compilation.finish(), Result::NO_ERROR);
|
|
|
|
float input = 0;
|
|
float output;
|
|
Execution execution(&compilation);
|
|
ASSERT_EQ(execution.setInput(0, &input), Result::NO_ERROR);
|
|
ASSERT_EQ(execution.setOutput(0, &output), Result::NO_ERROR);
|
|
ASSERT_EQ(execution.setLoopTimeout(1 * kMillisecondsInNanosecond), Result::NO_ERROR);
|
|
Result result = execution.compute();
|
|
ASSERT_TRUE(result == Result::MISSED_DEADLINE_TRANSIENT ||
|
|
result == Result::MISSED_DEADLINE_PERSISTENT)
|
|
<< "result = " << static_cast<int>(result);
|
|
}
|
|
|
|
TEST_F(ControlFlowTest, GetLoopTimeouts) {
|
|
uint64_t defaultTimeout = ANeuralNetworks_getDefaultLoopTimeout();
|
|
uint64_t maximumTimeout = ANeuralNetworks_getMaximumLoopTimeout();
|
|
ASSERT_EQ(defaultTimeout, operation_while::kTimeoutNsDefault);
|
|
ASSERT_EQ(maximumTimeout, operation_while::kTimeoutNsMaximum);
|
|
}
|
|
|
|
} // end namespace
|
|
} // namespace android::nn
|