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/*
* Copyright (C) 2017 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.
*/
#include "TestMemory.h"
#include "TestNeuralNetworksWrapper.h"
#include <gtest/gtest.h>
#include <sys/mman.h>
#include <sys/types.h>
#include <unistd.h>
using WrapperCompilation = ::android::nn::test_wrapper::Compilation;
using WrapperExecution = ::android::nn::test_wrapper::Execution;
using WrapperMemory = ::android::nn::test_wrapper::Memory;
using WrapperModel = ::android::nn::test_wrapper::Model;
using WrapperOperandType = ::android::nn::test_wrapper::OperandType;
using WrapperResult = ::android::nn::test_wrapper::Result;
using WrapperType = ::android::nn::test_wrapper::Type;
namespace {
// Tests the various ways to pass weights and input/output data.
class MemoryTest : public ::testing::Test {
protected:
void SetUp() override {}
};
TEST_F(MemoryTest, TestFd) {
// Create a file that contains matrix2 and matrix3.
char path[] = "/data/local/tmp/TestMemoryXXXXXX";
int fd = mkstemp(path);
const uint32_t offsetForMatrix2 = 20;
const uint32_t offsetForMatrix3 = 200;
static_assert(offsetForMatrix2 + sizeof(matrix2) < offsetForMatrix3, "matrices overlap");
lseek(fd, offsetForMatrix2, SEEK_SET);
write(fd, matrix2, sizeof(matrix2));
lseek(fd, offsetForMatrix3, SEEK_SET);
write(fd, matrix3, sizeof(matrix3));
fsync(fd);
WrapperMemory weights(offsetForMatrix3 + sizeof(matrix3), PROT_READ, fd, 0);
ASSERT_TRUE(weights.isValid());
WrapperModel model;
WrapperOperandType matrixType(WrapperType::TENSOR_FLOAT32, {3, 4});
WrapperOperandType scalarType(WrapperType::INT32, {});
int32_t activation(0);
auto a = model.addOperand(&matrixType);
auto b = model.addOperand(&matrixType);
auto c = model.addOperand(&matrixType);
auto d = model.addOperand(&matrixType);
auto e = model.addOperand(&matrixType);
auto f = model.addOperand(&scalarType);
model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4));
model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4));
model.setOperandValue(f, &activation, sizeof(activation));
model.addOperation(ANEURALNETWORKS_ADD, {a, c, f}, {b});
model.addOperation(ANEURALNETWORKS_ADD, {b, e, f}, {d});
model.identifyInputsAndOutputs({c}, {d});
ASSERT_TRUE(model.isValid());
model.finish();
// Test the three node model.
Matrix3x4 actual;
memset(&actual, 0, sizeof(actual));
WrapperCompilation compilation2(&model);
ASSERT_EQ(compilation2.finish(), WrapperResult::NO_ERROR);
WrapperExecution execution2(&compilation2);
ASSERT_EQ(execution2.setInput(0, matrix1, sizeof(Matrix3x4)), WrapperResult::NO_ERROR);
ASSERT_EQ(execution2.setOutput(0, actual, sizeof(Matrix3x4)), WrapperResult::NO_ERROR);
ASSERT_EQ(execution2.compute(), WrapperResult::NO_ERROR);
ASSERT_EQ(CompareMatrices(expected3, actual), 0);
close(fd);
unlink(path);
}
TEST_F(MemoryTest, TestAHardwareBuffer) {
const uint32_t offsetForMatrix2 = 20;
const uint32_t offsetForMatrix3 = 200;
AHardwareBuffer_Desc desc{
.width = offsetForMatrix3 + sizeof(matrix3),
.height = 1,
.layers = 1,
.format = AHARDWAREBUFFER_FORMAT_BLOB,
.usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN,
};
AHardwareBuffer* buffer = nullptr;
ASSERT_EQ(AHardwareBuffer_allocate(&desc, &buffer), 0);
void* bufferPtr = nullptr;
ASSERT_EQ(AHardwareBuffer_lock(buffer, desc.usage, -1, NULL, &bufferPtr), 0);
memcpy((uint8_t*)bufferPtr + offsetForMatrix2, matrix2, sizeof(matrix2));
memcpy((uint8_t*)bufferPtr + offsetForMatrix3, matrix3, sizeof(matrix3));
ASSERT_EQ(AHardwareBuffer_unlock(buffer, nullptr), 0);
WrapperMemory weights(buffer);
ASSERT_TRUE(weights.isValid());
WrapperModel model;
WrapperOperandType matrixType(WrapperType::TENSOR_FLOAT32, {3, 4});
WrapperOperandType scalarType(WrapperType::INT32, {});
int32_t activation(0);
auto a = model.addOperand(&matrixType);
auto b = model.addOperand(&matrixType);
auto c = model.addOperand(&matrixType);
auto d = model.addOperand(&matrixType);
auto e = model.addOperand(&matrixType);
auto f = model.addOperand(&scalarType);
model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4));
model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4));
model.setOperandValue(f, &activation, sizeof(activation));
model.addOperation(ANEURALNETWORKS_ADD, {a, c, f}, {b});
model.addOperation(ANEURALNETWORKS_ADD, {b, e, f}, {d});
model.identifyInputsAndOutputs({c}, {d});
ASSERT_TRUE(model.isValid());
model.finish();
// Test the three node model.
Matrix3x4 actual;
memset(&actual, 0, sizeof(actual));
WrapperCompilation compilation2(&model);
ASSERT_EQ(compilation2.finish(), WrapperResult::NO_ERROR);
WrapperExecution execution2(&compilation2);
ASSERT_EQ(execution2.setInput(0, matrix1, sizeof(Matrix3x4)), WrapperResult::NO_ERROR);
ASSERT_EQ(execution2.setOutput(0, actual, sizeof(Matrix3x4)), WrapperResult::NO_ERROR);
ASSERT_EQ(execution2.compute(), WrapperResult::NO_ERROR);
ASSERT_EQ(CompareMatrices(expected3, actual), 0);
AHardwareBuffer_release(buffer);
buffer = nullptr;
}
} // end namespace
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