238 lines
8.6 KiB
C++
238 lines
8.6 KiB
C++
/*
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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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#include "1.0/Utils.h"
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#include "MemoryUtils.h"
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#include "TestHarness.h"
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#include <android-base/logging.h>
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#include <android/hardware/neuralnetworks/1.0/types.h>
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#include <android/hardware_buffer.h>
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#include <android/hidl/allocator/1.0/IAllocator.h>
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#include <android/hidl/memory/1.0/IMemory.h>
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#include <hidlmemory/mapping.h>
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#include <vndk/hardware_buffer.h>
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <cstring>
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#include <functional>
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#include <iostream>
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#include <map>
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#include <numeric>
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#include <vector>
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namespace android::hardware::neuralnetworks {
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using namespace test_helper;
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using hidl::memory::V1_0::IMemory;
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using V1_0::DataLocation;
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using V1_0::Request;
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using V1_0::RequestArgument;
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std::unique_ptr<TestAshmem> TestAshmem::create(uint32_t size) {
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auto ashmem = std::make_unique<TestAshmem>(size);
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return ashmem->mIsValid ? std::move(ashmem) : nullptr;
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}
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void TestAshmem::initialize(uint32_t size) {
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mIsValid = false;
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ASSERT_GT(size, 0);
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mHidlMemory = nn::allocateSharedMemory(size);
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ASSERT_TRUE(mHidlMemory.valid());
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mMappedMemory = mapMemory(mHidlMemory);
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ASSERT_NE(mMappedMemory, nullptr);
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mPtr = static_cast<uint8_t*>(static_cast<void*>(mMappedMemory->getPointer()));
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ASSERT_NE(mPtr, nullptr);
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mIsValid = true;
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}
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std::unique_ptr<TestBlobAHWB> TestBlobAHWB::create(uint32_t size) {
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auto ahwb = std::make_unique<TestBlobAHWB>(size);
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return ahwb->mIsValid ? std::move(ahwb) : nullptr;
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}
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void TestBlobAHWB::initialize(uint32_t size) {
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mIsValid = false;
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ASSERT_GT(size, 0);
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const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
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const AHardwareBuffer_Desc desc = {
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.width = size,
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.height = 1,
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.layers = 1,
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.format = AHARDWAREBUFFER_FORMAT_BLOB,
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.usage = usage,
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.stride = size,
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};
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ASSERT_EQ(AHardwareBuffer_allocate(&desc, &mAhwb), 0);
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ASSERT_NE(mAhwb, nullptr);
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void* buffer = nullptr;
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ASSERT_EQ(AHardwareBuffer_lock(mAhwb, usage, -1, nullptr, &buffer), 0);
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ASSERT_NE(buffer, nullptr);
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mPtr = static_cast<uint8_t*>(buffer);
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const native_handle_t* handle = AHardwareBuffer_getNativeHandle(mAhwb);
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ASSERT_NE(handle, nullptr);
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mHidlMemory = hidl_memory("hardware_buffer_blob", handle, desc.width);
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mIsValid = true;
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}
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TestBlobAHWB::~TestBlobAHWB() {
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if (mAhwb) {
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AHardwareBuffer_unlock(mAhwb, nullptr);
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AHardwareBuffer_release(mAhwb);
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}
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}
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Request ExecutionContext::createRequest(const TestModel& testModel, MemoryType memoryType) {
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CHECK(memoryType == MemoryType::ASHMEM || memoryType == MemoryType::BLOB_AHWB);
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// Model inputs.
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hidl_vec<RequestArgument> inputs(testModel.main.inputIndexes.size());
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size_t inputSize = 0;
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for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
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const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
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if (op.data.size() == 0) {
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// Omitted input.
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inputs[i] = {.hasNoValue = true};
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} else {
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DataLocation loc = {.poolIndex = kInputPoolIndex,
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.offset = static_cast<uint32_t>(inputSize),
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.length = static_cast<uint32_t>(op.data.size())};
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inputSize += op.data.alignedSize();
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inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
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}
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}
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// Model outputs.
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hidl_vec<RequestArgument> outputs(testModel.main.outputIndexes.size());
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size_t outputSize = 0;
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for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {
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const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]];
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// In the case of zero-sized output, we should at least provide a one-byte buffer.
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// This is because zero-sized tensors are only supported internally to the driver, or
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// reported in output shapes. It is illegal for the client to pre-specify a zero-sized
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// tensor as model output. Otherwise, we will have two semantic conflicts:
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// - "Zero dimension" conflicts with "unspecified dimension".
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// - "Omitted operand buffer" conflicts with "zero-sized operand buffer".
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size_t bufferSize = std::max<size_t>(op.data.size(), 1);
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DataLocation loc = {.poolIndex = kOutputPoolIndex,
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.offset = static_cast<uint32_t>(outputSize),
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.length = static_cast<uint32_t>(bufferSize)};
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outputSize += op.data.size() == 0 ? TestBuffer::kAlignment : op.data.alignedSize();
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outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}};
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}
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// Allocate memory pools.
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if (memoryType == MemoryType::ASHMEM) {
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mInputMemory = TestAshmem::create(inputSize);
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mOutputMemory = TestAshmem::create(outputSize);
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} else {
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mInputMemory = TestBlobAHWB::create(inputSize);
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mOutputMemory = TestBlobAHWB::create(outputSize);
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}
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EXPECT_NE(mInputMemory, nullptr);
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EXPECT_NE(mOutputMemory, nullptr);
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hidl_vec<hidl_memory> pools = {mInputMemory->getHidlMemory(), mOutputMemory->getHidlMemory()};
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// Copy input data to the memory pool.
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uint8_t* inputPtr = mInputMemory->getPointer();
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for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
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const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]];
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if (op.data.size() > 0) {
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const uint8_t* begin = op.data.get<uint8_t>();
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const uint8_t* end = begin + op.data.size();
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std::copy(begin, end, inputPtr + inputs[i].location.offset);
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}
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}
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return {.inputs = std::move(inputs), .outputs = std::move(outputs), .pools = std::move(pools)};
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}
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std::vector<TestBuffer> ExecutionContext::getOutputBuffers(const Request& request) const {
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// Copy out output results.
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uint8_t* outputPtr = mOutputMemory->getPointer();
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std::vector<TestBuffer> outputBuffers;
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for (const auto& output : request.outputs) {
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outputBuffers.emplace_back(output.location.length, outputPtr + output.location.offset);
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}
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return outputBuffers;
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}
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uint32_t sizeOfData(V1_0::OperandType type) {
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switch (type) {
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case V1_0::OperandType::FLOAT32:
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case V1_0::OperandType::INT32:
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case V1_0::OperandType::UINT32:
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case V1_0::OperandType::TENSOR_FLOAT32:
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case V1_0::OperandType::TENSOR_INT32:
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return 4;
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case V1_0::OperandType::TENSOR_QUANT8_ASYMM:
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return 1;
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default:
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CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
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return 0;
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}
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}
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static bool isTensor(V1_0::OperandType type) {
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switch (type) {
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case V1_0::OperandType::FLOAT32:
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case V1_0::OperandType::INT32:
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case V1_0::OperandType::UINT32:
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return false;
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case V1_0::OperandType::TENSOR_FLOAT32:
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case V1_0::OperandType::TENSOR_INT32:
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case V1_0::OperandType::TENSOR_QUANT8_ASYMM:
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return true;
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default:
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CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
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return false;
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}
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}
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uint32_t sizeOfData(const V1_0::Operand& operand) {
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const uint32_t dataSize = sizeOfData(operand.type);
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if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0;
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return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize,
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std::multiplies<>{});
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}
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std::string gtestCompliantName(std::string name) {
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// gtest test names must only contain alphanumeric characters
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std::replace_if(
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name.begin(), name.end(), [](char c) { return !std::isalnum(c); }, '_');
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return name;
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}
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} // namespace android::hardware::neuralnetworks
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namespace android::hardware::neuralnetworks::V1_0 {
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::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
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return os << toString(errorStatus);
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}
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::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus) {
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return os << toString(deviceStatus);
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}
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} // namespace android::hardware::neuralnetworks::V1_0
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