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