mirror of
https://github.com/Evolution-X/hardware_interfaces
synced 2026-02-01 11:36:00 +00:00
To make it easier to create the next version of NNAPI, this change
removes the following nonsensical dependence:
- NNAPI 1.0 VTS depends on NNAPI 1.1 and 1.2
- NNAPI 1.1 VTS depends on NNAPI 1.2
In particular, I made the following changes:
- split GeneratedTestHarness.cpp into three separate implementations,
- created a restricted version of Callbacks.h for 1.0 and 1.1,
- removed the dependency on frameworks/ml/nn/HalInterfaces.h,
- refactored Android.bp files for more autonomy between 1.0, 1.1, and 1.2,
- consolidated some common code into Utils.h,
- created structure for sharing code between VTS versions (VtsHalNeuralNetworksV1_0_utils).
Bug: 74827824
Bug: 124462414
Test: VtsHalNeuralnetworksV1_0TargetTest
Test: VtsHalNeuralnetworksV1_1TargetTest
Test: VtsHalNeuralnetworksV1_1CompatV1_0TargetTest
Test: VtsHalNeuralnetworksV1_2TargetTest
Test: VtsHalNeuralnetworksV1_2CompatV1_0TargetTest
Test: VtsHalNeuralnetworksV1_2CompatV1_1TargetTest
Change-Id: I4243d0b5e574255cef1070850f4d0a284f65f54e
Merged-In: I4243d0b5e574255cef1070850f4d0a284f65f54e
(cherry picked from commit 1d6b465997)
284 lines
11 KiB
C++
284 lines
11 KiB
C++
/*
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* Copyright (C) 2018 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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#define LOG_TAG "neuralnetworks_hidl_hal_test"
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#include <android-base/logging.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 "1.0/Utils.h"
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#include "1.2/Callbacks.h"
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#include "ExecutionBurstController.h"
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#include "MemoryUtils.h"
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#include "TestHarness.h"
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#include "Utils.h"
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#include "VtsHalNeuralnetworks.h"
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namespace android {
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namespace hardware {
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namespace neuralnetworks {
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namespace V1_2 {
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namespace vts {
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namespace functional {
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using ::android::hardware::neuralnetworks::V1_2::implementation::ExecutionCallback;
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using ::android::hidl::memory::V1_0::IMemory;
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using test_helper::for_all;
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using test_helper::MixedTyped;
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using test_helper::MixedTypedExample;
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///////////////////////// UTILITY FUNCTIONS /////////////////////////
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static bool badTiming(Timing timing) {
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return timing.timeOnDevice == UINT64_MAX && timing.timeInDriver == UINT64_MAX;
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}
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// Primary validation function. This function will take a valid request, apply a
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// mutation to it to invalidate the request, then pass it to interface calls
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// that use the request. Note that the request here is passed by value, and any
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// mutation to the request does not leave this function.
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static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
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Request request, const std::function<void(Request*)>& mutation) {
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mutation(&request);
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// We'd like to test both with timing requested and without timing
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// requested. Rather than running each test both ways, we'll decide whether
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// to request timing by hashing the message. We do not use std::hash because
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// it is not guaranteed stable across executions.
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char hash = 0;
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for (auto c : message) {
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hash ^= c;
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};
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MeasureTiming measure = (hash & 1) ? MeasureTiming::YES : MeasureTiming::NO;
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// asynchronous
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{
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SCOPED_TRACE(message + " [execute_1_2]");
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sp<ExecutionCallback> executionCallback = new ExecutionCallback();
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ASSERT_NE(nullptr, executionCallback.get());
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Return<ErrorStatus> executeLaunchStatus =
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preparedModel->execute_1_2(request, measure, executionCallback);
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ASSERT_TRUE(executeLaunchStatus.isOk());
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ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
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executionCallback->wait();
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ErrorStatus executionReturnStatus = executionCallback->getStatus();
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const auto& outputShapes = executionCallback->getOutputShapes();
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Timing timing = executionCallback->getTiming();
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ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
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ASSERT_EQ(outputShapes.size(), 0);
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ASSERT_TRUE(badTiming(timing));
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}
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// synchronous
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{
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SCOPED_TRACE(message + " [executeSynchronously]");
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Return<void> executeStatus = preparedModel->executeSynchronously(
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request, measure,
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[](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes,
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const Timing& timing) {
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ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
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EXPECT_EQ(outputShapes.size(), 0);
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EXPECT_TRUE(badTiming(timing));
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});
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ASSERT_TRUE(executeStatus.isOk());
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}
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// burst
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{
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SCOPED_TRACE(message + " [burst]");
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// create burst
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std::shared_ptr<::android::nn::ExecutionBurstController> burst =
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::android::nn::ExecutionBurstController::create(preparedModel, /*blocking=*/true);
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ASSERT_NE(nullptr, burst.get());
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// create memory keys
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std::vector<intptr_t> keys(request.pools.size());
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for (size_t i = 0; i < keys.size(); ++i) {
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keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
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}
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// execute and verify
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ErrorStatus error;
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std::vector<OutputShape> outputShapes;
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Timing timing;
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std::tie(error, outputShapes, timing) = burst->compute(request, measure, keys);
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EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
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EXPECT_EQ(outputShapes.size(), 0);
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EXPECT_TRUE(badTiming(timing));
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// additional burst testing
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if (request.pools.size() > 0) {
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// valid free
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burst->freeMemory(keys.front());
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// negative test: invalid free of unknown (blank) memory
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burst->freeMemory(intptr_t{});
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// negative test: double free of memory
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burst->freeMemory(keys.front());
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}
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}
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}
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///////////////////////// REMOVE INPUT ////////////////////////////////////
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static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
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for (size_t input = 0; input < request.inputs.size(); ++input) {
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const std::string message = "removeInput: removed input " + std::to_string(input);
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validate(preparedModel, message, request,
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[input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
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}
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}
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///////////////////////// REMOVE OUTPUT ////////////////////////////////////
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static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
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for (size_t output = 0; output < request.outputs.size(); ++output) {
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const std::string message = "removeOutput: removed Output " + std::to_string(output);
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validate(preparedModel, message, request,
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[output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
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}
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}
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///////////////////////////// ENTRY POINT //////////////////////////////////
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std::vector<Request> createRequests(const std::vector<MixedTypedExample>& examples) {
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const uint32_t INPUT = 0;
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const uint32_t OUTPUT = 1;
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std::vector<Request> requests;
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for (auto& example : examples) {
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const MixedTyped& inputs = example.operands.first;
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const MixedTyped& outputs = example.operands.second;
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std::vector<RequestArgument> inputs_info, outputs_info;
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uint32_t inputSize = 0, outputSize = 0;
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// This function only partially specifies the metadata (vector of RequestArguments).
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// The contents are copied over below.
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for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
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if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
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RequestArgument arg = {
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.location = {.poolIndex = INPUT,
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.offset = 0,
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.length = static_cast<uint32_t>(s)},
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.dimensions = {},
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};
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RequestArgument arg_empty = {
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.hasNoValue = true,
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};
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inputs_info[index] = s ? arg : arg_empty;
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inputSize += s;
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});
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// Compute offset for inputs 1 and so on
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{
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size_t offset = 0;
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for (auto& i : inputs_info) {
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if (!i.hasNoValue) i.location.offset = offset;
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offset += i.location.length;
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}
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}
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// Go through all outputs, initialize RequestArgument descriptors
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for_all(outputs, [&outputs_info, &outputSize](int index, auto, auto s) {
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if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
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RequestArgument arg = {
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.location = {.poolIndex = OUTPUT,
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.offset = 0,
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.length = static_cast<uint32_t>(s)},
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.dimensions = {},
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};
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outputs_info[index] = arg;
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outputSize += s;
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});
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// Compute offset for outputs 1 and so on
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{
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size_t offset = 0;
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for (auto& i : outputs_info) {
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i.location.offset = offset;
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offset += i.location.length;
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}
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}
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std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
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nn::allocateSharedMemory(outputSize)};
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if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
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return {};
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}
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// map pool
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sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
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if (inputMemory == nullptr) {
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return {};
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}
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char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
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if (inputPtr == nullptr) {
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return {};
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}
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// initialize pool
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inputMemory->update();
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for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
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char* begin = (char*)p;
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char* end = begin + s;
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// TODO: handle more than one input
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std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
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});
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inputMemory->commit();
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requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
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}
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return requests;
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}
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void ValidationTest::validateRequests(const sp<IPreparedModel>& preparedModel,
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const std::vector<Request>& requests) {
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// validate each request
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for (const Request& request : requests) {
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removeInputTest(preparedModel, request);
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removeOutputTest(preparedModel, request);
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}
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}
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void ValidationTest::validateRequestFailure(const sp<IPreparedModel>& preparedModel,
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const std::vector<Request>& requests) {
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for (const Request& request : requests) {
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SCOPED_TRACE("Expecting request to fail [executeSynchronously]");
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Return<void> executeStatus = preparedModel->executeSynchronously(
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request, MeasureTiming::NO,
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[](ErrorStatus error, const hidl_vec<OutputShape>& outputShapes,
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const Timing& timing) {
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ASSERT_NE(ErrorStatus::NONE, error);
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EXPECT_EQ(outputShapes.size(), 0);
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EXPECT_TRUE(badTiming(timing));
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});
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ASSERT_TRUE(executeStatus.isOk());
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}
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}
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} // namespace functional
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} // namespace vts
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} // namespace V1_2
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} // namespace neuralnetworks
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} // namespace hardware
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} // namespace android
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