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// Copyright 2025 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "components/permissions/prediction_service/permissions_aiv3_handler.h"
#include "base/path_service.h"
#include "base/task/cancelable_task_tracker.h"
#include "base/test/metrics/histogram_tester.h"
#include "base/test/task_environment.h"
#include "base/test/test_future.h"
#include "components/optimization_guide/core/delivery/test_model_info_builder.h"
#include "components/optimization_guide/core/delivery/test_optimization_guide_model_provider.h"
#include "components/optimization_guide/core/inference/test_model_handler.h"
#include "components/optimization_guide/proto/common_types.pb.h"
#include "components/permissions/prediction_service/permissions_aiv3_encoder.h"
#include "testing/gmock/include/gmock/gmock.h"
#include "testing/gtest/include/gtest/gtest.h"
#include "third_party/skia/include/core/SkBitmap.h"
#include "third_party/tflite_support/src/tensorflow_lite_support/cc/task/core/task_utils.h"
namespace permissions {
namespace {
using ModelCallbackFuture =
::base::test::TestFuture<const std::optional<PermissionRequestRelevance>&>;
using ::optimization_guide::proto::OptimizationTarget;
using ::testing::SizeIs;
constexpr OptimizationTarget kOptTargetGeolocation = OptimizationTarget::
OPTIMIZATION_TARGET_GEOLOCATION_IMAGE_PERMISSION_RELEVANCE;
constexpr OptimizationTarget kOptTargetNotification = OptimizationTarget::
OPTIMIZATION_TARGET_NOTIFICATION_IMAGE_PERMISSION_RELEVANCE;
constexpr std::string_view kZeroReturnModel = "aiv3_ret_0.tflite";
constexpr std::string_view kZeroDotFiveReturnModel = "aiv3_ret_0_5.tflite";
constexpr std::string_view kOneReturnModel = "aiv3_ret_1.tflite";
constexpr SkColor kDefaultColor = SkColorSetRGB(0x1E, 0x1C, 0x0F);
auto& kModelInputWidth = PermissionsAiv3Encoder::kModelInputWidth;
auto& kModelInputHeight = PermissionsAiv3Encoder::kModelInputHeight;
base::FilePath ModelFilePath(std::string_view file_name) {
base::FilePath source_root_dir;
base::PathService::Get(base::DIR_SRC_TEST_DATA_ROOT, &source_root_dir);
return source_root_dir.AppendASCII("components")
.AppendASCII("test")
.AppendASCII("data")
.AppendASCII("permissions")
.AppendASCII(file_name);
}
void FillDataToBitmap(SkBitmap* bmp) {}
std::unique_ptr<SkBitmap> BuildBitmap(int width, int height) {
SkBitmap bitmap;
bitmap.allocN32Pixels(width, height);
bitmap.eraseColor(kDefaultColor);
return std::make_unique<SkBitmap>(std::move(bitmap));
}
class PermissionsAiv3EncoderFake : public PermissionsAiv3Encoder {
public:
explicit PermissionsAiv3EncoderFake(RequestType type)
: PermissionsAiv3Encoder(type) {}
void set_preprocess_hook(
base::OnceCallback<void(const std::vector<TfLiteTensor*>& input_tensors)>
hook) {
preprocess_hook_ = std::move(hook);
}
std::optional<PermissionRequestRelevance> relevance_;
base::OnceCallback<void(const std::vector<TfLiteTensor*>& input_tensors)>
preprocess_hook_;
protected:
bool Preprocess(const std::vector<TfLiteTensor*>& input_tensors,
const ModelInput& input) override {
auto ret = PermissionsAiv3Encoder::Preprocess(input_tensors, input);
if (preprocess_hook_) {
std::move(preprocess_hook_).Run(input_tensors);
}
return ret;
}
};
class Aiv3HandlerTestBase : public testing::Test {
public:
Aiv3HandlerTestBase() = default;
~Aiv3HandlerTestBase() override = default;
void SetUp() override {
model_provider_ = std::make_unique<
optimization_guide::TestOptimizationGuideModelProvider>();
auto geolocation_encoder_mock =
std::make_unique<PermissionsAiv3EncoderFake>(RequestType::kGeolocation);
geolocation_encoder_mock_ = geolocation_encoder_mock.get();
geolocation_model_handler_ = std::make_unique<PermissionsAiv3Handler>(
model_provider_.get(),
/*optimization_target=*/kOptTargetGeolocation,
/*request_type=*/RequestType::kGeolocation,
task_environment_.GetMainThreadTaskRunner(),
std::move(geolocation_encoder_mock));
auto notification_encoder_mock =
std::make_unique<PermissionsAiv3EncoderFake>(
RequestType::kNotifications);
notification_encoder_mock_ = notification_encoder_mock.get();
notification_model_handler_ = std::make_unique<PermissionsAiv3Handler>(
model_provider_.get(),
/*optimization_target=*/kOptTargetNotification,
/*request_type=*/RequestType::kNotifications,
task_environment_.GetMainThreadTaskRunner(),
std::move(notification_encoder_mock));
}
void TearDown() override {
geolocation_encoder_mock_ = nullptr;
notification_encoder_mock_ = nullptr;
geolocation_model_handler_.reset();
notification_model_handler_.reset();
model_provider_.reset();
task_environment_.RunUntilIdle();
}
void PushModelFileToModelExecutor(OptimizationTarget opt_target,
const base::FilePath& model_file_path) {
std::optional<optimization_guide::proto::Any> any;
auto model_metadata = optimization_guide::TestModelInfoBuilder()
.SetModelMetadata(any)
.SetModelFilePath(model_file_path)
.SetVersion(123)
.Build();
model_handler(opt_target)->OnModelUpdated(opt_target, *model_metadata);
task_environment_.RunUntilIdle();
}
PermissionsAiv3Handler* model_handler(OptimizationTarget target) {
return target == kOptTargetGeolocation ? geolocation_model_handler_.get()
: notification_model_handler_.get();
}
protected:
raw_ptr<PermissionsAiv3EncoderFake> geolocation_encoder_mock_;
raw_ptr<PermissionsAiv3EncoderFake> notification_encoder_mock_;
std::unique_ptr<PermissionsAiv3Handler> geolocation_model_handler_;
std::unique_ptr<PermissionsAiv3Handler> notification_model_handler_;
std::unique_ptr<optimization_guide::TestOptimizationGuideModelProvider>
model_provider_;
base::test::TaskEnvironment task_environment_;
};
class Aiv3HandlerTest : public Aiv3HandlerTestBase {};
struct RelevanceTestCase {
OptimizationTarget optimization_target;
base::FilePath model_file_path;
PermissionRequestRelevance expected_relevance;
};
class RelevanceAiv3HandlerTest
: public Aiv3HandlerTestBase,
public testing::WithParamInterface<RelevanceTestCase> {};
INSTANTIATE_TEST_SUITE_P(
ModelResults,
RelevanceAiv3HandlerTest,
testing::ValuesIn<RelevanceTestCase>({
{kOptTargetGeolocation, ModelFilePath(kZeroReturnModel),
PermissionRequestRelevance::kVeryLow},
{kOptTargetGeolocation, ModelFilePath(kZeroDotFiveReturnModel),
PermissionRequestRelevance::kHigh},
{kOptTargetGeolocation, ModelFilePath(kOneReturnModel),
PermissionRequestRelevance::kVeryHigh},
{kOptTargetNotification, ModelFilePath(kZeroReturnModel),
PermissionRequestRelevance::kVeryLow},
{kOptTargetNotification, ModelFilePath(kZeroDotFiveReturnModel),
PermissionRequestRelevance::kMedium},
{kOptTargetNotification, ModelFilePath(kOneReturnModel),
PermissionRequestRelevance::kVeryHigh},
}));
TEST_P(RelevanceAiv3HandlerTest,
RelevanceIsMatchedToTheCorrectModelThresholds) {
PushModelFileToModelExecutor(GetParam().optimization_target,
GetParam().model_file_path);
auto* aiv3_handler = model_handler(GetParam().optimization_target);
EXPECT_TRUE(aiv3_handler->ModelAvailable());
ModelCallbackFuture future;
aiv3_handler->ExecuteModel(
future.GetCallback(),
/*snapshot=*/BuildBitmap(kModelInputWidth, kModelInputHeight));
EXPECT_EQ(future.Take(), GetParam().expected_relevance);
}
TEST_F(Aiv3HandlerTest, BitmapGetsCopiedToTensor) {
PushModelFileToModelExecutor(kOptTargetGeolocation,
ModelFilePath(kZeroReturnModel));
auto snapshot = BuildBitmap(kModelInputWidth, kModelInputHeight);
FillDataToBitmap(snapshot.get());
bool flag = false;
geolocation_encoder_mock_->set_preprocess_hook(base::BindOnce(
[](bool* flag, const std::vector<TfLiteTensor*>& input_tensors) {
std::vector<float> data;
if (tflite::task::core::PopulateVector<float>(input_tensors[0], &data)
.ok()) {
EXPECT_THAT(data, SizeIs(kModelInputWidth * kModelInputHeight * 3));
for (int i = 0; i < kModelInputWidth * kModelInputHeight; i += 3) {
EXPECT_FLOAT_EQ(data[i], SkColorGetR(kDefaultColor) / 255.0f);
EXPECT_FLOAT_EQ(data[i + 1], SkColorGetG(kDefaultColor) / 255.0f);
EXPECT_FLOAT_EQ(data[i + 2], SkColorGetB(kDefaultColor) / 255.0f);
}
}
*flag = true;
},
&flag));
ModelCallbackFuture future;
auto* aiv3_handler = model_handler(kOptTargetGeolocation);
aiv3_handler->ExecuteModel(future.GetCallback(), std::move(snapshot));
EXPECT_EQ(future.Take(), PermissionRequestRelevance::kVeryLow);
EXPECT_TRUE(flag);
}
TEST_F(Aiv3HandlerTest, HandlesEmptyInputSnapshot) {
PushModelFileToModelExecutor(kOptTargetGeolocation,
ModelFilePath(kZeroReturnModel));
auto snapshot = BuildBitmap(/*width=*/0, /*height=*/0);
FillDataToBitmap(snapshot.get());
ModelCallbackFuture future;
auto* aiv3_handler = model_handler(kOptTargetGeolocation);
aiv3_handler->ExecuteModel(future.GetCallback(), std::move(snapshot));
EXPECT_EQ(future.Take(), std::nullopt);
}
struct ResizeTestCase {
int input_width;
int input_height;
};
class ResizeAiv3HandlerTest
: public Aiv3HandlerTestBase,
public testing::WithParamInterface<ResizeTestCase> {};
INSTANTIATE_TEST_SUITE_P(ResizeBitmapInternally,
ResizeAiv3HandlerTest,
testing::ValuesIn<ResizeTestCase>({
{/*input_width=*/32, /*input_height=*/32},
{/*input_width=*/32, /*input_height=*/64},
{/*input_width=*/64, /*input_height=*/32},
{/*input_width=*/128, /*input_height=*/128},
{/*input_width=*/64, /*input_height=*/128},
{/*input_width=*/128, /*input_height=*/64},
}));
TEST_P(ResizeAiv3HandlerTest, ResizesBitmapsForModelInput) {
PushModelFileToModelExecutor(kOptTargetGeolocation,
ModelFilePath(kZeroReturnModel));
auto snapshot = BuildBitmap(GetParam().input_width, GetParam().input_height);
FillDataToBitmap(snapshot.get());
bool flag = false;
geolocation_encoder_mock_->set_preprocess_hook(base::BindOnce(
[](bool* flag, const std::vector<TfLiteTensor*>& input_tensors) {
std::vector<float> data;
if (tflite::task::core::PopulateVector<float>(input_tensors[0], &data)
.ok()) {
EXPECT_THAT(data, SizeIs(kModelInputWidth * kModelInputHeight * 3));
for (int i = 0; i < kModelInputWidth * kModelInputHeight * 3; ++i) {
EXPECT_FALSE(std::isnan(data[i]));
}
}
*flag = true;
},
&flag));
ModelCallbackFuture future;
auto* aiv3_handler = model_handler(kOptTargetGeolocation);
aiv3_handler->ExecuteModel(future.GetCallback(), std::move(snapshot));
EXPECT_EQ(future.Take(), PermissionRequestRelevance::kVeryLow);
EXPECT_TRUE(flag);
}
} // namespace
} // namespace permissions
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