Merge "graphics.common: add HSV format definition to 1.2"
diff --git a/biometrics/face/1.0/types.hal b/biometrics/face/1.0/types.hal
index a488d67..2bcd3d5 100644
--- a/biometrics/face/1.0/types.hal
+++ b/biometrics/face/1.0/types.hal
@@ -281,9 +281,52 @@
TOO_SIMILAR = 15,
/**
+ * The magnitude of the pan angle of the user’s face with respect to the sensor’s
+ * capture plane is too high.
+ *
+ * The pan angle is defined as the angle swept out by the user’s face turning
+ * their neck left and right. The pan angle would be zero if the user faced the
+ * camera directly.
+ *
+ * The user should be informed to look more directly at the camera.
+ */
+ PAN_TOO_EXTREME = 16,
+
+ /**
+ * The magnitude of the tilt angle of the user’s face with respect to the sensor’s
+ * capture plane is too high.
+ *
+ * The tilt angle is defined as the angle swept out by the user’s face looking up
+ * and down. The pan angle would be zero if the user faced the camera directly.
+ *
+ * The user should be informed to look more directly at the camera.
+ */
+ TILT_TOO_EXTREME = 17,
+
+ /**
+ * The magnitude of the roll angle of the user’s face with respect to the sensor’s
+ * capture plane is too high.
+ *
+ * The roll angle is defined as the angle swept out by the user’s face tilting their head
+ * towards their shoulders to the left and right. The pan angle would be zero if the user
+ * faced the camera directly.
+ *
+ * The user should be informed to look more directly at the camera.
+ */
+ ROLL_TOO_EXTREME = 18,
+
+ /**
+ * The user’s face has been obscured by some object.
+ *
+ * The user should be informed to remove any objects from the line of sight from
+ * the sensor to the user’s face.
+ */
+ FACE_OBSCURED = 19,
+
+ /**
* Used to enable a vendor-specific acquisition message.
*/
- VENDOR = 16
+ VENDOR = 20
};
/**
diff --git a/camera/provider/2.4/default/CameraProvider.cpp b/camera/provider/2.4/default/CameraProvider.cpp
index 488b9af..f143dd7 100644
--- a/camera/provider/2.4/default/CameraProvider.cpp
+++ b/camera/provider/2.4/default/CameraProvider.cpp
@@ -143,7 +143,6 @@
int new_status) {
CameraProvider* cp = const_cast<CameraProvider*>(
static_cast<const CameraProvider*>(callbacks));
- bool found = false;
if (cp == nullptr) {
ALOGE("%s: callback ops is null", __FUNCTION__);
@@ -155,17 +154,23 @@
snprintf(cameraId, sizeof(cameraId), "%d", camera_id);
std::string cameraIdStr(cameraId);
cp->mCameraStatusMap[cameraIdStr] = (camera_device_status_t) new_status;
- if (cp->mCallbacks != nullptr) {
- CameraDeviceStatus status = (CameraDeviceStatus) new_status;
- for (auto const& deviceNamePair : cp->mCameraDeviceNames) {
- if (cameraIdStr.compare(deviceNamePair.first) == 0) {
- cp->mCallbacks->cameraDeviceStatusChange(
- deviceNamePair.second, status);
- found = true;
- }
- }
- switch (status) {
+ if (cp->mCallbacks == nullptr) {
+ // For camera connected before mCallbacks is set, the corresponding
+ // addDeviceNames() would be called later in setCallbacks().
+ return;
+ }
+
+ bool found = false;
+ CameraDeviceStatus status = (CameraDeviceStatus)new_status;
+ for (auto const& deviceNamePair : cp->mCameraDeviceNames) {
+ if (cameraIdStr.compare(deviceNamePair.first) == 0) {
+ cp->mCallbacks->cameraDeviceStatusChange(deviceNamePair.second, status);
+ found = true;
+ }
+ }
+
+ switch (status) {
case CameraDeviceStatus::PRESENT:
case CameraDeviceStatus::ENUMERATING:
if (!found) {
@@ -176,7 +181,6 @@
if (found) {
cp->removeDeviceNames(camera_id);
}
- }
}
}
@@ -439,8 +443,22 @@
// Methods from ::android::hardware::camera::provider::V2_4::ICameraProvider follow.
Return<Status> CameraProvider::setCallback(const sp<ICameraProviderCallback>& callback) {
+ if (callback == nullptr) {
+ return Status::ILLEGAL_ARGUMENT;
+ }
+
Mutex::Autolock _l(mCbLock);
mCallbacks = callback;
+
+ // Add and report all presenting external cameras.
+ for (auto const& statusPair : mCameraStatusMap) {
+ int id = std::stoi(statusPair.first);
+ auto status = static_cast<CameraDeviceStatus>(statusPair.second);
+ if (id >= mNumberOfLegacyCameras && status != CameraDeviceStatus::NOT_PRESENT) {
+ addDeviceNames(id, status, true);
+ }
+ }
+
return Status::OK;
}
@@ -452,6 +470,11 @@
Return<void> CameraProvider::getCameraIdList(getCameraIdList_cb _hidl_cb) {
std::vector<hidl_string> deviceNameList;
for (auto const& deviceNamePair : mCameraDeviceNames) {
+ if (std::stoi(deviceNamePair.first) >= mNumberOfLegacyCameras) {
+ // External camera devices must be reported through the device status change callback,
+ // not in this list.
+ continue;
+ }
if (mCameraStatusMap[deviceNamePair.first] == CAMERA_DEVICE_STATUS_PRESENT) {
deviceNameList.push_back(deviceNamePair.second);
}
diff --git a/neuralnetworks/1.0/vts/functional/Android.bp b/neuralnetworks/1.0/vts/functional/Android.bp
index 2920cec..52d6328 100644
--- a/neuralnetworks/1.0/vts/functional/Android.bp
+++ b/neuralnetworks/1.0/vts/functional/Android.bp
@@ -23,6 +23,7 @@
defaults: ["VtsHalTargetTestDefaults"],
export_include_dirs: ["."],
shared_libs: [
+ "libfmq",
"libnativewindow",
],
static_libs: [
@@ -51,6 +52,7 @@
"VtsHalNeuralnetworks.cpp",
],
shared_libs: [
+ "libfmq",
"libnativewindow",
],
static_libs: [
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 65c425e..8d427b1 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -15,6 +15,7 @@
*/
#include "Callbacks.h"
+#include "ExecutionBurstController.h"
#include "TestHarness.h"
#include "Utils.h"
@@ -109,14 +110,23 @@
}
return result;
}
-enum class Synchronously { NO, YES };
+static std::unique_ptr<::android::nn::ExecutionBurstController> CreateBurst(
+ const sp<V1_0::IPreparedModel>&) {
+ ADD_FAILURE() << "asking for burst execution at V1_0";
+ return nullptr;
+}
+static std::unique_ptr<::android::nn::ExecutionBurstController> CreateBurst(
+ const sp<V1_2::IPreparedModel>& preparedModel) {
+ return ::android::nn::createExecutionBurstController(preparedModel, /*blocking=*/true);
+}
+enum class Executor { ASYNC, SYNC, BURST };
const float kDefaultAtol = 1e-5f;
const float kDefaultRtol = 1e-5f;
template <typename T_IPreparedModel>
void EvaluatePreparedModel(sp<T_IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
const std::vector<MixedTypedExample>& examples,
bool hasRelaxedFloat32Model, float fpAtol, float fpRtol,
- Synchronously sync, MeasureTiming measure, bool testDynamicOutputShape) {
+ Executor executor, MeasureTiming measure, bool testDynamicOutputShape) {
const uint32_t INPUT = 0;
const uint32_t OUTPUT = 1;
@@ -209,35 +219,62 @@
inputMemory->commit();
outputMemory->commit();
+ const Request request = {.inputs = inputs_info, .outputs = outputs_info, .pools = pools};
+
ErrorStatus executionStatus;
hidl_vec<OutputShape> outputShapes;
Timing timing;
- if (sync == Synchronously::NO) {
- SCOPED_TRACE("asynchronous");
+ switch (executor) {
+ case Executor::ASYNC: {
+ SCOPED_TRACE("asynchronous");
- // launch execution
- sp<ExecutionCallback> executionCallback = new ExecutionCallback();
- ASSERT_NE(nullptr, executionCallback.get());
- Return<ErrorStatus> executionLaunchStatus = ExecutePreparedModel(
- preparedModel, {.inputs = inputs_info, .outputs = outputs_info, .pools = pools},
- measure, executionCallback);
- ASSERT_TRUE(executionLaunchStatus.isOk());
- EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+ // launch execution
+ sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+ ASSERT_NE(nullptr, executionCallback.get());
+ Return<ErrorStatus> executionLaunchStatus =
+ ExecutePreparedModel(preparedModel, request, measure, executionCallback);
+ ASSERT_TRUE(executionLaunchStatus.isOk());
+ EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
- // retrieve execution status
- executionCallback->wait();
- executionStatus = executionCallback->getStatus();
- outputShapes = executionCallback->getOutputShapes();
- timing = executionCallback->getTiming();
- } else {
- SCOPED_TRACE("synchronous");
+ // retrieve execution status
+ executionCallback->wait();
+ executionStatus = executionCallback->getStatus();
+ outputShapes = executionCallback->getOutputShapes();
+ timing = executionCallback->getTiming();
- // execute
- Return<ErrorStatus> executionReturnStatus = ExecutePreparedModel(
- preparedModel, {.inputs = inputs_info, .outputs = outputs_info, .pools = pools},
- measure, &outputShapes, &timing);
- ASSERT_TRUE(executionReturnStatus.isOk());
- executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
+ break;
+ }
+ case Executor::SYNC: {
+ SCOPED_TRACE("synchronous");
+
+ // execute
+ Return<ErrorStatus> executionReturnStatus = ExecutePreparedModel(
+ preparedModel, request, measure, &outputShapes, &timing);
+ ASSERT_TRUE(executionReturnStatus.isOk());
+ executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
+
+ break;
+ }
+ case Executor::BURST: {
+ SCOPED_TRACE("burst");
+
+ // create burst
+ const std::unique_ptr<::android::nn::ExecutionBurstController> controller =
+ CreateBurst(preparedModel);
+ ASSERT_NE(nullptr, controller.get());
+
+ // create memory keys
+ std::vector<intptr_t> keys(request.pools.size());
+ for (size_t i = 0; i < keys.size(); ++i) {
+ keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+ }
+
+ // execute burst
+ std::tie(executionStatus, outputShapes, timing) =
+ controller->compute(request, measure, keys);
+
+ break;
+ }
}
if (testDynamicOutputShape && executionStatus != ErrorStatus::NONE) {
@@ -286,10 +323,10 @@
template <typename T_IPreparedModel>
void EvaluatePreparedModel(sp<T_IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
const std::vector<MixedTypedExample>& examples,
- bool hasRelaxedFloat32Model, Synchronously sync, MeasureTiming measure,
+ bool hasRelaxedFloat32Model, Executor executor, MeasureTiming measure,
bool testDynamicOutputShape) {
EvaluatePreparedModel(preparedModel, is_ignored, examples, hasRelaxedFloat32Model, kDefaultAtol,
- kDefaultRtol, sync, measure, testDynamicOutputShape);
+ kDefaultRtol, executor, measure, testDynamicOutputShape);
}
static void getPreparedModel(sp<PreparedModelCallback> callback,
@@ -345,7 +382,7 @@
float fpAtol = 1e-5f, fpRtol = 5.0f * 1.1920928955078125e-7f;
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- /*hasRelaxedFloat32Model=*/false, fpAtol, fpRtol, Synchronously::NO,
+ /*hasRelaxedFloat32Model=*/false, fpAtol, fpRtol, Executor::ASYNC,
MeasureTiming::NO, /*testDynamicOutputShape=*/false);
}
@@ -392,7 +429,7 @@
ASSERT_NE(nullptr, preparedModel.get());
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- model.relaxComputationFloat32toFloat16, 1e-5f, 1e-5f, Synchronously::NO,
+ model.relaxComputationFloat32toFloat16, 1e-5f, 1e-5f, Executor::ASYNC,
MeasureTiming::NO, /*testDynamicOutputShape=*/false);
}
@@ -441,16 +478,22 @@
ASSERT_NE(nullptr, preparedModel.get());
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- model.relaxComputationFloat32toFloat16, Synchronously::NO,
+ model.relaxComputationFloat32toFloat16, Executor::ASYNC,
MeasureTiming::NO, testDynamicOutputShape);
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- model.relaxComputationFloat32toFloat16, Synchronously::YES,
+ model.relaxComputationFloat32toFloat16, Executor::SYNC, MeasureTiming::NO,
+ testDynamicOutputShape);
+ EvaluatePreparedModel(preparedModel, is_ignored, examples,
+ model.relaxComputationFloat32toFloat16, Executor::BURST,
MeasureTiming::NO, testDynamicOutputShape);
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- model.relaxComputationFloat32toFloat16, Synchronously::NO,
+ model.relaxComputationFloat32toFloat16, Executor::ASYNC,
MeasureTiming::YES, testDynamicOutputShape);
EvaluatePreparedModel(preparedModel, is_ignored, examples,
- model.relaxComputationFloat32toFloat16, Synchronously::YES,
+ model.relaxComputationFloat32toFloat16, Executor::SYNC,
+ MeasureTiming::YES, testDynamicOutputShape);
+ EvaluatePreparedModel(preparedModel, is_ignored, examples,
+ model.relaxComputationFloat32toFloat16, Executor::BURST,
MeasureTiming::YES, testDynamicOutputShape);
}
diff --git a/neuralnetworks/1.2/IDevice.hal b/neuralnetworks/1.2/IDevice.hal
index 6c3b483..de249b0 100644
--- a/neuralnetworks/1.2/IDevice.hal
+++ b/neuralnetworks/1.2/IDevice.hal
@@ -98,6 +98,25 @@
generates (ErrorStatus status, vec<bool> supportedOperations);
/**
+ * Gets whether the driver supports compilation caching.
+ *
+ * isCachingSupported indicates whether the driver supports compilation caching.
+ * Even if so, the driver may still choose not to cache certain compiled models.
+ *
+ * If the device reports the caching is not supported, the user may avoid calling
+ * IDevice::prepareModelFromCache and IPreparedModel::saveToCache.
+ *
+ * @return status Error status of the call, must be:
+ * - NONE if successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * @return supported A boolean indicating whether the driver supports compilation
+ * caching. Even on returning true, the driver may still choose
+ * not to cache certain compiled models.
+ */
+ isCachingSupported() generates (ErrorStatus status, bool supported);
+
+ /**
* Creates a prepared model for execution.
*
* prepareModel is used to make any necessary transformations or alternative
@@ -153,4 +172,84 @@
prepareModel_1_2(Model model, ExecutionPreference preference,
IPreparedModelCallback callback)
generates (ErrorStatus status);
+
+ /**
+ * Creates a prepared model from cache files for execution.
+ *
+ * prepareModelFromCache is used to retrieve a prepared model directly from
+ * cache files to avoid slow model compilation time. There are exactly two
+ * cache file descriptors provided to the driver: modelCache and dataCache.
+ *
+ * The dataCache is for caching constant data, possibly including preprocessed
+ * and transformed tensor buffers. Any modification to the dataCache should
+ * have no worse effect than generating bad output values at execution time.
+ *
+ * The modelCache is for caching security-sensitive data such as compiled
+ * executable machine code in the device's native binary format. A modification
+ * to the modelCache may affect the driver's execution behavior, and a malicious
+ * client could make use of this to execute beyond the granted permission. Thus,
+ * the driver must always check whether the modelCache is corrupted before preparing
+ * the model from cache.
+ *
+ * The two file descriptors may be closed by the client once the asynchronous
+ * preparation has finished. The driver has to copy all the data it needs.
+ *
+ * The model is prepared asynchronously with respect to the caller. The
+ * prepareModelFromCache function must verify the inputs to the
+ * prepareModelFromCache function are correct, and that the security-sensitive
+ * cache has not been modified since it was last written by the driver.
+ * If there is an error, or if compilation caching is not supported, or if the
+ * security-sensitive cache has been modified, prepareModelFromCache must
+ * immediately invoke the callback with the appropriate ErrorStatus value and
+ * nullptr for the IPreparedModel, then return with the same ErrorStatus. If
+ * the inputs to the prepareModelFromCache function are valid, the security-sensitive
+ * cache is not modified, and there is no error, prepareModelFromCache must launch an
+ * asynchronous task to prepare the model in the background, and immediately return
+ * from prepareModelFromCache with ErrorStatus::NONE. If the asynchronous task
+ * fails to launch, prepareModelFromCache must immediately invoke the callback
+ * with ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then
+ * return with ErrorStatus::GENERAL_FAILURE.
+ *
+ * When the asynchronous task has finished preparing the model, it must
+ * immediately invoke the callback function provided as an input to
+ * prepareModelFromCache. If the model was prepared successfully, the
+ * callback object must be invoked with an error status of ErrorStatus::NONE
+ * and the produced IPreparedModel object. If an error occurred preparing
+ * the model, the callback object must be invoked with the appropriate
+ * ErrorStatus value and nullptr for the IPreparedModel.
+ *
+ * The only information that may be unknown to the model at this stage is
+ * the shape of the tensors, which may only be known at execution time. As
+ * such, some driver services may return partially prepared models, where
+ * the prepared model may only be finished when it is paired with a set of
+ * inputs to the model. Note that the same prepared model object may be
+ * used with different shapes of inputs on different (possibly concurrent)
+ * executions.
+ *
+ * @param modelCache A handle holding exactly one cache file descriptor for the
+ * security-sensitive cache.
+ * @param dataCache A handle holding exactly one cache file descriptor for the
+ * constants' cache.
+ * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
+ * identifying the prepared model. It is the same token provided when saving
+ * the cache files with IPreparedModel::saveToCache. Tokens should be chosen
+ * to have a low rate of collision for a particular application. The driver
+ * cannot detect a collision; a collision will result in a failed execution
+ * or in a successful execution that produces incorrect output values.
+ * @param callback A callback object used to return the error status of
+ * preparing the model for execution and the prepared model if
+ * successful, nullptr otherwise. The callback object's notify function
+ * must be called exactly once, even if the model could not be prepared.
+ * @return status Error status of launching a task which prepares the model
+ * in the background; must be:
+ * - NONE if preparation task is successfully launched
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if caching is not supported or if there is an
+ * unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ */
+ prepareModelFromCache(handle modelCache, handle dataCache,
+ uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
+ IPreparedModelCallback callback)
+ generates (ErrorStatus status);
};
diff --git a/neuralnetworks/1.2/IPreparedModel.hal b/neuralnetworks/1.2/IPreparedModel.hal
index 5d2d80f..757d5f1 100644
--- a/neuralnetworks/1.2/IPreparedModel.hal
+++ b/neuralnetworks/1.2/IPreparedModel.hal
@@ -157,4 +157,62 @@
fmq_sync<FmqRequestDatum> requestChannel,
fmq_sync<FmqResultDatum> resultChannel)
generates (ErrorStatus status, IBurstContext context);
+
+ /*
+ * Saves the prepared model to cache files.
+ *
+ * saveToCache is used to save a prepared model to cache files for faster
+ * model compilation time when the same model preparation is requested in
+ * the future. There are exactly two cache file descriptors provided to the
+ * driver: modelCache and dataCache.
+ *
+ * The dataCache is for caching constant data, possibly including preprocessed
+ * and transformed tensor buffers. Any modification to the dataCache should
+ * have no worse effect than generating bad output values at execution time.
+ *
+ * The modelCache is for caching security-sensitive data such as compiled
+ * executable machine code in the device's native binary format. A modification
+ * to the modelCache may affect the driver's execution behavior, and a malicious
+ * client could make use of this to execute beyond the granted permission. Thus,
+ * the driver must always check whether the modelCache is corrupted before preparing
+ * the model from cache.
+ *
+ * The two file descriptors must point to two zero-length files with offset
+ * positioned at the beginning of the file. The file descriptors may be closed
+ * by the client once the method has returned.
+ *
+ * If the driver decides not to save the prepared model without looking at the
+ * input arguments to the saveToCache function, saveToCache must return with
+ * ErrorStatus::GENERAL_FAILURE. Otherwise, the saveToCache function must verify
+ * the input arguments to the saveToCache function are valid, and return with
+ * ErrorStatus::INVALID_ARGUMENT if not. If the inputs are valid but the driver
+ * could not save the prepared model, saveToCache must return with the appropriate
+ * ErrorStatus. Otherwise, it must write the cache files and return
+ * ErrorStatus::NONE. Unless saveToCache returns ErrorStatus::NONE, the contents
+ * of the cache files are undefined.
+ *
+ * @param modelCache A handle holding exactly one cache file descriptor for the
+ * security-sensitive cache.
+ * @param dataCache A handle holding exactly one cache file descriptor for the
+ * constants' cache.
+ * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
+ * identifying the prepared model. The same token will be provided
+ * when retrieving the prepared model from cache files with
+ * IDevice::prepareModelFromCache. Tokens should be chosen to have
+ * a low rate of collision for a particular application. The driver
+ * cannot detect a collision; a collision will result in a failed
+ * execution or in a successful execution that produces incorrect
+ * output values.
+ * @return status Error status of saveToCache, must be:
+ * - NONE if saveToCache is performed successfully
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the driver could not save the
+ * prepared model or if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid,
+ * unless the driver decides not to save the prepared model
+ * without looking at the input arguments
+ */
+ saveToCache(handle modelCache, handle dataCache,
+ uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token)
+ generates (ErrorStatus status);
};
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index bd8354f..0abe56d 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -25,6 +25,13 @@
import android.hidl.safe_union@1.0::Monostate;
+enum Constant : uint32_t {
+ /**
+ * The byte size of the cache token.
+ */
+ BYTE_SIZE_OF_CACHE_TOKEN = 32,
+};
+
enum OperandType : @1.0::OperandType {
/**
* An 8 bit boolean scalar value.
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
index 00a7c3e..d411da4 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
@@ -19,6 +19,7 @@
#include "VtsHalNeuralnetworks.h"
#include "Callbacks.h"
+#include "ExecutionBurstController.h"
#include "TestHarness.h"
#include "Utils.h"
@@ -112,6 +113,7 @@
};
MeasureTiming measure = (hash & 1) ? MeasureTiming::YES : MeasureTiming::NO;
+ // asynchronous
{
SCOPED_TRACE(message + " [execute_1_2]");
@@ -131,6 +133,7 @@
ASSERT_TRUE(badTiming(timing));
}
+ // synchronous
{
SCOPED_TRACE(message + " [executeSynchronously]");
@@ -144,6 +147,43 @@
});
ASSERT_TRUE(executeStatus.isOk());
}
+
+ // burst
+ {
+ SCOPED_TRACE(message + " [burst]");
+
+ // create burst
+ std::unique_ptr<::android::nn::ExecutionBurstController> burst =
+ ::android::nn::createExecutionBurstController(preparedModel, /*blocking=*/true);
+ ASSERT_NE(nullptr, burst.get());
+
+ // create memory keys
+ std::vector<intptr_t> keys(request.pools.size());
+ for (size_t i = 0; i < keys.size(); ++i) {
+ keys[i] = reinterpret_cast<intptr_t>(&request.pools[i]);
+ }
+
+ // execute and verify
+ ErrorStatus error;
+ std::vector<OutputShape> outputShapes;
+ Timing timing;
+ std::tie(error, outputShapes, timing) = burst->compute(request, measure, keys);
+ EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+ EXPECT_EQ(outputShapes.size(), 0);
+ EXPECT_TRUE(badTiming(timing));
+
+ // additional burst testing
+ if (request.pools.size() > 0) {
+ // valid free
+ burst->freeMemory(keys.front());
+
+ // negative test: invalid free of unknown (blank) memory
+ burst->freeMemory(intptr_t{});
+
+ // negative test: double free of memory
+ burst->freeMemory(keys.front());
+ }
+ }
}
// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,