Merge "vibrator: Support Async Callback APIs"
diff --git a/boot/1.1/default/Android.bp b/boot/1.1/default/Android.bp
new file mode 100644
index 0000000..dca5c26
--- /dev/null
+++ b/boot/1.1/default/Android.bp
@@ -0,0 +1,43 @@
+cc_library_shared {
+ name: "android.hardware.boot@1.1-impl",
+ defaults: [
+ "hidl_defaults",
+ "libboot_control_defaults",
+ ],
+ relative_install_path: "hw",
+ vendor: true,
+ recovery_available: true,
+ srcs: ["BootControl.cpp"],
+
+ shared_libs: [
+ "liblog",
+ "libhidlbase",
+ "libhardware",
+ "libutils",
+ "android.hardware.boot@1.0",
+ "android.hardware.boot@1.1",
+ ],
+ static_libs: [
+ "libboot_control",
+ "libfstab",
+ ],
+}
+
+cc_binary {
+ name: "android.hardware.boot@1.1-service",
+ defaults: ["hidl_defaults"],
+ relative_install_path: "hw",
+ vendor: true,
+ init_rc: ["android.hardware.boot@1.1-service.rc"],
+ srcs: ["service.cpp"],
+
+ shared_libs: [
+ "liblog",
+ "libhardware",
+ "libhidlbase",
+ "libutils",
+ "android.hardware.boot@1.0",
+ "android.hardware.boot@1.1",
+ ],
+
+}
diff --git a/boot/1.1/default/BootControl.cpp b/boot/1.1/default/BootControl.cpp
new file mode 100644
index 0000000..c9c62a4
--- /dev/null
+++ b/boot/1.1/default/BootControl.cpp
@@ -0,0 +1,129 @@
+/*
+ * 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.
+ */
+#define LOG_TAG "android.hardware.boot@1.1-impl"
+
+#include <memory>
+
+#include <log/log.h>
+
+#include "BootControl.h"
+
+namespace android {
+namespace hardware {
+namespace boot {
+namespace V1_1 {
+namespace implementation {
+
+using ::android::hardware::boot::V1_0::CommandResult;
+
+bool BootControl::Init() {
+ return impl_.Init();
+}
+
+// Methods from ::android::hardware::boot::V1_0::IBootControl follow.
+Return<uint32_t> BootControl::getNumberSlots() {
+ return impl_.GetNumberSlots();
+}
+
+Return<uint32_t> BootControl::getCurrentSlot() {
+ return impl_.GetCurrentSlot();
+}
+
+Return<void> BootControl::markBootSuccessful(markBootSuccessful_cb _hidl_cb) {
+ struct CommandResult cr;
+ if (impl_.MarkBootSuccessful()) {
+ cr.success = true;
+ cr.errMsg = "Success";
+ } else {
+ cr.success = false;
+ cr.errMsg = "Operation failed";
+ }
+ _hidl_cb(cr);
+ return Void();
+}
+
+Return<void> BootControl::setActiveBootSlot(uint32_t slot, setActiveBootSlot_cb _hidl_cb) {
+ struct CommandResult cr;
+ if (impl_.SetActiveBootSlot(slot)) {
+ cr.success = true;
+ cr.errMsg = "Success";
+ } else {
+ cr.success = false;
+ cr.errMsg = "Operation failed";
+ }
+ _hidl_cb(cr);
+ return Void();
+}
+
+Return<void> BootControl::setSlotAsUnbootable(uint32_t slot, setSlotAsUnbootable_cb _hidl_cb) {
+ struct CommandResult cr;
+ if (impl_.SetSlotAsUnbootable(slot)) {
+ cr.success = true;
+ cr.errMsg = "Success";
+ } else {
+ cr.success = false;
+ cr.errMsg = "Operation failed";
+ }
+ _hidl_cb(cr);
+ return Void();
+}
+
+Return<BoolResult> BootControl::isSlotBootable(uint32_t slot) {
+ if (!impl_.IsValidSlot(slot)) {
+ return BoolResult::INVALID_SLOT;
+ }
+ return impl_.IsSlotBootable(slot) ? BoolResult::TRUE : BoolResult::FALSE;
+}
+
+Return<BoolResult> BootControl::isSlotMarkedSuccessful(uint32_t slot) {
+ if (!impl_.IsValidSlot(slot)) {
+ return BoolResult::INVALID_SLOT;
+ }
+ return impl_.IsSlotMarkedSuccessful(slot) ? BoolResult::TRUE : BoolResult::FALSE;
+}
+
+Return<void> BootControl::getSuffix(uint32_t slot, getSuffix_cb _hidl_cb) {
+ hidl_string ans;
+ const char* suffix = impl_.GetSuffix(slot);
+ if (suffix) {
+ ans = suffix;
+ }
+ _hidl_cb(ans);
+ return Void();
+}
+
+Return<bool> BootControl::setSnapshotMergeStatus(MergeStatus status) {
+ return impl_.SetSnapshotMergeStatus(status);
+}
+
+Return<MergeStatus> BootControl::getSnapshotMergeStatus() {
+ return impl_.GetSnapshotMergeStatus();
+}
+
+IBootControl* HIDL_FETCH_IBootControl(const char* /* hal */) {
+ auto module = std::make_unique<BootControl>();
+ if (!module->Init()) {
+ ALOGE("Could not initialize BootControl module");
+ return nullptr;
+ }
+ return module.release();
+}
+
+} // namespace implementation
+} // namespace V1_1
+} // namespace boot
+} // namespace hardware
+} // namespace android
diff --git a/boot/1.1/default/BootControl.h b/boot/1.1/default/BootControl.h
new file mode 100644
index 0000000..75511b6
--- /dev/null
+++ b/boot/1.1/default/BootControl.h
@@ -0,0 +1,64 @@
+/*
+ * 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.
+ */
+
+#pragma once
+
+#include <android/hardware/boot/1.1/IBootControl.h>
+#include <hidl/MQDescriptor.h>
+#include <hidl/Status.h>
+#include <libboot_control/libboot_control.h>
+
+namespace android {
+namespace hardware {
+namespace boot {
+namespace V1_1 {
+namespace implementation {
+
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::boot::V1_0::BoolResult;
+using ::android::hardware::boot::V1_1::IBootControl;
+using ::android::hardware::boot::V1_1::MergeStatus;
+
+class BootControl : public IBootControl {
+ public:
+ bool Init();
+
+ // Methods from ::android::hardware::boot::V1_0::IBootControl follow.
+ Return<uint32_t> getNumberSlots() override;
+ Return<uint32_t> getCurrentSlot() override;
+ Return<void> markBootSuccessful(markBootSuccessful_cb _hidl_cb) override;
+ Return<void> setActiveBootSlot(uint32_t slot, setActiveBootSlot_cb _hidl_cb) override;
+ Return<void> setSlotAsUnbootable(uint32_t slot, setSlotAsUnbootable_cb _hidl_cb) override;
+ Return<BoolResult> isSlotBootable(uint32_t slot) override;
+ Return<BoolResult> isSlotMarkedSuccessful(uint32_t slot) override;
+ Return<void> getSuffix(uint32_t slot, getSuffix_cb _hidl_cb) override;
+
+ // Methods from ::android::hardware::boot::V1_1::IBootControl follow.
+ Return<bool> setSnapshotMergeStatus(MergeStatus status) override;
+ Return<MergeStatus> getSnapshotMergeStatus() override;
+
+ private:
+ android::bootable::BootControl impl_;
+};
+
+extern "C" IBootControl* HIDL_FETCH_IBootControl(const char* name);
+
+} // namespace implementation
+} // namespace V1_1
+} // namespace boot
+} // namespace hardware
+} // namespace android
diff --git a/boot/1.1/default/android.hardware.boot@1.1-service.rc b/boot/1.1/default/android.hardware.boot@1.1-service.rc
new file mode 100644
index 0000000..83fa9d0
--- /dev/null
+++ b/boot/1.1/default/android.hardware.boot@1.1-service.rc
@@ -0,0 +1,6 @@
+service vendor.boot-hal-1-1 /vendor/bin/hw/android.hardware.boot@1.1-service
+ interface android.hardware.boot@1.0::IBootControl default
+ interface android.hardware.boot@1.1::IBootControl default
+ class early_hal
+ user root
+ group root
diff --git a/boot/1.1/default/service.cpp b/boot/1.1/default/service.cpp
new file mode 100644
index 0000000..b24b464
--- /dev/null
+++ b/boot/1.1/default/service.cpp
@@ -0,0 +1,26 @@
+/*
+ * 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.
+ */
+#define LOG_TAG "android.hardware.boot@1.1-service"
+
+#include <android/hardware/boot/1.1/IBootControl.h>
+#include <hidl/LegacySupport.h>
+
+using android::hardware::defaultPassthroughServiceImplementation;
+using ::android::hardware::boot::V1_1::IBootControl;
+
+int main(int /* argc */, char* /* argv */[]) {
+ return defaultPassthroughServiceImplementation<IBootControl>();
+}
diff --git a/boot/1.1/vts/functional/Android.bp b/boot/1.1/vts/functional/Android.bp
new file mode 100644
index 0000000..49ea09a
--- /dev/null
+++ b/boot/1.1/vts/functional/Android.bp
@@ -0,0 +1,28 @@
+//
+// 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.
+//
+
+cc_test {
+ name: "VtsHalBootV1_1TargetTest",
+ defaults: ["VtsHalTargetTestDefaults"],
+ srcs: ["VtsHalBootV1_1TargetTest.cpp"],
+ static_libs: [
+ "android.hardware.boot@1.0",
+ "android.hardware.boot@1.1",
+ "libgmock",
+ ],
+ test_suites: ["device-tests"],
+}
+
diff --git a/boot/1.1/vts/functional/VtsHalBootV1_1TargetTest.cpp b/boot/1.1/vts/functional/VtsHalBootV1_1TargetTest.cpp
new file mode 100644
index 0000000..fba9a5e
--- /dev/null
+++ b/boot/1.1/vts/functional/VtsHalBootV1_1TargetTest.cpp
@@ -0,0 +1,86 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "boot_hidl_hal_test"
+
+#include <vector>
+
+#include <android-base/logging.h>
+#include <android/hardware/boot/1.1/IBootControl.h>
+#include <android/hardware/boot/1.1/types.h>
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
+
+#include <unistd.h>
+
+using ::android::sp;
+using ::android::hardware::hidl_enum_range;
+using ::android::hardware::hidl_vec;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::boot::V1_1::IBootControl;
+using ::android::hardware::boot::V1_1::MergeStatus;
+using ::testing::Contains;
+
+class BootHidlTest : public testing::TestWithParam<std::string> {
+ public:
+ virtual void SetUp() override {
+ boot = IBootControl::getService(GetParam());
+ ASSERT_NE(boot, nullptr);
+
+ LOG(INFO) << "Test is remote " << boot->isRemote();
+ }
+
+ sp<IBootControl> boot;
+};
+
+static std::vector<MergeStatus> ValidMergeStatusValues() {
+ std::vector<MergeStatus> values;
+ for (const auto value : hidl_enum_range<MergeStatus>()) {
+ if (value == MergeStatus::UNKNOWN) {
+ continue;
+ }
+ values.push_back(value);
+ }
+ return values;
+}
+
+/**
+ * Ensure merge status can be retrieved.
+ */
+TEST_P(BootHidlTest, GetSnapshotMergeStatus) {
+ auto values = ValidMergeStatusValues();
+ auto status = (MergeStatus)boot->getSnapshotMergeStatus();
+ EXPECT_THAT(values, Contains(status));
+}
+
+/**
+ * Ensure merge status can be set to arbitrary value.
+ */
+TEST_P(BootHidlTest, SetSnapshotMergeStatus) {
+ for (const auto value : ValidMergeStatusValues()) {
+ EXPECT_TRUE(boot->setSnapshotMergeStatus(value).withDefault(false));
+ auto status = boot->getSnapshotMergeStatus();
+ EXPECT_EQ(status, value);
+ }
+}
+
+INSTANTIATE_TEST_SUITE_P(
+ , BootHidlTest,
+ testing::ValuesIn(android::hardware::getAllHalInstanceNames(IBootControl::descriptor)),
+ android::hardware::PrintInstanceNameToString);
diff --git a/current.txt b/current.txt
index 66dbf65..972fe16 100644
--- a/current.txt
+++ b/current.txt
@@ -574,8 +574,11 @@
# ABI preserving changes to HALs during Android R
b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
+f1109cbb10297b7429a11fab42afa912710b303c9bf20bd5cdb8bd57b9c84186 android.hardware.neuralnetworks@1.0::types
+9d8ee57c490ffeaa28f702eaea8d198cb510e4bbfb99e6cb5f63e73341057c7c android.hardware.neuralnetworks@1.1::types
fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
+71c0f7127335e5b74d1615d5e7f129831b43ffbae5318ad0924d7d8d8910a859 android.hardware.neuralnetworks@1.2::types
a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
diff --git a/keymaster/4.0/support/include/keymasterV4_0/keymaster_tags.h b/keymaster/4.0/support/include/keymasterV4_0/keymaster_tags.h
index 97dab68..cb29c64 100644
--- a/keymaster/4.0/support/include/keymasterV4_0/keymaster_tags.h
+++ b/keymaster/4.0/support/include/keymasterV4_0/keymaster_tags.h
@@ -280,39 +280,50 @@
*/
template <typename ValueT>
class NullOr {
- template <typename T>
- struct reference_initializer {
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wnull-dereference"
- static T&& init() { return *static_cast<std::remove_reference_t<T>*>(nullptr); }
-#pragma GCC diagnostic pop
- };
- template <typename T>
- struct pointer_initializer {
- static T init() { return nullptr; }
- };
- template <typename T>
- struct value_initializer {
- static T init() { return T(); }
- };
- template <typename T>
- using initializer_t =
- std::conditional_t<std::is_lvalue_reference<T>::value, reference_initializer<T>,
- std::conditional_t<std::is_pointer<T>::value, pointer_initializer<T>,
- value_initializer<T>>>;
+ using internal_t = std::conditional_t<std::is_lvalue_reference<ValueT>::value,
+ std::remove_reference_t<ValueT>*, ValueT>;
- public:
- NullOr() : value_(initializer_t<ValueT>::init()), null_(true) {}
- NullOr(ValueT&& value) : value_(std::forward<ValueT>(value)), null_(false) {}
+ struct pointer_initializer {
+ static std::nullptr_t init() { return nullptr; }
+ };
+ struct value_initializer {
+ static ValueT init() { return ValueT(); }
+ };
+ struct value_pointer_deref_t {
+ static ValueT& deref(ValueT& v) { return v; }
+ };
+ struct reference_deref_t {
+ static auto& deref(internal_t v) { return *v; }
+ };
+ using initializer_t = std::conditional_t<std::is_lvalue_reference<ValueT>::value ||
+ std::is_pointer<ValueT>::value,
+ pointer_initializer, value_initializer>;
+ using deref_t = std::conditional_t<std::is_lvalue_reference<ValueT>::value, reference_deref_t,
+ value_pointer_deref_t>;
+
+ public:
+ NullOr() : value_(initializer_t::init()), null_(true) {}
+ template <typename T>
+ NullOr(T&& value, typename std::enable_if<
+ !std::is_lvalue_reference<ValueT>::value &&
+ std::is_same<std::decay_t<ValueT>, std::decay_t<T>>::value,
+ int>::type = 0)
+ : value_(std::forward<ValueT>(value)), null_(false) {}
+ template <typename T>
+ NullOr(T& value, typename std::enable_if<
+ std::is_lvalue_reference<ValueT>::value &&
+ std::is_same<std::decay_t<ValueT>, std::decay_t<T>>::value,
+ int>::type = 0)
+ : value_(&value), null_(false) {}
bool isOk() const { return !null_; }
- const ValueT& value() const & { return value_; }
- ValueT& value() & { return value_; }
- ValueT&& value() && { return std::move(value_); }
+ const ValueT& value() const& { return deref_t::deref(value_); }
+ ValueT& value() & { return deref_t::deref(value_); }
+ ValueT&& value() && { return std::move(deref_t::deref(value_)); }
- private:
- ValueT value_;
+ private:
+ internal_t value_;
bool null_;
};
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 02db063..ba9d068 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -25,25 +25,24 @@
* with at least one dimension). Types not prefaced by TENSOR_* represent
* scalar values and must have no dimensions.
*
- * Although many types are defined, most operators accept just a few
+ * Although we define many types, most operators accept just a few
* types. Most used are {@link OperandType::TENSOR_FLOAT32},
* {@link OperandType::TENSOR_QUANT8_ASYMM},
* and {@link OperandType::INT32}.
*/
enum OperandType : int32_t {
/** A 32 bit floating point scalar value. */
- FLOAT32 = 0,
+ FLOAT32 = 0,
/** A signed 32 bit integer scalar value. */
- INT32 = 1,
+ INT32 = 1,
/** An unsigned 32 bit integer scalar value. */
- UINT32 = 2,
-
+ UINT32 = 2,
/** A tensor of 32 bit floating point values. */
- TENSOR_FLOAT32 = 3,
+ TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
- TENSOR_INT32 = 4,
+ TENSOR_INT32 = 4,
/**
- * A tensor of 8 bit integers that represent real numbers.
+ * A tensor of 8 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 8 bit integer to the real value and vice versa. These two numbers are:
@@ -51,21 +50,21 @@
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
- * real_value = (integer_value - zeroPoint) * scale.
+ * real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM = 5,
/**
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* OEM specific scalar value.
*/
OEM = 10000,
/**
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
*/
@@ -78,7 +77,6 @@
* The type of an operation in a model.
*/
enum OperationType : int32_t {
-
/**
* Adds two tensors, element-wise.
*
@@ -110,14 +108,16 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = 0,
@@ -187,8 +187,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = 1,
@@ -206,22 +206,23 @@
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
- * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
- * must have the same scale and zeroPoint.
+ * [D0, D1, ..., Daxis(i), ..., Dm].
+ * All input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * must have the same scale and zeroPoint as the output tensor.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, the scale and zeroPoint
+ * values must be the same as the input tensors'.
*/
CONCATENATION = 2,
/**
- * Performs an 2-D convolution operation.
+ * Performs a 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
@@ -238,11 +239,17 @@
* filter[channel, di, dj, k]
* ) + bias[channel]
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -252,12 +259,12 @@
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
- * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
- * should also be of {@link OperandType::TENSOR_FLOAT32}. For input
- * tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale == input_scale * filter_scale.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -281,11 +288,11 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -299,11 +306,9 @@
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. For output tensor of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out].
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = 3,
@@ -329,11 +334,17 @@
* filter[1, di, dj, k * channel_multiplier + q]
* ) + bias[k * channel_multiplier + q]
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -343,11 +354,11 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -372,11 +383,11 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
- * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * the bias must be of the same
+ * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -392,11 +403,10 @@
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. For output tensor of
- * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out]. For
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
+ * output_scale > input_scale * filter_scale
*/
DEPTHWISE_CONV_2D = 4,
@@ -419,7 +429,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -431,8 +442,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = 5,
@@ -443,19 +454,19 @@
*
* output = (input - zeroPoint) * scale.
*
- * Supported tensor {@link OperandType}:
+ * Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}.
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor.
*
* Outputs:
- * * 0: The output tensor of same shape as input0, but with
- * {@link OperandType::TENSOR_FLOAT32}.
- *
- * Available since API level 27.
+ * * 0: A tensor with the same shape as input0.
*/
DEQUANTIZE = 6,
@@ -479,6 +490,13 @@
* If a value in Lookups is out of bounds, the operation must fail
* and an error must be reported.
*
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
* Inputs:
* * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
* The values are indices into the first dimension of Values.
@@ -489,8 +507,8 @@
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = 7,
@@ -508,8 +526,6 @@
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
- *
- * Available since API level 27.
*/
FLOOR = 8,
@@ -549,12 +565,9 @@
* invoke on the result.
*
* Outputs:
- * * 0: The output tensor, of shape [batch_size, num_units]. For output
- * tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
- * condition must be satisfied:
- * output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * * 0: The output tensor, of shape [batch_size, num_units]. For
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
@@ -585,6 +598,13 @@
* must be selected. If no entry in Keys has 123456, a slice of zeroes
* must be concatenated.
*
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
* Inputs:
* * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
* shape [ k ].
@@ -598,13 +618,13 @@
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input2.
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
* hits (True) or not (False).
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
- *
- * Available since API level 27.
*/
HASHTABLE_LOOKUP = 10,
@@ -617,9 +637,6 @@
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
- * For input tensor with more dimensions, independently normalizes each 1-D
- * slice along dimension dim.
- *
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
@@ -627,13 +644,10 @@
* Height, Width, and Channels).
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth].
+ * * 0: A 4-D tensor, specifying the tensor to be normalized.
*
* Outputs:
- * * 0: The output 4-D tensor, of the same shape as input
- * [batches, height, width, depth].
- *
- * Available since API level 27.
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
*/
L2_NORMALIZATION = 11,
@@ -652,7 +666,8 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -700,8 +715,6 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
*/
L2_POOL_2D = 12,
@@ -729,17 +742,18 @@
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the radius of
* the normalization window.
- * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
- * not be zero.
- * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
- * factor, alpha.
- * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
- * beta.
+ * * 2: A scalar, specifying the bias, must not be zero.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 3: A scalar, specifying the scale factor, alpha.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * alpha value must be of {@link OperandType::FLOAT32}.
+ * * 4: A scalar, specifying the exponent, beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
@@ -763,45 +777,53 @@
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported input tensor rank: from 1
+ *
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
- * Tensor[0].Dim[0]: Number of hash functions.
- * Tensor[0].Dim[1]: Number of seeds per hash functions.
- * Tensor[0].Dim[1] <= 32 in sparse case.
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of projected output bits generated by each
+ * hash function.
+ * If the projection type is Sparse:
+ * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
*
* * 1: Input. Dim.size >= 1, no restriction on DataType.
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
- * If not set, each input element is considered to have the same weight
- * of 1.0.
- * Tensor[1].Dim[0] == Tensor[2].Dim[0]
+ * If not set, each input element is considered to have the same weight
+ * of 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
- * Sparse: Value LSHProjectionType_SPARSE(=1).
+ * Sparse:
+ * Value LSHProjectionType_SPARSE(=1).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
- * Dense: Value LSHProjectionType_DENSE(=2).
+ * Dense:
+ * Value LSHProjectionType_DENSE(=2).
* Computed bit vector is considered to be dense. Each output
* element represents a bit and can take the value of either
* 0 or 1.
*
* Outputs:
- * * 0: If the projection type is sparse:
- * Output.Dim == { Tensor[0].Dim[0] }
- * A tensor of int32 that represents hash signatures.
- * If the projection type is Dense:
- * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
- * A flattened tensor that represents projected bit vectors.
+ * * 0: If the projection type is Sparse:
+ * Output.Dim == { Tensor[0].Dim[0] }
+ * A tensor of int32 that represents hash signatures.
*
- * Available since API level 27.
+ * If the projection type is Dense:
+ * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+ * A flattened tensor that represents projected bit vectors.
*/
LSH_PROJECTION = 15,
@@ -901,71 +923,54 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * All input and output tensors must be of the same type.
+ *
* Inputs:
* * 0: The input (\f$x_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of cell units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size], where “output_size” corresponds to either
- * the number of cell units (i.e., “num_units”), or the second
- * dimension of the “projection_weights”, if defined.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [output_size, num_units].
+ * A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [output_size].
+ * A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
@@ -984,21 +989,15 @@
*
* Outputs:
* * 0: The scratch buffer.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units * 3] with CIFG, or
+ * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
* [batch_size, num_units * 4] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * A 2-D tensor of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, output_size]. This is effectively the same as the
- * current “output state (out)” value.
- *
- * Available since API level 27.
+ * A 2-D tensor of shape [batch_size, output_size]. This is effectively
+ * the same as the current “output state (out)” value.
*/
LSTM = 16,
@@ -1019,7 +1018,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -1067,8 +1067,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = 17,
@@ -1106,8 +1106,6 @@
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
- *
- * Available since API level 27.
*/
MUL = 18,
@@ -1129,8 +1127,8 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU = 19,
@@ -1151,9 +1149,9 @@
* * 0: A tensor, specifying the input.
*
* Outputs:
- * * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU1 = 20,
@@ -1175,8 +1173,8 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU6 = 21,
@@ -1205,8 +1203,8 @@
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESHAPE = 22,
@@ -1220,9 +1218,10 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
- * Inputs:
+ * Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
@@ -1233,8 +1232,6 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 27.
*/
RESIZE_BILINEAR = 23,
@@ -1257,25 +1254,23 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * The input tensors must all be the same type.
+ *
* Inputs:
* * 0: input.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: weights.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
* * 2: recurrent_weights.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, num_units], with columns corresponding to the weights
- * from each unit.
+ * A 2-D tensor of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
* * 3: bias.
- * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FusedActivationFunc} value indicating the
* activation function. If “NONE” is specified then it results in a
@@ -1283,15 +1278,11 @@
*
* Outputs:
* * 0: hidden state (out).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
*
* * 1: output.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, num_units]. This is effectively the same as the
- * current state value.
- *
- * Available since API level 27.
+ * A 2-D tensor of shape [batch_size, num_units]. This is effectively
+ * the same as the current state value.
*/
RNN = 24,
@@ -1306,6 +1297,9 @@
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
+ * For input tensor with rank other than 2, the activation will be applied
+ * independently on each 1-D slice along specified dimension.
+ *
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1314,15 +1308,15 @@
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
- * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
- * scaling factor for the exponent, beta.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
+ * {@link OperandType::FLOAT32}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
SOFTMAX = 25,
@@ -1344,7 +1338,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1356,8 +1351,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = 26,
@@ -1403,25 +1398,23 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
+ * All input tensors must be the same type.
+ *
* Inputs:
* * 0: input.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
* * 1: weights_feature.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
* * 2: weights_time.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [num_units, memory_size], where “memory_size” corresponds to the
- * fixed-size of the memory.
+ * A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+ * corresponds to the fixed-size of the memory.
* * 3: bias.
- * An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
- * of shape [num_units].
+ * An optional 1-D tensor of shape [num_units].
* * 4: state (in).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
- * [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
@@ -1431,13 +1424,11 @@
*
* Outputs:
* * 0: state (out).
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
- * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
- *
- * Available since API level 27.
*/
SVDF = 27,
@@ -1458,8 +1449,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
TANH = 28,
diff --git a/neuralnetworks/1.0/types.t b/neuralnetworks/1.0/types.t
new file mode 100644
index 0000000..d7b26aa
--- /dev/null
+++ b/neuralnetworks/1.0/types.t
@@ -0,0 +1,431 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2017 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.
+ */
+
+package android.hardware.neuralnetworks@1.0;
+
+%insert Operand_1.0_Comment
+enum OperandType : int32_t {
+%insert Operand_1.0
+
+ /**
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * OEM specific scalar value.
+ */
+ OEM = 10000,
+
+ /**
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * A tensor of OEM specific values.
+ */
+ TENSOR_OEM_BYTE = 10001,
+};
+
+%insert Operation_1.0_Comment
+enum OperationType : int32_t {
+%insert Operation_1.0
+
+ /**
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
+ */
+ OEM_OPERATION = 10000,
+};
+
+/**
+ * Fused activation function types.
+ */
+enum FusedActivationFunc : int32_t {
+ NONE = 0,
+ RELU = 1,
+ RELU1 = 2,
+ RELU6 = 3,
+};
+
+/**
+ * How an operand is used.
+ */
+enum OperandLifeTime : int32_t {
+ /**
+ * The operand is internal to the model. It's created by an operation and
+ * consumed by other operations. It must be an output operand of
+ * exactly one operation.
+ */
+ TEMPORARY_VARIABLE,
+
+ /**
+ * The operand is an input of the model. It must not be an output
+ * operand of any operation.
+ *
+ * An operand can't be both input and output of a model.
+ */
+ MODEL_INPUT,
+
+ /**
+ * The operand is an output of the model. It must be an output
+ * operand of exactly one operation.
+ *
+ * An operand can't be both input and output of a model.
+ */
+ MODEL_OUTPUT,
+
+ /**
+ * The operand is a constant found in Model.operandValues. It must
+ * not be an output operand of any operation.
+ */
+ CONSTANT_COPY,
+
+ /**
+ * The operand is a constant that was specified via a Memory
+ * object. It must not be an output operand of any operation.
+ */
+ CONSTANT_REFERENCE,
+
+ /**
+ * The operand does not have a value. This is valid only for optional
+ * arguments of operations.
+ */
+ NO_VALUE,
+};
+
+/**
+ * Status of a device.
+ */
+enum DeviceStatus : int32_t {
+ AVAILABLE,
+ BUSY,
+ OFFLINE,
+ UNKNOWN,
+};
+
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+ /**
+ * Ratio of the time taken by the driver to execute the
+ * workload compared to the time the CPU would take for the
+ * same workload. A lower number is better.
+ */
+ float execTime;
+
+ /**
+ * Ratio of the energy used by the driver compared to what
+ * the CPU would use for doing the same workload. A lower number
+ * is better.
+ */
+ float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data.
+ */
+ PerformanceInfo float32Performance;
+
+ /**
+ * Driver performance when operating on asymmetric 8-bit quantized data.
+ */
+ PerformanceInfo quantized8Performance;
+};
+
+/**
+ * Describes the location of a data object.
+ */
+struct DataLocation {
+ /**
+ * The index of the memory pool where this location is found.
+ */
+ uint32_t poolIndex;
+
+ /**
+ * Offset in bytes from the start of the pool.
+ */
+ uint32_t offset;
+
+ /**
+ * The length of the data in bytes.
+ */
+ uint32_t length;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * Data type of the operand.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * For a tensor operand, dimensions.size() must be at least 1;
+ * however, any of the dimensions may be unspecified.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input or output is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * might not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+};
+
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
+struct RequestArgument {
+ /**
+ * If true, the argument does not have a value. This can be used for
+ * operations that take optional arguments. If true, the fields of location
+ * are set to 0 and the dimensions vector is left empty.
+ */
+ bool hasNoValue;
+
+ /**
+ * The location within one of the memory pools passed in the Request.
+ */
+ DataLocation location;
+
+ /**
+ * Updated dimension information.
+ *
+ * If dimensions.size() > 0, dimension information was provided
+ * along with the argument. This can be the case for models that
+ * accept inputs of varying size. This can't change the rank, just
+ * the value of the dimensions that were unspecified in the
+ * model. If dimensions.size() > 0, then all dimensions must be
+ * specified here; and any dimension that was specified in the
+ * model must have the same value here.
+ *
+ * If the dimensions in the model are not fully specified, then
+ * they must be fully specified here, unless hasNoValue is set to
+ * true. If the dimensions in the model are fully specified, then
+ * either dimensions.size() may be 0, or the dimensions in the
+ * model must be identical to the dimensions here.
+ */
+ vec<uint32_t> dimensions;
+};
+
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left
+ * unspecified at model preparation time.
+ *
+ * An output must not overlap with any other output, with an input, or
+ * with an operand of lifetime CONSTANT_REFERENCE.
+ */
+struct Request {
+ /**
+ * Input data and information to be used in the execution of a prepared
+ * model.
+ *
+ * The index of the input corresponds to the index in Model.inputIndexes.
+ * E.g., input[i] corresponds to Model.inputIndexes[i].
+ */
+ vec<RequestArgument> inputs;
+
+ /**
+ * Output data and information to be used in the execution of a prepared
+ * model.
+ *
+ * The index of the output corresponds to the index in Model.outputIndexes.
+ * E.g., output[i] corresponds to Model.outputIndexes[i].
+ */
+ vec<RequestArgument> outputs;
+
+ /**
+ * A collection of shared memory pools containing operand data for both the
+ * inputs and the outputs to a model.
+ */
+ vec<memory> pools;
+};
+
+/**
+ * Return status of a function.
+ */
+enum ErrorStatus : int32_t {
+ NONE,
+ DEVICE_UNAVAILABLE,
+ GENERAL_FAILURE,
+ OUTPUT_INSUFFICIENT_SIZE,
+ INVALID_ARGUMENT,
+};
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 73705bb..3d78fb6 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -26,7 +26,6 @@
* The type of an operation in a model.
*/
enum OperationType : @1.0::OperationType {
-
/**
* BatchToSpace for N-dimensional tensors.
*
@@ -41,7 +40,8 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -51,8 +51,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = 29,
@@ -91,8 +91,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
DIV = 30,
@@ -126,8 +124,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be same as input0.
*/
MEAN = 31,
@@ -138,7 +136,8 @@
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (the pad value is undefined)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (the pad value is undefined)
*
* Supported tensor rank: up to 4
*
@@ -160,11 +159,8 @@
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
- *
- * NOTE: The pad value for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
- * is undefined.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
PAD = 32,
@@ -182,8 +178,10 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (the pad value is undefined)
*
- * Supported tensor rank: 4
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+ * and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
@@ -201,8 +199,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_BATCH_ND = 33,
@@ -232,8 +230,8 @@
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SQUEEZE = 34,
@@ -278,8 +276,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
STRIDED_SLICE = 35,
@@ -318,8 +316,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
SUB = 36,
@@ -345,11 +341,10 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = 37,
-
};
/**
diff --git a/neuralnetworks/1.1/types.t b/neuralnetworks/1.1/types.t
new file mode 100644
index 0000000..75ac2e7
--- /dev/null
+++ b/neuralnetworks/1.1/types.t
@@ -0,0 +1,158 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 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.
+ */
+
+package android.hardware.neuralnetworks@1.1;
+
+import @1.0::Operand;
+import @1.0::OperationType;
+import @1.0::PerformanceInfo;
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : @1.0::OperationType {
+%insert Operation_1.1
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data.
+ */
+ PerformanceInfo float32Performance;
+
+ /**
+ * Driver performance when operating on asymmetric 8-bit quantized data.
+ */
+ PerformanceInfo quantized8Performance;
+
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16Performance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+};
+
+/**
+ * Execution preferences.
+ */
+enum ExecutionPreference : int32_t {
+ /**
+ * Prefer executing in a way that minimizes battery drain.
+ * This is desirable for compilations that will be executed often.
+ */
+ LOW_POWER = 0,
+ /**
+ * Prefer returning a single answer as fast as possible, even if this causes
+ * more power consumption.
+ */
+ FAST_SINGLE_ANSWER = 1,
+ /**
+ * Prefer maximizing the throughput of successive frames, for example when
+ * processing successive frames coming from the camera.
+ */
+ SUSTAINED_SPEED = 2,
+};
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f368ce2..837ced5 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -43,8 +43,6 @@
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
- *
- * Available since API level 29.
*/
BOOL = 6,
/**
@@ -55,14 +53,10 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
- *
- * Available since API level 29.
*/
TENSOR_QUANT16_SYMM = 7,
/**
* A tensor of IEEE 754 16 bit floating point values.
- *
- * Available since API level 29.
*/
TENSOR_FLOAT16 = 8,
/**
@@ -70,14 +64,10 @@
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
- *
- * Available since API level 29.
*/
TENSOR_BOOL8 = 9,
/**
* An IEEE 754 16 bit floating point scalar value.
- *
- * Available since API level 29.
*/
FLOAT16 = 10,
/**
@@ -90,14 +80,13 @@
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
*
+ *{@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
* The formula is:
* realValue[..., C, ...] =
* integerValue[..., C, ...] * scales[C]
* where C is an index in the Channel dimension.
- *
- * Available since API level 29.
*/
TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
/**
@@ -110,8 +99,6 @@
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
- *
- * Available since API level 29.
*/
TENSOR_QUANT16_ASYMM = 12,
/**
@@ -122,20 +109,19 @@
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
- *
- * Available since API level 29.
*/
TENSOR_QUANT8_SYMM = 13,
+
/*
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* OEM specific scalar value.
* OEM = 10000,
*/
/*
- * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
- * OEM operation and data types.
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
* TENSOR_OEM_BYTE = 10001,
@@ -166,6 +152,7 @@
* The type of an operation in a model.
*/
enum OperationType : int32_t {
+
/**
* Adds two tensors, element-wise.
*
@@ -187,12 +174,12 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -202,14 +189,16 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = @1.1::OperationType:ADD,
@@ -227,7 +216,7 @@
* ) / sum(1)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -235,13 +224,14 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -263,12 +253,12 @@
* invoke on the result.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -285,13 +275,13 @@
* invoke on the result.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D,
@@ -302,33 +292,34 @@
* dimensions except the dimension along the concatenation axis.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
- * level 29, see the input section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the input section)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm].
- * Before API level 29, all input tensors of
+ * Before HAL version 1.2, all input tensors of
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* must have the same scale and zeroPoint as the output tensor.
- * Since API level 29, zero-sized tensors are supported.
+ * Since HAL version 1.2, zero-sized tensors are supported.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
- *
- * Available since API level 27.
+ * Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint values can be different from
+ * input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
*/
CONCATENATION = @1.1::OperationType:CONCATENATION,
/**
- * Performs an 2-D convolution operation.
+ * Performs a 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
@@ -354,7 +345,7 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * Available since API level 29:
+ * Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -368,27 +359,29 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
- * specifying the input. Since API level 29, zero batches is supported
- * for this tensor.
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
- * filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
@@ -407,36 +400,37 @@
* invoke on the result.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 12 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 11 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
- * specifying the input. Since API level 29, zero batches is supported
- * for this tensor.
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
- * filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
@@ -450,26 +444,23 @@
* invoke on the result.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 9 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 8 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. Before API level 29,
- * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
- * following condition must be satisfied:
- * output_scale > input_scale * filter_scale
- *
- * Available since API level 27.
+ * [batches, out_height, out_width, depth_out].
+ * Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = @1.1::OperationType:CONV_2D,
@@ -504,7 +495,7 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * Available since API level 29:
+ * Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -518,6 +509,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
@@ -525,18 +517,19 @@
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input.
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
- * specifying the filter. For tensor of type
- * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 3.
+ * specifying the filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 3.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
@@ -557,17 +550,17 @@
* invoke on the result.
* * 11: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 13 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 12 (dilation factor for width) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -575,14 +568,14 @@
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of type {@link OperandType::TENSOR_FLOAT32} or
- * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
- * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
- * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
- * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
@@ -598,27 +591,24 @@
* invoke on the result.
* * 8: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 10 (dilation factor for height) must be specified as well.
- * Available since API level 29.
+ * Available since HAL version 1.2.
* * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 9 (dilation factor for width) must be specified as well.
- * Available since API level 29.
-
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
- * [batches, out_height, out_width, depth_out]. Before API level 29,
- * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
- * following condition must be satisfied:
+ * [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
* output_scale > input_scale * filter_scale
- *
- * Available since API level 27.
*/
DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D,
@@ -638,7 +628,7 @@
* be divisible by block_size * block_size
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -646,6 +636,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -655,13 +646,13 @@
* of the input depth.
* * 2: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE,
@@ -674,22 +665,21 @@
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
- * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29)
- * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
*
* Supported output tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}.
*
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor. Since API level 29, this tensor may be zero-sized.
+ * * 0: A tensor.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
- *
- * Available since API level 27.
*/
DEQUANTIZE = @1.1::OperationType:DEQUANTIZE,
@@ -730,8 +720,8 @@
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP,
@@ -739,7 +729,7 @@
* Computes element-wise floor() on the input tensor.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -750,8 +740,6 @@
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
- *
- * Available since API level 27.
*/
FLOOR = @1.1::OperationType:FLOOR,
@@ -764,7 +752,7 @@
* outputs = activation(inputs * weights’ + bias)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -777,8 +765,8 @@
* [batch_size, input_size], where "input_size" corresponds to the
* number of inputs to the layer, matching the second dimension of
* weights, and "batch_size" is calculated by dividing the number of
- * elements by "input_size". Since API level 29, zero batch_size is
- * supported for this tensor.
+ * elements by "input_size".
+ * Since HAL version 1.2, zero batch_size is supported for this tensor.
* * 1: A 2-D tensor, specifying the weights, of shape
* [num_units, input_size], where "num_units" corresponds to the number
* of output nodes.
@@ -793,12 +781,9 @@
* invoke on the result.
*
* Outputs:
- * * 0: The output tensor, of shape [batch_size, num_units]. Before API
- * level 29, For output tensor of {@link
- * OperandType::TENSOR_QUANT8_ASYMM}, the following condition must be
- * satisfied: output_scale > input_scale * filter_scale.
- *
- * Available since API level 27.
+ * * 0: The output tensor, of shape [batch_size, num_units]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED,
@@ -849,13 +834,13 @@
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input2.
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
* hits (True) or not (False).
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
- *
- * Available since API level 27.
*/
HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
@@ -872,12 +857,12 @@
* 1-D slice along dimension dim.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
- * Tensors with rank less than 4 are only supported since API level 29.
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
@@ -885,14 +870,12 @@
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
- *
- * Available since API level 27.
*/
L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION,
@@ -909,20 +892,21 @@
* sum(1))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -944,12 +928,12 @@
* invoke on the result.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -966,13 +950,11 @@
* invoke on the result.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
*/
L2_POOL_2D = @1.1::OperationType:L2_POOL_2D,
@@ -994,11 +976,11 @@
* 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
- * Tensors with rank less than 4 are only supported since API level 29.
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
@@ -1011,10 +993,10 @@
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
* value must be of {@link OperandType::FLOAT32}.
* * 3: A scalar, specifying the scale factor, alpha.
- * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the alpha
- * value must be of {@link OperandType::FLOAT16}.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the alpha
- * value must be of {@link OperandType::FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * alpha value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * alpha value must be of {@link OperandType::FLOAT32}.
* * 4: A scalar, specifying the exponent, beta.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
* value must be of {@link OperandType::FLOAT16}.
@@ -1024,12 +1006,10 @@
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
*/
LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION,
@@ -1041,22 +1021,20 @@
* output = 1 / (1 + exp(-input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
LOGISTIC = @1.1::OperationType:LOGISTIC,
@@ -1064,7 +1042,7 @@
* Projects an input to a bit vector via locality senstive hashing.
*
* Supported input tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1086,7 +1064,7 @@
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
* Sparse:
- * Value LSHProjectionType_SPARSE(=3) (since API level 29).
+ * Value LSHProjectionType_SPARSE(=3) (since HAL version 1.2).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
@@ -1107,14 +1085,12 @@
* Outputs:
* * 0: If the projection type is Sparse:
* Output.Dim == { Tensor[0].Dim[0] }
- * A tensor of int32 that represents hash signatures,
+ * A tensor of int32 that represents hash signatures.
*
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
- *
- * Available since API level 27.
- * The offset value for sparse projections was added in API level 29.
+ * The offset value for sparse projections was added in HAL version 1.2.
*/
LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION,
@@ -1170,7 +1146,7 @@
* matrix, each element of which is the product of the corresponding
* elements of the input matrices.
*
- * Since API level 29 LSTM supports layer normalization.
+ * Since HAL version 1.2 LSTM supports layer normalization.
* In case layer normalization is used, the inputs to internal activation
* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
* following an approach from section 3.1 from
@@ -1197,7 +1173,7 @@
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
* value if the recurrent projection layer exists, and should otherwise
* have no value.
- * * (API level >= 29) The four layer normalization weights either all have
+ * * (HAL version 1.2 or later) The four layer normalization weights either all have
* values or none of them have values. Additionally, if CIFG is used,
* input layer normalization weights tensor is omitted and the other layer
* normalization weights either all have values or none of them have
@@ -1228,7 +1204,7 @@
* Jimmy Ba et al. "Layer Normalization"
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input and output tensors must be of the same type.
@@ -1291,24 +1267,24 @@
* * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
* that values are bound within [-cell_clip, cell_clip]. If set to 0.0
* then clipping is disabled.
- * Until API level 29 this scalar must be of type {@link
- * FLOAT32}. Since API level 29, if all the input
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
- * Until API level 29 this scalar must be of type {@link
- * FLOAT32}. Since API level 29, if all the input
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
- * Since API level 29 there are additional inputs to this op:
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
+ * Since HAL version 1.2 there are additional inputs to this op:
* * 23:The input layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at input gate.
@@ -1333,8 +1309,6 @@
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of shape [batch_size, output_size]. This is effectively
* the same as the current “output state (out)” value.
- *
- * Available since API level 27.
*/
LSTM = @1.1::OperationType:LSTM,
@@ -1352,7 +1326,7 @@
* )
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1360,13 +1334,14 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -1388,12 +1363,12 @@
* invoke on the result.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -1410,13 +1385,13 @@
* invoke on the result.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D,
@@ -1435,15 +1410,15 @@
* of the input operands. It starts with the trailing dimensions, and works
* its way forward.
*
- * Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
- * * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM}
- *
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
@@ -1459,8 +1434,6 @@
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
- *
- * Available since API level 27.
*/
MUL = @1.1::OperationType:MUL,
@@ -1472,20 +1445,20 @@
* output = max(0, input)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU = @1.1::OperationType:RELU,
@@ -1497,20 +1470,20 @@
* output = min(1.f, max(-1.f, input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
- * * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU1 = @1.1::OperationType:RELU1,
@@ -1522,20 +1495,20 @@
* output = min(6, max(0, input))
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RELU6 = @1.1::OperationType:RELU6,
@@ -1546,7 +1519,7 @@
* tensor, but with a newly specified shape.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1565,8 +1538,8 @@
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESHAPE = @1.1::OperationType:RESHAPE,
@@ -1578,30 +1551,31 @@
* same as corner pixels of input.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Both resizing by shape and resizing by scale are supported.
*
* Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
- * the input. Since API level 29, zero batches is supported for this
- * tensor.
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
* * 2: An {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
- * Inputs (resizing by scale, since API level 29):
+ * Inputs (resizing by scale, since HAL version 1.2):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
* * 1: A scalar, specifying width_scale, the scaling factor of the width
@@ -1622,8 +1596,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR,
@@ -1644,7 +1618,7 @@
* argument (if not “NONE”).
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
@@ -1676,8 +1650,6 @@
* * 1: output.
* A 2-D tensor of shape [batch_size, num_units]. This is effectively
* the same as the current state value.
- *
- * Available since API level 27.
*/
RNN = @1.1::OperationType:RNN,
@@ -1696,34 +1668,32 @@
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
- * Tensors with rank other than 2 or 4 are only supported since API level 29.
+ * Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
*
* Inputs:
- * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since
- * API level 29, this tensor may be zero-sized.
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * Since HAL version 1.2, this tensor may be zero-sized.
* * 1: A scalar, specifying the positive scaling factor for the exponent,
* beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
- * {@link OperandType::FLOAT32}. If input0 is of {@link
- * OperandType::TENSOR_FLOAT16}, then the scalar must be of {@link
- * OperandType::FLOAT16}.
+ * {@link OperandType::FLOAT32}.
+ * If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
+ * scalar must be of {@link OperandType::FLOAT16}.
* * 2: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension the activation would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
- *
- * Available since API level 27.
*/
SOFTMAX = @1.1::OperationType:SOFTMAX,
@@ -1742,7 +1712,7 @@
* The input tensor's height and width must be divisible by block_size.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1750,6 +1720,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1759,13 +1730,13 @@
* input height and width.
* * 2: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
- *
- * Available since API level 27.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH,
@@ -1809,7 +1780,7 @@
* the filters.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input tensors must be the same type.
@@ -1843,8 +1814,6 @@
* * 1: output.
* A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
- *
- * Available since API level 27.
*/
SVDF = @1.1::OperationType:SVDF,
@@ -1856,22 +1825,20 @@
* output = tanh(input)
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor, specifying the input. Since API level 29, this tensor may
- * be zero-sized.
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
- *
- * Available since API level 27.
*/
TANH = @1.1::OperationType:TANH,
@@ -1886,7 +1853,7 @@
* This is the reverse of SpaceToBatch.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1894,6 +1861,7 @@
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -1906,8 +1874,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND,
@@ -1931,12 +1899,12 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -1951,8 +1919,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
*/
DIV = @1.1::OperationType:DIV,
@@ -1965,7 +1931,7 @@
* length 1.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1987,21 +1953,21 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be same as input0.
*/
MEAN = @1.1::OperationType:MEAN,
/**
- * Pads a tensor with zeros.
+ * Pads a tensor.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
- * level 29, see the output section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: up to 4
*
@@ -2023,12 +1989,12 @@
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*
- * NOTE: Before API level 29, the pad value for
- * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined.
- * Since API level 29, the pad value is always the logical zero.
- *
- * Available since API level 28.
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
*/
PAD = @1.1::OperationType:PAD,
@@ -2044,14 +2010,16 @@
* dimensions of the input are optionally zero padded according to paddings.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
@@ -2068,12 +2036,16 @@
* end of dimension i.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
- * Available since API level 29.
+ * Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*
- * Available since API level 28.
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
*/
SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND,
@@ -2086,7 +2058,7 @@
* dimensions by specifying the axes (input1).
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2104,8 +2076,8 @@
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SQUEEZE = @1.1::OperationType:SQUEEZE,
@@ -2119,7 +2091,7 @@
* reverse slice.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2151,8 +2123,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE,
@@ -2176,14 +2148,14 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Since API level 29, generic zero-sized input tensor is supported. Zero
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
- * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
*
@@ -2197,8 +2169,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SUB = @1.1::OperationType:SUB,
@@ -2212,7 +2184,7 @@
* regular matrix transpose on 2-D input Tensors.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -2220,14 +2192,14 @@
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be transposed.
- * Since API level 29, this tensor may be zero-sized.
+ * Since HAL version 1.2, this tensor may be zero-sized.
* * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
* the permutation of the dimensions of the input tensor.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 28.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = @1.1::OperationType:TRANSPOSE,
@@ -2245,8 +2217,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
ABS = 38,
@@ -2269,8 +2239,6 @@
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
- *
- * Available since API level 29.
*/
// There is no underscore in ARG_MAX to avoid name conflict with
// the macro defined in libc/kernel/uapi/linux/limits.h.
@@ -2295,8 +2263,6 @@
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
- *
- * Available since API level 29.
*/
ARGMIN = 40, // See ARGMAX for naming discussion.
@@ -2341,8 +2307,8 @@
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_rois, num_classes * 4], specifying the coordinates of each
* output bounding box for each class, with format [x1, y1, x2, y2].
- *
- * Available since API level 29.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
*/
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
@@ -2482,17 +2448,15 @@
* then clipping is disabled.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
- * otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+ * this scalar must be of type {@link OperandType::FLOAT16}.
* * 50: The clipping threshold for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
- * otherwise if all the input tensors have the type {@link
- * TENSOR_FLOAT16}, this scalar must be of type {@link
- * FLOAT16}.
+ * otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+ * this scalar must be of type {@link OperandType::FLOAT16}.
* * 51: merge_outputs
* An {@link OperandType::BOOL} scalar specifying if the outputs
* from forward and backward cells should be merged.
@@ -2539,8 +2503,6 @@
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, bw_output_size]
* If batch-major: [batch_size, max_time, bw_output_size]
- *
- * Available since API level 29.
*/
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
@@ -2658,8 +2620,6 @@
* (timeMajor). If it is set to true, then the shape is set to
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
* [batchSize, maxTime, bwNumUnits].
- *
- * Available since API level 29.
*/
BIDIRECTIONAL_SEQUENCE_RNN = 43,
@@ -2737,8 +2697,6 @@
* * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
- *
- * Available since API level 29.
*/
BOX_WITH_NMS_LIMIT = 44,
@@ -2762,8 +2720,6 @@
*
* Outputs:
* * 0: A tensor with the same shape as input0.
- *
- * Available since API level 29.
*/
CAST = 45,
@@ -2800,8 +2756,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
CHANNEL_SHUFFLE = 46,
@@ -2856,14 +2812,14 @@
* * 11: A scalar, score_threshold. Boxes with scores lower than the
* threshold are filtered before sending to the NMS algorithm. The
* scalar must be of {@link OperandType::FLOAT16} if input0 is of
- * {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
- * must be of {@link OperandType::FLOAT16} if input0 is of {@link
- * OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * must be of {@link OperandType::FLOAT16} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 13: An {@link OperandType::BOOL} scalar, set to true to include
* background class in the list of label map for the output, set
* to false to not include the background. When the background
@@ -2882,8 +2838,6 @@
* output detection.
* * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
* specifying the number of valid output detections for each batch.
- *
- * Available since API level 29.
*/
DETECTION_POSTPROCESSING = 47,
@@ -2908,8 +2862,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
EQUAL = 48,
@@ -2927,8 +2879,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
EXP = 49,
@@ -2956,8 +2906,8 @@
* Outputs:
* * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
* input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
EXPAND_DIMS = 50,
@@ -2994,8 +2944,8 @@
*
* Outputs:
* * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
GATHER = 51,
@@ -3074,8 +3024,6 @@
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
- *
- * Available since API level 29.
*/
GENERATE_PROPOSALS = 52,
@@ -3100,8 +3048,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
GREATER = 53,
/**
@@ -3125,8 +3071,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
GREATER_EQUAL = 54,
@@ -3191,7 +3135,8 @@
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
- * the channel dimension must be set to 0.
+ * the channel dimension (channelDim at
+ * {@link SymmPerChannelQuantParams}) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3229,7 +3174,8 @@
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
- * the channel dimension must be set to 0.
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3258,8 +3204,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
GROUPED_CONV_2D = 55,
@@ -3300,12 +3246,14 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_boxes, num_keypoints], specifying score of the keypoints.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from input0 scale and zeroPoint.
* * 1: A tensor of the same {@link OperandType} as input1, with shape
* [num_boxes, num_keypoints, 2], specifying the location of
* the keypoints, the second dimension is organized as
* [keypoint_x, keypoint_y].
- *
- * Available since API level 29.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
*/
HEATMAP_MAX_KEYPOINT = 56,
@@ -3339,26 +3287,24 @@
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: A scalar, specifying gamma, the scale applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 2: A scalar, specifying beta, the offset applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 3: A scalar, specifying epsilon, the small value added to variance to
* avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
- * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
- * OperandType::FLOAT32} if input0 is of {@link
- * OperandType::TENSOR_FLOAT32}.
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
* * 4: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
- *
- * Available since API level 29.
*/
INSTANCE_NORMALIZATION = 57,
@@ -3383,8 +3329,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LESS = 58,
@@ -3409,8 +3353,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LESS_EQUAL = 59,
@@ -3428,8 +3370,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
LOG = 60,
@@ -3450,8 +3390,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LOGICAL_AND = 61,
@@ -3468,8 +3406,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
LOGICAL_NOT = 62,
@@ -3490,8 +3426,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
LOGICAL_OR = 63,
@@ -3523,8 +3457,6 @@
* Outputs:
* * 0: The output tensor of the same {@link OperandType} and shape as
* input0.
- *
- * Available since API level 29.
*/
LOG_SOFTMAX = 64,
@@ -3543,11 +3475,13 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MAXIMUM = 65,
@@ -3566,11 +3500,13 @@
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MINIMUM = 66,
@@ -3589,8 +3525,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
NEG = 67,
@@ -3615,8 +3549,6 @@
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
- *
- * Available since API level 29.
*/
NOT_EQUAL = 68,
@@ -3657,8 +3589,8 @@
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
PAD_V2 = 69,
@@ -3689,8 +3621,6 @@
*
* Outputs:
* * 0: An output tensor.
- *
- * Available since API level 29.
*/
POW = 70,
@@ -3728,8 +3658,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be diffent from the input0 scale and zeroPoint.
*/
PRELU = 71,
@@ -3752,8 +3682,6 @@
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_QUANT8_ASYMM}.
- *
- * Available since API level 29.
*/
QUANTIZE = 72,
@@ -3879,8 +3807,6 @@
* Outputs:
* * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
* [batches, samples], containing the drawn samples.
- *
- * Available since API level 29.
*/
RANDOM_MULTINOMIAL = 74,
@@ -3906,8 +3832,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_ALL = 75,
@@ -3933,8 +3857,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_ANY = 76,
@@ -3962,8 +3884,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
REDUCE_MAX = 77,
@@ -3991,8 +3913,8 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
REDUCE_MIN = 78,
@@ -4018,8 +3940,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_PROD = 79,
@@ -4045,8 +3965,6 @@
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
- *
- * Available since API level 29.
*/
REDUCE_SUM = 80,
@@ -4064,7 +3982,7 @@
* interpolation.
*
* Supported tensor {@link OperandType}:
- * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+ * * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -4105,8 +4023,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from the input0 scale and zeroPoint.
*/
ROI_ALIGN = 81,
@@ -4156,8 +4074,8 @@
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
ROI_POOLING = 82,
@@ -4175,8 +4093,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
RSQRT = 83,
@@ -4201,9 +4117,13 @@
* true) or input2 (if false).
* * 1: An input tensor of the same shape as input0.
* * 2: An input tensor of the same shape and type as input1.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input1 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same type and shape as input1 and input2.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*
*/
SELECT = 84,
@@ -4222,8 +4142,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
SIN = 85,
@@ -4235,7 +4153,6 @@
* for each dimension. The size is specified as a 1-D tensor containing
* either size of a slice along corresponding dimension or -1. In the latter
* case, all the remaining elements in dimension are included in the slice.
- * Slice size in each dimension cannot be zero.
*
* A sum of begin offset and a size of a slice must not exceed size of a
* corresponding dimension.
@@ -4257,8 +4174,8 @@
*
* Outputs:
* * 0: An n-D tensor of the same type as the input containing the slice.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
*/
SLICE = 86,
@@ -4282,8 +4199,8 @@
*
* Outputs:
* * 0 ~ (num_splits - 1): Resulting subtensors.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
SPLIT = 87,
@@ -4301,8 +4218,6 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- *
- * Available since API level 29.
*/
SQRT = 88,
@@ -4330,8 +4245,8 @@
*
* Outputs:
* * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
TILE = 89,
@@ -4357,10 +4272,10 @@
* Outputs:
* * 0: An n-D tensor of the same type as the input, containing the k
* largest elements along each last dimensional slice.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
* * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
* containing the indices of values within the last dimension of input.
- *
- * Available since API level 29.
*/
TOPK_V2 = 90,
@@ -4374,7 +4289,7 @@
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
- * Supported tensor {@link OperandCode} configurations:
+ * Supported tensor {@link OperandType} configurations:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
@@ -4406,7 +4321,7 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4443,7 +4358,7 @@
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
- * dimension (extraParams.channelQuant.channelDim) must be set to 0.
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4473,8 +4388,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
TRANSPOSE_CONV_2D = 91,
@@ -4584,8 +4499,6 @@
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, output_size]
* If batch-major: [batch_size, max_time, output_size]
- *
- * Available since API level 29.
*/
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
@@ -4641,8 +4554,6 @@
* it is set to 1, then the output has a shape [maxTime, batchSize,
* numUnits], otherwise the output has a shape [batchSize, maxTime,
* numUnits].
- *
- * Available since API level 29.
*/
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
@@ -4696,8 +4607,8 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
- *
- * Available since API level 29.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input0.
*/
RESIZE_NEAREST_NEIGHBOR = 94,
diff --git a/neuralnetworks/1.2/types.t b/neuralnetworks/1.2/types.t
new file mode 100644
index 0000000..cab330d
--- /dev/null
+++ b/neuralnetworks/1.2/types.t
@@ -0,0 +1,745 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 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.
+ */
+
+package android.hardware.neuralnetworks@1.2;
+
+import @1.0::DataLocation;
+import @1.0::ErrorStatus;
+import @1.0::OperandLifeTime;
+import @1.0::OperandType;
+import @1.0::PerformanceInfo;
+import @1.1::OperationType;
+
+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,
+
+ /**
+ * The maximum number of files for each type of cache in compilation caching.
+ */
+ MAX_NUMBER_OF_CACHE_FILES = 32,
+};
+
+enum OperandType : @1.0::OperandType {
+%insert Operand_1.2
+
+ /*
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * OEM specific scalar value.
+ * OEM = 10000,
+ */
+ /*
+ * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+ * alternative to OEM operation and data types.
+ *
+ * A tensor of OEM specific values.
+ * TENSOR_OEM_BYTE = 10001,
+ */
+ /* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
+ * OperandTypeRange::FUNDAMENTAL_MAX.
+ */
+ /* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF
+ * OperandTypeRange::OEM_MAX.
+ */
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+%insert Operand_1.2_MAX
+ OEM_MIN = 10000,
+ OEM_MAX = 10001,
+ BASE_MAX = 0xFFFF,
+};
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+
+%insert Operation_1.0
+
+%insert Operation_1.1
+
+%insert Operation_1.2
+
+ /**
+ * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+ * OEM operation and data types.
+ *
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
+ */
+ OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
+ /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
+ * OperationTypeRange::FUNDAMENTAL_MAX.
+ */
+ /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
+ * OperationTypeRange::OEM_MAX.
+ */
+};
+
+/**
+ * The range of values in the OperationType enum.
+ */
+enum OperationTypeRange : uint32_t {
+ BASE_MIN = 0,
+ FUNDAMENTAL_MIN = 0,
+%insert Operation_1.2_MAX
+ OEM_MIN = 10000,
+ OEM_MAX = 10000,
+ BASE_MAX = 0xFFFF,
+};
+
+/**
+ * Device types.
+ *
+ * The type of NNAPI device.
+ */
+enum DeviceType : int32_t {
+ // Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
+ // HAL equivalent of unknown type and a 1.2 HAL implementation must belong
+ // to one of the categories below.
+ /** The device does not fall into any category below. */
+ OTHER = 1,
+ /** The device runs NNAPI models on single or multi-core CPU. */
+ CPU = 2,
+ /** The device can run NNAPI models and also accelerate graphics APIs such
+ * as OpenGL ES and Vulkan. */
+ GPU = 3,
+ /** Dedicated accelerator for Machine Learning workloads. */
+ ACCELERATOR = 4,
+};
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+ /**
+ * Driver performance when operating on float32 data but performing
+ * calculations with range and/or precision as low as that of the IEEE
+ * 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+ /**
+ * Driver performance when operating on a particular data type.
+ * In the case of float32 data, this is used when the calculations
+ * are not relaxed.
+ */
+ struct OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+ };
+
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ * If a particular OperandType is not present in operandPerformance,
+ * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ */
+ vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+ /**
+ * The operation type.
+ *
+ * Besides the values listed in {@link OperationType}, any value above
+ * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperationType type;
+
+ /**
+ * Describes the table that contains the indexes of the inputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> inputs;
+
+ /**
+ * Describes the table that contains the indexes of the outputs of the
+ * operation. The offset is the index in the operandIndexes table.
+ */
+ vec<uint32_t> outputs;
+};
+
+/**
+ * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
+ */
+struct SymmPerChannelQuantParams {
+ /** Array of scaling values for each channel. Each value must be greater than zero. */
+ vec<float> scales;
+ /** Index of the channel dimension */
+ uint32_t channelDim;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperandType type;
+
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * A tensor operand with all dimensions specified has "fully
+ * specified" dimensions. Whenever possible (i.e., whenever the
+ * dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully
+ * specified dimensions, in order to enable the best possible
+ * performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the
+ * dimensions of the operand are deduced from the operand
+ * dimensions and values of the operation for which that operand
+ * is an output.
+ *
+ * In the following situations, a tensor operand's dimensions must
+ * be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or
+ * CONSTANT_REFERENCE.
+ *
+ * . The operand has lifetime MODEL_INPUT. Fully
+ * specified dimensions must either be present in the
+ * Operand or they must be provided in the corresponding
+ * RequestArgument.
+ * EXCEPTION: If the input is optional and omitted
+ * (by setting the hasNoValue field of the corresponding
+ * RequestArgument to true) then it need not have fully
+ * specified dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is
+ * represented by setting each unspecified dimension to 0.
+ *
+ * A tensor operand with unspecified rank is represented by providing
+ * an empty dimensions vector.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * The number of times this operand appears as an operation input.
+ *
+ * (For example, if this operand appears once in one operation's
+ * input list, and three times in another operation's input list,
+ * then numberOfConsumers = 4.)
+ */
+ uint32_t numberOfConsumers;
+
+ /**
+ * Quantized scale of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+ * TENSOR_INT32.
+ */
+ float scale;
+
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+ */
+ int32_t zeroPoint;
+
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+ * NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_REFERENCE:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ */
+ DataLocation location;
+
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ safe_union ExtraParams {
+ /**
+ * No additional parameters.
+ */
+ Monostate none;
+
+ /**
+ * Symmetric per-channel quantization parameters.
+ *
+ * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+ */
+ SymmPerChannelQuantParams channelQuant;
+
+ /**
+ * Extension operand parameters.
+ *
+ * The framework treats this as an opaque data blob.
+ * The format is up to individual extensions.
+ */
+ vec<uint8_t> extension;
+ } extraParams;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+ /**
+ * All operands included in the model.
+ */
+ vec<Operand> operands;
+
+ /**
+ * All operations included in the model.
+ *
+ * The operations are sorted into execution order. Every operand
+ * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+ * written before it is read.
+ */
+ vec<Operation> operations;
+
+ /**
+ * Input indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> inputIndexes;
+
+ /**
+ * Output indexes of the model. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ vec<uint32_t> outputIndexes;
+
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_COPY.
+ */
+ vec<uint8_t> operandValues;
+
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime
+ * equals OperandLifeTime::CONSTANT_REFERENCE.
+ */
+ vec<memory> pools;
+
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+ * precision as low as that of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+ * range and precision of the IEEE 754 32-bit floating-point format.
+ */
+ bool relaxComputationFloat32toFloat16;
+
+ /**
+ * The mapping between extension names and prefixes of operand and
+ * operation type values.
+ *
+ * An operand or operation whose numeric type value is above
+ * {@link OperandTypeRange::BASE_MAX} or
+ * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+ * as an extension operand. The low
+ * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+ * the "prefix", which maps uniquely to the extension name.
+ *
+ * For example, if a model contains an operation whose value is
+ * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+ * prefix=0xAAAA and name="vendor.test.test_extension", then
+ * the operation should be interpreted as the operation 0xBBBB
+ * of the extension named vendor.test.test_extension.
+ *
+ * This is a one-to-one correspondence. That is, there must be at most one
+ * prefix corresponding to each extension name and at most one extension
+ * name corresponding to each prefix.
+ */
+ vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+ /**
+ * A correspondence between an extension name and a prefix of operand and
+ * operation type values.
+ */
+ struct ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ string name;
+
+ /**
+ * The unique extension identifier within the model.
+ *
+ * See {@link Model::extensionNameToPrefix}.
+ */
+ uint16_t prefix;
+ };
+
+ /**
+ * Numeric values of extension operand and operation types have the
+ * following structure:
+ * - 16 high bits represent the "prefix", which corresponds uniquely to the
+ * extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ enum ExtensionTypeEncoding : uint8_t {
+ HIGH_BITS_PREFIX = 16,
+ LOW_BITS_TYPE = 16,
+ };
+};
+
+/**
+ * Describes the shape information of an output operand after execution.
+ */
+struct OutputShape {
+ /**
+ * Dimensions of the operand.
+ */
+ vec<uint32_t> dimensions;
+
+ /**
+ * Whether the provided buffer size is sufficient for the output.
+ */
+ bool isSufficient;
+};
+
+/**
+ * Specifies whether or not to measure timing information during execution.
+ */
+enum MeasureTiming : int32_t {
+ NO = 0,
+ YES = 1,
+};
+
+/**
+
+ * Timing information measured during execution. Each time is a duration from
+ * the beginning of some task to the end of that task, including time when that
+ * task is not active (for example, preempted by some other task, or
+ * waiting for some resource to become available).
+ *
+ * Times are measured in microseconds.
+ * When a time is not available, it must be reported as UINT64_MAX.
+ */
+struct Timing {
+ /** Execution time on device (not driver, which runs on host processor). */
+ uint64_t timeOnDevice;
+ /** Execution time in driver (including time on device). */
+ uint64_t timeInDriver;
+};
+
+/**
+ * FmqRequestDatum is a single element of a serialized representation of an
+ * execution request (a {@link @1.0::Request} object and a {@link MeasureTiming}
+ * value) which is sent across FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The request is serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each input operand:
+ * 2.1) 'inputOperandInformation'
+ * 2.2) For each dimension element of the operand:
+ * 2.2.1) 'inputOperandDimensionValue'
+ * 3) For each output operand:
+ * 3.1) 'outputOperandInformation'
+ * 3.2) For each dimension element of the operand:
+ * 3.2.1) 'outputOperandDimensionValue'
+ * 4) For each pool:
+ * 4.1) 'poolIdentifier'
+ * 5) 'measureTiming'
+ */
+safe_union FmqRequestDatum {
+ /**
+ * Type to describe the high-level layout of the packet.
+ */
+ struct PacketInformation {
+ /**
+ * How many elements the packet contains, including the
+ * "packetInformation" datum.
+ */
+ uint32_t packetSize;
+
+ /**
+ * Number of input operands.
+ */
+ uint32_t numberOfInputOperands;
+
+ /**
+ * Number of output operands.
+ */
+ uint32_t numberOfOutputOperands;
+
+ /**
+ * Number of pool identifiers.
+ */
+ uint32_t numberOfPools;
+ };
+
+ /**
+ * Type representing the information for each operand.
+ */
+ struct OperandInformation {
+ /**
+ * If true, the argument does not have a value. This can be used for
+ * operations that take optional arguments. If true, the fields of
+ * 'location' are set to 0, 'numberOfDimensions' is set to 0, and the
+ * dimensions information is omitted from the serialization.
+ */
+ bool hasNoValue;
+
+ /**
+ * The location within one of the memory pools passed in the Request.
+ */
+ DataLocation location;
+
+ /**
+ * Number of subsequent elements that belong to the dimensions vector.
+ */
+ uint32_t numberOfDimensions;
+ };
+
+ /**
+ * packetInformation is the first element of the packet and describes the
+ * remainder of the packet.
+ */
+ PacketInformation packetInformation;
+
+ /**
+ * Information for each input operand.
+ */
+ OperandInformation inputOperandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t inputOperandDimensionValue;
+
+ /**
+ * Information for each output operand.
+ */
+ OperandInformation outputOperandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t outputOperandDimensionValue;
+
+ /**
+ * Unique identifier for a pool.
+ *
+ * A {@link @1.0::Request} passes across one or more pools of shared memory
+ * for the inputs and outputs of an execution. However, these memory pools
+ * are not able to be sent across FastMessageQueue directly. Instead, the
+ * producing side of the FMQ represents each different pool with a unique
+ * identifier, and sends this identifier across the FMQ. Whenever the
+ * consuming side of the FMQ needs the memory corresponding to this unique
+ * identifier, it can pass the identifier to
+ * {@link IBurstCallback::getMemories} to retreive the memory. Although this
+ * HIDL Binder call is expensive compared to communication across FMQ, it is
+ * only needed in the cases when the consumer does not recognize the unique
+ * identifier.
+ */
+ int32_t poolIdentifier;
+
+ /**
+ * Specifies whether or not to measure duration of the execution. The
+ * duration runs from the time the driver dequeues the request from a
+ * FastMessageQueue to the time the driver enqueues results to a
+ * FastMessageQueue.
+ */
+ MeasureTiming measureTiming;
+};
+
+/**
+ * FmqResultDatum is a single element of a serialized representation of the
+ * values returned from an execution ({@link @1.0::ErrorStatus},
+ * vec<{@link OutputShape}>, and {@link Timing}) which is returned via
+ * FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The execution return values ({@link @1.0::ErrorStatus} and
+ * vec<{@link OutputShape}>) are serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each returned operand:
+ * 2.1) 'operandInformation'
+ * 2.2) For each dimension element of the operand:
+ * 2.2.1) 'operandDimensionValue'
+ * 3) 'executionTiming'
+ */
+safe_union FmqResultDatum {
+ /**
+ * Type to describe the high-level layout of the packet.
+ */
+ struct PacketInformation {
+ /**
+ * How many elements the packet contains, including the
+ * "packetInformation" datum.
+ */
+ uint32_t packetSize;
+
+ /**
+ * Status of the execution.
+ */
+ ErrorStatus errorStatus;
+
+ /**
+ * Number of returned operands.
+ */
+ uint32_t numberOfOperands;
+ };
+
+ /**
+ * Type representing the information for each operand.
+ */
+ struct OperandInformation {
+ /**
+ * Indicates whether the operand's output buffer is large enough to
+ * store the operand's result data.
+ */
+ bool isSufficient;
+
+ /**
+ * Number of subsequent elements that belong to the dimensions vector.
+ */
+ uint32_t numberOfDimensions;
+ };
+
+ /**
+ * packetInformation is the first element of the packet and describes the
+ * remainder of the packet. It additionally includes the status of the
+ * execution.
+ */
+ PacketInformation packetInformation;
+
+ /**
+ * Information for each returned operand.
+ */
+ OperandInformation operandInformation;
+
+ /**
+ * Element of the dimensions vector.
+ */
+ uint32_t operandDimensionValue;
+
+ /**
+ * Duration of execution. Unless measurement was requested and execution
+ * succeeds, all times must be reported as UINT64_MAX. A driver may choose
+ * to report any time as UINT64_MAX, indicating that measurement is not
+ * available.
+ */
+ Timing executionTiming;
+};
+
+/**
+ * Information about an extension.
+ */
+struct Extension {
+ /**
+ * The extension name.
+ *
+ * The name must consist of lowercase latin letters, numbers, periods, and
+ * underscore signs. The name must contain at least one period.
+ *
+ * The name must start with the reverse domain name of the vendor.
+ *
+ * Example: com.google.test_extension
+ */
+ string name;
+
+ /**
+ * Information about an extension operand type.
+ */
+ struct OperandTypeInformation {
+ /**
+ * The extension operand type.
+ */
+ uint16_t type;
+
+ /**
+ * Indicates whether the extension operand type represents a tensor or
+ * a scalar.
+ */
+ bool isTensor;
+
+ /**
+ * The byte size of the operand (if scalar) or of a single element (if
+ * tensor).
+ */
+ uint32_t byteSize;
+ };
+
+ /**
+ * Information about operand types defined by the extension.
+ */
+ vec<OperandTypeInformation> operandTypes;
+};
diff --git a/radio/1.2/vts/functional/radio_hidl_hal_api.cpp b/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
index 5184ef9..a98f22a 100644
--- a/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
+++ b/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
@@ -46,7 +46,10 @@
::android::hardware::radio::V1_2::NetworkScanRequest request = {
.type = ScanType::ONE_SHOT,
.interval = 60,
- .specifiers = {::GERAN_SPECIFIER_P900, ::GERAN_SPECIFIER_850}};
+ .specifiers = {::GERAN_SPECIFIER_P900, ::GERAN_SPECIFIER_850},
+ .maxSearchTime = 60,
+ .incrementalResults = false,
+ .incrementalResultsPeriodicity = 1};
Return<void> res = radio_v1_2->startNetworkScan_1_2(serial, request);
ASSERT_OK(res);
diff --git a/radio/1.4/vts/functional/radio_hidl_hal_api.cpp b/radio/1.4/vts/functional/radio_hidl_hal_api.cpp
index b2d19a2..d3012bb 100644
--- a/radio/1.4/vts/functional/radio_hidl_hal_api.cpp
+++ b/radio/1.4/vts/functional/radio_hidl_hal_api.cpp
@@ -178,7 +178,12 @@
.channels = {1, 2}};
::android::hardware::radio::V1_2::NetworkScanRequest request = {
- .type = ScanType::ONE_SHOT, .interval = 60, .specifiers = {specifier}};
+ .type = ScanType::ONE_SHOT,
+ .interval = 60,
+ .specifiers = {specifier},
+ .maxSearchTime = 60,
+ .incrementalResults = false,
+ .incrementalResultsPeriodicity = 1};
Return<void> res = radio_v1_4->startNetworkScan_1_4(serial, request);
ASSERT_OK(res);