Merge changes I29bbf6bf,Ib0b03fb6 into pi-dev
* changes:
Audio HAL HIDL wrapper: Fix incorrect conversion of TTY Mode
Audio V4 VTS: test setter even if getter is NOT_SUPPORTED
diff --git a/audio/core/all-versions/default/include/core/all-versions/default/Conversions.impl.h b/audio/core/all-versions/default/include/core/all-versions/default/Conversions.impl.h
index 004a99e..5828c3f 100644
--- a/audio/core/all-versions/default/include/core/all-versions/default/Conversions.impl.h
+++ b/audio/core/all-versions/default/include/core/all-versions/default/Conversions.impl.h
@@ -18,6 +18,8 @@
#include <stdio.h>
+#include <log/log.h>
+
namespace android {
namespace hardware {
namespace audio {
@@ -108,6 +110,9 @@
return AudioMicrophoneChannelMapping::DIRECT;
case AUDIO_MICROPHONE_CHANNEL_MAPPING_PROCESSED:
return AudioMicrophoneChannelMapping::PROCESSED;
+ default:
+ ALOGE("Invalid channel mapping type: %d", mapping);
+ return AudioMicrophoneChannelMapping::UNUSED;
}
}
diff --git a/automotive/vehicle/2.0/types.hal b/automotive/vehicle/2.0/types.hal
index d4e4d46..faa1adc 100644
--- a/automotive/vehicle/2.0/types.hal
+++ b/automotive/vehicle/2.0/types.hal
@@ -966,19 +966,6 @@
| VehicleArea:GLOBAL),
/**
- * Cabin temperature
- *
- * @change_mode VehiclePropertyChangeMode:CONTINUOUS
- * @access VehiclePropertyAccess:READ
- * @unit VehicleUnit:CELSIUS
- */
- ENV_CABIN_TEMPERATURE = (
- 0x0704
- | VehiclePropertyGroup:SYSTEM
- | VehiclePropertyType:FLOAT
- | VehicleArea:SEAT),
-
- /**
* Property to control power state of application processor
*
* It is assumed that AP's power state is controller by separate power
diff --git a/broadcastradio/2.0/vts/functional/VtsHalBroadcastradioV2_0TargetTest.cpp b/broadcastradio/2.0/vts/functional/VtsHalBroadcastradioV2_0TargetTest.cpp
index 6877f07..571b80c 100644
--- a/broadcastradio/2.0/vts/functional/VtsHalBroadcastradioV2_0TargetTest.cpp
+++ b/broadcastradio/2.0/vts/functional/VtsHalBroadcastradioV2_0TargetTest.cpp
@@ -20,6 +20,7 @@
#include <VtsHalHidlTargetTestBase.h>
#include <android-base/logging.h>
+#include <android-base/strings.h>
#include <android/hardware/broadcastradio/2.0/IBroadcastRadio.h>
#include <android/hardware/broadcastradio/2.0/ITunerCallback.h>
#include <android/hardware/broadcastradio/2.0/ITunerSession.h>
@@ -66,6 +67,8 @@
} // namespace timeout
+static constexpr auto gTuneWorkaround = 200ms;
+
static const ConfigFlag gConfigFlagValues[] = {
ConfigFlag::FORCE_MONO,
ConfigFlag::FORCE_ANALOG,
@@ -158,6 +161,14 @@
physically > IdentifierType::SXM_CHANNEL);
}
+ if (logically == IdentifierType::AMFM_FREQUENCY) {
+ auto ps = utils::getMetadataString(info, MetadataKey::RDS_PS);
+ if (ps.has_value()) {
+ EXPECT_NE("", android::base::Trim(*ps))
+ << "Don't use empty RDS_PS as an indicator of missing RSD PS data.";
+ }
+ }
+
return onCurrentProgramInfoChanged_(info);
}
@@ -414,7 +425,7 @@
* This sleep workaround will fix default implementation, but the real HW tests will still be
* flaky. We probably need to implement egmock alternative based on actions.
*/
- std::this_thread::sleep_for(100ms);
+ std::this_thread::sleep_for(gTuneWorkaround);
// try tuning
ProgramInfo infoCb = {};
@@ -500,7 +511,7 @@
ASSERT_TRUE(openSession());
// TODO(b/69958777): see FmTune workaround
- std::this_thread::sleep_for(100ms);
+ std::this_thread::sleep_for(gTuneWorkaround);
EXPECT_TIMEOUT_CALL(*mCallback, onCurrentProgramInfoChanged_, _);
auto result = mSession->scan(true /* up */, true /* skip subchannel */);
@@ -525,7 +536,7 @@
ASSERT_TRUE(openSession());
// TODO(b/69958777): see FmTune workaround
- std::this_thread::sleep_for(100ms);
+ std::this_thread::sleep_for(gTuneWorkaround);
EXPECT_TIMEOUT_CALL(*mCallback, onCurrentProgramInfoChanged_, _).Times(AnyNumber());
auto result = mSession->step(true /* up */);
diff --git a/camera/device/3.4/default/CameraDeviceSession.cpp b/camera/device/3.4/default/CameraDeviceSession.cpp
index 550d65a..9722c75 100644
--- a/camera/device/3.4/default/CameraDeviceSession.cpp
+++ b/camera/device/3.4/default/CameraDeviceSession.cpp
@@ -52,6 +52,8 @@
}
}
+ mResultBatcher_3_4.setNumPartialResults(mNumPartialResults);
+
camera_metadata_entry_t capabilities =
mDeviceInfo.find(ANDROID_REQUEST_AVAILABLE_CAPABILITIES);
bool isLogicalMultiCamera = false;
diff --git a/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp b/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
index 637e280..baffc78 100644
--- a/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
+++ b/camera/provider/2.4/vts/functional/VtsHalCameraProviderV2_4TargetTest.cpp
@@ -1157,6 +1157,9 @@
TEST_F(CameraHidlTest, noHal1AfterP) {
constexpr int32_t HAL1_PHASE_OUT_API_LEVEL = 28;
int32_t firstApiLevel = property_get_int32("ro.product.first_api_level", /*default*/-1);
+ if (firstApiLevel < 0) {
+ firstApiLevel = property_get_int32("ro.build.version.sdk", /*default*/-1);
+ }
ASSERT_GT(firstApiLevel, 0); // first_api_level must exist
if (firstApiLevel >= HAL1_PHASE_OUT_API_LEVEL) {
diff --git a/compatibility_matrices/Android.mk b/compatibility_matrices/Android.mk
index ee97433..8904c68 100644
--- a/compatibility_matrices/Android.mk
+++ b/compatibility_matrices/Android.mk
@@ -18,68 +18,64 @@
BUILD_FRAMEWORK_COMPATIBILITY_MATRIX := $(LOCAL_PATH)/compatibility_matrix.mk
-# Clear potential input variables to BUILD_FRAMEWORK_COMPATIBILITY_MATRIX
-LOCAL_ADD_VBMETA_VERSION :=
-LOCAL_ASSEMBLE_VINTF_ENV_VARS :=
-LOCAL_ASSEMBLE_VINTF_ENV_VARS_OVERRIDE :=
-LOCAL_ASSEMBLE_VINTF_ERROR_MESSAGE :=
-LOCAL_ASSEMBLE_VINTF_FLAGS :=
-LOCAL_KERNEL_VERSIONS :=
-LOCAL_GEN_FILE_DEPENDENCIES :=
+my_kernel_config_data := kernel/configs
# Install all compatibility_matrix.*.xml to /system/etc/vintf
-
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.legacy.xml
LOCAL_MODULE_STEM := compatibility_matrix.legacy.xml
LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := \
- 3.18.0 \
- 4.4.0 \
- 4.9.0 \
- 4.14.0 \
+LOCAL_KERNEL_CONFIG_DATA_PATHS := \
+ 3.18.0:$(my_kernel_config_data)/o/android-3.18 \
+ 4.4.0:$(my_kernel_config_data)/o/android-4.4 \
+ 4.9.0:$(my_kernel_config_data)/o/android-4.9 \
include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.1.xml
LOCAL_MODULE_STEM := compatibility_matrix.1.xml
LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := \
- 3.18.0 \
- 4.4.0 \
- 4.9.0 \
- 4.14.0 \
+LOCAL_KERNEL_CONFIG_DATA_PATHS := \
+ 3.18.0:$(my_kernel_config_data)/o/android-3.18 \
+ 4.4.0:$(my_kernel_config_data)/o/android-4.4 \
+ 4.9.0:$(my_kernel_config_data)/o/android-4.9 \
include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.2.xml
LOCAL_MODULE_STEM := compatibility_matrix.2.xml
LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := \
- 3.18.0 \
- 4.4.0 \
- 4.9.0 \
- 4.14.0 \
+LOCAL_KERNEL_CONFIG_DATA_PATHS := \
+ 3.18.0:$(my_kernel_config_data)/o-mr1/android-3.18 \
+ 4.4.0:$(my_kernel_config_data)/o-mr1/android-4.4 \
+ 4.9.0:$(my_kernel_config_data)/o-mr1/android-4.9 \
include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.3.xml
LOCAL_MODULE_STEM := compatibility_matrix.3.xml
LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := \
- 4.4.0 \
- 4.9.0 \
- 4.14.0 \
+LOCAL_KERNEL_CONFIG_DATA_PATHS := \
+ 4.4.0:$(my_kernel_config_data)/p/android-4.4 \
+ 4.9.0:$(my_kernel_config_data)/p/android-4.9 \
+ 4.14.0:$(my_kernel_config_data)/p/android-4.14 \
include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
+my_kernel_config_data :=
+
# Framework Compatibility Matrix (common to all FCM versions)
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.device.xml
LOCAL_MODULE_STEM := compatibility_matrix.device.xml
# define LOCAL_MODULE_CLASS for local-generated-sources-dir.
@@ -126,6 +122,7 @@
# Framework Compatibility Matrix
include $(CLEAR_VARS)
+include $(LOCAL_PATH)/clear_vars.mk
LOCAL_MODULE := framework_compatibility_matrix.xml
LOCAL_MODULE_STEM := compatibility_matrix.xml
LOCAL_MODULE_PATH := $(TARGET_OUT)
diff --git a/compatibility_matrices/clear_vars.mk b/compatibility_matrices/clear_vars.mk
new file mode 100644
index 0000000..8fde301
--- /dev/null
+++ b/compatibility_matrices/clear_vars.mk
@@ -0,0 +1,24 @@
+#
+# 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.
+#
+
+# Clear input variables to BUILD_FRAMEWORK_COMPATIBILITY_MATRIX
+LOCAL_ADD_VBMETA_VERSION :=
+LOCAL_ASSEMBLE_VINTF_ENV_VARS :=
+LOCAL_ASSEMBLE_VINTF_ENV_VARS_OVERRIDE :=
+LOCAL_ASSEMBLE_VINTF_ERROR_MESSAGE :=
+LOCAL_ASSEMBLE_VINTF_FLAGS :=
+LOCAL_KERNEL_CONFIG_DATA_PATHS :=
+LOCAL_GEN_FILE_DEPENDENCIES :=
diff --git a/compatibility_matrices/compatibility_matrix.mk b/compatibility_matrices/compatibility_matrix.mk
index 6dc2b4f..1b6fd3b 100644
--- a/compatibility_matrices/compatibility_matrix.mk
+++ b/compatibility_matrices/compatibility_matrix.mk
@@ -14,17 +14,6 @@
# limitations under the License.
#
-###########################################################
-## Remove minor revision from a kernel version. For example,
-## 3.18.0 becomes 3.18.
-## $(1): kernel version
-###########################################################
-define remove-minor-revision
-$(strip $(subst $(space),.,$(wordlist 1,2,$(subst .,$(space),$(strip $(1))))))
-endef
-
-# $(warning $(call remove-minor-revision,3.18.0))
-
##### Input Variables:
# LOCAL_MODULE: required. Module name for the build system.
# LOCAL_MODULE_CLASS: optional. Default is ETC.
@@ -42,8 +31,8 @@
# LOCAL_ASSEMBLE_VINTF_ENV_VARS_OVERRIDE: Add a list of environment variables that is local to
# assemble_vintf invocation. Format is "VINTF_ENFORCE_NO_UNUSED_HALS=true".
# LOCAL_ASSEMBLE_VINTF_FLAGS: Add additional command line arguments to assemble_vintf invocation.
-# LOCAL_KERNEL_VERSIONS: Parse kernel configurations and add to the output matrix
-# (corresponds to <kernel> tags.)
+# LOCAL_KERNEL_CONFIG_DATA_PATHS: Paths to search for kernel config requirements. Format for each is
+# <kernel version x.y.z>:<path that contains android-base*.cfg>.
# LOCAL_GEN_FILE_DEPENDENCIES: A list of additional dependencies for the generated file.
ifndef LOCAL_MODULE
@@ -88,14 +77,13 @@
$(GEN): PRIVATE_ENV_VARS += FRAMEWORK_VBMETA_VERSION
endif # LOCAL_ADD_VBMETA_VERSION
-ifneq (,$(strip $(LOCAL_KERNEL_VERSIONS)))
-$(GEN): PRIVATE_KERNEL_CONFIG_DATA := kernel/configs
-$(GEN): PRIVATE_KERNEL_VERSIONS := $(LOCAL_KERNEL_VERSIONS)
-$(GEN): $(foreach version,$(PRIVATE_KERNEL_VERSIONS),\
- $(wildcard $(PRIVATE_KERNEL_CONFIG_DATA)/android-$(call remove-minor-revision,$(version))/android-base*.cfg))
-$(GEN): PRIVATE_FLAGS += $(foreach version,$(PRIVATE_KERNEL_VERSIONS),\
- --kernel=$(version):$(call normalize-path-list,\
- $(wildcard $(PRIVATE_KERNEL_CONFIG_DATA)/android-$(call remove-minor-revision,$(version))/android-base*.cfg)))
+ifneq (,$(strip $(LOCAL_KERNEL_CONFIG_DATA_PATHS)))
+$(GEN): PRIVATE_KERNEL_CONFIG_DATA_PATHS := $(LOCAL_KERNEL_CONFIG_DATA_PATHS)
+$(GEN): $(foreach pair,$(PRIVATE_KERNEL_CONFIG_DATA_PATHS),\
+ $(wildcard $(call word-colon,2,$(pair))/android-base*.cfg))
+$(GEN): PRIVATE_FLAGS += $(foreach pair,$(PRIVATE_KERNEL_CONFIG_DATA_PATHS),\
+ --kernel=$(call word-colon,1,$(pair)):$(call normalize-path-list,\
+ $(wildcard $(call word-colon,2,$(pair))/android-base*.cfg)))
endif
my_matrix_src_files := \
@@ -124,15 +112,7 @@
LOCAL_SRC_FILES :=
LOCAL_GENERATED_SOURCES :=
-LOCAL_ADD_VBMETA_VERSION :=
-LOCAL_ASSEMBLE_VINTF_ENV_VARS :=
-LOCAL_ASSEMBLE_VINTF_ENV_VARS_OVERRIDE :=
-LOCAL_ASSEMBLE_VINTF_ERROR_MESSAGE :=
-LOCAL_ASSEMBLE_VINTF_FLAGS :=
-LOCAL_KERNEL_VERSIONS :=
-LOCAL_GEN_FILE_DEPENDENCIES :=
+include $(LOCAL_PATH)/clear_vars.mk
my_matrix_src_files :=
include $(BUILD_PREBUILT)
-
-remove-minor-revision :=
diff --git a/current.txt b/current.txt
index 12cc6ff..5ccd6ab 100644
--- a/current.txt
+++ b/current.txt
@@ -260,7 +260,7 @@
4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
-934b9a0627080bca5dee83126d23ace31bdf1ed36fe192a2a7694f81b4f0c2af android.hardware.neuralnetworks@1.0::types
+18e6885e184fe48401c2c53f1d1b8bfb07240f40c81ae6b9d2e336fca6efdbb7 android.hardware.neuralnetworks@1.0::types
d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
@@ -299,7 +299,7 @@
3b17c1fdfc389e0abe626c37054954b07201127d890c2bc05d47613ec1f4de4f android.hardware.automotive.evs@1.0::types
b3caf524c46a47d67e6453a34419e1881942d059e146cda740502670e9a752c3 android.hardware.automotive.vehicle@2.0::IVehicle
7ce8728b27600e840cacf0a832f6942819fe535f9d3797ae052d5eef5065921c android.hardware.automotive.vehicle@2.0::IVehicleCallback
-962b18e414231b5f3930d63daba116347241b3606242b0606b3699ba25b14315 android.hardware.automotive.vehicle@2.0::types
+9cf9690f559f8425fa86e409137a42435ca225505152f03736ac8f3773ef4f89 android.hardware.automotive.vehicle@2.0::types
32cc50cc2a7658ec613c0c2dd2accbf6a05113b749852879e818b8b7b438db19 android.hardware.bluetooth.a2dp@1.0::IBluetoothAudioHost
ff4be64d7992f8bec97dff37f35450e79b3430c61f85f54322ce45bef229dc3b android.hardware.bluetooth.a2dp@1.0::IBluetoothAudioOffload
27f22d2e873e6201f9620cf4d8e2facb25bd0dd30a2b911e441b4600d560fa62 android.hardware.bluetooth.a2dp@1.0::types
@@ -339,7 +339,7 @@
4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
7698dc2382a2eeb43541840e3ee624f34108efdfb976b2bfa7c13ef15fb8c4c4 android.hardware.neuralnetworks@1.1::IDevice
-ce5dab4b2dd828bcff09acfb93fcd4846f847868b9e914d214095532c28dc0cf android.hardware.neuralnetworks@1.1::types
+72cc6126632456e8fbb8776fe50150c3c4dd5d09145653193affb70785211dfa android.hardware.neuralnetworks@1.1::types
8d3d86da0bfa4bf070970d8303c659f67f35d670c287d45a3f542e4fedadd578 android.hardware.nfc@1.1::INfc
e85f566698d2a2c28100e264fcf2c691a066756ddf8dd341d009ff50cfe10614 android.hardware.nfc@1.1::INfcClientCallback
5e278fcaa3287d397d8eebe1c22aaa28150f5caae1cf9381cd6dc32cb37899c5 android.hardware.nfc@1.1::types
diff --git a/graphics/allocator/2.0/utils/gralloc1-adapter/Gralloc1On0Adapter.cpp b/graphics/allocator/2.0/utils/gralloc1-adapter/Gralloc1On0Adapter.cpp
index b7ed58c..b2aab7b 100644
--- a/graphics/allocator/2.0/utils/gralloc1-adapter/Gralloc1On0Adapter.cpp
+++ b/graphics/allocator/2.0/utils/gralloc1-adapter/Gralloc1On0Adapter.cpp
@@ -161,6 +161,11 @@
case GRALLOC1_FUNCTION_INVALID:
ALOGE("Invalid function descriptor");
return nullptr;
+ case GRALLOC1_FUNCTION_VALIDATE_BUFFER_SIZE:
+ case GRALLOC1_FUNCTION_GET_TRANSPORT_SIZE:
+ case GRALLOC1_FUNCTION_IMPORT_BUFFER:
+ ALOGW("Not supported by gralloc 0");
+ return nullptr;
}
ALOGE("Unknown function descriptor: %d", intDescriptor);
diff --git a/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc1Hal.h b/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc1Hal.h
index 8b695e4..08d604e 100644
--- a/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc1Hal.h
+++ b/graphics/mapper/2.1/utils/passthrough/include/mapper-passthrough/2.1/Gralloc1Hal.h
@@ -28,6 +28,7 @@
using V2_0::BufferDescriptor;
using V2_0::Error;
+using android::hardware::graphics::common::V1_0::BufferUsage;
namespace detail {
@@ -38,48 +39,19 @@
Error validateBufferSize(const native_handle_t* bufferHandle,
const IMapper::BufferDescriptorInfo& descriptorInfo,
uint32_t stride) override {
- uint32_t bufferWidth;
- uint32_t bufferHeight;
- uint32_t bufferLayerCount;
- int32_t bufferFormat;
- uint64_t bufferProducerUsage;
- uint64_t bufferConsumerUsage;
- uint32_t bufferStride;
+ gralloc1_buffer_descriptor_info_t bufferDescriptorInfo;
- int32_t error = mDispatch.getDimensions(mDevice, bufferHandle, &bufferWidth, &bufferHeight);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
- error = mDispatch.getLayerCount(mDevice, bufferHandle, &bufferLayerCount);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
- error = mDispatch.getFormat(mDevice, bufferHandle, &bufferFormat);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
- error = mDispatch.getProducerUsage(mDevice, bufferHandle, &bufferProducerUsage);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
- error = mDispatch.getConsumerUsage(mDevice, bufferHandle, &bufferConsumerUsage);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
- error = mDispatch.getStride(mDevice, bufferHandle, &bufferStride);
- if (error != GRALLOC1_ERROR_NONE) {
- return toError(error);
- }
+ bufferDescriptorInfo.width = descriptorInfo.width;
+ bufferDescriptorInfo.height = descriptorInfo.height;
+ bufferDescriptorInfo.layerCount = descriptorInfo.layerCount;
+ bufferDescriptorInfo.format = static_cast<android_pixel_format_t>(descriptorInfo.format);
+ bufferDescriptorInfo.consumerUsage = toConsumerUsage(descriptorInfo.usage);
+ bufferDescriptorInfo.producerUsage = toProducerUsage(descriptorInfo.usage);
- // TODO format? usage? width > stride?
- // need a gralloc1 extension to really validate
- (void)bufferFormat;
- (void)bufferProducerUsage;
- (void)bufferConsumerUsage;
-
- if (descriptorInfo.width > bufferWidth || descriptorInfo.height > bufferHeight ||
- descriptorInfo.layerCount > bufferLayerCount || stride > bufferStride) {
- return Error::BAD_VALUE;
+ int32_t error =
+ mDispatch.validateBufferSize(mDevice, bufferHandle, &bufferDescriptorInfo, stride);
+ if (error != GRALLOC1_ERROR_NONE) {
+ return toError(error);
}
return Error::NONE;
@@ -87,10 +59,15 @@
Error getTransportSize(const native_handle_t* bufferHandle, uint32_t* outNumFds,
uint32_t* outNumInts) override {
- // need a gralloc1 extension to get the transport size
- *outNumFds = bufferHandle->numFds;
- *outNumInts = bufferHandle->numInts;
- return Error::NONE;
+ int32_t error = mDispatch.getTransportSize(mDevice, bufferHandle, outNumFds, outNumInts);
+ return toError(error);
+ }
+
+ Error importBuffer(const native_handle_t* rawHandle,
+ native_handle_t** outBufferHandle) override {
+ int32_t error = mDispatch.importBuffer(
+ mDevice, rawHandle, const_cast<const native_handle_t**>(outBufferHandle));
+ return toError(error);
}
Error createDescriptor_2_1(const IMapper::BufferDescriptorInfo& descriptorInfo,
@@ -109,12 +86,9 @@
return false;
}
- if (!initDispatch(GRALLOC1_FUNCTION_GET_DIMENSIONS, &mDispatch.getDimensions) ||
- !initDispatch(GRALLOC1_FUNCTION_GET_LAYER_COUNT, &mDispatch.getLayerCount) ||
- !initDispatch(GRALLOC1_FUNCTION_GET_FORMAT, &mDispatch.getFormat) ||
- !initDispatch(GRALLOC1_FUNCTION_GET_PRODUCER_USAGE, &mDispatch.getProducerUsage) ||
- !initDispatch(GRALLOC1_FUNCTION_GET_CONSUMER_USAGE, &mDispatch.getConsumerUsage) ||
- !initDispatch(GRALLOC1_FUNCTION_GET_STRIDE, &mDispatch.getStride)) {
+ if (!initDispatch(GRALLOC1_FUNCTION_VALIDATE_BUFFER_SIZE, &mDispatch.validateBufferSize) ||
+ !initDispatch(GRALLOC1_FUNCTION_GET_TRANSPORT_SIZE, &mDispatch.getTransportSize) ||
+ !initDispatch(GRALLOC1_FUNCTION_IMPORT_BUFFER, &mDispatch.importBuffer)) {
return false;
}
@@ -122,14 +96,73 @@
}
struct {
- GRALLOC1_PFN_GET_DIMENSIONS getDimensions;
- GRALLOC1_PFN_GET_LAYER_COUNT getLayerCount;
- GRALLOC1_PFN_GET_FORMAT getFormat;
- GRALLOC1_PFN_GET_PRODUCER_USAGE getProducerUsage;
- GRALLOC1_PFN_GET_CONSUMER_USAGE getConsumerUsage;
- GRALLOC1_PFN_GET_STRIDE getStride;
+ GRALLOC1_PFN_VALIDATE_BUFFER_SIZE validateBufferSize;
+ GRALLOC1_PFN_GET_TRANSPORT_SIZE getTransportSize;
+ GRALLOC1_PFN_IMPORT_BUFFER importBuffer;
} mDispatch = {};
+ static uint64_t toProducerUsage(uint64_t usage) {
+ // this is potentially broken as we have no idea which private flags
+ // should be filtered out
+ uint64_t producerUsage = usage & ~static_cast<uint64_t>(BufferUsage::CPU_READ_MASK |
+ BufferUsage::CPU_WRITE_MASK |
+ BufferUsage::GPU_DATA_BUFFER);
+
+ switch (usage & BufferUsage::CPU_WRITE_MASK) {
+ case static_cast<uint64_t>(BufferUsage::CPU_WRITE_RARELY):
+ producerUsage |= GRALLOC1_PRODUCER_USAGE_CPU_WRITE;
+ break;
+ case static_cast<uint64_t>(BufferUsage::CPU_WRITE_OFTEN):
+ producerUsage |= GRALLOC1_PRODUCER_USAGE_CPU_WRITE_OFTEN;
+ break;
+ default:
+ break;
+ }
+
+ switch (usage & BufferUsage::CPU_READ_MASK) {
+ case static_cast<uint64_t>(BufferUsage::CPU_READ_RARELY):
+ producerUsage |= GRALLOC1_PRODUCER_USAGE_CPU_READ;
+ break;
+ case static_cast<uint64_t>(BufferUsage::CPU_READ_OFTEN):
+ producerUsage |= GRALLOC1_PRODUCER_USAGE_CPU_READ_OFTEN;
+ break;
+ default:
+ break;
+ }
+
+ // BufferUsage::GPU_DATA_BUFFER is always filtered out
+
+ return producerUsage;
+ }
+
+ static uint64_t toConsumerUsage(uint64_t usage) {
+ // this is potentially broken as we have no idea which private flags
+ // should be filtered out
+ uint64_t consumerUsage =
+ usage &
+ ~static_cast<uint64_t>(BufferUsage::CPU_READ_MASK | BufferUsage::CPU_WRITE_MASK |
+ BufferUsage::SENSOR_DIRECT_DATA | BufferUsage::GPU_DATA_BUFFER);
+
+ switch (usage & BufferUsage::CPU_READ_MASK) {
+ case static_cast<uint64_t>(BufferUsage::CPU_READ_RARELY):
+ consumerUsage |= GRALLOC1_CONSUMER_USAGE_CPU_READ;
+ break;
+ case static_cast<uint64_t>(BufferUsage::CPU_READ_OFTEN):
+ consumerUsage |= GRALLOC1_CONSUMER_USAGE_CPU_READ_OFTEN;
+ break;
+ default:
+ break;
+ }
+
+ // BufferUsage::SENSOR_DIRECT_DATA is always filtered out
+
+ if (usage & BufferUsage::GPU_DATA_BUFFER) {
+ consumerUsage |= GRALLOC1_CONSUMER_USAGE_GPU_DATA_BUFFER;
+ }
+
+ return consumerUsage;
+ }
+
private:
using BaseType2_0 = V2_0::passthrough::detail::Gralloc1HalImpl<Hal>;
using BaseType2_0::createDescriptor;
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 802f6cb..4efa13a 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -42,7 +42,8 @@
TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
TENSOR_INT32 = 4,
- /** A tensor of 8 bit integers that represent real numbers.
+ /**
+ * A tensor of 8 bit 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:
@@ -70,15 +71,17 @@
/**
* Adds two tensors, element-wise.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
- * is the sum of both input tensors, optionally modified by an activation function.
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the sum of both input tensors, optionally
+ * modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
- * The size of the output is the maximum size along each dimension of the input operands.
- * It starts with the trailing dimensions, and works its way forward.
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its
+ * way forward.
*
* Example:
*
@@ -86,7 +89,7 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -94,98 +97,119 @@
*
* Inputs:
* * 0: A tensor.
- * * 1: A tensor of the same type, and compatible dimensions as input0.
- * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 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 type as input0.
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
*/
ADD = 0,
/**
* Performs a 2-D average pooling operation.
*
- * The output dimensions are functions of the filter dimensions, stride, and padding.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
* sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
- * 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.
*
* Inputs (explicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * * 5: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 7: An INT32 value, specifying the filter width.
- * * 8: An INT32 value, specifying the filter height.
- * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
*
* Inputs (implicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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)}.
- * * 2: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the filter width.
- * * 5: An INT32 value, specifying the filter height.
- * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: 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 output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * * 0: The output 4-D tensor, of shape
+ [batches, out_height, out_width, depth].
*/
AVERAGE_POOL_2D = 1,
/**
* Concatenates the input tensors along the given dimension.
*
- * The input tensors must have identical type and the same dimensions except the
- * dimension along the concatenation axis.
+ * The input tensors must have identical {@link OperandType} and the same
+ * dimensions except the dimension along the concatenation axis.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* 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} type, all
- * input tensors must have the same scale and zeroPoint.
- * * n: An INT32 value, specifying the concatenation axis.
+ * * 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.
+ * * n: An {@link OperandType::INT32} scalar, specifying the
+ * concatenation axis.
*
* Outputs:
- * * 0: The output, a tensor of the same type as the input tensors.
- * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ * * 0: The output, a tensor of the same {@link OperandType} as the input
+ * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*/
CONCATENATION = 2,
/**
* Performs an 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 appropriate size.
+ * 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
+ * appropriate size.
*
- * The output dimensions are functions of the filter dimensions, stride, and padding.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
*
* The values in the output tensor are computed as:
*
@@ -196,7 +220,7 @@
* bias[channel]
* )
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -205,63 +229,77 @@
* 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.
- * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
- * specifying the filter.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 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} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
- * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * * 7: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 8: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * 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.
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
*
* Inputs (implicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * * 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} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 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.
- * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * * 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)}.
- * * 4: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 5: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: 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 output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
- * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
- * condition must be satisfied: output_scale > input_scale * filter_scale.
+ * * 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.
*/
CONV_2D = 3,
/**
* Performs a depthwise 2-D convolution operation.
*
- * Given an input tensor of shape [batches, height, width, depth_in] and a filter
- * tensor of shape [1, filter_height, filter_width, depth_out] containing
- * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different
- * filter to each input channel (expanding from 1 channel to channel_multiplier channels
- * for each), then concatenates the results together.
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [1, filter_height, filter_width, depth_out]
+ * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
+ * applies a different filter to each input channel (expanding from 1
+ * channel to channel_multiplier channels for each), then concatenates the
+ * results together.
*
* The output has depth_out = depth_in * depth_multiplier channels.
- * The output dimensions are functions of the filter dimensions, stride, and padding.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
*
* The values in the output tensor are computed as:
*
@@ -271,7 +309,7 @@
* filter[1, di, dj, k * channel_multiplier + q]
* )
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -280,82 +318,97 @@
* 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.
+ * * 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.
- * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * * 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.
- * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * * 7: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 8: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 9: An INT32 value, specifying the depthwise multiplier.
- * * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
*
* Inputs (implicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 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.
- * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * * 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.
- * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * * 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)}.
- * * 4: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 5: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 6: An INT32 value, specifying the depthwise multiplier.
- * * 7: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 7: 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 output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
- * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
- * condition must be satisfied: output_scale > input_scale * filter_scale.
+ * * 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.
*/
DEPTHWISE_CONV_2D = 4,
/**
* Rearranges data from depth into blocks of spatial data.
*
- * More specifically, this op outputs a copy of the input tensor where values from
- * the depth dimension are moved in spatial blocks to the height and width dimensions.
- * The value block_size indicates the input block size and how the data is moved.
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the depth dimension are moved in spatial blocks to the height
+ * and width dimensions. The value block_size indicates the input block size
+ * and how the data is moved.
*
- * Chunks of data of size block_size * block_size from depth are rearranged into
- * non-overlapping blocks of size block_size x block_size.
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
*
- * The width of the output tensor is input_depth * block_size, whereas the height is
- * input_height * block_size.
- * The depth of the input tensor must be divisible by block_size * block_size
+ * The width of the output tensor is input_depth * block_size, whereas the
+ * height is input_height * block_size. The depth of the input tensor must
+ * be divisible by block_size * block_size
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
- * block_size * block_size must be a divisor of the input depth.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size * block_size must be a divisor
+ * of the input depth.
*
* Outputs:
- * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
- * depth/(block_size*block_size)].
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+ * width*block_size, depth/(block_size*block_size)].
*/
DEPTH_TO_SPACE = 5,
@@ -366,16 +419,16 @@
*
* output = (input - zeroPoint) * scale.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
*
* Outputs:
- * * 0: The output tensor of same shape as input0, but with type
+ * * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_FLOAT32}.
*/
DEQUANTIZE = 6,
@@ -401,7 +454,7 @@
* and an error must be reported.
*
* Inputs:
- * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+ * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
* The values are indices into the first dimension of Values.
* * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
* extracted.
@@ -416,7 +469,7 @@
/**
* Computes element-wise floor() on the input tensor.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -425,45 +478,51 @@
* * 0: A tensor.
*
* Outputs:
- * * 0: The output tensor, of the same type and dimensions as the input tensor.
+ * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+ * the input tensor.
*/
FLOOR = 8,
/**
- * Denotes a fully (densely) connected layer, which connects all elements in the input
- * tensor with each element in the output tensor.
+ * Denotes a fully (densely) connected layer, which connects all elements
+ * in the input tensor with each element in the output tensor.
*
* This layer implements the operation:
*
* outputs = activation(inputs * weights’ + bias)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
- * * 0: A tensor of at least rank 2, specifying the input. If rank is greater than 2,
- * then it gets flattened to a 2-D Tensor. The (flattened) 2-D Tensor is reshaped
- * (if necessary) to [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".
- * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
- * "num_units" corresponds to the number of output nodes.
- * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * * 0: A tensor of at least rank 2, specifying the input. If rank is
+ * greater than 2, then it gets flattened to a 2-D Tensor. The
+ * (flattened) 2-D Tensor is reshaped (if necessary) to
+ * [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".
+ * * 1: A 2-D tensor, specifying the weights, of shape
+ * [num_units, input_size], where "num_units" corresponds to the number
+ * of output nodes.
+ * * 2: A 1-D tensor, of shape [num_units], 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.
- * * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 3: 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 output tensor, of shape [batch_size, num_units].
- * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
- * condition must be satisfied: output_scale > input_scale * filter_scale.
+ * * 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,
@@ -495,19 +554,22 @@
* must be concatenated.
*
* Inputs:
- * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ k ].
- * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ n ];
- * Keys and Values pair represent a map, i.e., the ith element
- * in Keys (Keys[i]) is the key to select the ith sub-tensor
- * in Values (Values[i]), where 0 <= i <= n-1.
- * Keys tensor *MUST* be sorted in ascending order.
- * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.
+ * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
+ * shape [ k ].
+ * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [ n ]; Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
+ * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
+ * ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
+ * must be n.
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
* * 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.
+ * 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.
*/
HASHTABLE_LOOKUP = 10,
@@ -521,32 +583,37 @@
* 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.
+ * For input tensor with more dimensions, independently normalizes each 1-D
+ * slice along dimension dim.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
+ * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples,
+ * Height, Width, and Channels).
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth].
*
* Outputs:
- * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
*/
L2_NORMALIZATION = 11,
/**
* Performs an 2-D L2 pooling operation.
*
- * The output dimensions are functions of the filter dimensions, stride, and padding.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
- * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
+ * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) /
+ * sum(1))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
@@ -554,62 +621,82 @@
* 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.
- * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * * 5: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 7: An INT32 value, specifying the filter width.
- * * 8: An INT32 value, specifying the filter height.
- * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
*
* Inputs (implicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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)}.
- * * 2: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the filter width.
- * * 5: An INT32 value, specifying the filter height.
- * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: 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 output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
*/
L2_POOL_2D = 12,
/**
* Applies Local Response Normalization along the depth dimension.
*
- * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
- * dimension), and each vector is normalized independently. Within a given vector,
- * each component is divided by the weighted, squared sum of inputs within depth_radius.
+ * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+ * last dimension), and each vector is normalized independently. Within a
+ * given vector, each component is divided by the weighted, squared sum of
+ * inputs within depth_radius.
*
* The output is calculated using this formula:
*
- * sqr_sum[a, b, c, d] =
- * sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
+ * sqr_sum[a, b, c, d] = sum(
+ * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
* output = input / pow((bias + alpha * sqr_sum), beta)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the radius of the normalization window.
- * * 2: A FLOAT32 value, specifying the bias, must not be zero.
- * * 3: A FLOAT32 value, specifying the scale factor, alpha.
- * * 4: A FLOAT32 value, specifying the exponent, beta.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * 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.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
@@ -623,7 +710,7 @@
*
* output = 1 / (1 + exp(-input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -634,7 +721,7 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- * For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
LOGISTIC = 14,
@@ -650,18 +737,19 @@
*
* * 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.
+ * 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).
* Computed bit vector is considered to be sparse.
- * Each output element is an int32 made up of multiple bits computed from
- * hash functions.
+ * Each output element is an int32 made up of multiple bits
+ * computed from hash functions.
*
* 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.
+ * 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:
@@ -681,9 +769,12 @@
* \f{eqnarray*}{
* i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
* f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
- * C_t =& clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell})& \\
- * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o)& \\
- * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) & if\ there\ is\ a\ projection; \\
+ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot
+ * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
+ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
+ * & & \\
+ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
+ * & if\ there\ is\ a\ projection; \\
* h_t =& & \\
* & o_t \odot g(C_t) & otherwise. \\
* \f}
@@ -695,7 +786,8 @@
* * \f$o_t\f$ is the output,
* * \f$h_t\f$ is the output state,
* * \f$\sigma\f$ is the logistic sigmoid function,
- * * \f$g\f$ is the cell input and cell output activation function, usually \f$tahn\f$,
+ * * \f$g\f$ is the cell input and cell output activation function, usually
+ * \f$tahn\f$,
* * \f$W_{xi}\f$ is the input-to-input weight matrix,
* * \f$W_{hi}\f$ is the recurrent to input weight matrix,
* * \f$W_{ci}\f$ is the cell-to-input weight matrix,
@@ -715,29 +807,32 @@
* * \f$b_{proj}\f$ is the projection bias,
* * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
* * \f$t_{proj}\f$ is the threshold for clipping the projected output.
- * * \f$\odot\f$ is the <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+ * * \f$\odot\f$ is the
+ * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
* Hadamard product</a> that takes two matrices and produces another
* matrix, each element of which is the product of the corresponding
* elements of the input matrices.
*
* The operation has the following independently optional inputs:
- * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$),
- * cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values,
- * or none of them have values (i.e., all set to null). If they have no
- * values, coupling of input and forget gates (CIFG) is used, in which case
- * the input gate (\f$i_t\f$) is calculated using the following equation instead.
+ * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
+ * (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
+ * bias (\f$b_i\f$) either all have values, or none of them have values
+ * (i.e., all set to null). If they have no values, coupling of input and
+ * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
+ * is calculated using the following equation instead.
* \f{eqnarray*}{
* i_t = 1 - f_t
* \f}
- * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output
- * weights (\f$W_{co}\f$) either both have values or neither of them have values.
+ * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
+ * (\f$W_{co}\f$) either both have values or neither of them have values.
* If they have values, the peephole optimization is used. Additionally,
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
- * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection
- * layer, and should otherwise have no value.
- * * 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.
+ * * The projection weights (\f$W_{proj}\f$) is required only for the
+ * recurrent projection layer, and should otherwise have no value.
+ * * 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.
*
* References:
*
@@ -749,8 +844,8 @@
* The peephole implementation and projection layer is based on:
* https://research.google.com/pubs/archive/43905.pdf
* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
- * recurrent neural network architectures for large scale acoustic modeling."
- * INTERSPEECH, 2014.
+ * recurrent neural network architectures for large scale acoustic
+ * modeling." INTERSPEECH, 2014.
* (However, the concept of peephole optimization was introduced in work
* prior to this paper.)
*
@@ -758,56 +853,74 @@
* http://arxiv.org/pdf/1503.04069.pdf
* Greff et al. "LSTM: A Search Space Odyssey"
*
- * Supported tensor types (type T):
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: The input (\f$x_t\f$).
- * A 2-D tensor of type T, 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 {@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.
* * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
- * A 2-D tensor of type T, of shape [num_units, input_size], where
- * “num_units” corresponds to the number of cell units.
+ * 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.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
- * A 2-D tensor of type T, 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 {@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.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 14:The cell bias (\f$b_c\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 15:The output gate bias (\f$b_o\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
- * A 2-D tensor of type T, of shape [output_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
- * A 1-D tensor of type T, of shape [output_size].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
- * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
@@ -817,38 +930,43 @@
* <li>4: Tanh;
* <li>6: Sigmoid.
* </ul>
- * * 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.
- * * 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
+ * * 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.
+ * * 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.
*
* Outputs:
* * 0: The scratch buffer.
- * A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
- * CIFG, or [batch_size, num_units * 3] without CIFG.
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units * 4] with CIFG, or
+ * [batch_size, num_units * 3] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
- * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
- * A 2-D tensor of type T, of shape [batch_size, output_size]. This is
- * effectively the same as the current “output state (out)” value.
+ * 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.
*/
LSTM = 16,
/**
* Performs an 2-D max pooling operation.
*
- * The output dimensions are functions of the filter dimensions, stride, and padding.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
* max_{i, j} (input[batch, row + i, col + j, channel])
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -857,52 +975,68 @@
* 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.
- * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * * 5: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 7: An INT32 value, specifying the filter width.
- * * 8: An INT32 value, specifying the filter height.
- * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
*
* Inputs (implicit padding):
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 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)}.
- * * 2: An INT32 value, specifying the stride when walking through input
- * in the ‘width’ dimension.
- * * 3: An INT32 value, specifying the stride when walking through input
- * in the ‘height’ dimension.
- * * 4: An INT32 value, specifying the filter width.
- * * 5: An INT32 value, specifying the filter height.
- * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: 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 output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
*/
MAX_POOL_2D = 17,
/**
* Multiplies two tensors, element-wise.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
- * is the product of both input tensors, optionally modified by an activation function.
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the product of both input tensors, optionally
+ * modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
- * The size of the resulting output is the maximum size along each dimension of the
- * input operands. It starts with the trailing dimensions, and works its way forward.
+ * The size of the resulting output is the maximum size along each dimension
+ * of the input operands. It starts with the trailing dimensions, and works
+ * its way forward.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -910,14 +1044,17 @@
*
* Inputs:
* * 0: A tensor.
- * * 1: A tensor of the same type, and compatible dimensions as input0.
- * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 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 product, a tensor of the same type as input0.
- * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
- * condition must be satisfied: output_scale > input1_scale * input2_scale.
+ * * 0: The product, a tensor of the same {@link OperandType} as input0.
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
+ * output_scale > input1_scale * input2_scale.
*/
MUL = 18,
@@ -928,7 +1065,7 @@
*
* output = max(0, input)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -949,7 +1086,7 @@
*
* output = min(1.f, max(-1.f, input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -970,7 +1107,7 @@
*
* output = min(6, max(0, input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -987,10 +1124,10 @@
/**
* Reshapes a tensor.
*
- * Given tensor, this operation returns a tensor that has the same values as tensor,
- * but with a newly specified shape.
+ * Given tensor, this operation returns a tensor that has the same values as
+ * tensor, but with a newly specified shape.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -998,9 +1135,9 @@
*
* Inputs:
* * 0: A tensor, specifying the tensor to be reshaped.
- * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
- * of the output tensor. The number of elements implied by shape must be the same
- * as the number of elements in the input tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
+ * shape of the output tensor. The number of elements implied by shape
+ * must be the same as the number of elements in the input tensor.
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
@@ -1010,22 +1147,26 @@
/**
* Resizes images to given size using the bilinear interpretation.
*
- * Resized images must be distorted if their output aspect ratio is not the same as
- * input aspect ratio. The corner pixels of output may not be the same as
- * corner pixels of input.
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * * 1: An INT32 value, specifying the output height of the output tensor.
- * * 2: An INT32 value, specifying the output width of the output tensor.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
*
* Outputs:
- * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
*/
RESIZE_BILINEAR = 23,
@@ -1033,7 +1174,8 @@
* A basic recurrent neural network layer.
*
* This layer implements the operation:
- * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
+ * outputs = state = activation(inputs * input_weights +
+ * state * recurrent_weights + bias)
*
* Where:
* * “input_weights” is a weight matrix that multiplies the inputs;
@@ -1044,42 +1186,49 @@
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
- * Supported tensor types (Type T):
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
- * A 2-D tensor of type T, 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 {@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.
* * 1: weights.
- * A 2-D tensor of type T, of shape [num_units, input_size], where
- * “num_units” corresponds to the number of units.
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, input_size], where “num_units” corresponds to the
+ * number of units.
* * 2: recurrent_weights.
- * A 2-D tensor of type T, of shape [num_units, num_units], with columns
- * corresponding to the weights from each unit.
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, num_units], with columns corresponding to the weights
+ * from each unit.
* * 3: bias.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 4: hidden state (in).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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 linear
- * activation.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
*
* Outputs:
* * 0: hidden state (out).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
*
* * 1: output.
- * A 2-D tensor of type T, of shape [batch_size, num_units]. This is
- * effectively the same as the current state value.
+ * 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.
*/
RNN = 24,
/**
- * Computes the softmax activation on the input tensor element-wise, per batch, by
- * normalizing the input vector so the maximum coefficient is zero.
+ * Computes the softmax activation on the input tensor element-wise, per
+ * batch, by normalizing the input vector so the maximum coefficient is
+ * zero.
*
* The output is calculated using this formula:
*
@@ -1087,7 +1236,7 @@
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1095,11 +1244,12 @@
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
- * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
+ * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
+ * scaling factor for the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- * For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
SOFTMAX = 25,
@@ -1107,30 +1257,33 @@
/**
* Rearranges blocks of spatial data, into depth.
*
- * More specifically, this op outputs a copy of the input tensor where values from
- * the height and width dimensions are moved to the depth dimension.
- * The value block_size indicates the input block size and how the data is moved.
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the height and width dimensions are moved to the depth
+ * dimension. The value block_size indicates the input block size and how
+ * the data is moved.
*
- * Chunks of data of size block_size * block_size from depth are rearranged into
- * non-overlapping blocks of size block_size x block_size.
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
*
* The depth of the output tensor is input_depth * block_size * block_size.
* The input tensor's height and width must be divisible by block_size.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
- * block_size must be a divisor of both the input height and width.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size must be a divisor of both the
+ * input height and width.
*
* Outputs:
- * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
- * depth*block_size*block_size].
+ * * 0: The output 4-D tensor, of shape [batch, height/block_size,
+ * width/block_size, depth*block_size*block_size].
*/
SPACE_TO_DEPTH = 26,
@@ -1147,21 +1300,22 @@
* INTERSPEECH, 2015.
*
* It processes the incoming input using a 2-stage filtering mechanism:
- * * stage 1 performs filtering on the "features" dimension, whose outputs get
- * pushed into a memory of fixed-size memory_size.
+ * * stage 1 performs filtering on the "features" dimension, whose outputs
+ * get pushed into a memory of fixed-size memory_size.
* * stage 2 performs filtering on the "time" dimension of the memory_size
* memoized outputs of stage 1.
*
* Specifically, for rank 1, this layer implements the operation:
*
- * memory = push(conv1d(inputs, weights_feature, feature_dim, "PADDING_VALID"));
+ * memory = push(conv1d(inputs, weights_feature, feature_dim,
+ * "PADDING_VALID"));
* outputs = activation(memory * weights_time + bias);
*
* Where:
* * “weights_feature” is a weights matrix that processes the inputs (by
- * convolving the input with every “feature filter”), and whose outputs get
- * pushed, stacked in order, into the fixed-size “memory” (the oldest entry
- * gets dropped);
+ * convolving the input with every “feature filter”), and whose outputs
+ * get pushed, stacked in order, into the fixed-size “memory” (the oldest
+ * entry gets dropped);
* * “weights_time” is a weights matrix that processes the “memory” (by a
* batched matrix multiplication on the num_units);
* * “bias” is an optional bias vector (added to each output vector in the
@@ -1172,35 +1326,42 @@
* Each rank adds a dimension to the weights matrices by means of stacking
* the filters.
*
- * Supported tensor types (type T):
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
- * A 2-D tensor of type T, 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 {@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.
* * 1: weights_feature.
- * A 2-D tensor of type T, of shape [num_units, input_size], where
- * “num_units” corresponds to the number of units.
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [num_units, input_size], where “num_units” corresponds to the
+ * number of units.
* * 2: weights_time.
- * A 2-D tensor of type T, of shape [num_units, memory_size], where
- * “memory_size” corresponds to the fixed-size of the memory.
+ * 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.
* * 3: bias.
- * An optional 1-D tensor of type T, of shape [num_units].
+ * An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
+ * of shape [num_units].
* * 4: state (in).
- * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
- * An optional {@link FusedActivationFunc} value indicating the activation function.
- * If “NONE” is specified then it results in a linear activation.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
*
* Outputs:
* * 0: state (out).
- * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
*/
SVDF = 27,
@@ -1211,7 +1372,7 @@
*
* output = tanh(input)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4.
@@ -1227,7 +1388,8 @@
/**
* OEM specific operation.
*
- * This operation is OEM specific. It should only be used for OEM applications.
+ * This operation is OEM specific. It should only be used for OEM
+ * applications.
*/
OEM_OPERATION = 10000,
};
@@ -1274,8 +1436,8 @@
CONSTANT_REFERENCE,
/**
- * The operand does not have a value. This is valid only for optional arguments
- * of operations.
+ * The operand does not have a value. This is valid only for optional
+ * arguments of operations.
*/
NO_VALUE,
};
@@ -1391,7 +1553,8 @@
/**
* Where to find the data for this operand.
- * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
+ * 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.
@@ -1485,9 +1648,9 @@
*/
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.
+ * 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;
@@ -1499,10 +1662,10 @@
/**
* 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, 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.
*/
vec<uint32_t> dimensions;
};
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 3fa47a6..e4c656d 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -29,87 +29,95 @@
/**
* BatchToSpace for N-dimensional tensors.
*
- * This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions of shape
- * block_shape + [batch], interleaves these blocks back into the grid defined by the
- * spatial dimensions [1, ..., M], to obtain a result with the same rank as the input.
+ * This operation reshapes the batch dimension (dimension 0) into M + 1
+ * dimensions of shape block_shape + [batch], interleaves these blocks back
+ * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
+ * result with the same rank as the input.
*
* This is the reverse of SpaceToBatch.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
- * 0: An n-D tensor, specifying the tensor to be reshaped
- * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
- * input tensor. All values must be >= 1.
+ * * 0: An n-D tensor, specifying the tensor to be reshaped
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
BATCH_TO_SPACE_ND = 29,
/**
* Element-wise division of two tensors.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
- * is the result of dividing the first input tensor by the second, optionally
- * modified by an activation function.
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of dividing the first input tensor
+ * by the second, optionally modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
- * The size of the output is the maximum size along each dimension of the input operands.
- * It starts with the trailing dimensions, and works its way forward.
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the first input.
- * 1: A tensor of the same type, and compatible dimensions as input0.
- * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 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: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
DIV = 30,
/**
* Computes the mean of elements across dimensions of a tensor.
*
- * Reduces the input tensor along the given dimensions to reduce. Unless keep_dims
- * is true, the rank of the tensor is reduced by 1 for each entry in axis.
- * If keep_dims is true, the reduced dimensions are retained with length 1.
+ * Reduces the input tensor along the given dimensions to reduce. Unless
+ * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
+ * in axis. If keep_dims is true, the reduced dimensions are retained with
+ * length 1.
*
- * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
- * a single element is returned.
+ * If dimensions to reduce have no entries, all dimensions are reduced, and
+ * a tensor with a single element is returned.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor, specifying the input.
- * 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default),
- * reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
- * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
+ * * 0: A tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. If None (the default), reduces all dimensions. Must be in
+ * the range [-rank(input_tensor), rank(input_tensor)).
+ * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+ * retains reduced dimensions with length 1.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
MEAN = 31,
@@ -118,170 +126,193 @@
*
* This operation pads a tensor according to the specified paddings.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the tensor to be padded.
- * 1: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial dimension of the
- * input tensor. The shape of the tensor must be {rank(input0), 2}.
- * padding[i, 0] specifies the number of element to be padded in the front of dimension i.
- * padding[i, 1] specifies the number of element to be padded after the end of dimension i.
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of element to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the
+ * end of dimension i.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
PAD = 32,
/**
* SpaceToBatch for N-Dimensional tensors.
*
- * This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks
- * of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that
- * in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid,
- * and the batch dimension combines both the position within a spatial block and the original
- * batch position. Prior to division into blocks, the spatial dimensions of the input are
- * optionally zero padded according to paddings.
+ * This operation divides "spatial" dimensions [1, ..., M] of the input into
+ * a grid of blocks of shape block_shape, and interleaves these blocks with
+ * the "batch" dimension (0) such that in the output, the spatial dimensions
+ * [1, ..., M] correspond to the position within the grid, and the batch
+ * dimension combines both the position within a spatial block and the
+ * original batch position. Prior to division into blocks, the spatial
+ * dimensions of the input are optionally zero padded according to paddings.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
- * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
- * input tensor. All values must be >= 1.
- * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
- * input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
- * padding[i, 0] specifies the number of element to be padded in the front of dimension i.
- * padding[i, 1] specifies the number of element to be padded after the end of dimension i.
+ * * 0: An n-D tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. All values must be
+ * >= 0. The shape of the tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of element to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the
+ * end of dimension i.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
SPACE_TO_BATCH_ND = 33,
/**
* Removes dimensions of size 1 from the shape of a tensor.
*
- * Given a tensor input, this operation returns a tensor of the same type with all
- * dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
- * you can remove specific size 1 dimensions by specifying the axes (input1).
+ * Given a tensor input, this operation returns a tensor of the same
+ * {@link OperandType} with all dimensions of size 1 removed. If you don't
+ * want to remove all size 1 dimensions, you can remove specific size 1
+ * dimensions by specifying the axes (input1).
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, the tensor to be squeezed.
- * 1: An optional 1-D tensor of type TENSOR_INT32. The dimensions to squeeze. If specified
- * only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
- * The dimension index starts at 0. An error must be reported if squeezing a dimension that
- * is not 1.
+ * * 0: An n-D tensor, the tensor to be squeezed.
+ * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * dimensions to squeeze. If specified only squeezes the dimensions
+ * listed. Otherwise, squeezes all dimensions. The dimension index
+ * starts at 0. An error must be reported if squeezing a dimension that
+ * is not 1.
*
* Outputs:
- * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
- * dimensions of size 1 removed.
+ * * 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.
*/
SQUEEZE = 34,
/**
* Extracts a strided slice of a tensor.
*
- * Roughly speaking, this op extracts a slice of size (end - begin) / stride from the given
- * input tensor. Starting at the location specified by begin the slice continues by adding
- * stride to the index until all dimensions are not less than end. Note that a stride can
- * be negative, which causes a reverse slice.
+ * Roughly speaking, this op extracts a slice of size (end - begin) / stride
+ * from the given input tensor. Starting at the location specified by begin
+ * the slice continues by adding stride to the index until all dimensions
+ * are not less than end. Note that a stride can be negative, which causes a
+ * reverse slice.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the tensor to be sliced.
- * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
- * tensor to be sliced. The length must be of rank(input0).
- * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
- * tensor to be sliced. The length must be of rank(input0).
- * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
- * tensor to be sliced. The length must be of rank(input0).
- * 4: An INT32 value, begin_mask. If the ith bit of begin_mask is set, begin[i] is ignored
- * and the fullest possible range in that dimension is used instead.
- * 5: An INT32 value, end_mask. If the ith bit of end_mask is set, end[i] is ignored and
- * the fullest possible range in that dimension is used instead.
- * 6: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
- * set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
- * size 1 starting from begin[i] in the dimension must be preserved.
+ * * 0: An n-D tensor, specifying the tensor to be sliced.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the starts of
+ * the dimensions of the input tensor to be sliced. The length must be
+ * of rank(input0).
+ * * 2: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the ends of
+ * the dimensions of the input tensor to be sliced. The length must be
+ * of rank(input0).
+ * * 3: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the strides of
+ * the dimensions of the input tensor to be sliced. The length must be
+ * of rank(input0).
+ * * 4: An {@link OperandType::INT32} scalar, begin_mask. If the ith bit
+ * of begin_mask is set, begin[i] is ignored and the fullest possible
+ * range in that dimension is used instead.
+ * * 5: An {@link OperandType::INT32} scalar, end_mask. If the ith bit of
+ * end_mask is set, end[i] is ignored and the fullest possible range in
+ * that dimension is used instead.
+ * * 6: An {@link OperandType::INT32} scalar, shrink_axis_mask. An int32
+ * mask. If the ith bit of shrink_axis_mask is set, it implies that the
+ * ith specification shrinks the dimensionality by 1. A slice of size 1
+ * starting from begin[i] in the dimension must be preserved.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
STRIDED_SLICE = 35,
/**
* Element-wise subtraction of two tensors.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
- * is the result of subtracting the second input tensor from the first one, optionally
- * modified by an activation function.
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of subtracting the second input
+ * tensor from the first one, optionally modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
- * The size of the output is the maximum size along each dimension of the input operands.
- * It starts with the trailing dimensions, and works its way forward.
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the first input.
- * 1: A tensor of the same type, and compatible dimensions as input0.
- * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 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: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
SUB = 36,
/**
- * Transposes the input tensor, permuting the dimensions according to the perm tensor.
+ * Transposes the input tensor, permuting the dimensions according to the
+ * perm tensor.
*
- * The returned tensor's dimension i corresponds to the input dimension perm[i].
- * If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor.
- * Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
+ * The returned tensor's dimension i corresponds to the input dimension
+ * perm[i]. If perm is not given, it is set to (n-1...0), where n is the
+ * rank of the input tensor. Hence by default, this operation performs a
+ * regular matrix transpose on 2-D input Tensors.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the tensor to be transposed.
- * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
- * input tensor.
+ * * 0: An n-D tensor, specifying the tensor to be transposed.
+ * * 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 type as input0.
+ * * 0: A tensor of the same {@link OperandType} as input0.
*/
TRANSPOSE = 37,
};