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android / platform / packages / modules / NeuralNetworks / 257ee7a44c8db28a50a37a28e37faad85e296e8d / . / runtime / include / NeuralNetworks.h
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/*
* 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.
*/
/**
* @addtogroup NeuralNetworks
* @{
*/
/**
* @file NeuralNetworks.h
*/
#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/******************************************************************
*
* IMPORTANT NOTICE:
*
* This file is part of Android's set of stable system headers
* exposed by the Android NDK (Native Development Kit).
*
* Third-party source AND binary code relies on the definitions
* here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES.
*
* - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES)
* - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS
* - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY
* - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES
*/
#include <android/hardware_buffer.h>
#include <stddef.h>
#include <stdint.h>
#include <sys/cdefs.h>
__BEGIN_DECLS
/**
* Operand types.
*
* The type of operands that can be added to a model.
*
* Although we define many types, most operators accept just a few
* types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32},
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* and {@link ANEURALNETWORKS_INT32}.
*
* Available since API level 27.
*/
typedef enum {
/** A 32 bit floating point scalar value. */
ANEURALNETWORKS_FLOAT32 = 0,
/** A signed 32 bit integer scalar value. */
ANEURALNETWORKS_INT32 = 1,
/** An unsigned 32 bit integer scalar value. */
ANEURALNETWORKS_UINT32 = 2,
/** A tensor of 32 bit floating point values. */
ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
ANEURALNETWORKS_TENSOR_INT32 = 4,
/**
* A tensor of 8 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that be used to convert the
* 8 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit floating point value greater than zero.
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
*/
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
#if __ANDROID_API__ >= __ANDROID_API_Q__
/**
* An 8 bit boolean scalar value.
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
*
* Available since API level 29.
*/
ANEURALNETWORKS_BOOL = 6,
/**
* A tensor of 16 bit signed integers that represent real numbers.
*
* Attached to this tensor is a number representing real value scale that is
* used to convert the 16 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater then zero.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_QUANT16_SYMM = 7,
/**
* A tensor of IEEE 754 16 bit floating point values.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_FLOAT16 = 8,
/**
* A tensor of 8 bit boolean values.
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_BOOL8 = 9,
/**
* An IEEE 754 16 bit floating point scalar value.
*
* Available since API level 29.
*/
ANEURALNETWORKS_FLOAT16 = 10,
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
* This tensor is associated with additional fields that can
* be used to convert the 8 bit signed integer to the real value and vice versa.
* These fields are:
* - channelDim: a 32 bit unsigned integer indicating channel dimension.
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
*
* {@link ANeuralNetworksModel_setOperandSymmPerChannelQuantParams} must be used
* to set the parameters for an Operand of this type.
*
* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
* The formula is:
* realValue[..., C, ...] =
* integerValue[..., C, ...] * scales[C]
* where C is an index in the Channel dimension.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
/**
* A tensor of 16 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 16 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit floating point value greater than zero.
* - zeroPoint: a 32 bit integer, in range [0, 65535].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_QUANT16_ASYMM = 12,
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
* Attached to this tensor is a number representing real value scale that is
* used to convert the 8 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater then zero.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TENSOR_QUANT8_SYMM = 13,
#endif // __ANDROID_API__ >= __ANDROID_API_Q__
} OperandCode;
/**
* Operation types.
*
* The type of operations that can be added to a model.
*
* Available since API level 27.
*/
typedef enum {
// Operations below are available since API level 27.
/**
* Adds two tensors, element-wise.
*
* Takes two input tensors of identical {@link OperandCode} 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.
*
* Example:
*
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
* as input0.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandCode} as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_ADD = 0,
/**
* Performs a 2-D average pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, channel] =
* sum_{di, dj}(
* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
* ) / sum(1)
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
*/
ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
/**
* Concatenates the input tensors along the given dimension.
*
* The input tensors must have identical {@link OperandCode} and the same
* dimensions except the dimension along the concatenation axis.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API
* level 29, see the input section)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm].
* Before API level 29, all input tensors of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* must have the same scale and zeroPoint as the output tensor.
* * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandCode} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*
* Available since API level 27.
*/
ANEURALNETWORKS_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 output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, channel] =
* sum_{di, dj, k} (
* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
* filter[channel, di, dj, k]
* ) + bias[channel]
*
* Supported tensor {@link OperandCode} configurations:
* * 32 bit Floating point :
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Available since API level 29:
* * Quantized with symetric per channel quantization for the filter:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* * 16 bit Floating point:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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. For tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
* dimension (extraParams.channelQuant.channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
* * 11: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 12 (dilation factor for height) must be specified as well.
* Available since API level 29.
* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 11 (dilation factor for width) must be specified as well.
* Available since API level 29.
*
* 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. For tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
* dimension (extraParams.channelQuant.channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
* * 8: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 9 (dilation factor for height) must be specified as well.
* Available since API level 29.
* * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 8 (dilation factor for width) must be specified as well.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale (for
* filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
* this condition must be true for all filter scales).
*
* Available since API level 27.
*/
ANEURALNETWORKS_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.
*
* The output has depth_out = depth_in * depth_multiplier channels.
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, k * channel_multiplier + q] =
* sum_{di, dj} (
* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
* filter[1, di, dj, k * channel_multiplier + q]
* ) + bias[k * channel_multiplier + q]
*
* Supported tensor {@link OperandCode} configurations:
* * 32 bit Floating point :
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Available since API level 29:
* * Quantized with symetric per channel quantization for the filter:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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 [1, filter_height, filter_width, depth_out],
* specifying the filter. For tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
* dimension (extraParams.channelQuant.channelDim) must be set to 3.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise
* multiplier.
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 11: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 13 (dilation factor for height) must be specified as well.
* Available since API level 29.
* * 13: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 12 (dilation factor for width) must be specified as well.
* Available since API level 29.
*
* 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 [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise
* multiplier.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 8: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
* * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 10 (dilation factor for height) must be specified as well.
* Available since API level 29.
* * 10: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 9 (dilation factor for width) must be specified as well.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale (for
* filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
* this condition must be true for all filter scales).
*
* Available since API level 27.
*/
ANEURALNETWORKS_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.
*
* 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
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size.
* block_size must be >=1 and block_size * block_size must be a divisor
* of the input depth.
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
*
* Available since API level 27.
*/
ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
/**
* Dequantizes the input tensor.
*
* The formula is:
*
* output = (input - zeroPoint) * scale.
*
* Supported input tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29)
*
* Supported output tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_DEQUANTIZE = 6,
/**
* Looks up sub-tensors in the input tensor.
*
* This operator takes for input a tensor of values (Values) and
* a one-dimensional tensor of selection indices (Lookups).
* The output tensor is the concatenation of sub-tensors of Values as
* selected by Lookups.
*
* Think of Values as being sliced along its first dimension:
* The entries in Lookups select which slices are concatenated together
* to create the output tensor.
*
* For example, if Values has shape of [40, 200, 300] and
* Lookups has shape of [3], all three values found in Lookups are
* expected to be between 0 and 39. The resulting tensor must
* have shape of [3, 200, 300].
*
* If a value in Lookups is out of bounds, the operation must fail
* and an error must be reported.
*
* Supported value tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported value tensor rank: from 2
*
* Inputs:
* * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_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.
*
* Output:
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
*
* Available since API level 27.
*/
ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
/**
* Computes element-wise floor() on the input tensor.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor, of the same {@link OperandCode} and dimensions as
* the input tensor.
*
* Available since API level 27.
*/
ANEURALNETWORKS_FLOOR = 8,
/**
* 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 {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied:
* output_scale > input_scale * filter_scale.
*
* Available since API level 27.
*/
ANEURALNETWORKS_FULLY_CONNECTED = 9,
/**
* Looks up sub-tensors in the input tensor using a key-value map.
*
* This operator takes for input a tensor of values (Values),
* a one-dimensional tensor of selection values (Lookups) and
* a one-dimensional tensor that maps these values to Values
* indexes. The output tensor is the concatenation of sub-tensors of
* Values as selected by Lookups via Keys.
*
* Think of Values as being sliced along its outer-most dimension.
* The output is a concatenation of selected slices, with one slice
* for each entry of Lookups. The slice selected is the one at the
* same index as the Maps entry that matches the value in Lookups.
*
* For a hit, the corresponding sub-tensor of Values is included
* in the Output tensor. For a miss, the corresponding sub-tensor in
* Output must have zero values.
*
* For example, if Values has shape of [40, 200, 300],
* Keys should have a shape of [40]. If Lookups tensor has shape
* of [3], three slices are being concatenated, so the resulting tensor
* must have the shape of [3, 200, 300]. If the first entry in Lookups
* has the value 123456, that value must be located in Keys tensor.
* If the sixth entry of Keys contains 123456, the sixth slice of Values
* must be selected. If no entry in Keys has 123456, a slice of zeroes
* must be concatenated.
*
* Supported value tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported value tensor rank: from 2
*
* Inputs:
* * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with
* shape [ k ].
* * 1: Keys. A 1-D {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
*
* Available since API level 27.
*/
ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,
/**
* Applies L2 normalization along the depth dimension.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
* For input tensor with rank less than 4, independently normalizes each
* 1-D slice along dimension dim.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
* Tensors with rank less than 4 are only supported since API level 29.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since API level 29.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_L2_NORMALIZATION = 11,
/**
* Performs an 2-D L2 pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, c] =
* sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
* sum(1))
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
*/
ANEURALNETWORKS_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 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))
* output = input / pow((bias + alpha * sqr_sum), beta)
*
* For input tensor with rank less than 4, independently normalizes each
* 1-D slice along specified dimension.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
* Tensors with rank less than 4 are only supported since API level 29.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of
* the normalization window.
* * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must
* not be zero.
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale
* factor, alpha.
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the exponent,
* beta.
* * 5: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since API level 29.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,
/**
* Computes sigmoid activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = 1 / (1 + exp(-input))
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
* Supported input tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported input tensor rank: from 1
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of projected output bits generated by each
* hash function.
* If the projection type is Sparse:
* Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
*
* * 1: Input. Dim.size >= 1, no restriction on DataType.
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
* If not set, each input element is considered to have the same weight
* of 1.0.
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
* Sparse:
* Value LSHProjectionType_SPARSE(=3) (since API level 29).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
* NOTE: To avoid collisions across hash functions, an offset value
* of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
* where k is the index of the hash function.
*
* Value LSHProjectionType_SPARSE_DEPRECATED(=1).
* Legacy behavior that does not include the offset value.
*
* Dense:
* Value LSHProjectionType_DENSE(=2).
* Computed bit vector is considered to be dense. Each output
* element represents a bit and can take the value of either
* 0 or 1.
*
* Outputs:
* * 0: If the projection type is Sparse:
* Output.Dim == { Tensor[0].Dim[0] }
* A tensor of int32 that represents hash signatures,
*
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
*
* Available since API level 27.
* The offset value for sparse projections was added in API level 29.
*/
ANEURALNETWORKS_LSH_PROJECTION = 15,
/**
* Performs a single time step in a Long Short-Term Memory (LSTM) layer
*
* The LSTM operation is described by the following equations.
*
* \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; \\
* h_t =& & \\
* & o_t \odot g(C_t) & otherwise. \\
* \f}
* Where:
* * \f$x_t\f$ is the input,
* * \f$i_t\f$ is the input gate,
* * \f$f_t\f$ is the forget gate,
* * \f$C_t\f$ is the cell state,
* * \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$W_{xi}\f$ 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,
* * \f$b_i\f$ is the input gate bias,
* * \f$W_{xf}\f$ is the input-to-forget weight matrix,
* * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
* * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
* * \f$b_f\f$ is the forget gate bias,
* * \f$W_{xc}\f$ is the input-to-cell weight matrix,
* * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
* * \f$b_c\f$ is the cell bias,
* * \f$W_{xo}\f$ is the input-to-output weight matrix,
* * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
* * \f$W_{co}\f$ is the cell-to-output weight matrix,
* * \f$b_o\f$ is the output gate bias,
* * \f$W_{proj}\f$ is the projection weight matrix,
* * \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)">
* 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.
*
* Since API level 29 LSTM supports layer normalization.
* In case layer normalization is used, the inputs to internal activation
* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
* following an approach from section 3.1 from
* https://arxiv.org/pdf/1607.06450.pdf
*
* 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.
* \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.
* 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.
* * (API level >= 29) The four layer normalization weights either all have
* values or none of them have values. Layer normalization is used when
* values are present.
*
* References:
*
* The default non-peephole non-CIFG implementation is based on:
* http://www.bioinf.jku.at/publications/older/2604.pdf
* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
* Computation, 9(8):1735-1780, 1997.
*
* 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.
* (However, the concept of peephole optimization was introduced in work
* prior to this paper.)
*
* The coupling of input and forget gate (CIFG) is based on:
* http://arxiv.org/pdf/1503.04069.pdf
* Greff et al. "LSTM: A Search Space Odyssey"
*
* The layer normalization is based on:
* https://arxiv.org/pdf/1607.06450.pdf
* Jimmy Ba et al. "Layer Normalization"
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* All input and output tensors must be of the same type.
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 2-D tensor of 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 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 shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor of shape [num_units, output_size], where “output_size”
* corresponds to either the number of cell units (i.e., “num_units”),
* or the second dimension of the “projection_weights”, if defined.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor of shape [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
* <li>0: None;
* <li>1: Relu;
* <li>3: Relu6;
* <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.
* Until API level 29 this scalar must be of type {@link
* ANEURALNETWORKS_FLOAT32}. Since API level 29, if all the input
* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this
* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
* otherwise if all the input tensors have the type {@link
* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link
* ANEURALNETWORKS_FLOAT16}.
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
* Until API level 29 this scalar must be of type {@link
* ANEURALNETWORKS_FLOAT32}. Since API level 29, if all the input
* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this
* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
* otherwise if all the input tensors have the type {@link
* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link
* ANEURALNETWORKS_FLOAT16}.
* Since API level 29 there are additional inputs to this op:
* * 23:The input layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at input gate.
* * 24:The forget layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at forget gate.
* * 25:The cell layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at cell gate.
* * 26:The output layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at output gate.
*
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor 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 shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of shape [batch_size, output_size]. This is effectively
* the same as the current “output state (out)” value.
*
* Available since API level 27.
*/
ANEURALNETWORKS_LSTM = 16,
/**
* Performs an 2-D max pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, channel] =
* max_{di, dj} (
* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
* )
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* width.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
* height.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
*/
ANEURALNETWORKS_MAX_POOL_2D = 17,
/**
* Multiplies two tensors, element-wise.
*
* Takes two input tensors of identical {@link OperandCode} 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.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
* as input0.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The product, a tensor of the same {@link OperandCode} as input0.
* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
*
* Available since API level 27.
*/
ANEURALNETWORKS_MUL = 18,
/**
* Computes rectified linear activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = max(0, input)
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_RELU = 19,
/**
* Computes rectified linear 1 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(1.f, max(-1.f, input))
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_RELU1 = 20,
/**
* Computes rectified linear 6 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(6, max(0, input))
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_RELU6 = 21,
/**
* Reshapes a tensor.
*
* Given tensor, this operation returns a tensor that has the same values as
* tensor, but with a newly specified shape.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the tensor to be reshaped.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_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.
*
* Available since API level 27.
*/
ANEURALNETWORKS_RESHAPE = 22,
/**
* 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.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* height of the output tensor.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* width of the output tensor.
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
*
* Available since API level 27.
*/
ANEURALNETWORKS_RESIZE_BILINEAR = 23,
/**
* A basic recurrent neural network layer.
*
* This layer implements the operation:
* outputs = state = activation(inputs * input_weights +
* state * recurrent_weights + bias)
*
* Where:
* * “input_weights” is a weight matrix that multiplies the inputs;
* * “recurrent_weights” is a weight matrix that multiplies the current
* “state” which itself is the output from the previous time step
* computation;
* * “bias” is a bias vector (added to each output vector in the batch);
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor of shape [batch_size, input_size], where “batch_size”
* corresponds to the batching dimension, and “input_size” is the size
* of the input.
* * 1: weights.
* A 2-D tensor of shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: recurrent_weights.
* A 2-D tensor of shape [num_units, num_units], with columns
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
* A 2-D tensor of shape [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FuseCode} 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 shape [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of shape [batch_size, num_units]. This is effectively
* the same as the current state value.
*
* Available since API level 27.
*/
ANEURALNETWORKS_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.
*
* The output is calculated using this formula:
*
* output[batch, i] =
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
* For input tensor with rank other than 2, the activation will be applied
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
* Tensors with rank other than 2 or 4 are only supported since API level 29.
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
* * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive
* scaling factor for the exponent, beta.
* * 2: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
* specifying the dimension the activation would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since API level 29.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*
* Available since API level 27.
*/
ANEURALNETWORKS_SOFTMAX = 25,
/**
* 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.
*
* 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 {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input.
* * 1: An {@link ANEURALNETWORKS_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.
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
*
* Available since API level 27.
*/
ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
/**
* SVDF op is a kind of stateful layer derived from the notion that a
* densely connected layer that's processing a sequence of input frames can
* be approximated by using a singular value decomposition of each of its
* nodes. The implementation is based on:
*
* https://research.google.com/pubs/archive/43813.pdf
*
* P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
* “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
* 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 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,
* "ANEURALNETWORKS_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);
* * “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
* batch); and
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Each rank adds a dimension to the weights matrices by means of stacking
* the filters.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* All input tensors must be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor of shape [batch_size, input_size], where “batch_size”
* corresponds to the batching dimension, and “input_size” is the size
* of the input.
* * 1: weights_feature.
* A 2-D tensor of shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: weights_time.
* A 2-D tensor of shape [num_units, memory_size], where “memory_size”
* corresponds to the fixed-size of the memory.
* * 3: bias.
* An optional 1-D tensor of shape [num_units].
* * 4: state (in).
* A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
* An optional {@link FuseCode} 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 the same {@link OperandCode} as the inputs, with shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of the same {@link OperandCode} as the inputs, with shape
* [batch_size, num_units].
*
* Available since API level 27.
*/
ANEURALNETWORKS_SVDF = 27,
/**
* Computes hyperbolic tangent of input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = tanh(input)
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29)
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
*
* Available since API level 27.
*/
ANEURALNETWORKS_TANH = 28,
// Operations below are available since API level 28.
// TODO: make the description easier to understand.
/**
* 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 is the reverse of SpaceToBatch.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block
* sizes for each spatial dimension of the input tensor. All values
* must be >= 1.
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
/**
* Element-wise division of two tensors.
*
* Takes two input tensors of identical {@link OperandCode} 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.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the first input.
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
* as input0.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_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.
*
* If dimensions to reduce have no entries, all dimensions are reduced, and
* a tensor with a single element is returned.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor, specifying the input.
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_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 ANEURALNETWORKS_INT32} scalar, keep_dims. If positive,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_MEAN = 31,
/**
* Pads a tensor with zeros.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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 {@link ANEURALNETWORKS_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 elements to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of elements to be padded after the
* end of dimension i.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0. The
* output tensor has the same rank as input0, and each
* dimension of the output tensor has the same size as the
* corresponding dimension of the input tensor plus the size
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
*
* Available since API level 28.
*/
ANEURALNETWORKS_PAD = 32,
// TODO: make the description easier to understand.
/**
* 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.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_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 ANEURALNETWORKS_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.
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_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
* {@link OperandCode} 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 {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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 {@link ANEURALNETWORKS_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 {@link OperandCode} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
*
* Available since API level 28.
*/
ANEURALNETWORKS_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.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be sliced.
* * 1: begin, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
* starts of the dimensions of the input tensor to be sliced. The
* length must be of rank(input0).
* * 2: end, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
* ends of the dimensions of the input tensor to be sliced. The length
* must be of rank(input0).
* * 3: strides, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
* strides of the dimensions of the input tensor to be sliced. The
* length must be of rank(input0). The entries must be non-zero.
* * 4: begin_mask, an {@link ANEURALNETWORKS_INT32} scalar. 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: end_mask, an {@link ANEURALNETWORKS_INT32} scalar. 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: shrink_axis_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the
* ith bit of shrink_axis_mask is set, the ith dimension specification
* shrinks the dimensionality by 1, taking on the value at index
* begin[i]. In this case, the ith specification must define a
* slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
*
* Available since API level 28.
*/
ANEURALNETWORKS_STRIDED_SLICE = 35,
/**
* Element-wise subtraction of two tensors.
*
* Takes two input tensors of identical {@link OperandCode} 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.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the first input.
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
* as input0.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_SUB = 36,
/**
* 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.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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 {@link ANEURALNETWORKS_TENSOR_INT32},
* the permutation of the dimensions of the input tensor.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 28.
*/
ANEURALNETWORKS_TRANSPOSE = 37,
// Operations below are available since API level 29.
/**
* Computes the absolute value of a tensor, element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_ABS = 38,
/**
* Returns the index of the largest element along an axis.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor specifying the input. Must be non-empty.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor.
*
* Available since API level 29.
*/
// There is no underscore in ARG_MAX to avoid name conflict with
// the macro defined in libc/kernel/uapi/linux/limits.h.
ANEURALNETWORKS_ARGMAX = 39,
/**
* Returns the index of the smallest element along an axis.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor specifying the input. Must be non-empty.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor.
*
* Available since API level 29.
*/
ANEURALNETWORKS_ARGMIN = 40, // See ARGMAX for naming discussion.
/**
* Transform axis-aligned bounding box proposals using bounding box deltas.
*
* Given the positions of bounding box proposals and the corresponding
* bounding box deltas for each class, return the refined bounding box
* regions. The resulting bounding boxes are cliped against the edges of
* the image.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
* bounding box proposals, each line with format [x1, y1, x2, y2].
* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
* the zeroPoint must be 0 and the scale must be 0.125.
* * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
* bounding box delta for each region of interest and each class. The
* bounding box deltas are organized in the following order
* [dx, dy, dw, dh], where dx and dy is the relative correction factor
* for the center position of the bounding box with respect to the width
* and height, dw and dh is the log-scale relative correction factor
* for the width and height. For input0 of type
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, this tensor should be
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches], specifying the number of output boxes for each batch.
* * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
* each image in the batch, each line with format
* [image_height, image_width].
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0, with shape
* [num_rois, num_classes * 4], specifying the coordinates of each
* output bounding box for each class, with format [x1, y1, x2, y2].
*
* Available since API level 29.
*/
ANEURALNETWORKS_AXIS_ALIGNED_BBOX_TRANSFORM = 41,
ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM = 42,
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs in forward and backward directions.
*
* This Op unrolls the input along the sequence dimension, and implements
* the following operation for each element in the sequence s =
* 1...sequence_length:
* fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
* fw_state * fw_recurrent_weights’ + fw_bias)
*
* And for each element in sequence t = sequence_length : 1
* bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
* bw_state * bw_recurrent_weights’ + bw_bias)
*
* Where:
* * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
* * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
* current “state” which itself is the output from the previous time step
* computation;
* * “{fw,bw}_bias” is a bias vector (added to each output vector in the
* batch);
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* The op also supports an auxiliary input. Regular cell feeds one input
* into the two RNN cells in the following way:
*
* INPUT (INPUT_REVERSED)
* | |
* ---------------------
* | FW_RNN BW_RNN |
* ---------------------
* | |
* FW_OUT BW_OUT
*
* An op with an auxiliary input takes two inputs and feeds them into the
* RNN cells in the following way:
*
* AUX_INPUT (AUX_INPUT_REVERSED)
* | |
* INPUT | (INPUT_R'D.)|
* | | | |
* -----------------------
* | \ / \ / |
* | FW_RNN BW_RNN |
* -----------------------
* | |
* FW_OUT BW_OUT
*
* While stacking this op on top of itself, this allows to connect both
* forward and backward outputs from previous cell to the next cell's
* inputs.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to true, then the input has a shape [maxTime, batchSize,
* inputSize], otherwise the input has a shape [batchSize, maxTime,
* inputSize].
* * 1: fwWeights.
* A 2-D tensor of shape [fwNumUnits, inputSize].
* * 2: fwRecurrentWeights.
* A 2-D tensor of shape [fwNumUnits, fwNumUnits].
* * 3: fwBias.
* A 1-D tensor of shape [fwNumUnits].
* * 4: fwHiddenState.
* A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 5: bwWeights.
* A 2-D tensor of shape [bwNumUnits, inputSize].
* * 6: bwRecurrentWeights.
* A 2-D tensor of shape [bwNumUnits, bwNumUnits].
* * 7: bwBias.
* A 1-D tensor of shape [bwNumUnits].
* * 8: bwHiddenState
* A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 9: auxInput.
* A 3-D tensor. The shape is the same as of the input 0.
* * 10:fwAuxWeights.
* A 2-D tensor of shape [fwNumUnits, inputSize].
* * 11:bwAuxWeights.
* A 2-D tensor of shape [bwNumUnits, inputSize].
* * 12:fusedActivationFunction.
* A {@link FuseCode} value indicating the activation function. If
* “NONE” is specified then it results in a linear activation.
* * 13:timeMajor
* An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format
* of input and output tensors.
* * 14:mergeOutputs
* An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs
* from forward and backward cells are separate (if set to false) or
* concatenated (if set to true).
* Outputs:
* * 0: fwOutput.
* A 3-D tensor. The first two dimensions of the shape are defined by
* the input 6 (timeMajor) and the third dimension is defined by the
* input 14 (mergeOutputs). If timeMajor is set to true, then the first
* two dimensions are [maxTime, batchSize], otherwise they are set to
* [batchSize, maxTime]. If mergeOutputs is set to true, then the third
* dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
* to fwNumUnits.
* * 1: bwOutput.
* A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
* this tensor is not produced. The shape is defined by the input 6
* (timeMajor). If it is set to true, then the shape is set to
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
* [batchSize, maxTime, bwNumUnits].
*
* Available since API level 29.
*/
ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_RNN = 43,
/**
* Greedily selects a subset of bounding boxes in descending order of score.
*
* This op applies hard NMS algorithm to each class. In each loop of
* execution, the box with maximum score gets selected, and any boxes with
* the intersection-over-union (IOU) greater than a threshold are removed
* from the pending set.
*
* Axis-aligned bounding boxes are represented by its upper-left corner
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
* bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
* of each bounding box proposal. The boxes are grouped by batches in the
* first dimension.
* * 1: A 2-D Tensor specifying the bounding boxes of shape
* [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
* The boxes are grouped by batches in the first dimension. The sequential
* order of the boxes corresponds with input0. For input0 of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should be of
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
* scale of 0.125.
* * 2: A 1-D Tensor of shape [batches], specifying the number of boxes
* for each image in the batch.
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, score_threshold. Boxes
* with scores lower than the threshold are filtered before sending
* to the NMS algorithm.
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU
* threshold.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
* number of selected bounding boxes for each image. Set to a negative
* value for unlimited number of output bounding boxes.
*
* Outputs:
* * 0: A 1-D Tensor of the same {@link OperandCode} as input0, with shape
* [num_output_rois], specifying the score of each output box. The boxes
* are grouped by batches, but the sequential order in each batch is not
* guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale and zero point must be the same as input0.
* * 1: A 2-D Tensor of the same {@link OperandCode} as input1, with shape
* [num_output_rois, 4], specifying the coordinates of each
* output bounding box with the same format as input1. The sequential
* order of the boxes corresponds with output0. For type of
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the scale must be
* 0.125 and the zero point must be 0.
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the class of each output box. The
* sequential order of the boxes corresponds with output0.
* * 3: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches], specifying the number of output boxes for each image.
*
* Available since API level 29.
*/
ANEURALNETWORKS_BOX_WITH_NMS_LIMIT = 44,
/**
* Casts a tensor to a new type.
*
* This operation ignores the scale and zeroPoint of quanized tensors,
* e.g. it treats a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} input
* as a tensor of uint8 values.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_CAST = 45,
/**
* Shuffle the channels of the input tensor.
*
* Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
* divide the channel dimension into num_groups groups, and reorganize the
* channels by grouping channels with the same index in each group.
*
* Along the channel dimension, the output is calculated using this formula:
*
* output_channel[k * num_groups + g] = input_channel[g * group_size + k]
*
* where group_size = num_channels / num_groups
*
* The number of channels must be divisible by num_groups.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be shuffled.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* groups.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the dimension
* channel shuffle would be performed on. Negative index is used to
* specify axis from the end (e.g. -1 for the last axis). Must be in
* the range [-n, n).
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_CHANNEL_SHUFFLE = 46,
/**
* Apply postprocessing steps to bounding box detections.
*
* Bounding box detections are generated by applying transformation on a set
* of predefined anchors with the bounding box deltas from bounding box
* regression. A final step of hard NMS is applied to limit the number of
* returned boxes.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
* the score of each anchor with each class. Class 0 for each
* [batches, num_anchors, 0] is background and will be ignored.
* * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
* the first four values in length_box_encoding specifying the bounding
* box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
* where dy and dx is the linear-scale relative correction factor for the
* center position of the bounding box with respect to the width and height,
* dh and dw is the log-scale relative correction factor for the width and
* height. All the entries in length_box_encoding beyond the first four
* values are ignored in this operation.
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
* predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
* ctr_x are the center position of the box, and h and w are the height
* and the width.
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
* factor for dy in bounding box deltas.
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
* factor for dx in bounding box deltas.
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
* factor for dh in bounding box deltas.
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
* factor for dw in bounding box deltas.
* * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to use regular
* multi-class NMS algorithm that do NMS separately for each class,
* set to false for a faster algorithm that only do one single NMS
* using the highest class score..
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, max_num_detections, specifying
* the maximum number of boxes for the output. Boxes with the lowest
* scores are discarded to meet the limit.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
* number of classes per detection.
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is
* set to true, specifying the maximum number of detections when
* applying NMS algorithm for each single class.
* * 11: An {@link ANEURALNETWORKS_FLOAT32} scalar, score_threshold. Boxes
* with scores lower than the threshold are filtered before sending
* to the NMS algorithm.
* * 12: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU
* threshold for hard NMS.
* * 13: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to include
* background class in the list of label map for the output, set
* to false to not include the background. When the background
* class is included, it has label 0 and the output classes start
* at 1 in the label map, otherwise, the output classes start at 0.
*
* Outputs:
* * 0: A 2-D tensor of the same {@link OperandCode} as input0, with shape
* [batches, max_num_detections], specifying the score of each output
* detections.
* * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
* coordinates of each output bounding box, with format
* [y1, x1, y2, x2].
* * 2: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches, max_num_detections], specifying the class label for each
* output detection.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* valid output detections.
*
* Available since API level 29.
*/
ANEURALNETWORKS_DETECTION_POSTPROCESS = 47,
/**
* For input tensors x and y, computes x == y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_EQUAL = 48,
/**
* Computes exponential of x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_EXP = 49,
/**
* Inserts a dimension of 1 into a tensor's shape.
*
* Given a tensor input, this operation inserts a dimension of 1 at the
* given dimension index of input's shape. The dimension index starts at
* zero; if you specify a negative dimension index, it is counted backward
* from the end.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the dimension
* index to expand. Must be in the range [-(n + 1), (n + 1)).
*
* Outputs:
* * 0: An (n + 1)-D tensor with the same {@link OperandCode} and data as
* input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_EXPAND_DIMS = 50,
/**
* Gathers values along an axis.
*
* Produces an output tensor with shape
* input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
* where:
* # Vector indices (output is rank(input0)).
* output[a_0, ..., a_n, i, b_0, ..., b_n] =
* input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
*
* # Higher rank indices (output is rank(input0) + rank(indices) - 1).
* output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
* input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor from which to gather values.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis.
* Negative index is used to specify axis from the end
* (e.g. -1 for the last axis). Must be in the range [-n, n).
* * 2: A k-D tensor {@link ANEURALNETWORKS_TENSOR_INT32} of indices.
* The values must be in the bounds of the corresponding dimensions
* of input0.
*
* Outputs:
* * 0: An (n + k - 1)-D tensor with the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_GATHER = 51,
/**
* Generate aixs-aligned bounding box proposals.
*
* Bounding box proposals are generated by applying transformation on a set
* of predefined anchors with the bounding box deltas from bounding box
* regression. A final step of hard NMS is applied to limit the number of
* returned boxes.
*
* Axis-aligned bounding boxes are represented by its upper-left corner
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
* bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A 4-D Tensor specifying the score of each anchor at each
* location. With "NHWC" data layout, the tensor shape is
* [batches, height, width, num_anchors]. With "NCHW" data layout,
* the tensor shape is [batches, num_anchors, height, width].
* * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
* layout, the tensor shape is [batches, height, width, num_anchors * 4].
* With "NCHW" data layout, the tensor shape is
* [batches, num_anchors * 4, height, width]. The box deltas are encoded
* in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
* relative correction factor for the center position of the bounding box
* with respect to the width and height, dw and dh is the log-scale
* relative correction factor for the width and height. The last
* dimensions is the channel dimension.
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
* predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should be of
* {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with scale of 0.125.
* * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
* each image in the batch, with format [image_height, image_width].
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
* number of boxes before going into the hard NMS algorithm. Boxes
* with the lowest scores are discarded to meet the limit. Set to
* a non-positive value for unlimited number.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
* number of boxes returning from the hard NMS algorithm. Boxes
* with the lowest scores are discarded to meet the limit. Set to
* a non-positive value for unlimited number.
* * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU
* threshold for hard NMS.
* * 9: An {@link ANEURALNETWORKS_FLOAT32} scalar, min_size. Boxes with
* height or width lower than the absolute threshold are filtered out.
* * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and input1. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0, of shape
* [num_output_rois], specifying the score of each output box.
* The boxes are grouped by batches, but the sequential order in
* each batch is not guaranteed. For type of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the scale and zero
* point must be the same as input0.
* * 1: A tensor of the same {@link OperandCode} as input1, of shape
* [num_output_rois, 4], specifying the coordinates of each output
* bounding box for each class, with format [x1, y1, x2, y2].
* The sequential order of the boxes corresponds with output0.
* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the
* scale must be 0.125 and the zero point must be 0.
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches], specifying the number of output boxes for each image.
*
* Available since API level 29.
*/
ANEURALNETWORKS_GENERATE_PROPOSALS = 52,
/**
* For input tensors x and y, computes x > y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_GREATER = 53,
/**
* For input tensors x and y, computes x >= y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_GREATER_EQUAL = 54,
/**
* Performs a grouped 2-D convolution operation.
*
* Given an input tensor of shape [batches, height, width, depth_in] and a
* filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
* containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
* applies a group of different filters to each input channel group, then
* concatenates the results together.
*
* Specifically, the input channels are divided into num_groups groups, each with
* depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
* filters are also divided into num_groups groups, i.e. depth_out is divisible
* by num_groups. GROUPED_CONV applies each group of filters to the corresponding
* input channel group, and the result are concatenated together.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[b, i, j, g * channel_multiplier + q] =
* sum_{di, dj, dk} (
* input[b, strides[1] * i + di, strides[2] * j + dj,
* g * depth_group + dk] *
* filter[g * channel_multiplier + q, di, dj, dk]
* ) + bias[channel]
*
* where channel_multiplier = depth_out / num_groups
*
* Supported tensor {@link OperandCode} configurations:
* * 32 bit Floating point :
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * 16 bit Floating point:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* * Quantized with symetric per channel quantization for the filter:
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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, where depth_in = num_groups * depth_group.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (channelDim at
* {@link ANeuralNetworksSymmPerChannelQuantParams}) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
groups.
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 11: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input, where depth_in = num_groups * depth_group.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (channelDim at
* {@link ANeuralNetworksSymmPerChannelQuantParams}) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
* type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* groups.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale (for
* filter tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
* this condition must be true for all filter scales).
*
* Available since API level 29.
*/
ANEURALNETWORKS_GROUPED_CONV_2D = 55,
/**
* Localize the maximum keypoints from heatmaps.
*
* This operation approximates the accurate maximum keypoint scores and
* indices after bicubic upscaling by using Taylor expansion up to the
* quadratic term.
*
* The bounding box is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D Tensor of shape
* [num_boxes, heatmap_size, heatmap_size, num_keypoints],
* specifying the heatmaps, the height and width of heatmaps should
* be the same, and must be greater than or equal to 2.
* * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
* each with format [x1, y1, x2, y2]. For input0 of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should
* be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint
* of 0 and scale of 0.125.
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0, with shape
* [num_boxes, num_keypoints], specifying score of the keypoints.
* * 1: A tensor of the same {@link OperandCode} as input1, with shape
* [num_boxes, num_keypoints, 2], specifying the location of
* the keypoints, the second dimension is organized as
* [keypoint_x, keypoint_y].
*
* Available since API level 29.
*/
ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT = 56,
/**
* Applies instance normalization to the input tensor.
*
* The values in the output tensor are computed as:
*
* output[b, h, w, c] =
* (input[b, h, w, c] - mean[b, c]) * gamma /
* sqrt(var[b, c] + epsilon) + beta
*
* Where the mean and variance are computed across the spatial dimensions:
*
* mean[b, c] =
* sum_{h, w}(input[b, h, w, c]) / sum(1)
*
* var[b, c] =
* sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying gamma, the
* scale applied to the normalized tensor.
* * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying beta, the
* offset applied to the normalized tensor.
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying epsilon, the
* small value added to variance to avoid dividing by zero.
* * 4: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_INSTANCE_NORMALIZATION = 57,
/**
* For input tensors x and y, computes x < y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LESS = 58,
/**
* For input tensors x and y, computes x <= y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LESS_EQUAL = 59,
/**
* Computes natural logarithm of x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LOG = 60,
/**
* Returns the truth value of x AND y element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions
* compatible with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LOGICAL_AND = 61,
/**
* Computes the truth value of NOT x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LOGICAL_NOT = 62,
/**
* Returns the truth value of x OR y element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions
* compatible with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LOGICAL_OR = 63,
/**
* Computes the log softmax activations given logits.
*
* The output is calculated using this formula:
*
* output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor specifying the input logits.
* * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive
* scaling factor for the exponent, beta.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: The output tensor of the same {@link OperandCode} and shape as
* input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_LOG_SOFTMAX = 64,
/**
* Returns the element-wise maximum of two tensors.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and compatible dimensions
* with input0.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_MAXIMUM = 65,
/**
* Returns the element-wise minimum of two tensors.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and compatible dimensions
* with input0.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_MINIMUM = 66,
/**
* Computes numerical negative value element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_NEG = 67,
/**
* For input tensors x and y, computes x != y elementwise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_NOT_EQUAL = 68,
/**
* Pads a tensor with the given constant value according to the specified
* paddings.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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 {@link ANEURALNETWORKS_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 elements to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of elements to be padded after
* the end of dimension i.
* * 2: An scalar specifying the value to use for padding input0.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the
* pad value should be of {@link ANEURALNETWORKS_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the pad value should be of {@link ANEURALNETWORKS_INT32}. The
* scale and zeroPoint are assumed to be the same as in input0.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0. The
* output tensor has the same rank as input0, and each
* dimension of the output tensor has the same size as the
* corresponding dimension of the input tensor plus the size
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
*
* Available since API level 29.
*/
ANEURALNETWORKS_PAD_V2 = 69,
/**
* Computes the power of one value to another.
*
* Given a tensor base and a tensor exponent, this operation computes
* base^exponent elementwise.
*
* This operations supports broadcasting. 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.
*
* For example:
* base.dimension = {4, 1, 2}
* exponent.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor specifying the base.
* * 1: A tensor specifying the exponent.
*
* Outputs:
* * 0: An output tensor.
*
* Available since API level 29.
*/
ANEURALNETWORKS_POW = 70,
/**
* Parametric Rectified Linear Unit.
*
* It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
* is a learned array with the same {@link OperandCode} and compatible
* dimensions as input x.
*
* 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.
*
* Example:
* input.dimension = {4, 1, 2}
* alpha.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor, specifying the input.
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
* as input0, specifying the alpha.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_PRELU = 71,
/**
* Quantizes the input tensor.
*
* The formula is:
*
* output = max(0, min(255, round(input / scale) + zeroPoint)
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
*
* Available since API level 29.
*/
ANEURALNETWORKS_QUANTIZE = 72,
/**
* A version of quantized LSTM, using 16 bit quantization for internal
* state.
*
* There is no projection layer, so cell state size is equal to the output
* size.
*
* Inputs:
* * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [numBatches, inputSize] specifying the input to the LSTM
* cell. Tensor is quantized with a fixed quantization range of
* [-1, 127/128] (scale = 1/128, zeroPoint = 128).
* * 1: The input-to-input weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-input part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 2: The input-to-forget weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-forget part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 3: The input-to-cell weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-cell part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 4: The input-to-output weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-output part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 5: The recurrent-to-input weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying recurrent-to-input part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 6: The recurrent-to-forget weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying recurrent-to-forget
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 7: The recurrent-to-cell weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying recurrent-to-cell part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 8: The recurrent-to-output weights.
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying recurrent-to-output
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 9: The input gate bias.
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 10:The forget gate bias.
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 11:The cell bias.
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 12:The output gate bias.
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 13: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
* and shape [numBatches, outputSize] specifying the cell state from the
* previous time step of the LSTM cell. It is quantized using a
* quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
* 32768, zeroPoint = 0).
* * 14: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [numBathes, outputSize] specifying the output of the LSTM
* cell from previous time-step. Tensor is quantized with a fixed
* quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
* 128).
*
*
* Outputs:
* * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
* and shape [numBatches, outputSize] which contains a cell state from
* the current time step. Tensor is quantized using a quantization
* range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
* 0).
* * 1: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* and shape [numBathes, outputSize] which contains the output value.
* Tensor is quantized with a fixed quantization range of [-1, 127/128]
* (scale = 1/128, zeroPoint = 128).
*/
ANEURALNETWORKS_QUANTIZED_16BIT_LSTM = 73,
/**
* Draws samples from a multinomial distribution.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: A 2-D tensor with shape [batches, classes], specifying the
* unnormalized log-probabilities for all classes.
* * 1: A scalar {@link ANEURALNETWORKS_INT32}, specifying the number of
* independent samples to draw for each row slice.
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [2],
* specifying seeds used to initialize the random distribution.
* Outputs:
* * 0: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape
* [batches, samples], containing the drawn samples.
*
* Available since API level 29.
*/
ANEURALNETWORKS_RANDOM_MULTINOMIAL = 74,
/**
* Reduces a tensor by computing the "logical and" of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_ALL = 75,
/**
* Reduces a tensor by computing the "logical or" of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_ANY = 76,
/**
* Reduces a tensor by computing the maximum of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_MAX = 77,
/**
* Reduces a tensor by computing the minimum of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_MIN = 78,
/**
* Reduces a tensor by multiplying elements along given dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_PROD = 79,
/**
* Reduces a tensor by summing elements along given dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_REDUCE_SUM = 80,
/**
* Select and scale the feature map of each region of interest to a unified
* output size by average pooling sampling points from bilinear interpolation.
*
* The region of interest is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A spatial scaling factor is applied to map into feature map coordinate.
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
*
* No rounding is applied in this operation. The sampling points are unified
* distributed in the pooling bin and their values are calculated by bilinear
* interpolation.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, specifying the feature map.
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
* the regions of interest, each line with format [x1, y1, x2, y2].
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
* with zeroPoint of 0 and scale of 0.125.
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches], specifying the number of output boxes for each batch.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* height of the output tensor.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* width of the output tensor.
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* sampling points in height dimension used to compute the output.
* Set to 0 for adaptive value of ceil(roi_height/out_height).
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* sampling points in width dimension used to compute the output.
* Set to 0 for adaptive value of ceil(roi_width/out_width).
* * 9: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
*
* Available since API level 29.
*/
ANEURALNETWORKS_ROI_ALIGN = 81,
/**
* Select and scale the feature map of each region of interest to a unified
* output size by max-pooling.
*
* The region of interest is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A spatial scaling factor is applied to map into feature map coordinate.
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
*
* Rounding is applied in this operation to ensure integer boundary for
* regions of interest and pooling bins.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, specifying the feature map.
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
* the regions of interest, each line with format [x1, y1, x2, y2].
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
* with zeroPoint of 0 and scale of 0.125.
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
* [batches], specifying the number of output boxes for each batch.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* height of the output tensor.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* width of the output tensor.
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
*
* Available since API level 29.
*/
ANEURALNETWORKS_ROI_POOLING = 82,
/**
* Computes reciprocal of square root of x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_RSQRT = 83,
/**
* Using a tensor of booleans c and input tensors x and y select values
* elementwise from both input tensors:
*
* O[i] = C[i] ? x[i] : y[i].
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_BOOL8} acting as a
* mask that chooses, based on the value at each element, whether the
* corresponding element in the output should be taken from input1 (if
* true) or input2 (if false).
* * 1: An input tensor of the same shape as input0.
* * 2: An input tensor of the same shape and type as input1.
*
* Outputs:
* * 0: A tensor of the same type and shape as input1 and input2.
*
*/
ANEURALNETWORKS_SELECT = 84,
/**
* Computes sin of x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_SIN = 85,
/**
* Extracts a slice of specified size from the input tensor starting at a
* specified location.
*
* The starting location is specified as a 1-D tensor containing offsets
* for each dimension. The size is specified as a 1-D tensor containing
* either size of a slice along corresponding dimension or -1. In the latter
* case, all the remaining elements in dimension are included in the slice.
* Slice size in each dimension cannot be zero.
*
* A sum of begin offset and a size of a slice must not exceed size of a
* corresponding dimension.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor to take slice from.
* * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying
* the beginning indices of the slice in each dimension.
* * 2: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying
* the size of the slice in each dimension.
*
* Outputs:
* * 0: An n-D tensor of the same type as the input containing the slice.
*
* Available since API level 29.
*/
ANEURALNETWORKS_SLICE = 86,
/**
* Splits a tensor along a given axis into num_splits subtensors.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor to split.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis along
* which to split.
* * 2: An {@link ANEURALNETWORKS_INT32} scalar indicating the number of
* splits along given axis. Must evenly divide axis size.
*
* Outputs:
* * 0 ~ (num_splits - 1): Resulting subtensors.
*
* Available since API level 29.
*/
ANEURALNETWORKS_SPLIT = 87,
/**
* Computes square root of x element-wise.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 29.
*/
ANEURALNETWORKS_SQRT = 88,
/**
* Constructs a tensor by tiling a given tensor.
*
* This operation creates a new tensor by replicating `input` `multiples`
* times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
* elements, and the values of `input` are replicated `multiples[i]` times
* along the i-th dimension.
* For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: input, an n-D tensor specifying the input.
* * 1: multiples, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}.
* The length of multiples must be n.
*
* Outputs:
* * 0: A tiled tensor of the same {@link OperandCode} and rank as `input`.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TILE = 89,
/**
* Finds values and indices of the k largest entries for the last dimension.
*
* Resulting values in each dimensions are sorted in descending order. If
* two values are equal, the one with larger index appears first.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_INT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: input, an n-D tensor specifying the input.
* * 1: k, an {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
* top elements to look for along the last dimension.
*
* Outputs:
* * 0: An n-D tensor of the same type as the input, containing the k
* largest elements along each last dimensional slice.
* * 1: An n-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}
* containing the indices of values within the last dimension of input.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TOPK_V2 = 90,
/**
* Performs the tranpose of 2-D convolution operation.
*
* This operation is sometimes called "deconvolution" after Deconvolutional
* Networks, but is actually the transpose (gradient) of
* {@link ANEURALNETWORKS_CONV_2D} rather than an actual deconvolution.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias should be of the
* same type. For input tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* 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 type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias should be of the
* same type. For input tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link ANEURALNETWORKS_TENSOR_INT32} tensor, specifying the output
* tensor shape.
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
* {@link FuseCode} values. Specifies the activation to
* invoke on the result.
* * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale.
*
* Available since API level 29.
*/
ANEURALNETWORKS_TRANSPOSE_CONV_2D = 91,
ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs.
*
* This layer unrolls the input along the sequence dimension, and implements
* the following operation
* for each element in the sequence s = 1...sequence_length:
* outputs[s] = state = activation(inputs[s] * input_weights’ + state *
* recurrent_weights’ + bias)
*
* Where:
* * “input_weights” is a weight matrix that multiplies the inputs;
* * “recurrent_weights” is a weight matrix that multiplies the current
* “state” which itself is the output from the previous time step
* computation;
* * “bias” is a bias vector (added to each output vector in the batch);
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to 1, then the input has a shape [maxTime, batchSize,
* inputSize], otherwise the input has a shape [batchSize, maxTime,
* inputSize].
* * 1: weights.
* A 2-D tensor of shape [numUnits, inputSize].
* * 2: recurrent_weights.
* A 2-D tensor of shape [numUnits, numUnits].
* * 3: bias.
* A 1-D tensor of shape [numUnits].
* * 4: hidden state
* A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 5: fusedActivationFunction.
* A {@link FuseCode} value indicating the activation function. If
* “NONE” is specified then it results in a linear activation.
* * 6: timeMajor
* An {@link ANEURALNETWORKS_INT32} scalar specifying the shape format
* of input and output tensors. Must be set to either 0 or 1.
* Outputs:
* * 0: output.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to 1, then the output has a shape [maxTime, batchSize,
* numUnits], otherwise the output has a shape [batchSize, maxTime,
* numUnits].
*
* Available since API level 29.
*/
ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN = 93,
} OperationCode;
/**
* Fused activation function types.
*
*
* Available since API level 27.
*/
typedef enum {
/** NO fused activation function. */
ANEURALNETWORKS_FUSED_NONE = 0,
/** Fused ReLU activation function. */
ANEURALNETWORKS_FUSED_RELU = 1,
/** Fused ReLU1 activation function. */
ANEURALNETWORKS_FUSED_RELU1 = 2,
/** Fused ReLU6 activation function. */
ANEURALNETWORKS_FUSED_RELU6 = 3,
} FuseCode;
/**
* Implicit padding algorithms.
*
*
* Available since API level 27.
*/
typedef enum {
/**
* SAME padding.
* Padding on both ends are the "same":
* padding_to_beginning = total_padding / 2
* padding_to_end = (total_padding + 1)/2.
* i.e., for even number of padding, padding to both ends are exactly
* the same; for odd number of padding, padding to the ending is bigger
* than the padding to the beginning by 1.
*
* total_padding is a function of input, stride and filter size.
* It could be computed as follows:
* out_size = (input + stride - 1) / stride;
* needed_input = (out_size - 1) * stride + filter_size
* total_padding = max(0, needed_input - input_size)
* The computation is the same for the horizontal and vertical directions.
*/
ANEURALNETWORKS_PADDING_SAME = 1,
/**
* VALID padding.
* No padding. When the input size is not evenly divisible by
* the filter size, the input at the end that could not fill
* the whole filter tile will simply be ignored.
*/
ANEURALNETWORKS_PADDING_VALID = 2,
} PaddingCode;
/**
* Execution preferences.
*
* Available since API level 27.
*/
typedef enum {
/**
* Prefer executing in a way that minimizes battery drain.
* This is desirable for compilations that will be executed often.
*/
ANEURALNETWORKS_PREFER_LOW_POWER = 0,
/**
* Prefer returning a single answer as fast as possible, even if this causes
* more power consumption.
*/
ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
/**
* Prefer maximizing the throughput of successive frames, for example when
* processing successive frames coming from the camera.
*/
ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
} PreferenceCode;
/**
* Device types.
*
* The type of NNAPI device.
*/
typedef enum {
/** The device type cannot be provided. */
ANEURALNETWORKS_DEVICE_UNKNOWN = 0,
/** The device does not fall into any category below. */
ANEURALNETWORKS_DEVICE_OTHER = 1,
/** The device runs NNAPI models on single or multi-core CPU. */
ANEURALNETWORKS_DEVICE_CPU = 2,
/** The device can run NNAPI models and also accelerate graphics APIs such
* as OpenGL ES and Vulkan. */
ANEURALNETWORKS_DEVICE_GPU = 3,
/** Dedicated accelerator for Machine Learning workloads. */
ANEURALNETWORKS_DEVICE_ACCELERATOR = 4,
} DeviceTypeCode;
/**
* Result codes.
*
* <p>Any NNAPI function can return any result code, including result codes not
* currently documented. Any value other than {@link ANEURALNETWORKS_NO_ERROR}
* indicates a failure of some kind.</p>
*
* <p>Additional information about the nature of a failure can be obtained from
* the device log after enabling NNAPI debugging by setting the debug.nn.vlog
* property to 1, e.g., by calling "adb shell setprop debug.nn.vlog 1".</p>
*
* Available since API level 27.
*/
typedef enum {
/**
* Operation was succesful.
*/
ANEURALNETWORKS_NO_ERROR = 0,
/**
* Failure caused by not enough available memory.
*/
ANEURALNETWORKS_OUT_OF_MEMORY = 1,
ANEURALNETWORKS_INCOMPLETE = 2,
/**
* Failure caused by unexpected null argument.
*/
ANEURALNETWORKS_UNEXPECTED_NULL = 3,
/**
* Failure caused by invalid function arguments, invalid model definition,
* invalid execution definition or invalid data at execution time.
*/
ANEURALNETWORKS_BAD_DATA = 4,
/**
* Failure caused by failed model execution.
*/
ANEURALNETWORKS_OP_FAILED = 5,
/**
* Failure caused by object being in the wrong state.
*/
ANEURALNETWORKS_BAD_STATE = 6,
/**
* Failure caused by not being able to map a file into memory.
* This may be caused by a file descriptor not being mappable, or an AHardwareBuffer
* not supported by the device.
* Mitigate by reading its content into memory.
*/
ANEURALNETWORKS_UNMAPPABLE = 7,
/**
* Failure caused by insufficient buffer size provided to a model output.
*/
ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE = 8,
/**
* Failure caused by a device not being available.
*/
ANEURALNETWORKS_UNAVAILABLE_DEVICE = 9,
} ResultCode;
/**
* For {@link ANeuralNetworksModel_setOperandValue}, values with a
* length smaller or equal to this will be immediately copied into
* the model. The size is in bytes.
*
* Available since API level 27.
*/
enum { ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128 };
/**
* For {@link ANeuralNetworksCompilation_setCaching}, specify the size
* of the cache token expecting from the application. The size is in bytes.
*
* Available since API level 29.
*/
enum { ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN = 32 };
/**
* ANeuralNetworksMemory is an opaque type that represents memory.
*
* This type is used to represent shared memory, memory mapped files,
* and similar memories.
*
* By using shared memory, a program can efficiently communicate to the
* runtime and drivers the tensors that define a model. See
* {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
* should typically create one shared memory object that contains every constant tensor
* needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
* used to create shared memory from a file handle.
* {@link ANeuralNetworksMemory_createFromAHardwareBuffer} can be used to
* create shared memory from an AHardwareBuffer handle.
*
* Memory objects can also be used to specify the input and output arguments of
* an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
* and {@link ANeuralNetworksExecution_setOutputFromMemory}.
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;
/**
* ANeuralNetworksModel is an opaque type that contains a description of the
* mathematical operations that constitute the model.
*
* <p>Build the model by calling<ul>
* <li>{@link ANeuralNetworksModel_create}</li>
* <li>{@link ANeuralNetworksModel_addOperation}</li>
* <li>{@link ANeuralNetworksModel_addOperand}</li>
* </ul>
*
* This forms a graph in which each operation and operand is a node, a
* directed edge from an operand to an operation indicates that the
* operand is an input to the operation, and a directed edge from an
* operation to an operand indicates that the operand is an output
* from the operation. This graph must be acyclic.
*
* A model is completed by calling {@link ANeuralNetworksModel_finish}.
* A model is destroyed by calling {@link ANeuralNetworksModel_free}.
*
* <p>A model cannot be modified once {@link ANeuralNetworksModel_finish}
* has been called on it.</p>
*
* <p>It is the application's responsibility to make sure that only one thread
* modifies a model at a given time. It is however safe for more than one
* thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p>
*
* <p>It is also the application's responsibility to ensure that there are no other
* uses of the model after calling {@link ANeuralNetworksModel_free}.
* This includes any compilation or execution object created using the model.</p>
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksModel ANeuralNetworksModel;
/**
* ANeuralNetworksCompilation is an opaque type that can be used to compile
* a machine learning model.
*
* <p>To use:<ul>
* <li>Create a new compilation instance by calling the
* {@link ANeuralNetworksCompilation_create} function or
* {@link ANeuralNetworksCompilation_createForDevices}.</li>
* <li>Set any desired properties on the compilation (for example,
* {@link ANeuralNetworksCompilation_setPreference}).</li>
* <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li>
* <li>Use the compilation as many times as needed
* with {@link ANeuralNetworksExecution_create} and
* {@link ANeuralNetworksBurst_create}.</li>
* <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free}
* once all executions using the compilation have completed.</li></ul></p>
*
* A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}.
* A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}.
*
* <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish}
* has been called on it.</p>
*
* <p>It is the application's responsibility to make sure that only
* one thread modifies a compilation at a given time. It is however
* safe for more than one thread to use the compilation once
* {@link ANeuralNetworksCompilation_finish} has returned.</p>
*
* <p>It is also the application's responsibility to ensure that there are no other
* uses of the compilation after calling {@link ANeuralNetworksCompilation_free}.
* This includes any execution object created using the compilation.</p>
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation;
/**
* ANeuralNetworksExecution is an opaque type that can be used to apply a machine
* learning model to a set of inputs.
*
* <p>To use:<ul>
* <li>Create a new execution instance by calling the
* {@link ANeuralNetworksExecution_create} function.</li>
* <li>Associate input buffers or memory regions to the model inputs with
* {@link ANeuralNetworksExecution_setInput} or
* {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
* <li>Associate output buffers or memory regions to the model outputs with
* {@link ANeuralNetworksExecution_setOutput} or
* {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
* <li>Apply the model with one of the following:</li><ul>
* <li>Asynchronously with {@link ANeuralNetworksExecution_startCompute},
* waiting for the execution to complete with
* {@link ANeuralNetworksEvent_wait}.</li>
* <li>Synchronously with {@link ANeuralNetworksExecution_compute}.</li>
* <li>Synchronously as part of an execution burst with
* {@link ANeuralNetworksExecution_burstCompute}.</li></ul>
* <li>Destroy the execution with
* {@link ANeuralNetworksExecution_free}.</li></ul></p>
*
* <p>An output buffer or memory region must not overlap with any
* other output buffer or memory region, with an input buffer or
* memory region, or with an operand value in a memory object
* ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p>
*
* <p>An execution cannot be modified once
* {@link ANeuralNetworksExecution_compute} or
* {@link ANeuralNetworksExecution_startCompute} has been called on it.</p>
*
* <p>An execution can be applied to a model with
* {@link ANeuralNetworksExecution_compute} or
* {@link ANeuralNetworksExecution_startCompute} only once. Create new
* executions to do new evaluations of the model.</p>
*
* <p>It is the application's responsibility to make sure that only one thread
* modifies an execution at a given time. It is however safe for more than one
* thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p>
*
* <p>It is also the application's responsibility to ensure that there are no other
* uses of the execution after calling {@link ANeuralNetworksExecution_free}.</p>
*
* <p>Multiple executions can be scheduled and evaluated concurrently, either by
* means of {@link ANeuralNetworksExecution_compute} (which is synchronous) in
* different threads or by means of
* {@link ANeuralNetworksExecution_startCompute} (which is asynchronous). The
* runtime makes no guarantee on the ordering of completion of executions. If
* it's important to the application, the application should enforce the
* ordering by ensuring that one execution completes before the next is
* scheduled (for example, by scheduling all executions synchronously within a
* single thread, or by scheduling all executions asynchronously and using
* {@link ANeuralNetworksEvent_wait} between calls to
* {@link ANeuralNetworksExecution_startCompute}).</p>
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksExecution ANeuralNetworksExecution;
#if __ANDROID_API__ >= __ANDROID_API_Q__
/**
* Parameters for ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
*/
typedef struct ANeuralNetworksSymmPerChannelQuantParams {
/* The index of the channel dimension. */
uint32_t channelDim;
/** The size of the scale array. Should be equal to dimension[channelDim] of the Operand. */
uint32_t scaleCount;
/** The array of scaling values for each channel. Each value must be greater than zero. */
const float* scales;
} ANeuralNetworksSymmPerChannelQuantParams;
/**
* ANeuralNetworksBurst is an opaque type that can be used to reduce the latency
* of a rapid sequence of executions. It will likely cause overhead if only used
* for a single execution.
*
* ANeuralNetworksBurst serves as a context object for any number of inferences
* using {@link ANeuralNetworksExecution} objects. An ANeuralNetworksBurst
* object and the {@link ANeuralNetworksExecution} objects used with it must all
* have been created from the same {@link ANeuralNetworksCompilation} object.
*
* This object is also used as a hint to drivers, providing insight to the
* lifetime of a rapid sequence of executions. For example, a driver may choose
* to increase the clock frequency of its accelerator for the lifetime of a
* burst object.
*
* <p>To use:<ul>
* <li>Create a new burst object by calling the
* {@link ANeuralNetworksBurst_create} function.</li>
* <li>For each execution:</li><ul>
* <li>Create {@link ANeuralNetworksExecution} and configure its
* properties (see {@link ANeuralNetworksExecution} for details).</li>
* <li>Apply the model synchronously with
* {@link ANeuralNetworksExecution_burstCompute}, reusing the same
* {@link ANeuralNetworksBurst} with the new
* {@link ANeuralNetworksExecution}.</li>
* <li>Use and free the {@link ANeuralNetworksExecution}.</li></ul>
* <li>Destroy the burst with
* {@link ANeuralNetworksBurst_free}.</li></ul></p>
*
* Available since API level 29.
*/
typedef struct ANeuralNetworksBurst ANeuralNetworksBurst;
#endif // __ANDROID_API__ >= __ANDROID_API_Q__
/**
* ANeuralNetworksOperandType describes the type of an operand.
* This structure is used to describe both scalars and tensors.
*
* A tensor operand type must have a specified rank (number of
* dimensions) but may have any of its dimensions unspecified.
*
* A tensor operand type with all dimensions specified is "fully
* specified". Whenever possible (i.e., whenever the dimensions are
* known at model construction time), a tensor operand type should be
* (but is not required to be) fully specified, in order to enable the
* best possible performance.
*
* If a tensor operand's type is not fully specified, the dimensions
* of the operand are deduced from the operand types and values of the
* operation for which that operand is an output.
*
* <p>In the following situations, a tensor operand type must be fully
* specified:<ul>
* <li>The operand has a constant value, set by
* {@link ANeuralNetworksModel_setOperandValue} (with a
* non-nullptr buffer) or
* {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li>
* <li>The operand is a model input or model output (see
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}). A
* fully specified tensor operand type must either be provided
* to {@link ANeuralNetworksModel_addOperand}; or it must be
* provided to the corresponding
* {@link ANeuralNetworksExecution_setInput},
* {@link ANeuralNetworksExecution_setInputFromMemory},
* {@link ANeuralNetworksExecution_setOutput}, or
* {@link ANeuralNetworksModel_setOperandValueFromMemory}.
* EXCEPTION: If the input or output is optional and omitted
* (by passing nullptr for buffer to
* {@link ANeuralNetworksExecution_setInput} or
* {@link ANeuralNetworksExecution_setOutput}) then it need
* not have a fully specified tensor operand type.</li></ul>
*
* A tensor operand type with some number of unspecified dimensions is
* represented by setting each unspecified dimension to 0.
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksOperandType {
/** The data type, e.g ANEURALNETWORKS_INT8. */
int32_t type;
/** The number of dimensions (rank). It should be 0 for scalars. */
uint32_t dimensionCount;
/** The dimensions of the tensor. It should be nullptr for scalars. */
const uint32_t* dimensions;
/** These two fields are only used for quantized tensors.
* They should be zero for scalars and non-fixed point tensors.
* The dequantized value of each entry is (value - zeroPoint) * scale.
*/
float scale;
int32_t zeroPoint;
} ANeuralNetworksOperandType;
typedef int32_t ANeuralNetworksOperationType;
/**
* ANeuralNetworksEvent is an opaque type that represents an event
* that will be signaled once an execution completes.
*
* Available since API level 27.
*/
typedef struct ANeuralNetworksEvent ANeuralNetworksEvent;
#if __ANDROID_API__ >= __ANDROID_API_Q__
/**
* ANeuralNetworksDevice is an opaque type that represents a device.
*
* This type is used to query basic properties and supported operations of the corresponding
* device, and control which device(s) a model is to be run on.
*
* Available since API level 29.
*/
typedef struct ANeuralNetworksDevice ANeuralNetworksDevice;
/**
* Get the number of available devices.
*
* @param numDevices Used to return the number of devices.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworks_getDeviceCount(uint32_t* numDevices);
/**
* Get the representation of the specified device.
*
* @param devIndex The index of the specified device. Must be less than the
number of available devices.
* @param device The representation of the specified device.
* The same representation will always be returned for the specified
* device.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworks_getDevice(uint32_t devIndex, ANeuralNetworksDevice** device);
/**
* Get the name of the specified device.
*
* @param device The representation of the specified device.
* @param name The returned name of the specified device. The name will be in UTF-8
* and will be null-terminated. It will be recognizable as a known device name
* rather than a cryptic string. For devices with feature level 29 and above, the
* format of the name is {VENDOR}-{DEVICE}, e.g. “google-ipu”. For devices with
* feature level 28 or lower, the name will always be “unknown-device”.
* The name will remain valid for the duration of the application.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksDevice_getName(const ANeuralNetworksDevice* device, const char** name);
/**
* Get the type of a given device.
*
* The device type can be used to help application developers to distribute Machine Learning
* workloads and other workloads such as graphical rendering.
* E.g., for an app which renders AR scenes based on real time object detection results,
* the developer could choose an ACCELERATOR type device for ML workloads, and reserve GPU
* for graphical rendering.
*
* @param device The representation of the specified device.
* @param type The returned {@link DeviceTypeCode} of the specified device.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksDevice_getType(const ANeuralNetworksDevice* device, int32_t* type);
/**
* Get the version of the driver implementation of the specified device.
*
* It’s the responsibility of the driver implementor to insure that this version string
* uniquely distinguishes this implementation from all previous implementations.
*
* This version string must not be confused with the feature level which is solely defined
* by {@link ANeuralNetworksDevice_getFeatureLevel}. There is no implicit ordering of the versions.
* For example, it is not possible to filter all drivers older than a certain version.
*
* Application developers may use this version string to avoid or prefer specific driver
* implementations. For example, an application may want to do so because:
* - A specific version of the driver does not provide the required performance,
* perhaps because of a performance regression.
* - A specific version of the driver has a bug or returns results that don’t match
* the minimum precision requirement for the application.
*
* @param device The representation of the specified device.
* @param version The returned version string of the driver for the specified device. The
* string will be in UTF-8 and will be null-terminated. For devices with feature
* level 28 or lower, "UNKOWN" will be returned. The version string will remain
* valid for the duration of the application.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksDevice_getVersion(const ANeuralNetworksDevice* device, const char** version);
/**
* Get the supported NNAPI version of the specified device.
*
* Each device has a supported feature level, which is the most advanced feature this driver
* implements. For example, if the driver implements the features introduced in Android P,
* but does not implement the features introduced after Android P, the value would be 28.
* Developers could decide whether or not the specified device should be used for a Model that
* has certain feature requirements.
*
* @param device The representation of the specified device.
* @param featureLevel The API level of the most advanced feature this driver implements.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksDevice_getFeatureLevel(const ANeuralNetworksDevice* device,
int64_t* featureLevel);
/**
* Get the supported operations for a specified set of devices. If multiple devices
* are selected, the supported operation list is a union of supported operations of all
* selected devices.
*
* @param model The model to be queried.
* @param devices The set of devices. Must not contain duplicates.
* @param numDevices The number of devices in the set.
* @param supportedOps The boolean array to be filled. True means supported. The size of the
* boolean array must be at least as large as the number of operations
* in the model. The order of elements in the supportedOps array matches
* the order in which the corresponding operations were added to the model.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksModel_getSupportedOperationsForDevices(
const ANeuralNetworksModel* model, const ANeuralNetworksDevice* const* devices,
uint32_t numDevices, bool* supportedOps);
/**
* Create a {@link ANeuralNetworksCompilation} to compile the given model for a specified set
* of devices. If more than one device is specified, the compilation will
* distribute the workload automatically across the devices. The model must be fully
* supported by the specified set of devices. This means that
* ANeuralNetworksModel_getSupportedOperationsForDevices() must have returned true for every
* operation for that model/devices pair.
*
* @param model The {@link ANeuralNetworksModel} to be compiled.
* @param devices The set of devices. Must not contain duplicates.
* @param numDevices The number of devices in the set.
* @param compilation The newly created object or NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
* if the model is invalid.
*
* Available since API level 29.
*/
int ANeuralNetworksCompilation_createForDevices(ANeuralNetworksModel* model,
const ANeuralNetworksDevice* const* devices,
uint32_t numDevices,
ANeuralNetworksCompilation** compilation);
/**
* Sets the compilation caching signature and the cache directory.
*
* Provides optional caching information to the runtime for faster repeated
* compilation.
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* @param compilation The compilation to be modified.
* @param cacheDir The cache directory to store and retrieve caching data. It is
* recommended to use the code_cache provided by the Android runtime.
* If not using the code_cache, the user should choose a directory
* local to the application, and is responsible to manage and clean
* the cache entries.
* @param token The token provided by the user to specify a model, must be of length
* ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN. The user should ensure that
* the token is unique to a model within the application. The NNAPI
* runtime will not detected token collisions. If there is a collision,
* the compilation outcome may be incorrect without notifying with error.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*
* Available since API level 29.
*/
int ANeuralNetworksCompilation_setCaching(ANeuralNetworksCompilation* compilation,
const char* cacheDir, const uint8_t* token);
/**
* Schedule synchronous evaluation of the execution.
*
* <p>Schedules synchronous evaluation of the execution. Returns once the
* execution has completed and the outputs are ready to be consumed.
* </p>
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* See {@link ANeuralNetworksExecution_startCompute} for asynchronous execution.
* Synchronous execution incurs lower overhead than asynchronous execution.
*
* Available since API level 29.
*
* @param execution The execution to be scheduled and executed.
*
* @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
* ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot
* be properly mapped.
*/
int ANeuralNetworksExecution_compute(ANeuralNetworksExecution* execution);
/**
* Get the dimensional information of the specified output operand of the model of the
* {@link ANeuralNetworksExecution}.
*
* On asynchronous execution initiated by {@link ANeuralNetworksExecution_startCompute},
* {@link ANeuralNetworksEvent_wait} must be called prior to this function to recuperate
* the resources used by the execution.
*
* @param execution The execution to be queried.
* @param index The index of the output argument we are querying. It is
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
* @param rank The rank of the output operand.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE
* if the target output is provided an insufficient buffer at execution time,
* ANEURALNETWORKS_BAD_DATA if the index is invalid.
*
* Available since API level 29.
*/
int ANeuralNetworksExecution_getOutputOperandRank(ANeuralNetworksExecution* execution,
int32_t index, uint32_t* rank);
/**
* Get the dimensional information of the specified output operand of the model of the
* {@link ANeuralNetworksExecution}. The target output operand cannot be a scalar.
*
* On asynchronous execution initiated by {@link ANeuralNetworksExecution_startCompute},
* {@link ANeuralNetworksEvent_wait} must be called prior to this function to recuperate
* the resources used by the execution.
*
* @param execution The execution to be queried.
* @param index The index of the output argument we are querying. It is an index into the lists
* passed to {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
* @param dimensions The dimension array to be filled. The size of the array must be exactly as
* large as the rank of the output operand to be queried in the model.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE
* if the target output is provided an insufficient buffer at execution time,
* ANEURALNETWORKS_BAD_DATA if the index is invalid or if the target is a scalar.
*
* Available since API level 29.
*/
int ANeuralNetworksExecution_getOutputOperandDimensions(ANeuralNetworksExecution* execution,
int32_t index, uint32_t* dimensions);
/**
* Create a {@link ANeuralNetworksBurst} to apply the given compilation.
* This only creates the burst object. Computation is only performed once
* {@link ANeuralNetworksExecution_burstCompute} is invoked with a valid
* {@link ANeuralNetworksExecution} and {@link ANeuralNetworksBurst}.
*
* <p>The provided compilation must outlive the burst object.</p>
*
* Available since API level 29.
*
* @param compilation The {@link ANeuralNetworksCompilation} to be evaluated.
* @param burst The newly created object or NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
* if the compilation is invalid.
*/
int ANeuralNetworksBurst_create(ANeuralNetworksCompilation* compilation,
ANeuralNetworksBurst** burst) __INTRODUCED_IN(29);
/**
* Destroys the burst object.
*
* Available since API level 29.
*
* @param burst The burst object to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksBurst_free(ANeuralNetworksBurst* burst) __INTRODUCED_IN(29);
/**
* Schedule synchronous evaluation of the execution on a burst object.
*
* <p>Schedules synchronous evaluation of the execution. Returns once the
* execution has completed and the outputs are ready to be consumed.</p>
*
* <p>There must be at most one {@link ANeuralNetworksExecution} processing at
* any given time for any given burst object. Any
* {@link ANeuralNetworksExecution} launched before the previous has finished
* will result in ANEURALNETWORKS_BAD_STATE.</p>
*
* Available since API level 29.
*
* @param burst The burst object to execute on.
* @param execution The execution to be scheduled and executed. The execution
* must be created from the same {@link
* ANeuralNetworksCompilation} as the burst object.
*
* @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
*/
int ANeuralNetworksExecution_burstCompute(ANeuralNetworksExecution* execution,
ANeuralNetworksBurst* burst) __INTRODUCED_IN(29);
/**
* Creates a shared memory object from an AHardwareBuffer handle.
*
* If the shared memory is backed by an AHardwareBuffer of AHARDWAREBUFFER_FORMAT_BLOB
* format, it can be used the same way as shared memory created from a file handle. See
* {@link ANeuralNetworksMemory} for a description on how to use this shared memory.
*
* If the shared memory is backed by an AHardwareBuffer of a format other than
* AHARDWAREBUFFER_FORMAT_BLOB, it can only be used for Model inputs and outputs.
* When calling {@link ANeuralNetworksExecution_setInputFromMemory} or
* {@link ANeuralNetworksExecution_setOutputFromMemory} with the shared memory, both
* offset and length must be set to zero and the entire memory region will be
* associated with the specified input or output operand. There is no guarantee
* that an arbitrary AHardwareBuffer_Format and AHardwareBuffer_UsageFlags combination
* can be used by arbitrary devices. The execution will fail if selected set of devices
* cannot consume the buffer.
*
* Calling {@link ANeuralNetworksModel_setOperandValueFromMemory} with shared memory
* backed by an AHardwareBuffer of a format other than AHARDWAREBUFFER_FORMAT_BLOB is
* disallowed.
*
* TODO(miaowang): add documentation about intended usage with introspection API.
*
* Available since API level 29.
*
* @param ahwb The AHardwareBuffer handle.
* @param memory The memory object to be created.
* Set to NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if the request completed normally.
*
* @see AHardwareBuffer
*/
int ANeuralNetworksMemory_createFromAHardwareBuffer(const AHardwareBuffer* ahwb,
ANeuralNetworksMemory** memory);
/**
* Specifies whether duration of the {@link ANeuralNetworksExecution} is to be measured.
* By default, duration is not measured.
*
* The {@link ANeuralNetworksExecution} must have been created with
* {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 29.
*
* @param execution The execution to be modified.
* @param measure 'true' if duration is to be measured, 'false' if not.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksExecution_setMeasureTiming(ANeuralNetworksExecution* execution, bool measure);
/**
* Different duration measurements.
*
* Durations are measured in nanoseconds.
*
* Available since API level 29.
*/
typedef enum {
// Execution time on hardware (not driver, which runs on host processor).
ANEURALNETWORKS_DURATION_ON_HARDWARE = 0,
// Execution time in driver (including time on hardware). Excludes overhead
// such as that of the runtime itself and the IPC needed for the runtime to
// communicate with the driver.
ANEURALNETWORKS_DURATION_IN_DRIVER = 1,
} DurationCode;
/**
* Get the time spent in the specified {@link ANeuralNetworksExecution}, in nanoseconds.
* The execution must have completed.
*
* @param execution The execution to be queried.
* @param durationCode The measurement to be queried, specified by {@link DurationCode}.
* @param duration The returned duration. If no measurement was requested by
* {@link ANeuralNetworksExecution_setMeasureTiming}, or for some other
* reason the duration is not available, UINT64_MAX will be returned.
* A particular device need not support any given measurement.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksExecution_getDuration(const ANeuralNetworksExecution* execution,
int32_t durationCode, uint64_t* duration);
#endif // __ANDROID_API__ >= __ANDROID_API_Q__
#if __ANDROID_API__ >= 27
/**
* Creates a shared memory object from a file descriptor.
*
* The shared memory is backed by a file descriptor via mmap.
* See {@link ANeuralNetworksMemory} for a description on how to use
* this shared memory.
*
* Available since API level 27.
*
* @param size The requested size in bytes.
* Must not be larger than the file size.
* @param prot The desired memory protection for the mapping.
* It is either PROT_NONE or the bitwise OR of one or
* more of the following flags: PROT_READ, PROT_WRITE.
* @param fd The requested file descriptor.
* The file descriptor has to be mmap-able. The file
* descriptor will be duplicated.
* @param offset The offset to the beginning of the file of the area to map.
* The offset has to be aligned to a page size.
* @param memory The memory object to be created.
* Set to NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if the request completed normally.
*/
int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset,
ANeuralNetworksMemory** memory) __INTRODUCED_IN(27);
/**
* Delete a memory object.
*
* Destroys the object used by the run time to keep track of the memory.
* This will free the underlying actual memory if no other code has open
* handles to this memory.
*
* Available since API level 27.
*
* @param memory The memory object to be freed.
*/
void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) __INTRODUCED_IN(27);
/**
* Create an empty {@link ANeuralNetworksModel}.
*
* <p>This only creates the object. Computation is performed once
* {@link ANeuralNetworksExecution_compute} or
* {@link ANeuralNetworksExecution_startCompute} is invoked.
*
* The model should be constructed with calls to
* {@link ANeuralNetworksModel_addOperation} and
* {@link ANeuralNetworksModel_addOperand}
*
* <p>{@link ANeuralNetworksModel_finish} should be called once the model
* has been fully constructed.</p>
*
* <p>{@link ANeuralNetworksModel_free} should be called once the model
* is no longer needed.</p>
*
* Available since API level 27.
*
* @param model The {@link ANeuralNetworksModel} to be created.
* Set to NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_create(ANeuralNetworksModel** model) __INTRODUCED_IN(27);
/**
* Destroy a model.
*
* The model need not have been finished by a call to
* {@link ANeuralNetworksModel_finish}.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param model The model to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksModel_free(ANeuralNetworksModel* model) __INTRODUCED_IN(27);
/**
* Indicate that we have finished modifying a model. Required before
* calling {@link ANeuralNetworksCompilation_create} and
* {@link ANeuralNetworksCompilation_createForDevices}.
*
* An application is responsible to make sure that no other thread uses
* the model at the same time.
*
* This function must only be called once for a given model.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param model The model to be finished.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) __INTRODUCED_IN(27);
/**
* Add an operand to a model.
*
* The order in which the operands are added is important. The first one added
* to a model will have the index value 0, the second 1, etc. These indexes are
* used as operand identifiers in
* {@link ANeuralNetworksModel_addOperation},
* {@link ANeuralNetworksModel_identifyInputsAndOutputs},
* {@link ANeuralNetworksModel_setOperandValue},
* {@link ANeuralNetworksModel_setOperandValueFromMemory},
* {@link ANeuralNetworksExecution_setInput},
* {@link ANeuralNetworksExecution_setInputFromMemory},
* {@link ANeuralNetworksExecution_setOutput},
* {@link ANeuralNetworksExecution_setOutputFromMemory} and
* {@link ANeuralNetworksExecution_setOperandValue}.
*
* <p>Every operand must be referenced in exactly one of the following
* ways:<ul>
* <li>It is identified as a model input with
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</li>
* <li>It is identified as a constant with
* {@link ANeuralNetworksModel_setOperandValue} or
* {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li>
* <li>It is identified as an output of exactly one operation with
* {@link ANeuralNetworksModel_addOperation}.</li></p>
* <p>An operand that is identified as a model input or as a constant
* must not also be identified as a model output with
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</p>
*
* To build a model that can accommodate inputs of various sizes, as
* you may want to do for a CNN, leave unspecified the dimensions that
* will vary at run time. If you do so, fully specify dimensions
* when calling {@link ANeuralNetworksExecution_setInput} or
* {@link ANeuralNetworksExecution_setInputFromMemory}.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param model The model to be modified.
* @param type The {@link ANeuralNetworksOperandType} that describes the shape
* of the operand. Neither the {@link ANeuralNetworksOperandType}
* nor the dimensions it points to need to outlive the call to
* {@link ANeuralNetworksModel_addOperand}.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model,
const ANeuralNetworksOperandType* type) __INTRODUCED_IN(27);
/**
* Sets an operand to a constant value.
*
* Values of length smaller or equal to
* {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES}
* are immediately copied into the model.
*
* For values of length greater than {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES},
* a pointer to the buffer is stored within the model. The application is responsible
* for not changing the content of this region until all executions using this model
* have completed. As the data may be copied during processing, modifying the data
* after this call yields undefined results.
*
* For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory}
* is likely to be more efficient.
*
* To indicate that an optional operand should be considered missing,
* pass nullptr for buffer and 0 for length.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param model The model to be modified.
* @param index The index of the model operand we're setting.
* @param buffer A pointer to the data to use.
* @param length The size in bytes of the data value.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index,
const void* buffer, size_t length) __INTRODUCED_IN(27);
#if __ANDROID_API__ >= __ANDROID_API_Q__
/**
* Sets an operand's per channel quantization parameters.
*
* Sets parameters required by a tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}.
* This function must be called for every tensor of type
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} before
* calling {@link ANeuralNetworksModel_finish}.
*
* Available since API level 29.
*
* @param model The model to be modified.
* @param index The index of the model operand we're setting.
* @param channelQuant The per channel quantization parameters for the operand.
* No memory in this struct needs to outlive the call to
* this function.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_setOperandSymmPerChannelQuantParams(
ANeuralNetworksModel* model, int32_t index,
const ANeuralNetworksSymmPerChannelQuantParams* channelQuant) __INTRODUCED_IN(29);
#endif // __ANDROID_API__ >= __ANDROID_API_Q__
/**
* Sets an operand to a value stored in a memory object.
*
* The content of the memory is not copied. A reference to that memory is stored
* inside the model. The application is responsible for not changing the content
* of the memory region until all executions using this model have completed.
* As the data may be copied during processing, modifying the data after this call
* yields undefined results.
*
* To indicate that an optional operand should be considered missing,
* use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer.
*
* Is disallowed to set an operand value with shared memory backed by an AHardwareBuffer
* of a format other than AHARDWAREBUFFER_FORMAT_BLOB.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
* See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on
* AHardwareBuffer usage.
*
* Available since API level 27.
*
* @param model The model to be modified.
* @param index The index of the model operand we're setting.
* @param buffer A pointer to the data to use.
* @param memory The memory containing the data.
* @param offset This specifies the location of the data within the memory.
* The offset is in bytes from the start of memory.
* @param length The size in bytes of the data value.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index,
const ANeuralNetworksMemory* memory,
size_t offset, size_t length)
__INTRODUCED_IN(27);
/**
* Add an operation to a model.
*
* @param model The model to be modified.
* @param type The {@link ANeuralNetworksOperationType} of the operation.
* @param inputCount The number of entries in the inputs array.
* @param inputs An array of indexes identifying each operand.
* @param outputCount The number of entries in the outputs array.
* @param outputs An array of indexes identifying each operand.
*
* The operands specified by inputs and outputs must have been
* previously added by calls to {@link ANeuralNetworksModel_addOperand}.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model,
ANeuralNetworksOperationType type, uint32_t inputCount,
const uint32_t* inputs, uint32_t outputCount,
const uint32_t* outputs) __INTRODUCED_IN(27);
/**
* Specifies which operands will be the model's inputs and
* outputs. Every model must have at least one input and one output.
*
* An operand cannot be used for both input and output. Doing so will
* return an error.
*
* @param model The model to be modified.
* @param inputCount The number of entries in the inputs array.
* @param inputs An array of indexes identifying the input operands.
* @param outputCount The number of entries in the outputs array.
* @param outputs An array of indexes identifying the output operands.
*
* The operands specified by inputs and outputs must have been
* previously added by calls to {@link ANeuralNetworksModel_addOperand}.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* Available since API level 27.
*
*/
int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount,
const uint32_t* inputs, uint32_t outputCount,
const uint32_t* outputs) __INTRODUCED_IN(27);
#if __ANDROID_API__ >= 28
/**
* Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be
* calculated with range and/or precision as low as that of the IEEE 754 16-bit
* floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* must be calculated using at least the range and precision of the IEEE 754
* 32-bit floating-point format.
*
* @param model The model to be modified.
* @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be
* calculated with range and/or precision as low as that of the
* IEEE 754 16-bit floating point format. 'false' indicates
* {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using
* at least the range and precision of the IEEE 754 32-bit floating
* point format.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* Available since API level 28.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*/
int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow)
__INTRODUCED_IN(28);
#endif // __ANDROID_API__ >= 28
/**
* Create a {@link ANeuralNetworksCompilation} to compile the given model.
*
* <p>This only creates the object. Compilation is only performed once
* {@link ANeuralNetworksCompilation_finish} is invoked.</p>
*
* <p>{@link ANeuralNetworksCompilation_finish} should be called once
* all desired properties have been set on the compilation.</p>
*
* <p>{@link ANeuralNetworksModel_free} should be called once the compilation
* is no longer needed.</p>
*
* <p>The provided model must outlive the compilation.</p>
*
* The model must already have been finished by a call to
* {@link ANeuralNetworksModel_finish}.
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param model The {@link ANeuralNetworksModel} to be compiled.
* @param compilation The newly created object or NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
* if the model is invalid.
*/
int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model,
ANeuralNetworksCompilation** compilation) __INTRODUCED_IN(27);
/**
* Destroy a compilation.
*
* The compilation need not have been finished by a call to
* {@link ANeuralNetworksModel_finish}.
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param compilation The compilation to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation) __INTRODUCED_IN(27);
/**
* Sets the execution preference.
*
* <p>Provides guidance to the runtime when trade-offs are possible.</p>
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param compilation The compilation to be modified.
* @param preference Either {@link PREFER_LOW_POWER},
* {@link PREFER_SINGLE_FAST_ANSWER}, or
* {@link PREFER_SUSTAINED_SPEED}.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation,
int32_t preference) __INTRODUCED_IN(27);
/**
* Indicate that we have finished modifying a compilation. Required before
* calling {@link ANeuralNetworksExecution_create}.
*
* An application is responsible to make sure that no other thread uses
* the compilation at the same time.
*
* This function must only be called once for a given compilation.
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param compilation The compilation to be finished.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) __INTRODUCED_IN(27);
/**
* Create a {@link ANeuralNetworksExecution} to apply the given compilation.
* This only creates the object. Computation is only performed once
* {@link ANeuralNetworksExecution_compute} or
* {@link ANeuralNetworksExecution_startCompute} is invoked.
*
* <p>The provided compilation must outlive the execution.</p>
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param compilation The {@link ANeuralNetworksCompilation} to be evaluated.
* @param execution The newly created object or NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
* if the compilation is invalid.
*/
int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation,
ANeuralNetworksExecution** execution) __INTRODUCED_IN(27);
/**
* Destroy an execution.
*
* <p>If called on an execution for which
* {@link ANeuralNetworksExecution_startCompute} has been called, the
* function will return immediately but will mark the execution to be deleted
* once the computation completes. The related {@link ANeuralNetworksEvent}
* will be signaled and the {@link ANeuralNetworksEvent_wait} will return
* ANEURALNETWORKS_ERROR_DELETED.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param execution The execution to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) __INTRODUCED_IN(27);
/**
* Associate a user buffer with an input of the model of the
* {@link ANeuralNetworksExecution}.
*
* <p>The provided buffer must outlive the execution.</p>
*
* If the input is optional, you can indicate that it is omitted by
* passing nullptr for buffer and 0 for length.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param execution The execution to be modified.
* @param index The index of the input argument we are setting. It is
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with
* {@link ANeuralNetworksModel_addOperand}.
* @param type The {@link ANeuralNetworksOperandType} of the
* operand. Unless the input is omitted, this should be
* used to specify the dimensions that were left
* unspecified when the operand was added to the
* model. All other properties of the type must be the
* same as specified in the model. If the type is the same
* as specified when the model was built, NULL can be
* passed. Neither the {@link ANeuralNetworksOperandType}
* nor the dimensions it points to need to outlive the call
* to {@link ANeuralNetworksExecution_setInput}.
* @param buffer The buffer containing the data.
* @param length The length in bytes of the buffer.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
* name is not recognized or the buffer is too small for the input.
*/
int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type, const void* buffer,
size_t length) __INTRODUCED_IN(27);
/**
* Associate part of a memory object with an input of the model of the
* {@link ANeuralNetworksExecution}.
*
* <p>The provided memory must outlive the execution.</p>
*
* If the input is optional, you can indicate that it is omitted by
* using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer
* and 0 for length.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
* See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on
* AHardwareBuffer usage.
*
* Available since API level 27.
*
* @param execution The execution to be modified.
* @param index The index of the input argument we are setting. It is
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
* @param type The {@link ANeuralNetworksOperandType} of the
* operand. This should be used to specify the dimensions
* that were left unspecified when the operand was added
* to the model. All other properties of the type must be
* the same as specified in the model. If the type is the
* same as specified when the model was built, NULL can be
* passed. Neither the {@link ANeuralNetworksOperandType}
* nor the dimensions it points to need to outlive the call
* to {@link ANeuralNetworksExecution_setInputFromMemory}.
* @param memory The memory containing the data.
* @param offset This specifies the location of the data within the memory.
* The offset is in bytes from the start of memory.
* @param length The size in bytes of the data value.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
* name is not recognized or the buffer is too small for the input.
*/
int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type,
const ANeuralNetworksMemory* memory, size_t offset,
size_t length) __INTRODUCED_IN(27);
/**
* Associate a user buffer with an output of the model of the
* {@link ANeuralNetworksExecution}.
*
* If the output is optional, you can indicate that it is omitted by
* passing nullptr for buffer and 0 for length.
*
* <p>The provided buffer must outlive the execution.</p>
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*
* @param execution The execution to be modified.
* @param index The index of the output argument we are setting. It is
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
* @param type The {@link ANeuralNetworksOperandType} of the
* operand. Unless the output is omitted, this should be
* used to specify the dimensions that were left
* unspecified when the operand was added to the
* model. All other properties of the type must be the
* same as specified in the model. If the type is the same
* as specified when the model was built, NULL can be
* passed. Neither the {@link ANeuralNetworksOperandType}
* nor the dimensions it points to need to outlive the call
* to {@link ANeuralNetworksExecution_setOutput}.
* @param buffer The buffer where the data is to be written.
* @param length The length in bytes of the buffer.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
* name is not recognized or the buffer is too small for the output.
*/
int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type, void* buffer,
size_t length) __INTRODUCED_IN(27);
/**
* Associate part of a memory object with an output of the model of the
* {@link ANeuralNetworksExecution}.
*
* If the output is optional, you can indicate that it is omitted by
* using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer
* and 0 for length.
*
* <p>The provided memory must outlive the execution.</p>
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
* See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on
* AHardwareBuffer usage.
*
* Available since API level 27.
*
* @param execution The execution to be modified.
* @param index The index of the output argument we are setting. It is
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
* @param type The {@link ANeuralNetworksOperandType} of the operand. This should be
* used to specify the dimensions that were left
* unspecified when the operand was added to the
* model. All other properties of the type must be the
* same as specified in the model. If the type is the same
* as specified when the model was built, NULL can be
* passed. Neither the {@link ANeuralNetworksOperandType}
* nor the dimensions it points to need to outlive the call
* to {@link ANeuralNetworksExecution_setOutputFromMemory}.
* @param memory The memory where the data is to be stored.
* @param offset This specifies the location of the data within the memory.
* The offset is in bytes from the start of memory.
* @param length The length in bytes of the data value.
*
* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
* name is not recognized or the buffer is too small for the output.
*/
int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type,
const ANeuralNetworksMemory* memory, size_t offset,
size_t length) __INTRODUCED_IN(27);
/**
* Schedule asynchronous evaluation of the execution.
*
* <p>Schedules asynchronous evaluation of the execution. Once the model has
* been applied and the outputs are ready to be consumed, the returned event
* will be signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that
* event.
* </p>
*
* ANeuralNetworksEvent_wait must be called to recuperate the resources used
* by the execution.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* See {@link ANeuralNetworksExecution_compute} for synchronous execution.
* Synchronous execution incurs lower overhead than asynchronous execution.
*
* Available since API level 27.
*
* @param execution The execution to be scheduled and executed.
* @param event The event that will be signaled on completion. event is set to
* NULL if there's an error.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution,
ANeuralNetworksEvent** event) __INTRODUCED_IN(27);
/**
* Waits until the execution completes.
*
* More than one thread can wait on an event. When the execution completes,
* all threads will be released.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*
* @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
* ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot
* be properly mapped.
*/
int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) __INTRODUCED_IN(27);
/**
* Destroys the event.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* Available since API level 27.
*/
void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) __INTRODUCED_IN(27);
#endif // __ANDROID_API__ >= 27
__END_DECLS
#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/** @} */