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android / platform / frameworks / ml / 6cfad8f3f47371aa2a83677c2459a69688126fe7 / . / nn / 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
*/
#if __ANDROID_API__ >= __ANDROID_API_O_MR1__
#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}.
*/
typedef enum {
/** The following entries are used to declare scalars. */
/** 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,
/** The following entries are used to declare tensors. */
/** 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 integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert
* the 8 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit non-negative floating point value.
* - zeroPoint: an 32 bit integer, in range [0, 255].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
*/
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
} OperandCode;
/**
* Operation types.
*
* The type of operations that can be added to a model.
*/
typedef enum {
/** Adds two tensors, element-wise.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the sum of both input tensors, optionally modified by an activation function.
*
* 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 types:
* * {@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 type, and compatible dimensions as input0.
* * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The sum, a tensor of the same type as input0.
*/
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[batch, row, col, channel] =
* sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
* data layout.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 7: An INT32 value, specifying the filter width.
* * 8: An INT32 value, specifying the filter height.
* * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 3: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 4: An INT32 value, specifying the filter width.
* * 5: An INT32 value, specifying the filter height.
* * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
/** Concatenates the input tensors along the given dimension.
*
* The input tensors must have identical type and the same dimensions except the
* dimension along the concatenation axis.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
* For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all
* input tensors must have the same scale and zeroPoint.
* * n: An INT32 value, specifying the concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same type as the input tensors.
* The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*/
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[batch, row, col, channel] =
* sum_{i, j} (
* input[batch, row + i, col + j, k] *
* filter[channel, row + i, col + j, k] +
* bias[channel]
* )
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
* * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 4: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 5: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* 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} type, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
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]
* )
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* 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.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 9: An INT32 value, specifying the depthwise multiplier.
* * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* 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.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 4: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 5: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 6: An INT32 value, specifying the depthwise multiplier.
* * 7: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* 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} type, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
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 types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
* * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
* block_size * block_size must be a divisor of the input depth.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
* depth/(block_size*block_size)].
*/
ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
/** Dequantizes the input tensor.
*
* The formula is:
*
* output = (input - zeroPoint) * scale.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with type
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
*/
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], we would expect all three values
* found in Lookups to be between 0 and 39. The resulting tensor will
* have shape of [3, 200, 300].
*
* If a value in Lookups is out of bounds, the operation will fail
* and an error will be reported.
*
* Inputs:
* * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type.
* 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.
*/
ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
/** Computes element-wise floor() on the input tensor.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor, of the same type and dimensions as the input tensor.
*/
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 types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
* a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
* [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
* and “input_size” is the size of the input.
* * 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} type, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The output tensor, of shape [batch_size, num_units].
* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
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 will 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], we're concatenating three slices, so the resulting tensor
* will have the shape of [3, 200, 300]. If the first entry in
* Lookups has the value 123456, we'll look for that value in Keys tensor.
* If the sixth entry of Keys contains 123456, we'll select the sixth
* slice of Values. If no entry in Keys has 123456, a slice of zeroes
* will be concatenated.
*
* 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.
*/
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 more dimensions, independently normalizes each 1-D slice along dimension dim.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth].
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
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[batch, row, col, channel] =
* sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 7: An INT32 value, specifying the filter width.
* * 8: An INT32 value, specifying the filter height.
* * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 3: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 4: An INT32 value, specifying the filter width.
* * 5: An INT32 value, specifying the filter height.
* * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
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)
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the radius of the normalization window.
* * 2: A FLOAT32 value, specifying the bias, must not be zero.
* * 3: A FLOAT32 value, specifying the scale factor, alpha.
* * 4: A FLOAT32 value, specifying the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
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 types:
* * {@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} type,
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
ANEURALNETWORKS_LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of seeds per hash functions.
* Tensor[0].Dim[1] <= 32 in sparse case.
*
* * 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(=1).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits computed from
* hash functions.
*
* Dense: Value LSHProjectionType_DENSE(=2).
* 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.
*/
ANEURALNETWORKS_LSH_PROJECTION = 15,
/**
* Long short-term memory unit (LSTM) recurrent network layer.
*
* The default non-peephole implementation is based on:
* http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
* Computation, 9(8):1735-1780, 1997.
*
* The peephole implementation 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.
*
* 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 class has the following independently optional inputs:
* * If input gate (if CIFG): “input_to_forget_weights”,
* “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
* * If no peephole connections: “cell_to_input_weights”,
* “cell_to_forget_weights”, “cell_to_output_weights”.
* * If no projection layer: “projection_weights” and “projection_bias”.
* * If no projection bias: “projection_bias”.
*
* Supported tensor types (type T):
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: Input.
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size”
* is the size of the input.
* * 1: input_to_input_weights.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of cell units.
* * 2: input_to_forget_weights.
* A 2-D tensor of type T, of shape [num_units, input_size].
* * 3: input_to_cell_weights.
* A 2-D tensor of type T, of shape [num_units, input_size].
* * 4: input_to_output_weights.
* A 2-D tensor of type T, of shape [num_units, input_size].
* * 5: recurrent_to_input_weights.
* A 2-D tensor of type T, of shape [num_units, output_size], where
* “output_size” corresponds to either the number of cell units (i.e.,
* “num_units”), or the second dimension of the “projection_weights”, if
* defined.
* * 6: recurrent_to_forget_weights.
* A 2-D tensor of type T, of shape [num_units, output_size].
* * 7: recurrent_to_cell_weights.
* A 2-D tensor of type T, of shape [num_units, output_size].
* * 8: recurrent_to_output_weights.
* A 2-D tensor of type T, of shape [num_units, output_size].
* * 9: cell_to_input_weights.
* A 1-D tensor of type T, of shape [num_units].
* * 10:cell_to_forget_weights.
* A 1-D tensor of type T, of shape [num_units].
* * 11:cell_to_output_weights.
* A 1-D tensor of type T, of shape [num_units].
* * 12:input_gate_bias.
* A 1-D tensor of type T, of shape [num_units].
* * 13:forget_gate_bias.
* A 1-D tensor of type T, of shape [num_units].
* * 14:cell_bias.
* A 1-D tensor of type T, of shape [num_units].
* * 15:output_gate_bias.
* A 1-D tensor of type T, of shape [num_units].
* * 16:projection_weights.
* A 2-D tensor of type T, of shape [output_size, num_units].
* * 17:projection_bias.
* A 1-D tensor of type T, of shape [output_size].
* * 18: output_state (in).
* A 2-D tensor of type T, of shape [batch_size, output_size].
* * 19: cell_state (in).
* A 2-D tensor of type T, of shape [batch_size, num_units].
* * 20:fused_activation_function.
* An optional {@link FuseCode} value indicating the activation
* function.
* If “NONE” is specified then it results in a linear activation.
* * 21:cell_clip.
* A clipping threshold for the cell state, such that values are bound
* within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
* disabled.
* * 22:proj_clip.
* A clipping threshold for the output from the projection layer, such
* that values are bound within [-proj_clip, proj_clip]. If set to 0.0
* then clipping is disabled.
*
* Outputs:
* * 0: scratch_buffer.
* A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
* * 1: output_state (out).
* A 2-D tensor of type T, of shape [batch_size, output_size].
* * 2: cell_state (out).
* A 2-D tensor of type T, of shape [batch_size, num_units].
* * 3: output.
* A 2-D tensor of type T, of shape [batch_size, output_size]. This is
* effectively the same as the current “output_state” value.
*/
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[batch, row, col, channel] =
* max_{i, j} (input[batch, row + i, col + j, channel])
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 7: An INT32 value, specifying the filter width.
* * 8: An INT32 value, specifying the filter height.
* * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An INT32 value, specifying the output stride in the ‘width’ dimension.
* * 3: An INT32 value, specifying the output stride in the ‘height’ dimension.
* * 4: An INT32 value, specifying the filter width.
* * 5: An INT32 value, specifying the filter height.
* * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
ANEURALNETWORKS_MAX_POOL_2D = 17,
/** Multiplies two tensors, element-wise.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the product of both input tensors, optionally modified by an activation function.
*
* 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 types:
* * {@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 type, and compatible dimensions as input0.
* * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The product, a tensor of the same type as input0.
* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
* condition must be satisfied: output_scale > input1_scale * input2_scale.
*/
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 types:
* * {@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.
*/
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 types:
* * {@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.
*/
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 types:
* * {@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.
*/
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 types:
* * {@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 type {@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.
*/
ANEURALNETWORKS_RESHAPE = 22,
/** Resizes images to given size using the bilinear interpretation.
*
* Resized images will be distorted if their output aspect ratio is not the same as
* input aspect ratio.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the output height of the output tensor.
* * 2: An INT32 value, specifying the output width of the output tensor.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
*/
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 types (Type T):
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size” is
* the size of the input.
* * 1: weights.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of units.
* * 2: recurrent_weights.
* A 2-D tensor of type T, of shape [num_units, num_units], with columns
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of type T, of shape [num_units].
* * 4: hidden state (in).
* A 2-D tensor of type T, 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 type T, of shape [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units]. This is
* effectively the same as the current state value.
*/
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)}
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 2 or 4.
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
* * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
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 types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
* * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
* block_size must be a divisor of both the input height and width.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
* depth*block_size*block_size].
*/
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, "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 types (type T):
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size” is
* the size of the input.
* * 1: weights_feature.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of units.
* * 2: weights_time.
* A 2-D tensor of type T, of shape [num_units, memory_size], where
* “memory_size” corresponds to the fixed-size of the memory.
* * 3: bias.
* An optional 1-D tensor of type T, of shape [num_units].
* * 4: state (in).
* A 2-D tensor of type T, 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 type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units].
*/
ANEURALNETWORKS_SVDF = 27,
/** Computes hyperbolic tangent of input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = tanh(input)
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ANEURALNETWORKS_TANH = 28,
} OperationCode;
/**
* Fused activation function types.
*
*/
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.
*
*/
typedef enum {
/** SAME padding. */
ANEURALNETWORKS_PADDING_SAME = 1,
/** VALID padding. */
ANEURALNETWORKS_PADDING_VALID = 2,
} PaddingCode;
/**
* Execution preferences.
*/
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;
/**
* Result codes.
*/
typedef enum {
ANEURALNETWORKS_NO_ERROR = 0,
ANEURALNETWORKS_OUT_OF_MEMORY = 1,
ANEURALNETWORKS_INCOMPLETE = 2,
ANEURALNETWORKS_UNEXPECTED_NULL = 3,
ANEURALNETWORKS_BAD_DATA = 4,
ANEURALNETWORKS_OP_FAILED = 5,
ANEURALNETWORKS_UNMAPPABLE = 5,
ANEURALNETWORKS_BAD_STATE = 6,
} ResultCode;
/**
* 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 tensor
* needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
* used to create shared memory from a file handle. {@link ANeuralNetworksMemory_createShared}
* can be used to directly created shared memory.
*
* Memory objects can also be used to specify the input and output arguments of
* an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
* and {@link ANeuralNetworksExecution_setOutputFromMemory}.
*/
typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;
/**
* ANeuralNetworksModel is an opaque type that contains a description of the
* mathematical operations that constitute the model.
*
* <p>The model will be built by calling<ul>
* <li>{@link ANeuralNetworksModel_create},</li>
* <li>{@link ANeuralNetworksModel_addOperation},</li>
* <li>{@link ANeuralNetworksModel_addOperand},</li>
* </ul>
*
* 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>
*/
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.</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}.</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>
*/
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 data to the model inputs with
* {@link ANeuralNetworksExecution_setInput} or
* {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
* <li>Associate output buffers to the model outputs with
* {@link ANeuralNetworksExecution_setOutput} or
* {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
* <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li>
* <li>Wait for the execution to complete with {@link
* ANeuralNetworksEvent_wait}.</li>
* <li>Destroy the execution with
* {@link ANeuralNetworksExecution_free}.</li></ul></p>
*
* <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute}
* has been called on it.</p>
*
* <p>An execution can be applied to a model with
* {@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 request after calling {@link ANeuralNetworksExecution_free}.</p>
*/
typedef struct ANeuralNetworksExecution ANeuralNetworksExecution;
/**
* ANeuralNetworksOperandType describes the type of an operand.
* This structure is used to describe both scalars and tensors.
*/
typedef struct ANeuralNetworksOperandType {
/** The data type, e.g ANEURALNETWORKS_INT8. */
int32_t type;
/** The number of dimensions. 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.
*/
typedef struct ANeuralNetworksEvent ANeuralNetworksEvent;
/**
* 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.
*
* @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);
/**
* 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.
*
* @param memory The memory object to be freed.
*/
void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory);
/**
* Create an empty {@link ANeuralNetworksModel}.
*
* <p>This only creates the object. Computation is performed once
* {@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>
*
* @param model The {@link ANeuralNetworksModel} to be created.
* Set to NULL if unsuccessful.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_create(ANeuralNetworksModel** model);
/**
* 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.
*
* @param model The model to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksModel_free(ANeuralNetworksModel* model);
/**
* Indicate that we have finished modifying a model. Required before
* calling {@link ANeuralNetworksCompilation_create}.
*
* 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.
*
* @param model The model to be finished.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_finish(ANeuralNetworksModel* model);
/**
* 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 ANeuralNetworksExecution_setInput},
* {@link ANeuralNetworksExecution_setInputFromMemory},
* {@link ANeuralNetworksExecution_setOutput},
* {@link ANeuralNetworksExecution_setOutputFromMemory} and
* {@link ANeuralNetworksExecution_setOperandValue}.
*
* To build a model that can accomodate inputs of various sizes, as you may want
* to do for a CNN, set the size of the dimensions that will vary at run time to 0.
* If you do so, provide the full 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.
*
* @param model The model to be modified.
* @param type The {@link ANeuralNetworksOperandType} that describes the shape
* of the operand.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model,
const ANeuralNetworksOperandType* type);
/**
* Sets an operand to a constant value.
*
* For scalar values, the content of buffer is copied into the model.
*
* For tensor 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.
*
* 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.
*
* @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);
/**
* 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.
*
* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
* called will return an error.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*
* @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);
/**
* Add an operation to a model.
*
* @param model The model to be modified.
* @param type The type 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.
*
* @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);
/**
* Specfifies which operands will be the model's inputs and outputs.
*
* 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.
*
*/
int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount,
const uint32_t* inputs, uint32_t outputCount,
const uint32_t* outputs);
/**
* 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.
*
* @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);
/**
* 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.
*
* @param compilation The compilation to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation);
/**
* Sets the execution preference.
*
* <p>Provides guidance to the runtime when trade-offs are possible.</p>
*
* See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
*
* @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);
/**
* 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.
*
* @param compilation The compilation to be finished.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation);
/**
* Create a {@link ANeuralNetworksExecution} to apply the given compilation.
* This only creates the object. Computation is only performed once
* {@link ANeuralNetworksExecution_startCompute} is invoked.
*
* <p>The provided compilation must outlive the execution.</p>
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* @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);
/**
* 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.
*
* @param execution The execution to be destroyed. Passing NULL is acceptable and
* results in no operation.
*/
void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution);
/**
* 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.
*
* @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 type of the operand. This should be used to specify the
* dimensions that were set to 0 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.
* @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);
/**
* 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.
*
* @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 type of the operand. This can be used to specify the
* dimensions that were set to 0 when the operand was added to the
* model. All other values 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.
* @param memory The memory containing the data.
* @param offset This specifies the location of the data whithin 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);
/**
* 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.
*
* @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 type of the operand. This can be used to specify the
* dimensions that were set to 0 when the operand was added to the
* model. All other values 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.
* @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);
/**
* 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.
*
* @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 type of the operand. This can be used to specify the
* dimensions that were set to 0 when the operand was added to the
* model. All other values 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.
* @param memory The memory where the data is to be stored.
* @param offset This specifies the location of the data whithin 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);
/**
* Schedule evaluation of the execution.
*
* <p>Schedules 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>
*
* Multiple executions can be scheduled and evaluated concurrently. 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 using
* {@link ANeuralNetworksEvent_wait}.
*
* ANeuralNetworksEvent_wait must be called to recuperate the resources used
* by the execution.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
* @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);
/**
* 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.
*
* @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
*/
int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event);
/**
* Destroys the event.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*/
void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event);
__END_DECLS
#endif // __ANDROID_API__ >= 27
#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/** @} */