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android / platform / frameworks / ml / 84e960efd464cedbe9de31410bf82fdffe54e7f9 / . / 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 {
/** 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 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 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,
} 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 {@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_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.
*/
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 {@link OperandCode}:
* * {@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 {@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.
*
* 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.
*
* 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 {@link OperandCode} and the same
* dimensions except the dimension along the concatenation axis.
*
* Supported tensor {@link OperandCode}:
* * {@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}, all input tensors
* must have the same scale and zeroPoint.
* * 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].
*/
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 {@link OperandCode}:
* * {@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}, 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, 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.
*
* 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}, 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, 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.
*
* 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.
*/
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 {@link OperandCode}:
* * {@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}, 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, 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.
*
* 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 {@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, 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.
*
* 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.
*/
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_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 {@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.
*
* 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 {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@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], 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.
*
* 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.
*/
ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
/**
* Computes element-wise floor() on the input tensor.
*
* Supported tensor {@link OperandCode}:
* * {@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.
*/
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_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.
*/
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.
*
* 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 {@link OperandCode}:
* * {@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 {@link OperandCode}:
* * {@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 {@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.
*
* 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.
*
* 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 {@link OperandCode}:
* * {@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 {@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.
*
* 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 {@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.
*/
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,
/**
* 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.
*
* 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.
*
* 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"
*
* Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of cell units.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, output_size], where “output_size” corresponds to either
* the number of cell units (i.e., “num_units”), or the second
* dimension of the “projection_weights”, if defined.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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.
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
* projection layer, such that values are bound within
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
*
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, num_units * 4] with CIFG, or
* [batch_size, num_units * 3] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, output_size]. This is effectively the same as the
* current “output state (out)” 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 {@link OperandCode}:
* * {@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 {@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.
*
* 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.
*
* 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 {@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_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.
*/
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_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 {@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.
*/
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_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 {@link OperandCode}:
* * {@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.
*/
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_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 {@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.
*
* 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 {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* * 1: weights.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of units.
* * 2: recurrent_weights.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, num_units], with columns corresponding to the weights
* from each unit.
* * 3: bias.
* A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units].
* * 4: hidden state (in).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 {@link OperandCode}:
* * {@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: An {@link ANEURALNETWORKS_FLOAT32} scalar, 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},
* 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 {@link OperandCode}:
* * {@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 {@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.
*
* 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,
* "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_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* * 1: weights_feature.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of units.
* * 2: weights_time.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [num_units, memory_size], where “memory_size” corresponds to the
* fixed-size of the memory.
* * 3: bias.
* An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32},
* of shape [num_units].
* * 4: state (in).
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
* An optional {@link 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 {@link OperandCode}:
* * {@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,
#if __ANDROID_API__ >= __ANDROID_API_P__
// 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_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block
* sizes for each spatial dimension of the input tensor. All values
* must be >= 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*/
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_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.
*/
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_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.
*/
ANEURALNETWORKS_MEAN = 31,
/**
* Pads a tensor.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor {@link OperandCode}:
* * {@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 element to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the
* end of dimension i.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*/
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_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* 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.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*/
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_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.
*/
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_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: 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: 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: 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).
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit
* of begin_mask is set, begin[i] is ignored and the fullest possible
* range in that dimension is used instead.
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of
* end_mask is set, end[i] is ignored and the fullest possible range in
* that dimension is used instead.
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32
* mask. If the ith bit of shrink_axis_mask is set, it implies that the
* ith specification shrinks the dimensionality by 1. A slice of size 1
* starting from begin[i] in the dimension must be preserved.
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
*/
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_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.
*/
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_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.
*/
ANEURALNETWORKS_TRANSPOSE = 37,
#endif // __ANDROID_API__ >= __ANDROID_API_P__
} 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.
* 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 - output_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.
*/
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_BAD_STATE = 6,
ANEURALNETWORKS_UNMAPPABLE = 7,
} 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.
*/
enum {
ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128
};
/**
* 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.
*
* 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>Build the model 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 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 {@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 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_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 execution 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.
*
* 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.
*/
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.
*/
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 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.
*
* @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);
/**
* 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.
*
* @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 {@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.
*
* @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);
/**
* Specifies 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);
#if __ANDROID_API__ >= __ANDROID_API_P__
/**
* 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.
*
* See {@link ANeuralNetworksModel} for information on multithreaded usage.
*/
int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow);
#endif // __ANDROID_API__ >= __ANDROID_API_P__
/**
* 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 {@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);
/**
* 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 {@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);
/**
* 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 {@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);
/**
* 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 {@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);
/**
* 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__ >= __ANDROID_API_O_MR1__
#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/** @} */