/*
* 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 Build the model by calling A model cannot be modified once {@link ANeuralNetworksModel_finish}
* has been called on it. 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. 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. To use:
*
* * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such that values are bound
* within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
* disabled.
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the projection layer, such
* that values are bound within [-proj_clip, proj_clip]. If set to 0.0
* then clipping is disabled.
*
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
* CIFG, or [batch_size, num_units * 3] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of type T, of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of type T, of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of type T, of shape [batch_size, output_size]. This is
* effectively the same as the current “output state (out)” value.
*/
ANEURALNETWORKS_LSTM = 16,
/** Performs an 2-D max pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
* max_{i, j} (input[batch, row + i, col + j, channel])
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Both explicit padding and implicit padding are supported.
*
* Inputs (explicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
* * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
* * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
* * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
* * 5: An INT32 value, specifying the stride when walking through input
* in the ‘width’ dimension.
* * 6: An INT32 value, specifying the stride when walking through input
* in the ‘height’ dimension.
* * 7: An INT32 value, specifying the filter width.
* * 8: An INT32 value, specifying the filter height.
* * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
* {@link PaddingCode} values.
* * 2: An INT32 value, specifying the stride when walking through input
* in the ‘width’ dimension.
* * 3: An INT32 value, specifying the stride when walking through input
* in the ‘height’ dimension.
* * 4: An INT32 value, specifying the filter width.
* * 5: An INT32 value, specifying the filter height.
* * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
ANEURALNETWORKS_MAX_POOL_2D = 17,
/** Multiplies two tensors, element-wise.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the product of both input tensors, optionally modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
* The size of the resulting output is the maximum size along each dimension of the
* input operands. It starts with the trailing dimensions, and works its way forward.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same type, and compatible dimensions as input0.
* * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* * 0: The product, a tensor of the same type as input0.
* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
* condition must be satisfied: output_scale > input1_scale * input2_scale.
*/
ANEURALNETWORKS_MUL = 18,
/** Computes rectified linear activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = max(0, input)
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ANEURALNETWORKS_RELU = 19,
/** Computes rectified linear 1 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(1.f, max(-1.f, input))
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ANEURALNETWORKS_RELU1 = 20,
/** Computes rectified linear 6 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(6, max(0, input))
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ANEURALNETWORKS_RELU6 = 21,
/** Reshapes a tensor.
*
* Given tensor, this operation returns a tensor that has the same values as tensor,
* but with a newly specified shape.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the tensor to be reshaped.
* * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}, defining the shape
* of the output tensor. The number of elements implied by shape must be the same
* as the number of elements in the input tensor.
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
*/
ANEURALNETWORKS_RESHAPE = 22,
/** Resizes images to given size using the bilinear interpretation.
*
* Resized images will be distorted if their output aspect ratio is not the same as
* input aspect ratio. The corner pixels of output may not be the same as
* corner pixels of input.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
* * 1: An INT32 value, specifying the output height of the output tensor.
* * 2: An INT32 value, specifying the output width of the output tensor.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
*/
ANEURALNETWORKS_RESIZE_BILINEAR = 23,
/**
* A basic recurrent neural network layer.
*
* This layer implements the operation:
* outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
*
* Where:
* * “input_weights” is a weight matrix that multiplies the inputs;
* * “recurrent_weights” is a weight matrix that multiplies the current
* “state” which itself is the output from the previous time step
* computation;
* * “bias” is a bias vector (added to each output vector in the batch);
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Supported tensor types (Type T):
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size” is
* the size of the input.
* * 1: weights.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of units.
* * 2: recurrent_weights.
* A 2-D tensor of type T, of shape [num_units, num_units], with columns
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of type T, of shape [num_units].
* * 4: hidden state (in).
* A 2-D tensor of type T, of shape [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FuseCode} value indicating the activation
* function. If “NONE” is specified then it results in a linear
* activation.
*
* Outputs:
* * 0: hidden state (out).
* A 2-D tensor of type T, of shape [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units]. This is
* effectively the same as the current state value.
*/
ANEURALNETWORKS_RNN = 24,
/** Computes the softmax activation on the input tensor element-wise, per batch, by
* normalizing the input vector so the maximum coefficient is zero.
*
* The output is calculated using this formula:
*
* output[batch, i] =
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 2 or 4.
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
* * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
ANEURALNETWORKS_SOFTMAX = 25,
/** Rearranges blocks of spatial data, into depth.
*
* More specifically, this op outputs a copy of the input tensor where values from
* the height and width dimensions are moved to the depth dimension.
* The value block_size indicates the input block size and how the data is moved.
*
* Chunks of data of size block_size * block_size from depth are rearranged into
* non-overlapping blocks of size block_size x block_size.
*
* The depth of the output tensor is input_depth * block_size * block_size.
* The input tensor's height and width must be divisible by block_size.
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
* * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
* block_size must be a divisor of both the input height and width.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
* depth*block_size*block_size].
*/
ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
/**
* SVDF op is a kind of stateful layer derived from the notion that a
* densely connected layer that's processing a sequence of input frames can
* be approximated by using a singular value decomposition of each of its
* nodes. The implementation is based on:
*
* https://research.google.com/pubs/archive/43813.pdf
*
* P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
* “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
* INTERSPEECH, 2015.
*
* It processes the incoming input using a 2-stage filtering mechanism:
* * stage 1 performs filtering on the "features" dimension, whose outputs get
* pushed into a memory of fixed-size memory_size.
* * stage 2 performs filtering on the "time" dimension of the memory_size
* memoized outputs of stage 1.
*
* Specifically, for rank 1, this layer implements the operation:
*
* memory = push(conv1d(inputs, weights_feature, feature_dim, "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 types (type T):
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size” is
* the size of the input.
* * 1: weights_feature.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of units.
* * 2: weights_time.
* A 2-D tensor of type T, of shape [num_units, memory_size], where
* “memory_size” corresponds to the fixed-size of the memory.
* * 3: bias.
* An optional 1-D tensor of type T, of shape [num_units].
* * 4: state (in).
* A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
* An optional {@link FuseCode} value indicating the activation function.
* If “NONE” is specified then it results in a linear activation.
*
* Outputs:
* * 0: state (out).
* A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units].
*/
ANEURALNETWORKS_SVDF = 27,
/** Computes hyperbolic tangent of input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = tanh(input)
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ANEURALNETWORKS_TANH = 28,
// TODO: change to __ANDROID_API__ >= __ANDROID_API_P__ once available.
#if __ANDROID_API__ > __ANDROID_API_O_MR1__
// 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 types:
* * {@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 type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
*
* Outputs:
* 0: A tensor of the same type as input0.
*/
ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
/**
* Element-wise division of two tensors.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the result of dividing the first input tensor by the second, optionally
* modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
* The size of the output is the maximum size along each dimension of the input operands.
* It starts with the trailing dimensions, and works its way forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the first input.
* 1: A tensor of the same type, and compatible dimensions as input0.
* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* 0: A tensor of the same type 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 types:
* * {@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 type TENSOR_INT32. The dimensions to reduce. If None (the default),
* reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
* 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
*
* Outputs:
* 0: A tensor of the same type as input0.
*/
ANEURALNETWORKS_MEAN = 31,
/**
* Pads a tensor.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor types:
* * {@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 type TENSOR_INT32, the paddings for each spatial dimension of the
* input tensor. The shape of the tensor must be {rank(input0), 2}.
* padding[i, 0] specifies the number of element to be padded in the front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the end of dimension i.
*
* Outputs:
* 0: A tensor of the same type 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 types:
* * {@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 type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
* 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
* input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
* padding[i, 0] specifies the number of element to be padded in the front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the end of dimension i.
*
* Outputs:
* 0: A tensor of the same type 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 type with all
* dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
* you can remove specific size 1 dimensions by specifying the axes (input1).
*
* Supported tensor types:
* * {@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 type TENSOR_INT32. The dimensions to squeeze. If specified
* only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
* The dimension index starts at 0. An error will be reported if squeezing a dimension that
* is not 1.
*
* Outputs:
* 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
* dimensions of size 1 removed.
*/
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 types:
* * {@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 type TENSOR_INT32, the starts of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 4: An INT32 value, begin_mask. If the ith bit of begin_mask is set, begin[i] is ignored
* and the fullest possible range in that dimension is used instead.
* 5: An INT32 value, end_mask. If the ith bit of end_mask is set, end[i] is ignored and
* the fullest possible range in that dimension is used instead.
* 6: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
* set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
* size 1 starting from begin[i] in the dimension will be preserved.
*
* Outputs:
* 0: A tensor of the same type as input0.
*/
ANEURALNETWORKS_STRIDED_SLICE = 35,
/**
* Element-wise subtraction of two tensors.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the result of subtracting the second input tensor from the first one, optionally
* modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
* The size of the output is the maximum size along each dimension of the input operands.
* It starts with the trailing dimensions, and works its way forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the first input.
* 1: A tensor of the same type, and compatible dimensions as input0.
* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
* Specifies the activation to invoke on the result of each addition.
*
* Outputs:
* 0: A tensor of the same type 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 types:
* * {@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 type TENSOR_INT32, the permutation of the dimensions of the
* input tensor.
*
* Outputs:
* 0: A tensor of the same type as input0.
*/
ANEURALNETWORKS_TRANSPOSE = 37,
#endif
} 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_UNMAPPABLE = 5,
ANEURALNETWORKS_BAD_STATE = 6,
} 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.
*
*
*
*
* A model is completed by calling {@link ANeuralNetworksModel_finish}.
* A model is destroyed by calling {@link ANeuralNetworksModel_free}.
*
*
*
A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} * has been called on it.
* *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.
* *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.
*/ typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; /** * ANeuralNetworksExecution is an opaque type that can be used to apply a machine * learning model to a set of inputs. * *To use:
An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute} * has been called on it.
* *An execution can be applied to a model with * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions * to do new evaluations of the model.
* *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.
* *It is also the application's responsibility to ensure that there are no other * uses of the request after calling {@link ANeuralNetworksExecution_free}.
*/ typedef struct ANeuralNetworksExecution ANeuralNetworksExecution; /** * ANeuralNetworksOperandType describes the type of an operand. * This structure is used to describe both scalars and tensors. */ typedef struct ANeuralNetworksOperandType { /** The data type, e.g ANEURALNETWORKS_INT8. */ int32_t type; /** The number of dimensions. It should be 0 for scalars. */ uint32_t dimensionCount; /** The dimensions of the tensor. It should be nullptr for scalars. */ const uint32_t* dimensions; /** These two fields are only used for quantized tensors. * They should be zero for scalars and non-fixed point tensors. * The dequantized value of each entry is (value - zeroPoint) * scale. */ float scale; int32_t zeroPoint; } ANeuralNetworksOperandType; typedef int32_t ANeuralNetworksOperationType; /** * ANeuralNetworksEvent is an opaque type that represents an event * that will be signaled once an execution completes. */ typedef struct ANeuralNetworksEvent ANeuralNetworksEvent; /** * Creates a shared memory object from a file descriptor. * * The shared memory is backed by a file descriptor via mmap. * See {@link ANeuralNetworksMemory} for a description on how to use * this shared memory. * * @param size The requested size in bytes. * Must not be larger than the file size. * @param prot The desired memory protection for the mapping. * It is either PROT_NONE or the bitwise OR of one or * more of the following flags: PROT_READ, PROT_WRITE. * @param fd The requested file descriptor. * The file descriptor has to be mmap-able. The file * descriptor will be duplicated. * @param offset The offset to the beginning of the file of the area to map. * The offset has to be aligned to a page size. * @param memory The memory object to be created. * Set to NULL if unsuccessful. * * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. */ int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory** memory); /** * Delete a memory object. * * Destroys the object used by the run time to keep track of the memory. * This will free the underlying actual memory if no other code has open * handles to this memory. * * @param memory The memory object to be freed. */ void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory); /** * Create an empty {@link ANeuralNetworksModel}. * *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} * *
{@link ANeuralNetworksModel_finish} should be called once the model * has been fully constructed.
* *{@link ANeuralNetworksModel_free} should be called once the model * is no longer needed.
* * @param model The {@link ANeuralNetworksModel} to be created. * Set to NULL if unsuccessful. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ int ANeuralNetworksModel_create(ANeuralNetworksModel** model); /** * Destroy a model. * * The model need not have been finished by a call to * {@link ANeuralNetworksModel_finish}. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @param model The model to be destroyed. Passing NULL is acceptable and * results in no operation. */ void ANeuralNetworksModel_free(ANeuralNetworksModel* model); /** * Indicate that we have finished modifying a model. Required before * calling {@link ANeuralNetworksCompilation_create}. * * An application is responsible to make sure that no other thread uses * the model at the same time. * * This function must only be called once for a given model. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @param model The model to be finished. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ int ANeuralNetworksModel_finish(ANeuralNetworksModel* model); /** * Add an operand to a model. * * The order in which the operands are added is important. The first one added * to a model will have the index value 0, the second 1, etc. These indexes are * used as operand identifiers in {@link ANeuralNetworksModel_addOperation}, * {@link ANeuralNetworksExecution_setInput}, * {@link ANeuralNetworksExecution_setInputFromMemory}, * {@link ANeuralNetworksExecution_setOutput}, * {@link ANeuralNetworksExecution_setOutputFromMemory} and * {@link ANeuralNetworksExecution_setOperandValue}. * * To build a model that can accomodate inputs of various sizes, as you may want * to do for a CNN, set the size of the dimensions that will vary at run time to 0. * If you do so, provide the full dimensions when calling * {@link ANeuralNetworksExecution_setInput} or {@link ANeuralNetworksExecution_setInputFromMemory}. * * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been * called will return an error. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @param model The model to be modified. * @param type The {@link ANeuralNetworksOperandType} that describes the shape * of the operand. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, const ANeuralNetworksOperandType* type); /** * Sets an operand to a constant value. * * 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 type of the operation. * @param inputCount The number of entries in the inputs array. * @param inputs An array of indexes identifying each operand. * @param outputCount The number of entries in the outputs array. * @param outputs An array of indexes identifying each operand. * * The operands specified by inputs and outputs must have been * previously added by calls to {@link ANeuralNetworksModel_addOperand}. * * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been * called will return an error. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs); /** * 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); /** * 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); /** * Create a {@link ANeuralNetworksCompilation} to compile the given model. * *This only creates the object. Compilation is only performed once * {@link ANeuralNetworksCompilation_finish} is invoked.
* *{@link ANeuralNetworksCompilation_finish} should be called once * all desired properties have been set on the compilation.
* *{@link ANeuralNetworksModel_free} should be called once the compilation * is no longer needed.
* *The provided model must outlive the compilation.
* * 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. * *Provides guidance to the runtime when trade-offs are possible.
* * 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. * *The provided compilation must outlive the execution.
* * 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. * *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}. * *
The provided buffer must outlive the execution.
* * If the input is optional, you can indicate that it is omitted by * passing nullptr for buffer and 0 for length. * * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @param execution The execution to be modified. * @param index The index of the input argument we are setting. It is * an index into the lists passed to * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not * the index associated with {@link ANeuralNetworksModel_addOperand}. * @param type The type of the operand. This should be used to specify the * dimensions that were set to 0 when the operand was added to the * model. All other properties of the type must be the same as * specified in the model. If the type is the same as specified * when the model was built, NULL can be passed. * @param buffer The buffer containing the data. * @param length The length in bytes of the buffer. * * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the * name is not recognized or the buffer is too small for the input. */ int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length); /** * Associate part of a memory object with an input of the model of the * {@link ANeuralNetworksExecution}. * *The provided memory must outlive the execution.
* * If the input is optional, you can indicate that it is omitted by * using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer * and 0 for length. * * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @param execution The execution to be modified. * @param index The index of the input argument we are setting. It is * an index into the lists passed to * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not * the index associated with {@link ANeuralNetworksModel_addOperand}. * @param type The type of the operand. This can be used to specify the * dimensions that were set to 0 when the operand was added to the * model. All other values must be the same as specified in the * model. If the type is the same as specified when the model * was built, NULL can be passed. * @param memory The memory containing the data. * @param offset This specifies the location of the data 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. * *The provided buffer must outlive the execution.
* * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @param execution The execution to be modified. * @param index The index of the output argument we are setting. It is * an index into the lists passed to * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not * the index associated with {@link ANeuralNetworksModel_addOperand}. * @param type The type of the operand. This can be used to specify the * dimensions that were set to 0 when the operand was added to the * model. All other values must be the same as specified in the * model. If the type is the same as specified when the model * was built, NULL can be passed. * @param buffer The buffer where the data is to be written. * @param length The length in bytes of the buffer. * * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the * name is not recognized or the buffer is too small for the output. */ int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, void* buffer, size_t length); /** * Associate part of a memory object with an output of the model of the * {@link ANeuralNetworksExecution}. * * If the output is optional, you can indicate that it is omitted by * using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer * and 0 for length. * *The provided memory must outlive the execution.
* * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @param execution The execution to be modified. * @param index The index of the output argument we are setting. It is * an index into the lists passed to * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not * the index associated with {@link ANeuralNetworksModel_addOperand}. * @param type The type of the operand. This can be used to specify the * dimensions that were set to 0 when the operand was added to the * model. All other values must be the same as specified in the * model. If the type is the same as specified when the model * was built, NULL can be passed. * @param memory The memory where the data is to be stored. * @param offset This specifies the location of the data 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. * *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. *
* * Multiple executions can be scheduled and evaluated concurrently. The * runtime makes no guarantee on the ordering of completion of * executions. If it's important to the application, the application * should enforce the ordering by using * {@link ANeuralNetworksEvent_wait}. * * ANeuralNetworksEvent_wait must be called to recuperate the resources used * by the execution. * * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @param execution The execution to be scheduled and executed. * @param event The event that will be signaled on completion. event is set to * NULL if there's an error. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event); /** * Waits until the execution completes. * * More than one thread can wait on an event. When the execution completes, * all threads will be released. * * See {@link ANeuralNetworksExecution} for information on multithreaded usage. * * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. */ int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event); /** * Destroys the event. * * See {@link ANeuralNetworksExecution} for information on multithreaded usage. */ void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event); __END_DECLS #endif // __ANDROID_API__ >= 27 #endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H /** @} */