From: Hanjoung Lee Date: Tue, 20 Mar 2018 12:54:31 +0000 (+0900) Subject: Have NeuralNetworks.h in runtime/ref X-Git-Tag: 0.1~650 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=08885a77b1c341b16a8887c5b637d1bc9daca189;p=platform%2Fcore%2Fml%2Fnnfw.git Have NeuralNetworks.h in runtime/ref Add NN API header to runtime/ref. --- diff --git a/src/runtime/ref/nn/runtime/include/NeuralNetworks.h b/src/runtime/ref/nn/runtime/include/NeuralNetworks.h new file mode 100644 index 0000000..beaf6be --- /dev/null +++ b/src/runtime/ref/nn/runtime/include/NeuralNetworks.h @@ -0,0 +1,1929 @@ +/* + * 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 +#include +#include + +__BEGIN_DECLS + +/** + * Operand types. + * + * The type of operands that can be added to a model. + * + * Although we define many types, most operators accept just a few + * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * and {@link ANEURALNETWORKS_INT32}. + */ +typedef enum { + /** The following entries are used to declare scalars. */ + + /** A 32 bit floating point scalar value. */ + ANEURALNETWORKS_FLOAT32 = 0, + /** A signed 32 bit integer scalar value. */ + ANEURALNETWORKS_INT32 = 1, + /** An unsigned 32 bit integer scalar value. */ + ANEURALNETWORKS_UINT32 = 2, + + /** The following entries are used to declare tensors. */ + + /** A tensor of 32 bit floating point values. */ + ANEURALNETWORKS_TENSOR_FLOAT32 = 3, + /** A tensor of 32 bit integer values. */ + ANEURALNETWORKS_TENSOR_INT32 = 4, + /** A tensor of 8 bit integers that represent real numbers. + * + * Attached to this tensor are two numbers that can be used to convert + * the 8 bit integer to the real value and vice versa. These two numbers are: + * - scale: a 32 bit non-negative floating point value. + * - zeroPoint: an 32 bit integer, in range [0, 255]. + * + * The formula is: + * real_value = (integer_value - zeroPoint) * scale. + */ + ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5, +} OperandCode; + +/** + * Operation types. + * + * The type of operations that can be added to a model. + */ +typedef enum { + /** Adds two tensors, element-wise. + * + * Takes two input tensors of identical type and compatible dimensions. The output + * is the sum of both input tensors, optionally modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the input operands. + * It starts with the trailing dimensions, and works its way forward. + * + * Example: + * + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same type, and compatible dimensions as input0. + * * 2: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Outputs: + * * 0: The sum, a tensor of the same type as input0. + */ + ANEURALNETWORKS_ADD = 0, + + /** Performs a 2-D average pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and padding. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels) + * data layout. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. + * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. + * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. + * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. + * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. + * * 5: An INT32 value, specifying the 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_AVERAGE_POOL_2D = 1, + + /** Concatenates the input tensors along the given dimension. + * + * The input tensors must have identical type and the same dimensions except the + * dimension along the concatenation axis. + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. + * For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all + * input tensors must have the same scale and zeroPoint. + * * n: An INT32 value, specifying the concatenation axis. + * + * Outputs: + * * 0: The output, a tensor of the same type as the input tensors. + * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. + */ + ANEURALNETWORKS_CONCATENATION = 2, + + /** Performs an 2-D convolution operation. + * + * The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of + * images, applying the filter to each window of each image of the appropriate size. + * + * The output dimensions are functions of the filter dimensions, stride, and padding. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * sum_{i, j} ( + * input[batch, row + i, col + j, k] * + * filter[channel, row + i, col + j, k] + + * bias[channel] + * ) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. + * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. + * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. + * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. + * * 7: An INT32 value, specifying the stride when walking through input + * in the ‘width’ dimension. + * * 8: An INT32 value, specifying the stride when walking through input + * in the ‘height’ dimension. + * * 9: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 4: An INT32 value, specifying the stride when walking through input + * in the ‘width’ dimension. + * * 5: An INT32 value, specifying the stride when walking through input + * in the ‘height’ dimension. + * * 6: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following + * condition must be satisfied: output_scale > input_scale * filter_scale. + */ + ANEURALNETWORKS_CONV_2D = 3, + + /** Performs a depthwise 2-D convolution operation. + * + * Given an input tensor of shape [batches, height, width, depth_in] and a filter + * tensor of shape [1, filter_height, filter_width, depth_out] containing + * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different + * filter to each input channel (expanding from 1 channel to channel_multiplier channels + * for each), then concatenates the results together. + * + * The output has depth_out = depth_in * depth_multiplier channels. + * The output dimensions are functions of the filter dimensions, stride, and padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, k * channel_multiplier + q] = + * sum_{di, dj} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, k] * + * filter[1, di, dj, k * channel_multiplier + q] + * ) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. + * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. + * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. + * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. + * * 7: An INT32 value, specifying the stride when walking through input + * in the ‘width’ dimension. + * * 8: An INT32 value, specifying the stride when walking through input + * in the ‘height’ dimension. + * * 9: An INT32 value, specifying the depthwise multiplier. + * * 10: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 4: An INT32 value, specifying the stride when walking through input + * in the ‘width’ dimension. + * * 5: An INT32 value, specifying the stride when walking through input + * in the ‘height’ dimension. + * * 6: An INT32 value, specifying the depthwise multiplier. + * * 7: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following + * condition must be satisfied: output_scale > input_scale * filter_scale. + */ + ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, + + /** Rearranges data from depth into blocks of spatial data. + * + * More specifically, this op outputs a copy of the input tensor where values from + * the depth dimension are moved in spatial blocks to the height and width dimensions. + * The value block_size indicates the input block size and how the data is moved. + * + * Chunks of data of size block_size * block_size from depth are rearranged into + * non-overlapping blocks of size block_size x block_size. + * + * The width of the output tensor is input_depth * block_size, whereas the height is + * input_height * block_size. + * The depth of the input tensor must be divisible by block_size * block_size + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and + * block_size * block_size must be a divisor of the input depth. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size, + * depth/(block_size*block_size)]. + */ + ANEURALNETWORKS_DEPTH_TO_SPACE = 5, + + /** Dequantizes the input tensor. + * + * The formula is: + * + * output = (input - zeroPoint) * scale. + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. + * + * Outputs: + * * 0: The output tensor of same shape as input0, but with type + * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + */ + ANEURALNETWORKS_DEQUANTIZE = 6, + + /** Looks up sub-tensors in the input tensor. + * + * This operator takes for input a tensor of values (Values) and + * a one-dimensional tensor of selection indices (Lookups). + * The output tensor is the concatenation of sub-tensors of Values as + * selected by Lookups. + * + * Think of Values as being sliced along its first dimension: + * The entries in Lookups select which slices are concatenated together + * to create the output tensor. + * + * For example, if Values has shape of [40, 200, 300] and + * Lookups has shape of [3], we would expect all three values + * found in Lookups to be between 0 and 39. The resulting tensor will + * have shape of [3, 200, 300]. + * + * If a value in Lookups is out of bounds, the operation will fail + * and an error will be reported. + * + * Inputs: + * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type. + * The values are indices into the first dimension of Values. + * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are + * extracted. + * + * Output: + * * 0: A n-D tensor with the same rank and shape as the Values + * tensor, except for the first dimension which has the same size + * as Lookups' only dimension. + */ + ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, + + /** Computes element-wise floor() on the input tensor. + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor, of the same type and dimensions as the input tensor. + */ + ANEURALNETWORKS_FLOOR = 8, + + /** Denotes a fully (densely) connected layer, which connects all elements in the input + * tensor with each element in the output tensor. + * + * This layer implements the operation: + * + * outputs = activation(inputs * weights’ + bias) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to + * a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape + * [batch_size, input_size], where “batch_size” corresponds to the batching dimension, + * and “input_size” is the size of the input. + * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where + * "num_units" corresponds to the number of output nodes. + * * 2: A 1-D tensor, of shape [num_units], specifying the bias. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An INT32 value, and has to be one of the {@link FuseCode} values. + * Specifies the activation to invoke on the result of each addition. + * + * Outputs: + * * 0: The output tensor, of shape [batch_size, num_units]. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following + * condition must be satisfied: output_scale > input_scale * filter_scale. + */ + ANEURALNETWORKS_FULLY_CONNECTED = 9, + + /** Looks up sub-tensors in the input tensor using a key-value map. + * + * This operator takes for input a tensor of values (Values), + * a one-dimensional tensor of selection values (Lookups) and + * a one-dimensional tensor that maps these values to Values + * indexes. The output tensor is the concatenation of sub-tensors of + * Values as selected by Lookups via Keys. + * + * Think of Values as being sliced along its outer-most dimension. + * The output is a concatenation of selected slices, with one slice + * for each entry of Lookups. The slice selected is the one at the + * same index as the Maps entry that matches the value in Lookups. + * + * For a hit, the corresponding sub-tensor of Values is included + * in the Output tensor. For a miss, the corresponding sub-tensor in + * Output will have zero values. + * + * For example, if Values has shape of [40, 200, 300], + * Keys should have a shape of [40]. If Lookups tensor has shape + * of [3], we're concatenating three slices, so the resulting tensor + * will have the shape of [3, 200, 300]. If the first entry in + * Lookups has the value 123456, we'll look for that value in Keys tensor. + * If the sixth entry of Keys contains 123456, we'll select the sixth + * slice of Values. If no entry in Keys has 123456, a slice of zeroes + * will be concatenated. + * + * Inputs: + * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ k ]. + * * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ n ]; + * Keys and Values pair represent a map, i.e., the ith element + * in Keys (Keys[i]) is the key to select the ith sub-tensor + * in Values (Values[i]), where 0 <= i <= n-1. + * Keys tensor *MUST* be sorted in ascending order. + * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n. + * + * Outputs: + * * 0: Output. A tensor with shape [ k …]. + * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup + * hits (True) or not (False). + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + */ + ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, + + /** Applies L2 normalization along the depth dimension. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * input[batch, row, col, channel] / + * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) + * + * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim. + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels). + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth]. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. + */ + ANEURALNETWORKS_L2_NORMALIZATION = 11, + + /** Performs an 2-D L2 pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and padding. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1)) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. + * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. + * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. + * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. + * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. + * * 5: An INT32 value, specifying the 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_L2_POOL_2D = 12, + + /** Applies Local Response Normalization along the depth dimension. + * + * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last + * dimension), and each vector is normalized independently. Within a given vector, + * each component is divided by the weighted, squared sum of inputs within depth_radius. + * + * The output is calculated using this formula: + * + * sqr_sum[a, b, c, d] = + * sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2) + * output = input / pow((bias + alpha * sqr_sum), beta) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. + * * 1: An INT32 value, specifying the radius of the normalization window. + * * 2: A FLOAT32 value, specifying the bias, must not be zero. + * * 3: A FLOAT32 value, specifying the scale factor, alpha. + * * 4: A FLOAT32 value, specifying the exponent, beta. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + */ + ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13, + + /** Computes sigmoid activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = 1 / (1 + exp(-input)) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, + * the scale must be 1.f / 256 and the zeroPoint must be 0. + */ + ANEURALNETWORKS_LOGISTIC = 14, + + /** + * Projects an input to a bit vector via locality senstive hashing. + * + * Inputs: + * * 0: Hash functions. Dim.size == 2, DataType: Float. + * Tensor[0].Dim[0]: Number of hash functions. + * Tensor[0].Dim[1]: Number of seeds per hash functions. + * Tensor[0].Dim[1] <= 32 in sparse case. + * + * * 1: Input. Dim.size >= 1, no restriction on DataType. + * * 2: Weight. Optional. Dim.size == 1, DataType: Float. + * If not set, each input element is considered to have the same weight of + * 1.0. + * Tensor[1].Dim[0] == Tensor[2].Dim[0] + * * 3: Type: + * Sparse: Value LSHProjectionType_SPARSE(=1). + * Computed bit vector is considered to be sparse. + * Each output element is an int32 made up of multiple bits computed from + * hash functions. + * + * Dense: Value LSHProjectionType_DENSE(=2). + * Computed bit vector is considered to be dense. Each output element + * represents a bit and can take the value of either 0 or 1. + * + * Outputs: + * * 0: If the projection type is sparse: + * Output.Dim == { Tensor[0].Dim[0] } + * A tensor of int32 that represents hash signatures. + * If the projection type is Dense: + * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } + * A flattened tensor that represents projected bit vectors. + */ + ANEURALNETWORKS_LSH_PROJECTION = 15, + + /** + * Long short-term memory unit (LSTM) recurrent network layer. + * + * The default non-peephole implementation is based on: + * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural + * Computation, 9(8):1735-1780, 1997. + * + * The peephole implementation is based on: + * https://research.google.com/pubs/archive/43905.pdf + * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory + * recurrent neural network architectures for large scale acoustic modeling." + * INTERSPEECH, 2014. + * + * The coupling of input and forget gate (CIFG) is based on: + * http://arxiv.org/pdf/1503.04069.pdf + * Greff et al. "LSTM: A Search Space Odyssey" + * + * The class has the following independently optional inputs: + * * If input gate (if CIFG): “input_to_forget_weights”, + * “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”. + * * If no peephole connections: “cell_to_input_weights”, + * “cell_to_forget_weights”, “cell_to_output_weights”. + * * If no projection layer: “projection_weights” and “projection_bias”. + * * If no projection bias: “projection_bias”. + * + * Supported tensor types (type T): + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Inputs: + * * 0: Input. + * A 2-D tensor of type T, of shape [batch_size, input_size], where + * “batch_size” corresponds to the batching dimension, and “input_size” + * is the size of the input. + * * 1: input_to_input_weights. + * A 2-D tensor of type T, of shape [num_units, input_size], where + * “num_units” corresponds to the number of cell units. + * * 2: input_to_forget_weights. + * A 2-D tensor of type T, of shape [num_units, input_size]. + * * 3: input_to_cell_weights. + * A 2-D tensor of type T, of shape [num_units, input_size]. + * * 4: input_to_output_weights. + * A 2-D tensor of type T, of shape [num_units, input_size]. + * * 5: recurrent_to_input_weights. + * A 2-D tensor of type T, of shape [num_units, output_size], where + * “output_size” corresponds to either the number of cell units (i.e., + * “num_units”), or the second dimension of the “projection_weights”, if + * defined. + * * 6: recurrent_to_forget_weights. + * A 2-D tensor of type T, of shape [num_units, output_size]. + * * 7: recurrent_to_cell_weights. + * A 2-D tensor of type T, of shape [num_units, output_size]. + * * 8: recurrent_to_output_weights. + * A 2-D tensor of type T, of shape [num_units, output_size]. + * * 9: cell_to_input_weights. + * A 1-D tensor of type T, of shape [num_units]. + * * 10:cell_to_forget_weights. + * A 1-D tensor of type T, of shape [num_units]. + * * 11:cell_to_output_weights. + * A 1-D tensor of type T, of shape [num_units]. + * * 12:input_gate_bias. + * A 1-D tensor of type T, of shape [num_units]. + * * 13:forget_gate_bias. + * A 1-D tensor of type T, of shape [num_units]. + * * 14:cell_bias. + * A 1-D tensor of type T, of shape [num_units]. + * * 15:output_gate_bias. + * A 1-D tensor of type T, of shape [num_units]. + * * 16:projection_weights. + * A 2-D tensor of type T, of shape [output_size, num_units]. + * * 17:projection_bias. + * A 1-D tensor of type T, of shape [output_size]. + * * 18: output_state (in). + * A 2-D tensor of type T, of shape [batch_size, output_size]. + * * 19: cell_state (in). + * A 2-D tensor of type T, of shape [batch_size, num_units]. + * * 20:fused_activation_function. + * An optional {@link FuseCode} value indicating the activation + * function. + * If “NONE” is specified then it results in a linear activation. + * * 21:cell_clip. + * A clipping threshold for the cell state, such that values are bound + * within [-cell_clip, cell_clip]. If set to 0.0 then clipping is + * disabled. + * * 22:proj_clip. + * A clipping threshold for the output from the projection layer, such + * that values are bound within [-proj_clip, proj_clip]. If set to 0.0 + * then clipping is disabled. + * + * Outputs: + * * 0: scratch_buffer. + * A 3-D tensor of type T, of shape [batch_size, num_cell, 4]. + * * 1: output_state (out). + * A 2-D tensor of type T, of shape [batch_size, output_size]. + * * 2: cell_state (out). + * A 2-D tensor of type T, of shape [batch_size, num_units]. + * * 3: output. + * A 2-D tensor of type T, of shape [batch_size, output_size]. This is + * effectively the same as the current “output_state” value. + */ + ANEURALNETWORKS_LSTM = 16, + + /** Performs an 2-D max pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and padding. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * max_{i, j} (input[batch, row + i, col + j, channel]) + * + * Supported tensor types: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" data layout. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. + * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. + * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. + * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. + * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. + * * 5: An INT32 value, specifying the 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. + * + * 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, +} 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. {@link ANeuralNetworksMemory_createShared} + * can be used to directly created shared memory. + * + * Memory objects can also be used to specify the input and output arguments of + * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory} + * and {@link ANeuralNetworksExecution_setOutputFromMemory}. + */ +typedef struct ANeuralNetworksMemory ANeuralNetworksMemory; + +/** + * ANeuralNetworksModel is an opaque type that contains a description of the + * mathematical operations that constitute the model. + * + *

The model will be built by calling

    + *
  • {@link ANeuralNetworksModel_create},
    • + *
  • {@link ANeuralNetworksModel_addOperation},
    • + *
  • {@link ANeuralNetworksModel_addOperand},
    • + *
+ * + * A model is completed by calling {@link ANeuralNetworksModel_finish}. + * A model is destroyed by calling {@link ANeuralNetworksModel_free}. + * + *

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.

+ */ +typedef struct ANeuralNetworksModel ANeuralNetworksModel; + +/** + * ANeuralNetworksCompilation is an opaque type that can be used to compile + * a machine learning model. + * + *

To use:

    + *
  • Create a new compilation instance by calling the + * {@link ANeuralNetworksCompilation_create} function.
    • + *
  • Set any desired properties on the compilation (for example, + * {@link ANeuralNetworksCompilation_setPreference}).
    • + *
  • Complete the compilation with {@link ANeuralNetworksCompilation_finish}.
    • + *
  • Use the compilation as many times as needed + * with {@link ANeuralNetworksExecution_create}.
    • + *
  • Destroy the compilation with {@link ANeuralNetworksCompilation_free} + * once all executions using the compilation have completed.

+ * + * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. + * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_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:

    + *
  • Create a new execution instance by calling the + * {@link ANeuralNetworksExecution_create} function.
    • + *
  • Associate data to the model inputs with + * {@link ANeuralNetworksExecution_setInput} or + * {@link ANeuralNetworksExecution_setInputFromMemory}.
    • + *
  • Associate output buffers to the model outputs with + * {@link ANeuralNetworksExecution_setOutput} or + * {@link ANeuralNetworksExecution_setOutputFromMemory}.
    • + *
  • Apply the model with {@link ANeuralNetworksExecution_startCompute}.
    • + *
  • Wait for the execution to complete with {@link + * ANeuralNetworksEvent_wait}.
    • + *
  • Destroy the execution with + * {@link ANeuralNetworksExecution_free}.

+ * + *

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); + +/** + * Specfifies which operands will be the model's inputs and outputs. + * + * An operand cannot be used for both input and output. Doing so will + * return an error. + * + * @param model The model to be modified. + * @param inputCount The number of entries in the inputs array. + * @param inputs An array of indexes identifying the input operands. + * @param outputCount The number of entries in the outputs array. + * @param outputs An array of indexes identifying the output operands. + * + * The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + */ +int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, + const uint32_t* inputs, uint32_t outputCount, + const uint32_t* outputs); + +/** + * Create a {@link ANeuralNetworksCompilation} to compile the given model. + * + *

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 whithin the memory. + * The offset is in bytes from the start of memory. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the input. + */ +int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length); + +/** + * Associate a user buffer with an output of the model of the + * {@link ANeuralNetworksExecution}. + * + * If the output is optional, you can indicate that it is omitted by + * passing nullptr for buffer and 0 for length. + * + *

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 whithin the memory. + * The offset is in bytes from the start of memory. + * @param length The length in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the output. + */ +int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length); + +/** + * Schedule evaluation of the execution. + * + *

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 + +/** @} */