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diff --git a/include/NeuralNetworks.h b/include/NeuralNetworks.h deleted file mode 100644 index 6414af688..000000000 --- a/include/NeuralNetworks.h +++ /dev/null @@ -1,2578 +0,0 @@ -/* - * Copyright (C) 2017 The Android Open Source Project - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @addtogroup NeuralNetworks - * @{ - */ - -/** - * @file NeuralNetworks.h - */ - -#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H -#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H - -/****************************************************************** - * - * IMPORTANT NOTICE: - * - * This file is part of Android's set of stable system headers - * exposed by the Android NDK (Native Development Kit). - * - * Third-party source AND binary code relies on the definitions - * here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES. - * - * - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES) - * - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS - * - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY - * - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES - */ - -#include <stddef.h> -#include <stdint.h> -#include <sys/cdefs.h> - -__BEGIN_DECLS - -/** - * Operand types. - * - * The type of operands that can be added to a model. - * - * Although we define many types, most operators accept just a few - * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, - * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, - * and {@link ANEURALNETWORKS_INT32}. - */ -typedef enum { - /** A 32 bit floating point scalar value. */ - ANEURALNETWORKS_FLOAT32 = 0, - /** A signed 32 bit integer scalar value. */ - ANEURALNETWORKS_INT32 = 1, - /** An unsigned 32 bit integer scalar value. */ - ANEURALNETWORKS_UINT32 = 2, - - /** A tensor of 32 bit floating point values. */ - ANEURALNETWORKS_TENSOR_FLOAT32 = 3, - /** A tensor of 32 bit integer values. */ - ANEURALNETWORKS_TENSOR_INT32 = 4, - /** - * A tensor of 8 bit integers that represent real numbers. - * - * Attached to this tensor are two numbers that can be used to convert the - * 8 bit integer to the real value and vice versa. These two numbers are: - * - scale: a 32 bit floating point value greater than zero. - * - zeroPoint: a 32 bit integer, in range [0, 255]. - * - * The formula is: - * real_value = (integer_value - zeroPoint) * scale. - */ - ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5, -} OperandCode; - -/** - * Operation types. - * - * The type of operations that can be added to a model. - */ -typedef enum { - /** - * Adds two tensors, element-wise. - * - * Takes two input tensors of identical {@link OperandCode} and compatible - * dimensions. The output is the sum of both input tensors, optionally - * modified by an activation function. - * - * Two dimensions are compatible when: - * 1. they are equal, or - * 2. one of them is 1 - * - * The size of the output is the maximum size along each dimension of the - * input operands. It starts with the trailing dimensions, and works its - * way forward. - * - * Example: - * - * input1.dimension = {4, 1, 2} - * input2.dimension = {5, 4, 3, 1} - * output.dimension = {5, 4, 3, 2} - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: A tensor. - * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions - * as input0. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The sum, a tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_ADD = 0, - - /** - * Performs a 2-D average pooling operation. - * - * The output dimensions are functions of the filter dimensions, stride, and - * padding. - * - * The values in the output tensor are computed as: - * - * output[batch, row, col, channel] = - * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, - * and Channels) data layout. - * - * Both explicit padding and implicit padding are supported. - * - * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the left, in the ‘width’ dimension. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the right, in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the top, in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the bottom, in the ‘height’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit - * padding scheme, has to be one of the - * {@link PaddingCode} values. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - [batches, out_height, out_width, depth]. - */ - ANEURALNETWORKS_AVERAGE_POOL_2D = 1, - - /** - * Concatenates the input tensors along the given dimension. - * - * The input tensors must have identical {@link OperandCode} and the same - * dimensions except the dimension along the concatenation axis. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0 ~ n-1: The list of n input tensors, of shape - * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of - * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all input tensors - * must have the same scale and zeroPoint. - * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the - * concatenation axis. - * - * Outputs: - * * 0: The output, a tensor of the same {@link OperandCode} as the input - * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. - */ - ANEURALNETWORKS_CONCATENATION = 2, - - /** - * Performs an 2-D convolution operation. - * - * The CONV_2D op sweeps a 2-D filter that can mix channels together over a - * batch of images, applying the filter to each window of each image of the - * appropriate size. - * - * The output dimensions are functions of the filter dimensions, stride, and - * padding. - * - * The values in the output tensor are computed as: - * - * output[batch, row, col, channel] = - * sum_{i, j} ( - * input[batch, row + i, col + j, k] * - * filter[channel, row + i, col + j, k] + - * bias[channel] - * ) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Both explicit padding and implicit padding are supported. - * - * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: A 4-D tensor, of shape - * [depth_out, filter_height, filter_width, depth_in], specifying the - * filter. - * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. - * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias - * should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input - * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias - * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of - * 0 and bias_scale == input_scale * filter_scale. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the left, in the ‘width’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the right, in the ‘width’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the top, in the ‘height’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the bottom, in the ‘height’ dimension. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: A 4-D tensor, of shape - * [depth_out, filter_height, filter_width, depth_in], specifying the - * filter. - * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input - * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should - * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor - * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be - * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and - * bias_scale == input_scale * filter_scale. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit - * padding scheme, has to be one of the - * {@link PaddingCode} values. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - * [batches, out_height, out_width, depth_out]. For output tensor of - * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition - * must be satisfied: output_scale > input_scale * filter_scale. - */ - ANEURALNETWORKS_CONV_2D = 3, - - /** - * Performs a depthwise 2-D convolution operation. - * - * Given an input tensor of shape [batches, height, width, depth_in] and a - * filter tensor of shape [1, filter_height, filter_width, depth_out] - * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV - * applies a different filter to each input channel (expanding from 1 - * channel to channel_multiplier channels for each), then concatenates the - * results together. - * - * The output has depth_out = depth_in * depth_multiplier channels. - * The output dimensions are functions of the filter dimensions, stride, and - * padding. - * - * The values in the output tensor are computed as: - * - * output[b, i, j, k * channel_multiplier + q] = - * sum_{di, dj} ( - * input[b, strides[1] * i + di, strides[2] * j + dj, k] * - * filter[1, di, dj, k * channel_multiplier + q] - * ) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Both explicit padding and implicit padding are supported. - * - * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], - * specifying the filter. - * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input - * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should - * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor - * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be - * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and - * bias_scale == input_scale * filter_scale. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the left, in the ‘width’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the right, in the ‘width’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the top, in the ‘height’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the bottom, in the ‘height’ dimension. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise - * multiplier. - * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], - * specifying the filter. - * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input - * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should - * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor - * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be - * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and - * bias_scale == input_scale * filter_scale. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit - * padding scheme, has to be one of the - * {@link PaddingCode} values. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise - * multiplier. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - * [batches, out_height, out_width, depth_out]. For output tensor of - * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition - * must be satisfied: output_scale > input_scale * filter_scale. - */ - ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, - - /** - * Rearranges data from depth into blocks of spatial data. - * - * More specifically, this op outputs a copy of the input tensor where - * values from the depth dimension are moved in spatial blocks to the height - * and width dimensions. The value block_size indicates the input block size - * and how the data is moved. - * - * Chunks of data of size block_size * block_size from depth are rearranged - * into non-overlapping blocks of size block_size x block_size. - * - * The width of the output tensor is input_depth * block_size, whereas the - * height is input_height * block_size. The depth of the input tensor must - * be divisible by block_size * block_size - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Inputs: - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. - * block_size must be >=1 and block_size * block_size must be a divisor - * of the input depth. - * - * Outputs: - * * 0: The output 4-D tensor, of shape [batch, height*block_size, - * width*block_size, depth/(block_size*block_size)]. - */ - ANEURALNETWORKS_DEPTH_TO_SPACE = 5, - - /** - * Dequantizes the input tensor. - * - * The formula is: - * - * output = (input - zeroPoint) * scale. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. - * - * Outputs: - * * 0: The output tensor of same shape as input0, but with - * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. - */ - ANEURALNETWORKS_DEQUANTIZE = 6, - - /** - * Looks up sub-tensors in the input tensor. - * - * This operator takes for input a tensor of values (Values) and - * a one-dimensional tensor of selection indices (Lookups). - * The output tensor is the concatenation of sub-tensors of Values as - * selected by Lookups. - * - * Think of Values as being sliced along its first dimension: - * The entries in Lookups select which slices are concatenated together - * to create the output tensor. - * - * For example, if Values has shape of [40, 200, 300] and - * Lookups has shape of [3], all three values found in Lookups are - * expected to be between 0 and 39. The resulting tensor must - * have shape of [3, 200, 300]. - * - * If a value in Lookups is out of bounds, the operation must fail - * and an error must be reported. - * - * Inputs: - * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. - * The values are indices into the first dimension of Values. - * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are - * extracted. - * - * Output: - * * 0: A n-D tensor with the same rank and shape as the Values - * tensor, except for the first dimension which has the same size - * as Lookups' only dimension. - */ - ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, - - /** - * Computes element-wise floor() on the input tensor. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: A tensor. - * - * Outputs: - * * 0: The output tensor, of the same {@link OperandCode} and dimensions as - * the input tensor. - */ - ANEURALNETWORKS_FLOOR = 8, - - /** - * Denotes a fully (densely) connected layer, which connects all elements - * in the input tensor with each element in the output tensor. - * - * This layer implements the operation: - * - * outputs = activation(inputs * weights’ + bias) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor of at least rank 2, specifying the input. If rank is - * greater than 2, then it gets flattened to a 2-D Tensor. The - * (flattened) 2-D Tensor is reshaped (if necessary) to - * [batch_size, input_size], where "input_size" corresponds to the - * number of inputs to the layer, matching the second dimension of - * weights, and "batch_size" is calculated by dividing the number of - * elements by "input_size". - * * 1: A 2-D tensor, specifying the weights, of shape - * [num_units, input_size], where "num_units" corresponds to the number - * of output nodes. - * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input - * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should - * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor - * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be - * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and - * bias_scale == input_scale * filter_scale. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output tensor, of shape [batch_size, num_units]. For output - * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following - * condition must be satisfied: - * output_scale > input_scale * filter_scale. - */ - ANEURALNETWORKS_FULLY_CONNECTED = 9, - - /** - * Looks up sub-tensors in the input tensor using a key-value map. - * - * This operator takes for input a tensor of values (Values), - * a one-dimensional tensor of selection values (Lookups) and - * a one-dimensional tensor that maps these values to Values - * indexes. The output tensor is the concatenation of sub-tensors of - * Values as selected by Lookups via Keys. - * - * Think of Values as being sliced along its outer-most dimension. - * The output is a concatenation of selected slices, with one slice - * for each entry of Lookups. The slice selected is the one at the - * same index as the Maps entry that matches the value in Lookups. - * - * For a hit, the corresponding sub-tensor of Values is included - * in the Output tensor. For a miss, the corresponding sub-tensor in - * Output must have zero values. - * - * For example, if Values has shape of [40, 200, 300], - * Keys should have a shape of [40]. If Lookups tensor has shape - * of [3], three slices are being concatenated, so the resulting tensor - * must have the shape of [3, 200, 300]. If the first entry in Lookups - * has the value 123456, that value must be located in Keys tensor. - * If the sixth entry of Keys contains 123456, the sixth slice of Values - * must be selected. If no entry in Keys has 123456, a slice of zeroes - * must be concatenated. - * - * Inputs: - * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with - * shape [ k ]. - * * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape - * [ n ]; Keys and Values pair represent a map, i.e., the ith element - * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values - * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in - * ascending order. - * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension - * must be n. - * - * Outputs: - * * 0: Output. A tensor with shape [ k …]. - * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup - * hits (True) or not (False). - * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 - * and scale 1.0f. - * A non-zero byte represents True, a hit. A zero indicates otherwise. - */ - ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, - - /** - * Applies L2 normalization along the depth dimension. - * - * The values in the output tensor are computed as: - * - * output[batch, row, col, channel] = - * input[batch, row, col, channel] / - * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) - * - * For input tensor with more dimensions, independently normalizes each 1-D - * slice along dimension dim. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, - * Height, Width, and Channels). - * - * Inputs: - * * 0: A 4-D tensor, of shape [batches, height, width, depth]. - * - * Outputs: - * * 0: The output 4-D tensor, of the same shape as input - * [batches, height, width, depth]. - */ - ANEURALNETWORKS_L2_NORMALIZATION = 11, - - /** - * Performs an 2-D L2 pooling operation. - * - * The output dimensions are functions of the filter dimensions, stride, and - * padding. - * - * The values in the output tensor are computed as: - * - * output[batch, row, col, channel] = - * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / - * sum(1)) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Both explicit padding and implicit padding are supported. - * - * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the left, in the ‘width’ dimension. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the right, in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the top, in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the bottom, in the ‘height’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit - * padding scheme, has to be one of the - * {@link PaddingCode} values. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - * [batches, out_height, out_width, depth]. - */ - ANEURALNETWORKS_L2_POOL_2D = 12, - - /** - * Applies Local Response Normalization along the depth dimension. - * - * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the - * last dimension), and each vector is normalized independently. Within a - * given vector, each component is divided by the weighted, squared sum of - * inputs within depth_radius. - * - * The output is calculated using this formula: - * - * sqr_sum[a, b, c, d] = sum( - * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)) - * output = input / pow((bias + alpha * sqr_sum), beta) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Inputs: - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of - * the normalization window. - * * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must - * not be zero. - * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale - * factor, alpha. - * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the exponent, - * beta. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - */ - ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13, - - /** - * Computes sigmoid activation on the input tensor element-wise. - * - * The output is calculated using this formula: - * - * output = 1 / (1 + exp(-input)) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the input. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, - * the scale must be 1.f / 256 and the zeroPoint must be 0. - */ - ANEURALNETWORKS_LOGISTIC = 14, - - /** - * Projects an input to a bit vector via locality senstive hashing. - * - * Inputs: - * * 0: Hash functions. Dim.size == 2, DataType: Float. - * Tensor[0].Dim[0]: Number of hash functions. - * Tensor[0].Dim[1]: Number of seeds per hash functions. - * Tensor[0].Dim[1] <= 32 in sparse case. - * - * * 1: Input. Dim.size >= 1, no restriction on DataType. - * * 2: Weight. Optional. Dim.size == 1, DataType: Float. - * If not set, each input element is considered to have the same weight - * of 1.0. - * Tensor[1].Dim[0] == Tensor[2].Dim[0] - * * 3: Type: - * Sparse: Value LSHProjectionType_SPARSE(=1). - * Computed bit vector is considered to be sparse. - * Each output element is an int32 made up of multiple bits - * computed from hash functions. - * - * Dense: Value LSHProjectionType_DENSE(=2). - * Computed bit vector is considered to be dense. Each output - * element represents a bit and can take the value of either - * 0 or 1. - * - * Outputs: - * * 0: If the projection type is sparse: - * Output.Dim == { Tensor[0].Dim[0] } - * A tensor of int32 that represents hash signatures. - * If the projection type is Dense: - * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } - * A flattened tensor that represents projected bit vectors. - */ - ANEURALNETWORKS_LSH_PROJECTION = 15, - - /** - * Performs a single time step in a Long Short-Term Memory (LSTM) layer - * - * The LSTM operation is described by the following equations. - * - * \f{eqnarray*}{ - * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\ - * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\ - * C_t =& clip(f_t \odot C_{t-1} + i_t \odot - * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\ - * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\ - * & & \\ - * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) - * & if\ there\ is\ a\ projection; \\ - * h_t =& & \\ - * & o_t \odot g(C_t) & otherwise. \\ - * \f} - * Where: - * * \f$x_t\f$ is the input, - * * \f$i_t\f$ is the input gate, - * * \f$f_t\f$ is the forget gate, - * * \f$C_t\f$ is the cell state, - * * \f$o_t\f$ is the output, - * * \f$h_t\f$ is the output state, - * * \f$\sigma\f$ is the logistic sigmoid function, - * * \f$g\f$ is the cell input and cell output activation function, usually - * \f$tahn\f$, - * * \f$W_{xi}\f$ is the input-to-input weight matrix, - * * \f$W_{hi}\f$ is the recurrent to input weight matrix, - * * \f$W_{ci}\f$ is the cell-to-input weight matrix, - * * \f$b_i\f$ is the input gate bias, - * * \f$W_{xf}\f$ is the input-to-forget weight matrix, - * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix, - * * \f$W_{cf}\f$ is the cell-to-forget weight matrix, - * * \f$b_f\f$ is the forget gate bias, - * * \f$W_{xc}\f$ is the input-to-cell weight matrix, - * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix, - * * \f$b_c\f$ is the cell bias, - * * \f$W_{xo}\f$ is the input-to-output weight matrix, - * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix, - * * \f$W_{co}\f$ is the cell-to-output weight matrix, - * * \f$b_o\f$ is the output gate bias, - * * \f$W_{proj}\f$ is the projection weight matrix, - * * \f$b_{proj}\f$ is the projection bias, - * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and - * * \f$t_{proj}\f$ is the threshold for clipping the projected output. - * * \f$\odot\f$ is the - * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)"> - * Hadamard product</a> that takes two matrices and produces another - * matrix, each element of which is the product of the corresponding - * elements of the input matrices. - * - * The operation has the following independently optional inputs: - * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights - * (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate - * bias (\f$b_i\f$) either all have values, or none of them have values - * (i.e., all set to null). If they have no values, coupling of input and - * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$) - * is calculated using the following equation instead. - * \f{eqnarray*}{ - * i_t = 1 - f_t - * \f} - * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights - * (\f$W_{co}\f$) either both have values or neither of them have values. - * If they have values, the peephole optimization is used. Additionally, - * if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also - * required to have values for peephole optimization. - * * The projection weights (\f$W_{proj}\f$) is required only for the - * recurrent projection layer, and should otherwise have no value. - * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a - * value if the recurrent projection layer exists, and should otherwise - * have no value. - * - * References: - * - * The default non-peephole non-CIFG implementation is based on: - * http://www.bioinf.jku.at/publications/older/2604.pdf - * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural - * Computation, 9(8):1735-1780, 1997. - * - * The peephole implementation and projection layer is based on: - * https://research.google.com/pubs/archive/43905.pdf - * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory - * recurrent neural network architectures for large scale acoustic - * modeling." INTERSPEECH, 2014. - * (However, the concept of peephole optimization was introduced in work - * prior to this paper.) - * - * The coupling of input and forget gate (CIFG) is based on: - * http://arxiv.org/pdf/1503.04069.pdf - * Greff et al. "LSTM: A Search Space Odyssey" - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Inputs: - * * 0: The input (\f$x_t\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, input_size], where “batch_size” corresponds to the - * batching dimension, and “input_size” is the size of the input. - * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size], where “num_units” corresponds to the - * number of cell units. - * * 2: The input-to-forget weights (\f$W_{xf}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size]. - * * 3: The input-to-cell weights (\f$W_{xc}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size]. - * * 4: The input-to-output weights (\f$W_{xo}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size]. - * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, output_size], where “output_size” corresponds to either - * the number of cell units (i.e., “num_units”), or the second - * dimension of the “projection_weights”, if defined. - * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, output_size]. - * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, output_size]. - * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, output_size]. - * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 12:The input gate bias (\f$b_i\f$). Optional. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 13:The forget gate bias (\f$b_f\f$). - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 14:The cell bias (\f$b_c\f$). - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 15:The output gate bias (\f$b_o\f$). - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 16:The projection weights (\f$W_{proj}\f$). Optional. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [output_size, num_units]. - * * 17:The projection bias (\f$b_{proj}\f$). Optional. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [output_size]. - * * 18:The output state (in) (\f$h_{t-1}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, output_size]. - * * 19:The cell state (in) (\f$C_{t-1}\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. - * * 20:The activation function (\f$g\f$). - * A value indicating the activation function: - * <ul> - * <li>0: None; - * <li>1: Relu; - * <li>3: Relu6; - * <li>4: Tanh; - * <li>6: Sigmoid. - * </ul> - * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such - * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 - * then clipping is disabled. - * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the - * projection layer, such that values are bound within - * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - * - * Outputs: - * * 0: The scratch buffer. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units * 4] with CIFG, or - * [batch_size, num_units * 3] without CIFG. - * * 1: The output state (out) (\f$h_t\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, output_size]. - * * 2: The cell state (out) (\f$C_t\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. - * * 3: The output (\f$o_t\f$). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, output_size]. This is effectively the same as the - * current “output state (out)” value. - */ - ANEURALNETWORKS_LSTM = 16, - - /** - * Performs an 2-D max pooling operation. - * - * The output dimensions are functions of the filter dimensions, stride, and - * padding. - * - * The values in the output tensor are computed as: - * - * output[batch, row, col, channel] = - * max_{i, j} (input[batch, row + i, col + j, channel]) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Both explicit padding and implicit padding are supported. - * - * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the left, in the ‘width’ dimension. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the right, in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the top, in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on - * the bottom, in the ‘height’ dimension. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit - * padding scheme, has to be one of the - * {@link PaddingCode} values. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘width’ dimension. - * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when - * walking through input in the ‘height’ dimension. - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * width. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter - * height. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - * [batches, out_height, out_width, depth]. - */ - ANEURALNETWORKS_MAX_POOL_2D = 17, - - /** - * Multiplies two tensors, element-wise. - * - * Takes two input tensors of identical {@link OperandCode} and compatible - * dimensions. The output is the product of both input tensors, optionally - * modified by an activation function. - * - * Two dimensions are compatible when: - * 1. they are equal, or - * 2. one of them is 1 - * - * The size of the resulting output is the maximum size along each dimension - * of the input operands. It starts with the trailing dimensions, and works - * its way forward. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: A tensor. - * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions - * as input0. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: The product, a tensor of the same {@link OperandCode} as input0. - * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, - * the following condition must be satisfied: - * output_scale > input1_scale * input2_scale. - */ - ANEURALNETWORKS_MUL = 18, - - /** - * Computes rectified linear activation on the input tensor element-wise. - * - * The output is calculated using this formula: - * - * output = max(0, input) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the input. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - */ - ANEURALNETWORKS_RELU = 19, - - /** - * Computes rectified linear 1 activation on the input tensor element-wise. - * - * The output is calculated using this formula: - * - * output = min(1.f, max(-1.f, input)) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the input. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - */ - ANEURALNETWORKS_RELU1 = 20, - - /** - * Computes rectified linear 6 activation on the input tensor element-wise. - * - * The output is calculated using this formula: - * - * output = min(6, max(0, input)) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the input. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - */ - ANEURALNETWORKS_RELU6 = 21, - - /** - * Reshapes a tensor. - * - * Given tensor, this operation returns a tensor that has the same values as - * tensor, but with a newly specified shape. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the tensor to be reshaped. - * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, defining the - * shape of the output tensor. The number of elements implied by shape - * must be the same as the number of elements in the input tensor. - * - * Outputs: - * * 0: The output tensor, of shape specified by the input shape. - */ - ANEURALNETWORKS_RESHAPE = 22, - - /** - * Resizes images to given size using the bilinear interpretation. - * - * Resized images must be distorted if their output aspect ratio is not the - * same as input aspect ratio. The corner pixels of output may not be the - * same as corner pixels of input. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Inputs: - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying - * the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output - * height of the output tensor. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output - * width of the output tensor. - * - * Outputs: - * * 0: The output 4-D tensor, of shape - * [batches, new_height, new_width, depth]. - */ - ANEURALNETWORKS_RESIZE_BILINEAR = 23, - - /** - * A basic recurrent neural network layer. - * - * This layer implements the operation: - * outputs = state = activation(inputs * input_weights + - * state * recurrent_weights + bias) - * - * Where: - * * “input_weights” is a weight matrix that multiplies the inputs; - * * “recurrent_weights” is a weight matrix that multiplies the current - * “state” which itself is the output from the previous time step - * computation; - * * “bias” is a bias vector (added to each output vector in the batch); - * * “activation” is the function passed as the “fused_activation_function” - * argument (if not “NONE”). - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Inputs: - * * 0: input. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} of shape - * [batch_size, input_size], where “batch_size” corresponds to the - * batching dimension, and “input_size” is the size of the input. - * * 1: weights. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size], where “num_units” corresponds to the - * number of units. - * * 2: recurrent_weights. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, num_units], with columns corresponding to the weights - * from each unit. - * * 3: bias. - * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units]. - * * 4: hidden state (in). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. - * * 5: fused_activation_function. - * An optional {@link FuseCode} value indicating the - * activation function. If “NONE” is specified then it results in a - * linear activation. - * - * Outputs: - * * 0: hidden state (out). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. - * - * * 1: output. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. This is effectively the same as the - * current state value. - */ - ANEURALNETWORKS_RNN = 24, - - /** - * Computes the softmax activation on the input tensor element-wise, per - * batch, by normalizing the input vector so the maximum coefficient is - * zero. - * - * The output is calculated using this formula: - * - * output[batch, i] = - * exp((input[batch, i] - max(input[batch, :])) * beta) / - * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 2 or 4. - * - * Inputs: - * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. - * * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive - * scaling factor for the exponent, beta. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, - * the scale must be 1.f / 256 and the zeroPoint must be 0. - */ - ANEURALNETWORKS_SOFTMAX = 25, - - /** - * Rearranges blocks of spatial data, into depth. - * - * More specifically, this op outputs a copy of the input tensor where - * values from the height and width dimensions are moved to the depth - * dimension. The value block_size indicates the input block size and how - * the data is moved. - * - * Chunks of data of size block_size * block_size from depth are rearranged - * into non-overlapping blocks of size block_size x block_size. - * - * The depth of the output tensor is input_depth * block_size * block_size. - * The input tensor's height and width must be divisible by block_size. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4, with "NHWC" data layout. - * - * Inputs: - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], - * specifying the input. - * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. - * block_size must be >=1 and block_size must be a divisor of both the - * input height and width. - * - * Outputs: - * * 0: The output 4-D tensor, of shape [batches, height/block_size, - * width/block_size, depth_in*block_size*block_size]. - */ - ANEURALNETWORKS_SPACE_TO_DEPTH = 26, - - /** - * SVDF op is a kind of stateful layer derived from the notion that a - * densely connected layer that's processing a sequence of input frames can - * be approximated by using a singular value decomposition of each of its - * nodes. The implementation is based on: - * - * https://research.google.com/pubs/archive/43813.pdf - * - * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. - * “Compressing Deep Neural Networks using a Rank-Constrained Topology”. - * INTERSPEECH, 2015. - * - * It processes the incoming input using a 2-stage filtering mechanism: - * * stage 1 performs filtering on the "features" dimension, whose outputs - * get pushed into a memory of fixed-size memory_size. - * * stage 2 performs filtering on the "time" dimension of the memory_size - * memoized outputs of stage 1. - * - * Specifically, for rank 1, this layer implements the operation: - * - * memory = push(conv1d(inputs, weights_feature, feature_dim, - * "ANEURALNETWORKS_PADDING_VALID")); - * outputs = activation(memory * weights_time + bias); - * - * Where: - * * “weights_feature” is a weights matrix that processes the inputs (by - * convolving the input with every “feature filter”), and whose outputs - * get pushed, stacked in order, into the fixed-size “memory” (the oldest - * entry gets dropped); - * * “weights_time” is a weights matrix that processes the “memory” (by a - * batched matrix multiplication on the num_units); - * * “bias” is an optional bias vector (added to each output vector in the - * batch); and - * * “activation” is the function passed as the “fused_activation_function” - * argument (if not “NONE”). - * - * Each rank adds a dimension to the weights matrices by means of stacking - * the filters. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Inputs: - * * 0: input. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, input_size], where “batch_size” corresponds to the - * batching dimension, and “input_size” is the size of the input. - * * 1: weights_feature. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, input_size], where “num_units” corresponds to the - * number of units. - * * 2: weights_time. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [num_units, memory_size], where “memory_size” corresponds to the - * fixed-size of the memory. - * * 3: bias. - * An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, - * of shape [num_units]. - * * 4: state (in). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, (memory_size - 1) * num_units * rank]. - * * 5: rank. - * The rank of the SVD approximation. - * * 6: fused_activation_function. - * An optional {@link FuseCode} value indicating the - * activation function. If “NONE” is specified then it results in a - * linear activation. - * - * Outputs: - * * 0: state (out). - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, (memory_size - 1) * num_units * rank]. - * * 1: output. - * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape - * [batch_size, num_units]. - */ - ANEURALNETWORKS_SVDF = 27, - - /** - * Computes hyperbolic tangent of input tensor element-wise. - * - * The output is calculated using this formula: - * - * output = tanh(input) - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: up to 4. - * - * Inputs: - * * 0: A tensor, specifying the input. - * - * Outputs: - * * 0: The output tensor of same shape as input0. - */ - ANEURALNETWORKS_TANH = 28, - - // TODO: make the description easier to understand. - /** - * BatchToSpace for N-dimensional tensors. - * - * This operation reshapes the batch dimension (dimension 0) into M + 1 - * dimensions of shape block_shape + [batch], interleaves these blocks back - * into the grid defined by the spatial dimensions [1, ..., M], to obtain a - * result with the same rank as the input. - * - * This is the reverse of SpaceToBatch. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the tensor to be reshaped - * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block - * sizes for each spatial dimension of the input tensor. All values - * must be >= 1. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29, - - /** - * Element-wise division of two tensors. - * - * Takes two input tensors of identical {@link OperandCode} and compatible - * dimensions. The output is the result of dividing the first input tensor - * by the second, optionally modified by an activation function. - * - * Two dimensions are compatible when: - * 1. they are equal, or - * 2. one of them is 1 - * - * The size of the output is the maximum size along each dimension of the - * input operands. It starts with the trailing dimensions, and works its way - * forward. - * - * Example: - * input1.dimension = {4, 1, 2} - * input2.dimension = {5, 4, 3, 1} - * output.dimension = {5, 4, 3, 2} - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the first input. - * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions - * as input0. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_DIV = 30, - - /** - * Computes the mean of elements across dimensions of a tensor. - * - * Reduces the input tensor along the given dimensions to reduce. Unless - * keep_dims is true, the rank of the tensor is reduced by 1 for each entry - * in axis. If keep_dims is true, the reduced dimensions are retained with - * length 1. - * - * If dimensions to reduce have no entries, all dimensions are reduced, and - * a tensor with a single element is returned. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: A tensor, specifying the input. - * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions - * to reduce. If None (the default), reduces all dimensions. Must be in - * the range [-rank(input_tensor), rank(input_tensor)). - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, - * retains reduced dimensions with length 1. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_MEAN = 31, - - /** - * Pads a tensor. - * - * This operation pads a tensor according to the specified paddings. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the tensor to be padded. - * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings - * for each spatial dimension of the input tensor. The shape of the - * tensor must be {rank(input0), 2}. - * padding[i, 0] specifies the number of elements to be padded in the - * front of dimension i. - * padding[i, 1] specifies the number of elements to be padded after the - * end of dimension i. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. The - * output tensor has the same rank as input0, and each - * dimension of the output tensor has the same size as the - * corresponding dimension of the input tensor plus the size - * of the padding: - * output0.dimension[i] = - * padding[i, 0] + input0.dimension[i] + padding[i, 1] - */ - ANEURALNETWORKS_PAD = 32, - - // TODO: make the description easier to understand. - /** - * SpaceToBatch for N-Dimensional tensors. - * - * This operation divides "spatial" dimensions [1, ..., M] of the input into - * a grid of blocks of shape block_shape, and interleaves these blocks with - * the "batch" dimension (0) such that in the output, the spatial dimensions - * [1, ..., M] correspond to the position within the grid, and the batch - * dimension combines both the position within a spatial block and the - * original batch position. Prior to division into blocks, the spatial - * dimensions of the input are optionally zero padded according to paddings. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the input. - * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block - * sizes for each spatial dimension of the input tensor. All values - * must be >= 1. - * * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings - * for each spatial dimension of the input tensor. All values must be - * >= 0. The shape of the tensor must be {rank(input0), 2}. - * padding[i, 0] specifies the number of element to be padded in the - * front of dimension i. - * padding[i, 1] specifies the number of element to be padded after the - * end of dimension i. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33, - - /** - * Removes dimensions of size 1 from the shape of a tensor. - * - * Given a tensor input, this operation returns a tensor of the same - * {@link OperandCode} with all dimensions of size 1 removed. If you don't - * want to remove all size 1 dimensions, you can remove specific size 1 - * dimensions by specifying the axes (input1). - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, the tensor to be squeezed. - * * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The - * dimensions to squeeze. If specified only squeezes the dimensions - * listed. Otherwise, squeezes all dimensions. The dimension index - * starts at 0. An error must be reported if squeezing a dimension that - * is not 1. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. Contains the - * same data as input, but has one or more dimensions of size 1 - * removed. - */ - ANEURALNETWORKS_SQUEEZE = 34, - - /** - * Extracts a strided slice of a tensor. - * - * Roughly speaking, this op extracts a slice of size (end - begin) / stride - * from the given input tensor. Starting at the location specified by begin - * the slice continues by adding stride to the index until all dimensions - * are not less than end. Note that a stride can be negative, which causes a - * reverse slice. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the tensor to be sliced. - * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of - * the dimensions of the input tensor to be sliced. The length must be - * of rank(input0). - * * 2: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of - * the dimensions of the input tensor to be sliced. The length must be - * of rank(input0). - * * 3: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of - * the dimensions of the input tensor to be sliced. The length must be - * of rank(input0). - * * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit - * of begin_mask is set, begin[i] is ignored and the fullest possible - * range in that dimension is used instead. - * * 5: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of - * end_mask is set, end[i] is ignored and the fullest possible range in - * that dimension is used instead. - * * 6: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32 - * mask. If the ith bit of shrink_axis_mask is set, it implies that the - * ith specification shrinks the dimensionality by 1. A slice of size 1 - * starting from begin[i] in the dimension must be preserved. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_STRIDED_SLICE = 35, - - /** - * Element-wise subtraction of two tensors. - * - * Takes two input tensors of identical {@link OperandCode} and compatible - * dimensions. The output is the result of subtracting the second input - * tensor from the first one, optionally modified by an activation function. - * - * Two dimensions are compatible when: - * 1. they are equal, or - * 2. one of them is 1 - * - * The size of the output is the maximum size along each dimension of the - * input operands. It starts with the trailing dimensions, and works its way - * forward. - * - * Example: - * input1.dimension = {4, 1, 2} - * input2.dimension = {5, 4, 3, 1} - * output.dimension = {5, 4, 3, 2} - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the first input. - * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions - * as input0. - * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the - * {@link FuseCode} values. Specifies the activation to - * invoke on the result. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_SUB = 36, - - /** - * Transposes the input tensor, permuting the dimensions according to the - * perm tensor. - * - * The returned tensor's dimension i corresponds to the input dimension - * perm[i]. If perm is not given, it is set to (n-1...0), where n is the - * rank of the input tensor. Hence by default, this operation performs a - * regular matrix transpose on 2-D input Tensors. - * - * Supported tensor {@link OperandCode}: - * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} - * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} - * - * Supported tensor rank: up to 4 - * - * Inputs: - * * 0: An n-D tensor, specifying the tensor to be transposed. - * * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, - * the permutation of the dimensions of the input tensor. - * - * Outputs: - * * 0: A tensor of the same {@link OperandCode} as input0. - */ - ANEURALNETWORKS_TRANSPOSE = 37, -} OperationCode; - -/** - * Fused activation function types. - * - */ -typedef enum { - /** NO fused activation function. */ - ANEURALNETWORKS_FUSED_NONE = 0, - /** Fused ReLU activation function. */ - ANEURALNETWORKS_FUSED_RELU = 1, - /** Fused ReLU1 activation function. */ - ANEURALNETWORKS_FUSED_RELU1 = 2, - /** Fused ReLU6 activation function. */ - ANEURALNETWORKS_FUSED_RELU6 = 3, -} FuseCode; - -/** - * Implicit padding algorithms. - * - */ -typedef enum { - /** - * SAME padding. - * Padding on both ends are the "same": - * padding_to_beginning = total_padding / 2 - * padding_to_end = (total_padding + 1)/2. - * i.e., for even number of padding, padding to both ends are exactly - * the same; for odd number of padding, padding to the ending is bigger - * than the padding to the beginning by 1. - * - * total_padding is a function of input, stride and filter size. - * It could be computed as follows: - * out_size = (input + stride - 1) / stride; - * needed_input = (out_size - 1) * stride + filter_size - * total_padding = max(0, needed_input - output_size) - * The computation is the same for the horizontal and vertical directions. - */ - ANEURALNETWORKS_PADDING_SAME = 1, - - /** - * VALID padding. - * No padding. When the input size is not evenly divisible by - * the filter size, the input at the end that could not fill - * the whole filter tile will simply be ignored. - */ - ANEURALNETWORKS_PADDING_VALID = 2, -} PaddingCode; - -/** - * Execution preferences. - */ -typedef enum { - /** - * Prefer executing in a way that minimizes battery drain. - * This is desirable for compilations that will be executed often. - */ - ANEURALNETWORKS_PREFER_LOW_POWER = 0, - /** - * Prefer returning a single answer as fast as possible, even if this causes - * more power consumption. - */ - ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1, - /** - * Prefer maximizing the throughput of successive frames, for example when - * processing successive frames coming from the camera. - */ - ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2, -} PreferenceCode; - -/** - * Result codes. - */ -typedef enum { - ANEURALNETWORKS_NO_ERROR = 0, - ANEURALNETWORKS_OUT_OF_MEMORY = 1, - ANEURALNETWORKS_INCOMPLETE = 2, - ANEURALNETWORKS_UNEXPECTED_NULL = 3, - ANEURALNETWORKS_BAD_DATA = 4, - ANEURALNETWORKS_OP_FAILED = 5, - ANEURALNETWORKS_BAD_STATE = 6, - ANEURALNETWORKS_UNMAPPABLE = 7, -} ResultCode; - -/** - * For {@link ANeuralNetworksModel_setOperandValue}, values with a - * length smaller or equal to this will be immediately copied into - * the model. The size is in bytes. - */ -enum { - ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128 -}; - -/** - * ANeuralNetworksMemory is an opaque type that represents memory. - * - * This type is used to represent shared memory, memory mapped files, - * and similar memories. - * - * By using shared memory, a program can efficiently communicate to the - * runtime and drivers the tensors that define a model. See - * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application - * should typically create one shared memory object that contains every tensor - * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be - * used to create shared memory from a file handle. - * - * Memory objects can also be used to specify the input and output arguments of - * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory} - * and {@link ANeuralNetworksExecution_setOutputFromMemory}. - */ -typedef struct ANeuralNetworksMemory ANeuralNetworksMemory; - -/** - * ANeuralNetworksModel is an opaque type that contains a description of the - * mathematical operations that constitute the model. - * - * <p>Build the model by calling<ul> - * <li>{@link ANeuralNetworksModel_create}</li> - * <li>{@link ANeuralNetworksModel_addOperation}</li> - * <li>{@link ANeuralNetworksModel_addOperand}</li> - * </ul> - * - * This forms a graph in which each operation and operand is a node, a - * directed edge from an operand to an operation indicates that the - * operand is an input to the operation, and a directed edge from an - * operation to an operand indicates that the operand is an output - * from the operation. This graph must be acyclic. - * - * A model is completed by calling {@link ANeuralNetworksModel_finish}. - * A model is destroyed by calling {@link ANeuralNetworksModel_free}. - * - * <p>A model cannot be modified once {@link ANeuralNetworksModel_finish} - * has been called on it.</p> - * - * <p>It is the application's responsibility to make sure that only one thread - * modifies a model at a given time. It is however safe for more than one - * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p> - * - * <p>It is also the application's responsibility to ensure that there are no other - * uses of the model after calling {@link ANeuralNetworksModel_free}. - * This includes any compilation or execution object created using the model.</p> - */ -typedef struct ANeuralNetworksModel ANeuralNetworksModel; - -/** - * ANeuralNetworksCompilation is an opaque type that can be used to compile - * a machine learning model. - * - * <p>To use:<ul> - * <li>Create a new compilation instance by calling the - * {@link ANeuralNetworksCompilation_create} function.</li> - * <li>Set any desired properties on the compilation (for example, - * {@link ANeuralNetworksCompilation_setPreference}).</li> - * <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li> - * <li>Use the compilation as many times as needed - * with {@link ANeuralNetworksExecution_create}.</li> - * <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free} - * once all executions using the compilation have completed.</li></ul></p> - * - * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. - * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}. - * - * <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} - * has been called on it.</p> - * - * <p>It is the application's responsibility to make sure that only - * one thread modifies a compilation at a given time. It is however - * safe for more than one thread to use the compilation once - * {@link ANeuralNetworksCompilation_finish} has returned.</p> - * - * <p>It is also the application's responsibility to ensure that there are no other - * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}. - * This includes any execution object created using the compilation.</p> - */ -typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; - -/** - * ANeuralNetworksExecution is an opaque type that can be used to apply a machine - * learning model to a set of inputs. - * - * <p>To use:<ul> - * <li>Create a new execution instance by calling the - * {@link ANeuralNetworksExecution_create} function.</li> - * <li>Associate input buffers or memory regions to the model inputs with - * {@link ANeuralNetworksExecution_setInput} or - * {@link ANeuralNetworksExecution_setInputFromMemory}.</li> - * <li>Associate output buffers or memory regions to the model outputs with - * {@link ANeuralNetworksExecution_setOutput} or - * {@link ANeuralNetworksExecution_setOutputFromMemory}.</li> - * <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li> - * <li>Wait for the execution to complete with {@link - * ANeuralNetworksEvent_wait}.</li> - * <li>Destroy the execution with - * {@link ANeuralNetworksExecution_free}.</li></ul></p> - * - * <p>An output buffer or memory region must not overlap with any - * other output buffer or memory region, with an input buffer or - * memory region, or with an operand value in a memory object - * ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p> - * - * <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute} - * has been called on it.</p> - * - * <p>An execution can be applied to a model with - * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions - * to do new evaluations of the model.</p> - * - * <p>It is the application's responsibility to make sure that only one thread - * modifies an execution at a given time. It is however safe for more than one - * thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p> - * - * <p>It is also the application's responsibility to ensure that there are no other - * uses of the execution after calling {@link ANeuralNetworksExecution_free}.</p> - */ -typedef struct ANeuralNetworksExecution ANeuralNetworksExecution; - -/** - * ANeuralNetworksOperandType describes the type of an operand. - * This structure is used to describe both scalars and tensors. - * - * A tensor operand type must have a specified rank (number of - * dimensions) but may have any of its dimensions unspecified. - * - * A tensor operand type with all dimensions specified is "fully - * specified". Whenever possible (i.e., whenever the dimensions are - * known at model construction time), a tensor operand type should be - * (but is not required to be) fully specified, in order to enable the - * best possible performance. - * - * If a tensor operand's type is not fully specified, the dimensions - * of the operand are deduced from the operand types and values of the - * operation for which that operand is an output. - * - * <p>In the following situations, a tensor operand type must be fully - * specified:<ul> - * <li>The operand has a constant value, set by - * {@link ANeuralNetworksModel_setOperandValue} (with a - * non-nullptr buffer) or - * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> - * <li>The operand is a model input or model output (see - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}). A - * fully specified tensor operand type must either be provided - * to {@link ANeuralNetworksModel_addOperand}; or it must be - * provided to the corresponding - * {@link ANeuralNetworksExecution_setInput}, - * {@link ANeuralNetworksExecution_setInputFromMemory}, - * {@link ANeuralNetworksExecution_setOutput}, or - * {@link ANeuralNetworksModel_setOperandValueFromMemory}. - * EXCEPTION: If the input or output is optional and omitted - * (by passing nullptr for buffer to - * {@link ANeuralNetworksExecution_setInput} or - * {@link ANeuralNetworksExecution_setOutput}) then it need - * not have a fully specified tensor operand type.</li></ul> - * - * A tensor operand type with some number of unspecified dimensions is - * represented by setting each unspecified dimension to 0. - */ -typedef struct ANeuralNetworksOperandType { - /** The data type, e.g ANEURALNETWORKS_INT8. */ - int32_t type; - /** The number of dimensions (rank). It should be 0 for scalars. */ - uint32_t dimensionCount; - /** The dimensions of the tensor. It should be nullptr for scalars. */ - const uint32_t* dimensions; - /** These two fields are only used for quantized tensors. - * They should be zero for scalars and non-fixed point tensors. - * The dequantized value of each entry is (value - zeroPoint) * scale. - */ - float scale; - int32_t zeroPoint; -} ANeuralNetworksOperandType; - -typedef int32_t ANeuralNetworksOperationType; - -/** - * ANeuralNetworksEvent is an opaque type that represents an event - * that will be signaled once an execution completes. - */ -typedef struct ANeuralNetworksEvent ANeuralNetworksEvent; - - -/** - * Creates a shared memory object from a file descriptor. - * - * The shared memory is backed by a file descriptor via mmap. - * See {@link ANeuralNetworksMemory} for a description on how to use - * this shared memory. - * - * @param size The requested size in bytes. - * Must not be larger than the file size. - * @param prot The desired memory protection for the mapping. - * It is either PROT_NONE or the bitwise OR of one or - * more of the following flags: PROT_READ, PROT_WRITE. - * @param fd The requested file descriptor. - * The file descriptor has to be mmap-able. The file - * descriptor will be duplicated. - * @param offset The offset to the beginning of the file of the area to map. - * The offset has to be aligned to a page size. - * @param memory The memory object to be created. - * Set to NULL if unsuccessful. - * - * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. - */ -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, - ANeuralNetworksMemory** memory); - -/** - * Delete a memory object. - * - * Destroys the object used by the run time to keep track of the memory. - * This will free the underlying actual memory if no other code has open - * handles to this memory. - * - * @param memory The memory object to be freed. - */ -void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory); - -/** - * Create an empty {@link ANeuralNetworksModel}. - * - * <p>This only creates the object. Computation is performed once - * {@link ANeuralNetworksExecution_startCompute} is invoked. - * - * The model should be constructed with calls to - * {@link ANeuralNetworksModel_addOperation} and - * {@link ANeuralNetworksModel_addOperand} - * - * <p>{@link ANeuralNetworksModel_finish} should be called once the model - * has been fully constructed.</p> - * - * <p>{@link ANeuralNetworksModel_free} should be called once the model - * is no longer needed.</p> - * - * @param model The {@link ANeuralNetworksModel} to be created. - * Set to NULL if unsuccessful. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_create(ANeuralNetworksModel** model); - -/** - * Destroy a model. - * - * The model need not have been finished by a call to - * {@link ANeuralNetworksModel_finish}. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @param model The model to be destroyed. Passing NULL is acceptable and - * results in no operation. - */ -void ANeuralNetworksModel_free(ANeuralNetworksModel* model); - -/** - * Indicate that we have finished modifying a model. Required before - * calling {@link ANeuralNetworksCompilation_create}. - * - * An application is responsible to make sure that no other thread uses - * the model at the same time. - * - * This function must only be called once for a given model. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @param model The model to be finished. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_finish(ANeuralNetworksModel* model); - -/** - * Add an operand to a model. - * - * The order in which the operands are added is important. The first one added - * to a model will have the index value 0, the second 1, etc. These indexes are - * used as operand identifiers in - * {@link ANeuralNetworksModel_addOperation}, - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}, - * {@link ANeuralNetworksModel_setOperandValue}, - * {@link ANeuralNetworksModel_setOperandValueFromMemory}, - * {@link ANeuralNetworksExecution_setInput}, - * {@link ANeuralNetworksExecution_setInputFromMemory}, - * {@link ANeuralNetworksExecution_setOutput}, - * {@link ANeuralNetworksExecution_setOutputFromMemory} and - * {@link ANeuralNetworksExecution_setOperandValue}. - * - * <p>Every operand must be referenced in exactly one of the following - * ways:<ul> - * <li>It is identified as a model input with - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</li> - * <li>It is identified as a constant with - * {@link ANeuralNetworksModel_setOperandValue} or - * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> - * <li>It is identified as an output of exactly one operation with - * {@link ANeuralNetworksModel_addOperation}.</li></p> - * <p>An operand that is identified as a model input or as a constant - * must not also be identified as a model output with - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</p> - * - * To build a model that can accommodate inputs of various sizes, as - * you may want to do for a CNN, leave unspecified the dimensions that - * will vary at run time. If you do so, fully specify dimensions - * when calling {@link ANeuralNetworksExecution_setInput} or - * {@link ANeuralNetworksExecution_setInputFromMemory}. - * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @param model The model to be modified. - * @param type The {@link ANeuralNetworksOperandType} that describes the shape - * of the operand. Neither the {@link ANeuralNetworksOperandType} - * nor the dimensions it points to need to outlive the call to - * {@link ANeuralNetworksModel_addOperand}. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, - const ANeuralNetworksOperandType* type); - -/** - * Sets an operand to a constant value. - * - * Values of length smaller or equal to - * {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES} - * are immediately copied into the model. - * - * For values of length greater than {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES}, - * a pointer to the buffer is stored within the model. The application is responsible - * for not changing the content of this region until all executions using this model - * have completed. As the data may be copied during processing, modifying the data - * after this call yields undefined results. - * - * For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory} - * is likely to be more efficient. - * - * To indicate that an optional operand should be considered missing, - * pass nullptr for buffer and 0 for length. - * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @param model The model to be modified. - * @param index The index of the model operand we're setting. - * @param buffer A pointer to the data to use. - * @param length The size in bytes of the data value. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, - const void* buffer, size_t length); - -/** - * Sets an operand to a value stored in a memory object. - * - * The content of the memory is not copied. A reference to that memory is stored - * inside the model. The application is responsible for not changing the content - * of the memory region until all executions using this model have completed. - * As the data may be copied during processing, modifying the data after this call - * yields undefined results. - * - * To indicate that an optional operand should be considered missing, - * use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer. - * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @param model The model to be modified. - * @param index The index of the model operand we're setting. - * @param buffer A pointer to the data to use. - * @param memory The memory containing the data. - * @param offset This specifies the location of the data within the memory. - * The offset is in bytes from the start of memory. - * @param length The size in bytes of the data value. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, - const ANeuralNetworksMemory* memory, - size_t offset, size_t length); - -/** - * Add an operation to a model. - * - * @param model The model to be modified. - * @param type The {@link ANeuralNetworksOperationType} of the operation. - * @param inputCount The number of entries in the inputs array. - * @param inputs An array of indexes identifying each operand. - * @param outputCount The number of entries in the outputs array. - * @param outputs An array of indexes identifying each operand. - * - * The operands specified by inputs and outputs must have been - * previously added by calls to {@link ANeuralNetworksModel_addOperand}. - * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, - ANeuralNetworksOperationType type, uint32_t inputCount, - const uint32_t* inputs, uint32_t outputCount, - const uint32_t* outputs); - -/** - * Specifies which operands will be the model's inputs and - * outputs. Every model must have at least one input and one output. - * - * An operand cannot be used for both input and output. Doing so will - * return an error. - * - * @param model The model to be modified. - * @param inputCount The number of entries in the inputs array. - * @param inputs An array of indexes identifying the input operands. - * @param outputCount The number of entries in the outputs array. - * @param outputs An array of indexes identifying the output operands. - * - * The operands specified by inputs and outputs must have been - * previously added by calls to {@link ANeuralNetworksModel_addOperand}. - * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. - * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. - * - */ -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. - * - * <p>This only creates the object. Compilation is only performed once - * {@link ANeuralNetworksCompilation_finish} is invoked.</p> - * - * <p>{@link ANeuralNetworksCompilation_finish} should be called once - * all desired properties have been set on the compilation.</p> - * - * <p>{@link ANeuralNetworksModel_free} should be called once the compilation - * is no longer needed.</p> - * - * <p>The provided model must outlive the compilation.</p> - * - * The model must already have been finished by a call to - * {@link ANeuralNetworksModel_finish}. - * - * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. - * - * @param model The {@link ANeuralNetworksModel} to be compiled. - * @param compilation The newly created object or NULL if unsuccessful. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA - * if the model is invalid. - */ -int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, - ANeuralNetworksCompilation** compilation); - -/** - * Destroy a compilation. - * - * The compilation need not have been finished by a call to - * {@link ANeuralNetworksModel_finish}. - * - * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. - * - * @param compilation The compilation to be destroyed. Passing NULL is acceptable and - * results in no operation. - */ -void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation); - -/** - * Sets the execution preference. - * - * <p>Provides guidance to the runtime when trade-offs are possible.</p> - * - * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. - * - * @param compilation The compilation to be modified. - * @param preference Either {@link PREFER_LOW_POWER}, - * {@link PREFER_SINGLE_FAST_ANSWER}, or - * {@link PREFER_SUSTAINED_SPEED}. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, - int32_t preference); - -/** - * Indicate that we have finished modifying a compilation. Required before - * calling {@link ANeuralNetworksExecution_create}. - * - * An application is responsible to make sure that no other thread uses - * the compilation at the same time. - * - * This function must only be called once for a given compilation. - * - * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. - * - * @param compilation The compilation to be finished. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation); - -/** - * Create a {@link ANeuralNetworksExecution} to apply the given compilation. - * This only creates the object. Computation is only performed once - * {@link ANeuralNetworksExecution_startCompute} is invoked. - * - * <p>The provided compilation must outlive the execution.</p> - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. - * @param execution The newly created object or NULL if unsuccessful. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA - * if the compilation is invalid. - */ -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, - ANeuralNetworksExecution** execution); - -/** - * Destroy an execution. - * - * <p>If called on an execution for which - * {@link ANeuralNetworksExecution_startCompute} has been called, the - * function will return immediately but will mark the execution to be deleted - * once the computation completes. The related {@link ANeuralNetworksEvent} - * will be signaled and the {@link ANeuralNetworksEvent_wait} will return - * ANEURALNETWORKS_ERROR_DELETED. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be destroyed. Passing NULL is acceptable and - * results in no operation. - */ -void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution); - -/** - * Associate a user buffer with an input of the model of the - * {@link ANeuralNetworksExecution}. - * - * <p>The provided buffer must outlive the execution.</p> - * - * If the input is optional, you can indicate that it is omitted by - * passing nullptr for buffer and 0 for length. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be modified. - * @param index The index of the input argument we are setting. It is - * an index into the lists passed to - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not - * the index associated with - * {@link ANeuralNetworksModel_addOperand}. - * @param type The {@link ANeuralNetworksOperandType} of the - * operand. Unless the input is omitted, this should be - * used to specify the dimensions that were left - * unspecified when the operand was added to the - * model. All other properties of the type must be the - * same as specified in the model. If the type is the same - * as specified when the model was built, NULL can be - * passed. Neither the {@link ANeuralNetworksOperandType} - * nor the dimensions it points to need to outlive the call - * to {@link ANeuralNetworksExecution_setInput}. - * @param buffer The buffer containing the data. - * @param length The length in bytes of the buffer. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the - * name is not recognized or the buffer is too small for the input. - */ -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, - const ANeuralNetworksOperandType* type, const void* buffer, - size_t length); - -/** - * Associate part of a memory object with an input of the model of the - * {@link ANeuralNetworksExecution}. - * - * <p>The provided memory must outlive the execution.</p> - * - * If the input is optional, you can indicate that it is omitted by - * using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer - * and 0 for length. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be modified. - * @param index The index of the input argument we are setting. It is - * an index into the lists passed to - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not - * the index associated with {@link ANeuralNetworksModel_addOperand}. - * @param type The {@link ANeuralNetworksOperandType} of the - * operand. This should be used to specify the dimensions - * that were left unspecified when the operand was added - * to the model. All other properties of the type must be - * the same as specified in the model. If the type is the - * same as specified when the model was built, NULL can be - * passed. Neither the {@link ANeuralNetworksOperandType} - * nor the dimensions it points to need to outlive the call - * to {@link ANeuralNetworksExecution_setInputFromMemory}. - * @param memory The memory containing the data. - * @param offset This specifies the location of the data within the memory. - * The offset is in bytes from the start of memory. - * @param length The size in bytes of the data value. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the - * name is not recognized or the buffer is too small for the input. - */ -int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, - const ANeuralNetworksOperandType* type, - const ANeuralNetworksMemory* memory, size_t offset, - size_t length); - -/** - * Associate a user buffer with an output of the model of the - * {@link ANeuralNetworksExecution}. - * - * If the output is optional, you can indicate that it is omitted by - * passing nullptr for buffer and 0 for length. - * - * <p>The provided buffer must outlive the execution.</p> - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be modified. - * @param index The index of the output argument we are setting. It is - * an index into the lists passed to - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not - * the index associated with {@link ANeuralNetworksModel_addOperand}. - * @param type The {@link ANeuralNetworksOperandType} of the - * operand. Unless the output is omitted, this should be - * used to specify the dimensions that were left - * unspecified when the operand was added to the - * model. All other properties of the type must be the - * same as specified in the model. If the type is the same - * as specified when the model was built, NULL can be - * passed. Neither the {@link ANeuralNetworksOperandType} - * nor the dimensions it points to need to outlive the call - * to {@link ANeuralNetworksExecution_setOutput}. - * @param buffer The buffer where the data is to be written. - * @param length The length in bytes of the buffer. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the - * name is not recognized or the buffer is too small for the output. - */ -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, - const ANeuralNetworksOperandType* type, void* buffer, - size_t length); - -/** - * Associate part of a memory object with an output of the model of the - * {@link ANeuralNetworksExecution}. - * - * If the output is optional, you can indicate that it is omitted by - * using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer - * and 0 for length. - * - * <p>The provided memory must outlive the execution.</p> - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be modified. - * @param index The index of the output argument we are setting. It is - * an index into the lists passed to - * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not - * the index associated with {@link ANeuralNetworksModel_addOperand}. - * @param type The {@link ANeuralNetworksOperandType} of the operand. This should be - * used to specify the dimensions that were left - * unspecified when the operand was added to the - * model. All other properties of the type must be the - * same as specified in the model. If the type is the same - * as specified when the model was built, NULL can be - * passed. Neither the {@link ANeuralNetworksOperandType} - * nor the dimensions it points to need to outlive the call - * to {@link ANeuralNetworksExecution_setOutputFromMemory}. - * @param memory The memory where the data is to be stored. - * @param offset This specifies the location of the data within the memory. - * The offset is in bytes from the start of memory. - * @param length The length in bytes of the data value. - * - * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the - * name is not recognized or the buffer is too small for the output. - */ -int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, - const ANeuralNetworksOperandType* type, - const ANeuralNetworksMemory* memory, size_t offset, - size_t length); - -/** - * Schedule evaluation of the execution. - * - * <p>Schedules evaluation of the execution. Once the model has been - * applied and the outputs are ready to be consumed, the returned event will be - * signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event. - * </p> - * - * Multiple executions can be scheduled and evaluated concurrently. The - * runtime makes no guarantee on the ordering of completion of - * executions. If it's important to the application, the application - * should enforce the ordering by using - * {@link ANeuralNetworksEvent_wait}. - * - * ANeuralNetworksEvent_wait must be called to recuperate the resources used - * by the execution. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @param execution The execution to be scheduled and executed. - * @param event The event that will be signaled on completion. event is set to - * NULL if there's an error. - * - * @return ANEURALNETWORKS_NO_ERROR if successful. - */ -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, - ANeuralNetworksEvent** event); - -/** - * Waits until the execution completes. - * - * More than one thread can wait on an event. When the execution completes, - * all threads will be released. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - * - * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. - */ -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event); - -/** - * Destroys the event. - * - * See {@link ANeuralNetworksExecution} for information on multithreaded usage. - */ -void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event); - -__END_DECLS - -#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H - -/** @} */ |