android / platform / frameworks / ml / android-p-preview-4 / . / nn / runtime / include / NeuralNetworks.h

/* | |

* Copyright (C) 2017 The Android Open Source Project | |

* | |

* Licensed under the Apache License, Version 2.0 (the "License"); | |

* you may not use this file except in compliance with the License. | |

* You may obtain a copy of the License at | |

* | |

* http://www.apache.org/licenses/LICENSE-2.0 | |

* | |

* Unless required by applicable law or agreed to in writing, software | |

* distributed under the License is distributed on an "AS IS" BASIS, | |

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |

* See the License for the specific language governing permissions and | |

* limitations under the License. | |

*/ | |

/** | |

* @addtogroup NeuralNetworks | |

* @{ | |

*/ | |

/** | |

* @file NeuralNetworks.h | |

*/ | |

#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H | |

#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H | |

/****************************************************************** | |

* | |

* IMPORTANT NOTICE: | |

* | |

* This file is part of Android's set of stable system headers | |

* exposed by the Android NDK (Native Development Kit). | |

* | |

* Third-party source AND binary code relies on the definitions | |

* here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES. | |

* | |

* - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES) | |

* - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS | |

* - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY | |

* - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES | |

*/ | |

#if __ANDROID_API__ >= __ANDROID_API_O_MR1__ | |

#include <stddef.h> | |

#include <stdint.h> | |

#include <sys/cdefs.h> | |

__BEGIN_DECLS | |

/** | |

* Operand types. | |

* | |

* The type of operands that can be added to a model. | |

* | |

* Although we define many types, most operators accept just a few | |

* types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, | |

* and {@link ANEURALNETWORKS_INT32}. | |

*/ | |

typedef enum { | |

/** A 32 bit floating point scalar value. */ | |

ANEURALNETWORKS_FLOAT32 = 0, | |

/** A signed 32 bit integer scalar value. */ | |

ANEURALNETWORKS_INT32 = 1, | |

/** An unsigned 32 bit integer scalar value. */ | |

ANEURALNETWORKS_UINT32 = 2, | |

/** A tensor of 32 bit floating point values. */ | |

ANEURALNETWORKS_TENSOR_FLOAT32 = 3, | |

/** A tensor of 32 bit integer values. */ | |

ANEURALNETWORKS_TENSOR_INT32 = 4, | |

/** | |

* A tensor of 8 bit integers that represent real numbers. | |

* | |

* Attached to this tensor are two numbers that can be used to convert the | |

* 8 bit integer to the real value and vice versa. These two numbers are: | |

* - scale: a 32 bit floating point value greater than zero. | |

* - zeroPoint: a 32 bit integer, in range [0, 255]. | |

* | |

* The formula is: | |

* real_value = (integer_value - zeroPoint) * scale. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5, | |

} OperandCode; | |

/** | |

* Operation types. | |

* | |

* The type of operations that can be added to a model. | |

*/ | |

typedef enum { | |

/** | |

* Adds two tensors, element-wise. | |

* | |

* Takes two input tensors of identical {@link OperandCode} and compatible | |

* dimensions. The output is the sum of both input tensors, optionally | |

* modified by an activation function. | |

* | |

* Two dimensions are compatible when: | |

* 1. they are equal, or | |

* 2. one of them is 1 | |

* | |

* The size of the output is the maximum size along each dimension of the | |

* input operands. It starts with the trailing dimensions, and works its | |

* way forward. | |

* | |

* Example: | |

* | |

* input1.dimension = {4, 1, 2} | |

* input2.dimension = {5, 4, 3, 1} | |

* output.dimension = {5, 4, 3, 2} | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions | |

* as input0. | |

* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: The sum, a tensor of the same {@link OperandCode} as input0. | |

*/ | |

ANEURALNETWORKS_ADD = 0, | |

/** | |

* Performs a 2-D average pooling operation. | |

* | |

* The output dimensions are functions of the filter dimensions, stride, and | |

* padding. | |

* | |

* The values in the output tensor are computed as: | |

* | |

* output[batch, row, col, channel] = | |

* sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, | |

* and Channels) data layout. | |

* | |

* Both explicit padding and implicit padding are supported. | |

* | |

* Inputs (explicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying | |

* the input. | |

* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the left, in the ‘width’ dimension. | |

* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the right, in the ‘width’ dimension. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the top, in the ‘height’ dimension. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the bottom, in the ‘height’ dimension. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter | |

* width. | |

* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter | |

* height. | |

* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Inputs (implicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying | |

* the input. | |

* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit | |

* padding scheme, has to be one of the | |

* {@link PaddingCode} values. | |

* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter | |

* width. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter | |

* height. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: The output 4-D tensor, of shape | |

[batches, out_height, out_width, depth]. | |

*/ | |

ANEURALNETWORKS_AVERAGE_POOL_2D = 1, | |

/** | |

* Concatenates the input tensors along the given dimension. | |

* | |

* The input tensors must have identical {@link OperandCode} and the same | |

* dimensions except the dimension along the concatenation axis. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0 ~ n-1: The list of n input tensors, of shape | |

* [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all input tensors | |

* must have the same scale and zeroPoint. | |

* * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the | |

* concatenation axis. | |

* | |

* Outputs: | |

* * 0: The output, a tensor of the same {@link OperandCode} as the input | |

* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. | |

*/ | |

ANEURALNETWORKS_CONCATENATION = 2, | |

/** | |

* Performs an 2-D convolution operation. | |

* | |

* The CONV_2D op sweeps a 2-D filter that can mix channels together over a | |

* batch of images, applying the filter to each window of each image of the | |

* appropriate size. | |

* | |

* The output dimensions are functions of the filter dimensions, stride, and | |

* padding. | |

* | |

* The values in the output tensor are computed as: | |

* | |

* output[batch, row, col, channel] = | |

* sum_{i, j} ( | |

* input[batch, row + i, col + j, k] * | |

* filter[channel, row + i, col + j, k] + | |

* bias[channel] | |

* ) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: 4, with "NHWC" data layout. | |

* | |

* Both explicit padding and implicit padding are supported. | |

* | |

* Inputs (explicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], | |

* specifying the input. | |

* * 1: A 4-D tensor, of shape | |

* [depth_out, filter_height, filter_width, depth_in], specifying the | |

* filter. | |

* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. | |

* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias | |

* should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input | |

* tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias | |

* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of | |

* 0 and bias_scale == input_scale * filter_scale. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the left, in the ‘width’ dimension. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the right, in the ‘width’ dimension. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the top, in the ‘height’ dimension. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the bottom, in the ‘height’ dimension. | |

* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Inputs (implicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], | |

* specifying the input. | |

* * 1: A 4-D tensor, of shape | |

* [depth_out, filter_height, filter_width, depth_in], specifying the | |

* filter. | |

* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input | |

* tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should | |

* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be | |

* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and | |

* bias_scale == input_scale * filter_scale. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit | |

* padding scheme, has to be one of the | |

* {@link PaddingCode} values. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: The output 4-D tensor, of shape | |

* [batches, out_height, out_width, depth_out]. For output tensor of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition | |

* must be satisfied: output_scale > input_scale * filter_scale. | |

*/ | |

ANEURALNETWORKS_CONV_2D = 3, | |

/** | |

* Performs a depthwise 2-D convolution operation. | |

* | |

* Given an input tensor of shape [batches, height, width, depth_in] and a | |

* filter tensor of shape [1, filter_height, filter_width, depth_out] | |

* containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV | |

* applies a different filter to each input channel (expanding from 1 | |

* channel to channel_multiplier channels for each), then concatenates the | |

* results together. | |

* | |

* The output has depth_out = depth_in * depth_multiplier channels. | |

* The output dimensions are functions of the filter dimensions, stride, and | |

* padding. | |

* | |

* The values in the output tensor are computed as: | |

* | |

* output[b, i, j, k * channel_multiplier + q] = | |

* sum_{di, dj} ( | |

* input[b, strides[1] * i + di, strides[2] * j + dj, k] * | |

* filter[1, di, dj, k * channel_multiplier + q] | |

* ) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: 4, with "NHWC" data layout. | |

* | |

* Both explicit padding and implicit padding are supported. | |

* | |

* Inputs (explicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], | |

* specifying the input. | |

* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], | |

* specifying the filter. | |

* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input | |

* tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should | |

* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be | |

* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and | |

* bias_scale == input_scale * filter_scale. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the left, in the ‘width’ dimension. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the right, in the ‘width’ dimension. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the top, in the ‘height’ dimension. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on | |

* the bottom, in the ‘height’ dimension. | |

* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise | |

* multiplier. | |

* * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Inputs (implicit padding): | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], | |

* specifying the input. | |

* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], | |

* specifying the filter. | |

* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input | |

* tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should | |

* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be | |

* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and | |

* bias_scale == input_scale * filter_scale. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit | |

* padding scheme, has to be one of the | |

* {@link PaddingCode} values. | |

* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘width’ dimension. | |

* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when | |

* walking through input in the ‘height’ dimension. | |

* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise | |

* multiplier. | |

* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: The output 4-D tensor, of shape | |

* [batches, out_height, out_width, depth_out]. For output tensor of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition | |

* must be satisfied: output_scale > input_scale * filter_scale. | |

*/ | |

ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, | |

/** | |

* Rearranges data from depth into blocks of spatial data. | |

* | |

* More specifically, this op outputs a copy of the input tensor where | |

* values from the depth dimension are moved in spatial blocks to the height | |

* and width dimensions. The value block_size indicates the input block size | |

* and how the data is moved. | |

* | |

* Chunks of data of size block_size * block_size from depth are rearranged | |

* into non-overlapping blocks of size block_size x block_size. | |

* | |

* The width of the output tensor is input_depth * block_size, whereas the | |

* height is input_height * block_size. The depth of the input tensor must | |

* be divisible by block_size * block_size | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: 4, with "NHWC" data layout. | |

* | |

* Inputs: | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth_in], | |

* specifying the input. | |

* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. | |

* block_size must be >=1 and block_size * block_size must be a divisor | |

* of the input depth. | |

* | |

* Outputs: | |

* * 0: The output 4-D tensor, of shape [batch, height*block_size, | |

* width*block_size, depth/(block_size*block_size)]. | |

*/ | |

ANEURALNETWORKS_DEPTH_TO_SPACE = 5, | |

/** | |

* Dequantizes the input tensor. | |

* | |

* The formula is: | |

* | |

* output = (input - zeroPoint) * scale. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0, but with | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

*/ | |

ANEURALNETWORKS_DEQUANTIZE = 6, | |

/** | |

* Looks up sub-tensors in the input tensor. | |

* | |

* This operator takes for input a tensor of values (Values) and | |

* a one-dimensional tensor of selection indices (Lookups). | |

* The output tensor is the concatenation of sub-tensors of Values as | |

* selected by Lookups. | |

* | |

* Think of Values as being sliced along its first dimension: | |

* The entries in Lookups select which slices are concatenated together | |

* to create the output tensor. | |

* | |

* For example, if Values has shape of [40, 200, 300] and | |

* Lookups has shape of [3], all three values found in Lookups are | |

* expected to be between 0 and 39. The resulting tensor must | |

* have shape of [3, 200, 300]. | |

* | |

* If a value in Lookups is out of bounds, the operation must fail | |

* and an error must be reported. | |

* | |

* Inputs: | |

* * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. | |

* The values are indices into the first dimension of Values. | |

* * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are | |

* extracted. | |

* | |

* Output: | |

* * 0: A n-D tensor with the same rank and shape as the Values | |

* tensor, except for the first dimension which has the same size | |

* as Lookups' only dimension. | |

*/ | |

ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, | |

/** | |

* Computes element-wise floor() on the input tensor. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: The output tensor, of the same {@link OperandCode} and dimensions as | |

* the input tensor. | |

*/ | |

ANEURALNETWORKS_FLOOR = 8, | |

/** | |

* Denotes a fully (densely) connected layer, which connects all elements | |

* in the input tensor with each element in the output tensor. | |

* | |

* This layer implements the operation: | |

* | |

* outputs = activation(inputs * weights’ + bias) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

* * 0: A tensor of at least rank 2, specifying the input. If rank is | |

* greater than 2, then it gets flattened to a 2-D Tensor. The | |

* (flattened) 2-D Tensor is reshaped (if necessary) to | |

* [batch_size, input_size], where "input_size" corresponds to the | |

* number of inputs to the layer, matching the second dimension of | |

* weights, and "batch_size" is calculated by dividing the number of | |

* elements by "input_size". | |

* * 1: A 2-D tensor, specifying the weights, of shape | |

* [num_units, input_size], where "num_units" corresponds to the number | |

* of output nodes. | |

* * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input | |

* tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should | |

* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be | |

* of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and | |

* bias_scale == input_scale * filter_scale. | |

* * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: The output tensor, of shape [batch_size, num_units]. For output | |

* tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following | |

* condition must be satisfied: | |

* output_scale > input_scale * filter_scale. | |

*/ | |

ANEURALNETWORKS_FULLY_CONNECTED = 9, | |

/** | |

* Looks up sub-tensors in the input tensor using a key-value map. | |

* | |

* This operator takes for input a tensor of values (Values), | |

* a one-dimensional tensor of selection values (Lookups) and | |

* a one-dimensional tensor that maps these values to Values | |

* indexes. The output tensor is the concatenation of sub-tensors of | |

* Values as selected by Lookups via Keys. | |

* | |

* Think of Values as being sliced along its outer-most dimension. | |

* The output is a concatenation of selected slices, with one slice | |

* for each entry of Lookups. The slice selected is the one at the | |

* same index as the Maps entry that matches the value in Lookups. | |

* | |

* For a hit, the corresponding sub-tensor of Values is included | |

* in the Output tensor. For a miss, the corresponding sub-tensor in | |

* Output must have zero values. | |

* | |

* For example, if Values has shape of [40, 200, 300], | |

* Keys should have a shape of [40]. If Lookups tensor has shape | |

* of [3], three slices are being concatenated, so the resulting tensor | |

* must have the shape of [3, 200, 300]. If the first entry in Lookups | |

* has the value 123456, that value must be located in Keys tensor. | |

* If the sixth entry of Keys contains 123456, the sixth slice of Values | |

* must be selected. If no entry in Keys has 123456, a slice of zeroes | |

* must be concatenated. | |

* | |

* Inputs: | |

* * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with | |

* shape [ k ]. | |

* * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape | |

* [ n ]; Keys and Values pair represent a map, i.e., the ith element | |

* in Keys (Keys[i]) is the key to select the ith sub-tensor in Values | |

* (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in | |

* ascending order. | |

* * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension | |

* must be n. | |

* | |

* Outputs: | |

* * 0: Output. A tensor with shape [ k …]. | |

* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup | |

* hits (True) or not (False). | |

* Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 | |

* and scale 1.0f. | |

* A non-zero byte represents True, a hit. A zero indicates otherwise. | |

*/ | |

ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, | |

/** | |

* Applies L2 normalization along the depth dimension. | |

* | |

* The values in the output tensor are computed as: | |

* | |

* output[batch, row, col, channel] = | |

* input[batch, row, col, channel] / | |

* sqrt(sum_{c} pow(input[batch, row, col, c], 2)) | |

* | |

* For input tensor with more dimensions, independently normalizes each 1-D | |

* slice along dimension dim. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, | |

* Height, Width, and Channels). | |

* | |

* Inputs: | |

* * 0: A 4-D tensor, of shape [batches, height, width, depth]. | |

* | |

* Outputs: | |

* * 0: The output 4-D tensor, of 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, | |

#if __ANDROID_API__ >= __ANDROID_API_P__ | |

// TODO: make the description easier to understand. | |

/** | |

* BatchToSpace for N-dimensional tensors. | |

* | |

* This operation reshapes the batch dimension (dimension 0) into M + 1 | |

* dimensions of shape block_shape + [batch], interleaves these blocks back | |

* into the grid defined by the spatial dimensions [1, ..., M], to obtain a | |

* result with the same rank as the input. | |

* | |

* This is the reverse of SpaceToBatch. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: 4 | |

* | |

* Inputs: | |

* * 0: An n-D tensor, specifying the tensor to be reshaped | |

* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block | |

* sizes for each spatial dimension of the input tensor. All values | |

* must be >= 1. | |

* | |

* Outputs: | |

* * 0: A tensor of the same {@link OperandCode} as input0. | |

*/ | |

ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29, | |

/** | |

* Element-wise division of two tensors. | |

* | |

* Takes two input tensors of identical {@link OperandCode} and compatible | |

* dimensions. The output is the result of dividing the first input tensor | |

* by the second, optionally modified by an activation function. | |

* | |

* Two dimensions are compatible when: | |

* 1. they are equal, or | |

* 2. one of them is 1 | |

* | |

* The size of the output is the maximum size along each dimension of the | |

* input operands. It starts with the trailing dimensions, and works its way | |

* forward. | |

* | |

* Example: | |

* input1.dimension = {4, 1, 2} | |

* input2.dimension = {5, 4, 3, 1} | |

* output.dimension = {5, 4, 3, 2} | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: An n-D tensor, specifying the first input. | |

* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions | |

* as input0. | |

* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the | |

* {@link FuseCode} values. Specifies the activation to | |

* invoke on the result. | |

* | |

* Outputs: | |

* * 0: A tensor of the same {@link OperandCode} as input0. | |

*/ | |

ANEURALNETWORKS_DIV = 30, | |

/** | |

* Computes the mean of elements across dimensions of a tensor. | |

* | |

* Reduces the input tensor along the given dimensions to reduce. Unless | |

* keep_dims is true, the rank of the tensor is reduced by 1 for each entry | |

* in axis. If keep_dims is true, the reduced dimensions are retained with | |

* length 1. | |

* | |

* If dimensions to reduce have no entries, all dimensions are reduced, and | |

* a tensor with a single element is returned. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor, specifying the input. | |

* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions | |

* to reduce. If None (the default), reduces all dimensions. Must be in | |

* the range [-rank(input_tensor), rank(input_tensor)). | |

* * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, | |

* retains reduced dimensions with length 1. | |

* | |

* Outputs: | |

* * 0: A tensor of the same {@link OperandCode} as input0. | |

*/ | |

ANEURALNETWORKS_MEAN = 31, | |

/** | |

* Pads a tensor. | |

* | |

* This operation pads a tensor according to the specified paddings. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: An n-D tensor, specifying the tensor to be padded. | |

* * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings | |

* for each spatial dimension of the input tensor. The shape of the | |

* tensor must be {rank(input0), 2}. | |

* padding[i, 0] specifies the number of 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, | |

#endif // __ANDROID_API__ >= __ANDROID_API_P__ | |

} OperationCode; | |

/** | |

* Fused activation function types. | |

* | |

*/ | |

typedef enum { | |

/** NO fused activation function. */ | |

ANEURALNETWORKS_FUSED_NONE = 0, | |

/** Fused ReLU activation function. */ | |

ANEURALNETWORKS_FUSED_RELU = 1, | |

/** Fused ReLU1 activation function. */ | |

ANEURALNETWORKS_FUSED_RELU1 = 2, | |

/** Fused ReLU6 activation function. */ | |

ANEURALNETWORKS_FUSED_RELU6 = 3, | |

} FuseCode; | |

/** | |

* Implicit padding algorithms. | |

* | |

*/ | |

typedef enum { | |

/** | |

* SAME padding. | |

* Padding on both ends are the "same": | |

* padding_to_beginning = total_padding / 2 | |

* padding_to_end = (total_padding + 1)/2. | |

* i.e., for even number of padding, padding to both ends are exactly | |

* the same; for odd number of padding, padding to the ending is bigger | |

* than the padding to the beginning by 1. | |

* | |

* total_padding is a function of input, stride and filter size. | |

* It could be computed as follows: | |

* out_size = (input + stride - 1) / stride; | |

* needed_input = (out_size - 1) * stride + filter_size | |

* total_padding = max(0, needed_input - output_size) | |

* The computation is the same for the horizontal and vertical directions. | |

*/ | |

ANEURALNETWORKS_PADDING_SAME = 1, | |

/** | |

* VALID padding. | |

* No padding. When the input size is not evenly divisible by | |

* the filter size, the input at the end that could not fill | |

* the whole filter tile will simply be ignored. | |

*/ | |

ANEURALNETWORKS_PADDING_VALID = 2, | |

} PaddingCode; | |

/** | |

* Execution preferences. | |

*/ | |

typedef enum { | |

/** | |

* Prefer executing in a way that minimizes battery drain. | |

* This is desirable for compilations that will be executed often. | |

*/ | |

ANEURALNETWORKS_PREFER_LOW_POWER = 0, | |

/** | |

* Prefer returning a single answer as fast as possible, even if this causes | |

* more power consumption. | |

*/ | |

ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1, | |

/** | |

* Prefer maximizing the throughput of successive frames, for example when | |

* processing successive frames coming from the camera. | |

*/ | |

ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2, | |

} PreferenceCode; | |

/** | |

* Result codes. | |

*/ | |

typedef enum { | |

ANEURALNETWORKS_NO_ERROR = 0, | |

ANEURALNETWORKS_OUT_OF_MEMORY = 1, | |

ANEURALNETWORKS_INCOMPLETE = 2, | |

ANEURALNETWORKS_UNEXPECTED_NULL = 3, | |

ANEURALNETWORKS_BAD_DATA = 4, | |

ANEURALNETWORKS_OP_FAILED = 5, | |

ANEURALNETWORKS_BAD_STATE = 6, | |

ANEURALNETWORKS_UNMAPPABLE = 7, | |

} ResultCode; | |

/** | |

* For {@link ANeuralNetworksModel_setOperandValue}, values with a | |

* length smaller or equal to this will be immediately copied into | |

* the model. The size is in bytes. | |

*/ | |

enum { | |

ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128 | |

}; | |

/** | |

* ANeuralNetworksMemory is an opaque type that represents memory. | |

* | |

* This type is used to represent shared memory, memory mapped files, | |

* and similar memories. | |

* | |

* By using shared memory, a program can efficiently communicate to the | |

* runtime and drivers the tensors that define a model. See | |

* {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application | |

* should typically create one shared memory object that contains every tensor | |

* needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be | |

* used to create shared memory from a file handle. | |

* | |

* Memory objects can also be used to specify the input and output arguments of | |

* an execution. See {@link ANeuralNetworksExecution_setInputFromMemory} | |

* and {@link ANeuralNetworksExecution_setOutputFromMemory}. | |

*/ | |

typedef struct ANeuralNetworksMemory ANeuralNetworksMemory; | |

/** | |

* ANeuralNetworksModel is an opaque type that contains a description of the | |

* mathematical operations that constitute the model. | |

* | |

* <p>Build the model by calling<ul> | |

* <li>{@link ANeuralNetworksModel_create}</li> | |

* <li>{@link ANeuralNetworksModel_addOperation}</li> | |

* <li>{@link ANeuralNetworksModel_addOperand}</li> | |

* </ul> | |

* | |

* 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); | |

#if __ANDROID_API__ >= __ANDROID_API_P__ | |

/** | |

* Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be | |

* calculated with range and/or precision as low as that of the IEEE 754 16-bit | |

* floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* must be calculated using at least the range and precision of the IEEE 754 | |

* 32-bit floating-point format. | |

* | |

* @param model The model to be modified. | |

* @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be | |

* calculated with range and/or precision as low as that of the | |

* IEEE 754 16-bit floating point format. 'false' indicates | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using | |

* at least the range and precision of the IEEE 754 32-bit floating | |

* point format. | |

* | |

* Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been | |

* called will return an error. | |

* | |

* See {@link ANeuralNetworksModel} for information on multithreaded usage. | |

*/ | |

int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow); | |

#endif // __ANDROID_API__ >= __ANDROID_API_P__ | |

/** | |

* Create a {@link ANeuralNetworksCompilation} to compile the given model. | |

* | |

* <p>This only creates the object. Compilation is only performed once | |

* {@link ANeuralNetworksCompilation_finish} is invoked.</p> | |

* | |

* <p>{@link ANeuralNetworksCompilation_finish} should be called once | |

* all desired properties have been set on the compilation.</p> | |

* | |

* <p>{@link ANeuralNetworksModel_free} should be called once the compilation | |

* is no longer needed.</p> | |

* | |

* <p>The provided model must outlive the compilation.</p> | |

* | |

* The model must already have been finished by a call to | |

* {@link ANeuralNetworksModel_finish}. | |

* | |

* See {@link ANeuralNetworksCompilation} for information on multithreaded usage. | |

* | |

* @param model The {@link ANeuralNetworksModel} to be compiled. | |

* @param compilation The newly created object or NULL if unsuccessful. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA | |

* if the model is invalid. | |

*/ | |

int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, | |

ANeuralNetworksCompilation** compilation); | |

/** | |

* Destroy a compilation. | |

* | |

* The compilation need not have been finished by a call to | |

* {@link ANeuralNetworksModel_finish}. | |

* | |

* See {@link ANeuralNetworksCompilation} for information on multithreaded usage. | |

* | |

* @param compilation The compilation to be destroyed. Passing NULL is acceptable and | |

* results in no operation. | |

*/ | |

void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation); | |

/** | |

* Sets the execution preference. | |

* | |

* <p>Provides guidance to the runtime when trade-offs are possible.</p> | |

* | |

* See {@link ANeuralNetworksCompilation} for information on multithreaded usage. | |

* | |

* @param compilation The compilation to be modified. | |

* @param preference Either {@link PREFER_LOW_POWER}, | |

* {@link PREFER_SINGLE_FAST_ANSWER}, or | |

* {@link PREFER_SUSTAINED_SPEED}. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful. | |

*/ | |

int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, | |

int32_t preference); | |

/** | |

* Indicate that we have finished modifying a compilation. Required before | |

* calling {@link ANeuralNetworksExecution_create}. | |

* | |

* An application is responsible to make sure that no other thread uses | |

* the compilation at the same time. | |

* | |

* This function must only be called once for a given compilation. | |

* | |

* See {@link ANeuralNetworksCompilation} for information on multithreaded usage. | |

* | |

* @param compilation The compilation to be finished. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful. | |

*/ | |

int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation); | |

/** | |

* Create a {@link ANeuralNetworksExecution} to apply the given compilation. | |

* This only creates the object. Computation is only performed once | |

* {@link ANeuralNetworksExecution_startCompute} is invoked. | |

* | |

* <p>The provided compilation must outlive the execution.</p> | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. | |

* @param execution The newly created object or NULL if unsuccessful. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA | |

* if the compilation is invalid. | |

*/ | |

int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, | |

ANeuralNetworksExecution** execution); | |

/** | |

* Destroy an execution. | |

* | |

* <p>If called on an execution for which | |

* {@link ANeuralNetworksExecution_startCompute} has been called, the | |

* function will return immediately but will mark the execution to be deleted | |

* once the computation completes. The related {@link ANeuralNetworksEvent} | |

* will be signaled and the {@link ANeuralNetworksEvent_wait} will return | |

* ANEURALNETWORKS_ERROR_DELETED. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be destroyed. Passing NULL is acceptable and | |

* results in no operation. | |

*/ | |

void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution); | |

/** | |

* Associate a user buffer with an input of the model of the | |

* {@link ANeuralNetworksExecution}. | |

* | |

* <p>The provided buffer must outlive the execution.</p> | |

* | |

* If the input is optional, you can indicate that it is omitted by | |

* passing nullptr for buffer and 0 for length. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be modified. | |

* @param index The index of the input argument we are setting. It is | |

* an index into the lists passed to | |

* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not | |

* the index associated with | |

* {@link ANeuralNetworksModel_addOperand}. | |

* @param type The {@link ANeuralNetworksOperandType} of the | |

* operand. Unless the input is omitted, this should be | |

* used to specify the dimensions that were left | |

* unspecified when the operand was added to the | |

* model. All other properties of the type must be the | |

* same as specified in the model. If the type is the same | |

* as specified when the model was built, NULL can be | |

* passed. Neither the {@link ANeuralNetworksOperandType} | |

* nor the dimensions it points to need to outlive the call | |

* to {@link ANeuralNetworksExecution_setInput}. | |

* @param buffer The buffer containing the data. | |

* @param length The length in bytes of the buffer. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the | |

* name is not recognized or the buffer is too small for the input. | |

*/ | |

int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, | |

const ANeuralNetworksOperandType* type, const void* buffer, | |

size_t length); | |

/** | |

* Associate part of a memory object with an input of the model of the | |

* {@link ANeuralNetworksExecution}. | |

* | |

* <p>The provided memory must outlive the execution.</p> | |

* | |

* If the input is optional, you can indicate that it is omitted by | |

* using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer | |

* and 0 for length. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be modified. | |

* @param index The index of the input argument we are setting. It is | |

* an index into the lists passed to | |

* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not | |

* the index associated with {@link ANeuralNetworksModel_addOperand}. | |

* @param type The {@link ANeuralNetworksOperandType} of the | |

* operand. This should be used to specify the dimensions | |

* that were left unspecified when the operand was added | |

* to the model. All other properties of the type must be | |

* the same as specified in the model. If the type is the | |

* same as specified when the model was built, NULL can be | |

* passed. Neither the {@link ANeuralNetworksOperandType} | |

* nor the dimensions it points to need to outlive the call | |

* to {@link ANeuralNetworksExecution_setInputFromMemory}. | |

* @param memory The memory containing the data. | |

* @param offset This specifies the location of the data within the memory. | |

* The offset is in bytes from the start of memory. | |

* @param length The size in bytes of the data value. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the | |

* name is not recognized or the buffer is too small for the input. | |

*/ | |

int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, | |

const ANeuralNetworksOperandType* type, | |

const ANeuralNetworksMemory* memory, size_t offset, | |

size_t length); | |

/** | |

* Associate a user buffer with an output of the model of the | |

* {@link ANeuralNetworksExecution}. | |

* | |

* If the output is optional, you can indicate that it is omitted by | |

* passing nullptr for buffer and 0 for length. | |

* | |

* <p>The provided buffer must outlive the execution.</p> | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be modified. | |

* @param index The index of the output argument we are setting. It is | |

* an index into the lists passed to | |

* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not | |

* the index associated with {@link ANeuralNetworksModel_addOperand}. | |

* @param type The {@link ANeuralNetworksOperandType} of the | |

* operand. Unless the output is omitted, this should be | |

* used to specify the dimensions that were left | |

* unspecified when the operand was added to the | |

* model. All other properties of the type must be the | |

* same as specified in the model. If the type is the same | |

* as specified when the model was built, NULL can be | |

* passed. Neither the {@link ANeuralNetworksOperandType} | |

* nor the dimensions it points to need to outlive the call | |

* to {@link ANeuralNetworksExecution_setOutput}. | |

* @param buffer The buffer where the data is to be written. | |

* @param length The length in bytes of the buffer. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the | |

* name is not recognized or the buffer is too small for the output. | |

*/ | |

int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, | |

const ANeuralNetworksOperandType* type, void* buffer, | |

size_t length); | |

/** | |

* Associate part of a memory object with an output of the model of the | |

* {@link ANeuralNetworksExecution}. | |

* | |

* If the output is optional, you can indicate that it is omitted by | |

* using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer | |

* and 0 for length. | |

* | |

* <p>The provided memory must outlive the execution.</p> | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be modified. | |

* @param index The index of the output argument we are setting. It is | |

* an index into the lists passed to | |

* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not | |

* the index associated with {@link ANeuralNetworksModel_addOperand}. | |

* @param type The {@link ANeuralNetworksOperandType} of the operand. This should be | |

* used to specify the dimensions that were left | |

* unspecified when the operand was added to the | |

* model. All other properties of the type must be the | |

* same as specified in the model. If the type is the same | |

* as specified when the model was built, NULL can be | |

* passed. Neither the {@link ANeuralNetworksOperandType} | |

* nor the dimensions it points to need to outlive the call | |

* to {@link ANeuralNetworksExecution_setOutputFromMemory}. | |

* @param memory The memory where the data is to be stored. | |

* @param offset This specifies the location of the data within the memory. | |

* The offset is in bytes from the start of memory. | |

* @param length The length in bytes of the data value. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the | |

* name is not recognized or the buffer is too small for the output. | |

*/ | |

int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, | |

const ANeuralNetworksOperandType* type, | |

const ANeuralNetworksMemory* memory, size_t offset, | |

size_t length); | |

/** | |

* Schedule evaluation of the execution. | |

* | |

* <p>Schedules evaluation of the execution. Once the model has been | |

* applied and the outputs are ready to be consumed, the returned event will be | |

* signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event. | |

* </p> | |

* | |

* Multiple executions can be scheduled and evaluated concurrently. The | |

* runtime makes no guarantee on the ordering of completion of | |

* executions. If it's important to the application, the application | |

* should enforce the ordering by using | |

* {@link ANeuralNetworksEvent_wait}. | |

* | |

* ANeuralNetworksEvent_wait must be called to recuperate the resources used | |

* by the execution. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @param execution The execution to be scheduled and executed. | |

* @param event The event that will be signaled on completion. event is set to | |

* NULL if there's an error. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if successful. | |

*/ | |

int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, | |

ANeuralNetworksEvent** event); | |

/** | |

* Waits until the execution completes. | |

* | |

* More than one thread can wait on an event. When the execution completes, | |

* all threads will be released. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

* | |

* @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. | |

*/ | |

int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event); | |

/** | |

* Destroys the event. | |

* | |

* See {@link ANeuralNetworksExecution} for information on multithreaded usage. | |

*/ | |

void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event); | |

__END_DECLS | |

#endif // __ANDROID_API__ >= __ANDROID_API_O_MR1__ | |

#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H | |

/** @} */ |