android / platform / prebuilts / vndk / v33 / 7072fc286b3edae51f0a82d334cc185b16dc66c8 / . / x86 / include / packages / modules / NeuralNetworks / runtime / include / NeuralNetworksTypes.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 NeuralNetworksTypes.h | |

*/ | |

#ifndef ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_NEURAL_NETWORKS_TYPES_H | |

#define ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_NEURAL_NETWORKS_TYPES_H | |

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

* | |

* IMPORTANT NOTICE: | |

* | |

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

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

* | |

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

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

* | |

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

* - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS | |

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

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

*/ | |

#include <stdbool.h> | |

#include <stddef.h> | |

#include <stdint.h> | |

#include <sys/cdefs.h> | |

#ifdef __ANDROID__ | |

#include <android/hardware_buffer.h> | |

#endif // __ANDROID__ | |

__BEGIN_DECLS | |

/** | |

* Operand types. | |

* | |

* The type of an operand in a model. | |

* | |

* Types prefaced with ANEURALNETWORKS_TENSOR_* must be used for tensor data (i.e., tensors | |

* with at least one dimension). Types not prefaced by ANEURALNETWORKS_TENSOR_* represent | |

* scalar values and must have no dimensions. | |

* | |

* 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}. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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 unsigned 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, | |

/** | |

* An 8 bit boolean scalar value. | |

* | |

* Values of this operand type are either true or false. A zero value | |

* represents false; any other value represents true. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_BOOL = 6, | |

/** | |

* A tensor of 16 bit signed integers that represent real numbers. | |

* | |

* Attached to this tensor is a number representing real value scale that is | |

* used to convert the 16 bit number to a real value in the following way: | |

* realValue = integerValue * scale. | |

* | |

* scale is a 32 bit floating point with value greater than zero. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT16_SYMM = 7, | |

/** | |

* A tensor of IEEE 754 16 bit floating point values. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_FLOAT16 = 8, | |

/** | |

* A tensor of 8 bit boolean values. | |

* | |

* Values of this operand type are either true or false. A zero value | |

* represents false; any other value represents true. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_BOOL8 = 9, | |

/** | |

* An IEEE 754 16 bit floating point scalar value. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_FLOAT16 = 10, | |

/** | |

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

* | |

* This tensor is associated with additional fields that can | |

* be used to convert the 8 bit signed integer to the real value and vice versa. | |

* These fields are: | |

* - channelDim: a 32 bit unsigned integer indicating channel dimension. | |

* - scales: an array of positive 32 bit floating point values. | |

* The size of the scales array must be equal to dimensions[channelDim]. | |

* | |

* {@link ANeuralNetworksModel_setOperandSymmPerChannelQuantParams} must be used | |

* to set the parameters for an Operand of this type. | |

* | |

* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0). | |

* | |

* The formula is: | |

* realValue[..., C, ...] = | |

* integerValue[..., C, ...] * scales[C] | |

* where C is an index in the Channel dimension. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL = 11, | |

/** | |

* A tensor of 16 bit unsigned integers that represent real numbers. | |

* | |

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

* 16 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, 65535]. | |

* | |

* The formula is: | |

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

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT16_ASYMM = 12, | |

/** | |

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

* | |

* Attached to this tensor is a number representing real value scale that is | |

* used to convert the 8 bit number to a real value in the following way: | |

* realValue = integerValue * scale. | |

* | |

* scale is a 32 bit floating point with value greater than zero. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT8_SYMM = 13, | |

/** | |

* A tensor of 8 bit signed 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 [-128, 127]. | |

* | |

* The formula is: | |

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

* | |

* Available since NNAPI feature level 4. | |

*/ | |

ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED = 14, | |

/** | |

* A reference to a model. | |

* | |

* {@link ANeuralNetworksModel_setOperandValueFromModel} must be used to set | |

* the value for an Operand of this type. | |

* | |

* Available since NNAPI feature level 4. | |

*/ | |

ANEURALNETWORKS_MODEL = 15, | |

} OperandCode; | |

/** | |

* Operation types. | |

* | |

* The type of an operation in a model. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

typedef enum { | |

// Operations below are available since NNAPI feature level 1. | |

/** | |

* 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} | |

* | |

* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero | |

* dimension is only compatible with 0 or 1. The size of the output | |

* dimension is zero if either of corresponding input dimension is zero. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scales and zeroPoint can be different from input0 scale and zeroPoint. | |

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

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

* invoke on the result. | |

* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, | |

* the {@link FuseCode} must be "NONE". | |

* | |

* Outputs: | |

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint can be different from inputs' scale and zeroPoint. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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[b, i, j, channel] = | |

* sum_{di, dj}( | |

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

* ) / sum(1) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Inputs (implicit padding): | |

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

* the input. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* (full support since NNAPI feature level 3, see the input section) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

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

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

* Before NNAPI feature level 3, all input tensors of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* must have the same scale and zeroPoint as the output tensor. | |

* Input tensors of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} | |

* are allowed to have different scale and zeroPoint. | |

* Since NNAPI feature level 3, zero-sized tensors are supported. | |

* * 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]. | |

* Since NNAPI feature level 3, for a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, | |

* the scale and zeroPoint values can be different from | |

* input tensors. Before NNAPI feature level 3 they have to be the same as for the | |

* input tensors. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_CONCATENATION = 2, | |

/** | |

* Performs a 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[b, i, j, channel] = | |

* sum_{di, dj, k} ( | |

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

* filter[channel, di, dj, k] | |

* ) + bias[channel] | |

* | |

* Supported tensor {@link OperandCode} configurations: | |

* * 32 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. | |

* | |

* * Quantized: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* Available since NNAPI feature level 3: | |

* * 16 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. | |

* | |

* * Quantized with symmetric per channel quantization for the filter: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

* Available since NNAPI feature level 4: | |

* * Quantized signed (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* * Quantized signed with filter symmetric per channel quantization | |

* (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

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

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

* filter. | |

* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} | |

* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) | |

* must be set to 0. | |

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

* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

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

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

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, | |

* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 | |

* and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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. | |

* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* * 11: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation | |

* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on width dimension. If this input is set, | |

* input 12 (dilation factor for height) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation | |

* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on height dimension. If this input is set, | |

* input 11 (dilation factor for width) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* | |

* Inputs (implicit padding): | |

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

* specifying the input. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

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

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

* filter. | |

* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} | |

* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) | |

* must be set to 0. | |

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

* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same | |

* type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

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

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

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, | |

* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 | |

* and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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. | |

* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

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

* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on width dimension. If this input is set, | |

* input 9 (dilation factor for height) must be specified as well. | |

* Available since NNAPI feature level 3. | |

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

* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on height dimension. If this input is set, | |

* input 8 (dilation factor for width) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

* [batches, out_height, out_width, depth_out]. | |

* Before NNAPI feature level 3, for output tensor of | |

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

* be satisfied: output_scale > input_scale * filter_scale | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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] | |

* ) + bias[k * channel_multiplier + q] | |

* | |

* Supported tensor {@link OperandCode} configurations: | |

* * 32 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. | |

* | |

* * Quantized: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* Available since NNAPI feature level 3: | |

* * 16 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. | |

* | |

* * Quantized with symmetric per channel quantization for the filter: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

* Available since NNAPI feature level 4: | |

* * Quantized signed (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* * Quantized signed with filter symmetric per channel quantization | |

* (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} | |

* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) | |

* must be set to 3. | |

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

* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

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

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

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, | |

* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 | |

* and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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. | |

* * 11: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation | |

* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on width dimension. If this input is set, | |

* input 13 (dilation factor for height) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* * 13: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation | |

* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on height dimension. If this input is set, | |

* input 12 (dilation factor for width) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* | |

* 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 type {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

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

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

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, | |

* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 | |

* and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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. | |

* * 8: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

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

* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on width dimension. If this input is set, | |

* input 10 (dilation factor for height) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* * 10: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation | |

* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped | |

* cells between each filter element on height dimension. If this input is set, | |

* input 9 (dilation factor for width) must be specified as well. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

* [batches, out_height, out_width, depth_out]. Before NNAPI feature level 3, for | |

* output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, | |

* the following condition must be satisfied: | |

* output_scale > input_scale * filter_scale | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_DEPTH_TO_SPACE = 5, | |

/** | |

* Dequantizes the input tensor. | |

* | |

* The formula is: | |

* | |

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

* | |

* Supported input tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported output tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* Outputs: | |

* * 0: A tensor with the same shape as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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. | |

* | |

* Supported value tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 4) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported value tensor rank: from 2 | |

* | |

* 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input1. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, | |

/** | |

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

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@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. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* 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". | |

* Since NNAPI feature level 3, zero batch_size is supported for this tensor. | |

* * 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} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* 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]. Before NNAPI feature level 3, for | |

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

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

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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. | |

* | |

* Supported value tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported value tensor rank: from 2 | |

* | |

* 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 …]. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, | |

* the scale and zeroPoint must be the same as input2. | |

* * 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. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, | |

/** | |

* Applies L2 normalization along the axis 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)) | |

* | |

* By default the axis dimension is the last dimension of the input tensor. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* Tensors with rank less than 4 are only supported since NNAPI feature level 3. | |

* | |

* Inputs: | |

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

* * 1: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, | |

* specifying the dimension normalization would be performed on. | |

* Negative index is used to specify axis from the end (e.g. -1 for | |

* the last axis). Must be in the range [-n, n). | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, | |

* the scale must be 1.f / 128 and the zeroPoint must be 128. | |

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the scale must be 1.f / 128 and the zeroPoint must be 0. | |

* | |

* NOTE: Before NNAPI feature level 4, if the elements along an axis are all zeros, | |

* the result is undefined. Since NNAPI feature level 4, if the elements along an axis | |

* are all zeros, the result is logical zero. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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[b, i, j, c] = | |

* sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) / | |

* sum(1)) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Inputs (implicit padding): | |

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

* the input. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

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

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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) | |

* | |

* For input tensor with rank less than 4, independently normalizes each | |

* 1-D slice along specified dimension. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: up to 4 | |

* Tensors with rank less than 4 are only supported since NNAPI feature level 3. | |

* | |

* 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: A scalar, specifying the bias, must not be zero. | |

* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias | |

* value must be of {@link ANEURALNETWORKS_FLOAT16}. | |

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

* value must be of {@link ANEURALNETWORKS_FLOAT32}. | |

* * 3: A scalar, specifying the scale factor, alpha. | |

* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the | |

* alpha value must be of {@link ANEURALNETWORKS_FLOAT16}. | |

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

* alpha value must be of {@link ANEURALNETWORKS_FLOAT32}. | |

* * 4: A scalar, specifying the exponent, beta. | |

* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the beta | |

* value must be of {@link ANEURALNETWORKS_FLOAT16}. | |

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

* value must be of {@link ANEURALNETWORKS_FLOAT32}. | |

* * 5: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, | |

* specifying the dimension normalization would be performed on. | |

* Negative index is used to specify axis from the end (e.g. -1 for | |

* the last axis). Must be in the range [-n, n). | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* 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. | |

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the scale must be 1.f / 256 and the zeroPoint must be -128. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_LOGISTIC = 14, | |

/** | |

* Projects an input to a bit vector via locality senstive hashing. | |

* | |

* Supported input tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* | |

* Supported input tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: Hash functions. Dim.size == 2, DataType: Float. | |

* Tensor[0].Dim[0]: Number of hash functions. | |

* Tensor[0].Dim[1]: Number of projected output bits generated by each | |

* hash function. | |

* If the projection type is Sparse: | |

* Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32 | |

* | |

* * 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(=3) (since NNAPI feature level 3). | |

* Computed bit vector is considered to be sparse. | |

* Each output element is an int32 made up of multiple bits | |

* computed from hash functions. | |

* | |

* NOTE: To avoid collisions across hash functions, an offset value | |

* of k * (1 << Tensor[0].Dim[1]) will be added to each signature, | |

* where k is the index of the hash function. | |

* | |

* Value LSHProjectionType_SPARSE_DEPRECATED(=1). | |

* Legacy behavior that does not include the offset value. | |

* | |

* 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. | |

* | |

* Available since NNAPI feature level 1. | |

* The offset value for sparse projections was added in NNAPI feature level 3. | |

*/ | |

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. | |

* | |

* Since NNAPI feature level 3 LSTM supports layer normalization. | |

* In case layer normalization is used, the inputs to internal activation | |

* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered | |

* following an approach from section 3.1 from | |

* https://arxiv.org/pdf/1607.06450.pdf | |

* | |

* The operation has the following independently optional inputs: | |

* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights | |

* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all | |

* have values or neither of them have values (i.e., all set to null). If | |

* they have values, the peephole optimization is used. | |

* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights | |

* (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values, | |

* or none of them have values. 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} | |

* In case peephole optimization is used and CIFG is not used | |

* cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the | |

* cell-to-input weights must have no value. | |

* * 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. | |

* * (NNAPI feature level 3 or later) The four layer normalization weights either all have | |

* values or none of them have values. Additionally, if CIFG is used, | |

* input layer normalization weights tensor is omitted and the other layer | |

* normalization weights either all have values or none of them have | |

* values. Layer normalization is used when the values of all the layer | |

* normalization weights are present. | |

* | |

* 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" | |

* | |

* The layer normalization is based on: | |

* https://arxiv.org/pdf/1607.06450.pdf | |

* Jimmy Ba et al. "Layer Normalization" | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* All input and output tensors must be of the same type. | |

* | |

* Inputs: | |

* * 0: The input (\f$x_t\f$). | |

* A 2-D tensor 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 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 shape [num_units, input_size]. | |

* * 3: The input-to-cell weights (\f$W_{xc}\f$). | |

* A 2-D tensor of shape [num_units, input_size]. | |

* * 4: The input-to-output weights (\f$W_{xo}\f$). | |

* A 2-D tensor of shape [num_units, input_size]. | |

* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. | |

* A 2-D tensor 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 shape [num_units, output_size]. | |

* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). | |

* A 2-D tensor of shape [num_units, output_size]. | |

* * 8: The recurrent-to-output weights (\f$W_{ho}\f$). | |

* A 2-D tensor of shape [num_units, output_size]. | |

* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. | |

* A 1-D tensor of shape [num_units]. | |

* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. | |

* A 1-D tensor of shape [num_units]. | |

* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. | |

* A 1-D tensor of shape [num_units]. | |

* * 12:The input gate bias (\f$b_i\f$). Optional. | |

* A 1-D tensor of shape [num_units]. | |

* * 13:The forget gate bias (\f$b_f\f$). | |

* A 1-D tensor of shape [num_units]. | |

* * 14:The cell bias (\f$b_c\f$). | |

* A 1-D tensor of shape [num_units]. | |

* * 15:The output gate bias (\f$b_o\f$). | |

* A 1-D tensor of shape [num_units]. | |

* * 16:The projection weights (\f$W_{proj}\f$). Optional. | |

* A 2-D tensor of shape [output_size, num_units]. | |

* * 17:The projection bias (\f$b_{proj}\f$). Optional. | |

* A 1-D tensor of shape [output_size]. | |

* * 18:The output state (in) (\f$h_{t-1}\f$). | |

* A 2-D tensor of shape [batch_size, output_size]. | |

* * 19:The cell state (in) (\f$C_{t-1}\f$). | |

* A 2-D tensor 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. | |

* Until NNAPI feature level 3 this scalar must be of type {@link | |

* ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input | |

* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this | |

* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, | |

* otherwise if all the input tensors have the type {@link | |

* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link | |

* ANEURALNETWORKS_FLOAT16}. | |

* * 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. | |

* Until NNAPI feature level 3 this scalar must be of type {@link | |

* ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input | |

* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this | |

* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, | |

* otherwise if all the input tensors have the type {@link | |

* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link | |

* ANEURALNETWORKS_FLOAT16}. | |

* Since NNAPI feature level 3 there are additional inputs to this op: | |

* * 23:The input layer normalization weights. | |

* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs | |

* to activation at input gate. | |

* * 24:The forget layer normalization weights. | |

* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs | |

* to activation at forget gate. | |

* * 25:The cell layer normalization weights. | |

* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs | |

* to activation at cell gate. | |

* * 26:The output layer normalization weights. | |

* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs | |

* to activation at output gate. | |

* | |

* Outputs: | |

* * 0: The scratch buffer. | |

* A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or | |

* [batch_size, num_units * 4] without CIFG. | |

* * 1: The output state (out) (\f$h_t\f$). | |

* A 2-D tensor of shape [batch_size, output_size]. | |

* * 2: The cell state (out) (\f$C_t\f$). | |

* A 2-D tensor of shape [batch_size, num_units]. | |

* * 3: The output (\f$o_t\f$). | |

* A 2-D tensor of shape [batch_size, output_size]. This is effectively | |

* the same as the current “output state (out)” value. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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[b, i, j, channel] = | |

* max_{di, dj} ( | |

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

* ) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Inputs (implicit padding): | |

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

* the input. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

* * 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. | |

* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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. | |

* | |

* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero | |

* dimension is only compatible with 0 or 1. The size of the output | |

* dimension is zero if either of corresponding input dimension is zero. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) | |

* | |

* 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, | |

* the {@link FuseCode} must be "NONE". | |

* | |

* Outputs: | |

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

* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the following condition must be satisfied: | |

* output_scale > input1_scale * input2_scale. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* Outputs: | |

* * 0: The output tensor of the same shape as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 6) | |

* | |

* 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. | |

* | |

* If one component of shape is the special value -1, the size of that | |

* dimension is computed so that the total size remains constant. In | |

* particular, a shape of [-1] flattens into 1-D. At most one component | |

* of shape can be -1. | |

* | |

* Outputs: | |

* * 0: The output tensor, of shape specified by the input shape. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* Both resizing by shape and resizing by scale are supported. | |

* | |

* Inputs (resizing by shape): | |

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

* the input. | |

* Since NNAPI feature level 3, zero batches is supported for this tensor. | |

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

* width of the output tensor. | |

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

* height of the output tensor. | |

* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} | |

* scalar, default to false. If True, the centers of the 4 corner | |

* pixels of the input and output tensors are aligned, preserving the | |

* values at the corner pixels. | |

* Available since NNAPI feature level 4. | |

* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} | |

* scalar, default to false. If True, the pixel centers are assumed to | |

* be at (0.5, 0.5). This is the default behavior of image.resize in | |

* TF 2.0. If this parameter is True, then align_corners parameter | |

* must be False. | |

* Available since NNAPI feature level 4. | |

* | |

* Inputs (resizing by scale, since NNAPI feature level 3): | |

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

* the input. Zero batches is supported for this tensor. | |

* * 1: A scalar, specifying width_scale, the scaling factor of the width | |

* dimension from the input tensor to the output tensor. The output | |

* width is calculated as new_width = floor(width * width_scale). | |

* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is | |

* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} otherwise. | |

* * 2: A scalar, specifying height_scale, the scaling factor of the height | |

* dimension from the input tensor to the output tensor. The output | |

* height is calculated as new_height = floor(height * height_scale). | |

* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is | |

* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} otherwise. | |

* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL} | |

* scalar, default to false. If True, the centers of the 4 corner | |

* pixels of the input and output tensors are aligned, preserving the | |

* values at the corner pixels. | |

* Available since NNAPI feature level 4. | |

* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL} | |

* scalar, default to false. If True, the pixel centers are assumed to | |

* be at (0.5, 0.5). This is the default behavior of image.resize in | |

* TF 2.0. If this parameter is True, then align_corners parameter | |

* must be False. | |

* Available since NNAPI feature level 4. | |

* | |

* Outputs: | |

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

* [batches, new_height, new_width, depth]. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* The input tensors must all be the same type. | |

* | |

* Inputs: | |

* * 0: input. | |

* A 2-D tensor 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 shape [num_units, input_size], where “num_units” | |

* corresponds to the number of units. | |

* * 2: recurrent_weights. | |

* A 2-D tensor of shape [num_units, num_units], with columns | |

* corresponding to the weights from each unit. | |

* * 3: bias. | |

* A 1-D tensor of shape [num_units]. | |

* * 4: hidden state (in). | |

* A 2-D tensor 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 shape [batch_size, num_units]. | |

* | |

* * 1: output. | |

* A 2-D tensor of shape [batch_size, num_units]. This is effectively | |

* the same as the current state value. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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)} | |

* | |

* For input tensor with rank other than 2, the activation will be applied | |

* independently on each 1-D slice along specified dimension. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* Tensors with rank other than 2 or 4 are only supported since NNAPI feature level 3. | |

* | |

* Inputs: | |

* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. | |

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* * 1: A scalar, specifying the positive scaling factor for the exponent, | |

* beta. If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scalar | |

* must be of {@link ANEURALNETWORKS_FLOAT32}. | |

* If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, then the | |

* scalar must be of {@link ANEURALNETWORKS_FLOAT16}. | |

* * 2: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1, | |

* specifying the dimension the activation would be performed on. | |

* Negative index is used to specify axis from the end (e.g. -1 for | |

* the last axis). Must be in the range [-n, n). | |

* Available since NNAPI feature level 3. | |

* | |

* 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. | |

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the scale must be 1.f / 256 and the zeroPoint must be -128. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

* width/block_size, depth_in*block_size*block_size]. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* All input tensors must be the same type. | |

* | |

* Inputs: | |

* * 0: input. | |

* A 2-D tensor 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 shape [num_units, input_size], where “num_units” | |

* corresponds to the number of units. | |

* * 2: weights_time. | |

* A 2-D tensor 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 shape [num_units]. | |

* * 4: state (in). | |

* A 2-D tensor 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 the same {@link OperandCode} as the inputs, with shape | |

* [batch_size, (memory_size - 1) * num_units * rank]. | |

* * 1: output. | |

* A 2-D tensor of the same {@link OperandCode} as the inputs, with shape | |

* [batch_size, num_units]. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4. | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, | |

* the scale must be 1.f / 128 and the zeroPoint must be 128. | |

* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the scale must be 1.f / 128 and the zeroPoint must be 0. | |

* | |

* Available since NNAPI feature level 1. | |

*/ | |

ANEURALNETWORKS_TANH = 28, | |

// Operations below are available since NNAPI feature level 2. | |

/** | |

* 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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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. | |

* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since API level 29. | |

* | |

* Outputs: | |

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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. | |

* | |

* For inputs of {@link ANEURALNETWORKS_TENSOR_INT32}, performs | |

* "floor division" ("//" in Python). For example, | |

* 5 // 2 = 2 | |

* -5 // 2 = -3 | |

* | |

* 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} | |

* | |

* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero | |

* dimension is only compatible with 0 or 1. The size of the output | |

* dimension is zero if either of corresponding input dimension is zero. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) | |

* | |

* 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, | |

* the {@link FuseCode} must be "NONE". | |

* | |

* Outputs: | |

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

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* 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. Must be in the range | |

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

* | |

* NOTE: When the operation was introduced, the documentation | |

* incorrectly stated that if dimensions were empty, the operation | |

* would reduce across all dimensions. This behavior was never | |

* implemented. | |

* | |

* * 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* If all dimensions are reduced and keep_dims is false, the output | |

* shape is [1]. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

ANEURALNETWORKS_MEAN = 31, | |

/** | |

* Pads a tensor. | |

* | |

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

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* (full support since NNAPI feature level 3, see the output section) | |

* | |

* 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] | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* NOTE: Before NNAPI feature level 3, the pad value for | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. | |

* Since NNAPI feature level 3, the pad value is always the logical zero. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

ANEURALNETWORKS_PAD = 32, | |

/** | |

* 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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* (full support since NNAPI feature level 3, see the output section) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* NCHW is supported since NNAPI feature level 3. | |

* | |

* 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 {M, 2}, where M is the number | |

* of spatial dimensions. | |

* 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. | |

* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false. | |

* Set to true to specify NCHW data layout for input0 and output0. | |

* Available since NNAPI feature level 3. | |

* | |

* Outputs: | |

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* NOTE: Before NNAPI feature level 3, the pad value for | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. | |

* Since NNAPI feature level 3, the pad value is always the logical zero. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* If all input dimensions are equal to 1 and are to be squeezed, the | |

* output shape is [1]. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

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

* * 1: begin, 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: end, 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: strides, 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). The entries must be non-zero. | |

* * 4: begin_mask, an {@link ANEURALNETWORKS_INT32} scalar. 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: end_mask, an {@link ANEURALNETWORKS_INT32} scalar. 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: shrink_axis_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the | |

* ith bit of shrink_axis_mask is set, the ith dimension specification | |

* shrinks the dimensionality by 1, taking on the value at index | |

* begin[i]. In this case, the ith specification must define a | |

* slice of size 1, e.g. begin[i] = x, end[i] = x + 1. | |

* | |

* Outputs: | |

* * 0: A tensor of the same {@link OperandCode} as input0 and rank (n - k), | |

* where k is the number of bits set in shrink_axis_mask. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* If shrink_axis_mask is true for all input dimensions, the output | |

* shape is [1]. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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} | |

* | |

* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero | |

* dimension is only compatible with 0 or 1. The size of the output | |

* dimension is zero if either of corresponding input dimension is zero. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) | |

* | |

* 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor, | |

* the {@link FuseCode} must be "NONE". | |

* | |

* Outputs: | |

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint can be different from inputs' scale and zeroPoint. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

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_FLOAT16} (since NNAPI feature level 3) | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

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

* Since NNAPI feature level 3, this tensor may be zero-sized. | |

* * 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. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 2. | |

*/ | |

ANEURALNETWORKS_TRANSPOSE = 37, | |

// Operations below are available since NNAPI feature level 3. | |

/** | |

* Computes the absolute value of a tensor, element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_ABS = 38, | |

/** | |

* Returns the index of the largest element along an axis. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: An n-D tensor specifying the input. Must be non-empty. | |

* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to | |

* reduce across. Negative index is used to specify axis from the | |

* end (e.g. -1 for the last axis). Must be in the range [-n, n). | |

* | |

* Outputs: | |

* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor. | |

* If input is 1-dimensional, the output shape is [1]. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

// There is no underscore in ARG_MAX to avoid name conflict with | |

// the macro defined in libc/kernel/uapi/linux/limits.h. | |

ANEURALNETWORKS_ARGMAX = 39, | |

/** | |

* Returns the index of the smallest element along an axis. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: An n-D tensor specifying the input. Must be non-empty. | |

* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to | |

* reduce across. Negative index is used to specify axis from the | |

* end (e.g. -1 for the last axis). Must be in the range [-n, n). | |

* | |

* Outputs: | |

* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor. | |

* If input is 1-dimensional, the output shape is [1]. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_ARGMIN = 40, // See ARGMAX for naming discussion. | |

/** | |

* Transform axis-aligned bounding box proposals using bounding box deltas. | |

* | |

* Given the positions of bounding box proposals and the corresponding | |

* bounding box deltas for each class, return the refined bounding box | |

* regions. The resulting bounding boxes are cliped against the edges of | |

* the image. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM} | |

* | |

* Inputs: | |

* * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the | |

* bounding box proposals, each line with format [x1, y1, x2, y2]. | |

* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, | |

* the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois | |

* is supported for this tensor. | |

* * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the | |

* bounding box delta for each region of interest and each class. The | |

* bounding box deltas are organized in the following order | |

* [dx, dy, dw, dh], where dx and dy is the relative correction factor | |

* for the center position of the bounding box with respect to the width | |

* and height, dw and dh is the log-scale relative correction factor | |

* for the width and height. For input0 of type | |

* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, this tensor should be | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}. Zero num_rois is | |

* supported for this tensor. | |

* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [num_rois], specifying the batch index of each box. Boxes with | |

* the same batch index are grouped together. Zero num_rois is | |

* supported for this tensor. | |

* * 3: A 2-D Tensor of shape [batches, 2], specifying the information of | |

* each image in the batch, each line with format | |

* [image_height, image_width]. | |

* | |

* Outputs: | |

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

* [num_rois, num_classes * 4], specifying the coordinates of each | |

* output bounding box for each class, with format [x1, y1, x2, y2]. | |

* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the | |

* scale must be 0.125 and the zero point must be 0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_AXIS_ALIGNED_BBOX_TRANSFORM = 41, | |

/** | |

* A recurrent neural network layer that applies an LSTM cell to a | |

* sequence of inputs in forward and backward directions. | |

* | |

* The op supports cross-linking via an auxiliary input. Regular cell feeds | |

* one input into the two RNN cells in the following way: | |

* | |

* INPUT (INPUT_REVERSED) | |

* | | | |

* --------------------- | |

* | FW_LSTM BW_LSTM | | |

* --------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* An op with cross-linking takes two inputs and feeds them into the RNN | |

* cells in the following way: | |

* | |

* AUX_INPUT (AUX_INPUT_REVERSED) | |

* | | | |

* INPUT | (INPUT_R'D.)| | |

* | | | | | |

* ----------------------- | |

* | \ / \ / | | |

* | FW_LSTM BW_LSTM | | |

* ----------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* The cross-linking mode is enabled iff auxiliary input and auxiliary | |

* weights are present. While stacking this op on top of itself, this | |

* allows to connect both forward and backward outputs from previous cell | |

* to the next cell's input. | |

* | |

* Since NNAPI feature level 4 parallel linking mode is supported. The mode is | |

* enabled if auxiliary input is present but auxiliary weights are omitted. | |

* In this case, the cell feeds inputs into the RNN in the following way: | |

* | |

* INPUT (AUX_INPUT_REVERSED) | |

* | | | |

* --------------------- | |

* | FW_LSTM BW_LSTM | | |

* --------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* While stacking this op on top of itself, this allows to connect both | |

* forward and backward outputs from previous cell to the next cell's | |

* corresponding inputs. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: 3, either time-major or batch-major. | |

* | |

* All input and output tensors must be of the same type. | |

* | |

* Inputs: | |

* * 0: The input. | |

* A 3-D tensor of shape: | |

* If time-major: [max_time, batch_size, input_size] | |

* If batch-major: [batch_size, max_time, input_size] | |

* where "max_time" is the number of timesteps (sequence length), | |

* "batch_size" corresponds to the batching dimension, and | |

* "input_size" is the size of the input. | |

* * 1: The forward input-to-input weights. Optional. | |

* A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units” | |

* corresponds to the number of forward cell units. | |

* * 2: The forward input-to-forget weights. | |

* A 2-D tensor of shape [fw_num_units, input_size]. | |

* * 3: The forward input-to-cell weights. | |

* A 2-D tensor of shape [fw_num_units, input_size]. | |

* * 4: The forward input-to-output weights. | |

* A 2-D tensor of shape [fw_num_units, input_size]. | |

* * 5: The forward recurrent-to-input weights. Optional. | |

* A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size” | |

* corresponds to either the number of cell units (i.e., fw_num_units), | |

* or the second dimension of the “fw_projection_weights”, if defined. | |

* * 6: The forward recurrent-to-forget weights. | |

* A 2-D tensor of shape [fw_num_units, fw_output_size]. | |

* * 7: The forward recurrent-to-cell weights. | |

* A 2-D tensor of shape [fw_num_units, fw_output_size]. | |

* * 8: The forward recurrent-to-output weights. | |

* A 2-D tensor of shape [fw_num_units, fw_output_size]. | |

* * 9: The forward cell-to-input weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 10: The forward cell-to-forget weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 11: The forward cell-to-output weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 12: The forward input gate bias. Optional. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 13: The forward forget gate bias. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 14: The forward cell gate bias. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 15: The forward output gate bias. | |

* A 1-D tensor of shape [fw_num_units]. | |

* * 16: The forward projection weights. Optional. | |

* A 2-D tensor of shape [fw_output_size, fw_num_units]. | |

* * 17: The forward projection bias. Optional. | |

* A 1-D tensor of shape [fw_output_size]. | |

* * 18: The backward input-to-input weights. Optional. | |

* A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units” | |

* corresponds to the number of backward cell units. | |

* * 19: The backward input-to-forget weights. | |

* A 2-D tensor of shape [bw_num_units, input_size]. | |

* * 20: The backward input-to-cell weights. | |

* A 2-D tensor of shape [bw_num_units, input_size]. | |

* * 21: The backward input-to-output weights. | |

* A 2-D tensor of shape [bw_num_units, input_size]. | |

* * 22: The backward recurrent-to-input weights. Optional. | |

* A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size” | |

* corresponds to either the number of cell units (i.e., “bw_num_units”), | |

* or the second dimension of the “bw_projection_weights”, if defined. | |

* * 23: The backward recurrent-to-forget weights. | |

* A 2-D tensor of shape [bw_num_units, bw_output_size]. | |

* * 24: The backward recurrent-to-cell weights. | |

* A 2-D tensor of shape [bw_num_units, bw_output_size]. | |

* * 25: The backward recurrent-to-output weights. | |

* A 2-D tensor of shape [bw_num_units, bw_output_size]. | |

* * 26: The backward cell-to-input weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 27: The backward cell-to-forget weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 28: The backward cell-to-output weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 29: The backward input gate bias. Optional. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 30: The backward forget gate bias. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 31: The backward cell gate bias. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 32: The backward output gate bias. | |

* A 1-D tensor of shape [bw_num_units]. | |

* * 33: The backward projection weights. Optional. | |

* A 2-D tensor of shape [bw_output_size, bw_num_units]. | |

* * 34: The backward projection bias. Optional. | |

* A 1-D tensor of shape [bw_output_size]. | |

* * 35: The forward input activation state. | |

* A 2-D tensor of shape [batch_size, bw_output_size]. | |

* * 36: The forward input cell state. | |

* A 2-D tensor of shape [batch_size, bw_num_units]. | |

* * 37: The backward input activation state. | |

* A 2-D tensor of shape [batch_size, bw_output_size]. | |

* * 38: The backward input cell state. | |

* A 2-D tensor of shape [batch_size, bw_num_units]. | |

* * 39: The auxiliary input. Optional. | |

* A 3-D tensor of shape [max_time, batch_size, aux_input_size], | |

* where “batch_size” corresponds to the batching dimension, and | |

* “aux_input_size” is the size of the auxiliary input. Optional. See | |

* the docs above for the usage modes explanation. | |

* * 40: The forward auxiliary input-to-input weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [fw_num_units, aux_input_size]. | |

* * 41: The forward auxiliary input-to-forget weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [fw_num_units, aux_input_size]. | |

* * 42: The forward auxiliary input-to-cell weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [fw_num_units, aux_input_size]. | |

* * 43: The forward auxiliary input-to-output weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [fw_num_units, aux_input_size]. | |

* * 44: The backward auxiliary input-to-input weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [bw_num_units, aux_input_size]. | |

* * 45: The backward auxiliary input-to-forget weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [bw_num_units, aux_input_size]. | |

* * 46: The backward auxiliary input-to-cell weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [bw_num_units, aux_input_size]. | |

* * 47: The backward auxiliary input-to-output weights. | |

* Optional. See the docs above for the usage modes explanation. | |

* A 2-D tensor of shape [bw_num_units, aux_input_size]. | |

* * 48: The activation function. | |

* A value indicating the activation function: | |

* <ul> | |

* <li>0: None; | |

* <li>1: Relu; | |

* <li>3: Relu6; | |

* <li>4: Tanh; | |

* <li>6: Sigmoid. | |

* </ul> | |

* * 49: The clipping threshold for the cell state, such | |

* that values are bound within [-cell_clip, cell_clip]. If set to 0.0 | |

* then clipping is disabled. | |

* If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, | |

* this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, | |

* otherwise if all the input tensors have the type | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be | |

* of type {@link ANEURALNETWORKS_FLOAT16}. | |

* * 50: The clipping threshold for the output from the | |

* projection layer, such that values are bound within | |

* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. | |

* If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, | |

* this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32}, | |

* otherwise if all the input tensors have the type | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be | |

* of type {@link ANEURALNETWORKS_FLOAT16}. | |

* * 51: merge_outputs | |

* An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs | |

* from forward and backward cells should be merged. | |

* * 52: time_major | |

* An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format | |

* of input and output tensors. | |

* * 53: The forward input layer normalization weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs | |

* to activation at input gate. | |

* * 54: The forward forget layer normalization weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs | |

* to activation at forget gate. | |

* * 55: The forward cell layer normalization weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs | |

* to activation at cell gate. | |

* * 56: The forward output layer normalization weights. Optional. | |

* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs | |

* to activation at output gate. | |

* * 57: The backward input layer normalization weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs | |

* to activation at input gate. | |

* * 58: The backward forget layer normalization weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs | |

* to activation at forget gate. | |

* * 59: The backward cell layer normalization weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs | |

* to activation at cell gate. | |

* * 60: The backward output layer normalization weights. Optional. | |

* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs | |

* to activation at output gate. | |

* | |

* Outputs: | |

* * 0: The forward output. | |

* A 3-D tensor of shape: | |

* If time-major and not merge_outputs: | |

* [max_time, batch_size, fw_output_size] | |

* If time-major and merge_outputs: | |

* [max_time, batch_size, fw_output_size + bw_output_size] | |

* If batch-major and not merge_outputs: | |

* [batch_size, max_time, fw_output_size] | |

* If batch-major and merge_outputs: | |

* [batch_size, max_time, fw_output_size + bw_output_size] | |

* * 1: The backward output. Unused if merge_outputs is true. | |

* A 3-D tensor of shape: | |

* If time-major: [max_time, batch_size, bw_output_size] | |

* If batch-major: [batch_size, max_time, bw_output_size] | |

* * 2: The forward activation state output. | |

* A 2-D tensor of shape [batch_size, fw_output_size] containing an | |

* activation state from the last time step in the sequence. This | |

* output is optional and can be omitted. If this output is present | |

* then outputs 3-5 must be present as well. | |

* Available since NNAPI feature level 4. | |

* * 3: The forward cell state output. | |

* A tensor of shape [batch_size, fw_cell_size] containing a cell state | |

* from the last time step in the sequence. This output is optional | |

* and can be omitted. If this output is present | |

* then outputs 2, 4, 5 must be present as well. | |

* Available since NNAPI feature level 4. | |

* * 4: The backward activation state output. | |

* A 2-D tensor of shape [batch_size, bw_output_size] containing an | |

* activation state from the last time step in the sequence. This | |

* output is optional and can be omitted. If this output is present | |

* then outputs 2, 3, 5 must be present as well. | |

* Available since NNAPI feature level 4. | |

* * 5: The backward cell state output. | |

* A tensor of shape [batch_size, bw_cell_size] containing a cell state | |

* from the last time step in the sequence. This output is optional | |

* and can be omitted. If this output is present | |

* then outputs 2-4 must be present as well. | |

* Available since NNAPI feature level 4. | |

* | |

* Available since NNAPI feature level 3. | |

* | |

* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out | |

* and NNAPI does not maintain internal states. This operator does not support the usage pattern | |

* in which multiple cells are chained and state tensors are propagated. | |

*/ | |

ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM = 42, | |

/** | |

* A recurrent neural network layer that applies a basic RNN cell to a | |

* sequence of inputs in forward and backward directions. | |

* | |

* This Op unrolls the input along the sequence dimension, and implements | |

* the following operation for each element in the sequence s = | |

* 1...sequence_length: | |

* fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ + | |

* fw_state * fw_recurrent_weights’ + fw_bias) | |

* | |

* And for each element in sequence t = sequence_length : 1 | |

* bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ + | |

* bw_state * bw_recurrent_weights’ + bw_bias) | |

* | |

* Where: | |

* * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs; | |

* * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the | |

* current “state” which itself is the output from the previous time step | |

* computation; | |

* * “{fw,bw}_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”). | |

* | |

* The op supports cross-linking via an auxiliary input. Regular cell feeds | |

* one input into the two RNN cells in the following way: | |

* | |

* INPUT (INPUT_REVERSED) | |

* | | | |

* --------------------- | |

* | FW_RNN BW_RNN | | |

* --------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* An op with cross-linking takes two inputs and feeds them into the RNN | |

* cells in the following way: | |

* | |

* AUX_INPUT (AUX_INPUT_REVERSED) | |

* | | | |

* INPUT | (INPUT_R'D.)| | |

* | | | | | |

* ----------------------- | |

* | \ / \ / | | |

* | FW_RNN BW_RNN | | |

* ----------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* The cross-linking mode is enabled iff auxiliary input and auxiliary | |

* weights are present. While stacking this op on top of itself, this | |

* allows to connect both forward and backward outputs from previous cell | |

* to the next cell's input. | |

* | |

* Since NNAPI feature level 4 parallel linking mode is supported. The mode is | |

* enabled if auxiliary input is present but auxiliary weights are omitted. | |

* In this case, the cell feeds inputs into the RNN in the following way: | |

* | |

* INPUT (AUX_INPUT_REVERSED) | |

* | | | |

* --------------------- | |

* | FW_RNN BW_RNN | | |

* --------------------- | |

* | | | |

* FW_OUT BW_OUT | |

* | |

* While stacking this op on top of itself, this allows to connect both | |

* forward and backward outputs from previous cell to the next cell's | |

* corresponding inputs. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* The input tensors must all be the same type. | |

* | |

* Inputs: | |

* * 0: input. | |

* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If | |

* it is set to true, then the input has a shape [maxTime, batchSize, | |

* inputSize], otherwise the input has a shape [batchSize, maxTime, | |

* inputSize]. | |

* * 1: fwWeights. | |

* A 2-D tensor of shape [fwNumUnits, inputSize]. | |

* * 2: fwRecurrentWeights. | |

* A 2-D tensor of shape [fwNumUnits, fwNumUnits]. | |

* * 3: fwBias. | |

* A 1-D tensor of shape [fwNumUnits]. | |

* * 4: fwHiddenState. | |

* A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden | |

* state input for the first time step of the computation. | |

* * 5: bwWeights. | |

* A 2-D tensor of shape [bwNumUnits, inputSize]. | |

* * 6: bwRecurrentWeights. | |

* A 2-D tensor of shape [bwNumUnits, bwNumUnits]. | |

* * 7: bwBias. | |

* A 1-D tensor of shape [bwNumUnits]. | |

* * 8: bwHiddenState | |

* A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden | |

* state input for the first time step of the computation. | |

* * 9: auxInput. | |

* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If | |

* it is set to true, then the input has a shape [maxTime, batchSize, | |

* auxInputSize], otherwise the input has a shape [batchSize, maxTime, | |

* auxInputSize]. Can be omitted. See the docs above for the usage | |

* modes explanation. | |

* * 10:fwAuxWeights. | |

* A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted. | |

* See the docs above for the usage modes explanation. | |

* * 11:bwAuxWeights. | |

* A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted. | |

* See the docs above for the usage modes explanation. | |

* * 12:fusedActivationFunction. | |

* A {@link FuseCode} value indicating the activation function. If | |

* “NONE” is specified then it results in a linear activation. | |

* * 13:timeMajor | |

* An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format | |

* of input and output tensors. | |

* * 14:mergeOutputs | |

* An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs | |

* from forward and backward cells are separate (if set to false) or | |

* concatenated (if set to true). | |

* Outputs: | |

* * 0: fwOutput. | |

* A 3-D tensor. The first two dimensions of the shape are defined by | |

* the input 6 (timeMajor) and the third dimension is defined by the | |

* input 14 (mergeOutputs). If timeMajor is set to true, then the first | |

* two dimensions are [maxTime, batchSize], otherwise they are set to | |

* [batchSize, maxTime]. If mergeOutputs is set to true, then the third | |

* dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set | |

* to fwNumUnits. | |

* * 1: bwOutput. | |

* A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then | |

* this tensor is not produced. The shape is defined by the input 6 | |

* (timeMajor). If it is set to true, then the shape is set to | |

* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to | |

* [batchSize, maxTime, bwNumUnits]. | |

* * 2: The forward hidden state output. | |

* A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden | |

* state from the last time step in the sequence. This output is | |

* optional and can be omitted. If this output is present then output | |

* 3 must be present as well. | |

* Available since NNAPI feature level 4. | |

* * 3: The backward hidden state output. | |

* A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden | |

* state from the last time step in the sequence. This output is | |

* optional and can be omitted. If this output is present then output | |

* 2 must be present as well. | |

* Available since NNAPI feature level 4. | |

* | |

* Available since NNAPI feature level 3. | |

* | |

* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out | |

* and NNAPI does not maintain internal states. This operator does not support the usage pattern | |

* in which multiple cells are chained and state tensors are propagated. | |

*/ | |

ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_RNN = 43, | |

/** | |

* Greedily selects a subset of bounding boxes in descending order of score. | |

* | |

* This op applies NMS algorithm to each class. In each loop of execution, | |

* the box with maximum score gets selected and removed from the pending set. | |

* The scores of the rest of boxes are lowered according to the | |

* intersection-over-union (IOU) overlapping with the previously selected | |

* boxes and a specified NMS kernel method. Any boxes with score less | |

* than a threshold are removed from the pending set. | |

* | |

* Three NMS kernels are supported: | |

* * Hard: score_new = score_old * (1 if IoU < threshold else 0) | |

* * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU) | |

* * Gaussian: score_new = score_old * exp(- IoU^2 / sigma) | |

* | |

* Axis-aligned bounding boxes are represented by its upper-left corner | |

* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid | |

* bounding box should satisfy x1 <= x2 and y1 <= y2. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Inputs: | |

* * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score | |

* of each bounding box proposal. The boxes are grouped by batches in the | |

* first dimension. Zero num_rois is supported for this tensor. | |

* * 1: A 2-D Tensor specifying the bounding boxes of shape | |

* [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2]. | |

* The boxes are grouped by batches in the first dimension. The sequential | |

* order of the boxes corresponds with input0. For input0 of type | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should be of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and | |

* scale of 0.125. | |

* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, | |

* with zeroPoint of -128 and scale of 0.125. | |

* Zero num_rois is supported for this tensor. | |

* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [num_rois], specifying the batch index of each box. Boxes with | |

* the same batch index are grouped together. | |

* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, score_threshold. Boxes | |

* with scores lower than the threshold are filtered before sending | |

* to the NMS algorithm. | |

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

* number of selected bounding boxes for each image. Set to a negative | |

* value for unlimited number of output bounding boxes. | |

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

* kernel method, options are 0:hard, 1:linear, 2:gaussian. | |

* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU | |

* threshold in hard and linear NMS kernel. This field is ignored if | |

* gaussian kernel is selected. | |

* * 7: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the sigma in | |

* gaussian NMS kernel. This field is ignored if gaussian kernel is | |

* not selected. | |

* * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, nms_score_threshold. | |

* Boxes with scores lower than the threshold are dropped during the | |

* score updating phase in soft NMS. | |

* | |

* Outputs: | |

* * 0: A 1-D Tensor of the same {@link OperandCode} as input0, with shape | |

* [num_output_rois], specifying the score of each output box. The boxes | |

* are grouped by batches, but the sequential order in each batch is not | |

* guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, | |

* guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* or {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, | |

* the scale and zero point must be the same as input0. | |

* * 1: A 2-D Tensor of the same {@link OperandCode} as input1, with shape | |

* [num_output_rois, 4], specifying the coordinates of each | |

* output bounding box with the same format as input1. The sequential | |

* order of the boxes corresponds with output0. For type of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the scale must be | |

* 0.125 and the zero point must be 0. | |

* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [num_output_rois], specifying the class of each output box. The | |

* sequential order of the boxes corresponds with output0. | |

* * 3: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [num_output_rois], specifying the batch index of each box. Boxes | |

* with the same batch index are grouped together. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_BOX_WITH_NMS_LIMIT = 44, | |

/** | |

* Casts a tensor to a type. | |

* | |

* This operation ignores the scale and zeroPoint of quanized tensors, | |

* e.g. it treats a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} input | |

* as a tensor of uint8 values. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* Since NNAPI feature level 4, casting tensors of the following | |

* {@link OperandCode} to the same {@link OperandCode} is supported: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} | |

* | |

* Supported tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: A tensor with the same shape as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_CAST = 45, | |

/** | |

* Shuffle the channels of the input tensor. | |

* | |

* Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE | |

* divide the channel dimension into num_groups groups, and reorganize the | |

* channels by grouping channels with the same index in each group. | |

* | |

* Along the channel dimension, the output is calculated using this formula: | |

* | |

* output_channel[k * num_groups + g] = input_channel[g * group_size + k] | |

* | |

* where group_size = num_channels / num_groups | |

* | |

* The number of channels must be divisible by num_groups. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: up to 4 | |

* | |

* Inputs: | |

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

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

* groups. | |

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

* channel shuffle would be performed on. Negative index is used to | |

* specify axis from the end (e.g. -1 for the last axis). Must be in | |

* the range [-n, n). | |

* | |

* Outputs: | |

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

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_CHANNEL_SHUFFLE = 46, | |

/** | |

* Apply postprocessing steps to bounding box detections. | |

* | |

* Bounding box detections are generated by applying transformation on a set | |

* of predefined anchors with the bounding box deltas from bounding box | |

* regression. A final step of hard NMS is applied to limit the number of | |

* returned boxes. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Inputs: | |

* * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying | |

* the score of each anchor with each class. Class 0 for each | |

* [batches, num_anchors, 0] is background and will be ignored. | |

* * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with | |

* the first four values in length_box_encoding specifying the bounding | |

* box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw], | |

* where dy and dx is the linear-scale relative correction factor for the | |

* center position of the bounding box with respect to the width and height, | |

* dh and dw is the log-scale relative correction factor for the width and | |

* height. All the entries in length_box_encoding beyond the first four | |

* values are ignored in this operation. | |

* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each | |

* predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and | |

* ctr_x are the center position of the box, and h and w are the height | |

* and the width. | |

* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling | |

* factor for dy in bounding box deltas. | |

* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling | |

* factor for dx in bounding box deltas. | |

* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling | |

* factor for dh in bounding box deltas. | |

* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling | |

* factor for dw in bounding box deltas. | |

* * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to use regular | |

* multi-class NMS algorithm that do NMS separately for each class, | |

* set to false for a faster algorithm that only do one single NMS | |

* using the highest class score.. | |

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

* the maximum number of boxes for the output. Boxes with the lowest | |

* scores are discarded to meet the limit. | |

* * 9: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is | |

* set to false, specifying the maximum number of classes per detection. | |

* * 10: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is | |

* set to true, specifying the maximum number of detections when | |

* applying NMS algorithm for each single class. | |

* * 11: A scalar, score_threshold. Boxes with scores lower than the | |

* threshold are filtered before sending to the NMS algorithm. The | |

* scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar | |

* must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* * 13: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to include | |

* background class in the list of label map for the output, set | |

* to false to not include the background. When the background | |

* class is included, it has label 0 and the output classes start | |

* at 1 in the label map, otherwise, the output classes start at 0. | |

* | |

* Outputs: | |

* * 0: A 2-D tensor of the same {@link OperandCode} as input0, with shape | |

* [batches, max_num_detections], specifying the score of each output | |

* detections. | |

* * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the | |

* coordinates of each output bounding box, with format | |

* [y1, x1, y2, x2]. | |

* * 2: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [batches, max_num_detections], specifying the class label for each | |

* output detection. | |

* * 3: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape [batches], | |

* specifying the number of valid output detections for each batch. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_DETECTION_POSTPROCESSING = 47, | |

/** | |

* For input tensors x and y, computes x == y elementwise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_EQUAL = 48, | |

/** | |

* Computes exponential of x element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: from 1. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_EXP = 49, | |

/** | |

* Inserts a dimension of 1 into a tensor's shape. | |

* | |

* Given a tensor input, this operation inserts a dimension of 1 at the | |

* given dimension index of input's shape. The dimension index starts at | |

* zero; if you specify a negative dimension index, it is counted backward | |

* from the end. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: An n-D tensor. | |

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

* index to expand. Must be in the range [-(n + 1), (n + 1)). | |

* | |

* Outputs: | |

* * 0: An (n + 1)-D tensor with the same {@link OperandCode} and data as | |

* input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_EXPAND_DIMS = 50, | |

/** | |

* Gathers values along an axis. | |

* | |

* Produces an output tensor with shape | |

* input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] | |

* where: | |

* # Vector indices (output is rank(input0)). | |

* output[a_0, ..., a_n, i, b_0, ..., b_n] = | |

* input0[a_0, ..., a_n, indices[i], b_0, ..., b_n] | |

* | |

* # Higher rank indices (output is rank(input0) + rank(indices) - 1). | |

* output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = | |

* input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* Inputs: | |

* * 0: An n-D tensor from which to gather values. | |

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

* Negative index is used to specify axis from the end | |

* (e.g. -1 for the last axis). Must be in the range [-n, n). | |

* * 2: A k-D tensor {@link ANEURALNETWORKS_TENSOR_INT32} of indices. | |

* The values must be in the bounds of the corresponding dimensions | |

* of input0. | |

* | |

* Outputs: | |

* * 0: An (n + k - 1)-D tensor with the same {@link OperandCode} as input0. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint must be the same as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_GATHER = 51, | |

/** | |

* Generate aixs-aligned bounding box proposals. | |

* | |

* Bounding box proposals are generated by applying transformation on a set | |

* of predefined anchors with the bounding box deltas from bounding box | |

* regression. A final step of hard NMS is applied to limit the number of | |

* returned boxes. | |

* | |

* Axis-aligned bounding boxes are represented by its upper-left corner | |

* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid | |

* bounding box should satisfy x1 <= x2 and y1 <= y2. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Inputs: | |

* * 0: A 4-D Tensor specifying the score of each anchor at each | |

* location. With "NHWC" data layout, the tensor shape is | |

* [batches, height, width, num_anchors]. With "NCHW" data layout, | |

* the tensor shape is [batches, num_anchors, height, width]. | |

* * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data | |

* layout, the tensor shape is [batches, height, width, num_anchors * 4]. | |

* With "NCHW" data layout, the tensor shape is | |

* [batches, num_anchors * 4, height, width]. The box deltas are encoded | |

* in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale | |

* relative correction factor for the center position of the bounding box | |

* with respect to the width and height, dw and dh is the log-scale | |

* relative correction factor for the width and height. The last | |

* dimensions is the channel dimension. | |

* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each | |

* predefined anchor, with format [x1, y1, x2, y2]. For input0 of type | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor should be of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with scale of 0.125. | |

* * 3: A 2-D Tensor of shape [batches, 2], specifying the size of | |

* each image in the batch, with format [image_height, image_width]. | |

* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this | |

* tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with | |

* scale of 0.125. | |

* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio | |

* from the height of original image to the height of feature map. | |

* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio | |

* from the width of original image to the width of feature map. | |

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

* number of boxes before going into the hard NMS algorithm. Boxes | |

* with the lowest scores are discarded to meet the limit. Set to | |

* a non-positive value for unlimited number. | |

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

* number of boxes returning from the hard NMS algorithm. Boxes | |

* with the lowest scores are discarded to meet the limit. Set to | |

* a non-positive value for unlimited number. | |

* * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU | |

* threshold for hard NMS. | |

* * 9: An {@link ANEURALNETWORKS_FLOAT32} scalar, min_size. Boxes with | |

* height or width lower than the absolute threshold are filtered out. | |

* * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify | |

* NCHW data layout for input0 and input1. Set to false for NHWC. | |

* | |

* Outputs: | |

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

* [num_output_rois], specifying the score of each output box. | |

* The boxes are grouped by batches, but the sequential order in | |

* each batch is not guaranteed. For type of | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scale and zero | |

* point must be the same as input0. | |

* * 1: A tensor of the same {@link OperandCode} as input3, of shape | |

* [num_output_rois, 4], specifying the coordinates of each output | |

* bounding box for each class, with format [x1, y1, x2, y2]. | |

* The sequential order of the boxes corresponds with output0. | |

* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the | |

* scale must be 0.125 and the zero point must be 0. | |

* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape | |

* [num_output_rois], specifying the batch index of each box. Boxes | |

* with the same batch index are grouped together. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_GENERATE_PROPOSALS = 52, | |

/** | |

* For input tensors x and y, computes x > y elementwise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_GREATER = 53, | |

/** | |

* For input tensors x and y, computes x >= y elementwise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_GREATER_EQUAL = 54, | |

/** | |

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

* | |

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

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

* containing depth_out convolutional filters of depth depth_group, GROUPED_CONV | |

* applies a group of different filters to each input channel group, then | |

* concatenates the results together. | |

* | |

* Specifically, the input channels are divided into num_groups groups, each with | |

* depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional | |

* filters are also divided into num_groups groups, i.e. depth_out is divisible | |

* by num_groups. GROUPED_CONV applies each group of filters to the corresponding | |

* input channel group, and the result are concatenated together. | |

* | |

* 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, g * channel_multiplier + q] = | |

* sum_{di, dj, dk} ( | |

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

* g * depth_group + dk] * | |

* filter[g * channel_multiplier + q, di, dj, dk] | |

* ) + bias[channel] | |

* | |

* where channel_multiplier = depth_out / num_groups | |

* | |

* Supported tensor {@link OperandCode} configurations: | |

* * 16 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias. | |

* | |

* * 32 bit floating point: | |

* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias. | |

* | |

* * Quantized: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* * Quantized signed (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to | |

* * * input.scale * filter.scale). | |

* | |

* * Quantized with symmetric per channel quantization for the filter: | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

* * Quantized signed with filter symmetric per channel quantization | |

* (since NNAPI feature level 4): | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output. | |

* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. | |

* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0, | |

* * * each value scaling is separate and equal to input.scale * filter.scales[channel]). | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* | |

* 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, where depth_in = num_groups * depth_group. | |

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

* [depth_out, filter_height, filter_width, depth_group], specifying | |

* the filter, where depth_out must be divisible by num_groups. For | |

* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} | |

* the channel dimension (channelDim at | |

* {@link ANeuralNetworksSymmPerChannelQuantParams}) must be set to 0. | |

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

* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} | |

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

* of 0 and bias_scale == input_scale * filter_scale. For filter tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias | |

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

* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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 number of | |

* groups. | |

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

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

* invoke on the result. | |

* * 11: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify | |

* NCHW data layout for input0 and output0. Set to false for NHWC. | |

* | |

* Inputs (implicit padding): | |

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

* specifying the input, where depth_in = num_groups * depth_group. | |

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

* [depth_out, filter_height, filter_width, depth_group], specifying | |

* the filter, where depth_out must be divisible by num_groups. For | |

* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} | |

* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) | |

* must be set to 0. | |

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

* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type. | |

* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} | |

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

* of 0 and bias_scale == input_scale * filter_scale. For filter tensor | |

* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias | |

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

* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to | |

* bias_scale[i] = input_scale * filter_scale[i]. | |

* * 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 number of | |

* groups. | |

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

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

* invoke on the result. | |

* * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify | |

* NCHW data layout for input0 and output0. Set to false for NHWC. | |

* | |

* Outputs: | |

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

* [batches, out_height, out_width, depth_out]. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint can be different from inputs' scale and zeroPoint. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_GROUPED_CONV_2D = 55, | |

/** | |

* Localize the maximum keypoints from heatmaps. | |

* | |

* This operation approximates the accurate maximum keypoint scores and | |

* indices after bicubic upscaling by using Taylor expansion up to the | |

* quadratic term. | |

* | |

* The bounding box is represented by its upper-left corner coordinate | |

* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. | |

* A valid bounding box should satisfy x1 <= x2 and y1 <= y2. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* | |

* Inputs: | |

* * 0: A 4-D Tensor of shape | |

* [num_boxes, heatmap_size, heatmap_size, num_keypoints], | |

* specifying the heatmaps, the height and width of heatmaps should | |

* be the same, and must be greater than or equal to 2. | |

* * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes, | |

* each with format [x1, y1, x2, y2]. For input0 of type | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should | |

* be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint | |

* of 0 and scale of 0.125. | |

* For input0 of type | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor | |

* should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with | |

* zeroPoint of -128 and scale of 0.125. | |

* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify | |

* NCHW data layout for input0. Set to false for NHWC. | |

* | |

* Outputs: | |

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

* [num_boxes, num_keypoints], specifying score of the keypoints. | |

* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or | |

* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor, | |

* the scale and zeroPoint can be different from input0 scale and zeroPoint. | |

* * 1: A tensor of the same {@link OperandCode} as input1, with shape | |

* [num_boxes, num_keypoints, 2], specifying the location of | |

* the keypoints, the second dimension is organized as | |

* [keypoint_x, keypoint_y]. | |

* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the | |

* scale must be 0.125 and the zero point must be 0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT = 56, | |

/** | |

* Applies instance normalization to the input tensor. | |

* | |

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

* | |

* output[b, h, w, c] = | |

* (input[b, h, w, c] - mean[b, c]) * gamma / | |

* sqrt(var[b, c] + epsilon) + beta | |

* | |

* Where the mean and variance are computed across the spatial dimensions: | |

* | |

* mean[b, c] = | |

* sum_{h, w}(input[b, h, w, c]) / sum(1) | |

* | |

* var[b, c] = | |

* sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

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

* With the default data layout NHWC, the data is stored in the order of: | |

* [batch, height, width, channels]. Alternatively, the data layout could | |

* be NCHW, the data storage order of: [batch, channels, height, width]. | |

* | |

* Inputs: | |

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

* * 1: A scalar, specifying gamma, the scale applied to the normalized | |

* tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if | |

* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* * 2: A scalar, specifying beta, the offset applied to the normalized | |

* tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if | |

* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* * 3: A scalar, specifying epsilon, the small value added to variance to | |

* avoid dividing by zero. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if | |

* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of | |

* {@link ANEURALNETWORKS_FLOAT32} if input0 is of | |

* {@link ANEURALNETWORKS_TENSOR_FLOAT32}. | |

* * 4: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify | |

* NCHW data layout for input0 and output0. Set to false for NHWC. | |

* | |

* Outputs: | |

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

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_INSTANCE_NORMALIZATION = 57, | |

/** | |

* For input tensors x and y, computes x < y elementwise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LESS = 58, | |

/** | |

* For input tensors x and y, computes x <= y elementwise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* * {@link ANEURALNETWORKS_TENSOR_INT32} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} | |

* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4) | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

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

* with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LESS_EQUAL = 59, | |

/** | |

* Computes natural logarithm of x element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: from 1. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LOG = 60, | |

/** | |

* Returns the truth value of x AND y element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions | |

* compatible with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LOGICAL_AND = 61, | |

/** | |

* Computes the truth value of NOT x element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* | |

* Supported tensor rank: from 1. | |

* | |

* Inputs: | |

* * 0: A tensor. | |

* | |

* Outputs: | |

* * 0: The output tensor of same shape as input0. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LOGICAL_NOT = 62, | |

/** | |

* Returns the truth value of x OR y element-wise. | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_BOOL8} | |

* | |

* Supported tensor rank: from 1 | |

* | |

* This operation supports broadcasting. | |

* | |

* Inputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions | |

* compatible with input0. | |

* | |

* Outputs: | |

* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}. | |

* | |

* Available since NNAPI feature level 3. | |

*/ | |

ANEURALNETWORKS_LOGICAL_OR = 63, | |

/** | |

* Computes the log softmax activations given logits. | |

* | |

* The output is calculated using this formula: | |

* | |

* output = logits * beta - log(reduce_sum(exp(logits * beta), axis)) | |

* | |

* Supported tensor {@link OperandCode}: | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} | |

* * {@link ANEURALNETWORKS_TENSOR_FLOAT32} | |

* | |

* Supported tensor rank: from 1. | |

* | |

* Inputs: | |

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

* * 1: A scalar, specifying the positive scaling factor for the exponent, | |

* beta. | |

* For input tensor of {@link ANEURALNETWORKS_TEN |