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diff --git a/runtimes/include/NeuralNetworks.h b/runtimes/include/NeuralNetworks.h new file mode 100644 index 000000000..7400806d8 --- /dev/null +++ b/runtimes/include/NeuralNetworks.h @@ -0,0 +1,6444 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @addtogroup NeuralNetworks + * @{ + */ + +/** + * @file NeuralNetworks.h + */ + +#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H +#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H + +/****************************************************************** + * + * IMPORTANT NOTICE: + * + * This file is part of Android's set of stable system headers + * exposed by the Android NDK (Native Development Kit). + * + * Third-party source AND binary code relies on the definitions + * here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES. + * + * - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES) + * - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS + * - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY + * - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES + */ + +// For compatibility with android, check __ANDROID_API__ is defined +// If __ANDROID_API__ is pre-defined, this header may be used for android +#ifndef __ANDROID_API__ +#define __ANDROID_API__ 29 +#define __ANDROID_API_Q__ 29 +#define __INTRODUCED_IN(api_level) +typedef struct AHardwareBuffer AHardwareBuffer; +#else +#include <android/hardware_buffer.h> +#endif // __ANDROID_API__ +#include <stddef.h> +#include <stdint.h> +#include <sys/cdefs.h> + +__BEGIN_DECLS + +/** + * Operand types. + * + * The type of operands that can be added to a model. + * + * Although we define many types, most operators accept just a few + * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * and {@link ANEURALNETWORKS_INT32}. + * + * Available since API level 27. + */ +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, +#if __ANDROID_API__ >= __ANDROID_API_Q__ + /** + * 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 API level 29. + */ + 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 API level 29. + */ + ANEURALNETWORKS_TENSOR_QUANT16_SYMM = 7, + /** + * A tensor of IEEE 754 16 bit floating point values. + * + * Available since API level 29. + */ + 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 API level 29. + */ + ANEURALNETWORKS_TENSOR_BOOL8 = 9, + /** + * An IEEE 754 16 bit floating point scalar value. + * + * Available since API level 29. + */ + 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 API level 29. + */ + 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 API level 29. + */ + 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 API level 29. + */ + ANEURALNETWORKS_TENSOR_QUANT8_SYMM = 13, +#endif // __ANDROID_API__ >= __ANDROID_API_Q__ + +} OperandCode; + +/** + * Operation types. + * + * The type of operations that can be added to a model. + * + * Available since API level 27. + */ +typedef enum { + // Operations below are available since API level 27. + + /** + * 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 API level 29, 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} 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. + * + * Outputs: + * * 0: The sum, a tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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], specifying + * the input. Since API level 29, 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 API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Since API level 29, 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 API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API + * level 29, see the input section) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0 ~ n-1: The list of n input tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. + * Before API level 29, all input tensors of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * must have the same scale and zeroPoint as the output tensor. + * Since API level 29, 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 API level 29, for a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint values can be different from + * input tensors. Before API level 29 they have to be the same as for the input tensors. + * + * Available since API level 27. + */ + ANEURALNETWORKS_CONCATENATION = 2, + + /** + * Performs an 2-D convolution operation. + * + * The CONV_2D op sweeps a 2-D filter that can mix channels together over a + * batch of images, applying the filter to each window of each image of the + * appropriate size. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[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 API level 29: + * * 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]). + * + * 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. Since API level 29, 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 (extraParams.channelQuant.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}, + * 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 API level 29. + * * 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 API level 29. + * * 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 API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. Since API level 29, 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 (extraParams.channelQuant.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}, + * 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 API level 29. + * * 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 API level 29. + * * 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 API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. Before API level 29, + * for output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input_scale * filter_scale + * + * Available since API level 27. + */ + 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 API level 29: + * * 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]). + * + * 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. + * * 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 (extraParams.channelQuant.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}, + * 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 API level 29. + * * 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 API level 29. + * * 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 API level 29. + * + * 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}, + * 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 API level 29. + * * 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 API level 29. + * * 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 API level 29. + + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. Before API level 29, + * for output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input_scale * filter_scale + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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 [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 API level 29. + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29) + * + * Supported output tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. Since API level 29, this tensor may be zero-sized. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since API level 27. + */ + 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_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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} tensor, + * the scale and zeroPoint must be the same as input1. + * + * Available since API level 27. + */ + ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, + + /** + * Computes element-wise floor() on the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@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 API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor of at least rank 2, specifying the input. If rank is + * greater than 2, then it gets flattened to a 2-D Tensor. The + * (flattened) 2-D Tensor is reshaped (if necessary) to + * [batch_size, input_size], where "input_size" corresponds to the + * number of inputs to the layer, matching the second dimension of + * weights, and "batch_size" is calculated by dividing the number of + * elements by "input_size". Since API level 29, 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}, 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 API + * level 29, for output tensor of {@link + * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition must + * be satisfied: output_scale > input_scale * filter_scale. + * + * Available since API level 27. + */ + 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 API level 27. + */ + ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, + + /** + * Applies L2 normalization along the depth dimension. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * input[batch, row, col, channel] / + * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) + * + * For input tensor with rank less than 4, independently normalizes each + * 1-D slice along dimension dim. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29) + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since API level 29. + * + * 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 API level 29. + * + * 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. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@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]. + * + * 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 API level 29, 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 API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Since API level 29, 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 API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since API level 29. + * + * 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 API level 29. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. Since API level 29, 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. + * + * Available since API level 27. + */ + ANEURALNETWORKS_LOGISTIC = 14, + + /** + * Projects an input to a bit vector via locality senstive hashing. + * + * Supported input tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@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 API level 29). + * 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 API level 27. + * The offset value for sparse projections was added in API level 29. + */ + 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 API level 29 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. + * * (API level >= 29) 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 API level 29) + * * {@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 API level 29 this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT32}. Since API level 29, 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 API level 29 this scalar must be of type {@link + * ANEURALNETWORKS_FLOAT32}. Since API level 29, 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 API level 29 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 API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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], specifying + * the input. Since API level 29, 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 API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. Since API level 29, 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 API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29, 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: The product, a tensor of the same {@link OperandCode} as input0. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input1_scale * input2_scale. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. Since API level 29, this tensor may + * be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. Since API level 29, this tensor may + * be zero-sized. + * + * Outputs: + * * 0: The output tensor of the same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. Since API level 29, this tensor may + * be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the tensor to be reshaped. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, defining the + * shape of the output tensor. The number of elements implied by shape + * must be the same as the number of elements in the input tensor. + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29) + * + * 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 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 API level 29, 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 API level 29. + * + * Inputs (resizing by scale, since API level 29): + * * 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. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@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 API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * Tensors with rank other than 2 or 4 are only supported since API level 29. + * + * Inputs: + * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since + * API level 29, 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} or + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, 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 API level 29. + * + * 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. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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 [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 API level 29. + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 27. + */ + 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 API level 29) + * * {@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 API level 27. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. Since API level 29, 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. + * + * Available since API level 27. + */ + ANEURALNETWORKS_TANH = 28, + + // Operations below are available since API level 28. + + // TODO: make the description easier to understand. + /** + * BatchToSpace for N-dimensional tensors. + * + * This operation reshapes the batch dimension (dimension 0) into M + 1 + * dimensions of shape block_shape + [batch], interleaves these blocks back + * into the grid defined by the spatial dimensions [1, ..., M], to obtain a + * result with the same rank as the input. + * + * This is the reverse of SpaceToBatch. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 28. + */ + ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29, + + /** + * Element-wise division of two tensors. + * + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the result of dividing the first input tensor + * by the second, optionally modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Since API level 29, 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since API level 28. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. 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} tensor, + * the scale and zeroPoint must be same as input0. + * + * Available since API level 28. + */ + ANEURALNETWORKS_MEAN = 31, + + /** + * Pads a tensor with zeros. + * + * This operation pads a tensor according to the specified paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API + * level 29, 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * NOTE: Before API level 29, the pad value for + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. + * Since API level 29, the pad value is always the logical zero. + * + * Available since API level 28. + */ + ANEURALNETWORKS_PAD = 32, + + // TODO: make the description easier to understand. + /** + * SpaceToBatch for N-Dimensional tensors. + * + * This operation divides "spatial" dimensions [1, ..., M] of the input into + * a grid of blocks of shape block_shape, and interleaves these blocks with + * the "batch" dimension (0) such that in the output, the spatial dimensions + * [1, ..., M] correspond to the position within the grid, and the batch + * dimension combines both the position within a spatial block and the + * original batch position. Prior to division into blocks, the spatial + * dimensions of the input are optionally zero padded according to paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API + * level 29, 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]. + * + * 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 API level 29. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * NOTE: Before API level 29, the pad value for + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. + * Since API level 29, the pad value is always the logical zero. + * + * Available since API level 28. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, the tensor to be squeezed. + * * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The + * dimensions to squeeze. If specified only squeezes the dimensions + * listed. Otherwise, squeezes all dimensions. The dimension index + * starts at 0. An error must be reported if squeezing a dimension that + * is not 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. Contains the + * same data as input, but has one or more dimensions of size 1 + * removed. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 28. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be sliced. + * * 1: 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 28. + */ + 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 API level 29, 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since API level 29) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 28. + */ + 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 API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be transposed. + * Since API level 29, 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 28. + */ + ANEURALNETWORKS_TRANSPOSE = 37, + + // Operations below are available since API level 29. + + /** + * Computes the absolute value of a tensor, 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 API level 29. + */ + 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} + * + * 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. + * + * Available since API level 29. + */ + // 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} + * + * 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. + * + * Available since API level 29. + */ + 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}. 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 API level 29. + */ + ANEURALNETWORKS_AXIS_ALIGNED_BBOX_TRANSFORM = 41, + + /** + * Performs a forward LSTM on the input followed by a backward LSTM. + * + * 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, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 40: The forward auxiliary input-to-input weights. Optional. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 41: The forward auxiliary input-to-forget weights. Optional. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 42: The forward auxiliary input-to-cell weights. Optional. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 43: The forward auxiliary input-to-output weights. Optional. + * A 2-D tensor of shape [fw_num_units, input_size]. + * * 44: The backward auxiliary input-to-input weights. Optional. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 45: The backward auxiliary input-to-forget weights. Optional. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 46: The backward auxiliary input-to-cell weights. Optional. + * A 2-D tensor of shape [bw_num_units, input_size]. + * * 47: The backward auxiliary input-to-output weights. Optional. + * A 2-D tensor of shape [bw_num_units, 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] + * + * Available since API level 29. + */ + 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 also supports 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 an auxiliary input 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 + * + * 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 + * 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 the same as of the input 0. + * * 10:fwAuxWeights. + * A 2-D tensor of shape [fwNumUnits, inputSize]. + * * 11:bwAuxWeights. + * A 2-D tensor of shape [bwNumUnits, inputSize]. + * * 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]. + * + * Available since API level 29. + */ + 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} + * + * 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. 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}, + * 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 API level 29. + */ + ANEURALNETWORKS_BOX_WITH_NMS_LIMIT = 44, + + /** + * Casts a tensor to a new 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} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since API level 29. + */ + 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} + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + 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 API level 29. + */ + 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} + * + * 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 API level 29. + */ + 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 API level 29. + */ + 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} + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + 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} + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + 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} + * + * 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}, 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}, 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}, 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 API level 29. + */ + 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} + * + * 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 API level 29. + */ + 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} + * + * 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 API level 29. + */ + 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 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]). + * + * 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}, + * 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 (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}, + * 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} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 29. + */ + 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} + * + * 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. + * * 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} 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 API level 29. + */ + 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 API level 29. + */ + 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} + * + * 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 API level 29. + */ + 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} + * + * 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 API level 29. + */ + 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 API level 29. + */ + 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 API level 29. + */ + 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 API level 29. + */ + 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 API level 29. + */ + 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_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}. + * * 2: 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: The output tensor of the same {@link OperandCode} and shape as + * input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_LOG_SOFTMAX = 64, + + /** + * Returns the element-wise maximum of two tensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and compatible dimensions + * with input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_MAXIMUM = 65, + + /** + * Returns the element-wise minimum of two tensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandCode} and compatible dimensions + * with input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scales and zeroPoint can be different from input0 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_MINIMUM = 66, + + /** + * Computes numerical negative value element-wise. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_NEG = 67, + + /** + * 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} + * + * 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 API level 29. + */ + ANEURALNETWORKS_NOT_EQUAL = 68, + + /** + * Pads a tensor with the given constant value according to the specified + * paddings. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be padded. + * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. The shape of the + * tensor must be {rank(input0), 2}. + * padding[i, 0] specifies the number of elements to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of elements to be padded after + * the end of dimension i. + * * 2: An scalar specifying the value to use for padding input0. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the + * pad value must be of {@link ANEURALNETWORKS_FLOAT16}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the + * pad value must be of {@link ANEURALNETWORKS_FLOAT32}. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the pad value must be of {@link ANEURALNETWORKS_INT32}. The + * scale and zeroPoint are assumed to be the same as in input0. + * + * 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} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_PAD_V2 = 69, + + /** + * Computes the power of one value to another. + * + * Given a tensor base and a tensor exponent, this operation computes + * base^exponent elementwise. + * + * This operations supports broadcasting. 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. + * + * For example: + * base.dimension = {4, 1, 2} + * exponent.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor specifying the base. + * * 1: A tensor specifying the exponent. + * + * Outputs: + * * 0: An output tensor. + * + * Available since API level 29. + */ + ANEURALNETWORKS_POW = 70, + + /** + * Parametric Rectified Linear Unit. + * + * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha + * is a learned array with the same {@link OperandCode} and compatible + * dimensions as input x. + * + * 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: + * input.dimension = {4, 1, 2} + * alpha.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0, specifying the alpha. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be diffent from the input0 scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_PRELU = 71, + + /** + * Quantizes the input tensor. + * + * The formula is: + * + * output = max(0, min(255, round(input / scale) + zeroPoint) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor, may be zero-sized. + * + * Outputs: + * * 0: The output tensor of same shape as input0, but with + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. + * + * Available since API level 29. + */ + ANEURALNETWORKS_QUANTIZE = 72, + + /** + * A version of quantized LSTM, using 16 bit quantization for internal + * state. + * + * There is no projection layer, so cell state size is equal to the output + * size. + * + * Inputs: + * * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBatches, inputSize] specifying the input to the LSTM + * cell. Tensor is quantized with a fixed quantization range of + * [-1, 127/128] (scale = 1/128, zeroPoint = 128). + * * 1: The input-to-input weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-input part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 2: The input-to-forget weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-forget part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 3: The input-to-cell weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-cell part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 4: The input-to-output weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-output part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 5: The recurrent-to-input weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-input part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 6: The recurrent-to-forget weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-forget + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 7: The recurrent-to-cell weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-cell part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 8: The recurrent-to-output weights. + * A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [outputSize, outputSize] specifying recurrent-to-output + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 9: The input gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 10:The forget gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 11:The cell bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 12:The output gate bias. + * A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 13: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] specifying the cell state from the + * previous time step of the LSTM cell. It is quantized using a + * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / + * 32768, zeroPoint = 0). + * * 14: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] specifying the output of the LSTM + * cell from previous time-step. Tensor is quantized with a fixed + * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint = + * 128). + * + * + * Outputs: + * * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] which contains a cell state from + * the current time step. Tensor is quantized using a quantization + * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint = + * 0). + * * 1: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] which contains the output value. + * Tensor is quantized with a fixed quantization range of [-1, 127/128] + * (scale = 1/128, zeroPoint = 128). + */ + ANEURALNETWORKS_QUANTIZED_16BIT_LSTM = 73, + + /** + * Draws samples from a multinomial distribution. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 2-D tensor with shape [batches, classes], specifying the + * unnormalized log-probabilities for all classes. + * * 1: A scalar {@link ANEURALNETWORKS_INT32}, specifying the number of + * independent samples to draw for each row slice. + * * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [2], + * specifying seeds used to initialize the random distribution. + * Outputs: + * * 0: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape + * [batches, samples], containing the drawn samples. + * + * Available since API level 29. + */ + ANEURALNETWORKS_RANDOM_MULTINOMIAL = 74, + + /** + * Reduces a tensor by computing the "logical and" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_ALL = 75, + + /** + * Reduces a tensor by computing the "logical or" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_ANY = 76, + + /** + * Reduces a tensor by computing the maximum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_MAX = 77, + + /** + * Reduces a tensor by computing the minimum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_MIN = 78, + + /** + * Reduces a tensor by multiplying elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_PROD = 79, + + /** + * Reduces a tensor by summing elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_REDUCE_SUM = 80, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by average pooling sampling points from bilinear interpolation. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * No rounding is applied in this operation. The sampling points are unified + * distributed in the pooling bin and their values are calculated by bilinear + * interpolation. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29) + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line 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. 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: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * sampling points in height dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_height/out_height). + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * sampling points in width dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_width/out_width). + * * 9: 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} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from the input0 scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_ROI_ALIGN = 81, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by max-pooling. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * Rounding is applied in this operation to ensure integer boundary for + * regions of interest and pooling bins. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line 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. + * * 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. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: 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} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_ROI_POOLING = 82, + + /** + * Computes reciprocal of square root 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 API level 29. + */ + ANEURALNETWORKS_RSQRT = 83, + + /** + * Using a tensor of booleans c and input tensors x and y select values + * elementwise from both input tensors: + * + * O[i] = C[i] ? x[i] : y[i]. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_BOOL8} acting as a + * mask that chooses, based on the value at each element, whether the + * corresponding element in the output should be taken from input1 (if + * true) or input2 (if false). + * * 1: An input tensor of the same shape as input0. + * * 2: An input tensor of the same shape and type as input1. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scales and zeroPoint can be different from input1 scale and zeroPoint. + * + * Outputs: + * * 0: A tensor of the same type and shape as input1 and input2. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + */ + ANEURALNETWORKS_SELECT = 84, + + /** + * Computes sin 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 API level 29. + */ + ANEURALNETWORKS_SIN = 85, + + /** + * Extracts a slice of specified size from the input tensor starting at a + * specified location. + * + * The starting location is specified as a 1-D tensor containing offsets + * for each dimension. The size is specified as a 1-D tensor containing + * either size of a slice along corresponding dimension or -1. In the latter + * case, all the remaining elements in dimension are included in the slice. + * + * A sum of begin offset and a size of a slice must not exceed size of a + * corresponding dimension. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to take slice from, may be zero-sized. + * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying + * the beginning indices of the slice in each dimension. + * * 2: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying + * the size of the slice in each dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input containing the slice. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * its scale and zeroPoint has to be same as the input0 scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_SLICE = 86, + + /** + * Splits a tensor along a given axis into num_splits subtensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to split. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis along + * which to split. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar indicating the number of + * splits along given axis. Must evenly divide axis size. + * + * Outputs: + * * 0 ~ (num_splits - 1): Resulting subtensors. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_SPLIT = 87, + + /** + * Computes square root 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 API level 29. + */ + ANEURALNETWORKS_SQRT = 88, + + /** + * Constructs a tensor by tiling a given tensor. + * + * This operation creates a new tensor by replicating `input` `multiples` + * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]` + * elements, and the values of `input` are replicated `multiples[i]` times + * along the i-th dimension. + * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: multiples, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. + * The length of multiples must be n. + * + * Outputs: + * * 0: A tiled tensor of the same {@link OperandCode} and rank as `input`. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_TILE = 89, + + /** + * Finds values and indices of the k largest entries for the last dimension. + * + * Resulting values in each dimensions are sorted in descending order. If + * two values are equal, the one with larger index appears first. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: k, an {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * top elements to look for along the last dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input, containing the k + * largest elements along each last dimensional slice. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * * 1: An n-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} + * containing the indices of values within the last dimension of input. + * + * Available since API level 29. + */ + ANEURALNETWORKS_TOPK_V2 = 90, + + /** + * Performs the transpose of 2-D convolution operation. + * + * This operation is sometimes called "deconvolution" after Deconvolutional + * Networks, but is actually the transpose (gradient) of + * {@link ANEURALNETWORKS_CONV_2D} rather than an actual deconvolution. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * 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 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]). + * + * 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. Since API level 29, 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 (extraParams.channelQuant.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 should be of the + * same type. For input tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. For filter tensor of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * must 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 {@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. Since API level 29, 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 (extraParams.channelQuant.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 should be of the + * same type. For input tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. For filter tensor of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * must 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_TENSOR_INT32} tensor, specifying the output + * tensor shape. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * {@link PaddingCode} values. + * * 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, 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} tensor, + * the scale and zeroPoint can be different from inputs' scale and zeroPoint. + * + * Available since API level 29. + */ + ANEURALNETWORKS_TRANSPOSE_CONV_2D = 91, + + /** + * A recurrent neural network specified by an LSTM cell. + * + * Performs (fully) dynamic unrolling of input. + * + * This Op unrolls the input along the time dimension, and implements the + * following operation for each element in the sequence + * s = 1...sequence_length: + * outputs[s] = projection(state = activation(LSTMOp(inputs[s]))) + * + * Where LSTMOp is the LSTM op as in {@link ANEURALNETWORKS_LSTM}, + * the "projection" is an optional projection layer from state and output + * and the “activation” is the function passed as the + * “fused_activation_function” argument (if not “NONE”). + * + * 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 (\f$x_t\f$). + * 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 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. + * * 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. + * * 23:Time-major if true, batch-major if false. + * * 24:The input layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 25:The forget layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 26:The cell layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 27:The output layer normalization weights. Optional. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The output (\f$o_t\f$). + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * + * Available since API level 29. + */ + ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM = 92, + + /** + * A recurrent neural network layer that applies a basic RNN cell to a + * sequence of inputs. + * + * This layer unrolls the input along the sequence dimension, and implements + * the following operation + * for each element in the sequence s = 1...sequence_length: + * outputs[s] = state = activation(inputs[s] * 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} + * * {@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 1, then the input has a shape [maxTime, batchSize, + * inputSize], otherwise the input has a shape [batchSize, maxTime, + * inputSize]. + * * 1: weights. + * A 2-D tensor of shape [numUnits, inputSize]. + * * 2: recurrent_weights. + * A 2-D tensor of shape [numUnits, numUnits]. + * * 3: bias. + * A 1-D tensor of shape [numUnits]. + * * 4: hidden state + * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 5: fusedActivationFunction. + * A {@link FuseCode} value indicating the activation function. If + * “NONE” is specified then it results in a linear activation. + * * 6: timeMajor + * An {@link ANEURALNETWORKS_INT32} scalar specifying the shape format + * of input and output tensors. Must be set to either 0 or 1. + * Outputs: + * * 0: output. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to 1, then the output has a shape [maxTime, batchSize, + * numUnits], otherwise the output has a shape [batchSize, maxTime, + * numUnits]. + * + * Available since API level 29. + */ + ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN = 93, + + /** + * Resizes images to given size using the nearest neighbor 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} + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * 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 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. 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 {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * + * Inputs (resizing by scale): + * * 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 {@link ANEURALNETWORKS_BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. + * For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor, + * the scale and zeroPoint must be the same as input0. + * + * Available since API level 29. + */ + ANEURALNETWORKS_RESIZE_NEAREST_NEIGHBOR = 94, +} OperationCode; + +/** + * Fused activation function types. + * + * + * Available since API level 27. + */ +typedef enum { + /** NO fused activation function. */ + ANEURALNETWORKS_FUSED_NONE = 0, + /** Fused ReLU activation function. */ + ANEURALNETWORKS_FUSED_RELU = 1, + /** Fused ReLU1 activation function. */ + ANEURALNETWORKS_FUSED_RELU1 = 2, + /** Fused ReLU6 activation function. */ + ANEURALNETWORKS_FUSED_RELU6 = 3, +} FuseCode; + +/** + * Implicit padding algorithms. + * + * + * Available since API level 27. + */ +typedef enum { + /** + * SAME padding. + * Padding on both ends are the "same": + * padding_to_beginning = total_padding / 2 + * padding_to_end = (total_padding + 1)/2. + * i.e., for even number of padding, padding to both ends are exactly + * the same; for odd number of padding, padding to the ending is bigger + * than the padding to the beginning by 1. + * + * total_padding is a function of input, stride and filter size. + * It could be computed as follows: + * out_size = (input + stride - 1) / stride; + * needed_input = (out_size - 1) * stride + filter_size + * total_padding = max(0, needed_input - input_size) + * The computation is the same for the horizontal and vertical directions. + */ + ANEURALNETWORKS_PADDING_SAME = 1, + + /** + * VALID padding. + * No padding. When the input size is not evenly divisible by + * the filter size, the input at the end that could not fill + * the whole filter tile will simply be ignored. + */ + ANEURALNETWORKS_PADDING_VALID = 2, +} PaddingCode; + +/** + * Execution preferences. + * + * Available since API level 27. + */ +typedef enum { + /** + * Prefer executing in a way that minimizes battery drain. + * This is desirable for compilations that will be executed often. + */ + ANEURALNETWORKS_PREFER_LOW_POWER = 0, + /** + * Prefer returning a single answer as fast as possible, even if this causes + * more power consumption. + */ + ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1, + /** + * Prefer maximizing the throughput of successive frames, for example when + * processing successive frames coming from the camera. + */ + ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2, +} PreferenceCode; + +/** + * Device types. + * + * The type of NNAPI device. + */ +typedef enum { + /** The device type cannot be provided. */ + ANEURALNETWORKS_DEVICE_UNKNOWN = 0, + /** The device does not fall into any category below. */ + ANEURALNETWORKS_DEVICE_OTHER = 1, + /** The device runs NNAPI models on single or multi-core CPU. */ + ANEURALNETWORKS_DEVICE_CPU = 2, + /** The device can run NNAPI models and also accelerate graphics APIs such + * as OpenGL ES and Vulkan. */ + ANEURALNETWORKS_DEVICE_GPU = 3, + /** Dedicated accelerator for Machine Learning workloads. */ + ANEURALNETWORKS_DEVICE_ACCELERATOR = 4, +} DeviceTypeCode; + +/** + * Result codes. + * + * <p>Any NNAPI function can return any result code, including result codes not + * currently documented. Any value other than {@link ANEURALNETWORKS_NO_ERROR} + * indicates a failure of some kind.</p> + * + * <p>Additional information about the nature of a failure can be obtained from + * the device log after enabling NNAPI debugging by setting the debug.nn.vlog + * property to 1, e.g., by calling "adb shell setprop debug.nn.vlog 1".</p> + * + * Available since API level 27. + */ +typedef enum { + /** + * Operation was succesful. + */ + ANEURALNETWORKS_NO_ERROR = 0, + + /** + * Failure caused by not enough available memory. + */ + ANEURALNETWORKS_OUT_OF_MEMORY = 1, + + ANEURALNETWORKS_INCOMPLETE = 2, + + /** + * Failure caused by unexpected null argument. + */ + ANEURALNETWORKS_UNEXPECTED_NULL = 3, + + /** + * Failure caused by invalid function arguments, invalid model definition, + * invalid execution definition or invalid data at execution time. + */ + ANEURALNETWORKS_BAD_DATA = 4, + + /** + * Failure caused by failed model execution. + */ + ANEURALNETWORKS_OP_FAILED = 5, + + /** + * Failure caused by object being in the wrong state. + */ + ANEURALNETWORKS_BAD_STATE = 6, + + /** + * Failure caused by not being able to map a file into memory. + * This may be caused by a file descriptor not being mappable, or an AHardwareBuffer + * not supported by the device. + * Mitigate by reading its content into memory. + */ + ANEURALNETWORKS_UNMAPPABLE = 7, + + /** + * Failure caused by insufficient buffer size provided to a model output. + */ + ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE = 8, + + /** + * Failure caused by a device not being available. + */ + ANEURALNETWORKS_UNAVAILABLE_DEVICE = 9, +} ResultCode; + +/** + * For {@link ANeuralNetworksModel_setOperandValue}, values with a + * length smaller or equal to this will be immediately copied into + * the model. The size is in bytes. + * + * Available since API level 27. + */ +enum { ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128 }; + +/** + * For {@link ANeuralNetworksCompilation_setCaching}, specify the size + * of the cache token required from the application. The size is in bytes. + * + * Available since API level 29. + */ +enum { ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN = 32 }; + +/** + * ANeuralNetworksMemory is an opaque type that represents memory. + * + * This type is used to represent shared memory, memory mapped files, + * and similar memories. + * + * By using shared memory, a program can efficiently communicate to the + * runtime and drivers the tensors that define a model. See + * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application + * should typically create one shared memory object that contains every constant tensor + * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be used to + * create shared memory from a file handle. + * {@link ANeuralNetworksMemory_createFromAHardwareBuffer} can be used to + * create shared memory from an AHardwareBuffer handle. + * + * Memory objects can also be used to specify the input and output arguments of + * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory} + * and {@link ANeuralNetworksExecution_setOutputFromMemory}. + * + * When calling {@link ANeuralNetworksModel_setOperandValueFromMemory}, + * {@link ANeuralNetworksExecution_setInputFromMemory} and + * {@link ANeuralNetworksExecution_setOutputFromMemory}, each operand in the shared + * memory object must be aligned on a boundary of a byte size that is a multiple + * of the element type byte size, e.g., a tensor with + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} type must be aligned on 4-byte boundary. + * + * Available since API level 27. + */ +typedef struct ANeuralNetworksMemory ANeuralNetworksMemory; + +/** + * ANeuralNetworksModel is an opaque type that contains a description of the + * mathematical operations that constitute the model. + * + * <p>Build the model by calling<ul> + * <li>{@link ANeuralNetworksModel_create}</li> + * <li>{@link ANeuralNetworksModel_addOperation}</li> + * <li>{@link ANeuralNetworksModel_addOperand}</li> + * </ul> + * + * This forms a graph in which each operation and operand is a node, a + * directed edge from an operand to an operation indicates that the + * operand is an input to the operation, and a directed edge from an + * operation to an operand indicates that the operand is an output + * from the operation. This graph must be acyclic. + * + * A model is completed by calling {@link ANeuralNetworksModel_finish}. + * A model is destroyed by calling {@link ANeuralNetworksModel_free}. + * + * <p>A model cannot be modified once {@link ANeuralNetworksModel_finish} + * has been called on it.</p> + * + * <p>It is the application's responsibility to make sure that only one thread + * modifies a model at a given time. It is however safe for more than one + * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p> + * + * <p>It is also the application's responsibility to ensure that there are no other + * uses of the model after calling {@link ANeuralNetworksModel_free}. + * This includes any compilation or execution object created using the model.</p> + * + * Available since API level 27. + */ +typedef struct ANeuralNetworksModel ANeuralNetworksModel; + +/** + * ANeuralNetworksCompilation is an opaque type that can be used to compile + * a machine learning model. + * + * <p>To use:<ul> + * <li>Create a new compilation instance by calling the + * {@link ANeuralNetworksCompilation_create} function or + * {@link ANeuralNetworksCompilation_createForDevices}.</li> + * <li>Set any desired properties on the compilation (for example, + * {@link ANeuralNetworksCompilation_setPreference}).</li> + * <li>Optionally, set the caching signature and the cache directory on the + * compilation by calling {@link ANeuralNetworksCompilation_setCaching}.</li> + * <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li> + * <li>Use the compilation as many times as needed + * with {@link ANeuralNetworksExecution_create} and + * {@link ANeuralNetworksBurst_create}.</li> + * <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free} + * once all executions using the compilation have completed.</li></ul></p> + * + * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. + * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}. + * + * <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} + * has been called on it.</p> + * + * <p>It is the application's responsibility to make sure that only + * one thread modifies a compilation at a given time. It is however + * safe for more than one thread to use the compilation once + * {@link ANeuralNetworksCompilation_finish} has returned.</p> + * + * <p>It is also the application's responsibility to ensure that there are no other + * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}. + * This includes any execution object created using the compilation.</p> + * + * Available since API level 27. + */ +typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; + +/** + * ANeuralNetworksExecution is an opaque type that can be used to apply a machine + * learning model to a set of inputs. + * + * <p>To use:<ul> + * <li>Create a new execution instance by calling the + * {@link ANeuralNetworksExecution_create} function.</li> + * <li>Associate input buffers or memory regions to the model inputs with + * {@link ANeuralNetworksExecution_setInput} or + * {@link ANeuralNetworksExecution_setInputFromMemory}.</li> + * <li>Associate output buffers or memory regions to the model outputs with + * {@link ANeuralNetworksExecution_setOutput} or + * {@link ANeuralNetworksExecution_setOutputFromMemory}.</li> + * <li>Apply the model with one of the following:</li><ul> + * <li>Asynchronously with {@link ANeuralNetworksExecution_startCompute}, + * waiting for the execution to complete with + * {@link ANeuralNetworksEvent_wait}.</li> + * <li>Synchronously with {@link ANeuralNetworksExecution_compute}.</li> + * <li>Synchronously as part of an execution burst with + * {@link ANeuralNetworksExecution_burstCompute}.</li></ul> + * <li>Destroy the execution with + * {@link ANeuralNetworksExecution_free}.</li></ul></p> + * + * <p>An output buffer or memory region must not overlap with any + * other output buffer or memory region, with an input buffer or + * memory region, or with an operand value in a memory object + * ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p> + * + * <p>An execution cannot be modified once + * {@link ANeuralNetworksExecution_compute} or + * {@link ANeuralNetworksExecution_startCompute} has been called on it.</p> + * + * <p>An execution can be applied to a model with + * {@link ANeuralNetworksExecution_compute} or + * {@link ANeuralNetworksExecution_startCompute} only once. Create new + * executions to do new evaluations of the model.</p> + * + * <p>It is the application's responsibility to make sure that only one thread + * modifies an execution at a given time. It is however safe for more than one + * thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p> + * + * <p>It is also the application's responsibility to ensure that there are no other + * uses of the execution after calling {@link ANeuralNetworksExecution_free}.</p> + * + * <p>Multiple executions can be scheduled and evaluated concurrently, either by + * means of {@link ANeuralNetworksExecution_compute} (which is synchronous) in + * different threads or by means of + * {@link ANeuralNetworksExecution_startCompute} (which is asynchronous). The + * runtime makes no guarantee on the ordering of completion of executions. If + * it's important to the application, the application should enforce the + * ordering by ensuring that one execution completes before the next is + * scheduled (for example, by scheduling all executions synchronously within a + * single thread, or by scheduling all executions asynchronously and using + * {@link ANeuralNetworksEvent_wait} between calls to + * {@link ANeuralNetworksExecution_startCompute}).</p> + * + * Available since API level 27. + */ +typedef struct ANeuralNetworksExecution ANeuralNetworksExecution; + +#if __ANDROID_API__ >= __ANDROID_API_Q__ +/** + * Parameters for ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL operand. + */ +typedef struct ANeuralNetworksSymmPerChannelQuantParams { + /* The index of the channel dimension. */ + uint32_t channelDim; + /** The size of the scale array. Should be equal to dimension[channelDim] of the Operand. */ + uint32_t scaleCount; + /** The array of scaling values for each channel. Each value must be greater than zero. */ + const float* scales; +} ANeuralNetworksSymmPerChannelQuantParams; + +/** + * ANeuralNetworksBurst is an opaque type that can be used to reduce the latency + * of a rapid sequence of executions. It will likely cause overhead if only used + * for a single execution. + * + * ANeuralNetworksBurst serves as a context object for any number of inferences + * using {@link ANeuralNetworksExecution} objects. An ANeuralNetworksBurst + * object and the {@link ANeuralNetworksExecution} objects used with it must all + * have been created from the same {@link ANeuralNetworksCompilation} object. + * + * This object is also used as a hint to drivers, providing insight to the + * lifetime of a rapid sequence of executions. For example, a driver may choose + * to increase the clock frequency of its accelerator for the lifetime of a + * burst object. + * + * <p>To use:<ul> + * <li>Create a new burst object by calling the + * {@link ANeuralNetworksBurst_create} function.</li> + * <li>For each execution:</li><ul> + * <li>Create {@link ANeuralNetworksExecution} and configure its + * properties (see {@link ANeuralNetworksExecution} for details).</li> + * <li>Apply the model synchronously with + * {@link ANeuralNetworksExecution_burstCompute}, reusing the same + * {@link ANeuralNetworksBurst} with the new + * {@link ANeuralNetworksExecution}.</li> + * <li>Use and free the {@link ANeuralNetworksExecution}.</li></ul> + * <li>Destroy the burst with + * {@link ANeuralNetworksBurst_free}.</li></ul></p> + * + * Available since API level 29. + */ +typedef struct ANeuralNetworksBurst ANeuralNetworksBurst; +#endif // __ANDROID_API__ >= __ANDROID_API_Q__ + +/** + * ANeuralNetworksOperandType describes the type of an operand. + * + * This structure is used to describe both scalars and tensors. + * + * A tensor operand type with all dimensions specified is "fully + * specified". Whenever possible (i.e., whenever the dimensions are + * known at model construction time), a tensor operand type should be + * (but is not required to be) fully specified, in order to enable the + * best possible performance. + * + * If a tensor operand's type is not fully specified, the dimensions + * of the operand are deduced from the operand types and values of the + * operation for which that operand is an output. + * + * <p>In the following situations, a tensor operand type must be fully + * specified:<ul> + * <li>The operand has a constant value, set by + * {@link ANeuralNetworksModel_setOperandValue} (with a + * non-nullptr buffer) or + * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> + * <li>The operand is a model input (see + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}). A + * fully specified tensor operand type must either be provided + * to {@link ANeuralNetworksModel_addOperand}; or it must be + * provided to the corresponding + * {@link ANeuralNetworksExecution_setInput}, or + * {@link ANeuralNetworksExecution_setInputFromMemory}. + * EXCEPTION: If the input is optional and omitted + * (by passing nullptr for buffer to + * {@link ANeuralNetworksExecution_setInput}) then it need + * not have a fully specified tensor operand type.</li></ul> + * + * A tensor operand type of specified rank but some number of + * unspecified dimensions is represented by setting dimensionCount to + * the rank and each unspecified dimension to 0. + * + * Available since API level 27. + * + * Starting at API level 29, a tensor operand type of unspecified rank is + * represented by setting dimensionCount to 0 and dimensions to NULL (just as if + * it were a scalar operand type). + */ +typedef struct ANeuralNetworksOperandType { + /** + * The data type, e.g ANEURALNETWORKS_FLOAT32. + */ + int32_t type; + + /** + * The number of dimensions (rank). + * + * Must be 0 for scalars. + */ + uint32_t dimensionCount; + + /** + * The dimensions of the tensor. + * + * Must be nullptr for scalars. + */ + const uint32_t* dimensions; + + /** + * These two fields are only used for quantized tensors. + * They must be zero for all other types. + * The dequantized value of each entry is (value - zeroPoint) * scale. + */ + float scale; + int32_t zeroPoint; +} ANeuralNetworksOperandType; + +typedef int32_t ANeuralNetworksOperationType; + +/** + * ANeuralNetworksEvent is an opaque type that represents an event + * that will be signaled once an execution completes. + * + * Available since API level 27. + */ +typedef struct ANeuralNetworksEvent ANeuralNetworksEvent; + +#if __ANDROID_API__ >= __ANDROID_API_Q__ + +/** + * ANeuralNetworksDevice is an opaque type that represents a device. + * + * This type is used to query basic properties and supported operations of the corresponding + * device, and control which device(s) a model is to be run on. + * + * Available since API level 29. + */ +typedef struct ANeuralNetworksDevice ANeuralNetworksDevice; + +/** + * Get the number of available devices. + * + * @param numDevices Used to return the number of devices. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworks_getDeviceCount(uint32_t* numDevices) __INTRODUCED_IN(29); + +/** + * Get the representation of the specified device. + * + * @param devIndex The index of the specified device. Must be less than the + number of available devices. + * @param device The representation of the specified device. + * The same representation will always be returned for the specified + * device. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworks_getDevice(uint32_t devIndex, ANeuralNetworksDevice** device) + __INTRODUCED_IN(29); + +/** + * Get the name of the specified device. + * + * @param device The representation of the specified device. + * @param name The returned name of the specified device. The name will be in UTF-8 + * and will be null-terminated. It will be recognizable as a known device name + * rather than a cryptic string. For devices with feature level 29 and above, the + * format of the name is {VENDOR}-{DEVICE}. For devices with feature level 28 + * or lower, the format of the name is undefined. + * The name will remain valid for the duration of the application. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksDevice_getName(const ANeuralNetworksDevice* device, const char** name) + __INTRODUCED_IN(29); + +/** + * Get the type of a given device. + * + * The device type can be used to help application developers to distribute Machine Learning + * workloads and other workloads such as graphical rendering. + * E.g., for an app which renders AR scenes based on real time object detection results, + * the developer could choose an ACCELERATOR type device for ML workloads, and reserve GPU + * for graphical rendering. + * + * @param device The representation of the specified device. + * @param type The returned {@link DeviceTypeCode} of the specified device. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksDevice_getType(const ANeuralNetworksDevice* device, int32_t* type) + __INTRODUCED_IN(29); + +/** + * Get the version of the driver implementation of the specified device. + * + * It’s the responsibility of the driver implementor to insure that this version string + * uniquely distinguishes this implementation from all previous implementations. + * + * This version string must not be confused with the feature level which is solely defined + * by {@link ANeuralNetworksDevice_getFeatureLevel}. There is no implicit ordering of the versions. + * For example, it is not possible to filter all drivers older than a certain version. + * + * Application developers may use this version string to avoid or prefer specific driver + * implementations. For example, an application may want to do so because: + * - A specific version of the driver does not provide the required performance, + * perhaps because of a performance regression. + * - A specific version of the driver has a bug or returns results that don’t match + * the minimum precision requirement for the application. + * + * @param device The representation of the specified device. + * @param version The returned version string of the driver for the specified device. The + * string will be in UTF-8 and will be null-terminated. For devices with feature + * level 28 or lower, "UNKNOWN" will be returned. The version string will remain + * valid for the duration of the application. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksDevice_getVersion(const ANeuralNetworksDevice* device, const char** version) + __INTRODUCED_IN(29); + +/** + * Get the supported NNAPI version of the specified device. + * + * Each device has a supported feature level, which is the most advanced feature this driver + * implements. For example, if the driver implements the features introduced in Android P, + * but does not implement the features introduced after Android P, the value would be 28. + * Developers could decide whether or not the specified device should be used for a Model that + * has certain feature requirements. + * + * @param device The representation of the specified device. + * @param featureLevel The API level of the most advanced feature this driver implements. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksDevice_getFeatureLevel(const ANeuralNetworksDevice* device, + int64_t* featureLevel) __INTRODUCED_IN(29); + +/** + * Get the supported operations for a specified set of devices. If multiple devices + * are selected, the supported operation list is a union of supported operations of all + * selected devices. + * + * @param model The model to be queried. + * @param devices The set of devices. Must not contain duplicates. + * @param numDevices The number of devices in the set. + * @param supportedOps The boolean array to be filled. True means supported. The size of the + * boolean array must be at least as large as the number of operations + * in the model. The order of elements in the supportedOps array matches + * the order in which the corresponding operations were added to the model. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksModel_getSupportedOperationsForDevices( + const ANeuralNetworksModel* model, const ANeuralNetworksDevice* const* devices, + uint32_t numDevices, bool* supportedOps) __INTRODUCED_IN(29); + +/** + * Create a {@link ANeuralNetworksCompilation} to compile the given model for a specified set + * of devices. If more than one device is specified, the compilation will + * distribute the workload automatically across the devices. The model must be fully + * supported by the specified set of devices. This means that + * ANeuralNetworksModel_getSupportedOperationsForDevices() must have returned true for every + * operation for that model/devices pair. + * + * The user must handle all compilation and execution failures from the + * specified set of devices. This is in contrast to a use of {@link + * ANeuralNetworksCompilation_create}, where the runtime will attempt to recover + * from such failures. + * + * @param model The {@link ANeuralNetworksModel} to be compiled. + * @param devices The set of devices. Must not contain duplicates. + * @param numDevices The number of devices in the set. + * @param compilation The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the model is invalid. + * + * Available since API level 29. + */ +int ANeuralNetworksCompilation_createForDevices(ANeuralNetworksModel* model, + const ANeuralNetworksDevice* const* devices, + uint32_t numDevices, + ANeuralNetworksCompilation** compilation) + __INTRODUCED_IN(29); + +/** + * Sets the compilation caching signature and the cache directory. + * + * Provides optional caching information to the runtime for faster repeated + * compilation. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * @param compilation The compilation to be modified. + * @param cacheDir The cache directory for the runtime to store and retrieve caching + * data. It is recommended to use the code cache directory provided + * by the Android runtime. If not using the code cache directory, the + * user should choose a directory local to the application, and is + * responsible to managing the cache entries. + * @param token The token provided by the user to specify a model must be of length + * ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN. The user should ensure that + * the token is unique to a model within the application. The NNAPI + * runtime cannot detect token collisions; a collision will result in a + * failed execution or in a successful execution that produces incorrect + * output values. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + * + * Available since API level 29. + */ +int ANeuralNetworksCompilation_setCaching(ANeuralNetworksCompilation* compilation, + const char* cacheDir, const uint8_t* token) + __INTRODUCED_IN(29); + +/** + * Schedule synchronous evaluation of the execution. + * + * <p>Schedules synchronous evaluation of the execution. Returns once the + * execution has completed and the outputs are ready to be consumed. + * </p> + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * See {@link ANeuralNetworksExecution_startCompute} for asynchronous execution. + * Synchronous execution incurs lower overhead than asynchronous execution. + * + * Available since API level 29. + * + * @param execution The execution to be scheduled and executed. + * + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + * ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot + * be properly mapped. + */ +int ANeuralNetworksExecution_compute(ANeuralNetworksExecution* execution) __INTRODUCED_IN(29); + +/** + * Get the dimensional information of the specified output operand of the model of the + * {@link ANeuralNetworksExecution}. + * + * On asynchronous execution initiated by {@link ANeuralNetworksExecution_startCompute}, + * {@link ANeuralNetworksEvent_wait} must be called prior to this function to recuperate + * the resources used by the execution. + * + * @param execution The execution to be queried. + * @param index The index of the output argument we are querying. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param rank The rank of the output operand. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE + * if the target output is provided an insufficient buffer at execution time, + * ANEURALNETWORKS_BAD_DATA if the index is invalid. + * + * Available since API level 29. + */ +int ANeuralNetworksExecution_getOutputOperandRank(ANeuralNetworksExecution* execution, + int32_t index, uint32_t* rank) + __INTRODUCED_IN(29); + +/** + * Get the dimensional information of the specified output operand of the model of the + * {@link ANeuralNetworksExecution}. The target output operand cannot be a scalar. + * + * On asynchronous execution initiated by {@link ANeuralNetworksExecution_startCompute}, + * {@link ANeuralNetworksEvent_wait} must be called prior to this function to recuperate + * the resources used by the execution. + * + * @param execution The execution to be queried. + * @param index The index of the output argument we are querying. It is an index into the lists + * passed to {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param dimensions The dimension array to be filled. The size of the array must be exactly as + * large as the rank of the output operand to be queried in the model. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE + * if the target output is provided an insufficient buffer at execution time, + * ANEURALNETWORKS_BAD_DATA if the index is invalid or if the target is a scalar. + * + * Available since API level 29. + */ +int ANeuralNetworksExecution_getOutputOperandDimensions(ANeuralNetworksExecution* execution, + int32_t index, uint32_t* dimensions) + __INTRODUCED_IN(29); + +/** + * Create a {@link ANeuralNetworksBurst} to apply the given compilation. + * This only creates the burst object. Computation is only performed once + * {@link ANeuralNetworksExecution_burstCompute} is invoked with a valid + * {@link ANeuralNetworksExecution} and {@link ANeuralNetworksBurst}. + * + * <p>The provided compilation must outlive the burst object.</p> + * + * Available since API level 29. + * + * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. + * @param burst The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the compilation is invalid. + */ +int ANeuralNetworksBurst_create(ANeuralNetworksCompilation* compilation, + ANeuralNetworksBurst** burst) __INTRODUCED_IN(29); + +/** + * Destroys the burst object. + * + * Available since API level 29. + * + * @param burst The burst object to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ +void ANeuralNetworksBurst_free(ANeuralNetworksBurst* burst) __INTRODUCED_IN(29); + +/** + * Schedule synchronous evaluation of the execution on a burst object. + * + * <p>Schedules synchronous evaluation of the execution. Returns once the + * execution has completed and the outputs are ready to be consumed.</p> + * + * <p>There must be at most one {@link ANeuralNetworksExecution} processing at + * any given time for any given burst object. Any + * {@link ANeuralNetworksExecution} launched before the previous has finished + * will result in ANEURALNETWORKS_BAD_STATE.</p> + * + * Available since API level 29. + * + * @param burst The burst object to execute on. + * @param execution The execution to be scheduled and executed. The execution + * must be created from the same {@link + * ANeuralNetworksCompilation} as the burst object. + * + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + */ +int ANeuralNetworksExecution_burstCompute(ANeuralNetworksExecution* execution, + ANeuralNetworksBurst* burst) __INTRODUCED_IN(29); + +/** + * Creates a shared memory object from an AHardwareBuffer handle. + * + * If the shared memory is backed by an AHardwareBuffer of AHARDWAREBUFFER_FORMAT_BLOB + * format, it can be used the same way as shared memory created from a file handle. See + * {@link ANeuralNetworksMemory} for a description on how to use this shared memory. + * + * If the shared memory is backed by an AHardwareBuffer of a format other than + * AHARDWAREBUFFER_FORMAT_BLOB, it can only be used for Model inputs and outputs. + * When calling {@link ANeuralNetworksExecution_setInputFromMemory} or + * {@link ANeuralNetworksExecution_setOutputFromMemory} with the shared memory, both + * offset and length must be set to zero and the entire memory region will be + * associated with the specified input or output operand. There is no guarantee + * that an arbitrary AHardwareBuffer_Format and AHardwareBuffer_UsageFlags combination + * can be used by arbitrary devices. The execution will fail if selected set of devices + * cannot consume the buffer. + * + * Calling {@link ANeuralNetworksModel_setOperandValueFromMemory} with shared memory + * backed by an AHardwareBuffer of a format other than AHARDWAREBUFFER_FORMAT_BLOB is + * disallowed. + * + * TODO(miaowang): add documentation about intended usage with introspection API. + * + * Available since API level 29. + * + * @param ahwb The AHardwareBuffer handle. + * @param memory The memory object to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. + * + * @see AHardwareBuffer + */ +int ANeuralNetworksMemory_createFromAHardwareBuffer(const AHardwareBuffer* ahwb, + ANeuralNetworksMemory** memory) + __INTRODUCED_IN(29); + +/** + + * Specifies whether duration of the {@link ANeuralNetworksExecution} is to be + * measured. Evaluation of the execution must not have been scheduled. + * + * By default, duration is not measured. + * + * The {@link ANeuralNetworksExecution} must have been created with + * {@link ANeuralNetworksCompilation_createForDevices} with numDevices = 1. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 29. + * + * @param execution The execution to be modified. + * @param measure 'true' if duration is to be measured, 'false' if not. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksExecution_setMeasureTiming(ANeuralNetworksExecution* execution, bool measure) + __INTRODUCED_IN(29); + +/** + * Different duration measurements. + * + * Durations are measured in nanoseconds. + * + * Available since API level 29. + */ +typedef enum { + // Execution time on hardware (not driver, which runs on host processor). + ANEURALNETWORKS_DURATION_ON_HARDWARE = 0, + // Execution time in driver (including time on hardware). Excludes overhead + // such as that of the runtime itself and the IPC needed for the runtime to + // communicate with the driver. + ANEURALNETWORKS_DURATION_IN_DRIVER = 1, +} DurationCode; + +/** + * Get the time spent in the specified {@link ANeuralNetworksExecution}, in nanoseconds. + * The execution must have completed. + * + * Available since API level 29. + * + * @param execution The execution to be queried. + * @param durationCode The measurement to be queried, specified by {@link DurationCode}. + * @param duration The returned duration. If no measurement was requested by + * {@link ANeuralNetworksExecution_setMeasureTiming}, or for some other + * reason the duration is not available, UINT64_MAX will be returned. + * A particular device need not support any given measurement. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksExecution_getDuration(const ANeuralNetworksExecution* execution, + int32_t durationCode, uint64_t* duration) + __INTRODUCED_IN(29); + +#endif // __ANDROID_API__ >= __ANDROID_API_Q__ + +#if __ANDROID_API__ >= 27 + +/** + * Creates a shared memory object from a file descriptor. + * + * The shared memory is backed by a file descriptor via mmap. + * See {@link ANeuralNetworksMemory} for a description on how to use + * this shared memory. + * + * Available since API level 27. + * + * @param size The requested size in bytes. + * Must not be larger than the file size. + * @param prot The desired memory protection for the mapping. + * It is either PROT_NONE or the bitwise OR of one or + * more of the following flags: PROT_READ, PROT_WRITE. + * @param fd The requested file descriptor. + * The file descriptor has to be mmap-able. The file + * descriptor will be duplicated. + * @param offset The offset to the beginning of the file of the area to map. + * The offset has to be aligned to a page size. + * @param memory The memory object to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if the request completed normally. + */ +int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, + ANeuralNetworksMemory** memory) __INTRODUCED_IN(27); + +/** + * Delete a memory object. + * + * Destroys the object used by the run time to keep track of the memory. + * This will free the underlying actual memory if no other code has open + * handles to this memory. + * + * Available since API level 27. + * + * @param memory The memory object to be freed. + */ +void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) __INTRODUCED_IN(27); + +/** + * Create an empty {@link ANeuralNetworksModel}. + * + * <p>This only creates the object. Computation is performed once + * {@link ANeuralNetworksExecution_compute} or + * {@link ANeuralNetworksExecution_startCompute} is invoked. + * + * The model should be constructed with calls to + * {@link ANeuralNetworksModel_addOperation} and + * {@link ANeuralNetworksModel_addOperand} + * + * <p>{@link ANeuralNetworksModel_finish} should be called once the model + * has been fully constructed.</p> + * + * <p>{@link ANeuralNetworksModel_free} should be called once the model + * is no longer needed.</p> + * + * Available since API level 27. + * + * @param model The {@link ANeuralNetworksModel} to be created. + * Set to NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_create(ANeuralNetworksModel** model) __INTRODUCED_IN(27); + +/** + * Destroy a model. + * + * The model need not have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param model The model to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ +void ANeuralNetworksModel_free(ANeuralNetworksModel* model) __INTRODUCED_IN(27); + +/** + * Indicate that we have finished modifying a model. Required before + * calling {@link ANeuralNetworksCompilation_create} and + * {@link ANeuralNetworksCompilation_createForDevices}. + * + * An application is responsible to make sure that no other thread uses + * the model at the same time. + * + * This function must only be called once for a given model. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param model The model to be finished. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) __INTRODUCED_IN(27); + +/** + * Add an operand to a model. + * + * The order in which the operands are added is important. The first one added + * to a model will have the index value 0, the second 1, etc. These indexes are + * used as operand identifiers in + * {@link ANeuralNetworksModel_addOperation}, + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}, + * {@link ANeuralNetworksModel_setOperandValue}, + * {@link ANeuralNetworksModel_setOperandValueFromMemory}, + * {@link ANeuralNetworksExecution_setInput}, + * {@link ANeuralNetworksExecution_setInputFromMemory}, + * {@link ANeuralNetworksExecution_setOutput}, + * {@link ANeuralNetworksExecution_setOutputFromMemory} and + * {@link ANeuralNetworksExecution_setOperandValue}. + * + * <p>Every operand must be referenced in exactly one of the following + * ways:<ul> + * <li>It is identified as a model input with + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</li> + * <li>It is identified as a constant with + * {@link ANeuralNetworksModel_setOperandValue} or + * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> + * <li>It is identified as an output of exactly one operation with + * {@link ANeuralNetworksModel_addOperation}.</li></p> + * <p>An operand that is identified as a model input or as a constant + * must not also be identified as a model output with + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</p> + * + * To build a model that can accommodate inputs of various sizes, as + * you may want to do for a CNN, leave unspecified the dimensions that + * will vary at run time. If you do so, fully specify dimensions + * when calling {@link ANeuralNetworksExecution_setInput} or + * {@link ANeuralNetworksExecution_setInputFromMemory}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param model The model to be modified. + * @param type The {@link ANeuralNetworksOperandType} that describes the shape + * of the operand. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call to + * {@link ANeuralNetworksModel_addOperand}. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, + const ANeuralNetworksOperandType* type) __INTRODUCED_IN(27); + +/** + * Sets an operand to a constant value. + * + * Values of length smaller or equal to + * {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES} + * are immediately copied into the model. + * + * For values of length greater than {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES}, + * a pointer to the buffer is stored within the model. The application is responsible + * for not changing the content of this region until all executions using this model + * have completed. As the data may be copied during processing, modifying the data + * after this call yields undefined results. + * + * For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory} + * is likely to be more efficient. + * + * To indicate that an optional operand should be considered missing, + * pass nullptr for buffer and 0 for length. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param buffer A pointer to the data to use. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, + const void* buffer, size_t length) __INTRODUCED_IN(27); + +#if __ANDROID_API__ >= __ANDROID_API_Q__ + +/** + * Sets an operand's per channel quantization parameters. + * + * Sets parameters required by a tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}. + * This function must be called for every tensor of type + * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} before + * calling {@link ANeuralNetworksModel_finish}. + * + * Available since API level 29. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param channelQuant The per channel quantization parameters for the operand. + * No memory in this struct needs to outlive the call to + * this function. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_setOperandSymmPerChannelQuantParams( + ANeuralNetworksModel* model, int32_t index, + const ANeuralNetworksSymmPerChannelQuantParams* channelQuant) __INTRODUCED_IN(29); + +#endif // __ANDROID_API__ >= __ANDROID_API_Q__ + +/** + * Sets an operand to a value stored in a memory object. + * + * The content of the memory is not copied. A reference to that memory is stored + * inside the model. The application is responsible for not changing the content + * of the memory region until all executions using this model have completed. + * As the data may be copied during processing, modifying the data after this call + * yields undefined results. + * + * To indicate that an optional operand should be considered missing, + * use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer. + * + * Is disallowed to set an operand value with shared memory backed by an AHardwareBuffer + * of a format other than AHARDWAREBUFFER_FORMAT_BLOB. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on + * AHardwareBuffer usage. + * + * Available since API level 27. + * + * @param model The model to be modified. + * @param index The index of the model operand we're setting. + * @param buffer A pointer to the data to use. + * @param memory The memory containing the data. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, + const ANeuralNetworksMemory* memory, + size_t offset, size_t length) + __INTRODUCED_IN(27); + +/** + * Add an operation to a model. + * + * @param model The model to be modified. + * @param type The {@link ANeuralNetworksOperationType} of the operation. + * @param inputCount The number of entries in the inputs array. + * @param inputs An array of indexes identifying each operand. + * @param outputCount The number of entries in the outputs array. + * @param outputs An array of indexes identifying each operand. + * + * The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, + ANeuralNetworksOperationType type, uint32_t inputCount, + const uint32_t* inputs, uint32_t outputCount, + const uint32_t* outputs) __INTRODUCED_IN(27); + +/** + * Specifies which operands will be the model's inputs and + * outputs. Every model must have at least one input and one output. + * + * An operand cannot be used for both input and output. Doing so will + * return an error. + * + * @param model The model to be modified. + * @param inputCount The number of entries in the inputs array. + * @param inputs An array of indexes identifying the input operands. + * @param outputCount The number of entries in the outputs array. + * @param outputs An array of indexes identifying the output operands. + * + * The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * + * Available since API level 27. + * + */ +int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, + const uint32_t* inputs, uint32_t outputCount, + const uint32_t* outputs) __INTRODUCED_IN(27); + +#if __ANDROID_API__ >= 28 + +/** + * Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be + * calculated with range and/or precision as low as that of the IEEE 754 16-bit + * floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * must be calculated using at least the range and precision of the IEEE 754 + * 32-bit floating-point format. + * + * @param model The model to be modified. + * @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be + * calculated with range and/or precision as low as that of the + * IEEE 754 16-bit floating point format. 'false' indicates + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using + * at least the range and precision of the IEEE 754 32-bit floating + * point format. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error. + * + * Available since API level 28. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + */ +int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow) + __INTRODUCED_IN(28); + +#endif // __ANDROID_API__ >= 28 + +/** + * Create a {@link ANeuralNetworksCompilation} to compile the given model. + * + * <p>This only creates the object. Compilation is only performed once + * {@link ANeuralNetworksCompilation_finish} is invoked.</p> + * + * <p>{@link ANeuralNetworksCompilation_finish} should be called once + * all desired properties have been set on the compilation.</p> + * + * <p>{@link ANeuralNetworksModel_free} should be called once the compilation + * is no longer needed.</p> + * + * <p>The provided model must outlive the compilation.</p> + * + * The model must already have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param model The {@link ANeuralNetworksModel} to be compiled. + * @param compilation The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the model is invalid. + */ +int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, + ANeuralNetworksCompilation** compilation) __INTRODUCED_IN(27); + +/** + * Destroy a compilation. + * + * The compilation need not have been finished by a call to + * {@link ANeuralNetworksModel_finish}. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param compilation The compilation to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ +void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation) __INTRODUCED_IN(27); + +/** + * Sets the execution preference. + * + * <p>Provides guidance to the runtime when trade-offs are possible.</p> + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param compilation The compilation to be modified. + * @param preference Either {@link PREFER_LOW_POWER}, + * {@link PREFER_SINGLE_FAST_ANSWER}, or + * {@link PREFER_SUSTAINED_SPEED}. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, + int32_t preference) __INTRODUCED_IN(27); + +/** + * Indicate that we have finished modifying a compilation. Required before + * calling {@link ANeuralNetworksExecution_create}. + * + * An application is responsible to make sure that no other thread uses + * the compilation at the same time. + * + * This function must only be called once for a given compilation. + * + * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param compilation The compilation to be finished. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) __INTRODUCED_IN(27); + +/** + * Create a {@link ANeuralNetworksExecution} to apply the given compilation. + * This only creates the object. Computation is only performed once + * {@link ANeuralNetworksExecution_compute} or + * {@link ANeuralNetworksExecution_startCompute} is invoked. + * + * <p>The provided compilation must outlive the execution.</p> + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. + * @param execution The newly created object or NULL if unsuccessful. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA + * if the compilation is invalid. + */ +int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, + ANeuralNetworksExecution** execution) __INTRODUCED_IN(27); + +/** + * Destroy an execution. + * + * <p>If called on an execution for which + * {@link ANeuralNetworksExecution_startCompute} has been called, the + * function will return immediately but will mark the execution to be deleted + * once the computation completes. The related {@link ANeuralNetworksEvent} + * will be signaled and the {@link ANeuralNetworksEvent_wait} will return + * ANEURALNETWORKS_ERROR_DELETED. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param execution The execution to be destroyed. Passing NULL is acceptable and + * results in no operation. + */ +void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) __INTRODUCED_IN(27); + +/** + * Associate a user buffer with an input of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. + * + * <p>The provided buffer must outlive the execution.</p> + * + * If the input is optional, you can indicate that it is omitted by + * passing nullptr for buffer and 0 for length. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param execution The execution to be modified. + * @param index The index of the input argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with + * {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. Unless the input is omitted, this should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setInput}. + * @param buffer The buffer containing the data. + * @param length The length in bytes of the buffer. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the input. + */ +int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, const void* buffer, + size_t length) __INTRODUCED_IN(27); + +/** + * Associate part of a memory object with an input of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. + * + * <p>The provided memory must outlive the execution.</p> + * + * If the input is optional, you can indicate that it is omitted by + * using {@link ANeuralNetworksExecution_setInput} instead, passing nullptr for + * buffer and 0 for length. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on + * AHardwareBuffer usage. + * + * Available since API level 27. + * + * @param execution The execution to be modified. + * @param index The index of the input argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. This should be used to specify the dimensions + * that were left unspecified when the operand was added + * to the model. All other properties of the type must be + * the same as specified in the model. If the type is the + * same as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setInputFromMemory}. + * @param memory The memory containing the data. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The size in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the input. + */ +int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length) __INTRODUCED_IN(27); + +/** + * Associate a user buffer with an output of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. + * + * If the output is optional, you can indicate that it is omitted by + * passing nullptr for buffer and 0 for length. + * + * <p>The provided buffer must outlive the execution.</p> + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + * + * @param execution The execution to be modified. + * @param index The index of the output argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the + * operand. Unless the output is omitted, this should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setOutput}. + * Since API level 29, the output operand can have unspecified + * dimensions or rank to be deduced dynamically during the execution. + * However, the user must provide a large enough buffer. The user + * can retrieve the output dimensional information after the execution + * by {@link ANeuralNetworksExecution_getOutputOperandRank} and + * {@link ANeuralNetworksExecution_getOutputOperandDimensions}. + * @param buffer The buffer where the data is to be written. + * @param length The length in bytes of the buffer. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the output. + */ +int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, void* buffer, + size_t length) __INTRODUCED_IN(27); + +/** + * Associate part of a memory object with an output of the model of the + * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have + * been scheduled. + * + * If the output is optional, you can indicate that it is omitted by + * using {@link ANeuralNetworksExecution_setOutput} instead, passing nullptr for + * buffer and 0 for length. + * + * <p>The provided memory must outlive the execution.</p> + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * See {@link ANeuralNetworksMemory_createFromAHardwarBuffer} for information on + * AHardwareBuffer usage. + * + * Available since API level 27. + * + * @param execution The execution to be modified. + * @param index The index of the output argument we are setting. It is + * an index into the lists passed to + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not + * the index associated with {@link ANeuralNetworksModel_addOperand}. + * @param type The {@link ANeuralNetworksOperandType} of the operand. This should be + * used to specify the dimensions that were left + * unspecified when the operand was added to the + * model. All other properties of the type must be the + * same as specified in the model. If the type is the same + * as specified when the model was built, NULL can be + * passed. Neither the {@link ANeuralNetworksOperandType} + * nor the dimensions it points to need to outlive the call + * to {@link ANeuralNetworksExecution_setOutputFromMemory}. + * Since API level 29, the output operand can have unspecified + * dimensions or rank to be deduced dynamically during the execution. + * However, the user must provide a large enough memory. The user + * can retrieve the output dimensional information after the execution + * by {@link ANeuralNetworksExecution_getOutputOperandRank} and + * {@link ANeuralNetworksExecution_getOutputOperandDimensions}. + * @param memory The memory where the data is to be stored. + * @param offset This specifies the location of the data within the memory. + * The offset is in bytes from the start of memory. + * @param length The length in bytes of the data value. + * + * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the + * name is not recognized or the buffer is too small for the output. + */ +int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, + const ANeuralNetworksOperandType* type, + const ANeuralNetworksMemory* memory, size_t offset, + size_t length) __INTRODUCED_IN(27); + +/** + * Schedule asynchronous evaluation of the execution. + * + * <p>Schedules asynchronous evaluation of the execution. Once the model has + * been applied and the outputs are ready to be consumed, the returned event + * will be signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that + * event. + * </p> + * + * ANeuralNetworksEvent_wait must be called to recuperate the resources used + * by the execution. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * See {@link ANeuralNetworksExecution_compute} for synchronous execution. + * Synchronous execution incurs lower overhead than asynchronous execution. + * + * Available since API level 27. + * + * @param execution The execution to be scheduled and executed. + * @param event The event that will be signaled on completion. event is set to + * NULL if there's an error. + * + * @return ANEURALNETWORKS_NO_ERROR if successful. + */ +int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, + ANeuralNetworksEvent** event) __INTRODUCED_IN(27); + +/** + * Waits until the execution completes. + * + * More than one thread can wait on an event. When the execution completes, + * all threads will be released. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + * + * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. + * ANEURALNETWORKS_UNMAPPABLE if the execution input or output memory cannot + * be properly mapped. + */ +int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) __INTRODUCED_IN(27); + +/** + * Destroys the event. + * + * See {@link ANeuralNetworksExecution} for information on multithreaded usage. + * + * Available since API level 27. + */ +void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) __INTRODUCED_IN(27); + +#endif // __ANDROID_API__ >= 27 + +__END_DECLS + +#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H + +/** @} */ |