/*
+ * 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");
* @file NeuralNetworks.h
*/
-#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
-#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+#ifndef ANDROID_FRAMEWORKS_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+#define ANDROID_FRAMEWORKS_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/******************************************************************
*
* - 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>
* 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,
+ ANEURALNETWORKS_FLOAT32 = 0,
/** A signed 32 bit integer scalar value. */
- ANEURALNETWORKS_INT32 = 1,
+ ANEURALNETWORKS_INT32 = 1,
/** An unsigned 32 bit integer scalar value. */
- ANEURALNETWORKS_UINT32 = 2,
-
+ ANEURALNETWORKS_UINT32 = 2,
/** A tensor of 32 bit floating point values. */
- ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
+ ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
- ANEURALNETWORKS_TENSOR_INT32 = 4,
+ ANEURALNETWORKS_TENSOR_INT32 = 4,
/**
- * A tensor of 8 bit integers that represent real numbers.
+ * 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:
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
- * real_value = (integer_value - zeroPoint) * scale.
+ * 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.
*
* 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}
*
* * 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,
*
* The values in the output tensor are computed as:
*
- * output[batch, row, col, channel] =
- * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+ * 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" (i.e., Num_samples, Height, Width,
- * and Channels) data layout.
+ * 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.
+ * 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
* * 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.
+ * 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.
* * 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].
+ * [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,
* 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}
+ * * {@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]. For inputs of
- * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all input tensors
- * must have the same scale and zeroPoint.
+ * [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,
*
* The values in the output tensor are computed as:
*
- * output[batch, row, col, channel] =
- * sum_{i, j} (
- * input[batch, row + i, col + j, k] *
- * filter[channel, row + i, col + j, k] +
- * bias[channel]
- * )
+ * 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}:
- * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
- * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandCode} configurations:
+ * * 32 bit floating point:
+ * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * 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.
+ * 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.
- * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias
- * should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
- * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
+ * filter. For tensor of type
+ * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
+ * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
+ * type. For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
+ * 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 == input_scale * filter_scale.
+ * 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
* * 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.
+ * 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.
+ * filter. For tensor of type
+ * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@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.
+ * 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.
+ * 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]. For output tensor of
- * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
+ * [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,
* 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}:
- * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
- * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ * Supported tensor {@link OperandCode} configurations:
+ * * 32 bit floating point:
+ * * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
*
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * * 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.
*
* * 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.
+ * specifying the filter. For tensor of type
+ * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 3.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@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.
+ * 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
* * 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],
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
- * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
- * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor
- * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be
- * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
- * bias_scale == input_scale * filter_scale.
+ * 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.
* * 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]. For output tensor of
- * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition
- * must be satisfied: output_scale > input_scale * filter_scale.
+ * [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,
* 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" data layout.
+ * 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],
* * 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,
*
* output = (input - zeroPoint) * scale.
*
- * Supported tensor {@link OperandCode}:
+ * 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 of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor. Since API level 29, this tensor may be zero-sized.
*
* Outputs:
- * * 0: The output tensor of same shape as input0, but with
- * {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * * 0: A tensor with the same shape as input0.
+ *
+ * Available since API level 27.
*/
ANEURALNETWORKS_DEQUANTIZE = 6,
* 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.
* * 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
* Outputs:
* * 0: The output tensor, of the same {@link OperandCode} and dimensions as
* the input tensor.
+ *
+ * Available since API level 27.
*/
ANEURALNETWORKS_FLOOR = 8,
* 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}
*
* [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".
+ * 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.
* invoke on the result.
*
* Outputs:
- * * 0: The output tensor, of shape [batch_size, num_units]. For output
- * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
- * condition must be satisfied:
- * output_scale > input_scale * filter_scale.
+ * * 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,
* 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 ].
*
* 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,
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
- * For input tensor with more dimensions, independently normalizes each 1-D
- * slice along dimension dim.
+ * 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: 4, with "NHWC" data layout (i.e., Num_samples,
- * Height, Width, and Channels).
+ * 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].
+ * * 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: The output 4-D tensor, of the same shape as input
- * [batches, height, width, depth].
+ * * 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,
*
* The values in the output tensor are computed as:
*
- * output[batch, row, col, channel] =
- * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) /
+ * 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" data layout.
+ * 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.
+ * 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
* * 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.
+ * 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.
* * 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,
* 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: 4, with "NHWC" data layout.
+ * 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: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must
- * not be zero.
- * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale
- * factor, alpha.
- * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the exponent,
- * beta.
+ * * 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,
* 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.
+ * * 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 seeds per hash functions.
- * Tensor[0].Dim[1] <= 32 in sparse case.
+ * 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]
+ * If not set, each input element is considered to have the same weight
+ * of 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
- * Sparse: Value LSHProjectionType_SPARSE(=1).
+ * 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.
*
- * Dense: Value LSHProjectionType_DENSE(=2).
+ * 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.
+ * * 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.
+ * 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,
* 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$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
- * bias (\f$b_i\f$) either all have values, or none of them have values
- * (i.e., all set to null). If they have no values, coupling of input and
- * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
- * is calculated using the following equation instead.
+ * (\f$W_{hi}\f$) 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}
- * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
- * (\f$W_{co}\f$) either both have values or neither of them have values.
- * If they have values, the peephole optimization is used. Additionally,
- * if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
- * required to have values for peephole optimization.
+ * 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:
*
* 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of cell units.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, output_size], where “output_size” corresponds to either
- * the number of cell units (i.e., “num_units”), or the second
- * dimension of the “projection_weights”, if defined.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, output_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [output_size, num_units].
+ * A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
- * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [output_size].
+ * A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units * 4] with CIFG, or
- * [batch_size, num_units * 3] without CIFG.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, output_size].
+ * A 2-D tensor of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, output_size]. This is effectively the same as the
- * current “output state (out)” value.
+ * 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,
*
* The values in the output tensor are computed as:
*
- * output[batch, row, col, channel] =
- * max_{i, j} (input[batch, row + i, col + j, channel])
+ * 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" data layout.
+ * 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.
+ * 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
* * 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.
+ * 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.
* * 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,
* 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}
*
* 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,
* 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.
+ * * 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,
* 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.
+ * * 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.
+ * * 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,
* 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.
+ * * 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,
* 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}
*
* 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,
* 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" data layout.
+ * 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:
+ * 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.
+ * the input. Since API level 29, zero batches is supported for this
+ * tensor.
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
- * height of the output tensor.
- * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
* width of the output tensor.
+ * * 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,
* 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, num_units], with columns corresponding to the weights
- * from each unit.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units].
+ * A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * 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
*
* Outputs:
* * 0: hidden state (out).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * A 2-D tensor of shape [batch_size, num_units].
*
* * 1: output.
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units]. This is effectively the same as the
- * current state value.
+ * 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,
* 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: 2 or 4.
+ * 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.
- * * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive
- * scaling factor for the exponent, beta.
+ * * 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,
* 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" data layout.
+ * 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],
* * 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,
* 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, input_size], where “batch_size” corresponds to the
- * batching dimension, and “input_size” is the size of the input.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, input_size], where “num_units” corresponds to the
- * number of units.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [num_units, memory_size], where “memory_size” corresponds to the
- * fixed-size of the memory.
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32},
- * of shape [num_units].
+ * An optional 1-D tensor of shape [num_units].
* * 4: state (in).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, (memory_size - 1) * num_units * rank].
+ * 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.
*
* Outputs:
* * 0: state (out).
- * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
- * [batch_size, num_units].
+ * 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,
* 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.
+ * * 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,
- // TODO: make the description easier to understand.
+ // Operations below are available since API level 28.
+
/**
* BatchToSpace for N-dimensional tensors.
*
* 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
+ * 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,
* 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
*
* Outputs:
* * 0: A tensor of the same {@link OperandCode} as input0.
+ *
+ * Available since API level 28.
*/
ANEURALNETWORKS_DIV = 30,
* in axis. If keep_dims is true, the reduced dimensions are retained with
* length 1.
*
- * If dimensions to reduce have no entries, all dimensions are reduced, and
- * a tensor with a single element is returned.
- *
* Supported tensor {@link OperandCode}:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A tensor, specifying the input.
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
- * to reduce. If None (the default), reduces all dimensions. Must be in
- * the range [-rank(input_tensor), rank(input_tensor)).
+ * 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.
+ * 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}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API
+ * level 29, see the output section)
*
* Supported tensor rank: up to 4
*
* 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.
*
* 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}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (full support since API
+ * level 29, see the output section)
*
- * Supported tensor rank: 4
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
* must be >= 1.
* * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings
* for each spatial dimension of the input tensor. All values must be
- * >= 0. The shape of the tensor must be {rank(input0), 2}.
+ * >= 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,
* 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}
*
* * 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,
* reverse slice.
*
* Supported tensor {@link OperandCode}:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since API level 29)
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be sliced.
- * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of
- * the dimensions of the input tensor to be sliced. The length must be
- * of rank(input0).
- * * 2: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of
- * the dimensions of the input tensor to be sliced. The length must be
- * of rank(input0).
- * * 3: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of
- * the dimensions of the input tensor to be sliced. The length must be
- * of rank(input0).
- * * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit
+ * * 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: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of
+ * * 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: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32
- * mask. If the ith bit of shrink_axis_mask is set, it implies that the
- * ith specification shrinks the dimensionality by 1. A slice of size 1
- * starting from begin[i] in the dimension must be preserved.
+ * * 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.
+ * * 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,
* 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
*
*
* 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,
* 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}
*
*
* 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,
-} OperationCode;
-/**
- * Fused activation function types.
- *
- */
-typedef enum {
- /** NO fused activation function. */
- ANEURALNETWORKS_FUSED_NONE = 0,
- /** Fused ReLU activation function. */
- ANEURALNETWORKS_FUSED_RELU = 1,
- /** Fused ReLU1 activation function. */
- ANEURALNETWORKS_FUSED_RELU1 = 2,
- /** Fused ReLU6 activation function. */
- ANEURALNETWORKS_FUSED_RELU6 = 3,
-} FuseCode;
+ // Operations below are available since API level 29.
-/**
- * Implicit padding algorithms.
- *
- */
-typedef enum {
/**
- * SAME padding.
- * Padding on both ends are the "same":
- * padding_to_beginning = total_padding / 2
- * padding_to_end = (total_padding + 1)/2.
- * i.e., for even number of padding, padding to both ends are exactly
- * the same; for odd number of padding, padding to the ending is bigger
- * than the padding to the beginning by 1.
+ * Computes the absolute value of a tensor, element-wise.
*
- * total_padding is a function of input, stride and filter size.
- * It could be computed as follows:
- * out_size = (input + stride - 1) / stride;
- * needed_input = (out_size - 1) * stride + filter_size
- * total_padding = max(0, needed_input - output_size)
- * The computation is the same for the horizontal and vertical directions.
+ * 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_PADDING_SAME = 1,
+ ANEURALNETWORKS_ABS = 38,
/**
- * 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;
-
+ * 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 (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
+ * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias 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 (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
+ * {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias 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, dilation and filter size.
+ * It could be computed as follows:
+ * out_size = (input + stride - 1) / stride
+ * effective_filter_size = (filter_size - 1) * dilation + 1
+ * needed_input = (out_size - 1) * stride + effective_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 {
/**
} 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_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128
-};
+enum { ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN = 32 };
/**
* ANeuralNetworksMemory is an opaque type that represents memory.
* By using shared memory, a program can efficiently communicate to the
* runtime and drivers the tensors that define a model. See
* {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
- * should typically create one shared memory object that contains every tensor
- * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
- * used to create shared memory from a file handle.
+ * 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.
+ *
+ * It is the application's responsibility to ensure that there are no uses of
+ * the memory after calling {@link ANeuralNetworksMemory_free}. This includes
+ * any model which references this memory because of a call to
+ * {@link ANeuralNetworksModel_setOperandValueFromMemory}, any compilation
+ * created using such a model, any execution object or burst object created
+ * using such a compilation, or any execution which references this memory
+ * because of a call to {@link ANeuralNetworksExecution_setInputFromMemory} or
+ * {@link ANeuralNetworksExecution_setOutputFromMemory}.
+ *
+ * Available since API level 27.
*/
typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;
* 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>
+ * <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, execution object or burst object created using
+ * the model.</p>
+ *
+ * Available since API level 27.
*/
typedef struct ANeuralNetworksModel ANeuralNetworksModel;
*
* <p>To use:<ul>
* <li>Create a new compilation instance by calling the
- * {@link ANeuralNetworksCompilation_create} function.</li>
+ * {@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}.</li>
+ * 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>
*
*
* <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>
+ * This includes any execution object or burst object created using the compilation.</p>
+ *
+ * Available since API level 27.
*/
typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation;
* <li>Associate output buffers or memory regions to the model outputs with
* {@link ANeuralNetworksExecution_setOutput} or
* {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
- * <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li>
- * <li>Wait for the execution to complete with {@link
- * ANeuralNetworksEvent_wait}.</li>
+ * <li>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>
*
* memory region, or with an operand value in a memory object
* ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p>
*
- * <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute}
- * has been called on it.</p>
+ * <p>An execution cannot be modified once
+ * {@link ANeuralNetworksExecution_burstCompute},
+ * {@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_startCompute} only once. Create new executions
- * to do new evaluations of the model.</p>
+ * {@link ANeuralNetworksExecution_burstCompute},
+ * {@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 the execution
+ * either has never been scheduled or has completed (i.e., that
+ * {@link ANeuralNetworksExecution_burstCompute},
+ * {@link ANeuralNetworksExecution_compute}, or
+ * {@link ANeuralNetworksEvent_wait} has returned) before calling
+ * {@link ANeuralNetworksExecution_free}.</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} or
+ * {@link ANeuralNetworksExecution_burstCompute} (which are synchronous) in
+ * different threads, or by means of
+ * {@link ANeuralNetworksExecution_startCompute} (which is asynchronous).
+ * (Concurrent uses of {@link ANeuralNetworksExecution_burstCompute} must be on
+ * different burst objects.) 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 must have a specified rank (number of
- * dimensions) but may have any of its dimensions unspecified.
+ * 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
* {@link ANeuralNetworksModel_setOperandValue} (with a
* non-nullptr buffer) or
* {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li>
- * <li>The operand is a model input or model output (see
+ * <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},
- * {@link ANeuralNetworksExecution_setInputFromMemory},
- * {@link ANeuralNetworksExecution_setOutput}, or
- * {@link ANeuralNetworksModel_setOperandValueFromMemory}.
- * EXCEPTION: If the input or output is optional and omitted
+ * {@link ANeuralNetworksExecution_setInput}, or
+ * {@link ANeuralNetworksExecution_setInputFromMemory}.
+ * EXCEPTION: If the input is optional and omitted
* (by passing nullptr for buffer to
- * {@link ANeuralNetworksExecution_setInput} or
- * {@link ANeuralNetworksExecution_setOutput}) then it need
+ * {@link ANeuralNetworksExecution_setInput}) then it need
* not have a fully specified tensor operand type.</li></ul>
*
- * A tensor operand type with some number of unspecified dimensions is
- * represented by setting each unspecified dimension to 0.
+ * 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_INT8. */
+ /**
+ * The data type, e.g ANEURALNETWORKS_FLOAT32.
+ */
int32_t type;
- /** The number of dimensions (rank). It should be 0 for scalars. */
+
+ /**
+ * The number of dimensions (rank).
+ *
+ * Must be 0 for scalars.
+ */
uint32_t dimensionCount;
- /** The dimensions of the tensor. It should be nullptr for scalars. */
+
+ /**
+ * The dimensions of the tensor.
+ *
+ * Must be nullptr for scalars.
+ */
const uint32_t* dimensions;
- /** These two fields are only used for quantized tensors.
- * They should be zero for scalars and non-fixed point tensors.
+
+ /**
+ * 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;
/**
* 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 for 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.
+ *
+ * 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.
* 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.
* @return ANEURALNETWORKS_NO_ERROR if the request completed normally.
*/
int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset,
- ANeuralNetworksMemory** memory);
+ ANeuralNetworksMemory** memory) __INTRODUCED_IN(27);
/**
* Delete a memory object.
* This will free the underlying actual memory if no other code has open
* handles to this memory.
*
- * @param memory The memory object to be freed.
+ * Available since API level 27.
+ *
+ * @param memory The memory object to be freed. Passing NULL is acceptable and
+ * results in no operation.
*/
-void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory);
+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
* <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);
+int ANeuralNetworksModel_create(ANeuralNetworksModel** model) __INTRODUCED_IN(27);
/**
* Destroy a model.
*
* 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);
+void ANeuralNetworksModel_free(ANeuralNetworksModel* model) __INTRODUCED_IN(27);
/**
* Indicate that we have finished modifying a model. Required before
- * calling {@link ANeuralNetworksCompilation_create}.
+ * 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.
+ * An application must ensure 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);
+int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) __INTRODUCED_IN(27);
/**
* Add an operand to a model.
*
* 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}
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model,
- const ANeuralNetworksOperandType* type);
+ const ANeuralNetworksOperandType* type) __INTRODUCED_IN(27);
/**
* Sets an operand to a constant value.
* {@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 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 must not change 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. The provided buffer must outlive
+ * this model.
*
* For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory}
* is likely to be more efficient.
*
* 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.
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index,
- const void* buffer, size_t length);
+ 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.
+ * inside the model. The application must not change 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.
+ *
+ * <p>The provided memory must outlive this model.</p>
*
* 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.
*/
int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index,
const ANeuralNetworksMemory* memory,
- size_t offset, size_t length);
+ size_t offset, size_t length)
+ __INTRODUCED_IN(27);
/**
* Add an operation to a model.
*
* 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);
+ const uint32_t* outputs) __INTRODUCED_IN(27);
/**
* Specifies which operands will be the model's inputs and
*
* 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);
+ const uint32_t* outputs) __INTRODUCED_IN(27);
+
+#if __ANDROID_API__ >= 28
/**
* Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be
* 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);
+int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow)
+ __INTRODUCED_IN(28);
+
+#endif // __ANDROID_API__ >= 28
/**
* Create a {@link ANeuralNetworksCompilation} to compile the given model.
*
* 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.
*
* if the model is invalid.
*/
int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model,
- ANeuralNetworksCompilation** compilation);
+ ANeuralNetworksCompilation** compilation) __INTRODUCED_IN(27);
/**
* Destroy a compilation.
*
* The compilation need not have been finished by a call to
- * {@link ANeuralNetworksModel_finish}.
+ * {@link ANeuralNetworksCompilation_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);
+void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation) __INTRODUCED_IN(27);
/**
* Sets the execution preference.
*
* 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
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation,
- int32_t preference);
+ 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.
+ * An application must ensure 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);
+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.
*
* if the compilation is invalid.
*/
int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation,
- ANeuralNetworksExecution** execution);
+ 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.
+ * <p>The execution need not have been scheduled by a call to
+ * {@link ANeuralNetworksExecution_burstCompute},
+ * {@link ANeuralNetworksExecution_compute}, or
+ * {@link ANeuralNetworksExecution_startCompute}; but if it has been scheduled,
+ * then the application must not call {@link ANeuralNetworksExecution_free}
+ * until the execution has completed (i.e.,
+ * {@link ANeuralNetworksExecution_burstCompute},
+ * {@link ANeuralNetworksExecution_compute}, or
+ * {@link ANeuralNetworksEvent_wait} has returned).
*
* 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);
+void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) __INTRODUCED_IN(27);
/**
* Associate a user buffer with an input of the model of the
- * {@link ANeuralNetworksExecution}.
+ * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have
+ * been scheduled. Once evaluation of the execution has been scheduled, the
+ * application must not change the content of the buffer until the execution has
+ * completed. Evaluation of the execution will not change the content of the
+ * buffer.
*
* <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 input argument we are setting. It is
* an index into the lists passed to
*/
int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type, const void* buffer,
- size_t length);
+ size_t length) __INTRODUCED_IN(27);
/**
- * Associate part of a memory object with an input of the model of the
- * {@link ANeuralNetworksExecution}.
+ * Associate a region of a memory object with an input of the model of the
+ * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have
+ * been scheduled. Once evaluation of the execution has been scheduled, the
+ * application must not change the content of the region until the execution has
+ * completed. Evaluation of the execution will not change the content of the
+ * region.
*
* <p>The provided memory must outlive the execution.</p>
*
* If the input is optional, you can indicate that it is omitted by
- * using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer
- * and 0 for length.
+ * 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
int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type,
const ANeuralNetworksMemory* memory, size_t offset,
- size_t length);
+ size_t length) __INTRODUCED_IN(27);
/**
* Associate a user buffer with an output of the model of the
- * {@link ANeuralNetworksExecution}.
+ * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have
+ * been scheduled. Once evaluation of the execution has been scheduled, the
+ * application must not change the content of the buffer until the execution has
+ * completed.
*
* If the output 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 output argument we are setting. It is
* an index into the lists passed to
* 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.
*
*/
int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type, void* buffer,
- size_t length);
+ size_t length) __INTRODUCED_IN(27);
/**
- * Associate part of a memory object with an output of the model of the
- * {@link ANeuralNetworksExecution}.
+ * Associate a region of a memory object with an output of the model of the
+ * {@link ANeuralNetworksExecution}. Evaluation of the execution must not have
+ * been scheduled. Once evaluation of the execution has been scheduled, the
+ * application must not change the content of the region until the execution has
+ * completed.
*
* If the output is optional, you can indicate that it is omitted by
- * using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer
- * and 0 for length.
+ * 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
* 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.
int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
const ANeuralNetworksOperandType* type,
const ANeuralNetworksMemory* memory, size_t offset,
- size_t length);
+ size_t length) __INTRODUCED_IN(27);
/**
- * Schedule evaluation of the execution.
+ * Schedule asynchronous evaluation of the execution.
*
- * <p>Schedules evaluation of the execution. Once the model has been
- * applied and the outputs are ready to be consumed, the returned event will be
- * signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event.
+ * <p>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>
*
- * Multiple executions can be scheduled and evaluated concurrently. The
- * runtime makes no guarantee on the ordering of completion of
- * executions. If it's important to the application, the application
- * should enforce the ordering by using
- * {@link ANeuralNetworksEvent_wait}.
- *
* ANeuralNetworksEvent_wait must be called to recuperate the resources used
* by the execution.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
*
+ * 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);
+ ANeuralNetworksEvent** event) __INTRODUCED_IN(27);
/**
* Waits until the execution completes.
*
* 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);
+int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) __INTRODUCED_IN(27);
/**
* Destroys the event.
*
* See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * Available since API level 27.
+ *
+ * @param event The event object to be destroyed. Passing NULL is acceptable and
+ * results in no operation.
*/
-void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event);
+void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) __INTRODUCED_IN(27);
+
+#endif // __ANDROID_API__ >= 27
__END_DECLS
-#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+#endif // ANDROID_FRAMEWORKS_ML_NN_RUNTIME_NEURAL_NETWORKS_H
/** @} */
projects / platform / core / ml / nnfw.git / commitdiff