--- /dev/null
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @addtogroup NeuralNetworks
+ * @{
+ */
+
+/**
+ * @file NeuralNetworks.h
+ */
+
+#ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+#define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+
+/******************************************************************
+ *
+ * IMPORTANT NOTICE:
+ *
+ * This file is part of Android's set of stable system headers
+ * exposed by the Android NDK (Native Development Kit).
+ *
+ * Third-party source AND binary code relies on the definitions
+ * here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES.
+ *
+ * - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES)
+ * - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS
+ * - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY
+ * - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES
+ */
+
+#if __ANDROID_API__ >= __ANDROID_API_O_MR1__
+
+#include <stddef.h>
+#include <stdint.h>
+#include <sys/cdefs.h>
+
+__BEGIN_DECLS
+
+/**
+ * Operand types.
+ *
+ * The type of operands that can be added to a model.
+ *
+ * Although we define many types, most operators accept just a few
+ * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32},
+ * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
+ * and {@link ANEURALNETWORKS_INT32}.
+ */
+typedef enum {
+ /** The following entries are used to declare scalars. */
+
+ /** A 32 bit floating point scalar value. */
+ ANEURALNETWORKS_FLOAT32 = 0,
+ /** A signed 32 bit integer scalar value. */
+ ANEURALNETWORKS_INT32 = 1,
+ /** An unsigned 32 bit integer scalar value. */
+ ANEURALNETWORKS_UINT32 = 2,
+
+ /** The following entries are used to declare tensors. */
+
+ /** A tensor of 32 bit floating point values. */
+ ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
+ /** A tensor of 32 bit integer values. */
+ ANEURALNETWORKS_TENSOR_INT32 = 4,
+ /** A tensor of 8 bit integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert
+ * the 8 bit integer to the real value and vice versa. These two numbers are:
+ * - scale: a 32 bit non-negative floating point value.
+ * - zeroPoint: an 32 bit integer, in range [0, 255].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
+} OperandCode;
+
+/**
+ * Operation types.
+ *
+ * The type of operations that can be added to a model.
+ */
+typedef enum {
+ /** Adds two tensors, element-wise.
+ *
+ * Takes two input tensors of identical type and compatible dimensions. The output
+ * is the sum of both input tensors, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the input operands.
+ * It starts with the trailing dimensions, and works its way forward.
+ *
+ * Example:
+ *
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same type as input0.
+ */
+ ANEURALNETWORKS_ADD = 0,
+
+ /** Performs a 2-D average pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
+ * data layout.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * {@link PaddingCode} values.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ */
+ ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
+
+ /** Concatenates the input tensors along the given dimension.
+ *
+ * The input tensors must have identical type and the same dimensions except the
+ * dimension along the concatenation axis.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
+ * For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all
+ * input tensors must have the same scale and zeroPoint.
+ * * n: An INT32 value, specifying the concatenation axis.
+ *
+ * Outputs:
+ * * 0: The output, a tensor of the same type as the input tensors.
+ * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ */
+ ANEURALNETWORKS_CONCATENATION = 2,
+
+ /** Performs an 2-D convolution operation.
+ *
+ * The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of
+ * images, applying the filter to each window of each image of the appropriate size.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * sum_{i, j} (
+ * input[batch, row + i, col + j, k] *
+ * filter[channel, row + i, col + j, k] +
+ * bias[channel]
+ * )
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * {@link PaddingCode} values.
+ * * 4: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * 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} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
+ */
+ ANEURALNETWORKS_CONV_2D = 3,
+
+ /** Performs a depthwise 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a filter
+ * tensor of shape [1, filter_height, filter_width, depth_out] containing
+ * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different
+ * filter to each input channel (expanding from 1 channel to channel_multiplier channels
+ * for each), then concatenates the results together.
+ *
+ * The output has depth_out = depth_in * depth_multiplier channels.
+ * The output dimensions are functions of the filter dimensions, stride, and padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, k * channel_multiplier + q] =
+ * sum_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[1, di, dj, k * channel_multiplier + q]
+ * )
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 9: An INT32 value, specifying the depthwise multiplier.
+ * * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * {@link PaddingCode} values.
+ * * 4: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the depthwise multiplier.
+ * * 7: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * 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} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
+ */
+ ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,
+
+ /** Rearranges data from depth into blocks of spatial data.
+ *
+ * More specifically, this op outputs a copy of the input tensor where values from
+ * the depth dimension are moved in spatial blocks to the height and width dimensions.
+ * The value block_size indicates the input block size and how the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged into
+ * non-overlapping blocks of size block_size x block_size.
+ *
+ * The width of the output tensor is input_depth * block_size, whereas the height is
+ * input_height * block_size.
+ * The depth of the input tensor must be divisible by block_size * block_size
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size * block_size must be a divisor of the input depth.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+ * depth/(block_size*block_size)].
+ */
+ ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
+
+ /** Dequantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = (input - zeroPoint) * scale.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with type
+ * {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ */
+ ANEURALNETWORKS_DEQUANTIZE = 6,
+
+ /** Looks up sub-tensors in the input tensor.
+ *
+ * This operator takes for input a tensor of values (Values) and
+ * a one-dimensional tensor of selection indices (Lookups).
+ * The output tensor is the concatenation of sub-tensors of Values as
+ * selected by Lookups.
+ *
+ * Think of Values as being sliced along its first dimension:
+ * The entries in Lookups select which slices are concatenated together
+ * to create the output tensor.
+ *
+ * For example, if Values has shape of [40, 200, 300] and
+ * Lookups has shape of [3], we would expect all three values
+ * found in Lookups to be between 0 and 39. The resulting tensor will
+ * have shape of [3, 200, 300].
+ *
+ * If a value in Lookups is out of bounds, the operation will fail
+ * and an error will be reported.
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type.
+ * The values are indices into the first dimension of Values.
+ * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+ * extracted.
+ *
+ * Output:
+ * * 0: A n-D tensor with the same rank and shape as the Values
+ * tensor, except for the first dimension which has the same size
+ * as Lookups' only dimension.
+ */
+ ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
+
+ /** Computes element-wise floor() on the input tensor.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of the same type and dimensions as the input tensor.
+ */
+ ANEURALNETWORKS_FLOOR = 8,
+
+ /** Denotes a fully (densely) connected layer, which connects all elements in the input
+ * tensor with each element in the output tensor.
+ *
+ * This layer implements the operation:
+ *
+ * outputs = activation(inputs * weights’ + bias)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+ * a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+ * [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+ * and “input_size” is the size of the input.
+ * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
+ * "num_units" corresponds to the number of output nodes.
+ * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape [batch_size, num_units].
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
+ */
+ ANEURALNETWORKS_FULLY_CONNECTED = 9,
+
+ /** Looks up sub-tensors in the input tensor using a key-value map.
+ *
+ * This operator takes for input a tensor of values (Values),
+ * a one-dimensional tensor of selection values (Lookups) and
+ * a one-dimensional tensor that maps these values to Values
+ * indexes. The output tensor is the concatenation of sub-tensors of
+ * Values as selected by Lookups via Keys.
+ *
+ * Think of Values as being sliced along its outer-most dimension.
+ * The output is a concatenation of selected slices, with one slice
+ * for each entry of Lookups. The slice selected is the one at the
+ * same index as the Maps entry that matches the value in Lookups.
+ *
+ * For a hit, the corresponding sub-tensor of Values is included
+ * in the Output tensor. For a miss, the corresponding sub-tensor in
+ * Output will have zero values.
+ *
+ * For example, if Values has shape of [40, 200, 300],
+ * Keys should have a shape of [40]. If Lookups tensor has shape
+ * of [3], we're concatenating three slices, so the resulting tensor
+ * will have the shape of [3, 200, 300]. If the first entry in
+ * Lookups has the value 123456, we'll look for that value in Keys tensor.
+ * If the sixth entry of Keys contains 123456, we'll select the sixth
+ * slice of Values. If no entry in Keys has 123456, a slice of zeroes
+ * will be concatenated.
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ k ].
+ * * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ n ];
+ * Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor
+ * in Values (Values[i]), where 0 <= i <= n-1.
+ * Keys tensor *MUST* be sorted in ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.
+ *
+ * Outputs:
+ * * 0: Output. A tensor with shape [ k …].
+ * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+ * hits (True) or not (False).
+ * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f.
+ * A non-zero byte represents True, a hit. A zero indicates otherwise.
+ */
+ ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,
+
+ /** Applies L2 normalization along the depth dimension.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * input[batch, row, col, channel] /
+ * sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+ *
+ * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth].
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ */
+ ANEURALNETWORKS_L2_NORMALIZATION = 11,
+
+ /** Performs an 2-D L2 pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * {@link PaddingCode} values.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ */
+ ANEURALNETWORKS_L2_POOL_2D = 12,
+
+ /** Applies Local Response Normalization along the depth dimension.
+ *
+ * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
+ * dimension), and each vector is normalized independently. Within a given vector,
+ * each component is divided by the weighted, squared sum of inputs within depth_radius.
+ *
+ * The output is calculated using this formula:
+ *
+ * sqr_sum[a, b, c, d] =
+ * sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
+ * output = input / pow((bias + alpha * sqr_sum), beta)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the radius of the normalization window.
+ * * 2: A FLOAT32 value, specifying the bias, must not be zero.
+ * * 3: A FLOAT32 value, specifying the scale factor, alpha.
+ * * 4: A FLOAT32 value, specifying the exponent, beta.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,
+
+ /** Computes sigmoid activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = 1 / (1 + exp(-input))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ */
+ ANEURALNETWORKS_LOGISTIC = 14,
+
+ /**
+ * Projects an input to a bit vector via locality senstive hashing.
+ *
+ * Inputs:
+ * * 0: Hash functions. Dim.size == 2, DataType: Float.
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of seeds per hash functions.
+ * Tensor[0].Dim[1] <= 32 in sparse case.
+ *
+ * * 1: Input. Dim.size >= 1, no restriction on DataType.
+ * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
+ * If not set, each input element is considered to have the same weight of
+ * 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
+ * * 3: Type:
+ * Sparse: Value LSHProjectionType_SPARSE(=1).
+ * Computed bit vector is considered to be sparse.
+ * Each output element is an int32 made up of multiple bits computed from
+ * hash functions.
+ *
+ * Dense: Value LSHProjectionType_DENSE(=2).
+ * Computed bit vector is considered to be dense. Each output element
+ * represents a bit and can take the value of either 0 or 1.
+ *
+ * Outputs:
+ * * 0: If the projection type is sparse:
+ * Output.Dim == { Tensor[0].Dim[0] }
+ * A tensor of int32 that represents hash signatures.
+ * If the projection type is Dense:
+ * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+ * A flattened tensor that represents projected bit vectors.
+ */
+ ANEURALNETWORKS_LSH_PROJECTION = 15,
+
+ /**
+ * Long short-term memory unit (LSTM) recurrent network layer.
+ *
+ * The default non-peephole implementation is based on:
+ * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+ * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+ * Computation, 9(8):1735-1780, 1997.
+ *
+ * The peephole implementation is based on:
+ * https://research.google.com/pubs/archive/43905.pdf
+ * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+ * recurrent neural network architectures for large scale acoustic modeling."
+ * INTERSPEECH, 2014.
+ *
+ * The coupling of input and forget gate (CIFG) is based on:
+ * http://arxiv.org/pdf/1503.04069.pdf
+ * Greff et al. "LSTM: A Search Space Odyssey"
+ *
+ * The class has the following independently optional inputs:
+ * * If input gate (if CIFG): “input_to_forget_weights”,
+ * “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
+ * * If no peephole connections: “cell_to_input_weights”,
+ * “cell_to_forget_weights”, “cell_to_output_weights”.
+ * * If no projection layer: “projection_weights” and “projection_bias”.
+ * * If no projection bias: “projection_bias”.
+ *
+ * Supported tensor types (type T):
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: Input.
+ * A 2-D tensor of type T, of shape [batch_size, input_size], where
+ * “batch_size” corresponds to the batching dimension, and “input_size”
+ * is the size of the input.
+ * * 1: input_to_input_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of cell units.
+ * * 2: input_to_forget_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 3: input_to_cell_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 4: input_to_output_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 5: recurrent_to_input_weights.
+ * A 2-D tensor of type T, 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: recurrent_to_forget_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 7: recurrent_to_cell_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 8: recurrent_to_output_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 9: cell_to_input_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 10:cell_to_forget_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 11:cell_to_output_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 12:input_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 13:forget_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 14:cell_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 15:output_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 16:projection_weights.
+ * A 2-D tensor of type T, of shape [output_size, num_units].
+ * * 17:projection_bias.
+ * A 1-D tensor of type T, of shape [output_size].
+ * * 18: output_state (in).
+ * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * * 19: cell_state (in).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 20:fused_activation_function.
+ * An optional {@link FuseCode} value indicating the activation
+ * function.
+ * If “NONE” is specified then it results in a linear activation.
+ * * 21:cell_clip.
+ * A 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.
+ * * 22:proj_clip.
+ * A 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.
+ *
+ * Outputs:
+ * * 0: scratch_buffer.
+ * A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
+ * * 1: output_state (out).
+ * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * * 2: cell_state (out).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 3: output.
+ * A 2-D tensor of type T, of shape [batch_size, output_size]. This is
+ * effectively the same as the current “output_state” value.
+ */
+ ANEURALNETWORKS_LSTM = 16,
+
+ /** Performs an 2-D max pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * max_{i, j} (input[batch, row + i, col + j, channel])
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * {@link PaddingCode} values.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ */
+ ANEURALNETWORKS_MAX_POOL_2D = 17,
+
+ /** Multiplies two tensors, element-wise.
+ *
+ * Takes two input tensors of identical type and compatible dimensions. The output
+ * is the product of both input tensors, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the resulting output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way forward.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The product, a tensor of the same type as input0.
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input1_scale * input2_scale.
+ */
+ ANEURALNETWORKS_MUL = 18,
+
+ /** Computes rectified linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = max(0, input)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU = 19,
+
+ /** Computes rectified linear 1 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(1.f, max(-1.f, input))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU1 = 20,
+
+ /** Computes rectified linear 6 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(6, max(0, input))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU6 = 21,
+
+ /** Reshapes a tensor.
+ *
+ * Given tensor, this operation returns a tensor that has the same values as tensor,
+ * but with a newly specified shape.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}, defining the shape
+ * of the output tensor. The number of elements implied by shape must be the same
+ * as the number of elements in the input tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
+ */
+ ANEURALNETWORKS_RESHAPE = 22,
+
+ /** Resizes images to given size using the bilinear interpretation.
+ *
+ * Resized images will be distorted if their output aspect ratio is not the same as
+ * input aspect ratio.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the output height of the output tensor.
+ * * 2: An INT32 value, specifying the output width of the output tensor.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+ */
+ ANEURALNETWORKS_RESIZE_BILINEAR = 23,
+
+ /**
+ * A basic recurrent neural network layer.
+ *
+ * This layer implements the operation:
+ * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor types (Type T):
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of units.
+ * * 2: recurrent_weights.
+ * A 2-D tensor of type T, of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
+ * * 3: bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 4: hidden state (in).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 5: fused_activation_function.
+ * An optional {@link FuseCode} value indicating the activation
+ * function. If “NONE” is specified then it results in a linear
+ * activation.
+ *
+ * Outputs:
+ * * 0: hidden state (out).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ *
+ * * 1: output.
+ * A 2-D tensor of type T, of shape [batch_size, num_units]. This is
+ * effectively the same as the current state value.
+ */
+ ANEURALNETWORKS_RNN = 24,
+
+ /** Computes the softmax activation on the input tensor element-wise, per batch, by
+ * normalizing the input vector so the maximum coefficient is zero.
+ *
+ * The output is calculated using this formula:
+ *
+ * output[batch, i] =
+ * exp((input[batch, i] - max(input[batch, :])) * beta) /
+ * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 2 or 4.
+ *
+ * Inputs:
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ */
+ ANEURALNETWORKS_SOFTMAX = 25,
+
+ /** Rearranges blocks of spatial data, into depth.
+ *
+ * More specifically, this op outputs a copy of the input tensor where values from
+ * the height and width dimensions are moved to the depth dimension.
+ * The value block_size indicates the input block size and how the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged into
+ * non-overlapping blocks of size block_size x block_size.
+ *
+ * The depth of the output tensor is input_depth * block_size * block_size.
+ * The input tensor's height and width must be divisible by block_size.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size must be a divisor of both the input height and width.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+ * depth*block_size*block_size].
+ */
+ ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
+
+ /**
+ * SVDF op is a kind of stateful layer derived from the notion that a
+ * densely connected layer that's processing a sequence of input frames can
+ * be approximated by using a singular value decomposition of each of its
+ * nodes. The implementation is based on:
+ *
+ * https://research.google.com/pubs/archive/43813.pdf
+ *
+ * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+ * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+ * INTERSPEECH, 2015.
+ *
+ * It processes the incoming input using a 2-stage filtering mechanism:
+ * * stage 1 performs filtering on the "features" dimension, whose outputs get
+ * pushed into a memory of fixed-size memory_size.
+ * * stage 2 performs filtering on the "time" dimension of the memory_size
+ * memoized outputs of stage 1.
+ *
+ * Specifically, for rank 1, this layer implements the operation:
+ *
+ * memory = push(conv1d(inputs, weights_feature, feature_dim,
+ * "ANEURALNETWORKS_PADDING_VALID"));
+ * outputs = activation(memory * weights_time + bias);
+ *
+ * Where:
+ * * “weights_feature” is a weights matrix that processes the inputs (by
+ * convolving the input with every “feature filter”), and whose outputs get
+ * pushed, stacked in order, into the fixed-size “memory” (the oldest entry
+ * gets dropped);
+ * * “weights_time” is a weights matrix that processes the “memory” (by a
+ * batched matrix multiplication on the num_units);
+ * * “bias” is an optional bias vector (added to each output vector in the
+ * batch); and
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Each rank adds a dimension to the weights matrices by means of stacking
+ * the filters.
+ *
+ * Supported tensor types (type T):
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of units.
+ * * 2: weights_time.
+ * A 2-D tensor of type T, 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 type T, of shape [num_units].
+ * * 4: state (in).
+ * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * * 5: rank.
+ * The rank of the SVD approximation.
+ * * 6: fused_activation_function.
+ * An optional {@link FuseCode} value indicating the activation function.
+ * If “NONE” is specified then it results in a linear activation.
+ *
+ * Outputs:
+ * * 0: state (out).
+ * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * * 1: output.
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ */
+ ANEURALNETWORKS_SVDF = 27,
+
+ /** Computes hyperbolic tangent of input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = tanh(input)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_TANH = 28,
+} OperationCode;
+
+/**
+ * Fused activation function types.
+ *
+ */
+typedef enum {
+ /** NO fused activation function. */
+ ANEURALNETWORKS_FUSED_NONE = 0,
+ /** Fused ReLU activation function. */
+ ANEURALNETWORKS_FUSED_RELU = 1,
+ /** Fused ReLU1 activation function. */
+ ANEURALNETWORKS_FUSED_RELU1 = 2,
+ /** Fused ReLU6 activation function. */
+ ANEURALNETWORKS_FUSED_RELU6 = 3,
+} FuseCode;
+
+/**
+ * Implicit padding algorithms.
+ *
+ */
+typedef enum {
+ /**
+ * SAME padding.
+ * Padding on both ends are the "same":
+ * padding_to_beginning = total_padding / 2
+ * padding_to_end = (total_padding + 1)/2.
+ * i.e., for even number of padding, padding to both ends are exactly
+ * the same; for odd number of padding, padding to the ending is bigger
+ * than the padding to the beginning by 1.
+ *
+ * total_padding is a function of input, stride and filter size.
+ * It could be computed as follows:
+ * out_size = (input + stride - 1) / stride;
+ * needed_input = (out_size - 1) * stride + filter_size
+ * total_padding = max(0, needed_input - output_size)
+ * The computation is the same for the horizontal and vertical directions.
+ */
+ ANEURALNETWORKS_PADDING_SAME = 1,
+
+ /**
+ * VALID padding.
+ * No padding. When the input size is not evenly divisible by
+ * the filter size, the input at the end that could not fill
+ * the whole filter tile will simply be ignored.
+ */
+ ANEURALNETWORKS_PADDING_VALID = 2,
+} PaddingCode;
+
+/**
+ * Execution preferences.
+ */
+typedef enum {
+ /**
+ * Prefer executing in a way that minimizes battery drain.
+ * This is desirable for compilations that will be executed often.
+ */
+ ANEURALNETWORKS_PREFER_LOW_POWER = 0,
+ /**
+ * Prefer returning a single answer as fast as possible, even if this causes
+ * more power consumption.
+ */
+ ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
+ /**
+ * Prefer maximizing the throughput of successive frames, for example when
+ * processing successive frames coming from the camera.
+ */
+ ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
+} PreferenceCode;
+
+/**
+ * Result codes.
+ */
+typedef enum {
+ ANEURALNETWORKS_NO_ERROR = 0,
+ ANEURALNETWORKS_OUT_OF_MEMORY = 1,
+ ANEURALNETWORKS_INCOMPLETE = 2,
+ ANEURALNETWORKS_UNEXPECTED_NULL = 3,
+ ANEURALNETWORKS_BAD_DATA = 4,
+ ANEURALNETWORKS_OP_FAILED = 5,
+ ANEURALNETWORKS_UNMAPPABLE = 5,
+ ANEURALNETWORKS_BAD_STATE = 6,
+} ResultCode;
+
+/**
+ * For {@link ANeuralNetworksModel_setOperandValue}, values with a
+ * length smaller or equal to this will be immediately copied into
+ * the model. The size is in bytes.
+ */
+enum {
+ ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES = 128
+};
+
+/**
+ * ANeuralNetworksMemory is an opaque type that represents memory.
+ *
+ * This type is used to represent shared memory, memory mapped files,
+ * and similar memories.
+ *
+ * By using shared memory, a program can efficiently communicate to the
+ * runtime and drivers the tensors that define a model. See
+ * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
+ * should typically create one shared memory object that contains every tensor
+ * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
+ * used to create shared memory from a file handle. {@link ANeuralNetworksMemory_createShared}
+ * can be used to directly created shared memory.
+ *
+ * Memory objects can also be used to specify the input and output arguments of
+ * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
+ * and {@link ANeuralNetworksExecution_setOutputFromMemory}.
+ */
+typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;
+
+/**
+ * ANeuralNetworksModel is an opaque type that contains a description of the
+ * mathematical operations that constitute the model.
+ *
+ * <p>The model will be built by calling<ul>
+ * <li>{@link ANeuralNetworksModel_create},</li>
+ * <li>{@link ANeuralNetworksModel_addOperation},</li>
+ * <li>{@link ANeuralNetworksModel_addOperand},</li>
+ * </ul>
+ *
+ * A model is completed by calling {@link ANeuralNetworksModel_finish}.
+ * A model is destroyed by calling {@link ANeuralNetworksModel_free}.
+ *
+ * <p>A model cannot be modified once {@link ANeuralNetworksModel_finish}
+ * has been called on it.</p>
+ *
+ * <p>It is the application's responsibility to make sure that only one thread
+ * modifies a model at a given time. It is however safe for more than one
+ * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p>
+ *
+ * <p>It is also the application's responsibility to ensure that there are no other
+ * uses of the model after calling {@link ANeuralNetworksModel_free}.
+ * This includes any compilation or execution object created using the model.</p>
+ */
+typedef struct ANeuralNetworksModel ANeuralNetworksModel;
+
+/**
+ * ANeuralNetworksCompilation is an opaque type that can be used to compile
+ * a machine learning model.
+ *
+ * <p>To use:<ul>
+ * <li>Create a new compilation instance by calling the
+ * {@link ANeuralNetworksCompilation_create} function.</li>
+ * <li>Set any desired properties on the compilation (for example,
+ * {@link ANeuralNetworksCompilation_setPreference}).</li>
+ * <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li>
+ * <li>Use the compilation as many times as needed
+ * with {@link ANeuralNetworksExecution_create}.</li>
+ * <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free}
+ * once all executions using the compilation have completed.</li></ul></p>
+ *
+ * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}.
+ * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}.
+ *
+ * <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish}
+ * has been called on it.</p>
+ *
+ * <p>It is the application's responsibility to make sure that only
+ * one thread modifies a compilation at a given time. It is however
+ * safe for more than one thread to use the compilation once
+ * {@link ANeuralNetworksCompilation_finish} has returned.</p>
+ *
+ * <p>It is also the application's responsibility to ensure that there are no other
+ * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}.
+ * This includes any execution object created using the compilation.</p>
+ */
+typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation;
+
+/**
+ * ANeuralNetworksExecution is an opaque type that can be used to apply a machine
+ * learning model to a set of inputs.
+ *
+ * <p>To use:<ul>
+ * <li>Create a new execution instance by calling the
+ * {@link ANeuralNetworksExecution_create} function.</li>
+ * <li>Associate data to the model inputs with
+ * {@link ANeuralNetworksExecution_setInput} or
+ * {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
+ * <li>Associate output buffers to the model outputs with
+ * {@link ANeuralNetworksExecution_setOutput} or
+ * {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
+ * <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li>
+ * <li>Wait for the execution to complete with {@link
+ * ANeuralNetworksEvent_wait}.</li>
+ * <li>Destroy the execution with
+ * {@link ANeuralNetworksExecution_free}.</li></ul></p>
+ *
+ * <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute}
+ * has been called on it.</p>
+ *
+ * <p>An execution can be applied to a model with
+ * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions
+ * to do new evaluations of the model.</p>
+ *
+ * <p>It is the application's responsibility to make sure that only one thread
+ * modifies an execution at a given time. It is however safe for more than one
+ * thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p>
+ *
+ * <p>It is also the application's responsibility to ensure that there are no other
+ * uses of the request after calling {@link ANeuralNetworksExecution_free}.</p>
+ */
+typedef struct ANeuralNetworksExecution ANeuralNetworksExecution;
+
+/**
+ * ANeuralNetworksOperandType describes the type of an operand.
+ * This structure is used to describe both scalars and tensors.
+ */
+typedef struct ANeuralNetworksOperandType {
+ /** The data type, e.g ANEURALNETWORKS_INT8. */
+ int32_t type;
+ /** The number of dimensions. It should be 0 for scalars. */
+ uint32_t dimensionCount;
+ /** The dimensions of the tensor. It should be nullptr for scalars. */
+ const uint32_t* dimensions;
+ /** These two fields are only used for quantized tensors.
+ * They should be zero for scalars and non-fixed point tensors.
+ * The dequantized value of each entry is (value - zeroPoint) * scale.
+ */
+ float scale;
+ int32_t zeroPoint;
+} ANeuralNetworksOperandType;
+
+typedef int32_t ANeuralNetworksOperationType;
+
+/**
+ * ANeuralNetworksEvent is an opaque type that represents an event
+ * that will be signaled once an execution completes.
+ */
+typedef struct ANeuralNetworksEvent ANeuralNetworksEvent;
+
+
+/**
+ * Creates a shared memory object from a file descriptor.
+ *
+ * The shared memory is backed by a file descriptor via mmap.
+ * See {@link ANeuralNetworksMemory} for a description on how to use
+ * this shared memory.
+ *
+ * @param size The requested size in bytes.
+ * Must not be larger than the file size.
+ * @param prot The desired memory protection for the mapping.
+ * It is either PROT_NONE or the bitwise OR of one or
+ * more of the following flags: PROT_READ, PROT_WRITE.
+ * @param fd The requested file descriptor.
+ * The file descriptor has to be mmap-able. The file
+ * descriptor will be duplicated.
+ * @param offset The offset to the beginning of the file of the area to map.
+ * The offset has to be aligned to a page size.
+ * @param memory The memory object to be created.
+ * Set to NULL if unsuccessful.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if the request completed normally.
+ */
+int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset,
+ ANeuralNetworksMemory** memory);
+
+/**
+ * Delete a memory object.
+ *
+ * Destroys the object used by the run time to keep track of the memory.
+ * This will free the underlying actual memory if no other code has open
+ * handles to this memory.
+ *
+ * @param memory The memory object to be freed.
+ */
+void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory);
+
+/**
+ * Create an empty {@link ANeuralNetworksModel}.
+ *
+ * <p>This only creates the object. Computation is performed once
+ * {@link ANeuralNetworksExecution_startCompute} is invoked.
+ *
+ * The model should be constructed with calls to
+ * {@link ANeuralNetworksModel_addOperation} and
+ * {@link ANeuralNetworksModel_addOperand}
+ *
+ * <p>{@link ANeuralNetworksModel_finish} should be called once the model
+ * has been fully constructed.</p>
+ *
+ * <p>{@link ANeuralNetworksModel_free} should be called once the model
+ * is no longer needed.</p>
+ *
+ * @param model The {@link ANeuralNetworksModel} to be created.
+ * Set to NULL if unsuccessful.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_create(ANeuralNetworksModel** model);
+
+/**
+ * Destroy a model.
+ *
+ * The model need not have been finished by a call to
+ * {@link ANeuralNetworksModel_finish}.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @param model The model to be destroyed. Passing NULL is acceptable and
+ * results in no operation.
+ */
+void ANeuralNetworksModel_free(ANeuralNetworksModel* model);
+
+/**
+ * Indicate that we have finished modifying a model. Required before
+ * calling {@link ANeuralNetworksCompilation_create}.
+ *
+ * An application is responsible to make sure that no other thread uses
+ * the model at the same time.
+ *
+ * This function must only be called once for a given model.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @param model The model to be finished.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_finish(ANeuralNetworksModel* model);
+
+/**
+ * Add an operand to a model.
+ *
+ * The order in which the operands are added is important. The first one added
+ * to a model will have the index value 0, the second 1, etc. These indexes are
+ * used as operand identifiers in {@link ANeuralNetworksModel_addOperation},
+ * {@link ANeuralNetworksExecution_setInput},
+ * {@link ANeuralNetworksExecution_setInputFromMemory},
+ * {@link ANeuralNetworksExecution_setOutput},
+ * {@link ANeuralNetworksExecution_setOutputFromMemory} and
+ * {@link ANeuralNetworksExecution_setOperandValue}.
+ *
+ * To build a model that can accomodate inputs of various sizes, as you may want
+ * to do for a CNN, set the size of the dimensions that will vary at run time to 0.
+ * If you do so, provide the full dimensions when calling
+ * {@link ANeuralNetworksExecution_setInput} or {@link ANeuralNetworksExecution_setInputFromMemory}.
+ *
+ * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
+ * called will return an error.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @param model The model to be modified.
+ * @param type The {@link ANeuralNetworksOperandType} that describes the shape
+ * of the operand.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model,
+ const ANeuralNetworksOperandType* type);
+
+/**
+ * Sets an operand to a constant value.
+ *
+ * Values of length smaller or equal to
+ * {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES}
+ * are immediately copied into the model.
+ *
+ * For values of length greater than {@link ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES},
+ * a pointer to the buffer is stored within the model. The application is responsible
+ * for not changing the content of this region until all executions using this model
+ * have completed. As the data may be copied during processing, modifying the data
+ * after this call yields undefined results.
+ *
+ * For large tensors, using {@link ANeuralNetworksModel_setOperandValueFromMemory}
+ * is likely to be more efficient.
+ *
+ * To indicate that an optional operand should be considered missing,
+ * pass nullptr for buffer and 0 for length.
+ *
+ * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
+ * called will return an error.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @param model The model to be modified.
+ * @param index The index of the model operand we're setting.
+ * @param buffer A pointer to the data to use.
+ * @param length The size in bytes of the data value.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index,
+ const void* buffer, size_t length);
+
+/**
+ * Sets an operand to a value stored in a memory object.
+ *
+ * The content of the memory is not copied. A reference to that memory is stored
+ * inside the model. The application is responsible for not changing the content
+ * of the memory region until all executions using this model have completed.
+ * As the data may be copied during processing, modifying the data after this call
+ * yields undefined results.
+ *
+ * To indicate that an optional operand should be considered missing,
+ * use {@link ANeuralNetworksModel_setOperandValue} instead, passing nullptr for buffer.
+ *
+ * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
+ * called will return an error.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @param model The model to be modified.
+ * @param index The index of the model operand we're setting.
+ * @param buffer A pointer to the data to use.
+ * @param memory The memory containing the data.
+ * @param offset This specifies the location of the data within the memory.
+ * The offset is in bytes from the start of memory.
+ * @param length The size in bytes of the data value.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index,
+ const ANeuralNetworksMemory* memory,
+ size_t offset, size_t length);
+
+/**
+ * Add an operation to a model.
+ *
+ * @param model The model to be modified.
+ * @param type The type of the operation.
+ * @param inputCount The number of entries in the inputs array.
+ * @param inputs An array of indexes identifying each operand.
+ * @param outputCount The number of entries in the outputs array.
+ * @param outputs An array of indexes identifying each operand.
+ *
+ * The operands specified by inputs and outputs must have been
+ * previously added by calls to {@link ANeuralNetworksModel_addOperand}.
+ *
+ * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
+ * called will return an error.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model,
+ ANeuralNetworksOperationType type, uint32_t inputCount,
+ const uint32_t* inputs, uint32_t outputCount,
+ const uint32_t* outputs);
+
+/**
+ * Specfifies which operands will be the model's inputs and outputs.
+ *
+ * An operand cannot be used for both input and output. Doing so will
+ * return an error.
+ *
+ * @param model The model to be modified.
+ * @param inputCount The number of entries in the inputs array.
+ * @param inputs An array of indexes identifying the input operands.
+ * @param outputCount The number of entries in the outputs array.
+ * @param outputs An array of indexes identifying the output operands.
+ *
+ * The operands specified by inputs and outputs must have been
+ * previously added by calls to {@link ANeuralNetworksModel_addOperand}.
+ *
+ * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been
+ * called will return an error.
+ *
+ * See {@link ANeuralNetworksModel} for information on multithreaded usage.
+ *
+ */
+int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount,
+ const uint32_t* inputs, uint32_t outputCount,
+ const uint32_t* outputs);
+
+/**
+ * Create a {@link ANeuralNetworksCompilation} to compile the given model.
+ *
+ * <p>This only creates the object. Compilation is only performed once
+ * {@link ANeuralNetworksCompilation_finish} is invoked.</p>
+ *
+ * <p>{@link ANeuralNetworksCompilation_finish} should be called once
+ * all desired properties have been set on the compilation.</p>
+ *
+ * <p>{@link ANeuralNetworksModel_free} should be called once the compilation
+ * is no longer needed.</p>
+ *
+ * <p>The provided model must outlive the compilation.</p>
+ *
+ * The model must already have been finished by a call to
+ * {@link ANeuralNetworksModel_finish}.
+ *
+ * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
+ *
+ * @param model The {@link ANeuralNetworksModel} to be compiled.
+ * @param compilation The newly created object or NULL if unsuccessful.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
+ * if the model is invalid.
+ */
+int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model,
+ ANeuralNetworksCompilation** compilation);
+
+/**
+ * Destroy a compilation.
+ *
+ * The compilation need not have been finished by a call to
+ * {@link ANeuralNetworksModel_finish}.
+ *
+ * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
+ *
+ * @param compilation The compilation to be destroyed. Passing NULL is acceptable and
+ * results in no operation.
+ */
+void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation);
+
+/**
+ * Sets the execution preference.
+ *
+ * <p>Provides guidance to the runtime when trade-offs are possible.</p>
+ *
+ * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
+ *
+ * @param compilation The compilation to be modified.
+ * @param preference Either {@link PREFER_LOW_POWER},
+ * {@link PREFER_SINGLE_FAST_ANSWER}, or
+ * {@link PREFER_SUSTAINED_SPEED}.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation,
+ int32_t preference);
+
+/**
+ * Indicate that we have finished modifying a compilation. Required before
+ * calling {@link ANeuralNetworksExecution_create}.
+ *
+ * An application is responsible to make sure that no other thread uses
+ * the compilation at the same time.
+ *
+ * This function must only be called once for a given compilation.
+ *
+ * See {@link ANeuralNetworksCompilation} for information on multithreaded usage.
+ *
+ * @param compilation The compilation to be finished.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation);
+
+/**
+ * Create a {@link ANeuralNetworksExecution} to apply the given compilation.
+ * This only creates the object. Computation is only performed once
+ * {@link ANeuralNetworksExecution_startCompute} is invoked.
+ *
+ * <p>The provided compilation must outlive the execution.</p>
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated.
+ * @param execution The newly created object or NULL if unsuccessful.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA
+ * if the compilation is invalid.
+ */
+int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation,
+ ANeuralNetworksExecution** execution);
+
+/**
+ * Destroy an execution.
+ *
+ * <p>If called on an execution for which
+ * {@link ANeuralNetworksExecution_startCompute} has been called, the
+ * function will return immediately but will mark the execution to be deleted
+ * once the computation completes. The related {@link ANeuralNetworksEvent}
+ * will be signaled and the {@link ANeuralNetworksEvent_wait} will return
+ * ANEURALNETWORKS_ERROR_DELETED.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be destroyed. Passing NULL is acceptable and
+ * results in no operation.
+ */
+void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution);
+
+/**
+ * Associate a user buffer with an input of the model of the
+ * {@link ANeuralNetworksExecution}.
+ *
+ * <p>The provided buffer must outlive the execution.</p>
+ *
+ * If the input is optional, you can indicate that it is omitted by
+ * passing nullptr for buffer and 0 for length.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be modified.
+ * @param index The index of the input argument we are setting. It is
+ * an index into the lists passed to
+ * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
+ * the index associated with {@link ANeuralNetworksModel_addOperand}.
+ * @param type The type of the operand. This should be used to specify the
+ * dimensions that were set to 0 when the operand was added to the
+ * model. All other properties of the type must be the same as
+ * specified in the model. If the type is the same as specified
+ * when the model was built, NULL can be passed.
+ * @param buffer The buffer containing the data.
+ * @param length The length in bytes of the buffer.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
+ * name is not recognized or the buffer is too small for the input.
+ */
+int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index,
+ const ANeuralNetworksOperandType* type, const void* buffer,
+ size_t length);
+
+/**
+ * Associate part of a memory object with an input of the model of the
+ * {@link ANeuralNetworksExecution}.
+ *
+ * <p>The provided memory must outlive the execution.</p>
+ *
+ * If the input is optional, you can indicate that it is omitted by
+ * using @{Link ANeuralNetworks_setInput} instead, passing nullptr for buffer
+ * and 0 for length.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be modified.
+ * @param index The index of the input argument we are setting. It is
+ * an index into the lists passed to
+ * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
+ * the index associated with {@link ANeuralNetworksModel_addOperand}.
+ * @param type The type of the operand. This can be used to specify the
+ * dimensions that were set to 0 when the operand was added to the
+ * model. All other values must be the same as specified in the
+ * model. If the type is the same as specified when the model
+ * was built, NULL can be passed.
+ * @param memory The memory containing the data.
+ * @param offset This specifies the location of the data whithin the memory.
+ * The offset is in bytes from the start of memory.
+ * @param length The size in bytes of the data value.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
+ * name is not recognized or the buffer is too small for the input.
+ */
+int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
+ const ANeuralNetworksOperandType* type,
+ const ANeuralNetworksMemory* memory, size_t offset,
+ size_t length);
+
+/**
+ * Associate a user buffer with an output of the model of the
+ * {@link ANeuralNetworksExecution}.
+ *
+ * If the output is optional, you can indicate that it is omitted by
+ * passing nullptr for buffer and 0 for length.
+ *
+ * <p>The provided buffer must outlive the execution.</p>
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be modified.
+ * @param index The index of the output argument we are setting. It is
+ * an index into the lists passed to
+ * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
+ * the index associated with {@link ANeuralNetworksModel_addOperand}.
+ * @param type The type of the operand. This can be used to specify the
+ * dimensions that were set to 0 when the operand was added to the
+ * model. All other values must be the same as specified in the
+ * model. If the type is the same as specified when the model
+ * was built, NULL can be passed.
+ * @param buffer The buffer where the data is to be written.
+ * @param length The length in bytes of the buffer.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
+ * name is not recognized or the buffer is too small for the output.
+ */
+int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index,
+ const ANeuralNetworksOperandType* type, void* buffer,
+ size_t length);
+
+/**
+ * Associate part of a memory object with an output of the model of the
+ * {@link ANeuralNetworksExecution}.
+ *
+ * If the output is optional, you can indicate that it is omitted by
+ * using @{Link ANeuralNetworks_setOutput} instead, passing nullptr for buffer
+ * and 0 for length.
+ *
+ * <p>The provided memory must outlive the execution.</p>
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be modified.
+ * @param index The index of the output argument we are setting. It is
+ * an index into the lists passed to
+ * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
+ * the index associated with {@link ANeuralNetworksModel_addOperand}.
+ * @param type The type of the operand. This can be used to specify the
+ * dimensions that were set to 0 when the operand was added to the
+ * model. All other values must be the same as specified in the
+ * model. If the type is the same as specified when the model
+ * was built, NULL can be passed.
+ * @param memory The memory where the data is to be stored.
+ * @param offset This specifies the location of the data whithin the memory.
+ * The offset is in bytes from the start of memory.
+ * @param length The length in bytes of the data value.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the
+ * name is not recognized or the buffer is too small for the output.
+ */
+int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
+ const ANeuralNetworksOperandType* type,
+ const ANeuralNetworksMemory* memory, size_t offset,
+ size_t length);
+
+/**
+ * Schedule evaluation of the execution.
+ *
+ * <p>Schedules evaluation of the execution. Once the model has been
+ * applied and the outputs are ready to be consumed, the returned event will be
+ * signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event.
+ * </p>
+ *
+ * Multiple executions can be scheduled and evaluated concurrently. The
+ * runtime makes no guarantee on the ordering of completion of
+ * executions. If it's important to the application, the application
+ * should enforce the ordering by using
+ * {@link ANeuralNetworksEvent_wait}.
+ *
+ * ANeuralNetworksEvent_wait must be called to recuperate the resources used
+ * by the execution.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @param execution The execution to be scheduled and executed.
+ * @param event The event that will be signaled on completion. event is set to
+ * NULL if there's an error.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if successful.
+ */
+int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution,
+ ANeuralNetworksEvent** event);
+
+/**
+ * Waits until the execution completes.
+ *
+ * More than one thread can wait on an event. When the execution completes,
+ * all threads will be released.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ *
+ * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally.
+ */
+int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event);
+
+/**
+ * Destroys the event.
+ *
+ * See {@link ANeuralNetworksExecution} for information on multithreaded usage.
+ */
+void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event);
+
+__END_DECLS
+
+#endif // __ANDROID_API__ >= 27
+
+#endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H
+
+/** @} */
projects / platform / core / ml / nnfw.git / commitdiff