| /* |
| * 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" data layout. |
| * |
| * Inputs: |
| * * 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 output stride in the ‘width’ dimension. |
| * * 6: An INT32 value, specifying the output stride 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. |
| * |
| * 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: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. |
| * For inputs of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all |
| * input tensors must have the same scale and zeroPoint. |
| * * n+1: An INT32 value, specifying the concatenation axis. |
| * * n+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 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. |
| * |
| * Inputs: |
| * * 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 output stride in the ‘width’ dimension. |
| * * 8: An INT32 value, specifying the output stride 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. |
| * |
| * 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. |
| * |
| * Inputs: |
| * * 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 output stride in the ‘width’ dimension. |
| * * 8: An INT32 value, specifying the output stride 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. |
| * |
| * 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 items from a given tensor. |
| * |
| * Each item in the output is a raw copy of the corresponding item in |
| * the input “values”. If the given “lookup” indices are out of bounds, |
| * the op will fail and an error will be reported. |
| * |
| * Inputs: |
| * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2, |
| * then the shape would be [lookup_dimension, values_dimension], where |
| * “lookup_dimension” corresponds to the indexing dimension in the lookup |
| * table, and “values_dimension” to the contents. |
| * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where |
| * “lookup_size” is the number of elements to look for, and each entry |
| * corresponds to the first dimension of the “values” tensor. |
| * |
| * Output: |
| * * 0: A n-D tensor of type X and the same rank and shape as the “values” |
| * tensor, except for the first dimension which has size “lookup_size”. |
| */ |
| 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, a tensor of the same type and dimensions as input0. |
| */ |
| 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 [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, 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 values of a hash table with given keys. |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D int32 tensor with shape [ k ]. |
| * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in |
| * ascending order. |
| * * 2: Values. A tensor with shape [ n … ]. |
| * |
| * Outputs: |
| * * 0: Output. A tensor with shape [ k …]. |
| * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup |
| * hits or not. |
| */ |
| 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 x 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. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. |
| * |
| * 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. |
| * |
| * Inputs: |
| * * 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 output stride in the ‘width’ dimension. |
| * * 6: An INT32 value, specifying the output stride 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. |
| * |
| * 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: |
| * * {@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]. |
| * |
| * Parameters: |
| * * 18:fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation |
| * function. |
| * If “NONE” is specified then it results in a linear activation. |
| * * 19: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. |
| * * 20: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. |
| * A 2-D tensor of type T, of shape [batch_size, output_size]. |
| * * 2: cell_state. |
| * 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. |
| * |
| * Inputs: |
| * * 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 output stride in the ‘width’ dimension. |
| * * 6: An INT32 value, specifying the output stride 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. |
| * |
| * 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 original aspect ratio is not the same as input. |
| * |
| * 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 width of the output tensor. |
| * * 2: An INT32 value, specifying the output height 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: |
| * * {@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]. |
| * |
| * For FLOAT32 input tensor, bias must also be FLOAT32. |
| * For UINT8 input tensor, bias must be INT32. |
| * |
| * * 4: Hidden state. |
| * A 2-D tensor of type T, of shape [batch_size, num_units]. |
| * |
| * Parameters |
| * * 5: fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation |
| * function. If “NONE” is specified then it results in a linear |
| * activation. |
| * |
| * Outputs: |
| * * 0: 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, "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: |
| * * {@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. |
| * A optional 1-D tensor of type T, of shape [num_units]. |
| * |
| * For FLOAT32 input tensor, bias must also be FLOAT32. |
| * For UINT8 input tensor, bias must be INT32. |
| * |
| * Parameters: |
| * * 4: rank. |
| * The rank of the SVD approximation. |
| * * 5: fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation function. |
| * If “NONE” is specified then it results in a linear activation. |
| * |
| * Outputs: |
| * * 0: state. |
| * 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; |
| |
| /** |
| * 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; |
| |
| /** |
| * 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. |
| * |
| * For scalar values, the content of buffer is copied into the model. |
| * |
| * For tensor 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. |
| * |
| * 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. |
| * |
| * 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_setInputsAndOutputs(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> |
| * |
| * 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_setInputsAndOutputs}. 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> |
| * |
| * 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_setInputsAndOutputs}. 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}. |
| * |
| * <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_setInputsAndOutputs}. 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}. |
| * |
| * <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_setInputsAndOutputs}. 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 |
| |
| /** @} */ |