/* * 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 #include #include __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. * *

The model will be built by calling

    *
  • {@link ANeuralNetworksModel_create},
  • *
  • {@link ANeuralNetworksModel_addOperation},
  • *
  • {@link ANeuralNetworksModel_addOperand},
  • *
* * A model is completed by calling {@link ANeuralNetworksModel_finish}. * A model is destroyed by calling {@link ANeuralNetworksModel_free}. * *

A model cannot be modified once {@link ANeuralNetworksModel_finish} * has been called on it.

* *

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.

* *

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.

*/ typedef struct ANeuralNetworksModel ANeuralNetworksModel; /** * ANeuralNetworksCompilation is an opaque type that can be used to compile * a machine learning model. * *

To use:

    *
  • Create a new compilation instance by calling the * {@link ANeuralNetworksCompilation_create} function.
  • *
  • Set any desired properties on the compilation (for example, * {@link ANeuralNetworksCompilation_setPreference}).
  • *
  • Complete the compilation with {@link ANeuralNetworksCompilation_finish}.
  • *
  • Use the compilation as many times as needed * with {@link ANeuralNetworksExecution_create}.
  • *
  • Destroy the compilation with {@link ANeuralNetworksCompilation_free} * once all executions using the compilation have completed.

* * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}. * *

A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} * has been called on it.

* *

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.

* *

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.

*/ typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; /** * ANeuralNetworksExecution is an opaque type that can be used to apply a machine * learning model to a set of inputs. * *

To use:

    *
  • Create a new execution instance by calling the * {@link ANeuralNetworksExecution_create} function.
  • *
  • Associate data to the model inputs with * {@link ANeuralNetworksExecution_setInput} or * {@link ANeuralNetworksExecution_setInputFromMemory}.
  • *
  • Associate output buffers to the model outputs with * {@link ANeuralNetworksExecution_setOutput} or * {@link ANeuralNetworksExecution_setOutputFromMemory}.
  • *
  • Apply the model with {@link ANeuralNetworksExecution_startCompute}.
  • *
  • Wait for the execution to complete with {@link * ANeuralNetworksEvent_wait}.
  • *
  • Destroy the execution with * {@link ANeuralNetworksExecution_free}.

* *

An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute} * has been called on it.

* *

An execution can be applied to a model with * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions * to do new evaluations of the model.

* *

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.

* *

It is also the application's responsibility to ensure that there are no other * uses of the request after calling {@link ANeuralNetworksExecution_free}.

*/ 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}. * *

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} * *

{@link ANeuralNetworksModel_finish} should be called once the model * has been fully constructed.

* *

{@link ANeuralNetworksModel_free} should be called once the model * is no longer needed.

* * @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. * *

This only creates the object. Compilation is only performed once * {@link ANeuralNetworksCompilation_finish} is invoked.

* *

{@link ANeuralNetworksCompilation_finish} should be called once * all desired properties have been set on the compilation.

* *

{@link ANeuralNetworksModel_free} should be called once the compilation * is no longer needed.

* *

The provided model must outlive the compilation.

* * 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. * *

Provides guidance to the runtime when trade-offs are possible.

* * 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. * *

The provided compilation must outlive the execution.

* * 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. * *

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}. * *

The provided buffer must outlive the execution.

* * 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}. * *

The provided memory must outlive the execution.

* * 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. * *

The provided buffer must outlive the execution.

* * 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. * *

The provided memory must outlive the execution.

* * 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. * *

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. *

* * 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 /** @} */