/* * 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 { /** A 32 bit floating point scalar value. */ ANEURALNETWORKS_FLOAT32 = 0, /** A signed 32 bit integer scalar value. */ ANEURALNETWORKS_INT32 = 1, /** An unsigned 32 bit integer scalar value. */ ANEURALNETWORKS_UINT32 = 2, /** A tensor of 32 bit floating point values. */ ANEURALNETWORKS_TENSOR_FLOAT32 = 3, /** A tensor of 32 bit integer values. */ ANEURALNETWORKS_TENSOR_INT32 = 4, /** * A tensor of 8 bit integers that represent real numbers. * * Attached to this tensor are two numbers that can be used to convert the * 8 bit integer to the real value and vice versa. These two numbers are: * - scale: a 32 bit floating point value greater than zero. * - zeroPoint: a 32 bit integer, in range [0, 255]. * * The formula is: * real_value = (integer_value - zeroPoint) * scale. */ ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5, } 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 {@link OperandCode} and compatible * dimensions. The output is the sum of both input tensors, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the * input operands. It starts with the trailing dimensions, and works its * way forward. * * Example: * * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor. * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions * as input0. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: The sum, a tensor of the same {@link OperandCode} 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 {@link OperandCode}: * * {@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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the left, in the ‘width’ dimension. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the right, in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the top, in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the bottom, in the ‘height’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit * padding scheme, has to be one of the * {@link PaddingCode} values. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * 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 {@link OperandCode} and the same * dimensions except the dimension along the concatenation axis. * * Supported tensor {@link OperandCode}: * * {@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}, all input tensors * must have the same scale and zeroPoint. * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the * concatenation axis. * * Outputs: * * 0: The output, a tensor of the same {@link OperandCode} as the input * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. */ 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 {@link OperandCode}: * * {@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}, the bias * should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of * 0 and bias_scale == input_scale * filter_scale. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the left, in the ‘width’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the right, in the ‘width’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the top, in the ‘height’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the bottom, in the ‘height’ dimension. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * 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}, the bias should * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit * padding scheme, has to be one of the * {@link PaddingCode} values. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth_out]. For output tensor of * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition * must be satisfied: output_scale > input_scale * filter_scale. */ 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 {@link OperandCode}: * * {@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}, the bias should * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the left, in the ‘width’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the right, in the ‘width’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the top, in the ‘height’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the bottom, in the ‘height’ dimension. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise * multiplier. * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], * specifying the input. * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], * specifying the filter. * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit * padding scheme, has to be one of the * {@link PaddingCode} values. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise * multiplier. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth_out]. For output tensor of * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition * must be satisfied: output_scale > input_scale * filter_scale. */ ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, /** * Rearranges data from depth into blocks of spatial data. * * More specifically, this op outputs a copy of the input tensor where * values from the depth dimension are moved in spatial blocks to the height * and width dimensions. The value block_size indicates the input block size * and how the data is moved. * * Chunks of data of size block_size * block_size from depth are rearranged * into non-overlapping blocks of size block_size x block_size. * * The width of the output tensor is input_depth * block_size, whereas the * height is input_height * block_size. The depth of the input tensor must * be divisible by block_size * block_size * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_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 {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. * block_size must be >=1 and block_size * block_size must be a divisor * of the input depth. * * 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 {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. * * Outputs: * * 0: The output tensor of same shape as input0, but with * {@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], all three values found in Lookups are * expected to be between 0 and 39. The resulting tensor must * have shape of [3, 200, 300]. * * If a value in Lookups is out of bounds, the operation must fail * and an error must be reported. * * Inputs: * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. * The values are indices into the first dimension of Values. * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are * extracted. * * Output: * * 0: A n-D tensor with the same rank and shape as the Values * tensor, except for the first dimension which has the same size * as Lookups' only dimension. */ ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, /** * Computes element-wise floor() on the input tensor. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor. * * Outputs: * * 0: The output tensor, of the same {@link OperandCode} and dimensions as * the input tensor. */ ANEURALNETWORKS_FLOOR = 8, /** * Denotes a fully (densely) connected layer, which connects all elements * in the input tensor with each element in the output tensor. * * This layer implements the operation: * * outputs = activation(inputs * weights’ + bias) * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor of at least rank 2, specifying the input. If rank is * greater than 2, then it gets flattened to a 2-D Tensor. The * (flattened) 2-D Tensor is reshaped (if necessary) to * [batch_size, input_size], where "input_size" corresponds to the * number of inputs to the layer, matching the second dimension of * weights, and "batch_size" is calculated by dividing the number of * elements by "input_size". * * 1: A 2-D tensor, specifying the weights, of shape * [num_units, input_size], where "num_units" corresponds to the number * of output nodes. * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: The output tensor, of shape [batch_size, num_units]. For output * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, 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 must have zero values. * * For example, if Values has shape of [40, 200, 300], * Keys should have a shape of [40]. If Lookups tensor has shape * of [3], three slices are being concatenated, so the resulting tensor * must have the shape of [3, 200, 300]. If the first entry in Lookups * has the value 123456, that value must be located in Keys tensor. * If the sixth entry of Keys contains 123456, the sixth slice of Values * must be selected. If no entry in Keys has 123456, a slice of zeroes * must be concatenated. * * 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 {@link OperandCode}: * * {@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 {@link OperandCode}: * * {@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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the left, in the ‘width’ dimension. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the right, in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the top, in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the bottom, in the ‘height’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit * padding scheme, has to be one of the * {@link PaddingCode} values. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * 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 {@link OperandCode}: * * {@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 {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of * the normalization window. * * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must * not be zero. * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale * factor, alpha. * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the exponent, * beta. * * 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 {@link OperandCode}: * * {@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}, * 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, /** * Performs a single time step in a Long Short-Term Memory (LSTM) layer * * The LSTM operation is described by the following equations. * * \f{eqnarray*}{ * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\ * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\ * & & \\ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) * & if\ there\ is\ a\ projection; \\ * h_t =& & \\ * & o_t \odot g(C_t) & otherwise. \\ * \f} * Where: * * \f$x_t\f$ is the input, * * \f$i_t\f$ is the input gate, * * \f$f_t\f$ is the forget gate, * * \f$C_t\f$ is the cell state, * * \f$o_t\f$ is the output, * * \f$h_t\f$ is the output state, * * \f$\sigma\f$ is the logistic sigmoid function, * * \f$g\f$ is the cell input and cell output activation function, usually * \f$tahn\f$, * * \f$W_{xi}\f$ is the input-to-input weight matrix, * * \f$W_{hi}\f$ is the recurrent to input weight matrix, * * \f$W_{ci}\f$ is the cell-to-input weight matrix, * * \f$b_i\f$ is the input gate bias, * * \f$W_{xf}\f$ is the input-to-forget weight matrix, * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix, * * \f$W_{cf}\f$ is the cell-to-forget weight matrix, * * \f$b_f\f$ is the forget gate bias, * * \f$W_{xc}\f$ is the input-to-cell weight matrix, * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix, * * \f$b_c\f$ is the cell bias, * * \f$W_{xo}\f$ is the input-to-output weight matrix, * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix, * * \f$W_{co}\f$ is the cell-to-output weight matrix, * * \f$b_o\f$ is the output gate bias, * * \f$W_{proj}\f$ is the projection weight matrix, * * \f$b_{proj}\f$ is the projection bias, * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and * * \f$t_{proj}\f$ is the threshold for clipping the projected output. * * \f$\odot\f$ is the * * Hadamard product that takes two matrices and produces another * matrix, each element of which is the product of the corresponding * elements of the input matrices. * * The operation has the following independently optional inputs: * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights * (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate * bias (\f$b_i\f$) either all have values, or none of them have values * (i.e., all set to null). If they have no values, coupling of input and * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$) * is calculated using the following equation instead. * \f{eqnarray*}{ * i_t = 1 - f_t * \f} * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights * (\f$W_{co}\f$) either both have values or neither of them have values. * If they have values, the peephole optimization is used. Additionally, * if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also * required to have values for peephole optimization. * * The projection weights (\f$W_{proj}\f$) is required only for the * recurrent projection layer, and should otherwise have no value. * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a * value if the recurrent projection layer exists, and should otherwise * have no value. * * References: * * The default non-peephole non-CIFG implementation is based on: * http://www.bioinf.jku.at/publications/older/2604.pdf * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural * Computation, 9(8):1735-1780, 1997. * * The peephole implementation and projection layer is based on: * https://research.google.com/pubs/archive/43905.pdf * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory * recurrent neural network architectures for large scale acoustic * modeling." INTERSPEECH, 2014. * (However, the concept of peephole optimization was introduced in work * prior to this paper.) * * The coupling of input and forget gate (CIFG) is based on: * http://arxiv.org/pdf/1503.04069.pdf * Greff et al. "LSTM: A Search Space Odyssey" * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Inputs: * * 0: The input (\f$x_t\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, input_size], where “batch_size” corresponds to the * batching dimension, and “input_size” is the size of the input. * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size], where “num_units” corresponds to the * number of cell units. * * 2: The input-to-forget weights (\f$W_{xf}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size]. * * 3: The input-to-cell weights (\f$W_{xc}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size]. * * 4: The input-to-output weights (\f$W_{xo}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size]. * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, output_size], where “output_size” corresponds to either * the number of cell units (i.e., “num_units”), or the second * dimension of the “projection_weights”, if defined. * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, output_size]. * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, output_size]. * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, output_size]. * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 12:The input gate bias (\f$b_i\f$). Optional. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 13:The forget gate bias (\f$b_f\f$). * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 14:The cell bias (\f$b_c\f$). * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 15:The output gate bias (\f$b_o\f$). * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 16:The projection weights (\f$W_{proj}\f$). Optional. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [output_size, num_units]. * * 17:The projection bias (\f$b_{proj}\f$). Optional. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [output_size]. * * 18:The output state (in) (\f$h_{t-1}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, output_size]. * * 19:The cell state (in) (\f$C_{t-1}\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units]. * * 20:The activation function (\f$g\f$). * A value indicating the activation function: *
    *
  • 0: None; *
  • 1: Relu; *
  • 3: Relu6; *
  • 4: Tanh; *
  • 6: Sigmoid. *
* * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 * then clipping is disabled. * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the * projection layer, such that values are bound within * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. * * Outputs: * * 0: The scratch buffer. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units * 4] with CIFG, or * [batch_size, num_units * 3] without CIFG. * * 1: The output state (out) (\f$h_t\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, output_size]. * * 2: The cell state (out) (\f$C_t\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units]. * * 3: The output (\f$o_t\f$). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, output_size]. This is effectively the same as the * current “output state (out)” value. */ 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 {@link OperandCode}: * * {@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 {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the left, in the ‘width’ dimension. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the right, in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the top, in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on * the bottom, in the ‘height’ dimension. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit * padding scheme, has to be one of the * {@link PaddingCode} values. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘width’ dimension. * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when * walking through input in the ‘height’ dimension. * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * width. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter * height. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * 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 {@link OperandCode} and compatible * dimensions. The output is the product of both input tensors, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the resulting output is the maximum size along each dimension * of the input operands. It starts with the trailing dimensions, and works * its way forward. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor. * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions * as input0. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: The product, a tensor of the same {@link OperandCode} as input0. * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, * the following condition must be satisfied: * output_scale > input1_scale * input2_scale. */ ANEURALNETWORKS_MUL = 18, /** * Computes rectified linear activation on the input tensor element-wise. * * The output is calculated using this formula: * * output = max(0, input) * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_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 {@link OperandCode}: * * {@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 {@link OperandCode}: * * {@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 {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the tensor to be reshaped. * * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, defining the * shape of the output tensor. The number of elements implied by shape * must be the same as the number of elements in the input tensor. * * 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 must be distorted if their output aspect ratio is not the * same as input aspect ratio. The corner pixels of output may not be the * same as corner pixels of input. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_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 {@link ANEURALNETWORKS_INT32} scalar, specifying the output * height of the output tensor. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output * width of the output tensor. * * 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 {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Inputs: * * 0: input. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} of shape * [batch_size, input_size], where “batch_size” corresponds to the * batching dimension, and “input_size” is the size of the input. * * 1: weights. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size], where “num_units” corresponds to the * number of units. * * 2: recurrent_weights. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, num_units], with columns corresponding to the weights * from each unit. * * 3: bias. * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units]. * * 4: hidden state (in). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units]. * * * 1: output. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units]. This is effectively the same as the * current state value. */ 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 {@link OperandCode}: * * {@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: An {@link ANEURALNETWORKS_FLOAT32} scalar, 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}, * 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 {@link OperandCode}: * * {@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 {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. * block_size must be >=1 and block_size must be a divisor of both the * input height and width. * * 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 {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Inputs: * * 0: input. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, input_size], where “batch_size” corresponds to the * batching dimension, and “input_size” is the size of the input. * * 1: weights_feature. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, input_size], where “num_units” corresponds to the * number of units. * * 2: weights_time. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [num_units, memory_size], where “memory_size” corresponds to the * fixed-size of the memory. * * 3: bias. * An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, * of shape [num_units]. * * 4: state (in). * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, (memory_size - 1) * num_units * rank]. * * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, (memory_size - 1) * num_units * rank]. * * 1: output. * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape * [batch_size, num_units]. */ ANEURALNETWORKS_SVDF = 27, /** * Computes hyperbolic tangent of input tensor element-wise. * * The output is calculated using this formula: * * output = tanh(input) * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_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, #if __ANDROID_API__ >= __ANDROID_API_P__ // TODO: make the description easier to understand. /** * BatchToSpace for N-dimensional tensors. * * This operation reshapes the batch dimension (dimension 0) into M + 1 * dimensions of shape block_shape + [batch], interleaves these blocks back * into the grid defined by the spatial dimensions [1, ..., M], to obtain a * result with the same rank as the input. * * This is the reverse of SpaceToBatch. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: 4 * * Inputs: * * 0: An n-D tensor, specifying the tensor to be reshaped * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block * sizes for each spatial dimension of the input tensor. All values * must be >= 1. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29, /** * Element-wise division of two tensors. * * Takes two input tensors of identical {@link OperandCode} and compatible * dimensions. The output is the result of dividing the first input tensor * by the second, optionally modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the * input operands. It starts with the trailing dimensions, and works its way * forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the first input. * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions * as input0. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_DIV = 30, /** * Computes the mean of elements across dimensions of a tensor. * * Reduces the input tensor along the given dimensions to reduce. Unless * keep_dims is true, the rank of the tensor is reduced by 1 for each entry * in axis. If keep_dims is true, the reduced dimensions are retained with * length 1. * * If dimensions to reduce have no entries, all dimensions are reduced, and * a tensor with a single element is returned. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor, specifying the input. * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions * to reduce. If None (the default), reduces all dimensions. Must be in * the range [-rank(input_tensor), rank(input_tensor)). * * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, * retains reduced dimensions with length 1. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_MEAN = 31, /** * Pads a tensor. * * This operation pads a tensor according to the specified paddings. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the tensor to be padded. * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings * for each spatial dimension of the input tensor. The shape of the * tensor must be {rank(input0), 2}. * padding[i, 0] specifies the number of element to be padded in the * front of dimension i. * padding[i, 1] specifies the number of element to be padded after the * end of dimension i. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_PAD = 32, // TODO: make the description easier to understand. /** * SpaceToBatch for N-Dimensional tensors. * * This operation divides "spatial" dimensions [1, ..., M] of the input into * a grid of blocks of shape block_shape, and interleaves these blocks with * the "batch" dimension (0) such that in the output, the spatial dimensions * [1, ..., M] correspond to the position within the grid, and the batch * dimension combines both the position within a spatial block and the * original batch position. Prior to division into blocks, the spatial * dimensions of the input are optionally zero padded according to paddings. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: 4 * * Inputs: * * 0: An n-D tensor, specifying the input. * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block * sizes for each spatial dimension of the input tensor. All values * must be >= 1. * * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings * for each spatial dimension of the input tensor. All values must be * >= 0. The shape of the tensor must be {rank(input0), 2}. * padding[i, 0] specifies the number of element to be padded in the * front of dimension i. * padding[i, 1] specifies the number of element to be padded after the * end of dimension i. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33, /** * Removes dimensions of size 1 from the shape of a tensor. * * Given a tensor input, this operation returns a tensor of the same * {@link OperandCode} with all dimensions of size 1 removed. If you don't * want to remove all size 1 dimensions, you can remove specific size 1 * dimensions by specifying the axes (input1). * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, the tensor to be squeezed. * * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The * dimensions to squeeze. If specified only squeezes the dimensions * listed. Otherwise, squeezes all dimensions. The dimension index * starts at 0. An error must be reported if squeezing a dimension that * is not 1. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. Contains the * same data as input, but has one or more dimensions of size 1 * removed. */ ANEURALNETWORKS_SQUEEZE = 34, /** * Extracts a strided slice of a tensor. * * Roughly speaking, this op extracts a slice of size (end - begin) / stride * from the given input tensor. Starting at the location specified by begin * the slice continues by adding stride to the index until all dimensions * are not less than end. Note that a stride can be negative, which causes a * reverse slice. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the tensor to be sliced. * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of * the dimensions of the input tensor to be sliced. The length must be * of rank(input0). * * 2: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of * the dimensions of the input tensor to be sliced. The length must be * of rank(input0). * * 3: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of * the dimensions of the input tensor to be sliced. The length must be * of rank(input0). * * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit * of begin_mask is set, begin[i] is ignored and the fullest possible * range in that dimension is used instead. * * 5: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of * end_mask is set, end[i] is ignored and the fullest possible range in * that dimension is used instead. * * 6: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32 * mask. If the ith bit of shrink_axis_mask is set, it implies that the * ith specification shrinks the dimensionality by 1. A slice of size 1 * starting from begin[i] in the dimension must be preserved. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_STRIDED_SLICE = 35, /** * Element-wise subtraction of two tensors. * * Takes two input tensors of identical {@link OperandCode} and compatible * dimensions. The output is the result of subtracting the second input * tensor from the first one, optionally modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the * input operands. It starts with the trailing dimensions, and works its way * forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the first input. * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions * as input0. * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the * {@link FuseCode} values. Specifies the activation to * invoke on the result. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_SUB = 36, /** * Transposes the input tensor, permuting the dimensions according to the * perm tensor. * * The returned tensor's dimension i corresponds to the input dimension * perm[i]. If perm is not given, it is set to (n-1...0), where n is the * rank of the input tensor. Hence by default, this operation performs a * regular matrix transpose on 2-D input Tensors. * * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the tensor to be transposed. * * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, * the permutation of the dimensions of the input tensor. * * Outputs: * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_TRANSPOSE = 37, #endif // __ANDROID_API__ >= __ANDROID_API_P__ } 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_BAD_STATE = 6, ANEURALNETWORKS_UNMAPPABLE = 7, } 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. * * 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. * *

Build the model 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. * * A tensor operand type must have a specified rank (number of * dimensions) but may have any of its dimensions unspecified. * * A tensor operand type with all dimensions specified is "fully * specified". Whenever possible (i.e., whenever the dimensions are * known at model construction time), a tensor operand type should be * (but is not required to be) fully specified, in order to enable the * best possible performance. * * If a tensor operand's type is not fully specified, the dimensions * of the operand are deduced from the operand types and values of the * operation for which that operand is an output. * *

In the following situations, a tensor operand type must be fully * specified:

    *
  • The operand has a constant value, set by * {@link ANeuralNetworksModel_setOperandValue} (with a * non-nullptr buffer) or * {@link ANeuralNetworksModel_setOperandValueFromMemory}.
    • *
  • The operand is a model input or model output (see * {@link ANeuralNetworksModel_identifyInputsAndOutputs}). A * fully specified tensor operand type must either be provided * to {@link ANeuralNetworksModel_addOperand}; or it must be * provided to the corresponding * {@link ANeuralNetworksExecution_setInput}, * {@link ANeuralNetworksExecution_setInputFromMemory}, * {@link ANeuralNetworksExecution_setOutput}, or * {@link ANeuralNetworksModel_setOperandValueFromMemory}. * EXCEPTION: If the input or output is optional and omitted * (by passing nullptr for buffer to * {@link ANeuralNetworksExecution_setInput} or * {@link ANeuralNetworksExecution_setOutput}) then it need * not have a fully specified tensor operand type.
* * A tensor operand type with some number of unspecified dimensions is * represented by setting each unspecified dimension to 0. */ typedef struct ANeuralNetworksOperandType { /** The data type, e.g ANEURALNETWORKS_INT8. */ int32_t type; /** The number of dimensions (rank). 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 accommodate inputs of various sizes, as * you may want to do for a CNN, leave unspecified the dimensions that * will vary at run time. If you do so, fully specify dimensions * when calling {@link ANeuralNetworksExecution_setInput} or * {@link ANeuralNetworksExecution_setInputFromMemory}. * * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been * called will return an error. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @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 {@link ANeuralNetworksOperandType} 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); /** * Specifies 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); #if __ANDROID_API__ >= __ANDROID_API_P__ /** * Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be * calculated with range and/or precision as low as that of the IEEE 754 16-bit * floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32} * must be calculated using at least the range and precision of the IEEE 754 * 32-bit floating-point format. * * @param model The model to be modified. * @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be * calculated with range and/or precision as low as that of the * IEEE 754 16-bit floating point format. 'false' indicates * {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using * at least the range and precision of the IEEE 754 32-bit floating * point format. * * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been * called will return an error. * * See {@link ANeuralNetworksModel} for information on multithreaded usage. */ int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel* model, bool allow); #endif // __ANDROID_API__ >= __ANDROID_API_P__ /** * 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 {@link ANeuralNetworksOperandType} of the * operand. Unless the input is omitted, this should be * used to specify the dimensions that were left * unspecified when the operand was added to the * model. All other properties of the type must be the * same as specified in the model. If the type is the same * as specified when the model was built, NULL can be * passed. * @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 {@link ANeuralNetworksOperandType} of the * operand. This should be used to specify the dimensions * that were left unspecified when the operand was added * to the model. All other properties of the type must be * the same as specified in the model. If the type is the * same as specified when the model was built, NULL can be * passed. * @param memory The memory containing the data. * @param offset This specifies the location of the data within the memory. * The offset is in bytes from the start of memory. * @param length The size in bytes of the data value. * * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the * name is not recognized or the buffer is too small for the input. */ int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const ANeuralNetworksMemory* memory, size_t offset, size_t length); /** * 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 {@link ANeuralNetworksOperandType} of the * operand. Unless the output is omitted, this should be * used to specify the dimensions that were left * unspecified when the operand was added to the * model. All other properties of the type must be the * same as specified in the model. If the type is the same * as specified when the model was built, NULL can be * passed. * @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 {@link ANeuralNetworksOperandType} of the operand. This should be * used to specify the dimensions that were left * unspecified when the operand was added to the * model. All other properties of the type must be the * same as specified in the model. If the type is the same * as specified when the model was built, NULL can be * passed. * @param memory The memory where the data is to be stored. * @param offset This specifies the location of the data within the memory. * The offset is in bytes from the start of memory. * @param length The length in bytes of the data value. * * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the * name is not recognized or the buffer is too small for the output. */ int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const ANeuralNetworksMemory* memory, size_t offset, size_t length); /** * 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__ >= __ANDROID_API_O_MR1__ #endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H /** @} */