| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| /** |
| * @addtogroup NeuralNetworks |
| * @{ |
| */ |
| |
| /** |
| * @file NeuralNetworks.h |
| */ |
| |
| #ifndef ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H |
| #define ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H |
| |
| /****************************************************************** |
| * |
| * IMPORTANT NOTICE: |
| * |
| * This file is part of Android's set of stable system headers |
| * exposed by the Android NDK (Native Development Kit). |
| * |
| * Third-party source AND binary code relies on the definitions |
| * here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES. |
| * |
| * - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES) |
| * - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS |
| * - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY |
| * - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES |
| */ |
| |
| #if __ANDROID_API__ >= __ANDROID_API_O_MR1__ |
| |
| #include <stddef.h> |
| #include <stdint.h> |
| #include <sys/cdefs.h> |
| |
| __BEGIN_DECLS |
| |
| /** |
| * Operand types. |
| * |
| * The type of operands that can be added to a model. |
| * |
| * Although we define many types, most operators accept just a few |
| * types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32}, |
| * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, |
| * and {@link ANEURALNETWORKS_INT32}. |
| */ |
| typedef enum { |
| /** 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 the same shape as input |
| * [batches, height, 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 |
| * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)"> |
| * Hadamard product</a> 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: |
| * <ul> |
| * <li>0: None; |
| * <li>1: Relu; |
| * <li>3: Relu6; |
| * <li>4: Tanh; |
| * <li>6: Sigmoid. |
| * </ul> |
| * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such |
| * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 |
| * then clipping is disabled. |
| * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the |
| * projection layer, such that values are bound within |
| * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| * |
| * 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 [batches, height/block_size, |
| * width/block_size, depth_in*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 elements to be padded in the |
| * front of dimension i. |
| * padding[i, 1] specifies the number of elements to be padded after the |
| * end of dimension i. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandCode} as input0. The |
| * output tensor has the same rank as input0, and each |
| * dimension of the output tensor has the same size as the |
| * corresponding dimension of the input tensor plus the size |
| * of the padding: |
| * output0.dimension[i] = |
| * padding[i, 0] + input0.dimension[i] + padding[i, 1] |
| */ |
| 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. |
| * |
| * <p>Build the model by calling<ul> |
| * <li>{@link ANeuralNetworksModel_create}</li> |
| * <li>{@link ANeuralNetworksModel_addOperation}</li> |
| * <li>{@link ANeuralNetworksModel_addOperand}</li> |
| * </ul> |
| * |
| * This forms a graph in which each operation and operand is a node, a |
| * directed edge from an operand to an operation indicates that the |
| * operand is an input to the operation, and a directed edge from an |
| * operation to an operand indicates that the operand is an output |
| * from the operation. This graph must be acyclic. |
| * |
| * A model is completed by calling {@link ANeuralNetworksModel_finish}. |
| * A model is destroyed by calling {@link ANeuralNetworksModel_free}. |
| * |
| * <p>A model cannot be modified once {@link ANeuralNetworksModel_finish} |
| * has been called on it.</p> |
| * |
| * <p>It is the application's responsibility to make sure that only one thread |
| * modifies a model at a given time. It is however safe for more than one |
| * thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p> |
| * |
| * <p>It is also the application's responsibility to ensure that there are no other |
| * uses of the model after calling {@link ANeuralNetworksModel_free}. |
| * This includes any compilation or execution object created using the model.</p> |
| */ |
| typedef struct ANeuralNetworksModel ANeuralNetworksModel; |
| |
| /** |
| * ANeuralNetworksCompilation is an opaque type that can be used to compile |
| * a machine learning model. |
| * |
| * <p>To use:<ul> |
| * <li>Create a new compilation instance by calling the |
| * {@link ANeuralNetworksCompilation_create} function.</li> |
| * <li>Set any desired properties on the compilation (for example, |
| * {@link ANeuralNetworksCompilation_setPreference}).</li> |
| * <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li> |
| * <li>Use the compilation as many times as needed |
| * with {@link ANeuralNetworksExecution_create}.</li> |
| * <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free} |
| * once all executions using the compilation have completed.</li></ul></p> |
| * |
| * A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}. |
| * A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}. |
| * |
| * <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish} |
| * has been called on it.</p> |
| * |
| * <p>It is the application's responsibility to make sure that only |
| * one thread modifies a compilation at a given time. It is however |
| * safe for more than one thread to use the compilation once |
| * {@link ANeuralNetworksCompilation_finish} has returned.</p> |
| * |
| * <p>It is also the application's responsibility to ensure that there are no other |
| * uses of the compilation after calling {@link ANeuralNetworksCompilation_free}. |
| * This includes any execution object created using the compilation.</p> |
| */ |
| typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; |
| |
| /** |
| * ANeuralNetworksExecution is an opaque type that can be used to apply a machine |
| * learning model to a set of inputs. |
| * |
| * <p>To use:<ul> |
| * <li>Create a new execution instance by calling the |
| * {@link ANeuralNetworksExecution_create} function.</li> |
| * <li>Associate input buffers or memory regions to the model inputs with |
| * {@link ANeuralNetworksExecution_setInput} or |
| * {@link ANeuralNetworksExecution_setInputFromMemory}.</li> |
| * <li>Associate output buffers or memory regions to the model outputs with |
| * {@link ANeuralNetworksExecution_setOutput} or |
| * {@link ANeuralNetworksExecution_setOutputFromMemory}.</li> |
| * <li>Apply the model with {@link ANeuralNetworksExecution_startCompute}.</li> |
| * <li>Wait for the execution to complete with {@link |
| * ANeuralNetworksEvent_wait}.</li> |
| * <li>Destroy the execution with |
| * {@link ANeuralNetworksExecution_free}.</li></ul></p> |
| * |
| * <p>An output buffer or memory region must not overlap with any |
| * other output buffer or memory region, with an input buffer or |
| * memory region, or with an operand value in a memory object |
| * ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p> |
| * |
| * <p>An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute} |
| * has been called on it.</p> |
| * |
| * <p>An execution can be applied to a model with |
| * {@link ANeuralNetworksExecution_startCompute} only once. Create new executions |
| * to do new evaluations of the model.</p> |
| * |
| * <p>It is the application's responsibility to make sure that only one thread |
| * modifies an execution at a given time. It is however safe for more than one |
| * thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p> |
| * |
| * <p>It is also the application's responsibility to ensure that there are no other |
| * uses of the execution after calling {@link ANeuralNetworksExecution_free}.</p> |
| */ |
| typedef struct ANeuralNetworksExecution ANeuralNetworksExecution; |
| |
| /** |
| * ANeuralNetworksOperandType describes the type of an operand. |
| * This structure is used to describe both scalars and tensors. |
| * |
| * 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. |
| * |
| * <p>In the following situations, a tensor operand type must be fully |
| * specified:<ul> |
| * <li>The operand has a constant value, set by |
| * {@link ANeuralNetworksModel_setOperandValue} (with a |
| * non-nullptr buffer) or |
| * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> |
| * <li>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.</li></ul> |
| * |
| * 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}. |
| * |
| * <p>This only creates the object. Computation is performed once |
| * {@link ANeuralNetworksExecution_startCompute} is invoked. |
| * |
| * The model should be constructed with calls to |
| * {@link ANeuralNetworksModel_addOperation} and |
| * {@link ANeuralNetworksModel_addOperand} |
| * |
| * <p>{@link ANeuralNetworksModel_finish} should be called once the model |
| * has been fully constructed.</p> |
| * |
| * <p>{@link ANeuralNetworksModel_free} should be called once the model |
| * is no longer needed.</p> |
| * |
| * @param model The {@link ANeuralNetworksModel} to be created. |
| * Set to NULL if unsuccessful. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful. |
| */ |
| int ANeuralNetworksModel_create(ANeuralNetworksModel** model); |
| |
| /** |
| * Destroy a model. |
| * |
| * The model need not have been finished by a call to |
| * {@link ANeuralNetworksModel_finish}. |
| * |
| * See {@link ANeuralNetworksModel} for information on multithreaded usage. |
| * |
| * @param model The model to be destroyed. Passing NULL is acceptable and |
| * results in no operation. |
| */ |
| void ANeuralNetworksModel_free(ANeuralNetworksModel* model); |
| |
| /** |
| * Indicate that we have finished modifying a model. Required before |
| * calling {@link ANeuralNetworksCompilation_create}. |
| * |
| * An application is responsible to make sure that no other thread uses |
| * the model at the same time. |
| * |
| * This function must only be called once for a given model. |
| * |
| * See {@link ANeuralNetworksModel} for information on multithreaded usage. |
| * |
| * @param model The model to be finished. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful. |
| */ |
| int ANeuralNetworksModel_finish(ANeuralNetworksModel* model); |
| |
| /** |
| * Add an operand to a model. |
| * |
| * The order in which the operands are added is important. The first one added |
| * to a model will have the index value 0, the second 1, etc. These indexes are |
| * used as operand identifiers in |
| * {@link ANeuralNetworksModel_addOperation}, |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}, |
| * {@link ANeuralNetworksModel_setOperandValue}, |
| * {@link ANeuralNetworksModel_setOperandValueFromMemory}, |
| * {@link ANeuralNetworksExecution_setInput}, |
| * {@link ANeuralNetworksExecution_setInputFromMemory}, |
| * {@link ANeuralNetworksExecution_setOutput}, |
| * {@link ANeuralNetworksExecution_setOutputFromMemory} and |
| * {@link ANeuralNetworksExecution_setOperandValue}. |
| * |
| * <p>Every operand must be referenced in exactly one of the following |
| * ways:<ul> |
| * <li>It is identified as a model input with |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</li> |
| * <li>It is identified as a constant with |
| * {@link ANeuralNetworksModel_setOperandValue} or |
| * {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li> |
| * <li>It is identified as an output of exactly one operation with |
| * {@link ANeuralNetworksModel_addOperation}.</li></p> |
| * <p>An operand that is identified as a model input or as a constant |
| * must not also be identified as a model output with |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}.</p> |
| * |
| * 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. Neither the {@link ANeuralNetworksOperandType} |
| * nor the dimensions it points to need to outlive the call to |
| * {@link ANeuralNetworksModel_addOperand}. |
| * |
| * @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 ANeuralNetworksOperationType} 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. Every model must have at least one input and one output. |
| * |
| * 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. |
| * |
| * <p>This only creates the object. Compilation is only performed once |
| * {@link ANeuralNetworksCompilation_finish} is invoked.</p> |
| * |
| * <p>{@link ANeuralNetworksCompilation_finish} should be called once |
| * all desired properties have been set on the compilation.</p> |
| * |
| * <p>{@link ANeuralNetworksModel_free} should be called once the compilation |
| * is no longer needed.</p> |
| * |
| * <p>The provided model must outlive the compilation.</p> |
| * |
| * The model must already have been finished by a call to |
| * {@link ANeuralNetworksModel_finish}. |
| * |
| * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. |
| * |
| * @param model The {@link ANeuralNetworksModel} to be compiled. |
| * @param compilation The newly created object or NULL if unsuccessful. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA |
| * if the model is invalid. |
| */ |
| int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, |
| ANeuralNetworksCompilation** compilation); |
| |
| /** |
| * Destroy a compilation. |
| * |
| * The compilation need not have been finished by a call to |
| * {@link ANeuralNetworksModel_finish}. |
| * |
| * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. |
| * |
| * @param compilation The compilation to be destroyed. Passing NULL is acceptable and |
| * results in no operation. |
| */ |
| void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation* compilation); |
| |
| /** |
| * Sets the execution preference. |
| * |
| * <p>Provides guidance to the runtime when trade-offs are possible.</p> |
| * |
| * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. |
| * |
| * @param compilation The compilation to be modified. |
| * @param preference Either {@link PREFER_LOW_POWER}, |
| * {@link PREFER_SINGLE_FAST_ANSWER}, or |
| * {@link PREFER_SUSTAINED_SPEED}. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful. |
| */ |
| int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation* compilation, |
| int32_t preference); |
| |
| /** |
| * Indicate that we have finished modifying a compilation. Required before |
| * calling {@link ANeuralNetworksExecution_create}. |
| * |
| * An application is responsible to make sure that no other thread uses |
| * the compilation at the same time. |
| * |
| * This function must only be called once for a given compilation. |
| * |
| * See {@link ANeuralNetworksCompilation} for information on multithreaded usage. |
| * |
| * @param compilation The compilation to be finished. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful. |
| */ |
| int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation); |
| |
| /** |
| * Create a {@link ANeuralNetworksExecution} to apply the given compilation. |
| * This only creates the object. Computation is only performed once |
| * {@link ANeuralNetworksExecution_startCompute} is invoked. |
| * |
| * <p>The provided compilation must outlive the execution.</p> |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param compilation The {@link ANeuralNetworksCompilation} to be evaluated. |
| * @param execution The newly created object or NULL if unsuccessful. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA |
| * if the compilation is invalid. |
| */ |
| int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, |
| ANeuralNetworksExecution** execution); |
| |
| /** |
| * Destroy an execution. |
| * |
| * <p>If called on an execution for which |
| * {@link ANeuralNetworksExecution_startCompute} has been called, the |
| * function will return immediately but will mark the execution to be deleted |
| * once the computation completes. The related {@link ANeuralNetworksEvent} |
| * will be signaled and the {@link ANeuralNetworksEvent_wait} will return |
| * ANEURALNETWORKS_ERROR_DELETED. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be destroyed. Passing NULL is acceptable and |
| * results in no operation. |
| */ |
| void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution); |
| |
| /** |
| * Associate a user buffer with an input of the model of the |
| * {@link ANeuralNetworksExecution}. |
| * |
| * <p>The provided buffer must outlive the execution.</p> |
| * |
| * If the input is optional, you can indicate that it is omitted by |
| * passing nullptr for buffer and 0 for length. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be modified. |
| * @param index The index of the input argument we are setting. It is |
| * an index into the lists passed to |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not |
| * the index associated with |
| * {@link ANeuralNetworksModel_addOperand}. |
| * @param type The {@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. Neither the {@link ANeuralNetworksOperandType} |
| * nor the dimensions it points to need to outlive the call |
| * to {@link ANeuralNetworksExecution_setInput}. |
| * @param buffer The buffer containing the data. |
| * @param length The length in bytes of the buffer. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the |
| * name is not recognized or the buffer is too small for the input. |
| */ |
| int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, const void* buffer, |
| size_t length); |
| |
| /** |
| * Associate part of a memory object with an input of the model of the |
| * {@link ANeuralNetworksExecution}. |
| * |
| * <p>The provided memory must outlive the execution.</p> |
| * |
| * If the input is optional, you can indicate that it is omitted by |
| * using {@link ANeuralNetworks_setInput} instead, passing nullptr for buffer |
| * and 0 for length. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be modified. |
| * @param index The index of the input argument we are setting. It is |
| * an index into the lists passed to |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not |
| * the index associated with {@link ANeuralNetworksModel_addOperand}. |
| * @param type The {@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. Neither the {@link ANeuralNetworksOperandType} |
| * nor the dimensions it points to need to outlive the call |
| * to {@link ANeuralNetworksExecution_setInputFromMemory}. |
| * @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. |
| * |
| * <p>The provided buffer must outlive the execution.</p> |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be modified. |
| * @param index The index of the output argument we are setting. It is |
| * an index into the lists passed to |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not |
| * the index associated with {@link ANeuralNetworksModel_addOperand}. |
| * @param type The {@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. Neither the {@link ANeuralNetworksOperandType} |
| * nor the dimensions it points to need to outlive the call |
| * to {@link ANeuralNetworksExecution_setOutput}. |
| * @param buffer The buffer where the data is to be written. |
| * @param length The length in bytes of the buffer. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful, ANEURALNETWORKS_BAD_DATA if the |
| * name is not recognized or the buffer is too small for the output. |
| */ |
| int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, |
| const ANeuralNetworksOperandType* type, void* buffer, |
| size_t length); |
| |
| /** |
| * Associate part of a memory object with an output of the model of the |
| * {@link ANeuralNetworksExecution}. |
| * |
| * If the output is optional, you can indicate that it is omitted by |
| * using {@link ANeuralNetworks_setOutput} instead, passing nullptr for buffer |
| * and 0 for length. |
| * |
| * <p>The provided memory must outlive the execution.</p> |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be modified. |
| * @param index The index of the output argument we are setting. It is |
| * an index into the lists passed to |
| * {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not |
| * the index associated with {@link ANeuralNetworksModel_addOperand}. |
| * @param type The {@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. Neither the {@link ANeuralNetworksOperandType} |
| * nor the dimensions it points to need to outlive the call |
| * to {@link ANeuralNetworksExecution_setOutputFromMemory}. |
| * @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. |
| * |
| * <p>Schedules evaluation of the execution. Once the model has been |
| * applied and the outputs are ready to be consumed, the returned event will be |
| * signaled. Use {@link ANeuralNetworksEvent_wait} to wait for that event. |
| * </p> |
| * |
| * Multiple executions can be scheduled and evaluated concurrently. The |
| * runtime makes no guarantee on the ordering of completion of |
| * executions. If it's important to the application, the application |
| * should enforce the ordering by using |
| * {@link ANeuralNetworksEvent_wait}. |
| * |
| * ANeuralNetworksEvent_wait must be called to recuperate the resources used |
| * by the execution. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @param execution The execution to be scheduled and executed. |
| * @param event The event that will be signaled on completion. event is set to |
| * NULL if there's an error. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if successful. |
| */ |
| int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, |
| ANeuralNetworksEvent** event); |
| |
| /** |
| * Waits until the execution completes. |
| * |
| * More than one thread can wait on an event. When the execution completes, |
| * all threads will be released. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| * |
| * @return ANEURALNETWORKS_NO_ERROR if the execution completed normally. |
| */ |
| int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event); |
| |
| /** |
| * Destroys the event. |
| * |
| * See {@link ANeuralNetworksExecution} for information on multithreaded usage. |
| */ |
| void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event); |
| |
| __END_DECLS |
| |
| #endif // __ANDROID_API__ >= __ANDROID_API_O_MR1__ |
| |
| #endif // ANDROID_ML_NN_RUNTIME_NEURAL_NETWORKS_H |
| |
| /** @} */ |