| /* |
| * 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. |
| */ |
| |
| package android.hardware.neuralnetworks@1.0; |
| |
| /** |
| * Operand types. |
| * |
| * The type of an operand in a model. |
| * |
| * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors |
| * with at least one dimension). Types not prefaced by TENSOR_* represent |
| * scalar values and must have no dimensions. |
| * |
| * Although we define many types, most operators accept just a few |
| * types. Most used are {@link OperandType::TENSOR_FLOAT32}, |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * and {@link OperandType::INT32}. |
| */ |
| enum OperandType : int32_t { |
| /** A 32 bit floating point scalar value. */ |
| FLOAT32 = 0, |
| /** A signed 32 bit integer scalar value. */ |
| INT32 = 1, |
| /** An unsigned 32 bit integer scalar value. */ |
| UINT32 = 2, |
| /** A tensor of 32 bit floating point values. */ |
| TENSOR_FLOAT32 = 3, |
| /** A tensor of 32 bit integer values. */ |
| TENSOR_INT32 = 4, |
| /** |
| * A tensor of 8 bit unsigned 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. |
| */ |
| TENSOR_QUANT8_ASYMM = 5, |
| |
| /** |
| * DEPRECATED. Since HAL version 1.2, extensions are the preferred |
| * alternative to OEM operation and data types. |
| * |
| * OEM specific scalar value. |
| */ |
| OEM = 10000, |
| |
| /** |
| * DEPRECATED. Since HAL version 1.2, extensions are the preferred |
| * alternative to OEM operation and data types. |
| * |
| * A tensor of OEM specific values. |
| */ |
| TENSOR_OEM_BYTE = 10001, |
| }; |
| |
| /** |
| * Operation types. |
| * |
| * The type of an operation in a model. |
| */ |
| enum OperationType : int32_t { |
| /** |
| * Adds two tensors, element-wise. |
| * |
| * Takes two input tensors of identical {@link OperandType} 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scales and zeroPoint can be different from input0 scale and zeroPoint. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The sum, a tensor of the same {@link OperandType} as input0. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint can be different from inputs' scale and zeroPoint. |
| */ |
| 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[b, i, j, channel] = |
| * sum_{di, dj}( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, channel] |
| * ) / sum(1) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 9: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 6: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| AVERAGE_POOL_2D = 1, |
| |
| /** |
| * Concatenates the input tensors along the given dimension. |
| * |
| * The input tensors must have identical {@link OperandType} and the same |
| * dimensions except the dimension along the concatenation axis. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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]. |
| * All input tensors of |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * must have the same scale and zeroPoint as the output tensor. |
| * * n: An {@link OperandType::INT32} scalar, specifying the |
| * concatenation axis. |
| * |
| * Outputs: |
| * * 0: The output, a tensor of the same {@link OperandType} as the input |
| * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, the scale and zeroPoint |
| * values must be the same as the input tensors'. |
| */ |
| CONCATENATION = 2, |
| |
| /** |
| * Performs a 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[b, i, j, channel] = |
| * sum_{di, dj, k} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, k] * |
| * filter[channel, di, dj, k] |
| * ) + bias[channel] |
| * |
| * Supported tensor {@link OperandType} configurations: |
| * * 32 bit floating point: |
| * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. |
| * |
| * * Quantized: |
| * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. |
| * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to |
| * * * input.scale * filter.scale). |
| * |
| * 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_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 type {@link OperandType::TENSOR_FLOAT32} |
| * the bias must be of the same type. |
| * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 9: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 type {@link OperandType::TENSOR_FLOAT32} |
| * the bias must be of the same |
| * type. |
| * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::TENSOR_QUANT8_ASYMM}, |
| * the following condition must be satisfied: output_scale > input_scale * filter_scale |
| */ |
| 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] |
| * ) + bias[k * channel_multiplier + q] |
| * |
| * Supported tensor {@link OperandType} configurations: |
| * * 32 bit floating point: |
| * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. |
| * |
| * * Quantized: |
| * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. |
| * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to |
| * * * input.scale * filter.scale). |
| * |
| * 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_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 type {@link OperandType::TENSOR_FLOAT32} |
| * the bias must be of the same type. |
| * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise |
| * multiplier. |
| * * 10: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 type {@link OperandType::TENSOR_FLOAT32} |
| * the bias must be of the same type. |
| * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise |
| * multiplier. |
| * * 7: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::TENSOR_QUANT8_ASYMM}, |
| * the following condition must be satisfied: |
| * output_scale > input_scale * filter_scale |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, |
| * and Channels) data layout. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: An {@link OperandType::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)]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| DEPTH_TO_SPACE = 5, |
| |
| /** |
| * Dequantizes the input tensor. |
| * |
| * The formula is: |
| * |
| * output = (input - zeroPoint) * scale. |
| * |
| * Supported input tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported output tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32}. |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: A tensor with the same shape as input0. |
| */ |
| 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. |
| * |
| * Supported value tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported value tensor rank: from 2 |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D tensor of {@link OperandType::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. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input1. |
| */ |
| EMBEDDING_LOOKUP = 7, |
| |
| /** |
| * Computes element-wise floor() on the input tensor. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor, of the same {@link OperandType} and dimensions as |
| * the input tensor. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 OperandType::TENSOR_FLOAT32}, the bias should |
| * also be of {@link OperandType::TENSOR_FLOAT32}. |
| * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, |
| * with zeroPoint of 0 and bias_scale == input_scale * filter_scale. |
| * * 3: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::TENSOR_QUANT8_ASYMM}, the following |
| * condition must be satisfied: output_scale > input_scale * filter_scale. |
| */ |
| 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. |
| * |
| * Supported value tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported value tensor rank: from 2 |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with |
| * shape [ k ]. |
| * * 1: Keys. A 1-D {@link OperandType::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 …]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input2. |
| * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup |
| * hits (True) or not (False). |
| * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 |
| * and scale 1.0f. |
| * A non-zero byte represents True, a hit. A zero indicates otherwise. |
| */ |
| HASHTABLE_LOOKUP = 10, |
| |
| /** |
| * Applies L2 normalization along the axis 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)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, |
| * Height, Width, and Channels). |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, specifying the tensor to be normalized. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} and same shape as input0. |
| */ |
| 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[b, i, j, c] = |
| * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) / |
| * sum(1)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * 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 OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 9: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 6: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::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 OperandType::INT32} scalar, specifying the radius of |
| * the normalization window. |
| * * 2: A scalar, specifying the bias, must not be zero. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias |
| * value must be of {@link OperandType::FLOAT32}. |
| * * 3: A scalar, specifying the scale factor, alpha. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the |
| * alpha value must be of {@link OperandType::FLOAT32}. |
| * * 4: A scalar, specifying the exponent, beta. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta |
| * value must be of {@link OperandType::FLOAT32}. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 OperandType::TENSOR_QUANT8_ASYMM}, |
| * the scale must be 1.f / 256 and the zeroPoint must be 0. |
| */ |
| LOGISTIC = 14, |
| |
| /** |
| * Projects an input to a bit vector via locality senstive hashing. |
| * |
| * Supported input tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported input tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: Hash functions. Dim.size == 2, DataType: Float. |
| * Tensor[0].Dim[0]: Number of hash functions. |
| * Tensor[0].Dim[1]: Number of projected output bits generated by each |
| * hash function. |
| * If the projection type is Sparse: |
| * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32 |
| * |
| * * 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. |
| */ |
| 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 cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights |
| * (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all |
| * have values or neither of them have values (i.e., all set to null). If |
| * they have values, the peephole optimization is used. |
| * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights |
| * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values, |
| * or none of them have values. 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} |
| * In case peephole optimization is used and CIFG is not used |
| * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the |
| * cell-to-input weights must have no value. |
| * * 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * All input and output tensors must be of the same type. |
| * |
| * Inputs: |
| * * 0: The input (\f$x_t\f$). |
| * A 2-D tensor 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 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 shape [num_units, input_size]. |
| * * 3: The input-to-cell weights (\f$W_{xc}\f$). |
| * A 2-D tensor of shape [num_units, input_size]. |
| * * 4: The input-to-output weights (\f$W_{xo}\f$). |
| * A 2-D tensor of shape [num_units, input_size]. |
| * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. |
| * A 2-D tensor 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 shape [num_units, output_size]. |
| * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). |
| * A 2-D tensor of shape [num_units, output_size]. |
| * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). |
| * A 2-D tensor of shape [num_units, output_size]. |
| * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 12:The input gate bias (\f$b_i\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 13:The forget gate bias (\f$b_f\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 14:The cell bias (\f$b_c\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 15:The output gate bias (\f$b_o\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 16:The projection weights (\f$W_{proj}\f$). Optional. |
| * A 2-D tensor of shape [output_size, num_units]. |
| * * 17:The projection bias (\f$b_{proj}\f$). Optional. |
| * A 1-D tensor of shape [output_size]. |
| * * 18:The output state (in) (\f$h_{t-1}\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. |
| * * 19:The cell state (in) (\f$C_{t-1}\f$). |
| * A 2-D tensor 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 shape [batch_size, num_units * 3] with CIFG, or |
| * [batch_size, num_units * 4] without CIFG. |
| * * 1: The output state (out) (\f$h_t\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. |
| * * 2: The cell state (out) (\f$C_t\f$). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * * 3: The output (\f$o_t\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. This is effectively |
| * the same as the current “output state (out)” value. |
| */ |
| 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[b, i, j, channel] = |
| * max_{di, dj} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, channel] |
| * ) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 9: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} 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 OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the filter |
| * width. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the filter |
| * height. |
| * * 6: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| MAX_POOL_2D = 17, |
| |
| /** |
| * Multiplies two tensors, element-wise. |
| * |
| * Takes two input tensors of identical {@link OperandType} 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The product, a tensor of the same {@link OperandType} as input0. |
| * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the following condition must be satisfied: |
| * output_scale > input1_scale * input2_scale. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. |
| * |
| * Outputs: |
| * * 0: The output tensor of the same shape as input0. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::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 OperandType::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. |
| * |
| * If one component of shape is the special value -1, the size of that |
| * dimension is computed so that the total size remains constant. In |
| * particular, a shape of [-1] flattens into 1-D. At most one component |
| * of shape can be -1. |
| * |
| * Outputs: |
| * * 0: The output tensor, of shape specified by the input shape. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, |
| * and Channels) data layout. |
| * |
| * Inputs (resizing by shape): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. |
| * * 1: An {@link OperandType::INT32} scalar, specifying the output |
| * width of the output tensor. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the output |
| * height of the output tensor. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, new_height, new_width, depth]. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * The input tensors must all be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor 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 shape [num_units, input_size], where “num_units” |
| * corresponds to the number of units. |
| * * 2: recurrent_weights. |
| * A 2-D tensor of shape [num_units, num_units], with columns |
| * corresponding to the weights from each unit. |
| * * 3: bias. |
| * A 1-D tensor of shape [num_units]. |
| * * 4: hidden state (in). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * * 5: fused_activation_function. |
| * An optional {@link FusedActivationFunc} 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 shape [batch_size, num_units]. |
| * |
| * * 1: output. |
| * A 2-D tensor of shape [batch_size, num_units]. This is effectively |
| * the same as the current state value. |
| */ |
| 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)} |
| * |
| * For input tensor with rank other than 2, the activation will be applied |
| * independently on each 1-D slice along specified dimension. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 2 or 4. |
| * |
| * Inputs: |
| * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. |
| * * 1: A scalar, specifying the positive scaling factor for the exponent, |
| * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of |
| * {@link OperandType::FLOAT32}. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * For {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the scale must be 1.f / 256 and the zeroPoint must be 0. |
| */ |
| 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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, |
| * and Channels) data layout. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: An {@link OperandType::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]. |
| * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, |
| * the scale and zeroPoint must be the same as input0. |
| */ |
| 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, |
| * "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 OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * All input tensors must be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor 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 shape [num_units, input_size], where “num_units” |
| * corresponds to the number of units. |
| * * 2: weights_time. |
| * A 2-D tensor 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 shape [num_units]. |
| * * 4: state (in). |
| * A 2-D tensor 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 FusedActivationFunc} 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 the same {@link OperandType} as the inputs, with shape |
| * [batch_size, (memory_size - 1) * num_units * rank]. |
| * * 1: output. |
| * A 2-D tensor of the same {@link OperandType} as the inputs, with shape |
| * [batch_size, num_units]. |
| */ |
| SVDF = 27, |
| |
| /** |
| * Computes hyperbolic tangent of input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = tanh(input) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::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. |
| */ |
| TANH = 28, |
| |
| /** |
| * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to |
| * OEM operation and data types. |
| * |
| * This operation is OEM specific. It should only be used for OEM |
| * applications. |
| */ |
| OEM_OPERATION = 10000, |
| }; |
| |
| /** |
| * Fused activation function types. |
| */ |
| enum FusedActivationFunc : int32_t { |
| NONE = 0, |
| RELU = 1, |
| RELU1 = 2, |
| RELU6 = 3, |
| }; |
| |
| /** |
| * How an operand is used. |
| */ |
| enum OperandLifeTime : int32_t { |
| /** |
| * The operand is internal to the model. It's created by an operation and |
| * consumed by other operations. It must be an output operand of |
| * exactly one operation. |
| */ |
| TEMPORARY_VARIABLE, |
| |
| /** |
| * The operand is an input of the model. It must not be an output |
| * operand of any operation. |
| * |
| * An operand can't be both input and output of a model. |
| */ |
| MODEL_INPUT, |
| |
| /** |
| * The operand is an output of the model. It must be an output |
| * operand of exactly one operation. |
| * |
| * An operand can't be both input and output of a model. |
| */ |
| MODEL_OUTPUT, |
| |
| /** |
| * The operand is a constant found in Model.operandValues. It must |
| * not be an output operand of any operation. |
| */ |
| CONSTANT_COPY, |
| |
| /** |
| * The operand is a constant that was specified via a Memory |
| * object. It must not be an output operand of any operation. |
| */ |
| CONSTANT_REFERENCE, |
| |
| /** |
| * The operand does not have a value. This is valid only for optional |
| * arguments of operations. |
| */ |
| NO_VALUE, |
| }; |
| |
| /** |
| * Status of a device. |
| */ |
| enum DeviceStatus : int32_t { |
| AVAILABLE, |
| BUSY, |
| OFFLINE, |
| UNKNOWN, |
| }; |
| |
| /** |
| * Performance information for the reference workload. |
| * |
| * Used by a driver to report its performance characteristics. |
| */ |
| struct PerformanceInfo { |
| /** |
| * Ratio of the time taken by the driver to execute the |
| * workload compared to the time the CPU would take for the |
| * same workload. A lower number is better. |
| */ |
| float execTime; |
| |
| /** |
| * Ratio of the energy used by the driver compared to what |
| * the CPU would use for doing the same workload. A lower number |
| * is better. |
| */ |
| float powerUsage; |
| }; |
| |
| /** |
| * The capabilities of a driver. |
| */ |
| struct Capabilities { |
| /** |
| * Driver performance when operating on float32 data. |
| */ |
| PerformanceInfo float32Performance; |
| |
| /** |
| * Driver performance when operating on asymmetric 8-bit quantized data. |
| */ |
| PerformanceInfo quantized8Performance; |
| }; |
| |
| /** |
| * Describes the location of a data object. |
| */ |
| struct DataLocation { |
| /** |
| * The index of the memory pool where this location is found. |
| */ |
| uint32_t poolIndex; |
| |
| /** |
| * Offset in bytes from the start of the pool. |
| */ |
| uint32_t offset; |
| |
| /** |
| * The length of the data in bytes. |
| */ |
| uint32_t length; |
| }; |
| |
| /** |
| * Describes one operand of the model's graph. |
| */ |
| struct Operand { |
| /** |
| * Data type of the operand. |
| */ |
| OperandType type; |
| |
| /** |
| * Dimensions of the operand. |
| * |
| * For a scalar operand, dimensions.size() must be 0. |
| * |
| * For a tensor operand, dimensions.size() must be at least 1; |
| * however, any of the dimensions may be unspecified. |
| * |
| * A tensor operand with all dimensions specified has "fully |
| * specified" dimensions. Whenever possible (i.e., whenever the |
| * dimensions are known at model construction time), a tensor |
| * operand should have (but is not required to have) fully |
| * specified dimensions, in order to enable the best possible |
| * performance. |
| * |
| * If a tensor operand's dimensions are not fully specified, the |
| * dimensions of the operand are deduced from the operand |
| * dimensions and values of the operation for which that operand |
| * is an output. |
| * |
| * In the following situations, a tensor operand's dimensions must |
| * be fully specified: |
| * |
| * . The operand has lifetime CONSTANT_COPY or |
| * CONSTANT_REFERENCE. |
| * |
| * . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully |
| * specified dimensions must either be present in the |
| * Operand or they must be provided in the corresponding |
| * RequestArgument. |
| * EXCEPTION: If the input or output is optional and omitted |
| * (by setting the hasNoValue field of the corresponding |
| * RequestArgument to true) then it need not have fully |
| * specified dimensions. |
| * |
| * A tensor operand with some number of unspecified dimensions is |
| * represented by setting each unspecified dimension to 0. |
| */ |
| vec<uint32_t> dimensions; |
| |
| /** |
| * The number of times this operand appears as an operation input. |
| * |
| * (For example, if this operand appears once in one operation's |
| * input list, and three times in another operation's input list, |
| * then numberOfConsumers = 4.) |
| */ |
| uint32_t numberOfConsumers; |
| |
| /** |
| * Quantized scale of the operand. |
| * |
| * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or |
| * TENSOR_INT32. |
| */ |
| float scale; |
| |
| /** |
| * Quantized zero-point offset of the operand. |
| * |
| * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM. |
| */ |
| int32_t zeroPoint; |
| |
| /** |
| * How the operand is used. |
| */ |
| OperandLifeTime lifetime; |
| |
| /** |
| * Where to find the data for this operand. |
| * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or |
| * NO_VALUE: |
| * - All the fields must be 0. |
| * If the lifetime is CONSTANT_COPY: |
| * - location.poolIndex is 0. |
| * - location.offset is the offset in bytes into Model.operandValues. |
| * - location.length is set. |
| * If the lifetime is CONSTANT_REFERENCE: |
| * - location.poolIndex is set. |
| * - location.offset is the offset in bytes into the specified pool. |
| * - location.length is set. |
| */ |
| DataLocation location; |
| }; |
| |
| /** |
| * Describes one operation of the model's graph. |
| */ |
| struct Operation { |
| /** |
| * The operation type. |
| */ |
| OperationType type; |
| |
| /** |
| * Describes the table that contains the indexes of the inputs of the |
| * operation. The offset is the index in the operandIndexes table. |
| */ |
| vec<uint32_t> inputs; |
| |
| /** |
| * Describes the table that contains the indexes of the outputs of the |
| * operation. The offset is the index in the operandIndexes table. |
| */ |
| vec<uint32_t> outputs; |
| }; |
| |
| /** |
| * A Neural Network Model. |
| * |
| * This includes not only the execution graph, but also constant data such as |
| * weights or scalars added at construction time. The only information that |
| * might not be known is the shape of the input tensors. |
| */ |
| struct Model { |
| /** |
| * All operands included in the model. |
| */ |
| vec<Operand> operands; |
| |
| /** |
| * All operations included in the model. |
| * |
| * The operations are sorted into execution order. Every operand |
| * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be |
| * written before it is read. |
| */ |
| vec<Operation> operations; |
| |
| /** |
| * Input indexes of the model. There must be at least one. |
| * |
| * Each value corresponds to the index of the operand in "operands". |
| */ |
| vec<uint32_t> inputIndexes; |
| |
| /** |
| * Output indexes of the model. There must be at least one. |
| * |
| * Each value corresponds to the index of the operand in "operands". |
| */ |
| vec<uint32_t> outputIndexes; |
| |
| /** |
| * A byte buffer containing operand data that were copied into the model. |
| * |
| * An operand's value must be located here if and only if Operand::lifetime |
| * equals OperandLifeTime::CONSTANT_COPY. |
| */ |
| vec<uint8_t> operandValues; |
| |
| /** |
| * A collection of shared memory pools containing operand values. |
| * |
| * An operand's value must be located here if and only if Operand::lifetime |
| * equals OperandLifeTime::CONSTANT_REFERENCE. |
| */ |
| vec<memory> pools; |
| }; |
| |
| /** |
| * Metadata information specifying the location of the input or output data and |
| * any updates to the input or output operand. |
| */ |
| struct RequestArgument { |
| /** |
| * If true, the argument does not have a value. This can be used for |
| * operations that take optional arguments. If true, the fields of location |
| * are set to 0 and the dimensions vector is left empty. |
| */ |
| bool hasNoValue; |
| |
| /** |
| * The location within one of the memory pools passed in the Request. |
| */ |
| DataLocation location; |
| |
| /** |
| * Updated dimension information. |
| * |
| * If dimensions.size() > 0, dimension information was provided |
| * along with the argument. This can be the case for models that |
| * accept inputs of varying size. This can't change the rank, just |
| * the value of the dimensions that were unspecified in the |
| * model. If dimensions.size() > 0, then all dimensions must be |
| * specified here; and any dimension that was specified in the |
| * model must have the same value here. |
| * |
| * If the dimensions in the model are not fully specified, then |
| * they must be fully specified here, unless hasNoValue is set to |
| * true. If the dimensions in the model are fully specified, then |
| * either dimensions.size() may be 0, or the dimensions in the |
| * model must be identical to the dimensions here. |
| */ |
| vec<uint32_t> dimensions; |
| }; |
| |
| /** |
| * Inputs to be sent to and outputs to be retrieved from a prepared model. |
| * |
| * A Request serves two primary tasks: |
| * 1) Provides the input and output data to be used when executing the model. |
| * 2) Specifies any updates to the input operand metadata that were left |
| * unspecified at model preparation time. |
| * |
| * An output must not overlap with any other output, with an input, or |
| * with an operand of lifetime CONSTANT_REFERENCE. |
| */ |
| struct Request { |
| /** |
| * Input data and information to be used in the execution of a prepared |
| * model. |
| * |
| * The index of the input corresponds to the index in Model.inputIndexes. |
| * E.g., input[i] corresponds to Model.inputIndexes[i]. |
| */ |
| vec<RequestArgument> inputs; |
| |
| /** |
| * Output data and information to be used in the execution of a prepared |
| * model. |
| * |
| * The index of the output corresponds to the index in Model.outputIndexes. |
| * E.g., output[i] corresponds to Model.outputIndexes[i]. |
| */ |
| vec<RequestArgument> outputs; |
| |
| /** |
| * A collection of shared memory pools containing operand data for both the |
| * inputs and the outputs to a model. |
| */ |
| vec<memory> pools; |
| }; |
| |
| /** |
| * Return status of a function. |
| */ |
| enum ErrorStatus : int32_t { |
| NONE, |
| DEVICE_UNAVAILABLE, |
| GENERAL_FAILURE, |
| OUTPUT_INSUFFICIENT_SIZE, |
| INVALID_ARGUMENT, |
| }; |
