| /* |
| * 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. |
| */ |
| enum OperandType : int32_t { |
| /** |
| * The following entries are used to declare scalars. |
| */ |
| FLOAT32 = 0, |
| INT32 = 1, |
| UINT32 = 2, |
| |
| /** |
| * The following entries are used to declare tensors. |
| */ |
| TENSOR_FLOAT32 = 3, |
| 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 |
| * - zero_value: a 32 bit integer |
| * |
| * The formula is: |
| * real_value = (integer_value - zero_value) * scale. |
| */ |
| TENSOR_QUANT8_ASYMM = 5, |
| |
| /** |
| * The following entries are OEM specific operand types. |
| */ |
| OEM = 10000, |
| TENSOR_OEM_BYTE = 10001, |
| }; |
| |
| /** |
| * Operation types. |
| * |
| * The type of an operation in a model. |
| */ |
| enum OperationType : int32_t { |
| /** |
| * Adds two tensors, elment-wise. |
| * |
| * Takes two input tensors of identical type and compatible dimensions. The output |
| * is the sum of both input tensors, optionally modified by an activation function. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the output is the maximum size along each dimension of the input operands. |
| * It starts with the trailing dimensions, and works its way forward. |
| * |
| * Example: |
| * input1.dimension = {4, 1, 2} |
| * input2.dimension = {5, 4, 3, 1} |
| * output.dimension = {5, 4, 3, 2} |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * 0: A tensor. |
| * 1: A tensor of the same type, and compatible dimensions as input0. |
| * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The sum, a tensor of the same type as input0. |
| */ |
| ADD = 0, |
| |
| /** |
| * Performs a 2-D average pooling operation. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and padding. |
| * |
| * The values in output Tensor is computed as: |
| * output[batch, row, col, channel] = |
| * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. |
| * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. |
| * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. |
| * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. |
| * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. |
| * 5: An INT32 value, specifying the output stride in the ‘width’ dimension. |
| * 6: An INT32 value, specifying the output stride in the ‘height’ dimension. |
| * 7: An INT32 value, specifying the filter width. |
| * 8: An INT32 value, specifying the filter height. |
| * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. |
| */ |
| AVERAGE_POOL_2D = 1, |
| |
| /** |
| * Concatenates the input tensors along the given dimension. |
| * |
| * The input tensors must have identical type and the same dimensions except the |
| * dimension along the concatenation axis. |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm] |
| * n+1: An INT32 value, specifying the concatenation axis. |
| * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output, a tensor of the same type as the input tensors. |
| The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. |
| */ |
| 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 output Tensor is computed as: |
| * output[batch, row, col, channel] = |
| * sum_{i, j} ( |
| * input[batch, row + i, col + j, k] * |
| * filter[channel, row + i, col + j, k] + |
| * bias[channel] |
| * ) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. |
| * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], |
| * specifying the filter. |
| * 2: A 1-D tensor, of shape [depth_out], specifying the bias. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should |
| * also be of {@link OperandType::TENSOR_FLOAT32}. |
| * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias |
| * should be of {@link OperandType::TENSOR_INT32}. |
| * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. |
| * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. |
| * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. |
| * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. |
| * 7: An INT32 value, specifying the output stride in the ‘width’ dimension. |
| * 8: An INT32 value, specifying the output stride in the ‘height’ dimension. |
| * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. |
| */ |
| CONV_2D = 3, |
| |
| /** |
| * Performs an depthwise 2-D convolution operation. |
| * |
| * Given an input tensor of shape [batches, height, width, depth_in] and a filter |
| * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing |
| * in_channels 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 output Tensor is 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[di, dj, k, q] |
| * ) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. |
| * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], |
| * specifying the filter. |
| * 2: A 1-D tensor, of shape [depth_out], specifying the bias. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should |
| * also be of {@link OperandType::TENSOR_FLOAT32}. |
| * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias |
| * should be of {@link OperandType::TENSOR_INT32}. |
| * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. |
| * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. |
| * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. |
| * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. |
| * 7: An INT32 value, specifying the output stride in the ‘width’ dimension. |
| * 8: An INT32 value, specifying the output stride in the ‘height’ dimension. |
| * 9: An INT32 value, specifying the depthwise multiplier. |
| * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. |
| */ |
| DEPTHWISE_CONV_2D = 4, |
| |
| /** |
| * Rearranges data from depth into blocks of spatial data. |
| * |
| * More specifically, this op outputs a copy of the input tensor where values from |
| * the depth dimension are moved in spatial blocks to the height and width dimensions. |
| * The value block_size indicates the input block size and how the data is moved. |
| * |
| * Chunks of data of size block_size * block_size from depth are rearranged into |
| * non-overlapping blocks of size block_size x block_size. |
| * |
| * The width of the output tensor is input_depth * block_size, whereas the height is |
| * input_height * block_size. |
| * The depth of the input tensor must be divisible by block_size * block_size |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. |
| * 1: An INT32 value, specifying the block_size. block_size must be >=1 and |
| * block_size * block_size must be a divisor of the input depth. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size, |
| * depth/(block_size*block_size)]. |
| */ |
| DEPTH_TO_SPACE = 5, |
| |
| /** |
| * Dequantizes the input tensor. |
| * |
| * The formula is: |
| * output = (input - zero_value) * scale. |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0, but with type |
| {@link OperandType::TENSOR_FLOAT32}. |
| */ |
| DEQUANTIZE = 6, |
| |
| /** |
| * Looks up items from a given tensor. |
| * |
| * Each item in the output is a raw copy of the corresponding item in |
| * the input “values”. If the the given “lookup” indices are out of bounds, |
| * the op will fail and an error will be reported. |
| * |
| * Inputs: |
| * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2, |
| * then the shape would be [lookup_dimension, values_dimension], where |
| * “lookup_dimension” corresponds to the indexing dimension in the lookup |
| * table, and “values_dimension” to the contents. |
| * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where |
| * “lookup_size” is the number of elements to look for, and each entry |
| * corresponds to the first dimension of the “values” tensor. |
| * |
| * Output: |
| * * 0: A n-D tensor of type X and the same rank and shape as the “values” |
| * tensor, except for the first dimension which has size “lookup_size”. |
| */ |
| EMBEDDING_LOOKUP = 7, |
| |
| /** |
| * Computes element-wise floor() on the input tensor. |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * 0: A tensor. |
| * |
| * Ouputs: |
| * 0: The output, a tensor of the same type and dimensions as input0. |
| */ |
| FLOOR = 8, |
| |
| /** |
| * Denotes a fully (densely) connected layer, which connects all elements in the input |
| * tensor with each element in the output tensor. |
| * |
| * This layer implements the operation: |
| * outputs = activation(inputs * weights’ + bias) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to |
| * a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape |
| * [batch_size, input_size], where “batch_size” corresponds to the batching dimension, |
| * and “input_size” is the size of the input. |
| * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units” |
| * corresponds to the number of output nodes. |
| * 2: A 1-D tensor, of shape [num_units], specifying the bias. |
| * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should |
| * also be of {@link OperandType::TENSOR_FLOAT32}. |
| * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias |
| * should be of {@link OperandType::TENSOR_INT32}. |
| * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output tensor, of shape [batch_size, num_units]. |
| */ |
| FULLY_CONNECTED = 9, |
| |
| /** |
| * Looks up values of a hash table with given keys. |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D int32 tensor with shape [ k ]. |
| * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in |
| * ascending order. |
| * * 2: Values. A tensor with shape [ n … ]. |
| * |
| * Outputs: |
| * * 0: Output. A tensor with shape [ k …]. |
| * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup |
| * hits or not. |
| */ |
| HASHTABLE_LOOKUP = 10, |
| |
| /** |
| * Applies L2 normalization along a the depth dimension. |
| * |
| * The values in output Tensor is computed as: |
| * output[batch, row, col, channel] = |
| * input[batch, row, col, channel] / |
| * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) |
| * |
| * For x with more dimensions, independently normalizes each 1-D slice along dimension dim. |
| * |
| * Supported tensor types: {@link 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. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. |
| */ |
| 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 output Tensor is computed as: |
| * output[batch, row, col, channel] = |
| * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1)) |
| * |
| * Supported tensor types: {@link 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 INT32 value, specifying the padding on the left, in the ‘width’ dimension. |
| * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. |
| * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. |
| * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. |
| * 5: An INT32 value, specifying the output stride in the ‘width’ dimension. |
| * 6: An INT32 value, specifying the output stride in the ‘height’ dimension. |
| * 7: An INT32 value, specifying the filter width. |
| * 8: An INT32 value, specifying the filter height. |
| * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 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. |
| * |
| * In details: |
| * sqr_sum[a, b, c, d] = |
| * sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2) |
| * output = input / pow((bias + alpha * sqr_sum), beta) |
| * |
| * Supported tensor types: {@link 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 INT32 value, specifying the radius of the normalization window. |
| * 2: A FLOAT32 value, specifying the bias, must not be zero. |
| * 3: A FLOAT32 value, specifying the scale factor, alpha. |
| * 4: A FLOAT32 value, specifying the exponent, beta. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| LOCAL_RESPONSE_NORMALIZATION = 13, |
| |
| /** |
| * Computes sigmoid activation on the input tensor element-wise. |
| * |
| * In details: |
| * output = 1 / (1 + exp(-input)) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| 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. |
| */ |
| LSH_PROJECTION = 15, |
| |
| /** |
| * Long short-term memory unit (LSTM) recurrent network layer. |
| * |
| * The default non-peephole implementation is based on: |
| * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf |
| * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural |
| * Computation, 9(8):1735-1780, 1997. |
| * |
| * The peephole implementation is based on: |
| * https://research.google.com/pubs/archive/43905.pdf |
| * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory |
| * recurrent neural network architectures for large scale acoustic modeling." |
| * INTERSPEECH, 2014. |
| * |
| * The coupling of input and forget gate (CIFG) is based on: |
| * http://arxiv.org/pdf/1503.04069.pdf |
| * Greff et al. "LSTM: A Search Space Odyssey" |
| * |
| * The class has the following independently optional inputs: |
| * * If input gate (if CIFG): “input_to_forget_weights”, |
| * “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”. |
| * * If no peephole connections: “cell_to_input_weights”, |
| * “cell_to_forget_weights”, “cell_to_output_weights”. |
| * * If no projection layer: “projection_weights” and “projection_bias”. |
| * * If no projection bias: “projection_bias”. |
| * |
| * Supported tensor types: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Inputs: |
| * * 0: Input. |
| * A 2-D tensor of type T, of shape [batch_size, input_size], where |
| * “batch_size” corresponds to the batching dimension, and “input_size” |
| * is the size of the input. |
| * * 1: input_to_input_weights. |
| * A 2-D tensor of type T, of shape [num_units, input_size], where |
| * “num_units” corresponds to the number of cell units. |
| * * 2: input_to_forget_weights. |
| * A 2-D tensor of type T, of shape [num_units, input_size]. |
| * * 3: input_to_cell_weights. |
| * A 2-D tensor of type T, of shape [num_units, input_size]. |
| * * 4: input_to_output_weights. |
| * A 2-D tensor of type T, of shape [num_units, input_size]. |
| * * 5: recurrent_to_input_weights. |
| * A 2-D tensor of type T, of shape [num_units, output_size], where |
| * “output_size” corresponds to either the number of cell units (i.e., |
| * “num_units”), or the second dimension of the “projection_weights”, if |
| * defined. |
| * * 6: recurrent_to_forget_weights. |
| * A 2-D tensor of type T, of shape [num_units, output_size]. |
| * * 7: recurrent_to_cell_weights. |
| * A 2-D tensor of type T, of shape [num_units, output_size]. |
| * * 8: recurrent_to_output_weights. |
| * A 2-D tensor of type T, of shape [num_units, output_size]. |
| * * 9: cell_to_input_weights. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 10:cell_to_forget_weights. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 11:cell_to_output_weights. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 12:input_gate_bias. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 13:forget_gate_bias. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 14:cell_bias. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 15:output_gate_bias. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * * 16:projection_weights. |
| * A 2-D tensor of type T, of shape [output_size, num_units]. |
| * * 17:projection_bias. |
| * A 1-D tensor of type T, of shape [output_size]. |
| * |
| * Parameters: |
| * * 18:fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation |
| * function. |
| * If “NONE” is specified then it results in a linear activation. |
| * * 19:cell_clip. |
| * A clipping threshold for the cell state, such that values are bound |
| * within [-cell_clip, cell_clip]. If set to 0.0 then clipping is |
| * disabled. |
| * * 20:proj_clip. |
| * A clipping threshold for the output from the projection layer, such |
| * that values are bound within [-proj_clip, proj_clip]. If set to 0.0 |
| * then clipping is disabled. |
| * |
| * Outputs: |
| * * 0: scratch_buffer. |
| * A 3-D tensor of type T, of shape [batch_size, num_cell, 4]. |
| * * 1: output_state. |
| * A 2-D tensor of type T, of shape [batch_size, output_size]. |
| * * 2: cell_state. |
| * A 2-D tensor of type T, of shape [batch_size, num_units]. |
| * * 3: output. |
| * A 2-D tensor of type T, of shape [batch_size, output_size]. This is |
| * effectively the same as the current “output_state” value. |
| */ |
| LSTM = 16, |
| |
| /** |
| * Performs an 2-D max pooling operation. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and padding. |
| * |
| * The values in output Tensor is computed as: |
| * output[batch, row, col, channel] = |
| * max_{i, j} (input[batch, row + i, col + j, channel]) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. |
| * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension. |
| * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension. |
| * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension. |
| * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension. |
| * 5: An INT32 value, specifying the output stride in the ‘width’ dimension. |
| * 6: An INT32 value, specifying the output stride in the ‘height’ dimension. |
| * 7: An INT32 value, specifying the filter width. |
| * 8: An INT32 value, specifying the filter height. |
| * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. |
| */ |
| MAX_POOL_2D = 17, |
| |
| /** |
| * Multiplies two tensors, elment-wise. |
| * |
| * Takes two input tensors of identical type and compatible dimensions. The output |
| * is the product of both input tensors, optionally modified by an activation function. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the resulting output is the maximum size along each dimension of the |
| * input operands. It starts with the trailing dimensions, and works its way forward. |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * 0: A tensor. |
| * 1: A tensor of the same type, and compatible dimensions as input0. |
| * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. |
| * Specifies the activation to invoke on the result of each addition. |
| * |
| * Ouputs: |
| * 0: The product, a tensor of the same type as input0. |
| */ |
| MUL = 18, |
| |
| /** |
| * Computes rectified linear activation on the input tensor element-wise. |
| * |
| * In details: |
| * output = max(0, input) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| RELU = 19, |
| |
| /** |
| * Computes rectified linear 1 activation on the input tensor element-wise. |
| * |
| * In details: |
| * output = min(1.f, max(-1.f, input)) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| RELU1 = 20, |
| |
| /** |
| * Computes rectified linear 6 activation on the input tensor element-wise. |
| * |
| * In details: |
| * output = min(6, max(0, input)) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape 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 types: {@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 type {@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. |
| * |
| * Ouputs: |
| * 0: The output tensor, of shape specified by the input shape. |
| */ |
| RESHAPE = 22, |
| |
| /** |
| * Resizes images to given size using the bilinear interpretation. |
| * |
| * Resized images will be distorted if their original aspect ratio is not the same as input. |
| * |
| * Supported tensor types: {@link 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 INT32 value, specifying the output width of the output tensor. |
| * 2: An INT32 value, specifying the output height of the output tensor. |
| * |
| * Ouputs: |
| * 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 types: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor of type T, of shape [batch_size, input_size], where |
| * “batch_size” corresponds to the batching dimension, and “input_size” is |
| * the size of the input. |
| * * 1: weights. |
| * A 2-D tensor of type T, of shape [num_units, input_size], where |
| * “num_units” corresponds to the number of units. |
| * * 2: recurrent_weights. |
| * A 2-D tensor of type T, of shape [num_units, num_units], with columns |
| * corresponding to the weights from each unit. |
| * * 3: bias. |
| * A 1-D tensor of type T, of shape [num_units]. |
| * |
| * For FLOAT32 input tensor, bias must also be FLOAT32. |
| * For UINT8 input tensor, bias must be INT32. |
| * |
| * Parameters |
| * * 4: fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation |
| * function. If “NONE” is specified then it results in a linear |
| * activation. |
| * |
| * * 5: Hidden state. |
| * A 2-D tensor of type T, of shape [batch_size, num_units]. |
| * |
| * Outputs: |
| * * 0: output. |
| * A 2-D tensor of type T, of shape [batch_size, num_units]. This is |
| * effectively the same as the current state value. |
| */ |
| 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. |
| * |
| * In details: |
| * output[batch, i] = |
| * exp((input[batch, i] - max(input[batch, :])) * beta) / |
| * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} |
| * |
| * Supported tensor types: {@link 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 FLOAT32 value, specifying the scaling factor for the exponent, beta. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| SOFTMAX = 25, |
| |
| /** |
| * Rearranges blocks of spatial data, into depth. |
| * |
| * More specifically, this op outputs a copy of the input tensor where values from |
| * the height and width dimensions are moved to the depth dimension. |
| * The value block_size indicates the input block size and how the data is moved. |
| * |
| * Chunks of data of size block_size * block_size from depth are rearranged into |
| * non-overlapping blocks of size block_size x block_size. |
| * |
| * The depth of the output tensor is input_depth * block_size * block_size. |
| * The input tensor's height and width must be divisible by block_size. |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * Supported tensor rank: 4, with "NHWC" data layout. |
| * |
| * Inputs: |
| * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. |
| * 1: An INT32 value, specifying the block_size. block_size must be >=1 and |
| * block_size must be a divisor of both the input height and width. |
| * |
| * Ouputs: |
| * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size, |
| * depth*block_size*block_size]. |
| */ |
| SPACE_TO_DEPTH = 26, |
| |
| /** |
| * SVDF op is a kind of stateful layer derived from the notion that a |
| * densely connected layer that's processing a sequence of input frames can |
| * be approximated by using a singular value decomposition of each of its |
| * nodes. The implementation is based on: |
| * |
| * https://research.google.com/pubs/archive/43813.pdf |
| * |
| * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. |
| * “Compressing Deep Neural Networks using a Rank-Constrained Topology”. |
| * INTERSPEECH, 2015. |
| * |
| * It processes the incoming input using a 2-stage filtering mechanism: |
| * * stage 1 performs filtering on the "features" dimension, whose outputs get |
| * pushed into a memory of fixed-size memory_size. |
| * * stage 2 performs filtering on the "time" dimension of the memory_size |
| * memoized outputs of stage 1. |
| * |
| * Specifically, for rank 1, this layer implements the operation: |
| * |
| * memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID")); |
| * outputs = activation(memory * weights_time + bias); |
| * |
| * Where: |
| * * “weights_feature” is a weights matrix that processes the inputs (by |
| * convolving the input with every “feature filter”), and whose outputs get |
| * pushed, stacked in order, into the fixed-size “memory” (the oldest entry |
| * gets dropped); |
| * * “weights_time” is a weights matrix that processes the “memory” (by a |
| * batched matrix multiplication on the num_units); |
| * * “bias” is an optional bias vector (added to each output vector in the |
| * batch); and |
| * * “activation” is the function passed as the “fused_activation_function” |
| * argument (if not “NONE”). |
| * |
| * Each rank adds a dimension to the weights matrices by means of stacking |
| * the filters. |
| * |
| * Supported tensor types: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor of type T, of shape [batch_size, input_size], where |
| * “batch_size” corresponds to the batching dimension, and “input_size” is |
| * the size of the input. |
| * * 1: weights_feature. |
| * A 2-D tensor of type T, of shape [num_units, input_size], where |
| * “num_units” corresponds to the number of units. |
| * * 2: weights_time. |
| * A 2-D tensor of type T, of shape [num_units, memory_size], where |
| * “memory_size” corresponds to the fixed-size of the memory. |
| * * 3: bias. |
| * A optional 1-D tensor of type T, of shape [num_units]. |
| * |
| * For FLOAT32 input tensor, bias must also be FLOAT32. |
| * For UINT8 input tensor, bias must be INT32. |
| * |
| * Parameters: |
| * * 4: rank. |
| * The rank of the SVD approximation. |
| * * 5: fused_activation_function. |
| * An (optional) ActivationFunctionType indicating the activation function. |
| * If “NONE” is specified then it results in a linear activation. |
| * |
| * Outputs: |
| * * 0: state. |
| * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank]. |
| * * 1: output. |
| * A 2-D tensor of type T, of shape [batch_size, num_units]. |
| */ |
| SVDF = 27, |
| |
| /** |
| * Computes hyperbolic tangent of input tensor element-wise. |
| * |
| * In details: |
| * output = tanh(input) |
| * |
| * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * 0: A tensor, specifying the input. |
| * |
| * Ouputs: |
| * 0: The output tensor of same shape as input0. |
| */ |
| TANH = 28, |
| |
| /** |
| * OEM specific operation. |
| * |
| * 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. |
| */ |
| TEMPORARY_VARIABLE, |
| |
| /** |
| * The operand is an input of the model. An operand can't be both |
| * input and output of a model. |
| */ |
| MODEL_INPUT, |
| |
| /** |
| * The operand is an output of the model. |
| */ |
| MODEL_OUTPUT, |
| |
| /** |
| * The operand is a constant found in Model.operandValues. |
| */ |
| CONSTANT_COPY, |
| |
| /** |
| * The operand is a constant that was specified via a Memory object. |
| */ |
| 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. |
| */ |
| vec<uint32_t> dimensions; |
| |
| /** |
| * The number of operations that use this operand as input. |
| */ |
| 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 will 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. |
| */ |
| vec<Operation> operations; |
| |
| /** |
| * Input indexes of the model. |
| * |
| * Each value corresponds to the index of the operand in "operands". |
| */ |
| vec<uint32_t> inputIndexes; |
| |
| /** |
| * Output indexes of the model. |
| * |
| * 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 data that were |
| * registered by the model. |
| * |
| * 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. |
| */ |
| 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. |
| */ |
| 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, |
| }; |
