/* | |

* 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 depth dimension. | |

* | |

* The values in the output tensor are computed as: | |

* | |

* output[batch, row, col, channel] = | |

* input[batch, row, col, channel] / | |

* sqrt(sum_{c} pow(input[batch, row, col, c], 2)) | |

* | |

* 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, | |

}; |