mirror of
https://github.com/IllusionMan1212/ndk-odin.git
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6508 lines
295 KiB
Odin
6508 lines
295 KiB
Odin
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//+build android
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package android
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/**
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* For {@link ANeuralNetworksModel_setOperandValue}, values with a
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* length smaller or equal to this will be immediately copied into
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* the model. The size is in bytes.
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*
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* Available since NNAPI feature level 1.
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*/
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ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES :: 128
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/**
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* For {@link ANeuralNetworksCompilation_setCaching}, specify the size
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* of the cache token required from the application. The size is in bytes.
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*
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* Available since NNAPI feature level 3.
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*/
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ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN :: 32
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/**
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* Aliasing to {@link OperationCode}, used in function
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* {@link ANeuralNetworksModel_addOperation}.
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*/
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ANeuralNetworksOperationType :: i32
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/**
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* Operand types.
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*
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* The type of an operand in a model.
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*
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* Types prefaced with ANEURALNETWORKS_TENSOR_* must be used for tensor data (i.e., tensors
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* with at least one dimension). Types not prefaced by ANEURALNETWORKS_TENSOR_* represent
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* scalar values and must have no dimensions.
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*
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* Although we define many types, most operators accept just a few
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* types. Most used are {@link ANEURALNETWORKS_TENSOR_FLOAT32},
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* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
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* and {@link ANEURALNETWORKS_INT32}.
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*
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* Available since NNAPI feature level 1.
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*/
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OperandCode :: enum i32 {
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/** A 32 bit floating point scalar value. */
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FLOAT32 = 0,
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/** A signed 32 bit integer scalar value. */
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INT32 = 1,
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/** An unsigned 32 bit integer scalar value. */
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UINT32 = 2,
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/** A tensor of 32 bit floating point values. */
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TENSOR_FLOAT32 = 3,
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/** A tensor of 32 bit integer values. */
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TENSOR_INT32 = 4,
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/**
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* A tensor of 8 bit unsigned integers that represent real numbers.
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*
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* Attached to this tensor are two numbers that can be used to convert the
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* 8 bit integer to the real value and vice versa. These two numbers are:
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* - scale: a 32 bit floating point value greater than zero.
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* - zeroPoint: a 32 bit integer, in range [0, 255].
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*
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* The formula is:
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* real_value = (integer_value - zeroPoint) * scale.
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*/
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TENSOR_QUANT8_ASYMM = 5,
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/**
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* An 8 bit boolean scalar value.
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*
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* Values of this operand type are either true or false. A zero value
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* represents false; any other value represents true.
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*
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* Available since NNAPI feature level 3.
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*/
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BOOL = 6,
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/**
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* A tensor of 16 bit signed integers that represent real numbers.
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*
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* Attached to this tensor is a number representing real value scale that is
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* used to convert the 16 bit number to a real value in the following way:
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* realValue = integerValue * scale.
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*
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* scale is a 32 bit floating point with value greater than zero.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_QUANT16_SYMM = 7,
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/**
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* A tensor of IEEE 754 16 bit floating point values.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_FLOAT16 = 8,
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/**
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* A tensor of 8 bit boolean values.
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*
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* Values of this operand type are either true or false. A zero value
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* represents false; any other value represents true.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_BOOL8 = 9,
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/**
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* An IEEE 754 16 bit floating point scalar value.
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*
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* Available since NNAPI feature level 3.
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*/
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FLOAT16 = 10,
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/**
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* A tensor of 8 bit signed integers that represent real numbers.
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*
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* This tensor is associated with additional fields that can
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* be used to convert the 8 bit signed integer to the real value and vice versa.
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* These fields are:
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* - channelDim: a 32 bit unsigned integer indicating channel dimension.
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* - scales: an array of positive 32 bit floating point values.
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* The size of the scales array must be equal to dimensions[channelDim].
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*
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* {@link ANeuralNetworksModel_setOperandSymmPerChannelQuantParams} must be used
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* to set the parameters for an Operand of this type.
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*
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* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
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*
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* The formula is:
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* realValue[..., C, ...] =
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* integerValue[..., C, ...] * scales[C]
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* where C is an index in the Channel dimension.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
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/**
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* A tensor of 16 bit unsigned integers that represent real numbers.
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*
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* Attached to this tensor are two numbers that can be used to convert the
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* 16 bit integer to the real value and vice versa. These two numbers are:
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* - scale: a 32 bit floating point value greater than zero.
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* - zeroPoint: a 32 bit integer, in range [0, 65535].
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*
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* The formula is:
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* real_value = (integer_value - zeroPoint) * scale.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_QUANT16_ASYMM = 12,
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/**
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* A tensor of 8 bit signed integers that represent real numbers.
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*
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* Attached to this tensor is a number representing real value scale that is
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* used to convert the 8 bit number to a real value in the following way:
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* realValue = integerValue * scale.
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*
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* scale is a 32 bit floating point with value greater than zero.
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*
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* Available since NNAPI feature level 3.
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*/
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TENSOR_QUANT8_SYMM = 13,
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/**
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* A tensor of 8 bit signed integers that represent real numbers.
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*
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* Attached to this tensor are two numbers that can be used to convert the
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* 8 bit integer to the real value and vice versa. These two numbers are:
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* - scale: a 32 bit floating point value greater than zero.
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* - zeroPoint: a 32 bit integer, in range [-128, 127].
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*
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* The formula is:
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* real_value = (integer_value - zeroPoint) * scale.
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*
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* Available since NNAPI feature level 4.
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*/
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TENSOR_QUANT8_ASYMM_SIGNED = 14,
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/**
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* A reference to a model.
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*
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* {@link ANeuralNetworksModel_setOperandValueFromModel} must be used to set
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* the value for an Operand of this type.
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*
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* Available since NNAPI feature level 4.
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*/
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MODEL = 15,
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}
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/**
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* Operation types.
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*
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* The type of an operation in a model.
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*
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* Available since NNAPI feature level 1.
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*/
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OperationCode :: enum {
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// Operations below are available since NNAPI feature level 1.
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/**
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* Adds two tensors, element-wise.
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*
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* Takes two input tensors of identical {@link OperandCode} and compatible
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* dimensions. The output is the sum of both input tensors, optionally
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* modified by an activation function.
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*
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* Two dimensions are compatible when:
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* 1. they are equal, or
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* 2. one of them is 1
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*
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* The size of the output is the maximum size along each dimension of the
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* input operands. It starts with the trailing dimensions, and works its
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* way forward.
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*
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* Example:
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*
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* input1.dimension = {4, 1, 2}
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* input2.dimension = {5, 4, 3, 1}
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* output.dimension = {5, 4, 3, 2}
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*
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* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero
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* dimension is only compatible with 0 or 1. The size of the output
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* dimension is zero if either of corresponding input dimension is zero.
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*
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* Supported tensor {@link OperandCode}:
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
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* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4)
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*
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* Supported tensor rank: up to 4
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*
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* Inputs:
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* * 0: A tensor.
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* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
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* as input0.
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* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
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* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
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* the scales and zeroPoint can be different from input0 scale and zeroPoint.
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* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
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* {@link FuseCode} values. Specifies the activation to
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* invoke on the result.
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* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor,
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* the {@link FuseCode} must be "NONE".
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*
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* Outputs:
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* * 0: The sum, a tensor of the same {@link OperandCode} as input0.
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* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
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* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
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* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
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*
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* Available since NNAPI feature level 1.
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*/
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ADD = 0,
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/**
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* Performs a 2-D average pooling operation.
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*
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* The output dimensions are functions of the filter dimensions, stride, and
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* padding.
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*
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* The values in the output tensor are computed as:
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*
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* output[b, i, j, channel] =
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* sum_{di, dj}(
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* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
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* ) / sum(1)
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*
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* Supported tensor {@link OperandCode}:
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
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*
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* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
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* With the default data layout NHWC, the data is stored in the order of:
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* [batch, height, width, channels]. Alternatively, the data layout could
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* be NCHW, the data storage order of: [batch, channels, height, width].
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* NCHW is supported since NNAPI feature level 3.
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*
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* Both explicit padding and implicit padding are supported.
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*
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* Inputs (explicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
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* the input.
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* Since NNAPI feature level 3, zero batches is supported for this tensor.
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* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
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* the left, in the ‘width’ dimension.
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* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
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* the right, in the ‘width’ dimension.
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* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
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* the top, in the ‘height’ dimension.
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* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
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* the bottom, in the ‘height’ dimension.
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* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
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* width.
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* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
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* height.
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* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
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* {@link FuseCode} values. Specifies the activation to
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|||
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* invoke on the result.
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|||
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* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
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|||
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* Set to true to specify NCHW data layout for input0 and output0.
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|||
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* Available since NNAPI feature level 3.
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|||
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*
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|||
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* Inputs (implicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
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|||
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* the input.
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|||
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* Since NNAPI feature level 3, zero batches is supported for this tensor.
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* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
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* padding scheme, has to be one of the
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* {@link PaddingCode} values.
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* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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|||
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* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
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|||
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* width.
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|||
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* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
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|||
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* height.
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|||
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* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
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|||
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* {@link FuseCode} values. Specifies the activation to
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|||
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* invoke on the result.
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|||
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* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
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* Set to true to specify NCHW data layout for input0 and output0.
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|||
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|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
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* Outputs:
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|||
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|
* * 0: The output 4-D tensor, of shape
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|||
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* [batches, out_height, out_width, depth].
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|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
AVERAGE_POOL_2D = 1,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Concatenates the input tensors along the given dimension.
|
|||
|
|
*
|
|||
|
|
* The input tensors must have identical {@link OperandCode} and the same
|
|||
|
|
* dimensions except the dimension along the concatenation axis.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* (full support since NNAPI feature level 3, see the input section)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0 ~ n-1: The list of n input tensors, of shape
|
|||
|
|
* [D0, D1, ..., Daxis(i), ..., Dm].
|
|||
|
|
* Before NNAPI feature level 3, all input tensors of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* must have the same scale and zeroPoint as the output tensor.
|
|||
|
|
* Input tensors of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* are allowed to have different scale and zeroPoint.
|
|||
|
|
* Since NNAPI feature level 3, zero-sized tensors are supported.
|
|||
|
|
* * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the
|
|||
|
|
* concatenation axis.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output, a tensor of the same {@link OperandCode} as the input
|
|||
|
|
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
|
|||
|
|
* Since NNAPI feature level 3, for a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scale and zeroPoint values can be different from
|
|||
|
|
* input tensors. Before NNAPI feature level 3 they have to be the same as for the
|
|||
|
|
* input tensors.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode} configurations:
|
|||
|
|
* * 32 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3:
|
|||
|
|
* * 16 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized with symmetric per channel quantization for the filter:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4:
|
|||
|
|
* * Quantized signed (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized signed with filter symmetric per channel quantization
|
|||
|
|
* (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_in], specifying the
|
|||
|
|
* filter.
|
|||
|
|
* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim)
|
|||
|
|
* must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 11: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on width dimension. If this input is set,
|
|||
|
|
* input 12 (dilation factor for height) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on height dimension. If this input is set,
|
|||
|
|
* input 11 (dilation factor for width) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs (implicit padding):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
|||
|
|
* specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_in], specifying the
|
|||
|
|
* filter.
|
|||
|
|
* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim)
|
|||
|
|
* must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same
|
|||
|
|
* type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 8: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on width dimension. If this input is set,
|
|||
|
|
* input 9 (dilation factor for height) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on height dimension. If this input is set,
|
|||
|
|
* input 8 (dilation factor for width) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth_out].
|
|||
|
|
* Before NNAPI feature level 3, for output tensor of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition must
|
|||
|
|
* be satisfied: output_scale > input_scale * filter_scale
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode} configurations:
|
|||
|
|
* * 32 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3:
|
|||
|
|
* * 16 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized with symmetric per channel quantization for the filter:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4:
|
|||
|
|
* * Quantized signed (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized signed with filter symmetric per channel quantization
|
|||
|
|
* (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim)
|
|||
|
|
* must be set to 3.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise
|
|||
|
|
* multiplier.
|
|||
|
|
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 11: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 12: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on width dimension. If this input is set,
|
|||
|
|
* input 13 (dilation factor for height) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 13: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on height dimension. If this input is set,
|
|||
|
|
* input 12 (dilation factor for width) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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 ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* or {@link ANEURALNETWORKS_TENSOR_FLOAT16} the bias must be of the same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise
|
|||
|
|
* multiplier.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 8: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 9: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on width dimension. If this input is set,
|
|||
|
|
* input 10 (dilation factor for height) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 10: An optional {@link ANEURALNETWORKS_INT32} scalar, specifying the dilation
|
|||
|
|
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
|
|||
|
|
* cells between each filter element on height dimension. If this input is set,
|
|||
|
|
* input 9 (dilation factor for width) must be specified as well.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth_out]. Before NNAPI feature level 3, for
|
|||
|
|
* output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* the following condition must be satisfied:
|
|||
|
|
* output_scale > input_scale * filter_scale
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
DEPTHWISE_CONV_2D = 4,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Rearranges data from depth into blocks of spatial data.
|
|||
|
|
*
|
|||
|
|
* More specifically, this op outputs a copy of the input tensor where
|
|||
|
|
* values from the depth dimension are moved in spatial blocks to the height
|
|||
|
|
* and width dimensions. The value block_size indicates the input block size
|
|||
|
|
* and how the data is moved.
|
|||
|
|
*
|
|||
|
|
* Chunks of data of size block_size * block_size from depth are rearranged
|
|||
|
|
* into non-overlapping blocks of size block_size x block_size.
|
|||
|
|
*
|
|||
|
|
* The width of the output tensor is input_depth * block_size, whereas the
|
|||
|
|
* height is input_height * block_size. The depth of the input tensor must
|
|||
|
|
* be divisible by block_size * block_size
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
|||
|
|
* specifying the input.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size.
|
|||
|
|
* block_size must be >=1 and block_size * block_size must be a divisor
|
|||
|
|
* of the input depth.
|
|||
|
|
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
|
|||
|
|
* width*block_size, depth/(block_size*block_size)].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
DEPTH_TO_SPACE = 5,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Dequantizes the input tensor.
|
|||
|
|
*
|
|||
|
|
* The formula is:
|
|||
|
|
*
|
|||
|
|
* output = (input - zeroPoint) * scale.
|
|||
|
|
*
|
|||
|
|
* Supported input tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported output tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor with the same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 4)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported value tensor rank: from 2
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}.
|
|||
|
|
* The values are indices into the first dimension of Values.
|
|||
|
|
* * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
|
|||
|
|
* extracted.
|
|||
|
|
*
|
|||
|
|
* Output:
|
|||
|
|
* * 0: A n-D tensor with the same rank and shape as the Values
|
|||
|
|
* tensor, except for the first dimension which has the same size
|
|||
|
|
* as Lookups' only dimension.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input1.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
EMBEDDING_LOOKUP = 7,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes element-wise floor() on the input tensor.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor, of the same {@link OperandCode} and dimensions as
|
|||
|
|
* the input tensor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
FLOOR = 8,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Denotes a fully (densely) connected layer, which connects all elements
|
|||
|
|
* in the input tensor with each element in the output tensor.
|
|||
|
|
*
|
|||
|
|
* This layer implements the operation:
|
|||
|
|
*
|
|||
|
|
* outputs = activation(inputs * weights’ + bias)
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* 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".
|
|||
|
|
* Since NNAPI feature level 3, zero batch_size is supported for this tensor.
|
|||
|
|
* * 1: A 2-D tensor, specifying the weights, of shape
|
|||
|
|
* [num_units, input_size], where "num_units" corresponds to the number
|
|||
|
|
* of output nodes.
|
|||
|
|
* * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input
|
|||
|
|
* tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
|
|||
|
|
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32},
|
|||
|
|
* with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor, of shape [batch_size, num_units]. Before NNAPI feature level 3, for
|
|||
|
|
* output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
|
|||
|
|
* condition must be satisfied: output_scale > input_scale * filter_scale.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
*
|
|||
|
|
* Supported value tensor rank: from 2
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with
|
|||
|
|
* shape [ k ].
|
|||
|
|
* * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape
|
|||
|
|
* [ n ]; Keys and Values pair represent a map, i.e., the ith element
|
|||
|
|
* in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
|
|||
|
|
* (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
|
|||
|
|
* ascending order.
|
|||
|
|
* * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
|
|||
|
|
* must be n.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: Output. A tensor with shape [ k …].
|
|||
|
|
* For a {@link ANEURALNETWORKS_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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0
|
|||
|
|
* and scale 1.0f.
|
|||
|
|
* A non-zero byte represents True, a hit. A zero indicates otherwise.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
HASHTABLE_LOOKUP = 10,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Applies L2 normalization along the axis dimension.
|
|||
|
|
*
|
|||
|
|
* The values in the output tensor are computed as:
|
|||
|
|
*
|
|||
|
|
* output[batch, row, col, channel] =
|
|||
|
|
* input[batch, row, col, channel] /
|
|||
|
|
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
|
|||
|
|
*
|
|||
|
|
* By default the axis dimension is the last dimension of the input tensor.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
* Tensors with rank less than 4 are only supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be normalized.
|
|||
|
|
* * 1: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
|
|||
|
|
* specifying the dimension normalization would be performed on.
|
|||
|
|
* Negative index is used to specify axis from the end (e.g. -1 for
|
|||
|
|
* the last axis). Must be in the range [-n, n).
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* the scale must be 1.f / 128 and the zeroPoint must be 128.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the scale must be 1.f / 128 and the zeroPoint must be 0.
|
|||
|
|
*
|
|||
|
|
* NOTE: Before NNAPI feature level 4, if the elements along an axis are all zeros,
|
|||
|
|
* the result is undefined. Since NNAPI feature level 4, if the elements along an axis
|
|||
|
|
* are all zeros, the result is logical zero.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* width.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* height.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs (implicit padding):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* width.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* height.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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)
|
|||
|
|
*
|
|||
|
|
* For input tensor with rank less than 4, independently normalizes each
|
|||
|
|
* 1-D slice along specified dimension.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
* Tensors with rank less than 4 are only supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of
|
|||
|
|
* the normalization window.
|
|||
|
|
* * 2: A scalar, specifying the bias, must not be zero.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 3: A scalar, specifying the scale factor, alpha.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the
|
|||
|
|
* alpha value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the
|
|||
|
|
* alpha value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 4: A scalar, specifying the exponent, beta.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the beta
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the beta
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 5: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
|
|||
|
|
* specifying the dimension normalization would be performed on.
|
|||
|
|
* Negative index is used to specify axis from the end (e.g. -1 for
|
|||
|
|
* the last axis). Must be in the range [-n, n).
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
LOCAL_RESPONSE_NORMALIZATION = 13,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes sigmoid activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = 1 / (1 + exp(-input))
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* the scale must be 1.f / 256 and the zeroPoint must be 0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the scale must be 1.f / 256 and the zeroPoint must be -128.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
LOGISTIC = 14,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Projects an input to a bit vector via locality senstive hashing.
|
|||
|
|
*
|
|||
|
|
* Supported input tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_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(=3) (since NNAPI feature level 3).
|
|||
|
|
* Computed bit vector is considered to be sparse.
|
|||
|
|
* Each output element is an int32 made up of multiple bits
|
|||
|
|
* computed from hash functions.
|
|||
|
|
*
|
|||
|
|
* NOTE: To avoid collisions across hash functions, an offset value
|
|||
|
|
* of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
|
|||
|
|
* where k is the index of the hash function.
|
|||
|
|
*
|
|||
|
|
* Value LSHProjectionType_SPARSE_DEPRECATED(=1).
|
|||
|
|
* Legacy behavior that does not include the offset value.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
* The offset value for sparse projections was added in NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
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.
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 3 LSTM supports layer normalization.
|
|||
|
|
* In case layer normalization is used, the inputs to internal activation
|
|||
|
|
* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
|
|||
|
|
* following an approach from section 3.1 from
|
|||
|
|
* https://arxiv.org/pdf/1607.06450.pdf
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* * (NNAPI feature level 3 or later) The four layer normalization weights either all have
|
|||
|
|
* values or none of them have values. Additionally, if CIFG is used,
|
|||
|
|
* input layer normalization weights tensor is omitted and the other layer
|
|||
|
|
* normalization weights either all have values or none of them have
|
|||
|
|
* values. Layer normalization is used when the values of all the layer
|
|||
|
|
* normalization weights are present.
|
|||
|
|
*
|
|||
|
|
* 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"
|
|||
|
|
*
|
|||
|
|
* The layer normalization is based on:
|
|||
|
|
* https://arxiv.org/pdf/1607.06450.pdf
|
|||
|
|
* Jimmy Ba et al. "Layer Normalization"
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_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.
|
|||
|
|
* Until NNAPI feature level 3 this scalar must be of type {@link
|
|||
|
|
* ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input
|
|||
|
|
* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this
|
|||
|
|
* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
|
|||
|
|
* otherwise if all the input tensors have the type {@link
|
|||
|
|
* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link
|
|||
|
|
* ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* * 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.
|
|||
|
|
* Until NNAPI feature level 3 this scalar must be of type {@link
|
|||
|
|
* ANEURALNETWORKS_FLOAT32}. Since NNAPI feature level 3, if all the input
|
|||
|
|
* tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32}, this
|
|||
|
|
* scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
|
|||
|
|
* otherwise if all the input tensors have the type {@link
|
|||
|
|
* ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be of type {@link
|
|||
|
|
* ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* Since NNAPI feature level 3 there are additional inputs to this op:
|
|||
|
|
* * 23:The input layer normalization weights.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at input gate.
|
|||
|
|
* * 24:The forget layer normalization weights.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at forget gate.
|
|||
|
|
* * 25:The cell layer normalization weights.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at cell gate.
|
|||
|
|
* * 26:The output layer normalization weights.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at output gate.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* width.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* height.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 10: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs (implicit padding):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* width.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter
|
|||
|
|
* height.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 7: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
MAX_POOL_2D = 17,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Multiplies two tensors, element-wise.
|
|||
|
|
*
|
|||
|
|
* Takes two input tensors of identical {@link OperandCode} and compatible
|
|||
|
|
* dimensions. The output is the product of both input tensors, optionally
|
|||
|
|
* modified by an activation function.
|
|||
|
|
*
|
|||
|
|
* Two dimensions are compatible when:
|
|||
|
|
* 1. they are equal, or
|
|||
|
|
* 2. one of them is 1
|
|||
|
|
*
|
|||
|
|
* The size of the resulting output is the maximum size along each dimension
|
|||
|
|
* of the input operands. It starts with the trailing dimensions, and works
|
|||
|
|
* its way forward.
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero
|
|||
|
|
* dimension is only compatible with 0 or 1. The size of the output
|
|||
|
|
* dimension is zero if either of corresponding input dimension is zero.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
|
|||
|
|
* as input0.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor,
|
|||
|
|
* the {@link FuseCode} must be "NONE".
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The product, a tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the following condition must be satisfied:
|
|||
|
|
* output_scale > input1_scale * input2_scale.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
MUL = 18,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes rectified linear activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = max(0, input)
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
RELU = 19,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes rectified linear 1 activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = min(1.f, max(-1.f, input))
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of the same shape as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
RELU1 = 20,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes rectified linear 6 activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = min(6, max(0, input))
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
RELU6 = 21,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reshapes a tensor.
|
|||
|
|
*
|
|||
|
|
* Given tensor, this operation returns a tensor that has the same values as
|
|||
|
|
* tensor, but with a newly specified shape.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 6)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the tensor to be reshaped.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, defining the
|
|||
|
|
* shape of the output tensor. The number of elements implied by shape
|
|||
|
|
* must be the same as the number of elements in the input tensor.
|
|||
|
|
*
|
|||
|
|
* 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
RESHAPE = 22,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Resizes images to given size using the bilinear interpretation.
|
|||
|
|
*
|
|||
|
|
* Resized images must be distorted if their output aspect ratio is not the
|
|||
|
|
* same as input aspect ratio. The corner pixels of output may not be the
|
|||
|
|
* same as corner pixels of input.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Both resizing by shape and resizing by scale are supported.
|
|||
|
|
*
|
|||
|
|
* Inputs (resizing by shape):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input.
|
|||
|
|
* Since NNAPI feature level 3, zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* width of the output tensor.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* height of the output tensor.
|
|||
|
|
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the centers of the 4 corner
|
|||
|
|
* pixels of the input and output tensors are aligned, preserving the
|
|||
|
|
* values at the corner pixels.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the pixel centers are assumed to
|
|||
|
|
* be at (0.5, 0.5). This is the default behavior of image.resize in
|
|||
|
|
* TF 2.0. If this parameter is True, then align_corners parameter
|
|||
|
|
* must be False.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Inputs (resizing by scale, since NNAPI feature level 3):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input. Zero batches is supported for this tensor.
|
|||
|
|
* * 1: A scalar, specifying width_scale, the scaling factor of the width
|
|||
|
|
* dimension from the input tensor to the output tensor. The output
|
|||
|
|
* width is calculated as new_width = floor(width * width_scale).
|
|||
|
|
* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} otherwise.
|
|||
|
|
* * 2: A scalar, specifying height_scale, the scaling factor of the height
|
|||
|
|
* dimension from the input tensor to the output tensor. The output
|
|||
|
|
* height is calculated as new_height = floor(height * height_scale).
|
|||
|
|
* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} otherwise.
|
|||
|
|
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the centers of the 4 corner
|
|||
|
|
* pixels of the input and output tensors are aligned, preserving the
|
|||
|
|
* values at the corner pixels.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the pixel centers are assumed to
|
|||
|
|
* be at (0.5, 0.5). This is the default behavior of image.resize in
|
|||
|
|
* TF 2.0. If this parameter is True, then align_corners parameter
|
|||
|
|
* must be False.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, new_height, new_width, depth].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
RESIZE_BILINEAR = 23,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A basic recurrent neural network layer.
|
|||
|
|
*
|
|||
|
|
* This layer implements the operation:
|
|||
|
|
* outputs = state = activation(inputs * input_weights +
|
|||
|
|
* state * recurrent_weights + bias)
|
|||
|
|
*
|
|||
|
|
* Where:
|
|||
|
|
* * “input_weights” is a weight matrix that multiplies the inputs;
|
|||
|
|
* * “recurrent_weights” is a weight matrix that multiplies the current
|
|||
|
|
* “state” which itself is the output from the previous time step
|
|||
|
|
* computation;
|
|||
|
|
* * “bias” is a bias vector (added to each output vector in the batch);
|
|||
|
|
* * “activation” is the function passed as the “fused_activation_function”
|
|||
|
|
* argument (if not “NONE”).
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_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 FuseCode} value indicating the
|
|||
|
|
* activation function. If “NONE” is specified then it results in a
|
|||
|
|
* linear activation.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: hidden state (out).
|
|||
|
|
* A 2-D tensor of 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.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
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 OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
* Tensors with rank other than 2 or 4 are only supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
* * 1: A scalar, specifying the positive scaling factor for the exponent,
|
|||
|
|
* beta. If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT32},
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scalar
|
|||
|
|
* must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* If input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, then the
|
|||
|
|
* scalar must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* * 2: An optional {@link ANEURALNETWORKS_INT32} scalar, default to -1,
|
|||
|
|
* specifying the dimension the activation would be performed on.
|
|||
|
|
* Negative index is used to specify axis from the end (e.g. -1 for
|
|||
|
|
* the last axis). Must be in the range [-n, n).
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* the scale must be 1.f / 256 and the zeroPoint must be 0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the scale must be 1.f / 256 and the zeroPoint must be -128.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
SOFTMAX = 25,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Rearranges blocks of spatial data, into depth.
|
|||
|
|
*
|
|||
|
|
* More specifically, this op outputs a copy of the input tensor where
|
|||
|
|
* values from the height and width dimensions are moved to the depth
|
|||
|
|
* dimension. The value block_size indicates the input block size and how
|
|||
|
|
* the data is moved.
|
|||
|
|
*
|
|||
|
|
* Chunks of data of size block_size * block_size from depth are rearranged
|
|||
|
|
* into non-overlapping blocks of size block_size x block_size.
|
|||
|
|
*
|
|||
|
|
* The depth of the output tensor is input_depth * block_size * block_size.
|
|||
|
|
* The input tensor's height and width must be divisible by block_size.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
|||
|
|
* specifying the input.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size.
|
|||
|
|
* block_size must be >=1 and block_size must be a divisor of both the
|
|||
|
|
* input height and width.
|
|||
|
|
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
SPACE_TO_DEPTH = 26,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* SVDF op is a kind of stateful layer derived from the notion that a
|
|||
|
|
* densely connected layer that's processing a sequence of input frames can
|
|||
|
|
* be approximated by using a singular value decomposition of each of its
|
|||
|
|
* nodes. The implementation is based on:
|
|||
|
|
*
|
|||
|
|
* https://research.google.com/pubs/archive/43813.pdf
|
|||
|
|
*
|
|||
|
|
* P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
|
|||
|
|
* “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
|
|||
|
|
* INTERSPEECH, 2015.
|
|||
|
|
*
|
|||
|
|
* It processes the incoming input using a 2-stage filtering mechanism:
|
|||
|
|
* * stage 1 performs filtering on the "features" dimension, whose outputs
|
|||
|
|
* get pushed into a memory of fixed-size memory_size.
|
|||
|
|
* * stage 2 performs filtering on the "time" dimension of the memory_size
|
|||
|
|
* memoized outputs of stage 1.
|
|||
|
|
*
|
|||
|
|
* Specifically, for rank 1, this layer implements the operation:
|
|||
|
|
*
|
|||
|
|
* memory = push(conv1d(inputs, weights_feature, feature_dim,
|
|||
|
|
* "ANEURALNETWORKS_PADDING_VALID"));
|
|||
|
|
* outputs = activation(memory * weights_time + bias);
|
|||
|
|
*
|
|||
|
|
* Where:
|
|||
|
|
* * “weights_feature” is a weights matrix that processes the inputs (by
|
|||
|
|
* convolving the input with every “feature filter”), and whose outputs
|
|||
|
|
* get pushed, stacked in order, into the fixed-size “memory” (the oldest
|
|||
|
|
* entry gets dropped);
|
|||
|
|
* * “weights_time” is a weights matrix that processes the “memory” (by a
|
|||
|
|
* batched matrix multiplication on the num_units);
|
|||
|
|
* * “bias” is an optional bias vector (added to each output vector in the
|
|||
|
|
* batch); and
|
|||
|
|
* * “activation” is the function passed as the “fused_activation_function”
|
|||
|
|
* argument (if not “NONE”).
|
|||
|
|
*
|
|||
|
|
* Each rank adds a dimension to the weights matrices by means of stacking
|
|||
|
|
* the filters.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_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 FuseCode} value indicating the
|
|||
|
|
* activation function. If “NONE” is specified then it results in a
|
|||
|
|
* linear activation.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: state (out).
|
|||
|
|
* A 2-D tensor of the same {@link OperandCode} as the inputs, with shape
|
|||
|
|
* [batch_size, (memory_size - 1) * num_units * rank].
|
|||
|
|
* * 1: output.
|
|||
|
|
* A 2-D tensor of the same {@link OperandCode} as the inputs, with shape
|
|||
|
|
* [batch_size, num_units].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
SVDF = 27,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes hyperbolic tangent of input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = tanh(input)
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* the scale must be 1.f / 128 and the zeroPoint must be 128.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the scale must be 1.f / 128 and the zeroPoint must be 0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
TANH = 28,
|
|||
|
|
|
|||
|
|
// Operations below are available since NNAPI feature level 2.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* BatchToSpace for N-dimensional tensors.
|
|||
|
|
*
|
|||
|
|
* This operation reshapes the batch dimension (dimension 0) into M + 1
|
|||
|
|
* dimensions of shape block_shape + [batch], interleaves these blocks back
|
|||
|
|
* into the grid defined by the spatial dimensions [1, ..., M], to obtain a
|
|||
|
|
* result with the same rank as the input.
|
|||
|
|
*
|
|||
|
|
* This is the reverse of SpaceToBatch.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be reshaped
|
|||
|
|
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block
|
|||
|
|
* sizes for each spatial dimension of the input tensor. All values
|
|||
|
|
* must be >= 1.
|
|||
|
|
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since API level 29.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
BATCH_TO_SPACE_ND = 29,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Element-wise division of two tensors.
|
|||
|
|
*
|
|||
|
|
* Takes two input tensors of identical {@link OperandCode} and compatible
|
|||
|
|
* dimensions. The output is the result of dividing the first input tensor
|
|||
|
|
* by the second, optionally modified by an activation function.
|
|||
|
|
*
|
|||
|
|
* For inputs of {@link ANEURALNETWORKS_TENSOR_INT32}, performs
|
|||
|
|
* "floor division" ("//" in Python). For example,
|
|||
|
|
* 5 // 2 = 2
|
|||
|
|
* -5 // 2 = -3
|
|||
|
|
*
|
|||
|
|
* 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}
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero
|
|||
|
|
* dimension is only compatible with 0 or 1. The size of the output
|
|||
|
|
* dimension is zero if either of corresponding input dimension is zero.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the first input.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
|
|||
|
|
* as input0.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor,
|
|||
|
|
* the {@link FuseCode} must be "NONE".
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
DIV = 30,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the mean of elements across dimensions of a tensor.
|
|||
|
|
*
|
|||
|
|
* Reduces the input tensor along the given dimensions to reduce. Unless
|
|||
|
|
* keep_dims is true, the rank of the tensor is reduced by 1 for each entry
|
|||
|
|
* in axis. If keep_dims is true, the reduced dimensions are retained with
|
|||
|
|
* length 1.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Must be in the range
|
|||
|
|
* [-rank(input_tensor), rank(input_tensor)).
|
|||
|
|
*
|
|||
|
|
* NOTE: When the operation was introduced, the documentation
|
|||
|
|
* incorrectly stated that if dimensions were empty, the operation
|
|||
|
|
* would reduce across all dimensions. This behavior was never
|
|||
|
|
* implemented.
|
|||
|
|
*
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
MEAN = 31,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Pads a tensor.
|
|||
|
|
*
|
|||
|
|
* This operation pads a tensor according to the specified paddings.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
* (full support since NNAPI feature level 3, see the output section)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be padded.
|
|||
|
|
* * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings
|
|||
|
|
* for each spatial dimension of the input tensor. The shape of the
|
|||
|
|
* tensor must be {rank(input0), 2}.
|
|||
|
|
* padding[i, 0] specifies the number of elements to be padded in the
|
|||
|
|
* front of dimension i.
|
|||
|
|
* padding[i, 1] specifies the number of elements to be padded after the
|
|||
|
|
* end of dimension i.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. The
|
|||
|
|
* output tensor has the same rank as input0, and each
|
|||
|
|
* dimension of the output tensor has the same size as the
|
|||
|
|
* corresponding dimension of the input tensor plus the size
|
|||
|
|
* of the padding:
|
|||
|
|
* output0.dimension[i] =
|
|||
|
|
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* NOTE: Before NNAPI feature level 3, the pad value for
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined.
|
|||
|
|
* Since NNAPI feature level 3, the pad value is always the logical zero.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
PAD = 32,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* SpaceToBatch for N-Dimensional tensors.
|
|||
|
|
*
|
|||
|
|
* This operation divides "spatial" dimensions [1, ..., M] of the input into
|
|||
|
|
* a grid of blocks of shape block_shape, and interleaves these blocks with
|
|||
|
|
* the "batch" dimension (0) such that in the output, the spatial dimensions
|
|||
|
|
* [1, ..., M] correspond to the position within the grid, and the batch
|
|||
|
|
* dimension combines both the position within a spatial block and the
|
|||
|
|
* original batch position. Prior to division into blocks, the spatial
|
|||
|
|
* dimensions of the input are optionally zero padded according to paddings.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
* (full support since NNAPI feature level 3, see the output section)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
* NCHW is supported since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the input.
|
|||
|
|
* * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block
|
|||
|
|
* sizes for each spatial dimension of the input tensor. All values
|
|||
|
|
* must be >= 1.
|
|||
|
|
* * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings
|
|||
|
|
* for each spatial dimension of the input tensor. All values must be
|
|||
|
|
* >= 0. The shape of the tensor must be {M, 2}, where M is the number
|
|||
|
|
* of spatial dimensions.
|
|||
|
|
* padding[i, 0] specifies the number of element to be padded in the
|
|||
|
|
* front of dimension i.
|
|||
|
|
* padding[i, 1] specifies the number of element to be padded after the
|
|||
|
|
* end of dimension i.
|
|||
|
|
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* NOTE: Before NNAPI feature level 3, the pad value for
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined.
|
|||
|
|
* Since NNAPI feature level 3, the pad value is always the logical zero.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
SPACE_TO_BATCH_ND = 33,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Removes dimensions of size 1 from the shape of a tensor.
|
|||
|
|
*
|
|||
|
|
* Given a tensor input, this operation returns a tensor of the same
|
|||
|
|
* {@link OperandCode} with all dimensions of size 1 removed. If you don't
|
|||
|
|
* want to remove all size 1 dimensions, you can remove specific size 1
|
|||
|
|
* dimensions by specifying the axes (input1).
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, the tensor to be squeezed.
|
|||
|
|
* * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
|
|||
|
|
* dimensions to squeeze. If specified only squeezes the dimensions
|
|||
|
|
* listed. Otherwise, squeezes all dimensions. The dimension index
|
|||
|
|
* starts at 0. An error must be reported if squeezing a dimension that
|
|||
|
|
* is not 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. Contains the
|
|||
|
|
* same data as input, but has one or more dimensions of size 1
|
|||
|
|
* removed.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
* If all input dimensions are equal to 1 and are to be squeezed, the
|
|||
|
|
* output shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
SQUEEZE = 34,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Extracts a strided slice of a tensor.
|
|||
|
|
*
|
|||
|
|
* Roughly speaking, this op extracts a slice of size (end - begin) / stride
|
|||
|
|
* from the given input tensor. Starting at the location specified by begin
|
|||
|
|
* the slice continues by adding stride to the index until all dimensions
|
|||
|
|
* are not less than end. Note that a stride can be negative, which causes a
|
|||
|
|
* reverse slice.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be sliced.
|
|||
|
|
* * 1: begin, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
|
|||
|
|
* starts of the dimensions of the input tensor to be sliced. The
|
|||
|
|
* length must be of rank(input0).
|
|||
|
|
* * 2: end, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
|
|||
|
|
* ends of the dimensions of the input tensor to be sliced. The length
|
|||
|
|
* must be of rank(input0).
|
|||
|
|
* * 3: strides, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The
|
|||
|
|
* strides of the dimensions of the input tensor to be sliced. The
|
|||
|
|
* length must be of rank(input0). The entries must be non-zero.
|
|||
|
|
* * 4: begin_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the ith bit
|
|||
|
|
* of begin_mask is set, begin[i] is ignored and the fullest possible
|
|||
|
|
* range in that dimension is used instead.
|
|||
|
|
* * 5: end_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the ith bit of
|
|||
|
|
* end_mask is set, end[i] is ignored and the fullest possible range in
|
|||
|
|
* that dimension is used instead.
|
|||
|
|
* * 6: shrink_axis_mask, an {@link ANEURALNETWORKS_INT32} scalar. If the
|
|||
|
|
* ith bit of shrink_axis_mask is set, the ith dimension specification
|
|||
|
|
* shrinks the dimensionality by 1, taking on the value at index
|
|||
|
|
* begin[i]. In this case, the ith specification must define a
|
|||
|
|
* slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0 and rank (n - k),
|
|||
|
|
* where k is the number of bits set in shrink_axis_mask.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
* If shrink_axis_mask is true for all input dimensions, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
STRIDED_SLICE = 35,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Element-wise subtraction of two tensors.
|
|||
|
|
*
|
|||
|
|
* Takes two input tensors of identical {@link OperandCode} and compatible
|
|||
|
|
* dimensions. The output is the result of subtracting the second input
|
|||
|
|
* tensor from the first one, optionally modified by an activation function.
|
|||
|
|
*
|
|||
|
|
* Two dimensions are compatible when:
|
|||
|
|
* 1. they are equal, or
|
|||
|
|
* 2. one of them is 1
|
|||
|
|
*
|
|||
|
|
* The size of the output is the maximum size along each dimension of the
|
|||
|
|
* input operands. It starts with the trailing dimensions, and works its way
|
|||
|
|
* forward.
|
|||
|
|
*
|
|||
|
|
* Example:
|
|||
|
|
* input1.dimension = {4, 1, 2}
|
|||
|
|
* input2.dimension = {5, 4, 3, 1}
|
|||
|
|
* output.dimension = {5, 4, 3, 2}
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero
|
|||
|
|
* dimension is only compatible with 0 or 1. The size of the output
|
|||
|
|
* dimension is zero if either of corresponding input dimension is zero.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the first input.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
|
|||
|
|
* as input0.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_INT32} tensor,
|
|||
|
|
* the {@link FuseCode} must be "NONE".
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
SUB = 36,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Transposes the input tensor, permuting the dimensions according to the
|
|||
|
|
* perm tensor.
|
|||
|
|
*
|
|||
|
|
* The returned tensor's dimension i corresponds to the input dimension
|
|||
|
|
* perm[i]. If perm is not given, it is set to (n-1...0), where n is the
|
|||
|
|
* rank of the input tensor. Hence by default, this operation performs a
|
|||
|
|
* regular matrix transpose on 2-D input Tensors.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16} (since NNAPI feature level 3)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be transposed.
|
|||
|
|
* Since NNAPI feature level 3, this tensor may be zero-sized.
|
|||
|
|
* * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32},
|
|||
|
|
* the permutation of the dimensions of the input tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 2.
|
|||
|
|
*/
|
|||
|
|
TRANSPOSE = 37,
|
|||
|
|
|
|||
|
|
// Operations below are available since NNAPI feature level 3.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the absolute value of a tensor, element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ABS = 38,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the index of the largest element along an axis.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor specifying the input. Must be non-empty.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
|
|||
|
|
* reduce across. Negative index is used to specify axis from the
|
|||
|
|
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor.
|
|||
|
|
* If input is 1-dimensional, the output shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
// There is no underscore in ARG_MAX to avoid name conflict with
|
|||
|
|
// the macro defined in libc/kernel/uapi/linux/limits.h.
|
|||
|
|
ARGMAX = 39,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the index of the smallest element along an axis.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor specifying the input. Must be non-empty.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
|
|||
|
|
* reduce across. Negative index is used to specify axis from the
|
|||
|
|
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An (n - 1)-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor.
|
|||
|
|
* If input is 1-dimensional, the output shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ARGMIN = 40, // See ARGMAX for naming discussion.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Transform axis-aligned bounding box proposals using bounding box deltas.
|
|||
|
|
*
|
|||
|
|
* Given the positions of bounding box proposals and the corresponding
|
|||
|
|
* bounding box deltas for each class, return the refined bounding box
|
|||
|
|
* regions. The resulting bounding boxes are cliped against the edges of
|
|||
|
|
* the image.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
|
|||
|
|
* bounding box proposals, each line with format [x1, y1, x2, y2].
|
|||
|
|
* For tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
|
|||
|
|
* the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
|
|||
|
|
* is supported for this tensor.
|
|||
|
|
* * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
|
|||
|
|
* bounding box delta for each region of interest and each class. The
|
|||
|
|
* bounding box deltas are organized in the following order
|
|||
|
|
* [dx, dy, dw, dh], where dx and dy is the relative correction factor
|
|||
|
|
* for the center position of the bounding box with respect to the width
|
|||
|
|
* and height, dw and dh is the log-scale relative correction factor
|
|||
|
|
* for the width and height. For input0 of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, this tensor should be
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}. Zero num_rois is
|
|||
|
|
* supported for this tensor.
|
|||
|
|
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_rois], specifying the batch index of each box. Boxes with
|
|||
|
|
* the same batch index are grouped together. Zero num_rois is
|
|||
|
|
* supported for this tensor.
|
|||
|
|
* * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
|
|||
|
|
* each image in the batch, each line with format
|
|||
|
|
* [image_height, image_width].
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0, with shape
|
|||
|
|
* [num_rois, num_classes * 4], specifying the coordinates of each
|
|||
|
|
* output bounding box for each class, with format [x1, y1, x2, y2].
|
|||
|
|
* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the
|
|||
|
|
* scale must be 0.125 and the zero point must be 0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A recurrent neural network layer that applies an LSTM cell to a
|
|||
|
|
* sequence of inputs in forward and backward directions.
|
|||
|
|
*
|
|||
|
|
* The op supports cross-linking via an auxiliary input. Regular cell feeds
|
|||
|
|
* one input into the two RNN cells in the following way:
|
|||
|
|
*
|
|||
|
|
* INPUT (INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* ---------------------
|
|||
|
|
* | FW_LSTM BW_LSTM |
|
|||
|
|
* ---------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* An op with cross-linking takes two inputs and feeds them into the RNN
|
|||
|
|
* cells in the following way:
|
|||
|
|
*
|
|||
|
|
* AUX_INPUT (AUX_INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* INPUT | (INPUT_R'D.)|
|
|||
|
|
* | | | |
|
|||
|
|
* -----------------------
|
|||
|
|
* | \ / \ / |
|
|||
|
|
* | FW_LSTM BW_LSTM |
|
|||
|
|
* -----------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* The cross-linking mode is enabled iff auxiliary input and auxiliary
|
|||
|
|
* weights are present. While stacking this op on top of itself, this
|
|||
|
|
* allows to connect both forward and backward outputs from previous cell
|
|||
|
|
* to the next cell's input.
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 4 parallel linking mode is supported. The mode is
|
|||
|
|
* enabled if auxiliary input is present but auxiliary weights are omitted.
|
|||
|
|
* In this case, the cell feeds inputs into the RNN in the following way:
|
|||
|
|
*
|
|||
|
|
* INPUT (AUX_INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* ---------------------
|
|||
|
|
* | FW_LSTM BW_LSTM |
|
|||
|
|
* ---------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* While stacking this op on top of itself, this allows to connect both
|
|||
|
|
* forward and backward outputs from previous cell to the next cell's
|
|||
|
|
* corresponding inputs.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 3, either time-major or batch-major.
|
|||
|
|
*
|
|||
|
|
* All input and output tensors must be of the same type.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: The input.
|
|||
|
|
* A 3-D tensor of shape:
|
|||
|
|
* If time-major: [max_time, batch_size, input_size]
|
|||
|
|
* If batch-major: [batch_size, max_time, input_size]
|
|||
|
|
* where "max_time" is the number of timesteps (sequence length),
|
|||
|
|
* "batch_size" corresponds to the batching dimension, and
|
|||
|
|
* "input_size" is the size of the input.
|
|||
|
|
* * 1: The forward input-to-input weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
|
|||
|
|
* corresponds to the number of forward cell units.
|
|||
|
|
* * 2: The forward input-to-forget weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, input_size].
|
|||
|
|
* * 3: The forward input-to-cell weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, input_size].
|
|||
|
|
* * 4: The forward input-to-output weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, input_size].
|
|||
|
|
* * 5: The forward recurrent-to-input weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
|
|||
|
|
* corresponds to either the number of cell units (i.e., fw_num_units),
|
|||
|
|
* or the second dimension of the “fw_projection_weights”, if defined.
|
|||
|
|
* * 6: The forward recurrent-to-forget weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, fw_output_size].
|
|||
|
|
* * 7: The forward recurrent-to-cell weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, fw_output_size].
|
|||
|
|
* * 8: The forward recurrent-to-output weights.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, fw_output_size].
|
|||
|
|
* * 9: The forward cell-to-input weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 10: The forward cell-to-forget weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 11: The forward cell-to-output weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 12: The forward input gate bias. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 13: The forward forget gate bias.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 14: The forward cell gate bias.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 15: The forward output gate bias.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units].
|
|||
|
|
* * 16: The forward projection weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [fw_output_size, fw_num_units].
|
|||
|
|
* * 17: The forward projection bias. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_output_size].
|
|||
|
|
* * 18: The backward input-to-input weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
|
|||
|
|
* corresponds to the number of backward cell units.
|
|||
|
|
* * 19: The backward input-to-forget weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, input_size].
|
|||
|
|
* * 20: The backward input-to-cell weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, input_size].
|
|||
|
|
* * 21: The backward input-to-output weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, input_size].
|
|||
|
|
* * 22: The backward recurrent-to-input weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
|
|||
|
|
* corresponds to either the number of cell units (i.e., “bw_num_units”),
|
|||
|
|
* or the second dimension of the “bw_projection_weights”, if defined.
|
|||
|
|
* * 23: The backward recurrent-to-forget weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, bw_output_size].
|
|||
|
|
* * 24: The backward recurrent-to-cell weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, bw_output_size].
|
|||
|
|
* * 25: The backward recurrent-to-output weights.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, bw_output_size].
|
|||
|
|
* * 26: The backward cell-to-input weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 27: The backward cell-to-forget weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 28: The backward cell-to-output weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 29: The backward input gate bias. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 30: The backward forget gate bias.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 31: The backward cell gate bias.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 32: The backward output gate bias.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units].
|
|||
|
|
* * 33: The backward projection weights. Optional.
|
|||
|
|
* A 2-D tensor of shape [bw_output_size, bw_num_units].
|
|||
|
|
* * 34: The backward projection bias. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_output_size].
|
|||
|
|
* * 35: The forward input activation state.
|
|||
|
|
* A 2-D tensor of shape [batch_size, bw_output_size].
|
|||
|
|
* * 36: The forward input cell state.
|
|||
|
|
* A 2-D tensor of shape [batch_size, bw_num_units].
|
|||
|
|
* * 37: The backward input activation state.
|
|||
|
|
* A 2-D tensor of shape [batch_size, bw_output_size].
|
|||
|
|
* * 38: The backward input cell state.
|
|||
|
|
* A 2-D tensor of shape [batch_size, bw_num_units].
|
|||
|
|
* * 39: The auxiliary input. Optional.
|
|||
|
|
* A 3-D tensor of shape [max_time, batch_size, aux_input_size],
|
|||
|
|
* where “batch_size” corresponds to the batching dimension, and
|
|||
|
|
* “aux_input_size” is the size of the auxiliary input. Optional. See
|
|||
|
|
* the docs above for the usage modes explanation.
|
|||
|
|
* * 40: The forward auxiliary input-to-input weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, aux_input_size].
|
|||
|
|
* * 41: The forward auxiliary input-to-forget weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, aux_input_size].
|
|||
|
|
* * 42: The forward auxiliary input-to-cell weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, aux_input_size].
|
|||
|
|
* * 43: The forward auxiliary input-to-output weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [fw_num_units, aux_input_size].
|
|||
|
|
* * 44: The backward auxiliary input-to-input weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, aux_input_size].
|
|||
|
|
* * 45: The backward auxiliary input-to-forget weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, aux_input_size].
|
|||
|
|
* * 46: The backward auxiliary input-to-cell weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, aux_input_size].
|
|||
|
|
* * 47: The backward auxiliary input-to-output weights.
|
|||
|
|
* Optional. See the docs above for the usage modes explanation.
|
|||
|
|
* A 2-D tensor of shape [bw_num_units, aux_input_size].
|
|||
|
|
* * 48: The activation function.
|
|||
|
|
* A value indicating the activation function:
|
|||
|
|
* <ul>
|
|||
|
|
* <li>0: None;
|
|||
|
|
* <li>1: Relu;
|
|||
|
|
* <li>3: Relu6;
|
|||
|
|
* <li>4: Tanh;
|
|||
|
|
* <li>6: Sigmoid.
|
|||
|
|
* </ul>
|
|||
|
|
* * 49: The 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.
|
|||
|
|
* If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32},
|
|||
|
|
* this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
|
|||
|
|
* otherwise if all the input tensors have the type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be
|
|||
|
|
* of type {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* * 50: The 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.
|
|||
|
|
* If all the input tensors have type {@link ANEURALNETWORKS_TENSOR_FLOAT32},
|
|||
|
|
* this scalar must be of the type {@link ANEURALNETWORKS_FLOAT32},
|
|||
|
|
* otherwise if all the input tensors have the type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, this scalar must be
|
|||
|
|
* of type {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* * 51: merge_outputs
|
|||
|
|
* An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs
|
|||
|
|
* from forward and backward cells should be merged.
|
|||
|
|
* * 52: time_major
|
|||
|
|
* An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format
|
|||
|
|
* of input and output tensors.
|
|||
|
|
* * 53: The forward input layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at input gate.
|
|||
|
|
* * 54: The forward forget layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at forget gate.
|
|||
|
|
* * 55: The forward cell layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at cell gate.
|
|||
|
|
* * 56: The forward output layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at output gate.
|
|||
|
|
* * 57: The backward input layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at input gate.
|
|||
|
|
* * 58: The backward forget layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at forget gate.
|
|||
|
|
* * 59: The backward cell layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at cell gate.
|
|||
|
|
* * 60: The backward output layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at output gate.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The forward output.
|
|||
|
|
* A 3-D tensor of shape:
|
|||
|
|
* If time-major and not merge_outputs:
|
|||
|
|
* [max_time, batch_size, fw_output_size]
|
|||
|
|
* If time-major and merge_outputs:
|
|||
|
|
* [max_time, batch_size, fw_output_size + bw_output_size]
|
|||
|
|
* If batch-major and not merge_outputs:
|
|||
|
|
* [batch_size, max_time, fw_output_size]
|
|||
|
|
* If batch-major and merge_outputs:
|
|||
|
|
* [batch_size, max_time, fw_output_size + bw_output_size]
|
|||
|
|
* * 1: The backward output. Unused if merge_outputs is true.
|
|||
|
|
* A 3-D tensor of shape:
|
|||
|
|
* If time-major: [max_time, batch_size, bw_output_size]
|
|||
|
|
* If batch-major: [batch_size, max_time, bw_output_size]
|
|||
|
|
* * 2: The forward activation state output.
|
|||
|
|
* A 2-D tensor of shape [batch_size, fw_output_size] containing an
|
|||
|
|
* activation state from the last time step in the sequence. This
|
|||
|
|
* output is optional and can be omitted. If this output is present
|
|||
|
|
* then outputs 3-5 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 3: The forward cell state output.
|
|||
|
|
* A tensor of shape [batch_size, fw_cell_size] containing a cell state
|
|||
|
|
* from the last time step in the sequence. This output is optional
|
|||
|
|
* and can be omitted. If this output is present
|
|||
|
|
* then outputs 2, 4, 5 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 4: The backward activation state output.
|
|||
|
|
* A 2-D tensor of shape [batch_size, bw_output_size] containing an
|
|||
|
|
* activation state from the last time step in the sequence. This
|
|||
|
|
* output is optional and can be omitted. If this output is present
|
|||
|
|
* then outputs 2, 3, 5 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 5: The backward cell state output.
|
|||
|
|
* A tensor of shape [batch_size, bw_cell_size] containing a cell state
|
|||
|
|
* from the last time step in the sequence. This output is optional
|
|||
|
|
* and can be omitted. If this output is present
|
|||
|
|
* then outputs 2-4 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out
|
|||
|
|
* and NNAPI does not maintain internal states. This operator does not support the usage pattern
|
|||
|
|
* in which multiple cells are chained and state tensors are propagated.
|
|||
|
|
*/
|
|||
|
|
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A recurrent neural network layer that applies a basic RNN cell to a
|
|||
|
|
* sequence of inputs in forward and backward directions.
|
|||
|
|
*
|
|||
|
|
* This Op unrolls the input along the sequence dimension, and implements
|
|||
|
|
* the following operation for each element in the sequence s =
|
|||
|
|
* 1...sequence_length:
|
|||
|
|
* fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
|
|||
|
|
* fw_state * fw_recurrent_weights’ + fw_bias)
|
|||
|
|
*
|
|||
|
|
* And for each element in sequence t = sequence_length : 1
|
|||
|
|
* bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
|
|||
|
|
* bw_state * bw_recurrent_weights’ + bw_bias)
|
|||
|
|
*
|
|||
|
|
* Where:
|
|||
|
|
* * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
|
|||
|
|
* * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
|
|||
|
|
* current “state” which itself is the output from the previous time step
|
|||
|
|
* computation;
|
|||
|
|
* * “{fw,bw}_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”).
|
|||
|
|
*
|
|||
|
|
* The op supports cross-linking via an auxiliary input. Regular cell feeds
|
|||
|
|
* one input into the two RNN cells in the following way:
|
|||
|
|
*
|
|||
|
|
* INPUT (INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* ---------------------
|
|||
|
|
* | FW_RNN BW_RNN |
|
|||
|
|
* ---------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* An op with cross-linking takes two inputs and feeds them into the RNN
|
|||
|
|
* cells in the following way:
|
|||
|
|
*
|
|||
|
|
* AUX_INPUT (AUX_INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* INPUT | (INPUT_R'D.)|
|
|||
|
|
* | | | |
|
|||
|
|
* -----------------------
|
|||
|
|
* | \ / \ / |
|
|||
|
|
* | FW_RNN BW_RNN |
|
|||
|
|
* -----------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* The cross-linking mode is enabled iff auxiliary input and auxiliary
|
|||
|
|
* weights are present. While stacking this op on top of itself, this
|
|||
|
|
* allows to connect both forward and backward outputs from previous cell
|
|||
|
|
* to the next cell's input.
|
|||
|
|
*
|
|||
|
|
* Since NNAPI feature level 4 parallel linking mode is supported. The mode is
|
|||
|
|
* enabled if auxiliary input is present but auxiliary weights are omitted.
|
|||
|
|
* In this case, the cell feeds inputs into the RNN in the following way:
|
|||
|
|
*
|
|||
|
|
* INPUT (AUX_INPUT_REVERSED)
|
|||
|
|
* | |
|
|||
|
|
* ---------------------
|
|||
|
|
* | FW_RNN BW_RNN |
|
|||
|
|
* ---------------------
|
|||
|
|
* | |
|
|||
|
|
* FW_OUT BW_OUT
|
|||
|
|
*
|
|||
|
|
* While stacking this op on top of itself, this allows to connect both
|
|||
|
|
* forward and backward outputs from previous cell to the next cell's
|
|||
|
|
* corresponding inputs.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* The input tensors must all be the same type.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: input.
|
|||
|
|
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
|
|||
|
|
* it is set to true, then the input has a shape [maxTime, batchSize,
|
|||
|
|
* inputSize], otherwise the input has a shape [batchSize, maxTime,
|
|||
|
|
* inputSize].
|
|||
|
|
* * 1: fwWeights.
|
|||
|
|
* A 2-D tensor of shape [fwNumUnits, inputSize].
|
|||
|
|
* * 2: fwRecurrentWeights.
|
|||
|
|
* A 2-D tensor of shape [fwNumUnits, fwNumUnits].
|
|||
|
|
* * 3: fwBias.
|
|||
|
|
* A 1-D tensor of shape [fwNumUnits].
|
|||
|
|
* * 4: fwHiddenState.
|
|||
|
|
* A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
|
|||
|
|
* state input for the first time step of the computation.
|
|||
|
|
* * 5: bwWeights.
|
|||
|
|
* A 2-D tensor of shape [bwNumUnits, inputSize].
|
|||
|
|
* * 6: bwRecurrentWeights.
|
|||
|
|
* A 2-D tensor of shape [bwNumUnits, bwNumUnits].
|
|||
|
|
* * 7: bwBias.
|
|||
|
|
* A 1-D tensor of shape [bwNumUnits].
|
|||
|
|
* * 8: bwHiddenState
|
|||
|
|
* A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
|
|||
|
|
* state input for the first time step of the computation.
|
|||
|
|
* * 9: auxInput.
|
|||
|
|
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
|
|||
|
|
* it is set to true, then the input has a shape [maxTime, batchSize,
|
|||
|
|
* auxInputSize], otherwise the input has a shape [batchSize, maxTime,
|
|||
|
|
* auxInputSize]. Can be omitted. See the docs above for the usage
|
|||
|
|
* modes explanation.
|
|||
|
|
* * 10:fwAuxWeights.
|
|||
|
|
* A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted.
|
|||
|
|
* See the docs above for the usage modes explanation.
|
|||
|
|
* * 11:bwAuxWeights.
|
|||
|
|
* A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted.
|
|||
|
|
* See the docs above for the usage modes explanation.
|
|||
|
|
* * 12:fusedActivationFunction.
|
|||
|
|
* A {@link FuseCode} value indicating the activation function. If
|
|||
|
|
* “NONE” is specified then it results in a linear activation.
|
|||
|
|
* * 13:timeMajor
|
|||
|
|
* An {@link ANEURALNETWORKS_BOOL} scalar specifying the shape format
|
|||
|
|
* of input and output tensors.
|
|||
|
|
* * 14:mergeOutputs
|
|||
|
|
* An {@link ANEURALNETWORKS_BOOL} scalar specifying if the outputs
|
|||
|
|
* from forward and backward cells are separate (if set to false) or
|
|||
|
|
* concatenated (if set to true).
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: fwOutput.
|
|||
|
|
* A 3-D tensor. The first two dimensions of the shape are defined by
|
|||
|
|
* the input 6 (timeMajor) and the third dimension is defined by the
|
|||
|
|
* input 14 (mergeOutputs). If timeMajor is set to true, then the first
|
|||
|
|
* two dimensions are [maxTime, batchSize], otherwise they are set to
|
|||
|
|
* [batchSize, maxTime]. If mergeOutputs is set to true, then the third
|
|||
|
|
* dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
|
|||
|
|
* to fwNumUnits.
|
|||
|
|
* * 1: bwOutput.
|
|||
|
|
* A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
|
|||
|
|
* this tensor is not produced. The shape is defined by the input 6
|
|||
|
|
* (timeMajor). If it is set to true, then the shape is set to
|
|||
|
|
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
|
|||
|
|
* [batchSize, maxTime, bwNumUnits].
|
|||
|
|
* * 2: The forward hidden state output.
|
|||
|
|
* A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden
|
|||
|
|
* state from the last time step in the sequence. This output is
|
|||
|
|
* optional and can be omitted. If this output is present then output
|
|||
|
|
* 3 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 3: The backward hidden state output.
|
|||
|
|
* A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden
|
|||
|
|
* state from the last time step in the sequence. This output is
|
|||
|
|
* optional and can be omitted. If this output is present then output
|
|||
|
|
* 2 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out
|
|||
|
|
* and NNAPI does not maintain internal states. This operator does not support the usage pattern
|
|||
|
|
* in which multiple cells are chained and state tensors are propagated.
|
|||
|
|
*/
|
|||
|
|
BIDIRECTIONAL_SEQUENCE_RNN = 43,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Greedily selects a subset of bounding boxes in descending order of score.
|
|||
|
|
*
|
|||
|
|
* This op applies NMS algorithm to each class. In each loop of execution,
|
|||
|
|
* the box with maximum score gets selected and removed from the pending set.
|
|||
|
|
* The scores of the rest of boxes are lowered according to the
|
|||
|
|
* intersection-over-union (IOU) overlapping with the previously selected
|
|||
|
|
* boxes and a specified NMS kernel method. Any boxes with score less
|
|||
|
|
* than a threshold are removed from the pending set.
|
|||
|
|
*
|
|||
|
|
* Three NMS kernels are supported:
|
|||
|
|
* * Hard: score_new = score_old * (1 if IoU < threshold else 0)
|
|||
|
|
* * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU)
|
|||
|
|
* * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
|
|||
|
|
*
|
|||
|
|
* Axis-aligned bounding boxes are represented by its upper-left corner
|
|||
|
|
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
|
|||
|
|
* bounding box should satisfy x1 <= x2 and y1 <= y2.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
|
|||
|
|
* of each bounding box proposal. The boxes are grouped by batches in the
|
|||
|
|
* first dimension. Zero num_rois is supported for this tensor.
|
|||
|
|
* * 1: A 2-D Tensor specifying the bounding boxes of shape
|
|||
|
|
* [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
|
|||
|
|
* The boxes are grouped by batches in the first dimension. The sequential
|
|||
|
|
* order of the boxes corresponds with input0. For input0 of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should be of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
|
|||
|
|
* scale of 0.125.
|
|||
|
|
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
|
|||
|
|
* with zeroPoint of -128 and scale of 0.125.
|
|||
|
|
* Zero num_rois is supported for this tensor.
|
|||
|
|
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_rois], specifying the batch index of each box. Boxes with
|
|||
|
|
* the same batch index are grouped together.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, score_threshold. Boxes
|
|||
|
|
* with scores lower than the threshold are filtered before sending
|
|||
|
|
* to the NMS algorithm.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
|
|||
|
|
* number of selected bounding boxes for each image. Set to a negative
|
|||
|
|
* value for unlimited number of output bounding boxes.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the NMS
|
|||
|
|
* kernel method, options are 0:hard, 1:linear, 2:gaussian.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU
|
|||
|
|
* threshold in hard and linear NMS kernel. This field is ignored if
|
|||
|
|
* gaussian kernel is selected.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the sigma in
|
|||
|
|
* gaussian NMS kernel. This field is ignored if gaussian kernel is
|
|||
|
|
* not selected.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, nms_score_threshold.
|
|||
|
|
* Boxes with scores lower than the threshold are dropped during the
|
|||
|
|
* score updating phase in soft NMS.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A 1-D Tensor of the same {@link OperandCode} as input0, with shape
|
|||
|
|
* [num_output_rois], specifying the score of each output box. The boxes
|
|||
|
|
* are grouped by batches, but the sequential order in each batch is not
|
|||
|
|
* guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* guaranteed. For type of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* or {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the scale and zero point must be the same as input0.
|
|||
|
|
* * 1: A 2-D Tensor of the same {@link OperandCode} as input1, with shape
|
|||
|
|
* [num_output_rois, 4], specifying the coordinates of each
|
|||
|
|
* output bounding box with the same format as input1. The sequential
|
|||
|
|
* order of the boxes corresponds with output0. For type of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the scale must be
|
|||
|
|
* 0.125 and the zero point must be 0.
|
|||
|
|
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_output_rois], specifying the class of each output box. The
|
|||
|
|
* sequential order of the boxes corresponds with output0.
|
|||
|
|
* * 3: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_output_rois], specifying the batch index of each box. Boxes
|
|||
|
|
* with the same batch index are grouped together.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
BOX_WITH_NMS_LIMIT = 44,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Casts a tensor to a type.
|
|||
|
|
*
|
|||
|
|
* This operation ignores the scale and zeroPoint of quanized tensors,
|
|||
|
|
* e.g. it treats a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} input
|
|||
|
|
* as a tensor of uint8 values.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* Since NNAPI feature level 4, casting tensors of the following
|
|||
|
|
* {@link OperandCode} to the same {@link OperandCode} is supported:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor with the same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
CAST = 45,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Shuffle the channels of the input tensor.
|
|||
|
|
*
|
|||
|
|
* Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
|
|||
|
|
* divide the channel dimension into num_groups groups, and reorganize the
|
|||
|
|
* channels by grouping channels with the same index in each group.
|
|||
|
|
*
|
|||
|
|
* Along the channel dimension, the output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output_channel[k * num_groups + g] = input_channel[g * group_size + k]
|
|||
|
|
*
|
|||
|
|
* where group_size = num_channels / num_groups
|
|||
|
|
*
|
|||
|
|
* The number of channels must be divisible by num_groups.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be shuffled.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* groups.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the dimension
|
|||
|
|
* channel shuffle would be performed on. Negative index is used to
|
|||
|
|
* specify axis from the end (e.g. -1 for the last axis). Must be in
|
|||
|
|
* the range [-n, n).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
CHANNEL_SHUFFLE = 46,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Apply postprocessing steps to bounding box detections.
|
|||
|
|
*
|
|||
|
|
* Bounding box detections are generated by applying transformation on a set
|
|||
|
|
* of predefined anchors with the bounding box deltas from bounding box
|
|||
|
|
* regression. A final step of hard NMS is applied to limit the number of
|
|||
|
|
* returned boxes.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
|
|||
|
|
* the score of each anchor with each class. Class 0 for each
|
|||
|
|
* [batches, num_anchors, 0] is background and will be ignored.
|
|||
|
|
* * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
|
|||
|
|
* the first four values in length_box_encoding specifying the bounding
|
|||
|
|
* box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
|
|||
|
|
* where dy and dx is the linear-scale relative correction factor for the
|
|||
|
|
* center position of the bounding box with respect to the width and height,
|
|||
|
|
* dh and dw is the log-scale relative correction factor for the width and
|
|||
|
|
* height. All the entries in length_box_encoding beyond the first four
|
|||
|
|
* values are ignored in this operation.
|
|||
|
|
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
|
|||
|
|
* predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
|
|||
|
|
* ctr_x are the center position of the box, and h and w are the height
|
|||
|
|
* and the width.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
|
|||
|
|
* factor for dy in bounding box deltas.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
|
|||
|
|
* factor for dx in bounding box deltas.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
|
|||
|
|
* factor for dh in bounding box deltas.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scaling
|
|||
|
|
* factor for dw in bounding box deltas.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to use regular
|
|||
|
|
* multi-class NMS algorithm that do NMS separately for each class,
|
|||
|
|
* set to false for a faster algorithm that only do one single NMS
|
|||
|
|
* using the highest class score..
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, max_num_detections, specifying
|
|||
|
|
* the maximum number of boxes for the output. Boxes with the lowest
|
|||
|
|
* scores are discarded to meet the limit.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is
|
|||
|
|
* set to false, specifying the maximum number of classes per detection.
|
|||
|
|
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, only used when input7 is
|
|||
|
|
* set to true, specifying the maximum number of detections when
|
|||
|
|
* applying NMS algorithm for each single class.
|
|||
|
|
* * 11: A scalar, score_threshold. Boxes with scores lower than the
|
|||
|
|
* threshold are filtered before sending to the NMS algorithm. The
|
|||
|
|
* scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
|
|||
|
|
* must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* * 13: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to include
|
|||
|
|
* background class in the list of label map for the output, set
|
|||
|
|
* to false to not include the background. When the background
|
|||
|
|
* class is included, it has label 0 and the output classes start
|
|||
|
|
* at 1 in the label map, otherwise, the output classes start at 0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A 2-D tensor of the same {@link OperandCode} as input0, with shape
|
|||
|
|
* [batches, max_num_detections], specifying the score of each output
|
|||
|
|
* detections.
|
|||
|
|
* * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
|
|||
|
|
* coordinates of each output bounding box, with format
|
|||
|
|
* [y1, x1, y2, x2].
|
|||
|
|
* * 2: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [batches, max_num_detections], specifying the class label for each
|
|||
|
|
* output detection.
|
|||
|
|
* * 3: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape [batches],
|
|||
|
|
* specifying the number of valid output detections for each batch.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
DETECTION_POSTPROCESSING = 47,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x == y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
EQUAL = 48,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes exponential of x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
EXP = 49,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Inserts a dimension of 1 into a tensor's shape.
|
|||
|
|
*
|
|||
|
|
* Given a tensor input, this operation inserts a dimension of 1 at the
|
|||
|
|
* given dimension index of input's shape. The dimension index starts at
|
|||
|
|
* zero; if you specify a negative dimension index, it is counted backward
|
|||
|
|
* from the end.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the dimension
|
|||
|
|
* index to expand. Must be in the range [-(n + 1), (n + 1)).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An (n + 1)-D tensor with the same {@link OperandCode} and data as
|
|||
|
|
* input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
EXPAND_DIMS = 50,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Gathers values along an axis.
|
|||
|
|
*
|
|||
|
|
* Produces an output tensor with shape
|
|||
|
|
* input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
|
|||
|
|
* where:
|
|||
|
|
* # Vector indices (output is rank(input0)).
|
|||
|
|
* output[a_0, ..., a_n, i, b_0, ..., b_n] =
|
|||
|
|
* input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
|
|||
|
|
*
|
|||
|
|
* # Higher rank indices (output is rank(input0) + rank(indices) - 1).
|
|||
|
|
* output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
|
|||
|
|
* input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor from which to gather values.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis.
|
|||
|
|
* Negative index is used to specify axis from the end
|
|||
|
|
* (e.g. -1 for the last axis). Must be in the range [-n, n).
|
|||
|
|
* * 2: A k-D tensor {@link ANEURALNETWORKS_TENSOR_INT32} of indices.
|
|||
|
|
* The values must be in the bounds of the corresponding dimensions
|
|||
|
|
* of input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An (n + k - 1)-D tensor with the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
GATHER = 51,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Generate aixs-aligned bounding box proposals.
|
|||
|
|
*
|
|||
|
|
* Bounding box proposals are generated by applying transformation on a set
|
|||
|
|
* of predefined anchors with the bounding box deltas from bounding box
|
|||
|
|
* regression. A final step of hard NMS is applied to limit the number of
|
|||
|
|
* returned boxes.
|
|||
|
|
*
|
|||
|
|
* Axis-aligned bounding boxes are represented by its upper-left corner
|
|||
|
|
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
|
|||
|
|
* bounding box should satisfy x1 <= x2 and y1 <= y2.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D Tensor specifying the score of each anchor at each
|
|||
|
|
* location. With "NHWC" data layout, the tensor shape is
|
|||
|
|
* [batches, height, width, num_anchors]. With "NCHW" data layout,
|
|||
|
|
* the tensor shape is [batches, num_anchors, height, width].
|
|||
|
|
* * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
|
|||
|
|
* layout, the tensor shape is [batches, height, width, num_anchors * 4].
|
|||
|
|
* With "NCHW" data layout, the tensor shape is
|
|||
|
|
* [batches, num_anchors * 4, height, width]. The box deltas are encoded
|
|||
|
|
* in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
|
|||
|
|
* relative correction factor for the center position of the bounding box
|
|||
|
|
* with respect to the width and height, dw and dh is the log-scale
|
|||
|
|
* relative correction factor for the width and height. The last
|
|||
|
|
* dimensions is the channel dimension.
|
|||
|
|
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
|
|||
|
|
* predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor should be of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with scale of 0.125.
|
|||
|
|
* * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
|
|||
|
|
* each image in the batch, with format [image_height, image_width].
|
|||
|
|
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this
|
|||
|
|
* tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}, with
|
|||
|
|
* scale of 0.125.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the height of original image to the height of feature map.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the width of original image to the width of feature map.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
|
|||
|
|
* number of boxes before going into the hard NMS algorithm. Boxes
|
|||
|
|
* with the lowest scores are discarded to meet the limit. Set to
|
|||
|
|
* a non-positive value for unlimited number.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the maximum
|
|||
|
|
* number of boxes returning from the hard NMS algorithm. Boxes
|
|||
|
|
* with the lowest scores are discarded to meet the limit. Set to
|
|||
|
|
* a non-positive value for unlimited number.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the IoU
|
|||
|
|
* threshold for hard NMS.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_FLOAT32} scalar, min_size. Boxes with
|
|||
|
|
* height or width lower than the absolute threshold are filtered out.
|
|||
|
|
* * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and input1. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0, of shape
|
|||
|
|
* [num_output_rois], specifying the score of each output box.
|
|||
|
|
* The boxes are grouped by batches, but the sequential order in
|
|||
|
|
* each batch is not guaranteed. For type of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, the scale and zero
|
|||
|
|
* point must be the same as input0.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} as input3, of shape
|
|||
|
|
* [num_output_rois, 4], specifying the coordinates of each output
|
|||
|
|
* bounding box for each class, with format [x1, y1, x2, y2].
|
|||
|
|
* The sequential order of the boxes corresponds with output0.
|
|||
|
|
* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the
|
|||
|
|
* scale must be 0.125 and the zero point must be 0.
|
|||
|
|
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_output_rois], specifying the batch index of each box. Boxes
|
|||
|
|
* with the same batch index are grouped together.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
GENERATE_PROPOSALS = 52,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x > y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ANEURALNETWORKS_GREATER = 53,
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x >= y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
GREATER_EQUAL = 54,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Performs a grouped 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_group]
|
|||
|
|
* containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
|
|||
|
|
* applies a group of different filters to each input channel group, then
|
|||
|
|
* concatenates the results together.
|
|||
|
|
*
|
|||
|
|
* Specifically, the input channels are divided into num_groups groups, each with
|
|||
|
|
* depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
|
|||
|
|
* filters are also divided into num_groups groups, i.e. depth_out is divisible
|
|||
|
|
* by num_groups. GROUPED_CONV applies each group of filters to the corresponding
|
|||
|
|
* input channel group, and the result are concatenated together.
|
|||
|
|
*
|
|||
|
|
* 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, g * channel_multiplier + q] =
|
|||
|
|
* sum_{di, dj, dk} (
|
|||
|
|
* input[b, strides[1] * i + di, strides[2] * j + dj,
|
|||
|
|
* g * depth_group + dk] *
|
|||
|
|
* filter[g * channel_multiplier + q, di, dj, dk]
|
|||
|
|
* ) + bias[channel]
|
|||
|
|
*
|
|||
|
|
* where channel_multiplier = depth_out / num_groups
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode} configurations:
|
|||
|
|
* * 16 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * 32 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized signed (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized with symmetric per channel quantization for the filter:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* * Quantized signed with filter symmetric per channel quantization
|
|||
|
|
* (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* 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, where depth_in = num_groups * depth_group.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_group], specifying
|
|||
|
|
* the filter, where depth_out must be divisible by num_groups. For
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* the channel dimension (channelDim at
|
|||
|
|
* {@link ANeuralNetworksSymmPerChannelQuantParams}) must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
|
|||
|
|
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
|
|||
|
|
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* groups.
|
|||
|
|
* * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 11: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Inputs (implicit padding):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
|||
|
|
* specifying the input, where depth_in = num_groups * depth_group.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_group], specifying
|
|||
|
|
* the filter, where depth_out must be divisible by num_groups. For
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* the channel dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim)
|
|||
|
|
* must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint
|
|||
|
|
* of 0 and bias_scale == input_scale * filter_scale. For filter tensor
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
|
|||
|
|
* should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of
|
|||
|
|
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* groups.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth_out].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
GROUPED_CONV_2D = 55,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Localize the maximum keypoints from heatmaps.
|
|||
|
|
*
|
|||
|
|
* This operation approximates the accurate maximum keypoint scores and
|
|||
|
|
* indices after bicubic upscaling by using Taylor expansion up to the
|
|||
|
|
* quadratic term.
|
|||
|
|
*
|
|||
|
|
* The bounding box is represented by its upper-left corner coordinate
|
|||
|
|
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
|
|||
|
|
* A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D Tensor of shape
|
|||
|
|
* [num_boxes, heatmap_size, heatmap_size, num_keypoints],
|
|||
|
|
* specifying the heatmaps, the height and width of heatmaps should
|
|||
|
|
* be the same, and must be greater than or equal to 2.
|
|||
|
|
* * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
|
|||
|
|
* each with format [x1, y1, x2, y2]. For input0 of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, this tensor should
|
|||
|
|
* be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with zeroPoint
|
|||
|
|
* of 0 and scale of 0.125.
|
|||
|
|
* For input0 of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}, this tensor
|
|||
|
|
* should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, with
|
|||
|
|
* zeroPoint of -128 and scale of 0.125.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0, with shape
|
|||
|
|
* [num_boxes, num_keypoints], specifying score of the keypoints.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from input0 scale and zeroPoint.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} as input1, with shape
|
|||
|
|
* [num_boxes, num_keypoints, 2], specifying the location of
|
|||
|
|
* the keypoints, the second dimension is organized as
|
|||
|
|
* [keypoint_x, keypoint_y].
|
|||
|
|
* For type of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}, the
|
|||
|
|
* scale must be 0.125 and the zero point must be 0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
HEATMAP_MAX_KEYPOINT = 56,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Applies instance normalization to the input tensor.
|
|||
|
|
*
|
|||
|
|
* The values in the output tensor are computed as:
|
|||
|
|
*
|
|||
|
|
* output[b, h, w, c] =
|
|||
|
|
* (input[b, h, w, c] - mean[b, c]) * gamma /
|
|||
|
|
* sqrt(var[b, c] + epsilon) + beta
|
|||
|
|
*
|
|||
|
|
* Where the mean and variance are computed across the spatial dimensions:
|
|||
|
|
*
|
|||
|
|
* mean[b, c] =
|
|||
|
|
* sum_{h, w}(input[b, h, w, c]) / sum(1)
|
|||
|
|
*
|
|||
|
|
* var[b, c] =
|
|||
|
|
* sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be normalized.
|
|||
|
|
* * 1: A scalar, specifying gamma, the scale applied to the normalized
|
|||
|
|
* tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if
|
|||
|
|
* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* * 2: A scalar, specifying beta, the offset applied to the normalized
|
|||
|
|
* tensor. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if
|
|||
|
|
* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* * 3: A scalar, specifying epsilon, the small value added to variance to
|
|||
|
|
* avoid dividing by zero. The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if
|
|||
|
|
* input0 is of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} if input0 is of
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} and same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
INSTANCE_NORMALIZATION = 57,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x < y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LESS = 58,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x <= y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LESS_EQUAL = 59,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes natural logarithm of x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LOG = 60,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the truth value of x AND y element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions
|
|||
|
|
* compatible with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LOGICAL_AND = 61,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the truth value of NOT x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LOGICAL_NOT = 62,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the truth value of x OR y element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
* * 1: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8} and dimensions
|
|||
|
|
* compatible with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LOGICAL_OR = 63,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the log softmax activations given logits.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using this formula:
|
|||
|
|
*
|
|||
|
|
* output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor specifying the input logits.
|
|||
|
|
* * 1: A scalar, specifying the positive scaling factor for the exponent,
|
|||
|
|
* beta.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the beta
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the beta
|
|||
|
|
* value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis to
|
|||
|
|
* reduce across. Negative index is used to specify axis from the
|
|||
|
|
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of the same {@link OperandCode} and shape as
|
|||
|
|
* input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
LOG_SOFTMAX = 64,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the element-wise maximum of two tensors.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and compatible dimensions
|
|||
|
|
* with input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
MAXIMUM = 65,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the element-wise minimum of two tensors.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and compatible dimensions
|
|||
|
|
* with input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
MINIMUM = 66,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes numerical negative value element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
NEG = 67,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* For input tensors x and y, computes x != y elementwise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* This operation supports broadcasting.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode} and dimensions compatible
|
|||
|
|
* with input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_BOOL8}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
NOT_EQUAL = 68,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Pads a tensor with the given constant value according to the specified
|
|||
|
|
* paddings.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be padded.
|
|||
|
|
* * 1: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings
|
|||
|
|
* for each spatial dimension of the input tensor. The shape of the
|
|||
|
|
* tensor must be {rank(input0), 2}.
|
|||
|
|
* padding[i, 0] specifies the number of elements to be padded in the
|
|||
|
|
* front of dimension i.
|
|||
|
|
* padding[i, 1] specifies the number of elements to be padded after
|
|||
|
|
* the end of dimension i.
|
|||
|
|
* * 2: A scalar specifying the value to use for padding input0.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the
|
|||
|
|
* pad value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the
|
|||
|
|
* pad value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the pad value must be of {@link ANEURALNETWORKS_INT32}. The
|
|||
|
|
* scale and zeroPoint are assumed to be the same as in input0.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. The
|
|||
|
|
* output tensor has the same rank as input0, and each
|
|||
|
|
* dimension of the output tensor has the same size as the
|
|||
|
|
* corresponding dimension of the input tensor plus the size
|
|||
|
|
* of the padding:
|
|||
|
|
* output0.dimension[i] =
|
|||
|
|
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
PAD_V2 = 69,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the power of one value to another.
|
|||
|
|
*
|
|||
|
|
* Given a tensor base and a tensor exponent, this operation computes
|
|||
|
|
* base^exponent elementwise.
|
|||
|
|
*
|
|||
|
|
* This operations supports broadcasting. 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.
|
|||
|
|
*
|
|||
|
|
* For example:
|
|||
|
|
* base.dimension = {4, 1, 2}
|
|||
|
|
* exponent.dimension = {5, 4, 3, 1}
|
|||
|
|
* output.dimension = {5, 4, 3, 2}
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor specifying the base.
|
|||
|
|
* * 1: A tensor specifying the exponent.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An output tensor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
POW = 70,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Parametric Rectified Linear Unit.
|
|||
|
|
*
|
|||
|
|
* It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
|
|||
|
|
* is a learned array with the same {@link OperandCode} and compatible
|
|||
|
|
* dimensions as input x.
|
|||
|
|
*
|
|||
|
|
* 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:
|
|||
|
|
* input.dimension = {4, 1, 2}
|
|||
|
|
* alpha.dimension = {5, 4, 3, 1}
|
|||
|
|
* output.dimension = {5, 4, 3, 2}
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input.
|
|||
|
|
* * 1: A tensor of the same {@link OperandCode}, and compatible dimensions
|
|||
|
|
* as input0, specifying the alpha.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
PRELU = 71,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Quantizes the input tensor.
|
|||
|
|
*
|
|||
|
|
* The formula for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} output tensor is:
|
|||
|
|
*
|
|||
|
|
* output = max(0, min(255, round(input / scale) + zeroPoint)
|
|||
|
|
*
|
|||
|
|
* The formula for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} output
|
|||
|
|
* tensor is:
|
|||
|
|
*
|
|||
|
|
* output = max(-128, min(127, round(input / scale) + zeroPoint)
|
|||
|
|
*
|
|||
|
|
* Supported input tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported output tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, may be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0, but with
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} or.
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
QUANTIZE = 72,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A version of quantized LSTM, using 16 bit quantization for internal
|
|||
|
|
* state.
|
|||
|
|
*
|
|||
|
|
* There is no projection layer, so cell state size is equal to the output
|
|||
|
|
* size.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [numBatches, inputSize] specifying the input to the LSTM
|
|||
|
|
* cell. Tensor is quantized with a fixed quantization range of
|
|||
|
|
* [-1, 127/128] (scale = 1/128, zeroPoint = 128).
|
|||
|
|
* * 1: The input-to-input weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, inputSize] specifying input-to-input part of
|
|||
|
|
* weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 2: The input-to-forget weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, inputSize] specifying input-to-forget part of
|
|||
|
|
* weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 3: The input-to-cell weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, inputSize] specifying input-to-cell part of
|
|||
|
|
* weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 4: The input-to-output weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, inputSize] specifying input-to-output part of
|
|||
|
|
* weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 5: The recurrent-to-input weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, outputSize] specifying recurrent-to-input part
|
|||
|
|
* of weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 6: The recurrent-to-forget weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, outputSize] specifying recurrent-to-forget
|
|||
|
|
* part of weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 7: The recurrent-to-cell weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, outputSize] specifying recurrent-to-cell part
|
|||
|
|
* of weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 8: The recurrent-to-output weights.
|
|||
|
|
* A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [outputSize, outputSize] specifying recurrent-to-output
|
|||
|
|
* part of weights for fully-connected layer inside the LSTM cell.
|
|||
|
|
* Quantization zero point and scale must be the same across all the
|
|||
|
|
* weights.
|
|||
|
|
* * 9: The input gate bias.
|
|||
|
|
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
|
|||
|
|
* [outputSize] specifying the bias for the fully-connected layer
|
|||
|
|
* inside the LSTM cell. Bias is quantized with scale being a product
|
|||
|
|
* of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* * 10:The forget gate bias.
|
|||
|
|
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
|
|||
|
|
* [outputSize] specifying the bias for the fully-connected layer
|
|||
|
|
* inside the LSTM cell. Bias is quantized with scale being a product
|
|||
|
|
* of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* * 11:The cell bias.
|
|||
|
|
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
|
|||
|
|
* [outputSize] specifying the bias for the fully-connected layer
|
|||
|
|
* inside the LSTM cell. Bias is quantized with scale being a product
|
|||
|
|
* of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* * 12:The output gate bias.
|
|||
|
|
* A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape
|
|||
|
|
* [outputSize] specifying the bias for the fully-connected layer
|
|||
|
|
* inside the LSTM cell. Bias is quantized with scale being a product
|
|||
|
|
* of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* * 13: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* and shape [numBatches, outputSize] specifying the cell state from the
|
|||
|
|
* previous time step of the LSTM cell. It is quantized using a
|
|||
|
|
* quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
|
|||
|
|
* 32768, zeroPoint = 0).
|
|||
|
|
* * 14: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [numBathes, outputSize] specifying the output of the LSTM
|
|||
|
|
* cell from previous time-step. Tensor is quantized with a fixed
|
|||
|
|
* quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
|
|||
|
|
* 128).
|
|||
|
|
*
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* and shape [numBatches, outputSize] which contains a cell state from
|
|||
|
|
* the current time step. Tensor is quantized using a quantization
|
|||
|
|
* range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
|
|||
|
|
* 0).
|
|||
|
|
* * 1: A 2-D tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and shape [numBathes, outputSize] which contains the output value.
|
|||
|
|
* Tensor is quantized with a fixed quantization range of [-1, 127/128]
|
|||
|
|
* (scale = 1/128, zeroPoint = 128).
|
|||
|
|
*/
|
|||
|
|
QUANTIZED_16BIT_LSTM = 73,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Draws samples from a multinomial distribution.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 2-D tensor with shape [batches, classes], specifying the
|
|||
|
|
* unnormalized log-probabilities for all classes.
|
|||
|
|
* * 1: A scalar {@link ANEURALNETWORKS_INT32}, specifying the number of
|
|||
|
|
* independent samples to draw for each row slice.
|
|||
|
|
* * 2: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [2],
|
|||
|
|
* specifying seeds used to initialize the random distribution. If both
|
|||
|
|
* provided seeds are 0, both will be randomly generated.
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A 2-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape
|
|||
|
|
* [batches, samples], containing the drawn samples.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
RANDOM_MULTINOMIAL = 74,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by computing the "logical and" of elements along given
|
|||
|
|
* dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_ALL = 75,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by computing the "logical or" of elements along given
|
|||
|
|
* dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_ANY = 76,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by computing the maximum of elements along given
|
|||
|
|
* dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_MAX = 77,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by computing the minimum of elements along given
|
|||
|
|
* dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_MIN = 78,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by multiplying elements along given dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_PROD = 79,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reduces a tensor by summing elements along given dimensions.
|
|||
|
|
*
|
|||
|
|
* If keep_dims is true, the reduced dimensions are
|
|||
|
|
* retained with length 1. Otherwise, the rank of the tensor is reduced by
|
|||
|
|
* 1 for each entry in dimensions.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor.
|
|||
|
|
* * 1: A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions
|
|||
|
|
* to reduce. Dimension values must be in the range [-n, n).
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_BOOL} scalar, keep_dims. If true,
|
|||
|
|
* retains reduced dimensions with length 1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0.
|
|||
|
|
* If all dimensions are reduced and keep_dims is false, the output
|
|||
|
|
* shape is [1].
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
REDUCE_SUM = 80,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Select and scale the feature map of each region of interest to a unified
|
|||
|
|
* output size by average pooling sampling points from bilinear interpolation.
|
|||
|
|
*
|
|||
|
|
* The region of interest is represented by its upper-left corner coordinate
|
|||
|
|
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
|
|||
|
|
* A spatial scaling factor is applied to map into feature map coordinate.
|
|||
|
|
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
|
|||
|
|
*
|
|||
|
|
* No rounding is applied in this operation. The sampling points are unified
|
|||
|
|
* distributed in the pooling bin and their values are calculated by bilinear
|
|||
|
|
* interpolation.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D tensor, specifying the feature map.
|
|||
|
|
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
|
|||
|
|
* the regions of interest, each line with format [x1, y1, x2, y2].
|
|||
|
|
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
|
|||
|
|
* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
|
|||
|
|
* with zeroPoint of 0 and scale of 0.125. Zero num_rois is
|
|||
|
|
* supported for this tensor.
|
|||
|
|
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_rois], specifying the batch index of each box. Boxes with
|
|||
|
|
* the same batch index are grouped together. Zero num_rois is
|
|||
|
|
* supported for this tensor.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* height of the output tensor.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* width of the output tensor.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the height of original image to the height of feature map.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the width of original image to the width of feature map.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* sampling points in height dimension used to compute the output.
|
|||
|
|
* Set to 0 for adaptive value of ceil(roi_height/out_height).
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* sampling points in width dimension used to compute the output.
|
|||
|
|
* Set to 0 for adaptive value of ceil(roi_width/out_width).
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. The output
|
|||
|
|
* shape is [num_rois, out_height, out_width, depth].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from the input0 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ROI_ALIGN = 81,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Select and scale the feature map of each region of interest to a unified
|
|||
|
|
* output size by max-pooling.
|
|||
|
|
*
|
|||
|
|
* The region of interest is represented by its upper-left corner coordinate
|
|||
|
|
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
|
|||
|
|
* A spatial scaling factor is applied to map into feature map coordinate.
|
|||
|
|
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
|
|||
|
|
*
|
|||
|
|
* Rounding is applied in this operation to ensure integer boundary for
|
|||
|
|
* regions of interest and pooling bins.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 4-D tensor, specifying the feature map.
|
|||
|
|
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
|
|||
|
|
* the regions of interest, each line with format [x1, y1, x2, y2].
|
|||
|
|
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* this tensor should be of {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM},
|
|||
|
|
* with zeroPoint of 0 and scale of 0.125.
|
|||
|
|
* * 2: An 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor, of shape
|
|||
|
|
* [num_rois], specifying the batch index of each box. Boxes with
|
|||
|
|
* the same batch index are grouped together.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* height of the output tensor.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* width of the output tensor.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the height of original image to the height of feature map.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the ratio
|
|||
|
|
* from the width of original image to the width of feature map.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. The output
|
|||
|
|
* shape is [num_rois, out_height, out_width, depth].
|
|||
|
|
* For input0 of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ROI_POOLING = 82,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes reciprocal of square root of x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} (since NNAPI feature level 7)
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 7)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
RSQRT = 83,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Using a tensor of booleans c and input tensors x and y select values
|
|||
|
|
* elementwise from both input tensors:
|
|||
|
|
*
|
|||
|
|
* O[i] = C[i] ? x[i] : y[i].
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_BOOL8} acting as a
|
|||
|
|
* mask that chooses, based on the value at each element, whether the
|
|||
|
|
* corresponding element in the output should be taken from input1 (if
|
|||
|
|
* true) or input2 (if false).
|
|||
|
|
* * 1: An input tensor of the same shape as input0.
|
|||
|
|
* * 2: An input tensor of the same shape and type as input1.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scales and zeroPoint can be different from input1 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same type and shape as input1 and input2.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
SELECT = 84,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes sin of x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
SIN = 85,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Extracts a slice of specified size from the input tensor starting at a
|
|||
|
|
* specified location.
|
|||
|
|
*
|
|||
|
|
* The starting location is specified as a 1-D tensor containing offsets
|
|||
|
|
* for each dimension. The size is specified as a 1-D tensor containing
|
|||
|
|
* either size of a slice along corresponding dimension or -1. In the latter
|
|||
|
|
* case, all the remaining elements in dimension are included in the slice.
|
|||
|
|
*
|
|||
|
|
* A sum of begin offset and a size of a slice must not exceed size of a
|
|||
|
|
* corresponding dimension.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor to take slice from, may be zero-sized.
|
|||
|
|
* * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying
|
|||
|
|
* the beginning indices of the slice in each dimension.
|
|||
|
|
* * 2: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} specifying
|
|||
|
|
* the size of the slice in each dimension.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An n-D tensor of the same type as the input containing the slice.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
SLICE = 86,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Splits a tensor along a given axis into num_splits subtensors.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor to split.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis along
|
|||
|
|
* which to split.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar indicating the number of
|
|||
|
|
* splits along given axis. Must evenly divide axis size.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0 ~ (num_splits - 1): Resulting subtensors.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
SPLIT = 87,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes square root of x element-wise.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
SQRT = 88,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Constructs a tensor by tiling a given tensor.
|
|||
|
|
*
|
|||
|
|
* This operation creates a new tensor by replicating `input` `multiples`
|
|||
|
|
* times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
|
|||
|
|
* elements, and the values of `input` are replicated `multiples[i]` times
|
|||
|
|
* along the i-th dimension.
|
|||
|
|
* For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: input, an n-D tensor specifying the input.
|
|||
|
|
* * 1: multiples, a 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}.
|
|||
|
|
* The length of multiples must be n.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tiled tensor of the same {@link OperandCode} and rank as `input`.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
TILE = 89,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Finds values and indices of the k largest entries for the last dimension.
|
|||
|
|
*
|
|||
|
|
* Resulting values in each dimensions are sorted in descending order. If
|
|||
|
|
* two values are equal, the one with larger index appears first.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: input, an n-D tensor specifying the input.
|
|||
|
|
* * 1: k, an {@link ANEURALNETWORKS_INT32} scalar, specifying the number of
|
|||
|
|
* top elements to look for along the last dimension.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: An n-D tensor of the same type as the input, containing the k
|
|||
|
|
* largest elements along each last dimensional slice.
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
* * 1: An n-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* containing the indices of values within the last dimension of input.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
TOPK_V2 = 90,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Performs the transpose of 2-D convolution operation.
|
|||
|
|
*
|
|||
|
|
* This operation is sometimes called "deconvolution" after Deconvolutional
|
|||
|
|
* Networks, but is actually the transpose (gradient) of
|
|||
|
|
* {@link ANEURALNETWORKS_CONV_2D} rather than an actual deconvolution.
|
|||
|
|
*
|
|||
|
|
* The output dimensions are functions of the filter dimensions, stride, and
|
|||
|
|
* padding.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode} configurations:
|
|||
|
|
* * 16 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT16} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * 32 bit floating point:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} for input, filter, output, and bias.
|
|||
|
|
*
|
|||
|
|
* * Quantized:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized with symmetric per channel quantization for the filter:
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4:
|
|||
|
|
* * Quantized signed (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (with scale set to
|
|||
|
|
* * * input.scale * filter.scale).
|
|||
|
|
*
|
|||
|
|
* * Quantized signed with filter symmetric per channel quantization
|
|||
|
|
* (since NNAPI feature level 4):
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
|
|||
|
|
* * * {@link ANEURALNETWORKS_TENSOR_INT32} for bias (scale set to 0.0,
|
|||
|
|
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* 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.
|
|||
|
|
* Since API level 29, zero batches is supported for this tensor.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_in], specifying the
|
|||
|
|
* filter. For tensor of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
|
|||
|
|
* dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias must be of the
|
|||
|
|
* same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32},
|
|||
|
|
* with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias must be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the left, in the ‘width’ dimension.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the right, in the ‘width’ dimension.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the top, in the ‘height’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on
|
|||
|
|
* the bottom, in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 10: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Inputs (implicit padding):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
|||
|
|
* specifying the input.
|
|||
|
|
* Since API level 29, zero batches is supported for this tensor.
|
|||
|
|
* * 1: A 4-D tensor, of shape
|
|||
|
|
* [depth_out, filter_height, filter_width, depth_in], specifying the
|
|||
|
|
* filter. For tensor of type
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
|
|||
|
|
* dimension (ANeuralNetworksSymmPerChannelQuantParams::channelDim) must be set to 0.
|
|||
|
|
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
|
|||
|
|
* tensor of type {@link ANEURALNETWORKS_TENSOR_FLOAT32} or
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT16}, the bias should be of the
|
|||
|
|
* same type.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* and {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED},
|
|||
|
|
* the bias should be of {@link ANEURALNETWORKS_TENSOR_INT32},
|
|||
|
|
* with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
|
|||
|
|
* For filter tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL},
|
|||
|
|
* the bias must be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0
|
|||
|
|
* and bias_scale of 0. The actual scale of each value 'i' is equal to
|
|||
|
|
* bias_scale[i] = input_scale * filter_scale[i].
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_TENSOR_INT32} tensor, specifying the output
|
|||
|
|
* tensor shape.
|
|||
|
|
* * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit
|
|||
|
|
* padding scheme, has to be one of the
|
|||
|
|
* {@link PaddingCode} values.
|
|||
|
|
* * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘width’ dimension.
|
|||
|
|
* * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when
|
|||
|
|
* walking through input in the ‘height’ dimension.
|
|||
|
|
* * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
|
|||
|
|
* {@link FuseCode} values. Specifies the activation to
|
|||
|
|
* invoke on the result.
|
|||
|
|
* * 8: An {@link ANEURALNETWORKS_BOOL} scalar, set to true to specify
|
|||
|
|
* NCHW data layout for input0 and output0. Set to false for NHWC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, out_height, out_width, depth_out].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
TRANSPOSE_CONV_2D = 91,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A recurrent neural network specified by an LSTM cell.
|
|||
|
|
*
|
|||
|
|
* Performs (fully) dynamic unrolling of input.
|
|||
|
|
*
|
|||
|
|
* This Op unrolls the input along the time dimension, and implements the
|
|||
|
|
* following operation for each element in the sequence
|
|||
|
|
* s = 1...sequence_length:
|
|||
|
|
* outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
|
|||
|
|
*
|
|||
|
|
* Where LSTMOp is the LSTM op as in {@link ANEURALNETWORKS_LSTM},
|
|||
|
|
* the "projection" is an optional projection layer from state and output
|
|||
|
|
* and the “activation” is the function passed as the
|
|||
|
|
* “fused_activation_function” argument (if not “NONE”).
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 3, either time-major or batch-major.
|
|||
|
|
*
|
|||
|
|
* All input and output tensors must be of the same type.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: The input (\f$x_t\f$).
|
|||
|
|
* A 3-D tensor of shape:
|
|||
|
|
* If time-major: [max_time, batch_size, input_size]
|
|||
|
|
* If batch-major: [batch_size, max_time, input_size]
|
|||
|
|
* where “max_time” is the number of timesteps (sequence length),
|
|||
|
|
* “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.
|
|||
|
|
* * 23:Time-major if true, batch-major if false.
|
|||
|
|
* * 24:The input layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at input gate.
|
|||
|
|
* * 25:The forget layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at forget gate.
|
|||
|
|
* * 26:The cell layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at cell gate.
|
|||
|
|
* * 27:The output layer normalization weights. Optional.
|
|||
|
|
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
|
|||
|
|
* to activation at output gate.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output (\f$o_t\f$).
|
|||
|
|
* A 3-D tensor of shape:
|
|||
|
|
* If time-major: [max_time, batch_size, output_size]
|
|||
|
|
* If batch-major: [batch_size, max_time, output_size]
|
|||
|
|
* * 1: A tensor of shape [batch_size, output_size] containing a hidden
|
|||
|
|
* state from the last time step in the sequence. This output is
|
|||
|
|
* optional and can be omitted. If this output is present then
|
|||
|
|
* output #2 must be present as well.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 2: A tensor of shape [batch_size, cell_size] containing a cell state
|
|||
|
|
* from the last time step in the sequence. This output is optional
|
|||
|
|
* and can be omitted.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out
|
|||
|
|
* and NNAPI does not maintain internal states. This operator does not support the usage pattern
|
|||
|
|
* in which multiple cells are chained and state tensors are propagated.
|
|||
|
|
*/
|
|||
|
|
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* A recurrent neural network layer that applies a basic RNN cell to a
|
|||
|
|
* sequence of inputs.
|
|||
|
|
*
|
|||
|
|
* This layer unrolls the input along the sequence dimension, and implements
|
|||
|
|
* the following operation
|
|||
|
|
* for each element in the sequence s = 1...sequence_length:
|
|||
|
|
* outputs[s] = state = activation(inputs[s] * input_weights’ + state *
|
|||
|
|
* recurrent_weights’ + bias)
|
|||
|
|
*
|
|||
|
|
* Where:
|
|||
|
|
* * “input_weights” is a weight matrix that multiplies the inputs;
|
|||
|
|
* * “recurrent_weights” is a weight matrix that multiplies the current
|
|||
|
|
* “state” which itself is the output from the previous time step
|
|||
|
|
* computation;
|
|||
|
|
* * “bias” is a bias vector (added to each output vector in the batch);
|
|||
|
|
* * “activation” is the function passed as the “fused_activation_function”
|
|||
|
|
* argument (if not “NONE”).
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* The input tensors must all be the same type.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: input.
|
|||
|
|
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
|
|||
|
|
* it is set to 1, then the input has a shape [maxTime, batchSize,
|
|||
|
|
* inputSize], otherwise the input has a shape [batchSize, maxTime,
|
|||
|
|
* inputSize].
|
|||
|
|
* * 1: weights.
|
|||
|
|
* A 2-D tensor of shape [numUnits, inputSize].
|
|||
|
|
* * 2: recurrent_weights.
|
|||
|
|
* A 2-D tensor of shape [numUnits, numUnits].
|
|||
|
|
* * 3: bias.
|
|||
|
|
* A 1-D tensor of shape [numUnits].
|
|||
|
|
* * 4: hidden state
|
|||
|
|
* A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
|
|||
|
|
* state input for the first time step of the computation.
|
|||
|
|
* * 5: fusedActivationFunction.
|
|||
|
|
* A {@link FuseCode} value indicating the activation function. If
|
|||
|
|
* “NONE” is specified then it results in a linear activation.
|
|||
|
|
* * 6: timeMajor
|
|||
|
|
* An {@link ANEURALNETWORKS_INT32} scalar specifying the shape format
|
|||
|
|
* of input and output tensors. Must be set to either 0 or 1.
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: output.
|
|||
|
|
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
|
|||
|
|
* it is set to 1, then the output has a shape [maxTime, batchSize,
|
|||
|
|
* numUnits], otherwise the output has a shape [batchSize, maxTime,
|
|||
|
|
* numUnits].
|
|||
|
|
* * 1: A tensor of shape [batchSize, numUnits] containing hidden state
|
|||
|
|
* from the last time step in the sequence. This output is optional
|
|||
|
|
* and can be omitted.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*
|
|||
|
|
* Important: As of NNAPI feature level 3, there is no way to get the output state tensors out
|
|||
|
|
* and NNAPI does not maintain internal states. This operator does not support the usage pattern
|
|||
|
|
* in which multiple cells are chained and state tensors are propagated.
|
|||
|
|
*/
|
|||
|
|
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Resizes images to given size using the nearest neighbor interpretation.
|
|||
|
|
*
|
|||
|
|
* Resized images must be distorted if their output aspect ratio is not the
|
|||
|
|
* same as input aspect ratio. The corner pixels of output may not be the
|
|||
|
|
* same as corner pixels of input.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} (since NNAPI feature level 4)
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
|
|||
|
|
* With the default data layout NHWC, the data is stored in the order of:
|
|||
|
|
* [batch, height, width, channels]. Alternatively, the data layout could
|
|||
|
|
* be NCHW, the data storage order of: [batch, channels, height, width].
|
|||
|
|
*
|
|||
|
|
* Both resizing by shape and resizing by scale are supported.
|
|||
|
|
*
|
|||
|
|
* Inputs (resizing by shape):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input. Zero batches is supported for this tensor.
|
|||
|
|
* * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* width of the output tensor.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output
|
|||
|
|
* height of the output tensor.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the centers of the 4 corner
|
|||
|
|
* pixels of the input and output tensors are aligned, preserving the
|
|||
|
|
* values at the corner pixels.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the pixel centers are assumed to
|
|||
|
|
* be at (0.5, 0.5). This is the default behavior of image.resize in
|
|||
|
|
* TF 2.0. If this parameter is True, then align_corners parameter
|
|||
|
|
* must be False.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Inputs (resizing by scale):
|
|||
|
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
|||
|
|
* the input. Zero batches is supported for this tensor.
|
|||
|
|
* * 1: A scalar, specifying width_scale, the scaling factor of the width
|
|||
|
|
* dimension from the input tensor to the output tensor. The output
|
|||
|
|
* width is calculated as new_width = floor(width * width_scale).
|
|||
|
|
* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} otherwise.
|
|||
|
|
* * 2: A scalar, specifying height_scale, the scaling factor of the height
|
|||
|
|
* dimension from the input tensor to the output tensor. The output
|
|||
|
|
* height is calculated as new_height = floor(height * height_scale).
|
|||
|
|
* The scalar must be of {@link ANEURALNETWORKS_FLOAT16} if input0 is
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_FLOAT16} and of
|
|||
|
|
* {@link ANEURALNETWORKS_FLOAT32} otherwise.
|
|||
|
|
* * 3: An {@link ANEURALNETWORKS_BOOL} scalar, default to false.
|
|||
|
|
* Set to true to specify NCHW data layout for input0 and output0.
|
|||
|
|
* * 4: Align corners. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the centers of the 4 corner
|
|||
|
|
* pixels of the input and output tensors are aligned, preserving the
|
|||
|
|
* values at the corner pixels.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
* * 5: Half pixel centers. An optional {@link ANEURALNETWORKS_BOOL}
|
|||
|
|
* scalar, default to false. If True, the pixel centers are assumed to
|
|||
|
|
* be at (0.5, 0.5). This is the default behavior of image.resize in
|
|||
|
|
* TF 2.0. If this parameter is True, then align_corners parameter
|
|||
|
|
* must be False.
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output 4-D tensor, of shape
|
|||
|
|
* [batches, new_height, new_width, depth].
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
RESIZE_NEAREST_NEIGHBOR = 94,
|
|||
|
|
|
|||
|
|
// Operations below are available since NNAPI feature level 4.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Quantized version of {@link ANEURALNETWORKS_LSTM}.
|
|||
|
|
*
|
|||
|
|
* The input and the output use asymmetric quantized types, while the rest
|
|||
|
|
* use symmetric ones.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: The input to the LSTM cell.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* Shape: [batchSize, inputSize]
|
|||
|
|
* * 1: The input-to-input weights. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, inputSize]
|
|||
|
|
* * 2: The input-to-forget weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, inputSize]
|
|||
|
|
* * 3: The input-to-cell weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, inputSize]
|
|||
|
|
* * 4: The input-to-output weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, inputSize]
|
|||
|
|
* * 5: The recurrent-to-input weights. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, outputSize]
|
|||
|
|
* * 6: The recurrent-to-forget weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, outputSize]
|
|||
|
|
* * 7: The recurrent-to-cell weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, outputSize]
|
|||
|
|
* * 8: The recurrent-to-output weights.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [numUnits, outputSize]
|
|||
|
|
* * 9: The cell-to-input weights (for peephole). Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 10: The cell-to-forget weights (for peephole). Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 11: The cell-to-output weights (for peephole). Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 12: The input gate bias. Quantized with scale being the
|
|||
|
|
* product of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 13: The forget gate bias. Quantized with scale being the
|
|||
|
|
* product of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 14: The cell bias. Quantized with scale being the
|
|||
|
|
* product of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 15: The output gate bias. Quantized with scale being the
|
|||
|
|
* product of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 16: The projection weights. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* Shape: [outputSize, numUnits]
|
|||
|
|
* * 17: The projection bias. Quantized with scale being the
|
|||
|
|
* product of input and weights scales and zeroPoint equal to 0.
|
|||
|
|
* Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* Shape: [outputSize]
|
|||
|
|
* * 18: The output from the previous time step.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* Shape: [batchSize, outputSize]
|
|||
|
|
* * 19: The cell state from the previous time step.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [batchSize, numUnits]
|
|||
|
|
* * 20: The input layer normalization weights. Used to rescale
|
|||
|
|
* normalized inputs to activation at input gate. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 21: The forget layer normalization weights. Used to
|
|||
|
|
* rescale normalized inputs to activation at forget gate. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 22: The cell layer normalization weights. Used to rescale
|
|||
|
|
* normalized inputs to activation at cell gate. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 23: The output layer normalization weights. Used to
|
|||
|
|
* rescale normalized inputs to activation at output gate. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [numUnits]
|
|||
|
|
* * 24: The cell clip. If provided the cell state is clipped
|
|||
|
|
* by this value prior to the cell output activation. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 25: The projection clip. If provided and projection is enabled,
|
|||
|
|
* this is used for clipping the projected values. Optional.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 26: The scale of the intermediate result of matmul,
|
|||
|
|
* i.e. input to layer normalization, at input gate.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 27: The scale of the intermediate result of matmul,
|
|||
|
|
* i.e. input to layer normalization, at forget gate.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 28: The scale of the intermediate result of matmul,
|
|||
|
|
* i.e. input to layer normalization, at cell gate.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 29: The scale of the intermediate result of matmul,
|
|||
|
|
* i.e. input to layer normalization, at output gate.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* * 30: The zero point of the hidden state, i.e. input to
|
|||
|
|
* projection.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_INT32}.
|
|||
|
|
* * 31: The scale of the hidden state, i.e. input to
|
|||
|
|
* projection.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output state (out).
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* Shape: [batchSize, outputSize]
|
|||
|
|
* * 1: The cell state (out).
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* Shape: [batchSize, numUnits]
|
|||
|
|
* * 2: The output. This is effectively the same as the current
|
|||
|
|
* "output state (out)" value.
|
|||
|
|
* Type: {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* Shape: [batchSize, outputSize]
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
QUANTIZED_LSTM = 95,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Executes one of the two referenced models as determined by a boolean
|
|||
|
|
* value.
|
|||
|
|
*
|
|||
|
|
* The inputs and outputs of the two referenced models must agree with the
|
|||
|
|
* signature of this operation. That is, if the operation has (3 + n) inputs
|
|||
|
|
* and m outputs, both models must have n inputs and m outputs with the same
|
|||
|
|
* types, ranks (if specified), dimensions (if specified), scales,
|
|||
|
|
* zeroPoints, and other operand parameters as the corresponding operation
|
|||
|
|
* inputs and outputs.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A value of type {@link ANEURALNETWORKS_TENSOR_BOOL8} and shape [1]
|
|||
|
|
* that determines which of the two referenced models to execute.
|
|||
|
|
* The operand must have fully specified dimensions.
|
|||
|
|
* * 1: A {@link ANEURALNETWORKS_MODEL} reference to the model to be
|
|||
|
|
* executed if the condition is true.
|
|||
|
|
* * 2: A {@link ANEURALNETWORKS_MODEL} reference to the model to be
|
|||
|
|
* executed if the condition is false.
|
|||
|
|
* * 3 ~ (n + 2): Inputs to be passed to the model selected for execution.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0 ~ (m - 1): Outputs produced by the selected model.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
IF = 96,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Executes the body model until the condition model outputs false.
|
|||
|
|
*
|
|||
|
|
* The inputs to this operation are the condition model, the body model,
|
|||
|
|
* and operand values for the first iteration of the loop. The values are
|
|||
|
|
* implicitly split into three groups of input-output, state-only, and
|
|||
|
|
* input-only values, as described below.
|
|||
|
|
*
|
|||
|
|
* The outputs of this operation are the final values of input-output
|
|||
|
|
* operands.
|
|||
|
|
*
|
|||
|
|
* Both the condition and body model receive (m + k + n) inputs.
|
|||
|
|
* * The first m (m >= 1) inputs are input-output operands. For the first
|
|||
|
|
* iteration, these are initialized from the corresponding inputs of the
|
|||
|
|
* WHILE operation. In subsequent iterations, their values come from the
|
|||
|
|
* corresponding outputs of the body model produced during the previous
|
|||
|
|
* iteration.
|
|||
|
|
* * The next k (k >= 0) inputs are state-only operands. They are similar to
|
|||
|
|
* the input-output operands, except that their values are no longer
|
|||
|
|
* available after the loop terminates.
|
|||
|
|
* * The last n (n >= 0) inputs are input-only operands. Their values come
|
|||
|
|
* from the corresponding inputs of the WHILE operation.
|
|||
|
|
*
|
|||
|
|
* The body model produces (m + k) outputs.
|
|||
|
|
* * The first m outputs are input-output operands. They become the outputs
|
|||
|
|
* of the WHILE operation when a termination condition is reached.
|
|||
|
|
* * The last k outputs are state-only operands. Their values are no longer
|
|||
|
|
* available after the loop terminates.
|
|||
|
|
*
|
|||
|
|
* The numbers m, k, and n are inferred by the runtime as follows:
|
|||
|
|
* m = (WHILE operation output count)
|
|||
|
|
* k = (body model output count) - m
|
|||
|
|
* n = (body model input count) - m - k
|
|||
|
|
*
|
|||
|
|
* The pseudo-code below illustrates the flow of a WHILE operation with
|
|||
|
|
* inputs condition, body, initial_input_output, initial_state, input_only
|
|||
|
|
* (m = 1, k = 1, n = 1):
|
|||
|
|
*
|
|||
|
|
* input_output = initial_input_output
|
|||
|
|
* state = initial_state
|
|||
|
|
* while condition(input_output, state, input_only):
|
|||
|
|
* input_output, state = body(input_output, state, input_only)
|
|||
|
|
* return input_output
|
|||
|
|
*
|
|||
|
|
* To prevent infinite loops, there is an implicit execution timeout
|
|||
|
|
* associated with each loop ("loop timeout duration"). See {@link
|
|||
|
|
* ANeuralNetworksExecution_setLoopTimeout}.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A {@link ANEURALNETWORKS_MODEL} reference to the condition
|
|||
|
|
* model. The model must have (m + k + n) inputs with
|
|||
|
|
* the same types, ranks (if specified), dimensions (if specified),
|
|||
|
|
* scales, zeroPoints, and other operand parameters as the
|
|||
|
|
* corresponding inputs of the WHILE operation and exactly one output
|
|||
|
|
* of {@link ANEURALNETWORKS_TENSOR_BOOL8} and shape [1].
|
|||
|
|
* The output operand must have fully specified dimensions.
|
|||
|
|
* * 1: A {@link ANEURALNETWORKS_MODEL} reference to the body model.
|
|||
|
|
* The model must have (m + k + n) inputs and (m + k) outputs with
|
|||
|
|
* the same types, ranks (if specified), dimensions (if specified),
|
|||
|
|
* scales, zeroPoints, and other operand parameters as the
|
|||
|
|
* corresponding inputs and outputs of the WHILE operation.
|
|||
|
|
* * (m inputs): Initial values for input-output operands.
|
|||
|
|
* * (k inputs): Initial values for state-only operands.
|
|||
|
|
* * (n inputs): Values for input-only operands.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0 ~ (m - 1): Outputs produced by the loop.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
WHILE = 97,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes exponential linear activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* The output is calculated using the following formula:
|
|||
|
|
*
|
|||
|
|
* ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1))
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input. May be zero-sized.
|
|||
|
|
* * 1: A scalar, specifying the alpha parameter.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16},
|
|||
|
|
* the alpha value must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32},
|
|||
|
|
* the alpha value must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape and type as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
ELU = 98,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes hard-swish activation on the input tensor element-wise.
|
|||
|
|
*
|
|||
|
|
* Hard swish activation is introduced in
|
|||
|
|
* https://arxiv.org/pdf/1905.02244.pdf
|
|||
|
|
*
|
|||
|
|
* The output is calculated using the following formula:
|
|||
|
|
*
|
|||
|
|
* h-swish(x) = x * max(0, min(6, (x + 3))) / 6
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor, specifying the input. May be zero-sized.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor of same shape and type as input0.
|
|||
|
|
* Scale and zero point of this tensor may be different from the input
|
|||
|
|
* tensor's parameters.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
HARD_SWISH = 99,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Creates a tensor filled with a scalar value.
|
|||
|
|
*
|
|||
|
|
* Supported output tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A 1-D tensor, specifying the desired output tensor shape.
|
|||
|
|
* * 1: A scalar, specifying the value to fill the output tensors with.
|
|||
|
|
* For output tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT16},
|
|||
|
|
* the scalar must be of {@link ANEURALNETWORKS_FLOAT16}.
|
|||
|
|
* For output tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32},
|
|||
|
|
* the scalar must be of {@link ANEURALNETWORKS_FLOAT32}.
|
|||
|
|
* For output tensor of {@link ANEURALNETWORKS_TENSOR_INT32},
|
|||
|
|
* the scalar must be of {@link ANEURALNETWORKS_INT32}.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The output tensor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
FILL = 100,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Returns the rank of a tensor.
|
|||
|
|
*
|
|||
|
|
* The rank of a tensor is the number of dimensions in it. Also known as
|
|||
|
|
* "order", "degree", "ndims".
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: The input tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A scalar of {@link ANEURALNETWORKS_INT32}, specifying the rank
|
|||
|
|
* of the input tensor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
RANK = 101,
|
|||
|
|
|
|||
|
|
// Operations below are available since NNAPI feature level 6.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Performs multiplication of two tensors in batches.
|
|||
|
|
*
|
|||
|
|
* Multiplies all slices of two input tensors and arranges the individual
|
|||
|
|
* results in a single output tensor of the same batch size. Each pair of
|
|||
|
|
* slices in the same batch have identical {@link OperandCode}. Each
|
|||
|
|
* slice can optionally be adjointed (transpose and conjugate) before
|
|||
|
|
* multiplication.
|
|||
|
|
*
|
|||
|
|
* The two input tensors and the output tensor must be 2-D or higher and
|
|||
|
|
* have the same batch size.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: at least 2 and up to 4
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A tensor with 2-D or higher shape [..., r_x, c_x].
|
|||
|
|
* * 1: A tensor with 2-D or higher shape [..., r_y, c_y]. It has the same
|
|||
|
|
* {@link OperandCode} and batch size as input0.
|
|||
|
|
* * 2: An optional {@link ANEURALNETWORKS_BOOL} scalar adj_x, default
|
|||
|
|
* to false. Set to true to adjoint the slices of input0.
|
|||
|
|
* * 3: An optional {@link ANEURALNETWORKS_BOOL} scalar adj_y, default
|
|||
|
|
* to false. Set to true to adjoint the slices of input1.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor with 2-D or higher shape [..., r_o, c_o], where
|
|||
|
|
* r_o = c_x if adj_x else r_x
|
|||
|
|
* c_o = r_y if adj_y else c_y
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 6.
|
|||
|
|
*/
|
|||
|
|
BATCH_MATMUL = 102,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Packs N input tensors (N >= 1) of rank R into one output tensor of rank R+1.
|
|||
|
|
* The tensors are packed along a given axis.
|
|||
|
|
*
|
|||
|
|
* The input tensors must have identical {@link OperandCode} and dimensions.
|
|||
|
|
*
|
|||
|
|
* For example, suppose there are N input tensors of shape (A, B, C).
|
|||
|
|
* If axis is 0, the output tensor will have shape (N, A, B, C).
|
|||
|
|
* If axis is 1, the output tensor will have shape (A, N, B, C).
|
|||
|
|
*
|
|||
|
|
* All dimensions through the axis dimension determine the output tile count;
|
|||
|
|
* the remaining dimensions determine the tile shape.
|
|||
|
|
*
|
|||
|
|
* Return to the example of N input tensors of shape (A, B, C).
|
|||
|
|
* If axis is 0, there are N tiles in the output, each of shape (A, B, C).
|
|||
|
|
* If axis is 1, there are A*N tiles in the output, each of shape (B, C).
|
|||
|
|
*
|
|||
|
|
* The coordinates of a tile within the output tensor are (t[0],...,t[axis]).
|
|||
|
|
* The coordinates of a tile within an input tensor are (t[0],...,t[axis-1]).
|
|||
|
|
* (If axis is 0, an input tensor consists of a single tile.)
|
|||
|
|
* If we index input tensors starting with 0 (rather than by operand number),
|
|||
|
|
* then output_tile[t[0],...,t[axis]] = input_tile[t[axis]][t[0],...,t[axis-1]].
|
|||
|
|
* That is, all output tile coordinates except for the axis coordinate select
|
|||
|
|
* the corresponding location within some input tensor; and the axis coordinate
|
|||
|
|
* selects the input tensor.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported input tensor rank: from 1
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: A scalar of type {@link ANEURALNETWORKS_INT32}, specifying
|
|||
|
|
* the axis along which to pack. The valid range is [0, R+1).
|
|||
|
|
* * 1 ~ N: Input tensors to be packed together.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensors,
|
|||
|
|
* the scales and zeroPoint must be the same for all input tensors,
|
|||
|
|
* and will be the same for the output tensor.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The packed tensor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 6.
|
|||
|
|
*/
|
|||
|
|
PACK = 103,
|
|||
|
|
|
|||
|
|
// Operations below are available since NNAPI feature level 7.
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Pads a tensor with mirrored values.
|
|||
|
|
*
|
|||
|
|
* This operator specifies one of two padding modes: REFLECT or SYMMETRIC.
|
|||
|
|
* In the case of REFLECT mode, the mirroring excludes the border element
|
|||
|
|
* on the padding side.
|
|||
|
|
* In the case of SYMMETRIC mode, the mirroring includes the border element
|
|||
|
|
* on the padding side.
|
|||
|
|
*
|
|||
|
|
* For example, if the input is the 1-D tensor `[1, 2, 3]` and the padding
|
|||
|
|
* is `[0, 2]` (i.e., pad no elements before the first (and only) dimension,
|
|||
|
|
* and two elements after the first (and only) dimension), then:
|
|||
|
|
* - REFLECT mode produces the output `[1, 2, 3, 2, 1]`
|
|||
|
|
* - SYMMETRIC mode produces the output `[1, 2, 3, 3, 2]`
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: from 1.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: An n-D tensor, specifying the tensor to be padded.
|
|||
|
|
* * 1: A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings
|
|||
|
|
* for each spatial dimension of the input tensor. The shape of the
|
|||
|
|
* tensor must be {rank(input0), 2}.
|
|||
|
|
* padding[i, 0] specifies the number of elements to be padded in the
|
|||
|
|
* front of dimension i.
|
|||
|
|
* padding[i, 1] specifies the number of elements to be padded after the
|
|||
|
|
* end of dimension i.
|
|||
|
|
* Each padding value must be nonnegative.
|
|||
|
|
* In the case of REFLECT mode, each padding value must be less than the
|
|||
|
|
* corresponding dimension.
|
|||
|
|
* In the case of SYMMETRIC mode, each padding value must be less than or
|
|||
|
|
* equal to the corresponding dimension.
|
|||
|
|
* * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the mode.
|
|||
|
|
* Options are 0:REFLECT and 1:SYMMETRIC.
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: A tensor of the same {@link OperandCode} as input0. The
|
|||
|
|
* output tensor has the same rank as input0, and each
|
|||
|
|
* dimension of the output tensor has the same size as the
|
|||
|
|
* corresponding dimension of the input tensor plus the size
|
|||
|
|
* of the padding:
|
|||
|
|
* output0.dimension[i] =
|
|||
|
|
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
|
|||
|
|
* For a {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensor,
|
|||
|
|
* the scale and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 7.
|
|||
|
|
*/
|
|||
|
|
MIRROR_PAD = 104,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Reverses a specified dimension of a tensor.
|
|||
|
|
*
|
|||
|
|
* Supported tensor {@link OperandCode}:
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
|
|||
|
|
* * {@link ANEURALNETWORKS_TENSOR_INT32}
|
|||
|
|
*
|
|||
|
|
* Supported tensor rank: up to 8.
|
|||
|
|
*
|
|||
|
|
* Inputs:
|
|||
|
|
* * 0: Input tensor of rank n.
|
|||
|
|
* * 1: Axis tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} and shape [1],
|
|||
|
|
* specifying which dimension of the input tensor is to be reversed. The dimension
|
|||
|
|
* must be in the range [0, n).
|
|||
|
|
*
|
|||
|
|
* Outputs:
|
|||
|
|
* * 0: The reversed tensor of the same shape as the input tensor.
|
|||
|
|
* For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} and
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED} tensors,
|
|||
|
|
* the scales and zeroPoint must be the same as input0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 7.
|
|||
|
|
*/
|
|||
|
|
REVERSE = 105,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Fused activation function types.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
FuseCode :: enum {
|
|||
|
|
/** NO fused activation function. */
|
|||
|
|
NONE = 0,
|
|||
|
|
/** Fused ReLU activation function. */
|
|||
|
|
RELU = 1,
|
|||
|
|
/** Fused ReLU1 activation function. */
|
|||
|
|
RELU1 = 2,
|
|||
|
|
/** Fused ReLU6 activation function. */
|
|||
|
|
RELU6 = 3,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Implicit padding algorithms.
|
|||
|
|
*
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
PaddingCode :: enum {
|
|||
|
|
/**
|
|||
|
|
* SAME padding.
|
|||
|
|
* Padding on both ends are the "same":
|
|||
|
|
* padding_to_beginning = total_padding / 2
|
|||
|
|
* padding_to_end = (total_padding + 1)/2.
|
|||
|
|
* i.e., for even number of padding, padding to both ends are exactly
|
|||
|
|
* the same; for odd number of padding, padding to the ending is bigger
|
|||
|
|
* than the padding to the beginning by 1.
|
|||
|
|
*
|
|||
|
|
* total_padding is a function of input, stride, dilation and filter size.
|
|||
|
|
* It could be computed as follows:
|
|||
|
|
* out_size = (input + stride - 1) / stride
|
|||
|
|
* effective_filter_size = (filter_size - 1) * dilation + 1
|
|||
|
|
* needed_input = (out_size - 1) * stride + effective_filter_size
|
|||
|
|
* total_padding = max(0, needed_input - input_size)
|
|||
|
|
* The computation is the same for the horizontal and vertical directions.
|
|||
|
|
*/
|
|||
|
|
SAME = 1,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* VALID padding.
|
|||
|
|
* No padding. When the input size is not evenly divisible by
|
|||
|
|
* the filter size, the input at the end that could not fill
|
|||
|
|
* the whole filter tile will simply be ignored.
|
|||
|
|
*/
|
|||
|
|
VALID = 2,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Execution preferences.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
PreferenceCode :: enum {
|
|||
|
|
/**
|
|||
|
|
* Prefer executing in a way that minimizes battery drain.
|
|||
|
|
* This is desirable for compilations that will be executed often.
|
|||
|
|
*/
|
|||
|
|
PREFER_LOW_POWER = 0,
|
|||
|
|
/**
|
|||
|
|
* Prefer returning a single answer as fast as possible, even if this causes
|
|||
|
|
* more power consumption.
|
|||
|
|
*/
|
|||
|
|
PREFER_FAST_SINGLE_ANSWER = 1,
|
|||
|
|
/**
|
|||
|
|
* Prefer maximizing the throughput of successive frames, for example when
|
|||
|
|
* processing successive frames coming from the camera.
|
|||
|
|
*/
|
|||
|
|
PREFER_SUSTAINED_SPEED = 2,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Device types.
|
|||
|
|
*
|
|||
|
|
* The type of NNAPI device.
|
|||
|
|
*/
|
|||
|
|
DeviceTypeCode :: enum {
|
|||
|
|
/** The device type cannot be provided. */
|
|||
|
|
UNKNOWN = 0,
|
|||
|
|
/** The device does not fall into any category below. */
|
|||
|
|
OTHER = 1,
|
|||
|
|
/** The device runs NNAPI models on single or multi-core CPU. */
|
|||
|
|
CPU = 2,
|
|||
|
|
/** The device can run NNAPI models and also accelerate graphics APIs such
|
|||
|
|
* as OpenGL ES and Vulkan. */
|
|||
|
|
GPU = 3,
|
|||
|
|
/** Dedicated accelerator for Machine Learning workloads. */
|
|||
|
|
ACCELERATOR = 4,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* NNAPI feature levels.
|
|||
|
|
*
|
|||
|
|
* Each update of the NNAPI specification yields a new NNAPI feature level enum value.
|
|||
|
|
* NNAPI feature level corrseponds to an NNAPI specification version that a driver
|
|||
|
|
* and/or the NNAPI runtime can implement.
|
|||
|
|
*
|
|||
|
|
* A feature level up to and including "FEATURE_LEVEL_5" maps directly to
|
|||
|
|
* the Android API level that introduced the corresponding update of the NNAPI
|
|||
|
|
* specification. Feature levels after Android API level 31 have no association with
|
|||
|
|
* API level because the NNAPI specification can be updated between Android API
|
|||
|
|
* releases. Outputs of {@link ANeuralNetworksDevice_getFeatureLevel} and
|
|||
|
|
* {@link ANeuralNetworks_getRuntimeFeatureLevel} must be compared against
|
|||
|
|
* these enum values instead of the Android API level.
|
|||
|
|
*/
|
|||
|
|
FeatureLevelCode :: enum {
|
|||
|
|
/** NNAPI specification available in Android O-MR1, Android NNAPI feature level 1 */
|
|||
|
|
LEVEL_1 = 27,
|
|||
|
|
/** NNAPI specification available in Android P, Android NNAPI feature level 2 */
|
|||
|
|
LEVEL_2 = 28,
|
|||
|
|
/** NNAPI specification available in Android Q, Android NNAPI feature level 3 */
|
|||
|
|
LEVEL_3 = 29,
|
|||
|
|
/** NNAPI specification available in Android R, Android NNAPI feature level 4 */
|
|||
|
|
LEVEL_4 = 30,
|
|||
|
|
/**
|
|||
|
|
* NNAPI specification available in Android S, Android NNAPI feature level 5.
|
|||
|
|
* After Android S, the NNAPI specification can be updated between Android
|
|||
|
|
* API releases.
|
|||
|
|
*/
|
|||
|
|
LEVEL_5 = 31,
|
|||
|
|
/** Android NNAPI feature level 6 */
|
|||
|
|
LEVEL_6 = 1000006,
|
|||
|
|
/** Android NNAPI feature level 7 */
|
|||
|
|
LEVEL_7 = 1000007,
|
|||
|
|
/** Android NNAPI feature level 8 */
|
|||
|
|
LEVEL_8 = 1000008,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Result codes.
|
|||
|
|
*
|
|||
|
|
* <p>Any NNAPI function can return any result code, including result codes not
|
|||
|
|
* currently documented. Any value other than {@link ANEURALNETWORKS_NO_ERROR}
|
|||
|
|
* indicates a failure of some kind.</p>
|
|||
|
|
*
|
|||
|
|
* <p>Additional information about the nature of a failure can be obtained from
|
|||
|
|
* the device log after enabling NNAPI debugging by setting the debug.nn.vlog
|
|||
|
|
* property to 1, e.g., by calling "adb shell setprop debug.nn.vlog 1".</p>
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
NNResultCode :: enum {
|
|||
|
|
/**
|
|||
|
|
* Operation was successful.
|
|||
|
|
*/
|
|||
|
|
NO_ERROR = 0,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by not enough available memory.
|
|||
|
|
*/
|
|||
|
|
OUT_OF_MEMORY = 1,
|
|||
|
|
|
|||
|
|
INCOMPLETE = 2,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by unexpected null argument.
|
|||
|
|
*/
|
|||
|
|
UNEXPECTED_NULL = 3,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by invalid function arguments, invalid model definition,
|
|||
|
|
* invalid execution definition or invalid data at execution time.
|
|||
|
|
*/
|
|||
|
|
BAD_DATA = 4,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by failed model execution.
|
|||
|
|
*/
|
|||
|
|
OP_FAILED = 5,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by object being in the wrong state.
|
|||
|
|
*/
|
|||
|
|
BAD_STATE = 6,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by not being able to map a file into memory.
|
|||
|
|
* This may be caused by a file descriptor not being mappable, or an AHardwareBuffer
|
|||
|
|
* not supported by the device.
|
|||
|
|
* Mitigate by reading its content into memory.
|
|||
|
|
*/
|
|||
|
|
UNMAPPABLE = 7,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by insufficient buffer size provided to a model output.
|
|||
|
|
*/
|
|||
|
|
OUTPUT_INSUFFICIENT_SIZE = 8,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure caused by a device not being available.
|
|||
|
|
*/
|
|||
|
|
UNAVAILABLE_DEVICE = 9,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure because a deadline could not be met for a task, but future
|
|||
|
|
* deadlines may still be met for the same task after a short delay.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
MISSED_DEADLINE_TRANSIENT = 10,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure because a deadline could not be met for a task, and future
|
|||
|
|
* deadlines will likely also not be met for the same task even after a
|
|||
|
|
* short delay.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
MISSED_DEADLINE_PERSISTENT = 11,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure because of a resource limitation within the driver, but future
|
|||
|
|
* calls for the same task may still succeed after a short delay.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
RESOURCE_EXHAUSTED_TRANSIENT = 12,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure because of a resource limitation within the driver, and future
|
|||
|
|
* calls for the same task will likely also fail even after a short
|
|||
|
|
* delay.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
RESOURCE_EXHAUSTED_PERSISTENT = 13,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Failure indicating an object is in a dead state.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
DEAD_OBJECT = 14,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Different duration measurements.
|
|||
|
|
*
|
|||
|
|
* Durations are measured in nanoseconds.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
DurationCode :: enum {
|
|||
|
|
// Execution time on hardware (not driver, which runs on host processor).
|
|||
|
|
DURATION_ON_HARDWARE = 0,
|
|||
|
|
// Execution time in driver (including time on hardware). Excludes overhead
|
|||
|
|
// such as that of the runtime itself and the IPC needed for the runtime to
|
|||
|
|
// communicate with the driver.
|
|||
|
|
DURATION_IN_DRIVER = 1,
|
|||
|
|
// Execution time on hardware, after all dependencies have been signaled.
|
|||
|
|
// If no dependencies specified (for example, if the execution was scheduled other
|
|||
|
|
// than with {@link ANeuralNetworksExecution_startComputeWithDependencies}), the
|
|||
|
|
// reported time will be the same as ANEURALNETWORKS_DURATION_ON_HARDWARE.
|
|||
|
|
// Available since NNAPI feature level 4.
|
|||
|
|
FENCED_DURATION_ON_HARDWARE = 2,
|
|||
|
|
// Execution time in driver, after all dependencies have been signaled. Excludes
|
|||
|
|
// overhead such as that of the runtime itself and the IPC needed for the runtime
|
|||
|
|
// to communicate with the driver.
|
|||
|
|
// If no dependencies specified (for example, if the execution was scheduled other
|
|||
|
|
// than with {@link ANeuralNetworksExecution_startComputeWithDependencies}), the
|
|||
|
|
// reported time will be the same as ANEURALNETWORKS_DURATION_IN_DRIVER.
|
|||
|
|
// Available since NNAPI feature level 4.
|
|||
|
|
FENCED_DURATION_IN_DRIVER = 3,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Relative execution priority.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
PriorityCode :: enum {
|
|||
|
|
LOW = 90,
|
|||
|
|
MEDIUM = 100,
|
|||
|
|
HIGH = 110,
|
|||
|
|
DEFAULT = MEDIUM,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksMemory is an opaque type that represents memory.
|
|||
|
|
*
|
|||
|
|
* This type is used to represent shared memory, memory mapped files,
|
|||
|
|
* and similar memories.
|
|||
|
|
*
|
|||
|
|
* By using shared memory, a program can efficiently communicate to the
|
|||
|
|
* runtime and drivers the tensors that define a model. See
|
|||
|
|
* {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
|
|||
|
|
* should typically create one shared memory object that contains every constant tensor
|
|||
|
|
* needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be used to
|
|||
|
|
* create shared memory from a file handle.
|
|||
|
|
* {@link ANeuralNetworksMemory_createFromAHardwareBuffer} can be used to
|
|||
|
|
* create shared memory from an AHardwareBuffer handle.
|
|||
|
|
*
|
|||
|
|
* Memory objects can also be used to specify the input and output arguments of
|
|||
|
|
* an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
|
|||
|
|
* and {@link ANeuralNetworksExecution_setOutputFromMemory}.
|
|||
|
|
*
|
|||
|
|
* When calling {@link ANeuralNetworksModel_setOperandValueFromMemory},
|
|||
|
|
* {@link ANeuralNetworksExecution_setInputFromMemory} and
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutputFromMemory}, each operand in the shared
|
|||
|
|
* memory object must be aligned on a boundary of a byte size that is a multiple
|
|||
|
|
* of the element type byte size, e.g., a tensor with
|
|||
|
|
* {@link ANEURALNETWORKS_TENSOR_FLOAT32} type must be aligned on 4-byte boundary.
|
|||
|
|
*
|
|||
|
|
* It is the application's responsibility to ensure that there are no uses of
|
|||
|
|
* the memory after calling {@link ANeuralNetworksMemory_free}. This includes
|
|||
|
|
* any model which references this memory because of a call to
|
|||
|
|
* {@link ANeuralNetworksModel_setOperandValueFromMemory}, any compilation
|
|||
|
|
* created using such a model, any execution object or burst object created
|
|||
|
|
* using such a compilation, or any execution which references this memory
|
|||
|
|
* because of a call to {@link ANeuralNetworksExecution_setInputFromMemory} or
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutputFromMemory}.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*
|
|||
|
|
* Starting at NNAPI feature level 4, the application may request creation of device native memory
|
|||
|
|
* from {@link ANeuralNetworksMemoryDesc} to avoid potential memory copying and transformation
|
|||
|
|
* overhead between executions. See also {@link ANeuralNetworksMemoryDesc} and
|
|||
|
|
* {@link ANeuralNetworksMemory_createFromDesc}.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksMemory :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksModel is an opaque type that contains a description of the
|
|||
|
|
* mathematical operations that constitute the model.
|
|||
|
|
*
|
|||
|
|
* <p>Build the model by calling<ul>
|
|||
|
|
* <li>{@link ANeuralNetworksModel_create}</li>
|
|||
|
|
* <li>{@link ANeuralNetworksModel_addOperation}</li>
|
|||
|
|
* <li>{@link ANeuralNetworksModel_addOperand}</li>
|
|||
|
|
* </ul>
|
|||
|
|
*
|
|||
|
|
* This forms a graph in which each operation and operand is a node, a
|
|||
|
|
* directed edge from an operand to an operation indicates that the
|
|||
|
|
* operand is an input to the operation, and a directed edge from an
|
|||
|
|
* operation to an operand indicates that the operand is an output
|
|||
|
|
* from the operation. This graph must be acyclic.
|
|||
|
|
*
|
|||
|
|
* A model is completed by calling {@link ANeuralNetworksModel_finish}.
|
|||
|
|
* A model is destroyed by calling {@link ANeuralNetworksModel_free}.
|
|||
|
|
*
|
|||
|
|
* <p>A model cannot be modified once {@link ANeuralNetworksModel_finish}
|
|||
|
|
* has been called on it.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is the application's responsibility to make sure that only one thread
|
|||
|
|
* modifies a model at a given time. It is however safe for more than one
|
|||
|
|
* thread to use the model once {@link ANeuralNetworksModel_finish} has returned.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is also the application's responsibility to ensure that there are no
|
|||
|
|
* other uses of the model after calling {@link ANeuralNetworksModel_free}.
|
|||
|
|
* This includes any compilation, execution object or burst object created using
|
|||
|
|
* the model.</p>
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksModel :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksCompilation is an opaque type that can be used to compile
|
|||
|
|
* a machine learning model.
|
|||
|
|
*
|
|||
|
|
* <p>To use:<ul>
|
|||
|
|
* <li>Create a new compilation instance by calling the
|
|||
|
|
* {@link ANeuralNetworksCompilation_create} function or
|
|||
|
|
* {@link ANeuralNetworksCompilation_createForDevices}.</li>
|
|||
|
|
* <li>Set any desired properties on the compilation (for example,
|
|||
|
|
* {@link ANeuralNetworksCompilation_setPreference}).</li>
|
|||
|
|
* <li>Optionally, set the caching signature and the cache directory on the
|
|||
|
|
* compilation by calling {@link ANeuralNetworksCompilation_setCaching}.</li>
|
|||
|
|
* <li>Complete the compilation with {@link ANeuralNetworksCompilation_finish}.</li>
|
|||
|
|
* <li>Use the compilation as many times as needed
|
|||
|
|
* with {@link ANeuralNetworksExecution_create} and
|
|||
|
|
* {@link ANeuralNetworksBurst_create}.</li>
|
|||
|
|
* <li>Destroy the compilation with {@link ANeuralNetworksCompilation_free}
|
|||
|
|
* once all executions using the compilation have completed.</li></ul></p>
|
|||
|
|
*
|
|||
|
|
* A compilation is completed by calling {@link ANeuralNetworksCompilation_finish}.
|
|||
|
|
* A compilation is destroyed by calling {@link ANeuralNetworksCompilation_free}.
|
|||
|
|
*
|
|||
|
|
* <p>A compilation cannot be modified once {@link ANeuralNetworksCompilation_finish}
|
|||
|
|
* has been called on it.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is the application's responsibility to make sure that only
|
|||
|
|
* one thread modifies a compilation at a given time. It is however
|
|||
|
|
* safe for more than one thread to use the compilation once
|
|||
|
|
* {@link ANeuralNetworksCompilation_finish} has returned.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is also the application's responsibility to ensure that there are no other
|
|||
|
|
* uses of the compilation after calling {@link ANeuralNetworksCompilation_free}.
|
|||
|
|
* This includes any execution object or burst object created using the compilation,
|
|||
|
|
* or any memory descriptor with the compilation as part of one of the roles specified by
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_addInputRole} or
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_addOutputRole}.</p>
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksCompilation :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksExecution is an opaque type that can be used to apply a machine
|
|||
|
|
* learning model to a set of inputs.
|
|||
|
|
*
|
|||
|
|
* <p>To use:<ul>
|
|||
|
|
* <li>Create a new execution instance by calling the
|
|||
|
|
* {@link ANeuralNetworksExecution_create} function.</li>
|
|||
|
|
* <li>Associate input buffers or memory regions to the model inputs with
|
|||
|
|
* {@link ANeuralNetworksExecution_setInput} or
|
|||
|
|
* {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
|
|||
|
|
* <li>Associate output buffers or memory regions to the model outputs with
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutput} or
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
|
|||
|
|
* <li>Optionally, configure the execution with
|
|||
|
|
* {@link ANeuralNetworksExecution_setLoopTimeout},
|
|||
|
|
* {@link ANeuralNetworksExecution_setMeasureTiming},
|
|||
|
|
* {@link ANeuralNetworksExecution_setReusable}, or
|
|||
|
|
* {@link ANeuralNetworksExecution_setTimeout}.
|
|||
|
|
* <li>Apply the model with one of the following:</li><ul>
|
|||
|
|
* <li>Asynchronously with {@link ANeuralNetworksExecution_startCompute}
|
|||
|
|
* or with {@link ANeuralNetworksExecution_startComputeWithDependencies},
|
|||
|
|
* waiting for the execution to complete with
|
|||
|
|
* {@link ANeuralNetworksEvent_wait}.</li>
|
|||
|
|
* <li>Synchronously with {@link ANeuralNetworksExecution_compute}.</li>
|
|||
|
|
* <li>Synchronously as part of an execution burst with
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute}.</li></ul>
|
|||
|
|
* If the execution has been marked as reusable, then you can
|
|||
|
|
* apply the model more than once.
|
|||
|
|
* <li>Destroy the execution with
|
|||
|
|
* {@link ANeuralNetworksExecution_free}.</li></ul></p>
|
|||
|
|
*
|
|||
|
|
* <p>An output buffer or memory region must not overlap with any
|
|||
|
|
* other output buffer or memory region, with an input buffer or
|
|||
|
|
* memory region, or with an operand value in a memory object
|
|||
|
|
* ({@link ANeuralNetworksModel_setOperandValueFromMemory}).</p>
|
|||
|
|
*
|
|||
|
|
* <p>An execution is in the preparation state after it is created by
|
|||
|
|
* {@link ANeuralNetworksExecution_create}. An execution may only be modified in the preparation
|
|||
|
|
* state. Scheduling a computation by calling {@link ANeuralNetworksExecution_burstCompute},
|
|||
|
|
* {@link ANeuralNetworksExecution_compute}, {@link ANeuralNetworksExecution_startCompute},
|
|||
|
|
* or {@link ANeuralNetworksExecution_startComputeWithDependencies} will change the state of
|
|||
|
|
* the execution object to the computation state. When the computation completes, the state of
|
|||
|
|
* the execution object will change from the computation state to the completed state.
|
|||
|
|
* The computation is completed when {@link ANeuralNetworksExecution_compute},
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute}, or {@link ANeuralNetworksEvent_wait}
|
|||
|
|
* has returned.</p>
|
|||
|
|
*
|
|||
|
|
* <p>An execution can be applied to a model with
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute},
|
|||
|
|
* {@link ANeuralNetworksExecution_compute},
|
|||
|
|
* {@link ANeuralNetworksExecution_startCompute} or
|
|||
|
|
* {@link ANeuralNetworksExecution_startComputeWithDependencies} only once. Create new
|
|||
|
|
* executions to do new evaluations of the model.</p>
|
|||
|
|
*
|
|||
|
|
* <p>Starting at NNAPI feature level 5, the application may call
|
|||
|
|
* {@link ANeuralNetworksExecution_setReusable} to set an execution to be reusable for multiple
|
|||
|
|
* computations. The application may schedule and evaluate a computation again from the completed
|
|||
|
|
* state of a reusable execution. The execution cannot be modified between computations.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is the application's responsibility to make sure that only one thread
|
|||
|
|
* modifies an execution at a given time. It is however safe for more than one
|
|||
|
|
* thread to use {@link ANeuralNetworksEvent_wait} at the same time.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is also the application's responsibility to ensure that the execution
|
|||
|
|
* either has never been scheduled or has completed (i.e., that
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute},
|
|||
|
|
* {@link ANeuralNetworksExecution_compute}, or
|
|||
|
|
* {@link ANeuralNetworksEvent_wait} has returned) before calling
|
|||
|
|
* {@link ANeuralNetworksExecution_free}.</p>.
|
|||
|
|
*
|
|||
|
|
* <p>It is also the application's responsibility to ensure that there are no other
|
|||
|
|
* uses of the execution after calling {@link ANeuralNetworksExecution_free}.</p>
|
|||
|
|
*
|
|||
|
|
* <p>It is the application's responsibility to ensure that there are no concurrent computations
|
|||
|
|
* scheduled and evaluated on the same execution, either by means of
|
|||
|
|
* {@link ANeuralNetworksExecution_compute} or
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute} (which are synchronous)
|
|||
|
|
* in different threads, or by means of
|
|||
|
|
* {@link ANeuralNetworksExecution_startCompute} or
|
|||
|
|
* {@link ANeuralNetworksExecution_startComputeWithDependencies} (which are asynchronous).
|
|||
|
|
* It is however safe to schedule and evaluate multiple computations on different executions
|
|||
|
|
* concurrently. (Concurrent uses of {@link ANeuralNetworksExecution_burstCompute} must be on
|
|||
|
|
* different burst objects.) The runtime makes no guarantee on the ordering of
|
|||
|
|
* completion of executions. If it's important to the application, the
|
|||
|
|
* application should enforce the ordering by ensuring that one execution
|
|||
|
|
* completes before the next is scheduled (for example, by scheduling all
|
|||
|
|
* executions synchronously within a single thread, or by scheduling all
|
|||
|
|
* executions asynchronously and using {@link ANeuralNetworksEvent_wait} between
|
|||
|
|
* calls to {@link ANeuralNetworksExecution_startCompute}); or by using
|
|||
|
|
* {@link ANeuralNetworksExecution_startComputeWithDependencies} to make the execution wait for a
|
|||
|
|
* list of events to be signaled before starting the actual evaluation.</p>
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksExecution :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Parameters for ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksSymmPerChannelQuantParams :: struct {
|
|||
|
|
/** The index of the channel dimension. */
|
|||
|
|
channelDim: u32,
|
|||
|
|
/** The size of the scale array. Should be equal to dimension[channelDim] of the Operand. */
|
|||
|
|
scaleCount: u32,
|
|||
|
|
/** The array of scaling values for each channel. Each value must be greater than zero. */
|
|||
|
|
scales: [^]f32,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksBurst is an opaque type that can be used to reduce the latency
|
|||
|
|
* of a rapid sequence of executions. It will likely cause overhead if only used
|
|||
|
|
* for a single execution.
|
|||
|
|
*
|
|||
|
|
* ANeuralNetworksBurst serves as a context object for any number of inferences
|
|||
|
|
* using {@link ANeuralNetworksExecution} objects. An ANeuralNetworksBurst
|
|||
|
|
* object and the {@link ANeuralNetworksExecution} objects used with it must all
|
|||
|
|
* have been created from the same {@link ANeuralNetworksCompilation} object.
|
|||
|
|
*
|
|||
|
|
* This object is also used as a hint to drivers, providing insight to the
|
|||
|
|
* lifetime of a rapid sequence of executions. For example, a driver may choose
|
|||
|
|
* to increase the clock frequency of its accelerator for the lifetime of a
|
|||
|
|
* burst object.
|
|||
|
|
*
|
|||
|
|
* <p>To use:<ul>
|
|||
|
|
* <li>Create a new burst object by calling the
|
|||
|
|
* {@link ANeuralNetworksBurst_create} function.</li>
|
|||
|
|
* <li>For each execution:</li><ul>
|
|||
|
|
* <li>Create {@link ANeuralNetworksExecution} and configure its
|
|||
|
|
* properties (see {@link ANeuralNetworksExecution} for details).</li>
|
|||
|
|
* <li>Apply the model synchronously with
|
|||
|
|
* {@link ANeuralNetworksExecution_burstCompute}, reusing the same
|
|||
|
|
* {@link ANeuralNetworksBurst} with the new
|
|||
|
|
* {@link ANeuralNetworksExecution}.</li>
|
|||
|
|
* <li>Use and free the {@link ANeuralNetworksExecution}.</li></ul>
|
|||
|
|
* <li>Destroy the burst with
|
|||
|
|
* {@link ANeuralNetworksBurst_free}.</li></ul></p>
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksBurst :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksOperandType describes the type of an operand.
|
|||
|
|
*
|
|||
|
|
* This structure is used to describe both scalars and tensors.
|
|||
|
|
*
|
|||
|
|
* A tensor operand type with all dimensions specified is "fully
|
|||
|
|
* specified". Whenever possible (i.e., whenever the dimensions are
|
|||
|
|
* known at model construction time), a tensor operand type should be
|
|||
|
|
* (but is not required to be) fully specified, in order to enable the
|
|||
|
|
* best possible performance.
|
|||
|
|
*
|
|||
|
|
* If a tensor operand's type is not fully specified, the dimensions
|
|||
|
|
* of the operand are deduced from the operand types and values of the
|
|||
|
|
* operation for which that operand is an output or from the corresponding
|
|||
|
|
* {@link ANEURALNETWORKS_IF} or {@link ANEURALNETWORKS_WHILE} operation input
|
|||
|
|
* operand type in the case of referenced model input operands.
|
|||
|
|
*
|
|||
|
|
* <p>In the following situations, a tensor operand type must be fully
|
|||
|
|
* specified:<ul>
|
|||
|
|
* <li>The operand has a constant value, set by
|
|||
|
|
* {@link ANeuralNetworksModel_setOperandValue} (with a
|
|||
|
|
* non-nullptr buffer) or
|
|||
|
|
* {@link ANeuralNetworksModel_setOperandValueFromMemory}.</li>
|
|||
|
|
* <li>The operand is a model input (see
|
|||
|
|
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}) of the main
|
|||
|
|
* model within a compilation. A fully specified tensor operand type
|
|||
|
|
* must either be provided to {@link ANeuralNetworksModel_addOperand};
|
|||
|
|
* or it must be provided to the corresponding
|
|||
|
|
* {@link ANeuralNetworksExecution_setInput}, or
|
|||
|
|
* {@link ANeuralNetworksExecution_setInputFromMemory}.
|
|||
|
|
* EXCEPTION: If the input is optional and omitted
|
|||
|
|
* (by passing nullptr for buffer to
|
|||
|
|
* {@link ANeuralNetworksExecution_setInput}) then it need
|
|||
|
|
* not have a fully specified tensor operand type.</li>
|
|||
|
|
* <li>The operand is a model output (see
|
|||
|
|
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}) of the main
|
|||
|
|
* model within a compilation and is to be used with {@link
|
|||
|
|
* ANeuralNetworksExecution_startComputeWithDependencies}.
|
|||
|
|
* A fully specified tensor operand type must either be provided
|
|||
|
|
* to {@link ANeuralNetworksModel_addOperand}; or it must be
|
|||
|
|
* provided to the corresponding
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutput}, or
|
|||
|
|
* {@link ANeuralNetworksExecution_setOutputFromMemory}.</li></ul>
|
|||
|
|
*
|
|||
|
|
* A tensor operand type of specified rank but some number of
|
|||
|
|
* unspecified dimensions is represented by setting dimensionCount to
|
|||
|
|
* the rank and each unspecified dimension to 0.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*
|
|||
|
|
* Starting at NNAPI feature level 3, a tensor operand type of unspecified rank is
|
|||
|
|
* represented by setting dimensionCount to 0 and dimensions to NULL (just as if
|
|||
|
|
* it were a scalar operand type).
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksOperandType :: struct {
|
|||
|
|
/**
|
|||
|
|
* The data type, e.g ANEURALNETWORKS_FLOAT32.
|
|||
|
|
*/
|
|||
|
|
type: OperandCode,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* The number of dimensions (rank).
|
|||
|
|
*
|
|||
|
|
* Must be 0 for scalars.
|
|||
|
|
*/
|
|||
|
|
dimensionCount: u32,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* The dimensions of the tensor.
|
|||
|
|
*
|
|||
|
|
* Must be nullptr for scalars.
|
|||
|
|
*/
|
|||
|
|
dimensions: [^]u32,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* The quantization scale.
|
|||
|
|
*
|
|||
|
|
* Must be 0 when not applicable to an operand type.
|
|||
|
|
*
|
|||
|
|
* See {@link OperandCode}.
|
|||
|
|
*/
|
|||
|
|
scale: f32,
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* The quantization zero point.
|
|||
|
|
*
|
|||
|
|
* Must be 0 when not applicable to an operand type.
|
|||
|
|
*
|
|||
|
|
* See {@link OperandCode}.
|
|||
|
|
*/
|
|||
|
|
zeroPoint: i32,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksEvent is an opaque type that represents an event
|
|||
|
|
* that will be signaled once an execution completes.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 1.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksEvent :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksDevice is an opaque type that represents a device.
|
|||
|
|
*
|
|||
|
|
* This type is used to query basic properties and supported operations of the corresponding
|
|||
|
|
* device, and control which device(s) a model is to be run on.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 3.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksDevice :: struct{}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* ANeuralNetworksMemoryDesc is an opaque type that represents a memory descriptor.
|
|||
|
|
*
|
|||
|
|
* A memory descriptor describes the properties of a memory object, and is used by
|
|||
|
|
* {@link ANeuralNetworksMemory_createFromDesc}.
|
|||
|
|
*
|
|||
|
|
* To use:
|
|||
|
|
* - Create a new memory descriptor by calling {@link ANeuralNetworksMemoryDesc_create}.
|
|||
|
|
* - Specify all of the intended input and output roles by calling
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_addInputRole} and
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_addOutputRole}.
|
|||
|
|
* - Optionally, specify the memory dimensions by calling
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_setDimensions}.
|
|||
|
|
* - Complete the memory descriptor with {@link ANeuralNetworksMemoryDesc_finish}.
|
|||
|
|
* - Use the memory descriptor as many times as needed with
|
|||
|
|
* {@link ANeuralNetworksMemory_createFromDesc}.
|
|||
|
|
* - Destroy the memory descriptor with {@link ANeuralNetworksMemoryDesc_free}.
|
|||
|
|
*
|
|||
|
|
* A memory descriptor is completed by calling {@link ANeuralNetworksMemoryDesc_finish}.
|
|||
|
|
* A memory descriptor is destroyed by calling {@link ANeuralNetworksMemoryDesc_free}.
|
|||
|
|
*
|
|||
|
|
* A memory descriptor must not be modified once {@link ANeuralNetworksMemoryDesc_finish}
|
|||
|
|
* has been called on it.
|
|||
|
|
*
|
|||
|
|
* It is the application's responsibility to make sure that only
|
|||
|
|
* one thread modifies a memory descriptor at a given time. It is however
|
|||
|
|
* safe for more than one thread to use the memory descriptor once
|
|||
|
|
* {@link ANeuralNetworksMemoryDesc_finish} has returned.
|
|||
|
|
*
|
|||
|
|
* It is also the application's responsibility to ensure that there are no other
|
|||
|
|
* uses of the memory descriptor after calling {@link ANeuralNetworksMemoryDesc_free}.
|
|||
|
|
* It is however safe to continue using a {@link ANeuralNetworksMemory} object created
|
|||
|
|
* from the memory descriptor.
|
|||
|
|
*
|
|||
|
|
* Available since NNAPI feature level 4.
|
|||
|
|
*/
|
|||
|
|
ANeuralNetworksMemoryDesc :: struct{}
|
|||
|
|
|