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NNAPI: sync NDK and HAL documentation

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Bug: 72650109
Test: mma
Change-Id: I3e0a3680b89d80be500d8975f587f2d7c33fea10
This commit is contained in:
Michael Butler 2018-04-26 18:41:24 -07:00
parent 0051181ac7
commit e98352806b
3 changed files with 700 additions and 506 deletions
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4
current.txt
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@ -260,7 +260,7 @@ fb92e2b40f8e9d494e8fd3b4ac18499a3216342e7cff160714c3bbf3660b6e79 android.hardwar
4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
934b9a0627080bca5dee83126d23ace31bdf1ed36fe192a2a7694f81b4f0c2af android.hardware.neuralnetworks@1.0::types
18e6885e184fe48401c2c53f1d1b8bfb07240f40c81ae6b9d2e336fca6efdbb7 android.hardware.neuralnetworks@1.0::types
d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
@ -339,7 +339,7 @@ b8c7ed58aa8740361e63d0ce9e7c94227572a629f356958840b34809d2393a7c android.hardwar
4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
7698dc2382a2eeb43541840e3ee624f34108efdfb976b2bfa7c13ef15fb8c4c4 android.hardware.neuralnetworks@1.1::IDevice
ce5dab4b2dd828bcff09acfb93fcd4846f847868b9e914d214095532c28dc0cf android.hardware.neuralnetworks@1.1::types
72cc6126632456e8fbb8776fe50150c3c4dd5d09145653193affb70785211dfa android.hardware.neuralnetworks@1.1::types
8d3d86da0bfa4bf070970d8303c659f67f35d670c287d45a3f542e4fedadd578 android.hardware.nfc@1.1::INfc
e85f566698d2a2c28100e264fcf2c691a066756ddf8dd341d009ff50cfe10614 android.hardware.nfc@1.1::INfcClientCallback
5e278fcaa3287d397d8eebe1c22aaa28150f5caae1cf9381cd6dc32cb37899c5 android.hardware.nfc@1.1::types

965
neuralnetworks/1.0/types.hal
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237
neuralnetworks/1.1/types.hal
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@ -29,87 +29,95 @@ enum OperationType : @1.0::OperationType {
/**
* 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 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 types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
* 0: An n-D tensor, specifying the tensor to be reshaped
* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
* * 0: An n-D tensor, specifying the tensor to be reshaped
* * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
* sizes for each spatial dimension of the input tensor. All values
* must be >= 1.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
BATCH_TO_SPACE_ND = 29,
/**
* Element-wise division of two tensors.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the result of dividing the first input tensor by the second, optionally
* modified by an activation function.
* Takes two input tensors of identical {@link OperandType} and compatible
* dimensions. The output is the result of dividing the first input tensor
* by the second, optionally modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
*
* The size of the output is the maximum size along each dimension of the input operands.
* It starts with the trailing dimensions, and works its way forward.
* The size of the output is the maximum size along each dimension of the
* input operands. It starts with the trailing dimensions, and works its way
* forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the first input.
* 1: A tensor of the same type, and compatible dimensions as input0.
* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
* Specifies the activation to invoke on the result of each addition.
* * 0: An n-D tensor, specifying the first input.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
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.
* Reduces the input tensor along the given dimensions to reduce. Unless
* keep_dims is true, the rank of the tensor is reduced by 1 for each entry
* in axis. If keep_dims is true, the reduced dimensions are retained with
* length 1.
*
* If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
* a single element is returned.
* If dimensions to reduce have no entries, all dimensions are reduced, and
* a tensor with a single element is returned.
*
* Supported tensor types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: A tensor, specifying the input.
* 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default),
* reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
* 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
* * 0: A tensor, specifying the input.
* * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. If None (the default), reduces all dimensions. Must be in
* the range [-rank(input_tensor), rank(input_tensor)).
* * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
* retains reduced dimensions with length 1.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
MEAN = 31,
@ -118,170 +126,193 @@ enum OperationType : @1.0::OperationType {
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the tensor to be padded.
* 1: A 2-D Tensor of type 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 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.
* * 0: An n-D tensor, specifying the tensor to be padded.
* * 1: A 2-D Tensor of {@link OperandType::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 element to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the
* end of dimension i.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
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.
* 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 types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
* 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
* input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
* padding[i, 0] specifies the number of element to be padded in the front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the end of dimension i.
* * 0: An n-D tensor, specifying the input.
* * 1: A 1-D Tensor of {@link OperandType::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 OperandType::TENSOR_INT32}, the paddings
* for each spatial dimension of the input tensor. All values must be
* >= 0. The shape of the tensor must be {rank(input0), 2}.
* padding[i, 0] specifies the number of element to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of element to be padded after the
* end of dimension i.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
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 type 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).
* Given a tensor input, this operation returns a tensor of the same
* {@link OperandType} 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 types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, the tensor to be squeezed.
* 1: An optional 1-D tensor of type 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.
* * 0: An n-D tensor, the tensor to be squeezed.
* * 1: An optional 1-D tensor of {@link OperandType::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 type as input0. Contains the same data as input, but has one or more
* dimensions of size 1 removed.
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
*/
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.
* 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 types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the tensor to be sliced.
* 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
* tensor to be sliced. The length must be of rank(input0).
* 4: An INT32 value, begin_mask. If the ith bit of begin_mask is set, begin[i] is ignored
* and the fullest possible range in that dimension is used instead.
* 5: An INT32 value, end_mask. If the ith bit of end_mask is set, end[i] is ignored and
* the fullest possible range in that dimension is used instead.
* 6: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
* set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
* size 1 starting from begin[i] in the dimension must be preserved.
* * 0: An n-D tensor, specifying the tensor to be sliced.
* * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the starts of
* the dimensions of the input tensor to be sliced. The length must be
* of rank(input0).
* * 2: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the ends of
* the dimensions of the input tensor to be sliced. The length must be
* of rank(input0).
* * 3: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the strides of
* the dimensions of the input tensor to be sliced. The length must be
* of rank(input0).
* * 4: An {@link OperandType::INT32} scalar, begin_mask. If the ith bit
* of begin_mask is set, begin[i] is ignored and the fullest possible
* range in that dimension is used instead.
* * 5: An {@link OperandType::INT32} scalar, end_mask. If the ith bit of
* end_mask is set, end[i] is ignored and the fullest possible range in
* that dimension is used instead.
* * 6: An {@link OperandType::INT32} scalar, shrink_axis_mask. An int32
* mask. If the ith bit of shrink_axis_mask is set, it implies that the
* ith specification shrinks the dimensionality by 1. A slice of size 1
* starting from begin[i] in the dimension must be preserved.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
STRIDED_SLICE = 35,
/**
* Element-wise subtraction of two tensors.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the result of subtracting the second input tensor from the first one, optionally
* modified by an activation function.
* Takes two input tensors of identical {@link OperandType} 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.
* The size of the output is the maximum size along each dimension of the
* input operands. It starts with the trailing dimensions, and works its way
* forward.
*
* Example:
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the first input.
* 1: A tensor of the same type, and compatible dimensions as input0.
* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
* Specifies the activation to invoke on the result of each addition.
* * 0: An n-D tensor, specifying the first input.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
SUB = 36,
/**
* Transposes the input tensor, permuting the dimensions according to the perm tensor.
* 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.
* 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 types:
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the tensor to be transposed.
* 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
* input tensor.
* * 0: An n-D tensor, specifying the tensor to be transposed.
* * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
* the permutation of the dimensions of the input tensor.
*
* Outputs:
* 0: A tensor of the same type as input0.
* * 0: A tensor of the same {@link OperandType} as input0.
*/
TRANSPOSE = 37,
};