NNAPI: sync NDK and HAL documentation (e1a235e5) · Commits · e / os / android_hardware_interfaces

current.txt

+1 −1

Original line number	Diff line number	Diff line
		@@ -249,5 +249,5 @@ c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardwar
		# Future changes to HALs
		5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
		12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
		702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
		18e6885e184fe48401c2c53f1d1b8bfb07240f40c81ae6b9d2e336fca6efdbb7 android.hardware.neuralnetworks@1.0::types

neuralnetworks/1.0/types.hal

+564 −401

File changed.

Preview size limit exceeded, changes collapsed.

neuralnetworks/1.1/types.hal

+134 −103

Original line number	Diff line number	Diff line
		@@ -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
		* * 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,
		};