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Commit 89300b25 authored by Michael Butler's avatar Michael Butler Committed by Mika Raento
Browse files

NNAPI: sync NDK and HAL documentation 

Bug: 72650109
Test: mma
Merged-In: I3e0a3680b89d80be500d8975f587f2d7c33fea10
Change-Id: I3e0a3680b89d80be500d8975f587f2d7c33fea10
(cherry picked from commit e9835280)
parent c5c7bfe1

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,

};