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Sync NNAPI Operand and Operation documentation fixes

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This CL adds the typo fixes, bug fixes, and missing descriptions added
to the NNAPI but not to the NN HAL. This CL also adds description of
implicit padding, which was present but not documented in 1.0.

Bug: 77541934
Bug: 75459529
Test: mma
Merged-In: I7baa9e515057c43e759849284b3bb0d420a71f1b
Change-Id: I7baa9e515057c43e759849284b3bb0d420a71f1b
(cherry picked from commit 25f3ad328b)
This commit is contained in:
Michael Butler
2018-04-03 13:03:50 -07:00
parent e12088cd64
commit 1e9f62d4f2
4 changed files with 704 additions and 407 deletions
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5
current.txt
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@@ -238,15 +238,16 @@ a432d6d9200248dc2126827bcd6cdea31dd65eff39b939f64585d27d915a5857 android.hardwar
619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
0a159f81359cd4f71bbe00972ee8403ea79351fb7c0cd48be72ebb3e424dbaef android.hardware.radio@1.0::types
09342041e17c429fce0034b9096d17849122111436a5f0053e7e59500e1cb89c android.hardware.media.omx@1.0::IOmxStore
246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
93eb3757ceaf21590fa4cd1d4a7dfe3b3794af5396100a6d25630879352abce9 android.hardware.neuralnetworks@1.0::IDevice
f66f9a38541bf92001d3adcce678cd7e3da2262124befb460b1c9aea9492813b android.hardware.neuralnetworks@1.0::IExecutionCallback
953607822954435874f4b81686440a604e2a88cdd2d9164c6293f3d5772510d7 android.hardware.neuralnetworks@1.0::IPreparedModel
73e03573494ba96f0e711ab7f1956c5b2d54c3da690cd7ecf4d6d0f287447730 android.hardware.neuralnetworks@1.0::IPreparedModelCallback
246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
# Future changes to HALs
5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
1488db5ffb8a7979488d1084761aab8bca2f59bc9a02d75cdefc296afeaf591b android.hardware.neuralnetworks@1.0::types
12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types

2
neuralnetworks/1.0/IExecutionCallback.hal
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@@ -28,7 +28,7 @@ interface IExecutionCallback {
* ErrorStatus resulting from the execution. If the asynchronous task
* is not launched, notify must be invoked with the appropriate error.
*
* @return param Error status returned from launching the asynchronous task
* @param status Error status returned from launching the asynchronous task
* (if the launch fails) or from the asynchronous task itself
* (if the launch succeeds). Must be:
* - NONE if the asynchronous execution was successful

978
neuralnetworks/1.0/types.hal
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File diff suppressed because it is too large Load Diff

126
neuralnetworks/1.1/types.hal
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@@ -27,25 +27,24 @@ import @1.0::PerformanceInfo;
*/
enum OperationType : @1.0::OperationType {
/**
* BatchToSpace for N-D tensors.
* BatchToSpace for N-dimensional tensors.
*
* This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
* 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.
* The spatial dimensions of this intermediate result are then optionally cropped
* according to the amount to crop to produce the output.
*
* This is the reverse of SpaceToBatch.
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor rank: up to 4
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 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.
* 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
* input tensor. All values must be >= 0.
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -53,9 +52,9 @@ enum OperationType : @1.0::OperationType {
BATCH_TO_SPACE_ND = 29,
/**
* Divides the second tensor from the first tensor, element-wise.
* Element-wise division of two tensors.
*
* Takes two input tensors of identical OperandType and compatible dimensions. The output
* 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.
*
@@ -71,7 +70,9 @@ enum OperationType : @1.0::OperationType {
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
@@ -88,15 +89,17 @@ enum OperationType : @1.0::OperationType {
/**
* Computes the mean of elements across dimensions of a tensor.
*
* Reduces input tensor along the dimensions given in axis. 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 axis has 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:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor rank: up to 4
*
* Inputs:
@@ -115,14 +118,18 @@ enum OperationType : @1.0::OperationType {
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
* of the input tensor.
* 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.
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -130,7 +137,7 @@ enum OperationType : @1.0::OperationType {
PAD = 32,
/**
* SpaceToBatch for N-D tensors.
* 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
@@ -139,16 +146,20 @@ enum OperationType : @1.0::OperationType {
* batch position. Prior to division into blocks, the spatial dimensions of the input are
* optionally zero padded according to paddings.
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor rank: up to 4
* Supported tensor types:
* * {@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.
* 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.
@@ -160,17 +171,20 @@ enum OperationType : @1.0::OperationType {
*
* 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 axis.
* you can remove specific size 1 dimensions by specifying the axes (input1).
*
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
* squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
* index starts at 0. It is an error to squeeze a dimension that is not 1.
* 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.
*
* Outputs:
* 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
@@ -181,23 +195,25 @@ enum OperationType : @1.0::OperationType {
/**
* Extracts a strided slice of a tensor.
*
* 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
* 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: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 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.
* 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.
* 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.
* tensor to be sliced. The length must be of rank(input0).
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -205,7 +221,7 @@ enum OperationType : @1.0::OperationType {
STRIDED_SLICE = 35,
/**
* Subtracts the second tensor from the first tensor, element-wise.
* 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
@@ -223,7 +239,9 @@ enum OperationType : @1.0::OperationType {
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
@@ -240,18 +258,20 @@ enum OperationType : @1.0::OperationType {
/**
* Transposes the input tensor, permuting the dimensions according to the perm tensor.
*
* The returned tensor's dimension i must correspond to the input dimension perm[i].
* 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: {@link OperandType::TENSOR_FLOAT32}
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor types:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 1: A 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 type TENSOR_INT32, the permutation of the dimensions of the
* input tensor.
*
* Outputs:
* 0: A tensor of the same type as input0.