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Merge "Sync NNAPI Operand and Operation documentation fixes" into pi-dev

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This commit is contained in:
Michael Butler
2018-04-11 23:22:05 +00:00
committed by Android (Google) Code Review
parent c6567b1a96 25f3ad328b
commit 1f88e2092f
4 changed files with 705 additions and 408 deletions
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7
current.txt
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@@ -241,11 +241,11 @@ a432d6d9200248dc2126827bcd6cdea31dd65eff39b939f64585d27d915a5857 android.hardwar
86ba9c03978b79a742e990420bc5ced0673d25a939f82572996bef92621e2014 android.hardware.cas@1.0::IMediaCasService
503da837d1a67cbdb7c08a033e927e5430ae1b159d98bf72c6336b4dcc5e76f5 android.hardware.cas.native@1.0::types
619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
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
@@ -259,7 +259,8 @@ fb92e2b40f8e9d494e8fd3b4ac18499a3216342e7cff160714c3bbf3660b6e79 android.hardwar
251594ea9b27447bfa005ebd806e58fb0ae4aad84a69938129c9800ec0c64eda android.hardware.gnss@1.0::IGnssMeasurementCallback
4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
6721fc5b64d997f3eda15b762a0dd9f3fa414926219dbca58312972d565b4bee android.hardware.neuralnetworks@1.0::types
12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
@@ -338,7 +339,7 @@ b8c7ed58aa8740361e63d0ce9e7c94227572a629f356958840b34809d2393a7c android.hardwar
4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
3e4d8e0085ebe8549efb8ad4b8b400a141a3fa3f47ae23696b3e05a1612eb003 android.hardware.neuralnetworks@1.1::IDevice
e808a6f61cd7b47887c599d8843e67a2dcbf4ec5aadd5d22fdce93020070ef1b android.hardware.neuralnetworks@1.1::types
50db076b03a6760557fc60ef433ba9dd2ff983cf3305eeb504b0fff3eaa604ff 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

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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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.