Fix HAL and AIDL files to be consistent with API generation tool.
Bug: 161426966
Test: generate_api.sh
Change-Id: I4f4cc108ccdf2f37a1a7fa154ba8e8a186295276
diff --git a/current.txt b/current.txt
index 88b067d..cc93f31 100644
--- a/current.txt
+++ b/current.txt
@@ -838,5 +838,7 @@
# ABI preserving changes to HALs during Android T
62ace52d9c3ff1f60f94118557a2aaf0b953513e59dcd34d5f94ae28d4c7e780 android.hardware.fastboot@1.0::IFastboot
+ca62a2a95d173ed323309e5e00f653ad3cceec82a6e5e4976a249cb5aafe2515 android.hardware.neuralnetworks@1.2::types
+fa76bced6b1b71c40fc706c508a9011284c57f57831cd0cf5f45653ed4ea463e android.hardware.neuralnetworks@1.3::types
# There should be no more HIDL HALs - please use AIDL instead.
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f5b6ead..ccb0f30 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -80,7 +80,7 @@
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
*
- *{@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
+ * {@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
* The formula is:
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
index a26b858..9c3bbf7 100644
--- a/neuralnetworks/1.3/types.hal
+++ b/neuralnetworks/1.3/types.hal
@@ -41,7 +41,6 @@
* real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM_SIGNED = 14,
-
/**
* A reference to a subgraph.
*
@@ -5230,7 +5229,7 @@
* The output is calculated using the following formula:
*
* h-swish(x) = x * max(0, min(6, (x + 3))) / 6
-
+ *
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
index 12edc0f..dfda1d2 100644
--- a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
@@ -24,35 +24,30 @@
* Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
* with at least one dimension). Types not prefaced by TENSOR_* represent
* scalar values and must have no dimensions.
+ *
+ * Although we define many types, most operators accept just a few
+ * types. Most used are {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * and {@link OperandType::INT32}.
*/
@VintfStability
@Backing(type="int")
enum OperandType {
- /**
- * A 32 bit floating point scalar value.
- */
+ /** A 32 bit floating point scalar value. */
FLOAT32 = 0,
- /**
- * A signed 32 bit integer scalar value.
- */
+ /** A signed 32 bit integer scalar value. */
INT32 = 1,
- /**
- * An unsigned 32 bit integer scalar value.
- */
+ /** An unsigned 32 bit integer scalar value. */
UINT32 = 2,
- /**
- * A tensor of 32 bit floating point values.
- */
+ /** A tensor of 32 bit floating point values. */
TENSOR_FLOAT32 = 3,
- /**
- * A tensor of 32 bit integer values.
- */
+ /** A tensor of 32 bit integer values. */
TENSOR_INT32 = 4,
/**
* A tensor of 8 bit unsigned integers that represent real numbers.
*
- * Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the
- * real value and vice versa. These two numbers are:
+ * Attached to this tensor are two numbers that can be used to convert the
+ * 8 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit floating point value greater than zero.
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
@@ -63,15 +58,15 @@
/**
* An 8 bit boolean scalar value.
*
- * Values of this operand type are either true or false. A zero value represents false; any
- * other value represents true.
+ * Values of this operand type are either true or false. A zero value
+ * represents false; any other value represents true.
*/
BOOL = 6,
/**
* A tensor of 16 bit signed integers that represent real numbers.
*
- * Attached to this tensor is a number representing real value scale that is used to convert the
- * 16 bit number to a real value in the following way:
+ * Attached to this tensor is a number representing real value scale that is
+ * used to convert the 16 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
@@ -84,8 +79,8 @@
/**
* A tensor of 8 bit boolean values.
*
- * Values of this operand type are either true or false. A zero value represents false; any
- * other value represents true.
+ * Values of this operand type are either true or false. A zero value
+ * represents false; any other value represents true.
*/
TENSOR_BOOL8 = 9,
/**
@@ -95,8 +90,9 @@
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
- * This tensor is associated with additional fields that can be used to convert the 8 bit signed
- * integer to the real value and vice versa. These fields are:
+ * This tensor is associated with additional fields that can
+ * be used to convert the 8 bit signed integer to the real value and vice versa.
+ * These fields are:
* - channelDim: a 32 bit unsigned integer indicating channel dimension.
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
@@ -113,8 +109,8 @@
/**
* A tensor of 16 bit unsigned integers that represent real numbers.
*
- * Attached to this tensor are two numbers that can be used to convert the 16 bit integer to the
- * real value and vice versa. These two numbers are:
+ * Attached to this tensor are two numbers that can be used to convert the
+ * 16 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit floating point value greater than zero.
* - zeroPoint: a 32 bit integer, in range [0, 65535].
*
@@ -125,8 +121,8 @@
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
- * Attached to this tensor is a number representing real value scale that is used to convert the
- * 8 bit number to a real value in the following way:
+ * Attached to this tensor is a number representing real value scale that is
+ * used to convert the 8 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
@@ -135,8 +131,8 @@
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
- * Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the
- * real value and vice versa. These two numbers are:
+ * Attached to this tensor are two numbers that can be used to convert the
+ * 8 bit integer to the real value and vice versa. These two numbers are:
* - scale: a 32 bit floating point value greater than zero.
* - zeroPoint: a 32 bit integer, in range [-128, 127].
*
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
index e7fb90d..8585bda 100644
--- a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
@@ -78,6 +78,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = 0,
+
/**
* Performs a 2-D average pooling operation.
*
@@ -162,6 +163,7 @@
* the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = 1,
+
/**
* Concatenates the input tensors along the given dimension.
*
@@ -195,11 +197,13 @@
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
* Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint values can be different from
- * input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
+ * input tensors. Before HAL version 1.2 they have to be the same as for the
+ * input tensors.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
* the scale and zeroPoint values can be different from input tensors.
*/
CONCATENATION = 2,
+
/**
* Performs a 2-D convolution operation.
*
@@ -243,7 +247,8 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * Quantized signed with filter symmetric per channel quantization
+ * (since HAL version 1.3):
* * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -356,10 +361,12 @@
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
- * Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
- * the following condition must be satisfied: output_scale > input_scale * filter_scale
+ * Before HAL version 1.2, for output tensor of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition must
+ * be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = 3,
+
/**
* Performs a depthwise 2-D convolution operation.
*
@@ -407,7 +414,8 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * Quantized signed with filter symmetric per channel quantization
+ * (since HAL version 1.3):
* * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -521,6 +529,7 @@
* output_scale > input_scale * filter_scale
*/
DEPTHWISE_CONV_2D = 4,
+
/**
* Rearranges data from depth into blocks of spatial data.
*
@@ -566,6 +575,7 @@
* the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = 5,
+
/**
* Dequantizes the input tensor.
*
@@ -593,6 +603,7 @@
* * 0: A tensor with the same shape as input0.
*/
DEQUANTIZE = 6,
+
/**
* Looks up sub-tensors in the input tensor.
*
@@ -637,6 +648,7 @@
* the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = 7,
+
/**
* Computes element-wise floor() on the input tensor.
*
@@ -654,6 +666,7 @@
* the input tensor.
*/
FLOOR = 8,
+
/**
* Denotes a fully (densely) connected layer, which connects all elements
* in the input tensor with each element in the output tensor.
@@ -699,6 +712,7 @@
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
+
/**
* Looks up sub-tensors in the input tensor using a key-value map.
*
@@ -755,6 +769,7 @@
* A non-zero byte represents True, a hit. A zero indicates otherwise.
*/
HASHTABLE_LOOKUP = 10,
+
/**
* Applies L2 normalization along the axis dimension.
*
@@ -795,6 +810,7 @@
* are all zeros, the result is logical zero.
*/
L2_NORMALIZATION = 11,
+
/**
* Performs an 2-D L2 pooling operation.
*
@@ -873,6 +889,7 @@
* [batches, out_height, out_width, depth].
*/
L2_POOL_2D = 12,
+
/**
* Applies Local Response Normalization along the depth dimension.
*
@@ -927,6 +944,7 @@
* * 0: The output tensor of same shape as input0.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
+
/**
* Computes sigmoid activation on the input tensor element-wise.
*
@@ -954,6 +972,7 @@
* the scale must be 1.f / 256 and the zeroPoint must be -128.
*/
LOGISTIC = 14,
+
/**
* Projects an input to a bit vector via locality senstive hashing.
*
@@ -967,8 +986,8 @@
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
- * Tensor[0].Dim[0]: 15 of hash functions.
- * Tensor[0].Dim[1]: 16 of projected output bits generated by each
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of projected output bits generated by each
* hash function.
* If the projection type is Sparse:
* Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
@@ -1009,6 +1028,7 @@
* The offset value for sparse projections was added in HAL version 1.2.
*/
LSH_PROJECTION = 15,
+
/**
* Performs a single time step in a Long Short-Term Memory (LSTM) layer
*
@@ -1226,6 +1246,7 @@
* the same as the current “output state (out)” value.
*/
LSTM = 16,
+
/**
* Performs an 2-D max pooling operation.
*
@@ -1310,6 +1331,7 @@
* the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = 17,
+
/**
* Multiplies two tensors, element-wise.
*
@@ -1356,6 +1378,7 @@
* output_scale > input1_scale * input2_scale.
*/
MUL = 18,
+
/**
* Computes rectified linear activation on the input tensor element-wise.
*
@@ -1382,6 +1405,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RELU = 19,
+
/**
* Computes rectified linear 1 activation on the input tensor element-wise.
*
@@ -1408,6 +1432,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RELU1 = 20,
+
/**
* Computes rectified linear 6 activation on the input tensor element-wise.
*
@@ -1434,6 +1459,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RELU6 = 21,
+
/**
* Reshapes a tensor.
*
@@ -1466,6 +1492,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RESHAPE = 22,
+
/**
* Resizes images to given size using the bilinear interpretation.
*
@@ -1547,6 +1574,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RESIZE_BILINEAR = 23,
+
/**
* A basic recurrent neural network layer.
*
@@ -1598,6 +1626,7 @@
* the same as the current state value.
*/
RNN = 24,
+
/**
* Computes the softmax activation on the input tensor element-wise, per
* batch, by normalizing the input vector so the maximum coefficient is
@@ -1645,6 +1674,7 @@
* the scale must be 1.f / 256 and the zeroPoint must be -128.
*/
SOFTMAX = 25,
+
/**
* Rearranges blocks of spatial data, into depth.
*
@@ -1689,6 +1719,7 @@
* the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = 26,
+
/**
* SVDF op is a kind of stateful layer derived from the notion that a
* densely connected layer that's processing a sequence of input frames can
@@ -1765,6 +1796,7 @@
* [batch_size, num_units].
*/
SVDF = 27,
+
/**
* Computes hyperbolic tangent of input tensor element-wise.
*
@@ -1792,6 +1824,7 @@
* the scale must be 1.f / 128 and the zeroPoint must be 0.
*/
TANH = 28,
+
/**
* BatchToSpace for N-dimensional tensors.
*
@@ -1830,6 +1863,7 @@
* the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = 29,
+
/**
* Element-wise division of two tensors.
*
@@ -1880,6 +1914,7 @@
* * 0: A tensor of the same {@link OperandType} as input0.
*/
DIV = 30,
+
/**
* Computes the mean of elements across dimensions of a tensor.
*
@@ -1919,6 +1954,7 @@
* shape is [1].
*/
MEAN = 31,
+
/**
* Pads a tensor.
*
@@ -1960,6 +1996,7 @@
* Since HAL version 1.2, the pad value is always the logical zero.
*/
PAD = 32,
+
/**
* SpaceToBatch for N-Dimensional tensors.
*
@@ -2012,6 +2049,7 @@
* Since HAL version 1.2, the pad value is always the logical zero.
*/
SPACE_TO_BATCH_ND = 33,
+
/**
* Removes dimensions of size 1 from the shape of a tensor.
*
@@ -2047,6 +2085,7 @@
* output shape is [1].
*/
SQUEEZE = 34,
+
/**
* Extracts a strided slice of a tensor.
*
@@ -2097,6 +2136,7 @@
* shape is [1].
*/
STRIDED_SLICE = 35,
+
/**
* Element-wise subtraction of two tensors.
*
@@ -2147,6 +2187,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SUB = 36,
+
/**
* Transposes the input tensor, permuting the dimensions according to the
* perm tensor.
@@ -2177,6 +2218,7 @@
* the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = 37,
+
/**
* Computes the absolute value of a tensor, element-wise.
*
@@ -2194,6 +2236,7 @@
* * 0: The output tensor of same shape as input0.
*/
ABS = 38,
+
/**
* Returns the index of the largest element along an axis.
*
@@ -2216,7 +2259,10 @@
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
* If input is 1-dimensional, the output shape is [1].
*/
+ // There is no underscore in ARG_MAX to avoid name conflict with
+ // the macro defined in libc/kernel/uapi/linux/limits.h.
ARGMAX = 39,
+
/**
* Returns the index of the smallest element along an axis.
*
@@ -2239,7 +2285,8 @@
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
* If input is 1-dimensional, the output shape is [1].
*/
- ARGMIN = 40,
+ ARGMIN = 40, // See ARGMAX for naming discussion.
+
/**
* Transform axis-aligned bounding box proposals using bounding box deltas.
*
@@ -2286,6 +2333,7 @@
* scale must be 0.125 and the zero point must be 0.
*/
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+
/**
* A recurrent neural network layer that applies an LSTM cell to a
* sequence of inputs in forward and backward directions.
@@ -2560,6 +2608,7 @@
* Available since HAL version 1.3.
*/
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs in forward and backward directions.
@@ -2711,6 +2760,7 @@
* Available since HAL version 1.3.
*/
BIDIRECTIONAL_SEQUENCE_RNN = 43,
+
/**
* Greedily selects a subset of bounding boxes in descending order of score.
*
@@ -2794,6 +2844,7 @@
* with the same batch index are grouped together.
*/
BOX_WITH_NMS_LIMIT = 44,
+
/**
* Casts a tensor to a type.
*
@@ -2824,6 +2875,7 @@
* * 0: A tensor with the same shape as input0.
*/
CAST = 45,
+
/**
* Shuffle the channels of the input tensor.
*
@@ -2863,6 +2915,7 @@
* the scale and zeroPoint must be the same as input0.
*/
CHANNEL_SHUFFLE = 46,
+
/**
* Apply postprocessing steps to bounding box detections.
*
@@ -2942,6 +2995,7 @@
* specifying the number of valid output detections for each batch.
*/
DETECTION_POSTPROCESSING = 47,
+
/**
* For input tensors x and y, computes x == y elementwise.
*
@@ -2966,6 +3020,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
EQUAL = 48,
+
/**
* Computes exponential of x element-wise.
*
@@ -2982,6 +3037,7 @@
* * 0: The output tensor of same shape as input0.
*/
EXP = 49,
+
/**
* Inserts a dimension of 1 into a tensor's shape.
*
@@ -3012,6 +3068,7 @@
* the scale and zeroPoint must be the same as input0.
*/
EXPAND_DIMS = 50,
+
/**
* Gathers values along an axis.
*
@@ -3051,6 +3108,7 @@
* the scale and zeroPoint must be the same as input0.
*/
GATHER = 51,
+
/**
* Generate aixs-aligned bounding box proposals.
*
@@ -3132,6 +3190,7 @@
* with the same batch index are grouped together.
*/
GENERATE_PROPOSALS = 52,
+
/**
* For input tensors x and y, computes x > y elementwise.
*
@@ -3180,6 +3239,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
GREATER_EQUAL = 54,
+
/**
* Performs a grouped 2-D convolution operation.
*
@@ -3232,7 +3292,8 @@
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
- * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * Quantized signed with filter symmetric per channel quantization
+ * (since HAL version 1.3):
* * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -3329,6 +3390,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
GROUPED_CONV_2D = 55,
+
/**
* Localize the maximum keypoints from heatmaps.
*
@@ -3382,6 +3444,7 @@
* scale must be 0.125 and the zero point must be 0.
*/
HEATMAP_MAX_KEYPOINT = 56,
+
/**
* Applies instance normalization to the input tensor.
*
@@ -3432,6 +3495,7 @@
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
*/
INSTANCE_NORMALIZATION = 57,
+
/**
* For input tensors x and y, computes x < y elementwise.
*
@@ -3456,6 +3520,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LESS = 58,
+
/**
* For input tensors x and y, computes x <= y elementwise.
*
@@ -3480,6 +3545,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LESS_EQUAL = 59,
+
/**
* Computes natural logarithm of x element-wise.
*
@@ -3496,6 +3562,7 @@
* * 0: The output tensor of same shape as input0.
*/
LOG = 60,
+
/**
* Returns the truth value of x AND y element-wise.
*
@@ -3515,6 +3582,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LOGICAL_AND = 61,
+
/**
* Computes the truth value of NOT x element-wise.
*
@@ -3530,6 +3598,7 @@
* * 0: The output tensor of same shape as input0.
*/
LOGICAL_NOT = 62,
+
/**
* Returns the truth value of x OR y element-wise.
*
@@ -3549,6 +3618,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LOGICAL_OR = 63,
+
/**
* Computes the log softmax activations given logits.
*
@@ -3579,6 +3649,7 @@
* input0.
*/
LOG_SOFTMAX = 64,
+
/**
* Returns the element-wise maximum of two tensors.
*
@@ -3605,6 +3676,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MAXIMUM = 65,
+
/**
* Returns the element-wise minimum of two tensors.
*
@@ -3631,6 +3703,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MINIMUM = 66,
+
/**
* Computes numerical negative value element-wise.
*
@@ -3648,6 +3721,7 @@
* * 0: The output tensor of same shape as input0.
*/
NEG = 67,
+
/**
* For input tensors x and y, computes x != y elementwise.
*
@@ -3672,6 +3746,7 @@
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
NOT_EQUAL = 68,
+
/**
* Pads a tensor with the given constant value according to the specified
* paddings.
@@ -3716,6 +3791,7 @@
* the scale and zeroPoint must be the same as input0.
*/
PAD_V2 = 69,
+
/**
* Computes the power of one value to another.
*
@@ -3745,6 +3821,7 @@
* * 0: An output tensor.
*/
POW = 70,
+
/**
* Parametric Rectified Linear Unit.
*
@@ -3785,6 +3862,7 @@
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
*/
PRELU = 71,
+
/**
* Quantizes the input tensor.
*
@@ -3816,6 +3894,7 @@
* {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}.
*/
QUANTIZE = 72,
+
/**
* A version of quantized LSTM, using 16 bit quantization for internal
* state.
@@ -3920,6 +3999,7 @@
* (scale = 1/128, zeroPoint = 128).
*/
QUANTIZED_16BIT_LSTM = 73,
+
/**
* Draws samples from a multinomial distribution.
*
@@ -3940,6 +4020,7 @@
* [batches, samples], containing the drawn samples.
*/
RANDOM_MULTINOMIAL = 74,
+
/**
* Reduces a tensor by computing the "logical and" of elements along given
* dimensions.
@@ -3966,6 +4047,7 @@
* shape is [1].
*/
REDUCE_ALL = 75,
+
/**
* Reduces a tensor by computing the "logical or" of elements along given
* dimensions.
@@ -3992,6 +4074,7 @@
* shape is [1].
*/
REDUCE_ANY = 76,
+
/**
* Reduces a tensor by computing the maximum of elements along given
* dimensions.
@@ -4024,6 +4107,7 @@
* the scale and zeroPoint must be the same as input0.
*/
REDUCE_MAX = 77,
+
/**
* Reduces a tensor by computing the minimum of elements along given
* dimensions.
@@ -4056,6 +4140,7 @@
* the scale and zeroPoint must be the same as input0.
*/
REDUCE_MIN = 78,
+
/**
* Reduces a tensor by multiplying elements along given dimensions.
*
@@ -4082,6 +4167,7 @@
* shape is [1].
*/
REDUCE_PROD = 79,
+
/**
* Reduces a tensor by summing elements along given dimensions.
*
@@ -4108,6 +4194,7 @@
* shape is [1].
*/
REDUCE_SUM = 80,
+
/**
* Select and scale the feature map of each region of interest to a unified
* output size by average pooling sampling points from bilinear interpolation.
@@ -4169,6 +4256,7 @@
* the scale and zeroPoint can be different from the input0 scale and zeroPoint.
*/
ROI_ALIGN = 81,
+
/**
* Select and scale the feature map of each region of interest to a unified
* output size by max-pooling.
@@ -4222,6 +4310,7 @@
* the scale and zeroPoint must be the same as input0.
*/
ROI_POOLING = 82,
+
/**
* Computes reciprocal of square root of x element-wise.
*
@@ -4238,6 +4327,7 @@
* * 0: The output tensor of same shape as input0.
*/
RSQRT = 83,
+
/**
* Using a tensor of booleans c and input tensors x and y select values
* elementwise from both input tensors:
@@ -4270,6 +4360,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SELECT = 84,
+
/**
* Computes sin of x element-wise.
*
@@ -4286,6 +4377,7 @@
* * 0: The output tensor of same shape as input0.
*/
SIN = 85,
+
/**
* Extracts a slice of specified size from the input tensor starting at a
* specified location.
@@ -4321,6 +4413,7 @@
* its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
*/
SLICE = 86,
+
/**
* Splits a tensor along a given axis into num_splits subtensors.
*
@@ -4347,6 +4440,7 @@
* the scale and zeroPoint must be the same as input0.
*/
SPLIT = 87,
+
/**
* Computes square root of x element-wise.
*
@@ -4363,6 +4457,7 @@
* * 0: The output tensor of same shape as input0.
*/
SQRT = 88,
+
/**
* Constructs a tensor by tiling a given tensor.
*
@@ -4393,6 +4488,7 @@
* the scale and zeroPoint must be the same as input0.
*/
TILE = 89,
+
/**
* Finds values and indices of the k largest entries for the last dimension.
*
@@ -4423,6 +4519,7 @@
* containing the indices of values within the last dimension of input.
*/
TOPK_V2 = 90,
+
/**
* Performs the transpose of 2-D convolution operation.
*
@@ -4457,7 +4554,8 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * Quantized signed with filter symmetric per channel quantization
+ * (since HAL version 1.3):
* * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -4551,6 +4649,7 @@
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
TRANSPOSE_CONV_2D = 91,
+
/**
* A recurrent neural network specified by an LSTM cell.
*
@@ -4668,6 +4767,7 @@
* Available since HAL version 1.3.
*/
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs.
@@ -4726,6 +4826,7 @@
* Available since HAL version 1.3.
*/
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+
/**
* Resizes images to given size using the nearest neighbor interpretation.
*
@@ -4804,6 +4905,7 @@
* the scale and zeroPoint must be the same as input0.
*/
RESIZE_NEAREST_NEIGHBOR = 94,
+
/**
* Quantized version of {@link OperationType::LSTM}.
*
@@ -4932,6 +5034,7 @@
* Shape: [batchSize, outputSize]
*/
QUANTIZED_LSTM = 95,
+
/**
* Executes one of the two referenced subgraphs as determined by a boolean
* value.
@@ -4958,6 +5061,7 @@
* * 0 ~ (m - 1): Outputs produced by the selected subgraph.
*/
IF = 96,
+
/**
* Executes the body subgraph until the condition subgraph outputs false.
*
@@ -5024,6 +5128,7 @@
* * 0 ~ (m - 1): Outputs produced by the loop.
*/
WHILE = 97,
+
/**
* Computes exponential linear activation on the input tensor element-wise.
*
@@ -5049,6 +5154,7 @@
* * 0: The output tensor of same shape and type as input0.
*/
ELU = 98,
+
/**
* Computes hard-swish activation on the input tensor element-wise.
*
@@ -5076,6 +5182,7 @@
* tensor's parameters.
*/
HARD_SWISH = 99,
+
/**
* Creates a tensor filled with a scalar value.
*
@@ -5100,6 +5207,7 @@
* * 0: The output tensor.
*/
FILL = 100,
+
/**
* Returns the rank of a tensor.
*