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Commit efc791ed authored by Lev Proleev's avatar Lev Proleev Committed by android-build-merger
Browse files

Fix operations docs am: fdf3c036 

am: 0775d27f

Change-Id: Id7027a64a93e8be6ca8b494c14f04a45c74260b4
parents 7224bc9b 0775d27f

current.txt

+1 −1
Original line number Diff line number Diff line
@@ -449,7 +449,7 @@ dd1ec219f5d2e2b33c6c0bcb92e63bbedb36f7c716413462848f6b6ae74fc864 android.hardwar 
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback

83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel

e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback

769f8650631eef7a3ceedc8cf130f4b99eb52fe698a11609d55de32985a3dddf android.hardware.neuralnetworks@1.2::types

c752cff336d86762c26dc82e7e037f4962b815b1a068d2319d40a3d068e26f68 android.hardware.neuralnetworks@1.2::types

cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc

abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types

4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats

neuralnetworks/1.2/types.hal

+60 −30
Original line number Diff line number Diff line
@@ -218,6 +218,7 @@ enum OperationType : int32_t { 
     *         ) / sum(1)

     *

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)

     * * {@link OperandType::TENSOR_FLOAT32}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM}

     *
@@ -333,7 +334,7 @@ enum OperationType : int32_t { 
     *         ) + bias[channel]

     *

     * Supported tensor {@link OperandType} configurations:

     * * 32 bit Floating point :

     * * 32 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.

     *

     * * Quantized:
@@ -342,15 +343,15 @@ enum OperationType : int32_t { 
     * * * input.scale * filter.scale).

     *

     * Available since API level 29:

     * * 16 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     *

     * * Quantized with symmetric per channel quantization for the filter:

     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.

     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.

     * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,

     * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).

     *

     * * 16 bit Floating point:

     * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     *

     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.

     * With the default data layout NHWC, the data is stored in the order of:

     * [batch, height, width, channels]. Alternatively, the data layout could
@@ -482,7 +483,7 @@ enum OperationType : int32_t { 
     *         ) + bias[k * channel_multiplier + q]

     *

     * Supported tensor {@link OperandType} configurations:

     * * 32 bit Floating point :

     * * 32 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.

     *

     * * Quantized:
@@ -491,6 +492,9 @@ enum OperationType : int32_t { 
     * * * input.scale * filter.scale).

     *

     * Available since API level 29:

     * * 16 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     *

     * * Quantized with symmetric per channel quantization for the filter:

     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.

     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
@@ -1010,6 +1014,7 @@ enum OperationType : int32_t { 
     *     output = 1 / (1 + exp(-input))

     *

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)

     * * {@link OperandType::TENSOR_FLOAT32}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM}

     *
@@ -1315,6 +1320,7 @@ enum OperationType : int32_t { 
     *         )

     *

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)

     * * {@link OperandType::TENSOR_FLOAT32}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM}

     *
@@ -1623,6 +1629,7 @@ enum OperationType : int32_t { 
     * independently on each 1-D slice along specified dimension.

     *

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)

     * * {@link OperandType::TENSOR_FLOAT32}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM}

     *
@@ -1631,8 +1638,12 @@ enum OperationType : int32_t { 
     *

     * Inputs:

     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.

     * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive

     *      scaling factor for the exponent, beta.

     * * 1: A scalar, specifying the positive scaling factor for the exponent,

     *      beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or

     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of

     *      {@link OperandType::FLOAT32}. If input0 is of {@link

     *      OperandType::TENSOR_FLOAT16}, then the scalar must be of {@link

     *      OperandType::FLOAT16}.

     * * 2: An optional {@link OperandType::INT32} scalar, default to -1,

     *      specifying the dimension the activation would be performed on.

     *      Negative index is used to specify axis from the end (e.g. -1 for
@@ -2706,11 +2717,17 @@ enum OperationType : int32_t { 
     * * 10: An {@link OperandType::INT32} scalar, only used when input7 is

     *       set to true, specifying the maximum number of detections when

     *       applying NMS algorithm for each single class.

     * * 11: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes

     *       with scores lower than the threshold are filtered before sending

     *       to the NMS algorithm.

     * * 12: An {@link OperandType::FLOAT32} scalar, specifying the IoU

     *       threshold for hard NMS.

     * * 11: A scalar, score_threshold. Boxes with scores lower than the

     *       threshold are filtered before sending to the NMS algorithm. The

     *       scalar must be of {@link OperandType::FLOAT16} if input0 is of

     *       {@link OperandType::TENSOR_FLOAT16} and of {@link

     *       OperandType::FLOAT32} if input0 is of {@link

     *       OperandType::TENSOR_FLOAT32}.

     * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar

     *       must be of {@link OperandType::FLOAT16} if input0 is of {@link

     *       OperandType::TENSOR_FLOAT16} and of {@link

     *       OperandType::FLOAT32} if input0 is of {@link

     *       OperandType::TENSOR_FLOAT32}.

     * * 13: An {@link OperandType::BOOL} scalar, set to true to include

     *       background class in the list of label map for the output, set

     *       to false to not include the background. When the background
@@ -3007,11 +3024,11 @@ enum OperationType : int32_t { 
     * where channel_multiplier = depth_out / num_groups

     *

     * Supported tensor {@link OperandType} configurations:

     * * 32 bit Floating point :

     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.

     * * 16 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     *

     * * 16 bit Floating point:

     * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     * * 32 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.

     *

     * * Quantized:

     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
@@ -3188,12 +3205,21 @@ enum OperationType : int32_t { 
     *

     * Inputs:

     * * 0: An n-D tensor, specifying the tensor to be normalized.

     * * 1: An {@link OperandType::FLOAT32} scalar, specifying gamma, the

     *      scale applied to the normalized tensor.

     * * 2: An {@link OperandType::FLOAT32} scalar, specifying beta, the

     *      offset applied to the normalized tensor.

     * * 3: An {@link OperandType::FLOAT32} scalar, specifying epsilon, the

     *      small value added to variance to avoid dividing by zero.

     * * 1: A scalar, specifying gamma, the scale applied to the normalized

     *      tensor. The scalar must be of {@link OperandType::FLOAT16} if

     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link

     *      OperandType::FLOAT32} if input0 is of {@link

     *      OperandType::TENSOR_FLOAT32}.

     * * 2: A scalar, specifying beta, the offset applied to the normalized

     *      tensor. The scalar must be of {@link OperandType::FLOAT16} if

     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link

     *      OperandType::FLOAT32} if input0 is of {@link

     *      OperandType::TENSOR_FLOAT32}.

     * * 3: A scalar, specifying epsilon, the small value added to variance to

     *      avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if

     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link

     *      OperandType::FLOAT32} if input0 is of {@link

     *      OperandType::TENSOR_FLOAT32}.

     * * 4: An {@link OperandType::BOOL} scalar, set to true to specify

     *      NCHW data layout for input0 and output0. Set to false for NHWC.

     *
@@ -3475,10 +3501,12 @@ enum OperationType : int32_t { 
     *      padding[i, 1] specifies the number of elements to be padded after

     *      the end of dimension i.

     * * 2: An scalar specifying the value to use for padding input0.

     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the

     *      pad value must be of {@link OperandType::FLOAT16}.

     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the

     *      pad value should be of {@link OperandType::FLOAT32}.

     *      pad value must be of {@link OperandType::FLOAT32}.

     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},

     *      the pad value should be of {@link OperandType::INT32}. The

     *      the pad value must be of {@link OperandType::INT32}. The

     *      scale and zeroPoint are assumed to be the same as in input0.

     *

     * Outputs:
@@ -3627,25 +3655,25 @@ enum OperationType : int32_t { 
     *      weights.

     * * 5: The recurrent-to-input weights.

     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}

     *      and shape [outputSize, inputSize] specifying recurrent-to-input part

     *      and shape [outputSize, outputSize] specifying recurrent-to-input part

     *      of weights for fully-connected layer inside the LSTM cell.

     *      Quantization zero point and scale must be the same across all the

     *      weights.

     * * 6: The recurrent-to-forget weights.

     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}

     *      and shape [outputSize, inputSize] specifying recurrent-to-forget

     *      and shape [outputSize, outputSize] specifying recurrent-to-forget

     *      part of weights for fully-connected layer inside the LSTM cell.

     *      Quantization zero point and scale must be the same across all the

     *      weights.

     * * 7: The recurrent-to-cell weights.

     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}

     *      and shape [outputSize, inputSize] specifying recurrent-to-cell part

     *      and shape [outputSize, outputSize] specifying recurrent-to-cell part

     *      of weights for fully-connected layer inside the LSTM cell.

     *      Quantization zero point and scale must be the same across all the

     *      weights.

     * * 8: The recurrent-to-output weights.

     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}

     *      and shape [outputSize, inputSize] specifying recurrent-to-output

     *      and shape [outputSize, outputSize] specifying recurrent-to-output

     *      part of weights for fully-connected layer inside the LSTM cell.

     *      Quantization zero point and scale must be the same across all the

     *      weights.
@@ -4205,7 +4233,10 @@ enum OperationType : int32_t { 
     * padding.

     *

     * Supported tensor {@link OperandCode} configurations:

     * * 32 bit Floating point :

     * * 16 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.

     *

     * * 32 bit floating point:

     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.

     *

     * * Quantized:
@@ -4213,7 +4244,6 @@ enum OperationType : int32_t { 
     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to

     * * * input.scale * filter.scale).

     *

     * Available since API level 29:

     * * Quantized with symmetric per channel quantization for the filter:

     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.

     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.