tequilaOS/platform_hardware_interfaces
2
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Fix operations docs

Browse source 
am: fdf3c0363a

Change-Id: Ibb6c8aab16e379f295298b0f2ebb1e83cfdd8fc6
This commit is contained in:
Lev Proleev 2019-03-05 04:18:47 -08:00 committed by android-build-merger
commit 0775d27fe6
2 changed files with 61 additions and 31 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
current.txt
View file

@ -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

90
neuralnetworks/1.2/types.hal
View file

@ -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.