RPPT Tensor Operations - Statistical Operations.

RPPT Tensor Operations - Statistical Operations.#

RPP: RPPT Tensor Operations - Statistical Operations.

RPPT Tensor Operations - Statistical Operations. More...

Functions
RppStatus	rppt_tensor_sum (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t tensorSumArr, Rpp32u tensorSumArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Tensor sum operation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_tensor_min (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t minArr, Rpp32u minArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Tensor min operation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_tensor_max (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t maxArr, Rpp32u maxArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Tensor max operation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_normalize (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u axisMask, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp8u computeMeanStddev, Rpp32f scale, Rpp32f shift, Rpp32u *roiTensor, rppHandle_t rppHandle, RppBackend executionBackend)
	Normalize Generic augmentation on HIP/HOST backend. More...

RppStatus	rppt_tensor_mean (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t tensorMeanArr, Rpp32u tensorMeanArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Tensor mean operation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_tensor_stddev (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t tensorStddevArr, Rpp32u tensorStddevArrLength, Rpp32f *meanTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Tensor stddev operation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_threshold (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f minTensor, Rpp32f maxTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Threshold augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

Detailed Description

RPPT Tensor Operations - Statistical Operations.

Function Documentation

◆ rppt_normalize()

RppStatus rppt_normalize	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u	axisMask,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp8u	computeMeanStddev,
		Rpp32f	scale,
		Rpp32f	shift,
		Rpp32u *	roiTensor,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Normalize Generic augmentation on HIP/HOST backend.

Normalizes the input generic ND buffer by removing the mean and dividing by the standard deviation for a given ND Tensor. Supports u8->f32, i8->f32, f16->f16 and f32->f32 datatypes. Also has toggle variant(NHWC->NCHW) support for 3D.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	axisMask	axis along which normalization needs to be done
[in]	meanTensor	values to be subtracted from input (in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend))
[in]	stdDevTensor	standard deviation values to scale the input (in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend))
[in]	computeMeanStddev	flag to represent internal computation of mean, stddev (Wherein 0th bit used to represent computeMean and 1st bit for computeStddev, 0- Externally provided)
[in]	scale	value to be multiplied with data after subtracting from mean
[in]	shift	value to be added finally
[in]	roiTensor	values to represent dimensions of input tensor (in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend))
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_max()

RppStatus rppt_tensor_max	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	maxArr,
		Rpp32u	maxArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Tensor max operation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The tensor max is a reduction operation that finds the channel-wise (R max / G max / B max) and overall max for each image in a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	maxArr	destination array in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	maxArrLength	length of provided destination array (Restrictions - if srcDescPtr->c == 1 then maxArrLength >= srcDescPtr->n, and if srcDescPtr->c == 3 then maxArrLength >= srcDescPtr->n * 4)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorPtrSrc[i].xywhROI.roiWidth <= 3840 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 2160)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_mean()

RppStatus rppt_tensor_mean	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	tensorMeanArr,
		Rpp32u	tensorMeanArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Tensor mean operation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The tensor mean is a reduction operation that finds the channel-wise (R mean / G mean / B mean) and total mean for each image in a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	tensorMeanArr	destination array in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	tensorMeanArrLength	length of provided destination array (Restrictions - if srcDescPtr->c == 1 then tensorMeanArrLength = srcDescPtr->n, and if srcDescPtr->c == 3 then tensorMeanArrLength = srcDescPtr->n * 4)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorPtrSrc[i].xywhROI.roiWidth <= 3840 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 2160)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_min()

RppStatus rppt_tensor_min	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	minArr,
		Rpp32u	minArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Tensor min operation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The tensor min is a reduction operation that finds the channel-wise (R min / G min / B min) and overall min for each image in a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	minArr	destination array in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	minArrLength	length of provided destination array (Restrictions - if srcDescPtr->c == 1 then minArrLength >= srcDescPtr->n, and if srcDescPtr->c == 3 then minArrLength >= srcDescPtr->n * 4)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorPtrSrc[i].xywhROI.roiWidth <= 3840 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 2160)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_stddev()

RppStatus rppt_tensor_stddev	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	tensorStddevArr,
		Rpp32u	tensorStddevArrLength,
		Rpp32f *	meanTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Tensor stddev operation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The tensor stddev is a reduction operation that finds the channel-wise (R stddev / G stddev / B stddev) and total standard deviation for each image with respect to meanTensor passed.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	tensorStddevArr	destination array in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	tensorStddevArrLength	length of provided destination array (Restrictions - if srcDescPtr->c == 1 then tensorStddevArrLength = srcDescPtr->n, and if srcDescPtr->c == 3 then tensorStddevArrLength = srcDescPtr->n * 4)
[in]	meanTensor	mean values for stddev calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize * 4 in format (MeanR, MeanG, MeanB, MeanImage) for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorPtrSrc[i].xywhROI.roiWidth <= 3840 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 2160)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_sum()

RppStatus rppt_tensor_sum	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	tensorSumArr,
		Rpp32u	tensorSumArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Tensor sum operation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The tensor sum is a reduction operation that finds the channel-wise (R sum / G sum / B sum) and total sum for each image in a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	tensorSumArr	destination array in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	tensorSumArrLength	length of provided destination array (Restrictions - if srcDescPtr->c == 1 then tensorSumArrLength >= srcDescPtr->n, and if srcDescPtr->c == 3 then tensorSumArrLength >= srcDescPtr->n * 4)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorPtrSrc[i].xywhROI.roiWidth <= 3840 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 2160)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	execution backend to run the operation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_threshold()

RppStatus rppt_threshold	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	minTensor,
		Rpp32f *	maxTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Threshold augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The Threshold augmentation outputs a black/white binary mask image, based on whether or not each pixel is within the user-specified pixel-range bounds, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.
Note: Returns a black image for below 2 cases:

If the minimum cutoff value greater than the maximum cutoff value for the given input in a batch.

Values provided for minimum cutoff value, maximum cutoff value are beyond the below specified min and max values.
Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	minTensor	minimum cutoff value (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize * channels) - minTensor ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).
[in]	maxTensor	maximum cutoff value (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize * channels) - maxTensor ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreateWithBatchSize()`
[in]	executionBackend	execution backend to run the augmentation on (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

RPPT Tensor Operations - Statistical Operations.

RPPT Tensor Operations - Statistical Operations.#

Functions

Detailed Description

Function Documentation

◆ rppt_normalize()

◆ rppt_tensor_max()

◆ rppt_tensor_mean()

◆ rppt_tensor_min()

◆ rppt_tensor_stddev()

◆ rppt_tensor_sum()

◆ rppt_threshold()