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

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

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

RppStatus	rppt_tensor_min_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t imageMinArr, Rpp32u imageMinArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Tensor min operation on HIP backend for a NCHW/NHWC layout tensor. More...

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

RppStatus	rppt_tensor_max_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t imageMaxArr, Rpp32u imageMaxArrLength, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Tensor max operation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_normalize_host (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)
	Normalize Generic augmentation on HOST backend. More...

RppStatus	rppt_normalize_gpu (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)
	Normalize Generic augmentation on HIP backend. More...

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

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

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

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

Detailed Description

RPPT Tensor Operations - Statistical Operations.

Function Documentation

◆ rppt_normalize_gpu()

RppStatus rppt_normalize_gpu	(	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
	)

Normalize Generic augmentation on HIP backend.

Normalizes the input generic ND buffer by removing the mean and dividing by the standard deviation for a given ND Tensor.

Parameters

[in]	srcPtr	source tensor memory in HIP memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor memory in HIP memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	axisMask	axis along which normalization needs to be done
[in]	meanTensor	values to be subtracted from input
[in]	stdDevTensor	standard deviation values to scale the input
[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]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_normalize_host()

RppStatus rppt_normalize_host	(	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
	)

Normalize Generic augmentation on 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 memory in HOST memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor memory in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	axisMask	axis along which normalization needs to be done
[in]	meanTensor	values to be subtracted from input
[in]	stdDevTensor	standard deviation values to scale the input
[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]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_max_gpu()

RppStatus rppt_tensor_max_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	imageMaxArr,
		Rpp32u	imageMaxArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Tensor max operation on HIP 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
[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
[in]	maxArrLength	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 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 handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_max_host()

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

Tensor max operation on 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 HOST memory
[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 HOST memory
[in]	maxArrLength	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 HOST memory, 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 HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_mean_gpu()

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

Tensor mean operation on HIP 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
[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
[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 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 handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_mean_host()

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

Tensor mean operation on 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 HOST memory
[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 HOST memory
[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 HOST memory, 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 HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_min_gpu()

RppStatus rppt_tensor_min_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	imageMinArr,
		Rpp32u	imageMinArrLength,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Tensor min operation on HIP 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
[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
[in]	minArrLength	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 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 handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_min_host()

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

Tensor min operation on 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 HOST memory
[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 HOST memory
[in]	minArrLength	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 HOST memory, 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 HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_stddev_gpu()

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

Tensor stddev operation on HIP 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
[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
[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 of size batchSize * 4 in format (MeanR, MeanG, MeanB, MeanImage) for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory, 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 handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_stddev_host()

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

Tensor stddev operation on 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 HOST memory
[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 HOST memory
[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 of size batchSize * 4 in format (MeanR, MeanG, MeanB, MeanImage) for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HOST memory, 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 HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_sum_gpu()

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

Tensor sum operation on HIP 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
[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
[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 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 handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_tensor_sum_host()

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

Tensor sum operation on 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 HOST memory
[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 HOST memory
[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 HOST memory, 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 HOST handle created with `rppCreateWithBatchSize()`

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_gpu()

◆ rppt_normalize_host()

◆ rppt_tensor_max_gpu()

◆ rppt_tensor_max_host()

◆ rppt_tensor_mean_gpu()

◆ rppt_tensor_mean_host()

◆ rppt_tensor_min_gpu()

◆ rppt_tensor_min_host()

◆ rppt_tensor_stddev_gpu()

◆ rppt_tensor_stddev_host()

◆ rppt_tensor_sum_gpu()

◆ rppt_tensor_sum_host()