Other accelerated functions used in Synet Framework. More...

Functions
SIMD_API void	SimdSynetAddBias (const float bias, size_t channels, size_t spatial, float dst, SimdTensorFormatType format)
	Adds a bias to given vector. More...

SIMD_API void	SimdSynetAdd8i (const uint8_t aData, const float aScale, const float aShift, const uint8_t bData, const float bScale, const float bShift, uint8_t cData, const float cScale, const float *cShift, size_t batch, size_t channels, size_t spatial, SimdTensorFormatType format, SimdSynetCompatibilityType compatibility)
	Adds two INT8 tensors. More...

SIMD_API void	SimdSynetEltwiseLayerForward (float const const src, const float weight, size_t count, size_t size, SimdSynetEltwiseOperationType type, float dst)
	This function is used for forward propagation of EltwiseLayer. More...

SIMD_API void	SimdSynetLrnLayerCrossChannels (const float src, size_t half, size_t channels, size_t spatial, const float k, float *dst, SimdTensorFormatType format)
	This function is used for forward propagation of LrnLayer (cross channels normalization). More...

SIMD_API void	SimdSynetShuffleLayerForward (const float src0, const float src1, size_t channels0, size_t channels1, size_t spatial, float dst0, float dst1, SimdTensorFormatType format, int type)
	This function is used for forward propagation of ShuffleLayer. More...

SIMD_API void	SimdSynetSoftmaxLayerForward (const float src, size_t outer, size_t count, size_t inner, float dst)
	This function is used for forward propagation of SoftmaxLayer. More...

SIMD_API void	SimdSynetUnaryOperation32f (const float src, size_t size, SimdSynetUnaryOperation32fType type, float dst)
	This function is used for forward propagation of UnaryOperationLayer. More...

Detailed Description

Other accelerated functions used in Synet Framework.

Function Documentation

◆ SimdSynetAddBias()

void SimdSynetAddBias	(	const float *	bias,
		size_t	channels,
		size_t	spatial,
		float *	dst,
		SimdTensorFormatType	format
	)

Adds a bias to given vector.

Algorithm's details (example for NCHW tensor format):

for(c = 0; c < channels; ++c)
    for(j = 0; j < spatial; ++j)
         dst[c*spatial + s] += bias[c];

Note: This function is used in Synet Framework.

Parameters

[in]	bias	- a pointer to the 32-bit float array with bias coefficients. The size of the array is eqial to channels.
[in]	channels	- a number of channels in the image tensor.
[in]	spatial	- a spatial size of image tensor.
[in,out]	dst	- a pointer to cumulative 32-bit image tensor. The size of the array is equal to channels * spatial.
[in]	format	- a format of image tensor.

◆ SimdSynetAdd8i()

void SimdSynetAdd8i	(	const uint8_t *	aData,
		const float *	aScale,
		const float *	aShift,
		const uint8_t *	bData,
		const float *	bScale,
		const float *	bShift,
		uint8_t *	cData,
		const float *	cScale,
		const float *	cShift,
		size_t	batch,
		size_t	channels,
		size_t	spatial,
		SimdTensorFormatType	format,
		SimdSynetCompatibilityType	compatibility
	)

Adds two INT8 tensors.

Algorithm's details (example for NCHW tensor format):

for(b = 0; b < batch; ++b)
    for(c = 0; c < channels; ++c)
        for(s = 0; s < spatial; ++s)
        {
             offs = (b*channels + c)*spatial + s;
             A = aData[offs]*aScale[c] + aShift[c]; 
             B = bData[offs]*bScale[c] + bShift[c];
             cData[offs] = round((A + B)*cScale[c] + cShift[c]);
        }

Note: This function is used in Synet Framework.

Parameters

[in]	aData	- a pointer to the first input 8-bit integer tensor.
[in]	aScale	- a pointer to the 32-bit float array with scale coefficients of the first input tensor.
[in]	aShift	- a pointer to the 32-bit float array with shift coefficients of the first input tensor.
[in]	bData	- a pointer to the second input 8-bit integer tensor.
[in]	bScale	- a pointer to the 32-bit float array with scale coefficients of the second input tensor.
[in]	bShift	- a pointer to the 32-bit float array with shift coefficients of the second input tensor.
[out]	cData	- a pointer to the output 8-bit integer tensor.
[in]	cScale	- a pointer to the 32-bit float array with scale coefficients of the output tensor.
[in]	cShift	- a pointer to the 32-bit float array with shift coefficients of the output tensor.
[in]	batch	- a batch size of input and output image tensors.
[in]	channels	- a number of channels in input and output image tensors.
[in]	spatial	- a spatial size of input and output image tensors.
[in]	format	- a format of input and output image tensors.
[in]	compatibility	- a flags of calculation compatibility.

◆ SimdSynetEltwiseLayerForward()

void SimdSynetEltwiseLayerForward	(	float const const	src,
		const float *	weight,
		size_t	count,
		size_t	size,
		SimdSynetEltwiseOperationType	type,
		float *	dst
	)

This function is used for forward propagation of EltwiseLayer.

Algorithm's details for SimdSynetEltwiseOperationProduct:

for(j = 0; j < size; ++j)
    dst[j] = 1;
for(i = 0; i < count; ++i)
    for(j = 0; j < size; ++j)
        dst[j] *= src[i][j];

Algorithm's details for SimdSynetEltwiseOperationSum:

for(j = 0; j < size; ++j)
    dst[j] = 0;
for(i = 0; i < count; ++i)
    for(j = 0; j < size; ++j)
        dst[j] += src[i][j]*weight[i];

Algorithm's details for SimdSynetEltwiseOperationMax:

for(j = 0; j < size; ++j)
    dst[j] = -FLT_MAX;
for(i = 0; i < count; ++i)
    for(j = 0; j < size; ++j)
        dst[j] = Max(dst[j], src[i][j]);

Algorithm's details for SimdSynetEltwiseOperationMin:

for(j = 0; j < size; ++j)
    dst[j] = FLT_MAX;
for(i = 0; i < count; ++i)
    for(j = 0; j < size; ++j)
        dst[j] = Min(dst[j], src[i][j]);

Note: This function is used in Synet Framework.

Parameters

[in]	src	- a pointer to poitres to the input 32-bit float arrays.
[in]	weight	- a pointer to the 32-bit float array with sum coefficients. It is need only for SimdSynetEltwiseOperationSum operation type otherwise it can be NULL.
[in]	count	- a count of input arrays. Must be at least 2.
[in]	size	- a size of the input and output arrays.
[in]	type	- a type of operation (see SimdSynetEltwiseOperationType).
[out]	dst	- a pointer to the output 32-bit float array.

◆ SimdSynetLrnLayerCrossChannels()

void SimdSynetLrnLayerCrossChannels	(	const float *	src,
		size_t	half,
		size_t	channels,
		size_t	spatial,
		const float *	k,
		float *	dst,
		SimdTensorFormatType	format
	)

This function is used for forward propagation of LrnLayer (cross channels normalization).

Algorithm's details (example for NCHW tensor format):

for(c = 0; c < channels; ++c)
    for(s = 0; s < spatial; ++s)
    {
        lo = Max(0, c - half);
        hi = Min(channels, c + half + 1);
        sum = 0;
        for(i = lo; i < ln; ++i)
            sum += Square(src[i*spatial + s]);
        dst[c*spatial + s] = src[c*spatial + s]*Pow(k[0] + sum*k[1], k[2]);
    }

Note: This function is used in Synet Framework.

Parameters

[in]	src	- a pointer to the 32-bit float array with input image tensor. The size of the array is equal to channels * spatial.
[in]	half	- a local normalization half size.
[in]	channels	- a number of channels in the (input/output) image tensor
[in]	spatial	- a spatial size of (input/output) image tensor.
[in]	k	- a pointer to the 32-bit float array with 3 coefficients (see algorithm details).
[out]	dst	- a pointer to the 32-bit float array with output image tensor. The size of the array is equal to channels * spatial.
[in]	format	- a format of (input/output) image tensor.

◆ SimdSynetShuffleLayerForward()

void SimdSynetShuffleLayerForward	(	const float *	src0,
		const float *	src1,
		size_t	channels0,
		size_t	channels1,
		size_t	spatial,
		float *	dst0,
		float *	dst1,
		SimdTensorFormatType	format,
		int	type
	)

This function is used for forward propagation of ShuffleLayer.

Note: This function is used in Synet Framework.

Parameters

[in]	src0	- a pointer to the 32-bit float array with the first input image tensor.
[in]	src1	- a pointer to the 32-bit float array with the second input image tensor.
[in]	channels0	- a number of channels in the first input (type == 0) or output (type == 1) image tensor. It must be even number.
[in]	channels1	- a number of channels in the second input (type == 0) or output (type == 1) image tensor. It must be even number.
[in]	spatial	- a spatial size of (input/output) image tensors.
[out]	dst0	- a pointer to the 32-bit float array with the first output image tensor.
[out]	dst1	- a pointer to the 32-bit float array with the second output image tensor.
[in]	format	- a format of (input/output) image tensors.
[in]	type	- a shuffle type (it can be 0 or 1).

◆ SimdSynetSoftmaxLayerForward()

void SimdSynetSoftmaxLayerForward	(	const float *	src,
		size_t	outer,
		size_t	count,
		size_t	inner,
		float *	dst
	)

This function is used for forward propagation of SoftmaxLayer.

Note: This function is used in Synet Framework.

Parameters

[in]	src	- a pointer to the input 32-bit float array. The size of the array must be equal to outercountinner.
[in]	outer	- an outer size of input and output arrays.
[in]	count	- a size of softmax dimmension.
[in]	inner	- an inner size of input and output arrays.
[out]	dst	- a pointer to the output 32-bit float array. The size of the array must be equal to outercountinner.

◆ SimdSynetUnaryOperation32f()

void SimdSynetUnaryOperation32f	(	const float *	src,
		size_t	size,
		SimdSynetUnaryOperation32fType	type,
		float *	dst
	)

This function is used for forward propagation of UnaryOperationLayer.

Note: This function is used in Synet Framework.

Parameters

[in]	src	- a pointer to poitres to the input 32-bit float arrays.
[in]	size	- a size of the input and output arrays.
[in]	type	- an unary operation type (see SimdSynetUnaryOperation32fType).
[out]	dst	- a pointer to the output 32-bit float array.

Simd Library Documentation.

Functions

Detailed Description

Function Documentation

◆ SimdSynetAddBias()

◆ SimdSynetAdd8i()

◆ SimdSynetEltwiseLayerForward()

◆ SimdSynetLrnLayerCrossChannels()

◆ SimdSynetShuffleLayerForward()

◆ SimdSynetSoftmaxLayerForward()

◆ SimdSynetUnaryOperation32f()