blas.cpp File Reference

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Functions
void	axpy_cpu (int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
void	backward_shortcut_multilayer_cpu (int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalization)
int	check_sim (size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
void	const_cpu (int N, float ALPHA, float *X, int INCX)
void	constrain_cpu (int size, float ALPHA, float *X)
void	copy_cpu (int N, float *X, int INCX, float *Y, int INCY)
float	cosine_similarity (float *A, float *B, unsigned int feature_size)
void	deinter_cpu (int NX, float *X, int NY, float *Y, int B, float *OUT)
float	dot_cpu (int N, float *X, int INCX, float *Y, int INCY)
void	fill_cpu (int N, float ALPHA, float *X, int INCX)
float	find_P_constrastive (size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
float	find_sim (size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
void	fix_nan_and_inf_cpu (float *input, size_t size)
void	flatten (float *x, int size, int layers, int batch, int forward)
void	get_embedding (float *src, int src_w, int src_h, int src_c, int embedding_size, int cur_w, int cur_h, int cur_n, int cur_b, float *dst)
int	get_sim_P_index (size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
void	grad_contrastive_loss_negative (size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh)
void	grad_contrastive_loss_negative_f (size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size, int neg_max)
void	grad_contrastive_loss_positive (size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh)
void	grad_contrastive_loss_positive_f (size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size)
void	inter_cpu (int NX, float *X, int NY, float *Y, int B, float *OUT)
void	l1_cpu (int n, float *pred, float *truth, float *delta, float *error)
void	l2_cpu (int n, float *pred, float *truth, float *delta, float *error)
void	logistic_x_ent_cpu (int n, float *pred, float *truth, float *delta, float *error)
float	math_vector_length (float *A, unsigned int feature_size)
void	mean_cpu (float *x, int batch, int filters, int spatial, float *mean)
void	mul_cpu (int N, float *X, int INCX, float *Y, int INCY)
void	mult_add_into_cpu (int N, float *X, float *Y, float *Z)
void	normalize_cpu (float *x, float *mean, float *variance, int batch, int filters, int spatial)
float	P_constrastive (size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim)
float	P_constrastive_f (size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size)
float	P_constrastive_f_det (size_t il, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size)
void	pow_cpu (int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
static float	relu (float src)
void	reorg_cpu (float *x, int out_w, int out_h, int out_c, int batch, int stride, int forward, float *out)
void	scal_add_cpu (int N, float ALPHA, float BETA, float *X, int INCX)
void	scal_cpu (int N, float ALPHA, float *X, int INCX)
void	shortcut_cpu (int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
void	shortcut_multilayer_cpu (int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization)
void	smooth_l1_cpu (int n, float *pred, float *truth, float *delta, float *error)
void	softmax (float *input, int n, float temp, float *output, int stride)
void	softmax_cpu (float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
void	softmax_x_ent_cpu (int n, float *pred, float *truth, float *delta, float *error)
void	upsample_cpu (float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
void	variance_cpu (float *x, float *mean, int batch, int filters, int spatial, float *variance)
void	weighted_delta_cpu (float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc)
void	weighted_sum_cpu (float *a, float *b, float *s, int n, float *c)

Function Documentation

axpy_cpu()

void axpy_cpu	(	int	N,
		float	ALPHA,
		float *	X,
		int	INCX,
		float *	Y,
		int	INCY
	)

backward_shortcut_multilayer_cpu()

void backward_shortcut_multilayer_cpu	(	int	size,
		int	src_outputs,
		int	batch,
		int	n,
		int *	outputs_of_layers,
		float **	layers_delta,
		float *	delta_out,
		float *	delta_in,
		float *	weights,
		float *	weight_updates,
		int	nweights,
		float *	in,
		float **	layers_output,
		WEIGHTS_NORMALIZATION_T	weights_normalization
	)

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check_sim()

int check_sim	(	size_t	i,
		size_t	j,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

const_cpu()

void const_cpu	(	int	N,
		float	ALPHA,
		float *	X,
		int	INCX
	)

constrain_cpu()

void constrain_cpu	(	int	size,
		float	ALPHA,
		float *	X
	)

copy_cpu()

void copy_cpu	(	int	N,
		float *	X,
		int	INCX,
		float *	Y,
		int	INCY
	)

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cosine_similarity()

float cosine_similarity	(	float *	A,
		float *	B,
		unsigned int	feature_size
	)

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deinter_cpu()

void deinter_cpu	(	int	NX,
		float *	X,
		int	NY,
		float *	Y,
		int	B,
		float *	OUT
	)

dot_cpu()

float dot_cpu	(	int	N,
		float *	X,
		int	INCX,
		float *	Y,
		int	INCY
	)

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fill_cpu()

void fill_cpu	(	int	N,
		float	ALPHA,
		float *	X,
		int	INCX
	)

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find_P_constrastive()

float find_P_constrastive	(	size_t	i,
		size_t	j,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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find_sim()

float find_sim	(	size_t	i,
		size_t	j,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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fix_nan_and_inf_cpu()

void fix_nan_and_inf_cpu	(	float *	input,
		size_t	size
	)

flatten()

void flatten	(	float *	x,
		int	size,
		int	layers,
		int	batch,
		int	forward
	)

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get_embedding()

void get_embedding	(	float *	src,
		int	src_w,
		int	src_h,
		int	src_c,
		int	embedding_size,
		int	cur_w,
		int	cur_h,
		int	cur_n,
		int	cur_b,
		float *	dst
	)

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get_sim_P_index()

int get_sim_P_index	(	size_t	i,
		size_t	j,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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grad_contrastive_loss_negative()

void grad_contrastive_loss_negative	(	size_t	i,
		int *	labels,
		size_t	num_of_samples,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		float *	cos_sim,
		float *	p_constrastive,
		float *	delta,
		int	wh
	)

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grad_contrastive_loss_negative_f()

void grad_contrastive_loss_negative_f	(	size_t	i,
		int *	class_ids,
		int *	labels,
		size_t	num_of_samples,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		float *	delta,
		int	wh,
		contrastive_params *	contrast_p,
		int	contrast_p_size,
		int	neg_max
	)

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grad_contrastive_loss_positive()

void grad_contrastive_loss_positive	(	size_t	i,
		int *	labels,
		size_t	num_of_samples,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		float *	cos_sim,
		float *	p_constrastive,
		float *	delta,
		int	wh
	)

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grad_contrastive_loss_positive_f()

void grad_contrastive_loss_positive_f	(	size_t	i,
		int *	class_ids,
		int *	labels,
		size_t	num_of_samples,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		float *	delta,
		int	wh,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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inter_cpu()

void inter_cpu	(	int	NX,
		float *	X,
		int	NY,
		float *	Y,
		int	B,
		float *	OUT
	)

l1_cpu()

void l1_cpu	(	int	n,
		float *	pred,
		float *	truth,
		float *	delta,
		float *	error
	)

l2_cpu()

void l2_cpu	(	int	n,
		float *	pred,
		float *	truth,
		float *	delta,
		float *	error
	)

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logistic_x_ent_cpu()

void logistic_x_ent_cpu	(	int	n,
		float *	pred,
		float *	truth,
		float *	delta,
		float *	error
	)

math_vector_length()

float math_vector_length	(	float *	A,
		unsigned int	feature_size
	)

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mean_cpu()

void mean_cpu	(	float *	x,
		int	batch,
		int	filters,
		int	spatial,
		float *	mean
	)

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mul_cpu()

void mul_cpu	(	int	N,
		float *	X,
		int	INCX,
		float *	Y,
		int	INCY
	)

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mult_add_into_cpu()

void mult_add_into_cpu	(	int	N,
		float *	X,
		float *	Y,
		float *	Z
	)

normalize_cpu()

void normalize_cpu	(	float *	x,
		float *	mean,
		float *	variance,
		int	batch,
		int	filters,
		int	spatial
	)

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P_constrastive()

float P_constrastive	(	size_t	i,
		size_t	l,
		int *	labels,
		size_t	num_of_samples,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		float *	cos_sim,
		float *	exp_cos_sim
	)

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P_constrastive_f()

float P_constrastive_f	(	size_t	i,
		size_t	l,
		int *	labels,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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P_constrastive_f_det()

float P_constrastive_f_det	(	size_t	il,
		int *	labels,
		float **	z,
		unsigned int	feature_size,
		float	temperature,
		contrastive_params *	contrast_p,
		int	contrast_p_size
	)

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pow_cpu()

void pow_cpu	(	int	N,
		float	ALPHA,
		float *	X,
		int	INCX,
		float *	Y,
		int	INCY
	)

relu()

static float relu ( float  src )

static

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reorg_cpu()

void reorg_cpu	(	float *	x,
		int	out_w,
		int	out_h,
		int	out_c,
		int	batch,
		int	stride,
		int	forward,
		float *	out
	)

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scal_add_cpu()

void scal_add_cpu	(	int	N,
		float	ALPHA,
		float	BETA,
		float *	X,
		int	INCX
	)

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scal_cpu()

void scal_cpu	(	int	N,
		float	ALPHA,
		float *	X,
		int	INCX
	)

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shortcut_cpu()

void shortcut_cpu	(	int	batch,
		int	w1,
		int	h1,
		int	c1,
		float *	add,
		int	w2,
		int	h2,
		int	c2,
		float *	out
	)

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shortcut_multilayer_cpu()

void shortcut_multilayer_cpu	(	int	size,
		int	src_outputs,
		int	batch,
		int	n,
		int *	outputs_of_layers,
		float **	layers_output,
		float *	out,
		float *	in,
		float *	weights,
		int	nweights,
		WEIGHTS_NORMALIZATION_T	weights_normalization
	)

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smooth_l1_cpu()

void smooth_l1_cpu	(	int	n,
		float *	pred,
		float *	truth,
		float *	delta,
		float *	error
	)

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softmax()

void softmax	(	float *	input,
		int	n,
		float	temp,
		float *	output,
		int	stride
	)

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softmax_cpu()

void softmax_cpu	(	float *	input,
		int	n,
		int	batch,
		int	batch_offset,
		int	groups,
		int	group_offset,
		int	stride,
		float	temp,
		float *	output
	)

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softmax_x_ent_cpu()

void softmax_x_ent_cpu	(	int	n,
		float *	pred,
		float *	truth,
		float *	delta,
		float *	error
	)

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upsample_cpu()

void upsample_cpu	(	float *	in,
		int	w,
		int	h,
		int	c,
		int	batch,
		int	stride,
		int	forward,
		float	scale,
		float *	out
	)

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variance_cpu()

void variance_cpu	(	float *	x,
		float *	mean,
		int	batch,
		int	filters,
		int	spatial,
		float *	variance
	)

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weighted_delta_cpu()

void weighted_delta_cpu	(	float *	a,
		float *	b,
		float *	s,
		float *	da,
		float *	db,
		float *	ds,
		int	n,
		float *	dc
	)

weighted_sum_cpu()

void weighted_sum_cpu	(	float *	a,
		float *	b,
		float *	s,
		int	n,
		float *	c
	)