convolutional_layer.cpp File Reference

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Functions
void	add_bias (float *output, float *biases, int batch, int n, int size)
void	assisted_excitation_forward (Darknet::Layer &l, Darknet::NetworkState state)
void	backward_bias (float *bias_updates, float *delta, int batch, int n, int size)
void	backward_convolutional_layer (Darknet::Layer &l, Darknet::NetworkState state)
void	binarize_input (float *input, int n, int size, float *binary)
void	binarize_weights (float *weights, int n, int size, float *binary)
void	binary_align_weights (Darknet::Layer *l)
int	convolutional_out_height (const Darknet::Layer &l)
int	convolutional_out_width (const Darknet::Layer &l)
void	create_convolutional_cudnn_tensors (Darknet::Layer *l)
void	cudnn_convolutional_setup (Darknet::Layer *l, int cudnn_preference, size_t workspace_size_specify)
void	denormalize_convolutional_layer (Darknet::Layer &l)
void	forward_convolutional_layer (Darknet::Layer &l, Darknet::NetworkState state)
void	free_convolutional_batchnorm (Darknet::Layer *l)
void	gemm_nn_custom (int M, int N, int K, float ALPHA, float *A, int lda, float *B, int ldb, float *C, int ldc)
Darknet::Image	get_convolutional_delta (const Darknet::Layer &l)
Darknet::Image	get_convolutional_image (const Darknet::Layer &l)
Darknet::Image	get_convolutional_weight (const Darknet::Layer &l, int i)
size_t	get_convolutional_workspace_size (const Darknet::Layer &l)
Darknet::Layer	make_convolutional_layer (int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, Darknet::Layer *share_layer, int assisted_excitation, int deform, int train)
void	rescale_weights (Darknet::Layer &l, float scale, float trans)
void	resize_convolutional_layer (Darknet::Layer *l, int w, int h)
void	rgbgr_weights (const Darknet::Layer &l)
void	scale_bias (float *output, float *scales, int batch, int n, int size)
void	set_specified_workspace_limit (Darknet::Layer *l, size_t workspace_size_limit)
void	swap_binary (Darknet::Layer *l)
void	test_convolutional_layer ()
void	update_convolutional_layer (Darknet::Layer &l, int batch, float learning_rate_init, float momentum, float decay)
Darknet::Image *	visualize_convolutional_layer (const Darknet::Layer &l, const char *window, Darknet::Image *prev_weights)

Function Documentation

add_bias()

void add_bias	(	float *	output,
		float *	biases,
		int	batch,
		int	n,
		int	size
	)

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assisted_excitation_forward()

void assisted_excitation_forward	(	Darknet::Layer &	l,
		Darknet::NetworkState	state
	)

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backward_bias()

void backward_bias	(	float *	bias_updates,
		float *	delta,
		int	batch,
		int	n,
		int	size
	)

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backward_convolutional_layer()

void backward_convolutional_layer	(	Darknet::Layer &	l,
		Darknet::NetworkState	state
	)

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binarize_input()

void binarize_input	(	float *	input,
		int	n,
		int	size,
		float *	binary
	)

binarize_weights()

void binarize_weights	(	float *	weights,
		int	n,
		int	size,
		float *	binary
	)

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binary_align_weights()

void binary_align_weights

(

Darknet::Layer *

l

)

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convolutional_out_height()

int convolutional_out_height

(

const Darknet::Layer &

l

)

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convolutional_out_width()

int convolutional_out_width

(

const Darknet::Layer &

l

)

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create_convolutional_cudnn_tensors()

void create_convolutional_cudnn_tensors

(

Darknet::Layer *

l

)

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cudnn_convolutional_setup()

void cudnn_convolutional_setup	(	Darknet::Layer *	l,
		int	cudnn_preference,
		size_t	workspace_size_specify
	)

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denormalize_convolutional_layer()

void denormalize_convolutional_layer

(

Darknet::Layer &

l

)

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forward_convolutional_layer()

void forward_convolutional_layer	(	Darknet::Layer &	l,
		Darknet::NetworkState	state
	)

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free_convolutional_batchnorm()

void free_convolutional_batchnorm

(

Darknet::Layer *

l

)

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gemm_nn_custom()

void gemm_nn_custom	(	int	M,
		int	N,
		int	K,
		float	ALPHA,
		float *	A,
		int	lda,
		float *	B,
		int	ldb,
		float *	C,
		int	ldc
	)

get_convolutional_delta()

Darknet::Image get_convolutional_delta

(

const Darknet::Layer &

l

)

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get_convolutional_image()

Darknet::Image get_convolutional_image

(

const Darknet::Layer &

l

)

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get_convolutional_weight()

Darknet::Image get_convolutional_weight	(	const Darknet::Layer &	l,
		int	i
	)

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get_convolutional_workspace_size()

size_t get_convolutional_workspace_size

(

const Darknet::Layer &

l

)

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make_convolutional_layer()

Darknet::Layer make_convolutional_layer	(	int	batch,
		int	steps,
		int	h,
		int	w,
		int	c,
		int	n,
		int	groups,
		int	size,
		int	stride_x,
		int	stride_y,
		int	dilation,
		int	padding,
		ACTIVATION	activation,
		int	batch_normalize,
		int	binary,
		int	xnor,
		int	adam,
		int	use_bin_output,
		int	index,
		int	antialiasing,
		Darknet::Layer *	share_layer,
		int	assisted_excitation,
		int	deform,
		int	train
	)

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rescale_weights()

void rescale_weights	(	Darknet::Layer &	l,
		float	scale,
		float	trans
	)

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resize_convolutional_layer()

void resize_convolutional_layer	(	Darknet::Layer *	l,
		int	w,
		int	h
	)

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rgbgr_weights()

void rgbgr_weights

(

const Darknet::Layer &

l

)

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scale_bias()

void scale_bias	(	float *	output,
		float *	scales,
		int	batch,
		int	n,
		int	size
	)

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set_specified_workspace_limit()

void set_specified_workspace_limit	(	Darknet::Layer *	l,
		size_t	workspace_size_limit
	)

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swap_binary()

void swap_binary

(

Darknet::Layer *

l

)

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test_convolutional_layer()

void test_convolutional_layer

(

)

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update_convolutional_layer()

void update_convolutional_layer	(	Darknet::Layer &	l,
		int	batch,
		float	learning_rate_init,
		float	momentum,
		float	decay
	)

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visualize_convolutional_layer()

Darknet::Image * visualize_convolutional_layer	(	const Darknet::Layer &	l,
		const char *	window,
		Darknet::Image *	prev_weights
	)

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