network_kernels.cu File Reference

#include "darknet_internal.hpp"

Classes
struct	time_benchmark_layers

Typedefs
typedef struct time_benchmark_layers	time_benchmark_layers

Functions
void	backward_network_gpu (Darknet::Network &net, Darknet::NetworkState state)
void	distribute_updates (Darknet::Layer &l, Darknet::Layer &base)
void	distribute_weights (Darknet::Layer &l, Darknet::Layer &base)
void	forward_backward_network_gpu (Darknet::Network &net, float *x, float *y)
void	forward_backward_network_gpu_dev (Darknet::Network &net, float *x_gpu, float *y_gpu)
void	forward_network_gpu (Darknet::Network &net, Darknet::NetworkState state)
void	free_training_state (Darknet::Network &net)
float *	get_network_output_gpu (Darknet::Network &net)
float *	get_network_output_layer_gpu (Darknet::Network &net, int i)
void	merge_updates (Darknet::Layer &l, Darknet::Layer &base)
void	merge_weights (Darknet::Layer &l, Darknet::Layer &base)
float *	network_predict_gpu (Darknet::Network &net, float *input)
void	pull_updates (Darknet::Layer &l)
void	pull_weights (Darknet::Layer &l)
void	push_updates (Darknet::Layer &l)
void	push_weights (Darknet::Layer &l)
void	scale_weights (Darknet::Layer &l, float s)
void	start_background_task (Darknet::Network &net, const int layer_index, std::function< void()> task)
	Run task on the network's background thread. Tasks run one at a time, in order. Needs Darknet::TrainingState.
void	sync_layer (Darknet::Network *nets, int n, int j)
void	sync_nets (Darknet::Network *nets, int n, int interval)
int	time_comparator (const void *pa, const void *pb)
float	train_network_datum_gpu (Darknet::Network &net, float *x, float *y)
float	train_network_subdivisions_gpu (Darknet::Network &net, data d, int wait_key)
float	train_networks (Darknet::Network *nets, int n, data d, int interval)
void	update_layer (Darknet::Layer &l, Darknet::Network net)
void	update_network_gpu (Darknet::Network &net)
bool	wait_for_background_task (Darknet::Network &net, const int layer_index)
	Wait for the task started for this layer. Returns false if there was none.

Typedef Documentation

time_benchmark_layers

typedef struct time_benchmark_layers time_benchmark_layers

Function Documentation

backward_network_gpu()

void backward_network_gpu	(	Darknet::Network &	net,
		Darknet::NetworkState	state
	)

Todo:

replace qsort() unknown priority

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distribute_updates()

void distribute_updates	(	Darknet::Layer &	l,
		Darknet::Layer &	base
	)

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distribute_weights()

void distribute_weights	(	Darknet::Layer &	l,
		Darknet::Layer &	base
	)

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forward_backward_network_gpu()

void forward_backward_network_gpu	(	Darknet::Network &	net,
		float *	x,
		float *	y
	)

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forward_backward_network_gpu_dev()

void forward_backward_network_gpu_dev	(	Darknet::Network &	net,
		float *	x_gpu,
		float *	y_gpu
	)

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forward_network_gpu()

void forward_network_gpu	(	Darknet::Network &	net,
		Darknet::NetworkState	state
	)

Todo:

in previous versions we did not CHECK_CUDA here – was that intentional?

Todo:

replace qsort() low priority

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free_training_state()

void free_training_state

(

Darknet::Network &

net

)

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get_network_output_gpu()

float * get_network_output_gpu

(

Darknet::Network &

net

)

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get_network_output_layer_gpu()

float * get_network_output_layer_gpu	(	Darknet::Network &	net,
		int	i
	)

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merge_updates()

void merge_updates	(	Darknet::Layer &	l,
		Darknet::Layer &	base
	)

merge_weights()

void merge_weights	(	Darknet::Layer &	l,
		Darknet::Layer &	base
	)

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network_predict_gpu()

float * network_predict_gpu	(	Darknet::Network &	net,
		float *	input
	)

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pull_updates()

void pull_updates

(

Darknet::Layer &

l

)

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pull_weights()

void pull_weights

(

Darknet::Layer &

l

)

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push_updates()

void push_updates

(

Darknet::Layer &

l

)

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push_weights()

void push_weights

(

Darknet::Layer &

l

)

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scale_weights()

void scale_weights	(	Darknet::Layer &	l,
		float	s
	)

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start_background_task()

void start_background_task	(	Darknet::Network &	net,
		const int	layer_index,
		std::function< void()>	task
	)

Run task on the network's background thread. Tasks run one at a time, in order. Needs Darknet::TrainingState.

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sync_layer()

void sync_layer	(	Darknet::Network *	nets,
		int	n,
		int	j
	)

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sync_nets()

void sync_nets	(	Darknet::Network *	nets,
		int	n,
		int	interval
	)

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time_comparator()

int time_comparator	(	const void *	pa,
		const void *	pb
	)

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train_network_datum_gpu()

float train_network_datum_gpu	(	Darknet::Network &	net,
		float *	x,
		float *	y
	)

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train_network_subdivisions_gpu()

float train_network_subdivisions_gpu	(	Darknet::Network &	net,
		data	d,
		int	wait_key
	)

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train_networks()

float train_networks	(	Darknet::Network *	nets,
		int	n,
		data	d,
		int	interval
	)

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update_layer()

void update_layer	(	Darknet::Layer &	l,
		Darknet::Network	net
	)

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update_network_gpu()

void update_network_gpu

(

Darknet::Network &

net

)

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wait_for_background_task()

bool wait_for_background_task	(	Darknet::Network &	net,
		const int	layer_index
	)

Wait for the task started for this layer. Returns false if there was none.

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