darknet_network.hpp File Reference

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Classes
struct	Darknet::Network
	Neural network structure. Contains all of the layers. Created by Darknet::CfgFile::create_network(). More...
struct	Darknet::NetworkDetails
	A place to store other details related to the neural network which we cannot easily add to the usual Darknet::Network structure. More...
struct	Darknet::NetworkState

Namespaces
namespace	Darknet
	The namespace for the C++ Darknet API.

Functions
void	backward_network (Darknet::Network &net, Darknet::NetworkState state)
void	copy_weights_net (const Darknet::Network &net_train, Darknet::Network *net_map)
char *	Darknet::detection_to_json (Darknet::Detection *dets, int nboxes, int classes, const Darknet::VStr &names, long long int frame_id, char *filename)
void	ema_apply (Darknet::Network &net)
void	ema_update (Darknet::Network &net, float ema_alpha)
void	forward_network (Darknet::Network &net, Darknet::NetworkState state)
void	free_batch_detections (det_num_pair *det_num_pairs, int n)
void	free_network (Darknet::Network &net)
	Free all memory allocations for the given neural network.
void	free_network_recurrent_state (Darknet::Network &net)
void	fuse_conv_batchnorm (Darknet::Network &net)
	ChatGPT: The function fuse_conv_batchnorm() in the Darknet/YOLO codebase is responsible for fusing the convolutional layer and batch normalization layer into a single operation.
int	get_current_batch (const Darknet::Network &net)
int64_t	get_current_iteration (const Darknet::Network &net)
float	get_current_rate (const Darknet::Network &net)
float	get_current_seq_subdivisions (const Darknet::Network &net)
float	get_network_cost (const Darknet::Network &net)
Darknet::Image	get_network_image (Darknet::Network &net)
Darknet::Image	get_network_image_layer (Darknet::Network &net, int i)
int	get_network_input_size (Darknet::Network &net)
float *	get_network_output (Darknet::Network &net)
int	get_network_output_size (Darknet::Network &net)
int	get_sequence_value (const Darknet::Network &net)
int	is_ema_initialized (const Darknet::Network &net)
Darknet::Network	make_network (int n)
	Think of this as the constructor for the Darknet::Network object.
int	network_height (Darknet::Network *net)
float *	network_predict (Darknet::Network &net, float *input)
det_num_pair *	network_predict_batch (Darknet::Network *net, Darknet::Image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
matrix	network_predict_data (Darknet::Network &net, data test)
int	network_width (Darknet::Network *net)
void	optimize_picture (Darknet::Network *net, Darknet::Image orig, int max_layer, float scale, float rate, float thresh, int norm)
void	reject_similar_weights (Darknet::Network &net, float sim_threshold)
int	resize_network (Darknet::Network *net, int w, int h)
void	restore_network_recurrent_state (Darknet::Network &net)
void	set_batch_network (Darknet::Network *net, int b)
void	test_detector (const char *datacfg, const char *cfgfile, const char *weightfile, const char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, const char *outfile, int letter_box, int benchmark_layers)
void	train_detector (const char *datacfg, const char *cfgfile, const char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int show_imgs, int benchmark_layers, const char *chart_path)
float	train_network (Darknet::Network &net, data d)
float	train_network_batch (Darknet::Network &net, data d, int n)
float	train_network_datum (Darknet::Network &net, float *x, float *y)
float	train_network_waitkey (Darknet::Network &net, data d, int wait_key)
void	update_network (Darknet::Network &net)
float	validate_detector_map (const char *datacfg, const char *cfgfile, const char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, Darknet::Network *existing_net)
void	visualize_network (Darknet::Network &net)

Function Documentation

backward_network()

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

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copy_weights_net()

void copy_weights_net	(	const Darknet::Network &	net_train,
		Darknet::Network *	net_map
	)

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ema_apply()

void ema_apply

(

Darknet::Network &

net

)

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ema_update()

void ema_update	(	Darknet::Network &	net,
		float	ema_alpha
	)

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forward_network()

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

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free_batch_detections()

void free_batch_detections	(	det_num_pair *	det_num_pairs,
		int	n
	)

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free_network()

void free_network

(

Darknet::Network &

net

)

Free all memory allocations for the given neural network.

All of these functions perform the exact same task, so use the one that makes the most sense given your application. Some of these are C++ calls, some are C, some pass the network by refrence, some pass a pointer to the neural network.

See also

darknet_free_neural_network()

Darknet::free_neural_network()

free_network_ptr()

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free_network_recurrent_state()

void free_network_recurrent_state

(

Darknet::Network &

net

)

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fuse_conv_batchnorm()

void fuse_conv_batchnorm

(

Darknet::Network &

net

)

ChatGPT: The function fuse_conv_batchnorm() in the Darknet/YOLO codebase is responsible for fusing the convolutional layer and batch normalization layer into a single operation.

This process is essential for both model optimization and inference efficiency, as it reduces the number of operations needed during model execution.

In Darknet, when you have a convolutional layer followed by a batch normalization layer, the two layers can be fused into a single convolution-like operation that combines both the convolution and normalization. This reduces the need for the separate batch normalization step during inference, thus improving speed.

The fused operation allows the model to perform one pass of convolution instead of:

• Convolution operation.
• Batch normalization (normalization, scaling, shifting).
• Activation function (like ReLU).

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get_current_batch()

int get_current_batch

(

const Darknet::Network &

net

)

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get_current_iteration()

int64_t get_current_iteration

(

const Darknet::Network &

net

)

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get_current_rate()

float get_current_rate

(

const Darknet::Network &

net

)

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get_current_seq_subdivisions()

float get_current_seq_subdivisions

(

const Darknet::Network &

net

)

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get_network_cost()

float get_network_cost

(

const Darknet::Network &

net

)

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get_network_image()

Darknet::Image get_network_image

(

Darknet::Network &

net

)

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get_network_image_layer()

Darknet::Image get_network_image_layer	(	Darknet::Network &	net,
		int	i
	)

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get_network_input_size()

int get_network_input_size

(

Darknet::Network &

net

)

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get_network_output()

float * get_network_output

(

Darknet::Network &

net

)

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get_network_output_size()

int get_network_output_size

(

Darknet::Network &

net

)

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get_sequence_value()

int get_sequence_value

(

const Darknet::Network &

net

)

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is_ema_initialized()

int is_ema_initialized

(

const Darknet::Network &

net

)

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make_network()

Darknet::Network make_network

(

int

n

)

Think of this as the constructor for the Darknet::Network object.

Parameters

[in]

n

The number of network layers to initialize.

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network_height()

int network_height

(

Darknet::Network *

net

)

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network_predict()

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

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network_predict_batch()

det_num_pair * network_predict_batch	(	Darknet::Network *	net,
		Darknet::Image	im,
		int	batch_size,
		int	w,
		int	h,
		float	thresh,
		float	hier,
		int *	map,
		int	relative,
		int	letter
	)

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network_predict_data()

matrix network_predict_data	(	Darknet::Network &	net,
		data	test
	)

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network_width()

int network_width

(

Darknet::Network *

net

)

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optimize_picture()

void optimize_picture	(	Darknet::Network *	net,
		Darknet::Image	orig,
		int	max_layer,
		float	scale,
		float	rate,
		float	thresh,
		int	norm
	)

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reject_similar_weights()

void reject_similar_weights	(	Darknet::Network &	net,
		float	sim_threshold
	)

Todo:

V5: would be nice if we could call rand_uniform_many() instead

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resize_network()

int resize_network	(	Darknet::Network *	net,
		int	w,
		int	h
	)

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restore_network_recurrent_state()

void restore_network_recurrent_state

(

Darknet::Network &

net

)

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set_batch_network()

void set_batch_network	(	Darknet::Network *	net,
		int	b
	)

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test_detector()

void test_detector	(	const char *	datacfg,
		const char *	cfgfile,
		const char *	weightfile,
		const char *	filename,
		float	thresh,
		float	hier_thresh,
		int	dont_show,
		int	ext_output,
		int	save_labels,
		const char *	outfile,
		int	letter_box,
		int	benchmark_layers
	)

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train_detector()

void train_detector	(	const char *	datacfg,
		const char *	cfgfile,
		const char *	weightfile,
		int *	gpus,
		int	ngpus,
		int	clear,
		int	dont_show,
		int	calc_map,
		float	thresh,
		float	iou_thresh,
		int	show_imgs,
		int	benchmark_layers,
		const char *	chart_path
	)

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train_network()

float train_network	(	Darknet::Network &	net,
		data	d
	)

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train_network_batch()

float train_network_batch	(	Darknet::Network &	net,
		data	d,
		int	n
	)

train_network_datum()

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

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train_network_waitkey()

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

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update_network()

void update_network

(

Darknet::Network &

net

)

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validate_detector_map()

float validate_detector_map	(	const char *	datacfg,
		const char *	cfgfile,
		const char *	weightfile,
		float	thresh_calc_avg_iou,
		const float	iou_thresh,
		const int	map_points,
		int	letter_box,
		Darknet::Network *	existing_net
	)

Todo:

how many cores do we have available?

Todo:

I think this is TP + FN (where the object actually exists, and we either found it, or missed it)

Todo:

would we save anything if net was passed in by reference?

Todo:

try this again in 2026? std::execution::par_unseq,

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visualize_network()

void visualize_network

(

Darknet::Network &

net

)

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