#include "darknet_layers.hpp"

Public Attributes
float *	a_avg_gpu

int	absolute

ACTIVATION	activation

float *	activation_input

float *	activation_input_gpu

int	adam

char *	align_bit_weights

char *	align_bit_weights_gpu

int	align_bit_weights_size

float *	align_workspace_gpu

int	align_workspace_size

float	alpha

float	angle

int	antialiasing

int	assisted_excitation

int	avgpool

float	B1

float	B2

int	background

void(*	backward )(Layer &l, Darknet::NetworkState network_state)

void(*	backward_gpu )(Layer &l, Darknet::NetworkState network_state)

int	batch

int	batch_normalize

cudnnConvolutionBwdDataAlgo_t	bd_algo

cudnnConvolutionBwdDataAlgo_t	bd_algo16

float	beta

float	beta_nms

cudnnConvolutionBwdFilterAlgo_t	bf_algo

cudnnConvolutionBwdFilterAlgo_t	bf_algo16

float	bflops

float *	bias_change_gpu

float *	bias_m

float *	bias_m_gpu

int	bias_match

float *	bias_updates
	biases loaded here by load_convolutional_weights() and load_connected_weights(), see n

float *	bias_updates_gpu

float *	bias_v

float *	bias_v_gpu

float *	biases

float *	biases_ema

float *	biases_gpu

float *	bin_conv_shortcut_in_gpu

float *	bin_conv_shortcut_out_gpu

uint32_t *	bin_re_packed_input

int	binary

float *	binary_input

float *	binary_input_gpu

float *	binary_weights

float *	binary_weights_gpu

int	bit_align

float *	bottelneck_delta_gpu

float *	bottelneck_hi_gpu

int	bottleneck

int	burnin_update

int	c
	channels

float *	c_cpu

float *	c_gpu

float *	cell_cpu

float *	cell_gpu

int *	class_ids

float	class_scale

int	classes

float *	classes_multipliers

int	classfix

float	clip

float	cls_normalizer

float *	col_image

float *	col_image_gpu

float *	combine_cpu_unused

float *	combine_delta_cpu_unused

float *	combine_delta_gpu

float *	combine_gpu

float *	concat

float *	concat_delta

float *	concat_delta_gpu

float *	concat_gpu

contrastive_params *	contrast_p_gpu

int	contrastive_neg_max

cudnnConvolutionDescriptor_t	convDesc

float	coord_scale

int	coordconv

int	coords

float *	cos_sim

float *	cos_sim_gpu

float *	cost

COST_TYPE	cost_type

int *	counts

char *	cweights

float *	dc_cpu

float *	dc_gpu

cudnnTensorDescriptor_t	ddstTensorDesc

cudnnTensorDescriptor_t	ddstTensorDesc16

int	deform

float *	delta

float *	delta_gpu

float	delta_normalizer

int	delta_pinned

int	detection

int	dets_for_show

int	dets_for_track

float *	dh_cpu

float *	dh_gpu

int	dilation

int	does_cost

int	dont_update

int	dontload

int	dontloadscales

int	dontsave

float	dot

float *	drop_blocks_scale

float *	drop_blocks_scale_gpu

int	dropblock

int	dropblock_size_abs

float	dropblock_size_rel

cudnnTensorDescriptor_t	dsrcTensorDesc

cudnnTensorDescriptor_t	dsrcTensorDesc16

cudnnTensorDescriptor_t	dstTensorDesc

cudnnTensorDescriptor_t	dstTensorDesc16

cudnnFilterDescriptor_t	dweightDesc

cudnnFilterDescriptor_t	dweightDesc16

int	dynamic_minibatch

int	embedding_layer_id

float *	embedding_output

int	embedding_size

float	eps

float *	exp_cos_sim

float	exposure

int	extra

float *	f_cpu

float *	f_gpu

int	flatten

int	flip

int	flipped

int	focal_loss

float	focus

int	forced

float *	forgot_delta

float *	forgot_delta_gpu

float *	forgot_state

float *	forgot_state_gpu

void(*	forward )(Layer &l, Darknet::NetworkState network_state)

void(*	forward_gpu )(Layer &l, Darknet::NetworkState network_state)

cudnnConvolutionFwdAlgo_t	fw_algo

cudnnConvolutionFwdAlgo_t	fw_algo16

float *	g_cpu

float *	g_gpu

float *	gate_delta_gpu

float *	gate_gpu

int	grad_centr

int	group_id

int	groups

float *	gt_gpu

int	h
	height

float *	h_cpu

float *	h_gpu

float *	hh_cpu

float *	hh_gpu

int	hidden

int	history_size

float *	i_cpu

float *	i_gpu

float	ignore_thresh

int	index
	layer number starting at zero ([net] does not count)

int *	indexes

int *	indexes_gpu

float *	input_antialiasing_gpu

Layer *	input_gate_layer_unused

Layer *	input_h_layer_unused

Layer *	input_layer

int *	input_layers

Layer *	input_r_layer_unused

Layer *	input_save_layer_unused

int *	input_sizes

int *	input_sizes_gpu

Layer *	input_state_layer_unused

Layer *	input_z_layer_unused

int	inputs

IOU_LOSS	iou_loss

float	iou_normalizer

float	iou_thresh

IOU_LOSS	iou_thresh_kind

float	jitter

int	joint

float	kappa

int	keep_delta_gpu

float	label_smooth_eps

int *	labels

float *	last_prev_cell_gpu

float *	last_prev_state_gpu

float **	layers_delta

float **	layers_delta_gpu

float **	layers_output

float **	layers_output_gpu

int	lda_align

float	learning_rate_scale

int	log

float *	loss

float *	loss_gpu

ACTIVATION	lstm_activation

float *	m

float *	m_cbn_avg_gpu

float *	m_gpu

int *	map

int *	mask

float	mask_scale

int	max_boxes

float	max_delta

int	maxpool_depth

int	maxpool_zero_nonmax

float *	mean

float	mean_alpha

float *	mean_arr

float *	mean_arr_gpu

float *	mean_delta

float *	mean_delta_gpu

float *	mean_gpu

int	n
	number of anchors, masks (?), weights (?); for example, with YOLOv4-tiny this is set to `3`

int	nbiases
	unused? Seems to be no references to this in the codebase.

int	new_coords

int	new_lda

NMS_KIND	nms_kind

int	noadjust

int	noloss

float	noobject_scale

cudnnTensorDescriptor_t	normDstTensorDesc

cudnnTensorDescriptor_t	normDstTensorDescF16

float *	norms

float *	norms_gpu

cudnnTensorDescriptor_t	normTensorDesc

int	numload

int	nweights
	number of floats stored in weights

float *	o_cpu

float *	o_gpu

float	obj_normalizer

float	object_scale

int	objectness

int	objectness_smooth

int	onlyforward

int	optimized_memory

int	out_c

int	out_channels

int	out_h

int	out_w

float *	output

float *	output_avg_gpu

float *	output_gpu

Layer *	output_layer

int	output_pinned

int	outputs

float *	p_constrastive

int	pad

int	peephole

cudnnPoolingDescriptor_t	poolingDesc

float *	prev_cell_cpu

float *	prev_cell_gpu

float *	prev_state

float *	prev_state_cpu

float *	prev_state_gpu

float	probability

float *	r_cpu

float *	r_gpu

float *	rand

float *	rand_gpu

float	random

float	ratio

int	receptive_h

int	receptive_h_scale

int	receptive_w

int	receptive_w_scale

int	reorg

int	rescore

Layer *	reset_layer_unused

float	resize

float	reverse

float *	rolling_mean

float *	rolling_mean_gpu

float *	rolling_variance
	rolling means loaded here by load_convolutional_weights() and load_connected_weights() when batch_normalize is set

float *	rolling_variance_gpu

int	rotate

float	saturation

float *	save_delta_gpu

float *	save_gpu

float	scale

float *	scale_change_gpu

float *	scale_m

float *	scale_m_gpu

float *	scale_updates
	scales loaded here by load_convolutional_weights() when batch_normalize is set

float *	scale_updates_gpu

float *	scale_v

float *	scale_v_gpu

int	scale_wh

float	scale_x_y

float *	scales

float *	scales_ema

float *	scales_gpu

Layer *	self_layer

Layer *	share_layer

float	shift

int	shortcut

int	show_details

int	side

float	sim_thresh

int	size

float	smooth

int	softmax

Darknet::Tree *	softmax_tree

int	spatial

float *	spatial_mean

int	sqrt

float *	squared

float *	squared_gpu

cudnnTensorDescriptor_t	srcTensorDesc

cudnnTensorDescriptor_t	srcTensorDesc16

float *	state

int	state_constrain

float *	state_delta

float *	state_delta_gpu

Layer *	state_gate_layer_unused

float *	state_gpu

Layer *	state_h_layer_unused

Layer *	state_layer_unused

Layer *	state_r_layer_unused

Layer *	state_save_layer_unused

Layer *	state_state_layer_unused

Layer *	state_z_layer_unused

int	steps

int	stopbackward

float *	stored_c_cpu

float *	stored_c_gpu

float *	stored_h_cpu

float *	stored_h_gpu

int	stream

int	stretch

int	stretch_sway

int	stride

int	stride_x

int	stride_y

float **	sums

int	sway

int	t

char *	t_bit_input

int	tanh

float *	temp2_cpu

float *	temp2_gpu

float *	temp3_cpu

float *	temp3_gpu

float *	temp_cpu

float *	temp_gpu

float	temperature

float	thresh

float	time_normalizer

int	total

float	track_ciou_norm

int	track_history_size

int	train

int	train_only_bn

float *	transposed_align_workspace_gpu

int	truth

int	truth_size

float	truth_thresh

int	truths

Darknet::ELayerType	type

float	uc_normalizer

Layer *	uf
	used in lstm (update for forget gate?)

Layer *	ug
	used in lstm (update gradient?)

Layer *	uh_unused

Layer *	ui
	used in lstm (update input gate?)

Layer *	uo
	used in lstm (update for output gate?)

void(*	update )(Layer &l, int, float, float, float)

void(*	update_gpu )(Layer &l, int, float, float, float, float)

Layer *	update_layer_unused

Layer *	ur_unused

int	use_bin_output

Layer *	uz_unused

float *	v

float *	v_cbn_avg_gpu

float *	v_gpu

float *	variance

float *	variance_delta

float *	variance_delta_gpu

float *	variance_gpu

Layer *	vf_unused

Layer *	vi_unused

Layer *	vo_unused

int	w
	width

int	wait_stream_id

float *	weight_change_gpu

float *	weight_deform_gpu

float *	weight_updates
	weights loaded here by load_convolutional_weights(), load_connected_weights(), and load_shortcut_weights()

float *	weight_updates_gpu

float *	weight_updates_gpu16

cudnnFilterDescriptor_t	weightDesc

cudnnFilterDescriptor_t	weightDesc16

float *	weights

float *	weights_ema

float *	weights_gpu

float *	weights_gpu16

WEIGHTS_NORMALIZATION_T	weights_normalization

WEIGHTS_TYPE_T	weights_type

Layer *	wf
	used in lstm (weights for forget gate?)

Layer *	wg
	used in lstm (weight gradient?)

Layer *	wh_unused

Layer *	wi
	used in lstm (weight for input connections?)

Layer *	wo
	used in lstm (weights for output forget gate?)

size_t	workspace_size

Layer *	wr_unused

Layer *	wz_unused

float *	x
	rolling variance loaded here by load_convolutional_weights() and load_connected_weights() when batch_normalize is set

float *	x_gpu

float *	x_norm

float *	x_norm_gpu

int	xnor

YOLO_POINT	yolo_point

float *	z_cpu

float *	z_gpu

Member Data Documentation

◆ a_avg_gpu

float* Darknet::Layer::a_avg_gpu

◆ absolute

int Darknet::Layer::absolute

◆ activation

ACTIVATION Darknet::Layer::activation

◆ activation_input

float* Darknet::Layer::activation_input

◆ activation_input_gpu

float* Darknet::Layer::activation_input_gpu

◆ adam

int Darknet::Layer::adam

◆ align_bit_weights

char* Darknet::Layer::align_bit_weights

◆ align_bit_weights_gpu

char* Darknet::Layer::align_bit_weights_gpu

◆ align_bit_weights_size

int Darknet::Layer::align_bit_weights_size

◆ align_workspace_gpu

float* Darknet::Layer::align_workspace_gpu

◆ align_workspace_size

int Darknet::Layer::align_workspace_size

◆ alpha

float Darknet::Layer::alpha

◆ angle

float Darknet::Layer::angle

◆ antialiasing

int Darknet::Layer::antialiasing

◆ assisted_excitation

int Darknet::Layer::assisted_excitation

◆ avgpool

int Darknet::Layer::avgpool

◆ B1

float Darknet::Layer::B1

◆ B2

float Darknet::Layer::B2

◆ background

int Darknet::Layer::background

◆ backward

void(* Darknet::Layer::backward) (Layer &l, Darknet::NetworkState network_state)

◆ backward_gpu

void(* Darknet::Layer::backward_gpu) (Layer &l, Darknet::NetworkState network_state)

◆ batch

int Darknet::Layer::batch

◆ batch_normalize

int Darknet::Layer::batch_normalize

◆ bd_algo

cudnnConvolutionBwdDataAlgo_t Darknet::Layer::bd_algo

◆ bd_algo16

cudnnConvolutionBwdDataAlgo_t Darknet::Layer::bd_algo16

◆ beta

float Darknet::Layer::beta

◆ beta_nms

float Darknet::Layer::beta_nms

◆ bf_algo

cudnnConvolutionBwdFilterAlgo_t Darknet::Layer::bf_algo

◆ bf_algo16

cudnnConvolutionBwdFilterAlgo_t Darknet::Layer::bf_algo16

◆ bflops

float Darknet::Layer::bflops

◆ bias_change_gpu

float* Darknet::Layer::bias_change_gpu

◆ bias_m

float* Darknet::Layer::bias_m

◆ bias_m_gpu

float* Darknet::Layer::bias_m_gpu

Todo:: V5: possibly unused?

◆ bias_match

int Darknet::Layer::bias_match

◆ bias_updates

float* Darknet::Layer::bias_updates

biases loaded here by load_convolutional_weights() and load_connected_weights(), see n

◆ bias_updates_gpu

float* Darknet::Layer::bias_updates_gpu

◆ bias_v

float* Darknet::Layer::bias_v

◆ bias_v_gpu

float* Darknet::Layer::bias_v_gpu

Todo:: V5: possibly unused?

◆ biases

float* Darknet::Layer::biases

◆ biases_ema

float* Darknet::Layer::biases_ema

◆ biases_gpu

float* Darknet::Layer::biases_gpu

◆ bin_conv_shortcut_in_gpu

float* Darknet::Layer::bin_conv_shortcut_in_gpu

Todo:: V5: possibly unused?

◆ bin_conv_shortcut_out_gpu

float* Darknet::Layer::bin_conv_shortcut_out_gpu

Todo:: V5: possibly unused?

◆ bin_re_packed_input

uint32_t* Darknet::Layer::bin_re_packed_input

◆ binary

int Darknet::Layer::binary

◆ binary_input

float* Darknet::Layer::binary_input

◆ binary_input_gpu

float* Darknet::Layer::binary_input_gpu

◆ binary_weights

float* Darknet::Layer::binary_weights

◆ binary_weights_gpu

float* Darknet::Layer::binary_weights_gpu

◆ bit_align

int Darknet::Layer::bit_align

◆ bottelneck_delta_gpu

float* Darknet::Layer::bottelneck_delta_gpu

◆ bottelneck_hi_gpu

float* Darknet::Layer::bottelneck_hi_gpu

◆ bottleneck

int Darknet::Layer::bottleneck

◆ burnin_update

int Darknet::Layer::burnin_update

◆ c

int Darknet::Layer::c

channels

◆ c_cpu

float* Darknet::Layer::c_cpu

◆ c_gpu

float* Darknet::Layer::c_gpu

◆ cell_cpu

float* Darknet::Layer::cell_cpu

◆ cell_gpu

float* Darknet::Layer::cell_gpu

◆ class_ids

int* Darknet::Layer::class_ids

◆ class_scale

float Darknet::Layer::class_scale

◆ classes

int Darknet::Layer::classes

◆ classes_multipliers

float* Darknet::Layer::classes_multipliers

◆ classfix

int Darknet::Layer::classfix

◆ clip

float Darknet::Layer::clip

◆ cls_normalizer

float Darknet::Layer::cls_normalizer

◆ col_image

float* Darknet::Layer::col_image

◆ col_image_gpu

float* Darknet::Layer::col_image_gpu

◆ combine_cpu_unused

float* Darknet::Layer::combine_cpu_unused

Todo:: V5: unused?

◆ combine_delta_cpu_unused

float* Darknet::Layer::combine_delta_cpu_unused

Todo:: V5: unused?

◆ combine_delta_gpu

float* Darknet::Layer::combine_delta_gpu

Todo:: V5: unused?

◆ combine_gpu

float* Darknet::Layer::combine_gpu

Todo:: V5: unused?

◆ concat

float* Darknet::Layer::concat

◆ concat_delta

float* Darknet::Layer::concat_delta

◆ concat_delta_gpu

float* Darknet::Layer::concat_delta_gpu

Todo:: V5: unused?

◆ concat_gpu

float* Darknet::Layer::concat_gpu

Todo:: V5: unused?

◆ contrast_p_gpu

contrastive_params* Darknet::Layer::contrast_p_gpu

◆ contrastive_neg_max

int Darknet::Layer::contrastive_neg_max

Todo:: V5: possibly unused?

◆ convDesc

cudnnConvolutionDescriptor_t Darknet::Layer::convDesc

◆ coord_scale

float Darknet::Layer::coord_scale

◆ coordconv

int Darknet::Layer::coordconv

◆ coords

int Darknet::Layer::coords

◆ cos_sim

float* Darknet::Layer::cos_sim

◆ cos_sim_gpu

float* Darknet::Layer::cos_sim_gpu

◆ cost

float* Darknet::Layer::cost

◆ cost_type

COST_TYPE Darknet::Layer::cost_type

◆ counts

int* Darknet::Layer::counts

◆ cweights

char* Darknet::Layer::cweights

Todo:: V5: possibly unused?

◆ dc_cpu

float* Darknet::Layer::dc_cpu

◆ dc_gpu

float* Darknet::Layer::dc_gpu

◆ ddstTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::ddstTensorDesc

◆ ddstTensorDesc16

cudnnTensorDescriptor_t Darknet::Layer::ddstTensorDesc16

◆ deform

int Darknet::Layer::deform

◆ delta

float* Darknet::Layer::delta

◆ delta_gpu

float* Darknet::Layer::delta_gpu

◆ delta_normalizer

float Darknet::Layer::delta_normalizer

◆ delta_pinned

int Darknet::Layer::delta_pinned

◆ detection

int Darknet::Layer::detection

◆ dets_for_show

int Darknet::Layer::dets_for_show

◆ dets_for_track

int Darknet::Layer::dets_for_track

◆ dh_cpu

float* Darknet::Layer::dh_cpu

◆ dh_gpu

float* Darknet::Layer::dh_gpu

◆ dilation

int Darknet::Layer::dilation

◆ does_cost

int Darknet::Layer::does_cost

◆ dont_update

int Darknet::Layer::dont_update

◆ dontload

int Darknet::Layer::dontload

◆ dontloadscales

int Darknet::Layer::dontloadscales

◆ dontsave

int Darknet::Layer::dontsave

◆ dot

float Darknet::Layer::dot

◆ drop_blocks_scale

float* Darknet::Layer::drop_blocks_scale

◆ drop_blocks_scale_gpu

float* Darknet::Layer::drop_blocks_scale_gpu

◆ dropblock

int Darknet::Layer::dropblock

◆ dropblock_size_abs

int Darknet::Layer::dropblock_size_abs

◆ dropblock_size_rel

float Darknet::Layer::dropblock_size_rel

◆ dsrcTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::dsrcTensorDesc

◆ dsrcTensorDesc16

cudnnTensorDescriptor_t Darknet::Layer::dsrcTensorDesc16

◆ dstTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::dstTensorDesc

◆ dstTensorDesc16

cudnnTensorDescriptor_t Darknet::Layer::dstTensorDesc16

◆ dweightDesc

cudnnFilterDescriptor_t Darknet::Layer::dweightDesc

◆ dweightDesc16

cudnnFilterDescriptor_t Darknet::Layer::dweightDesc16

◆ dynamic_minibatch

int Darknet::Layer::dynamic_minibatch

◆ embedding_layer_id

int Darknet::Layer::embedding_layer_id

◆ embedding_output

float* Darknet::Layer::embedding_output

◆ embedding_size

int Darknet::Layer::embedding_size

◆ eps

float Darknet::Layer::eps

◆ exp_cos_sim

float* Darknet::Layer::exp_cos_sim

◆ exposure

float Darknet::Layer::exposure

◆ extra

int Darknet::Layer::extra

◆ f_cpu

float* Darknet::Layer::f_cpu

◆ f_gpu

float* Darknet::Layer::f_gpu

◆ flatten

int Darknet::Layer::flatten

◆ flip

int Darknet::Layer::flip

◆ flipped

int Darknet::Layer::flipped

◆ focal_loss

int Darknet::Layer::focal_loss

◆ focus

float Darknet::Layer::focus

◆ forced

int Darknet::Layer::forced

◆ forgot_delta

float* Darknet::Layer::forgot_delta

Todo:: V5: unused?

◆ forgot_delta_gpu

float* Darknet::Layer::forgot_delta_gpu

Todo:: V5: possibly unused?

◆ forgot_state

float* Darknet::Layer::forgot_state

Todo:: V5: unused?

◆ forgot_state_gpu

float* Darknet::Layer::forgot_state_gpu

Todo:: V5: possibly unused?

◆ forward

void(* Darknet::Layer::forward) (Layer &l, Darknet::NetworkState network_state)

◆ forward_gpu

void(* Darknet::Layer::forward_gpu) (Layer &l, Darknet::NetworkState network_state)

◆ fw_algo

cudnnConvolutionFwdAlgo_t Darknet::Layer::fw_algo

◆ fw_algo16

cudnnConvolutionFwdAlgo_t Darknet::Layer::fw_algo16

◆ g_cpu

float* Darknet::Layer::g_cpu

◆ g_gpu

float* Darknet::Layer::g_gpu

◆ gate_delta_gpu

float* Darknet::Layer::gate_delta_gpu

Todo:: V5: possibly unused?

◆ gate_gpu

float* Darknet::Layer::gate_gpu

Todo:: V5: possibly unused?

◆ grad_centr

int Darknet::Layer::grad_centr

◆ group_id

int Darknet::Layer::group_id

◆ groups

int Darknet::Layer::groups

◆ gt_gpu

float* Darknet::Layer::gt_gpu

◆ h

int Darknet::Layer::h

height

◆ h_cpu

float* Darknet::Layer::h_cpu

◆ h_gpu

float* Darknet::Layer::h_gpu

◆ hh_cpu

float* Darknet::Layer::hh_cpu

◆ hh_gpu

float* Darknet::Layer::hh_gpu

Todo:: V5: possibly unused?

◆ hidden

int Darknet::Layer::hidden

◆ history_size

int Darknet::Layer::history_size

◆ i_cpu

float* Darknet::Layer::i_cpu

◆ i_gpu

float* Darknet::Layer::i_gpu

◆ ignore_thresh

float Darknet::Layer::ignore_thresh

◆ index

int Darknet::Layer::index

layer number starting at zero ([net] does not count)

◆ indexes

int* Darknet::Layer::indexes

◆ indexes_gpu

int* Darknet::Layer::indexes_gpu

◆ input_antialiasing_gpu

float* Darknet::Layer::input_antialiasing_gpu

◆ input_gate_layer_unused

Layer* Darknet::Layer::input_gate_layer_unused

Todo:: V5: unused?

◆ input_h_layer_unused

Layer* Darknet::Layer::input_h_layer_unused

Todo:: V5: unused?

◆ input_layer

Layer* Darknet::Layer::input_layer

◆ input_layers

int* Darknet::Layer::input_layers

◆ input_r_layer_unused

Layer* Darknet::Layer::input_r_layer_unused

Todo:: V5: unused?

◆ input_save_layer_unused

Layer* Darknet::Layer::input_save_layer_unused

Todo:: V5: unused?

◆ input_sizes

int* Darknet::Layer::input_sizes

◆ input_sizes_gpu

int* Darknet::Layer::input_sizes_gpu

◆ input_state_layer_unused

Layer* Darknet::Layer::input_state_layer_unused

Todo:: V5: unused?

◆ input_z_layer_unused

Layer* Darknet::Layer::input_z_layer_unused

Todo:: V5: unused?

◆ inputs

int Darknet::Layer::inputs

◆ iou_loss

IOU_LOSS Darknet::Layer::iou_loss

◆ iou_normalizer

float Darknet::Layer::iou_normalizer

◆ iou_thresh

float Darknet::Layer::iou_thresh

◆ iou_thresh_kind

IOU_LOSS Darknet::Layer::iou_thresh_kind

◆ jitter

float Darknet::Layer::jitter

◆ joint

int Darknet::Layer::joint

◆ kappa

float Darknet::Layer::kappa

◆ keep_delta_gpu

int Darknet::Layer::keep_delta_gpu

◆ label_smooth_eps

float Darknet::Layer::label_smooth_eps

◆ labels

int* Darknet::Layer::labels

◆ last_prev_cell_gpu

float* Darknet::Layer::last_prev_cell_gpu

Todo:: V5: unused?

◆ last_prev_state_gpu

float* Darknet::Layer::last_prev_state_gpu

Todo:: V5: unused?

◆ layers_delta

float** Darknet::Layer::layers_delta

◆ layers_delta_gpu

float** Darknet::Layer::layers_delta_gpu

◆ layers_output

float** Darknet::Layer::layers_output

◆ layers_output_gpu

float** Darknet::Layer::layers_output_gpu

◆ lda_align

int Darknet::Layer::lda_align

◆ learning_rate_scale

float Darknet::Layer::learning_rate_scale

◆ log

int Darknet::Layer::log

◆ loss

float* Darknet::Layer::loss

◆ loss_gpu

float* Darknet::Layer::loss_gpu

◆ lstm_activation

ACTIVATION Darknet::Layer::lstm_activation

◆ m

float* Darknet::Layer::m

◆ m_cbn_avg_gpu

float* Darknet::Layer::m_cbn_avg_gpu

Todo:: V5: possibly unused?

◆ m_gpu

float* Darknet::Layer::m_gpu

◆ map

int* Darknet::Layer::map

◆ mask

int* Darknet::Layer::mask

◆ mask_scale

float Darknet::Layer::mask_scale

◆ max_boxes

int Darknet::Layer::max_boxes

◆ max_delta

float Darknet::Layer::max_delta

◆ maxpool_depth

int Darknet::Layer::maxpool_depth

◆ maxpool_zero_nonmax

int Darknet::Layer::maxpool_zero_nonmax

◆ mean

float* Darknet::Layer::mean

◆ mean_alpha

float Darknet::Layer::mean_alpha

◆ mean_arr

float* Darknet::Layer::mean_arr

◆ mean_arr_gpu

float* Darknet::Layer::mean_arr_gpu

◆ mean_delta

float* Darknet::Layer::mean_delta

◆ mean_delta_gpu

float* Darknet::Layer::mean_delta_gpu

◆ mean_gpu

float* Darknet::Layer::mean_gpu

◆ n

int Darknet::Layer::n

number of anchors, masks (?), weights (?); for example, with YOLOv4-tiny this is set to 3

◆ nbiases

int Darknet::Layer::nbiases

unused? Seems to be no references to this in the codebase.

◆ new_coords

int Darknet::Layer::new_coords

◆ new_lda

int Darknet::Layer::new_lda

◆ nms_kind

NMS_KIND Darknet::Layer::nms_kind

◆ noadjust

int Darknet::Layer::noadjust

◆ noloss

int Darknet::Layer::noloss

◆ noobject_scale

float Darknet::Layer::noobject_scale

◆ normDstTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::normDstTensorDesc

◆ normDstTensorDescF16

cudnnTensorDescriptor_t Darknet::Layer::normDstTensorDescF16

◆ norms

float* Darknet::Layer::norms

◆ norms_gpu

float* Darknet::Layer::norms_gpu

◆ normTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::normTensorDesc

◆ numload

int Darknet::Layer::numload

◆ nweights

int Darknet::Layer::nweights

number of floats stored in weights

◆ o_cpu

float* Darknet::Layer::o_cpu

◆ o_gpu

float* Darknet::Layer::o_gpu

◆ obj_normalizer

float Darknet::Layer::obj_normalizer

◆ object_scale

float Darknet::Layer::object_scale

◆ objectness

int Darknet::Layer::objectness

◆ objectness_smooth

int Darknet::Layer::objectness_smooth

◆ onlyforward

int Darknet::Layer::onlyforward

◆ optimized_memory

int Darknet::Layer::optimized_memory

◆ out_c

int Darknet::Layer::out_c

◆ out_channels

int Darknet::Layer::out_channels

◆ out_h

int Darknet::Layer::out_h

◆ out_w

int Darknet::Layer::out_w

◆ output

float* Darknet::Layer::output

◆ output_avg_gpu

float* Darknet::Layer::output_avg_gpu

◆ output_gpu

float* Darknet::Layer::output_gpu

◆ output_layer

Layer* Darknet::Layer::output_layer

◆ output_pinned

int Darknet::Layer::output_pinned

◆ outputs

int Darknet::Layer::outputs

◆ p_constrastive

float* Darknet::Layer::p_constrastive

◆ pad

int Darknet::Layer::pad

◆ peephole

int Darknet::Layer::peephole

◆ poolingDesc

cudnnPoolingDescriptor_t Darknet::Layer::poolingDesc

◆ prev_cell_cpu

float* Darknet::Layer::prev_cell_cpu

◆ prev_cell_gpu

float* Darknet::Layer::prev_cell_gpu

◆ prev_state

float* Darknet::Layer::prev_state

◆ prev_state_cpu

float* Darknet::Layer::prev_state_cpu

◆ prev_state_gpu

float* Darknet::Layer::prev_state_gpu

◆ probability

float Darknet::Layer::probability

◆ r_cpu

float* Darknet::Layer::r_cpu

◆ r_gpu

float* Darknet::Layer::r_gpu

◆ rand

float* Darknet::Layer::rand

◆ rand_gpu

float* Darknet::Layer::rand_gpu

◆ random

float Darknet::Layer::random

◆ ratio

float Darknet::Layer::ratio

◆ receptive_h

int Darknet::Layer::receptive_h

◆ receptive_h_scale

int Darknet::Layer::receptive_h_scale

◆ receptive_w

int Darknet::Layer::receptive_w

◆ receptive_w_scale

int Darknet::Layer::receptive_w_scale

◆ reorg

int Darknet::Layer::reorg

◆ rescore

int Darknet::Layer::rescore

◆ reset_layer_unused

Layer* Darknet::Layer::reset_layer_unused

Todo:: V5: unused?

◆ resize

float Darknet::Layer::resize

◆ reverse

float Darknet::Layer::reverse

◆ rolling_mean

float* Darknet::Layer::rolling_mean

◆ rolling_mean_gpu

float* Darknet::Layer::rolling_mean_gpu

◆ rolling_variance

float* Darknet::Layer::rolling_variance

rolling means loaded here by load_convolutional_weights() and load_connected_weights() when batch_normalize is set

◆ rolling_variance_gpu

float* Darknet::Layer::rolling_variance_gpu

◆ rotate

int Darknet::Layer::rotate

◆ saturation

float Darknet::Layer::saturation

◆ save_delta_gpu

float* Darknet::Layer::save_delta_gpu

Todo:: V5: unused?

◆ save_gpu

float* Darknet::Layer::save_gpu

Todo:: V5: possibly unused?

◆ scale

float Darknet::Layer::scale

◆ scale_change_gpu

float* Darknet::Layer::scale_change_gpu

◆ scale_m

float* Darknet::Layer::scale_m

◆ scale_m_gpu

float* Darknet::Layer::scale_m_gpu

Todo:: V5: possibly unused?

◆ scale_updates

float* Darknet::Layer::scale_updates

scales loaded here by load_convolutional_weights() when batch_normalize is set

◆ scale_updates_gpu

float* Darknet::Layer::scale_updates_gpu

◆ scale_v

float* Darknet::Layer::scale_v

◆ scale_v_gpu

float* Darknet::Layer::scale_v_gpu

Todo:: V5: possibly unused?

◆ scale_wh

int Darknet::Layer::scale_wh

◆ scale_x_y

float Darknet::Layer::scale_x_y

◆ scales

float* Darknet::Layer::scales

◆ scales_ema

float* Darknet::Layer::scales_ema

◆ scales_gpu

float* Darknet::Layer::scales_gpu

◆ self_layer

Layer* Darknet::Layer::self_layer

◆ share_layer

Layer* Darknet::Layer::share_layer

◆ shift

float Darknet::Layer::shift

◆ shortcut

int Darknet::Layer::shortcut

◆ show_details

int Darknet::Layer::show_details

◆ side

int Darknet::Layer::side

◆ sim_thresh

float Darknet::Layer::sim_thresh

◆ size

int Darknet::Layer::size

◆ smooth

float Darknet::Layer::smooth

◆ softmax

int Darknet::Layer::softmax

◆ softmax_tree

Darknet::Tree* Darknet::Layer::softmax_tree

◆ spatial

int Darknet::Layer::spatial

◆ spatial_mean

float* Darknet::Layer::spatial_mean

◆ sqrt

int Darknet::Layer::sqrt

◆ squared

float* Darknet::Layer::squared

◆ squared_gpu

float* Darknet::Layer::squared_gpu

◆ srcTensorDesc

cudnnTensorDescriptor_t Darknet::Layer::srcTensorDesc

◆ srcTensorDesc16

cudnnTensorDescriptor_t Darknet::Layer::srcTensorDesc16

◆ state

float* Darknet::Layer::state

◆ state_constrain

int Darknet::Layer::state_constrain

◆ state_delta

float* Darknet::Layer::state_delta

Todo:: V5: possibly unused?

◆ state_delta_gpu

float* Darknet::Layer::state_delta_gpu

Todo:: V5: possibly unused?

◆ state_gate_layer_unused

Layer* Darknet::Layer::state_gate_layer_unused

Todo:: V5: unused?

◆ state_gpu

float* Darknet::Layer::state_gpu

◆ state_h_layer_unused

Layer* Darknet::Layer::state_h_layer_unused

Todo:: V5: unused?

◆ state_layer_unused

Layer* Darknet::Layer::state_layer_unused

Todo:: V5: unused?

◆ state_r_layer_unused

Layer* Darknet::Layer::state_r_layer_unused

Todo:: V5: unused?

◆ state_save_layer_unused

Layer* Darknet::Layer::state_save_layer_unused

Todo:: V5: unused?

◆ state_state_layer_unused

Layer* Darknet::Layer::state_state_layer_unused

Todo:: V5: unused?

◆ state_z_layer_unused

Layer* Darknet::Layer::state_z_layer_unused

Todo:: V5: unused?

◆ steps

int Darknet::Layer::steps

◆ stopbackward

int Darknet::Layer::stopbackward

◆ stored_c_cpu

float* Darknet::Layer::stored_c_cpu

Todo:: V5: unused?

◆ stored_c_gpu

float* Darknet::Layer::stored_c_gpu

Todo:: V5: possibly unused?

◆ stored_h_cpu

float* Darknet::Layer::stored_h_cpu

◆ stored_h_gpu

float* Darknet::Layer::stored_h_gpu

◆ stream

int Darknet::Layer::stream

◆ stretch

int Darknet::Layer::stretch

◆ stretch_sway

int Darknet::Layer::stretch_sway

◆ stride

int Darknet::Layer::stride

◆ stride_x

int Darknet::Layer::stride_x

◆ stride_y

int Darknet::Layer::stride_y

◆ sums

float** Darknet::Layer::sums

◆ sway

int Darknet::Layer::sway

◆ t

int Darknet::Layer::t

◆ t_bit_input

char* Darknet::Layer::t_bit_input

◆ tanh

int Darknet::Layer::tanh

◆ temp2_cpu

float* Darknet::Layer::temp2_cpu

◆ temp2_gpu

float* Darknet::Layer::temp2_gpu

◆ temp3_cpu

float* Darknet::Layer::temp3_cpu

◆ temp3_gpu

float* Darknet::Layer::temp3_gpu

◆ temp_cpu

float* Darknet::Layer::temp_cpu

◆ temp_gpu

float* Darknet::Layer::temp_gpu

◆ temperature

float Darknet::Layer::temperature

◆ thresh

float Darknet::Layer::thresh

◆ time_normalizer

float Darknet::Layer::time_normalizer

◆ total

int Darknet::Layer::total

◆ track_ciou_norm

float Darknet::Layer::track_ciou_norm

◆ track_history_size

int Darknet::Layer::track_history_size

◆ train

int Darknet::Layer::train

◆ train_only_bn

int Darknet::Layer::train_only_bn

◆ transposed_align_workspace_gpu

float* Darknet::Layer::transposed_align_workspace_gpu

◆ truth

int Darknet::Layer::truth

◆ truth_size

int Darknet::Layer::truth_size

◆ truth_thresh

float Darknet::Layer::truth_thresh

◆ truths

int Darknet::Layer::truths

◆ type

Darknet::ELayerType Darknet::Layer::type

See also: Darknet::to_string()

◆ uc_normalizer

float Darknet::Layer::uc_normalizer

◆ uf

Layer* Darknet::Layer::uf

used in lstm (update for forget gate?)

◆ ug

Layer* Darknet::Layer::ug

used in lstm (update gradient?)

◆ uh_unused

Layer* Darknet::Layer::uh_unused

Todo:: V5: unused?

◆ ui

Layer* Darknet::Layer::ui

used in lstm (update input gate?)

◆ uo

Layer* Darknet::Layer::uo

used in lstm (update for output gate?)

◆ update

void(* Darknet::Layer::update) (Layer &l, int, float, float, float)

◆ update_gpu

void(* Darknet::Layer::update_gpu) (Layer &l, int, float, float, float, float)

◆ update_layer_unused

Layer* Darknet::Layer::update_layer_unused

Todo:: V5: unused?

◆ ur_unused

Layer* Darknet::Layer::ur_unused

Todo:: V5: unused?

◆ use_bin_output

int Darknet::Layer::use_bin_output

◆ uz_unused

Layer* Darknet::Layer::uz_unused

Todo:: V5: unused?

◆ v

float* Darknet::Layer::v

◆ v_cbn_avg_gpu

float* Darknet::Layer::v_cbn_avg_gpu

Todo:: V5: possibly unused?

◆ v_gpu

float* Darknet::Layer::v_gpu

◆ variance

float* Darknet::Layer::variance

◆ variance_delta

float* Darknet::Layer::variance_delta

◆ variance_delta_gpu

float* Darknet::Layer::variance_delta_gpu

◆ variance_gpu

float* Darknet::Layer::variance_gpu

◆ vf_unused

Layer* Darknet::Layer::vf_unused

Todo:: V5: unused?

◆ vi_unused

Layer* Darknet::Layer::vi_unused

Todo:: V5: unused?

◆ vo_unused

Layer* Darknet::Layer::vo_unused

Todo:: V5: unused?

◆ w

int Darknet::Layer::w

width

◆ wait_stream_id

int Darknet::Layer::wait_stream_id

◆ weight_change_gpu

float* Darknet::Layer::weight_change_gpu

◆ weight_deform_gpu

float* Darknet::Layer::weight_deform_gpu

◆ weight_updates

float* Darknet::Layer::weight_updates

weights loaded here by load_convolutional_weights(), load_connected_weights(), and load_shortcut_weights()

◆ weight_updates_gpu

float* Darknet::Layer::weight_updates_gpu

◆ weight_updates_gpu16

float* Darknet::Layer::weight_updates_gpu16

◆ weightDesc

cudnnFilterDescriptor_t Darknet::Layer::weightDesc

◆ weightDesc16

cudnnFilterDescriptor_t Darknet::Layer::weightDesc16

◆ weights

float* Darknet::Layer::weights

◆ weights_ema

float* Darknet::Layer::weights_ema

◆ weights_gpu

float* Darknet::Layer::weights_gpu

◆ weights_gpu16

float* Darknet::Layer::weights_gpu16

◆ weights_normalization

WEIGHTS_NORMALIZATION_T Darknet::Layer::weights_normalization

◆ weights_type

WEIGHTS_TYPE_T Darknet::Layer::weights_type

◆ wf

Layer* Darknet::Layer::wf

used in lstm (weights for forget gate?)

◆ wg

Layer* Darknet::Layer::wg

used in lstm (weight gradient?)

◆ wh_unused

Layer* Darknet::Layer::wh_unused

Todo:: V5: unused?

◆ wi

Layer* Darknet::Layer::wi

used in lstm (weight for input connections?)

◆ wo

Layer* Darknet::Layer::wo

used in lstm (weights for output forget gate?)

◆ workspace_size

size_t Darknet::Layer::workspace_size

◆ wr_unused

Layer* Darknet::Layer::wr_unused

Todo:: V5: unused?

◆ wz_unused

Layer* Darknet::Layer::wz_unused

Todo:: V5: unused?

◆ x

float* Darknet::Layer::x

rolling variance loaded here by load_convolutional_weights() and load_connected_weights() when batch_normalize is set

◆ x_gpu

float* Darknet::Layer::x_gpu

◆ x_norm

float* Darknet::Layer::x_norm

◆ x_norm_gpu

float* Darknet::Layer::x_norm_gpu

◆ xnor

int Darknet::Layer::xnor

◆ yolo_point

YOLO_POINT Darknet::Layer::yolo_point

◆ z_cpu

float* Darknet::Layer::z_cpu

◆ z_gpu

float* Darknet::Layer::z_gpu

The documentation for this struct was generated from the following file:

darknet_layers.hpp

Public Attributes