train Namespace Reference

Functions
def	save_checkpoint (save_path, epoch, depthNet, optimizer, is_best, filename='checkpoint.pth')
	model save method #### More...
def	save_urgentstop (save_path, depthNet_state, filename='urgentstop.pth')
def	save_path_formatter (args, parser)
def	main ()
def	np2Depth (input_tensor, invaild_mask)
def	showdepth (predict_depths, gt_depth, b_idx)
def	train (epoch, train_loader, HCDepthNet, optimizer, epoch_length, train_writer)
def	validate (epoch, val_loader, HCDepthNet, epoch_length, validating_writer)

Variables
	parser
	metavar
	help
	type
	int
	default
	float
	dest
	None
	action
bool	scaling = True
int	best_error = -1
	device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
int	scaled_width = 480
int	scaled_height = 352
float	dmin = 0.5
float	dmax = 32.0
int	level = 64

Function Documentation

main()

def train.main

(

)

np2Depth()

def train.np2Depth	(		input_tensor,
			invaild_mask
	)

save_checkpoint()

def train.save_checkpoint	(		save_path,
			epoch,
			depthNet,
			optimizer,
			is_best,
			filename = 'checkpoint.pth'
	)

model save method ####

save_path_formatter()

def train.save_path_formatter	(		args,
			parser
	)

save_urgentstop()

def train.save_urgentstop	(		save_path,
			depthNet_state,
			filename = 'urgentstop.pth'
	)

showdepth()

def train.showdepth	(		predict_depths,
			gt_depth,
			b_idx
	)

train()

def train.train	(		epoch,
			train_loader,
			HCDepthNet,
			optimizer,
			epoch_length,
			train_writer
	)

validate()

def train.validate	(		epoch,
			val_loader,
			HCDepthNet,
			epoch_length,
			validating_writer
	)

Variable Documentation

action

train.action

best_error

int train.best_error = -1

default

train.default

dest

train.dest

device

train.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

dmax

float train.dmax = 32.0

dmin

float train.dmin = 0.5

float

train.float

help

train.help

int

train.int

level

int train.level = 64

metavar

train.metavar

None

train.None

parser

train.parser

Initial value:

=  argparse.ArgumentParser(description='Multi-view depth estimation',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)

scaled_height

int train.scaled_height = 352

scaled_width

int train.scaled_width = 480

scaling

bool train.scaling = True

type

train.type