196 lines
11 KiB
Python
196 lines
11 KiB
Python
# pylint: disable=too-many-branches, too-many-statements
|
|
|
|
import argparse
|
|
|
|
from openpifpaf.network import nets
|
|
from openpifpaf import decoder
|
|
|
|
|
|
def cli():
|
|
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
|
|
|
# Subparser definition
|
|
subparsers = parser.add_subparsers(help='Different parsers for main actions', dest='command')
|
|
predict_parser = subparsers.add_parser("predict")
|
|
prep_parser = subparsers.add_parser("prep")
|
|
training_parser = subparsers.add_parser("train")
|
|
eval_parser = subparsers.add_parser("eval")
|
|
|
|
# Preprocess input data
|
|
prep_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints', required=True)
|
|
prep_parser.add_argument('--dataset',
|
|
help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti',
|
|
default='kitti')
|
|
prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit', default='data/nuscenes/')
|
|
prep_parser.add_argument('--iou_min', help='minimum iou to match ground truth', type=float, default=0.3)
|
|
prep_parser.add_argument('--variance', help='new', action='store_true')
|
|
prep_parser.add_argument('--activity', help='new', action='store_true')
|
|
prep_parser.add_argument('--monocular', help='new', action='store_true')
|
|
|
|
# Predict (2D pose and/or 3D location from images)
|
|
# General
|
|
predict_parser.add_argument('--mode', help='pifpaf, mono, stereo', default='stereo')
|
|
predict_parser.add_argument('images', nargs='*', help='input images')
|
|
predict_parser.add_argument('--glob', help='glob expression for input images (for many images)')
|
|
predict_parser.add_argument('-o', '--output-directory', help='Output directory')
|
|
predict_parser.add_argument('--output_types', nargs='+', default=['json'],
|
|
help='what to output: json keypoints skeleton for Pifpaf'
|
|
'json bird front combined for Monoloco')
|
|
predict_parser.add_argument('--show', help='to show images', action='store_true')
|
|
|
|
# Pifpaf
|
|
nets.cli(predict_parser)
|
|
decoder.cli(predict_parser, force_complete_pose=True, instance_threshold=0.15)
|
|
predict_parser.add_argument('--scale', default=1.0, type=float, help='change the scale of the image to preprocess')
|
|
|
|
# Monoloco
|
|
predict_parser.add_argument('--model', help='path of MonoLoco model to load', required=True)
|
|
predict_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=512)
|
|
predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization',
|
|
default='data/arrays/names-kitti-200615-1022.json')
|
|
predict_parser.add_argument('--transform', help='transformation for the pose', default='None')
|
|
predict_parser.add_argument('--draw_box', help='to draw box in the images', action='store_true')
|
|
predict_parser.add_argument('--z_max', type=int, help='maximum meters distance for predictions', default=22)
|
|
predict_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0)
|
|
predict_parser.add_argument('--dropout', type=float, help='dropout parameter', default=0.2)
|
|
predict_parser.add_argument('--show_all', help='only predict ground-truth matches or all', action='store_true')
|
|
|
|
# Social distancing and social interactions
|
|
predict_parser.add_argument('--social', help='social', action='store_true')
|
|
predict_parser.add_argument('--activity', help='activity', action='store_true')
|
|
predict_parser.add_argument('--json_dir', help='for social')
|
|
predict_parser.add_argument('--threshold_prob', type=float, help='concordance for samples', default=0.25)
|
|
predict_parser.add_argument('--threshold_dist', type=float, help='min distance of people', default=2)
|
|
predict_parser.add_argument('--margin', type=float, help='conservative for noise in orientation', default=1.5)
|
|
predict_parser.add_argument('--radii', type=tuple, help='o-space radii', default=(0.25, 1, 2))
|
|
|
|
# Training
|
|
training_parser.add_argument('--joints', help='Json file with input joints',
|
|
default='data/arrays/joints-nuscenes_teaser-190513-1846.json')
|
|
training_parser.add_argument('--save', help='whether to not save model and log file', action='store_true')
|
|
training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=500)
|
|
training_parser.add_argument('--bs', type=int, default=512, help='input batch size')
|
|
training_parser.add_argument('--monocular', help='whether to train monoloco', action='store_true')
|
|
training_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2)
|
|
training_parser.add_argument('--lr', type=float, help='learning rate', default=0.001)
|
|
training_parser.add_argument('--sched_step', type=float, help='scheduler step time (epochs)', default=30)
|
|
training_parser.add_argument('--sched_gamma', type=float, help='Scheduler multiplication every step', default=0.98)
|
|
training_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=1024)
|
|
training_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3)
|
|
training_parser.add_argument('--hyp', help='run hyperparameters tuning', action='store_true')
|
|
training_parser.add_argument('--multiplier', type=int, help='Size of the grid of hyp search', default=1)
|
|
training_parser.add_argument('--r_seed', type=int, help='specify the seed for training and hyp tuning', default=1)
|
|
training_parser.add_argument('--activity', help='new', action='store_true')
|
|
|
|
# Evaluation
|
|
eval_parser.add_argument('--dataset', help='datasets to evaluate, kitti or nuscenes', default='kitti')
|
|
eval_parser.add_argument('--geometric', help='to evaluate geometric distance', action='store_true')
|
|
eval_parser.add_argument('--generate', help='create txt files for KITTI evaluation', action='store_true')
|
|
eval_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints (for KITTI evaluation)')
|
|
eval_parser.add_argument('--model', help='path of MonoLoco model to load')
|
|
eval_parser.add_argument('--joints', help='Json file with input joints to evaluate (for nuScenes evaluation)')
|
|
eval_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0)
|
|
eval_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2)
|
|
eval_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=1024)
|
|
eval_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3)
|
|
eval_parser.add_argument('--show', help='whether to show statistic graphs', action='store_true')
|
|
eval_parser.add_argument('--save', help='whether to save statistic graphs', action='store_true')
|
|
eval_parser.add_argument('--verbose', help='verbosity of statistics', action='store_true')
|
|
eval_parser.add_argument('--monocular', help='whether to train using the baseline', action='store_true')
|
|
eval_parser.add_argument('--new', help='new', action='store_true')
|
|
eval_parser.add_argument('--variance', help='evaluate keypoints variance', action='store_true')
|
|
eval_parser.add_argument('--activity', help='evaluate activities', action='store_true')
|
|
eval_parser.add_argument('--net', help='Choose network: monoloco, monoloco_p, monoloco_pp, monstereo')
|
|
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = cli()
|
|
if args.command == 'predict':
|
|
if args.activity:
|
|
from .activity import predict
|
|
else:
|
|
from .predict import predict
|
|
predict(args)
|
|
|
|
elif args.command == 'prep':
|
|
|
|
if 'nuscenes' in args.dataset:
|
|
from .prep.preprocess_nu import PreprocessNuscenes
|
|
prep = PreprocessNuscenes(args.dir_ann, args.dir_nuscenes, args.dataset, args.iou_min)
|
|
prep.run()
|
|
else:
|
|
from .prep.prep_kitti import PreprocessKitti
|
|
prep = PreprocessKitti(args.dir_ann, args.iou_min, args.monocular)
|
|
if args.activity:
|
|
prep.prep_activity()
|
|
else:
|
|
prep.run()
|
|
|
|
elif args.command == 'train':
|
|
from .train import HypTuning
|
|
if args.hyp:
|
|
hyp_tuning = HypTuning(joints=args.joints, epochs=args.epochs,
|
|
monocular=args.monocular, dropout=args.dropout,
|
|
multiplier=args.multiplier, r_seed=args.r_seed)
|
|
hyp_tuning.train()
|
|
else:
|
|
|
|
from .train import Trainer
|
|
training = Trainer(joints=args.joints, epochs=args.epochs, bs=args.bs,
|
|
monocular=args.monocular, dropout=args.dropout, lr=args.lr, sched_step=args.sched_step,
|
|
n_stage=args.n_stage, sched_gamma=args.sched_gamma, hidden_size=args.hidden_size,
|
|
r_seed=args.r_seed, save=args.save)
|
|
|
|
_ = training.train()
|
|
_ = training.evaluate()
|
|
|
|
elif args.command == 'eval':
|
|
|
|
if args.activity:
|
|
from .eval.eval_activity import ActivityEvaluator
|
|
evaluator = ActivityEvaluator(args)
|
|
if 'collective' in args.dataset:
|
|
evaluator.eval_collective()
|
|
else:
|
|
evaluator.eval_kitti()
|
|
|
|
elif args.geometric:
|
|
assert args.joints, "joints argument not provided"
|
|
from .network.geom_baseline import geometric_baseline
|
|
geometric_baseline(args.joints)
|
|
|
|
elif args.variance:
|
|
from .eval.eval_variance import joints_variance
|
|
joints_variance(args.joints, clusters=None, dic_ms=None)
|
|
|
|
else:
|
|
if args.generate:
|
|
from .eval.generate_kitti import GenerateKitti
|
|
kitti_txt = GenerateKitti(args.model, args.dir_ann, p_dropout=args.dropout, n_dropout=args.n_dropout,
|
|
hidden_size=args.hidden_size)
|
|
kitti_txt.run()
|
|
|
|
if args.dataset == 'kitti':
|
|
from .eval import EvalKitti
|
|
kitti_eval = EvalKitti(verbose=args.verbose)
|
|
kitti_eval.run()
|
|
kitti_eval.printer(show=args.show, save=args.save)
|
|
|
|
elif 'nuscenes' in args.dataset:
|
|
from .train import Trainer
|
|
training = Trainer(joints=args.joints, hidden_size=args.hidden_size)
|
|
_ = training.evaluate(load=True, model=args.model, debug=False)
|
|
|
|
else:
|
|
raise ValueError("Option not recognized")
|
|
|
|
else:
|
|
raise ValueError("Main subparser not recognized or not provided")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|