160 lines
8.4 KiB
Python
160 lines
8.4 KiB
Python
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import argparse
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import os
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import sys
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sys.path.insert(0, os.path.join('.', 'features'))
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sys.path.insert(0, os.path.join('.', 'models'))
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from openpifpaf.network import nets
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from openpifpaf import decoder
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from features.preprocess_nu import PreprocessNuscenes
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from features.preprocess_ki import PreprocessKitti
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from predict.predict import predict
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from models.trainer import Trainer
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from eval.generate_kitti import generate_kitti
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from eval.geom_baseline import geometric_baseline
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from models.hyp_tuning import HypTuning
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from eval.kitti_eval import KittiEval
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from visuals.webcam import webcam
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def cli():
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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# Subparser definition
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subparsers = parser.add_subparsers(help='Different parsers for main actions', dest='command')
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predict_parser = subparsers.add_parser("predict")
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prep_parser = subparsers.add_parser("prep")
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training_parser = subparsers.add_parser("train")
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eval_parser = subparsers.add_parser("eval")
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# Preprocess input data
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prep_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints', required=True)
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prep_parser.add_argument('--dataset',
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help='datasets to preprocess: nuscenes, nuscenes_teaser, nuscenes_mini, kitti',
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default='nuscenes')
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prep_parser.add_argument('--dir_nuscenes', help='directory of nuscenes devkit',
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default='data/nuscenes/')
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# Predict (2D pose and/or 3D location from images)
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# 0) General arguments
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predict_parser.add_argument('--networks', nargs='+', help='Run pifpaf and/or monoloco', default=['monoloco'])
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predict_parser.add_argument('images', nargs='*', help='input images')
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predict_parser.add_argument('--glob', help='glob expression for input images (for many images)')
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predict_parser.add_argument('-o', '--output-directory', help='Output directory')
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predict_parser.add_argument('--output_types', nargs='+', default=['json'],
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help='what to output: json keypoints skeleton for Pifpaf'
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'json bird front combined for Monoloco')
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predict_parser.add_argument('--show', help='to show images', action='store_true')
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# 1)Pifpaf arguments
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nets.cli(predict_parser)
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decoder.cli(predict_parser, force_complete_pose=True, instance_threshold=0.1)
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predict_parser.add_argument('--checkpoint', help='pifpaf model to load')
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predict_parser.add_argument('--scale', default=1.0, type=float, help='change the scale of the image to preprocess')
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# 2) Monoloco argument
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predict_parser.add_argument('--model', help='path of MonoLoco model to load',
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default="data/models/monoloco-190513-1437.pkl")
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predict_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=256)
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predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization',
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default='data/arrays/names-kitti-190513-1754.json')
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predict_parser.add_argument('--transform', help='transformation for the pose', default='None')
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predict_parser.add_argument('--draw_kps', help='to draw kps in the images', action='store_true')
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predict_parser.add_argument('--predict', help='whether to make prediction', action='store_true')
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predict_parser.add_argument('--z_max', type=int, help='maximum meters distance for predictions', default=22)
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predict_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0)
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predict_parser.add_argument('--dropout', type=float, help='dropout parameter', default=0.2)
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predict_parser.add_argument('--webcam', help='monoloco streaming', action='store_true')
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# Training
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training_parser.add_argument('--joints', help='Json file with input joints',
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default='data/arrays/joints-nuscenes_teaser-190513-1846.json')
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training_parser.add_argument('--save', help='whether to not save model and log file', action='store_false')
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training_parser.add_argument('-e', '--epochs', type=int, help='number of epochs to train for', default=150)
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training_parser.add_argument('--bs', type=int, default=256, help='input batch size')
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training_parser.add_argument('--baseline', help='whether to train using the baseline', action='store_true')
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training_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2)
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training_parser.add_argument('--lr', type=float, help='learning rate', default=0.002)
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training_parser.add_argument('--sched_step', type=float, help='scheduler step time (epochs)', default=20)
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training_parser.add_argument('--sched_gamma', type=float, help='Scheduler multiplication every step', default=0.9)
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training_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=256)
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training_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3)
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training_parser.add_argument('--hyp', help='run hyperparameters tuning', action='store_true')
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training_parser.add_argument('--multiplier', type=int, help='Size of the grid of hyp search', default=1)
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training_parser.add_argument('--r_seed', type=int, help='specify the seed for training and hyp tuning', default=1)
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# Evaluation
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eval_parser.add_argument('--dataset', help='datasets to evaluate, kitti or nuscenes', default='kitti')
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eval_parser.add_argument('--geometric', help='to evaluate geometric distance', action='store_true')
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eval_parser.add_argument('--generate', help='create txt files for KITTI evaluation', action='store_true')
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eval_parser.add_argument('--dir_ann', help='directory of annotations of 2d joints (for KITTI evaluation')
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eval_parser.add_argument('--model', help='path of MonoLoco model to load', required=True)
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eval_parser.add_argument('--joints', help='Json file with input joints to evaluate (for nuScenes evaluation)')
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eval_parser.add_argument('--n_dropout', type=int, help='Epistemic uncertainty evaluation', default=0)
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eval_parser.add_argument('--dropout', type=float, help='dropout. Default no dropout', default=0.2)
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eval_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=256)
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eval_parser.add_argument('--n_stage', type=int, help='Number of stages in the model', default=3)
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eval_parser.add_argument('--show', help='whether to show eval statistics', action='store_true')
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args = parser.parse_args()
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return args
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def main():
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args = cli()
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if args.command == 'predict':
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if args.webcam:
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webcam(args)
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else:
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predict(args)
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elif args.command == 'prep':
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if 'nuscenes' in args.dataset:
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prep = PreprocessNuscenes(args.dir_ann, args.dir_nuscenes, args.dataset)
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prep.run()
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if 'kitti' in args.dataset:
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prep = PreprocessKitti(args.dir_ann)
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prep.run()
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elif args.command == 'train':
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if args.hyp:
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hyp_tuning = HypTuning(joints=args.joints, epochs=args.epochs,
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baseline=args.baseline, dropout=args.dropout,
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multiplier=args.multiplier, r_seed=args.r_seed)
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hyp_tuning.train()
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else:
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training = Trainer(joints=args.joints, epochs=args.epochs, bs=args.bs,
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baseline=args.baseline, dropout=args.dropout, lr=args.lr, sched_step=args.sched_step,
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n_stage=args.n_stage, sched_gamma=args.sched_gamma, hidden_size=args.hidden_size,
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r_seed=args.r_seed, save=args.save)
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_ = training.train()
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_ = training.evaluate()
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elif args.command == 'eval':
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if args.geometric:
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geometric_baseline(args.joints)
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if args.generate:
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generate_kitti(args.model, args.dir_ann, p_dropout=args.dropout, n_dropout=args.n_dropout)
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if args.dataset == 'kitti':
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kitti_eval = KittiEval()
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kitti_eval.run()
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kitti_eval.printer(show=args.show)
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if 'nuscenes' in args.dataset:
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training = Trainer(joints=args.joints)
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_ = training.evaluate(load=True, model=args.model, debug=False)
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else:
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raise ValueError("Main subparser not recognized or not provided")
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if __name__ == '__main__':
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main()
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