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options.py
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options.py
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# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
import configargparse
class MonodepthOptions:
def __init__(self):
self.parser = configargparse.ArgumentParser()
self.parser.add_argument('--config', is_config_file=True,
help='config file path')
self.parser.add_argument("--debug", action="store_true")
self.parser.add_argument("--eval_only",
help="if set, only evaluation",
action="store_true")
self.parser.add_argument("--local_rank", default=0, type=int)
# paths
self.parser.add_argument("--dataroot",
type=str,
help="the root for the ddad and nuscenes dataset",
default='data/nuscenes')
self.parser.add_argument("--model_name",
type=str,
help="the name of the folder to save the model in",
default="nusc-depth")
self.parser.add_argument("--log_dir",
type=str,
help="log directory",
default='logs')
# method options
self.parser.add_argument("--volume_depth",
action="store_true",
help="if set, using the depth from volume rendering, rather than the depthdecoder")
self.parser.add_argument("--voxels_size",
type=int, nargs='+',
default=[24, 300, 300],
help='the resolution of the voxel for rendering： Z, Y, X = 24, 300, 300')
self.parser.add_argument("--real_size",
type=float, nargs='+',
default=[-40, 40, -40, 40, -1, 5.4],
help='the real scale of the voxel: XMIN, XMAX, ZMIN, ZMAX, YMIN, YMAX')
self.parser.add_argument("--self_supervise",
action="store_true",
help="if set, using the self-supervised mothod")
self.parser.add_argument("--eval_occ",
action="store_true",
help="if set, eval the occupancy score")
self.parser.add_argument("--contracted_coord",
action="store_true",
help="if set, using the contracted coordinate")
self.parser.add_argument("--contracted_ratio",
type=float, default=0.8,
help="the threshold for the contracted coordinate")
self.parser.add_argument("--infinite_range",
action="store_true",
help="sampling strategy for contracted coordinate")
self.parser.add_argument("--auxiliary_frame",
action="store_true",
help="if set, using auxiliary images")
self.parser.add_argument("--use_semantic",
help="if set, use semantic segmentation for training",
action="store_true")
self.parser.add_argument("--semantic_classes",
type=int, default=17,
help="the output channel of the semantic_head")
self.parser.add_argument("--class_frequencies",
nargs="+", type=int,
default= [0, 18164955800, 734218842, 2336187448, 10996756106, 395414611,
638889260, 651023279, 117046208, 985341947, 7303776233, 43131984997,
0, 9342674867, 9525824742, 51204832885, 22848065525, 31841090018])
self.parser.add_argument("--semantic_sample_ratio",
type=float, default=0.25,
help="sample less points for semantic to accelerate the training")
self.parser.add_argument("--last_free",
action="store_true",
help="if the last class is free space")
# DATASET options
self.parser.add_argument("--dataset",
type=str,
help="dataset to train on",
default="nusc")
self.parser.add_argument("--cam_N",
type=int,
help="THE NUM OF CAM",
default=6)
self.parser.add_argument("--use_fix_mask",
help="if set, use self-occlusion mask (only for DDAD)",
action="store_true")
# OPTIMIZATION options
self.parser.add_argument("--use_fp16",
action="store_true",
help="if set, using mixed precision training")
self.parser.add_argument("--batch_size",
type=int,
help="batch size",
default=6)
self.parser.add_argument("--B",
type=int,
help="real batch size",
default=1)
self.parser.add_argument("--learning_rate",
type=float,
help="learning rate",
default=1e-4)
self.parser.add_argument("--weight_decay",
type=float,
help="weight decay",
default=0.0)
self.parser.add_argument("--num_epochs",
type=int,
help="number of epochs",
default=12)
self.parser.add_argument("--scheduler_step_size",
type=int,
help="step size of the scheduler",
default=10)
# DEPTH ESTIMATION options
self.parser.add_argument("--frame_ids",
nargs="+",
type=int,
help="frames to load, currently only support for 3 frames",
default=[0, -1, 1])
self.parser.add_argument("--v1_multiscale",
help="if set, uses monodepth v1 multiscale",
action="store_true")
self.parser.add_argument("--avg_reprojection",
help="if set, uses average reprojection loss",
action="store_true")
self.parser.add_argument("--disable_automasking",
help="if set, doesn't do auto-masking",
action="store_true")
self.parser.add_argument("--predictive_mask",
help="if set, uses a predictive masking scheme as in Zhou et al",
action="store_true")
self.parser.add_argument("--no_ssim",
help="if set, disables ssim in the loss",
action="store_true")
self.parser.add_argument("--weights_init",
type=str,
help="pretrained or scratch",
default="pretrained",
choices=["pretrained", "scratch"])
self.parser.add_argument("--scales",
type=int, nargs="+",
help="scales used in the loss",
default=[0])
# self.parser.add_argument("--pose_model_input",
# type=str,
# help="how many images the pose network gets",
# default="pairs",
# choices=["pairs", "all"])
# self.parser.add_argument("--pose_model_type",
# type=str,
# help="normal or shared",
# default="separate_resnet")
# SYSTEM options
self.parser.add_argument("--num_workers",
type=int,
help="number of dataloader workers",
default=4)
# LOADING options
self.parser.add_argument("--load_weights_folder",
type=str,
help="name of model to load")
self.parser.add_argument("--models_to_load",
nargs="+",
type=str,
help="models to load",
default=["encoder", "depth"])
# LOGGING options
self.parser.add_argument("--log_frequency",
type=int,
help="number of steps between each log",
default=25)
self.parser.add_argument("--save_frequency",
type=int,
help="save frequency for visualization",
default=100)
self.parser.add_argument("--eval_frequency",
type=int,
help="number of steps between each save",
default=1000)
# RENDERING options
self.parser.add_argument("--render_type",
type=str,
help="rednering by the density or probability [density, prob, neus, volsdf]",
default='prob')
self.parser.add_argument("--stepsize",
type=float,
help="stepsize (in voxel) for rendering",
default=0.5)
# HYPERPARAMETERS
self.parser.add_argument("--semantic_loss_weight",
type=float, default=0.05,
help="the weight for the semantic loss")
self.parser.add_argument("--disparity_smoothness",
type=float,
help="disparity smoothness weight",
default=0.001)
self.parser.add_argument("--height_ori",
type=int,
help="original input image height",
default=1216)
self.parser.add_argument("--width_ori",
type=int,
help="original input image width",
default=1936)
self.parser.add_argument("--height",
type=int, default=336,
help="input image height")
self.parser.add_argument("--width",
type=int, default=672,
help="input image width")
self.parser.add_argument("--render_h",
type=int, default=224,
help="input image height")
self.parser.add_argument("--render_w",
type=int, default=352,
help="input image width")
self.parser.add_argument("--weight_entropy_last",
type=float, default=0.0)
self.parser.add_argument("--weight_distortion",
type=float, default=0.0)
self.parser.add_argument("--weight_sparse_reg",
type=float, default=0.0)
self.parser.add_argument("--min_depth",
type=float,
help="minimum depth",
default=0.1)
self.parser.add_argument("--max_depth",
type=float,
help="maximum depth",
default=80.0)
self.parser.add_argument("--min_depth_test",
type=float,
help="the min depth for the evaluation",
default=0.1)
self.parser.add_argument("--max_depth_test",
type=float,
help="the max depth for the evaluation",
default=80.0)
self.parser.add_argument("--en_lr",
type=float,
help="learning rate for encoder in volume rendering",
default=0.0001)
self.parser.add_argument("--de_lr",
type=float,
help="learning rate for decoder (3D CNN) in volume rendering",
default=0.001)
self.parser.add_argument("--aggregation",
type=str,
help="the type of the feature aggregation [mlp 3dcnn 2dcnn]",
default= '3dcnn')
self.parser.add_argument("--position", type=str,
help="rednering by the density or probability [No, embedding, embedding1]",
default='embedding')
self.parser.add_argument("--data_type", type=str,
help=" data size for traing and testing - > [train_all, all, mini, tiny]",
default='all')
self.parser.add_argument("--input_channel", type=int, help="the final feature channel in the encoder",
default=64)
self.parser.add_argument("--con_channel", type=int, help="the final feature channel in the encoder",
default=16)
self.parser.add_argument("--out_channel", type=int, help="the output channel of the voxel",
default=1)
self.parser.add_argument("--encoder", type=str,
help="the method for the comparison [101, 50]", default='101')
def parse(self):
self.options = self.parser.parse_args()
return self.options