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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.
from __future__ import absolute_import, division, print_function
import os
import configargparse
file_dir = os.path.dirname(__file__) # the directory that options.py resides in
class MonodepthOptions:
def __init__(self):
self.parser = configargparse.ArgumentParser()
self.parser.add_argument('--config', is_config_file=True,
help='config file path')
# PATHS
self.parser.add_argument("--data_path",
type=str,
help="path to the training data",
default=os.path.join(file_dir, "kitti_data"))
self.parser.add_argument("--log_dir",
type=str,
help="log directory",
default='./logs')
# TRAINING options
self.parser.add_argument("--model_name",
type=str,
help="the name of the folder to save the model in",
default="mdp")
self.parser.add_argument("--split",
type=str,
help="which training split to use",
choices=["eigen_zhou", "eigen_full", "odom", "benchmark"],
default="eigen_zhou")
self.parser.add_argument("--num_layers",
type=int,
help="number of resnet layers",
default=34,
choices=[18, 34, 50, 101, 152])
self.parser.add_argument("--dataset",
type=str,
help="dataset to train on",
default="kitti"
)
self.parser.add_argument("--png",
help="if set, trains from raw KITTI png files (instead of jpgs)",
action="store_true")
self.parser.add_argument("--height",
type=int,
help="input image height",
default=336)
self.parser.add_argument("--width",
type=int,
help="input image width",
default=672)
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("--disparity_smoothness",
type=float,
help="disparity smoothness weight",
default=2.0)
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=100.0)
self.parser.add_argument("--use_stereo",
help="if set, uses stereo pair for training",
action="store_true")
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("--eval_only",
help="if set, only evaluation",
action="store_true")
self.parser.add_argument("--use_fix_mask",
help="if set, use self-occlusion mask (only for DDAD)",
action="store_true")
self.parser.add_argument("--spatial", type=lambda x: x.lower() == 'true', default=False,
help="if set, use spatial photometric loss")
self.parser.add_argument("--joint_pose",
help="if set, use joint pose estimation",
action="store_true")
self.parser.add_argument("--model_type",
type=str,
default="unet")
self.parser.add_argument("--use_sfm_spatial", type=lambda x: x.lower() == 'true', default=False,
help="if set, use sfm pseudo label")
self.parser.add_argument("--thr_dis",
type=float,
help="epipolar geometry threshold",
default=1.0)
self.parser.add_argument("--match_spatial_weight",
type=float,
help="sfm pretraining loss weight",
default=0.1)
self.parser.add_argument("--spatial_weight",
type=float,
help="spatial photometric loss weight",
default=0.1)
self.parser.add_argument("--skip",
help="if set, use skip connection in CVT",
action="store_true")
self.parser.add_argument("--focal", type=lambda x: x.lower() == 'true', default=False,
help="if set, use sfm pseudo label")
self.parser.add_argument("--focal_scale",
type=float,
help="the global focal length to normalize depth",
default=500)
# OPTIMIZATION options
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("--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)
# ABLATION options
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("--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("--no_cuda",
help="if set disables CUDA",
action="store_true")
self.parser.add_argument("--num_workers",
type=int,
help="number of dataloader workers",
default=20)
# 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 batches between each tensorboard log",
default=25)
self.parser.add_argument("--save_frequency",
type=int,
help="number of epochs between each save",
default=1)
self.parser.add_argument("--eval_frequency",
type=int,
help="number of epochs between each save",
default=1000)
# EVALUATION options
self.parser.add_argument("--eval_stereo",
help="if set evaluates in stereo mode",
action="store_true")
self.parser.add_argument("--eval_mono",
help="if set evaluates in mono mode",
action="store_true")
self.parser.add_argument("--disable_median_scaling",
help="if set disables median scaling in evaluation",
action="store_true")
self.parser.add_argument("--pred_depth_scale_factor",
help="if set multiplies predictions by this number",
type=float,
default=1)
self.parser.add_argument("--ext_disp_to_eval",
type=str,
help="optional path to a .npy disparities file to evaluate")
self.parser.add_argument("--eval_split",
type=str,
default="eigen",
choices=[
"eigen", "eigen_benchmark", "benchmark", "odom_9", "odom_10"],
help="which split to run eval on")
self.parser.add_argument("--save_pred_disps",
help="if set saves predicted disparities",
action="store_true")
self.parser.add_argument("--no_eval",
help="if set disables evaluation",
action="store_true")
self.parser.add_argument("--eval_eigen_to_benchmark",
help="if set assume we are loading eigen results from npy but "
"we want to evaluate using the new benchmark.",
action="store_true")
self.parser.add_argument("--eval_out_dir",
help="if set will output the disparities to this folder",
type=str)
self.parser.add_argument("--post_process",
help="if set will perform the flipping post processing "
"from the original monodepth paper",
action="store_true")
self.parser.add_argument("--local_rank", default=0, type=int)
# customized
self.parser.add_argument("--volume_depth",
type=lambda x: x.lower() == 'true', default = True,
help="if set, using the depth from volume rendering, rather than the depthdecoder", )
self.parser.add_argument("--loss_type", type=str,
help="the loss for training [self, semi, gt]", default='gt')
self.parser.add_argument("--trans2voxel", type=str,
help="the manner from 2d feature to 3D voxel：[interpolation, transformer]",
default = "interpolation")
self.parser.add_argument("--voxels_size", type=int, action='append', default=[16, 256, 256],
help='the resolution of the voxel for rendering： Z, Y, X = 200, 8, 200')
self.parser.add_argument("--real_size", type=int, action='append', default=[-52, 52, -52, 52, 0, 6],
help='the real scale of the voxel: XMIN, XMAX, ZMIN, ZMAX, YMIN, YMAX')
self.parser.add_argument("--scales", action='append', type=int, help="scales used in the loss",
default=[0])
self.parser.add_argument("--stepsize", help="stepsize for rendering", type=float, default=0.5)
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("--pose_aug", type=str,
help="do the augmentation on the camera pose for image level or car level [image, car, No]",
default='No')
self.parser.add_argument("--render_type", type=str,
help="rednering by the density or probability [density, prob]", default='prob')
self.parser.add_argument("--position", type=str,
help="rednering by the density or probability [No, embedding, embedding1]",
default='No')
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("--log", type=lambda x: x.lower() == 'true', default = False,
help="if set, using line space sample")
self.parser.add_argument("--render_h", type=int, help="input image height",
default=224)
self.parser.add_argument("--render_w", type=int, help="input image width",
default=352)
self.parser.add_argument("--view_trans", type=str,
help="the manner for image space to 3D volume space [simple, lift, bevformer]",
default='simple')
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("--cam_N", type=int, help="THE NUM OF CAM", default=6)
self.parser.add_argument("--method", type=str,
help="the method for the comparison [surrounddepth, CRF, monodepth2]",
default='rendering')
self.parser.add_argument("--encoder", type=str,
help="the method for the comparison [101, 50]", default='50')
self.parser.add_argument("--loss", type=str,
help="activation in the decoder [l1, sml1, silog, rl1]",
default='silog')
self.parser.add_argument("--evl_score", type=lambda x: x.lower() == 'true', default=True,
help="if set, eval the occupancy score!")
self.parser.add_argument("--surfaceloss", type=float, default=1.0,
help="if tvloss > 0, using the surface loss", )
self.parser.add_argument("--empty_w", type=float, default=5.0,
help="the weight of the empty point loss for the l1 grid loss", )
self.parser.add_argument("--l1_voxel", type=str,
help="activation in the decoder [No, ce, l1, ce_only, l1_only]", default='No')
self.parser.add_argument("--val_reso", type=float, default=0.4, help="the resolution of the voxel in the evaluation [0.2, 0.4]")
self.parser.add_argument("--N_trian", type=int, help="THE NUM OF sample point in the voxel training", default=30)
self.parser.add_argument("--val_depth", type=lambda x: x.lower() == 'true', default=True, help="if set, do the depth voxel evaluation!")
self.parser.add_argument("--use_t", type=str, default='No', help="if [No, 2d, 3d], do the temporal information fusion!")
self.parser.add_argument("--surround_view", type=lambda x: x.lower() == 'true', default=False, help="if set, eval the surrounding view depth!")
self.parser.add_argument("--pretrain_path", type=str, help="pretrain path for the encoder", default='No')
self.parser.add_argument("--ground_prior", type=lambda x: x.lower() == 'true', default=False, help="if set, set the ground as 1!")
self.parser.add_argument("--downsample_val", type=lambda x: x.lower() == 'true', default=True, help="if set")
self.parser.add_argument("--val_binary", type=lambda x: x.lower() == 'true', default=False, help="if set")
self.parser.add_argument("--abs", type=lambda x: x.lower() == 'true', default=False, help="if set, use the abs scale")
self.parser.add_argument("--gt_pose", type=lambda x: x.lower() == 'true', default=False, help="if set, use the gt pose")
self.parser.add_argument("--dataroot", type=str, help="the root for the ddad and nuscenes dataset", default='/data/ggeoinfo/Wanshui_BEV/data/ddad')
# sdf
self.parser.add_argument("--sdf", type=str, default='No', help="if set, model the sky with mlp")
self.parser.add_argument("--beta", type=float, default=1.0, help="the initial weight of beta in sdf")
self.parser.add_argument("--vis_sdf", type=lambda x: x.lower() == 'true', default=False, help="if set, vis sdf")
def parse(self):
self.options = self.parser.parse_args()
return self.options