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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 argparse
file_dir = os.path.dirname(__file__) # the directory that options.py resides in
class MonodepthOptions:
def __init__(self):
self.parser = argparse.ArgumentParser(description="Monodepthv2 options")
# 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=os.path.join(os.path.expanduser("~"), "tmp"))
# 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", "scannet"],
default="eigen_zhou")
self.parser.add_argument("--num_layers",
type=int,
help="number of resnet layers",
default=18,
choices=[18, 34, 50, 101, 152])
self.parser.add_argument("--dataset",
type=str,
help="dataset to train on",
default="kitti",
choices=["kitti", "kitti_odom", "kitti_depth", "kitti_test", "scannet", "nuscenes", "make3d", "vkitti"])
# 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=192)
self.parser.add_argument("--width",
type=int,
help="input image width",
default=640)
self.parser.add_argument("--disparity_smoothness",
type=float,
help="disparity smoothness weight",
default=1e-3)
self.parser.add_argument("--scales",
nargs="+",
type=int,
help="scales used in the loss",
default=[0, 1, 2, 3])
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",
default=[0, -1, 1])
# OPTIMIZATION options
self.parser.add_argument("--batch_size",
type=int,
help="batch size",
default=12)
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=20)
self.parser.add_argument("--scheduler_step_size",
type=int,
help="step size of the scheduler",
default=15)
# 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",
choices=["posecnn", "separate_resnet", "shared"])
# 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=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", "pose_encoder", "pose"])
# LOGGING options
self.parser.add_argument("--log_frequency",
type=int,
help="number of batches between each tensorboard log",
default=250)
self.parser.add_argument("--save_frequency",
type=int,
help="number of epochs between each save",
default=1)
# 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", "make3d", "vkitti"],
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("--visu_dir",
help="Path where to save visualisation",
type=str,
default='')
# UNCERTAINTY options
self.parser.add_argument("--uncertainty",
help="Predict uncertainty",
action="store_true")
self.parser.add_argument("--uncert_act",
help="Activation function that output the uncertainty",
type=str,
choices=['sigmoid', 'exp', 'no'],
default='sigmoid')
self.parser.add_argument("--sample_size",
help="Predict uncertainty",
type=int,
default=1)
self.parser.add_argument("--masking_strategy",
help="Masking either out means or out samples",
type=str,
choices=['no', 'out_samples', 'out_dists'],
default='no')
self.parser.add_argument("--uncert_as_a_fraction_of_depth",
help="Predict uncertainty as a fraction of depth",
action="store_true")
self.parser.add_argument("--distribution",
help="The distribution of the depth",
type=str,
choices=['normal', 'laplace'])
# SELF-UNCERTAINTY options
self.parser.add_argument("--self",
help="Train self",
action="store_true")
self.parser.add_argument("--self_scaling",
help="Scale loss based on scale",
action="store_true")
self.parser.add_argument("--dist_self",
help="Distribution of the student depth prediction",
type=str,
choices=['normal', 'laplace'],
default='normal')
self.parser.add_argument("--uncert_act_stud",
help="Activation function that output the uncertainty of the student",
type=str,
choices=['sigmoid', 'exp', 'no'],
default='no')
self.parser.add_argument("--stud_uncert_as_a_fraction_of_depth",
help="Predict uncertainty as a fraction of depth",
action="store_true")
self.parser.add_argument("--kldiv",
help="Compute KLDiv instead of NLL",
action="store_true")
self.parser.add_argument("--finetune",
help="Finetune loaded checkpoints",
action="store_true")
# MONO-UNCERTAINTY options
self.parser.add_argument("--custom_scale", type=float, default=100., help="custom scale factor for depth maps")
self.parser.add_argument("--log", help="if set, adds the variance output to monodepth2 according to log-likelihood maximization technique", action="store_true")
self.parser.add_argument("--repr", help="if set, adds the Repr output to monodepth2", action="store_true")
self.parser.add_argument("--dropout", help="if set enables dropout inference", action="store_true")
self.parser.add_argument("--bootstraps", type=int, default=1, help="if > 1, loads multiple checkpoints from different trainings to build a bootstrapped ensamble")
self.parser.add_argument("--snapshots", type=int, default=1, help="if > 1, loads the last N checkpoints to build a snapshots ensemble")
self.parser.add_argument("--output_dir", type=str, default="output", help="output directory for predicted depth and uncertainty maps")
self.parser.add_argument("--qual", help="if set save colored depth and uncertainty maps", action="store_true")
def parse(self):
self.options = self.parser.parse_args()
self.options.save_visu = False if self.options.visu_dir == '' else True
return self.options