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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("data", "kitti", "kitti_raw"))
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="dynadepth")
self.parser.add_argument("--split",
type=str,
help="which training split to use",
choices=["eigen_quater", "eigen_half", "eigen_three_quater", "eigen_zhou", "eigen_full", "odom", "benchmark", "test"],
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("--num_layers_imu",
type=int,
help="number of resnet layers, for velo and gravity networks",
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"])
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=8)
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=30)
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"])
# ABLATION STUDY for DynaDepth
self.parser.add_argument("--imu_filename",
type=str,
default="matched_oxts.txt",
help="matched_oxts means clean imu data, while we can use other names for noisy oxts (e.g., matched_oxts_gyro/acc/both_1.0/0.1/0.01.txt)")
self.parser.add_argument("--img_noise_type",
type=str,
default="clean",
choices=["clean", "bcsh", "gg", "mask"],
help="(1) bcsh: brightness/contrast/saturation/hue (2) gg: gamma/gain (3) mask" )
self.parser.add_argument("--img_noise_brightness",
type=float,
default=0.5)
self.parser.add_argument("--img_noise_contrast",
type=float,
default=0.5)
self.parser.add_argument("--img_noise_saturation",
type=float,
default=0.5)
self.parser.add_argument("--img_noise_hue",
type=float,
default=0.5)
self.parser.add_argument("--img_noise_gamma",
type=float,
default=0.5)
self.parser.add_argument("--img_noise_gain",
type=float,
default=0.5)
self.parser.add_argument("--img_mask_num",
type=int,
default=1)
self.parser.add_argument("--img_mask_size",
type=int,
default=50,
help="square and masked on raw image size (inputs[(n,im,-1)] before resize): e.g. 1241x376")
self.parser.add_argument("--avoid_quat_check",
help="if set, will not check quat.norm to be close to 1 (useful for noisy imu)",
action="store_true")
# IMU options
self.parser.add_argument("--display_velo_scale",
help="if set, will log the abs diff of velo norm in tensorboard",
action="store_true")
self.parser.add_argument("--imu_l2_weight",
type=float,
help="l2 loss for imu pre-integrated poses and camera-predicted poses",
default=0)
self.parser.add_argument("--imu_warp_weight",
type=float,
help="imu warping loss weight",
default=0.5)
self.parser.add_argument("--imu_consistency_weight",
type=float,
help="imu consistency loss weight",
default=0.01)
self.parser.add_argument("--trans_scale_factor",
type=float,
help="real baseline: 0.54m, baseline in the code: 0.1m",
default=5.4)
self.parser.add_argument("--no_grad_imu_consistency",
type=str,
help="disable gradient backpropagation of the specified option",
default="none",
choices=["network", "imu", "none"])
self.parser.add_argument("--first_save_epoch",
type=int,
help="the first epoch to save models",
default=10)
self.parser.add_argument("--predict_velo_residue",
help="if set predict velo residual based on predicted translation / dt",
action="store_true")
self.parser.add_argument("--velo_weight",
type=float,
help="loss weight for velocity_norm",
default=0.001)
self.parser.add_argument("--gravity_weight",
type=float,
help="loss weight for gravity_norm",
default=0.001)
## EKF options
self.parser.add_argument("--use_ekf",
help="online denoising and integrating IMU data",
action="store_true")
self.parser.add_argument("--ekf_warming_epochs",
type=int,
help="when --use_ekf, pretraining epochs before adding ekf",
default=0)
self.parser.add_argument("--ekf_velo_weight",
type=float,
help="loss weight for L2 norm of ekf_v and v_ck",
default=0)
self.parser.add_argument("--ekf_gravity_weight",
type=float,
help="loss weight for L2 norm of ekf_g and g_ck",
default=0)
self.parser.add_argument("--train_init_covar",
help="train the imu init covar, otherwise fixed",
action="store_true")
self.parser.add_argument("--train_imu_noise_covar",
help="train the imu noise covar, otherwise fixed",
action="store_true")
self.parser.add_argument("--vis_covar_use_fixed",
help="use fixed vis_covar, otherwise predict by CNN",
action="store_true")
self.parser.add_argument("--sample_vis_pose",
help="Sample the vis_pose for warping from the normal distribution",
action="store_true")
self.parser.add_argument("--naive_vis_covar",
help="directly regress vis_covar rather than using 10^(3*tanh(x))",
action="store_true")
self.parser.add_argument("--resume_imu",
help="resume from our imu-only checkpoint after --resume_epochs",
action="store_true")
self.parser.add_argument("--resume_gravity",
help="resume from our imu+gravity checkpoint after --resume_epochs",
action="store_true")
self.parser.add_argument("--resume_velo",
help="resume from our imu+velo+gravity checkpoint after --resume_epochs",
action="store_true")
self.parser.add_argument("--resume_epochs",
type=int,
help="pretrain epochs when --resume_imu/gravity/velo",
default=0)
self.parser.add_argument("--k_imu_clip",
type=int,
help="k_imu_clip x 12 IMU records will be fed into CNN",
default=5)
# 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=12)
# 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_mono",
help="if set evaluates in mono mode that uses median scaling, otherwise uses the scale-aware evaluation",
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("--show_scale_hist",
action="store_true")
self.parser.add_argument("--save_make3d",
action="store_true")
def parse(self):
self.options = self.parser.parse_args()
return self.options