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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="DCL options")
# PATHS
self.parser.add_argument("--data_path",
type=str,
help="path to the training data",
default=os.path.join(file_dir, "data/gopro_data"))
self.parser.add_argument("--log_dir",
type=str,
help="log directory",
default=os.path.join(file_dir, "logger"))
# TRAINING options
self.parser.add_argument("--model_name",
type=str,
help="the name of the folder to save the model in",
default="DCL")
self.parser.add_argument("--split",
type=str,
help="which training split to use",
choices=["dense_fog", "gopro_fan"],
default="gopro_fan")
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="gopro",
choices=["gopro", "densefog", "lightfog"])
self.parser.add_argument("--png",
help="if set, trains from raw KITTI png files (instead of jpgs)",
action="store_true")
self.parser.add_argument("--input_frames",
type = int,
help = "the number of input hazy image " ,
default = 2)
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])
self.parser.add_argument('--gan_mode',
type=str,
default='lsgan',
help='the type of GAN objective. [vanilla| lsgan | wgangp]. ')
self.parser.add_argument('--norm',
type=str,
default='instance',
help='instance normalization or batch normalization [instance | batch]')
self.parser.add_argument('--use_position_map',
type=bool,
default=True,
help='if set, use position prior for discrimiator')
# OPTIMIZATION options
self.parser.add_argument("--batch_size",
type=int,
help="batch size",
default=2)
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=50)
# ABLATION options
self.parser.add_argument("--weights_init",
type=str,
help="pretrained or scratch",
default="pretrained",
choices=["pretrained", "scratch"])
# 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", "dehaze_network"])
# LOGGING options
self.parser.add_argument("--log_frequency",
type=int,
help="number of batches between each tensorboard log",
default=50)
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",
# 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")
def parse(self):
self.options = self.parser.parse_args()
return self.options