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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")
# 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("--height",
type=int,
help="input image height",
default=288) # 改成nyu的288
self.parser.add_argument("--width",
type=int,
help="input image width",
default=384)
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=10.0) # 改成NYU的10.0
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=41) # change from 20 to 41
self.parser.add_argument("--scheduler_step_size",
type=int,
help="step size of the scheduler",
default=15)
# SYSTEM options
self.parser.add_argument("--no_cuda",
help="if set disables CUDA",
action="store_true")
# 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"],
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("--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("--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