mirror of
https://github.com/kritiksoman/GIMP-ML
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209 lines
10 KiB
Python
209 lines
10 KiB
Python
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# Copyright Niantic 2019. Patent Pending. All rights reserved.
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#
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# This software is licensed under the terms of the Monodepth2 licence
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# which allows for non-commercial use only, the full terms of which are made
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# available in the LICENSE file.
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from __future__ import absolute_import, division, print_function
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import os
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import argparse
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file_dir = os.path.dirname(__file__) # the directory that options.py resides in
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class MonodepthOptions:
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def __init__(self):
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self.parser = argparse.ArgumentParser(description="Monodepthv2 options")
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# PATHS
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self.parser.add_argument("--data_path",
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type=str,
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help="path to the training data",
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default=os.path.join(file_dir, "kitti_data"))
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self.parser.add_argument("--log_dir",
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type=str,
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help="log directory",
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default=os.path.join(os.path.expanduser("~"), "tmp"))
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# TRAINING options
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self.parser.add_argument("--model_name",
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type=str,
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help="the name of the folder to save the model in",
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default="mdp")
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self.parser.add_argument("--split",
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type=str,
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help="which training split to use",
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choices=["eigen_zhou", "eigen_full", "odom", "benchmark"],
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default="eigen_zhou")
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self.parser.add_argument("--num_layers",
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type=int,
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help="number of resnet layers",
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default=18,
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choices=[18, 34, 50, 101, 152])
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self.parser.add_argument("--dataset",
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type=str,
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help="dataset to train on",
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default="kitti",
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choices=["kitti", "kitti_odom", "kitti_depth", "kitti_test"])
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self.parser.add_argument("--png",
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help="if set, trains from raw KITTI png files (instead of jpgs)",
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action="store_true")
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self.parser.add_argument("--height",
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type=int,
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help="input image height",
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default=192)
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self.parser.add_argument("--width",
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type=int,
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help="input image width",
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default=640)
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self.parser.add_argument("--disparity_smoothness",
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type=float,
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help="disparity smoothness weight",
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default=1e-3)
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self.parser.add_argument("--scales",
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nargs="+",
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type=int,
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help="scales used in the loss",
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default=[0, 1, 2, 3])
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self.parser.add_argument("--min_depth",
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type=float,
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help="minimum depth",
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default=0.1)
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self.parser.add_argument("--max_depth",
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type=float,
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help="maximum depth",
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default=100.0)
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self.parser.add_argument("--use_stereo",
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help="if set, uses stereo pair for training",
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action="store_true")
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self.parser.add_argument("--frame_ids",
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nargs="+",
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type=int,
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help="frames to load",
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default=[0, -1, 1])
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# OPTIMIZATION options
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self.parser.add_argument("--batch_size",
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type=int,
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help="batch size",
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default=12)
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self.parser.add_argument("--learning_rate",
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type=float,
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help="learning rate",
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default=1e-4)
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self.parser.add_argument("--num_epochs",
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type=int,
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help="number of epochs",
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default=20)
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self.parser.add_argument("--scheduler_step_size",
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type=int,
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help="step size of the scheduler",
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default=15)
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# ABLATION options
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self.parser.add_argument("--v1_multiscale",
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help="if set, uses monodepth v1 multiscale",
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action="store_true")
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self.parser.add_argument("--avg_reprojection",
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help="if set, uses average reprojection loss",
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action="store_true")
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self.parser.add_argument("--disable_automasking",
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help="if set, doesn't do auto-masking",
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action="store_true")
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self.parser.add_argument("--predictive_mask",
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help="if set, uses a predictive masking scheme as in Zhou et al",
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action="store_true")
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self.parser.add_argument("--no_ssim",
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help="if set, disables ssim in the loss",
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action="store_true")
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self.parser.add_argument("--weights_init",
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type=str,
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help="pretrained or scratch",
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default="pretrained",
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choices=["pretrained", "scratch"])
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self.parser.add_argument("--pose_model_input",
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type=str,
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help="how many images the pose network gets",
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default="pairs",
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choices=["pairs", "all"])
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self.parser.add_argument("--pose_model_type",
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type=str,
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help="normal or shared",
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default="separate_resnet",
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choices=["posecnn", "separate_resnet", "shared"])
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# SYSTEM options
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self.parser.add_argument("--no_cuda",
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help="if set disables CUDA",
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action="store_true")
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self.parser.add_argument("--num_workers",
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type=int,
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help="number of dataloader workers",
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default=12)
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# LOADING options
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self.parser.add_argument("--load_weights_folder",
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type=str,
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help="name of model to load")
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self.parser.add_argument("--models_to_load",
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nargs="+",
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type=str,
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help="models to load",
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default=["encoder", "depth", "pose_encoder", "pose"])
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# LOGGING options
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self.parser.add_argument("--log_frequency",
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type=int,
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help="number of batches between each tensorboard log",
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default=250)
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self.parser.add_argument("--save_frequency",
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type=int,
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help="number of epochs between each save",
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default=1)
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# EVALUATION options
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self.parser.add_argument("--eval_stereo",
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help="if set evaluates in stereo mode",
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action="store_true")
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self.parser.add_argument("--eval_mono",
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help="if set evaluates in mono mode",
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action="store_true")
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self.parser.add_argument("--disable_median_scaling",
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help="if set disables median scaling in evaluation",
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action="store_true")
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self.parser.add_argument("--pred_depth_scale_factor",
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help="if set multiplies predictions by this number",
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type=float,
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default=1)
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self.parser.add_argument("--ext_disp_to_eval",
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type=str,
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help="optional path to a .npy disparities file to evaluate")
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self.parser.add_argument("--eval_split",
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type=str,
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default="eigen",
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choices=[
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"eigen", "eigen_benchmark", "benchmark", "odom_9", "odom_10"],
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help="which split to run eval on")
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self.parser.add_argument("--save_pred_disps",
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help="if set saves predicted disparities",
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action="store_true")
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self.parser.add_argument("--no_eval",
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help="if set disables evaluation",
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action="store_true")
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self.parser.add_argument("--eval_eigen_to_benchmark",
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help="if set assume we are loading eigen results from npy but "
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"we want to evaluate using the new benchmark.",
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action="store_true")
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self.parser.add_argument("--eval_out_dir",
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help="if set will output the disparities to this folder",
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type=str)
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self.parser.add_argument("--post_process",
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help="if set will perform the flipping post processing "
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"from the original monodepth paper",
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action="store_true")
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def parse(self):
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self.options = self.parser.parse_args()
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return self.options
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