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Initial media depth project backup

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2026-05-20 12:25:12 +08:00
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# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import json
import os
from zoedepth.utils.easydict import EasyDict as edict
from zoedepth.utils.arg_utils import infer_type
import pathlib
import platform
ROOT = pathlib.Path(__file__).parent.parent.resolve()
HOME_DIR = os.path.expanduser("./data")
COMMON_CONFIG = {
"save_dir": os.path.expanduser("./depth_anything_finetune"),
"project": "ZoeDepth",
"tags": '',
"notes": "",
"gpu": None,
"root": ".",
"uid": None,
"print_losses": False
}
DATASETS_CONFIG = {
"kitti": {
"dataset": "kitti",
"min_depth": 0.001,
"max_depth": 80,
"data_path": os.path.join(HOME_DIR, "Kitti/raw_data"),
"gt_path": os.path.join(HOME_DIR, "Kitti/data_depth_annotated_zoedepth"),
"filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt",
"input_height": 352,
"input_width": 1216, # 704
"data_path_eval": os.path.join(HOME_DIR, "Kitti/raw_data"),
"gt_path_eval": os.path.join(HOME_DIR, "Kitti/data_depth_annotated_zoedepth"),
"filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt",
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"do_random_rotate": True,
"degree": 1.0,
"do_kb_crop": True,
"garg_crop": True,
"eigen_crop": False,
"use_right": False
},
"kitti_test": {
"dataset": "kitti",
"min_depth": 0.001,
"max_depth": 80,
"data_path": os.path.join(HOME_DIR, "Kitti/raw_data"),
"gt_path": os.path.join(HOME_DIR, "Kitti/data_depth_annotated_zoedepth"),
"filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt",
"input_height": 352,
"input_width": 1216,
"data_path_eval": os.path.join(HOME_DIR, "Kitti/raw_data"),
"gt_path_eval": os.path.join(HOME_DIR, "Kitti/data_depth_annotated_zoedepth"),
"filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt",
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"do_random_rotate": False,
"degree": 1.0,
"do_kb_crop": True,
"garg_crop": True,
"eigen_crop": False,
"use_right": False
},
"nyu": {
"dataset": "nyu",
"avoid_boundary": False,
"min_depth": 1e-3, # originally 0.1
"max_depth": 10,
"data_path": os.path.join(HOME_DIR, "nyu"),
"gt_path": os.path.join(HOME_DIR, "nyu"),
"filenames_file": "./train_test_inputs/nyudepthv2_train_files_with_gt.txt",
"input_height": 480,
"input_width": 640,
"data_path_eval": os.path.join(HOME_DIR, "nyu"),
"gt_path_eval": os.path.join(HOME_DIR, "nyu"),
"filenames_file_eval": "./train_test_inputs/nyudepthv2_test_files_with_gt.txt",
"min_depth_eval": 1e-3,
"max_depth_eval": 10,
"min_depth_diff": -10,
"max_depth_diff": 10,
"do_random_rotate": True,
"degree": 1.0,
"do_kb_crop": False,
"garg_crop": False,
"eigen_crop": True
},
"ibims": {
"dataset": "ibims",
"ibims_root": os.path.join(HOME_DIR, "iBims1/m1455541/ibims1_core_raw/"),
"eigen_crop": True,
"garg_crop": False,
"do_kb_crop": False,
"min_depth_eval": 0,
"max_depth_eval": 10,
"min_depth": 1e-3,
"max_depth": 10
},
"sunrgbd": {
"dataset": "sunrgbd",
"sunrgbd_root": os.path.join(HOME_DIR, "SUNRGB-D"),
"eigen_crop": True,
"garg_crop": False,
"do_kb_crop": False,
"min_depth_eval": 0,
"max_depth_eval": 8,
"min_depth": 1e-3,
"max_depth": 10
},
"diml_indoor": {
"dataset": "diml_indoor",
"diml_indoor_root": os.path.join(HOME_DIR, "DIML/indoor/sample/testset/"),
"eigen_crop": True,
"garg_crop": False,
"do_kb_crop": False,
"min_depth_eval": 0,
"max_depth_eval": 10,
"min_depth": 1e-3,
"max_depth": 10
},
"diml_outdoor": {
"dataset": "diml_outdoor",
"diml_outdoor_root": os.path.join(HOME_DIR, "DIML/outdoor/test/LR"),
"eigen_crop": False,
"garg_crop": True,
"do_kb_crop": False,
"min_depth_eval": 2,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 80
},
"diode_indoor": {
"dataset": "diode_indoor",
"diode_indoor_root": os.path.join(HOME_DIR, "DIODE/val/indoors/"),
"eigen_crop": True,
"garg_crop": False,
"do_kb_crop": False,
"min_depth_eval": 1e-3,
"max_depth_eval": 10,
"min_depth": 1e-3,
"max_depth": 10
},
"diode_outdoor": {
"dataset": "diode_outdoor",
"diode_outdoor_root": os.path.join(HOME_DIR, "DIODE/val/outdoor/"),
"eigen_crop": False,
"garg_crop": True,
"do_kb_crop": False,
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 80
},
"hypersim_test": {
"dataset": "hypersim_test",
"hypersim_test_root": os.path.join(HOME_DIR, "HyperSim/"),
"eigen_crop": True,
"garg_crop": False,
"do_kb_crop": False,
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 10
},
"vkitti": {
"dataset": "vkitti",
"vkitti_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti_test/"),
"eigen_crop": False,
"garg_crop": True,
"do_kb_crop": True,
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 80
},
"vkitti2": {
"dataset": "vkitti2",
"vkitti2_root": os.path.join(HOME_DIR, "vKitti2/"),
"eigen_crop": False,
"garg_crop": True,
"do_kb_crop": True,
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 80,
},
"ddad": {
"dataset": "ddad",
"ddad_root": os.path.join(HOME_DIR, "shortcuts/datasets/ddad/ddad_val/"),
"eigen_crop": False,
"garg_crop": True,
"do_kb_crop": True,
"min_depth_eval": 1e-3,
"max_depth_eval": 80,
"min_depth": 1e-3,
"max_depth": 80,
},
}
ALL_INDOOR = ["nyu", "ibims", "sunrgbd", "diode_indoor", "hypersim_test"]
ALL_OUTDOOR = ["kitti", "diml_outdoor", "diode_outdoor", "vkitti2", "ddad"]
ALL_EVAL_DATASETS = ALL_INDOOR + ALL_OUTDOOR
COMMON_TRAINING_CONFIG = {
"dataset": "nyu",
"distributed": True,
"workers": 16,
"clip_grad": 0.1,
"use_shared_dict": False,
"shared_dict": None,
"use_amp": False,
"aug": True,
"random_crop": False,
"random_translate": False,
"translate_prob": 0.2,
"max_translation": 100,
"validate_every": 0.25,
"log_images_every": 0.1,
"prefetch": False,
}
def flatten(config, except_keys=('bin_conf')):
def recurse(inp):
if isinstance(inp, dict):
for key, value in inp.items():
if key in except_keys:
yield (key, value)
if isinstance(value, dict):
yield from recurse(value)
else:
yield (key, value)
return dict(list(recurse(config)))
def split_combined_args(kwargs):
"""Splits the arguments that are combined with '__' into multiple arguments.
Combined arguments should have equal number of keys and values.
Keys are separated by '__' and Values are separated with ';'.
For example, '__n_bins__lr=256;0.001'
Args:
kwargs (dict): key-value pairs of arguments where key-value is optionally combined according to the above format.
Returns:
dict: Parsed dict with the combined arguments split into individual key-value pairs.
"""
new_kwargs = dict(kwargs)
for key, value in kwargs.items():
if key.startswith("__"):
keys = key.split("__")[1:]
values = value.split(";")
assert len(keys) == len(
values), f"Combined arguments should have equal number of keys and values. Keys are separated by '__' and Values are separated with ';'. For example, '__n_bins__lr=256;0.001. Given (keys,values) is ({keys}, {values})"
for k, v in zip(keys, values):
new_kwargs[k] = v
return new_kwargs
def parse_list(config, key, dtype=int):
"""Parse a list of values for the key if the value is a string. The values are separated by a comma.
Modifies the config in place.
"""
if key in config:
if isinstance(config[key], str):
config[key] = list(map(dtype, config[key].split(',')))
assert isinstance(config[key], list) and all([isinstance(e, dtype) for e in config[key]]
), f"{key} should be a list of values dtype {dtype}. Given {config[key]} of type {type(config[key])} with values of type {[type(e) for e in config[key]]}."
def get_model_config(model_name, model_version=None):
"""Find and parse the .json config file for the model.
Args:
model_name (str): name of the model. The config file should be named config_{model_name}[_{model_version}].json under the models/{model_name} directory.
model_version (str, optional): Specific config version. If specified config_{model_name}_{model_version}.json is searched for and used. Otherwise config_{model_name}.json is used. Defaults to None.
Returns:
easydict: the config dictionary for the model.
"""
config_fname = f"config_{model_name}_{model_version}.json" if model_version is not None else f"config_{model_name}.json"
config_file = os.path.join(ROOT, "models", model_name, config_fname)
if not os.path.exists(config_file):
return None
with open(config_file, "r") as f:
config = edict(json.load(f))
# handle dictionary inheritance
# only training config is supported for inheritance
if "inherit" in config.train and config.train.inherit is not None:
inherit_config = get_model_config(config.train["inherit"]).train
for key, value in inherit_config.items():
if key not in config.train:
config.train[key] = value
return edict(config)
def update_model_config(config, mode, model_name, model_version=None, strict=False):
model_config = get_model_config(model_name, model_version)
if model_config is not None:
config = {**config, **
flatten({**model_config.model, **model_config[mode]})}
elif strict:
raise ValueError(f"Config file for model {model_name} not found.")
return config
def check_choices(name, value, choices):
# return # No checks in dev branch
if value not in choices:
raise ValueError(f"{name} {value} not in supported choices {choices}")
KEYS_TYPE_BOOL = ["use_amp", "distributed", "use_shared_dict", "same_lr", "aug", "three_phase",
"prefetch", "cycle_momentum"] # Casting is not necessary as their int casted values in config are 0 or 1
def get_config(model_name, mode='train', dataset=None, **overwrite_kwargs):
"""Main entry point to get the config for the model.
Args:
model_name (str): name of the desired model.
mode (str, optional): "train" or "infer". Defaults to 'train'.
dataset (str, optional): If specified, the corresponding dataset configuration is loaded as well. Defaults to None.
Keyword Args: key-value pairs of arguments to overwrite the default config.
The order of precedence for overwriting the config is (Higher precedence first):
# 1. overwrite_kwargs
# 2. "config_version": Config file version if specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{config_version}.json
# 3. "version_name": Default Model version specific config specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{version_name}.json
# 4. common_config: Default config for all models specified in COMMON_CONFIG
Returns:
easydict: The config dictionary for the model.
"""
check_choices("Model", model_name, ["zoedepth", "zoedepth_nk"])
check_choices("Mode", mode, ["train", "infer", "eval"])
if mode == "train":
check_choices("Dataset", dataset, ["nyu", "kitti", "mix", None])
config = flatten({**COMMON_CONFIG, **COMMON_TRAINING_CONFIG})
config = update_model_config(config, mode, model_name)
# update with model version specific config
version_name = overwrite_kwargs.get("version_name", config["version_name"])
config = update_model_config(config, mode, model_name, version_name)
# update with config version if specified
config_version = overwrite_kwargs.get("config_version", None)
if config_version is not None:
print("Overwriting config with config_version", config_version)
config = update_model_config(config, mode, model_name, config_version)
# update with overwrite_kwargs
# Combined args are useful for hyperparameter search
overwrite_kwargs = split_combined_args(overwrite_kwargs)
config = {**config, **overwrite_kwargs}
# Casting to bool # TODO: Not necessary. Remove and test
for key in KEYS_TYPE_BOOL:
if key in config:
config[key] = bool(config[key])
# Model specific post processing of config
parse_list(config, "n_attractors")
# adjust n_bins for each bin configuration if bin_conf is given and n_bins is passed in overwrite_kwargs
if 'bin_conf' in config and 'n_bins' in overwrite_kwargs:
bin_conf = config['bin_conf'] # list of dicts
n_bins = overwrite_kwargs['n_bins']
new_bin_conf = []
for conf in bin_conf:
conf['n_bins'] = n_bins
new_bin_conf.append(conf)
config['bin_conf'] = new_bin_conf
if mode == "train":
orig_dataset = dataset
if dataset == "mix":
dataset = 'nyu' # Use nyu as default for mix. Dataset config is changed accordingly while loading the dataloader
if dataset is not None:
config['project'] = f"MonoDepth3-{orig_dataset}" # Set project for wandb
if dataset is not None:
config['dataset'] = dataset
config = {**DATASETS_CONFIG[dataset], **config}
config['model'] = model_name
typed_config = {k: infer_type(v) for k, v in config.items()}
# add hostname to config
config['hostname'] = platform.node()
return edict(typed_config)
def change_dataset(config, new_dataset):
config.update(DATASETS_CONFIG[new_dataset])
return config