linear_evaluate_depth.py

import argparse
import copy
import logging
import os
import os.path as osp
import time

import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import init_dist
from mmcv.utils import Config, DictAction
from mmseg.apis import set_random_seed
from mmseg.utils import collect_env, get_root_logger

from utils.model_utils import build_2d_model
from linear_evaluate_fit3d import FiT3D
from evaluation.depth.apis import train_depther
from evaluation.depth.datasets import build_dataset
from evaluation.eval_utils.misc import create_depther

logger = logging.getLogger()
current_time = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())


def parse_args():
    parser = argparse.ArgumentParser(description="Linear Probing for Depth Estimation")
    parser.add_argument("config", default="evaluation/configs/vitb_scannetpp_depth_linear_config.py", help="train config file path")
    
    parser.add_argument("--work-dir", default="work_dirs/depth_eval/scannetpp/dinov2_small",  help="the dir to save logs and models")

    parser.add_argument("--resume-from", default='', help="the checkpoint file to resume from")

    parser.add_argument(
        "--backbone-type",
        default="dinov2_small",
        help="model type",
    )
    parser.add_argument("--eval_baseline", action="store_true", default=False)
    parser.add_argument(
        "--no-validate",
        action="store_true",
        help="whether not to evaluate the checkpoint during training",
    )
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        "--gpus",
        type=int,
        help="number of gpus to use " "(only applicable to non-distributed training)",
    )
    group_gpus.add_argument(
        "--gpu-ids",
        type=int,
        nargs="+",
        help="ids of gpus to use " "(only applicable to non-distributed training)",
    )
    parser.add_argument("--seed", type=int, default=None, help="random seed")
    parser.add_argument(
        "--deterministic",
        action="store_true",
        help="whether to set deterministic options for CUDNN backend.",
    )
    parser.add_argument(
        "--options", nargs="+", action=DictAction, help="custom options"
    )
    parser.add_argument(
        "--launcher",
        choices=["none", "pytorch", "slurm", "mpi"],
        default="none",
        help="job launcher",
    )
    parser.add_argument("--local-rank", type=int, default=0)
    args = parser.parse_args()
    if "LOCAL_RANK" not in os.environ:
        os.environ["LOCAL_RANK"] = str(args.local_rank)

    return args


def main(args):
    cfg = Config.fromfile(args.config)
    if args.options is not None:
        cfg.merge_from_dict(args.options)
    # set cudnn_benchmark
    if cfg.get("cudnn_benchmark", False):
        torch.backends.cudnn.benchmark = True

    # work_dir is determined in this priority: CLI > segment in file > filename
    if args.work_dir is not None:
        # update configs according to CLI args if args.work_dir is not None
        cfg.work_dir = args.work_dir
    elif cfg.get("work_dir", None) is None:
        # use config filename as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join(
            "./work_dirs", osp.splitext(osp.basename(args.config))[0]
        )

    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
    else:
        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == "none":
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # create work_dir
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # dump config
    # cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
    # init the logger before other steps
    timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
    log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # init the meta dict to record some important information such as
    # environment info and seed, which will be logged
    meta = dict()
    # log env info
    env_info_dict = collect_env()
    env_info = "\n".join([f"{k}: {v}" for k, v in env_info_dict.items()])
    dash_line = "-" * 60 + "\n"
    logger.info("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
    meta["env_info"] = env_info

    # log some basic info
    logger.info(f"Distributed training: {distributed}")
    logger.info(f"Config:\n{cfg}")

    # set random seeds
    if args.seed is not None:
        logger.info(
            f"Set random seed to {args.seed}, deterministic: " f"{args.deterministic}"
        )
        set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = args.seed
    meta["seed"] = args.seed
    meta["exp_name"] = osp.basename(args.config)


    device = torch.device("cuda")

    if args.eval_baseline:
        vit = build_2d_model(args.backbone_type)
        vit = vit.to(device)
        backbone_model = vit
    else:
        fit3d = FiT3D(args.backbone_type)
        fit3d.eval()
        fit3d.to(device)
        backbone_model = fit3d

    backbone_model.eval()

    model = create_depther(cfg, backbone_model=backbone_model)

    if cfg.get("SyncBN", False):
        model = nn.SyncBatchNorm.convert_sync_batchnorm(model)

    logger.info(model)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        val_dataset = copy.deepcopy(cfg.data.val)
        val_dataset.pipeline = cfg.data.train.pipeline
        datasets.append(build_dataset(val_dataset))

    train_depther(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta,
    )


if __name__ == "__main__":
    args = parse_args()
    main(args)