<filename>src/run.py
#!/usr/bin/env python3
# Author: <NAME>

from typing import List, Union
import os.path as osp
import shutil
import random

import argparse
import numpy as np
import torch

from src.config.default import get_config
from src.runner import Runner

DO_PRESERVE_RUNS = False

def get_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--run-type",
        choices=["train", "eval"],
        required=True,
        help="run type of the experiment (train or eval)",
    )

    parser.add_argument(
        "--exp-config",
        type=str,
        required=True,
        help="path to config yaml containing info about experiment",
    )

    parser.add_argument(
        "--ckpt-path",
        default=None,
        type=str,
        help="full path to a ckpt (for eval or resumption)"
    )

    parser.add_argument(
        "--clear-only",
        default=False,
        type=bool,
    )

    parser.add_argument(
        "opts",
        default=None,
        nargs=argparse.REMAINDER,
        help="Modify config options from command line",
    )
    return parser

def main():
    parser = get_parser()
    args = parser.parse_args()
    run_exp(**vars(args))

def check_exists(path, preserve=DO_PRESERVE_RUNS):
    if osp.exists(path):
        # logger.warn(f"{path} exists")
        print(f"{path} exists")
        if not preserve:
            # logger.warn(f"removing {path}")
            print(f"removing {path}")
            shutil.rmtree(path, ignore_errors=True)
        return True
    return False


def prepare_config(exp_config: Union[List[str], str], run_type: str, ckpt_path="", opts=None, suffix=None) -> None:
    r"""Prepare config node / do some preprocessing

    Args:
        exp_config: path to config file.
        run_type: "train" or "eval.
        ckpt_path: If training, ckpt to resume. If evaluating, ckpt to evaluate.
        opts: list of strings of additional config options.

    Returns:
        Runner, config, ckpt_path
    """
    config = get_config(exp_config, opts)

    # Default behavior is to pull experiment name from config file
    # Bind variant name to directories
    if isinstance(exp_config, str):
        variant_config = exp_config
    else:
        variant_config = exp_config[-1]
    variant_name = osp.split(variant_config)[1].split('.')[0]
    config.defrost()
    config.VARIANT = variant_name
    if suffix is not None:
        config.TENSORBOARD_DIR = osp.join(config.TENSORBOARD_DIR, suffix)
        config.CHECKPOINT_DIR = osp.join(config.CHECKPOINT_DIR, suffix)
        config.LOG_DIR = osp.join(config.LOG_DIR, suffix)
    config.TENSORBOARD_DIR = osp.join(config.TENSORBOARD_DIR, config.VARIANT)
    config.CHECKPOINT_DIR = osp.join(config.CHECKPOINT_DIR, config.VARIANT)
    config.LOG_DIR = osp.join(config.LOG_DIR, config.VARIANT)
    config.freeze()

    if ckpt_path is not None:
        if not osp.exists(ckpt_path):
            ckpt_path = osp.join(config.CHECKPOINT_DIR, ckpt_path)

    np.random.seed(config.SEED)
    random.seed(config.SEED)
    torch.random.manual_seed(config.SEED)
    torch.backends.cudnn.deterministic = True

    return config, ckpt_path

def run_exp(exp_config: Union[List[str], str], run_type: str, ckpt_path="", clear_only=False, opts=None, suffix=None) -> None:
    config, ckpt_path = prepare_config(exp_config, run_type, ckpt_path, opts, suffix=suffix)
    if clear_only:
        check_exists(config.TENSORBOARD_DIR, preserve=False)
        check_exists(config.CHECKPOINT_DIR, preserve=False)
        check_exists(config.LOG_DIR, preserve=False)
        exit(0)
    if run_type == "train":
        if ckpt_path is not None:
            runner = Runner(config)
            runner.train(checkpoint_path=ckpt_path)
        else:
            if DO_PRESERVE_RUNS:
                if check_exists(config.TENSORBOARD_DIR) or \
                    check_exists(config.CHECKPOINT_DIR) or \
                    check_exists(config.LOG_DIR):
                    exit(1)
            else:
                check_exists(config.TENSORBOARD_DIR)
                check_exists(config.CHECKPOINT_DIR)
                check_exists(config.LOG_DIR)
            runner = Runner(config)
            runner.train()
    # * The evaluation code path has legacy code (and will not run).
    # * Evaluation / analysis is done in analysis scripts.
    # elif run_type == "eval":
    #     runner.eval(checkpoint_path=ckpt_path)

if __name__ == "__main__":
    main()

<filename>src/run.py
#!/usr/bin/env python3
# Author: <NAME>

from typing import List, Union
import os.path as osp
import shutil
import random

import argparse
import numpy as np
import torch

from src.config.default import get_config
from src.runner import Runner

DO_PRESERVE_RUNS = False

def get_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--run-type",
        choices=["train", "eval"],
        required=True,
        help="run type of the experiment (train or eval)",
    )

    parser.add_argument(
        "--exp-config",
        type=str,
        required=True,
        help="path to config yaml containing info about experiment",
    )

    parser.add_argument(
        "--ckpt-path",
        default=None,
        type=str,
        help="full path to a ckpt (for eval or resumption)"
    )

    parser.add_argument(
        "--clear-only",
        default=False,
        type=bool,
    )

    parser.add_argument(
        "opts",
        default=None,
        nargs=argparse.REMAINDER,
        help="Modify config options from command line",
    )
    return parser

def main():
    parser = get_parser()
    args = parser.parse_args()
    run_exp(**vars(args))

def check_exists(path, preserve=DO_PRESERVE_RUNS):
    if osp.exists(path):
        # logger.warn(f"{path} exists")
        print(f"{path} exists")
        if not preserve:
            # logger.warn(f"removing {path}")
            print(f"removing {path}")
            shutil.rmtree(path, ignore_errors=True)
        return True
    return False


def prepare_config(exp_config: Union[List[str], str], run_type: str, ckpt_path="", opts=None, suffix=None) -> None:
    r"""Prepare config node / do some preprocessing

    Args:
        exp_config: path to config file.
        run_type: "train" or "eval.
        ckpt_path: If training, ckpt to resume. If evaluating, ckpt to evaluate.
        opts: list of strings of additional config options.

    Returns:
        Runner, config, ckpt_path
    """
    config = get_config(exp_config, opts)

    # Default behavior is to pull experiment name from config file
    # Bind variant name to directories
    if isinstance(exp_config, str):
        variant_config = exp_config
    else:
        variant_config = exp_config[-1]
    variant_name = osp.split(variant_config)[1].split('.')[0]
    config.defrost()
    config.VARIANT = variant_name
    if suffix is not None:
        config.TENSORBOARD_DIR = osp.join(config.TENSORBOARD_DIR, suffix)
        config.CHECKPOINT_DIR = osp.join(config.CHECKPOINT_DIR, suffix)
        config.LOG_DIR = osp.join(config.LOG_DIR, suffix)
    config.TENSORBOARD_DIR = osp.join(config.TENSORBOARD_DIR, config.VARIANT)
    config.CHECKPOINT_DIR = osp.join(config.CHECKPOINT_DIR, config.VARIANT)
    config.LOG_DIR = osp.join(config.LOG_DIR, config.VARIANT)
    config.freeze()

    if ckpt_path is not None:
        if not osp.exists(ckpt_path):
            ckpt_path = osp.join(config.CHECKPOINT_DIR, ckpt_path)

    np.random.seed(config.SEED)
    random.seed(config.SEED)
    torch.random.manual_seed(config.SEED)
    torch.backends.cudnn.deterministic = True

    return config, ckpt_path

def run_exp(exp_config: Union[List[str], str], run_type: str, ckpt_path="", clear_only=False, opts=None, suffix=None) -> None:
    config, ckpt_path = prepare_config(exp_config, run_type, ckpt_path, opts, suffix=suffix)
    if clear_only:
        check_exists(config.TENSORBOARD_DIR, preserve=False)
        check_exists(config.CHECKPOINT_DIR, preserve=False)
        check_exists(config.LOG_DIR, preserve=False)
        exit(0)
    if run_type == "train":
        if ckpt_path is not None:
            runner = Runner(config)
            runner.train(checkpoint_path=ckpt_path)
        else:
            if DO_PRESERVE_RUNS:
                if check_exists(config.TENSORBOARD_DIR) or \
                    check_exists(config.CHECKPOINT_DIR) or \
                    check_exists(config.LOG_DIR):
                    exit(1)
            else:
                check_exists(config.TENSORBOARD_DIR)
                check_exists(config.CHECKPOINT_DIR)
                check_exists(config.LOG_DIR)
            runner = Runner(config)
            runner.train()
    # * The evaluation code path has legacy code (and will not run).
    # * Evaluation / analysis is done in analysis scripts.
    # elif run_type == "eval":
    #     runner.eval(checkpoint_path=ckpt_path)

if __name__ == "__main__":
    main()

#!/usr/bin/env python3 # Author: <NAME> # logger.warn(f"{path} exists") # logger.warn(f"removing {path}") Prepare config node / do some preprocessing      Args:         exp_config: path to config file.         run_type: "train" or "eval.         ckpt_path: If training, ckpt to resume. If evaluating, ckpt to evaluate.         opts: list of strings of additional config options.      Returns:         Runner, config, ckpt_path # Default behavior is to pull experiment name from config file # Bind variant name to directories # * The evaluation code path has legacy code (and will not run). # * Evaluation / analysis is done in analysis scripts. # elif run_type == "eval": #     runner.eval(checkpoint_path=ckpt_path)