utils/loggers/comet/hpo.py

import argparse
import json
import logging
import os
import sys
from pathlib import Path

import comet_ml

logger = logging.getLogger(__name__)

FILE = Path(__file__).resolve()
ROOT = FILE.parents[3]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH

from train import train
from utils.callbacks import Callbacks
from utils.general import increment_path
from utils.torch_utils import select_device

# Project Configuration
config = comet_ml.config.get_config()
COMET_PROJECT_NAME = config.get_string(
    os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5"
)


def get_args(known=False):
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--weights",
        type=str,
        default=ROOT / "yolov5s.pt",
        help="initial weights path",
    )
    parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
    parser.add_argument(
        "--data",
        type=str,
        default=ROOT / "data/coco128.yaml",
        help="dataset.yaml path",
    )
    parser.add_argument(
        "--hyp",
        type=str,
        default=ROOT / "data/hyps/hyp.scratch-low.yaml",
        help="hyperparameters path",
    )
    parser.add_argument(
        "--epochs", type=int, default=300, help="total training epochs"
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        default=16,
        help="total batch size for all GPUs, -1 for autobatch",
    )
    parser.add_argument(
        "--imgsz",
        "--img",
        "--img-size",
        type=int,
        default=640,
        help="train, val image size (pixels)",
    )
    parser.add_argument(
        "--rect", action="store_true", help="rectangular training"
    )
    parser.add_argument(
        "--resume",
        nargs="?",
        const=True,
        default=False,
        help="resume most recent training",
    )
    parser.add_argument(
        "--nosave", action="store_true", help="only save final checkpoint"
    )
    parser.add_argument(
        "--noval", action="store_true", help="only validate final epoch"
    )
    parser.add_argument(
        "--noautoanchor", action="store_true", help="disable AutoAnchor"
    )
    parser.add_argument(
        "--noplots", action="store_true", help="save no plot files"
    )
    parser.add_argument(
        "--evolve",
        type=int,
        nargs="?",
        const=300,
        help="evolve hyperparameters for x generations",
    )
    parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
    parser.add_argument(
        "--cache",
        type=str,
        nargs="?",
        const="ram",
        help='--cache images in "ram" (default) or "disk"',
    )
    parser.add_argument(
        "--image-weights",
        action="store_true",
        help="use weighted image selection for training",
    )
    parser.add_argument(
        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
    )
    parser.add_argument(
        "--multi-scale", action="store_true", help="vary img-size +/- 50%%"
    )
    parser.add_argument(
        "--single-cls",
        action="store_true",
        help="train multi-class data as single-class",
    )
    parser.add_argument(
        "--optimizer",
        type=str,
        choices=["SGD", "Adam", "AdamW"],
        default="SGD",
        help="optimizer",
    )
    parser.add_argument(
        "--sync-bn",
        action="store_true",
        help="use SyncBatchNorm, only available in DDP mode",
    )
    parser.add_argument(
        "--workers",
        type=int,
        default=8,
        help="max dataloader workers (per RANK in DDP mode)",
    )
    parser.add_argument(
        "--project", default=ROOT / "runs/train", help="save to project/name"
    )
    parser.add_argument("--name", default="exp", help="save to project/name")
    parser.add_argument(
        "--exist-ok",
        action="store_true",
        help="existing project/name ok, do not increment",
    )
    parser.add_argument("--quad", action="store_true", help="quad dataloader")
    parser.add_argument(
        "--cos-lr", action="store_true", help="cosine LR scheduler"
    )
    parser.add_argument(
        "--label-smoothing",
        type=float,
        default=0.0,
        help="Label smoothing epsilon",
    )
    parser.add_argument(
        "--patience",
        type=int,
        default=100,
        help="EarlyStopping patience (epochs without improvement)",
    )
    parser.add_argument(
        "--freeze",
        nargs="+",
        type=int,
        default=[0],
        help="Freeze layers: backbone=10, first3=0 1 2",
    )
    parser.add_argument(
        "--save-period",
        type=int,
        default=-1,
        help="Save checkpoint every x epochs (disabled if < 1)",
    )
    parser.add_argument(
        "--seed", type=int, default=0, help="Global training seed"
    )
    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="Automatic DDP Multi-GPU argument, do not modify",
    )

    # Weights & Biases arguments
    parser.add_argument("--entity", default=None, help="W&B: Entity")
    parser.add_argument(
        "--upload_dataset",
        nargs="?",
        const=True,
        default=False,
        help='W&B: Upload data, "val" option',
    )
    parser.add_argument(
        "--bbox_interval",
        type=int,
        default=-1,
        help="W&B: Set bounding-box image logging interval",
    )
    parser.add_argument(
        "--artifact_alias",
        type=str,
        default="latest",
        help="W&B: Version of dataset artifact to use",
    )

    # Comet Arguments
    parser.add_argument(
        "--comet_optimizer_config",
        type=str,
        help="Comet: Path to a Comet Optimizer Config File.",
    )
    parser.add_argument(
        "--comet_optimizer_id",
        type=str,
        help="Comet: ID of the Comet Optimizer sweep.",
    )
    parser.add_argument(
        "--comet_optimizer_objective",
        type=str,
        help="Comet: Set to 'minimize' or 'maximize'.",
    )
    parser.add_argument(
        "--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize."
    )
    parser.add_argument(
        "--comet_optimizer_workers",
        type=int,
        default=1,
        help="Comet: Number of Parallel Workers to use with the Comet Optimizer.",
    )

    return parser.parse_known_args()[0] if known else parser.parse_args()


def run(parameters, opt):
    hyp_dict = {
        k: v
        for k, v in parameters.items()
        if k not in ["epochs", "batch_size"]
    }

    opt.save_dir = str(
        increment_path(
            Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve
        )
    )
    opt.batch_size = parameters.get("batch_size")
    opt.epochs = parameters.get("epochs")

    device = select_device(opt.device, batch_size=opt.batch_size)
    train(hyp_dict, opt, device, callbacks=Callbacks())


if __name__ == "__main__":
    opt = get_args(known=True)

    opt.weights = str(opt.weights)
    opt.cfg = str(opt.cfg)
    opt.data = str(opt.data)
    opt.project = str(opt.project)

    optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
    if optimizer_id is None:
        with open(opt.comet_optimizer_config) as f:
            optimizer_config = json.load(f)
        optimizer = comet_ml.Optimizer(optimizer_config)
    else:
        optimizer = comet_ml.Optimizer(optimizer_id)

    opt.comet_optimizer_id = optimizer.id
    status = optimizer.status()

    opt.comet_optimizer_objective = status["spec"]["objective"]
    opt.comet_optimizer_metric = status["spec"]["metric"]

    logger.info("COMET INFO: Starting Hyperparameter Sweep")
    for parameter in optimizer.get_parameters():
        run(parameter["parameters"], opt)