models/yolov5/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)