DenseMammogram/detection/train.py

r"""PyTorch Detection Training.

To run in a multi-gpu environment, use the distributed launcher::

    python -m torch.distributed.launch --nproc_per_node=$NGPU --use_env \
        train.py ... --world-size $NGPU

The default hyperparameters are tuned for training on 8 gpus and 2 images per gpu.
    --lr 0.02 --batch-size 2 --world-size 8
If you use different number of gpus, the learning rate should be changed to 0.02/8*$NGPU.

On top of that, for training Faster/Mask R-CNN, the default hyperparameters are
    --epochs 26 --lr-steps 16 22 --aspect-ratio-group-factor 3

Also, if you train Keypoint R-CNN, the default hyperparameters are
    --epochs 46 --lr-steps 36 43 --aspect-ratio-group-factor 3
Because the number of images is smaller in the person keypoint subset of COCO,
the number of epochs should be adapted so that we have the same number of iterations.
"""
import datetime
import os
import time

import detection.presets
import torch
import torch.utils.data
import torchvision
import torchvision.models.detection
import torchvision.models.detection.mask_rcnn
import detection.utils as utils
from detection.coco_utils import get_coco, get_coco_kp
from detection.engine import train_one_epoch, evaluate
from detection.group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups


try:
    from torchvision.prototype import models as PM
except ImportError:
    PM = None


def get_dataset(name, image_set, transform, data_path):
    paths = {"coco": (data_path, get_coco, 91), "coco_kp": (data_path, get_coco_kp, 2)}
    p, ds_fn, num_classes = paths[name]

    ds = ds_fn(p, image_set=image_set, transforms=transform)
    return ds, num_classes


def get_transform(train, args):
    if train:
        return presets.DetectionPresetTrain(args.data_augmentation)
    elif not args.weights:
        return presets.DetectionPresetEval()
    else:
        weights = PM.get_weight(args.weights)
        return weights.transforms()


def get_args_parser(add_help=True):
    import argparse

    parser = argparse.ArgumentParser(description="PyTorch Detection Training", add_help=add_help)

    parser.add_argument("--data-path", default="/datasets01/COCO/022719/", type=str, help="dataset path")
    parser.add_argument("--dataset", default="coco", type=str, help="dataset name")
    parser.add_argument("--model", default="maskrcnn_resnet50_fpn", type=str, help="model name")
    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
    parser.add_argument(
        "-b", "--batch-size", default=2, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
    )
    parser.add_argument("--epochs", default=26, type=int, metavar="N", help="number of total epochs to run")
    parser.add_argument(
        "-j", "--workers", default=4, type=int, metavar="N", help="number of data loading workers (default: 4)"
    )
    parser.add_argument(
        "--lr",
        default=0.02,
        type=float,
        help="initial learning rate, 0.02 is the default value for training on 8 gpus and 2 images_per_gpu",
    )
    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
    parser.add_argument(
        "--wd",
        "--weight-decay",
        default=1e-4,
        type=float,
        metavar="W",
        help="weight decay (default: 1e-4)",
        dest="weight_decay",
    )
    parser.add_argument(
        "--lr-scheduler", default="multisteplr", type=str, help="name of lr scheduler (default: multisteplr)"
    )
    parser.add_argument(
        "--lr-step-size", default=8, type=int, help="decrease lr every step-size epochs (multisteplr scheduler only)"
    )
    parser.add_argument(
        "--lr-steps",
        default=[16, 22],
        nargs="+",
        type=int,
        help="decrease lr every step-size epochs (multisteplr scheduler only)",
    )
    parser.add_argument(
        "--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma (multisteplr scheduler only)"
    )
    parser.add_argument("--print-freq", default=20, type=int, help="print frequency")
    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
    parser.add_argument("--start_epoch", default=0, type=int, help="start epoch")
    parser.add_argument("--aspect-ratio-group-factor", default=3, type=int)
    parser.add_argument("--rpn-score-thresh", default=None, type=float, help="rpn score threshold for faster-rcnn")
    parser.add_argument(
        "--trainable-backbone-layers", default=None, type=int, help="number of trainable layers of backbone"
    )
    parser.add_argument(
        "--data-augmentation", default="hflip", type=str, help="data augmentation policy (default: hflip)"
    )
    parser.add_argument(
        "--sync-bn",
        dest="sync_bn",
        help="Use sync batch norm",
        action="store_true",
    )
    parser.add_argument(
        "--test-only",
        dest="test_only",
        help="Only test the model",
        action="store_true",
    )
    parser.add_argument(
        "--pretrained",
        dest="pretrained",
        help="Use pre-trained models from the modelzoo",
        action="store_true",
    )

    # distributed training parameters
    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")

    # Prototype models only
    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")

    # Mixed precision training parameters
    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")

    return parser


def main(args):
    if args.weights and PM is None:
        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
    if args.output_dir:
        utils.mkdir(args.output_dir)

    utils.init_distributed_mode(args)
    print(args)

    device = torch.device(args.device)

    # Data loading code
    print("Loading data")

    dataset, num_classes = get_dataset(args.dataset, "train", get_transform(True, args), args.data_path)
    dataset_test, _ = get_dataset(args.dataset, "val", get_transform(False, args), args.data_path)

    print("Creating data loaders")
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)
    else:
        train_sampler = torch.utils.data.RandomSampler(dataset)
        test_sampler = torch.utils.data.SequentialSampler(dataset_test)

    if args.aspect_ratio_group_factor >= 0:
        group_ids = create_aspect_ratio_groups(dataset, k=args.aspect_ratio_group_factor)
        train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, args.batch_size)
    else:
        train_batch_sampler = torch.utils.data.BatchSampler(train_sampler, args.batch_size, drop_last=True)

    data_loader = torch.utils.data.DataLoader(
        dataset, batch_sampler=train_batch_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
    )

    data_loader_test = torch.utils.data.DataLoader(
        dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
    )

    print("Creating model")
    kwargs = {"trainable_backbone_layers": args.trainable_backbone_layers}
    if "rcnn" in args.model:
        if args.rpn_score_thresh is not None:
            kwargs["rpn_score_thresh"] = args.rpn_score_thresh
    if not args.weights:
        model = torchvision.models.detection.__dict__[args.model](
            pretrained=args.pretrained, num_classes=num_classes, **kwargs
        )
    else:
        model = PM.detection.__dict__[args.model](weights=args.weights, num_classes=num_classes, **kwargs)
    model.to(device)
    if args.distributed and args.sync_bn:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    params = [p for p in model.parameters() if p.requires_grad]
    optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)

    scaler = torch.cuda.amp.GradScaler() if args.amp else None

    args.lr_scheduler = args.lr_scheduler.lower()
    if args.lr_scheduler == "multisteplr":
        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
    elif args.lr_scheduler == "cosineannealinglr":
        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
    else:
        raise RuntimeError(
            f"Invalid lr scheduler '{args.lr_scheduler}'. Only MultiStepLR and CosineAnnealingLR are supported."
        )

    if args.resume:
        checkpoint = torch.load(args.resume, map_location="cpu")
        model_without_ddp.load_state_dict(checkpoint["model"])
        optimizer.load_state_dict(checkpoint["optimizer"])
        lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
        args.start_epoch = checkpoint["epoch"] + 1
        if args.amp:
            scaler.load_state_dict(checkpoint["scaler"])

    if args.test_only:
        evaluate(model, data_loader_test, device=device)
        return

    print("Start training")
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        train_one_epoch(model, optimizer, data_loader, device, epoch, args.print_freq, scaler)
        lr_scheduler.step()
        if args.output_dir:
            checkpoint = {
                "model": model_without_ddp.state_dict(),
                "optimizer": optimizer.state_dict(),
                "lr_scheduler": lr_scheduler.state_dict(),
                "args": args,
                "epoch": epoch,
            }
            if args.amp:
                checkpoint["scaler"] = scaler.state_dict()
            utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
            utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))

        # evaluate after every epoch
        evaluate(model, data_loader_test, device=device)

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print(f"Training time {total_time_str}")


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