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 presets import torch import torch.utils.data import torchvision import torchvision.models.detection import torchvision.models.detection.mask_rcnn import utils from coco_utils import get_coco, get_coco_kp from engine import train_one_epoch, evaluate from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups 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(data_augmentation=args.data_augmentation) elif args.weights and args.test_only: weights = torchvision.models.get_weight(args.weights) trans = weights.transforms() return lambda img, target: (trans(img), target) else: return presets.DetectionPresetEval() 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", ) # 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") parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load") parser.add_argument("--weights-backbone", default=None, type=str, help="the backbone 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.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 model = torchvision.models.detection.__dict__[args.model]( weights=args.weights, weights_backbone=args.weights_backbone, 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)