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Ishan S. Patel
2021-07-01 15:22:44 -04:00
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train.py
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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 torch
import torch.utils.data
import torchvision
import torchvision.models.detection
import torchvision.models.detection.mask_rcnn
from coco_utils import get_coco, get_coco_kp
from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
from engine import train_one_epoch, evaluate
import presets
import utils
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, data_augmentation):
return presets.DetectionPresetTrain(data_augmentation) if train else 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/', help='dataset')
parser.add_argument('--dataset', default='coco', help='dataset')
parser.add_argument('--model', default='maskrcnn_resnet50_fpn', help='model')
parser.add_argument('--device', default='cuda', help='device')
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", help='the 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='.', help='path where to save')
parser.add_argument('--resume', default='', help='resume from 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", 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://', help='url used to set up distributed 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.data_augmentation),
args.data_path)
dataset_test, _ = get_dataset(args.dataset, "val", get_transform(False, args.data_augmentation), 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](num_classes=num_classes, pretrained=args.pretrained,
**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)
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("Invalid lr scheduler '{}'. Only MultiStepLR and CosineAnnealingLR "
"are supported.".format(args.lr_scheduler))
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.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)
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
}
utils.save_on_master(
checkpoint,
os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
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('Training time {}'.format(total_time_str))
if __name__ == "__main__":
args = get_args_parser().parse_args()
main(args)