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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.
"""
import datetime
import os
import time
import torch
import torch.utils.data
from torch import nn
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 utils
import transforms as T
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):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def main(args):
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(train=True), args.data_path)
dataset_test, _ = get_dataset(args.dataset, "val", get_transform(train=False), 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")
model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes,
pretrained=args.pretrained)
model.to(device)
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)
# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
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'])
if args.test_only:
evaluate(model, data_loader_test, device=device)
return
print("Start training")
start_time = time.time()
for epoch in range(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:
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args},
os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
# 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__":
import argparse
parser = argparse.ArgumentParser(
description=__doc__)
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=13, 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: 16)')
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-step-size', default=8, type=int, help='decrease lr every step-size epochs')
parser.add_argument('--lr-steps', default=[8, 11], nargs='+', type=int, help='decrease lr every step-size epochs')
parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')
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('--aspect-ratio-group-factor', default=0, type=int)
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')
args = parser.parse_args()
if args.output_dir:
utils.mkdir(args.output_dir)
main(args)