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" if args.dataset == 'coco' else 'trainval',
get_transform(train=True), args.data_path)
dataset_test, _ = get_dataset(args.dataset,
"val" if args.dataset == 'coco' else 'test',
get_transform(train=False), args.data_path)
print("Creating data loaderssss")
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 = get_model(num_classes=num_classes)
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:
voc_evaluate(model, data_loader_test, device=device)
return
pytorch_total_params = sum(p.numel() for p in model.parameters())
print("model params", pytorch_total_params)
temp = torch.randn(1, 3, 500, 353, device='cuda')
model.eval()
macs, params = flop_count(model, (temp, ))
# macs, params = clever_format([macs, params], "%.3f")
print("macs", macs.items())
# print("macs", params.items())
print("Start training")
start_time = time.time()
best_map = 0
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 and epoch % 9 == 0:
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
if 'coco' in args.dataset:
coco_evaluate(model, data_loader_test, device=device)
elif 'voc' in args.dataset:
map = voc_evaluate(model, data_loader_test, device=device)
if map > best_map:
best_map = map
print("Best Mean AP")
print(best_map)
else:
print(
f'No evaluation method available for the dataset {args.dataset}'
)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
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("final")))
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 = get_model(num_classes=num_classes)
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:
print("----------------------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:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args,
'epoch': epoch
}, 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))
data_loader_test = torch.utils.data.DataLoader(dataset_test,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=utils.collate_fn)
#############################################################
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
# our dataset has two classes only - background and person
num_classes = 2
# get the model using our helper function
model = get_model(num_classes)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.005,
momentum=0.9,
weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)