def main():
model = se_resnet50(num_classes=1000)
optimizer = optim.SGD(lr=0.6 / 1024 * args.batch_size, momentum=0.9, weight_decay=1e-4)
scheduler = lr_scheduler.MultiStepLR([50, 70])
c = [callbacks.AccuracyCallback(), callbacks.LossCallback()]
r = reporter.TQDMReporter(range(args.epochs), callbacks=c)
tb = reporter.TensorboardReporter(c)
rep = callbacks.CallbackList(r, tb, callbacks.WeightSave("checkpoints"))
if args.distributed:
# DistributedSupervisedTrainer sets up torch.distributed
if args.local_rank == 0:
print("\nuse DistributedDataParallel")
trainer = DistributedSupervisedTrainer(model, optimizer, F.cross_entropy, callbacks=rep, scheduler=scheduler,
init_method=args.init_method, backend=args.backend)
else:
multi_gpus = torch.cuda.device_count() > 1
if multi_gpus:
print("\nuse DataParallel")
trainer = SupervisedTrainer(model, optimizer, F.cross_entropy, callbacks=rep,
scheduler=scheduler, data_parallel=multi_gpus)
# if distributed, need to setup loaders after DistributedSupervisedTrainer
train_loader, test_loader = imagenet_loaders(args.root, args.batch_size, distributed=args.distributed,
num_train_samples=args.batch_size * 10 if args.debug else None,
num_test_samples=args.batch_size * 10 if args.debug else None)
for _ in r:
trainer.train(train_loader)
trainer.test(test_loader)
def main():
if args.distributed:
init_distributed()
model = se_resnet50(num_classes=1000)
optimizer = optim.SGD(lr=0.6 / 1024 * args.batch_size,
momentum=0.9, weight_decay=1e-4)
scheduler = lr_scheduler.MultiStepLR([50, 70])
train_loader, test_loader = imagenet_loaders(args.root, args.batch_size, distributed=args.distributed,
num_train_samples=args.batch_size * 10 if args.debug else None,
num_test_samples=args.batch_size * 10 if args.debug else None)
c = [callbacks.AccuracyCallback(), callbacks.AccuracyCallback(k=5),
callbacks.LossCallback(),
callbacks.WeightSave('.'),
reporters.TensorboardReporter('.'),
reporters.TQDMReporter(range(args.epochs))]
with SupervisedTrainer(model, optimizer, F.cross_entropy,
callbacks=c,
scheduler=scheduler,
) as trainer:
for _ in c[-1]:
trainer.train(train_loader)
trainer.test(test_loader)
def main():
train_loader, test_loader = imagenet_loaders(
args.root,
args.batch_size,
num_train_samples=args.batch_size * args.train_max_iter,
num_test_samples=args.batch_size * args.test_max_iter)
pretrained_model = resnet50(pretrained=True)
for p in pretrained_model.parameters():
p.requires_grad = False
pretrained_model.eval()
generator = ResNetGenerator(3, 3, args.num_filters)
generator.cuda()
optimizer = optim.Adam(lr=args.lr, betas=(args.beta1, 0.999))
trainer = Trainer({
"generator": generator,
"classifier": pretrained_model
},
optimizer,
reporter.TensorboardReporter([
adv_accuracy, fooling_rate,
callbacks.AccuracyCallback(),
callbacks.LossCallback()
],
save_dir="results"),
noise=torch.randn(3, 224,
224).expand(args.batch_size, -1, -1,
-1))
for ep in range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
def main():
if args.distributed:
init_distributed()
if args.enable_accimage:
enable_accimage()
model = resnet50()
optimizer = optim.SGD(lr=1e-1 * args.batch_size * get_num_nodes() / 256,
momentum=0.9,
weight_decay=1e-4)
scheduler = lr_scheduler.MultiStepLR([30, 60, 80])
c = [callbacks.AccuracyCallback(), callbacks.LossCallback()]
r = reporters.TQDMReporter(range(args.epochs), callbacks=c)
tb = reporters.TensorboardReporter(c)
rep = callbacks.CallbackList(r, tb, callbacks.WeightSave("checkpoints"))
_train_loader, _test_loader = imagenet_loaders(
args.root,
args.batch_size,
distributed=args.distributed,
num_train_samples=args.batch_size * 10 if args.debug else None,
num_test_samples=args.batch_size * 10 if args.debug else None)
if args.distributed:
# DistributedSupervisedTrainer sets up torch.distributed
if args.local_rank == 0:
print("\nuse DistributedDataParallel\n")
trainer = DistributedSupervisedTrainer(model,
optimizer,
F.cross_entropy,
callbacks=rep,
scheduler=scheduler,
init_method=args.init_method,
backend=args.backend,
enable_amp=args.enable_amp)
else:
use_multi_gpus = torch.cuda.device_count() > 1
if use_multi_gpus:
print("\nuse DataParallel\n")
trainer = SupervisedTrainer(model,
optimizer,
F.cross_entropy,
callbacks=rep,
data_parallel=use_multi_gpus)
for epoch in r:
if args.use_prefetcher:
train_loader = prefetcher.DataPrefetcher(_train_loader)
test_loader = prefetcher.DataPrefetcher(_test_loader)
else:
train_loader, test_loader = _train_loader, _test_loader
# following apex's training scheme
trainer.train(train_loader)
trainer.test(test_loader)
rep.close()
def main(cfg):
if cfg.distributed.enable:
init_distributed(use_horovod=cfg.distributed.use_horovod,
backend=cfg.distributed.backend,
init_method=cfg.distributed.init_method)
if cfg.enable_accimage:
enable_accimage()
model = resnet50()
optimizer = optim.SGD(lr=1e-1 * cfg.batch_size * get_num_nodes() / 256,
momentum=0.9,
weight_decay=1e-4)
scheduler = lr_scheduler.MultiStepLR([30, 60, 80])
tq = reporters.TQDMReporter(range(cfg.epochs))
c = [
callbacks.AccuracyCallback(),
callbacks.AccuracyCallback(k=5),
callbacks.LossCallback(), tq,
reporters.TensorboardReporter("."),
reporters.IOReporter(".")
]
_train_loader, _test_loader = imagenet_loaders(
cfg.root,
cfg.batch_size,
distributed=cfg.distributed.enable,
num_train_samples=cfg.batch_size * 10 if cfg.debug else None,
num_test_samples=cfg.batch_size * 10 if cfg.debug else None)
use_multi_gpus = not cfg.distributed.enable and torch.cuda.device_count(
) > 1
with SupervisedTrainer(model,
optimizer,
F.cross_entropy,
callbacks=c,
scheduler=scheduler,
data_parallel=use_multi_gpus,
use_horovod=cfg.distributed.use_horovod) as trainer:
for epoch in tq:
if cfg.use_prefetcher:
train_loader = prefetcher.DataPrefetcher(_train_loader)
test_loader = prefetcher.DataPrefetcher(_test_loader)
else:
train_loader, test_loader = _train_loader, _test_loader
# following apex's training scheme
trainer.train(train_loader)
trainer.test(test_loader)