def main():
global args, config, best_prec1
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
config = EasyDict(config['common'])
config.save_path = os.path.dirname(args.config)
rank, world_size = dist_init()
# create model
bn_group_size = config.model.kwargs.bn_group_size
bn_var_mode = config.model.kwargs.get('bn_var_mode', 'L2')
if bn_group_size == 1:
bn_group = None
else:
assert world_size % bn_group_size == 0
bn_group = simple_group_split(world_size, rank,
world_size // bn_group_size)
config.model.kwargs.bn_group = bn_group
config.model.kwargs.bn_var_mode = (link.syncbnVarMode_t.L1 if bn_var_mode
== 'L1' else link.syncbnVarMode_t.L2)
model = model_entry(config.model)
if rank == 0:
print(model)
model.cuda()
if config.optimizer.type == 'FP16SGD' or config.optimizer.type == 'FusedFP16SGD':
args.fp16 = True
else:
args.fp16 = False
if args.fp16:
# if you have modules that must use fp32 parameters, and need fp32 input
# try use link.fp16.register_float_module(your_module)
# if you only need fp32 parameters set cast_args=False when call this
# function, then call link.fp16.init() before call model.half()
if config.optimizer.get('fp16_normal_bn', False):
print('using normal bn for fp16')
link.fp16.register_float_module(link.nn.SyncBatchNorm2d,
cast_args=False)
link.fp16.register_float_module(torch.nn.BatchNorm2d,
cast_args=False)
link.fp16.init()
model.half()
model = DistModule(model, args.sync)
# create optimizer
opt_config = config.optimizer
opt_config.kwargs.lr = config.lr_scheduler.base_lr
if config.get('no_wd', False):
param_group, type2num = param_group_no_wd(model)
opt_config.kwargs.params = param_group
else:
opt_config.kwargs.params = model.parameters()
optimizer = optim_entry(opt_config)
# optionally resume from a checkpoint
last_iter = -1
best_prec1 = 0
if args.load_path:
if args.recover:
best_prec1, last_iter = load_state(args.load_path,
model,
optimizer=optimizer)
else:
load_state(args.load_path, model)
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# augmentation
aug = [
transforms.RandomResizedCrop(config.augmentation.input_size),
transforms.RandomHorizontalFlip()
]
for k in config.augmentation.keys():
assert k in [
'input_size', 'test_resize', 'rotation', 'colorjitter', 'colorold'
]
rotation = config.augmentation.get('rotation', 0)
colorjitter = config.augmentation.get('colorjitter', None)
colorold = config.augmentation.get('colorold', False)
if rotation > 0:
aug.append(transforms.RandomRotation(rotation))
if colorjitter is not None:
aug.append(transforms.ColorJitter(*colorjitter))
aug.append(transforms.ToTensor())
if colorold:
aug.append(ColorAugmentation())
aug.append(normalize)
# train
train_dataset = McDataset(config.train_root,
config.train_source,
transforms.Compose(aug),
fake=args.fake)
# val
val_dataset = McDataset(
config.val_root, config.val_source,
transforms.Compose([
transforms.Resize(config.augmentation.test_resize),
transforms.CenterCrop(config.augmentation.input_size),
transforms.ToTensor(),
normalize,
]), args.fake)
train_sampler = DistributedGivenIterationSampler(
train_dataset,
config.lr_scheduler.max_iter,
config.batch_size,
last_iter=last_iter)
val_sampler = DistributedSampler(val_dataset, round_up=False)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=val_sampler)
config.lr_scheduler['optimizer'] = optimizer.optimizer if isinstance(
optimizer, FP16SGD) else optimizer
config.lr_scheduler['last_iter'] = last_iter
lr_scheduler = get_scheduler(config.lr_scheduler)
if rank == 0:
tb_logger = SummaryWriter(config.save_path + '/events')
logger = create_logger('global_logger', config.save_path + '/log.txt')
logger.info('args: {}'.format(pprint.pformat(args)))
logger.info('config: {}'.format(pprint.pformat(config)))
else:
tb_logger = None
if args.evaluate:
if args.fusion_list is not None:
validate(val_loader,
model,
fusion_list=args.fusion_list,
fuse_prob=args.fuse_prob)
else:
validate(val_loader, model)
link.finalize()
return
train(train_loader, val_loader, model, optimizer, lr_scheduler,
last_iter + 1, tb_logger)
link.finalize()
def dist_finalize():
link.finalize()