def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# Visualizer
self.visualizer = TensorboardVisualizer(args, sys.argv)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
data_kwargs = {
'transform': input_transform,
'base_size': args.base_size,
'crop_size': args.crop_size
}
train_dataset = get_segmentation_dataset(args.dataset,
split='train',
mode='train',
**data_kwargs)
val_dataset = get_segmentation_dataset(args.dataset,
split='val',
mode='val',
**data_kwargs)
args.iters_per_epoch = len(train_dataset) // (args.num_gpus *
args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(train_dataset,
shuffle=True,
distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler,
args.batch_size,
args.max_iters)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
args.batch_size)
self.train_loader = data.DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(model=args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
norm_layer=BatchNorm2d).to(
self.device) # jpu=args.jpu
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert ext == '.pkl' or '.pth', 'Sorry only .pth and .pkl files supported.'
print('Resuming training, loading {}...'.format(args.resume))
self.model.load_state_dict(
torch.load(args.resume,
map_location=lambda storage, loc: storage))
# create criterion
self.criterion = get_segmentation_loss(args.model,
use_ohem=args.use_ohem,
aux=args.aux,
aux_weight=args.aux_weight,
ignore_index=-1).to(self.device)
# optimizer, for model just includes pretrained, head and auxlayer
params_list = list()
if hasattr(self.model, 'pretrained'):
params_list.append({
'params': self.model.pretrained.parameters(),
'lr': args.lr
})
if hasattr(self.model, 'exclusive'):
for module in self.model.exclusive:
params_list.append({
'params':
getattr(self.model, module).parameters(),
'lr':
args.lr * 10
})
self.optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lr scheduling
self.lr_scheduler = WarmupPolyLR(self.optimizer,
max_iters=args.max_iters,
power=0.9,
warmup_factor=args.warmup_factor,
warmup_iters=args.warmup_iters,
warmup_method=args.warmup_method)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
def __init__(self, args, logger):
self.args = args
self.logger = logger
if get_rank() == 0:
TBWriter.init(
os.path.join(args.project_dir, args.task_dir, "tbevents")
)
self.device = torch.device(args.device)
self.meters = MetricLogger(delimiter=" ")
# image transform
input_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
),
]
)
# dataset and dataloader
data_kwargs = {
"transform": input_transform,
"base_size": args.base_size,
"crop_size": args.crop_size,
"root": args.dataroot,
}
train_dataset = get_segmentation_dataset(
args.dataset, split="train", mode="train", **data_kwargs
)
val_dataset = get_segmentation_dataset(
args.dataset, split="val", mode="val", **data_kwargs
)
args.iters_per_epoch = len(train_dataset) // (
args.num_gpus * args.batch_size
)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(
train_dataset, shuffle=True, distributed=args.distributed
)
train_batch_sampler = make_batch_data_sampler(
train_sampler, args.batch_size, args.max_iters
)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(
val_sampler, args.batch_size
)
self.train_loader = data.DataLoader(
dataset=train_dataset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True,
)
self.val_loader = data.DataLoader(
dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True,
)
# create network
BatchNorm2d = nn.SyncBatchNorm if args.distributed else nn.BatchNorm2d
self.model = get_segmentation_model(
model=args.model,
dataset=args.dataset,
backbone=args.backbone,
aux=args.aux,
jpu=args.jpu,
norm_layer=BatchNorm2d,
).to(self.device)
# resume checkpoint if needed
if args.resume:
if os.path.isfile(args.resume):
name, ext = os.path.splitext(args.resume)
assert (
ext == ".pkl" or ".pth"
), "Sorry only .pth and .pkl files supported."
print("Resuming training, loading {}...".format(args.resume))
self.model.load_state_dict(
torch.load(
args.resume, map_location=lambda storage, loc: storage
)
)
# create criterion
self.criterion = get_segmentation_loss(
args.model,
use_ohem=args.use_ohem,
aux=args.aux,
aux_weight=args.aux_weight,
ignore_index=-1,
).to(self.device)
# optimizer, for model just includes pretrained, head and auxlayer
params_list = list()
if hasattr(self.model, "pretrained"):
params_list.append(
{"params": self.model.pretrained.parameters(), "lr": args.lr}
)
if hasattr(self.model, "exclusive"):
for module in self.model.exclusive:
params_list.append(
{
"params": getattr(self.model, module).parameters(),
"lr": args.lr * args.lr_scale,
}
)
self.optimizer = torch.optim.SGD(
params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
# lr scheduling
self.lr_scheduler = get_lr_scheduler(self.optimizer, args)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(
self.model,
device_ids=[args.local_rank],
output_device=args.local_rank,
)
# evaluation metrics
self.metric = SegmentationMetric(train_dataset.num_class)
self.best_pred = 0.0
