def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# image transform
input_transform = transforms.Compose([
transforms.ToTensor(),
#transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
])
# dataset and dataloader
val_dataset = get_segmentation_dataset(root=args.dataset,
split='val',
mode='val',
transform=input_transform)
val_sampler = make_data_sampler(val_dataset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler,
images_per_batch=1)
self.val_loader = data.DataLoader(dataset=val_dataset,
batch_sampler=val_batch_sampler,
num_workers=args.workers,
pin_memory=True)
# create network
self.model = get_segmentation_model(model=args.model,
classnum=args.classnum,
aux=args.aux,
pretrained=True,
pretrained_base=False)
if args.distributed:
self.model = self.model.module
self.model.to(self.device)
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
# 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}
trainset = get_segmentation_dataset(args.dataset, split='train', mode='train', **data_kwargs)
args.iters_per_epoch = len(trainset) // (args.num_gpus * args.batch_size)
args.max_iters = args.epochs * args.iters_per_epoch
train_sampler = make_data_sampler(trainset, shuffle=True, distributed=args.distributed)
train_batch_sampler = make_batch_data_sampler(train_sampler, args.batch_size, args.max_iters)
self.train_loader = data.DataLoader(dataset=trainset,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
pin_memory=True)
if not args.skip_val:
valset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs)
val_sampler = make_data_sampler(valset, False, args.distributed)
val_batch_sampler = make_batch_data_sampler(val_sampler, args.batch_size)
self.val_loader = data.DataLoader(dataset=valset,
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(args.model, dataset=args.dataset,
aux=args.aux, norm_layer=BatchNorm2d)
if args.distributed:
self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[args.local_rank],
output_device=args.local_rank)
self.model = self.model.to(args.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
if args.ohem:
min_kept = int(args.batch_size // args.num_gpus * args.crop_size ** 2 // 16)
self.criterion = MixSoftmaxCrossEntropyOHEMLoss(args.aux, args.aux_weight, min_kept=min_kept,
ignore_index=-1).to(self.device)
else:
self.criterion = MixSoftmaxCrossEntropyLoss(args.aux, args.aux_weight, ignore_index=-1).to(self.device)
# optimizer
self.optimizer = torch.optim.SGD(self.model.parameters(),
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)
# evaluation metrics
self.metric = SegmentationMetric(trainset.num_class)
self.best_pred = 0.0