def __new__(cls, root, train=True, transform=None, download=False):
if train:
return datasets.SBDataset(root,
image_set='train_noval',
mode='segmentation',
download=download,
transforms=transform)
else:
return datasets.VOCSegmentation(root,
image_set='val',
download=download,
transforms=transform)
def __new__(cls, root, train=True, transform=None, download=False):
root = pathlib.Path(root).parent
if train:
return VD.SBDataset(root / "sbd",
image_set='train_noval',
mode='segmentation',
transforms=transform,
download=download)
else:
return VD.VOCSegmentation(root / "voc",
image_set="val",
transforms=transform,
download=download)
def sbdataset():
return collect_download_configs(
lambda: datasets.SBDataset(ROOT, download=True),
name="SBDataset",
file="voc",
)
data_root_dir = os.environ["DATA_PATH"]
def get_summary_transforms():
transforms = []
transforms.append(T.ToTensor())
transforms.append(
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
return T.Compose(transforms)
sbd_train = datasets.SBDataset(data_root_dir + "/sbd/",
image_set='train',
mode='segmentation',
download=False,
transforms=get_summary_transforms())
sbd_val = datasets.SBDataset(data_root_dir + "/sbd/",
image_set='val',
mode='segmentation',
download=False,
transforms=get_summary_transforms())
cityscapes_train = datasets.Cityscapes(data_root_dir + "/cityscapes/",
split='train',
mode='fine',
target_type='semantic',
transforms=get_summary_transforms())
cityscapes_val = datasets.Cityscapes(data_root_dir + "/cityscapes/",
split='val',
