def __init__(self, data_path: str, batch_size: int, lr: float,
num_layers: int, features_start: int, bilinear: bool,
**kwargs):
super().__init__()
self.data_path = data_path
self.batch_size = batch_size
self.lr = lr
self.num_layers = num_layers
self.features_start = features_start
self.bilinear = bilinear
self.net = UNet(num_classes=19,
num_layers=self.num_layers,
features_start=self.features_start,
bilinear=self.bilinear)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753],
std=[0.32064945, 0.32098866, 0.32325324])
])
self.trainset = KITTI(self.data_path,
split='train',
transform=self.transform)
self.validset = KITTI(self.data_path,
split='valid',
transform=self.transform)
def __init__(self, hparams):
super().__init__()
self.root_path = hparams.root
self.batch_size = hparams.batch_size
self.learning_rate = hparams.lr
self.net = UNet(num_classes=19)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753],
std=[0.32064945, 0.32098866, 0.32325324])
])
self.trainset = KITTI(self.root_path,
split='train',
transform=self.transform)
self.testset = KITTI(self.root_path,
split='test',
transform=self.transform)
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
self.data_path = hparams.data_path
self.batch_size = hparams.batch_size
self.learning_rate = hparams.lr
self.net = UNet(num_classes=19,
num_layers=hparams.num_layers,
features_start=hparams.features_start,
bilinear=hparams.bilinear)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753],
std=[0.32064945, 0.32098866, 0.32325324])
])
self.trainset = KITTI(self.data_path,
split='train',
transform=self.transform)
self.validset = KITTI(self.data_path,
split='valid',
transform=self.transform)