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)