def __init__(self, loader_params, dataset_params):
super().__init__(loader_params, dataset_params)
self.image_augment_with_target = ImgAug(patching_seq(crop_size=(self.dataset_params.h,
self.dataset_params.w)))
self.image_augment = ImgAug(color_seq)
self.dataset = None
def __init__(self, loader_params, dataset_params):
super().__init__()
self.loader_params = AttrDict(loader_params)
self.dataset_params = AttrDict(dataset_params)
self.dataset = MetadataImageSegmentationDataset
self.image_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.2, 0.2, 0.2]),
])
self.mask_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.Lambda(binarize),
transforms.Lambda(to_tensor),
])
self.image_augment_with_target = ImgAug(affine_seq)
self.image_augment = ImgAug(color_seq)
def __init__(self, loader_params, dataset_params):
super().__init__()
self.loader_params = AttrDict(loader_params)
self.dataset_params = AttrDict(dataset_params)
self.image_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.0], std=[1.0]),
])
self.mask_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.Lambda(to_monochrome),
transforms.Lambda(to_tensor),
])
self.image_augment_with_target = ImgAug(affine_seq)
self.image_augment = ImgAug(color_seq)
self.dataset = None
def __init__(self, loader_params, dataset_params):
super().__init__(loader_params, dataset_params)
self.image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD),
])
self.mask_transform = transforms.Compose([
transforms.Lambda(to_monochrome),
transforms.Lambda(to_tensor),
])
self.image_augment_with_target_train = ImgAug(
crop_seq(crop_size=(self.dataset_params.h, self.dataset_params.w)))
self.image_augment_with_target_inference = ImgAug(
padding_seq(pad_size=(self.dataset_params.h_pad,
self.dataset_params.w_pad),
pad_method='replicate'))
self.dataset = MetadataImageSegmentationDataset
def __init__(self, loader_params, dataset_params):
super().__init__(loader_params, dataset_params)
self.image_augment_inference = ImgAug(
padding_seq(pad_size=(self.dataset_params.h_pad,
self.dataset_params.w_pad),
pad_method='replicate'))
self.image_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD),
])
self.dataset = MetadataImageSegmentationTTA
def __init__(self, loader_params, dataset_params):
super().__init__(loader_params, dataset_params)
self.image_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD),
])
self.mask_transform = transforms.Compose([
transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
transforms.Lambda(to_monochrome),
transforms.Lambda(to_tensor),
])
self.image_augment_with_target_train = ImgAug(fast_seq)
self.dataset = MetadataImageSegmentationDataset
