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