def __init__(self, args): super().__init__() self.args = args self.train_imgs = [] self.train_lbls = [] self.val_imgs = [] self.val_lbls = [] self.test_imgs = [] self.kfold = KFold(n_splits=self.args.nfolds, shuffle=True, random_state=12345) self.data_path = get_path(args) configs = get_config_file(self.args) self.kwargs = { "dim": self.args.dim, "patch_size": configs["patch_size"], "seed": self.args.seed, "gpus": self.args.gpus, "num_workers": self.args.num_workers, "oversampling": self.args.oversampling, "benchmark": self.args.benchmark, "nvol": self.args.nvol, "train_batches": self.args.train_batches, "test_batches": self.args.test_batches, "meta": load_data(self.data_path, "*_meta.npy"), }
def get_unet_params(self): config = get_config_file(self.args) in_channels = config["in_channels"] patch_size = config["patch_size"] spacings = config["spacings"] n_class = config["n_class"] strides, kernels, sizes = [], [], patch_size[:] while True: spacing_ratio = [spacing / min(spacings) for spacing in spacings] stride = [ 2 if ratio <= 2 and size >= 8 else 1 for (ratio, size) in zip(spacing_ratio, sizes) ] kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio] if all(s == 1 for s in stride): break sizes = [i / j for i, j in zip(sizes, stride)] spacings = [i * j for i, j in zip(spacings, stride)] kernels.append(kernel) strides.append(stride) if len(strides) == 5: break strides.insert(0, len(spacings) * [1]) kernels.append(len(spacings) * [3]) return in_channels, n_class, kernels, strides, patch_size
def __init__(self, args): super().__init__() self.args = args self.tfrecords_train = [] self.tfrecords_val = [] self.tfrecords_test = [] self.train_idx = [] self.val_idx = [] self.test_idx = [] self.kfold = KFold(n_splits=self.args.nfolds, shuffle=True, random_state=12345) self.data_path = os.path.join(self.args.data, get_task_code(self.args)) if self.args.exec_mode == "predict" and not args.benchmark: self.data_path = os.path.join(self.data_path, "test") configs = get_config_file(self.args) self.kwargs = { "dim": self.args.dim, "patch_size": configs["patch_size"], "seed": self.args.seed, "gpus": self.args.gpus, "num_workers": self.args.num_workers, "oversampling": self.args.oversampling, "create_idx": self.args.create_idx, "benchmark": self.args.benchmark, }
def __init__(self, args): super().__init__() self.args = args self.data_path = get_data_path(args) self.kfold = get_kfold_splitter(args.nfolds) self.kwargs = { "dim": self.args.dim, "seed": self.args.seed, "gpus": self.args.gpus, "nvol": self.args.nvol, "overlap": self.args.overlap, "benchmark": self.args.benchmark, "num_workers": self.args.num_workers, "oversampling": self.args.oversampling, "test_batches": self.args.test_batches, "train_batches": self.args.train_batches, "invert_resampled_y": self.args.invert_resampled_y, "patch_size": get_config_file(self.args)["patch_size"], } self.train_imgs, self.train_lbls, self.val_imgs, self.val_lbls, self.test_imgs = ( [], ) * 5
def __init__(self, args): super().__init__() self.args = args self.train_imgs = [] self.train_lbls = [] self.val_imgs = [] self.val_lbls = [] self.test_imgs = [] self.kfold = KFold(n_splits=self.args.nfolds, shuffle=True, random_state=12345) self.data_path = get_path(args) configs = get_config_file(self.args) if self.args.gpus: device_count = self.args.gpus device = 'gpu' elif self.args.hpus: device_count = self.args.hpus device = 'hpu' else: device_count = 1 device = 'cpu' self.kwargs = { "dim": self.args.dim, "patch_size": configs["patch_size"], "seed": self.args.seed, "num_device": device_count, "num_workers": self.args.num_workers, "oversampling": self.args.oversampling, "benchmark": self.args.benchmark, "nvol": self.args.nvol, "train_batches": self.args.train_batches, "test_batches": self.args.test_batches, "meta": load_data(self.data_path, "*_meta.npy"), "device": device, "augment": self.args.augment, "set_aug_seed": self.args.set_aug_seed, }