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,
}