def __init__(self, args):
self.args = args
self.ct_min = 0
self.ct_max = 0
self.ct_mean = 0
self.ct_std = 0
self.target_spacing = None
self.task = args.task
self.task_code = get_task_code(args)
self.verbose = args.verbose
self.patch_size = patch_size[self.task_code]
self.training = args.exec_mode == "training"
self.data_path = os.path.join(args.data, task[args.task])
metadata_path = os.path.join(self.data_path, "dataset.json")
self.metadata = json.load(open(metadata_path, "r"))
self.modality = self.metadata["modality"]["0"]
self.results = os.path.join(args.results, self.task_code)
if not self.training:
self.results = os.path.join(self.results, self.args.exec_mode)
self.crop_foreg = transforms.CropForegroundd(keys=["image", "label"],
source_key="image")
nonzero = True if self.modality != "CT" else False # normalize only non-zero region for MRI
self.normalize_intensity = transforms.NormalizeIntensity(
nonzero=nonzero, channel_wise=True)
if self.args.exec_mode == "val":
dataset_json = json.load(open(metadata_path, "r"))
dataset_json["val"] = dataset_json["training"]
with open(metadata_path, "w") as outfile:
json.dump(dataset_json, outfile)
def __init__(self, args):
self.args = args
self.ct_min = 0
self.ct_max = 0
self.ct_mean = 0
self.ct_std = 0
self.target_spacing = None
self.task = args.task
self.task_code = get_task_code(args)
self.patch_size = patch_size[self.task_code]
self.training = args.exec_mode == "training"
self.data_path = os.path.join(args.data, task[args.task])
self.results = os.path.join(args.results, self.task_code)
if not self.training:
self.results = os.path.join(self.results, "test")
self.metadata = json.load(open(os.path.join(self.data_path, "dataset.json"), "r"))
self.modality = self.metadata["modality"]["0"]
self.crop_foreg = transforms.CropForegroundd(keys=["image", "label"], source_key="image")
self.normalize_intensity = transforms.NormalizeIntensity(nonzero=True, channel_wise=True)