def __init__(self, config, result): self.config = config self.result = result self.setup = self._nothing_ self.cleanup = self._nothing_ self.case = [] self.name = utils.get_script_name()
def gen_file_name(self): """ """ m = hashlib.md5() m.update(sys.argv[0]) m.update(time.strftime("%d_%m_%Y_%H_%M_%S", self.gtime.start_date)) name_script = utils.get_script_name() name_hash = m.hexdigest()[0:16].upper() return"%s_%s.log" % (name_script, name_hash)
def set_doc(self, doc): des = dict() des[KEY_TYPE] = TYPE_SCRIPT des[KEY_NAME] = utils.get_script_name() if doc is None : des[KEY_DOC] = "" else: des[KEY_DOC] = Pydoc.clean(doc) self.result.doc(des)
p_transform_augment=None, p_transform=p_transform, world_coord_system=False, luna_origin=None) rng = patch_class_config.rng # candidates after segmentations path predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH) segmentation_outputs_path = predictions_dir + '/%s' % seg_config_name id2candidates_path = utils_lung.get_candidates_paths(segmentation_outputs_path) # filter our those, who are already generated predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH) \ + '/' + utils.get_script_name(__file__) exclude_pids = utils_lung.get_generated_pids(predictions_dir) data_iterator = data_iterators.CandidatesDSBDataGenerator( data_path=pathfinder.DATA_PATH, transform_params=p_transform, data_prep_fun=data_prep_function, id2candidates_path=id2candidates_path, exclude_pids=exclude_pids) def build_model(): metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH) metadata_path = utils.find_model_metadata( metadata_dir, patch_class_config.__name__.split('.')[-1])
data_prep_function = patch_class_config.partial(patch_class_config.data_prep_function, p_transform_augment=None, p_transform=p_transform, world_coord_system=False, luna_origin=None) rng = patch_class_config.rng # candidates after segmentations path predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH) segmentation_outputs_path = predictions_dir + '/%s' % seg_config_name id2candidates_path = utils_lung.get_candidates_paths(segmentation_outputs_path) # filter our those, who are already generated predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH) \ + '/' + utils.get_script_name(__file__) exclude_pids = utils_lung.get_generated_pids(predictions_dir) data_iterator = data_iterators.CandidatesDSBDataGenerator(data_path=pathfinder.DATA_PATH, transform_params=p_transform, data_prep_fun=data_prep_function, id2candidates_path=id2candidates_path, exclude_pids=exclude_pids) def build_model(): metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH) metadata_path = utils.find_model_metadata(metadata_dir, patch_class_config.__name__.split('.')[-1]) metadata = utils.load_pkl(metadata_path) print 'Build model'