Using the data block API, is there a way to create labels (segmented image), using label_from_func as and when required and not for all training images at once?
I have a DataFrame of image paths and their run length encoded segmentation labels. There are about 5 rle segmentations strings that segment different objects in one image.
So, I subclassed the SegmentationItemList and modified the label_from_func attribute such that it accepts a data frame input also. The function combines all the 5 rle segmentations strings into one segmented image for 1 input image. However, the process of creating the itemlist takes too long (2.5 hours) because it is creating these labels for ALL the training images once. Is there a way that it only runs label_from_func and creates the labels when it is asked to do so for the required training examples?
Class SegMasksItemList(SegmentationItemList): def label_from_func(self, func:Callable, label_cls:Callable=None,df: Callable=None, **kwargs)->'LabelList': return self._label_from_list([func(o, df) for o in self.items], label_cls=label_cls, **kwargs) src=SegMasksItemList.from_folder('../input/train/').split_by_rand_pct(valid_pct=0.10).label_from_func(func=func_label, df=df)
My defined function ‘func’ essentially takes in item, which is the image_path, loads segmentations of its 5 rle encoded strings using vision.image.open_mask_rle, and then it combines all these 5 segmentations into 1 and returns that array.