def view_dataset(self, mode="train"): assert mode == "train" or mode == "valid", "Invalid view mode" if self.model_mode == "seg_gland" or self.model_mode == "seg_nuc": datagen = self.get_datagen(4, mode=mode, view=True) loader.visualize(datagen, 4) else: # visualise more for classification- don't need to show label datagen = self.get_datagen(8, mode=mode, view=True) loader.visualize(datagen, 8) return
def view_dataset(self, mode='train'): assert mode == 'train' or mode == 'valid', "Invalid view mode" datagen = self.get_datagen(4, mode=mode, view=True) loader.visualize(datagen, 4) return
def view_dataset(self, mode="train"): assert mode == "train" or mode == "valid", "Invalid view mode" datagen = self.get_datagen(4, mode=mode, view=True) loader.visualize(datagen, 4, aug_only=True, preview=True) return
def view_dataset(self, mode='train'): assert mode == 'train' or mode == 'valid', "Invalid view mode" datagen = self.get_datagen(4, mode=mode, view=True) loader.visualize(datagen, 4) # 4 is any value <= batch size of model trainer return