#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np from dataset import DatasetFactory, Config from dataset.images import Image Config().verbose = True Config().dataset_type = 'wrapper_dataset' ops = ('cropping', ) # val_ind factory = DatasetFactory() factory.add_image_type('image', 'label', 'mask', 'bounding_box') factory.add_dataset(dataset_id='1', dirname='t_data_1', val_ind=[0, 2]) factory.add_dataset(dataset_id='2', dirname='t_data_2') factory.add_dataset(dataset_id='3', dirname='t_data_3', val_ind=[0]) factory.add_training_operation(*ops) t_dataset, v_dataset = factory.create() t_keys = ['1/at1006', '2/at1025', '2/at1029', '3/at1034', '3/at1040'] v_keys = ['1/at1000', '1/at1007', '3/at1033'] assert list(t_dataset.images.keys()) == t_keys assert list(v_dataset.images.keys()) == v_keys for im in t_dataset[0]: if hasattr(im, 'labels'): print(im.labels) assert isinstance(im, Image) Config().dataset_type = 'dataset' t_dataset, v_dataset = factory.create() for im in t_dataset[0]:
#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np from dataset import DatasetFactory, Config from dataset.trees import TensorTree # Config().dataset_type = 'wrapper_dataset' t_ops = ('cropping', 'label_normalization') v_ops = ('cropping', 'label_normalization') factory = DatasetFactory() factory.add_image_type('image', 'hierachical_label', 'mask') factory.add_dataset(dataset_id='tmc', dirname='data') factory.add_dataset(dataset_id='kki', dirname='ped_data') factory.add_training_operation(*t_ops) t_dataset, v_dataset = factory.create() indices = [0, 1, len(t_dataset) - 1] tensor_trees = [t_dataset[ind][1] for ind in indices] print(tensor_trees[-1]) tensor_tree = TensorTree.stack(tensor_trees) print(tensor_tree)