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| import numpy as np | |
| import raven_utils.group as group | |
| import raven_utils.entity as entity | |
| import raven_utils.rules as rules | |
| import raven_utils.properties as properties | |
| ENTITY_INDEX = entity.INDEX + 1 | |
| ENTITY_DICT = dict(zip(group.NAMES, ENTITY_INDEX[:-1])) | |
| NAMES_ORDER = dict(zip(group.NAMES, np.arange(len(group.NAMES)))) | |
| PROPERTIES_INDEXES = np.cumsum(np.array(list(entity.NO.values())) * properties.NO) | |
| INDEX = np.concatenate([[0], PROPERTIES_INDEXES]) + entity.SUM + 1 # +2 type and uniformity | |
| SECOND_LAYOUT = [i - 1 for i in [ | |
| ENTITY_DICT["in_center_single_out_center_single"] + 1, | |
| ENTITY_DICT["in_distribute_four_out_center_single"] + 1, | |
| ENTITY_DICT["in_distribute_four_out_center_single"] + 2, | |
| ENTITY_DICT["in_distribute_four_out_center_single"] + 3, | |
| ENTITY_DICT["left_center_single_right_center_single"] + 1, | |
| ENTITY_DICT["up_center_single_down_center_single"] + 1 | |
| ]] | |
| FIRST_LAYOUT = list(set(range(entity.SUM)) - set(SECOND_LAYOUT)) | |
| LAYOUT_NO = 2 | |
| START_INDEX = dict(zip(group.NAMES, INDEX[:-1])) | |
| END_INDEX = INDEX[-1] | |
| RULES_ATTRIBUTES_ALL_LEN = rules.ATTRIBUTES_LEN * LAYOUT_NO | |
| UNIFORMITY_NO = 2 | |
| UNIFORMITY_INDEX = END_INDEX + RULES_ATTRIBUTES_ALL_LEN | |
| SIZE = UNIFORMITY_INDEX + UNIFORMITY_NO | |
| def take(target): | |
| return target[1], target[2] | |
| def create(images, index, pattern_index=(2, 5), full_index=False, arrange=np.arange, shape=lambda x: x.shape): | |
| return [images[:, pattern_index[0]], images[:, pattern_index[1]], | |
| images[arrange(shape(index)[0]), (0 if full_index else 8) + index[:, 0]]] | |
| def take_simple(target): | |
| return target[1], target[0] | |
| def create_simple(images, target, index=slice(None), pattern_index=(2, 5)): | |
| return [images[:, pattern_index[0]], images[:, pattern_index[1]], target][index] |