xautodl/lib/datasets/SearchDatasetWrap.py

##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch, copy, random
import torch.utils.data as data


class SearchDataset(data.Dataset):
    def __init__(self, name, data, train_split, valid_split, check=True):
        self.datasetname = name
        if isinstance(data, (list, tuple)):  # new type of SearchDataset
            assert len(data) == 2, "invalid length: {:}".format(len(data))
            self.train_data = data[0]
            self.valid_data = data[1]
            self.train_split = train_split.copy()
            self.valid_split = valid_split.copy()
            self.mode_str = "V2"  # new mode
        else:
            self.mode_str = "V1"  # old mode
            self.data = data
            self.train_split = train_split.copy()
            self.valid_split = valid_split.copy()
            if check:
                intersection = set(train_split).intersection(set(valid_split))
                assert (
                    len(intersection) == 0
                ), "the splitted train and validation sets should have no intersection"
        self.length = len(self.train_split)

    def __repr__(self):
        return "{name}(name={datasetname}, train={tr_L}, valid={val_L}, version={ver})".format(
            name=self.__class__.__name__,
            datasetname=self.datasetname,
            tr_L=len(self.train_split),
            val_L=len(self.valid_split),
            ver=self.mode_str,
        )

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        assert index >= 0 and index < self.length, "invalid index = {:}".format(index)
        train_index = self.train_split[index]
        valid_index = random.choice(self.valid_split)
        if self.mode_str == "V1":
            train_image, train_label = self.data[train_index]
            valid_image, valid_label = self.data[valid_index]
        elif self.mode_str == "V2":
            train_image, train_label = self.train_data[train_index]
            valid_image, valid_label = self.valid_data[valid_index]
        else:
            raise ValueError("invalid mode : {:}".format(self.mode_str))
        return train_image, train_label, valid_image, valid_label