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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| import bisect | |
| import numpy as np | |
| from torch.utils.data.dataset import ConcatDataset as _ConcatDataset | |
| class ConcatDataset(_ConcatDataset): | |
| """ | |
| Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra | |
| method for querying the sizes of the image | |
| """ | |
| def get_idxs(self, idx): | |
| dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) | |
| if dataset_idx == 0: | |
| sample_idx = idx | |
| else: | |
| sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] | |
| return dataset_idx, sample_idx | |
| def get_img_info(self, idx): | |
| dataset_idx, sample_idx = self.get_idxs(idx) | |
| return self.datasets[dataset_idx].get_img_info(sample_idx) | |
| class MixDataset(object): | |
| def __init__(self, datasets, ratios): | |
| self.datasets = datasets | |
| self.ratios = ratios | |
| self.lengths = [] | |
| for dataset in self.datasets: | |
| self.lengths.append(len(dataset)) | |
| self.lengths = np.array(self.lengths) | |
| self.seperate_inds = [] | |
| s = 0 | |
| for i in self.ratios[:-1]: | |
| s += i | |
| self.seperate_inds.append(s) | |
| def __len__(self): | |
| return self.lengths.sum() | |
| def __getitem__(self, item): | |
| i = np.random.rand() | |
| ind = bisect.bisect_right(self.seperate_inds, i) | |
| b_ind = np.random.randint(self.lengths[ind]) | |
| return self.datasets[ind][b_ind] | |