Created
February 19, 2018 22:35
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PyTorch Sampler for Intensional Overfitting!
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| from torch.utils.data.sampler import Sampler | |
| class MySampler(Sampler): | |
| def __init__(self, main_source, indices): | |
| self.main_source = main_source | |
| self.indices = indices | |
| main_source_len = len(self.main_source) | |
| how_many = int(round(main_source_len / len(self.indices))) | |
| self.to_iter_from = [] | |
| for _ in range(how_many): | |
| self.to_iter_from.extend(self.indices) | |
| def __iter__(self): | |
| return iter(self.to_iter_from) | |
| def __len__(self): | |
| return len(self.main_source) |
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