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With this weird little trick the new fixed_unigram_candidate_sampler() finally seems to be fully robust and to actually work.
master
Stanislaw Adaszewski
4 years ago
1 changed files with 5 additions and 4 deletions
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+ 5
- 4
src/triacontagon/sampling.py
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| @@ -21,7 +21,7 @@ from itertools import product, \ | |||||
| from functools import reduce | from functools import reduce | ||||
| def fixed_unigram_candidate_sampler_new( | |||||
| def fixed_unigram_candidate_sampler( | |||||
| true_classes: torch.Tensor, | true_classes: torch.Tensor, | ||||
| num_repeats: torch.Tensor, | num_repeats: torch.Tensor, | ||||
| unigrams: torch.Tensor, | unigrams: torch.Tensor, | ||||
| @@ -72,9 +72,10 @@ def fixed_unigram_candidate_sampler_new( | |||||
| mask = (true_classes[indices[:, 1]] == candidates).sum(dim=1).to(torch.bool) | mask = (true_classes[indices[:, 1]] == candidates).sum(dim=1).to(torch.bool) | ||||
| can_cum = cumcount(candidates[:, 0]) | |||||
| # can_cum = cumcount(candidates[:, 0]) | |||||
| can_diff = torch.cat([ torch.tensor([1]), candidates[1:, 0] - candidates[:-1, 0] ]) | |||||
| ind_cum = cumcount(indices[:, 1]) | ind_cum = cumcount(indices[:, 1]) | ||||
| repeated = (can_cum > 0) & (ind_cum > 0) | |||||
| repeated = (can_diff == 0) & (ind_cum > 0) | |||||
| # TODO: this is wrong, still requires work | # TODO: this is wrong, still requires work | ||||
| mask = mask | repeated | mask = mask | repeated | ||||
| @@ -141,7 +142,7 @@ def fixed_unigram_candidate_sampler_slow( | |||||
| return torch.tensor(res) | return torch.tensor(res) | ||||
| def fixed_unigram_candidate_sampler( | |||||
| def fixed_unigram_candidate_sampler_old( | |||||
| true_classes: torch.Tensor, | true_classes: torch.Tensor, | ||||
| num_repeats: torch.Tensor, | num_repeats: torch.Tensor, | ||||
| unigrams: torch.Tensor, | unigrams: torch.Tensor, | ||||