IF YOU WOULD LIKE TO GET AN ACCOUNT, please write an email to s dot adaszewski at gmail dot com. User accounts are meant only to report issues and/or generate pull requests. This is a purpose-specific Git hosting for ADARED projects. Thank you for your understanding!
Você não pode selecionar mais de 25 tópicos Os tópicos devem começar com uma letra ou um número, podem incluir traços ('-') e podem ter até 35 caracteres.

sampling.py 1.4KB

123456789101112131415161718192021222324252627282930313233343536
  1. import numpy as np
  2. import torch
  3. import torch.utils.data
  4. from typing import List, \
  5. Union
  6. def fixed_unigram_candidate_sampler(
  7. true_classes: Union[np.array, torch.Tensor],
  8. num_samples: int,
  9. unigrams: List[Union[int, float]],
  10. distortion: float = 1.):
  11. if isinstance(true_classes, torch.Tensor):
  12. true_classes = true_classes.detach().cpu().numpy()
  13. if true_classes.shape[0] != num_samples:
  14. raise ValueError('true_classes must be a 2D matrix with shape (num_samples, num_true)')
  15. unigrams = np.array(unigrams)
  16. if distortion != 1.:
  17. unigrams = unigrams.astype(np.float64) ** distortion
  18. # print('unigrams:', unigrams)
  19. indices = np.arange(num_samples)
  20. result = np.zeros(num_samples, dtype=np.int64)
  21. while len(indices) > 0:
  22. # print('len(indices):', len(indices))
  23. sampler = torch.utils.data.WeightedRandomSampler(unigrams, len(indices))
  24. candidates = np.array(list(sampler))
  25. candidates = np.reshape(candidates, (len(indices), 1))
  26. # print('candidates:', candidates)
  27. # print('true_classes:', true_classes[indices, :])
  28. result[indices] = candidates.T
  29. mask = (candidates == true_classes[indices, :])
  30. mask = mask.sum(1).astype(np.bool)
  31. # print('mask:', mask)
  32. indices = indices[mask]
  33. return result