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- from icosagon.dropout import dropout_sparse, \
- dropout_dense
- import torch
- import numpy as np
-
-
- def test_dropout_01():
- for i in range(11):
- torch.random.manual_seed(i)
- a = torch.rand((5, 10))
- a[a < .5] = 0
-
- keep_prob=i/10. + np.finfo(np.float32).eps
-
- torch.random.manual_seed(i)
- b = dropout_dense(a, keep_prob=keep_prob)
-
- torch.random.manual_seed(i)
- c = dropout_sparse(a.to_sparse(), keep_prob=keep_prob)
-
- print('keep_prob:', keep_prob)
- print('a:', a.detach().cpu().numpy())
- print('b:', b.detach().cpu().numpy())
- print('c:', c, c.to_dense().detach().cpu().numpy())
-
- assert torch.all(b == c.to_dense())
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