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Optimize temporal dropout using vectorized masked_fill #51

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Optimize temporal dropout using vectorized masked_fill #51

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5 changes: 2 additions & 3 deletions tribev2/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,9 +219,8 @@ def aggregate_features(self, batch):
elif self.config.extractor_aggregation == "sum":
out = sum(tensors)
if self.config.temporal_dropout > 0 and self.training:
for batch_idx in range(out.shape[0]):
mask = torch.rand(out.shape[1]) < self.config.temporal_dropout
out[batch_idx, mask, :] = torch.zeros_like(out[batch_idx, mask, :])
mask = torch.rand(out.shape[:2], device=out.device) < self.config.temporal_dropout
out = out.masked_fill_(mask.unsqueeze(-1), 0.0)
return out

def transformer_forward(self, x, subject_id=None):
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