Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Test Plan:
The notebook showed no diff for id score list
https://our.intern.facebook.com/intern/anp/view/?id=154764
Reviewed By: alyssawangqq
Differential Revision: D17649974
fbshipit-source-id: 84cb4ae372fc215295c2d0b139d65f4eacafae4a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20673
Add option to bucket-weighted pooling to hash the bucket so that any cardinality score can be used.
Reviewed By: huginhuangfb
Differential Revision: D15003509
fbshipit-source-id: 575a149de395f18fd7759f3edb485619f8aa5363
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13004
Implement BucketWeighted model layer, which learns a weight for each possible score in an IdScoreList. Here, we assume that the scores in the IdScoreList have already been converted into the appropriate 'buckets'. If this is not done, then essentially each score represents its own bucket.
We assume that the scores/buckets are integers, and if max_score is not set, we assume that the maximum cardinality of the score is less than or equal to the cardinality of the ids.
Reviewed By: chonglinsun
Differential Revision: D10413186
fbshipit-source-id: 743e643a1b36adf124502a8b6b29976158cdb130
