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
Summary:
To achive this, I modified the blob name scheme defined in a layer.
Before it was scope/fc_w and scope/fc_w_auto_0 (if there is another fc
within the same scope).
Now I change it to scope/fc/w and scope/fc_auto_0/w.
That is, we rely on the uniqueness of the scoped layer name to define
names for blobs.
I also overwrote the create_param method in LayerModelHelper to let it
use the resolved name for blobs given the sharingparameter context.
There are some details such as making the initializer more structured
that I need to finalize.
Reviewed By: kennyhorror
Differential Revision: D5435132
fbshipit-source-id: a0525f5ea0977e255dd5ea765b38913f5951d455
Summary:
- Integrated RFF into the preprocessing workflow for dense features
- Developed Flow interface to input RFF parameters
- Created unit test for using RFF with sparseNN
Reviewed By: chocjy
Differential Revision: D5367534
fbshipit-source-id: 07307259c501a614d9ee68a731f0cc8ecd17db68
Summary:
- Created the random fourier features layer
- Generated a unit test to test the random fourier features layer is built correctly
- Inspired by the paper [[ https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf | Random Features for Large-Scale Kernel Machines]]
Reviewed By: chocjy
Differential Revision: D5318105
fbshipit-source-id: c3885cb5ad1358853d4fc13c780fec3141609176
