fast path for CUDNN global average pooling

Summary:
KaimingHe  debugged slow model, and found out that global average pooling was hideously slow, even with CUDNN. Turns out CUDNN pooling op (especially backward pass) is not optimized for global pooling.

This adds a fast path for global average pooling with NCHW. This is about 30x faster than CUDNN with 56 x 56 pooling, Compared to equivalent ReduceBackSum, this is about 3x faster.

I will bootcamp the max pooling.

Reviewed By: asaadaldien

Differential Revision: D5533059

fbshipit-source-id: 2d590693d737fa92184603663031d96f6145f304
2 files changed
tree: 073a849a1e627dbaa798db610ef5ad1363cb4991
  1. .travis/
  2. caffe/
  3. caffe2/
  4. cmake/
  5. docs/
  6. scripts/
  7. third_party/
  8. .Doxyfile
  9. .Doxyfile-c
  10. .Doxyfile-python
  11. .gitignore
  12. .gitmodules
  13. .travis.yml
  14. appveyor.yml
  15. CMakeLists.txt
  16. LICENSE
  17. Makefile
  18. PATENTS
  19. README.md
  20. release-notes.md
README.md

Caffe2

TravisCI Build Status Appveyor Build Status

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.

News and Events

Caffe2 research award competition request for proposals

Questions and Feedback

Please use Github issues (https://github.com/caffe2/caffe2/issues) to ask questions, report bugs, and request new features.

Please participate in our survey (https://www.surveymonkey.com/r/caffe2). We will send you information about new releases and special developer events/webinars.

License and Citation

Caffe2 is released under the BSD 2-Clause license.

Further Resources on Caffe2.ai