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android / platform / external / pytorch / 4ed85d1d00
commit4ed85d1d006af2b23684ce34a4a7eff93828e3c8[log] [tgz]
authorMatt Uyttendaele <uyt@fb.com>Wed Feb 01 21:13:58 2017 -0800
committerFacebook Github Bot <facebook-github-bot@users.noreply.github.com>Wed Feb 01 21:29:56 2017 -0800
treed998e14c15d28ec6e018d4a2114b96e6166e0d2d
parent77fd7c2b6f1299419a5665ee6e97989bf27a7def [diff]
modified behavior of input_op to convert to desired channel depth if source is different

Summary:
The color_ flag to image_input_op now indicates the desired number of output channels. If the source
DB has a different number of channels then color to grayscale or vice-versa is done.

Reviewed By: Yangqing

Differential Revision: D4498455

fbshipit-source-id: da8c39eccd06b9158f320a05663658e502905ae5
1 file changed
tree: d998e14c15d28ec6e018d4a2114b96e6166e0d2d
  1. .travis/
  2. caffe/
  3. caffe2/
  4. cmake/
  5. docs/
  6. scripts/
  7. third_party/
  8. .Doxyfile
  9. .gitignore
  10. .gitmodules
  11. .travis.yml
  12. CMakeLists.txt
  13. LICENSE
  14. Makefile
  15. README.md
README.md

Caffe2

Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.

License and Citation

Caffe2 is released under the BSD 2-Clause license.

Building Caffe2

Build Status

git clone --recursive https://github.com/bwasti/caffe2.git
cd caffe2

OS X

brew install automake protobuf
mkdir build && cd build
cmake ..
make

Ubuntu

sudo apt-get install libprotobuf-dev protobuf-compiler libatlas-base-dev libgoogle-glog-dev libgtest-dev liblmdb-dev libleveldb-dev libsnappy-dev python-dev python-pip libiomp-dev libopencv-dev libpthread-stubs0-dev cmake
sudo pip install numpy
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install git

CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz" &&
curl -fsSL ${CUDNN_URL} -O &&
sudo tar -xzf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local &&
rm cudnn-8.0-linux-x64-v5.1.tgz &&
sudo ldconfig

mkdir build && cd build
cmake ..
make

Python support

To run the tutorials you'll need ipython-notebooks and matplotlib, which can be installed on OS X with:

brew install matplotlib --with-python3
pip install ipython notebook

Build status (known working)

Ubuntu 14.04 (GCC)

  • [x] Default CPU build
  • [x] Default GPU build

OS X (Clang)

  • [x] Default CPU build
  • [x] Default GPU build

Options (both Clang and GCC)

  • [ ] Nervana GPU
  • [ ] ZMQ
  • [ ] RocksDB
  • [ ] MPI
  • [ ] OpenMP
  • [x] No LMDB
  • [x] No LevelDB
  • [x] No OpenCV

BLAS

  • [x] OpenBLAS
  • [x] ATLAS
  • [ ] MKL

Other

  • [x] CMake 2.8 support
  • [x] List of dependencies for Ubuntu 14.04
  • [x] List of dependencies for OS X