| commit | e67425647a725bebcfdcce7eed24d97dea3a5638 | [log] [tgz] |
|---|---|---|
| author | Viswanath Sivakumar <viswanath@fb.com> | Wed Jan 18 09:56:22 2017 -0800 |
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | Wed Jan 18 09:59:21 2017 -0800 |
| tree | bbf2d4a534b94ef2ef7347bb45bcc7b282e2cfbf | |
| parent | bfca2b86c3b3d6832c3b6d2242d74a8bbef65943 [diff] |
Support bias for Scale layer in caffe_translate Summary: Turns out xray models have some independent Scale layers (with bias) besides the Conv-Scale pairs. We could still fuse it with previous layers with some work, but for simplicity, including Add op followed by Mul for bias if needed. We could revisit optimizations layer fusion in the future once we have something working for xray. Reviewed By: Yangqing Differential Revision: D4427266 fbshipit-source-id: ef7d8677ccd7d10dbd20759eeed378d9bc4522d1
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.
Caffe2 is released under the BSD 2-Clause license.
git clone --recursive https://github.com/bwasti/caffe2.git cd caffe2
brew install automake protobuf mkdir build && cd build cmake .. make
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
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
Ubuntu 14.04 (GCC)
OS X (Clang)
Options (both Clang and GCC)
BLAS
Other