Resnet50 native module for Node.js

| My experiments with pytorch glow

Compiling Glow

For more detailed info see the glow documentation found here.

• download glow:

  git clone https://github.com/pytorch/glow.git
  cd glow
  git submodule update --init --recursive

• build glow:

  mkdir build_Release && cd $_
  cmake -G Ninja .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=/usr/local/opt/llvm
  ninja all

  Note: You might need to install some dependencies (see main docs).

Creating a bundle to use in our node module

Detailed glow documentation for standalone executable bundles found here.

• getting the resnet50 model:

  ../utils/download_caffe2_models.sh

  This is probably the easiest way but it will take a while since it downloads a lot more than just the resnet50 model. If you want just the resnet model then open up the file above and run the specific wget commands...

  wget -nc http://fb-glow-assets.s3.amazonaws.com/models/resnet50/predict_net.pbtxt -P resnet50
  wget -nc http://fb-glow-assets.s3.amazonaws.com/models/resnet50/predict_net.pb -P resnet50
  wget -nc http://fb-glow-assets.s3.amazonaws.com/models/resnet50/init_net.pb -P resnet50

  Even this may still take a while since the init_net.pb file is ~120M in size.

• getting sample image:

  ls ../tests/images/imagenet

  There are several in the tests dir however any 224x224 png image should work.

• creating the bundle:

  mkdir bundle
  ./bin/image-classifier ../tests/images/imagenet/dog_207.png \
    -image_mode=0to1 -m resnet50 -cpu -emit-bundle bundle -g

And now we should have our object and weights files to be used in our node module. The bundle/resnet50.o and bundle/resnet50.weights should be pretty much the same as the files currently in the lib dir.