tclw123 / caffe_spatial Goto Github PK

• Ubuntu 1404
• Caffe
• Python 2.7

1. Download this repo and compile: make -j24, see Caffe's official guide. Make sure you get it through.
2. Here we show how to run the code, taking lenet5 as an example:
   • Preparation:
     • Data: Create your mnist training and testing lmdb (either you can download ours), put them in compression_experiments/mnist/.
     • Pretrained model: We provide a pretrained lenet5 model in compression_experiments/mnist/weights/baseline_lenet5.caffemodel (test accuracy = 0.991).
   • (We have set up an experiment folder in compression_experiments/lenet5, where there are three files: train.sh, solver.prototxt, train_val.prototxt. There are some path settings in them and pruning configs in solver.prototxt, where we have done that for you, but you are free to change them.)
   • In your caffe root path, run nohup sh compression_experiments/lenet5/train.sh <gpu_id> > /dev/null &, then check your log at compression_experiments/lenet5/weights.

For vgg16, resnet50, we also provided their experiment folders in compression_experiments, check them out and have a try!

There are two logs generated during pruning: log_<TimeID>_acc.txt and log_<TimeID>_prune.txt. The former saves the logs printed by the original Caffe; the latter saves the logs printed by our added codes.

Go to the project folder, e.g., compression_experiments/lenet5 for lenet5, then run cat weights/*prune.txt | grep app you will see the pruning and retraining course.

• check file: we provide a check file in 'compression_experiments/check/val_spa_lenet.py'.
• In your caffe root path, run 'python compression_experiments/check/val_spa_lenet.py', then you will obtain the shape of kernel.

• target_reg:
• IF_eswpf: