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What's hidden in a randomly weighted neural network?

by Vivek Ramanujan*, Mitchell Wortsman*, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari

arxiv link: https://arxiv.org/abs/1911.13299

News & Updates

  • Simple one file example! Check out simple_mnist_example.py.
  • Faster version of GetSubNet written by Suchin Gururangan! Feel free to replace the old version with this:
def percentile(t, q):
    k = 1 + round(.01 * float(q) * (t.numel() - 1))
    return t.view(-1).kthvalue(k).values.item()
    
class GetSubnetFaster(torch.autograd.Function):
    @staticmethod
    def forward(ctx, scores, zeros, ones, sparsity):
        k_val = percentile(scores, sparsity*100)
        return torch.where(scores < k_val, zeros.to(scores.device), ones.to(scores.device))

    @staticmethod
    def backward(ctx, g):
        return g, None, None, None

Setup

  1. Set up a virtualenv with python 3.7.4. You can use pyvenv or conda for this.
  2. Run pip install -r requirements.txt to get requirements
  3. Create a data directory as a base for all datasets. For example, if your base directory is /mnt/datasets then imagenet would be located at /mnt/datasets/imagenet and CIFAR-10 would be located at /mnt/datasets/cifar10

Starting an Experiment

We use config files located in the configs/ folder to organize our experiments. The basic setup for any experiment is:

python main.py --config <path/to/config> <override-args>

Common example override-args include --multigpu=<gpu-ids seperated by commas, no spaces> to run on GPUs, and --prune-rate to set the prune rate, weights_remaining in our paper, for an experiment. Run python main --help for more details.

YAML Name Key

(u)uc -> (unscaled) unsigned constant
(u)sc -> (unscaled) signed constant
(u)pt -> (unscaled) pretrained init
(u)kn -> (unscaled) kaiming normal

Example Run

python main.py --config configs/smallscale/conv4/conv4_usc_unsigned.yml \
               --multigpu 0 \
               --name example \
               --data <path/to/data-dir> \
               --prune-rate 0.5

Expected Results and Pretrained Models

Model Params % Weights Remaining Initialization Accuracy (ImageNet)
ResNet-50 7.7M 30% Kaiming Normal 61.7
ResNet-50 7.7M 30% Signed Kaiming Constant 68.6
ResNet-101 13.3M 30% Kaiming Normal 66.15
ResNet-101 13.3M 30% Signed Kaiming Constant 72.3
Wide ResNet-50 20.6M 30% Kaiming Normal 67.9
Wide ResNet-50 20.6M 30% Signed Kaiming Constant 73.3

To use a pretrained model use the --pretrained=<path/to/pretrained-checkpoint> flag.

Tracking

tensorboard --logdir runs/ --bind_all

When your experiment is done, a CSV entry will be written (or appended) to runs/results.csv. Your experiment base directory will automatically be written to runs/<config-name>/prune-rate=<prune-rate>/<experiment-name> with checkpoints/ and logs/ subdirectories. If your experiment happens to match a previously created experiment base directory then an integer increment will be added to the filepath (eg. /0, /1, etc.). Checkpoints by default will have the first, best, and last models. To change this behavior, use the --save-every flag.

Requirements

Python 3.7.4, CUDA Version 10.1 (also works with 9.2 and 10.0):

absl-py==0.8.1
grpcio==1.24.3
Markdown==3.1.1
numpy==1.17.3
Pillow==6.2.1
protobuf==3.10.0
PyYAML==5.1.2
six==1.12.0
tensorboard==2.0.0
torch==1.3.0
torchvision==0.4.1
tqdm==4.36.1
Werkzeug==0.16.0

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