README.md

DelugeNets

Deluge Networks (DelugeNets) are a novel class of neural networks facilitating massive and flexible cross-layer information inflows from preceding layers to succeeding layers. For more technical details of DelugeNets, please refer to the paper: https://arxiv.org/abs/1611.05552.

Prerequisite

fb.resnet.torch: https://github.com/facebook/fb.resnet.torch
optnet: https://github.com/fmassa/optimize-net

Further Memory Optimization

Insert these lines to the M.setup function in models/init.lua:

local cache = {}
model:apply(function(m)
  local moduleType = torch.type(m)
  if moduleType == 'nn.CrossLayerDepthwiseConvolution' then
     if cache['cldc-o'] == nil then
        cache['cldc-o'] = torch.CudaStorage(1)
     end
     m.SBatchNorm.output = torch.CudaTensor(cache['cldc-o'], 1, 0)
  end
end)

Training Divergence

There are occassional divergence issues when training the networks. For stability, please set cuDNN to deterministic mode:

-cudnn deterministic

CIFAR-10 & CIFAR-100

Model	#Params	CIFAR-10 error	CIFAR-100 error
DelugeNet-146	6.7M	3.98	19.72
DelugeNet-218	10.0M	3.88	19.31
Wide-DelugeNet-146	20.2M	3.76	19.02

How to run

1. Make sure that fb.resnet.torch runs well on your machine.
   
   
2. Copy delugenet.lua and CrossLayerDepthwiseConvolution.lua to fb.resnet.torch/models.
   
   
3. Modify the learning rate schedule codes in train.lua for CIFAR-10 and CIFAR-100 to:
   decay = epoch >= 225 and 2 or epoch >= 150 and 1 or 0
   
   
4. Train DelugeNets:

DelugeNet-146 on CIFAR-10
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar10 -depth 146.1

DelugeNet-146 on CIFAR-100
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar100 -depth 146.1

DelugeNet-218 on CIFAR-10
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar10 -depth 218

DelugeNet-218 on CIFAR-100
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar100 -depth 218

Wide-DelugeNet-146 on CIFAR-10
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar10 -depth 146.2

Wide-DelugeNet-146 on CIFAR-100
th main.lua -batchSize 64 -nEpochs 300 -optnet true -netType delugenet -dataset cifar100 -depth 146.2

ImageNet

Validation errors (single-crop 224x224)

Model	#Params	GigaFLOPs	top-1 error	top-5 error
DelugeNet-92	43.4M	11.8	22.05	6.03
DelugeNet-104	51.4M	13.2	21.86	5.98
DelugeNet-122	63.6M	15.2	21.53	5.86

How to run

1. Follow the guide at fb.resnet.torch repository on how to set up ImageNet dataset.
   
   
2. To train ImageNet-based models, you need a minimum of 8 TITAN X GPUs:

DelugeNet-92
th main.lua -nGPU 8 -batchSize 256 -nEpochs 100 -optnet true -netType delugenet -dataset imagenet -data IMAGENET_PATH -nThreads 13 -depth 92

DelugeNet-104
th main.lua -nGPU 8 -batchSize 256 -nEpochs 100 -optnet true -netType delugenet -dataset imagenet -data IMAGENET_PATH -nThreads 13 -depth 104

DelugeNet-122
th main.lua -nGPU 8 -batchSize 256 -nEpochs 100 -optnet true -netType delugenet -dataset imagenet -data IMAGENET_PATH -nThreads 13 -depth 122

Replace IMAGENET_PATH with ImageNet dataset path.