GRAMTorch: PyTorch implementation of GRAM-nets

This example implements the paper Generative Ratio Matching Networks.

After every 100 training iterations, the files $DATASET-$MODEL-data.png and $DATASET-$MODEL-samples-epoch=$EPOCH.png are written to disk with the samples from the generative model.

After every epoch, models are saved to: $DATASET-$MODEL-netG-epoch=$EPOCH.pth and $DATASET-$MODEL-netD-epoch=$EPOCH.pth

NOTE: This repo is NOT the code used to produce the results in the paper. For reproducibility, see JuliaGRAM and GRAMFlow instead.

Downloading the dataset

You can download the LSUN dataset by cloning this repo and running

python download.py -c bedroom

Usage

usage: main.py [-h] --dataset DATASET --model MODEL --dataroot DATAROOT
               [--workers WORKERS] [--batchSize BATCHSIZE]
               [--showSize SHOWSIZE] [--imageSize IMAGESIZE] [--nz NZ]
               [--nk NK] [--ngf NGF] [--ncf NCF] [--n_epochs N_EPOCHS]
               [--lr LR] [--beta1 BETA1] [--clip_ratio]
               [--eps_ratio EPS_RATIO] [--cuda] [--gpu_id GPU_ID]
               [--ngpu NGPU] [--netG NETG] [--netD NETD] [--netF NETF]
               [--outf OUTF] [--monitor_heuristic] [--manualSeed MANUALSEED]
               [--nowandb] [--classes CLASSES]

optional arguments:
  -h, --help              show this help message and exit
  --dataset DATASET       cifar10 | lsun | mnist |imagenet | folder | lfw | fake
  --model MODEL           gramnet | gan
  --dataroot DATAROOT     path to dataset
  --workers WORKERS       number of data loading workers
  --batchSize BATCHSIZE   input batch size
  --showSize SHOWSIZE     size of display batch
  --imageSize IMAGESIZE   the height / width of the input image to network
  --nz NZ                 size of the latent z vector
  --nk NK                 size of the projected k vector
  --ngf NGF 
  --ncf NCF 
  --n_epochs N_EPOCHS     number of epochs to train for
  --lr LR                 learning rate, default=0.0002
  --beta1 BETA1           beta1 for adam. default=0.5
  --clip_ratio            apply ratio clipping as suggested by one of the reviewer
  --eps_ratio EPS_RATIO   add eps to the diagonal before solving
  --cuda                  enables cuda
  --gpu_id GPU_ID         default GPU ID to use
  --ngpu NGPU             number of GPUs to use
  --netG NETG             path to netG (to continue training)
  --netD NETD             path to netD (to continue training)
  --netF NETF             path to netF (to continue training)
  --outf OUTF             folder to output images and model checkpoints
  --monitor_heuristic     monitor heuristic σ
  --manualSeed MANUALSEED manual seed
  --nowandb               disables wandb
  --classes CLASSES       comma separated list of classes for the lsun data set

Note that by default Weights & Biases is used for logging, but you can disable it by --nowandb.

Acknowledgement

The code is heavily based on the DCGAN example of PyTorch.

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Maintainer
Kai Xu