DCGAN.torch: Train your own image generator

1. Train your own network
   1. Train a face generator using the Celeb-A dataset
   2. Train Bedrooms, Bridges, Churches etc. using the LSUN dataset
   3. Train a generator on your own set of images.
   4. Train on the ImageNet dataset
2. Use a pre-trained generator to generate images.
   1. Generate samples of 64x64 pixels
   2. Generate large artsy images (tried up to 4096 x 4096 pixels)
   3. Walk in the space of samples
3. Vector Arithmetic of images in latent space

Prerequisites

• Computer with Linux or OSX
• Torch-7
• For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.

Installing dependencies

Without GPU

• Install Torch: http://torch.ch/docs/getting-started.html#_

With NVIDIA GPU

• Install CUDA, and preferably CuDNN (optional).
  • Instructions for Ubuntu are here: INSTALL.md
• Install Torch: http://torch.ch/docs/getting-started.html#_
• Install optnet to reduce memory footprint for large images: luarocks install optnet
• Optional, if you installed CuDNN, install cudnn bindings with luarocks install cudnn

Display UI

Optionally, for displaying images during training and generation, we will use the display package.

• Install it with: luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
• Then start the server with: th -ldisplay.start
• Open this URL in your browser: http://localhost:8000

You can see training progress in your browser window. It will look something like this:

1. Train your own network

1.1 Train a face generator using the Celeb-A dataset

Preprocessing

mkdir celebA; cd celebA

Download img_align_celeba.zip from http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html under the link "Align&Cropped Images".

unzip img_align_celeba.zip; cd ..
DATA_ROOT=celebA th data/crop_celebA.lua

Training

DATA_ROOT=celebA dataset=folder th main.lua

1.2. Train Bedrooms, Bridges, Churches etc. using the LSUN dataset

LSUN dataset is shipped as an LMDB database. First, install LMDB on your system.

• On OSX with Homebrew: brew install lmdb
• On Ubuntu: sudo apt-get install liblmdb-dev

Then install a couple of Torch packages.

luarocks install lmdb.torch
luarocks install tds

Preprocessing (with bedroom class as an example)

Download bedroom_train_lmdb from the LSUN website.

Generate an index file:

DATA_ROOT=[path_to_lmdb] th data/lsun_index_generator.lua

Training

DATA_ROOT=[path-to-lmdb] dataset=lsun th main.lua

The code for the LSUN data loader is hardcoded for bedrooms. Change this line to another LSUN class to generate other classes.

1.3. Train a generator on your own set of images.

Preprocessing

• Create a folder called myimages.
• Inside that folder, create a folder called images and place all your images inside it.

Training

DATA_ROOT=myimages dataset=folder th main.lua

1.4. Train on the ImageNet dataset

Preprocessing

Follow instructions from this link.

Training

DATA_ROOT=[PATH_TO_IMAGENET]/train dataset=folder th main.lua

All training options:

   dataset = 'lsun',       -- imagenet / lsun / folder
   batchSize = 64,
   loadSize = 96,
   fineSize = 64,
   nz = 100,               -- #  of dim for Z
   ngf = 64,               -- #  of gen filters in first conv layer
   ndf = 64,               -- #  of discrim filters in first conv layer
   nThreads = 1,           -- #  of data loading threads to use
   niter = 25,             -- #  of iter at starting learning rate
   lr = 0.0002,            -- initial learning rate for adam
   beta1 = 0.5,            -- momentum term of adam
   ntrain = math.huge,     -- #  of examples per epoch. math.huge for full dataset
   display = 1,            -- display samples while training. 0 = false
   display_id = 10,        -- display window id.
   gpu = 1,                -- gpu = 0 is CPU mode. gpu=X is GPU mode on GPU X
   name = 'experiment1',
   noise = 'normal',       -- uniform / normal
   epoch_save_modulo = 1,  -- save checkpoint ever # of epoch

2. Use a pre-trained generator to generate images.

The generate script can operate in CPU or GPU mode.

to run it on the CPU, use:

gpu=0 net=[checkpoint-path] th generate.lua

for using a GPU, use:

gpu=1 net=[checkpoint-path] th generate.lua

Pre-trained network can be downloaded from here:

• for faces (celeb-A dataset): celebA_25_net_G.t7
• for bedrooms (LSUN dataset): bedrooms_4_net_G.t7

##2.1. Generate samples of 64x64 pixels

gpu=0 batchSize=64 net=celebA_25_net_G.t7 th generate.lua

The batchSize parameter controls the number of images to generate. If you have display running, the image will be shown there. The image is also saved to generation1.png in the same folder.

##2.2. Generate large artsy images (tried up to 4096 x 4096 pixels)

gpu=0 batchSize=1 imsize=10 noisemode=linefull net=bedrooms_4_net_G.t7 th generate.lua

Controlling the imsize parameter will control the size of the output image. Larger the imsize, larger the output image.

##2.3. Walk in the space of samples

gpu=0 batchSize=16 noisemode=line net=bedrooms_4_net_G.t7 th generate.lua

controlling the batchSize parameter changes how big of a step you take.

Vector Arithmetic

net=[modelfile] gpu=0 qlua arithmetic.lua