- 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.
- Install Torch: http://torch.ch/docs/getting-started.html#_
- Install CUDA, and preferably CuDNN (optional).
- Instructions for Ubuntu are here: INSTALL.md
- Install Torch: http://torch.ch/docs/getting-started.html#_
- Optional, if you installed CuDNN, install cudnn bindings with
luarocks install cudnn
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:
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
DATA_ROOT=celebA dataset=folder th main.lua
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
Download bedroom_train_lmdb
from the LSUN website.
Generate an index file:
DATA_ROOT=[path_to_lmdb] th data/lsun_index_generator.lua
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.
- Create a folder called
myimages
. - Inside that folder, create a folder called
images
and place all your images inside it.
DATA_ROOT=myimages dataset=folder th main.lua
Follow instructions from this link.
DATA_ROOT=[PATH_TO_IMAGENET]/train dataset=folder th main.lua
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
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
- 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.