[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

git clone https://github.com/pathak22/context-encoder.git

cd context-encoder
bash ./models/scripts/download_inpaintCenter_models.sh
# This will populate the `./models/` folder with trained models.

net=models/inpaintCenter/paris_inpaintCenter.t7 name=paris_result imDir=images/paris overlapPred=4 manualSeed=222 batchSize=21 gpu=1 th demo.lua
net=models/inpaintCenter/imagenet_inpaintCenter.t7 name=imagenet_result imDir=images/imagenet overlapPred=4 manualSeed=222 batchSize=21 gpu=1 th demo.lua
net=models/inpaintCenter/paris_inpaintCenter.t7 name=ucberkeley_result imDir=images/ucberkeley overlapPred=4 manualSeed=222 batchSize=4 gpu=1 th demo.lua
# Note: If you are running on cpu, use gpu=0
# Note: samples given in ./images/* are held-out images

luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
cd ~
th -ldisplay.start 8000
# if working on server machine create tunnel: ssh -f -L 8000:localhost:8000 -N server_address.com
# on client side, open in browser: http://localhost:8000/

mkdir -p /path_to_wherever_you_want/mydataset/train/images/
# put all training images inside mydataset/train/images/
mkdir -p /path_to_wherever_you_want/mydataset/val/images/
# put all val images inside mydataset/val/images/
cd context-encoder/
ln -sf /path_to_wherever_you_want/mydataset dataset

# For training center region inpainting model, run:
DATA_ROOT=dataset/train display_id=11 name=inpaintCenter overlapPred=4 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=128 gpu=1 th train.lua

# For training random region inpainting model, run:
DATA_ROOT=dataset/train display_id=11 name=inpaintRandomNoOverlap useOverlapPred=0 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=128 gpu=1 th train_random.lua
# or use fineSize=64 to train to generate 64x64 sized image (results are better):
DATA_ROOT=dataset/train display_id=11 name=inpaintRandomNoOverlap useOverlapPred=0 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=64 gpu=1 th train_random.lua

# For training center region inpainting model, run:
DATA_ROOT=dataset/val net=checkpoints/inpaintCenter_500_net_G.t7 name=test_patch overlapPred=4 manualSeed=222 batchSize=30 loadSize=350 gpu=1 th test.lua
DATA_ROOT=dataset/val net=checkpoints/inpaintCenter_500_net_G.t7 name=test_full overlapPred=4 manualSeed=222 batchSize=30 loadSize=129 gpu=1 th test.lua

# For testing random region inpainting model, run (with fineSize=64 or 124, same as training):
DATA_ROOT=dataset/val net=checkpoints/inpaintRandomNoOverlap_500_net_G.t7 name=test_patch_random useOverlapPred=0 manualSeed=222 batchSize=30 loadSize=350 gpu=1 th test_random.lua
DATA_ROOT=dataset/val net=checkpoints/inpaintRandomNoOverlap_500_net_G.t7 name=test_full_random useOverlapPred=0 manualSeed=222 batchSize=30 loadSize=129 gpu=1 th test_random.lua