GuoxiangQu / caffe_GAN

This project uses GAN to implement gray image colorization

Deep Convolutional Generative Adversarial Nerworks with Caffe Implementation

This project uses DCGAN to implement gray image colorization. The network copys from aleju/colorizer.

Changed Files:

caffe.proto:
	Add the new line:
		  optional bool param_propagate_down = 6;

net.cpp
	Add the following lines:
		if(param_spec->has_param_propagate_down()){
   	 		param_need_backward = param_spec->param_propagate_down();
  		}

Usage:

1. The Installation completely the same as Caffe. Please follow the installation instructions.
   • Make sure you uncomment WITH_PYTHON_LAYER := 1 to support for python layer. And don't forget make pycaffe
   • Add the ~/caffe_GAN/caffe_GAN/gan_example/lib/layers path to $PYTHONPATH
2. Building the dataset:
   • Download Labeled Faces in the Wild and extract it somewhere
   • In gan_example/ run mkdir out_unaug_64x64 and run python lib/utils/generate_dataset.py --path="lfw", where lfw is the path to your LFW dataset
   • Generate the train.txt file:
   • In out_unaug_64x64/ run ls -1 > ../train.txt
3. Train
   • gan_example/ run ./train.sh begin training.
4. The generate images will be putted in output directory.