@@ -39,21 +39,21 @@ Please see the [Installation_Guide](https://github.com/anguyen8/opencv_contrib/b
## Usage
* An MNIST experiment (Fig. 4, 5 in the paper) can be run directly on a local machine (4-core) within a reasonable amount of time (around ~5 minutes or less for 200 generations).
* An ImageNet experiment needs to be run on a cluster environment. It took us ~4 days x 128 cores to run 5000 generations and produce 1000 images (Fig. 8 in the paper).
* An ImageNet experiment needs to be run on a cluster environment. It took us ~4 days x 128 cores to run 5000 generations and produce 1000 images (Fig. 8 in the paper).
*[How to configure an experiment to test the evolutionary framework quickly](https://github.com/Evolving-AI-Lab/fooling/wiki/How-to-test-the-evolutionary-framework-quickly)
* To reproduce the gradient ascent fooling images (Figures 13, S3, S4, S5, S6, and S7 from the paper), see the [documentation in the caffe/ascent directory](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/caffe/ascent). You'll need to download the correct Caffe version for this experiment using `./download_caffe_gradient_ascent.sh` script.
## Troubleshooting
1. If Sferes (Waf) can't find your CUDA and Caffe dynamic libraries
1. If Sferes (Waf) can't find your CUDA and Caffe dynamic libraries
> Add obj.libpath to the wscript for exp/images to find libcudart and libcaffe or you can use LD_LIBRARY_PATH (for Linux).
2. Is there a way to monitor the progress of the experiments?
> There is a flag for printing out results (fitness + images) every N generations.
You can adjust the dump_period setting [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images.cpp#L159).
You can adjust the dump_period setting [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images.cpp#L159).
3. Where do I get the pre-trained Caffe models?
> For AlexNet, please download on Caffe's Model Zoo.
> For LeNet, you can grab it [here](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/model/lenet).
> For LeNet, you can grab it [here](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/model/lenet).
4. How do I run the experiments on my local machine without MPI?
> You can enable MPI or non-MPI mode by commenting/uncommenting a line [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images_mnist.cpp#L190-L191). It can be simple eval::Eval (single-core), eval::Mpi (distributed for clusters).
