This repo includes tools used for conducting experiments of image retrieval by using CUDA.

Install Python 2.7.

Install Python modules:

• pip install numpy
• pip install bitarray
• pip install redis

Other prerequisites:

• PyCuda
• Boost
• Boost::Python
• Boost::Serialization

Download and compile yael library with the --enable-numpy option. Copy generated yael.py, _yael.so under the same path of these scripts.

This repo uses modified LSHash.

To build fastdict:

mkdir build
cd build
cmake path_to_fastdict/
make
cp fastdict.so path_to_repo/

ANN_SIFT1B

python fast_binary.py -f sift_base.fvecs

python fast_binary.py -f bigann_base.bvecs -v bvecs -n 100000000 -k 10 -o 0 -s random -i y -e bigann_100000000.npz

python fast_binary_for_indexonly.py -f ../bigann_base.bvecs -v bvecs -n 500000000 -k 10 -o 0 -s random -i y -e bigann_500000000_random_k8_b64 -r 8

Parameters:

• -f: image feature file
• -v: feature file format
• -n: number of image features to read
• -k: retrieve top-k neighbors
• -o: offset of reading features (begin from offset)
• -s: storage method (dict, redis, random)
• -i: whether runing indexing (y/n), default is 'n'
• -e: indexing file for writing (when -i 'y') and reading (when -i 'n')
• -c: whether to perform dict compressing, default is 'n'
• -r: the number of sampled dimensions
• -q: performing sequential sampling, default is 'n' (meaning that default is ramdom sampling)
• -p: querying in compressed domain, default is 'n' (meaning that plain querying mode)
• -g: 'y' for GPU-based uncompression. 'n' for CPU-based.
• -l: 'y' for VLQ base64 mode. default is 'n'
• -b: the level of bucket expansion
• -t: the type of FastDict component. It can be 'int32', 'int8' or 'string'. default is 'int32'
• -u: 'net' or 'local'; how the cuda computation engine is called.
• -host: when -u is 'net', indicating the cuda server location.
• -title: the title string that will be logged at cuda server.
• -gt: the feature file of ground truth.

R --slave --args <binary code length> <number of binary codes> <bit width of bit counts> <number of sampled dimensions> <weight of worst-case> <weight of best-case> < cal_compress_effect.R

For example:

R --slave --args 64 1000000000 32 32 0.19 0.81 < cal_compress_effect.R