This is a faster and improved version of diffusion retrieval, inspired by diffusion-retrieval.
Reference:
If you would like to understand further details of our method, these slides may provide some help.
All random walk processes are moved to offline, making the online search remarkably fast
In contrast to previous works, we achieved better performance by applying late truncation instead of early truncation to the graph
Install Facebook FAISS by running conda install faiss-cpu -c pytorch
Optional: install the faiss-gpu under the instruction according to your CUDA version
Install joblib by running conda install joblib
Install tqdm by running conda install tqdm
All parameters can be modified in Makefile
. You may want to edit DATASET and FEATURE_TYPE to test all combinations of each dataset and each feature type.
Another parameter truncation_size is set to 1000 by default, for large datasets like Oxford105k and Paris106k, changing it to 5000 will improve the performance.
Run make download
to download files needed in experiments;
Run make mat2npy
to convert .mat files to .npy files;
Run make rank
to get the results. If you have GPUs, try using commands like CUDA_VISIBLE_DEVICES=0,1 make rank
, 0,1
are examples of GPU ids.
Note: on Oxford5k and Paris6k datasets, the
truncation_size
parameter should be no larger than 1024 when using GPUs according to FAISS's limitation. You can use CPUs instead.
search_old
in rank.py
.