b8fe92dfee
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2217 This diff introduces a new Faiss contrib module that contains: - generic k-means implemented in python (was in distributed_ondisk) - the two-level clustering code, including a simple function that runs it on a Faiss IVF index. - sparse clustering code (new) The main idea is that that code is often re-used so better have it in contrib. Reviewed By: beauby Differential Revision: D34170932 fbshipit-source-id: cc297cc56d241b5ef421500ed410d8e2be0f1b77 |
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| .. | ||
| README.md | ||
| __init__.py | ||
| client_server.py | ||
| clustering.py | ||
| datasets.py | ||
| evaluation.py | ||
| exhaustive_search.py | ||
| factory_tools.py | ||
| inspect_tools.py | ||
| ivf_tools.py | ||
| ondisk.py | ||
| rpc.py | ||
| torch_utils.py | ||
| vecs_io.py | ||
README.md
The contrib modules
The contrib directory contains helper modules for Faiss for various tasks.
Code structure
The contrib directory gets compiled in the module faiss.contrib. Note that although some of the modules may depend on additional modules (eg. GPU Faiss, pytorch, hdf5), they are not necessarily compiled in to avoid adding dependencies. It is the user's responsibility to provide them.
In contrib, we are progressively dropping python2 support.
List of contrib modules
rpc.py
A very simple Remote Procedure Call library, where function parameters and results are pickled, for use with client_server.py
client_server.py
The server handles requests to a Faiss index. The client calls the remote index. This is mainly to shard datasets over several machines, see Distributd index
ondisk.py
Encloses the main logic to merge indexes into an on-disk index. See On-disk storage
exhaustive_search.py
Computes the ground-truth search results for a dataset that possibly does not fit in RAM. Uses GPU if available.
Tested in tests/test_contrib.TestComputeGT
torch_utils.py
Interoperability functions for pytorch and Faiss: Importing this will allow pytorch Tensors (CPU or GPU) to be used as arguments to Faiss indexes and other functions. Torch GPU tensors can only be used with Faiss GPU indexes. If this is imported with a package that supports Faiss GPU, the necessary stream synchronization with the current pytorch stream will be automatically performed.
Numpy ndarrays can continue to be used in the Faiss python interface after importing this file. All arguments must be uniformly either numpy ndarrays or Torch tensors; no mixing is allowed.
Tested in tests/test_contrib_torch.py (CPU) and gpu/test/test_contrib_torch_gpu.py (GPU).
inspect_tools.py
Functions to inspect C++ objects wrapped by SWIG. Most often this just means reading fields and converting them to the proper python array.
ivf_tools.py
A few functions to override the coarse quantizer in IVF, providing additional flexibility for assignment.
datasets.py
(may require h5py)
Defintion of how to access data for some standard datsets.
factory_tools.py
Functions related to factory strings.
evaluation.py
A few non-trivial evaluation functions for search results
clustering.py
Contains:
-
a Python implementation of kmeans, that can be used for special datatypes (eg. sparse matrices).
-
a 2-level clustering routine and a function that can apply it to train an IndexIVF