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6baebe2cee
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3872 The contrib.torch subdirectory is intended to receive modules in python that are useful for similarity search and that apply to CPU or GPU pytorch tensors. The current version includes CPU clustering on torch tensors. To be added: * implementation of PQ Reviewed By: asadoughi Differential Revision: D62759207 fbshipit-source-id: 87dbaa5083e3f2f4f60526815e22ded4e83e8559
54 lines
1.0 KiB
Python
54 lines
1.0 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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This contrib module contains Pytorch code for quantization.
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"""
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import numpy as np
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import torch
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import faiss
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from faiss.contrib import torch_utils
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class Quantizer:
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def __init__(self, d, code_size):
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self.d = d
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self.code_size = code_size
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def train(self, x):
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pass
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def encode(self, x):
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pass
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def decode(self, x):
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pass
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class VectorQuantizer(Quantizer):
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def __init__(self, d, k):
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code_size = int(torch.ceil(torch.log2(k) / 8))
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Quantizer.__init__(d, code_size)
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self.k = k
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def train(self, x):
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pass
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class ProductQuantizer(Quantizer):
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def __init__(self, d, M, nbits):
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code_size = int(torch.ceil(M * nbits / 8))
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Quantizer.__init__(d, code_size)
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self.M = M
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self.nbits = nbits
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def train(self, x):
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pass
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