From 68cda1e6abf76312eb13f45ca3c8565a0b00d745 Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Mon, 27 Jun 2022 18:17:16 +0200 Subject: Add quantizer --- text_recognizer/networks/quantizer/kmeans.py | 32 ++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 text_recognizer/networks/quantizer/kmeans.py (limited to 'text_recognizer/networks/quantizer/kmeans.py') diff --git a/text_recognizer/networks/quantizer/kmeans.py b/text_recognizer/networks/quantizer/kmeans.py new file mode 100644 index 0000000..a34c381 --- /dev/null +++ b/text_recognizer/networks/quantizer/kmeans.py @@ -0,0 +1,32 @@ +"""K-means clustering for embeddings.""" +from typing import Tuple + +from einops import repeat +import torch +from torch import Tensor + +from text_recognizer.networks.quantizer.utils import norm, sample_vectors + + +def kmeans( + samples: Tensor, num_clusters: int, num_iters: int = 10 +) -> Tuple[Tensor, Tensor]: + """Compute k-means clusters.""" + D = samples.shape[-1] + + means = sample_vectors(samples, num_clusters) + + for _ in range(num_iters): + dists = samples @ means.t() + buckets = dists.max(dim=-1).indices + bins = torch.bincount(buckets, minlength=num_clusters) + zero_mask = bins == 0 + bins_min_clamped = bins.masked_fill(zero_mask, 1) + + new_means = buckets.new_zeros(num_clusters, D).type_as(samples) + new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=D), samples) + new_means /= bins_min_clamped[..., None] + new_means = norm(new_means) + means = torch.where(zero_mask[..., None], means, new_means) + + return means, bins -- cgit v1.2.3-70-g09d2