3.2 VectorQuantize初始化 class VectorQuantize(nn.Module): def __init__( self, dim, codebook_size, codebook_dim = None, heads = 1, separate_codebook_per_head = False, decay = 0.8, eps = 1e-5, kmeans_init = False, kmeans_iters = 10, sync_kmeans = True, use_cosine_sim = False...
vector_quantize_pytorch api文档 _vectors 从这篇文章开始,依次详解各个组件,自然是从容器开始说起。 vector将元素复制到内部的dynamic array中,元素之间总是存在某种顺序,所以vectors是一种有序群集(ordered collection)。vector支持随机存取,因此只要知道位置,你可以在常数时间内存取任何一个元素。vector的迭代器是随机...
51CTO博客已为您找到关于vector_quantize_pytorch api文档的相关内容,包含IT学习相关文档代码介绍、相关教程视频课程,以及vector_quantize_pytorch api文档问答内容。更多vector_quantize_pytorch api文档相关解答可以来51CTO博客参与分享和学习,帮助广大IT技术人实现成长和
import torch from vector_quantize_pytorch import VectorQuantize vq = VectorQuantize( dim = 256, codebook_size = 512, # codebook size decay = 0.8, # the exponential moving average decay, lower means the dictionary will change faster commitment_weight = 1. # the weight on the commitment loss ...
import torch from vector_quantize_pytorch import VectorQuantize vq = VectorQuantize( dim = 256, codebook_size = 512, # codebook size decay = 0.8, # the exponential moving average decay, lower means the dictionary will change faster commitment_weight = 1. # the weight on the commitment loss ...
This layered approach continues, with each stage focusing on the residuals from the previous stage, as illustrated in the figure above. Thus, rather than trying to quantize a high-dimensional vector with a single mammoth codebook directly, RVQ breaks down the problem, achieving high precision with...
vector-quantize-pytorch lucidrains Fetched on 2024/09/23 05:16 lucidrains/vector-quantize-pytorch Vector (and Scalar) Quantization, in Pytorch -View it on GitHub Star 2416 Rank 13994
from vector_quantize_pytorch import LFQ # you can specify either dim or codebook_size @@ -212,7 +216,8 @@ def test_lfq(spherical): dim = 16, # this is the input feature dimension, defaults to log2(codebook_size) if not defined entropy_loss_weight = 0.1, # how much weight ...
--dtype=bfloat16 - Now supported by vector_quantize_pytorchTraining OutputIn Weights & Biases, we can see the training progress. In validation, we generate a video from the compressed poses (right) and compare it to the original video (left). (This is the output using 4 codebooks of siz...
vector-quantize-pytorch/vector_quantize_pytorch/vector_quantize_pytorch.py Line 33 in 1155588 return (rearrange(x2, 'b i -> b i 1') + rearrange(y2, 'b j -> b 1 j') + xy).sqrt() (x-y)^2 could be negative if computed as x^2 + y^2 - 2xy due...