样例代码 #from vector_quantize_pytorch import kmeansimporttorchimporttorch.nn.functionalasFimportnumpyasnp# A no-operation function that does nothingdefnoop(*args,**kwargs):pass# Function to normalize a tensor using L2 normalizationdefl2norm(t,dim=-1,eps=1e-6):returnF.normalize(t,p=2,dim=...
VectorQuantize是将每一个vector与内部的codebook做计算,然后得出一个quantized vector 下面是一个例子,可以通过debug来看一下内部具体的流程。下面的内容,也是根据debug走读代码分析而来。 importvector_quantize_pytorchasvqimporttorcha=torch.FloatTensor([-0.1,0.5,0.2,0.33,-0.6,0.2]).view(1,3,2)print('a=',...
vector_quantize_pytorch 是一个用于向量量化的PyTorch库。你可以通过以下步骤来安装它: 打开你的命令行工具(例如:CMD、Terminal或Anaconda Prompt)。 输入以下pip命令来安装vector_quantize_pytorch: bash pip install vector_quantize_pytorch 等待安装完成。安装过程中,pip会自动下载并安装所需的依赖项。 验证安装:你...
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 ...
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...
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@@ -1,6 +1,6 @@ [project] name = "vector-quantize-pytorch" version = "1.18.4" version = "1.18.5" description = "Vector Quantization - Pytorch" authors = [ { name = "Phil Wang", email = "lucidrains@gmail.com" } 0 comments on commit ca90db2 Please sign in to comment. ...
An alternative would be to quantize a block (preferably large) of the source outputs at once. This way, we can make use of the correlation in the source to reduce the transmission rate. Indeed, in the proof of the source coding theorem subject to a fidelity criterion, we did resort to ...
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...
fromvector_quantize_pytorchimportLFQ 206210 207211 # you can specify either dim or codebook_size @@ -212,7 +216,8 @@ def test_lfq(spherical): 212216 dim=16,# this is the input feature dimension, defaults to log2(codebook_size) if not defined ...