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...
从上面的例子中可以清晰地看出来,vector插入10个元素之后,capacity()函数返回13,当插入第14个元素时,begin()所代表的内存位置发生了变化。 回到“如果超过了capacity()的数量时,vector就有必要重新配置内部存储器。” vector的容量之所以很重要,有一下两个原因 一旦重新分配内存,与vector相关的所有references, pointers...
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import torch from vector_quantize_pytorch import ResidualVQ residual_vq = ResidualVQ( dim = 256, codebook_size = 256, num_quantizers = 4, kmeans_init = True, # set to True kmeans_iters = 10 # number of kmeans iterations to calculate the centroids for the codebook on init ) x = to...
vector-quantize-pytorch/vector_quantize_pytorch/residual_sim_vq.py Line 153 in ac5d631 should_quantize_dropout = self.training and self.quantize_dropout and not return_loss [rank2]: File "/home/ubuntu/sky_workdir/my_cool_project/sim_vq.py", line 313, in forward [rank2]: should_qua...
@@ -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. ...
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 ...
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...