codebook_size指定了codebook的大小,所有的vector最终会quantized入这个大小之内。比如这里是6,那么最后每一个vector就会被映射到0至5,共6个数之内,也就是indices输出。 quantized是根据indices找出codebook中的值。所以它跟embedding很大不同是,最后输出的维度(最后一维)是不会变的。 3. 简析VectorQuan
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=...
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...
可以通过pip命令安装vector_quantize_pytorch。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 ...
vector_quantize_pytorch api文档 我在探索vector_quantize_pytorch的 API 文档时,发现了一个重要话题,它关乎数据备份与恢复的策略、流程和最佳实践。基于这个主题,我决定记录下我参考的备份策略、恢复流程和一些相关的最佳实践,以便分享给需要的人。 备份策略
$ pip install vector-quantize-pytorch Usage 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 =...
Once the conda-forge channel has been enabled, vector-quantize-pytorch can be installed with: conda install vector-quantize-pytorch It is possible to list all of the versions of vector-quantize-pytorch available on your platform with: conda search vector-quantize-pytorch --channel conda-forge ...
vector-quantize-pytorch lucidrains Fetched on 2025/04/30 22:55 lucidrains/vector-quantize-pytorch Vector (and Scalar) Quantization, in Pytorch -View it on GitHub Star 3194 Rank 11343
We examine how the performance of a memoryless vector quantizer (VQ) changes as a function of its training set size. By relating the training distortion of such a codebook to its test (true) distortion, we demonstrate that one may obtain "good" codebooks at a fraction of the computational ...
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...