import vector_quantize_pytorch as vq import torch a = torch.FloatTensor([-0.1, 0.5, 0.2, 0.33, -0.6, 0.2]).view(1, 3, 2) print('a=', a) quantizer = vq.VectorQuantize(dim=2, codebook_size=6) quantized, indices, loss = quantizer(a) print('quantized', quantized) print('indices',...
import torch from vector_quantize_pytorch import ResidualVQ residual_vq = ResidualVQ( dim = 256, num_quantizers = 8, # specify number of quantizers codebook_size = 1024, # codebook size ) x = torch.randn(1, 1024, 256) quantized, indices, commit_loss = residual_vq(x) # (1, 1024,...
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...
一、用VIM提升image generation和image understanding任务的关键点在于一个好的image quantizer 二、发现在stage2用更大的计算量并且保持stage1中transformer的轻量级是有益的 Method 一、Vector-Quantized Images with ViT-VQGAN 二、Vector-Quantized Image Modeling Experiment 一、重建 二、生成 三、无监督学习 论文地...
scikit-learn, pytorch, tensorflow. The only file that client need is client.py. Copy this file to your project and import it, then you are ready to go. Q: Can I use multilingual BERT model provided by Google? A: Yes. Q: Can I use my own fine-tuned BERT model? A: Yes. In ...
The first quantizer layer has a codebook table containing a fixed number of learnable vectors of the same dimension. The input vector is compared with those in the codebook, and the index of the most similar one is extracted. This is the compression stage: We've just replaced a vector of ...
def train_coarse_quantizer(data, quantizer_path, num_clusters, hnsw=False, niter=10, cuda=False): d = data.shape[1] index_flat = faiss.IndexFlatL2(d) # make it into a gpu index if cuda: res = faiss.StandardGpuResources() index_flat = faiss.index_cpu_to_gpu(res,...
A: No. Think of BertClient as a general feature extractor, whose output can be fed to any ML models, e.g. scikit-learn, pytorch, tensorflow. The only file that client need is client.py. Copy this file to your project and import it, then you are ready to go....
edited by pytorch-botbot 🐛 Describe the bug Minimum reproduction importtorch.nn.functionalasFimporttorchfromtorchimportnnclassGumbelVectorQuantizer(nn.Module):def__init__(self):super().__init__()self.num_groups=32self.num_vars=320self.weight_proj=nn.Linear(256,self.num_groups*self.num_vars...
--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...