vits | Conditional Variational Autoencoder with Adversarial | Speech library

import torch
from vit_pytorch.cvt import CvT
from vit_pytorch.es_vit import EsViTTrainer

cvt = CvT(
    num_classes = 1000,
    s1_emb_dim = 64,
    s1_emb_kernel = 7,
    s1_emb_stride = 4,
    s1_proj_kernel = 3,
    s1_kv_proj_stride = 2,
    s1_  

import torch
from vit_pytorch.vit_3d import ViT

v = ViT(
    image_size = 128,          # image size
    frames = 16,               # number of frames
    image_patch_size = 16,     # image patch size
    frame_patch_size = 2,      # frame patch siz  

@Article{prabhu2022adapting,
  author  = {Viraj Prabhu and Sriram Yenamandra and Aaditya Singh and Judy Hoffman},
  journal = {arXiv:2206.08222},
  title   = {Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consi