encA = EncoderA(args.wseed, zShared_dim=args.n_shared)
decA = DecoderA(args.wseed, zShared_dim=args.n_shared)
encB = EncoderB(args.wseed, zShared_dim=args.n_shared)
decB = DecoderB(args.wseed, zShared_dim=args.n_shared)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
optimizer = torch.optim.Adam(
list(encB.parameters()) + list(decB.parameters()) + list(encA.parameters()) + list(decA.parameters()),
lr=args.lr)
#
# def elbo(q, pA, pB, lamb=1.0, annealing_factor=1.0):
# muA_own = q['sharedA'].dist.loc.squeeze(0)
# stdA_own = q['sharedA'].dist.scale.squeeze(0)
# muB_own = q['sharedB'].dist.loc.squeeze(0)
# stdB_own = q['sharedB'].dist.scale.squeeze(0)
#
# # from each of modality
# reconst_loss_A = pA['images_own'].loss.mean()
# kl_A = 0.5 * torch.sum(1 + torch.log(stdA_own ** 2 + EPS) - muA_own ** 2 - stdA_own ** 2,
# dim=1).mean()
#
encA = EncoderA(args.wseed, zPrivate_dim=args.n_private)
decA = DecoderA(args.wseed, zPrivate_dim=args.n_private)
encB = EncoderB(args.wseed)
decB = DecoderB(args.wseed)
if CUDA:
encA.cuda()
decA.cuda()
encB.cuda()
decB.cuda()
cuda_tensors(encA)
cuda_tensors(decA)
cuda_tensors(encB)
cuda_tensors(decB)
optimizer = torch.optim.Adam(list(encB.parameters()) +
list(decB.parameters()) +
list(encA.parameters()) + list(decA.parameters()),
lr=args.lr)
def elbo(q,
pA,
pB,
lamb=1.0,
beta1=(1.0, 1.0, 1.0),
beta2=(1.0, 1.0, 1.0),
bias=1.0):
# from each of modality
reconst_loss_A, kl_A = probtorch.objectives.mws_tcvae.elbo(
q,