def __init__(self, hid_dim, latent_dim, enc_layers, dec_layers, dropout,
enc_bi, dec_max_len, beam_size, WEAtt_type, encoder_emb,
decoder_emb, pad_id):
super(VAE, self).__init__()
assert encoder_emb.num_embeddings == decoder_emb.num_embeddings
assert encoder_emb.embedding_dim == decoder_emb.embedding_dim
self.voc_size = encoder_emb.num_embeddings
self.emb_dim = encoder_emb.embedding_dim
self.hid_dim = hid_dim
self.enc_layers = enc_layers
self.dec_layers = dec_layers
self.dropout = dropout
self.enc_bi = enc_bi
self.n_dir = 2 if self.enc_bi else 1
self.dec_max_len = dec_max_len
self.beam_size = beam_size
self.WEAtt_type = WEAtt_type
self.latent_dim = latent_dim
self.PostEncoder = Encoder(emb_dim=self.emb_dim,
hid_dim=self.hid_dim,
n_layer=self.enc_layers,
dropout=self.dropout,
bi=self.enc_bi,
embedding=encoder_emb)
self.RespEncoder = Encoder(emb_dim=self.emb_dim,
hid_dim=self.hid_dim,
n_layer=self.enc_layers,
dropout=self.dropout,
bi=self.enc_bi,
embedding=encoder_emb)
self.PriorGaussian = Gaussian(in_dim=self.hid_dim * self.n_dir *
self.enc_layers,
out_dim=self.latent_dim)
self.PosteriorGaussian = Gaussian(in_dim=2 * self.hid_dim *
self.n_dir * self.enc_layers,
out_dim=self.latent_dim)
self.PostRepr = nn.Linear(self.hid_dim * self.n_dir * self.enc_layers,
self.emb_dim)
self.Decoder = Decoder(voc_size=self.voc_size,
latent_dim=self.latent_dim,
emb_dim=self.emb_dim,
hid_dim=self.hid_dim * self.n_dir,
n_layer=self.dec_layers,
dropout=self.dropout,
max_len=self.dec_max_len,
beam_size=self.beam_size,
WEAtt_type=self.WEAtt_type,
embedding=decoder_emb)
self.BoW = nn.Linear(self.latent_dim, self.voc_size)
self.criterionSeq = SeqLoss(voc_size=self.voc_size,
pad=pad_id,
end=None,
unk=None)
def __init__(self, hid_dim, latent_dim, enc_layers, dec_layers, dropout,
enc_bi, dec_max_len, beam_size, WEAtt_type, encoder_emb,
decoder_emb, pad_id, n_flows, flow_type):
super(VAE_NF, self).__init__()
assert encoder_emb.num_embeddings == decoder_emb.num_embeddings
assert encoder_emb.embedding_dim == decoder_emb.embedding_dim
self.voc_size = encoder_emb.num_embeddings
self.emb_dim = encoder_emb.embedding_dim
self.hid_dim = hid_dim
self.enc_layers = enc_layers
self.dec_layers = dec_layers
self.dropout = dropout
self.enc_bi = enc_bi
self.n_dir = 2 if self.enc_bi else 1
self.dec_max_len = dec_max_len
self.beam_size = beam_size
self.WEAtt_type = WEAtt_type
self.latent_dim = latent_dim
self.n_flows = n_flows
self.flow_type = flow_type
self.Encoder = Encoder(emb_dim=self.emb_dim,
hid_dim=self.hid_dim,
n_layer=self.enc_layers,
dropout=self.dropout,
bi=self.enc_bi,
embedding=encoder_emb)
self.PriorGaussian = torch.distributions.Normal(
gpu_wrapper(torch.zeros(self.latent_dim)),
gpu_wrapper(torch.ones(self.latent_dim)))
self.PosteriorGaussian = Gaussian(in_dim=self.hid_dim * self.n_dir *
self.enc_layers,
out_dim=self.latent_dim)
self.Decoder = Decoder(voc_size=self.voc_size,
latent_dim=self.latent_dim,
emb_dim=self.emb_dim,
hid_dim=self.hid_dim * self.n_dir,
n_layer=self.dec_layers,
dropout=self.dropout,
max_len=self.dec_max_len,
beam_size=self.beam_size,
WEAtt_type=self.WEAtt_type,
embedding=decoder_emb)
self.BoW = nn.Linear(self.latent_dim, self.voc_size)
self.Flows = NormalizingFlows(cond_dim=self.hid_dim * self.n_dir *
self.enc_layers,
latent_dim=self.latent_dim,
n_flows=self.n_flows,
flow_type=self.flow_type)
self.criterionSeq = SeqLoss(voc_size=self.voc_size,
pad=pad_id,
end=None,
unk=None)
def __init__(self, hid_dim, latent_dim, enc_layers, dec_layers, dropout, enc_bi, dec_max_len, beam_size, WEAtt_type, encoder_emb, decoder_emb, pad_id):
super(S_VAE, self).__init__()
assert encoder_emb.num_embeddings == decoder_emb.num_embeddings
assert encoder_emb.embedding_dim == decoder_emb.embedding_dim
self.voc_size = encoder_emb.num_embeddings
self.emb_dim = encoder_emb.embedding_dim
self.hid_dim = hid_dim
self.enc_layers = enc_layers
self.dec_layers = dec_layers
self.dropout = dropout
self.enc_bi = enc_bi
self.n_dir = 2 if self.enc_bi else 1
self.dec_max_len = dec_max_len
self.beam_size = beam_size
self.WEAtt_type = WEAtt_type
self.latent_dim = latent_dim
self.Encoder = Encoder(emb_dim=self.emb_dim,
hid_dim=self.hid_dim,
n_layer=self.enc_layers,
dropout=self.dropout,
bi=self.enc_bi,
embedding=encoder_emb)
self.PriorUniform = HypersphericalUniform(dim=self.latent_dim)
self.PosteriorVMF = VonMisesFisherModule(in_dim=self.hid_dim * self.n_dir * self.enc_layers, out_dim=self.latent_dim)
self.Decoder = Decoder(voc_size=self.voc_size,
latent_dim=self.latent_dim,
emb_dim=self.emb_dim,
hid_dim=self.hid_dim * self.n_dir,
n_layer=self.dec_layers,
dropout=self.dropout,
max_len=self.dec_max_len,
beam_size=self.beam_size,
WEAtt_type=self.WEAtt_type,
embedding=decoder_emb)
self.criterionSeq = SeqLoss(voc_size=self.voc_size, pad=pad_id, end=None, unk=None)