Decoders
- class multimodal_compare.models.decoders.Dec_CNN(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_FNN(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_MNIST(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_MNIST2(latent_dim, data_dim, latent_private, num_hidden_layers=1)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_PolyMNIST(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_SVHN(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_SVHN2(latent_dim, data_dim, latent_private)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
z (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_Transformer(latent_dim, data_dim, latent_private, ff_size=1024, num_layers=4, num_heads=2, dropout=0.1, activation='gelu')
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(batch)
Forward pass
- Parameters:
batch (list, torch.tensor) – list with sampled latent vectors z and (optionally) boolean masks for desired lengths
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_TransformerIMG(latent_dim, data_dim, latent_private, ff_size=1024, num_layers=4, num_heads=4, dropout=0.1, activation='gelu')
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(batch)
Forward pass
- Parameters:
batch (list, torch.tensor) – list with sampled latent vectors z and (optionally) boolean masks for desired lengths
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_TxtTransformer(latent_dim, data_dim, latent_private, ff_size=1024, num_layers=8, num_heads=2, dropout=0.1, activation='gelu')
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(batch)
Forward pass
- Parameters:
batch (list, torch.tensor) – list with sampled latent vectors z and (optionally) boolean masks for desired lengths
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.Dec_VideoGPT(latent_dim, data_dim, latent_private, n_res_layers=4)
Bases:
VaeDecoder- _is_full_backward_hook: bool | None
- forward(z)
Forward pass
- Parameters:
x (torch.tensor) – sampled latent vectors z
- Returns:
output reconstructions, log variance
- Return type:
tuple(torch.tensor, torch.tensor)
- training: bool
- class multimodal_compare.models.decoders.VaeDecoder(latent_dim, data_dim, latent_private, net_type: NetworkTypes)
Bases:
VaeComponent- _is_full_backward_hook: bool | None
- training: bool