ConvNets

class pythae.models.nn.benchmarks.cifar.Encoder_Conv_AE_CIFAR(args)

A Convolutional encoder Neural net suited for CIFAR and Autoencoder-based models.

It can be built as follows:

>>> from pythae.models.nn.benchmarks.cifar import Encoder_Conv_AE_CIFAR
>>> from pythae.models import AEConfig
>>> model_config = AEConfig(input_dim=(3, 32, 32), latent_dim=16)
>>> encoder = Encoder_Conv_AE_CIFAR(model_config)
>>> encoder
... Encoder_Conv_AE_CIFAR(
...   (layers): ModuleList(
...     (0): Sequential(
...       (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (1): Sequential(
...       (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (2): Sequential(
...       (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (3): Sequential(
...       (0): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...   )
...   (embedding): Linear(in_features=4096, out_features=16, bias=True)
... )

and then passed to a pythae.models instance

>>> from pythae.models import AE
>>> model = AE(model_config=model_config, encoder=encoder)
>>> model.encoder == encoder
... True

Note

Please note that this encoder is only suitable for Autoencoder based models since it only outputs the embeddings of the input data under the key embedding.

>>> import torch
>>> input = torch.rand(2, 3, 32, 32)
>>> out = encoder(input)
>>> out.embedding.shape
... torch.Size([2, 16])

forward(x, output_layer_levels=None)

Forward method

Parameters

output_layer_levels (List[int]) – The levels of the layers where the outputs are extracted. If None, the last layer’s output is returned. Default: None.

Returns

An instance of ModelOutput containing the embeddings of the input data under the key embedding. Optional: The outputs of the layers specified in output_layer_levels arguments are available under the keys embedding_layer_i where i is the layer’s level.

Return type

ModelOutput

class pythae.models.nn.benchmarks.cifar.Encoder_Conv_VAE_CIFAR(args)

A Convolutional encoder Neural net suited for CIFAR and Variational Autoencoder-based models.

It can be built as follows:

>>> from pythae.models.nn.benchmarks.cifar import Encoder_Conv_VAE_CIFAR
>>> from pythae.models import VAEConfig
>>> model_config = VAEConfig(input_dim=(3, 32, 32), latent_dim=16)
>>> encoder = Encoder_Conv_VAE_CIFAR(model_config)
>>> encoder
... Encoder_Conv_VAE_CIFAR(
...   (layers): ModuleList(
...     (0): Sequential(
...       (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (1): Sequential(
...       (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (2): Sequential(
...       (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (3): Sequential(
...       (0): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...   )
...   (embedding): Linear(in_features=4096, out_features=16, bias=True)
...   (log_var): Linear(in_features=4096, out_features=16, bias=True)
... )

and then passed to a pythae.models instance

>>> from pythae.models import VAE
>>> model = VAE(model_config=model_config, encoder=encoder)
>>> model.encoder == encoder
... True

Note

Please note that this encoder is only suitable for Variational Autoencoder based models since it outputs the embeddings and the log of the covariance diagonal coefficients of the input data under the key embedding and log_covariance.

>>> import torch
>>> input = torch.rand(2, 3, 32, 32)
>>> out = encoder(input)
>>> out.embedding.shape
... torch.Size([2, 16])
>>> out.log_covariance.shape
... torch.Size([2, 16])

forward(x, output_layer_levels=None)

Forward method

Parameters

output_layer_levels (List[int]) – The levels of the layers where the outputs are extracted. If None, the last layer’s output is returned. Default: None.

Returns

An instance of ModelOutput containing the embeddings of the input data under the key embedding and the log of the diagonal coefficient of the covariance matrices under the key log_covariance. Optional: The outputs of the layers specified in output_layer_levels arguments are available under the keys embedding_layer_i where i is the layer’s level.

Return type

ModelOutput

class pythae.models.nn.benchmarks.cifar.Encoder_Conv_SVAE_CIFAR(args)

A Convolutional encoder Neural net suited for CIFAR and Hyperspherical Variational Autoencoder. models.

It can be built as follows:

>>> from pythae.models.nn.benchmarks.cifar import Encoder_Conv_SVAE_CIFAR
>>> from pythae.models import SVAEConfig
>>> model_config = SVAEConfig(input_dim=(3, 32, 32), latent_dim=16)
>>> encoder = Encoder_Conv_SVAE_CIFAR(model_config)
>>> encoder
... Encoder_Conv_SVAE_CIFAR(
...   (layers): ModuleList(
...     (0): Sequential(
...       (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (1): Sequential(
...       (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (2): Sequential(
...       (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (3): Sequential(
...       (0): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...   )
...   (embedding): Linear(in_features=4096, out_features=16, bias=True)
...   (log_concentration): Linear(in_features=4096, out_features=1, bias=True)
... )

and then passed to a pythae.models instance

>>> from pythae.models import SVAE
>>> model = SVAE(model_config=model_config, encoder=encoder)
>>> model.encoder == encoder
... True

Note

Please note that this encoder is only suitable for Variational Autoencoder based models since it outputs the embeddings and the log of the covariance diagonal coefficients of the input data under the key embedding and log_covariance.

>>> import torch
>>> input = torch.rand(2, 3, 32, 32)
>>> out = encoder(input)
>>> out.embedding.shape
... torch.Size([2, 16])
>>> out.log_concentration.shape
... torch.Size([2, 1])

forward(x, output_layer_levels=None)

Forward method

Parameters

output_layer_levels (List[int]) – The levels of the layers where the outputs are extracted. If None, the last layer’s output is returned. Default: None.

Returns

An instance of ModelOutput containing the embeddings of the input data under the key embedding and the log of the diagonal coefficient of the covariance matrices under the key log_covariance. Optional: The outputs of the layers specified in output_layer_levels arguments are available under the keys embedding_layer_i where i is the layer’s level.

Return type

ModelOutput

class pythae.models.nn.benchmarks.cifar.Decoder_Conv_AE_CIFAR(args)

A Convolutional decoder Neural net suited for CIFAR and Autoencoder-based models.

It can be built as follows:

>>> from pythae.models.nn.benchmarks.cifar import Decoder_Conv_AE_CIFAR
>>> from pythae.models import VAEConfig
>>> model_config = VAEConfig(input_dim=(3, 32, 32), latent_dim=16)
>>> decoder = Decoder_Conv_AE_CIFAR(model_config)
>>> decoder
... Decoder_Conv_AE_CIFAR(
...   (layers): ModuleList(
...     (0): Linear(in_features=16, out_features=65536, bias=True)
...     (1): Sequential(
...       (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (2): Sequential(
...       (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
...       (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
...       (2): ReLU()
...     )
...     (3): Sequential(
...       (0): ConvTranspose2d(256, 3, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2))
...       (1): Sigmoid()
...     )
...   )
... )

and then passed to a pythae.models instance

>>> from pythae.models import VAE
>>> model = VAE(model_config=model_config, decoder=decoder)
>>> model.decoder == decoder
... True

Note

Please note that this decoder is suitable for all models.

>>> import torch
>>> input = torch.randn(2, 16)
>>> out = decoder(input)
>>> out.reconstruction.shape
... torch.Size([2, 3, 32, 32])

forward(z, output_layer_levels=None)

Forward method

Parameters

output_layer_levels (List[int]) – The levels of the layers where the outputs are extracted. If None, the last layer’s output is returned. Default: None.

Returns

An instance of ModelOutput containing the reconstruction of the latent code under the key reconstruction. Optional: The outputs of the layers specified in output_layer_levels arguments are available under the keys reconstruction_layer_i where i is the layer’s level.

Return type

ModelOutput

class pythae.models.nn.benchmarks.cifar.Discriminator_Conv_CIFAR(args)

A Convolutional discriminator Neural net suited for CIFAR.

It can be built as follows:

>>> from pythae.models.nn.benchmarks.cifar import Discriminator_Conv_CIFAR
>>> from pythae.models import VAEGANConfig
>>> model_config = VAEGANConfig(input_dim=(3, 32, 32), latent_dim=16)
>>> discriminator = Discriminator_Conv_CIFAR(model_config)
>>> discriminator
... Discriminator_Conv_CIFAR(
...   (layers): ModuleList(
...     (0): Sequential(
...       (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): ReLU()
...     )
...     (1): Sequential(
...       (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): Tanh()
...     )
...     (2): Sequential(
...       (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): ReLU()
...     )
...     (3): Sequential(
...       (0): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
...       (1): ReLU()
...     )
...     (4): Sequential(
...       (0): Linear(in_features=4096, out_features=1, bias=True)
...       (1): Sigmoid()
...     )
...   )
... )

and then passed to a pythae.models instance

>>> from pythae.models import VAEGAN
>>> model = VAEGAN(model_config=model_config, discriminator=discriminator)
>>> model.discriminator == discriminator
... True

forward(x, output_layer_levels=None)

Forward method

Parameters

output_layer_levels (List[int]) – The levels of the layers where the outputs are extracted. If None, the last layer’s output is returned. Default: None.

Returns

An instance of ModelOutput containing the adversarial score of the input under the key embedding. Optional: The outputs of the layers specified in output_layer_levels arguments are available under the keys embedding_layer_i where i is the layer’s level.

Return type

ModelOutput