"""Proposed Neural nets architectures suited for CIFAR"""
from typing import List
import torch
import torch.nn as nn
from ....base import BaseAEConfig
from ....base.base_utils import ModelOutput
from ...base_architectures import BaseDecoder, BaseDiscriminator, BaseEncoder
[docs]class Encoder_Conv_AE_CIFAR(BaseEncoder):
"""
A Convolutional encoder Neural net suited for CIFAR and Autoencoder-based models.
It can be built as follows:
.. code-block::
>>> 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 :class:`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`.
.. code-block::
>>> import torch
>>> input = torch.rand(2, 3, 32, 32)
>>> out = encoder(input)
>>> out.embedding.shape
... torch.Size([2, 16])
"""
def __init__(self, args: BaseAEConfig):
BaseEncoder.__init__(self)
self.input_dim = (3, 32, 32)
self.latent_dim = args.latent_dim
self.n_channels = 3
layers = nn.ModuleList()
layers.append(
nn.Sequential(
nn.Conv2d(self.n_channels, 128, 4, 2, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(128, 256, 4, 2, padding=1), nn.BatchNorm2d(256), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(256, 512, 4, 2, padding=1), nn.BatchNorm2d(512), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(512, 1024, 4, 2, padding=1), nn.BatchNorm2d(1024), nn.ReLU()
)
)
self.layers = layers
self.depth = len(layers)
self.embedding = nn.Linear(1024 * 2 * 2, args.latent_dim)
[docs] def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None):
"""Forward method
Args:
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:
ModelOutput: 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."""
output = ModelOutput()
max_depth = self.depth
if output_layer_levels is not None:
assert all(
self.depth >= levels > 0 or levels == -1
for levels in output_layer_levels
), (
f"Cannot output layer deeper than depth ({self.depth}). "
f"Got ({output_layer_levels})."
)
if -1 in output_layer_levels:
max_depth = self.depth
else:
max_depth = max(output_layer_levels)
out = x
for i in range(max_depth):
out = self.layers[i](out)
if output_layer_levels is not None:
if i + 1 in output_layer_levels:
output[f"embedding_layer_{i+1}"] = out
if i + 1 == self.depth:
output["embedding"] = self.embedding(out.reshape(x.shape[0], -1))
return output
[docs]class Encoder_Conv_VAE_CIFAR(BaseEncoder):
"""
A Convolutional encoder Neural net suited for CIFAR and Variational Autoencoder-based
models.
It can be built as follows:
.. code-block::
>>> 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 :class:`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`.
.. code-block::
>>> 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])
"""
def __init__(self, args: BaseAEConfig):
BaseEncoder.__init__(self)
self.input_dim = (3, 32, 32)
self.latent_dim = args.latent_dim
self.n_channels = 3
layers = nn.ModuleList()
layers.append(
nn.Sequential(
nn.Conv2d(self.n_channels, 128, 4, 2, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(128, 256, 4, 2, padding=1), nn.BatchNorm2d(256), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(256, 512, 4, 2, padding=1), nn.BatchNorm2d(512), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(512, 1024, 4, 2, padding=1), nn.BatchNorm2d(1024), nn.ReLU()
)
)
self.layers = layers
self.depth = len(layers)
self.embedding = nn.Linear(1024 * 2 * 2, args.latent_dim)
self.log_var = nn.Linear(1024 * 2 * 2, args.latent_dim)
[docs] def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None):
"""Forward method
Args:
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:
ModelOutput: 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.
"""
output = ModelOutput()
max_depth = self.depth
if output_layer_levels is not None:
assert all(
self.depth >= levels > 0 or levels == -1
for levels in output_layer_levels
), (
f"Cannot output layer deeper than depth ({self.depth}). "
f"Got ({output_layer_levels})."
)
if -1 in output_layer_levels:
max_depth = self.depth
else:
max_depth = max(output_layer_levels)
out = x
for i in range(max_depth):
out = self.layers[i](out)
if output_layer_levels is not None:
if i + 1 in output_layer_levels:
output[f"embedding_layer_{i+1}"] = out
if i + 1 == self.depth:
output["embedding"] = self.embedding(out.reshape(x.shape[0], -1))
output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1))
return output
[docs]class Encoder_Conv_SVAE_CIFAR(BaseEncoder):
"""
A Convolutional encoder Neural net suited for CIFAR and Hyperspherical Variational
Autoencoder.
models.
It can be built as follows:
.. code-block::
>>> 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 :class:`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`.
.. code-block::
>>> 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])
"""
def __init__(self, args: BaseAEConfig):
BaseEncoder.__init__(self)
self.input_dim = (3, 32, 32)
self.latent_dim = args.latent_dim
self.n_channels = 3
layers = nn.ModuleList()
layers.append(
nn.Sequential(
nn.Conv2d(self.n_channels, 128, 4, 2, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(128, 256, 4, 2, padding=1), nn.BatchNorm2d(256), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(256, 512, 4, 2, padding=1), nn.BatchNorm2d(512), nn.ReLU()
)
)
layers.append(
nn.Sequential(
nn.Conv2d(512, 1024, 4, 2, padding=1), nn.BatchNorm2d(1024), nn.ReLU()
)
)
self.layers = layers
self.depth = len(layers)
self.embedding = nn.Linear(1024 * 2 * 2, args.latent_dim)
self.log_concentration = nn.Linear(1024 * 2 * 2, 1)
[docs] def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None):
"""Forward method
Args:
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:
ModelOutput: 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.
"""
output = ModelOutput()
max_depth = self.depth
if output_layer_levels is not None:
assert all(
self.depth >= levels > 0 or levels == -1
for levels in output_layer_levels
), (
f"Cannot output layer deeper than depth ({self.depth}). "
f"Got ({output_layer_levels})."
)
if -1 in output_layer_levels:
max_depth = self.depth
else:
max_depth = max(output_layer_levels)
out = x
for i in range(max_depth):
out = self.layers[i](out)
if output_layer_levels is not None:
if i + 1 in output_layer_levels:
output[f"embedding_layer_{i+1}"] = out
if i + 1 == self.depth:
output["embedding"] = self.embedding(out.reshape(x.shape[0], -1))
output["log_concentration"] = self.log_concentration(
out.reshape(x.shape[0], -1)
)
return output
[docs]class Decoder_Conv_AE_CIFAR(BaseDecoder):
"""
A Convolutional decoder Neural net suited for CIFAR and Autoencoder-based
models.
It can be built as follows:
.. code-block::
>>> 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 :class:`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.
.. code-block::
>>> import torch
>>> input = torch.randn(2, 16)
>>> out = decoder(input)
>>> out.reconstruction.shape
... torch.Size([2, 3, 32, 32])
"""
def __init__(self, args: dict):
BaseDecoder.__init__(self)
self.input_dim = (3, 32, 32)
self.latent_dim = args.latent_dim
self.n_channels = 3
layers = nn.ModuleList()
layers.append(nn.Linear(args.latent_dim, 1024 * 8 * 8))
layers.append(
nn.Sequential(
nn.ConvTranspose2d(1024, 512, 4, 2, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.ConvTranspose2d(512, 256, 4, 2, padding=1, output_padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.ConvTranspose2d(256, self.n_channels, 4, 1, padding=2), nn.Sigmoid()
)
)
self.layers = layers
self.depth = len(layers)
[docs] def forward(self, z: torch.Tensor, output_layer_levels: List[int] = None):
"""Forward method
Args:
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:
ModelOutput: 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.
"""
output = ModelOutput()
max_depth = self.depth
if output_layer_levels is not None:
assert all(
self.depth >= levels > 0 or levels == -1
for levels in output_layer_levels
), (
f"Cannot output layer deeper than depth ({self.depth}). "
f"Got ({output_layer_levels})."
)
if -1 in output_layer_levels:
max_depth = self.depth
else:
max_depth = max(output_layer_levels)
out = z
for i in range(max_depth):
out = self.layers[i](out)
if i == 0:
out = out.reshape(z.shape[0], 1024, 8, 8)
if output_layer_levels is not None:
if i + 1 in output_layer_levels:
output[f"reconstruction_layer_{i+1}"] = out
if i + 1 == self.depth:
output["reconstruction"] = out
return output
[docs]class Discriminator_Conv_CIFAR(BaseDiscriminator):
"""
A Convolutional discriminator Neural net suited for CIFAR.
It can be built as follows:
.. code-block::
>>> 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 :class:`pythae.models` instance
>>> from pythae.models import VAEGAN
>>> model = VAEGAN(model_config=model_config, discriminator=discriminator)
>>> model.discriminator == discriminator
... True
"""
def __init__(self, args: dict):
BaseDiscriminator.__init__(self)
self.input_dim = (3, 32, 32)
self.latent_dim = args.latent_dim
self.n_channels = 3
layers = nn.ModuleList()
layers.append(
nn.Sequential(
nn.Conv2d(self.n_channels, 128, 4, 2, padding=1),
# nn.BatchNorm2d(128),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(128, 256, 4, 2, padding=1),
# nn.BatchNorm2d(256),
nn.Tanh(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(256, 512, 4, 2, padding=1),
# nn.BatchNorm2d(512),
nn.ReLU(),
)
)
layers.append(
nn.Sequential(
nn.Conv2d(512, 1024, 4, 2, padding=1),
# nn.BatchNorm2d(1024),
nn.ReLU(),
)
)
layers.append(nn.Sequential(nn.Linear(1024 * 2 * 2, 1), nn.Sigmoid()))
self.layers = layers
self.depth = len(layers)
[docs] def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None):
"""Forward method
Args:
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:
ModelOutput: 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.
"""
output = ModelOutput()
max_depth = self.depth
if output_layer_levels is not None:
assert all(
self.depth >= levels > 0 or levels == -1
for levels in output_layer_levels
), (
f"Cannot output layer deeper than depth ({self.depth}). "
f"Got ({output_layer_levels})."
)
if -1 in output_layer_levels:
max_depth = self.depth
else:
max_depth = max(output_layer_levels)
out = x
for i in range(max_depth):
if i == 4:
out = out.reshape(x.shape[0], -1)
out = self.layers[i](out)
if output_layer_levels is not None:
if i + 1 in output_layer_levels:
output[f"embedding_layer_{i+1}"] = out
if i + 1 == self.depth:
output["embedding"] = out
return output