Module minder_utils.models.feature_extractors.autoencoder

Classes

class AutoEncoder

Helper class that provides a standard way to create an ABC using inheritance.

Expand source code

class AutoEncoder(Feature_extractor):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        self.encoder = Encoder(**self.config['model'])
        self.decoder = Decoder(**self.config['model'])
        self.model = nn.Sequential(self.encoder, self.decoder)
        self.criterion = nn.BCELoss() if self.config['loss']['func'] == 'bce' else nn.MSELoss()

    def forward(self, inputs):
        codes = self.encoder(inputs)
        decoded = self.decoder(codes)
        return codes, decoded

    def step(self, data):
        data, label = data
        if self.config['model']['base_model'] in ['nn']:
            data = data.view(data.size(0), -1)
        return self.criterion(self.decoder(self.encoder(data)), data)

Ancestors

• Feature_extractor
• abc.ABC
• torch.nn.modules.module.Module

Class variables

var dump_patches : bool

var training : bool

Methods

def step(self, data)

Expand source code

def step(self, data):
    data, label = data
    if self.config['model']['base_model'] in ['nn']:
        data = data.view(data.size(0), -1)
    return self.criterion(self.decoder(self.encoder(data)), data)

Inherited members

• Feature_extractor:
  • forward
  • transform

class Decoder (base_model, input_dim, latent_dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class Decoder(nn.Module):
    def __init__(self, base_model, input_dim, latent_dim):
        super(Decoder, self).__init__()
        # Decoder
        if base_model == 'conv':
            self.decoder = nn.Sequential(
                nn.Conv2d(3, 8, kernel_size=2, padding=1),
                nn.Tanh(),
                nn.Conv2d(8, 16, kernel_size=2, padding=1),
                nn.Tanh(),
                nn.Conv2d(16, 8, kernel_size=2),
                nn.Tanh(),
                nn.Conv2d(8, 3, kernel_size=2),
                nn.Sigmoid()
            )
        else:
            self.decoder = nn.Sequential(
                nn.Linear(latent_dim, 64),
                nn.Tanh(),
                nn.Linear(64, 128),
                nn.Tanh(),
                nn.Linear(128, 256),
                nn.Tanh(),
                nn.Linear(256, input_dim),
                nn.Sigmoid()
            )

    def forward(self, inputs):
        outputs = self.decoder(inputs)
        return outputs

Ancestors

• torch.nn.modules.module.Module

Class variables

var dump_patches : bool

var training : bool

Methods

def forward(self, inputs) ‑> Callable[..., Any]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, inputs):
    outputs = self.decoder(inputs)
    return outputs

class Encoder (base_model, input_dim, latent_dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class Encoder(nn.Module):
    def __init__(self, base_model, input_dim, latent_dim):
        super(Encoder, self).__init__()
        # Encoder
        if base_model == 'conv':
            self.encoder = nn.Sequential(
                nn.Conv2d(3, 8, kernel_size=2, padding=1),
                nn.Tanh(),
                nn.Conv2d(8, 16, kernel_size=2, padding=1),
                nn.Tanh(),
                nn.Conv2d(16, 8, kernel_size=2),
                nn.Tanh(),
                nn.Conv2d(8, 3, kernel_size=2),
                nn.Tanh()
            )
        else:
            self.encoder = nn.Sequential(
                nn.Flatten(start_dim=1),
                nn.Linear(input_dim, 256),
                nn.Tanh(),
                nn.Linear(256, 128),
                nn.Tanh(),
                nn.Linear(128, 64),
                nn.Tanh(),
                nn.Linear(64, latent_dim),
                nn.Tanh()
            )

    def forward(self, inputs):
        codes = self.encoder(inputs)
        return codes

Ancestors

• torch.nn.modules.module.Module

Class variables

var dump_patches : bool

var training : bool

Methods

def forward(self, inputs) ‑> Callable[..., Any]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, inputs):
    codes = self.encoder(inputs)
    return codes