Module flowcon.nn.nets.resnet

Functions

def main()

Classes

class ConvResidualBlock (channels, context_channels=None, activation=<function relu>, dropout_probability=0.0, use_batch_norm=False, zero_initialization=True)

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().__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.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

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

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

Expand source code 
class ConvResidualBlock(nn.Module):
    def __init__(
        self,
        channels,
        context_channels=None,
        activation=F.relu,
        dropout_probability=0.0,
        use_batch_norm=False,
        zero_initialization=True,
    ):
        super().__init__()
        self.activation = activation

        if context_channels is not None:
            self.context_layer = nn.Conv2d(
                in_channels=context_channels,
                out_channels=channels,
                kernel_size=1,
                padding=0,
            )
        self.use_batch_norm = use_batch_norm
        if use_batch_norm:
            self.batch_norm_layers = nn.ModuleList(
                [nn.BatchNorm2d(channels, eps=1e-3) for _ in range(2)]
            )
        self.conv_layers = nn.ModuleList(
            [nn.Conv2d(channels, channels, kernel_size=3, padding=1) for _ in range(2)]
        )
        self.dropout = nn.Dropout(p=dropout_probability)
        if zero_initialization:
            init.uniform_(self.conv_layers[-1].weight, -1e-3, 1e-3)
            init.uniform_(self.conv_layers[-1].bias, -1e-3, 1e-3)

    def forward(self, inputs, context=None):
        temps = inputs
        if self.use_batch_norm:
            temps = self.batch_norm_layers[0](temps)
        temps = self.activation(temps)
        temps = self.conv_layers[0](temps)
        if self.use_batch_norm:
            temps = self.batch_norm_layers[1](temps)
        temps = self.activation(temps)
        temps = self.dropout(temps)
        temps = self.conv_layers[1](temps)
        if context is not None:
            temps = F.glu(torch.cat((temps, self.context_layer(context)), dim=1), dim=1)
        return inputs + temps

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Methods

def forward(self, inputs, context=None) ‑> Callable[..., Any]

Define 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.

class ConvResidualNet (in_channels, out_channels, hidden_channels, context_channels=None, num_blocks=2, activation=<function relu>, dropout_probability=0.0, use_batch_norm=False)

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().__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.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

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

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

Expand source code 
class ConvResidualNet(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        hidden_channels,
        context_channels=None,
        num_blocks=2,
        activation=F.relu,
        dropout_probability=0.0,
        use_batch_norm=False,
    ):
        super().__init__()
        self.context_channels = context_channels
        self.hidden_channels = hidden_channels
        if context_channels is not None:
            self.initial_layer = nn.Conv2d(
                in_channels=in_channels + context_channels,
                out_channels=hidden_channels,
                kernel_size=1,
                padding=0,
            )
        else:
            self.initial_layer = nn.Conv2d(
                in_channels=in_channels,
                out_channels=hidden_channels,
                kernel_size=1,
                padding=0,
            )
        self.blocks = nn.ModuleList(
            [
                ConvResidualBlock(
                    channels=hidden_channels,
                    context_channels=context_channels,
                    activation=activation,
                    dropout_probability=dropout_probability,
                    use_batch_norm=use_batch_norm,
                )
                for _ in range(num_blocks)
            ]
        )
        self.final_layer = nn.Conv2d(
            hidden_channels, out_channels, kernel_size=1, padding=0
        )

    def forward(self, inputs, context=None):
        if context is None:
            temps = self.initial_layer(inputs)
        else:
            temps = self.initial_layer(torch.cat((inputs, context), dim=1))
        for block in self.blocks:
            temps = block(temps, context)
        outputs = self.final_layer(temps)
        return outputs

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Methods

def forward(self, inputs, context=None) ‑> Callable[..., Any]

Define 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.

class ResidualBlock (features, context_features, activation=ReLU(), dropout_probability=0.0, use_batch_norm=False, zero_initialization=True)

A general-purpose residual block. Works only with 1-dim inputs.

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

Expand source code 
class ResidualBlock(nn.Module):
    """A general-purpose residual block. Works only with 1-dim inputs."""

    def __init__(
        self,
        features,
        context_features,
        activation=torch.nn.ReLU(),
        dropout_probability=0.0,
        use_batch_norm=False,
        zero_initialization=True,
    ):
        super().__init__()
        self.activation = activation

        self.use_batch_norm = use_batch_norm
        if use_batch_norm:
            self.batch_norm_layers = nn.ModuleList(
                [nn.BatchNorm1d(features, eps=1e-3) for _ in range(2)]
            )
        if context_features is not None:
            self.context_layer = nn.Linear(context_features, features)
        self.linear_layers = nn.ModuleList(
            [nn.Linear(features, features) for _ in range(2)]
        )
        self.dropout = nn.Dropout(p=dropout_probability)
        if zero_initialization:
            init.uniform_(self.linear_layers[-1].weight, -1e-3, 1e-3)
            init.uniform_(self.linear_layers[-1].bias, -1e-3, 1e-3)

    def forward(self, inputs, context=None):
        temps = inputs
        if self.use_batch_norm:
            temps = self.batch_norm_layers[0](temps)
        temps = self.activation(temps)
        temps = self.linear_layers[0](temps)
        if self.use_batch_norm:
            temps = self.batch_norm_layers[1](temps)
        temps = self.activation(temps)
        temps = self.dropout(temps)
        temps = self.linear_layers[1](temps)
        if context is not None:
            temps = F.glu(torch.cat((temps, self.context_layer(context)), dim=1), dim=1)
        return inputs + temps

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Methods

def forward(self, inputs, context=None) ‑> Callable[..., Any]

Define 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.

class ResidualNet (in_features, out_features, hidden_features, context_features=None, num_blocks=2, activation=ReLU(), dropout_probability=0.0, use_batch_norm=False)

A general-purpose residual network. Works only with 1-dim inputs.

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

Expand source code 
class ResidualNet(nn.Module):
    """A general-purpose residual network. Works only with 1-dim inputs."""

    def __init__(
        self,
        in_features,
        out_features,
        hidden_features,
        context_features=None,
        num_blocks=2,
        activation=torch.nn.ReLU(),
        dropout_probability=0.0,
        use_batch_norm=False,
    ):
        super().__init__()
        self.hidden_features = hidden_features
        self.context_features = context_features
        if context_features is not None:
            self.initial_layer = nn.Linear(
                in_features + context_features, hidden_features
            )
        else:
            self.initial_layer = nn.Linear(in_features, hidden_features)
        self.blocks = nn.ModuleList(
            [
                ResidualBlock(
                    features=hidden_features,
                    context_features=context_features,
                    activation=activation,
                    dropout_probability=dropout_probability,
                    use_batch_norm=use_batch_norm,
                )
                for _ in range(num_blocks)
            ]
        )
        self.final_layer = nn.Linear(hidden_features, out_features)

    def forward(self, inputs, context=None):
        if context is None:
            temps = self.initial_layer(inputs)
        else:
            temps = self.initial_layer(torch.cat((inputs, context), dim=1))
        for block in self.blocks:
            temps = block(temps, context=context)
        outputs = self.final_layer(temps)
        return outputs

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Methods

def forward(self, inputs, context=None) ‑> Callable[..., Any]

Define 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.