dasf.ml.dl.models

Submodules

• dasf.ml.dl.models.devconvnet

Classes

TorchPatchDeConvNet	Base class for all neural network modules.
TorchPatchDeConvNetSkip	Base class for all neural network modules.
TorchSectionDeConvNet	Base class for all neural network modules.
TorchSectionDeConvNetSkip	Base class for all neural network modules.

Package Contents

class dasf.ml.dl.models.TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)

Bases: NNModule

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 to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

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

forward(x)

Same as torch.nn.Module.forward().

Args:

*args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.

Return:

Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)

load()

This is just a no-op load method.

class dasf.ml.dl.models.TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)

Bases: NNModule

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 to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

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

forward(x)

Same as torch.nn.Module.forward().

Args:

*args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.

Return:

Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)

load()

This is just a no-op load method.

class dasf.ml.dl.models.TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False)

Bases: NNModule

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 to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

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

forward(x)

Same as torch.nn.Module.forward().

Args:

*args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.

Return:

Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)

load()

This is just a no-op load method.

class dasf.ml.dl.models.TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)

Bases: NNModule

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 to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

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

forward(x)

Same as torch.nn.Module.forward().

Args:

*args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.

Return:

Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)

load()

This is just a no-op load method.