trw.layers.unet_2d

Module Contents

Classes

UNet_2d	Base class for all neural network modules.
UNetConvBlock	Base class for all neural network modules.
UNetUpBlock	Base class for all neural network modules.

class trw.layers.unet_2d.UNet_2d(in_channels=1, n_classes=2, depth=5, wf=6, padding=True, batch_norm=False, up_mode='upconv')

Bases: torch.nn.Module

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 to(), etc.

Variables

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

forward(self, x)

class trw.layers.unet_2d.UNetConvBlock(in_size, out_size, padding, batch_norm)

Bases: torch.nn.Module

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 to(), etc.

Variables

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

forward(self, x)

class trw.layers.unet_2d.UNetUpBlock(in_size, out_size, up_mode, padding, batch_norm)

Bases: torch.nn.Module

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 to(), etc.

Variables

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

center_crop(self, layer, target_size)

forward(self, x, bridge)