minerva.utils.upsample

Classes

Upsample

Base class for all neural network modules.

Functions

resize(input[, size, scale_factor, mode, ...])

Module Contents

class minerva.utils.upsample.Upsample(size=None, scale_factor=None, mode='nearest', align_corners=None)

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 them to be nested 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) -> None:
        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 also have their parameters converted 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.

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

align_corners = None

forward(x)

mode = 'nearest'

size = None

minerva.utils.upsample.resize(input, size=None, scale_factor=None, mode='nearest', align_corners=None, warning=True)