credit.models.unet404

credit.models.unet404#

Attributes#

logger

supported_models

supported_encoders

Classes#

SegmentationModel404

Base class for all neural network modules.

Functions#

load_premade_encoder_model(model_conf)

Module Contents#

credit.models.unet404.logger#
credit.models.unet404.supported_models#
credit.models.unet404.supported_encoders = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d',...#
credit.models.unet404.load_premade_encoder_model(model_conf)#
class credit.models.unet404.SegmentationModel404(conf)#

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.

variables#
frames#
static_variables#
model#
forward(x)#
Contents