credit.models.unet_downscaling

Attributes

logger
supported_models
supported_encoders

Classes

DownscalingSegmentationModel

Base class for all neural network modules.

Functions

load_premade_encoder_model(model_conf)

Module Contents

credit.models.unet_downscaling.logger

credit.models.unet_downscaling.supported_models

credit.models.unet_downscaling.supported_encoders = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d',...

credit.models.unet_downscaling.load_premade_encoder_model(model_conf)

class credit.models.unet_downscaling.DownscalingSegmentationModel(channels: dict, image_height=640, image_width=1280, frames=2, rk4_integration=False, architecture=None, post_conf=None, **kwargs)

Bases: credit.models.base_model.BaseModel

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.

image_height = 640

image_width = 1280

frames = 2

channels

rk4_integration = False

input_channels

output_channels

model

use_post_block

forward(x)

rk4(x)