credit.models.debugger_model

Attributes

logger
image_height

Classes

DebuggerModel

Base class for all neural network modules.

Module Contents

credit.models.debugger_model.logger

class credit.models.debugger_model.DebuggerModel(image_height: int = 640, image_width: int = 1280, frames: int = 2, channels: int = 4, surface_channels: int = 7, input_only_channels: int = 3, output_only_channels: int = 0, levels: int = 15, post_conf: dict = 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 = 4

surface_channels = 7

levels = 15

input_only_channels = 3

output_only_channels = 0

linear

use_post_block

forward(x)

forward that multiplies self.coef to the input used to test postblock and other model parts

credit.models.debugger_model.image_height = 640