core.modules.scaling

core.modules.scaling#

Submodules#

Classes#

ScaleFactor

Base class for all neural network modules.

Package Contents#

class core.modules.scaling.ScaleFactor(name: str | None = None, enforce_consistency: bool = True)#

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().__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.

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.

scale_factor: torch.Tensor#
name: str | None = None#
index_fn: IndexFn | None = None#
stats: _Stats | None = None#
_enforce_consistency(state_dict, prefix, _local_metadata, _strict, _missing_keys, _unexpected_keys, _error_msgs) → None#
property fitted: bool#
reset_() → None#
set_(scale: float | torch.Tensor) → None#
initialize_(*, index_fn: IndexFn | None = None) → None#
fit_context_()#
fit_()#
_observe(x: torch.Tensor, ref: torch.Tensor | None = None) → None#
forward(x: torch.Tensor, *, ref: torch.Tensor | None = None) → torch.Tensor#
Contents