core.modules.scaling.scale_factor

core.modules.scaling.scale_factor#

Attributes#

IndexFn

Classes#

_Stats

dict() -> new empty dictionary

ScaleFactor

Base class for all neural network modules.

Functions#

_check_consistency(→ None)

Module Contents#

class core.modules.scaling.scale_factor._Stats#

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {} for k, v in iterable:

d[k] = v

dict(**kwargs) -> new dictionary initialized with the name=value pairs

in the keyword argument list. For example: dict(one=1, two=2)

variance_in: float#
variance_out: float#
n_samples: int#
core.modules.scaling.scale_factor.IndexFn#
core.modules.scaling.scale_factor._check_consistency(old: torch.Tensor, new: torch.Tensor, key: str) → None#
class core.modules.scaling.scale_factor.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