# Source code for imaginaire.layers.misc

```# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import torch
from torch import nn

[docs]class ApplyNoise(nn.Module):
r"""Add Gaussian noise to the input tensor."""

def __init__(self):
super().__init__()
# scale of the noise
self.scale = nn.Parameter(torch.zeros(1))
self.conditional = True

[docs]    def forward(self, x, *_args, noise=None, **_kwargs):
r"""

Args:
x (tensor): Input tensor.
noise (tensor, optional, default=``None``) : Noise tensor to be
added to the input.
"""
if noise is None:
sz = x.size()
noise = x.new_empty(sz[0], 1, *sz[2:]).normal_()

return x + self.scale * noise

[docs]class PartialSequential(nn.Sequential):
r"""Sequential block for partial convolutions."""
def __init__(self, *modules):
super(PartialSequential, self).__init__(*modules)

[docs]    def forward(self, x):
r"""

Args:
x (tensor): Input tensor.
"""
act = x[:, :-1]
mask = x[:, -1].unsqueeze(1)
for module in self:
return act

[docs]class ConstantInput(nn.Module):
def __init__(self, channel, size=4):
super().__init__()
if isinstance(size, int):
h, w = size, size
else:
h, w = size
self.input = nn.Parameter(torch.randn(1, channel, h, w))

[docs]    def forward(self):
return self.input
```