Shortcuts

mmedit.models.editors.stylegan1.stylegan1_modules

Module Contents

Classes

EqualLinearActModule

Equalized LR Linear Module with Activation Layer.

NoiseInjection

Noise Injection Module.

ConstantInput

Constant Input.

Blur

Blur module.

AdaptiveInstanceNorm

Adaptive Instance Normalization Module.

StyleConv

Base class for all neural network modules.

Functions

make_kernel(k)

class mmedit.models.editors.stylegan1.stylegan1_modules.EqualLinearActModule(*args, equalized_lr_cfg=dict(gain=1.0, lr_mul=1.0), bias=True, bias_init=0.0, act_cfg=None, **kwargs)[源代码]

Bases: torch.nn.Module

Equalized LR Linear Module with Activation Layer.

This module is modified from EqualizedLRLinearModule defined in PGGAN. The major features updated in this module is adding support for activation layers used in StyleGAN2.

参数
  • equalized_lr_cfg (dict | None, optional) – Config for equalized lr. Defaults to dict(gain=1., lr_mul=1.).

  • bias (bool, optional) – Whether to use bias item. Defaults to True.

  • bias_init (float, optional) – The value for bias initialization. Defaults to 0..

  • act_cfg (dict | None, optional) – Config for activation layer. Defaults to None.

forward(x)[源代码]

Forward function.

参数

x (Tensor) – Input feature map with shape of (N, C, …).

返回

Output feature map.

返回类型

Tensor

class mmedit.models.editors.stylegan1.stylegan1_modules.NoiseInjection(noise_weight_init=0.0)[源代码]

Bases: torch.nn.Module

Noise Injection Module.

In StyleGAN2, they adopt this module to inject spatial random noise map in the generators.

参数

noise_weight_init (float, optional) – Initialization weight for noise injection. Defaults to 0..

forward(image, noise=None, return_noise=False)[源代码]

Forward Function.

参数
  • image (Tensor) – Spatial features with a shape of (N, C, H, W).

  • noise (Tensor, optional) – Noises from the outside. Defaults to None.

  • return_noise (bool, optional) – Whether to return noise tensor. Defaults to False.

返回

Output features.

返回类型

Tensor

class mmedit.models.editors.stylegan1.stylegan1_modules.ConstantInput(channel, size=4)[源代码]

Bases: torch.nn.Module

Constant Input.

In StyleGAN2, they substitute the original head noise input with such a constant input module.

参数
  • channel (int) – Channels for the constant input tensor.

  • size (int, optional) – Spatial size for the constant input. Defaults to 4.

forward(x)[源代码]

Forward function.

参数

x (Tensor) – Input feature map with shape of (N, C, …).

返回

Output feature map.

返回类型

Tensor

mmedit.models.editors.stylegan1.stylegan1_modules.make_kernel(k)[源代码]
class mmedit.models.editors.stylegan1.stylegan1_modules.Blur(kernel, pad, upsample_factor=1)[源代码]

Bases: torch.nn.Module

Blur module.

This module is adopted rightly after upsampling operation in StyleGAN2.

参数
  • kernel (Array) – Blur kernel/filter used in UpFIRDn.

  • pad (list[int]) – Padding for features.

  • upsample_factor (int, optional) – Upsampling factor. Defaults to 1.

forward(x)[源代码]

Forward function.

参数

x (Tensor) – Input feature map with shape of (N, C, H, W).

返回

Output feature map.

返回类型

Tensor

class mmedit.models.editors.stylegan1.stylegan1_modules.AdaptiveInstanceNorm(in_channel, style_dim)[源代码]

Bases: torch.nn.Module

Adaptive Instance Normalization Module.

Ref: https://github.com/rosinality/style-based-gan-pytorch/blob/master/model.py # noqa

参数
  • in_channel (int) – The number of input’s channel.

  • style_dim (int) – Style latent dimension.

forward(input, style)[源代码]

Forward function.

参数
  • input (Tensor) – Input tensor with shape (n, c, h, w).

  • style (Tensor) – Input style tensor with shape (n, c).

返回

Forward results.

返回类型

Tensor

class mmedit.models.editors.stylegan1.stylegan1_modules.StyleConv(in_channels, out_channels, kernel_size, style_channels, padding=1, initial=False, blur_kernel=[1, 2, 1], upsample=False, fused=False)[源代码]

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.

备注

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

变量

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(x, style1, style2, noise1=None, noise2=None, return_noise=False)[源代码]

Forward function.

参数
  • x (Tensor) – Input tensor.

  • style1 (Tensor) – Input style tensor with shape (n, c).

  • style2 (Tensor) – Input style tensor with shape (n, c).

  • noise1 (Tensor, optional) – Noise tensor with shape (n, c, h, w). Defaults to None.

  • noise2 (Tensor, optional) – Noise tensor with shape (n, c, h, w). Defaults to None.

  • return_noise (bool, optional) – If True, noise1 and noise2

  • False. (will be returned with out. Defaults to) –

返回

Forward results.

返回类型

Tensor | tuple[Tensor]

Read the Docs v: latest
Versions
master
latest
stable
zyh-doc-notfound-extend
Downloads
pdf
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.