mmagic.models.editors.deblurganv2.deblurganv2_discriminator

Module Contents

Classes

NLayerDiscriminator	Defines the PatchGAN discriminator with the specified arguments.
DicsriminatorTail	Base class for all neural network modules.
MultiScaleDiscriminator	Defines the MultiScale PatchGAN discriminator with the specified
DoubleGan	Get a discriminator with a patch gan and a full gan.
PatchGan	A patch gan discriminator with the specified arguments.
MultiScale	A multiscale patch gan discriminator with the specified arguments.
DeblurGanV2Discriminator	Defines the discriminator for DeblurGanv2 with the specified arguments..

Functions

get_fullD(norm_layer)

Get a full gan discriminator.

Attributes

backbone_list

mmagic.models.editors.deblurganv2.deblurganv2_discriminator.backbone_list = ['DoubleGan', 'MultiScale', 'NoGan', 'PatchGan']

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.NLayerDiscriminator(input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True)

Bases: torch.nn.Module

Defines the PatchGAN discriminator with the specified arguments.

forward(input)

Forward function.

参数

input (torch.Tensor) – You can directly input a torch.Tensor.

返回

torch.tensor will be returned.

返回类型

torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.DicsriminatorTail(nf_mult, n_layers, ndf=64, norm_layer=nn.BatchNorm2d, use_parallel=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.

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(input)

Forward function.

参数

input (torch.Tensor) – You can directly input a torch.Tensor.

返回

torch.tensor will be returned.

返回类型

torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.MultiScaleDiscriminator(input_nc=3, ndf=64, norm_layer=nn.BatchNorm2d, use_parallel=True)

Bases: torch.nn.Module

Defines the MultiScale PatchGAN discriminator with the specified arguments.

forward(input)

Forward function.

参数

input (torch.Tensor) – You can directly input a torch.Tensor.

返回

torch.tensor will be returned.

返回类型

torch.Tensor

mmagic.models.editors.deblurganv2.deblurganv2_discriminator.get_fullD(norm_layer)

Get a full gan discriminator.

参数

norm_layer (Str) – norm type

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.DoubleGan(norm_layer='instance', d_layers=3)

Bases: torch.nn.Module

Get a discriminator with a patch gan and a full gan.

forward(x)

Forward function.

参数

x (torch.Tensor) – You can directly input a torch.Tensor.

返回

List(torch.tensor) will be returned.

返回类型

List(torch.Tensor)

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.PatchGan(norm_layer='instance', d_layers=3)

Bases: torch.nn.Module

A patch gan discriminator with the specified arguments.

forward(x)

Forward function.

参数

x (torch.Tensor) – You can directly input a torch.Tensor.

返回

torch.tensor will be returned.

返回类型

torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.MultiScale(norm_layer='instance', d_layers=3)

Bases: torch.nn.Module

A multiscale patch gan discriminator with the specified arguments.

forward(x)

Forward function.

参数

x (torch.Tensor) – You can directly input a torch.Tensor.

返回

torch.tensor will be returned.

返回类型

torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_discriminator.DeblurGanV2Discriminator

Defines the discriminator for DeblurGanv2 with the specified arguments..

参数

model (Str) – Type of the discriminator model