`mmagic.models.editors.deblurganv2.deblurganv2_util`¶

Module Contents¶

Classes¶

`BasicConv2d`	Base class for all neural network modules.
`Mixed_5b`	Base class for all neural network modules.
`Block35`	Base class for all neural network modules.
`Mixed_6a`	Base class for all neural network modules.
`Block17`	Base class for all neural network modules.
`Mixed_7a`	Base class for all neural network modules.
`Block8`	Base class for all neural network modules.
`InceptionResNetV2`	Define a inceptionresnetv2 model.
`InvertedResidual`	Base class for all neural network modules.
`MobileNetV2`	Base class for all neural network modules.

Functions¶

`inceptionresnetv2`([num_classes, pretrained])	return a inceptionresnetv2 network.
`conv_bn`(inp, oup, stride)
`conv_1x1_bn`(inp, oup)
`get_norm_layer`([norm_type])	Returns a norm layer of the specified type.

Attributes¶

pretrained_settings

mmagic.models.editors.deblurganv2.deblurganv2_util.pretrained_settings[source]¶

class mmagic.models.editors.deblurganv2.deblurganv2_util.BasicConv2d(in_planes, out_planes, kernel_size, stride, padding=0)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Mixed_5b[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Block35(scale=1.0)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Mixed_6a[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Block17(scale=1.0)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Mixed_7a[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.Block8(scale=1.0, noReLU=False)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.InceptionResNetV2(num_classes=1001)[source]¶

Bases: torch.nn.Module

Define a inceptionresnetv2 model.

features(input)[source]¶

Get network features.

Parameters: input (torch.tensor) – You can directly input a torch.Tensor.

logits(features)[source]¶

Get features logits.

Parameters: features (torch.tensor) – You can directly input a torch.Tensor.

forward(input)[source]¶

Forward function.

Parameters: input (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

mmagic.models.editors.deblurganv2.deblurganv2_util.inceptionresnetv2(num_classes=1000, pretrained='imagenet')[source]¶: return a inceptionresnetv2 network.

mmagic.models.editors.deblurganv2.deblurganv2_util.conv_bn(inp, oup, stride)[source]¶

mmagic.models.editors.deblurganv2.deblurganv2_util.conv_1x1_bn(inp, oup)[source]¶

class mmagic.models.editors.deblurganv2.deblurganv2_util.InvertedResidual(inp, oup, stride, expand_ratio)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

class mmagic.models.editors.deblurganv2.deblurganv2_util.MobileNetV2(n_class=1000, input_size=224, width_mult=1.0)[source]¶

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.

forward(x)[source]¶

Forward function.

Parameters: x (torch.Tensor) – You can directly input a torch.Tensor.
Returns: torch.tensor will be returned.
Return type: torch.Tensor

_initialize_weights()[source]¶

mmagic.models.editors.deblurganv2.deblurganv2_util.get_norm_layer(norm_type='instance')[source]¶

Returns a norm layer of the specified type.

Parameters: norm_type (Str) – norm layer type

mmagic.models.editors.deblurganv2.deblurganv2_util¶

Module Contents¶

Classes¶

Functions¶

Attributes¶

`mmagic.models.editors.deblurganv2.deblurganv2_util`¶