basicsr.ops.dcn.__init__

class basicsr.ops.dcn.__init__.DeformConv(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, bias=False)

Bases: Module

forward(x, offset)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()

training: bool

class basicsr.ops.dcn.__init__.DeformConvPack(*args, **kwargs)

Bases: DeformConv

A Deformable Conv Encapsulation that acts as normal Conv layers.

Parameters:

• in_channels (int) – Same as nn.Conv2d.

• out_channels (int) – Same as nn.Conv2d.

• kernel_size (int or tuple[int]) – Same as nn.Conv2d.

• stride (int or tuple[int]) – Same as nn.Conv2d.

• padding (int or tuple[int]) – Same as nn.Conv2d.

• dilation (int or tuple[int]) – Same as nn.Conv2d.

• groups (int) – Same as nn.Conv2d.

• bias (bool or str) – If specified as auto, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_offset()

training: bool

class basicsr.ops.dcn.__init__.ModulatedDeformConv(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, bias=True)

Bases: Module

forward(x, offset, mask)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_weights()

training: bool

class basicsr.ops.dcn.__init__.ModulatedDeformConvPack(*args, **kwargs)

Bases: ModulatedDeformConv

A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers.

Parameters:

• in_channels (int) – Same as nn.Conv2d.

• out_channels (int) – Same as nn.Conv2d.

• kernel_size (int or tuple[int]) – Same as nn.Conv2d.

• stride (int or tuple[int]) – Same as nn.Conv2d.

• padding (int or tuple[int]) – Same as nn.Conv2d.

• dilation (int or tuple[int]) – Same as nn.Conv2d.

• groups (int) – Same as nn.Conv2d.

• bias (bool or str) – If specified as auto, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_weights()

training: bool

basicsr.ops.dcn.__init__.deform_conv()

basicsr.ops.dcn.__init__.modulated_deform_conv()