limap.line2d.SOLD2.model.nets package

Submodules

limap.line2d.SOLD2.model.nets.backbone module

class limap.line2d.SOLD2.model.nets.backbone.HourglassBackbone(input_channel=1, depth=4, num_stacks=2, num_blocks=1, num_classes=5)

Bases: Module

Hourglass backbone.

forward(input_images)

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.

training: bool
class limap.line2d.SOLD2.model.nets.backbone.SuperpointBackbone

Bases: Module

SuperPoint backbone.

forward(input_images)

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.

training: bool

limap.line2d.SOLD2.model.nets.descriptor_decoder module

class limap.line2d.SOLD2.model.nets.descriptor_decoder.SuperpointDescriptor(input_feat_dim=128)

Bases: Module

Descriptor decoder based on the SuperPoint arcihtecture.

forward(input_features)

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.

training: bool

limap.line2d.SOLD2.model.nets.heatmap_decoder module

class limap.line2d.SOLD2.model.nets.heatmap_decoder.PixelShuffleDecoder(input_feat_dim=128, num_upsample=2, output_channel=2)

Bases: Module

Pixel shuffle decoder.

forward(input_features)

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.

get_channel_conf(num_upsample)
training: bool

limap.line2d.SOLD2.model.nets.junction_decoder module

class limap.line2d.SOLD2.model.nets.junction_decoder.SuperpointDecoder(input_feat_dim=128, backbone_name='lcnn')

Bases: Module

Junction decoder based on the SuperPoint architecture.

forward(input_features)

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.

training: bool

limap.line2d.SOLD2.model.nets.lcnn_hourglass module

Hourglass network, taken from https://github.com/zhou13/lcnn

class limap.line2d.SOLD2.model.nets.lcnn_hourglass.HourglassNet(block, head, depth, num_stacks, num_blocks, num_classes, input_channels)

Bases: Module

Hourglass model from Newell et al ECCV 2016

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.

training: bool
limap.line2d.SOLD2.model.nets.lcnn_hourglass.hg(**kwargs)

Module contents