model_with_bridge

ModelWithBridge

Bases: torch.nn.Module

Implementation of the bridge architecture described in Gradually Vanishing Bridge for Adversarial Domain Adaptation.

Source code in pytorch_adapt\layers\model_with_bridge.py

class ModelWithBridge(torch.nn.Module):
    """
    Implementation of the bridge architecture described in
    [Gradually Vanishing Bridge for Adversarial Domain Adaptation](https://arxiv.org/abs/2003.13183).
    """

    def __init__(self, model: torch.nn.Module, bridge: torch.nn.Module = None):
        """
        Arguments:
            model: Any pytorch model.
            bridge: A model which has the same input/output sizes as ```model```.
                If ```None```, then the bridge is formed by copying ```model```,
                and randomly reinitialization all its parameters.
        """
        super().__init__()
        self.model = model
        if bridge is None:
            bridge = c_f.reinit(copy.deepcopy(model))
        self.bridge = bridge

    def forward(
        self, x: torch.Tensor, return_bridge: bool = False
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """
        Arguments:
            x: The input to both ```self.model``` and ```self.bridge```.
            return_bridge: Whether or not to return the bridge output
                in addition to the ```model - bridge``` output
        Returns:
            If ```return_bridge = False```, then return just ```model - bridge```.
                If ```return_bridge = True```, then return a tuple of ```(model - bridge), bridge```
        """
        y = self.model(x)
        z = self.bridge(x)
        output = y - z
        if return_bridge:
            return output, z
        return output

__init__(model, bridge=None)

Parameters:

Name	Type	Description	Default
model	torch.nn.Module	Any pytorch model.	required
bridge	torch.nn.Module	A model which has the same input/output sizes as model. If None, then the bridge is formed by copying model, and randomly reinitialization all its parameters.	None

Source code in pytorch_adapt\layers\model_with_bridge.py

def __init__(self, model: torch.nn.Module, bridge: torch.nn.Module = None):
    """
    Arguments:
        model: Any pytorch model.
        bridge: A model which has the same input/output sizes as ```model```.
            If ```None```, then the bridge is formed by copying ```model```,
            and randomly reinitialization all its parameters.
    """
    super().__init__()
    self.model = model
    if bridge is None:
        bridge = c_f.reinit(copy.deepcopy(model))
    self.bridge = bridge

forward(x, return_bridge=False)

Parameters:

Name	Type	Description	Default
x	torch.Tensor	The input to both self.model and self.bridge.	required
return_bridge	bool	Whether or not to return the bridge output in addition to the model - bridge output	False

Returns:

Type	Description
Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]	If return_bridge = False, then return just model - bridge. If return_bridge = True, then return a tuple of (model - bridge), bridge

Source code in pytorch_adapt\layers\model_with_bridge.py

def forward(
    self, x: torch.Tensor, return_bridge: bool = False
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    Arguments:
        x: The input to both ```self.model``` and ```self.bridge```.
        return_bridge: Whether or not to return the bridge output
            in addition to the ```model - bridge``` output
    Returns:
        If ```return_bridge = False```, then return just ```model - bridge```.
            If ```return_bridge = True```, then return a tuple of ```(model - bridge), bridge```
    """
    y = self.model(x)
    z = self.bridge(x)
    output = y - z
    if return_bridge:
        return output, z
    return output