rslearn.models.upsample

upsample

An upsampling layer.

Upsample

Bases: IntermediateComponent

Upsamples each input feature map by the same factor.

Source code in rslearn/models/upsample.py

class Upsample(IntermediateComponent):
    """Upsamples each input feature map by the same factor."""

    def __init__(
        self,
        scale_factor: int,
        mode: str = "bilinear",
    ):
        """Initialize an Upsample.

        Args:
            scale_factor: the upsampling factor, e.g. 2 to double the size.
            mode: "nearest" or "bilinear".
        """
        super().__init__()
        self.layer = torch.nn.Upsample(scale_factor=scale_factor, mode=mode)

    def forward(self, intermediates: Any, context: ModelContext) -> FeatureMaps:
        """Upsample each feature map by scale_factor.

        Args:
            intermediates: the output from the previous component, which must be a FeatureMaps.
            context: the model context.

        Returns:
            upsampled feature maps.
        """
        if not isinstance(intermediates, FeatureMaps):
            raise ValueError("input to Upsample must be a FeatureMaps")

        upsampled_feat_maps = [
            self.layer(feat_map) for feat_map in intermediates.feature_maps
        ]
        return FeatureMaps(upsampled_feat_maps)

forward

forward(intermediates: Any, context: ModelContext) -> FeatureMaps

Upsample each feature map by scale_factor.

Parameters:

Name	Type	Description	Default
intermediates	Any	the output from the previous component, which must be a FeatureMaps.	required
context	ModelContext	the model context.	required

Returns:

Type	Description
FeatureMaps	upsampled feature maps.

Source code in rslearn/models/upsample.py

def forward(self, intermediates: Any, context: ModelContext) -> FeatureMaps:
    """Upsample each feature map by scale_factor.

    Args:
        intermediates: the output from the previous component, which must be a FeatureMaps.
        context: the model context.

    Returns:
        upsampled feature maps.
    """
    if not isinstance(intermediates, FeatureMaps):
        raise ValueError("input to Upsample must be a FeatureMaps")

    upsampled_feat_maps = [
        self.layer(feat_map) for feat_map in intermediates.feature_maps
    ]
    return FeatureMaps(upsampled_feat_maps)