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rslearn.models.conv

conv

A single convolutional layer.

Conv

Bases: IntermediateComponent

A single convolutional layer.

It inputs a set of feature maps; the conv layer is applied to each feature map independently, and list of outputs is returned.

Source code in rslearn/models/conv.py 
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class Conv(IntermediateComponent):
    """A single convolutional layer.

    It inputs a set of feature maps; the conv layer is applied to each feature map
    independently, and list of outputs is returned.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        padding: str | int = "same",
        stride: int = 1,
        activation: torch.nn.Module = torch.nn.ReLU(inplace=True),
        batch_norm: bool = False,
    ):
        """Initialize a Conv.

        Args:
            in_channels: number of input channels.
            out_channels: number of output channels.
            kernel_size: kernel size, see torch.nn.Conv2D.
            padding: padding to apply, see torch.nn.Conv2D.
            stride: stride to apply, see torch.nn.Conv2D.
            activation: activation to apply after convolution
            batch_norm: whether to apply batch normalization before activation
        """
        super().__init__()

        self.layer = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size, padding=padding, stride=stride
        )
        self.batch_norm = torch.nn.BatchNorm2d(out_channels) if batch_norm else None
        self.activation = activation

    def forward(self, intermediates: Any, context: ModelContext) -> FeatureMaps:
        """Apply conv layer on each feature map.

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

        Returns:
            the resulting feature maps after applying the same Conv2d on each one.
        """
        if not isinstance(intermediates, FeatureMaps):
            raise ValueError("input to Conv must be FeatureMaps")

        new_features = []
        for feat_map in intermediates.feature_maps:
            feat_map = self.layer(feat_map)
            if self.batch_norm is not None:
                feat_map = self.batch_norm(feat_map)
            feat_map = self.activation(feat_map)
            new_features.append(feat_map)
        return FeatureMaps(new_features)

forward 

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

Apply conv layer on each feature map.

Parameters:

Name Type Description Default
intermediates Any

the previous output, which must be a FeatureMaps.

 required
context ModelContext

the model context.

 required

Returns:

Type Description
FeatureMaps

the resulting feature maps after applying the same Conv2d on each one.
Source code in rslearn/models/conv.py 
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def forward(self, intermediates: Any, context: ModelContext) -> FeatureMaps:
    """Apply conv layer on each feature map.

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

    Returns:
        the resulting feature maps after applying the same Conv2d on each one.
    """
    if not isinstance(intermediates, FeatureMaps):
        raise ValueError("input to Conv must be FeatureMaps")

    new_features = []
    for feat_map in intermediates.feature_maps:
        feat_map = self.layer(feat_map)
        if self.batch_norm is not None:
            feat_map = self.batch_norm(feat_map)
        feat_map = self.activation(feat_map)
        new_features.append(feat_map)
    return FeatureMaps(new_features)