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Decoders

terratorch.models.decoders.fcn_decoder #

FCNDecoder #

Bases: Module

Fully Convolutional Decoder

__init__(embed_dim, channels=256, num_convs=4, in_index=-1) #

Constructor

Parameters:

Name Type Description Default
embed_dim _type_

Input embedding dimension

required
channels int

Number of channels for each conv. Defaults to 256.

256
num_convs int

Number of convs. Defaults to 4.

4
in_index int

Index of the input list to take. Defaults to -1.

-1

terratorch.models.decoders.identity_decoder #

Pass the features straight through

IdentityDecoder #

Bases: Module

Identity decoder. Useful to pass the feature straight to the head.

__init__(embed_dim, out_index=-1) #

Constructor

Parameters:

Name Type Description Default
embed_dim int

Input embedding dimension

required
out_index int

Index of the input list to take.. Defaults to -1.

-1

terratorch.models.decoders.upernet_decoder #

PPM #

Bases: ModuleList

Pooling Pyramid Module used in PSPNet.

__init__(pool_scales, in_channels, channels, align_corners) #

Constructor

Parameters:

Name Type Description Default
pool_scales tuple[int]

Pooling scales used in Pooling Pyramid Module.

required
in_channels int

Input channels.

required
channels int

Channels after modules, before conv_seg.

required
align_corners bool

align_corners argument of F.interpolate.

required

forward(x) #

Forward function.

UperNetDecoder #

Bases: Module

UperNetDecoder. Adapted from MMSegmentation.

__init__(embed_dim, pool_scales=(1, 2, 3, 6), channels=256, align_corners=True, scale_modules=False) #

Constructor

Parameters:

Name Type Description Default
embed_dim list[int]

Input embedding dimension for each input.

required
pool_scales tuple[int]

Pooling scales used in Pooling Pyramid Module applied on the last feature. Default: (1, 2, 3, 6).

(1, 2, 3, 6)
channels int

Channels used in the decoder. Defaults to 256.

256
align_corners bool

Wheter to align corners in rescaling. Defaults to True.

True
scale_modules bool

Whether to apply scale modules to the inputs. Needed for plain ViT. Defaults to False.

False

forward(inputs) #

Forward function for feature maps before classifying each pixel with Args: inputs (list[Tensor]): List of multi-level img features.

Returns:

Name Type Description
feats Tensor

A tensor of shape (batch_size, self.channels, H, W) which is feature map for last layer of decoder head.

psp_forward(inputs) #

Forward function of PSP module.


Last update: March 23, 2025