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docs: add missing upsample_mode parameter documentation to SegResNetDS and SegResNetDS2 #9131

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docs: add missing upsample_mode parameter documentation to SegResNetDS and SegResNetDS2 #9131

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10 changes: 10 additions & 0 deletions monai/networks/nets/segresnet_ds.py
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Expand Up @@ -250,6 +250,11 @@ class SegResNetDS(nn.Module):
dsdepth: number of levels for deep supervision. This will be the length of the list of outputs at each scale level.
At dsdepth==1,only a single output is returned.
preprocess: optional callable function to apply before the model's forward pass
upsample_mode: upsampling method used in the decoder path. Defaults to ``"deconv"``.
Supported values: ``"deconv"`` (transposed convolution), ``"deconvgroup"``
(group transposed convolution), ``"nontrainable"`` (interpolation-based, e.g.
:py:class:`torch.nn.Upsample`), and ``"pixelshuffle"``.
See also: :py:class:`monai.utils.enums.UpsampleMode`.
resolution: optional input image resolution. When provided, the network will first use non-isotropic kernels to bring
image spacing into an approximately isotropic space.
Otherwise, by default, the kernel size and downsampling is always isotropic.
Expand Down Expand Up @@ -458,6 +463,11 @@ class SegResNetDS2(SegResNetDS):
dsdepth: number of levels for deep supervision. This will be the length of the list of outputs at each scale level.
At dsdepth==1,only a single output is returned.
preprocess: optional callable function to apply before the model's forward pass
upsample_mode: upsampling method used in the decoder path. Defaults to ``"deconv"``.
Supported values: ``"deconv"`` (transposed convolution), ``"deconvgroup"``
(group transposed convolution), ``"nontrainable"`` (interpolation-based, e.g.
:py:class:`torch.nn.Upsample`), and ``"pixelshuffle"``.
See also: :py:class:`monai.utils.enums.UpsampleMode`.
resolution: optional input image resolution. When provided, the network will first use non-isotropic kernels to bring
image spacing into an approximately isotropic space.
Otherwise, by default, the kernel size and downsampling is always isotropic.
Expand Down
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