fix SegResNetVAE docstring to match args
@@ -181,6 +181,12 @@ class SegResNetVAE(SegResNet):
The model supports 2D or 3D inputs.
Args:
+ input_image_size: the size of images to input into the network. It is used to
+ determine the in_features of the fc layer in VAE.
+ vae_estimate_std: whether to estimate the standard deviations in VAE. Defaults to ``False``.
+ vae_default_std: if not to estimate the std, use the default value. Defaults to 0.3.
+ vae_nz: number of latent variables in VAE. Defaults to 256.
+ Where, 128 to represent mean, and 128 to represent std.
spatial_dims: spatial dimension of the input data. Defaults to 3.
init_filters: number of output channels for initial convolution layer. Defaults to 8.
in_channels: number of input channels for the network. Defaults to 1.
@@ -199,15 +205,6 @@ class SegResNetVAE(SegResNet):
- ``deconv``, uses transposed convolution layers.
- ``nontrainable``, uses non-trainable `linear` interpolation.
- ``pixelshuffle``, uses :py:class:`monai.networks.blocks.SubpixelUpsample`.
-
- use_vae: if use the variational autoencoder (VAE) during training. Defaults to ``False``.
- input_image_size: the size of images to input into the network. It is used to
- determine the in_features of the fc layer in VAE. When ``use_vae == True``, please
- ensure that this parameter is set. Defaults to ``None``.
- vae_estimate_std: whether to estimate the standard deviations in VAE. Defaults to ``False``.
- vae_default_std: if not to estimate the std, use the default value. Defaults to 0.3.
- vae_nz: number of latent variables in VAE. Defaults to 256.
- Where, 128 to represent mean, and 128 to represent std.
"""
def __init__(
