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2825 add hints for norm_name in dynunet_blocks.py #2826

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2825 add hints for norm_name in dynunet_blocks.py #2826

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c7feb19
update dynunet block norm layers
yiheng-wang-nv Aug 24, 2021
6bae738
add more notifications in docstrings
yiheng-wang-nv Aug 24, 2021
5f8a882
remove unexpected changes
yiheng-wang-nv Aug 24, 2021
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12 changes: 9 additions & 3 deletions monai/networks/blocks/dynunet_block.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,9 @@ class UnetResBlock(nn.Module):
out_channels: number of output channels.
kernel_size: convolution kernel size.
stride: convolution stride.
norm_name: feature normalization type and arguments.
norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
`norm_name=("instance", {"affine": True})`.

"""

Expand Down Expand Up @@ -106,7 +108,9 @@ class UnetBasicBlock(nn.Module):
out_channels: number of output channels.
kernel_size: convolution kernel size.
stride: convolution stride.
norm_name: feature normalization type and arguments.
norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
`norm_name=("instance", {"affine": True})`.

"""

Expand Down Expand Up @@ -163,7 +167,9 @@ class UnetUpBlock(nn.Module):
kernel_size: convolution kernel size.
stride: convolution stride.
upsample_kernel_size: convolution kernel size for transposed convolution layers.
norm_name: feature normalization type and arguments.
norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
`norm_name=("instance", {"affine": True})`.

"""

Expand Down
2 changes: 1 addition & 1 deletion monai/networks/nets/dynunet.py
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Expand Up @@ -86,7 +86,7 @@ class DynUNet(nn.Module):
strides: convolution strides for each blocks.
upsample_kernel_size: convolution kernel size for transposed convolution layers. The values should
equal to strides[1:].
norm_name: feature normalization type and arguments. Defaults to ``INSTANCE``.
norm_name: feature normalization type and arguments. Defaults to ``("INSTANCE", {"affine": True})``.
deep_supervision: whether to add deep supervision head before output. Defaults to ``False``.
If ``True``, in training mode, the forward function will output not only the last feature
map, but also the previous feature maps that come from the intermediate up sample layers.
Expand Down