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Fix argument names in QuickNAT docstrings #9148

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Fix argument names in QuickNAT docstrings #9148

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a76cac2
ix argument names in QuickNAT docstrings
caitlin-leonard Oct 2, 2026
7628d39
Fix se_layer casing and typo in QuickNAT docstring
caitlin-leonard Oct 2, 2026
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10 changes: 5 additions & 5 deletions monai/networks/nets/quicknat.py
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Expand Up @@ -128,15 +128,15 @@ class ConvConcatDenseBlock(ConvDenseBlock):
Every convolutional layer is preceded by a batch-normalization layer and a Rectifier Linear Unit (ReLU) layer.
The first two convolutional layers are followed by a concatenation layer that concatenates
the input feature map with outputs of the current and previous convolutional blocks.
Kernel size of two convolutional layers kept small to limit number of paramters.
Kernel size of two convolutional layers kept small to limit number of parameters.
Appropriate padding is provided so that the size of feature maps before and after convolution remains constant.
The output channels for each convolution layer is set to 64, which acts as a bottle- neck for feature map selectivity.
The input channel size is variable, depending on the number of dense connections.
The third convolutional layer is also preceded by a batch normalization and ReLU,
but has a 1 * 1 kernel size to compress the feature map size to 64.
Args:
in_channles: variable depending on depth of the network
seLayer: Squeeze and Excite block to be included, defaults to None, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'},
in_channels: variable depending on depth of the network
se_layer: Squeeze and Excite block to be included, defaults to None, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'},
dropout_layer: Dropout block to be included, defaults to None.
:return: forward passed tensor
"""
Expand Down Expand Up @@ -343,11 +343,11 @@ class Quicknat(nn.Module):
num_filters: number of output channels for each convolutional layer in a Dense Block.
kernel_size: size of the kernel of each convolutional layer in a Dense Block.
kernel_c: convolution kernel size of classifier block kernel.
stride_convolution: convolution stride. Defaults to 1.
stride_conv: convolution stride. Defaults to 1.
pool: kernel size of the pooling layer,
stride_pool: stride for the pooling layer.
se_block: Squeeze and Excite block type to include. Use ``None`` or ``"None"`` to disable it; valid enabled values are ``"CSE"``, ``"SSE"``, and ``"CSSE"``.
droup_out: dropout ratio. Defaults to no dropout.
drop_out: dropout ratio. Defaults to no dropout.
act: activation type and arguments. Defaults to PReLU.
norm: feature normalization type and arguments. Defaults to instance norm.
adn_ordering: a string representing the ordering of activation (A), normalization (N), and dropout (D).
Expand Down