From a76cac29f15f56a1401e92725c3676f0124983e1 Mon Sep 17 00:00:00 2001 From: Caitlin Leonard <157518754+caitlin-leonard@users.noreply.github.com> Date: Fri, 2 Oct 2026 18:40:03 +0530 Subject: [PATCH 1/2] ix argument names in QuickNAT docstrings Signed-off-by: Caitlin Leonard <157518754+caitlin-leonard@users.noreply.github.com> --- monai/networks/nets/quicknat.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/monai/networks/nets/quicknat.py b/monai/networks/nets/quicknat.py index a934b26c31..849923b340 100644 --- a/monai/networks/nets/quicknat.py +++ b/monai/networks/nets/quicknat.py @@ -135,8 +135,8 @@ class ConvConcatDenseBlock(ConvDenseBlock): 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 """ @@ -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). From 7628d392ae2a499656fb5c6fa8726eb29aee3c16 Mon Sep 17 00:00:00 2001 From: Caitlin Leonard <157518754+caitlin-leonard@users.noreply.github.com> Date: Fri, 2 Oct 2026 18:53:33 +0530 Subject: [PATCH 2/2] Fix se_layer casing and typo in QuickNAT docstring Signed-off-by: Caitlin Leonard <157518754+caitlin-leonard@users.noreply.github.com> --- monai/networks/nets/quicknat.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/monai/networks/nets/quicknat.py b/monai/networks/nets/quicknat.py index 849923b340..d3641911b4 100644 --- a/monai/networks/nets/quicknat.py +++ b/monai/networks/nets/quicknat.py @@ -128,7 +128,7 @@ 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. @@ -136,7 +136,7 @@ class ConvConcatDenseBlock(ConvDenseBlock): but has a 1 * 1 kernel size to compress the feature map size to 64. Args: 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'}, + 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 """