depth-wise convolution layer in cmMamba block

[maitreyamaity]

Dear Authors,

It is mentioned in the paper - "A second branch embeds fj via depth-wise convolution and passes it through an SSM layer". I failed to understand the DWConv layer after I printed the model summary.

For example, the below block is the summary of a cm-mamba bottleneck layer.

(bottleneck_5): cmMambaWithCNN(
(mamba_layer): MambaLayerOnlyspiral(
(norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mamba1): Mamba(
(in_proj): Linear(in_features=256, out_features=1024, bias=False)
(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512)
(act): SiLU()
(x_proj): Linear(in_features=512, out_features=48, bias=False)
(dt_proj): Linear(in_features=16, out_features=512, bias=True)
(out_proj): Linear(in_features=512, out_features=256, bias=False)
)
(mamba2): Mamba(
(in_proj): Linear(in_features=256, out_features=1024, bias=False)
(conv1d): Conv1d(512, 512, kernel_size=(4,), stride=(1,), padding=(3,), groups=512)
(act): SiLU()
(x_proj): Linear(in_features=512, out_features=48, bias=False)
(dt_proj): Linear(in_features=16, out_features=512, bias=True)
(out_proj): Linear(in_features=512, out_features=256, bias=False)
)
(conv1d): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(cc): ChannelCompression(
(skip): Sequential(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(block): Sequential(
(0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(residual_cnn): Sequential(
(0): ResnetBlock(
(conv_block): Sequential(
(0): ReflectionPad2d((1, 1, 1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): ReflectionPad2d((1, 1, 1, 1))
(5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)

Kindly share more information on my query.

Thanks