I don't know how to create the model in the figure

YuhskeHujisaki: <p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/e/1/e1f8250b86f5ee09816ad18fa964db0527480fbe.png" data-download-href="https://discuss.pytorch.org/uploads/default/e1f8250b86f5ee09816ad18fa964db0527480fbe" title="スクリーンショット 2022-12-01 102403"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/e/1/e1f8250b86f5ee09816ad18fa964db0527480fbe_2_636x500.png" alt="スクリーンショット 2022-12-01 102403" data-base62-sha1="wf14Rv3pGJ0RwiEITfsoQmFxnDE" width="636" height="500" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/e/1/e1f8250b86f5ee09816ad18fa964db0527480fbe_2_636x500.png, https://discuss.pytorch.org/uploads/default/original/3X/e/1/e1f8250b86f5ee09816ad18fa964db0527480fbe.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/e/1/e1f8250b86f5ee09816ad18fa964db0527480fbe.png 2x" data-dominant-color="E6E2E8"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">スクリーンショット 2022-12-01 102403</span><span class="informations">892×701 166 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p><aside class="onebox allowlistedgeneric" data-onebox-src="https://link.springer.com/chapter/10.1007/978-3-030-51935-3_11">
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<h3><a href="https://link.springer.com/chapter/10.1007/978-3-030-51935-3_11" target="_blank" rel="noopener nofollow ugc">Incep-EEGNet: A ConvNet for Motor Imagery Decoding</a></h3>

  <p>The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalography that is mainly used to measure...</p>


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<p>
I created a model with reference to the above figure in this paper. However, how to create the A branch with a pointwise convolution with a kernel size of 1, 1 with a stride of (1, 2) and A branch with an average pooling with a kernel size of parts in the diagram above? I don’t know. Below is the model I’ve written so far.</p>
<pre><code class="lang-auto">import torch
from torch import nn
import torch.nn.functional as F
class DepthwiseConv2d(torch.nn.Conv2d):
    def __init__(self,
                 in_channels,
                 depth_multiplier=1,
                 kernel_size=3,
                 stride=1,
                 padding=0,
                 dilation=1,
                 bias=True,
                 padding_mode='zeros'
                 ):
        out_channels = in_channels * depth_multiplier
        super().__init__(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=in_channels,
            bias=bias,
            padding_mode=padding_mode
        )
class InceptionEEGNet(nn.Module):
  def __init__(self,bathsize): # input = (1,22,256)
    super().__init__()
    self.bathsize = bathsize
    self.conv2d_1 = nn.Conv2d(1,64,(1,16),padding='same')
    self.batchnorm2d_1 = nn.BatchNorm2d(64)
    self.conv2d_2 = DepthwiseConv2d(64,depth_multiplier=4,kernel_size=(16,1),padding='valid')
    self.batchnorm2d_elu_1 = nn.Sequential(
        nn.BatchNorm2d(256),
        nn.ELU()
        )
    self.averagepooling = nn.Sequential(
        nn.AvgPool2d((1,2)),
        nn.Dropout2d(p=0.5)
    )
    self.inception_block_1 = nn.Sequential(
        nn.Conv2d(256,64,(1,1),padding='same'),
        nn.Conv2d(64,64,(1,7),padding='same'),
    )
    self.inception_block_2 = nn.Sequential(
        nn.Conv2d(256,64,(1,1),padding='same'),
        nn.Conv2d(64,64,(1,9),padding='same'),
    )
    self.inception_block_3 = nn.Sequential(
        nn.Conv2d(256,64,(1,1),padding='same'),
    )
    self.inception_block_4 =  nn.Conv2d(256,64,(1,1),padding ="same")
    self.batchnorm2d_elu_2 = nn.Sequential(
        nn.BatchNorm2d(256),
        nn.ELU(),
        nn.Dropout2d(p=0.5)
        )
    self.conv2d_3 = nn.Conv2d(256,256,(1,5),padding='same')

  def forward(self,x):
    x = self.conv2d_1(x)
    x = self.batchnorm2d_1(x)
    x = self.conv2d_2(x)
    x = self.batchnorm2d_elu_1(x)
    x = self.averagepooling(x)
    x1 = self.inception_block_1(x)
    x2 = self.inception_block_2(x)
    x3 = self.inception_block_3(x)
    x4 = self.inception_block_4(x)
    x = torch.cat((x1, x2, x3, x4), 1)
    x = self.batchnorm2d_elu_2(x)
    x = self.conv2d_3(x)
    x = self.batchnorm2d_elu_2(x)
    x = F.adaptive_avg_pool3d(x, (1,1,4))
    x = x.squeeze()
    return x

net = InceptionEEGNet(10)
x = torch.rand(10,1,16,125)
print(net(x).shape)
</code></pre>

ptrblck: <p>Double post from <a href="https://discuss.pytorch.org/t/i-cant-concat-how-should-i-do-it/167205">here</a>.</p>