[GitHub] kice opened a new issue #9264: Some questions about gluon

[GitBox <gi...@apache.org>]

kice opened a new issue #9264: Some questions about gluon
URL: https://github.com/apache/incubator-mxnet/issues/9264
 
 
   I want to do this by using list `self.conv.append(nn.Conv2D())`, but it does work.
   Can someone give me a suggestion for this case? I have on idea for it.
   
   The network is created with skip connections.
   
   <pre><code>
   class Layer(nn.HybridBlock):
       def __init__(self, depth=15, channels=3, **kwargs):
           super(Layer, self).__init__(**kwargs)
           self.depth = depth
   
           self.conv01 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv02 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv03 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv04 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv05 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv06 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv07 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv08 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv09 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv10 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv11 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv12 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv13 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv14 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.conv15 = nn.Conv2D(128, kernel_size=3, strides=1, padding=1, use_bias=True)
   
           self.deconv01 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv02 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv03 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv04 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv05 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv06 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv07 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv08 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv09 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv10 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv11 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv12 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv13 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv14 = nn.Conv2DTranspose(128, kernel_size=3, strides=1, padding=1, use_bias=True)
           self.deconv15 = nn.Conv2DTranspose(channels, kernel_size=3, strides=1, padding=1, use_bias=True)
     
       def hybrid_forward(self, F, x):
           encode01 = F.relu(self.conv01(x))
           encode02 = F.relu(self.conv02(encode01))
           encode03 = F.relu(self.conv03(encode02))
           encode04 = F.relu(self.conv04(encode03))
           encode05 = F.relu(self.conv05(encode04))
           encode06 = F.relu(self.conv06(encode05))
           encode07 = F.relu(self.conv07(encode06))
           encode08 = F.relu(self.conv08(encode07))
           encode09 = F.relu(self.conv09(encode08))
           encode10 = F.relu(self.conv10(encode09))
           encode11 = F.relu(self.conv11(encode10))
           encode12 = F.relu(self.conv12(encode11))
           encode13 = F.relu(self.conv13(encode12))
           encode14 = F.relu(self.conv14(encode13))
           encode15 = F.relu(self.conv15(encode14))
   
           decode00 = encode15
   
           decode01 = F.relu(self.deconv01(decode00))
           decode01 = F.relu(encode14+decode01)
   
           decode02 = F.relu(self.deconv02(decode01))
   
           decode03 = F.relu(self.deconv03(decode02))
           decode03 = F.relu(encode12+decode03)
   
           decode04 = F.relu(self.deconv04(decode03))
   
           decode05 = F.relu(self.deconv05(decode04))
           decode05 = F.relu(encode10+decode05)
   
           decode06 = F.relu(self.deconv06(decode05))
   
           decode07 = F.relu(self.deconv07(decode06))
           decode07 = F.relu(encode08+decode07)
   
           decode08 = F.relu(self.deconv08(decode07))
   
           decode09 = F.relu(self.deconv09(decode08))
           decode09 = F.relu(encode06+decode09)
   
           decode10 = F.relu(self.deconv10(decode09))
   
           decode11 = F.relu(self.deconv11(decode10))
           decode11 = F.relu(encode04+decode11)
   
           decode12 = F.relu(self.deconv12(decode11))
   
           decode13 = F.relu(self.deconv13(decode12))
           decode13 = F.relu(encode02+decode13)
   
           decode14 = F.relu(self.deconv14(decode13))
   
           decode15 = self.deconv15(decode14)
   
           return decode15
   </code></pre>

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