About Resi-connection

[mushding]

Hi there, thanks for the amazing work!
In the section 'Impact of residual connection and convolution layer in RSTB' of the paper said that would add a 1x1 conv or 3x3 conv at the residual connection.
the result shows that 3x3 is better than 1x1, also the inverted-bottleneck 3x3.

back to the code itself.
when I first read the 'resi_connection' argument in SwinIR class, I thought that '1conv' means 1x1 conv and '3conv' means 3x3 conv
after a while reading more code, I realized that '1conv' actually means 'one 3x3 conv' and '3conv' means 'three 3x3 conv'.

# build the last conv layer in deep feature extraction
if resi_connection == '1conv':
    self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == '3conv':
    # to save parameters and memory
    self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
                                         nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                         nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
                                         nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                         nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

I think the way of naming could be a little bit confusing.
It could be better if it calls '1conv3' '3conv3' or something else.

just want to tell you the confusing part.
thanks.

[Jingyun Liang]

Sorry for the confusion. We will fix it later. Thank you!