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The pytorch implement for Attention Residual Learning for Skin Lesion Classification
Hey there,
I'm not completely sure Softmax2d does exactly what you what (apply the softmax spatially).
I've implemented this paper by doing using that function at the start, but later I found out that that was a mistake.
I believe that we should be doing this:
self.softmaxLayer = nn.Softmax(2) #it will apply softmax along the third dimension (we use 2 because we start indexing from 0)
and later override the forward function by doing this:
out = out + (identity
* self.softmaxLayer(out.view(*out.size()[:2], -1)).view_as(out)
* self.alpha) + (identity)
Tell me what you think
因为中间那个数据集链接进不去 所以我是自己下载的数据集 但可能自己比较小白 真的找不出问题在哪
如果有时间的话 谢谢解答了
Hello,
I felt confued about your BasicBlock module:
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, alpha =0.001):
...
def forward(self, x):
...
out = out + residual + self.alpha * attention * residual
if in_channels != out_channels, the last line can running?
residual
: inplanes, out
: planes
Looking forward to your reply. Thanks.
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