def forward(self, X):
# input X is a 4D tensor
assert(X.size(1)==self.D,"Encoding Layer wrong channels!")
if X.dim() == 3:
# BxDxN
B, N, K, D = X.size(0), X.size(2), self.K, self.D
X = X.transpose(1,2).contiguous()
elif X.dim() == 4:
# BxDxHxW
B, N, K, D = X.size(0), X.size(2)*X.size(3), self.K, self.D
X = X.view(B,D,-1).transpose(1,2).contiguous()
else:
raise RuntimeError('Encoding Layer unknown input dims!')
# assignment weights
A = F.softmax(scaledL2()(X, self.codewords, self.scale))
# aggregate
E = aggregate()(A, X, self.codewords)
return E
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