Comments (1)
depth-wise conv
x = torch.einsum('nctv,cvw->nctw', (x, dw_gcn_weight))
point-wise conv
x = torch.einsum('nctw,cd->ndtw', (x, self.pw_gcn_weight))
these can be replaced by conv with groups?
belease I want to test speed on phone, so I need to use ncnn / mnn / tnn, thus, need to convert model from pth to onnx, then to
ncnn / mnn / tnn, BUT einsum NOT supported by these framework
torch.einsum
is a simple way to implement complex matrix multiplication. You can simply replace it by matmul
with tensor reshape and permutation.
'nctv,cvw->nctw'
: for each channel in c
, you can first reshape two tensors into sizes [nt, v]
and [v, w]
, then perform matmul
between them. finally, we get [c, nt, w] -> nctw
.
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