@zhang-tao-whu hello, as for the tracker part, I have a question about the function of match_embds, in this function, why is the cosine similarity calculated from only one sample in the batch, as shown in the following code?
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def match_embds(self, ref_embds, cur_embds):
# embeds (q, b, c)
ref_embds, cur_embds = ref_embds.detach()[:, 0, :], cur_embds.detach()[:, 0, :] # only one sample in a batch
ref_embds = ref_embds / (ref_embds.norm(dim=1)[:, None] + 1e-6)
cur_embds = cur_embds / (cur_embds.norm(dim=1)[:, None] + 1e-6)
cos_sim = torch.mm(ref_embds, cur_embds.transpose(0, 1))
C = 1 - cos_sim
C = C.cpu()
C = torch.where(torch.isnan(C), torch.full_like(C, 0), C)
indices = linear_sum_assignment(C.transpose(0, 1))
indices = indices[1]
return indices
@zhang-tao-whu hello, as for the tracker part, I have a question about the function of match_embds, in this function, why is the cosine similarity calculated from only one sample in the batch, as shown in the following code? `
`