40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import torch
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def pairwise_dist(x, y):
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xx, yy, zz = torch.mm(x, x.t()), torch.mm(y, y.t()), torch.mm(x, y.t())
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rx = xx.diag().unsqueeze(0).expand_as(xx)
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ry = yy.diag().unsqueeze(0).expand_as(yy)
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P = rx.t() + ry - 2 * zz
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return P
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def NN_loss(x, y, dim=0):
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dist = pairwise_dist(x, y)
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values, indices = dist.min(dim=dim)
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return values.mean()
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def distChamfer(a, b):
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"""
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:param a: Pointclouds Batch x nul_points x dim
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:param b: Pointclouds Batch x nul_points x dim
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:return:
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-closest point on b of points from a
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-closest point on a of points from b
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-idx of closest point on b of points from a
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-idx of closest point on a of points from b
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Works for pointcloud of any dimension
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"""
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x, y = a.double(), b.double()
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bs, num_points_x, points_dim = x.size()
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bs, num_points_y, points_dim = y.size()
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xx = torch.pow(x, 2).sum(2)
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yy = torch.pow(y, 2).sum(2)
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zz = torch.bmm(x, y.transpose(2, 1))
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rx = xx.unsqueeze(1).expand(bs, num_points_y, num_points_x) # Diagonal elements xx
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ry = yy.unsqueeze(1).expand(bs, num_points_x, num_points_y) # Diagonal elements yy
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P = rx.transpose(2, 1) + ry - 2 * zz
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return torch.min(P, 2)[0].float(), torch.min(P, 1)[0].float(), torch.min(P, 2)[1].int(), torch.min(P, 1)[1].int()
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