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| # Copyright 2021 by Haozhe Wu, Tsinghua University, Department of Computer Science and Technology. | |
| # All rights reserved. | |
| # This file is part of the pytorch-nicp, | |
| # and is released under the "MIT License Agreement". Please see the LICENSE | |
| # file that should have been included as part of this package. | |
| import torch | |
| import torch.nn as nn | |
| import torch.sparse as sp | |
| # reference: https://github.com/wuhaozhe/pytorch-nicp | |
| class LocalAffine(nn.Module): | |
| def __init__(self, num_points, batch_size=1, edges=None): | |
| ''' | |
| specify the number of points, the number of points should be constant across the batch | |
| and the edges torch.Longtensor() with shape N * 2 | |
| the local affine operator supports batch operation | |
| batch size must be constant | |
| add additional pooling on top of w matrix | |
| ''' | |
| super(LocalAffine, self).__init__() | |
| self.A = nn.Parameter(torch.eye(3).unsqueeze( | |
| 0).unsqueeze(0).repeat(batch_size, num_points, 1, 1)) | |
| self.b = nn.Parameter(torch.zeros(3).unsqueeze(0).unsqueeze( | |
| 0).unsqueeze(3).repeat(batch_size, num_points, 1, 1)) | |
| self.edges = edges | |
| self.num_points = num_points | |
| def stiffness(self): | |
| ''' | |
| calculate the stiffness of local affine transformation | |
| f norm get infinity gradient when w is zero matrix, | |
| ''' | |
| if self.edges is None: | |
| raise Exception("edges cannot be none when calculate stiff") | |
| idx1 = self.edges[:, 0] | |
| idx2 = self.edges[:, 1] | |
| affine_weight = torch.cat((self.A, self.b), dim=3) | |
| w1 = torch.index_select(affine_weight, dim=1, index=idx1) | |
| w2 = torch.index_select(affine_weight, dim=1, index=idx2) | |
| w_diff = (w1 - w2) ** 2 | |
| w_rigid = (torch.linalg.det(self.A) - 1.0) ** 2 | |
| return w_diff, w_rigid | |
| def forward(self, x, return_stiff=False): | |
| ''' | |
| x should have shape of B * N * 3 | |
| ''' | |
| x = x.unsqueeze(3) | |
| out_x = torch.matmul(self.A, x) | |
| out_x = out_x + self.b | |
| out_x.squeeze_(3) | |
| if return_stiff: | |
| stiffness, rigid = self.stiffness() | |
| return out_x, stiffness, rigid | |
| else: | |
| return out_x | |