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from torch.nn.modules.loss import _Loss
from torch.autograd import Variable
import torch
import time
import numpy as np
import torch.nn as nn
import random
import torch.backends.cudnn as cudnn
from lib.knn.__init__ import KNearestNeighbor
def loss_calculation(pred_r, pred_t, target, model_points, idx, points, num_point_mesh, sym_list):
knn = KNearestNeighbor(1)
pred_r = pred_r.view(1, 1, -1)
pred_t = pred_t.view(1, 1, -1)
bs, num_p, _ = pred_r.size()
num_input_points = len(points[0])
pred_r = pred_r / (torch.norm(pred_r, dim=2).view(bs, num_p, 1))
base = torch.cat(((1.0 - 2.0*(pred_r[:, :, 2]**2 + pred_r[:, :, 3]**2)).view(bs, num_p, 1),\
(2.0*pred_r[:, :, 1]*pred_r[:, :, 2] - 2.0*pred_r[:, :, 0]*pred_r[:, :, 3]).view(bs, num_p, 1), \
(2.0*pred_r[:, :, 0]*pred_r[:, :, 2] + 2.0*pred_r[:, :, 1]*pred_r[:, :, 3]).view(bs, num_p, 1), \
(2.0*pred_r[:, :, 1]*pred_r[:, :, 2] + 2.0*pred_r[:, :, 3]*pred_r[:, :, 0]).view(bs, num_p, 1), \
(1.0 - 2.0*(pred_r[:, :, 1]**2 + pred_r[:, :, 3]**2)).view(bs, num_p, 1), \
(-2.0*pred_r[:, :, 0]*pred_r[:, :, 1] + 2.0*pred_r[:, :, 2]*pred_r[:, :, 3]).view(bs, num_p, 1), \
(-2.0*pred_r[:, :, 0]*pred_r[:, :, 2] + 2.0*pred_r[:, :, 1]*pred_r[:, :, 3]).view(bs, num_p, 1), \
(2.0*pred_r[:, :, 0]*pred_r[:, :, 1] + 2.0*pred_r[:, :, 2]*pred_r[:, :, 3]).view(bs, num_p, 1), \
(1.0 - 2.0*(pred_r[:, :, 1]**2 + pred_r[:, :, 2]**2)).view(bs, num_p, 1)), dim=2).contiguous().view(bs * num_p, 3, 3)
ori_base = base
base = base.contiguous().transpose(2, 1).contiguous()
model_points = model_points.view(bs, 1, num_point_mesh, 3).repeat(1, num_p, 1, 1).view(bs * num_p, num_point_mesh, 3)
target = target.view(bs, 1, num_point_mesh, 3).repeat(1, num_p, 1, 1).view(bs * num_p, num_point_mesh, 3)
ori_target = target
pred_t = pred_t.contiguous().view(bs * num_p, 1, 3)
ori_t = pred_t
pred = torch.add(torch.bmm(model_points, base), pred_t)
if idx[0].item() in sym_list:
target = target[0].transpose(1, 0).contiguous().view(3, -1)
pred = pred.permute(2, 0, 1).contiguous().view(3, -1)
inds = knn(target.unsqueeze(0), pred.unsqueeze(0))
target = torch.index_select(target, 1, inds.view(-1) - 1)
target = target.view(3, bs * num_p, num_point_mesh).permute(1, 2, 0).contiguous()
pred = pred.view(3, bs * num_p, num_point_mesh).permute(1, 2, 0).contiguous()
dis = torch.mean(torch.norm((pred - target), dim=2), dim=1)
t = ori_t[0]
points = points.view(1, num_input_points, 3)
ori_base = ori_base[0].view(1, 3, 3).contiguous()
ori_t = t.repeat(bs * num_input_points, 1).contiguous().view(1, bs * num_input_points, 3)
new_points = torch.bmm((points - ori_t), ori_base).contiguous()
new_target = ori_target[0].view(1, num_point_mesh, 3).contiguous()
ori_t = t.repeat(num_point_mesh, 1).contiguous().view(1, num_point_mesh, 3)
new_target = torch.bmm((new_target - ori_t), ori_base).contiguous()
# print('------------> ', dis.item(), idx[0].item())
del knn
return dis, new_points.detach(), new_target.detach()
class Loss_refine(_Loss):
def __init__(self, num_points_mesh, sym_list):
super(Loss_refine, self).__init__(True)
self.num_pt_mesh = num_points_mesh
self.sym_list = sym_list
def forward(self, pred_r, pred_t, target, model_points, idx, points):
return loss_calculation(pred_r, pred_t, target, model_points, idx, points, self.num_pt_mesh, self.sym_list)
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