def compute_loss_cycle3(self):
P1_P3_P2_cycle = loss.batch_cycle_3(self.P1_P3, self.idx1_P2_P1,
self.idx1_P3_P2, self.batchs)
P2_P1_P3_cycle = loss.batch_cycle_3(self.P2_P1, self.idx1_P3_P2,
self.idx1_P1_P3, self.batchs)
self.loss_train_cycleL2_3 = (1 / 6.0) * (loss.L2(
P1_P3_P2_cycle, self.P1) + loss.L2(P2_P1_P3_cycle, self.P2))
def compute_loss_selfReconstruction(self, network):
rot_matrix = self.rot_matrix.cuda().float().transpose(2,
1).contiguous()
P4 = self.P3.bmm(rot_matrix).detach()
scale = torch.rand(P4.size(0), 1,
3).cuda() - 0.5 # uniform sampling -0.5, 0.5
P4 = P4 + scale * P4 # Anisotropic scaling
P3 = self.P3.transpose(2, 1).contiguous()
P4 = P4.transpose(2, 1).contiguous()
P4_P3 = network(P3, P4) # forward pass
P3_P4 = network(P4, P3) # forward pass
self.loss_train_selfReconstruction_L2 = (1 / 2.0) * (
loss.L2(P4_P3, P4) + loss.L2(P3_P4, P3))
def test_iteration(self):
self.couple.separate_points_normal_labels()
batchs = self.couple.P1.size(0)
self.couple.add_P2P1(
*loss.forward_chamfer(self.network,
self.couple.P1,
self.couple.P2,
local_fix=self.fix,
distChamfer=self.distChamfer))
loss_val_Deformation_ChamferL2 = loss.chamferL2(
self.couple.dist1_P2_P1, self.couple.dist2_P2_P1)
loss_val_Reconstruction_L2 = loss.L2(self.couple.P2_P1, self.couple.P2)
self.log.update("loss_val_Deformation_ChamferL2",
loss_val_Deformation_ChamferL2)
self.log.update("loss_val_Reconstruction_L2",
loss_val_Reconstruction_L2)
print('\r' + colored(
'[%d: %d/%d]' %
(self.epoch, self.iteration, self.len_dataset_test /
(self.opt.batch_size)), 'red') + colored(
'loss_val_Deformation_ChamferL2: %f' %
loss_val_Deformation_ChamferL2.item(), 'yellow'),
end='')
if self.iteration % 60 == 1 and self.opt.display:
self.visualizer.show_pointclouds(points=self.couple.P2[0],
Y=self.couple.label2[0],
title="val_B")
self.visualizer.show_pointclouds(points=self.couple.P1[0],
Y=self.couple.label1[0],
title="val_A")
self.visualizer.show_pointclouds(points=self.couple.P2_P1[0],
Y=self.couple.label1[0],
title="val_B_reconstructed")
# Compute Miou when labels are tranfered from P1 to P2.
predicted_target = self.couple.label1.view(-1)[
self.couple.idx2_P2_P1].view(batchs, -1)
for shape in range(batchs):
if self.couple.cat_1 == self.couple.cat_2:
target = self.couple.label2[shape].squeeze().data.cpu().numpy()
iou_val = miou_shape.miou_shape(
predicted_target[shape].squeeze().cpu().numpy(), target,
self.dataset_train.part_category[self.couple.cat_1[shape]])
self.log.update("iou_val", iou_val)