Evaluate a data point :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors. :return:
(self, data, use_octree=False)
| 88 | } |
| 89 | |
| 90 | def eval(self, data, use_octree=False): |
| 91 | ''' |
| 92 | Evaluate a data point |
| 93 | :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors. |
| 94 | :return: |
| 95 | ''' |
| 96 | opt = self.opt |
| 97 | with torch.no_grad(): |
| 98 | self.netG.eval() |
| 99 | if self.netC: |
| 100 | self.netC.eval() |
| 101 | save_path = '%s/%s/result_%s.obj' % (opt.results_path, opt.name, data['name']) |
| 102 | if self.netC: |
| 103 | gen_mesh_color(opt, self.netG, self.netC, self.cuda, data, save_path, use_octree=use_octree) |
| 104 | else: |
| 105 | gen_mesh(opt, self.netG, self.cuda, data, save_path, use_octree=use_octree) |
| 106 | |
| 107 | |
| 108 | if __name__ == '__main__': |