leonardozcm / asfm-net-pytorch Goto Github PK

                # downsample gt to 2048
                partial = fps_subsample(gt, 2048)
                coarse_gt = fps_subsample(gt, 1024)

                # preprocess transpose
                partial = partial.permute(0, 2, 1)

                v, y_coarse, y_detail = model(partial)

                # y_coarse = y_coarse.permute(0, 2, 1)
                y_coarse = fps_subsample(
                    gt[:, torch.randperm(gt.shape[1]), :], 1024)
                y_detail = y_detail.permute(0, 2, 1)

                loss_coarse = chamfer_sqrt(coarse_gt, y_coarse)
                loss_fine = chamfer_sqrt(
                    gt, gt[:, torch.randperm(gt.shape[1]), :])
                loss = loss_coarse + 0.1 * loss_fine

(ML) chriskafka@bigshot:~/PycharmProjects/ASFM-Net-Review$ python main_pcn.py --test --backbone
cuda available  True
Loaded compiled 3D CUDA chamfer distance
Test[1200/1200] Taxonomy = 04530566 Sample = 294644520ccc2ce27795dd28016933fc Losses = ['7.0097', '5.2598', '7.5357'] Metrics = ['0.0053']: 100%|████████████████████████████████████████████████████████████████████████████████| 1200/1200 [02:24<00:00,  8.31it/s]
============================ TEST RESULTS ============================
Taxonomy        #Sample ChamferDistance
02691156        150     0.0056
02933112        150     0.0108
02958343        150     0.0091
03001627        150     0.0102
03636649        150     0.0126
04256520        150     0.0104
04379243        150     0.0099
04530566        150     0.0091
Overall                 0.0097

Epoch  -1       12.8742 9.6951  13.8437

                    4096            16384