无法保存网格,其他一切都正常。

NeRFNetwork( (encoder): GridEncoder: input_dim=3 num_levels=16 level_dim=2 resolution=16 -> 2048 per_level_scale=1.3819 params=(6098120, 2) gridtype=hash align_corners=False interpolation=smoothstep (sigma_net): MLP( (net): ModuleList( (0): Linear(in_features=32, out_features=64, bias=True) (1): Linear(in_features=64, out_features=64, bias=True) (2): Linear(in_features=64, out_features=64, bias=True) (3): Linear(in_features=64, out_features=64, bias=True) (4): Linear(in_features=64, out_features=64, bias=True) (5): Linear(in_features=64, out_features=64, bias=True) (6): Linear(in_features=64, out_features=64, bias=True) (7): Linear(in_features=64, out_features=64, bias=True) (8): Linear(in_features=64, out_features=64, bias=True) (9): Linear(in_features=64, out_features=64, bias=True) (10): Linear(in_features=64, out_features=64, bias=True) (11): Linear(in_features=64, out_features=64, bias=True) (12): Linear(in_features=64, out_features=64, bias=True) (13): Linear(in_features=64, out_features=64, bias=True) (14): Linear(in_features=64, out_features=64, bias=True) (15): Linear(in_features=64, out_features=64, bias=True) (16): Linear(in_features=64, out_features=64, bias=True) (17): Linear(in_features=64, out_features=64, bias=True) (18): Linear(in_features=64, out_features=64, bias=True) (19): Linear(in_features=64, out_features=64, bias=True) (20): Linear(in_features=64, out_features=64, bias=True) (21): Linear(in_features=64, out_features=64, bias=True) (22): Linear(in_features=64, out_features=64, bias=True) (23): Linear(in_features=64, out_features=64, bias=True) (24): Linear(in_features=64, out_features=64, bias=True) (25): Linear(in_features=64, out_features=64, bias=True) (26): Linear(in_features=64, out_features=64, bias=True) (27): Linear(in_features=64, out_features=64, bias=True) (28): Linear(in_features=64, out_features=64, bias=True) (29): Linear(in_features=64, out_features=64, bias=True) (30): Linear(in_features=64, out_features=64, bias=True) (31): Linear(in_features=64, out_features=64, bias=True) (32): Linear(in_features=64, out_features=64, bias=True) (33): Linear(in_features=64, out_features=64, bias=True) (34): Linear(in_features=64, out_features=64, bias=True) (35): Linear(in_features=64, out_features=64, bias=True) (36): Linear(in_features=64, out_features=64, bias=True) (37): Linear(in_features=64, out_features=64, bias=True) (38): Linear(in_features=64, out_features=64, bias=True) (39): Linear(in_features=64, out_features=64, bias=True) (40): Linear(in_features=64, out_features=64, bias=True) (41): Linear(in_features=64, out_features=64, bias=True) (42): Linear(in_features=64, out_features=64, bias=True) (43): Linear(in_features=64, out_features=64, bias=True) (44): Linear(in_features=64, out_features=64, bias=True) (45): Linear(in_features=64, out_features=64, bias=True) (46): Linear(in_features=64, out_features=64, bias=True) (47): Linear(in_features=64, out_features=64, bias=True) (48): Linear(in_features=64, out_features=64, bias=True) (49): Linear(in_features=64, out_features=64, bias=True) (50): Linear(in_features=64, out_features=64, bias=True) (51): Linear(in_features=64, out_features=64, bias=True) (52): Linear(in_features=64, out_features=64, bias=True) (53): Linear(in_features=64, out_features=64, bias=True) (54): Linear(in_features=64, out_features=64, bias=True) (55): Linear(in_features=64, out_features=64, bias=True) (56): Linear(in_features=64, out_features=64, bias=True) (57): Linear(in_features=64, out_features=64, bias=True) (58): Linear(in_features=64, out_features=64, bias=True) (59): Linear(in_features=64, out_features=64, bias=True) (60): Linear(in_features=64, out_features=64, bias=True) (61): Linear(in_features=64, out_features=64, bias=True) (62): Linear(in_features=64, out_features=64, bias=True) (63): Linear(in_features=64, out_features=64, bias=True) (64): Linear(in_features=64, out_features=64, bias=True) (65): Linear(in_features=64, out_features=64, bias=True) (66): Linear(in_features=64, out_features=64, bias=True) (67): Linear(in_features=64, out_features=64, bias=True) (68): Linear(in_features=64, out_features=64, bias=True) (69): Linear(in_features=64, out_features=64, bias=True) (70): Linear(in_features=64, out_features=64, bias=True) (71): Linear(in_features=64, out_features=64, bias=True) (72): Linear(in_features=64, out_features=64, bias=True) (73): Linear(in_features=64, out_features=64, bias=True) (74): Linear(in_features=64, out_features=64, bias=True) (75): Linear(in_features=64, out_features=64, bias=True) (76): Linear(in_features=64, out_features=64, bias=True) (77): Linear(in_features=64, out_features=64, bias=True) (78): Linear(in_features=64, out_features=64, bias=True) (79): Linear(in_features=64, out_features=64, bias=True) (80): Linear(in_features=64, out_features=64, bias=True) (81): Linear(in_features=64, out_features=64, bias=True) (82): Linear(in_features=64, out_features=64, bias=True) (83): Linear(in_features=64, out_features=64, bias=True) (84): Linear(in_features=64, out_features=64, bias=True) (85): Linear(in_features=64, out_features=64, bias=True) (86): Linear(in_features=64, out_features=64, bias=True) (87): Linear(in_features=64, out_features=64, bias=True) (88): Linear(in_features=64, out_features=64, bias=True) (89): Linear(in_features=64, out_features=64, bias=True) (90): Linear(in_features=64, out_features=64, bias=True) (91): Linear(in_features=64, out_features=64, bias=True) (92): Linear(in_features=64, out_features=64, bias=True) (93): Linear(in_features=64, out_features=64, bias=True) (94): Linear(in_features=64, out_features=64, bias=True) (95): Linear(in_features=64, out_features=64, bias=True) (96): Linear(in_features=64, out_features=64, bias=True) (97): Linear(in_features=64, out_features=64, bias=True) (98): Linear(in_features=64, out_features=64, bias=True) (99): Linear(in_features=64, out_features=64, bias=True) (100): Linear(in_features=64, out_features=64, bias=True) 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