| 4 | |
| 5 | |
| 6 | class Decoder(nn.Module): |
| 7 | def __init__(self, |
| 8 | bound=None, |
| 9 | voxel_size=None, |
| 10 | L=None, |
| 11 | F_entry=None, |
| 12 | log2_T=None, |
| 13 | b=None, |
| 14 | **kwargs): |
| 15 | super().__init__() |
| 16 | |
| 17 | self.bound = torch.FloatTensor(bound) |
| 18 | self.bound_dis = self.bound[:, 1] - self.bound[:, 0] |
| 19 | self.max_dis = torch.ceil(torch.max(self.bound_dis)) |
| 20 | N_min = int(self.max_dis / voxel_size) |
| 21 | self.hash_sdf_out = \ |
| 22 | tcnn.NetworkWithInputEncoding( |
| 23 | n_input_dims=3, n_output_dims=1, |
| 24 | encoding_config={ |
| 25 | "otype": "Grid", |
| 26 | "type": "Hash", |
| 27 | "n_levels": L, |
| 28 | "n_features_per_level": F_entry, |
| 29 | "log2_hashmap_size": log2_T, |
| 30 | "base_resolution": N_min, # 1/base_resolution is the grid_size |
| 31 | "per_level_scale": b, |
| 32 | "interpolation": "Linear" |
| 33 | }, |
| 34 | network_config={ |
| 35 | "otype": "FullyFusedMLP", |
| 36 | "activation": "ReLU", |
| 37 | "output_activation": "None", |
| 38 | "n_neurons": 64, |
| 39 | "n_hidden_la2yers": 1, |
| 40 | } |
| 41 | ) |
| 42 | |
| 43 | self.hash_color_out = \ |
| 44 | tcnn.NetworkWithInputEncoding( |
| 45 | n_input_dims=3, n_output_dims=3, |
| 46 | encoding_config={ |
| 47 | "otype": "Grid", |
| 48 | "type": "Hash", |
| 49 | "n_levels": L, |
| 50 | "n_features_per_level": F_entry, |
| 51 | "log2_hashmap_size": log2_T, |
| 52 | "base_resolution": N_min, # 1/base_resolution is the grid_size |
| 53 | "per_level_scale": b, |
| 54 | "interpolation": "Linear" |
| 55 | }, |
| 56 | network_config={ |
| 57 | "otype": "FullyFusedMLP", |
| 58 | "activation": "ReLU", |
| 59 | "output_activation": "Sigmoid", |
| 60 | "n_neurons": 64, |
| 61 | "n_hidden_layers": 2, |
| 62 | } |
| 63 | ) |