Spaces:
Runtime error
Runtime error
File size: 6,207 Bytes
19c4ddf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
from typing import Any, Dict, Optional, Tuple
import torch
from shap_e.models.nn.ops import get_act
from shap_e.models.query import Query
from shap_e.models.stf.mlp import MLPModel
from shap_e.util.collections import AttrDict
class MLPDensitySDFModel(MLPModel):
def __init__(
self,
initial_bias: float = -0.1,
sdf_activation="tanh",
density_activation="exp",
**kwargs,
):
super().__init__(
n_output=2,
output_activation="identity",
**kwargs,
)
self.mlp[-1].bias[0].data.fill_(initial_bias)
self.sdf_activation = get_act(sdf_activation)
self.density_activation = get_act(density_activation)
def forward(
self,
query: Query,
params: Optional[Dict[str, torch.Tensor]] = None,
options: Optional[Dict[str, Any]] = None,
) -> AttrDict[str, Any]:
# query.direction is None typically for SDF models and training
h, _h_directionless = self._mlp(
query.position, query.direction, params=params, options=options
)
h_sdf, h_density = h.split(1, dim=-1)
return AttrDict(
density=self.density_activation(h_density),
signed_distance=self.sdf_activation(h_sdf),
)
class MLPNeRSTFModel(MLPModel):
def __init__(
self,
sdf_activation="tanh",
density_activation="exp",
channel_activation="sigmoid",
direction_dependent_shape: bool = True, # To be able to load old models. Set this to be False in future models.
separate_nerf_channels: bool = False,
separate_coarse_channels: bool = False,
initial_density_bias: float = 0.0,
initial_sdf_bias: float = -0.1,
**kwargs,
):
h_map, h_directionless_map = indices_for_output_mode(
direction_dependent_shape=direction_dependent_shape,
separate_nerf_channels=separate_nerf_channels,
separate_coarse_channels=separate_coarse_channels,
)
n_output = index_mapping_max(h_map)
super().__init__(
n_output=n_output,
output_activation="identity",
**kwargs,
)
self.direction_dependent_shape = direction_dependent_shape
self.separate_nerf_channels = separate_nerf_channels
self.separate_coarse_channels = separate_coarse_channels
self.sdf_activation = get_act(sdf_activation)
self.density_activation = get_act(density_activation)
self.channel_activation = get_act(channel_activation)
self.h_map = h_map
self.h_directionless_map = h_directionless_map
self.mlp[-1].bias.data.zero_()
layer = -1 if self.direction_dependent_shape else self.insert_direction_at
self.mlp[layer].bias[0].data.fill_(initial_sdf_bias)
self.mlp[layer].bias[1].data.fill_(initial_density_bias)
def forward(
self,
query: Query,
params: Optional[Dict[str, torch.Tensor]] = None,
options: Optional[Dict[str, Any]] = None,
) -> AttrDict[str, Any]:
options = AttrDict() if options is None else AttrDict(options)
h, h_directionless = self._mlp(
query.position, query.direction, params=params, options=options
)
activations = map_indices_to_keys(self.h_map, h)
activations.update(map_indices_to_keys(self.h_directionless_map, h_directionless))
if options.nerf_level == "coarse":
h_density = activations.density_coarse
else:
h_density = activations.density_fine
if options.get("rendering_mode", "stf") == "nerf":
if options.nerf_level == "coarse":
h_channels = activations.nerf_coarse
else:
h_channels = activations.nerf_fine
else:
h_channels = activations.stf
return AttrDict(
density=self.density_activation(h_density),
signed_distance=self.sdf_activation(activations.sdf),
channels=self.channel_activation(h_channels),
)
IndexMapping = AttrDict[str, Tuple[int, int]]
def indices_for_output_mode(
direction_dependent_shape: bool,
separate_nerf_channels: bool,
separate_coarse_channels: bool,
) -> Tuple[IndexMapping, IndexMapping]:
"""
Get output mappings for (h, h_directionless).
"""
h_map = AttrDict()
h_directionless_map = AttrDict()
if direction_dependent_shape:
h_map.sdf = (0, 1)
if separate_coarse_channels:
assert separate_nerf_channels
h_map.density_coarse = (1, 2)
h_map.density_fine = (2, 3)
h_map.stf = (3, 6)
h_map.nerf_coarse = (6, 9)
h_map.nerf_fine = (9, 12)
else:
h_map.density_coarse = (1, 2)
h_map.density_fine = (1, 2)
if separate_nerf_channels:
h_map.stf = (2, 5)
h_map.nerf_coarse = (5, 8)
h_map.nerf_fine = (5, 8)
else:
h_map.stf = (2, 5)
h_map.nerf_coarse = (2, 5)
h_map.nerf_fine = (2, 5)
else:
h_directionless_map.sdf = (0, 1)
h_directionless_map.density_coarse = (1, 2)
if separate_coarse_channels:
h_directionless_map.density_fine = (2, 3)
else:
h_directionless_map.density_fine = h_directionless_map.density_coarse
h_map.stf = (0, 3)
if separate_coarse_channels:
assert separate_nerf_channels
h_map.nerf_coarse = (3, 6)
h_map.nerf_fine = (6, 9)
else:
if separate_nerf_channels:
h_map.nerf_coarse = (3, 6)
else:
h_map.nerf_coarse = (0, 3)
h_map.nerf_fine = h_map.nerf_coarse
return h_map, h_directionless_map
def map_indices_to_keys(mapping: IndexMapping, data: torch.Tensor) -> AttrDict[str, torch.Tensor]:
return AttrDict({k: data[..., start:end] for k, (start, end) in mapping.items()})
def index_mapping_max(mapping: IndexMapping) -> int:
return max(end for _, (_, end) in mapping.items())
|