Spaces:
Runtime error
Runtime error
File size: 3,880 Bytes
c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 c24da45 31ec2b7 |
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 |
# import torch
# import torch.nn as nn
# import nvdiffrast.torch as dr
# from util.flexicubes_geometry import FlexiCubesGeometry
# class Renderer(nn.Module):
# def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type):
# super().__init__()
# self.tet_grid_size = tet_grid_size
# self.camera_angle_num = camera_angle_num
# self.scale = scale
# self.geo_type = geo_type
# self.glctx = dr.RasterizeCudaContext()
# if self.geo_type == "flex":
# self.flexicubes = FlexiCubesGeometry(grid_res = self.tet_grid_size)
# def forward(self, data, sdf, deform, verts, tets, training=False, weight = None):
# results = {}
# deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95
# if self.geo_type == "flex":
# deform = deform *0.5
# v_deformed = verts + deform
# verts_list = []
# faces_list = []
# reg_list = []
# n_shape = verts.shape[0]
# for i in range(n_shape):
# verts_i, faces_i, reg_i = self.flexicubes.get_mesh(v_deformed[i], sdf[i].squeeze(dim=-1),
# with_uv=False, indices=tets, weight_n=weight[i], is_training=training)
# verts_list.append(verts_i)
# faces_list.append(faces_i)
# reg_list.append(reg_i)
# verts = verts_list
# faces = faces_list
# flexicubes_surface_reg = torch.cat(reg_list).mean()
# flexicubes_weight_reg = (weight ** 2).mean()
# results["flex_surf_loss"] = flexicubes_surface_reg
# results["flex_weight_loss"] = flexicubes_weight_reg
# return results, verts, faces
import torch
import torch.nn as nn
# import nvdiffrast.torch as dr # Comentado porque no se usará en CPU
from util.flexicubes_geometry import FlexiCubesGeometry
class Renderer(nn.Module):
def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type):
super().__init__()
self.tet_grid_size = tet_grid_size
self.camera_angle_num = camera_angle_num
self.scale = scale
self.geo_type = geo_type
# Eliminar el contexto de GPU y usar una alternativa o desactivarlo
# self.glctx = dr.RasterizeCudaContext() # Comentado porque se usa GPU
if self.geo_type == "flex":
self.flexicubes = FlexiCubesGeometry(grid_res=self.tet_grid_size)
def forward(self, data, sdf, deform, verts, tets, training=False, weight=None):
results = {}
deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95
if self.geo_type == "flex":
deform = deform * 0.5
v_deformed = verts + deform
verts_list = []
faces_list = []
reg_list = []
n_shape = verts.shape[0]
for i in range(n_shape):
# Aquí deberás adaptar el uso de FlexiCubesGeometry para que funcione sin GPU.
verts_i, faces_i, reg_i = self.flexicubes.get_mesh(
v_deformed[i], sdf[i].squeeze(dim=-1),
with_uv=False, indices=tets, weight_n=weight[i], is_training=training
)
verts_list.append(verts_i)
faces_list.append(faces_i)
reg_list.append(reg_i)
verts = verts_list
faces = faces_list
flexicubes_surface_reg = torch.cat(reg_list).mean()
flexicubes_weight_reg = (weight ** 2).mean()
results["flex_surf_loss"] = flexicubes_surface_reg
results["flex_weight_loss"] = flexicubes_weight_reg
return results, verts, faces
|