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3718945 | 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 | from diffusionGS.models.gsrenderer.gs_core import (
GaussianModel,
render_opencv_cam,
render_turntable,
RGB2SH,
deferred_gaussian_render,
deferred_gaussian_render_scene,
imageseq2video,
)
import torch
import torch.nn as nn
import torch.nn.functional as F
from easydict import EasyDict as edict
'''
调用时输入的是 Gaussian Model 的参数
随后 init Gaussian model, 用 set_data 传参
最后调用 render_opencv_cam 渲染
返回的是渲染出来的 multi-view images
'''
class Renderer(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.scaling_modifier = None #onfig.model.gaussians.get("scaling_modifier", None)
self.gaussians_model = GaussianModel(
config.gaussians_sh_degree, self.scaling_modifier
)
print(f"in Renderer, scaling_modifier: {self.scaling_modifier}")
print(
f"in Renderer, gaussians_model.scaling_modifier: {self.gaussians_model.scaling_modifier}"
)
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
def forward(
self,
xyz,
features,
scaling,
rotation,
opacity,
height,
width,
C2W,
fxfycxcy,
deferred=True,
):
"""
高斯常规的一些 attribute
xyz: [b, n_gaussians, 3]
features: [b, n_gaussians, (sh_degree+1)^2, 3]
scaling: [b, n_gaussians, 3]
rotation: [b, n_gaussians, 4]
opacity: [b, n_gaussians, 1]
height: int
width: int
C2W: [b, v, 4, 4]
fxfycxcy: [b, v, 4]
output: [b, v, 3, height, width]
"""
if deferred:
renderings = deferred_gaussian_render(
xyz,
features,
scaling,
rotation,
opacity,
height,
width,
C2W,
fxfycxcy,
self.scaling_modifier,
self.config.use_gssplat,
)
else:
b, v = C2W.size(0), C2W.size(1)
renderings = torch.zeros(
b, v, 3, height, width, dtype=torch.float32, device=xyz.device
)
for i in range(b):
pc = self.gaussians_model.set_data(
xyz[i], features[i], scaling[i], rotation[i], opacity[i]
)
for j in range(v):
renderings[i, j] = render_opencv_cam(
pc, height, width, C2W[i, j], fxfycxcy[i, j]
)["render"]
return renderings
class SceneRenderer(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
#self.sh_degree = config.model.gaussians.sh_degree
self.scaling_modifier = None #config.model.gaussians.get("scaling_modifier", None)
self.gaussians_model = GaussianModel(
config.gaussians_sh_degree, self.scaling_modifier
)
print(f"in Renderer, scaling_modifier: {self.scaling_modifier}")
print(f"in Renderer, gaussians_model.scaling_modifier: {self.gaussians_model.scaling_modifier}")
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
def forward(
self,
xyz,
features,
scaling,
rotation,
opacity,
height,
width,
C2W,
fxfycxcy,
defer_render=False,
):
"""
xyz: [b, n_gaussians, 3]
features: [b, n_gaussians, (sh_degree+1)^2, 3]
scaling: [b, n_gaussians, 3]
rotation: [b, n_gaussians, 4]
opacity: [b, n_gaussians, 1]
height: int
width: int
C2W: [b, v, 4, 4]
fxfycxcy: [b, v, 4]
output: [b, v, 3, height, width]
"""
if defer_render == True:
# if False:
renderings, depth, alpha = deferred_gaussian_render_scene(
xyz, features, scaling, rotation, opacity, height, width, C2W, fxfycxcy, self.scaling_modifier
)
else:
b, v = C2W.size(0), C2W.size(1)
renderings = torch.zeros(
b, v, 3, height, width, dtype=torch.float32, device=xyz.device
)
depth = torch.zeros(
b, v, 1, height, width, dtype=torch.float32, device=xyz.device
)
alpha = torch.zeros(
b, v, 1, height, width, dtype=torch.float32, device=xyz.device
)
for i in range(b):
pc = self.gaussians_model.set_data(
xyz[i], features[i], scaling[i], rotation[i], opacity[i]
)
for j in range(v):
# renderings[i, j] = render_opencv_cam(
# pc, height, width, C2W[i, j], fxfycxcy[i, j]
# )["render"]
buffers = render_opencv_cam(
pc, height, width, C2W[i, j], fxfycxcy[i, j]
)
renderings[i, j] = buffers["render"]
if "depth" in buffers and buffers["depth"] is not None:
depth[i, j] = buffers["depth"]
if "alpha" in buffers and buffers["alpha"] is not None:
alpha[i, j] = buffers["alpha"]
# return renderings
return renderings #edict(render=renderings, depth=depth, alpha=alpha) |