-
Notifications
You must be signed in to change notification settings - Fork 86
/
project_gaussians.cpp
123 lines (107 loc) · 4.57 KB
/
project_gaussians.cpp
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
#include "project_gaussians.hpp"
#if defined(USE_HIP) || defined(USE_CUDA) || defined(USE_MPS)
variable_list ProjectGaussians::forward(AutogradContext *ctx,
torch::Tensor means,
torch::Tensor scales,
float globScale,
torch::Tensor quats,
torch::Tensor viewMat,
torch::Tensor projMat,
float fx,
float fy,
float cx,
float cy,
int imgHeight,
int imgWidth,
TileBounds tileBounds,
float clipThresh
){
int numPoints = means.size(0);
auto t = project_gaussians_forward_tensor(numPoints, means, scales, globScale,
quats, viewMat, projMat, fx, fy,
cx, cy, imgHeight, imgWidth, tileBounds, clipThresh);
torch::Tensor cov3d = std::get<0>(t);
torch::Tensor xys = std::get<1>(t);
torch::Tensor depths = std::get<2>(t);
torch::Tensor radii = std::get<3>(t);
torch::Tensor conics = std::get<4>(t);
torch::Tensor numTilesHit = std::get<5>(t);
ctx->saved_data["imgHeight"] = imgHeight;
ctx->saved_data["imgWidth"] = imgWidth;
ctx->saved_data["numPoints"] = numPoints;
ctx->saved_data["globScale"] = globScale;
ctx->saved_data["fx"] = fx;
ctx->saved_data["fy"] = fy;
ctx->saved_data["cx"] = cx;
ctx->saved_data["cy"] = cy;
ctx->save_for_backward({ means, scales, quats, viewMat, projMat, cov3d, radii, conics });
return { xys, depths, radii, conics, numTilesHit, cov3d };
}
tensor_list ProjectGaussians::backward(AutogradContext *ctx, tensor_list grad_outputs) {
torch::Tensor v_xys = grad_outputs[0];
torch::Tensor v_depths = grad_outputs[1];
torch::Tensor v_radii = grad_outputs[2];
torch::Tensor v_conics = grad_outputs[3];
torch::Tensor v_numTiles = grad_outputs[4];
torch::Tensor v_cov3d = grad_outputs[5];
variable_list saved = ctx->get_saved_variables();
torch::Tensor means = saved[0];
torch::Tensor scales = saved[1];
torch::Tensor quats = saved[2];
torch::Tensor viewMat = saved[3];
torch::Tensor projMat = saved[4];
torch::Tensor cov3d = saved[5];
torch::Tensor radii = saved[6];
torch::Tensor conics = saved[7];
auto t = project_gaussians_backward_tensor(ctx->saved_data["numPoints"].toInt(),
means, scales, ctx->saved_data["globScale"].toDouble(),
quats, viewMat, projMat,
ctx->saved_data["fx"].toDouble(), ctx->saved_data["fy"].toDouble(),
ctx->saved_data["cx"].toDouble(), ctx->saved_data["cy"].toDouble(),
ctx->saved_data["imgHeight"].toInt(), ctx->saved_data["imgWidth"].toInt(),
cov3d, radii,
conics, v_xys, v_depths, v_conics);
torch::Tensor none;
return {std::get<2>(t), // v_mean
std::get<3>(t), // v_scale
none, // globScale
std::get<4>(t), // v_quat
none, // viewMat
none, // projMat
none, // fx
none, // fy
none, // cx
none, // cy
none, // imgHeight
none, // imgWidth
none, // tileBounds
none // clipThresh
};
}
#endif
variable_list ProjectGaussiansCPU::apply(
torch::Tensor means,
torch::Tensor scales,
float globScale,
torch::Tensor quats,
torch::Tensor viewMat,
torch::Tensor projMat,
float fx,
float fy,
float cx,
float cy,
int imgHeight,
int imgWidth,
float clipThresh
){
int numPoints = means.size(0);
auto t = project_gaussians_forward_tensor_cpu(numPoints, means, scales, globScale,
quats, viewMat, projMat, fx, fy,
cx, cy, imgHeight, imgWidth, clipThresh);
torch::Tensor xys = std::get<0>(t);
torch::Tensor radii = std::get<1>(t);
torch::Tensor conics = std::get<2>(t);
torch::Tensor cov2d = std::get<3>(t);
torch::Tensor camDepths = std::get<4>(t);
return { xys, radii, conics, cov2d, camDepths };
}