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23e73f9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import os
import sys
import torch
import random
import numpy as np
import torch.nn.functional as F
from argparse import ArgumentParser
from core.registry import register_method
from core.base_method import BaseMethod
SURFEL_ROOT = "/root/autodl-tmp/gaussian_surfels"
if SURFEL_ROOT not in sys.path:
sys.path.insert(0, SURFEL_ROOT)
@register_method("gaussian_surfel")
class SurfelWrapper(BaseMethod):
def __init__(self, dataset_config, hyperparams):
from scene import Scene, GaussianModel
from arguments import ModelParams, PipelineParams, OptimizationParams
self.parser = ArgumentParser()
self.lp = ModelParams(self.parser)
self.op = OptimizationParams(self.parser)
self.pp = PipelineParams(self.parser)
self.args = self.parser.parse_args([])
self.args.source_path = dataset_config["source_path"]
self.args.model_path = dataset_config["model_path"]
self.args.resolution = dataset_config.get("resolution", 1)
self.args.eval = True
self.args.surface = True
if any(x in self.args.source_path.lower() for x in ["tnt", "360", "tanks"]):
self.args.images = "images"
else:
self.args.images = "image"
self.dataset = self.lp.extract(self.args)
self.opt = self.op.extract(self.args)
self.pipe = self.pp.extract(self.args)
_prev_cwd = os.getcwd(); os.chdir("/root/autodl-tmp/gaussian_surfels")
self.gaussians = GaussianModel(self.dataset)
os.chdir(_prev_cwd)
# INJECTED_RES_FIX begin
import sys as _sys
_scene, _explicit_res = None, None
for _i, _a in enumerate(_sys.argv[:-1]):
_v = _sys.argv[_i + 1]
if _a == "--scene": _scene = _v
elif _a == "--source_path": _scene = _v.rstrip("/").split("/")[-1]
elif _a == "--resolution":
try: _explicit_res = int(_v)
except: pass
_OUTDOOR_360 = {"bicycle", "flowers", "garden", "stump", "treehill"}
if _explicit_res is not None and _explicit_res > 0:
_res = _explicit_res
elif _scene is not None:
_res = 4 if _scene in _OUTDOOR_360 else 2
else:
_res = None
try:
if _res is not None:
self.dataset.resolution = _res
print("[res-fix] scene=%s explicit=%s -> res=%s (%s)" % (_scene, _explicit_res, _res, __file__))
except Exception as _e:
print("[res-fix] FAILED:", _e)
# INJECTED_RES_FIX end
self.scene = Scene(self.dataset, self.gaussians, self.opt.camera_lr, shuffle=False, resolution_scales=[1, 2, 4])
self.gaussians.training_setup(self.opt)
bg_color = [1, 1, 1] if self.dataset.white_background else [0, 0, 0]
self.background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
self.viewpoint_stack = None
self.last_n_gaussians = len(self.gaussians.get_xyz)
self.track_decoupling = hyperparams.get("track_decoupling", False)
self.cap_gaussians = hyperparams.get("cap_gaussians", None)
def train_iteration(self, step):
from gaussian_renderer import render as native_render
from utils.loss_utils import l1_loss, ssim, cos_loss
from utils.image_utils import depth2normal
self.gaussians.update_learning_rate(step)
if step % 1000 == 0:
self.gaussians.oneupSHdegree()
scale = 1
if step < 2000:
scale = 4
elif step < 5000:
scale = 2
if not self.viewpoint_stack:
self.viewpoint_stack = self.scene.getTrainCameras(scale).copy()
viewpoint_cam = self.viewpoint_stack.pop(random.randint(0, len(self.viewpoint_stack) - 1))
render_pkg = native_render(viewpoint_cam, self.gaussians, self.pipe, self.background, [float('inf'), float('inf')])
image, normal, depth, opac = render_pkg["render"], render_pkg["normal"], render_pkg["depth"], render_pkg["opac"]
gt_image = viewpoint_cam.get_gtImage(self.background, self.dataset.use_mask)
mask_vis = (opac.detach() > 1e-5)
loss_target = (1.0 - self.opt.lambda_dssim) * l1_loss(image, gt_image)
loss_parasitic = self.opt.lambda_dssim * (1.0 - ssim(image, gt_image))
d2n = depth2normal(depth, mask_vis, viewpoint_cam)
normal_norm = F.normalize(normal, dim=0) * mask_vis
loss_surface = cos_loss(normal_norm, d2n)
loss = loss_target + loss_parasitic
loss += (0.01 + 0.1 * min(2 * step / self.opt.iterations, 1)) * loss_surface
grad_cos_sim = 0.0
p_ratio = 0.0
if self.track_decoupling and step % 100 == 0:
self.gaussians.optimizer.zero_grad()
loss_target.backward(retain_graph=True)
g_t = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else None
self.gaussians.optimizer.zero_grad()
loss_parasitic.backward(retain_graph=True)
g_p = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else None
if g_t is not None and g_p is not None:
mask = (g_t.norm(dim=1) > 1e-8) & (g_p.norm(dim=1) > 1e-8)
if mask.any():
grad_cos_sim = float(F.cosine_similarity(g_t[mask], g_p[mask], dim=1).mean())
p_ratio = float(g_p[mask].norm(dim=1).mean() / (g_t[mask].norm(dim=1).mean() + 1e-8))
self.gaussians.optimizer.zero_grad()
loss.backward()
else:
loss.backward()
# 修复点：包裹在 no_grad() 中，且在 optimizer.step() 之前执行
with torch.no_grad():
if step > self.opt.densify_from_iter:
self.gaussians.max_radii2D[render_pkg["visibility_filter"]] = torch.max(self.gaussians.max_radii2D[render_pkg["visibility_filter"]], render_pkg["radii"][render_pkg["visibility_filter"]])
self.gaussians.add_densification_stats(render_pkg["viewspace_points"], render_pkg["visibility_filter"])
if step % self.opt.densification_interval == 0:
self.gaussians.adaptive_prune(0.1, self.scene.cameras_extent)
if self.cap_gaussians is None or len(self.gaussians.get_xyz) < self.cap_gaussians:
self.gaussians.adaptive_densify(self.opt.densify_grad_threshold, self.scene.cameras_extent)
if step % self.opt.opacity_reset_interval == 0:
self.gaussians.reset_opacity(0.12, step)
# 统计完致密化状态后，再进行优化更新并清空梯度
self.gaussians.optimizer.step()
self.gaussians.optimizer.zero_grad(set_to_none=True)
num_gaussians = len(self.gaussians.get_xyz)
metrics = {
"loss": float(loss),
"loss_l1": float(loss_target),
"loss_ssim": float(loss_parasitic),
"num_gaussians": num_gaussians,
"delta_N": num_gaussians - self.last_n_gaussians,
"peak_vram_GB": float(torch.cuda.max_memory_allocated() / (1024**3)),
"grad_cos_sim": grad_cos_sim,
"parasitic_ratio": p_ratio
}
self.last_n_gaussians = num_gaussians
histograms = {}
if step % 1000 == 0:
histograms["opacity"] = torch.sigmoid(self.gaussians._opacity).detach()
scales = torch.exp(self.gaussians._scaling).detach()
histograms["scaling"] = scales
scales_2d = scales[:, :2] if scales.shape[1] >= 2 else scales
gamma = scales_2d.max(dim=-1)[0] / (scales_2d.min(dim=-1)[0] + 1e-7)
histograms["anisotropy"] = gamma
histograms["sh_dc_mag"] = self.gaussians._features_dc.detach().norm(dim=-1)
return metrics, histograms
def render(self, camera):
from gaussian_renderer import render as native_render
with torch.no_grad():
pkg = native_render(camera, self.gaussians, self.pipe, self.background, [float('inf'), float('inf')])
return {"image": pkg["render"], "depth": pkg["depth"]}
def save(self, save_dir, step):
self.scene.save(step)
def load(self, model_path, iteration):
self.gaussians.load_ply(os.path.join(model_path, f"point_cloud/iteration_{iteration}/point_cloud.ply"))
def get_spatial_centers(self):
return self.gaussians._xyz.detach()
def compute_physical_metrics(self, cameras=None):
scales = self.gaussians.get_scaling.detach()
return {
"gamma_median": float(torch.median(scales[:, 0] / (scales[:, 1] + 1e-7))),
"alpha_mean": float(self.gaussians.get_opacity.detach().mean()),
"z_scale_mean": float(scales[:, 2].mean())
}
def evaluate_spatial_field(self, query_points, cameras=None):
from pytorch3d.ops import knn_points
xyz = self.gaussians.get_xyz.detach()
opac = self.gaussians.get_opacity.detach()
dist_sq, _, _ = knn_points(query_points.unsqueeze(0), xyz.unsqueeze(0), K=1)
dist = torch.sqrt(dist_sq.squeeze(0))
weights = torch.exp(-0.5 * (dist / 0.01)**2)
return (weights * opac.T).sum(dim=1)
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