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import sys
import random
import torch
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
def force_unload_conflicts():
conflicts = ['render', 'scene', 'utils', 'arguments', 'model']
for name in list(sys.modules.keys()):
parts = name.split('.')
if parts[0] in conflicts:
del sys.modules[name]
@register_method("bgtriangle")
class BGTriangleWrapper(BaseMethod):
def __init__(self, dataset_config, hyperparams):
force_unload_conflicts()
self.method_path = os.path.abspath("/root/autodl-tmp/bg_triangle_official")
if self.method_path not in sys.path:
sys.path.insert(0, self.method_path)
try:
from utils.loss_utils import l1_loss, ssim
from arguments import ModelParams, PipelineParams, OptimizationParams
from model.bprimitive_bezier import BPrimitiveBezier
from render.renderer import Renderer
from scene import Scene
self.l1_loss = l1_loss
self.ssim = ssim
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.eval = True
self.args.resolution = dataset_config.get("resolution", 1)
self.track_decoupling = hyperparams.get("track_decoupling", False)
self.dataset = self.lp.extract(self.args)
self.opt = self.op.extract(self.args)
self.pipe = self.pp.extract(self.args)
self.order = hyperparams.get("order", 1)
self.bprimitive = BPrimitiveBezier(self.order, self.dataset.sh_degree)
self.bprimitive.boundary_mode = 1
# 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.bprimitive)
self.bprimitive.training_setup(self.opt)
N = self.bprimitive.control_point.shape[0]
if not hasattr(self.bprimitive, 'vis_map'):
self.bprimitive.vis_map = torch.zeros((N, 6, 6), device="cuda")
if not hasattr(self.bprimitive, 'vis_accum'):
self.bprimitive.vis_accum = torch.zeros((N, 1), device="cuda")
if not hasattr(self.bprimitive, 'denom_vis'):
self.bprimitive.denom_vis = torch.zeros((N, 1), device="cuda")
if not hasattr(self.bprimitive, 'edge_accum'):
self.bprimitive.edge_accum = torch.zeros((N, 1), device="cuda")
if not hasattr(self.bprimitive, 'denom_edge'):
self.bprimitive.denom_edge = torch.zeros((N, 1), device="cuda")
if not hasattr(self.bprimitive, 'gradient_accum'):
self.bprimitive.gradient_accum = torch.zeros((N, 1), device="cuda")
if not hasattr(self.bprimitive, 'denom'):
self.bprimitive.denom = torch.zeros((N, 1), device="cuda")
self.renderer = Renderer()
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 = self.scene.getTrainCameras().copy()
self.last_n_primitives = self.bprimitive.control_point.shape[0]
finally:
if self.method_path in sys.path:
sys.path.remove(self.method_path)
def train_iteration(self, step):
force_unload_conflicts()
if self.method_path not in sys.path:
sys.path.insert(0, self.method_path)
if not self.viewpoint_stack:
self.viewpoint_stack = self.scene.getTrainCameras().copy()
viewpoint_cam = self.viewpoint_stack.pop(random.randint(0, len(self.viewpoint_stack) - 1))
render_pkg, debug_info = self.renderer(
self.bprimitive, viewpoint_cam, self.background, None,
self.pipe.num_segments_per_bprimitive_edge, -5.0, 1.0, "version_1"
)
image = render_pkg
gt_image = viewpoint_cam.original_image.cuda()
loss_l1 = self.l1_loss(image, gt_image)
loss_ssim = 1.0 - self.ssim(image, gt_image)
loss_target = (1.0 - 0.2) * loss_l1
loss_parasitic = 0.2 * loss_ssim
loss = loss_target + loss_parasitic
grad_cos_sim = 0.0
parasitic_ratio = 0.0
if self.track_decoupling and step % 100 == 0:
self.bprimitive.optimizer.zero_grad(set_to_none=True)
loss_target.backward(retain_graph=True)
# 使用 reshape 替换 view
grad_target = self.bprimitive.control_point.grad.clone().reshape(self.bprimitive.control_point.shape[0], -1) if self.bprimitive.control_point.grad is not None else torch.zeros_like(self.bprimitive.control_point).reshape(self.bprimitive.control_point.shape[0], -1)
self.bprimitive.optimizer.zero_grad(set_to_none=True)
loss_parasitic.backward(retain_graph=True)
# 使用 reshape 替换 view
grad_parasitic = self.bprimitive.control_point.grad.clone().reshape(self.bprimitive.control_point.shape[0], -1) if self.bprimitive.control_point.grad is not None else torch.zeros_like(self.bprimitive.control_point).reshape(self.bprimitive.control_point.shape[0], -1)
valid_mask = (torch.norm(grad_target, dim=1) > 0) & (torch.norm(grad_parasitic, dim=1) > 0)
if valid_mask.any():
grad_cos_sim = float(F.cosine_similarity(grad_target[valid_mask], grad_parasitic[valid_mask], dim=1).mean())
parasitic_ratio = float(torch.norm(grad_parasitic, dim=1).mean() / (torch.norm(grad_target, dim=1).mean() + 1e-7))
self.bprimitive.optimizer.zero_grad(set_to_none=True)
loss.backward()
else:
loss.backward()
with torch.no_grad():
self.bprimitive.optimizer.step()
self.bprimitive.optimizer.zero_grad(set_to_none=True)
num_primitives = self.bprimitive.control_point.shape[0]
metrics = {
"loss": float(loss),
"loss_l1": float(loss_target),
"loss_ssim": float(loss_parasitic),
"num_gaussians": int(num_primitives),
"delta_N": int(num_primitives - self.last_n_primitives),
"peak_vram_GB": float(torch.cuda.max_memory_allocated() / (1024 ** 3)),
"grad_cos_sim": float(grad_cos_sim),
"parasitic_ratio": float(parasitic_ratio)
}
self.last_n_primitives = num_primitives
histograms = {}
if step % 1000 == 0:
histograms["opacity"] = torch.sigmoid(self.bprimitive.opacity).clone().detach()
scales = torch.exp(self.bprimitive.scaling).clone().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.bprimitive.control_point_dc.detach().norm(dim=-1).mean(dim=1)
if self.method_path in sys.path:
sys.path.remove(self.method_path)
return metrics, histograms
def render(self, camera):
force_unload_conflicts()
if self.method_path not in sys.path:
sys.path.insert(0, self.method_path)
with torch.no_grad():
image, _ = self.renderer(
self.bprimitive, camera, self.background, None,
self.pipe.num_segments_per_bprimitive_edge, -5.0, 1.0, "version_1"
)
if self.method_path in sys.path:
sys.path.remove(self.method_path)
return {"image": image, "depth": None}
def save(self, save_dir, step):
force_unload_conflicts()
if self.method_path not in sys.path:
sys.path.insert(0, self.method_path)
self.scene.save(step)
if self.method_path in sys.path:
sys.path.remove(self.method_path)
def load(self, model_path, iteration):
pass
def get_spatial_centers(self):
return self.bprimitive.control_point.mean(dim=1)
def compute_physical_metrics(self, cameras=None):
metrics = {}
with torch.no_grad():
raw_scales = self.bprimitive.scaling
scales = torch.exp(raw_scales)
scales_2d = scales[:, :2] if scales.dim() > 1 and scales.shape[1] >= 2 else scales.unsqueeze(-1).expand(-1, 2)
max_S, _ = torch.max(scales_2d, dim=1)
min_S, _ = torch.min(scales_2d, dim=1)
gamma = max_S / (min_S + 1e-7)
metrics["gamma_median"] = float(torch.median(gamma))
metrics["gamma_90th_percentile"] = float(torch.quantile(gamma, 0.90))
metrics["scale_mean"] = float(torch.mean(scales_2d))
metrics["alpha_mean"] = float(torch.mean(torch.sigmoid(self.bprimitive.opacity)))
dc = self.bprimitive.control_point_dc
rest = self.bprimitive.control_point_rest
if rest is not None and rest.shape[2] > 0:
metrics["sh_energy_ratio"] = float(rest.norm(dim=-1).mean() / (dc.norm(dim=-1).mean() + 1e-7))
return metrics
def evaluate_spatial_field(self, query_points: torch.Tensor, cameras=None) -> torch.Tensor:
with torch.no_grad():
V = query_points.shape[0]
densities = torch.zeros(V, device="cuda")
xyz = self.bprimitive.control_point.mean(dim=1)
opacities = torch.sigmoid(self.bprimitive.opacity).squeeze()
scales = torch.exp(self.bprimitive.scaling)
sigma_sq = (scales[:, :2].max(dim=1)[0].pow(2)) if scales.shape[1] >= 2 else scales.squeeze().pow(2)
N_gaussians = xyz.shape[0]
chunk_size = max(1, 30_000_000 // (N_gaussians + 1))
for i in range(0, V, chunk_size):
end = min(i + chunk_size, V)
dist_sq = torch.cdist(query_points[i:end], xyz, p=2).pow(2)
weights = torch.exp(-0.5 * dist_sq / (sigma_sq.unsqueeze(0) + 1e-7))
densities[i:end] = torch.sum(weights * opacities.unsqueeze(0), dim=1)
return densities
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