| import os |
| import sys |
| import random |
| import torch |
| import torch.nn.functional as F |
| from argparse import ArgumentParser |
|
|
| from core.registry import register_method |
| from core.base_method import BaseMethod |
|
|
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../CompGS_offy'))) |
| from Modules.TrainerCompGS import TrainerCompGS |
| from Modules.Common import RenderSettings |
| from pytorch_msssim import ssim |
|
|
| @register_method("compgs") |
| class CompGSWrapper(BaseMethod): |
| def __init__(self, dataset_config, hyperparams): |
| self.source_path = dataset_config["source_path"] |
| self.model_path = dataset_config["model_path"] |
| self.resolution = dataset_config.get("resolution", 1) |
| self.track_decoupling = hyperparams.get("track_decoupling", False) |
|
|
| dummy_config = os.path.join(os.path.dirname(__file__), '../../CompGS_offy/Configs/TanksAndTemplates.yaml') |
| import yaml |
| base_cfg_path = "/root/autodl-tmp/CompGS_offy/Configs/TanksAndTemplates.yaml" |
| with open(base_cfg_path, "r") as f: |
| cfg = yaml.safe_load(f) |
| |
| |
| cfg["dataset"]["root"] = dataset_config["source_path"] |
| cfg["training"]["save_directory"] = dataset_config["model_path"] |
| |
| |
| iters = hyperparams.get("iterations", 30000) |
| cfg["training"]["max_iterations"] = iters |
| |
| os.makedirs(dataset_config["model_path"], exist_ok=True) |
| runtime_cfg_path = os.path.join(dataset_config["model_path"], "runtime_config.yaml") |
| with open(runtime_cfg_path, "w") as f: |
| yaml.dump(cfg, f) |
| |
| self.trainer = TrainerCompGS(config_path=runtime_cfg_path, override_cfgs={}) |
| self.gaussian_model = self.trainer.gaussian_model |
| self.dataset = self.trainer.dataset |
| self.optimizer = self.trainer.gaussian_optimizer |
| self.aux_optimizer = self.trainer.aux_optimizer |
| |
| self.last_n_gaussians = self.gaussian_model.num_coupled_primitive |
|
|
| def train_iteration(self, step): |
| self.trainer.gaussian_lr_scheduler(iteration=step) |
| |
| sample = self.dataset[random.randint(0, len(self.dataset) - 1)] |
| |
| render_settings = RenderSettings( |
| cam_idx=sample.cam_idx, image_height=sample.image_height, image_width=sample.image_width, |
| tanfovx=sample.tan_half_fov_x, tanfovy=sample.tan_half_fov_y, campos=sample.camera_center, |
| viewmatrix=sample.world_to_view_proj_mat, projmatrix=sample.world_to_image_proj_mat) |
|
|
| retain_grad = step < self.trainer.configs['adaptive_control']['stop_iteration'] |
| render_results = self.gaussian_model.render(render_settings=render_settings, retain_grad=retain_grad) |
|
|
| ssim_weight = self.trainer.configs['training']['ssim_weight'] |
| l1_loss = F.l1_loss(render_results.rendered_img, sample.img) |
| ssim_loss = 1 - ssim(render_results.rendered_img.unsqueeze(dim=0), sample.img.unsqueeze(dim=0), data_range=1., size_average=True) |
| rendering_loss = (1 - ssim_weight) * l1_loss + ssim_weight * ssim_loss |
|
|
| reg_loss = 0.01 * render_results.scales.prod(dim=1).mean() |
| bpp = render_results.bpp |
| rate_loss = self.trainer.configs['training']['lambda_weight'] * sum(v for v in bpp.values()) if step > self.trainer.configs['training']['rate_loss_start_iteration'] else torch.tensor(0.0, device=self.trainer.device) |
|
|
| loss_target = rendering_loss |
| loss_parasitic = reg_loss + rate_loss |
| loss = loss_target + loss_parasitic |
|
|
| grad_cos_sim = 0.0 |
| parasitic_ratio = 0.0 |
| stats = {} |
|
|
| if self.track_decoupling and step % 100 == 0: |
| params = self.gaussian_model.gaussian_params |
| param_groups_map = { |
| "spatial": [params.means], |
| "geometry": [params.scales_before_exp, params.rotations_before_norm], |
| "opacity": [params.ref_feats], |
| "appearance": [params.res_feats], |
| } |
|
|
| self.optimizer.zero_grad(set_to_none=True) |
| self.aux_optimizer.zero_grad(set_to_none=True) |
| loss_target.backward(retain_graph=True) |
| |
| grad_target = params.means.grad.clone() if params.means.grad is not None else torch.zeros_like(params.means) |
| grads_target = {} |
| for group_name, plist in param_groups_map.items(): |
| grads_target[group_name] = torch.cat([p.grad.clone().reshape(-1) for p in plist if p.grad is not None]) |
|
|
| self.optimizer.zero_grad(set_to_none=True) |
| self.aux_optimizer.zero_grad(set_to_none=True) |
| loss_parasitic.backward(retain_graph=True) |
| |
| grad_parasitic = params.means.grad.clone() if params.means.grad is not None else torch.zeros_like(params.means) |
| grads_parasitic = {} |
| for group_name, plist in param_groups_map.items(): |
| grads_parasitic[group_name] = torch.cat([p.grad.clone().reshape(-1) for p in plist if p.grad is not None]) |
|
|
| 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)) |
|
|
| for group_name in param_groups_map: |
| gt, gp = grads_target.get(group_name), grads_parasitic.get(group_name) |
| if gt is not None and gp is not None and gt.norm() > 0 and gp.norm() > 0: |
| cos = float(F.cosine_similarity(gt.unsqueeze(0), gp.unsqueeze(0))) |
| r = float(gp.norm() / (gt.norm() + gp.norm() + 1e-7)) |
| ti = r * max(0.0, -cos) |
| else: |
| ti = 0.0 |
| stats[f"sti_{group_name}"] = ti |
|
|
| self.optimizer.zero_grad(set_to_none=True) |
| self.aux_optimizer.zero_grad(set_to_none=True) |
| loss.backward() |
| aux_loss = self.gaussian_model.aux_loss |
| aux_loss.backward() |
| else: |
| loss.backward() |
| aux_loss = self.gaussian_model.aux_loss |
| aux_loss.backward() |
|
|
| self.trainer.optimize(iteration=step, render_results=render_results) |
|
|
| num_gaussians = self.gaussian_model.num_coupled_primitive |
| metrics = { |
| "loss": float(loss), "loss_l1": float(l1_loss), "loss_ssim": float(ssim_loss), |
| "num_gaussians": int(num_gaussians), "delta_N": int(num_gaussians - self.last_n_gaussians), |
| "peak_vram_GB": float(torch.cuda.max_memory_allocated() / (1024 ** 3)), |
| "grad_cos_sim": float(grad_cos_sim), "parasitic_ratio": float(parasitic_ratio) |
| } |
| metrics.update(stats) |
| self.last_n_gaussians = num_gaussians |
| |
| histograms = {} |
| if step % 1000 == 0: |
| histograms["scaling"] = torch.exp(self.gaussian_model.gaussian_params.scales_before_exp).clone().detach() |
| scales_2d = histograms["scaling"][:, :2] |
| histograms["anisotropy"] = scales_2d.max(dim=-1)[0] / (scales_2d.min(dim=-1)[0] + 1e-7) |
| histograms["sh_dc_mag"] = self.gaussian_model.gaussian_params.ref_feats.detach().norm(dim=-1) |
|
|
| return metrics, histograms |
| |
| def render(self, camera): |
| with torch.no_grad(): |
| render_settings = RenderSettings( |
| cam_idx=camera.cam_idx, image_height=camera.image_height, image_width=camera.image_width, |
| tanfovx=camera.tan_half_fov_x, tanfovy=camera.tan_half_fov_y, campos=camera.camera_center, |
| viewmatrix=camera.world_to_view_proj_mat, projmatrix=camera.world_to_image_proj_mat) |
| rendered_img, _, _ = self.gaussian_model.render_inference(render_settings=render_settings) |
| return {"image": rendered_img, "depth": None} |
|
|
| def save(self, save_dir, step): |
| ckpt_folder = os.path.join(save_dir, f'iteration_{step}') |
| os.makedirs(ckpt_folder, exist_ok=True) |
| self.gaussian_model.save_uncompressed_params(os.path.join(ckpt_folder, 'point_cloud.ply')) |
| self.gaussian_model.save_weights(os.path.join(ckpt_folder, 'weights.pth')) |
|
|
| def load(self, model_path, iteration): |
| ckpt_folder = os.path.join(model_path, f'iteration_{iteration}') |
| self.gaussian_model.load_uncompressed_params(os.path.join(ckpt_folder, 'point_cloud.ply')) |
| self.gaussian_model.load_weights(os.path.join(ckpt_folder, 'weights.pth')) |
|
|
| def get_spatial_centers(self): |
| return self.gaussian_model.means |
|
|
| def compute_physical_metrics(self, cameras=None): |
| metrics = {} |
| with torch.no_grad(): |
| scales = torch.exp(self.gaussian_model.gaussian_params.scales_before_exp) |
| 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)) |
| |
| ref_f = self.gaussian_model.gaussian_params.ref_feats |
| res_f = self.gaussian_model.gaussian_params.res_feats |
| if res_f is not None and res_f.shape[1] > 0: |
| metrics["sh_energy_ratio"] = float(res_f.norm(dim=-1).mean() / (ref_f.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=self.trainer.device) |
| xyz = self.gaussian_model.means |
| scales = torch.exp(self.gaussian_model.gaussian_params.scales_before_exp) |
| 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, 30000000 // (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, dim=1) |
| return densities |
|
|
|
|
