#!/usr/bin/env python3 """Causal global/high branch ablation for a RenderMe INS checkpoint.""" from __future__ import annotations import argparse import json import sys from contextlib import contextmanager from pathlib import Path from types import SimpleNamespace from typing import Iterator, Optional import torch import torch.nn.functional as F from torch import Tensor from torchvision.utils import make_grid, save_image REPOSITORY_ROOT = Path(__file__).resolve().parents[1] if str(REPOSITORY_ROOT) not in sys.path: sys.path.insert(0, str(REPOSITORY_ROOT)) from datasets.renderme_ins_dataset import RenderMeINSViewDataset from scripts.evaluate_ins_stage1_renderme import ( build_model_from_checkpoint, foreground_metrics, read_checkpoint, ) from scripts.train_ins_stage1 import ( DistributedContext, build_auxiliary_losses, resize_target, ) CONDITIONS = ("baseline", "global_zero", "high_zero") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Render baseline/global=0/high=0 from one INS checkpoint." ) parser.add_argument("--checkpoint", type=Path, required=True) parser.add_argument("--data-root", type=Path, default=None) parser.add_argument("--frame-index", type=int, default=None) parser.add_argument("--camera-id", default=None) parser.add_argument("--source-camera-id", default=None) parser.add_argument( "--mode", choices=("auto", "same-view", "fixed-source-reuse"), default="auto", ) parser.add_argument("--resolution", type=int, default=512) parser.add_argument("--amp", choices=("auto", "none", "bf16"), default="auto") parser.add_argument("--lpips", action=argparse.BooleanOptionalAction, default=True) parser.add_argument( "--lpips-net", choices=("alex", "squeeze", "vgg"), default="alex" ) parser.add_argument("--device", default="cuda:0") parser.add_argument( "--output-dir", type=Path, default=Path("/workspace/cpfs-data/tmp/ins_branch_ablation"), ) args = parser.parse_args() if not args.checkpoint.is_file(): raise FileNotFoundError(args.checkpoint) if args.data_root is not None and not args.data_root.is_dir(): raise FileNotFoundError(args.data_root) if args.frame_index is not None and args.frame_index < 0: raise ValueError("--frame-index cannot be negative.") if args.resolution <= 0: raise ValueError("--resolution must be positive.") return args def saved_args(checkpoint: dict[str, object]) -> dict[str, object]: value = checkpoint.get("args", {}) return value if isinstance(value, dict) else {} def resolve_config( args: argparse.Namespace, checkpoint: dict[str, object], ) -> dict[str, object]: saved = saved_args(checkpoint) root = args.data_root or Path( saved.get( "data_root", "/workspace/cpfs-data/data/renderme_all/0026/0026_e0", ) ) frame_index = ( int(saved.get("frame_index", 0)) if args.frame_index is None else args.frame_index ) mode = str(saved.get("mode", "same-view")) if args.mode == "auto" else args.mode if mode not in {"same-view", "fixed-source-reuse"}: raise ValueError(f"Unsupported checkpoint mode: {mode}") source_camera_id = args.source_camera_id if source_camera_id is None: source_camera_id = saved.get("source_camera_id") checkpoint_amp = str(saved.get("amp", "none")) amp = checkpoint_amp if args.amp == "auto" else args.amp if amp not in {"none", "bf16"}: raise ValueError( f"Ablation supports none/bf16, but checkpoint requested {amp!r}." ) return { "root": Path(root), "frame_index": frame_index, "mode": mode, "source_camera_id": ( None if source_camera_id is None else str(source_camera_id) ), "canonical_radius": float(saved.get("canonical_radius", 2.7)), "canonical_focus": tuple( float(value) for value in saved.get("canonical_focus", (0.0, 0.0, 0.2)) ), "background": tuple( float(value) for value in saved.get("background", (1.0, 1.0, 1.0)) ), "amp": amp, } @contextmanager def ablate_branch(model, condition: str) -> Iterator[None]: """Temporarily replace one branch output with an all-zero tensor.""" if condition not in CONDITIONS: raise ValueError(f"Unknown branch condition: {condition}") if condition == "baseline": yield return module = ( model.encoder.f_encoder if condition == "global_zero" else model.encoder.high_encoder ) def zero_output(_module, _inputs, output): if not torch.is_tensor(output): raise TypeError("INS encoder branch output must be a tensor.") return torch.zeros_like(output) handle = module.register_forward_hook(zero_output) try: yield finally: handle.remove() def masked_mean(value: Tensor, mask: Tensor) -> Tensor: if mask.shape[1] == 1 and value.shape[1] != 1: mask = mask.expand(-1, value.shape[1], -1, -1) return (value * mask).sum() / mask.sum().clamp_min(1.0) @torch.inference_mode() def evaluate(args: argparse.Namespace) -> dict[str, object]: device = torch.device(args.device) if device.type != "cuda" or not torch.cuda.is_available(): raise RuntimeError("INS branch ablation requires CUDA.") torch.cuda.set_device(device) checkpoint = read_checkpoint(args.checkpoint) config = resolve_config(args, checkpoint) model = build_model_from_checkpoint(checkpoint, args.resolution, device) dataset = RenderMeINSViewDataset( root=config["root"], frame_index=config["frame_index"], image_size=512, camera_ids=None if args.camera_id is None else [args.camera_id], source_camera_id=config["source_camera_id"], canonical_radius=config["canonical_radius"], canonical_focus=config["canonical_focus"], background=config["background"], ) target_camera_id = str(args.camera_id or dataset.source_camera_id) target_sample = dataset.get_view(target_camera_id) source_camera_id = str(dataset.source_camera_id) source_sample = dataset.get_view(source_camera_id) if config["mode"] == "same-view": encoder_sample = target_sample else: encoder_sample = source_sample encoder_image = encoder_sample["image"].unsqueeze(0).to(device) encoder_camera = encoder_sample["camera"].unsqueeze(0).to(device) render_camera = target_sample["camera"].unsqueeze(0).to(device) target_source = target_sample["image"].unsqueeze(0).to(device) target = resize_target(target_source, args.resolution) foreground_mask = target_sample["foreground_mask"].unsqueeze(0).to(device) background = torch.tensor(config["background"], device=device) context = DistributedContext( rank=0, local_rank=device.index or 0, world_size=1, distributed=False, device=device, ) metric_args = SimpleNamespace( lpips_weight=1.0 if args.lpips else 0.0, id_weight=0.0, lpips_net=args.lpips_net, ) lpips_metric, _ = build_auxiliary_losses(metric_args, context) predictions: dict[str, Tensor] = {} masks: dict[str, Tensor] = {} metrics: dict[str, dict[str, float]] = {} for condition in CONDITIONS: with ablate_branch(model, condition): with torch.autocast( device_type="cuda", dtype=torch.bfloat16, enabled=config["amp"] == "bf16", ): output = model( encoder_image, render_camera, sampling_camera=encoder_camera, encoder_camera=encoder_camera, render_resolution=args.resolution, deterministic_sampling=True, ) raw_prediction = output["image"].float().clamp(0.0, 1.0) condition_metrics, composited, resized_mask = foreground_metrics( raw_prediction, target, foreground_mask, background, lpips_metric, identity_metric=None, ) gaussians = output["gaussians"] condition_metrics.update( { "opacity_mean": float(gaussians["opacities"].float().mean()), "scale_mean": float(gaussians["scales"].float().mean()), "scale_max": float(gaussians["scales"].float().max()), } ) predictions[condition] = composited masks[condition] = resized_mask metrics[condition] = condition_metrics baseline = predictions["baseline"] mask = masks["baseline"] for condition in ("global_zero", "high_zero"): metrics[condition]["foreground_render_change_mae"] = float( masked_mean((predictions[condition] - baseline).abs(), mask) ) metrics[condition]["delta_foreground_psnr"] = ( metrics[condition]["foreground_psnr"] - metrics["baseline"]["foreground_psnr"] ) if "lpips" in metrics[condition]: metrics[condition]["delta_lpips"] = ( metrics[condition]["lpips"] - metrics["baseline"]["lpips"] ) args.output_dir.mkdir(parents=True, exist_ok=True) encoder_01 = resize_target(encoder_image, args.resolution).cpu() target_cpu = target.cpu() mask_rgb = mask.expand(-1, 3, -1, -1).cpu() prediction_row = [predictions[name].cpu() for name in CONDITIONS] error_row = [(prediction - target_cpu).abs() * mask_rgb for prediction in prediction_row] overview = make_grid( torch.cat( [ encoder_01, target_cpu, prediction_row[0], prediction_row[1], mask_rgb, error_row[0], error_row[1], error_row[2], prediction_row[2], ], dim=0, ), nrow=4, padding=3, ) # The final high-zero prediction occupies a third row because the first two # rows are kept as four aligned columns. Individual renders below are the # unambiguous source for numerical/visual comparison. overview_path = args.output_dir / "ablation_overview.png" save_image(overview, overview_path) for name, prediction in predictions.items(): save_image(prediction.cpu(), args.output_dir / f"{name}.png") save_image( (prediction.cpu() - target_cpu).abs() * mask_rgb, args.output_dir / f"{name}_error.png", ) result: dict[str, object] = { "checkpoint": str(args.checkpoint.resolve()), "checkpoint_step": int(checkpoint.get("global_step", 0)), "protocol": config["mode"], "amp": config["amp"], "encoder_camera_id": str(encoder_sample["camera_id"]), "target_camera_id": target_camera_id, "resolution": args.resolution, "metrics": metrics, "interpretation": { "foreground_render_change_mae": ( "Mean absolute foreground render change relative to baseline." ), "delta_foreground_psnr": ( "A large negative value means the ablated branch was important." ), "delta_lpips": ( "A large positive value means the ablated branch was important." ), }, "outputs": { "overview": str(overview_path), **{ name: str(args.output_dir / f"{name}.png") for name in CONDITIONS }, }, } metrics_path = args.output_dir / "metrics.json" with metrics_path.open("w", encoding="utf-8") as file: json.dump(result, file, indent=2) file.write("\n") print(json.dumps(result, indent=2), flush=True) print(f"overview: {overview_path}", flush=True) print(f"metrics: {metrics_path}", flush=True) return result def main() -> int: evaluate(parse_args()) return 0 if __name__ == "__main__": raise SystemExit(main())