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"""Visualize the global/high INS encoder branches on a RenderMe view.
The images are activation diagnostics. They show spatial response, PCA
projections, and representative channels; they do not assign semantic labels
to the features.
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
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,
read_checkpoint,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Visualize global and high-resolution INS branch activations."
)
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("--resolution", type=int, default=512)
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--amp", choices=("none", "bf16"), default="none")
parser.add_argument(
"--output-dir",
type=Path,
default=Path("/workspace/cpfs-data/tmp/ins_branch_viz"),
)
parser.add_argument("--top-channels", type=int, default=12)
parser.add_argument("--save-features", action="store_true")
args = parser.parse_args()
if not args.checkpoint.is_file():
raise FileNotFoundError(args.checkpoint)
if args.resolution <= 0 or args.top_channels <= 0:
raise ValueError("resolution and top-channels must be positive.")
return args
def robust_normalize(value: Tensor) -> Tensor:
value = value.float()
low = torch.quantile(value, 0.01)
high = torch.quantile(value, 0.99)
return ((value - low) / (high - low).clamp_min(1e-8)).clamp(0.0, 1.0)
def feature_mean_map(features: Tensor) -> Tensor:
value = robust_normalize(features.abs().mean(dim=1, keepdim=True))
return value.expand(-1, 3, -1, -1)
def feature_pca_rgb(features: Tensor) -> Tensor:
features = features.float()
batch, channels, height, width = features.shape
if batch != 1:
raise ValueError("Branch visualization expects batch size one.")
samples = features[0].permute(1, 2, 0).reshape(height * width, channels)
samples = samples - samples.mean(dim=0, keepdim=True)
_, _, vectors = torch.pca_lowrank(samples, q=3, center=False, niter=2)
projected = samples @ vectors[:, :3]
projected = projected.reshape(height, width, 3).permute(2, 0, 1).unsqueeze(0)
return robust_normalize(projected)
def top_channel_grid(features: Tensor, count: int) -> tuple[Tensor, list[int]]:
features = features.float()
count = min(count, features.shape[1])
scores = features.abs().flatten(2).mean(dim=2)[0]
indices = torch.topk(scores, k=count).indices.tolist()
maps = [
robust_normalize(features[:, index : index + 1]).expand(-1, 3, -1, -1)
for index in indices
]
return make_grid(torch.cat(maps, dim=0), nrow=min(4, count), padding=2), indices
def gradient_energy(features: Tensor) -> Tensor:
features = features.float()
horizontal = (features[:, :, :, 1:] - features[:, :, :, :-1]).abs().mean()
vertical = (features[:, :, 1:, :] - features[:, :, :-1, :]).abs().mean()
return 0.5 * (horizontal + vertical)
def capture_branches(model) -> tuple[dict[str, Tensor], list[object]]:
captured: dict[str, Tensor] = {}
def make_hook(name: str):
def hook(_module, _inputs, output):
captured[name] = output.detach().float()
return hook
handles = [
model.encoder.f_encoder.register_forward_hook(make_hook("global")),
model.encoder.high_encoder.register_forward_hook(make_hook("high")),
model.encoder.final_encoder.register_forward_hook(make_hook("fused")),
]
return captured, handles
def resolve_data_config(args: argparse.Namespace, checkpoint: dict) -> dict:
saved = checkpoint.get("args", {})
if not isinstance(saved, dict):
saved = {}
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
source = args.source_camera_id
if source is None:
source = saved.get("source_camera_id")
canonical_radius = float(saved.get("canonical_radius", 2.7))
canonical_focus = tuple(float(v) for v in saved.get("canonical_focus", (0.0, 0.0, 0.2)))
background = tuple(float(v) for v in saved.get("background", (1.0, 1.0, 1.0)))
return {
"root": root,
"frame_index": frame_index,
"source": None if source is None else str(source),
"canonical_radius": canonical_radius,
"canonical_focus": canonical_focus,
"background": background,
}
@torch.inference_mode()
def visualize(args: argparse.Namespace) -> dict[str, object]:
device = torch.device(args.device)
if device.type != "cuda" or not torch.cuda.is_available():
raise RuntimeError("Branch visualization requires CUDA.")
torch.cuda.set_device(device)
checkpoint = read_checkpoint(args.checkpoint)
model = build_model_from_checkpoint(checkpoint, args.resolution, device)
config = resolve_data_config(args, checkpoint)
# The geometric center is resolved by the dataset when source is omitted.
camera_id = args.camera_id or config["source"]
dataset = RenderMeINSViewDataset(
root=config["root"],
frame_index=config["frame_index"],
image_size=512,
camera_ids=None if camera_id is None else [camera_id],
source_camera_id=config["source"],
canonical_radius=config["canonical_radius"],
canonical_focus=config["canonical_focus"],
background=config["background"],
)
camera_id = str(camera_id or dataset.source_camera_id)
sample = dataset.get_view(camera_id)
image = sample["image"].unsqueeze(0).to(device)
captured, handles = capture_branches(model)
with torch.autocast(
device_type="cuda",
dtype=torch.bfloat16,
enabled=args.amp == "bf16",
):
triplanes = model.encoder(image)
for handle in handles:
handle.remove()
if set(captured) != {"global", "high", "fused"}:
raise RuntimeError(f"Missing branch activations: {sorted(captured)}")
args.output_dir.mkdir(parents=True, exist_ok=True)
source = ((image.float() + 1.0) * 0.5).clamp(0.0, 1.0)
global_features = captured["global"]
high_features = captured["high"]
fused_features = captured["fused"]
global_mean = feature_mean_map(global_features)
high_mean = feature_mean_map(high_features)
global_pca = feature_pca_rgb(global_features)
high_pca = feature_pca_rgb(high_features)
fused_mean = feature_mean_map(fused_features)
global_channels, global_indices = top_channel_grid(global_features, args.top_channels)
high_channels, high_indices = top_channel_grid(high_features, args.top_channels)
source_small = F.interpolate(
source, size=global_mean.shape[-2:], mode="bilinear", align_corners=False
)
overview = make_grid(
torch.cat(
[source_small, global_mean, global_pca, high_mean, high_pca, fused_mean],
dim=0,
),
nrow=3,
padding=3,
)
overview_path = args.output_dir / "branch_overview.png"
global_path = args.output_dir / "global_top_channels.png"
high_path = args.output_dir / "high_top_channels.png"
save_image(overview, overview_path)
save_image(global_channels, global_path)
save_image(high_channels, high_path)
stats: dict[str, object] = {
"checkpoint": str(args.checkpoint.resolve()),
"camera_id": camera_id,
"global_shape": list(global_features.shape),
"high_shape": list(high_features.shape),
"fused_shape": list(fused_features.shape),
"triplane_shape": list(triplanes.shape),
"global_top_channels": global_indices,
"high_top_channels": high_indices,
"global_mean_abs": float(global_features.abs().mean()),
"high_mean_abs": float(high_features.abs().mean()),
"global_gradient_energy": float(gradient_energy(global_features)),
"high_gradient_energy": float(gradient_energy(high_features)),
"global_spatial_std": float(global_features.flatten(2).std(dim=2).mean()),
"high_spatial_std": float(high_features.flatten(2).std(dim=2).mean()),
"outputs": {
"overview": str(overview_path),
"global_channels": str(global_path),
"high_channels": str(high_path),
},
}
if args.save_features:
feature_path = args.output_dir / "branch_features.pt"
torch.save(
{
"global": global_features.cpu(),
"high": high_features.cpu(),
"fused": fused_features.cpu(),
},
feature_path,
)
stats["outputs"]["features"] = str(feature_path)
with (args.output_dir / "stats.json").open("w", encoding="utf-8") as file:
json.dump(stats, file, indent=2)
file.write("\n")
print(json.dumps(stats, indent=2), flush=True)
return stats
def main() -> int:
visualize(parse_args())
return 0
if __name__ == "__main__":
raise SystemExit(main())
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