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# import os
# import json
# import torch
# from huggingface_hub import list_repo_files, hf_hub_download
# from safetensors.torch import load_file
# REPO_ID = "MatchLab/PointMapVerse"
# SUBFOLDER = "light_arkitscenes"
# def pointmap_to_points(pm: torch.Tensor, max_points: int = 4096) -> torch.Tensor:
# """
# Convert [H, W, 3] point map to [N, 3] points, with subsampling.
# """
# pm = pm.float()
# pts = pm.reshape(-1, 3)
# pts = pts[pts.norm(dim=-1) > 0] # remove zero points
# if pts.shape[0] > max_points:
# pts = pts[torch.randperm(pts.shape[0])[:max_points]]
# return pts
# def chamfer_distance(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
# """
# Symmetric Chamfer distance between two point sets a, b: [N,3], [M,3]
# """
# d = torch.cdist(a, b, p=2)
# return d.min(dim=1).values.mean() + d.min(dim=0).values.mean()
# def compute_chamfer_rankings_for_scene(scene_path: str, repo_id: str):
# """
# For a single safetensors scene file, compute sorted Chamfer rankings
# for each view.
# Returns a list of dicts for JSON.
# """
# local_path = hf_hub_download(repo_id, scene_path, repo_type="dataset")
# data = load_file(local_path)
# point_maps = data["point_map"] # [V, H, W, 3]
# num_views = point_maps.shape[0]
# # Precompute point clouds per view
# pcs = [pointmap_to_points(point_maps[v]) for v in range(num_views)]
# scene_id = os.path.basename(scene_path).split(".")[0] # e.g. scene0000_01
# results = []
# for i in range(num_views):
# pts_i = pcs[i]
# dists = []
# for j in range(num_views):
# if i == j:
# continue
# d = chamfer_distance(pts_i, pcs[j]).item()
# dists.append((j, d))
# # sort by Chamfer distance
# dists_sorted = sorted(dists, key=lambda x: x[1])
# sorted_views = [j for (j, _) in dists_sorted]
# item = {
# "scene_id": scene_id,
# "cur_view": i,
# "sorted_views": sorted_views,
# }
# results.append(
# item
# )
# print(item)
# return results
# def load_all_scenes_and_dump_json(
# repo_id: str = REPO_ID,
# subfolder: str = SUBFOLDER,
# output_json: str = "light_arkitscenes_chamfer_rankings.json",
# ):
# print("📂 Listing files…")
# files = list_repo_files(repo_id, repo_type="dataset")
# # Filter for safetensors inside subfolder/
# scene_files = [f for f in files if f.startswith(subfolder) and f.endswith(".safetensors")]
# print(f"🔍 Found {len(scene_files)} scenes")
# all_entries = []
# for fname in sorted(scene_files):
# print(f"➡️ Processing scene: {fname}")
# scene_entries = compute_chamfer_rankings_for_scene(fname, repo_id)
# all_entries.extend(scene_entries)
# print(f"💾 Saving JSON to: {output_json}")
# with open(output_json, "w") as f:
# json.dump(all_entries, f, indent=2)
# print("✅ Finished computing Chamfer rankings for all scenes")
# return all_entries
# if __name__ == "__main__":
# load_all_scenes_and_dump_json(REPO_ID, SUBFOLDER)
# from transformers import AutoModel, AutoTokenizer
# text_encoder = AutoModel.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True)
# tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True)
# from safetensors.torch import load_file
# from safetensors.torch import load
# from huggingface_hub import hf_hub_download
# def load_safetensor_from_hf(repo_id, filename, repo_type="dataset"):
# if filename.startswith('cc3m'):
# with open(filename, "rb") as f:
# return load(f.read())
# else:
# cached_path = hf_hub_download(
# repo_id=repo_id,
# filename=filename,
# repo_type=repo_type,
# local_files_only=True
# )
# return load_file(cached_path)
from huggingface_hub import snapshot_download
from pathlib import Path
# ---- Config ----
REPO_ID = "MatchLab/ScenePointv2" # e.g. "openai/light_lsun"
SUBFOLDER = "light_lsun"
LOCAL_DIR = "./light_lsun"
# ---- Download ----
snapshot_download(
repo_id=REPO_ID,
repo_type="dataset",
local_dir=LOCAL_DIR,
allow_patterns=[f"{SUBFOLDER}/**"],
local_dir_use_symlinks=False,
)
print(f"✅ Downloaded {SUBFOLDER} to {Path(LOCAL_DIR).resolve()}")
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