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# import os
# import torch
# import json
# import re
# from PIL import Image
# import torch.nn.functional as F
# from safetensors.torch import load_file
# from huggingface_hub import hf_hub_download
# import sys
# sys.path.append("/gpfs/home/ym621/UniPointMap")
# import open_clip
# # ---------------------------
# # Helpers
# # ---------------------------
# def load_safetensor_from_hf(repo_id, filename, repo_type="dataset"):
# cached_path = hf_hub_download(
# repo_id=repo_id,
# filename=filename,
# revision='7bb7c7f3d379c5145bb06d2cf0949c66ac9a2c4e',
# repo_type=repo_type,
# local_files_only=True
# )
# return load_file(cached_path)
# def load_json(data_path: str):
# with open(data_path, "r", encoding="utf-8") as f:
# return json.load(f)
# def load_jsonl(path):
# data = []
# with open(path, "r", encoding="utf-8") as f:
# for line in f:
# if line.strip():
# data.append(json.loads(line))
# return data
# # ---------------------------
# # Load CLIP model
# # ---------------------------
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model, _, preprocess = open_clip.create_model_and_transforms(
# 'ViT-B-16', pretrained='datacomp_xl_s13b_b90k'
# )
# tokenizer = open_clip.get_tokenizer('ViT-B-16')
# model = model.to(device).eval()
# # ---------------------------
# # Preload reference captions
# # ---------------------------
# scannet_data = load_jsonl('/gpfs/home/ym621/UniPointMap/PointMapVerse/existing_datasets/ScanNet/annotations/scannet_caption_per_view.jsonl')
# arkitscenes_data = load_jsonl('/gpfs/home/ym621/UniPointMap/PointMapVerse/existing_datasets/Arkitscenes/annotations/arkitscenes_caption_per_view.jsonl')
# rscan_data = load_jsonl('/gpfs/home/ym621/UniPointMap/PointMapVerse/existing_datasets/3RScan/annotations/3rscan_caption_per_view.jsonl')
# org_data = {}
# cur_scan_id = ''
# for idx, data in enumerate([scannet_data, arkitscenes_data, rscan_data]):
# if idx == 0:
# root = 'light_scannet'
# elif idx == 1:
# root = 'light_arkitscenes'
# else:
# root = 'light_3rscan'
# local_idx = 0
# for item in data:
# if item['scan_id'] != cur_scan_id:
# cur_scan_id = item['scan_id']
# local_idx = 0
# scan_id = f"{root}/{item['scan_id']}_{local_idx}"
# org_data[scan_id] = item['utterance'].split('.')
# local_idx += 1
# # ---------------------------
# # Caching safetensors
# # ---------------------------
# safetensor_cache = {}
# def get_image_from_safetensor(image_path, idx):
# if image_path not in safetensor_cache:
# safetensor_cache[image_path] = load_safetensor_from_hf(
# 'MatchLab/PointMapVerse', image_path
# )
# return safetensor_cache[image_path]['color_images'][idx]
# # ---------------------------
# # Process captions
# # ---------------------------
# caption_dir = "../captions"
# captions = [f for f in os.listdir(caption_dir) if f.endswith('.json')]
# filtered_captions = {}
# count, total_count = 0, 0
# for cap in captions:
# cap_path = os.path.join(caption_dir, cap)
# caption_data = load_json(cap_path)
# for k, v in caption_data.items():
# image_path = f"{'_'.join(k.split('_')[:-1])}.safetensors"
# idx = int(k.split('_')[-1])
# # --- load + preprocess image ---
# img_tensor = get_image_from_safetensor(image_path, idx)
# img_tensor = img_tensor.cpu().numpy()
# pil_img = Image.fromarray(img_tensor.astype("uint8")).convert("RGB")
# image = preprocess(pil_img).unsqueeze(0).to(device)
# with torch.no_grad():
# image_features = model.encode_image(image)
# image_features = F.normalize(image_features, dim=-1)
# # --- clean captions ---
# if "1." in v:
# v = v.split("1.", 1)[-1].strip()
# v = "1." + v
# if not v.startswith('1.'):
# v = ["An image showing an indoor scene."]
# count += 1
# else:
# v = re.split(r'\s*\d+\.\s*', v)
# v = [c.strip().replace('*', '') for c in v if c.strip()]
# if len(v) < 4:
# v = ["An image showing an indoor scene."]
# count += 1
# # --- combine old + new captions ---
# old_v = org_data.get(k, [])
# all_v = old_v + v
# # --- encode captions ---
# with torch.no_grad():
# text_tokens = tokenizer(all_v).to(device)
# text_features = model.encode_text(text_tokens)
# text_features = F.normalize(text_features, dim=-1)
# sims = (image_features @ text_features.T).squeeze(0) # [num_caps]
# # --- sort captions (fast torch.topk instead of sorted) ---
# topk_vals, topk_idx = torch.topk(sims, k=len(all_v))
# # print(topk_vals)
# sorted_captions = [all_v[i] for i in topk_idx.tolist()]
# # print(sorted_captions)
# filtered_captions[k] = sorted_captions
# total_count += 1
# if total_count % 50 == 0:
# print(f"Processed {total_count} files...")
# # ---------------------------
# # Save results
# # ---------------------------
# output_path = os.path.join(caption_dir, "filtered_captions_sorted.json")
# with open(output_path, "w", encoding="utf-8") as f:
# json.dump(filtered_captions, f, indent=4)
# print(f'Total captions not starting with "1.": {count} out of {total_count} captions.')
# print(f"Sorted captions saved to {output_path}")
# ---------------------------
# Save results as JSONL
# ---------------------------
import os
import json
from transformers import AutoTokenizer
# ---------------------------
# Paths
# ---------------------------
caption_dir = "../captions"
filtered_json_path = os.path.join(caption_dir, "filtered_captions_sorted.json")
# Output files for each dataset
output_paths = {
"scannet": os.path.join(caption_dir, "filtered_captions_scannet.jsonl"),
"arkitscenes": os.path.join(caption_dir, "filtered_captions_arkitscenes.jsonl"),
"3rscan": os.path.join(caption_dir, "filtered_captions_3rscan.jsonl"),
}
# ---------------------------
# Load filtered captions
# ---------------------------
with open(filtered_json_path, "r", encoding="utf-8") as f:
filtered_captions = json.load(f)
print(f"Loaded {len(filtered_captions)} scan entries.")
# ---------------------------
# Setup tokenizer (bert-base-uncased)
# ---------------------------
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# ---------------------------
# Open three output files
# ---------------------------
files = {k: open(path, "w", encoding="utf-8") for k, path in output_paths.items()}
line_ids = {"scannet": 1, "arkitscenes": 1, "3rscan": 1}
# ---------------------------
# Convert and write entries
# ---------------------------
for k, sorted_captions in filtered_captions.items():
# Determine dataset type
if k.startswith("light_scannet"):
dataset = "scannet"
elif k.startswith("light_arkitscenes"):
dataset = "arkitscenes"
elif k.startswith("light_3rscan"):
dataset = "3rscan"
else:
continue # skip unknown dataset keys
image_path = f"{'_'.join(k.split('_')[:-1])}.safetensors"
scan_id = "_".join(k.split("_")[:-1]).split("/")[-1] # e.g. scene0000_00
# Clean and join top-5 captions
sorted_captions = [cap.replace('.', '').strip() for cap in sorted_captions]
entry = {
"item_id": f"{dataset}_train_{line_ids[dataset]:06d}",
"scan_id": scan_id,
"utterance": sorted_captions,
"safetensors_path": image_path,
}
# Write to the correct file
files[dataset].write(json.dumps(entry) + "\n")
line_ids[dataset] += 1
# ---------------------------
# Close files
# ---------------------------
for f in files.values():
f.close()
print(f"✅ Saved entries to:")
for k, path in output_paths.items():
print(f" {k}: {path} ({line_ids[k]-1} entries)")
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