DISEF CALTECH101 reproducibility deliverables: caltech101_disef_20261005T073721Z_262ae8436d11
57b4b9f verified | diff --git a/DataSplit/split_coop_Caltech101.csv b/DataSplit/split_coop_Caltech101.csv | |
| new file mode 100644 | |
| index 0000000..61460a2 | |
| --- /dev/null | |
| +++ b/DataSplit/split_coop_Caltech101.csv | |
| +split,filename | |
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| +test,watch/image_0064.jpg | |
| +test,watch/image_0013.jpg | |
| +test,watch/image_0139.jpg | |
| +test,watch/image_0170.jpg | |
| +test,watch/image_0112.jpg | |
| +test,watch/image_0055.jpg | |
| +test,watch/image_0032.jpg | |
| +test,watch/image_0221.jpg | |
| +test,watch/image_0171.jpg | |
| +test,watch/image_0036.jpg | |
| +test,watch/image_0060.jpg | |
| +test,watch/image_0134.jpg | |
| +test,watch/image_0118.jpg | |
| +test,watch/image_0070.jpg | |
| +test,watch/image_0111.jpg | |
| +test,watch/image_0071.jpg | |
| +test,watch/image_0204.jpg | |
| +test,watch/image_0006.jpg | |
| +test,watch/image_0203.jpg | |
| +test,watch/image_0015.jpg | |
| +test,watch/image_0235.jpg | |
| +test,watch/image_0144.jpg | |
| +test,watch/image_0211.jpg | |
| +test,watch/image_0220.jpg | |
| +test,watch/image_0003.jpg | |
| +test,watch/image_0115.jpg | |
| +test,watch/image_0050.jpg | |
| +test,watch/image_0040.jpg | |
| +test,watch/image_0218.jpg | |
| +test,watch/image_0210.jpg | |
| +test,watch/image_0150.jpg | |
| +test,water_lilly/image_0002.jpg | |
| +test,water_lilly/image_0017.jpg | |
| +test,water_lilly/image_0003.jpg | |
| +test,water_lilly/image_0026.jpg | |
| +test,water_lilly/image_0018.jpg | |
| +test,water_lilly/image_0027.jpg | |
| +test,water_lilly/image_0033.jpg | |
| +test,water_lilly/image_0034.jpg | |
| +test,water_lilly/image_0016.jpg | |
| +test,water_lilly/image_0004.jpg | |
| +test,water_lilly/image_0008.jpg | |
| +test,water_lilly/image_0031.jpg | |
| +test,wheelchair/image_0041.jpg | |
| +test,wheelchair/image_0048.jpg | |
| +test,wheelchair/image_0017.jpg | |
| +test,wheelchair/image_0040.jpg | |
| +test,wheelchair/image_0034.jpg | |
| +test,wheelchair/image_0056.jpg | |
| +test,wheelchair/image_0021.jpg | |
| +test,wheelchair/image_0007.jpg | |
| +test,wheelchair/image_0003.jpg | |
| +test,wheelchair/image_0049.jpg | |
| +test,wheelchair/image_0010.jpg | |
| +test,wheelchair/image_0029.jpg | |
| +test,wheelchair/image_0033.jpg | |
| +test,wheelchair/image_0031.jpg | |
| +test,wheelchair/image_0030.jpg | |
| +test,wheelchair/image_0039.jpg | |
| +test,wheelchair/image_0047.jpg | |
| +test,wild_cat/image_0008.jpg | |
| +test,wild_cat/image_0002.jpg | |
| +test,wild_cat/image_0026.jpg | |
| +test,wild_cat/image_0013.jpg | |
| +test,wild_cat/image_0014.jpg | |
| +test,wild_cat/image_0019.jpg | |
| +test,wild_cat/image_0012.jpg | |
| +test,wild_cat/image_0016.jpg | |
| +test,wild_cat/image_0034.jpg | |
| +test,wild_cat/image_0021.jpg | |
| +test,windsor_chair/image_0037.jpg | |
| +test,windsor_chair/image_0002.jpg | |
| +test,windsor_chair/image_0050.jpg | |
| +test,windsor_chair/image_0009.jpg | |
| +test,windsor_chair/image_0023.jpg | |
| +test,windsor_chair/image_0053.jpg | |
| +test,windsor_chair/image_0021.jpg | |
| +test,windsor_chair/image_0011.jpg | |
| +test,windsor_chair/image_0036.jpg | |
| +test,windsor_chair/image_0054.jpg | |
| +test,windsor_chair/image_0055.jpg | |
| +test,windsor_chair/image_0026.jpg | |
| +test,windsor_chair/image_0022.jpg | |
| +test,windsor_chair/image_0043.jpg | |
| +test,windsor_chair/image_0044.jpg | |
| +test,windsor_chair/image_0045.jpg | |
| +test,windsor_chair/image_0038.jpg | |
| +test,wrench/image_0009.jpg | |
| +test,wrench/image_0027.jpg | |
| +test,wrench/image_0008.jpg | |
| +test,wrench/image_0005.jpg | |
| +test,wrench/image_0028.jpg | |
| +test,wrench/image_0039.jpg | |
| +test,wrench/image_0023.jpg | |
| +test,wrench/image_0002.jpg | |
| +test,wrench/image_0007.jpg | |
| +test,wrench/image_0003.jpg | |
| +test,wrench/image_0035.jpg | |
| +test,yin_yang/image_0010.jpg | |
| +test,yin_yang/image_0007.jpg | |
| +test,yin_yang/image_0051.jpg | |
| +test,yin_yang/image_0038.jpg | |
| +test,yin_yang/image_0030.jpg | |
| +test,yin_yang/image_0024.jpg | |
| +test,yin_yang/image_0033.jpg | |
| +test,yin_yang/image_0039.jpg | |
| +test,yin_yang/image_0046.jpg | |
| +test,yin_yang/image_0041.jpg | |
| +test,yin_yang/image_0042.jpg | |
| +test,yin_yang/image_0001.jpg | |
| +test,yin_yang/image_0044.jpg | |
| +test,yin_yang/image_0022.jpg | |
| +test,yin_yang/image_0059.jpg | |
| +test,yin_yang/image_0004.jpg | |
| +test,yin_yang/image_0028.jpg | |
| +test,yin_yang/image_0021.jpg | |
| diff --git a/DisEF/caltech101_checkpoint_daemon.py b/DisEF/caltech101_checkpoint_daemon.py | |
| new file mode 100644 | |
| index 0000000..71ba094 | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_checkpoint_daemon.py | |
| +#!/usr/bin/env python3 | |
| +import argparse,json,sys,time,traceback | |
| +from pathlib import Path | |
| +sys.path.insert(0,str(Path(__file__).resolve().parent)) | |
| +import caltech101_hf | |
| + | |
| +def backed_count(root): | |
| + index=root/'metadata/chunk_index.json' | |
| + if not index.exists(): return 0 | |
| + data=json.loads(index.read_text()) | |
| + return sum(int(c.get('image_count',0)) for c in data.get('chunks',[])) | |
| + | |
| +p=argparse.ArgumentParser() | |
| +p.add_argument('--run-root',default='/marimo/runs/disef_caltech101') | |
| +p.add_argument('--interval',type=int,default=300) | |
| +p.add_argument('--max-chunk-images',type=int,default=500) | |
| +a=p.parse_args(); root=Path(a.run_root) | |
| +while True: | |
| + sleep_for=a.interval | |
| + try: | |
| + s=json.loads((root/'state/caltech101_disef_state.json').read_text()) | |
| + backed=backed_count(root) | |
| + pending=max(s.get('completed',0)-backed,0) | |
| + index=root/'metadata/chunk_index.json' | |
| + age=time.time()-(index.stat().st_mtime if index.exists() else 0) | |
| + terminal=s.get('status') in {'COMPLETED','PARTIAL','BLOCKED'} | |
| + if (pending>=50 and age>=300) or (pending>0 and age>=900) or (terminal and pending>0): | |
| + size=min(max(pending,100),a.max_chunk_images) | |
| + result=caltech101_hf.checkpoint(root,force=terminal,max_chunk_images=size) | |
| + print(json.dumps(result),flush=True) | |
| + pending=max(s.get('completed',0)-backed_count(root),0) | |
| + if terminal and pending<=0: | |
| + break | |
| + if terminal and pending>0: | |
| + sleep_for=15 | |
| + except Exception: | |
| + print(traceback.format_exc(),flush=True) | |
| + time.sleep(sleep_for) | |
| diff --git a/DisEF/caltech101_disef.py b/DisEF/caltech101_disef.py | |
| new file mode 100644 | |
| index 0000000..5e8ea00 | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_disef.py | |
| +#!/usr/bin/env python3 | |
| +"""Crash-safe DISEF/SPC synthetic generation for Caltech101. | |
| + | |
| +Scientific basis: vturrisi/disef (arXiv:2312.03046). This SPC variant keeps | |
| +Qwen3-VL-Embedding filtering while restoring the paper's cross-sample caption | |
| +conditioning and CALTECH101 diffusion configuration. | |
| +""" | |
| +from __future__ import annotations | |
| + | |
| +import argparse | |
| +import csv | |
| +import gc | |
| +import hashlib | |
| +import json | |
| +import os | |
| +import random | |
| +import shutil | |
| +import socket | |
| +import subprocess | |
| +import sys | |
| +import time | |
| +import traceback | |
| +from collections import Counter, defaultdict | |
| +from contextlib import contextmanager | |
| +from datetime import datetime, timezone | |
| +from pathlib import Path | |
| +from typing import Any | |
| + | |
| +import pandas as pd | |
| +from PIL import Image, ImageOps | |
| + | |
| +sys.path.insert(0, str(Path(__file__).resolve().parent)) | |
| +SPC_ROOT = Path(__file__).resolve().parents[1] | |
| +DEFAULT_DATA_ROOT = Path("/marimo/data/caltech101/train") | |
| +DEFAULT_SPLIT = SPC_ROOT / "DataSplit/split_coop_Caltech101.csv" | |
| +DEFAULT_QWEN_SOURCE = Path("/marimo/Qwen3-VL-Embedding") | |
| +DEFAULT_CACHE_ROOT = Path("/marimo/runs/disef_caltech101_cache") | |
| +IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"} | |
| +CLASS_FOLDERS = ['face', 'leopard', 'motorbike', 'accordion', 'airplane', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang'] | |
| +FOLDER_TO_LABEL = {'face': 'face', 'leopard': 'leopard', 'motorbike': 'motorbike', 'accordion': 'accordion', 'airplane': 'airplane', 'anchor': 'anchor', 'ant': 'ant', 'barrel': 'barrel', 'bass': 'bass', 'beaver': 'beaver', 'binocular': 'binocular', 'bonsai': 'bonsai', 'brain': 'brain', 'brontosaurus': 'brontosaurus', 'buddha': 'buddha', 'butterfly': 'butterfly', 'camera': 'camera', 'cannon': 'cannon', 'car_side': 'car side', 'ceiling_fan': 'ceiling fan', 'cellphone': 'cellphone', 'chair': 'chair', 'chandelier': 'chandelier', 'cougar_body': 'cougar body', 'cougar_face': 'cougar face', 'crab': 'crab', 'crayfish': 'crayfish', 'crocodile': 'crocodile', 'crocodile_head': 'crocodile head', 'cup': 'cup', 'dalmatian': 'dalmatian', 'dollar_bill': 'dollar bill', 'dolphin': 'dolphin', 'dragonfly': 'dragonfly', 'electric_guitar': 'electric guitar', 'elephant': 'elephant', 'emu': 'emu', 'euphonium': 'euphonium', 'ewer': 'ewer', 'ferry': 'ferry', 'flamingo': 'flamingo', 'flamingo_head': 'flamingo head', 'garfield': 'garfield', 'gerenuk': 'gerenuk', 'gramophone': 'gramophone', 'grand_piano': 'grand piano', 'hawksbill': 'hawksbill', 'headphone': 'headphone', 'hedgehog': 'hedgehog', 'helicopter': 'helicopter', 'ibis': 'ibis', 'inline_skate': 'inline skate', 'joshua_tree': 'joshua tree', 'kangaroo': 'kangaroo', 'ketch': 'ketch', 'lamp': 'lamp', 'laptop': 'laptop', 'llama': 'llama', 'lobster': 'lobster', 'lotus': 'lotus', 'mandolin': 'mandolin', 'mayfly': 'mayfly', 'menorah': 'menorah', 'metronome': 'metronome', 'minaret': 'minaret', 'nautilus': 'nautilus', 'octopus': 'octopus', 'okapi': 'okapi', 'pagoda': 'pagoda', 'panda': 'panda', 'pigeon': 'pigeon', 'pizza': 'pizza', 'platypus': 'platypus', 'pyramid': 'pyramid', 'revolver': 'revolver', 'rhino': 'rhino', 'rooster': 'rooster', 'saxophone': 'saxophone', 'schooner': 'schooner', 'scissors': 'scissors', 'scorpion': 'scorpion', 'sea_horse': 'sea horse', 'snoopy': 'snoopy', 'soccer_ball': 'soccer ball', 'stapler': 'stapler', 'starfish': 'starfish', 'stegosaurus': 'stegosaurus', 'stop_sign': 'stop sign', 'strawberry': 'strawberry', 'sunflower': 'sunflower', 'tick': 'tick', 'trilobite': 'trilobite', 'umbrella': 'umbrella', 'watch': 'watch', 'water_lilly': 'water lilly', 'wheelchair': 'wheelchair', 'wild_cat': 'wild cat', 'windsor_chair': 'windsor chair', 'wrench': 'wrench', 'yin_yang': 'yin yang'} | |
| +FOLDER_TO_SOURCE = {'face': 'Faces', 'leopard': 'Leopards', 'motorbike': 'Motorbikes', 'accordion': 'accordion', 'airplane': 'airplanes', 'anchor': 'anchor', 'ant': 'ant', 'barrel': 'barrel', 'bass': 'bass', 'beaver': 'beaver', 'binocular': 'binocular', 'bonsai': 'bonsai', 'brain': 'brain', 'brontosaurus': 'brontosaurus', 'buddha': 'buddha', 'butterfly': 'butterfly', 'camera': 'camera', 'cannon': 'cannon', 'car_side': 'car_side', 'ceiling_fan': 'ceiling_fan', 'cellphone': 'cellphone', 'chair': 'chair', 'chandelier': 'chandelier', 'cougar_body': 'cougar_body', 'cougar_face': 'cougar_face', 'crab': 'crab', 'crayfish': 'crayfish', 'crocodile': 'crocodile', 'crocodile_head': 'crocodile_head', 'cup': 'cup', 'dalmatian': 'dalmatian', 'dollar_bill': 'dollar_bill', 'dolphin': 'dolphin', 'dragonfly': 'dragonfly', 'electric_guitar': 'electric_guitar', 'elephant': 'elephant', 'emu': 'emu', 'euphonium': 'euphonium', 'ewer': 'ewer', 'ferry': 'ferry', 'flamingo': 'flamingo', 'flamingo_head': 'flamingo_head', 'garfield': 'garfield', 'gerenuk': 'gerenuk', 'gramophone': 'gramophone', 'grand_piano': 'grand_piano', 'hawksbill': 'hawksbill', 'headphone': 'headphone', 'hedgehog': 'hedgehog', 'helicopter': 'helicopter', 'ibis': 'ibis', 'inline_skate': 'inline_skate', 'joshua_tree': 'joshua_tree', 'kangaroo': 'kangaroo', 'ketch': 'ketch', 'lamp': 'lamp', 'laptop': 'laptop', 'llama': 'llama', 'lobster': 'lobster', 'lotus': 'lotus', 'mandolin': 'mandolin', 'mayfly': 'mayfly', 'menorah': 'menorah', 'metronome': 'metronome', 'minaret': 'minaret', 'nautilus': 'nautilus', 'octopus': 'octopus', 'okapi': 'okapi', 'pagoda': 'pagoda', 'panda': 'panda', 'pigeon': 'pigeon', 'pizza': 'pizza', 'platypus': 'platypus', 'pyramid': 'pyramid', 'revolver': 'revolver', 'rhino': 'rhino', 'rooster': 'rooster', 'saxophone': 'saxophone', 'schooner': 'schooner', 'scissors': 'scissors', 'scorpion': 'scorpion', 'sea_horse': 'sea_horse', 'snoopy': 'snoopy', 'soccer_ball': 'soccer_ball', 'stapler': 'stapler', 'starfish': 'starfish', 'stegosaurus': 'stegosaurus', 'stop_sign': 'stop_sign', 'strawberry': 'strawberry', 'sunflower': 'sunflower', 'tick': 'tick', 'trilobite': 'trilobite', 'umbrella': 'umbrella', 'watch': 'watch', 'water_lilly': 'water_lilly', 'wheelchair': 'wheelchair', 'wild_cat': 'wild_cat', 'windsor_chair': 'windsor_chair', 'wrench': 'wrench', 'yin_yang': 'yin_yang'} | |
| +SOURCE_TO_FOLDER = {v:k for k,v in FOLDER_TO_SOURCE.items()} | |
| +TEMPLATES = [ | |
| + "a photo of a {}", "a rendering of a {}", "a cropped photo of the {}", | |
| + "the photo of a {}", "a clean photo of a {}", "a detailed photo of a {}", | |
| + "a dark photo of the {}", "a close-up photo of a {}", "a bright photo of the {}", | |
| + "a good photo of the {}", "a photo of one {}", "a close-up rendering of the {}", | |
| + "a photo of a nice {}", "a photo of the small {}", "a photo of the large {}", | |
| + "a photo of a cool {}", "a photo of a small {}", | |
| +] | |
| + | |
| + | |
| + | |
| +def now_iso() -> str: | |
| + return datetime.now(timezone.utc).isoformat() | |
| + | |
| + | |
| +def atomic_json(path: Path, obj: Any) -> None: | |
| + # The generator is the single writer for scientific progress, while the | |
| + # checkpoint daemon writes immutable remote-chunk metadata. Merge those | |
| + # markers before every state commit so resume cannot erase durability data. | |
| + if path.name == "caltech101_disef_state.json" and isinstance(obj, dict) and "records" in obj: | |
| + chunk_index = path.parent.parent / "metadata" / "chunk_index.json" | |
| + if chunk_index.exists(): | |
| + try: | |
| + index = json.loads(chunk_index.read_text()) | |
| + if (index.get("run_id") == obj.get("run_id") | |
| + and index.get("config_fingerprint") == obj.get("config_fingerprint")): | |
| + backed = 0 | |
| + for chunk in index.get("chunks", []): | |
| + for rec in chunk.get("records", []): | |
| + task_id = rec.get("task_id") | |
| + if task_id in obj["records"]: | |
| + obj["records"][task_id]["remote_chunk"] = chunk["filename"] | |
| + backed += 1 | |
| + obj["remote_backed_up_count"] = backed | |
| + if index.get("updated_at"): | |
| + obj["latest_remote_checkpoint"] = index["updated_at"] | |
| + except (OSError, ValueError, KeyError, TypeError): | |
| + pass | |
| + path.parent.mkdir(parents=True, exist_ok=True) | |
| + tmp = path.with_name(path.name + f".tmp.{os.getpid()}") | |
| + with tmp.open("w", encoding="utf-8") as f: | |
| + json.dump(obj, f, indent=2, sort_keys=True) | |
| + f.flush() | |
| + os.fsync(f.fileno()) | |
| + os.replace(tmp, path) | |
| + | |
| + | |
| +def append_jsonl(path: Path, record: dict[str, Any]) -> None: | |
| + path.parent.mkdir(parents=True, exist_ok=True) | |
| + with path.open("a", encoding="utf-8") as f: | |
| + f.write(json.dumps(record, sort_keys=True) + "\n") | |
| + f.flush() | |
| + os.fsync(f.fileno()) | |
| + | |
| + | |
| +def sha256_file(path: Path) -> str: | |
| + h = hashlib.sha256() | |
| + with path.open("rb") as f: | |
| + for block in iter(lambda: f.read(8 << 20), b""): | |
| + h.update(block) | |
| + return h.hexdigest() | |
| + | |
| + | |
| +def validate_image(path: Path, expected_size: tuple[int, int] = (512, 512), class_folder: str | None = None) -> tuple[bool, str]: | |
| + try: | |
| + if not path.is_file() or path.stat().st_size <= 0: | |
| + return False, "missing-or-empty" | |
| + if class_folder is not None and path.parent.name != class_folder: | |
| + return False, "wrong-class-directory" | |
| + with Image.open(path) as im: | |
| + fmt = im.format | |
| + size = im.size | |
| + im.load() | |
| + if fmt not in {"JPEG", "PNG"}: | |
| + return False, f"unexpected-format:{fmt}" | |
| + if size != expected_size: | |
| + return False, f"unexpected-size:{size}" | |
| + return True, "ok" | |
| + except Exception as exc: | |
| + return False, f"{type(exc).__name__}:{exc}" | |
| + | |
| + | |
| +def padded_image(path: Path, size: int = 512) -> Image.Image: | |
| + with Image.open(path) as src: | |
| + image = src.convert("RGB") | |
| + width, height = image.size | |
| + side = max(width, height) | |
| + canvas = Image.new("RGB", (side, side), (0, 0, 0)) | |
| + canvas.paste(image, ((side - width) // 2, (side - height) // 2)) | |
| + return canvas.resize((size, size), Image.Resampling.LANCZOS) | |
| + | |
| + | |
| +def pid_alive(pid: int | None) -> bool: | |
| + if not pid: | |
| + return False | |
| + try: | |
| + n = int(pid) | |
| + os.kill(n, 0) | |
| + stat = Path(f"/proc/{n}/stat") | |
| + if stat.exists() and stat.read_text().split()[2] == "Z": return False | |
| + return True | |
| + except (OSError, ValueError): | |
| + return False | |
| + | |
| + | |
| +def canonical_scientific_config(args: argparse.Namespace, selected_sha: str) -> dict[str, Any]: | |
| + return {"dataset":"Caltech101","dataset_version":"torchvision Caltech101 / official DISEF CoOp split", | |
| + "split_sha256":sha256_file(Path(args.split_csv)),"selected_sources_sha256":selected_sha,"classes":CLASS_FOLDERS, | |
| + "fewshot":16,"split_seed":args.split_seed,"global_seed":args.global_seed,"images_per_class":args.images_per_class, | |
| + "diffusion_model":args.diffusion_model,"caption_model":args.caption_model,"filter_model":args.filter_model, | |
| + "filter_backend":"Qwen3-VL-Embedding-2B (SPC adaptation)","resolution":512, | |
| + "scheduler":"DPMSolverMultistepScheduler / DPM-Solver++","num_inference_steps":20,"starting_step":15, | |
| + "strength":0.25,"guidance_scale":8.0,"caption_temperature":0.2,"cross_sample_caption":True, | |
| + "dtype":args.dtype,"batch_size":args.batch_size,"attention_backend":"diffusers default SDPA", | |
| + "jpeg_quality":95,"spc_commit":subprocess.check_output(["git","-C",str(SPC_ROOT),"rev-parse","HEAD"],text=True).strip()} | |
| + | |
| +def fingerprint(config: dict[str, Any]) -> str: | |
| + payload = json.dumps(config, sort_keys=True, separators=(",", ":")).encode() | |
| + return hashlib.sha256(payload).hexdigest() | |
| + | |
| + | |
| +def load_sources(data_root: Path, split_csv: Path, split_seed: int, limit_classes: int | None = None) -> tuple[dict[str,list[Path]],str]: | |
| + source_root=data_root/"caltech101"/"101_ObjectCategories" | |
| + if not source_root.is_dir(): raise FileNotFoundError(source_root) | |
| + df=pd.read_csv(split_csv) | |
| + if list(df.columns)!=["split","filename"]: raise ValueError(df.columns.tolist()) | |
| + train=df[df["split"]=="train"]; folders=CLASS_FOLDERS[:limit_classes] if limit_classes else CLASS_FOLDERS | |
| + rng=random.Random(split_seed); selected={}; selected_rel=[] | |
| + for folder in folders: | |
| + sf=FOLDER_TO_SOURCE[folder]; rows=[x for x in train["filename"].tolist() if Path(x).parent.name==sf] | |
| + if len(rows)<16: raise ValueError(f"{folder} has only {len(rows)} train rows") | |
| + chosen=rng.sample(rows,16); paths=[source_root/rel for rel in chosen]; missing=[str(p) for p in paths if not p.is_file()] | |
| + if missing: raise FileNotFoundError(missing[:3]) | |
| + selected[folder]=paths; selected_rel.extend([f"{folder}/{p.name}" for p in paths]) | |
| + return selected,hashlib.sha256(json.dumps(selected_rel,separators=(",",":")).encode()).hexdigest() | |
| + | |
| +def ensure_layout(run_root: Path) -> dict[str, Path]: | |
| + paths = { | |
| + "root": run_root, | |
| + "images": run_root / "images", | |
| + "metadata": run_root / "metadata", | |
| + "state": run_root / "state", | |
| + "logs": run_root / "logs", | |
| + "artifacts": run_root / "artifacts", | |
| + "figures": run_root / "artifacts/figures", | |
| + "checkpoints": run_root / "checkpoints", | |
| + "quarantine": run_root / "state/quarantine", | |
| + } | |
| + for p in paths.values(): | |
| + p.mkdir(parents=True, exist_ok=True) | |
| + return paths | |
| + | |
| + | |
| +def base_state(run_id: str, fp: str, target: int) -> dict[str, Any]: | |
| + return { | |
| + "schema_version": 1, "run_id": run_id, "config_fingerprint": fp, | |
| + "hostname": socket.gethostname(), "status": "INITIALIZING", "created_at": now_iso(), | |
| + "updated_at": now_iso(), "generator_pid": None, "supervisor_pid": None, | |
| + "completed": 0, "target": target, "failed": 0, "retried": 0, | |
| + "remote_backed_up_count": 0, "latest_remote_checkpoint": None, | |
| + "current_class": None, "current_source": None, "records": {}, "last_event": "initialized", | |
| + } | |
| + | |
| + | |
| +def read_state(path: Path) -> dict[str, Any] | None: | |
| + if not path.exists(): | |
| + return None | |
| + return json.loads(path.read_text()) | |
| + | |
| + | |
| +def save_heartbeat(paths: dict[str, Path], state: dict[str, Any]) -> None: | |
| + heartbeat = {k: state.get(k) for k in [ | |
| + "run_id", "status", "completed", "target", "current_class", "current_source", | |
| + "failed", "retried", "latest_remote_checkpoint", "remote_backed_up_count", | |
| + "generator_pid", "supervisor_pid", "hostname", "last_event" | |
| + ]} | |
| + heartbeat["timestamp"] = now_iso() | |
| + atomic_json(paths["state"] / "heartbeat.json", heartbeat) | |
| + | |
| + | |
| +def append_metric(paths: dict[str, Path], state: dict[str, Any], started: float) -> None: | |
| + metric_path = paths["metadata"] / "runtime_metrics.csv" | |
| + elapsed = max(time.time() - started, 0.001) | |
| + gpu_util = vram_used = vram_total = 0.0 | |
| + try: | |
| + line = subprocess.check_output([ | |
| + "nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total", | |
| + "--format=csv,noheader,nounits" | |
| + ], text=True).splitlines()[0] | |
| + gpu_util, vram_used, vram_total = [float(x.strip()) for x in line.split(",")] | |
| + except Exception: | |
| + pass | |
| + usage = shutil.disk_usage(paths["root"]) | |
| + row = { | |
| + "timestamp": now_iso(), "elapsed_seconds": round(elapsed, 3), | |
| + "completed": state["completed"], "failed": state["failed"], "retried": state["retried"], | |
| + "images_per_minute": round(state["completed"] / elapsed * 60, 4), | |
| + "average_images_per_minute": round(state["completed"] / elapsed * 60, 4), | |
| + "gpu_util_percent": gpu_util, "vram_used_mb": vram_used, "vram_total_mb": vram_total, | |
| + "disk_used_gb": round((usage.total - usage.free) / 1e9, 3), | |
| + "disk_free_gb": round(usage.free / 1e9, 3), | |
| + "remote_backed_up_count": state.get("remote_backed_up_count", 0), | |
| + } | |
| + exists = metric_path.exists() | |
| + with metric_path.open("a", newline="") as f: | |
| + w = csv.DictWriter(f, fieldnames=list(row)) | |
| + if not exists: | |
| + w.writeheader() | |
| + w.writerow(row) | |
| + f.flush(); os.fsync(f.fileno()) | |
| + | |
| + | |
| +def dataset_smoke(args: argparse.Namespace) -> None: | |
| + selected,digest=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,args.limit_classes); df=pd.read_csv(args.split_csv) | |
| + print(json.dumps({"status":"PASS","dataset":"Caltech101","class_count":len(CLASS_FOLDERS),"selected_class_count":len(selected), | |
| + "split_counts":dict(Counter(df["split"])),"shots_per_class":{k:len(v) for k,v in selected.items()}, | |
| + "selected_sources_sha256":digest,"source_categories":{k:FOLDER_TO_SOURCE[k] for k in list(selected)[:8]}, | |
| + "sample_paths":[str(v[0]) for v in selected.values()][:5],"prompt_examples":[f"a photo of a {FOLDER_TO_LABEL[k]}." for k in list(selected)[:5]], | |
| + "expected_total":len(selected)*args.images_per_class},indent=2)) | |
| + | |
| +def resolve_model_revisions(args: argparse.Namespace) -> dict[str, str]: | |
| + from huggingface_hub import HfApi | |
| + api = HfApi(token=os.environ.get("HF_TOKEN")) | |
| + revisions = {} | |
| + for key, model in [("diffusion", args.diffusion_model), ("caption", args.caption_model), ("filter", args.filter_model)]: | |
| + revisions[key] = api.model_info(model).sha | |
| + return revisions | |
| + | |
| + | |
| +def load_captioner(args: argparse.Namespace): | |
| + import torch | |
| + from transformers import AutoProcessor, LlavaForConditionalGeneration | |
| + dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16 | |
| + processor = AutoProcessor.from_pretrained(args.caption_model) | |
| + model = LlavaForConditionalGeneration.from_pretrained( | |
| + args.caption_model, torch_dtype=dtype, low_cpu_mem_usage=True | |
| + ).eval().to("cuda") | |
| + return processor, model | |
| + | |
| + | |
| +def caption_one(processor, model, path: Path, class_label: str, seed: int) -> str: | |
| + import torch | |
| + query = f"Provide a detailed caption for the image focusing on the object, knowing it's a {class_label}." | |
| + prompt = f"USER: <image>\n{query}\nASSISTANT:" | |
| + image = padded_image(path) | |
| + inputs = processor(images=image, text=prompt, return_tensors="pt") | |
| + inputs = {k: v.to(model.device) for k, v in inputs.items()} | |
| + torch.manual_seed(seed); torch.cuda.manual_seed_all(seed) | |
| + with torch.inference_mode(): | |
| + output = model.generate(**inputs, do_sample=True, temperature=0.2, max_new_tokens=160, use_cache=True) | |
| + new_tokens = output[:, inputs["input_ids"].shape[1]:] | |
| + text = processor.batch_decode(new_tokens, skip_special_tokens=True)[0].strip() | |
| + if len(text) < 10: | |
| + text = f"A detailed photograph of a {class_label}, a type of pet." | |
| + return text | |
| + | |
| + | |
| +def ensure_captions(args: argparse.Namespace, selected: dict[str, list[Path]], paths: dict[str, Path]) -> dict[str, str]: | |
| + cache_path = Path(args.caption_cache) | |
| + cache_path.parent.mkdir(parents=True, exist_ok=True) | |
| + cache = json.loads(cache_path.read_text()) if cache_path.exists() else {} | |
| + needed = [(folder, p) for folder, items in selected.items() for p in items if str(p) not in cache] | |
| + if not needed: | |
| + return cache | |
| + state_path = paths["state"] / "caltech101_disef_state.json" | |
| + state = read_state(state_path) or {} | |
| + state["last_event"] = f"captioning {len(needed)} uncached support images" | |
| + state["updated_at"] = now_iso(); atomic_json(state_path, state); save_heartbeat(paths, state) | |
| + processor, model = load_captioner(args) | |
| + try: | |
| + for i, (folder, source) in enumerate(needed): | |
| + cache[str(source)] = caption_one(processor, model, source, FOLDER_TO_LABEL[folder], args.global_seed + i) | |
| + atomic_json(cache_path, cache) | |
| + finally: | |
| + del model, processor | |
| + gc.collect() | |
| + import torch | |
| + torch.cuda.empty_cache() | |
| + return cache | |
| + | |
| + | |
| +def load_diffusion(args: argparse.Namespace): | |
| + import torch | |
| + from diffusers import DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline | |
| + dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16 | |
| + pipe = StableDiffusionImg2ImgPipeline.from_pretrained(args.diffusion_model, torch_dtype=dtype) | |
| + pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type="dpmsolver++") | |
| + pipe.set_progress_bar_config(disable=True) | |
| + pipe.to("cuda") | |
| + return pipe | |
| + | |
| + | |
| +def load_qwen(args: argparse.Namespace): | |
| + import torch | |
| + source = Path(args.qwen_source) | |
| + if not source.is_dir(): | |
| + raise FileNotFoundError(f"Qwen source repository missing: {source}") | |
| + sys.path.insert(0, str(source)) | |
| + from src.models.qwen3_vl_embedding import Qwen3VLEmbedder | |
| + dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16 | |
| + model = Qwen3VLEmbedder( | |
| + model_name_or_path=args.filter_model, torch_dtype=dtype, | |
| + attn_implementation="sdpa", max_length=512, max_pixels=512 * 512, | |
| + ) | |
| + model.model.eval() | |
| + return model | |
| + | |
| + | |
| +def build_prototypes(qwen, folders: list[str]): | |
| + import torch | |
| + import torch.nn.functional as F | |
| + vectors = [] | |
| + with torch.inference_mode(): | |
| + for folder in folders: | |
| + label = FOLDER_TO_LABEL[folder] | |
| + embeddings = qwen.process([{"text": template.format(label) + "."} for template in TEMPLATES]) | |
| + vectors.append(F.normalize(embeddings.mean(dim=0), dim=-1)) | |
| + return torch.stack(vectors) | |
| + | |
| + | |
| +def qwen_predict(qwen, prototypes, images: list[Image.Image]) -> tuple[list[int], list[float]]: | |
| + import torch | |
| + import torch.nn.functional as F | |
| + with torch.inference_mode(): | |
| + features = qwen.process([{"image": image} for image in images]) | |
| + features = F.normalize(features, dim=-1) | |
| + sims = features.to(prototypes.dtype) @ prototypes.T | |
| + pred = sims.argmax(dim=-1) | |
| + return pred.cpu().tolist(), [float(sims[i, pred[i]].item()) for i in range(len(images))] | |
| + | |
| + | |
| +def model_smoke(args: argparse.Namespace) -> None: | |
| + revisions = resolve_model_revisions(args) | |
| + processor, captioner = load_captioner(args) | |
| + del captioner, processor; gc.collect() | |
| + import torch | |
| + torch.cuda.empty_cache() | |
| + pipe = load_diffusion(args) | |
| + del pipe; gc.collect(); torch.cuda.empty_cache() | |
| + qwen = load_qwen(args) | |
| + prototypes = build_prototypes(qwen, CLASS_FOLDERS[:2]) | |
| + print(json.dumps({"status": "PASS", "revisions": revisions, "qwen_prototype_shape": list(prototypes.shape), | |
| + "gpu": torch.cuda.get_device_name(0), "dtype": args.dtype, | |
| + "vram_allocated_mb": round(torch.cuda.memory_allocated()/2**20, 1)}, indent=2)) | |
| + del qwen, prototypes; gc.collect(); torch.cuda.empty_cache() | |
| + | |
| + | |
| +def task_seed(global_seed: int, class_idx: int, synth_idx: int, attempt: int) -> int: | |
| + return global_seed + class_idx * 1_000_000 + synth_idx * 1_000 + attempt | |
| + | |
| + | |
| +def save_candidate(image: Image.Image, final_path: Path) -> None: | |
| + final_path.parent.mkdir(parents=True, exist_ok=True) | |
| + tmp = final_path.with_name(final_path.name + f".tmp.{os.getpid()}") | |
| + image.convert("RGB").save(tmp, format="JPEG", quality=95, optimize=True) | |
| + with tmp.open("rb") as f: | |
| + os.fsync(f.fileno()) | |
| + os.replace(tmp, final_path) | |
| + ok, reason = validate_image(final_path, class_folder=final_path.parent.name) | |
| + if not ok: | |
| + raise RuntimeError(f"Saved image failed validation: {reason}") | |
| + | |
| + | |
| +def reconcile_existing(paths: dict[str, Path], state: dict[str, Any]) -> None: | |
| + records = state.setdefault("records", {}) | |
| + completed = 0 | |
| + for task_id, rec in list(records.items()): | |
| + if rec.get("completion_status") != "COMPLETE": | |
| + continue | |
| + out = paths["root"] / rec["synthetic_filename"] | |
| + ok, reason = validate_image(out, class_folder=rec["class_folder"]) | |
| + if ok: | |
| + completed += 1 | |
| + else: | |
| + if out.exists(): | |
| + quarantine = paths["quarantine"] / f"{out.name}.{int(time.time())}.corrupt" | |
| + quarantine.parent.mkdir(parents=True, exist_ok=True) | |
| + os.replace(out, quarantine) | |
| + rec["completion_status"] = "INVALIDATED" | |
| + rec["local_validation"] = reason | |
| + state["completed"] = completed | |
| + | |
| + | |
| +def maybe_checkpoint(run_root: Path, state: dict[str, Any], force: bool = False) -> None: | |
| + pending = state["completed"] - state.get("remote_backed_up_count", 0) | |
| + last_ts = state.get("latest_remote_checkpoint_epoch") or time.time() | |
| + age = time.time() - last_ts | |
| + should = force or (pending >= 50 and age >= 300) or (pending > 0 and age >= 900) | |
| + if not should or not os.environ.get("HF_TOKEN"): | |
| + return | |
| + try: | |
| + import caltech101_hf | |
| + caltech101_hf.checkpoint(run_root, force=force) | |
| + refreshed = json.loads((run_root / "state" / "caltech101_disef_state.json").read_text()) | |
| + state.clear() | |
| + state.update(refreshed) | |
| + except Exception as exc: | |
| + state["last_event"] = f"HF checkpoint deferred: {type(exc).__name__}: {exc}" | |
| + | |
| + | |
| +def _save_record(paths,state,manifest_path,folder,class_idx,synth_idx,source,caption_source,prompt,seed,pred,score,image,attempt,args): | |
| + out_dir=paths["images"]/folder; out_dir.mkdir(parents=True,exist_ok=True) | |
| + out=out_dir/f"{folder}__src-{source.stem}__syn-{synth_idx:03d}__seed-{seed}.jpg" | |
| + if out.exists(): | |
| + ok,reason=validate_image(out,class_folder=folder) | |
| + if ok: raise RuntimeError(f"valid output collision: {out}") | |
| + os.replace(out,paths["quarantine"]/(out.name+".corrupt")) | |
| + save_candidate(image,out); tid=f"{class_idx:03d}:{synth_idx:03d}" | |
| + rec={"event":"LOCAL_COMPLETE","timestamp":now_iso(),"task_id":tid,"synthetic_filename":str(out.relative_to(paths["root"])), | |
| + "class_id":class_idx,"class_folder":folder,"class_name":FOLDER_TO_LABEL[folder],"source_image":str(source), | |
| + "caption_source_image":str(caption_source),"caption":args._captions[str(caption_source)],"prompt":prompt,"random_seed":seed, | |
| + "generation_parameters":{"model":args.diffusion_model,"steps":20,"starting_step":15,"strength":0.25,"guidance_scale":8.0, | |
| + "scheduler":"DPM-Solver++","resolution":[512,512],"batch_size":args.batch_size}, | |
| + "filter_model":args.filter_model,"filter_predicted_class":CLASS_FOLDERS[pred],"filter_similarity":score, | |
| + "filter_pass":pred==class_idx,"fallback_accept":pred!=class_idx,"completion_status":"COMPLETE","local_validation":"ok", | |
| + "retry_count":attempt,"remote_chunk":None,"sha256":sha256_file(out),"bytes":out.stat().st_size} | |
| + append_jsonl(manifest_path,rec); state["records"][tid]=rec; state["completed"]+=1; state.update(updated_at=now_iso(),last_event=f"completed {tid}") | |
| + atomic_json(paths["state"]/"caltech101_disef_state.json",state); save_heartbeat(paths,state); return rec | |
| + | |
| +def benchmark(args): | |
| + import torch | |
| + selected,_=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,1); folder=list(selected)[0] | |
| + pipe=load_diffusion(args); qwen=load_qwen(args); prototypes=build_prototypes(qwen,[folder]) | |
| + base = (selected[folder] * ((max(args.benchmark_batches) + len(selected[folder]) - 1) // len(selected[folder])))[:max(args.benchmark_batches)] | |
| + rows=[]; | |
| + for bs in args.benchmark_batches: | |
| + try: | |
| + imgs=[padded_image(p) for p in base[:bs]]; ps=[f"a detailed photo of a {FOLDER_TO_LABEL[folder]}." for _ in imgs] | |
| + with torch.inference_mode(): pipe(prompt=ps,image=imgs,strength=0.25,guidance_scale=8.0,num_inference_steps=20, | |
| + generator=[torch.Generator(device="cuda").manual_seed(args.global_seed+i) for i in range(bs)],output_type="pil") | |
| + torch.cuda.synchronize(); torch.cuda.reset_peak_memory_stats(); start=time.perf_counter() | |
| + for _ in range(2): | |
| + with torch.inference_mode(): out=pipe(prompt=ps,image=imgs,strength=0.25,guidance_scale=8.0,num_inference_steps=20, | |
| + generator=[torch.Generator(device="cuda").manual_seed(args.global_seed+1000*_+i) for i in range(bs)],output_type="pil").images | |
| + qwen_predict(qwen,prototypes,out) | |
| + torch.cuda.synchronize(); elapsed=time.perf_counter()-start | |
| + util=float(subprocess.check_output(["nvidia-smi","--query-gpu=utilization.gpu","--format=csv,noheader,nounits"],text=True).strip()) | |
| + rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1), | |
| + "gpu_util_percent":util,"images_per_second":2*bs/elapsed,"images_per_minute":120*bs/elapsed,"duration_seconds":elapsed,"status":"PASS"}) | |
| + del out; torch.cuda.empty_cache() | |
| + except torch.cuda.OutOfMemoryError: | |
| + rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1), | |
| + "gpu_util_percent":0,"images_per_second":0,"images_per_minute":0,"duration_seconds":0,"status":"OOM"}); torch.cuda.empty_cache(); break | |
| + except Exception as exc: | |
| + rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1), | |
| + "gpu_util_percent":0,"images_per_second":0,"images_per_minute":0,"duration_seconds":0,"status":type(exc).__name__}); break | |
| + out_path=Path(args.benchmark_output); out_path.parent.mkdir(parents=True,exist_ok=True); pd.DataFrame(rows).to_csv(out_path,index=False); print(json.dumps({"status":"PASS","rows":rows},indent=2)) | |
| + del pipe,qwen,prototypes; gc.collect(); torch.cuda.empty_cache() | |
| + | |
| +def generate(args): | |
| + import torch | |
| + run_root=Path(args.run_root).resolve(); paths=ensure_layout(run_root); selected,selected_sha=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,args.limit_classes) | |
| + folders=list(selected); scientific=canonical_scientific_config(args,selected_sha); scientific["classes"]=folders; fp=fingerprint(scientific); config_path=paths["metadata"]/"generation_config.json" | |
| + if config_path.exists(): | |
| + prior=json.loads(config_path.read_text()) | |
| + if prior.get("config_fingerprint")!=fp: raise RuntimeError(f"incompatible resume: {prior.get('config_fingerprint')} != {fp}") | |
| + run_id=prior["run_id"] | |
| + else: | |
| + run_id=args.run_id or f"caltech101_disef_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}_{fp[:12]}" | |
| + atomic_json(config_path,dict(scientific,config_fingerprint=fp,run_id=run_id,expected_total=len(folders)*args.images_per_class, | |
| + model_revisions=resolve_model_revisions(args),created_at=now_iso())) | |
| + target=len(folders)*args.images_per_class; state_path=paths["state"]/"caltech101_disef_state.json"; state=read_state(state_path) or base_state(run_id,fp,target) | |
| + if state["config_fingerprint"]!=fp: raise RuntimeError("state/config fingerprint mismatch") | |
| + lock_path=paths["state"]/"run.lock"; old_lock=read_state(lock_path) | |
| + if old_lock and pid_alive(old_lock.get("generator_pid")) and old_lock.get("generator_pid")!=os.getpid(): raise RuntimeError(f"live generator {old_lock.get('generator_pid')}") | |
| + supervisor_pid=old_lock.get("supervisor_pid") if old_lock else os.environ.get("DISEF_SUPERVISOR_PID") | |
| + atomic_json(lock_path,{"run_id":run_id,"config_fingerprint":fp,"generator_pid":os.getpid(),"supervisor_pid":int(supervisor_pid) if supervisor_pid else None,"hostname":socket.gethostname(),"updated_at":now_iso()}) | |
| + state.update(status="RUNNING",generator_pid=os.getpid(),supervisor_pid=int(supervisor_pid) if supervisor_pid else None,hostname=socket.gethostname(),updated_at=now_iso(),last_event="reconciling outputs") | |
| + reconcile_existing(paths,state); atomic_json(state_path,state); save_heartbeat(paths,state) | |
| + if args.init_only: | |
| + state.update(status="INITIALIZED",updated_at=now_iso(),last_event="initialized"); atomic_json(state_path,state); save_heartbeat(paths,state); return | |
| + manifest_path=paths["metadata"]/"caltech101_disef_manifest.jsonl"; started=time.time(); last_metric=0.0; captions=ensure_captions(args,selected,paths); args._captions=captions | |
| + state["last_event"]="loading diffusion and Qwen models"; atomic_json(state_path,state); save_heartbeat(paths,state); pipe=load_diffusion(args); qwen=load_qwen(args); prototypes=build_prototypes(qwen,folders) | |
| + state["last_event"]="models loaded; batched generation active"; atomic_json(state_path,state); save_heartbeat(paths,state); attempts={}; new_count=0 | |
| + try: | |
| + for class_idx,folder in enumerate(folders): | |
| + sources=selected[folder]; pending=[] | |
| + for i in range(args.images_per_class): | |
| + tid=f"{class_idx:03d}:{i:03d}"; rec=state["records"].get(tid) | |
| + if not (rec and rec.get("completion_status")=="COMPLETE" and validate_image(paths["root"]/rec["synthetic_filename"],class_folder=folder)[0]): pending.append(i) | |
| + while pending: | |
| + if args.max_new is not None and new_count>=args.max_new: | |
| + state.update(status="STOPPED",updated_at=now_iso(),last_event=f"intentional max-new stop at {new_count}"); atomic_json(state_path,state); save_heartbeat(paths,state); maybe_checkpoint(run_root,state,True); return | |
| + remaining_cap = (args.max_new - new_count) if args.max_new is not None else args.batch_size | |
| + batch_ids=pending[:min(args.batch_size, remaining_cap)]; rest=pending[len(batch_ids):]; tasks=[]; images=[]; prompts=[]; gens=[] | |
| + for synth_idx in batch_ids: | |
| + tid=f"{class_idx:03d}:{synth_idx:03d}"; attempt=attempts.get(tid,0); seed=task_seed(args.global_seed,class_idx,synth_idx,attempt); rng=random.Random(seed); ai=rng.randrange(len(sources)); choices=[j for j in range(len(sources)) if j!=ai]; ci=choices[rng.randrange(len(choices))]; source=sources[ai]; caption_source=sources[ci] | |
| + tasks.append((synth_idx,tid,attempt,seed,source,caption_source)); images.append(padded_image(source)); prompts.append(captions[str(caption_source)]); gens.append(torch.Generator(device="cuda").manual_seed(seed)) | |
| + state.update(current_class=folder,current_source=str(tasks[0][4]),updated_at=now_iso(),last_event=f"generating batch {len(tasks)}"); save_heartbeat(paths,state) | |
| + try: | |
| + with torch.inference_mode(): result=pipe(prompt=prompts,image=images,strength=0.25,guidance_scale=8.0,num_inference_steps=20,generator=gens,output_type="pil") | |
| + pred,scores=qwen_predict(qwen,prototypes,result.images); retry=[] | |
| + for j,(synth_idx,tid,attempt,seed,source,caption_source) in enumerate(tasks): | |
| + if pred[j]!=class_idx and attempt<args.max_filter_attempts-1: | |
| + attempts[tid]=attempt+1; state["retried"]+=1; retry.append(synth_idx); continue | |
| + _save_record(paths,state,manifest_path,folder,class_idx,synth_idx,source,caption_source,prompts[j],seed,pred[j],scores[j],result.images[j],attempt,args) | |
| + new_count += 1 | |
| + pending=retry+rest; atomic_json(state_path,state); save_heartbeat(paths,state) | |
| + if time.time()-last_metric>=60 or state["completed"]==target: append_metric(paths,state,started); last_metric=time.time() | |
| + maybe_checkpoint(run_root,state) | |
| + except torch.cuda.OutOfMemoryError: | |
| + torch.cuda.empty_cache(); gc.collect() | |
| + if len(tasks)>1: | |
| + args.batch_size=max(1,len(tasks)//2); state["last_event"]=f"OOM; operational batch reduced to {args.batch_size}"; state["retried"]+=len(tasks); attempts.update({t[1]:t[2]+1 for t in tasks}); pending=batch_ids+rest; save_heartbeat(paths,state); continue | |
| + raise | |
| + except Exception as exc: | |
| + state["last_event"]=f"batch error {type(exc).__name__}: {exc}"; state["retried"]+=len(tasks); attempts.update({t[1]:t[2]+1 for t in tasks}); pending=batch_ids+rest; (paths["logs"]/"generation_errors.log").open("a").write(f"[{now_iso()}] {state['last_event']}\\n{traceback.format_exc()}\\n") | |
| + if any(attempts[t[1]]>=3 for t in tasks): state["failed"]+=len(tasks); pending=rest | |
| + save_heartbeat(paths,state) | |
| + state.update(status="COMPLETED" if state["completed"]==target else "PARTIAL",updated_at=now_iso(),current_class=None,current_source=None,last_event="generation loop finished"); atomic_json(state_path,state); save_heartbeat(paths,state); append_metric(paths,state,started); maybe_checkpoint(run_root,state,True) | |
| + except BaseException as exc: | |
| + state.update(status="BLOCKED" if isinstance(exc,RuntimeError) else "STOPPED",updated_at=now_iso(),last_event=f"{type(exc).__name__}: {exc}"); atomic_json(state_path,state); save_heartbeat(paths,state) | |
| + try: maybe_checkpoint(run_root,state,True) | |
| + except Exception: pass | |
| + raise | |
| + finally: | |
| + del pipe,qwen,prototypes; gc.collect(); torch.cuda.empty_cache() | |
| + | |
| +def build_parser(): | |
| + p=argparse.ArgumentParser(description=__doc__); p.add_argument("--mode",choices=["dataset-smoke","model-smoke","benchmark","generate"],default="generate") | |
| + p.add_argument("--data-root",default=str(DEFAULT_DATA_ROOT)); p.add_argument("--split-csv",default=str(DEFAULT_SPLIT)); p.add_argument("--run-root",default="/marimo/runs/disef_caltech101") | |
| + p.add_argument("--caption-cache",default=str(DEFAULT_CACHE_ROOT/"caltech101_llava_captions_seed1.json")); p.add_argument("--qwen-source",default=str(DEFAULT_QWEN_SOURCE)) | |
| + p.add_argument("--diffusion-model",default="SG161222/Realistic_Vision_V2.0"); p.add_argument("--caption-model",default="llava-hf/llava-1.5-7b-hf"); p.add_argument("--filter-model",default="Qwen/Qwen3-VL-Embedding-2B") | |
| + p.add_argument("--split-seed",type=int,default=1); p.add_argument("--global-seed",type=int,default=20261005); p.add_argument("--images-per-class",type=int,default=64); p.add_argument("--batch-size",type=int,default=1) | |
| + p.add_argument("--dtype",choices=["float16","bfloat16"],default="bfloat16"); p.add_argument("--limit-classes",type=int); p.add_argument("--max-new",type=int); p.add_argument("--max-filter-attempts",type=int,default=100); p.add_argument("--run-id"); p.add_argument("--init-only",action="store_true") | |
| + p.add_argument("--benchmark-batches",type=int,nargs="+",default=[1,2,4,8,16,32]); p.add_argument("--benchmark-output",default="/marimo/runs/disef_caltech101/metadata/optimization_benchmark.csv"); return p | |
| +def torch_cuda_available(): | |
| + try: import torch; return torch.cuda.is_available() | |
| + except: return False | |
| +def main(): | |
| + args=build_parser().parse_args() | |
| + if args.mode in {"model-smoke","benchmark","generate"} and not torch_cuda_available(): raise RuntimeError("CUDA required") | |
| + if args.mode=="dataset-smoke": dataset_smoke(args) | |
| + elif args.mode=="model-smoke": model_smoke(args) | |
| + elif args.mode=="benchmark": benchmark(args) | |
| + else: generate(args) | |
| +if __name__=="__main__": main() | |
| diff --git a/DisEF/caltech101_finalize.py b/DisEF/caltech101_finalize.py | |
| new file mode 100644 | |
| index 0000000..a510010 | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_finalize.py | |
| +#!/usr/bin/env python3 | |
| +from __future__ import annotations | |
| +import argparse,csv,hashlib,json,os,shutil,sys,tempfile,zipfile | |
| +from collections import Counter | |
| +from datetime import datetime,timezone | |
| +from pathlib import Path | |
| +sys.path.insert(0,str(Path(__file__).resolve().parent)) | |
| +from caltech101_disef import validate_image,atomic_json | |
| + | |
| +def sha(p): | |
| + h=hashlib.sha256() | |
| + with p.open('rb') as f: | |
| + for b in iter(lambda:f.read(8<<20),b''): h.update(b) | |
| + return h.hexdigest() | |
| +def add(z,src,arc): | |
| + if src.exists() and src.is_file(): z.write(src,arcname=str(arc)) | |
| +def main(): | |
| + ap=argparse.ArgumentParser(); ap.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); ap.add_argument('--output-dir',default='/marimo/final'); ap.add_argument('--report',default='/marimo/DISEF_CALTECH101_RUN_REPORT.md'); ap.add_argument('--patch',default='/marimo/DISEF_CALTECH101_PATCH.diff'); ap.add_argument('--upload',action='store_true'); a=ap.parse_args() | |
| + root=Path(a.run_root).resolve(); out=Path(a.output_dir).resolve(); out.mkdir(parents=True,exist_ok=True) | |
| + state=json.loads((root/'state/caltech101_disef_state.json').read_text()); config=json.loads((root/'metadata/generation_config.json').read_text()) | |
| + if state['status']!='COMPLETED' or state['completed']!=state['target']: raise RuntimeError(f"run incomplete: {state['status']} {state['completed']}/{state['target']}") | |
| + records=[r for _,r in sorted(state['records'].items()) if r.get('completion_status')=='COMPLETE'] | |
| + if len(records)!=state['target']: raise RuntimeError('complete record count mismatch') | |
| + invalid=[]; names=[]; counts=Counter(); sizes=Counter(); formats=Counter() | |
| + for r in records: | |
| + q=root/r['synthetic_filename']; ok,reason=validate_image(q,class_folder=r['class_folder']); | |
| + if not ok: invalid.append((str(q),reason)) | |
| + names.append(r['synthetic_filename']); counts[r['class_folder']]+=1 | |
| + from PIL import Image | |
| + with Image.open(q) as im: im.load(); sizes[str(im.size)]+=1; formats[im.format]+=1 | |
| + expected=config['images_per_class']; bad_counts={c:counts.get(c,0) for c in config['classes'] if counts.get(c,0)!=expected} | |
| + if invalid or bad_counts or len(names)!=len(set(names)): raise RuntimeError(f'integrity failure invalid={invalid[:3]} bad_counts={bad_counts} duplicate={len(names)-len(set(names))}') | |
| + # Final manifest CSV from authoritative state records. | |
| + csv_path=root/'metadata/caltech101_disef_manifest.csv'; fields=['synthetic_filename','class_id','class_folder','class_name','source_image','caption_source_image','caption','prompt','random_seed','filter_model','filter_predicted_class','filter_similarity','filter_pass','fallback_accept','completion_status','local_validation','retry_count','remote_chunk','sha256','bytes'] | |
| + with csv_path.open('w',newline='') as f: | |
| + w=csv.DictWriter(f,fieldnames=fields,extrasaction='ignore'); w.writeheader(); w.writerows(records) | |
| + # Ensure figures/previews current. | |
| + import subprocess | |
| + subprocess.run([sys.executable,str(Path(__file__).with_name('caltech101_monitor.py')),'--run-root',str(root)],check=True) | |
| + images_zip=out/'caltech101_disef_images.zip' | |
| + with zipfile.ZipFile(images_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=1) as z: | |
| + for r in records: | |
| + q=root/r['synthetic_filename']; z.write(q,arcname=str(Path(r['class_folder'])/q.name)) | |
| + # Strict extraction and ImageFolder validation. | |
| + with tempfile.TemporaryDirectory(prefix='caltech101_zip_validate_',dir='/marimo') as td: | |
| + td=Path(td) | |
| + with zipfile.ZipFile(images_zip) as z: | |
| + members=z.infolist() | |
| + if any(Path(m.filename).suffix.lower() not in {'.jpg','.jpeg','.png'} for m in members if not m.is_dir()): raise RuntimeError('non-image member in image zip') | |
| + if any(len(Path(m.filename).parts)!=2 for m in members if not m.is_dir()): raise RuntimeError('image zip root layout invalid') | |
| + z.extractall(td) | |
| + from torchvision.datasets import ImageFolder | |
| + ds=ImageFolder(td) | |
| + if len(ds)!=state['target'] or ds.classes!=sorted(config['classes']): raise RuntimeError(f'ImageFolder mismatch {len(ds)} classes={ds.classes}') | |
| + for q,_ in ds.samples: | |
| + ok,reason=validate_image(Path(q),class_folder=Path(q).parent.name) | |
| + if not ok: raise RuntimeError(f'extracted image invalid: {q}: {reason}') | |
| + checksums=out/'caltech101_disef_checksums.txt' | |
| + # Artifacts ZIP. | |
| + artifacts_zip=out/'caltech101_disef_artifacts.zip' | |
| + amap={ | |
| + root/'metadata/caltech101_disef_manifest.jsonl':'caltech101_disef_artifacts/metadata/caltech101_disef_manifest.jsonl', csv_path:'caltech101_disef_artifacts/metadata/caltech101_disef_manifest.csv', | |
| + root/'metadata/generation_config.json':'caltech101_disef_artifacts/metadata/generation_config.json', root/'metadata/run_metadata.json':'caltech101_disef_artifacts/metadata/run_metadata.json', | |
| + root/'metadata/runtime_metrics.csv':'caltech101_disef_artifacts/metadata/runtime_metrics.csv', Path(a.report):'caltech101_disef_artifacts/reports/DISEF_CALTECH101_RUN_REPORT.md', | |
| + root/'logs/generation.log':'caltech101_disef_artifacts/logs/generation.log', root/'logs/supervisor.log':'caltech101_disef_artifacts/logs/supervisor.log', | |
| + root/'logs/optimization.log':'caltech101_disef_artifacts/logs/optimization.log', | |
| + } | |
| + for fig in (root/'artifacts/figures').glob('*.png'): amap[fig]=f'caltech101_disef_artifacts/figures/{fig.name}' | |
| + for name in ['requirements_molab.txt','run_caltech101_disef.sh','resume_caltech101_disef.sh','restore_caltech101_disef_from_hf.sh','status_caltech101_disef.sh']: | |
| + amap[Path(__file__).with_name(name)]=f'caltech101_disef_artifacts/reproducibility/{name}' | |
| + with zipfile.ZipFile(artifacts_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z: | |
| + for src,arc in amap.items(): add(z,src,arc) | |
| + # Corrected source ZIP. | |
| + code_zip=out/'caltech101_disef_code.zip'; spc=Path(__file__).resolve().parents[1] | |
| + code_files=[spc/'README.md',spc/'data_train.py',spc/'util_data.py',spc/'utils.py',spc/'DataSplit/split_coop_Caltech101.csv',spc/'DisEF/main_disef.py'] | |
| + code_files += [q for q in (spc/'DisEF').iterdir() if q.is_file() and q.suffix in {'.py','.sh','.txt','.md'}] | |
| + with zipfile.ZipFile(code_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z: | |
| + for src in sorted(set(code_files)): add(z,src,Path('caltech101_disef_code/SPC')/src.relative_to(spc)) | |
| + add(z,Path(a.patch),'caltech101_disef_code/DISEF_CALTECH101_PATCH.diff') | |
| + checksums.write_text(f"{sha(images_zip)} {images_zip.name}\n{sha(code_zip)} {code_zip.name}\n") | |
| + # The artifacts archive contains hashes of the two independent deliverables; | |
| + # its own final hash is recorded externally to avoid a self-referential hash. | |
| + with zipfile.ZipFile(artifacts_zip,'a',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z: z.write(checksums,'caltech101_disef_artifacts/checksums/caltech101_disef_checksums.txt') | |
| + lines=[f"{sha(q)} {q.name}" for q in [images_zip,artifacts_zip,code_zip]]; checksums.write_text('\n'.join(lines)+'\n') | |
| + summary={"status":"SUCCESS","run_id":state['run_id'],"config_fingerprint":state['config_fingerprint'],"expected":state['target'],"actual":len(records),"missing":0,"failed":state['failed'],"corrupt":0,"classes":len(counts),"per_class":dict(counts),"sizes":dict(sizes),"formats":dict(formats),"dataset_bytes":sum((root/r['synthetic_filename']).stat().st_size for r in records),"archives":{q.name:{"path":str(q),"bytes":q.stat().st_size,"sha256":sha(q)} for q in [images_zip,artifacts_zip,code_zip]},"validated_at":datetime.now(timezone.utc).isoformat()} | |
| + atomic_json(root/'metadata/final_validation.json',summary) | |
| + if a.upload: | |
| + from huggingface_hub import CommitOperationAdd,HfApi,hf_hub_download | |
| + api=HfApi(token=os.environ.get('HF_TOKEN')) | |
| + image_repo='DanielNguyen121/DisEF-datasets' | |
| + image_commit=api.create_commit(repo_id=image_repo,repo_type='dataset',operations=[CommitOperationAdd(path_in_repo=images_zip.name,path_or_fileobj=str(images_zip))],commit_message=f"DISEF CALTECH101 image dataset: {state['run_id']}") | |
| + if images_zip.name not in set(api.list_repo_files(image_repo,repo_type='dataset',revision=image_commit.oid)): raise RuntimeError('final image archive missing remotely') | |
| + downloaded=Path(hf_hub_download(image_repo,images_zip.name,repo_type='dataset',token=os.environ.get('HF_TOKEN'),revision=image_commit.oid,force_download=True)) | |
| + if sha(downloaded)!=sha(images_zip): raise RuntimeError('remote image checksum mismatch') | |
| + support_repo='DanielNguyen121/DisEF-Caltech101' | |
| + support=[artifacts_zip,code_zip,Path(a.report),Path(a.patch),csv_path,root/'metadata/generation_config.json',checksums] | |
| + support_commit=api.create_commit(repo_id=support_repo,repo_type='dataset',operations=[CommitOperationAdd(path_in_repo=q.name,path_or_fileobj=str(q)) for q in support],commit_message=f"DISEF CALTECH101 reproducibility deliverables: {state['run_id']}") | |
| + remote=set(api.list_repo_files(support_repo,repo_type='dataset',revision=support_commit.oid)); missing=[q.name for q in support if q.name not in remote] | |
| + if missing: raise RuntimeError(f'support remote files missing: {missing}') | |
| + summary['hf_image_commit']=image_commit.oid; summary['hf_support_commit']=support_commit.oid | |
| + summary['hf_image_path']=f'{image_repo}/{images_zip.name}'; summary['hf_support_paths']=[f'{support_repo}/{q.name}' for q in support] | |
| + atomic_json(root/'metadata/final_validation.json',summary) | |
| + print(json.dumps(summary,indent=2)) | |
| +if __name__=='__main__': main() | |
| diff --git a/DisEF/caltech101_hf.py b/DisEF/caltech101_hf.py | |
| new file mode 100644 | |
| index 0000000..f19f7e3 | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_hf.py | |
| +#!/usr/bin/env python3 | |
| +"""Immutable Hugging Face checkpoint and restore utilities for DISEF CALTECH101.""" | |
| +from __future__ import annotations | |
| +import argparse | |
| +import fcntl | |
| +import hashlib | |
| +import json | |
| +import os | |
| +import shutil | |
| +import tempfile | |
| +import time | |
| +import sys | |
| +import zipfile | |
| +from datetime import datetime, timezone | |
| +from pathlib import Path | |
| +from typing import Any | |
| + | |
| +from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download | |
| + | |
| +sys.path.insert(0, str(Path(__file__).resolve().parent)) | |
| + | |
| +REPO_ID = "DanielNguyen121/DisEF-Caltech101" | |
| +REPO_TYPE = "dataset" | |
| + | |
| + | |
| +def now_iso() -> str: | |
| + return datetime.now(timezone.utc).isoformat() | |
| + | |
| + | |
| +def atomic_json(path: Path, obj: Any) -> None: | |
| + path.parent.mkdir(parents=True, exist_ok=True) | |
| + tmp = path.with_name(path.name + f".tmp.{os.getpid()}") | |
| + with tmp.open("w") as f: | |
| + json.dump(obj, f, indent=2, sort_keys=True); f.flush(); os.fsync(f.fileno()) | |
| + os.replace(tmp, path) | |
| + | |
| + | |
| +def sha(path: Path) -> str: | |
| + h=hashlib.sha256() | |
| + with path.open("rb") as f: | |
| + for b in iter(lambda:f.read(8<<20),b""): h.update(b) | |
| + return h.hexdigest() | |
| + | |
| + | |
| +def append_backup_events(manifest: Path, records: list[dict], chunk_name: str) -> bytes: | |
| + content = manifest.read_bytes() if manifest.exists() else b"" | |
| + lines=[] | |
| + for rec in records: | |
| + lines.append(json.dumps({"event":"REMOTE_BACKED_UP","timestamp":now_iso(),"task_id":rec["task_id"], | |
| + "synthetic_filename":rec["synthetic_filename"],"remote_chunk":chunk_name},sort_keys=True)) | |
| + return content + (("\n" if content and not content.endswith(b"\n") else "") + "\n".join(lines) + "\n").encode() | |
| + | |
| + | |
| +def _checkpoint_unlocked(run_root: str | Path, force: bool=False, max_chunk_images: int=100) -> dict[str,Any]: | |
| + root=Path(run_root).resolve(); state_path=root/'state/caltech101_disef_state.json' | |
| + config=json.loads((root/'metadata/generation_config.json').read_text()) | |
| + state=json.loads(state_path.read_text()); run_id=state['run_id']; fp=state['config_fingerprint'] | |
| + if config['config_fingerprint']!=fp: raise RuntimeError('config/state fingerprint mismatch') | |
| + chunk_index_path=root/'metadata/chunk_index.json' | |
| + index=json.loads(chunk_index_path.read_text()) if chunk_index_path.exists() else {"run_id":run_id,"config_fingerprint":fp,"chunks":[]} | |
| + backed={r["task_id"] for c in index["chunks"] for r in c.get("records",[])} | |
| + pending=[r for tid,r in sorted(state.get('records',{}).items()) if r.get('completion_status')=='COMPLETE' and tid not in backed] | |
| + if not pending: | |
| + return {"status":"NOOP","remote_backed_up_count":len(backed)} | |
| + selected=pending[:max_chunk_images] | |
| + chunk_num=len(index['chunks'])+1; chunk_name=f"caltech101_disef_chunk_{chunk_num:06d}.zip" | |
| + chunk_path=root/'checkpoints'/chunk_name; chunk_path.parent.mkdir(parents=True,exist_ok=True) | |
| + if chunk_path.exists(): raise RuntimeError(f'immutable chunk already exists: {chunk_path}') | |
| + from caltech101_disef import validate_image | |
| + with zipfile.ZipFile(chunk_path,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=1) as z: | |
| + for rec in selected: | |
| + p=root/rec['synthetic_filename']; ok,reason=validate_image(p,class_folder=rec['class_folder']) | |
| + if not ok: raise RuntimeError(f'cannot checkpoint invalid image {p}: {reason}') | |
| + z.write(p,arcname=rec['synthetic_filename'].removeprefix('images/')) | |
| + chunk_sha=sha(chunk_path) | |
| + chunk_record={"chunk_id":chunk_num,"filename":chunk_name,"image_count":len(selected),"sha256":chunk_sha, | |
| + "uploaded_at":now_iso(),"records":[{"task_id":r['task_id'],"synthetic_filename":r['synthetic_filename']} for r in selected]} | |
| + candidate_index=json.loads(json.dumps(index)); candidate_index['chunks'].append(chunk_record); candidate_index['updated_at']=now_iso() | |
| + candidate_state=json.loads(json.dumps(state)); | |
| + for rec in selected: candidate_state['records'][rec['task_id']]['remote_chunk']=chunk_name | |
| + candidate_state['remote_backed_up_count']=len(backed)+len(selected) | |
| + candidate_state['latest_remote_checkpoint']=now_iso(); candidate_state['latest_remote_checkpoint_epoch']=time.time() | |
| + summary={"run_id":run_id,"config_fingerprint":fp,"status":candidate_state['status'],"completed":candidate_state['completed'], | |
| + "target":candidate_state['target'],"remote_backed_up_count":candidate_state['remote_backed_up_count'], | |
| + "latest_chunk":chunk_name,"updated_at":now_iso()} | |
| + with tempfile.TemporaryDirectory(prefix='caltech101_hf_') as td: | |
| + td=Path(td) | |
| + c_state=td/'caltech101_disef_state.json'; c_index=td/'chunk_index.json'; c_summary=td/'progress_summary.json'; c_manifest=td/'caltech101_disef_manifest.jsonl' | |
| + atomic_json(c_state,candidate_state); atomic_json(c_index,candidate_index); atomic_json(c_summary,summary) | |
| + c_manifest.write_bytes(append_backup_events(root/'metadata/caltech101_disef_manifest.jsonl',selected,chunk_name)) | |
| + prefix=f"_progress/{run_id}" | |
| + operations=[ | |
| + CommitOperationAdd(path_in_repo=f"{prefix}/chunks/{chunk_name}",path_or_fileobj=str(chunk_path)), | |
| + CommitOperationAdd(path_in_repo=f"{prefix}/current/caltech101_disef_state.json",path_or_fileobj=str(c_state)), | |
| + CommitOperationAdd(path_in_repo=f"{prefix}/current/caltech101_disef_manifest.jsonl",path_or_fileobj=str(c_manifest)), | |
| + CommitOperationAdd(path_in_repo=f"{prefix}/current/chunk_index.json",path_or_fileobj=str(c_index)), | |
| + CommitOperationAdd(path_in_repo=f"{prefix}/current/progress_summary.json",path_or_fileobj=str(c_summary)), | |
| + ] | |
| + api=HfApi(token=os.environ.get('HF_TOKEN')) | |
| + commit=api.create_commit(repo_id=REPO_ID,repo_type=REPO_TYPE,operations=operations, | |
| + commit_message=f"CALTECH101 {run_id}: immutable chunk {chunk_num:06d} ({len(selected)} images)") | |
| + files=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE,revision=commit.oid)) | |
| + remote_chunk=f"{prefix}/chunks/{chunk_name}" | |
| + if remote_chunk not in files: raise RuntimeError('remote chunk verification failed') | |
| + # Commit succeeded. Merge backup markers into the newest local state; | |
| + # generation may have advanced while the network upload was in flight. | |
| + local_manifest = root/'metadata/caltech101_disef_manifest.jsonl' | |
| + with local_manifest.open('a',encoding='utf-8') as manifest_out: | |
| + for rec in selected: | |
| + manifest_out.write(json.dumps({"event":"REMOTE_BACKED_UP","timestamp":now_iso(),"task_id":rec["task_id"], | |
| + "synthetic_filename":rec["synthetic_filename"],"remote_chunk":chunk_name},sort_keys=True)+"\n") | |
| + manifest_out.flush(); os.fsync(manifest_out.fileno()) | |
| + latest_state=json.loads(state_path.read_text()) | |
| + if latest_state['run_id']!=run_id or latest_state['config_fingerprint']!=fp: | |
| + raise RuntimeError('local state identity changed during checkpoint') | |
| + for rec in selected: | |
| + if rec['task_id'] in latest_state.get('records',{}): latest_state['records'][rec['task_id']]['remote_chunk']=chunk_name | |
| + latest_state['remote_backed_up_count']=len(backed)+len(selected) | |
| + latest_state['latest_remote_checkpoint']=candidate_state['latest_remote_checkpoint'] | |
| + latest_state['latest_remote_checkpoint_epoch']=candidate_state['latest_remote_checkpoint_epoch'] | |
| + atomic_json(chunk_index_path,candidate_index); atomic_json(state_path,latest_state) | |
| + return {"status":"UPLOADED","chunk":chunk_name,"sha256":chunk_sha,"count":len(selected), | |
| + "remote_backed_up_count":len(backed)+len(selected),"commit":commit.oid} | |
| + | |
| + | |
| +def checkpoint(run_root: str | Path, force: bool=False, max_chunk_images: int=100) -> dict[str,Any]: | |
| + root=Path(run_root).resolve(); lock_path=root/'state/hf_checkpoint.lock'; lock_path.parent.mkdir(parents=True,exist_ok=True) | |
| + with lock_path.open('a+') as lock: | |
| + fcntl.flock(lock.fileno(),fcntl.LOCK_EX) | |
| + return _checkpoint_unlocked(root,force=force,max_chunk_images=max_chunk_images) | |
| + | |
| + | |
| +def upload_bootstrap(run_root: str|Path, files: list[str|Path]) -> dict[str,Any]: | |
| + root=Path(run_root).resolve(); state=json.loads((root/'state/caltech101_disef_state.json').read_text()); run_id=state['run_id'] | |
| + prefix=f"_progress/{run_id}/bootstrap" | |
| + api=HfApi(token=os.environ.get('HF_TOKEN')); operations=[] | |
| + for file in files: | |
| + p=Path(file) | |
| + if not p.is_file(): raise FileNotFoundError(p) | |
| + operations.append(CommitOperationAdd(path_in_repo=f"{prefix}/{p.name}",path_or_fileobj=str(p))) | |
| + commit=api.create_commit(repo_id=REPO_ID,repo_type=REPO_TYPE,operations=operations, | |
| + commit_message=f"CALTECH101 {run_id}: recovery bootstrap") | |
| + remote=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE,revision=commit.oid)) | |
| + missing=[f"{prefix}/{Path(p).name}" for p in files if f"{prefix}/{Path(p).name}" not in remote] | |
| + if missing: raise RuntimeError(f'bootstrap verification failed: {missing}') | |
| + return {"status":"UPLOADED","commit":commit.oid,"prefix":prefix,"file_count":len(files)} | |
| + | |
| + | |
| +def restore(run_id: str, dest: str|Path, validate_only: bool=False) -> dict[str,Any]: | |
| + dest=Path(dest).resolve(); dest.mkdir(parents=True,exist_ok=True) | |
| + prefix=f"_progress/{run_id}" | |
| + token=os.environ.get('HF_TOKEN'); api=HfApi(token=token) | |
| + files=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE)) | |
| + needed=[f"{prefix}/current/caltech101_disef_state.json",f"{prefix}/current/caltech101_disef_manifest.jsonl",f"{prefix}/current/chunk_index.json",f"{prefix}/current/progress_summary.json"] | |
| + missing=[p for p in needed if p not in files] | |
| + if missing: raise RuntimeError(f'missing remote current files: {missing}') | |
| + state_dir=dest/'state'; metadata=dest/'metadata'; images=dest/'images' | |
| + for directory in (state_dir,metadata,images,dest/'checkpoints',dest/'logs',dest/'artifacts/figures'): | |
| + directory.mkdir(parents=True,exist_ok=True) | |
| + mapping={ | |
| + 'caltech101_disef_state.json':state_dir/'caltech101_disef_state.json', | |
| + 'caltech101_disef_manifest.jsonl':metadata/'caltech101_disef_manifest.jsonl', | |
| + 'chunk_index.json':metadata/'chunk_index.json', | |
| + 'progress_summary.json':metadata/'progress_summary.json', | |
| + } | |
| + for remote in needed: | |
| + src=Path(hf_hub_download(REPO_ID,remote,repo_type=REPO_TYPE,token=token)) | |
| + shutil.copy2(src,mapping[Path(remote).name]) | |
| + bootstrap_config=f"{prefix}/bootstrap/generation_config.json" | |
| + if bootstrap_config in files: | |
| + src=Path(hf_hub_download(REPO_ID,bootstrap_config,repo_type=REPO_TYPE,token=token)) | |
| + shutil.copy2(src,metadata/'generation_config.json') | |
| + index=json.loads((metadata/'chunk_index.json').read_text()) | |
| + state=json.loads((state_dir/'caltech101_disef_state.json').read_text()) | |
| + if state['run_id']!=run_id or state['config_fingerprint']!=index['config_fingerprint']: | |
| + raise RuntimeError('remote state/index identity mismatch') | |
| + restored=0 | |
| + for chunk in index['chunks']: | |
| + remote=f"{prefix}/chunks/{chunk['filename']}" | |
| + src=Path(hf_hub_download(REPO_ID,remote,repo_type=REPO_TYPE,token=token)) | |
| + if sha(src)!=chunk['sha256']: raise RuntimeError(f"chunk checksum mismatch: {chunk['filename']}") | |
| + shutil.copy2(src,dest/'checkpoints'/chunk['filename']) | |
| + with zipfile.ZipFile(src) as z: | |
| + for member in z.infolist(): | |
| + target=(images/member.filename).resolve() | |
| + if images.resolve() not in target.parents: raise RuntimeError('unsafe zip member') | |
| + z.extractall(images) | |
| + restored += chunk['image_count'] | |
| + from caltech101_disef import validate_image | |
| + invalid=[] | |
| + for chunk in index['chunks']: | |
| + for rec in chunk['records']: | |
| + rel=rec['synthetic_filename'].removeprefix('images/') | |
| + p=images/rel; ok,reason=validate_image(p,class_folder=Path(rel).parent.name) | |
| + if not ok: invalid.append((rel,reason)) | |
| + if invalid: raise RuntimeError(f'invalid restored images: {invalid[:3]}') | |
| + missing_count=max(0,int(state['target'])-restored) | |
| + atomic_json(state_dir/'restore_validation.json',{"run_id":run_id,"config_fingerprint":state['config_fingerprint'], | |
| + "restored":restored,"target":state['target'],"remaining":missing_count,"invalid":invalid,"validated_at":now_iso()}) | |
| + return {"status":"PASS","run_id":run_id,"restored":restored,"remaining":missing_count, | |
| + "chunks":len(index['chunks']),"dest":str(dest),"config_fingerprint":state['config_fingerprint']} | |
| + | |
| + | |
| +def main(): | |
| + p=argparse.ArgumentParser(); sub=p.add_subparsers(dest='cmd',required=True) | |
| + c=sub.add_parser('checkpoint'); c.add_argument('--run-root',required=True); c.add_argument('--force',action='store_true') | |
| + r=sub.add_parser('restore'); r.add_argument('--run-id',required=True); r.add_argument('--dest',required=True) | |
| + b=sub.add_parser('bootstrap'); b.add_argument('--run-root',required=True); b.add_argument('files',nargs='+') | |
| + a=p.parse_args() | |
| + if a.cmd=='checkpoint': result=checkpoint(a.run_root,a.force) | |
| + elif a.cmd=='restore': result=restore(a.run_id,a.dest) | |
| + else: result=upload_bootstrap(a.run_root,a.files) | |
| + print(json.dumps(result,indent=2)) | |
| + | |
| +if __name__=='__main__': main() | |
| diff --git a/DisEF/caltech101_monitor.py b/DisEF/caltech101_monitor.py | |
| new file mode 100644 | |
| index 0000000..e4e722e | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_monitor.py | |
| +#!/usr/bin/env python3 | |
| +"""CPU-only metrics figures and qualitative previews; never loads generation models.""" | |
| +import argparse,json,time | |
| +from pathlib import Path | |
| +import matplotlib | |
| +matplotlib.use('Agg') | |
| +import matplotlib.pyplot as plt | |
| +import pandas as pd | |
| +from PIL import Image,ImageOps,ImageDraw | |
| + | |
| +def savefig(fig,path): | |
| + path.parent.mkdir(parents=True,exist_ok=True); tmp=path.with_suffix('.tmp.png'); fig.tight_layout(); fig.savefig(tmp,dpi=140); plt.close(fig); tmp.replace(path) | |
| +def refresh(root): | |
| + state_path=root/'state/caltech101_disef_state.json' | |
| + if not state_path.exists(): return 'NOT INITIALIZED' | |
| + s=json.loads(state_path.read_text()); figs=root/'artifacts/figures'; figs.mkdir(parents=True,exist_ok=True) | |
| + metrics=root/'metadata/runtime_metrics.csv' | |
| + if metrics.exists() and metrics.stat().st_size: | |
| + df=pd.read_csv(metrics) | |
| + x=df.elapsed_seconds/60 | |
| + fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.completed,label='completed'); ax.axhline(s['target'],ls='--',color='tab:red',label='target'); ax.set(xlabel='Elapsed (min)',ylabel='Completed images',title='DISEF CALTECH101 generation progress'); ax.legend(); ax.grid(alpha=.25); savefig(fig,figs/'generation_progress.png') | |
| + fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.images_per_minute); ax.set(xlabel='Elapsed (min)',ylabel='Images/min',title='Generation throughput'); ax.grid(alpha=.25); savefig(fig,figs/'generation_throughput.png') | |
| + fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.vram_used_mb/1024,label='VRAM GiB'); ax2=ax.twinx(); ax2.plot(x,df.gpu_util_percent,color='tab:orange',alpha=.7,label='GPU %'); ax.set(xlabel='Elapsed (min)',ylabel='VRAM (GiB)',title='GPU and VRAM usage'); ax2.set_ylabel('GPU utilization (%)'); ax.grid(alpha=.25); savefig(fig,figs/'gpu_vram_usage.png') | |
| + counts={} | |
| + for rec in s.get('records',{}).values(): | |
| + if rec.get('completion_status')=='COMPLETE': counts[rec['class_folder']]=counts.get(rec['class_folder'],0)+1 | |
| + config=json.loads((root/'metadata/generation_config.json').read_text()); classes=config['classes']; expected=config['images_per_class'] | |
| + fig,ax=plt.subplots(figsize=(14,5)); vals=[counts.get(c,0) for c in classes]; ax.bar(range(len(classes)),[expected]*len(classes),color='lightgray',label='expected'); ax.bar(range(len(classes)),vals,color='tab:blue',label='actual'); ax.set_xticks(range(len(classes)),classes,rotation=75,ha='right',fontsize=7); ax.set(ylabel='Images',title='Expected vs actual images per class'); ax.legend(); savefig(fig,figs/'images_per_class.png') | |
| + records=[r for r in s.get('records',{}).values() if r.get('completion_status')=='COMPLETE' and (root/r['synthetic_filename']).exists()] | |
| + if records: | |
| + chosen=[]; seen=set() | |
| + for rec in records: | |
| + if rec['class_folder'] not in seen: chosen.append(rec); seen.add(rec['class_folder']) | |
| + if len(chosen)>=16: break | |
| + if len(chosen)<16: chosen=(chosen+records)[:16] | |
| + canvas=Image.new('RGB',(4*260,4*290),'white'); draw=ImageDraw.Draw(canvas) | |
| + for i,rec in enumerate(chosen): | |
| + im=Image.open(root/rec['synthetic_filename']).convert('RGB'); im=ImageOps.fit(im,(250,250)); x=(i%4)*260+5; y=(i//4)*290+5; canvas.paste(im,(x,y)); draw.text((x,y+252),rec['class_folder'][:34],fill='black') | |
| + canvas.save(figs/'caltech101_disef_preview.png') | |
| + pairs=chosen[:8]; canvas=Image.new('RGB',(2*260,8*285),'white'); draw=ImageDraw.Draw(canvas) | |
| + for i,rec in enumerate(pairs): | |
| + for j,key in enumerate(['source_image','synthetic_filename']): | |
| + q=Path(rec[key]) if key=='source_image' else root/rec[key]; im=Image.open(q).convert('RGB'); im=ImageOps.fit(im,(250,250)); x=j*260+5; y=i*285+5; canvas.paste(im,(x,y)); draw.text((x,y+252),('REAL | ' if j==0 else 'DISEF | ')+rec['class_folder'][:25],fill='black') | |
| + canvas.save(figs/'caltech101_real_vs_disef_preview.png') | |
| + return s.get('status') | |
| +def main(): | |
| + p=argparse.ArgumentParser(); p.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); p.add_argument('--watch',action='store_true'); p.add_argument('--interval',type=int,default=300); a=p.parse_args(); root=Path(a.run_root) | |
| + while True: | |
| + status=refresh(root); print(f'{time.strftime("%FT%T%z")} monitor status={status}',flush=True) | |
| + if not a.watch or status in {'COMPLETED','BLOCKED','PARTIAL'}: return | |
| + time.sleep(a.interval) | |
| +if __name__=='__main__': main() | |
| diff --git a/DisEF/caltech101_status.py b/DisEF/caltech101_status.py | |
| new file mode 100644 | |
| index 0000000..4015b2a | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_status.py | |
| +#!/usr/bin/env python3 | |
| +import argparse,json,os,shutil,subprocess,time | |
| +from pathlib import Path | |
| +from datetime import datetime | |
| +from PIL import Image | |
| + | |
| +def alive(pid): | |
| + try: | |
| + n=int(pid); os.kill(n,0) | |
| + stat=Path(f'/proc/{n}/stat') | |
| + return not stat.exists() or stat.read_text().split()[2] != 'Z' | |
| + except: return False | |
| +p=argparse.ArgumentParser(); p.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); a=p.parse_args(); root=Path(a.run_root) | |
| +sp=root/'state/caltech101_disef_state.json'; lockp=root/'state/run.lock' | |
| +if not sp.exists(): raise SystemExit('Status: NOT INITIALIZED') | |
| +s=json.loads(sp.read_text()); lock=json.loads(lockp.read_text()) if lockp.exists() else {} | |
| +remote_count=s.get('remote_backed_up_count',0); remote_time=s.get('latest_remote_checkpoint') | |
| +chunk_index=root/'metadata/chunk_index.json' | |
| +if chunk_index.exists(): | |
| + try: | |
| + ci=json.loads(chunk_index.read_text()) | |
| + if ci.get('run_id')==s.get('run_id') and ci.get('config_fingerprint')==s.get('config_fingerprint'): | |
| + remote_count=sum(int(c.get('image_count',0)) for c in ci.get('chunks',[])) | |
| + remote_time=ci.get('updated_at',remote_time) | |
| + except (OSError,ValueError,TypeError): | |
| + pass | |
| +status=s.get('status','UNKNOWN') | |
| +if status=='RUNNING' and not alive(lock.get('generator_pid')): status='STOPPED' | |
| +valid=[] | |
| +for q in (root/'images').rglob('*.jpg'): | |
| + try: | |
| + with Image.open(q) as im: | |
| + im.load() | |
| + if im.format=='JPEG' and im.size==(512,512): valid.append(q) | |
| + except Exception: | |
| + pass | |
| +completed=len(valid) | |
| +latest_image=max(valid,key=lambda q:q.stat().st_mtime) if valid else None | |
| +current_class=latest_image.parent.name if latest_image else s.get('current_class') | |
| +current_source=latest_image.name if latest_image else s.get('current_source') | |
| +now=time.time() | |
| +current_rate=sum(q.stat().st_mtime>=now-60 for q in valid) | |
| +elapsed=max(time.time()-datetime.fromisoformat(s['created_at']).timestamp(),1) | |
| +average_rate=completed/elapsed*60; remaining=max(s.get('target',0)-completed,0); eta=remaining/current_rate*60 if current_rate else 0 | |
| +try: gpu=subprocess.check_output(['nvidia-smi','--query-gpu=utilization.gpu,memory.used,memory.total','--format=csv,noheader,nounits'],text=True).splitlines()[0] | |
| +except: gpu='unavailable' | |
| +du=shutil.disk_usage(root); log=root/'logs/generation.log'; latest='' | |
| +if log.exists(): | |
| + lines=log.read_text(errors='replace').splitlines(); latest=lines[-1] if lines else '' | |
| +lines=[ | |
| + f"Status: {status}", f"Run ID: {s.get('run_id')}", | |
| + f"PID: {lock.get('generator_pid')} ({'alive' if alive(lock.get('generator_pid')) else 'dead'})", | |
| + f"Supervisor: {lock.get('supervisor_pid')} ({'alive' if alive(lock.get('supervisor_pid')) else 'dead'})", | |
| + f"Progress: {completed}/{s.get('target',0)} ({100*completed/max(s.get('target',1),1):.2f}%)", | |
| + f"Current class: {current_class}", f"Current source: {current_source}", | |
| + f"Failed: {s.get('failed',0)}", f"Retried: {s.get('retried',0)}", f"Rate: {current_rate:.2f} img/min; average {average_rate:.2f}", | |
| + f"Elapsed: {elapsed/3600:.2f} h", f"Estimated remaining: {eta/3600:.2f} h", f"Disk free: {du.free/1e9:.1f} GB", | |
| + f"GPU util, VRAM used/total MiB: {gpu}", f"Remote backed up: {remote_count}", | |
| + f"Last HF checkpoint: {remote_time}", f"Latest event: {latest_image.name if latest_image else (s.get('last_event') or latest)}"] | |
| +print('\n'.join(lines)) | |
| diff --git a/DisEF/caltech101_supervisor.py b/DisEF/caltech101_supervisor.py | |
| new file mode 100644 | |
| index 0000000..31338fe | |
| --- /dev/null | |
| +++ b/DisEF/caltech101_supervisor.py | |
| +#!/usr/bin/env python3 | |
| +from __future__ import annotations | |
| +import argparse, json, os, subprocess, sys, time, traceback | |
| +from pathlib import Path | |
| +sys.path.insert(0,str(Path(__file__).resolve().parent)) | |
| +from caltech101_disef import atomic_json, now_iso, pid_alive | |
| + | |
| +def main(): | |
| + p=argparse.ArgumentParser(); p.add_argument('--run-root',required=True); p.add_argument('--max-restarts',type=int,default=3); p.add_argument('--batch-size',type=int,default=16); p.add_argument('generator_args',nargs=argparse.REMAINDER); a=p.parse_args() | |
| + root=Path(a.run_root).resolve(); (root/'logs').mkdir(parents=True,exist_ok=True); (root/'state').mkdir(parents=True,exist_ok=True) | |
| + lock_path=root/'state/run.lock'; state_path=root/'state/caltech101_disef_state.json'; log_path=root/'logs/generation.log' | |
| + if lock_path.exists(): | |
| + lock=json.loads(lock_path.read_text()) | |
| + if pid_alive(lock.get('supervisor_pid')) and lock.get('supervisor_pid')!=os.getpid(): raise SystemExit(f"supervisor already alive: {lock['supervisor_pid']}") | |
| + if pid_alive(lock.get('generator_pid')): raise SystemExit(f"generator already alive: {lock['generator_pid']}") | |
| + cmd=[sys.executable,str(Path(__file__).with_name('caltech101_disef.py')),'--mode','generate','--run-root',str(root),'--batch-size',str(a.batch_size),*a.generator_args] | |
| + for attempt in range(a.max_restarts+1): | |
| + env=os.environ.copy(); env['DISEF_SUPERVISOR_PID']=str(os.getpid()) | |
| + with log_path.open('a',buffering=1) as log: | |
| + log.write(f"[{now_iso()}] supervisor start attempt={attempt} cmd={cmd!r}\n") | |
| + child=subprocess.Popen(cmd,stdout=log,stderr=subprocess.STDOUT,text=True,env=env) | |
| + state=json.loads(state_path.read_text()) if state_path.exists() else {} | |
| + atomic_json(lock_path,{"run_id":state.get('run_id'),"config_fingerprint":state.get('config_fingerprint'),"supervisor_pid":os.getpid(),"generator_pid":child.pid,"hostname":os.uname().nodename,"updated_at":now_iso()}) | |
| + rc=child.wait(); log.write(f"[{now_iso()}] generator exit rc={rc}\n") | |
| + state=json.loads(state_path.read_text()) if state_path.exists() else {} | |
| + if rc==0 and state.get('status') in {'COMPLETED','PARTIAL','STOPPED'}: return | |
| + if state.get('status')=='COMPLETED': return | |
| + try: | |
| + import caltech101_hf; caltech101_hf.checkpoint(root,force=True) | |
| + except Exception: | |
| + with (root/'logs/supervisor.log').open('a') as f: f.write(traceback.format_exc()+"\n") | |
| + if attempt>=a.max_restarts: | |
| + state.update(status='BLOCKED',updated_at=now_iso(),last_event=f'generator failed after {a.max_restarts+1} attempts; last rc={rc}') | |
| + atomic_json(state_path,state); return | |
| + time.sleep(20) | |
| +if __name__=='__main__': main() | |
| diff --git a/DisEF/requirements_molab.txt b/DisEF/requirements_molab.txt | |
| new file mode 100644 | |
| index 0000000..e9cbc04 | |
| --- /dev/null | |
| +++ b/DisEF/requirements_molab.txt | |
| +# Validated Molab environment (Python 3.13, CUDA 13 / Blackwell) | |
| +torch==2.14.1 | |
| +torchvision==0.29.1 | |
| +diffusers==0.40.0 | |
| +transformers==5.18.0 | |
| +accelerate==1.15.0 | |
| +huggingface-hub==1.33.0 | |
| +qwen-vl-utils==0.0.14 | |
| +sentencepiece==0.2.2 | |
| +open-clip-torch==3.3.0 | |
| +peft==0.21.2 | |
| +Pillow==12.3.0 | |
| +numpy==2.5.3 | |
| +pandas==3.0.3 | |
| +matplotlib==3.11.1 | |
| diff --git a/DisEF/restore_caltech101_disef_from_hf.sh b/DisEF/restore_caltech101_disef_from_hf.sh | |
| new file mode 100644 | |
| index 0000000..b284aac | |
| --- /dev/null | |
| +++ b/DisEF/restore_caltech101_disef_from_hf.sh | |
| +#!/usr/bin/env bash | |
| +set -euo pipefail | |
| +RUN_ID="${1:?Usage: $0 RUN_ID [RUN_ROOT]}" | |
| +RUN_ROOT="${2:-/marimo/runs/disef_caltech101}" | |
| +SPC_DIR="${SPC_DIR:-/marimo/SPC}" | |
| +BOOT_DIR="/marimo/caltech101_recovery_bootstrap/$RUN_ID" | |
| +PYTHON_BIN="${PYTHON_BIN:-/tmp/uv-venv/bin/python}" | |
| +if [[ ! -d "$SPC_DIR/.git" ]]; then git clone https://github.com/thanhhai1988/SPC.git "$SPC_DIR"; fi | |
| +mkdir -p "$BOOT_DIR" | |
| +"$PYTHON_BIN" - "$RUN_ID" "$BOOT_DIR" <<'PY' | |
| +import os,sys,shutil | |
| +from pathlib import Path | |
| +from huggingface_hub import snapshot_download | |
| +run_id,dest=sys.argv[1],Path(sys.argv[2]) | |
| +snap=Path(snapshot_download('DanielNguyen121/DisEF-Caltech101',repo_type='dataset',token=os.environ.get('HF_TOKEN'),allow_patterns=[f'_progress/{run_id}/bootstrap/*'])) | |
| +src=snap/f'_progress/{run_id}/bootstrap' | |
| +for p in src.iterdir(): shutil.copy2(p,dest/p.name) | |
| +PY | |
| +if [[ -f "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" ]]; then | |
| + git -C "$SPC_DIR" apply --check "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" 2>/dev/null && git -C "$SPC_DIR" apply "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" || true | |
| +fi | |
| +uv pip install -r "$BOOT_DIR/requirements_molab.txt" -p "$PYTHON_BIN" | |
| +if [[ ! -d /marimo/Qwen3-VL-Embedding ]]; then git clone https://github.com/QwenLM/Qwen3-VL-Embedding.git /marimo/Qwen3-VL-Embedding; fi | |
| +"$PYTHON_BIN" - "$SPC_DIR" <<'PY' | |
| +import hashlib, shutil, subprocess, sys, zipfile | |
| +from pathlib import Path | |
| +spc=Path(sys.argv[1]) | |
| +root=Path('/marimo/data/caltech101/train') | |
| +archive=root/'caltech-101.zip' | |
| +url='https://data.caltech.edu/records/mzrjq-6wc02/files/caltech-101.zip' | |
| +if not archive.exists(): | |
| + root.mkdir(parents=True,exist_ok=True) | |
| + subprocess.run(['curl','-k','-fL',url,'-o',str(archive)],check=True) | |
| +if hashlib.md5(archive.read_bytes()).hexdigest() != '3138e1922a9193bfa496528edbbc45d0': | |
| + raise RuntimeError('Caltech101 archive MD5 mismatch') | |
| +categories=root/'caltech101'/'101_ObjectCategories' | |
| +if not categories.exists(): | |
| + with zipfile.ZipFile(archive) as zf: zf.extractall(root) | |
| +split_source=spc/'DataSplit'/'split_coop_Caltech101.csv' | |
| +if not split_source.exists(): | |
| + raise RuntimeError(f'missing patched DISEF split: {split_source}') | |
| +shutil.copy2(split_source,root/'split_coop.csv') | |
| +PY | |
| +"$PYTHON_BIN" "$SPC_DIR/DisEF/caltech101_hf.py" restore --run-id "$RUN_ID" --dest "$RUN_ROOT" | |
| +exec bash "$SPC_DIR/DisEF/resume_caltech101_disef.sh" "$RUN_ROOT" | |
| diff --git a/DisEF/resume_caltech101_disef.sh b/DisEF/resume_caltech101_disef.sh | |
| new file mode 100644 | |
| index 0000000..2570ced | |
| --- /dev/null | |
| +++ b/DisEF/resume_caltech101_disef.sh | |
| +#!/usr/bin/env bash | |
| +set -euo pipefail | |
| +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| +exec bash "$SCRIPT_DIR/run_caltech101_disef.sh" "${1:-/marimo/runs/disef_caltech101}" | |
| diff --git a/DisEF/run_caltech101_disef.sh b/DisEF/run_caltech101_disef.sh | |
| new file mode 100644 | |
| index 0000000..e98f638 | |
| --- /dev/null | |
| +++ b/DisEF/run_caltech101_disef.sh | |
| +#!/usr/bin/env bash | |
| +set -euo pipefail | |
| +RUN_ROOT="${1:-/marimo/runs/disef_caltech101}" | |
| +BATCH_SIZE="${DISEF_BATCH_SIZE:-${2:-16}}" | |
| +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| +PYTHON_BIN="${PYTHON_BIN:-/tmp/uv-venv/bin/python}" | |
| +mkdir -p "$RUN_ROOT/logs" "$RUN_ROOT/state" | |
| +"$PYTHON_BIN" "$SCRIPT_DIR/caltech101_status.py" --run-root "$RUN_ROOT" 2>/dev/null | head -4 || true | |
| +nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_supervisor.py" --run-root "$RUN_ROOT" --max-restarts 3 --batch-size "$BATCH_SIZE" \ | |
| + >>"$RUN_ROOT/logs/supervisor.log" 2>&1 </dev/null & | |
| +SUPERVISOR_PID=$! | |
| +nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_monitor.py" --run-root "$RUN_ROOT" --watch --interval 300 \ | |
| + >>"$RUN_ROOT/logs/monitor.log" 2>&1 </dev/null & | |
| +MONITOR_PID=$! | |
| +nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_checkpoint_daemon.py" --run-root "$RUN_ROOT" --interval 300 \ | |
| + >>"$RUN_ROOT/logs/checkpoint_daemon.log" 2>&1 </dev/null & | |
| +CHECKPOINT_PID=$! | |
| +echo "supervisor_pid=$SUPERVISOR_PID monitor_pid=$MONITOR_PID checkpoint_pid=$CHECKPOINT_PID run_root=$RUN_ROOT" | |
| diff --git a/DisEF/status_caltech101_disef.sh b/DisEF/status_caltech101_disef.sh | |
| new file mode 100644 | |
| index 0000000..90479b9 | |
| --- /dev/null | |
| +++ b/DisEF/status_caltech101_disef.sh | |
| +#!/usr/bin/env bash | |
| +set -euo pipefail | |
| +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| +exec "${PYTHON_BIN:-/tmp/uv-venv/bin/python}" "$SCRIPT_DIR/caltech101_status.py" --run-root "${1:-/marimo/runs/disef_caltech101}" | |