Add files using upload-large-folder tool

197d46b verified 2 months ago

9.05 kB

"""单点调试入口：每个阶段都能单独跑一次，方便下断点。
配合 .vscode/launch.json 里的 evo2-2 / evo2-3 / evo2-4 / evo2-5 使用。
  python evo2/debug_one.py qgen     --dataset egoschema --space ACTION --time GLOBAL
  python evo2/debug_one.py retrieve --question "Where did I put the screwdriver?"
  python evo2/debug_one.py episode  --pool data/pool_egoschema.json --index 0
  python evo2/debug_one.py curate   --kind fail
"""

import argparse
import json
import logging
import os
import sys

sys.path.insert(0, "/mnt/tokyo-ai/gst/EvoStream")
sys.path.insert(0, "/mnt/tokyo-ai/gst/EvoStream/cons_memory")

logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s")


# ---------------------------------------------------------------- 出题

def cmd_qgen(a):
    from infer.llm_clients import make_client
    from memory_store import VideoMemory
    from evo2.qgen import QuestionGenerator, phrasing_rule
    from evo2.style_cards import build_style_card
    from evo2.validate_qgen import list_banks, sample_frames

    banks = list_banks(a.dataset, 3)
    vid, bank = banks[a.video_idx]
    mem = VideoMemory(bank)
    mode = "global" if a.time == "GLOBAL" else "window"
    frames, ts = sample_frames(mem, mode)
    print(f"视频 {vid} | 采样 {mode} {len(frames)} 帧 @ "
          f"{[f'{t:.0f}s' for t in ts]}")
    print(f"\n--- 该格子的措辞约束 ---\n{phrasing_rule(a.time)}\n")

    qg = QuestionGenerator(make_client(a.gen_ep, temperature=0.7),
                           make_client(a.verify_ep, temperature=0.0),
                           style_card=build_style_card(a.dataset))
    rec = qg.make_one(frames, ts, a.space, a.time)   # ← 断点打这里
    print(json.dumps(rec, ensure_ascii=False, indent=1)[:2500])
    print(f"\n结果: stage={rec['stage']} passed={rec['passed']} "
          f"reject={rec.get('reject')}")


# ---------------------------------------------------------------- 检索

def cmd_retrieve(a):
    from infer.llm_clients import embed_texts
    from evo2.classifier import classify_lexical
    from evo2.seed_lib import build_seed
    from evo2.skill import SkillLibrary, keyword_score

    lib = SkillLibrary(build_seed(a.seed_mode), embed_fn=embed_texts)
    lib.index_library()
    cell = classify_lexical(a.question, None)        # ← 断点：题型判定
    print(f"题目: {a.question}")
    print(f"题型判定: {cell}")
    cards, scores = lib.retrieve(a.question, cell, topk=len(lib.cards),
                                 return_scores=True)   # ← 断点：打分
    print(f"\n全部卡片打分（前8）:")
    for s in scores[:8]:
        print(f"  {s['total']:.4f} = kw {s['kw']:.3f}*0.5 + sem {s['sem']:.3f}*0.5 "
              f"(+先验)  [{s['card_id']}]")
    print(f"\n实际注入(top2): {[c['card_id'] for c in cards[:2]]}")
    print("\n渲染给 executor 的文本:")
    print(SkillLibrary.render(cards[:2]))


# ---------------------------------------------------------------- 单题推理

def cmd_episode(a):
    from infer.config import RunConfig
    from infer.llm_clients import embed_texts, make_client
    from infer.tools import ToolRegistry
    from evo2.pipeline import run_one_episode
    from evo2.seed_lib import build_seed
    from evo2.skill import SkillLibrary
    from evo2.tracelog import TraceLogger

    pool = json.load(open(a.pool))
    qs = pool["train"] + pool["frozen_val"]
    q = qs[a.index]
    print(f"题目[{a.index}]: {q['question'][:140]}")
    print(f"  格子={q['cell']} gt={q.get('gt')} video={q['video_id']}")

    cfg = RunConfig()
    cfg.executor = a.executor_ep
    lib = SkillLibrary(build_seed(a.seed_mode), embed_fn=embed_texts)
    lib.index_library()
    vlm = make_client(cfg.vlm_tool, temperature=0.0)
    tl = TraceLogger("/mnt/tokyo-ai/gst/EvoStream/runs/DEBUG_one")
    log = run_one_episode(q, lib, make_client(a.executor_ep, temperature=0.0),
                          lambda m: ToolRegistry(m, cfg, vlm_client=vlm),
                          cfg, tl=tl, meta={"phase": "debug"})   # ← 断点
    print("\n--- 工具轨迹 ---")
    for s in (log.get("trace") or []):
        print(f"  {s['step']}. {s['tool']}({json.dumps(s['args'], ensure_ascii=False)[:90]})")
        print(f"     → {(s.get('result_digest') or '')[:180]}")
    print(f"\n思考: {(log.get('reasoning') or '')[:400]}")
    print(f"答案: {log.get('choice')}  (gt={q.get('gt')})  正确={log.get('correct')}")
    print(f"注入的卡: {log.get('skills_injected')}  adherence={log.get('adherence')}")
    tl.close()


# ---------------------------------------------------------------- 归纳改卡

def cmd_curate(a):
    from infer.llm_clients import make_client
    from infer.tools import TOOL_SPECS
    from evo2.curator import build_prompt, catalog_of
    from evo2.gates import gate_all
    from evo2.seed_lib import build_seed
    from evo2.skill import new_card

    # 构造轨迹：库里已有一张"只用文本检索"的缺陷卡，照做仍答错 → 应 modify
    existing = [new_card("count-caption-only", ["how many", "count", "number of"],
                         "询问数量时用文本检索caption统计",
                         [{"tool": "get_topk_caption", "params": {"k": 5}}],
                         ["COUNT"], ["NOW", "PAST"], "caption中提到了数量")] \
        if a.with_existing else build_seed("empty")

    fails = [
        {"question": "How many times did the person rinse the pan?", "cell": "COUNT-PAST",
         "trace": [{"tool": "get_topk_caption", "args": {"query": "rinse pan", "k": 5}}],
         "skills_injected": [c["card_id"] for c in existing], "adherence": 1.0,
         "failure_mode": "照策略执行了但caption未给出精确数量"},
        {"question": "How many plates were on the rack?", "cell": "COUNT-NOW",
         "trace": [{"tool": "get_topk_caption", "args": {"query": "plates", "k": 5}}],
         "skills_injected": [c["card_id"] for c in existing], "adherence": 1.0,
         "failure_mode": "照策略执行了但caption未给出精确数量"},
    ]
    contrast = [
        {"question": "Where did I put the blue screwdriver?", "cell": "SPATIAL-PAST",
         "trace": [{"tool": "get_topk_caption", "args": {"query": "screwdriver"}}],
         "outcome": "答错", "skills_injected": []},
        {"question": "Where did I put the blue screwdriver?", "cell": "SPATIAL-PAST",
         "trace": [{"tool": "get_topk_frame", "args": {"query": "screwdriver on bench"}},
                   {"tool": "rewatch_frames", "args": {"t_start": 88, "t_end": 108}}],
         "outcome": "答对", "skills_injected": []},
    ]
    eps = fails if a.kind == "fail" else contrast
    specs = TOOL_SPECS if isinstance(TOOL_SPECS, str) else \
        json.dumps(TOOL_SPECS, ensure_ascii=False)[:2500]
    prompt = build_prompt(a.kind, eps, catalog_of(existing), specs,
                          n_cards=len(existing))    # ← 断点：看完整 prompt
    print(f"--- prompt 长度 {len(prompt)} 字符（前1200）---\n{prompt[:1200]}\n...")
    r = make_client(a.curator_ep, temperature=0.5).chat_json(prompt, max_tokens=4000) or {}
    print(f"\n--- curator reasoning ---\n{str(r.get('reasoning'))[:600]}")
    for op in (r.get("ops") or []):
        card = op.get("card") or {}
        if op.get("op") == "modify":
            base = next((c for c in existing if c["card_id"] == op.get("card_id")), {})
            card = {**base, **(op.get("changes") or {})}
        ok, why = gate_all(card, existing, op.get("op", "add"))   # ← 断点：过闸
        print(f"\n[{op.get('op')}] {card.get('card_id')}  过闸={ok} {why}")
        print(json.dumps(card, ensure_ascii=False, indent=1)[:900])


def main():
    ap = argparse.ArgumentParser()
    sub = ap.add_subparsers(dest="cmd", required=True)

    p = sub.add_parser("qgen")
    p.add_argument("--dataset", default="egoschema")
    p.add_argument("--space", default="ACTION")
    p.add_argument("--time", default="GLOBAL")
    p.add_argument("--video-idx", type=int, default=0)
    p.add_argument("--gen-ep", default="qwen38_pool")
    p.add_argument("--verify-ep", default="qwen_local")
    p.set_defaults(fn=cmd_qgen)

    p = sub.add_parser("retrieve")
    p.add_argument("--question", required=True)
    p.add_argument("--seed-mode", default="minimal")
    p.set_defaults(fn=cmd_retrieve)

    p = sub.add_parser("episode")
    p.add_argument("--pool", default="/mnt/tokyo-ai/gst/EvoStream/data/pool_egoschema.json")
    p.add_argument("--index", type=int, default=0)
    p.add_argument("--seed-mode", default="minimal")
    p.add_argument("--executor-ep", default="qwen38_pool")
    p.set_defaults(fn=cmd_episode)

    p = sub.add_parser("curate")
    p.add_argument("--kind", default="fail", choices=["fail", "success"])
    p.add_argument("--with-existing", action="store_true", default=True)
    p.add_argument("--curator-ep", default="qwen38_pool")
    p.set_defaults(fn=cmd_curate)

    a = ap.parse_args()
    a.fn(a)


if __name__ == "__main__":
    main()

	"""单点调试入口：每个阶段都能单独跑一次，方便下断点。

	配合 .vscode/launch.json 里的 evo2-2 / evo2-3 / evo2-4 / evo2-5 使用。

	python evo2/debug_one.py qgen --dataset egoschema --space ACTION --time GLOBAL
	python evo2/debug_one.py retrieve --question "Where did I put the screwdriver?"
	python evo2/debug_one.py episode --pool data/pool_egoschema.json --index 0
	python evo2/debug_one.py curate --kind fail
	"""

	import argparse
	import json
	import logging
	import os
	import sys

	sys.path.insert(0, "/mnt/tokyo-ai/gst/EvoStream")
	sys.path.insert(0, "/mnt/tokyo-ai/gst/EvoStream/cons_memory")

	logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s")


	# ---------------------------------------------------------------- 出题

	def cmd_qgen(a):
	from infer.llm_clients import make_client
	from memory_store import VideoMemory
	from evo2.qgen import QuestionGenerator, phrasing_rule
	from evo2.style_cards import build_style_card
	from evo2.validate_qgen import list_banks, sample_frames

	banks = list_banks(a.dataset, 3)
	vid, bank = banks[a.video_idx]
	mem = VideoMemory(bank)
	mode = "global" if a.time == "GLOBAL" else "window"
	frames, ts = sample_frames(mem, mode)
	print(f"视频 {vid} \| 采样 {mode} {len(frames)} 帧 @ "
	f"{[f'{t:.0f}s' for t in ts]}")
	print(f"\n--- 该格子的措辞约束 ---\n{phrasing_rule(a.time)}\n")

	qg = QuestionGenerator(make_client(a.gen_ep, temperature=0.7),
	make_client(a.verify_ep, temperature=0.0),
	style_card=build_style_card(a.dataset))
	rec = qg.make_one(frames, ts, a.space, a.time) # ← 断点打这里
	print(json.dumps(rec, ensure_ascii=False, indent=1)[:2500])
	print(f"\n结果: stage={rec['stage']} passed={rec['passed']} "
	f"reject={rec.get('reject')}")


	# ---------------------------------------------------------------- 检索

	def cmd_retrieve(a):
	from infer.llm_clients import embed_texts
	from evo2.classifier import classify_lexical
	from evo2.seed_lib import build_seed
	from evo2.skill import SkillLibrary, keyword_score

	lib = SkillLibrary(build_seed(a.seed_mode), embed_fn=embed_texts)
	lib.index_library()
	cell = classify_lexical(a.question, None) # ← 断点：题型判定
	print(f"题目: {a.question}")
	print(f"题型判定: {cell}")
	cards, scores = lib.retrieve(a.question, cell, topk=len(lib.cards),
	return_scores=True) # ← 断点：打分
	print(f"\n全部卡片打分（前8）:")
	for s in scores[:8]:
	print(f" {s['total']:.4f} = kw {s['kw']:.3f}0.5 + sem {s['sem']:.3f}0.5 "
	f"(+先验) [{s['card_id']}]")
	print(f"\n实际注入(top2): {[c['card_id'] for c in cards[:2]]}")
	print("\n渲染给 executor 的文本:")
	print(SkillLibrary.render(cards[:2]))


	# ---------------------------------------------------------------- 单题推理

	def cmd_episode(a):
	from infer.config import RunConfig
	from infer.llm_clients import embed_texts, make_client
	from infer.tools import ToolRegistry
	from evo2.pipeline import run_one_episode
	from evo2.seed_lib import build_seed
	from evo2.skill import SkillLibrary
	from evo2.tracelog import TraceLogger

	pool = json.load(open(a.pool))
	qs = pool["train"] + pool["frozen_val"]
	q = qs[a.index]
	print(f"题目[{a.index}]: {q['question'][:140]}")
	print(f" 格子={q['cell']} gt={q.get('gt')} video={q['video_id']}")

	cfg = RunConfig()
	cfg.executor = a.executor_ep
	lib = SkillLibrary(build_seed(a.seed_mode), embed_fn=embed_texts)
	lib.index_library()
	vlm = make_client(cfg.vlm_tool, temperature=0.0)
	tl = TraceLogger("/mnt/tokyo-ai/gst/EvoStream/runs/DEBUG_one")
	log = run_one_episode(q, lib, make_client(a.executor_ep, temperature=0.0),
	lambda m: ToolRegistry(m, cfg, vlm_client=vlm),
	cfg, tl=tl, meta={"phase": "debug"}) # ← 断点
	print("\n--- 工具轨迹 ---")
	for s in (log.get("trace") or []):
	print(f" {s['step']}. {s['tool']}({json.dumps(s['args'], ensure_ascii=False)[:90]})")
	print(f" → {(s.get('result_digest') or '')[:180]}")
	print(f"\n思考: {(log.get('reasoning') or '')[:400]}")
	print(f"答案: {log.get('choice')} (gt={q.get('gt')}) 正确={log.get('correct')}")
	print(f"注入的卡: {log.get('skills_injected')} adherence={log.get('adherence')}")
	tl.close()


	# ---------------------------------------------------------------- 归纳改卡

	def cmd_curate(a):
	from infer.llm_clients import make_client
	from infer.tools import TOOL_SPECS
	from evo2.curator import build_prompt, catalog_of
	from evo2.gates import gate_all
	from evo2.seed_lib import build_seed
	from evo2.skill import new_card

	# 构造轨迹：库里已有一张"只用文本检索"的缺陷卡，照做仍答错 → 应 modify
	existing = [new_card("count-caption-only", ["how many", "count", "number of"],
	"询问数量时用文本检索caption统计",
	[{"tool": "get_topk_caption", "params": {"k": 5}}],
	["COUNT"], ["NOW", "PAST"], "caption中提到了数量")] \
	if a.with_existing else build_seed("empty")

	fails = [
	{"question": "How many times did the person rinse the pan?", "cell": "COUNT-PAST",
	"trace": [{"tool": "get_topk_caption", "args": {"query": "rinse pan", "k": 5}}],
	"skills_injected": [c["card_id"] for c in existing], "adherence": 1.0,
	"failure_mode": "照策略执行了但caption未给出精确数量"},
	{"question": "How many plates were on the rack?", "cell": "COUNT-NOW",
	"trace": [{"tool": "get_topk_caption", "args": {"query": "plates", "k": 5}}],
	"skills_injected": [c["card_id"] for c in existing], "adherence": 1.0,
	"failure_mode": "照策略执行了但caption未给出精确数量"},
	]
	contrast = [
	{"question": "Where did I put the blue screwdriver?", "cell": "SPATIAL-PAST",
	"trace": [{"tool": "get_topk_caption", "args": {"query": "screwdriver"}}],
	"outcome": "答错", "skills_injected": []},
	{"question": "Where did I put the blue screwdriver?", "cell": "SPATIAL-PAST",
	"trace": [{"tool": "get_topk_frame", "args": {"query": "screwdriver on bench"}},
	{"tool": "rewatch_frames", "args": {"t_start": 88, "t_end": 108}}],
	"outcome": "答对", "skills_injected": []},
	]
	eps = fails if a.kind == "fail" else contrast
	specs = TOOL_SPECS if isinstance(TOOL_SPECS, str) else \
	json.dumps(TOOL_SPECS, ensure_ascii=False)[:2500]
	prompt = build_prompt(a.kind, eps, catalog_of(existing), specs,
	n_cards=len(existing)) # ← 断点：看完整 prompt
	print(f"--- prompt 长度 {len(prompt)} 字符（前1200）---\n{prompt[:1200]}\n...")
	r = make_client(a.curator_ep, temperature=0.5).chat_json(prompt, max_tokens=4000) or {}
	print(f"\n--- curator reasoning ---\n{str(r.get('reasoning'))[:600]}")
	for op in (r.get("ops") or []):
	card = op.get("card") or {}
	if op.get("op") == "modify":
	base = next((c for c in existing if c["card_id"] == op.get("card_id")), {})
	card = {base, (op.get("changes") or {})}
	ok, why = gate_all(card, existing, op.get("op", "add")) # ← 断点：过闸
	print(f"\n[{op.get('op')}] {card.get('card_id')} 过闸={ok} {why}")
	print(json.dumps(card, ensure_ascii=False, indent=1)[:900])


	def main():
	ap = argparse.ArgumentParser()
	sub = ap.add_subparsers(dest="cmd", required=True)

	p = sub.add_parser("qgen")
	p.add_argument("--dataset", default="egoschema")
	p.add_argument("--space", default="ACTION")
	p.add_argument("--time", default="GLOBAL")
	p.add_argument("--video-idx", type=int, default=0)
	p.add_argument("--gen-ep", default="qwen38_pool")
	p.add_argument("--verify-ep", default="qwen_local")
	p.set_defaults(fn=cmd_qgen)

	p = sub.add_parser("retrieve")
	p.add_argument("--question", required=True)
	p.add_argument("--seed-mode", default="minimal")
	p.set_defaults(fn=cmd_retrieve)

	p = sub.add_parser("episode")
	p.add_argument("--pool", default="/mnt/tokyo-ai/gst/EvoStream/data/pool_egoschema.json")
	p.add_argument("--index", type=int, default=0)
	p.add_argument("--seed-mode", default="minimal")
	p.add_argument("--executor-ep", default="qwen38_pool")
	p.set_defaults(fn=cmd_episode)

	p = sub.add_parser("curate")
	p.add_argument("--kind", default="fail", choices=["fail", "success"])
	p.add_argument("--with-existing", action="store_true", default=True)
	p.add_argument("--curator-ep", default="qwen38_pool")
	p.set_defaults(fn=cmd_curate)

	a = ap.parse_args()
	a.fn(a)


	if __name__ == "__main__":
	main()