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"""Compare how v3 and v4 replay pipelines read multi_choice actions.
v3 source:
- EpisodeDatasetResolver.get_step("multi_choice", step)
v4-noresolver source:
- scripts.dataset_replay._build_action_sequence(..., "multi_choice")
- then _parse_oracle_command() in replay loop
"""
import argparse
import importlib.util
import json
import re
import sys
from pathlib import Path
from typing import Any, Optional
import h5py
import numpy as np
REPO_ROOT = Path(__file__).resolve().parents[2]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
SRC_ROOT = REPO_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
sys.path.insert(0, str(SRC_ROOT))
def _load_episode_dataset_resolver_cls():
resolver_path = SRC_ROOT / "robomme" / "env_record_wrapper" / "episode_dataset_resolver.py"
spec = importlib.util.spec_from_file_location(
"episode_dataset_resolver_direct",
resolver_path,
)
if spec is None or spec.loader is None:
raise RuntimeError(f"Failed to load resolver module from {resolver_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
resolver_cls = getattr(module, "EpisodeDatasetResolver", None)
if resolver_cls is None:
raise RuntimeError(f"EpisodeDatasetResolver not found in {resolver_path}")
return resolver_cls
EpisodeDatasetResolver = _load_episode_dataset_resolver_cls()
DEFAULT_ENV_ID = "PatternLock"
DEFAULT_DATASET_ROOT = "/data/hongzefu/data_0226-test"
def _parse_oracle_command_v4(choice_action: Optional[Any]) -> Optional[dict[str, Any]]:
"""Exact validation logic used in evaluate_dataset_replay-parallelv4-noresolver.py."""
if not isinstance(choice_action, dict):
return None
choice = choice_action.get("choice")
if not isinstance(choice, str) or not choice.strip():
return None
point = choice_action.get("point")
if not isinstance(point, (list, tuple, np.ndarray)) or len(point) != 2:
return None
return choice_action
def _is_video_demo_v4(ts: h5py.Group) -> bool:
info = ts.get("info")
if info is None or "is_video_demo" not in info:
return False
return bool(np.reshape(np.asarray(info["is_video_demo"][()]), -1)[0])
def _is_subgoal_boundary_v4(ts: h5py.Group) -> bool:
info = ts.get("info")
if info is None or "is_subgoal_boundary" not in info:
return False
return bool(np.reshape(np.asarray(info["is_subgoal_boundary"][()]), -1)[0])
def _decode_h5_str_v4(raw: Any) -> str:
if isinstance(raw, np.ndarray):
raw = raw.flatten()[0]
if isinstance(raw, (bytes, np.bytes_)):
raw = raw.decode("utf-8")
return raw
def _build_multi_choice_sequence_v4(episode_data: h5py.Group) -> list[Any]:
"""
Re-implementation of dataset_replay._build_action_sequence(..., \"multi_choice\")
without importing cv2/imageio/torch dependencies.
"""
timestep_keys = sorted(
(k for k in episode_data.keys() if k.startswith("timestep_")),
key=lambda k: int(k.split("_")[1]),
)
out: list[Any] = []
for key in timestep_keys:
ts = episode_data[key]
if _is_video_demo_v4(ts):
continue
action_grp = ts.get("action")
if action_grp is None:
continue
if not _is_subgoal_boundary_v4(ts):
continue
if "choice_action" not in action_grp:
continue
raw = _decode_h5_str_v4(action_grp["choice_action"][()])
try:
out.append(json.loads(raw))
except (TypeError, ValueError, json.JSONDecodeError):
continue
return out
def _resolve_h5_path(env_id: str, dataset_root: Optional[str], h5_path: Optional[str]) -> Path:
if h5_path:
return Path(h5_path)
if not dataset_root:
raise ValueError("Either --h5_path or --dataset_root must be provided")
return Path(dataset_root) / f"record_dataset_{env_id}.h5"
def _episode_indices(data: h5py.File) -> list[int]:
return sorted(
int(m.group(1))
for key in data.keys()
for m in [re.match(r"episode_(\d+)$", key)]
if m
)
def _parse_episode_filter(raw: Optional[str], all_eps: list[int]) -> list[int]:
if not raw:
return all_eps
selected: set[int] = set()
for token in [x.strip() for x in raw.split(",") if x.strip()]:
if "-" in token:
lo_s, hi_s = token.split("-", 1)
lo = int(lo_s)
hi = int(hi_s)
if lo > hi:
lo, hi = hi, lo
selected.update(range(lo, hi + 1))
else:
selected.add(int(token))
return [ep for ep in all_eps if ep in selected]
def _canonical_command(cmd: Any) -> str:
"""Stable string form for diffing and readable output."""
try:
return json.dumps(cmd, ensure_ascii=False, sort_keys=True)
except TypeError:
if isinstance(cmd, dict):
safe = {
str(k): (v.tolist() if isinstance(v, np.ndarray) else v)
for k, v in cmd.items()
}
return json.dumps(safe, ensure_ascii=False, sort_keys=True)
return repr(cmd)
def _read_v4_commands(episode_group: h5py.Group) -> tuple[list[Any], list[dict[str, Any]], int]:
raw_list = _build_multi_choice_sequence_v4(episode_group)
parsed_list: list[dict[str, Any]] = []
skipped = 0
for item in raw_list:
parsed = _parse_oracle_command_v4(item)
if parsed is None:
skipped += 1
continue
parsed_list.append(parsed)
return raw_list, parsed_list, skipped
def _read_v3_commands(env_id: str, episode: int, dataset_ref: str) -> list[dict[str, Any]]:
out: list[dict[str, Any]] = []
with EpisodeDatasetResolver(
env_id=env_id,
episode=episode,
dataset_directory=dataset_ref,
) as resolver:
step = 0
while True:
cmd = resolver.get_step("multi_choice", step)
if cmd is None:
break
if isinstance(cmd, dict):
out.append(cmd)
step += 1
return out
def compare_episode(
env_id: str,
episode: int,
episode_group: h5py.Group,
dataset_ref: str,
max_show: int,
) -> None:
v4_raw, v4_effective, v4_skipped = _read_v4_commands(episode_group)
v3_resolver = _read_v3_commands(env_id=env_id, episode=episode, dataset_ref=dataset_ref)
print(f"\n=== episode_{episode} ===")
print(
"counts: "
f"v4_raw={len(v4_raw)}, "
f"v4_effective={len(v4_effective)} (skipped_by_parse={v4_skipped}), "
f"v3_resolver={len(v3_resolver)}"
)
v4_effective_c = [_canonical_command(x) for x in v4_effective]
v3_c = [_canonical_command(x) for x in v3_resolver]
if v4_effective_c == v3_c:
print("effective sequence compare: SAME")
else:
print("effective sequence compare: DIFFERENT")
max_len = max(len(v4_effective_c), len(v3_c))
shown = 0
for idx in range(max_len):
left = v4_effective_c[idx] if idx < len(v4_effective_c) else "<MISSING>"
right = v3_c[idx] if idx < len(v3_c) else "<MISSING>"
if left == right:
continue
print(f" idx={idx}")
print(f" v4_effective: {left}")
print(f" v3_resolver : {right}")
shown += 1
if shown >= max_show:
remaining = max_len - idx - 1
if remaining > 0:
print(f" ... more differences omitted ({remaining} remaining positions)")
break
print(f"sample v4_raw (first {max_show}):")
for i, item in enumerate(v4_raw[:max_show]):
print(f" [{i}] {_canonical_command(item)}")
print(f"sample v4_effective (first {max_show}):")
for i, item in enumerate(v4_effective[:max_show]):
print(f" [{i}] {_canonical_command(item)}")
print(f"sample v3_resolver (first {max_show}):")
for i, item in enumerate(v3_resolver[:max_show]):
print(f" [{i}] {_canonical_command(item)}")
def main() -> None:
parser = argparse.ArgumentParser(
description=(
"Compare multi_choice read results between "
"evaluate_dataset_replay-parallelv3 and parallelv4-noresolver."
)
)
parser.add_argument(
"--env_id",
type=str,
default=DEFAULT_ENV_ID,
help=f"Task/env id. Default: {DEFAULT_ENV_ID}",
)
parser.add_argument(
"--dataset_root",
type=str,
default=DEFAULT_DATASET_ROOT,
help=(
"Directory that contains record_dataset_<env_id>.h5. "
f"Default: {DEFAULT_DATASET_ROOT}"
),
)
parser.add_argument(
"--h5_path",
type=str,
default=None,
help="Direct path to .h5 file (overrides --dataset_root)",
)
parser.add_argument(
"--episodes",
type=str,
default=0,
help="Episode filter, e.g. '0,3,8-10'. Default: all episodes in h5",
)
parser.add_argument(
"--max_show",
type=int,
default=50,
help="Max number of diff/sample rows per episode",
)
args = parser.parse_args()
h5_file = _resolve_h5_path(args.env_id, args.dataset_root, args.h5_path)
if not h5_file.exists():
raise FileNotFoundError(f"h5 file not found: {h5_file}")
dataset_ref = str(h5_file) if h5_file.suffix == ".h5" else str(h5_file.parent)
print(f"env_id={args.env_id}")
print(f"h5={h5_file}")
with h5py.File(h5_file, "r") as data:
all_eps = _episode_indices(data)
selected_eps = _parse_episode_filter(args.episodes, all_eps)
if not selected_eps:
print("No episodes selected.")
return
print(f"episodes={selected_eps}")
for ep in selected_eps:
key = f"episode_{ep}"
if key not in data:
print(f"\n=== episode_{ep} ===")
print("missing in h5, skip")
continue
compare_episode(
env_id=args.env_id,
episode=ep,
episode_group=data[key],
dataset_ref=dataset_ref,
max_show=args.max_show,
)
if __name__ == "__main__":
main()
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