File size: 2,581 Bytes
45e45cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | #!/usr/bin/env python3
"""Plot the 5 sampling-path accumulated values (at 4 target frames) for one episode.
Usage:
python plot_one_episode.py chunk-000_episode_000039
python plot_one_episode.py chunk-000_episode_000039 --out /tmp/x.png
"""
import argparse
import json
from pathlib import Path
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
MODES = ["dense_all", "stride2", "stride4", "front_dense", "back_dense"]
REFERENCE_MODE = "dense_all"
COLORS = {"dense_all": "tab:blue", "stride2": "tab:orange",
"stride4": "tab:green", "front_dense": "tab:red",
"back_dense": "tab:purple"}
FRACS = ["1/4", "2/4", "3/4", "end"]
p = argparse.ArgumentParser()
p.add_argument("episode_dir", help="e.g. chunk-000_episode_000039")
p.add_argument("--results-root",
default=str(Path(__file__).resolve().parent.parent / "results_full"))
p.add_argument("--out", default=None)
a = p.parse_args()
ep_dir = Path(a.results_root) / "episode_results" / a.episode_dir
out = Path(a.out) if a.out else ep_dir / "curves.png"
data = {}
for m in MODES:
f = ep_dir / f"{m}.json"
if f.exists():
data[m] = json.loads(f.read_text())
if not data:
raise SystemExit(f"no mode json found in {ep_dir}")
meta0 = next(iter(data.values()))
xt = np.arange(len(FRACS))
fig, ax = plt.subplots(figsize=(11, 6))
for m in MODES:
if m not in data:
continue
ys = [data[m]["checkpoints"][frac]["value"] for frac in FRACS]
ax.plot(xt, ys, "-o", color=COLORS[m], lw=1.8, ms=7, label=m)
# gray min-max spread bar at each checkpoint
for i, frac in enumerate(FRACS):
vals = [data[m]["checkpoints"][frac]["value"] for m in MODES if m in data]
ax.plot([i, i], [min(vals), max(vals)], color="0.8", lw=2, zorder=0)
scores = {frac: [data[m]["checkpoints"][frac]["value"] for m in MODES if m in data]
for frac in FRACS}
rngs = ", ".join(f"{frac}: {max(v)-min(v):.1f}" for frac, v in scores.items())
ax.set_xticks(xt)
ax.set_xticklabels([f"{frac}\n(t={meta0['checkpoints'][frac]['target_t']})" for frac in FRACS])
ax.set_title(f"{a.episode_dir} ({meta0['camera']}, seq={meta0['pool_n']} @ "
f"{meta0['compressed_fps']:.0f}fps/{meta0['target_size'][0]}px)\n"
f"task: {meta0['task'][:100]}\nPrefix Range @ checkpoints -> {rngs}",
fontsize=10)
ax.set_xlabel("target frame (checkpoint)")
ax.set_ylabel("accumulated value (0-100)")
ax.set_ylim(0, 100)
ax.grid(alpha=0.25)
ax.legend(fontsize=9)
fig.tight_layout()
fig.savefig(out, dpi=140)
print("saved:", out)
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