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dd253d4 | 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 | #!/usr/bin/env python3
"""Diagnose why Stack-Can tableHF_ep002.mp4 (a SUCCESS) scored track_max=0.0.
Re-runs the EXACT runtime TrackFlowScorer used by compare_robo_trackflow.py
on ep002 (and ep000 as a healthy reference), verbose, a few times — SAM3 is
non-deterministic so we want to see how often the can/plate detection misses.
"""
import sys, os, numpy as np
RL = "/home/hongyi/PWM/policy_rl/rl"
for p in [RL, f"{RL}/old/online"]:
if p not in sys.path:
sys.path.insert(0, p)
from rewards import TrackFlowScorer
M = "/home/hongyi/PWM/reward_model/3pointr_models"
SC = "/home/hongyi/PWM/external/DiffSynth-Studio/data/robot_new/Stack-Can"
TF_GPU = os.environ.get("TF_GPU", "0")
def ckpt():
for c in ("stack_can_canplate_150pts_384_flow_backbone_final.pth",
"stack_can_canplate_150pts_384_flow_backbone.pth"):
if os.path.exists(f"{M}/{c}"):
return f"{M}/{c}"
raise FileNotFoundError
tf = TrackFlowScorer(
pointr_ckpt=ckpt(),
prompt="red can", prompt_fallbacks=["can", "orange can"],
mark_prompt="white round plate",
mark_prompt_fallbacks=["white circle", "white disc", "white object on the table"],
subprocess_gpu=TF_GPU, verbose=True)
NRUNS = int(os.environ.get("NRUNS", "3"))
for ep in ("ep002", "ep000"):
v = f"{SC}/tableHF_{ep}.mp4"
print(f"\n########## {ep} {v} ##########", flush=True)
for i in range(NRUNS):
tp = tf.score(v, mode="progress_gated", k_pixel=50.0)
tp = np.asarray(tp)
print(f">>> {ep} run{i}: track_max={tp.max():.4f} nonzero={int((tp>0).sum())}/{tp.size}",
flush=True)
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