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Script to find best cases for different visualizations.
"""
import os
import sys
import glob
import numpy as np
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "src"))
def main():
# Setup
ckpt_path = "/root/githubs/gliomasam3_moe/logs/segmamba/model/ckpt_step600.pt"
data_path = "/data/yty/brats23_segmamba_processed"
# Get all cases
npz_files = sorted(glob.glob(os.path.join(data_path, "*.npz")))
case_ids = [os.path.basename(f).replace(".npz", "") for f in npz_files]
print(f"Found {len(case_ids)} cases")
# Use ModelRunner from vis_publication
from vis_publication import ModelRunner
import yaml
with open("visualizations/vis_config.yaml", "r") as f:
vis_cfg = yaml.safe_load(f)
runner = ModelRunner(vis_cfg, "configs/train.yaml", ckpt_path, "cuda")
model = runner.model
print("Model loaded")
# Scan cases for best examples
et_absent_candidates = []
moe_routing_candidates = []
test_cases = case_ids[:50] # Test first 50
for case_id in test_cases:
try:
# Load data using runner
img_t, _ = runner.load_case_tensor(case_id)
# Run model
with torch.no_grad():
out = runner.forward_intermediate(img_t)
pi_et = float(out["pi_et"].cpu().numpy().reshape(-1)[0])
et_pre = out["et_pre"].cpu().numpy()[0, 0]
et_post = out["et_post"].cpu().numpy()[0, 0]
moe_gamma = out["moe_gamma"].cpu().numpy()[0] # [3, n_experts]
# Check ET-absent: want low pi_et AND difference between pre/post
pre_sum = (et_pre > 0.5).sum()
post_sum = (et_post > 0.5).sum()
diff_ratio = abs(pre_sum - post_sum) / max(pre_sum, 1)
et_absent_candidates.append({
"case_id": case_id,
"pi_et": pi_et,
"pre_sum": int(pre_sum),
"post_sum": int(post_sum),
"diff_ratio": diff_ratio,
"score": (1 - pi_et) * diff_ratio # High score = low pi_et + big diff
})
# Check MoE routing: want non-zero and diverse weights
gamma_mean = moe_gamma.mean(axis=0) # Average over regions
nonzero_count = (gamma_mean > 0.05).sum()
gamma_std = gamma_mean.std()
moe_routing_candidates.append({
"case_id": case_id,
"gamma_mean": gamma_mean.tolist(),
"nonzero_count": int(nonzero_count),
"gamma_std": float(gamma_std),
"score": nonzero_count * gamma_std # High score = diverse weights
})
print(f"{case_id}: pi_et={pi_et:.3f}, pre={pre_sum}, post={post_sum}, diff={diff_ratio:.2f}, moe_nonzero={nonzero_count}")
except Exception as e:
print(f"Error on {case_id}: {e}")
continue
# Sort and report
print("\n" + "="*60)
print("Best ET-absent candidates (high score = low pi_et + big diff):")
et_absent_candidates.sort(key=lambda x: x["score"], reverse=True)
for c in et_absent_candidates[:10]:
print(f" {c['case_id']}: pi_et={c['pi_et']:.3f}, pre={c['pre_sum']}, post={c['post_sum']}, score={c['score']:.3f}")
print("\n" + "="*60)
print("Best MoE routing candidates (high score = diverse weights):")
moe_routing_candidates.sort(key=lambda x: x["score"], reverse=True)
for c in moe_routing_candidates[:10]:
print(f" {c['case_id']}: nonzero={c['nonzero_count']}, std={c['gamma_std']:.3f}, weights={[f'{w:.2f}' for w in c['gamma_mean']]}")
if __name__ == "__main__":
main()
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