moved_repo_first_update

2026-02-24 10:39:48 +01:00
commit 9894a23f09
98 changed files with 35387 additions and 0 deletions
@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+import argparse, subprocess, sys, time, json
+from pathlib import Path
+
+# Backbones in the paper that torchvision supports
+BACKBONES = [
+    "efficientnet_b0",
+    "resnet50",
+    "densenet121",
+    "vgg16",
+    "mobilenet_v2",
+    "inception_v3",
+    # (Xception omitted; not in torchvision — add via timm later if needed)
+]
+
+MODES = [
+    ("multiclass", ["Healthy", "Glaucoma", "Suspect"]),
+    ("binary", ["Healthy", "Glaucoma"]),
+]
+
+def run(cmd):
+    print("\n$ " + " ".join(map(str, cmd)))
+    res = subprocess.run(cmd, check=True)
+    return res.returncode
+
+def main():
+    ap = argparse.ArgumentParser(description="Run all paper CNNs across folds in multiclass + binary, then compile plots.")
+    ap.add_argument("--epochs", type=int, default=5, help="Epochs per fold (fast sanity first).")
+    ap.add_argument("--shortname", type=str, default="papergrid", help="Prefix for run IDs.")
+    ap.add_argument("--n-splits", type=int, default=5, help="Number of folds.")
+    ap.add_argument("--fusion-mode", type=str, default="fused", choices=["image_only","fused","metadata_only","vote"],
+                    help="Paper CNNs are image-only; leave as image_only unless you’re testing others.")
+    ap.add_argument("--freeze-ratio", type=float, default=0.0, help="0.0 = full fine-tune (as in the paper).")
+    # You can override data roots if needed
+    ap.add_argument("--image-dir", default="Papila/FundusImages")
+    ap.add_argument("--clinical-dir", default="Papila/ClinicalData")
+    ap.add_argument("--label-col", default="Diagnosis")
+    ap.add_argument("--cat-cols", nargs="*", default=["Gender", "Phakic/Pseudophakic"])
+    args = ap.parse_args()
+
+    ts = time.strftime("%Y%m%d_%H%M%S")
+    master_tag = f"{args.shortname}_{ts}"
+    master_dir = Path("analysis_data") / master_tag
+    master_dir.mkdir(parents=True, exist_ok=True)
+
+    # Keep a log of all subruns for the master report
+    index = []
+
+    for backbone in BACKBONES:
+        for eval_mode, class_names in MODES:
+            # build a child shortname per (backbone, mode)
+            sub_prefix = f"{args.shortname}_{backbone}_{eval_mode}"
+            cmd = [
+                sys.executable, "scripts/run_multifold.py",
+                "--backbone", backbone,
+                "--freeze-ratio", str(args.freeze_ratio),
+                "--fusion-mode", args.fusion_mode,
+                "--epochs", str(args.epochs),
+                "--n-splits", str(args.n_splits),
+                "--shortname", sub_prefix,
+                "--eval_mode", eval_mode,
+                "--image-dir", args.image_dir,
+                "--clinical-dir", args.clinical_dir,
+                "--label-col", args.label_col,
+            ]
+
+            # class names by mode (ensures plot legends are correct)
+            cmd += ["--class-names", *class_names]
+
+            plot_head_map = {
+                "image_only": "image",
+                "fused" : "fused",
+                "metadata_only": "metadata",
+                "vote": "fused",
+            }
+            # We always aggregate/plot the image head for paper CNNs
+            cmd += ["--plot-head", plot_head_map.get(args.fusion_mode)]
+
+            # Delegate the whole run to run_multifold.py
+            run(cmd)
+
+            # Discover the child run folder (the newest folder matching the shortname prefix)
+            # We do this because run_multifold appends its own timestamp.
+            adir = Path("analysis_data")
+            children = sorted([p for p in adir.glob(f"{sub_prefix}_*") if p.is_dir()])
+            if not children:
+                print(f"[WARN] No analysis_data folder found for {sub_prefix}; skipping index entry.")
+                continue
+            run_dir = children[-1]
+            summary_json = run_dir / "summary.json"
+            plots_dir = run_dir / "plots"
+
+            # Record entry
+            entry = {
+                "backbone": backbone,
+                "eval_mode": eval_mode,
+                "run_dir": str(run_dir),
+                "summary_json": str(summary_json) if summary_json.exists() else None,
+                "plots": {
+                    "mean": str(plots_dir / "roc_image_mean_ovr.png"),
+                    "overlay": str(plots_dir / "roc_image_perfold_overlay.png"),
+                }
+            }
+            # Try to read AUCs
+            try:
+                if summary_json.exists():
+                    entry.update(json.loads(summary_json.read_text()))
+            except Exception:
+                pass
+            index.append(entry)
+
+    # Write a master JSON + markdown report
+    (master_dir / "index.json").write_text(json.dumps(index, indent=2), encoding="utf-8")
+
+    # Simple markdown table of results with links
+    lines = [
+        f"# Multimodel grid — {master_tag}",
+        "",
+        f"- Epochs per fold: **{args.epochs}**",
+        f"- Folds: **{args.n_splits}**",
+        f"- Fusion mode: **{args.fusion_mode}** (paper CNNs = image-only)",
+        f"- Freeze ratio: **{args.freeze_ratio}**",
+        "",
+        "| Backbone | Mode | Mean AUC (macro/mc or ROC-AUC/bin) | Plots | Run folder |",
+        "|---|---|---:|---|---|",
+    ]
+    for e in index:
+        auc_mean = e.get("macro_ovr_auc_mean", None)
+        if auc_mean is not None:
+            auc_str = f"{auc_mean:.3f}"
+        else:
+            auc_str = "—"
+        mean_png = e["plots"]["mean"]
+        overlay_png = e["plots"]["overlay"]
+        plots_md = f"[mean]({mean_png}) / [overlay]({overlay_png})"
+        lines.append(
+            f"| `{e['backbone']}` | `{e['eval_mode']}` | {auc_str} | {plots_md} | `{e['run_dir']}` |"
+        )
+    (master_dir / "README.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+    print(f"\nAll done.\n- Master index: {master_dir/'index.json'}\n- Report: {master_dir/'README.md'}")
+    print(f"- Individual runs live under analysis_data/<shortname_backbone_mode_*> with plots and summaries.")
+
+if __name__ == "__main__":
+    main()