Add nnU-Net EVAR models

Dataset100_SegmentationPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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Dataset100_SegmentationPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_2/checkpoint_best.pth

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Dataset100_SegmentationPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/checkpoint_best.pth

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Dataset100_SegmentationPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_4/checkpoint_best.pth

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Dataset100_SegmentationPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

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Dataset101_LandmarksPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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Dataset101_LandmarksPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/checkpoint_best.pth

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Dataset101_LandmarksPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/checkpoint_best.pth

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Dataset101_LandmarksPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_4/checkpoint_best.pth

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Dataset101_LandmarksPreEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

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Dataset200_SegmentationPostEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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+        "ExAorta": 2,
+        "endoleak": 3
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+}

Dataset200_SegmentationPostEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth

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+            "previous_stage": "3d_lowres"
+        }
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+}

Dataset201_LandmarksPostEVAR/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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+    "file_ending": ".nii.gz"
+}

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+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
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+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "order": 1,
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+                "force_separate_z": null
+            },
+            "batch_dice": false
+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
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+}

formatting_models.py

@@ -0,0 +1,350 @@
+from pathlib import Path
+import json
+import shutil
+import torch
+import re
+
+SOURCE_ROOT = Path(r"\\NAS3_Z\all\Juan\Documents\MATLAB\AutoAGM\AutoAGM_files\pythonfiles\folders\nnunet_results")
+DEST_ROOT = Path(r"\\NAS3_Z\all\Juan\EVARAI\HuggingFace_models")
+
+DATASETS = {
+    "Dataset370_PreInterventionV3": "Dataset100_SegmentationPreEVAR",
+    "Dataset380_LandmarksPreInterventionV2": "Dataset101_LandmarksPreEVAR",
+    "Dataset371_PostInterventionV3": "Dataset200_SegmentationPostEVAR",
+    "Dataset381_LandmarksPostInterventionV2": "Dataset201_LandmarksPostEVAR",
+}
+
+
+
+OVERWRITE = True
+
+
+def get_dataset_id(name):
+    match = re.match(r"Dataset(\d{3})_", name)
+    if not match:
+        raise ValueError(f"Nombre de dataset no válido: {name}")
+    return int(match.group(1))
+
+
+def normalize_key(key):
+    return str(key).lower().replace("-", "_").replace(" ", "_")
+
+
+def is_dataset_id_key(key):
+    key = normalize_key(key)
+    return key in {"dataset_id", "datasetid", "dataset_name_or_id"} or key.endswith("_dataset_id")
+
+
+def is_dataset_name_key(key):
+    key = normalize_key(key)
+    return key == "dataset_name" or key.endswith("_dataset_name")
+
+
+def load_checkpoint(path):
+    try:
+        return torch.load(path, map_location="cpu", weights_only=False)
+    except TypeError:
+        return torch.load(path, map_location="cpu")
+
+
+def find_dataset_references(obj, old_name, old_id, path="root", parent_key=None):
+    """Busca referencias al nombre/ID antiguo sin modificar nada."""
+    found = []
+
+    if isinstance(obj, dict):
+        for key, value in obj.items():
+            current_path = f"{path}.{key}"
+
+            if isinstance(key, str) and (old_name in key or f"Dataset{old_id:03d}" in key):
+                found.append((current_path + " [KEY]", key))
+
+            # No tiene sentido recorrer millones de parámetros
+            if key == "network_weights":
+                continue
+
+            found.extend(find_dataset_references(value, old_name, old_id, current_path, key))
+
+    elif isinstance(obj, (list, tuple)):
+        for i, value in enumerate(obj):
+            found.extend(find_dataset_references(value, old_name, old_id, f"{path}[{i}]", parent_key))
+
+    elif isinstance(obj, str):
+        if old_name in obj or f"Dataset{old_id:03d}" in obj:
+            found.append((path, obj))
+        elif parent_key is not None and is_dataset_id_key(parent_key) and obj == str(old_id):
+            found.append((path, obj))
+
+    elif isinstance(obj, int) and not isinstance(obj, bool):
+        if parent_key is not None and is_dataset_id_key(parent_key) and obj == old_id:
+            found.append((path, obj))
+
+    return found
+
+
+def rewrite_metadata(obj, old_name, new_name, old_id, new_id, parent_key=None):
+    """
+    Cambia únicamente referencias identificables del dataset.
+    No hace un reemplazo global old_id -> new_id.
+    """
+    if isinstance(obj, dict):
+        result = {}
+
+        for key, value in obj.items():
+            new_key = key
+
+            if isinstance(key, str):
+                new_key = key.replace(old_name, new_name)
+
+            if new_key == "network_weights":
+                result[new_key] = value
+                continue
+
+            result[new_key] = rewrite_metadata(value, old_name, new_name, old_id, new_id, new_key)
+
+        return result
+
+    if isinstance(obj, list):
+        return [rewrite_metadata(v, old_name, new_name, old_id, new_id, parent_key) for v in obj]
+
+    if isinstance(obj, tuple):
+        return tuple(rewrite_metadata(v, old_name, new_name, old_id, new_id, parent_key) for v in obj)
+
+    if isinstance(obj, str):
+        value = obj.replace(old_name, new_name)
+
+        if parent_key is not None and is_dataset_name_key(parent_key):
+            if value.startswith(f"Dataset{old_id:03d}_") or value == f"Dataset{old_id:03d}":
+                value = new_name
+
+        if parent_key is not None and is_dataset_id_key(parent_key) and value == str(old_id):
+            value = str(new_id)
+
+        return value
+
+    if isinstance(obj, int) and not isinstance(obj, bool):
+        if parent_key is not None and is_dataset_id_key(parent_key) and obj == old_id:
+            return new_id
+
+    return obj
+
+
+def verify_weights_identical(original, modified):
+    """Comprueba que network_weights no ha cambiado."""
+    if "network_weights" not in original or "network_weights" not in modified:
+        raise RuntimeError("El checkpoint no contiene 'network_weights'.")
+
+    weights_a = original["network_weights"]
+    weights_b = modified["network_weights"]
+
+    if weights_a.keys() != weights_b.keys():
+        raise RuntimeError("Las claves de network_weights han cambiado.")
+
+    for key in weights_a:
+        a, b = weights_a[key], weights_b[key]
+
+        if torch.is_tensor(a) and torch.is_tensor(b):
+            if not torch.equal(a, b):
+                raise RuntimeError(f"Los pesos han cambiado en: {key}")
+        elif a != b:
+            raise RuntimeError(f"Ha cambiado un elemento de network_weights: {key}")
+
+
+def audit_json(path, old_name, old_id):
+    with open(path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    return find_dataset_references(data, old_name, old_id, path.name)
+
+
+def audit_checkpoint(path, old_name, old_id):
+    checkpoint = load_checkpoint(path)
+    return find_dataset_references(checkpoint, old_name, old_id, path.name)
+
+
+def save_modified_json(src, dst, old_name, new_name, old_id, new_id):
+    with open(src, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    data = rewrite_metadata(data, old_name, new_name, old_id, new_id)
+
+    dst.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(dst, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=4, ensure_ascii=False)
+
+
+def save_modified_checkpoint(src, dst, old_name, new_name, old_id, new_id):
+    checkpoint = load_checkpoint(src)
+
+    # Guardamos una referencia a los pesos originales.
+    original_weights = checkpoint["network_weights"]
+
+    # Modificamos todos los metadatos excepto network_weights.
+    modified = rewrite_metadata(checkpoint, old_name, new_name, old_id, new_id)
+
+    # Comprobación antes de guardar.
+    verify_weights_identical({"network_weights": original_weights}, modified)
+
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(modified, dst)
+
+    # Volvemos a cargar el archivo generado y comprobamos de nuevo.
+    reloaded = load_checkpoint(dst)
+    verify_weights_identical({"network_weights": original_weights}, reloaded)
+
+
+def find_trainer_dirs(dataset_dir):
+    trainers = []
+
+    for plans_file in dataset_dir.rglob("plans.json"):
+        trainer_dir = plans_file.parent
+
+        if (trainer_dir / "dataset.json").exists() and any(trainer_dir.glob("fold_*")):
+            trainers.append(trainer_dir)
+
+    return sorted(set(trainers))
+
+
+def print_references(refs, indent="      "):
+    if not refs:
+        print(f"{indent}No se encontraron referencias.")
+        return
+
+    for path, value in refs:
+        value = str(value)
+        if len(value) > 150:
+            value = value[:147] + "..."
+        print(f"{indent}{path}: {value}")
+
+
+def export_dataset(old_name, new_name):
+    old_id = get_dataset_id(old_name)
+    new_id = get_dataset_id(new_name)
+
+    src_dataset = SOURCE_ROOT / old_name
+    dst_dataset = DEST_ROOT / new_name
+
+    print("\n" + "=" * 80)
+    print(f"{old_name}  ->  {new_name}")
+    print(f"ID: {old_id} -> {new_id}")
+
+    if not src_dataset.exists():
+        raise FileNotFoundError(f"No existe: {src_dataset}")
+
+    if dst_dataset.exists() and not OVERWRITE:
+        raise FileExistsError(
+            f"Ya existe {dst_dataset}. Bórralo o cambia OVERWRITE = True."
+        )
+
+    if dst_dataset.exists() and OVERWRITE:
+        shutil.rmtree(dst_dataset)
+
+    trainer_dirs = find_trainer_dirs(src_dataset)
+
+    if not trainer_dirs:
+        raise RuntimeError(f"No se encontraron trainers dentro de {src_dataset}")
+
+    for trainer_dir in trainer_dirs:
+        relative = trainer_dir.relative_to(src_dataset)
+        dst_trainer = dst_dataset / relative
+
+        print(f"\n  Trainer: {relative}")
+
+        # ------------------------------------------------------------------
+        # AUDITORÍA ORIGINAL
+        # ------------------------------------------------------------------
+
+        for filename in ("dataset.json", "plans.json"):
+            src = trainer_dir / filename
+
+            print(f"\n    Auditoría {filename}:")
+            refs = audit_json(src, old_name, old_id)
+            print_references(refs)
+
+            save_modified_json(
+                src, dst_trainer / filename,
+                old_name, new_name, old_id, new_id
+            )
+
+        # ------------------------------------------------------------------
+        # FOLDS
+        # ------------------------------------------------------------------
+
+        fold_dirs = sorted(
+            [p for p in trainer_dir.glob("fold_*") if p.is_dir()],
+            key=lambda p: p.name
+        )
+
+        for fold_dir in fold_dirs:
+            checkpoint = fold_dir / "checkpoint_best.pth"
+
+            if not checkpoint.exists():
+                print(f"\n    [WARNING] {fold_dir.name}: no existe checkpoint_best.pth")
+                continue
+
+            print(f"\n    Auditoría {fold_dir.name}/checkpoint_best.pth:")
+
+            refs = audit_checkpoint(checkpoint, old_name, old_id)
+            print_references(refs)
+
+            dst_checkpoint = dst_trainer / fold_dir.name / "checkpoint_best.pth"
+
+            save_modified_checkpoint(
+                checkpoint, dst_checkpoint,
+                old_name, new_name, old_id, new_id
+            )
+
+            print("      ✓ checkpoint copiado")
+            print("      ✓ metadatos actualizados")
+            print("      ✓ network_weights idénticos")
+
+        # ------------------------------------------------------------------
+        # AUDITORÍA FINAL
+        # ------------------------------------------------------------------
+
+        print("\n    Comprobación final...")
+
+        residual_refs = []
+
+        for filename in ("dataset.json", "plans.json"):
+            residual_refs.extend(
+                audit_json(dst_trainer / filename, old_name, old_id)
+            )
+
+        for checkpoint in dst_trainer.glob("fold_*/checkpoint_best.pth"):
+            residual_refs.extend(
+                audit_checkpoint(checkpoint, old_name, old_id)
+            )
+
+        if residual_refs:
+            print("\n    [ERROR] Quedan referencias al dataset antiguo:")
+            print_references(residual_refs)
+            raise RuntimeError(
+                f"La exportación de {old_name} contiene referencias antiguas."
+            )
+
+        print("    ✓ No quedan referencias al nombre/ID antiguo.")
+
+
+def main():
+    if not SOURCE_ROOT.exists():
+        raise FileNotFoundError(f"No existe SOURCE_ROOT: {SOURCE_ROOT}")
+
+    DEST_ROOT.mkdir(parents=True, exist_ok=True)
+
+    print("SOURCE:", SOURCE_ROOT)
+    print("DEST:  ", DEST_ROOT)
+
+    for old_name, new_name in DATASETS.items():
+        export_dataset(old_name, new_name)
+
+    print("\n" + "=" * 80)
+    print("EXPORTACIÓN COMPLETADA")
+    print("=" * 80)
+
+    for new_name in DATASETS.values():
+        print(f"  {DEST_ROOT / new_name}")
+
+
+if __name__ == "__main__":
+    main()