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milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md

@@ -124,6 +124,22 @@ Metadata fusion can be combined with every image fusion mode:
 
 Normal online image transforms keep the original metadata vector. If you materialize offline transform augmentations, duplicate the original row metadata unchanged. Do not invent metadata for generated synthetic lesions in this trainer; use real-row metadata or disable metadata for synthetic-only experiments.
 
+For synthetic paired augmentations, prefer appending a small train-only subset instead of replacing the whole training CSV:
+
+```bash
+python train_milk10k_effb2_dual_metadata.py \
+  --data-dir /marimo/milk10k \
+  --augmented-data-dir /marimo/milk10k_augmented \
+  --augmented-classes INF BEN_OTH DF VASC \
+  --augmented-max-per-class 25 \
+  --zero-augmented-metadata \
+  --loss ce_f1 \
+  --f1-weight 0.3 \
+  --f1-ignore-classes MAL_OTH
+```
+
+`--zero-augmented-metadata` only zeros metadata vectors for appended augmented rows; real rows still use metadata normally.
+
 ## 3. Class Weight Only
 
 ```bash

milk10k_effb2_metadata/cli.py

@@ -95,6 +95,29 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Keep synthetic lesion IDs containing __sdpair_ in train only; validation is split from real lesions.",
     )
+    parser.add_argument(
+        "--augmented-data-dir",
+        type=Path,
+        default=None,
+        help="Optional augmented MILK10k-style data dir. Only extra lesion IDs are appended to the train split.",
+    )
+    parser.add_argument(
+        "--augmented-max-per-class",
+        type=int,
+        default=0,
+        help="Cap extra augmented lesions per class. 0 keeps all extra rows from --augmented-data-dir.",
+    )
+    parser.add_argument(
+        "--augmented-classes",
+        nargs="*",
+        default=[],
+        help="Optional class-name allowlist for appended augmented lesions, e.g. --augmented-classes INF BEN_OTH DF VASC.",
+    )
+    parser.add_argument(
+        "--zero-augmented-metadata",
+        action="store_true",
+        help="Set metadata vectors to all zeros for rows appended from --augmented-data-dir.",
+    )
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--branch-dim", type=int, default=512)
     parser.add_argument("--metadata-dim", type=int, default=64)

milk10k_effb2_metadata/data.py

@@ -32,6 +32,9 @@ class PairedMilk10kMetadataDataset(Dataset):
         self.df = df.reset_index(drop=True)
         self.labels = [label_to_idx[label] for label in self.df["label"].tolist()]
         self.metadata = np.stack([metadata_vector(row, metadata_spec) for _, row in self.df.iterrows()])
+        if "ignore_metadata" in self.df.columns:
+            ignore_mask = self.df["ignore_metadata"].fillna(False).astype(bool).to_numpy()
+            self.metadata[ignore_mask] = 0.0
         self.transform = transform
 
     def __len__(self) -> int:

milk10k_effb2_metadata/runner.py

@@ -14,6 +14,7 @@ from milk10k_effb2_metadata.data import (
     fit_metadata_spec,
     kfold_splits,
     lesion_split,
+    load_paired_dataframe,
     make_loaders,
     metadata_vector,
 )
@@ -45,6 +46,67 @@ def build_tail_tracking_config(
     }
 
 
+def resolve_label_name(class_names: list[str], name: str) -> str:
+    normalized = {label.upper(): label for label in class_names}
+    key = name.strip().upper()
+    if key not in normalized:
+        raise ValueError(f"Unknown augmented class name: {name!r}. Choices: {class_names}")
+    return normalized[key]
+
+
+def load_augmented_subset(
+    base_df: pd.DataFrame,
+    class_names: list[str],
+    args: argparse.Namespace,
+) -> pd.DataFrame:
+    augmented_data_dir = getattr(args, "augmented_data_dir", None)
+    if augmented_data_dir is None:
+        return pd.DataFrame(columns=base_df.columns)
+    augmented_dir = augmented_data_dir.expanduser().resolve()
+    augmented_df = load_paired_dataframe(augmented_dir)
+    base_lesion_ids = set(base_df["lesion_id"].astype(str))
+    augmented_df = augmented_df[~augmented_df["lesion_id"].astype(str).isin(base_lesion_ids)].copy()
+    augmented_classes = getattr(args, "augmented_classes", [])
+    if augmented_classes:
+        allowed = {resolve_label_name(class_names, name) for name in augmented_classes}
+        augmented_df = augmented_df[augmented_df["label"].isin(allowed)].copy()
+    augmented_max_per_class = getattr(args, "augmented_max_per_class", 0)
+    if augmented_max_per_class < 0:
+        raise ValueError("--augmented-max-per-class must be >= 0.")
+    if augmented_max_per_class > 0 and not augmented_df.empty:
+        augmented_df = (
+            augmented_df.sample(frac=1.0, random_state=args.seed)
+            .groupby("label", group_keys=False)
+            .head(augmented_max_per_class)
+            .sort_values(["label", "lesion_id"])
+            .reset_index(drop=True)
+        )
+    augmented_df["is_augmented"] = True
+    augmented_df["ignore_metadata"] = bool(getattr(args, "zero_augmented_metadata", False))
+    return augmented_df
+
+
+def append_augmented_train_rows(
+    base_df: pd.DataFrame,
+    train_df: pd.DataFrame,
+    class_names: list[str],
+    args: argparse.Namespace,
+) -> pd.DataFrame:
+    augmented_df = load_augmented_subset(base_df, class_names, args)
+    if augmented_df.empty:
+        if getattr(args, "augmented_data_dir", None) is not None:
+            print("Augmented data: no extra rows selected.")
+        return train_df
+    counts = augmented_df["label"].value_counts().sort_index().to_dict()
+    print(
+        "Augmented train append: "
+        f"rows={len(augmented_df)}, counts={counts}, "
+        f"zero_metadata={getattr(args, 'zero_augmented_metadata', False)}, "
+        f"source={getattr(args, 'augmented_data_dir', None)}"
+    )
+    return pd.concat([train_df, augmented_df], ignore_index=True, sort=False)
+
+
 def run_training_split(
     df: pd.DataFrame,
     train_df: pd.DataFrame,
@@ -233,6 +295,9 @@ def train_single_run(
     clinical_backbone_backend: str,
     dermoscopic_backbone_backend: str,
 ) -> dict[str, Any]:
+    df = df.copy()
+    df["is_augmented"] = False
+    df["ignore_metadata"] = False
     if args.synthetic_train_only:
         synthetic_mask = df["lesion_id"].astype(str).str.contains("__sdpair_", regex=False)
         real_df = df[~synthetic_mask].copy()
@@ -245,6 +310,7 @@ def train_single_run(
         )
     else:
         train_df, val_df = lesion_split(df, args.val_size, args.seed)
+    train_df = append_augmented_train_rows(df, train_df, class_names, args)
     return run_training_split(
         df,
         train_df,
@@ -268,9 +334,13 @@ def train_kfold(
     clinical_backbone_backend: str,
     dermoscopic_backbone_backend: str,
 ) -> list[dict[str, Any]]:
+    df = df.copy()
+    df["is_augmented"] = False
+    df["ignore_metadata"] = False
     fold_metrics = []
     for fold_idx, (train_df, val_df) in enumerate(kfold_splits(df, args.k_folds, args.seed)):
         print(f"\nK-fold {fold_idx + 1}/{args.k_folds}")
+        train_df = append_augmented_train_rows(df, train_df, class_names, args)
         metrics = run_training_split(
             df,
             train_df,