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Stable_diffusion_augmentation/plan_and_materialize_balanced_milk10k.py

@@ -31,7 +31,9 @@ def parse_args(argv=None):
     p.add_argument("--augmented-metadata", type=Path, default=None)
     p.add_argument("--synthetic-input-dir", type=Path, default=None)
     p.add_argument("--qc-summary", type=Path, default=None)
+    p.add_argument("--fresh-start", action="store_true", help="Ignore every existing synthetic CSV and plan from base real data only.")
     p.add_argument("--report-dir", type=Path, required=True)
+    p.add_argument("--scripts-dir", type=Path, default=None, help="Command output folder; defaults to Stable_diffusion_augmentation/.")
     p.add_argument("--materialize-dir", type=Path, default=None)
     p.add_argument("--bcc-cap-ratio", type=float, default=1.5)
     p.add_argument("--tail-floor", type=int, default=150)
@@ -56,7 +58,7 @@ def resolve_paths(args):
     info = base / "augmented_info"
     aug_gt = args.augmented_groundtruth or info / "MILK10k_Training_GroundTruth(2).csv"
     aug_meta = args.augmented_metadata or info / "MILK10k_Training_Metadata(3).csv"
-    required = [gt, meta, input_dir, aug_gt, aug_meta]
+    required = [gt, meta, input_dir] + ([] if args.fresh_start else [aug_gt, aug_meta])
     missing = [str(path) for path in required if not path.exists()]
     if missing: raise FileNotFoundError("Missing inputs: " + ", ".join(missing))
     return gt, meta, input_dir, aug_gt.expanduser().resolve(), aug_meta.expanduser().resolve()
@@ -75,11 +77,16 @@ def source_id(lesion_id): return str(lesion_id).split("__sdpair_", 1)[0]
 
 def load_inventory(args):
     gt_path, meta_path, input_dir, aug_gt_path, aug_meta_path = resolve_paths(args)
-    base_gt_raw = pd.read_csv(gt_path); base_meta = pd.read_csv(meta_path); aug_gt_raw = pd.read_csv(aug_gt_path); aug_meta = pd.read_csv(aug_meta_path)
-    base_gt = attach_labels(base_gt_raw); aug_gt = attach_labels(aug_gt_raw)
-    base_ids = set(base_gt.lesion_id.astype(str)); synth_gt = aug_gt[~aug_gt.lesion_id.astype(str).isin(base_ids)].copy()
+    base_gt_raw = pd.read_csv(gt_path); base_meta = pd.read_csv(meta_path); base_gt = attach_labels(base_gt_raw)
+    base_ids = set(base_gt.lesion_id.astype(str))
+    if args.fresh_start:
+        synth_gt = pd.DataFrame(columns=[*base_gt.columns, "source_lesion_id", "pair_metadata_complete"])
+        synth_meta = pd.DataFrame(columns=base_meta.columns)
+        return base_gt_raw, base_gt, base_meta, synth_gt, synth_meta, input_dir
+    aug_gt_raw = pd.read_csv(aug_gt_path); aug_meta = pd.read_csv(aug_meta_path); aug_gt = attach_labels(aug_gt_raw)
+    synth_gt = aug_gt[~aug_gt.lesion_id.astype(str).isin(base_ids)].copy()
     synth_meta = aug_meta[aug_meta.lesion_id.astype(str).isin(set(synth_gt.lesion_id.astype(str)))].copy()
-    if set(base_gt.lesion_id.astype(str)) - set(aug_gt.lesion_id.astype(str)): raise ValueError("Augmented ground truth omits base lesions.")
+    if base_ids - set(aug_gt.lesion_id.astype(str)): raise ValueError("Augmented ground truth omits base lesions.")
     if synth_gt.lesion_id.duplicated().any(): raise ValueError("Duplicate synthetic lesion IDs.")
     synth_gt["source_lesion_id"] = synth_gt.lesion_id.map(source_id)
     modality_counts = synth_meta.groupby("lesion_id").image_type.agg(lambda values: set(map(str, values)))
@@ -267,10 +274,12 @@ def command_script(args, plan, report_dir):
               "python Stable_diffusion_augmentation/plan_and_materialize_balanced_milk10k.py --base-data-dir \"$BASE_DATA\" --augmented-groundtruth \"$CANDIDATE_DIR/MILK10k_Training_GroundTruth.csv\" --augmented-metadata \"$CANDIDATE_DIR/MILK10k_Training_Metadata.csv\" --synthetic-input-dir \"$CANDIDATE_DIR/MILK10k_Training_Input\" --qc-summary \"$GEN_DIR/effb2_qc_summary.csv\" --report-dir \"$REPORT_DIR/final_audit\" --materialize-dir \"$FINAL_DIR\" --require-target-pred --overwrite", "",
               "# Train safely: synthetic IDs stay train-only.",
               "# python milk10k_effb2_dermoscopic_metadata/train_milk10k_effb2_dermoscopic_metadata.py --data-dir \"$FINAL_DIR\" --output-dir /path/to/run --split-manifest /path/to/run/split_v2.json --synthetic-train-only --metadata-mode none --loss ldam", ""]
-    scripts={"01_generate_pairs.sh":header+generate,"02_qc_and_materialize.sh":header+qc_materialize,
-             "run_balance_pipeline.sh":header+generate+qc_materialize}
-    for name,lines in scripts.items():
-        path=report_dir/name;path.write_text("\n".join(lines),encoding="utf-8");path.chmod(0o755)
+    code_dir=(args.scripts_dir.expanduser().resolve() if args.scripts_dir else Path(__file__).resolve().parent)
+    code_dir.mkdir(parents=True,exist_ok=True)
+    exported={"run_fresh_balance_01_generate.sh":header+generate,
+              "run_fresh_balance_02_qc_materialize.sh":header+qc_materialize}
+    for name,lines in exported.items():
+        path=code_dir/name;path.write_text("\n".join(lines),encoding="utf-8");path.chmod(0o755)
 
 
 def report_markdown(args, plan, sources, manifest, bcc_cap, usable_count, missing_pairs, missing_image_files):

Stable_diffusion_augmentation/run_fresh_balance_01_generate.sh

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+BASE_DATA="/mnt/data/imp301/data_related"
+BASE_INPUT="/mnt/data/imp301/MILK10k_Training_Input"
+REPORT_DIR="/mnt/data/imp301/data_related/augmented_info/fresh_balance_plan"
+GEN_DIR="$REPORT_DIR/generated_balance_pairs"
+
+python Stable_diffusion_augmentation/generate_milk10k_sd_pairs.py \
+  --data-dir "$BASE_DATA" \
+  --input-dir "$BASE_INPUT" \
+  --output-dir "$GEN_DIR" \
+  --class-names BEN_OTH DF INF VASC \
+  --num-per-lesion 3 \
+  --max-source-lesions 34 \
+  --shuffle \
+  --skip-existing

Stable_diffusion_augmentation/run_fresh_balance_02_qc_materialize.sh

@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+BASE_DATA="/mnt/data/imp301/data_related"
+BASE_INPUT="/mnt/data/imp301/MILK10k_Training_Input"
+REPORT_DIR="/mnt/data/imp301/data_related/augmented_info/fresh_balance_plan"
+CHECKPOINT_DIR="/path/to/convnext_5fold_run"
+GEN_DIR="$REPORT_DIR/generated_balance_pairs"
+CANDIDATE_DIR="$REPORT_DIR/candidate_augmented"
+FINAL_DIR="/path/to/milk10k_balanced_augmented"
+
+python Stable_diffusion_augmentation/run_effb2_qc.py \
+  --checkpoint-dir "$CHECKPOINT_DIR" \
+  --output-dir "$GEN_DIR"
+
+python Stable_diffusion_augmentation/filter_paired_augmentation_by_qc.py \
+  --manifest "$GEN_DIR/paired_augmentation_manifest.csv" \
+  --qc-summary "$GEN_DIR/effb2_qc_summary.csv" \
+  --output "$GEN_DIR/filtered_manifest.csv" \
+  --min-target-prob 0.4 \
+  --require-target-pred
+
+python Stable_diffusion_augmentation/materialize_augmented_milk10k_dataset.py \
+  --input-dir "$BASE_INPUT" \
+  --metadata-csv "$BASE_DATA/MILK10k_Training_Metadata.csv" \
+  --groundtruth-csv "$BASE_DATA/MILK10k_Training_GroundTruth.csv" \
+  --augmentation-manifest "$GEN_DIR/filtered_manifest.csv" \
+  --output-dir "$CANDIDATE_DIR" \
+  --symlink \
+  --synthetic-metadata neutral \
+  --overwrite
+
+python Stable_diffusion_augmentation/plan_and_materialize_balanced_milk10k.py \
+  --base-data-dir "$BASE_DATA" \
+  --augmented-groundtruth "$CANDIDATE_DIR/MILK10k_Training_GroundTruth.csv" \
+  --augmented-metadata "$CANDIDATE_DIR/MILK10k_Training_Metadata.csv" \
+  --synthetic-input-dir "$CANDIDATE_DIR/MILK10k_Training_Input" \
+  --qc-summary "$GEN_DIR/effb2_qc_summary.csv" \
+  --report-dir "$REPORT_DIR/final_audit" \
+  --materialize-dir "$FINAL_DIR" \
+  --require-target-pred \
+  --overwrite

Stable_diffusion_augmentation/tests/test_balance_planner.py

@@ -63,5 +63,12 @@ class BalancePlannerTests(unittest.TestCase):
             result=run(parse_args(["--base-data-dir",str(base),"--report-dir",str(root/"report")]))
             self.assertEqual(len(result["manifest"]),4);self.assertEqual(len(result["usable"]),0)
 
+    def test_fresh_start_ignores_existing_synthetic_inventory(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root=Path(tmp);base,_,_=fixture(root)
+            result=run(parse_args(["--base-data-dir",str(base),"--fresh-start","--report-dir",str(root/"fresh")]))
+            self.assertEqual(len(result["manifest"]),0)
+            self.assertEqual(int(result["plan"].raw_synthetic_inventory.sum()),0)
+
 
 if __name__=="__main__":unittest.main()