Export all splits (incl density splits) using streaming

tools/export_coco.py

@@ -1,10 +1,10 @@
 import argparse
 from pathlib import Path
 import orjson
-from datasets import load_dataset
+from datasets import load_dataset, get_dataset_split_names
 
 
-def export_streaming(split_iter, out_dir: Path, split_name: str):
+def export_split(ds, out_dir: Path, split_name: str):
     images_dir = out_dir / split_name / "images"
     ann_dir = out_dir / split_name / "annotations"
     images_dir.mkdir(parents=True, exist_ok=True)
@@ -14,9 +14,8 @@ def export_streaming(split_iter, out_dir: Path, split_name: str):
     annotations = []
     categories = None
     ann_id = 1
-    n_images = 0
 
-    for ex in split_iter:
+    for i, ex in enumerate(ds, 1):
         image_id = int(ex["image_id"])
         filename = ex["filename"]
         width = int(ex["width"])
@@ -24,16 +23,13 @@ def export_streaming(split_iter, out_dir: Path, split_name: str):
 
         (images_dir / filename).write_bytes(ex["image_bytes"])
 
-        images.append({
-            "id": image_id,
-            "file_name": filename,
-            "width": width,
-            "height": height,
-        })
+        images.append(
+            {"id": image_id, "file_name": filename, "width": width, "height": height}
+        )
 
-        annos = orjson.loads(ex["coco_annotations"].encode("utf-8"))
+        annos = orjson.loads(ex["coco_annotations"])
         if categories is None:
-            categories = orjson.loads(ex["coco_categories"].encode("utf-8"))
+            categories = orjson.loads(ex["coco_categories"])
 
         for a in annos:
             a = dict(a)
@@ -41,46 +37,39 @@ def export_streaming(split_iter, out_dir: Path, split_name: str):
             ann_id += 1
             annotations.append(a)
 
-        n_images += 1
-        if n_images % 500 == 0:
-            print(f"[{split_name}] exported {n_images} images...")
+        if i % 1000 == 0:
+            print(f"[{split_name}] exported {i} images...")
 
     coco = {"images": images, "annotations": annotations, "categories": categories or []}
     (ann_dir / f"instances_{split_name}.json").write_bytes(orjson.dumps(coco))
-    return len(images), len(annotations)
-
-
-def try_load(repo: str, config: str, split: str):
-    try:
-        return load_dataset(repo, config, split=split, streaming=True)
-    except Exception:
-        return None
+    print(f"[{split_name}] images={len(images)} annotations={len(annotations)}")
 
 
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--repo", required=True)
-    ap.add_argument("--config", required=True)
-    ap.add_argument("--out", required=True)
+    ap.add_argument("--repo", required=True, help="HF dataset repo id (e.g. aadityabuilds/tree-distribution-shift)")
+    ap.add_argument("--config", required=True, help="Config name (e.g. in_state_train_Karnataka__ood_Rajasthan)")
+    ap.add_argument("--out", required=True, help="Output directory (writes <out>/<config>/...)")
+    ap.add_argument("--revision", default=None, help="Optional HF revision/commit sha")
     args = ap.parse_args()
 
     out_dir = Path(args.out) / args.config
     out_dir.mkdir(parents=True, exist_ok=True)
 
-    # core splits
-    for split in ["train", "id_test", "ood_test"]:
-        ds = load_dataset(args.repo, args.config, split=split, streaming=True)
-        n_i, n_a = export_streaming(ds, out_dir, split)
-        print(f"[{split}] images={n_i} annotations={n_a}")
-
-    # optional density splits
-    for split in ["ood_same_density", "ood_diff_density"]:
-        ds = try_load(args.repo, args.config, split)
-        if ds is None:
-            print(f"[{split}] not present (skipping)")
-            continue
-        n_i, n_a = export_streaming(ds, out_dir, split)
-        print(f"[{split}] images={n_i} annotations={n_a}")
+    # Discover splits from the Hub (so we don't hardcode)
+    splits = get_dataset_split_names(args.repo, args.config, revision=args.revision)
+    print("Splits:", splits)
+
+    for split in splits:
+        # streaming=True avoids huge RAM mmap / pyarrow pressure
+        ds = load_dataset(
+            args.repo,
+            args.config,
+            split=split,
+            streaming=True,
+            revision=args.revision,
+        )
+        export_split(ds, out_dir, split)
 
     print(f"COCO export written to: {out_dir}")