Create README.md

README.md

@@ -0,0 +1,91 @@
+---
+license: mit
+---
+
+"""
+Download and unpack merged dataset in Colab/Kaggle
+"""
+
+import numpy as np
+import json
+import os
+from huggingface_hub import snapshot_download
+from tqdm import tqdm
+
+# ═══════════════════════════════════════════════════════════
+#  CONFIG
+# ═══════════════════════════════════════════════════════════
+
+HF_REPO = "nnsohamnn/runner-game-dataset"  # ← Change this!
+DOWNLOAD_DIR = "runner_dataset_merged"
+OUTPUT_DIR = "runner_dataset"
+
+# ═══════════════════════════════════════════════════════════
+#  DOWNLOAD
+# ═══════════════════════════════════════════════════════════
+
+print("📥 Downloading from Hugging Face...")
+snapshot_download(
+    repo_id=HF_REPO,
+    repo_type="dataset",
+    local_dir=DOWNLOAD_DIR
+)
+print("✅ Download complete!")
+
+# ═══════════════════════════════════════════════════════════
+#  OPTION A: USE MERGED FILES DIRECTLY (RECOMMENDED)
+# ═══════════════════════════════════════════════════════════
+
+# You can use the merged files directly in training!
+# This is actually MORE efficient than individual files.
+
+# Example loading:
+print("\n📊 Dataset info:")
+with open(os.path.join(DOWNLOAD_DIR, "metadata.json"), 'r') as f:
+    metadata = json.load(f)
+    print(f"   Total frames: {metadata['total_frames']:,}")
+    print(f"   Chunks: {metadata['num_chunks']}")
+    print(f"   Actions: {metadata['actions']}")
+
+# ═══════════════════════════════════════════════════════════
+#  OPTION B: UNPACK TO INDIVIDUAL FILES (if needed)
+# ═══════════════════════════════════════════════════════════
+
+def unpack_dataset():
+    """Unpack merged files back to individual files (optional)"""
+    
+    print("\n📦 Unpacking to individual files...")
+    
+    os.makedirs(os.path.join(OUTPUT_DIR, "frames"), exist_ok=True)
+    os.makedirs(os.path.join(OUTPUT_DIR, "actions"), exist_ok=True)
+    
+    # Unpack frames
+    chunk_files = sorted([f for f in os.listdir(DOWNLOAD_DIR) if f.startswith("frames_chunk")])
+    
+    frame_idx = 0
+    for chunk_file in chunk_files:
+        print(f"   Unpacking {chunk_file}...")
+        data = np.load(os.path.join(DOWNLOAD_DIR, chunk_file))
+        frames = data['frames']
+        
+        for i in tqdm(range(len(frames)), desc=f"   {chunk_file}"):
+            np.save(
+                os.path.join(OUTPUT_DIR, "frames", f"{frame_idx:06d}.npy"),
+                frames[i]
+            )
+            frame_idx += 1
+        
+        data.close()
+    
+    # Unpack actions
+    print("   Unpacking actions.jsonl...")
+    with open(os.path.join(DOWNLOAD_DIR, "actions.jsonl"), 'r') as f:
+        for idx, line in enumerate(tqdm(f, desc="   actions")):
+            action_data = json.loads(line)
+            with open(os.path.join(OUTPUT_DIR, "actions", f"{idx:06d}.json"), 'w') as out_f:
+                json.dump(action_data, out_f)
+    
+    print(f"\n✅ Unpacked to {OUTPUT_DIR}/")
+
+# Uncomment to unpack:
+# unpack_dataset()