Upload extract_weights.py with huggingface_hub

extract_weights.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+"""
+PyTorch 체크포인트에서 가중치를 추출하여 binary 형식으로 저장
+"""
+import torch
+import numpy as np
+import struct
+import sys
+from pathlib import Path
+
+def extract_weights(checkpoint_path, output_path):
+    """체크포인트에서 가중치 추출"""
+    print(f"Loading checkpoint: {checkpoint_path}")
+    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    
+    # state_dict 추출
+    if 'model_state_dict' in checkpoint:
+        state_dict = checkpoint['model_state_dict']
+    elif 'state_dict' in checkpoint:
+        state_dict = checkpoint['state_dict']
+    else:
+        state_dict = checkpoint
+    
+    print(f"Found {len(state_dict)} parameters")
+    
+    # Binary 파일로 저장
+    with open(output_path, 'wb') as f:
+        # 매직 넘버와 버전 정보
+        f.write(b'LCNN')  # Magic number
+        f.write(struct.pack('I', 1))  # Version
+        
+        # 파라미터 개수
+        f.write(struct.pack('I', len(state_dict)))
+        
+        for name, param in state_dict.items():
+            print(f"  {name}: {param.shape}")
+            
+            # 파라미터 이름 (최대 256 bytes)
+            name_bytes = name.encode('utf-8')[:256]
+            f.write(struct.pack('I', len(name_bytes)))
+            f.write(name_bytes)
+            
+            # 텐서 데이터
+            data = param.cpu().numpy().astype(np.float32)
+            
+            # Shape 정보
+            ndim = len(data.shape)
+            f.write(struct.pack('I', ndim))
+            for dim in data.shape:
+                f.write(struct.pack('I', dim))
+            
+            # 데이터 (C-contiguous order)
+            data_flat = data.flatten('C')
+            f.write(struct.pack(f'{len(data_flat)}f', *data_flat))
+    
+    print(f"\nWeights saved to: {output_path}")
+    print(f"File size: {Path(output_path).stat().st_size / 1024 / 1024:.2f} MB")
+
+if __name__ == '__main__':
+    checkpoint_path = sys.argv[1] if len(sys.argv) > 1 else '~/mycnn/checkpoints/LiteCNNPro_best.pth'
+    output_path = sys.argv[2] if len(sys.argv) > 2 else './model_weights.bin'
+    
+    checkpoint_path = Path(checkpoint_path).expanduser()
+    extract_weights(checkpoint_path, output_path)