fix: predict_batch에 batch_size 청킹 추가 - GPU OOM 방지

- batch_size 파라미터 추가 (기본값: 32)
- 대량 윈도우 입력 시 배치 청킹으로 메모리 사용 제어
- predict 메서드에 .float() 추가 - dtype 일관성 수정
- CodeRabbit 리뷰 반영

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

models/stgcn_classifier.py

@@ -103,7 +103,7 @@ class STGCNClassifier:
             window_input = window
 
         # ST-GCN inference
-        window_tensor = torch.from_numpy(window_input).unsqueeze(0).to(self.device)  # (1, C, T, V, M)
+        window_tensor = torch.from_numpy(window_input).float().unsqueeze(0).to(self.device)  # (1, C, T, V, M)
 
         with torch.no_grad():
             outputs = self.model(window_tensor)
@@ -121,6 +121,7 @@ class STGCNClassifier:
     def predict_batch(
         self,
         windows: list[np.ndarray],
+        batch_size: int = 32,
         normalize: bool = True,
         debug: bool = False
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
@@ -129,6 +130,7 @@ class STGCNClassifier:
 
         Args:
             windows: [(C, T, V, M), ...] ST-GCN 입력 윈도우 리스트
+            batch_size: GPU 배치 크기 (기본값: 32, OOM 방지용)
             normalize: hip center 정규화 적용 여부
             debug: 디버그 로그 출력 여부
 
@@ -140,34 +142,46 @@ class STGCNClassifier:
         if not windows:
             return np.array([]), np.array([]), np.array([])
 
-        # 정규화 및 배치 텐서 준비
-        batch_list = []
-        for window in windows:
-            if normalize:
-                window_input = normalize_skeleton(window, method='hip_center')
-            else:
-                window_input = window
-            batch_list.append(torch.from_numpy(window_input).float())
-
-        # 배치 텐서 생성 (N, C, T, V, M)
-        batch_tensor = torch.stack(batch_list).to(self.device)
-
-        with torch.no_grad():
-            outputs = self.model(batch_tensor)
-            probs = torch.softmax(outputs, dim=1)
-            preds = torch.argmax(outputs, dim=1)
-
-            predictions = preds.cpu().numpy()
-            # 각 예측에 대해 해당 클래스의 확률을 신뢰도로 사용
-            confidences = probs[torch.arange(len(preds)), preds].cpu().numpy()
-            # Fall 클래스(class 1)의 확률 - 그래프 표시용
-            fall_probs = probs[:, 1].cpu().numpy()
-
-        if debug:
-            for i, (pred, conf, fall_p) in enumerate(zip(predictions, confidences, fall_probs)):
-                self.logger.debug(f"  Batch[{i}] ST-GCN: pred={pred}, conf={conf:.3f}, fall_prob={fall_p:.3f}")
-
-        return predictions, confidences, fall_probs
+        all_predictions = []
+        all_confidences = []
+        all_fall_probs = []
+
+        for chunk_start in range(0, len(windows), batch_size):
+            chunk_windows = windows[chunk_start:chunk_start + batch_size]
+
+            batch_list = []
+            for window in chunk_windows:
+                if normalize:
+                    window_input = normalize_skeleton(window, method='hip_center')
+                else:
+                    window_input = window
+                batch_list.append(torch.from_numpy(window_input).float())
+
+            batch_tensor = torch.stack(batch_list).to(self.device)
+
+            with torch.no_grad():
+                outputs = self.model(batch_tensor)
+                probs = torch.softmax(outputs, dim=1)
+                preds = torch.argmax(outputs, dim=1)
+
+                predictions = preds.cpu().numpy()
+                confidences = probs[torch.arange(len(preds)), preds].cpu().numpy()
+                fall_probs = probs[:, 1].cpu().numpy()
+
+                all_predictions.append(predictions)
+                all_confidences.append(confidences)
+                all_fall_probs.append(fall_probs)
+
+            if debug:
+                for i, (pred, conf, fall_p) in enumerate(zip(predictions, confidences, fall_probs)):
+                    global_idx = chunk_start + i
+                    self.logger.debug(f"  Batch[{global_idx}] ST-GCN: pred={pred}, conf={conf:.3f}, fall_prob={fall_p:.3f}")
+
+        return (
+            np.concatenate(all_predictions),
+            np.concatenate(all_confidences),
+            np.concatenate(all_fall_probs)
+        )
 
     def is_fall(self, prediction: int, confidence: float) -> bool:
         """