f

model_training_nnn_tpu/dataset_tf.py

@@ -110,21 +110,35 @@ class BrainToTextDatasetTF:
             self._preload_all_data()
             print(f"✅ Preloading completed - {len(self.data_cache)} trials cached")
 
-        # ========================= 特征维度自动检测 =========================
+        # ========================= 特征维度自动检测 (更稳健的版本) =========================
         # 明确地计算并存储特征维度，避免 padded_batch 时的维度不匹配
         if self.feature_subset:
             self.feature_dim = len(self.feature_subset)
             print(f"✅ Using feature subset dimension: {self.feature_dim}")
         else:
-            # 不要硬编码！尝试从数据中推断实际特征维度
-            try:
-                first_day = next(iter(self.trial_indices))
-                first_trial = self.trial_indices[first_day]['trials'][0]
-                first_sample = self._load_single_trial_data(first_day, first_trial)
-                self.feature_dim = first_sample['input_features'].shape[1]
-                print(f"✅ Auto-detected feature dimension: {self.feature_dim}")
-            except Exception as e:
-                print(f"⚠️ Could not auto-detect feature dimension, falling back to 512. Error: {e}")
+            # 稳健地从数据中推断实际特征维度
+            # 遍历所有 day，直到找到第一个包含 trial 的 day
+            detected_dim = None
+            for day in self.trial_indices:
+                # 检查 trial 列表是否非空
+                if self.trial_indices[day]['trials']:
+                    try:
+                        # 列表非空，尝试加载第一个 trial
+                        first_valid_trial = self.trial_indices[day]['trials'][0]
+                        first_sample = self._load_single_trial_data(day, first_valid_trial)
+                        detected_dim = first_sample['input_features'].shape[1]
+                        print(f"✅ Auto-detected feature dimension from day {day}: {detected_dim}")
+                        break  # 成功检测到维度，跳出循环
+                    except Exception as e:
+                        # 如果加载这个 trial 失败，则继续尝试下一个 day
+                        print(f"⚠️ Warning: Could not load trial {first_valid_trial} from day {day} for dimension check. Error: {e}")
+                        continue
+
+            if detected_dim is not None:
+                self.feature_dim = detected_dim
+            else:
+                # 如果遍历完所有 day 都没有找到任何有效的 trial，则报错或回退
+                print(f"⚠️ CRITICAL: Could not auto-detect feature dimension after checking all days. No valid trials found in the dataset split '{self.split}'. Falling back to 512.")
                 self.feature_dim = 512  # 作为最后的备用方案
         # ========================= 特征维度检测结束 =========================