dssm train

recommend-model-produce/src/main/python/tools/static_ps_infer_v2.py

@@ -70,22 +70,22 @@ class InferenceFetchHandler(FetchHandler):
         super().handler(res_dict=fetch_vars)
         """处理每批次的推理结果"""
         result_dict = {}
-        logger.info("InferenceFetchHandler fetch_vars {}".format(fetch_vars))
-        for var_name, var_value in fetch_vars.items():
-            # 转换数据类型
-            if isinstance(var_value, np.ndarray):
-                result = var_value.tolist()
-            else:
-                result = var_value
-            result_dict[var_name] = result
+        # logger.info("InferenceFetchHandler fetch_vars {}".format(fetch_vars))
+        # for var_name, var_value in fetch_vars.items():
+        #     # 转换数据类型
+        #     if isinstance(var_value, np.ndarray):
+        #         result = var_value.tolist()
+        #     else:
+        #         result = var_value
+        #     result_dict[var_name] = result
         
-        self.current_batch.append(result_dict)
-        self.total_samples += len(result_dict.get(list(result_dict.keys())[0], []))
+        # self.current_batch.append(result_dict)
+        # self.total_samples += len(result_dict.get(list(result_dict.keys())[0], []))
         
-        # 当累积足够的结果时，写入文件
-        if len(self.current_batch) >= self.batch_size:
-            self._write_batch()
-            logger.info(f"Saved {self.total_samples} samples to {self.output_file}")
+        # # 当累积足够的结果时，写入文件
+        # if len(self.current_batch) >= self.batch_size:
+        #     self._write_batch()
+        #     logger.info(f"Saved {self.total_samples} samples to {self.output_file}")
     
     def _write_batch(self):
         """将批次结果写入文件"""