From 5d422d6f22874ace9ca62291de6e14fb801b7969 Mon Sep 17 00:00:00 2001
From: hiha3456 <744762298@qq.com>
Date: Thu, 16 Jun 2022 13:58:00 +0800
Subject: [PATCH] add log

---
 ding/framework/middleware/league_actor.py       |  4 ++--
 ding/framework/middleware/league_coordinator.py | 10 +++++-----
 ding/framework/middleware/league_learner.py     |  8 ++++----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/ding/framework/middleware/league_actor.py b/ding/framework/middleware/league_actor.py
index 39228ecc36..66b978f545 100644
--- a/ding/framework/middleware/league_actor.py
+++ b/ding/framework/middleware/league_actor.py
@@ -181,7 +181,7 @@ def _on_learner_model(self, learner_model: "LearnerModel"):
         """
         If get newest learner model, put it inside model_queue.
         """
-        print('Actor {} recieved model \n'.format(task.router.node_id), flush=True)
+        print('Actor {} recieved model {} \n'.format(task.router.node_id, learner_model.player_id), flush=True)
         with self.model_dict_lock:
             self.model_dict[learner_model.player_id] = learner_model
 
@@ -252,7 +252,7 @@ def __call__(self, ctx: "BattleContext"):
         job = self._get_job()
         if job is None:
             return
-        print('For actor {}, a job begin \n'.format(task.router.node_id), flush=True)
+        print('For actor {}, a job {} begin \n'.format(task.router.node_id, job.launch_player), flush=True)
 
         ctx.player_id_list = [player.player_id for player in job.players]
         self.agent_num = len(job.players)
diff --git a/ding/framework/middleware/league_coordinator.py b/ding/framework/middleware/league_coordinator.py
index 43300d8711..ec85766a8d 100644
--- a/ding/framework/middleware/league_coordinator.py
+++ b/ding/framework/middleware/league_coordinator.py
@@ -27,7 +27,7 @@ def __init__(self, league: "BaseLeague") -> None:
         task.on(EventEnum.ACTOR_FINISH_JOB, self._on_actor_job)
 
     def _on_actor_greeting(self, actor_id):
-        print('coordinator recieve actor greeting\n', flush=True)
+        print('coordinator recieve actor {} greeting\n'.format(actor_id), flush=True)
         with self._lock:
             player_num = len(self.league.active_players_ids)
             player_id = self.league.active_players_ids[self._total_send_jobs % player_num]
@@ -37,22 +37,22 @@ def _on_actor_greeting(self, actor_id):
         if job.job_no > 0 and job.job_no % self._eval_frequency == 0:
             job.is_eval = True
         job.actor_id = actor_id
-        print('coordinator emit job\n', flush=True)
+        print('coordinator emit job (main_player: {}) to actor {}\n'.format(job.launch_player, actor_id), flush=True)
         task.emit(EventEnum.COORDINATOR_DISPATCH_ACTOR_JOB.format(actor_id=actor_id), job)
 
     def _on_learner_meta(self, player_meta: "PlayerMeta"):
-        print('coordinator recieve learner meta\n', flush=True)
+        print('coordinator recieve learner meta for player{}\n'.format(player_meta.player_id), flush=True)
         # print("on_learner_meta {}".format(player_meta))
         self.league.update_active_player(player_meta)
         self.league.create_historical_player(player_meta)
 
     def _on_actor_job(self, job: "Job"):
-        print('coordinator recieve actor finished job\n', flush=True)
+        print('coordinator recieve actor finished job, palyer {}\n'.format(job.launch_player), flush=True)
         print("on_actor_job {}".format(job.launch_player))  # right
         self.league.update_payoff(job)
 
     def __del__(self):
-        print('task finished, coordinator closed', flush=True)
+        print('task finished, coordinator {} closed'.format(task.router.node_id), flush=True)
 
     def __call__(self, ctx: "Context") -> None:
         sleep(1)
diff --git a/ding/framework/middleware/league_learner.py b/ding/framework/middleware/league_learner.py
index af7ec59039..cb02eb25e6 100644
--- a/ding/framework/middleware/league_learner.py
+++ b/ding/framework/middleware/league_learner.py
@@ -37,7 +37,7 @@ def __init__(self, cfg: dict, policy_fn: Callable, player: "ActivePlayer") -> No
         self._step = 0
 
     def _on_actor_send_data(self, actor_data: "ActorData"):
-        print("learner receive data from actor! \n", flush=True)
+        print("learner {} receive data from actor! \n".format(task.router.node_id), flush=True)
         with self._lock:
             cfg = self.cfg
             for _ in range(cfg.policy.learn.update_per_collect):
@@ -50,7 +50,7 @@ def _on_actor_send_data(self, actor_data: "ActorData"):
         # print("save checkpoint")
         checkpoint = self._save_checkpoint() if self.player.is_trained_enough() else None
 
-        print('learner send player meta\n', flush=True)
+        print('learner {} send player meta {}\n'.format(task.router.node_id, self.player_id), flush=True)
         task.emit(
             EventEnum.LEARNER_SEND_META,
             PlayerMeta(player_id=self.player_id, checkpoint=checkpoint, total_agent_step=self._learner.train_iter)
@@ -60,7 +60,7 @@ def _on_actor_send_data(self, actor_data: "ActorData"):
         learner_model = LearnerModel(
             player_id=self.player_id, state_dict=self._learner.policy.state_dict(), train_iter=self._learner.train_iter
         )
-        print('learner send model\n', flush=True)
+        print('learner {} send model\n'.format(task.router.node_id), flush=True)
         task.emit(EventEnum.LEARNER_SEND_MODEL, learner_model)
 
     def _get_learner(self) -> BaseLearner:
@@ -84,7 +84,7 @@ def _save_checkpoint(self) -> Optional[Storage]:
         return storage
 
     def __del__(self):
-        print('task finished, learner closed', flush=True)
+        print('task finished, learner {} closed\n'.format(task.router.node_id), flush=True)
 
     def __call__(self, _: "Context") -> None:
         sleep(1)