polish(lxy): add wandb anonymous mode parameter (#528)

* add wandb anonymous mode parameter

* polish style

* polish style

* polish unittest for wandb

* polish unittest for wandb

* polish unittest for wandb

* change anonymous mode to must in logger

ding/framework/middleware/functional/logger.py

@@ -104,7 +104,9 @@ def _logger(ctx: "OfflineRLContext"):
     return _logger
 
 
-def wandb_online_logger(cfg: EasyDict, env: BaseEnvManagerV2, model: torch.nn.Module) -> Callable:
+def wandb_online_logger(
+        cfg: EasyDict, env: BaseEnvManagerV2, model: torch.nn.Module, anonymous: bool = False
+) -> Callable:
     '''
     Overview:
         Wandb visualizer to track the experiment.
@@ -116,13 +118,18 @@ def wandb_online_logger(cfg: EasyDict, env: BaseEnvManagerV2, model: torch.nn.Mo
             - action_logger: `q_value` or `action probability`.
         - env (:obj:`BaseEnvManagerV2`): Evaluator environment.
         - model (:obj:`nn.Module`): Model.
+        - anonymous (:obj:`bool`): Open the anonymous mode of wandb or not.
+            The anonymous mode allows visualization of data without wandb count.
     '''
 
     color_list = ["orange", "red", "blue", "purple", "green", "darkcyan"]
     metric_list = ["q_value", "target q_value", "loss", "lr", "entropy"]
     # Initialize wandb with default settings
     # Settings can be covered by calling wandb.init() at the top of the script
-    wandb.init()
+    if anonymous:
+        wandb.init(anonymous="must")
+    else:
+        wandb.init()
     # The visualizer is called to save the replay of the simulation
     # which will be uploaded to wandb later
     env.enable_save_replay(replay_path=cfg.record_path)
@@ -192,7 +199,13 @@ def _plot(ctx: "OnlineRLContext"):
     return _plot
 
 
-def wandb_offline_logger(cfg: EasyDict, env: BaseEnvManagerV2, model: torch.nn.Module, datasetpath: str) -> Callable:
+def wandb_offline_logger(
+        cfg: EasyDict,
+        env: BaseEnvManagerV2,
+        model: torch.nn.Module,
+        datasetpath: str,
+        anonymous: bool = False
+) -> Callable:
     '''
     Overview:
         Wandb visualizer to track the experiment.
@@ -205,13 +218,18 @@ def wandb_offline_logger(cfg: EasyDict, env: BaseEnvManagerV2, model: torch.nn.M
         - env (:obj:`BaseEnvManagerV2`): Evaluator environment.
         - model (:obj:`nn.Module`): Model.
         - datasetpath (:obj:`str`): The path of offline dataset.
+        - anonymous (:obj:`bool`): Open the anonymous mode of wandb or not.
+            The anonymous mode allows visualization of data without wandb count.
     '''
 
     color_list = ["orange", "red", "blue", "purple", "green", "darkcyan"]
     metric_list = ["q_value", "target q_value", "loss", "lr", "entropy", "target_q_value", "td_error"]
     # Initialize wandb with default settings
     # Settings can be covered by calling wandb.init() at the top of the script
-    wandb.init()
+    if anonymous:
+        wandb.init(anonymous="must")
+    else:
+        wandb.init()
     # The visualizer is called to save the replay of the simulation
     # which will be uploaded to wandb later
     env.enable_save_replay(replay_path=cfg.record_path)

ding/framework/middleware/tests/test_logger.py

@@ -185,7 +185,7 @@ def __getitem__(self, index):
         return [[1]] * 50
 
 
-@pytest.mark.other  # due to no api key in github now
+@pytest.mark.unittest
 def test_wandb_online_logger():
 
     cfg = EasyDict(
@@ -197,7 +197,7 @@ def test_wandb_online_logger():
     ctx = OnlineRLContext()
     ctx.train_output = [{'reward': 1, 'q_value': [1.0]}]
     model = TheModelClass()
-    wandb.init(config=cfg)
+    wandb.init(config=cfg, anonymous="must")
 
     def mock_metric_logger(metric_dict):
         metric_list = [
@@ -211,17 +211,17 @@ def mock_gradient_logger(input_model):
 
     def test_wandb_online_logger_metric():
         with patch.object(wandb, 'log', new=mock_metric_logger):
-            wandb_online_logger(cfg, env, model)(ctx)
+            wandb_online_logger(cfg, env, model, anonymous=True)(ctx)
 
     def test_wandb_online_logger_gradient():
         with patch.object(wandb, 'watch', new=mock_gradient_logger):
-            wandb_online_logger(cfg, env, model)(ctx)
+            wandb_online_logger(cfg, env, model, anonymous=True)(ctx)
 
     test_wandb_online_logger_metric()
     test_wandb_online_logger_gradient()
 
 
-@pytest.mark.other  # due to no api key in github now
+@pytest.mark.unittest
 def test_wandb_offline_logger(mocker):
 
     cfg = EasyDict(
@@ -237,7 +237,7 @@ def test_wandb_offline_logger(mocker):
     ctx = OnlineRLContext()
     ctx.train_output = [{'reward': 1, 'q_value': [1.0]}]
     model = TheModelClass()
-    wandb.init(config=cfg)
+    wandb.init(config=cfg, anonymous="must")
 
     def mock_metric_logger(metric_dict):
         metric_list = [
@@ -255,7 +255,7 @@ def mock_image_logger(imagepath):
     def test_wandb_offline_logger_gradient():
         cfg.vis_dataset = False
         with patch.object(wandb, 'watch', new=mock_gradient_logger):
-            wandb_offline_logger(cfg, env, model, 'dataset.h5')(ctx)
+            wandb_offline_logger(cfg, env, model, 'dataset.h5', anonymous=True)(ctx)
 
     def test_wandb_offline_logger_dataset():
         cfg.vis_dataset = True
@@ -264,7 +264,7 @@ def test_wandb_offline_logger_dataset():
         with patch.object(wandb, 'log', new=mock_metric_logger):
             with patch.object(wandb, 'Image', new=mock_image_logger):
                 mocker.patch('h5py.File', return_value=m)
-                wandb_offline_logger(cfg, env, model, 'dataset.h5')(ctx)
+                wandb_offline_logger(cfg, env, model, 'dataset.h5', anonymous=True)(ctx)
 
     test_wandb_offline_logger_gradient()
     test_wandb_offline_logger_dataset()