Add show_progress option for trainer (#641)

- A DummyTqdm class added to utils: it replicates the interface used by trainers, but does not show the progress bar;
- Added a show_progress argument to the base trainer: when show_progress == True, dummy_tqdm is used in place of tqdm.

examples/atari/atari_wrapper.py

@@ -8,13 +8,13 @@
 import gym
 import numpy as np
 
+from tianshou.env import ShmemVectorEnv
+
 try:
     import envpool
 except ImportError:
     envpool = None
 
-from tianshou.env import ShmemVectorEnv
-
 
 class NoopResetEnv(gym.Wrapper):
     """Sample initial states by taking random number of no-ops on reset.

examples/mujoco/mujoco_env.py

@@ -1,14 +1,14 @@
 import warnings
 
+import gym
+
+from tianshou.env import ShmemVectorEnv, VectorEnvNormObs
+
 try:
     import envpool
 except ImportError:
     envpool = None
 
-import gym
-
-from tianshou.env import ShmemVectorEnv, VectorEnvNormObs
-
 
 def make_mujoco_env(task, seed, training_num, test_num, obs_norm):
     """Wrapper function for Mujoco env.

test/discrete/test_pg.py

@@ -80,7 +80,7 @@ def test_pg(args=get_args()):
         dist,
         args.gamma,
         reward_normalization=args.rew_norm,
-        action_space=env.action_space
+        action_space=env.action_space,
     )
     for m in net.modules():
         if isinstance(m, torch.nn.Linear):
@@ -116,7 +116,7 @@ def stop_fn(mean_rewards):
         episode_per_collect=args.episode_per_collect,
         stop_fn=stop_fn,
         save_best_fn=save_best_fn,
-        logger=logger
+        logger=logger,
     )
     assert stop_fn(result['best_reward'])
 

test/offline/test_bcq.py

@@ -58,6 +58,7 @@ def get_args():
         help='watch the play of pre-trained policy only',
     )
     parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
+    parser.add_argument("--show-progress", action="store_true")
     args = parser.parse_known_args()[0]
     return args
 
@@ -209,6 +210,7 @@ def watch():
         save_best_fn=save_best_fn,
         stop_fn=stop_fn,
         logger=logger,
+        show_progress=args.show_progress,
     )
     assert stop_fn(result['best_reward'])
 

tianshou/trainer/base.py

@@ -9,7 +9,14 @@
 from tianshou.data import Collector, ReplayBuffer
 from tianshou.policy import BasePolicy
 from tianshou.trainer.utils import gather_info, test_episode
-from tianshou.utils import BaseLogger, LazyLogger, MovAvg, deprecation, tqdm_config
+from tianshou.utils import (
+    BaseLogger,
+    DummyTqdm,
+    LazyLogger,
+    MovAvg,
+    deprecation,
+    tqdm_config,
+)
 
 
 class BaseTrainer(ABC):
@@ -68,6 +75,8 @@ class BaseTrainer(ABC):
     :param BaseLogger logger: A logger that logs statistics during
         training/testing/updating. Default to a logger that doesn't log anything.
     :param bool verbose: whether to print the information. Default to True.
+    :param bool show_progress: whether to display a progress bar when training.
+        Default to True.
     :param bool test_in_train: whether to test in the training phase.
         Default to True.
     """
@@ -143,6 +152,7 @@ def __init__(
         reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
         logger: BaseLogger = LazyLogger(),
         verbose: bool = True,
+        show_progress: bool = True,
         test_in_train: bool = True,
         save_fn: Optional[Callable[[BasePolicy], None]] = None,
     ):
@@ -190,6 +200,7 @@ def __init__(
 
         self.reward_metric = reward_metric
         self.verbose = verbose
+        self.show_progress = show_progress
         self.test_in_train = test_in_train
         self.resume_from_log = resume_from_log
 
@@ -259,8 +270,14 @@ def __next__(self) -> Union[None, Tuple[int, Dict[str, Any], Dict[str, Any]]]:
         self.policy.train()
 
         epoch_stat: Dict[str, Any] = dict()
+
+        if self.show_progress:
+            progress = tqdm.tqdm
+        else:
+            progress = DummyTqdm
+
         # perform n step_per_epoch
-        with tqdm.tqdm(
+        with progress(
             total=self.step_per_epoch, desc=f"Epoch #{self.epoch}", **tqdm_config
         ) as t:
             while t.n < t.total and not self.stop_fn_flag:

tianshou/trainer/offline.py

@@ -49,6 +49,8 @@ class OfflineTrainer(BaseTrainer):
     :param BaseLogger logger: A logger that logs statistics during
         updating/testing. Default to a logger that doesn't log anything.
     :param bool verbose: whether to print the information. Default to True.
+    :param bool show_progress: whether to display a progress bar when training.
+        Default to True.
     """
 
     __doc__ = BaseTrainer.gen_doc("offline") + "\n".join(__doc__.split("\n")[1:])
@@ -70,6 +72,7 @@ def __init__(
         reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
         logger: BaseLogger = LazyLogger(),
         verbose: bool = True,
+        show_progress: bool = True,
         **kwargs: Any,
     ):
         super().__init__(
@@ -90,6 +93,7 @@ def __init__(
             reward_metric=reward_metric,
             logger=logger,
             verbose=verbose,
+            show_progress=show_progress,
             **kwargs,
         )
 

tianshou/trainer/offpolicy.py

@@ -57,6 +57,8 @@ class OffpolicyTrainer(BaseTrainer):
     :param BaseLogger logger: A logger that logs statistics during
         training/testing/updating. Default to a logger that doesn't log anything.
     :param bool verbose: whether to print the information. Default to True.
+    :param bool show_progress: whether to display a progress bar when training.
+        Default to True.
     :param bool test_in_train: whether to test in the training phase.
         Default to True.
     """
@@ -83,6 +85,7 @@ def __init__(
         reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
         logger: BaseLogger = LazyLogger(),
         verbose: bool = True,
+        show_progress: bool = True,
         test_in_train: bool = True,
         **kwargs: Any,
     ):
@@ -106,6 +109,7 @@ def __init__(
             reward_metric=reward_metric,
             logger=logger,
             verbose=verbose,
+            show_progress=show_progress,
             test_in_train=test_in_train,
             **kwargs,
         )

tianshou/trainer/onpolicy.py

@@ -60,6 +60,8 @@ class OnpolicyTrainer(BaseTrainer):
     :param BaseLogger logger: A logger that logs statistics during
         training/testing/updating. Default to a logger that doesn't log anything.
     :param bool verbose: whether to print the information. Default to True.
+    :param bool show_progress: whether to display a progress bar when training.
+        Default to True.
     :param bool test_in_train: whether to test in the training phase. Default to
         True.
 
@@ -91,6 +93,7 @@ def __init__(
         reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
         logger: BaseLogger = LazyLogger(),
         verbose: bool = True,
+        show_progress: bool = True,
         test_in_train: bool = True,
         **kwargs: Any,
     ):
@@ -115,6 +118,7 @@ def __init__(
             reward_metric=reward_metric,
             logger=logger,
             verbose=verbose,
+            show_progress=show_progress,
             test_in_train=test_in_train,
             **kwargs,
         )

tianshou/utils/__init__.py

@@ -1,17 +1,18 @@
 """Utils package."""
 
-from tianshou.utils.config import tqdm_config
 from tianshou.utils.logger.base import BaseLogger, LazyLogger
 from tianshou.utils.logger.tensorboard import BasicLogger, TensorboardLogger
 from tianshou.utils.logger.wandb import WandbLogger
 from tianshou.utils.lr_scheduler import MultipleLRSchedulers
+from tianshou.utils.progress_bar import DummyTqdm, tqdm_config
 from tianshou.utils.statistics import MovAvg, RunningMeanStd
 from tianshou.utils.warning import deprecation
 
 __all__ = [
     "MovAvg",
     "RunningMeanStd",
     "tqdm_config",
+    "DummyTqdm",
     "BaseLogger",
     "TensorboardLogger",
     "BasicLogger",

tianshou/utils/progress_bar.py

@@ -0,0 +1,35 @@
+from typing import Any
+
+tqdm_config = {
+    "dynamic_ncols": True,
+    "ascii": True,
+}
+
+
+class DummyTqdm:
+    """A dummy tqdm class that keeps stats but without progress bar.
+
+    It supports ``__enter__`` and ``__exit__``, update and a dummy
+    ``set_postfix``, which is the interface that trainers use.
+
+    .. note::
+
+        Using ``disable=True`` in tqdm config results in infinite loop, thus
+        this class is created. See the discussion at #641 for details.
+    """
+
+    def __init__(self, total: int, **kwargs: Any):
+        self.total = total
+        self.n = 0
+
+    def set_postfix(self, **kwargs: Any) -> None:
+        pass
+
+    def update(self, n: int = 1) -> None:
+        self.n += n
+
+    def __enter__(self) -> "DummyTqdm":
+        return self
+
+    def __exit__(self, *args: Any, **kwargs: Any) -> None:
+        pass