Refactoring/mypy issues test (#1017)

Improves typing in examples and tests, towards mypy passing there.

Introduces the SpaceInfo utility

examples/atari/atari_c51.py

@@ -8,15 +8,15 @@
 import torch
 from atari_network import C51
 from atari_wrapper import make_atari_env
-from torch.utils.tensorboard import SummaryWriter
 
+from examples.common import logger_factory
 from tianshou.data import Collector, VectorReplayBuffer
 from tianshou.policy import C51Policy
+from tianshou.policy.base import BasePolicy
 from tianshou.trainer import OffpolicyTrainer
-from tianshou.utils import TensorboardLogger, WandbLogger
 
 
-def get_args():
+def get_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
     parser.add_argument("--seed", type=int, default=0)
@@ -66,7 +66,7 @@ def get_args():
     return parser.parse_args()
 
 
-def test_c51(args=get_args()):
+def test_c51(args: argparse.Namespace = get_args()) -> None:
     env, train_envs, test_envs = make_atari_env(
         args.task,
         args.seed,
@@ -87,7 +87,7 @@ def test_c51(args=get_args()):
     net = C51(*args.state_shape, args.action_shape, args.num_atoms, args.device)
     optim = torch.optim.Adam(net.parameters(), lr=args.lr)
     # define policy
-    policy = C51Policy(
+    policy: C51Policy = C51Policy(
         model=net,
         optim=optim,
         discount_factor=args.gamma,
@@ -123,21 +123,19 @@ def test_c51(args=get_args()):
 
     # logger
     if args.logger == "wandb":
-        logger = WandbLogger(
-            save_interval=1,
-            name=log_name.replace(os.path.sep, "__"),
-            run_id=args.resume_id,
-            config=args,
-            project=args.wandb_project,
-        )
-    writer = SummaryWriter(log_path)
-    writer.add_text("args", str(args))
-    if args.logger == "tensorboard":
-        logger = TensorboardLogger(writer)
-    else:  # wandb
-        logger.load(writer)
-
-    def save_best_fn(policy):
+        logger_factory.logger_type = "wandb"
+        logger_factory.wandb_project = args.wandb_project
+    else:
+        logger_factory.logger_type = "tensorboard"
+
+    logger = logger_factory.create_logger(
+        log_dir=log_path,
+        experiment_name=log_name,
+        run_id=args.resume_id,
+        config_dict=vars(args),
+    )
+
+    def save_best_fn(policy: BasePolicy) -> None:
         torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
 
     def stop_fn(mean_rewards: float) -> bool:
@@ -147,7 +145,7 @@ def stop_fn(mean_rewards: float) -> bool:
             return mean_rewards >= 20
         return False
 
-    def train_fn(epoch, env_step):
+    def train_fn(epoch: int, env_step: int) -> None:
         # nature DQN setting, linear decay in the first 1M steps
         if env_step <= 1e6:
             eps = args.eps_train - env_step / 1e6 * (args.eps_train - args.eps_train_final)
@@ -157,11 +155,11 @@ def train_fn(epoch, env_step):
         if env_step % 1000 == 0:
             logger.write("train/env_step", env_step, {"train/eps": eps})
 
-    def test_fn(epoch, env_step):
+    def test_fn(epoch: int, env_step: int | None) -> None:
         policy.set_eps(args.eps_test)
 
     # watch agent's performance
-    def watch():
+    def watch() -> None:
         print("Setup test envs ...")
         policy.eval()
         policy.set_eps(args.eps_test)

examples/atari/atari_dqn.py

@@ -8,17 +8,17 @@
 import torch
 from atari_network import DQN
 from atari_wrapper import make_atari_env
-from torch.utils.tensorboard import SummaryWriter
 
+from examples.common import logger_factory
 from tianshou.data import Collector, VectorReplayBuffer
 from tianshou.policy import DQNPolicy
+from tianshou.policy.base import BasePolicy
 from tianshou.policy.modelbased.icm import ICMPolicy
 from tianshou.trainer import OffpolicyTrainer
-from tianshou.utils import TensorboardLogger, WandbLogger
 from tianshou.utils.net.discrete import IntrinsicCuriosityModule
 
 
-def get_args():
+def get_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
     parser.add_argument("--seed", type=int, default=0)
@@ -83,7 +83,7 @@ def get_args():
     return parser.parse_args()
 
 
-def test_dqn(args=get_args()):
+def test_dqn(args: argparse.Namespace = get_args()) -> None:
     env, train_envs, test_envs = make_atari_env(
         args.task,
         args.seed,
@@ -104,7 +104,7 @@ def test_dqn(args=get_args()):
     net = DQN(*args.state_shape, args.action_shape, args.device).to(args.device)
     optim = torch.optim.Adam(net.parameters(), lr=args.lr)
     # define policy
-    policy = DQNPolicy(
+    policy: DQNPolicy = DQNPolicy(
         model=net,
         optim=optim,
         action_space=env.action_space,
@@ -158,21 +158,19 @@ def test_dqn(args=get_args()):
 
     # logger
     if args.logger == "wandb":
-        logger = WandbLogger(
-            save_interval=1,
-            name=log_name.replace(os.path.sep, "__"),
-            run_id=args.resume_id,
-            config=args,
-            project=args.wandb_project,
-        )
-    writer = SummaryWriter(log_path)
-    writer.add_text("args", str(args))
-    if args.logger == "tensorboard":
-        logger = TensorboardLogger(writer)
-    else:  # wandb
-        logger.load(writer)
+        logger_factory.logger_type = "wandb"
+        logger_factory.wandb_project = args.wandb_project
+    else:
+        logger_factory.logger_type = "tensorboard"
+
+    logger = logger_factory.create_logger(
+        log_dir=log_path,
+        experiment_name=log_name,
+        run_id=args.resume_id,
+        config_dict=vars(args),
+    )
 
-    def save_best_fn(policy):
+    def save_best_fn(policy: BasePolicy) -> None:
         torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
 
     def stop_fn(mean_rewards: float) -> bool:
@@ -182,7 +180,7 @@ def stop_fn(mean_rewards: float) -> bool:
             return mean_rewards >= 20
         return False
 
-    def train_fn(epoch, env_step):
+    def train_fn(epoch: int, env_step: int) -> None:
         # nature DQN setting, linear decay in the first 1M steps
         if env_step <= 1e6:
             eps = args.eps_train - env_step / 1e6 * (args.eps_train - args.eps_train_final)
@@ -192,17 +190,17 @@ def train_fn(epoch, env_step):
         if env_step % 1000 == 0:
             logger.write("train/env_step", env_step, {"train/eps": eps})
 
-    def test_fn(epoch, env_step):
+    def test_fn(epoch: int, env_step: int | None) -> None:
         policy.set_eps(args.eps_test)
 
-    def save_checkpoint_fn(epoch, env_step, gradient_step):
+    def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str:
         # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
         ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
         torch.save({"model": policy.state_dict()}, ckpt_path)
         return ckpt_path
 
     # watch agent's performance
-    def watch():
+    def watch() -> None:
         print("Setup test envs ...")
         policy.eval()
         policy.set_eps(args.eps_test)

examples/atari/atari_dqn_hl.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 
+import functools
 import os
 
 from examples.atari.atari_network import (
@@ -48,7 +49,7 @@ def main(
     icm_lr_scale: float = 0.0,
     icm_reward_scale: float = 0.01,
     icm_forward_loss_weight: float = 0.2,
-):
+) -> None:
     log_name = os.path.join(task, "dqn", str(experiment_config.seed), datetime_tag())
 
     sampling_config = SamplingConfig(
@@ -102,4 +103,5 @@ def main(
 
 
 if __name__ == "__main__":
-    logging.run_cli(main)
+    run_with_default_config = functools.partial(main, experiment_config=ExperimentConfig())
+    logging.run_cli(run_with_default_config)

examples/atari/atari_fqf.py

@@ -8,16 +8,16 @@
 import torch
 from atari_network import DQN
 from atari_wrapper import make_atari_env
-from torch.utils.tensorboard import SummaryWriter
 
+from examples.common import logger_factory
 from tianshou.data import Collector, VectorReplayBuffer
 from tianshou.policy import FQFPolicy
+from tianshou.policy.base import BasePolicy
 from tianshou.trainer import OffpolicyTrainer
-from tianshou.utils import TensorboardLogger, WandbLogger
 from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
 
 
-def get_args():
+def get_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
     parser.add_argument("--seed", type=int, default=3128)
@@ -69,7 +69,7 @@ def get_args():
     return parser.parse_args()
 
 
-def test_fqf(args=get_args()):
+def test_fqf(args: argparse.Namespace = get_args()) -> None:
     env, train_envs, test_envs = make_atari_env(
         args.task,
         args.seed,
@@ -99,7 +99,7 @@ def test_fqf(args=get_args()):
     fraction_net = FractionProposalNetwork(args.num_fractions, net.input_dim)
     fraction_optim = torch.optim.RMSprop(fraction_net.parameters(), lr=args.fraction_lr)
     # define policy
-    policy = FQFPolicy(
+    policy: FQFPolicy = FQFPolicy(
         model=net,
         optim=optim,
         fraction_model=fraction_net,
@@ -136,21 +136,19 @@ def test_fqf(args=get_args()):
 
     # logger
     if args.logger == "wandb":
-        logger = WandbLogger(
-            save_interval=1,
-            name=log_name.replace(os.path.sep, "__"),
-            run_id=args.resume_id,
-            config=args,
-            project=args.wandb_project,
-        )
-    writer = SummaryWriter(log_path)
-    writer.add_text("args", str(args))
-    if args.logger == "tensorboard":
-        logger = TensorboardLogger(writer)
-    else:  # wandb
-        logger.load(writer)
-
-    def save_best_fn(policy):
+        logger_factory.logger_type = "wandb"
+        logger_factory.wandb_project = args.wandb_project
+    else:
+        logger_factory.logger_type = "tensorboard"
+
+    logger = logger_factory.create_logger(
+        log_dir=log_path,
+        experiment_name=log_name,
+        run_id=args.resume_id,
+        config_dict=vars(args),
+    )
+
+    def save_best_fn(policy: BasePolicy) -> None:
         torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
 
     def stop_fn(mean_rewards: float) -> bool:
@@ -160,7 +158,7 @@ def stop_fn(mean_rewards: float) -> bool:
             return mean_rewards >= 20
         return False
 
-    def train_fn(epoch, env_step):
+    def train_fn(epoch: int, env_step: int) -> None:
         # nature DQN setting, linear decay in the first 1M steps
         if env_step <= 1e6:
             eps = args.eps_train - env_step / 1e6 * (args.eps_train - args.eps_train_final)
@@ -170,11 +168,11 @@ def train_fn(epoch, env_step):
         if env_step % 1000 == 0:
             logger.write("train/env_step", env_step, {"train/eps": eps})
 
-    def test_fn(epoch, env_step):
+    def test_fn(epoch: int, env_step: int | None) -> None:
         policy.set_eps(args.eps_test)
 
     # watch agent's performance
-    def watch():
+    def watch() -> None:
         print("Setup test envs ...")
         policy.eval()
         policy.set_eps(args.eps_test)