Remove PyType and upgrade to latest SB3 version (#215)

.github/workflows/ci.yml

@@ -58,8 +58,6 @@ jobs:
     - name: Type check
       run: |
         make type
-      # skip PyType, doesn't support 3.11 yet
-      if: "!(matrix.python-version == '3.11')"
     - name: Test with pytest
       run: |
         make pytest

Makefile

@@ -4,13 +4,10 @@ LINT_PATHS=sb3_contrib/ tests/ setup.py docs/conf.py
 pytest:
 	./scripts/run_tests.sh
 
-pytype:
-	pytype -j auto
-
 mypy:
 	mypy ${LINT_PATHS}
 
-type: pytype mypy
+type: mypy
 
 lint:
 	# stop the build if there are Python syntax errors or undefined names

docs/misc/changelog.rst

@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 2.2.0a9 (WIP)
+Release 2.2.0a11 (WIP)
 --------------------------
 
 Breaking Changes:

pyproject.toml

@@ -19,9 +19,6 @@ max-complexity = 15
 [tool.black]
 line-length = 127
 
-[tool.pytype]
-inputs = ['sb3_contrib']
-
 [tool.mypy]
 ignore_missing_imports = true
 follow_imports = "silent"

sb3_contrib/ars/policies.py

@@ -4,7 +4,8 @@ import torch as th
 from gymnasium import spaces
 from stable_baselines3.common.policies import BasePolicy
 from stable_baselines3.common.preprocessing import get_action_dim
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, create_mlp
+from stable_baselines3.common.torch_layers import create_mlp
+from stable_baselines3.common.type_aliases import PyTorchObs
 from torch import nn
 
 
@@ -66,10 +67,7 @@ class ARSPolicy(BasePolicy):
         )
         return data
 
-    def forward(self, obs: th.Tensor) -> th.Tensor:
-        # Make mypy happy:
-        assert isinstance(self.features_extractor, BaseFeaturesExtractor)
-
+    def forward(self, obs: PyTorchObs) -> th.Tensor:
         features = self.extract_features(obs, self.features_extractor)
         if isinstance(self.action_space, spaces.Box):
             return self.action_net(features)
@@ -79,7 +77,7 @@ class ARSPolicy(BasePolicy):
         else:
             raise NotImplementedError()
 
-    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
+    def _predict(self, observation: PyTorchObs, deterministic: bool = True) -> th.Tensor:
         # Non deterministic action does not really make sense for ARS, we ignore this parameter for now..
         return self(observation)
 

sb3_contrib/qrdqn/policies.py

@@ -10,7 +10,7 @@ from stable_baselines3.common.torch_layers import (
     NatureCNN,
     create_mlp,
 )
-from stable_baselines3.common.type_aliases import Schedule
+from stable_baselines3.common.type_aliases import PyTorchObs, Schedule
 from torch import nn
 
 
@@ -58,7 +58,7 @@ class QuantileNetwork(BasePolicy):
         quantile_net = create_mlp(self.features_dim, action_dim * self.n_quantiles, self.net_arch, self.activation_fn)
         self.quantile_net = nn.Sequential(*quantile_net)
 
-    def forward(self, obs: th.Tensor) -> th.Tensor:
+    def forward(self, obs: PyTorchObs) -> th.Tensor:
         """
         Predict the quantiles.
 
@@ -68,7 +68,7 @@ class QuantileNetwork(BasePolicy):
         quantiles = self.quantile_net(self.extract_features(obs, self.features_extractor))
         return quantiles.view(-1, self.n_quantiles, int(self.action_space.n))
 
-    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
+    def _predict(self, observation: PyTorchObs, deterministic: bool = True) -> th.Tensor:
         q_values = self(observation).mean(dim=1)
         # Greedy action
         action = q_values.argmax(dim=1).reshape(-1)
@@ -181,10 +181,10 @@ class QRDQNPolicy(BasePolicy):
         net_args = self._update_features_extractor(self.net_args, features_extractor=None)
         return QuantileNetwork(**net_args).to(self.device)
 
-    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+    def forward(self, obs: PyTorchObs, deterministic: bool = True) -> th.Tensor:
         return self._predict(obs, deterministic=deterministic)
 
-    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+    def _predict(self, obs: PyTorchObs, deterministic: bool = True) -> th.Tensor:
         return self.quantile_net._predict(obs, deterministic=deterministic)
 
     def _get_constructor_parameters(self) -> Dict[str, Any]:

sb3_contrib/tqc/policies.py

@@ -13,7 +13,7 @@ from stable_baselines3.common.torch_layers import (
     create_mlp,
     get_actor_critic_arch,
 )
-from stable_baselines3.common.type_aliases import Schedule
+from stable_baselines3.common.type_aliases import PyTorchObs, Schedule
 from torch import nn as nn
 
 # CAP the standard deviation of the actor
@@ -143,7 +143,7 @@ class Actor(BasePolicy):
         assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
         self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
 
-    def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
+    def get_action_dist_params(self, obs: PyTorchObs) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
         """
         Get the parameters for the action distribution.
 
@@ -163,17 +163,17 @@ class Actor(BasePolicy):
         log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
         return mean_actions, log_std, {}
 
-    def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
+    def forward(self, obs: PyTorchObs, deterministic: bool = False) -> th.Tensor:
         mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
         # Note: the action is squashed
         return self.action_dist.actions_from_params(mean_actions, log_std, deterministic=deterministic, **kwargs)
 
-    def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
+    def action_log_prob(self, obs: PyTorchObs) -> Tuple[th.Tensor, th.Tensor]:
         mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
         # return action and associated log prob
         return self.action_dist.log_prob_from_params(mean_actions, log_std, **kwargs)
 
-    def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
+    def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
         return self(observation, deterministic)
 
 
@@ -231,7 +231,7 @@ class Critic(BaseModel):
             self.add_module(f"qf{i}", qf_net)
             self.q_networks.append(qf_net)
 
-    def forward(self, obs: th.Tensor, action: th.Tensor) -> th.Tensor:
+    def forward(self, obs: PyTorchObs, action: th.Tensor) -> th.Tensor:
         # Learn the features extractor using the policy loss only
         # when the features_extractor is shared with the actor
         with th.set_grad_enabled(not self.share_features_extractor):
@@ -411,10 +411,10 @@ class TQCPolicy(BasePolicy):
         critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor)
         return Critic(**critic_kwargs).to(self.device)
 
-    def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
+    def forward(self, obs: PyTorchObs, deterministic: bool = False) -> th.Tensor:
         return self._predict(obs, deterministic=deterministic)
 
-    def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
+    def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
         return self.actor(observation, deterministic)
 
     def set_training_mode(self, mode: bool) -> None:

sb3_contrib/version.txt

@@ -1 +1 @@
-2.2.0a9
+2.2.0a11