Revert all changes for python 2

+ Add makefile and pytype

.gitignore

@@ -12,6 +12,7 @@ __pycache__/
 _build/
 *.npz
 *.pth
+.pytype/
 
 # Setuptools distribution and build folders.
 /dist/

Makefile

@@ -0,0 +1,13 @@
+SHELL=/bin/bash
+
+pytest:
+	./scripts/run_tests.sh
+
+pytype:
+	pytype
+
+doc:
+	cd docs && make html
+
+spelling:
+	cd docs && make spelling

setup.cfg

@@ -15,3 +15,6 @@ filterwarnings =
 		# Gym warnings
 		ignore:Parameters to load are deprecated.:DeprecationWarning
 		ignore:the imp module is deprecated in favour of importlib:PendingDeprecationWarning
+
+[pytype]
+inputs = torchy_baselines

setup.py

@@ -18,6 +18,7 @@ setup(name='torchy_baselines',
             'pytest-cov',
             'pytest-env',
             'pytest-xdist',
+            'pytype',
         ],
         'docs': [
             'sphinx',

torchy_baselines/common/base_class.py

@@ -1,9 +1,9 @@
 import time
-from abc import ABCMeta, abstractmethod
-from collections import deque
 import os
 import io
 import zipfile
+from abc import ABC, abstractmethod
+from collections import deque
 
 import gym
 import torch as th
@@ -18,7 +18,7 @@ from torchy_baselines.common.evaluation import evaluate_policy
 from torchy_baselines.common.save_util import data_to_json, json_to_data
 
 
-class BaseRLModel(object):
+class BaseRLModel(ABC):
     """
     The base RL model
 
@@ -43,8 +43,6 @@ class BaseRLModel(object):
     :param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
         Default: -1 (only sample at the beginning of the rollout)
     """
-    __metaclass__ = ABCMeta
-
     def __init__(self, policy, env, policy_base, policy_kwargs=None,
                  verbose=0, device='auto', support_multi_env=False,
                  create_eval_env=False, monitor_wrapper=True, seed=None,

torchy_baselines/common/logger.py

@@ -272,7 +272,7 @@ def getkvs():
     return Logger.CURRENT.name2val
 
 
-def log(*args, **kwargs):
+def log(*args, level=INFO):
     """
     Write the sequence of args, with no separators,
     to the console and output files (if you've configured an output file).
@@ -283,7 +283,6 @@ def log(*args, **kwargs):
     :param args: (list) log the arguments
     :param level: (int) the logging level (can be DEBUG=10, INFO=20, WARN=30, ERROR=40, DISABLED=50)
     """
-    level = kwargs.get('level', INFO)
     Logger.CURRENT.log(*args, level=level)
 
 
@@ -424,7 +423,7 @@ class Logger(object):
         self.name2val.clear()
         self.name2cnt.clear()
 
-    def log(self, *args, **kwargs):
+    def log(self, *args, level=INFO):
         """
         Write the sequence of args, with no separators,
         to the console and output files (if you've configured an output file).
@@ -435,7 +434,6 @@ class Logger(object):
         :param args: (list) log the arguments
         :param level: (int) the logging level (can be DEBUG=10, INFO=20, WARN=30, ERROR=40, DISABLED=50)
         """
-        level = kwargs.get('level', INFO)
         if self.level <= level:
             self._do_log(args)
 

torchy_baselines/common/vec_env/base_vec_env.py

@@ -1,6 +1,6 @@
-from abc import ABCMeta, abstractmethod
 import inspect
 import pickle
+from abc import ABC, abstractmethod
 
 import cloudpickle
 
@@ -27,7 +27,7 @@ class NotSteppingError(Exception):
         Exception.__init__(self, msg)
 
 
-class VecEnv(object):
+class VecEnv(ABC):
     """
     An abstract asynchronous, vectorized environment.
 
@@ -39,8 +39,6 @@ class VecEnv(object):
         'render.modes': ['human', 'rgb_array']
     }
 
-    __metaclass__ = ABCMeta
-
     def __init__(self, num_envs, observation_space, action_space):
         self.num_envs = num_envs
         self.observation_space = observation_space
@@ -112,7 +110,7 @@ class VecEnv(object):
         raise NotImplementedError()
 
     @abstractmethod
-    def env_method(self, method_name, *method_args, **method_kwargs):
+    def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
         """
         Call instance methods of vectorized environments.
 
@@ -222,8 +220,8 @@ class VecEnvWrapper(VecEnv):
     def set_attr(self, attr_name, value, indices=None):
         return self.venv.set_attr(attr_name, value, indices)
 
-    def env_method(self, method_name, *method_args, **method_kwargs):
-        return self.venv.env_method(method_name, *method_args, **method_kwargs)
+    def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
+        return self.venv.env_method(method_name, *method_args, indices=indices, **method_kwargs)
 
     def __getattr__(self, name):
         """Find attribute from wrapped venv(s) if this wrapper does not have it.

torchy_baselines/common/vec_env/dummy_vec_env.py

@@ -99,11 +99,8 @@ class DummyVecEnv(VecEnv):
         for env_i in target_envs:
             setattr(env_i, attr_name, value)
 
-    def env_method(self, method_name, *method_args, **method_kwargs):
+    def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
         """Call instance methods of vectorized environments."""
-        indices = method_kwargs.get('indices')
-        if 'indices' in method_kwargs:
-            del method_kwargs['indices']
         target_envs = self._get_target_envs(indices)
         return [getattr(env_i, method_name)(*method_args, **method_kwargs) for env_i in target_envs]
 

torchy_baselines/common/vec_env/subproc_vec_env.py

@@ -153,8 +153,7 @@ class SubprocVecEnv(VecEnv):
         for pipe in self.remotes:
             # gather images from subprocesses
             # `mode` will be taken into account later
-            kwargs.update({'mode': 'rgb_array'})
-            pipe.send(('render', (args, kwargs)))
+            pipe.send(('render', (args, {'mode': 'rgb_array', **kwargs})))
         imgs = [pipe.recv() for pipe in self.remotes]
         # Create a big image by tiling images from subprocesses
         bigimg = tile_images(imgs)
@@ -199,11 +198,8 @@ class SubprocVecEnv(VecEnv):
         assert len(seed) == len(indices)
         return [self.env_method('seed', seed[i], indices=i) for i in indices]
 
-    def env_method(self, method_name, *method_args, **method_kwargs):
+    def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
         """Call instance methods of vectorized environments."""
-        indices = method_kwargs.get('indices')
-        if 'indices' in method_kwargs:
-            del method_kwargs['indices']
         target_remotes = self._get_target_remotes(indices)
         for remote in target_remotes:
             remote.send(('env_method', (method_name, method_args, method_kwargs)))