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* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
"""Tests for the filter observation wrapper."""
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from typing import Optional
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import numpy as np
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import pytest
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import gym
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from gym.spaces import Box, Dict, Discrete, Tuple
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from gym.wrappers import FilterObservation, FlattenObservation
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class FakeEnvironment(gym.Env):
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def __init__(self, observation_space):
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self.observation_space = observation_space
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self.obs_keys = self.observation_space.spaces.keys()
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self.action_space = Box(shape=(1,), low=-1, high=1, dtype=np.float32)
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def render(self, width=32, height=32, *args, **kwargs):
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del args
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del kwargs
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image_shape = (height, width, 3)
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return np.zeros(image_shape, dtype=np.uint8)
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def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
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super().reset(seed=seed)
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observation = self.observation_space.sample()
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return observation
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def step(self, action):
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del action
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observation = self.observation_space.sample()
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reward, terminal, info = 0.0, False, {}
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return observation, reward, terminal, info
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NESTED_DICT_TEST_CASES = (
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(
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Dict(
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{
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"key1": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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"key2": Dict(
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{
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"subkey1": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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"subkey2": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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}
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),
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}
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),
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(6,),
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),
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(
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Dict(
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{
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"key1": Box(shape=(2, 3), low=-1, high=1, dtype=np.float32),
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"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
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"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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}
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),
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(9,),
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),
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(
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Dict(
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{
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"key1": Tuple(
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(
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Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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)
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),
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"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
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"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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}
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),
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(7,),
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),
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(
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Dict(
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{
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"key1": Tuple((Box(shape=(2,), low=-1, high=1, dtype=np.float32),)),
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"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
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"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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}
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),
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(5,),
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),
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(
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Dict(
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{
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"key1": Tuple(
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(Dict({"key9": Box(shape=(2,), low=-1, high=1, dtype=np.float32)}),)
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),
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"key2": Box(shape=(), low=-1, high=1, dtype=np.float32),
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"key3": Box(shape=(2,), low=-1, high=1, dtype=np.float32),
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}
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),
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(5,),
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),
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)
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class TestNestedDictWrapper:
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@pytest.mark.parametrize("observation_space, flat_shape", NESTED_DICT_TEST_CASES)
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def test_nested_dicts_size(self, observation_space, flat_shape):
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env = FakeEnvironment(observation_space=observation_space)
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# Make sure we are testing the right environment for the test.
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observation_space = env.observation_space
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assert isinstance(observation_space, Dict)
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wrapped_env = FlattenObservation(FilterObservation(env, env.obs_keys))
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assert wrapped_env.observation_space.shape == flat_shape
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assert wrapped_env.observation_space.dtype == np.float32
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@pytest.mark.parametrize("observation_space, flat_shape", NESTED_DICT_TEST_CASES)
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def test_nested_dicts_ravel(self, observation_space, flat_shape):
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env = FakeEnvironment(observation_space=observation_space)
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wrapped_env = FlattenObservation(FilterObservation(env, env.obs_keys))
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obs = wrapped_env.reset()
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assert obs.shape == wrapped_env.observation_space.shape
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