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99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
"""Tests for the flatten observation wrapper."""
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from collections import OrderedDict
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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, flatten, unflatten
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from gym.wrappers import 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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def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
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super().reset(seed=seed)
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self.observation = self.observation_space.sample()
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return self.observation
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OBSERVATION_SPACES = (
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(
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Dict(
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OrderedDict(
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[
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("key1", Box(shape=(2, 3), low=0, high=0, 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=2, high=2, dtype=np.float32)),
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]
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)
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),
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True,
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),
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(
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Dict(
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OrderedDict(
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[
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("key2", Box(shape=(), low=0, high=0, dtype=np.float32)),
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("key3", Box(shape=(2,), low=1, high=1, dtype=np.float32)),
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("key1", Box(shape=(2, 3), low=2, high=2, dtype=np.float32)),
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]
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)
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),
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True,
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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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False,
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),
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)
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class TestFlattenEnvironment:
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@pytest.mark.parametrize("observation_space, ordered_values", OBSERVATION_SPACES)
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def test_flattened_environment(self, observation_space, ordered_values):
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"""
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make sure that flattened observations occur in the order expected
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"""
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env = FakeEnvironment(observation_space=observation_space)
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wrapped_env = FlattenObservation(env)
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flattened = wrapped_env.reset()
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unflattened = unflatten(env.observation_space, flattened)
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original = env.observation
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self._check_observations(original, flattened, unflattened, ordered_values)
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@pytest.mark.parametrize("observation_space, ordered_values", OBSERVATION_SPACES)
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def test_flatten_unflatten(self, observation_space, ordered_values):
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"""
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test flatten and unflatten functions directly
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"""
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original = observation_space.sample()
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flattened = flatten(observation_space, original)
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unflattened = unflatten(observation_space, flattened)
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self._check_observations(original, flattened, unflattened, ordered_values)
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def _check_observations(self, original, flattened, unflattened, ordered_values):
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# make sure that unflatten(flatten(original)) == original
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assert set(unflattened.keys()) == set(original.keys())
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for k, v in original.items():
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np.testing.assert_allclose(unflattened[k], v)
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if ordered_values:
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# make sure that the values were flattened in the order they appeared in the
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# OrderedDict
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np.testing.assert_allclose(sorted(flattened), flattened)
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