Behron
3e39a34a7a
More merge conflicts. Taking main changes then will integrate mine.
127 lines
3.7 KiB
Python
127 lines
3.7 KiB
Python
# Copyright 2022 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for all models."""
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import pathlib
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from typing import List
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from absl.testing import absltest
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from absl.testing import parameterized
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import jax
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import jax.numpy as jp
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import mujoco
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from mujoco import mjx
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# Internal import.
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_ROOT_DIR = pathlib.Path(__file__).parent.parent
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_MODEL_DIRS = [f for f in _ROOT_DIR.iterdir() if f.is_dir()]
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_MODEL_XMLS: List[pathlib.Path] = []
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_MJX_MODEL_XMLS: List[pathlib.Path] = []
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def _get_xmls(pattern: str) -> List[pathlib.Path]:
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for d in _MODEL_DIRS:
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# Produce tuples of test name and XML path.
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for f in d.glob(pattern):
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test_name = str(f).removeprefix(str(f.parent.parent))
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yield (test_name, f)
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_MODEL_XMLS = list(_get_xmls('scene*.xml'))
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_MJX_MODEL_XMLS = list(_get_xmls('scene*mjx.xml'))
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# Total simulation duration, in seconds.
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_MAX_SIM_TIME = 0.1
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# Scale for the pseudorandom control noise.
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_NOISE_SCALE = 1.0
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def _pseudorandom_ctrlnoise(
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model: mujoco.MjModel,
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data: mujoco.MjData,
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i: int,
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noise: float,
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) -> None:
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for j in range(model.nu):
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ctrlrange = model.actuator_ctrlrange[j]
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if model.actuator_ctrllimited[j]:
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center = 0.5 * (ctrlrange[1] + ctrlrange[0])
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radius = 0.5 * (ctrlrange[1] - ctrlrange[0])
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else:
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center = 0.0
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radius = 1.0
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data.ctrl[j] = center + radius * noise * (2*mujoco.mju_Halton(i, j+2) - 1)
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class ModelsTest(parameterized.TestCase):
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"""Tests that MuJoCo models load and do not emit warnings."""
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@parameterized.named_parameters(_MODEL_XMLS)
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def test_compiles_and_steps(self, xml_path: pathlib.Path) -> None:
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model = mujoco.MjModel.from_xml_path(str(xml_path))
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data = mujoco.MjData(model)
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i = 0
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while data.time < _MAX_SIM_TIME:
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_pseudorandom_ctrlnoise(model, data, i, _NOISE_SCALE)
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mujoco.mj_step(model, data)
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i += 1
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# Check no warnings were triggered during the simulation.
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if not all(data.warning.number == 0):
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warning_info = '\n'.join([
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f'{mujoco.mjtWarning(enum_value).name}: count={count}'
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for enum_value, count in enumerate(data.warning.number) if count
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])
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self.fail(f'MuJoCo warning(s) encountered:\n{warning_info}')
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class MjxModelsTest(parameterized.TestCase):
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"""Tests that MJX models load and do not return NaNs."""
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@parameterized.named_parameters(_MJX_MODEL_XMLS)
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def test_compiles_and_steps(self, xml_path: pathlib.Path) -> None:
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model = mujoco.MjModel.from_xml_path(str(xml_path))
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model = mjx.put_model(model)
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data = mjx.make_data(model)
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ctrlrange = jp.where(
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model.actuator_ctrllimited[:, None],
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model.actuator_ctrlrange,
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jp.array([-10.0, 10.0]),
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)
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def step(x, _):
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data, rng = x
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rng, key = jax.random.split(rng)
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ctrl = jax.random.uniform(
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key,
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shape=(model.nu,),
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minval=ctrlrange[:, 0],
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maxval=ctrlrange[:, 1],
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)
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data = mjx.step(model, data.replace(ctrl=ctrl))
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return (data, rng), ()
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(data, _), _ = jax.lax.scan(
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step,
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(data, jax.random.PRNGKey(0)),
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(),
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length=min(_MAX_SIM_TIME // model.opt.timestep, 100),
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)
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self.assertFalse(jp.isnan(data.qpos).any())
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if __name__ == '__main__':
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absltest.main()
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