Saran Tunyasuvunakool
3577e2cf8b
PiperOrigin-RevId: 434731612 Change-Id: I0cfda3e7a3d1c72036764986efc252ffa1b8c6b0
489 lines
18 KiB
Python
489 lines
18 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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# ==============================================================================
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"""tests for rollout function."""
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from absl.testing import absltest
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from absl.testing import parameterized
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import mujoco
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import numpy as np
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import concurrent.futures
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import threading
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from mujoco import rollout
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#--------------------------- models used for testing ---------------------------
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TEST_XML = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<actuator>
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<general joint="pitch" gainprm="100"/>
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<general joint="yaw" dyntype="filter" dynprm="1" gainprm="100"/>
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</actuator>
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<sensor>
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<accelerometer site="site"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_NO_SENSORS = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<actuator>
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<general joint="pitch" gainprm="100"/>
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<general joint="yaw" dyntype="filter" dynprm="1" gainprm="100"/>
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</actuator>
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</mujoco>
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"""
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TEST_XML_NO_ACTUATORS = r"""
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<mujoco>
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<worldbody>
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<light pos="0 0 2"/>
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<geom type="plane" size="5 5 .1"/>
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<body pos="0 0 .1">
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<joint name="yaw" axis="0 0 1"/>
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<joint name="pitch" axis="0 1 0"/>
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<geom type="capsule" size=".02" fromto="0 0 0 1 0 0"/>
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<geom type="box" pos="1 0 0" size=".1 .1 .1"/>
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<site name="site" pos="1 0 0"/>
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</body>
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</worldbody>
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<sensor>
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<accelerometer site="site"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_MOCAP = r"""
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<mujoco>
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<worldbody>
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<body name="1" mocap="true">
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</body>
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<body name="2" mocap="true">
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</body>
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</worldbody>
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<sensor>
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<framepos objtype="xbody" objname="1"/>
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<framequat objtype="xbody" objname="2"/>
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</sensor>
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</mujoco>
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"""
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TEST_XML_EMPTY = r"""
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<mujoco>
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</mujoco>
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"""
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ALL_MODELS = {'TEST_XML': TEST_XML,
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'TEST_XML_NO_SENSORS': TEST_XML_NO_SENSORS,
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'TEST_XML_NO_ACTUATORS': TEST_XML_NO_ACTUATORS,
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'TEST_XML_EMPTY': TEST_XML_EMPTY}
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#------------------------------- tests -----------------------------------------
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class MuJoCoRolloutTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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np.random.seed(42)
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#----------------------------- test basic operation
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@parameterized.parameters(ALL_MODELS.keys())
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def test_single_step(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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initial_state = np.random.randn(model.nq + model.nv + model.na)
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ctrl = np.random.randn(model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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mujoco.mj_resetData(model, data)
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py_state, py_sensordata = step(model, data, initial_state, ctrl=ctrl)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_single_rollout(self, model_name):
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nstep = 3 # number of timesteps
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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initial_state = np.random.randn(model.nq + model.nv + model.na)
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ctrl = np.random.randn(nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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py_state, py_sensordata = single_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(state, np.asarray(py_state))
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np.testing.assert_array_equal(sensordata, np.asarray(py_sensordata))
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_step(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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nstate = 5 # number of initial states
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initial_state = np.random.randn(nstate, model.nq + model.nv + model.na)
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ctrl = np.random.randn(nstate, 1, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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mujoco.mj_resetData(model, data)
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py_state, py_sensordata = multi_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_single_rollout_fixed_ctrl(self, model_name):
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nstep = 3
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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initial_state = np.random.randn(model.nq + model.nv + model.na)
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ctrl = np.random.randn(model.nu)
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state = np.empty((nstep, model.nq + model.nv + model.na))
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sensordata = np.empty((nstep, model.nsensordata))
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rollout.rollout(model, data, initial_state, ctrl,
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state=state, sensordata=sensordata)
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ctrl = np.tile(ctrl, (nstep, 1)) # repeat??
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py_state, py_sensordata = single_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_rollout(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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nstate = 2 # number of initial states
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nstep = 3 # number of timesteps
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initial_state = np.random.randn(nstate, model.nq + model.nv + model.na)
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ctrl = np.random.randn(nstate, nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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py_state, py_sensordata = multi_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(py_state, py_state)
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np.testing.assert_array_equal(py_sensordata, py_sensordata)
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@parameterized.parameters(ALL_MODELS.keys())
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def test_multi_rollout_fixed_ctrl_infer_from_output(self, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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nstate = 2 # number of initial states
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nstep = 3 # number of timesteps
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initial_state = np.random.randn(nstate, model.nq + model.nv + model.na)
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ctrl = np.random.randn(nstate, 1, model.nu) # 1 control in the time dimension
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state = np.empty((nstate, nstep, model.nq + model.nv + model.na))
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl,
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state=state)
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ctrl = np.repeat(ctrl, nstep, axis=1)
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py_state, py_sensordata = multi_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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@parameterized.product(arg_nstep=[[3, 1, 1], [3, 3, 1], [3, 1, 3]],
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model_name=list(ALL_MODELS.keys()))
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def test_multi_rollout_multiple_inputs(self, arg_nstep, model_name):
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model = mujoco.MjModel.from_xml_string(ALL_MODELS[model_name])
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data = mujoco.MjData(model)
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nstate = 4 # number of initial states
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initial_state = np.random.randn(nstate, model.nq + model.nv + model.na)
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# arg_nstep is the horizon for {ctrl, qfrc_applied, xfrc_applied}, respectively
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ctrl = np.random.randn(nstate, arg_nstep[0], model.nu)
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qfrc_applied = np.random.randn(nstate, arg_nstep[1], model.nv)
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xfrc_applied = np.random.randn(nstate, arg_nstep[2], model.nbody*6)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl,
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qfrc_applied=qfrc_applied,
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xfrc_applied=xfrc_applied)
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# tile singleton arguments
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nstep = max(arg_nstep)
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if arg_nstep[0] == 1:
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ctrl = np.repeat(ctrl, nstep, axis=1)
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if arg_nstep[1] == 1:
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qfrc_applied = np.repeat(qfrc_applied, nstep, axis=1)
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if arg_nstep[2] == 1:
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xfrc_applied = np.repeat(xfrc_applied, nstep, axis=1)
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py_state, py_sensordata = multi_rollout(model, data, initial_state,
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ctrl=ctrl,
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qfrc_applied=qfrc_applied,
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xfrc_applied=xfrc_applied)
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np.testing.assert_array_equal(state, py_state)
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np.testing.assert_array_equal(sensordata, py_sensordata)
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#----------------------------- test threaded operation
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def test_threading(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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num_workers = 32
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nstate = 10000
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nstep = 5
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initial_state = np.random.randn(nstate, model.nq+model.nv+model.na)
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state = np.zeros((nstate, nstep, model.nq+model.nv+model.na))
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sensordata = np.zeros((nstate, nstep, model.nsensordata))
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ctrl = np.random.randn(nstate, nstep, model.nu)
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thread_local = threading.local()
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def thread_initializer():
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thread_local.data = mujoco.MjData(model)
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def call_rollout(initial_state, ctrl, state):
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rollout.rollout(model, thread_local.data, skip_checks=True,
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nstate=initial_state.shape[0], nstep=nstep,
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initial_state=initial_state, ctrl=ctrl, state=state)
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n = initial_state.shape[0] // num_workers # integer division
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chunks = [] # a list of tuples, one per worker
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for i in range(num_workers-1):
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chunks.append(
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(initial_state[i*n:(i+1)*n], ctrl[i*n:(i+1)*n], state[i*n:(i+1)*n]))
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# last chunk, absorbing the remainder:
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chunks.append(
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(initial_state[(num_workers-1)*n:], ctrl[(num_workers-1)*n:],
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state[(num_workers-1)*n:]))
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with concurrent.futures.ThreadPoolExecutor(
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max_workers=num_workers, initializer=thread_initializer) as executor:
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futures = []
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for chunk in chunks:
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futures.append(executor.submit(call_rollout, *chunk))
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for future in concurrent.futures.as_completed(futures):
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future.result()
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data = mujoco.MjData(model)
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py_state, py_sensordata = multi_rollout(model, data, initial_state,
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ctrl=ctrl)
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np.testing.assert_array_equal(state, py_state)
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#----------------------------- test advanced operation
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def test_time(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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data = mujoco.MjData(model)
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nstate = 1
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nstep = 3
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initial_time = np.array([[2.]])
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initial_state = np.random.randn(nstate, model.nq + model.nv + model.na)
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ctrl = np.random.randn(nstate, nstep, model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl,
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initial_time=initial_time)
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self.assertAlmostEqual(data.time, 2 + nstep*model.opt.timestep)
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def test_warmstart(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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data = mujoco.MjData(model)
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state0 = np.zeros(model.nq + model.nv + model.na)
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ctrl = np.zeros(model.nu)
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state1, _ = step(model, data, state0, ctrl=ctrl)
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initial_warmstart = data.qacc_warmstart.copy()
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state2, _ = step(model, data, state1, ctrl=ctrl)
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state, _ = rollout.rollout(model, data, state1, ctrl)
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assert np.linalg.norm(state-state2) > 0
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state, _ = rollout.rollout(model, data, state1, ctrl,
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initial_warmstart=initial_warmstart)
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np.testing.assert_array_equal(state, state2)
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def test_mocap(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML_MOCAP)
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data = mujoco.MjData(model)
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initial_state = np.zeros(model.nq + model.nv + model.na)
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pos1 = np.array((1., 2., 3.))
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quat1 = np.array((1., 2., 3., 4.))
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quat1 /= np.linalg.norm(quat1)
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pos2 = np.array((2., 3., 4.))
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quat2 = np.array((2., 3., 4., 5.))
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quat2 /= np.linalg.norm(quat2)
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mocap = np.hstack((pos1, quat1, pos2, quat2))
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state, sensordata = rollout.rollout(model, data, initial_state, mocap=mocap)
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np.testing.assert_array_almost_equal(sensordata[:3], pos1)
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np.testing.assert_array_almost_equal(sensordata[3:], quat2)
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#----------------------------- test correctness
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def test_intercept_mj_errors(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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data = mujoco.MjData(model)
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initial_state = np.zeros(model.nq + model.nv + model.na)
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ctrl = np.zeros((3, model.nu))
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model.opt.solver = 10 # invalid solver type
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with self.assertRaisesWithLiteralMatch(mujoco.FatalError,
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'Unknown solver type 10'):
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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def test_invalid(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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data = mujoco.MjData(model)
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initial_state = np.zeros(model.nq + model.nv + model.na)
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ctrl = 'string'
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with self.assertRaisesWithLiteralMatch(
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ValueError, 'ctrl must be a numpy array or float'):
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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qfrc_applied = np.zeros((2, 3, 4, 5))
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with self.assertRaisesWithLiteralMatch(
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ValueError, 'qfrc_applied can have at most 3 dimensions'):
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state, sensordata = rollout.rollout(model, data, initial_state,
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qfrc_applied=qfrc_applied)
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def test_bad_sizes(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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data = mujoco.MjData(model)
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initial_state = np.random.randn(model.nq + model.nv + model.na+1)
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with self.assertRaisesWithLiteralMatch(
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ValueError, 'trailing dimension of initial_state must be 5, got 6'):
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state, sensordata = rollout.rollout(model, data, initial_state)
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initial_state = np.random.randn(model.nq + model.nv + model.na)
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ctrl = np.random.randn(model.nu+1)
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with self.assertRaisesWithLiteralMatch(
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ValueError, 'trailing dimension of ctrl must be 2, got 3'):
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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ctrl = np.random.randn(2, model.nu)
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qfrc_applied = np.random.randn(3, model.nv) # incompatible horizon
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with self.assertRaisesWithLiteralMatch(
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ValueError, 'dimension 1 inferred as 2 but qfrc_applied has 3'):
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl,
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qfrc_applied=qfrc_applied)
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def test_stateless(self):
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model = mujoco.MjModel.from_xml_string(TEST_XML)
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model.opt.disableflags |= mujoco.mjtDisableBit.mjDSBL_WARMSTART.value
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data = mujoco.MjData(model)
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# call step with a clean mjData
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initial_state = np.random.randn(model.nq + model.nv + model.na)
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ctrl = np.random.randn(model.nu)
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state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
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# fill mjData with some debug value, see that we still get the same outputs
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mujoco.mj_resetDataDebug(model, data, 255)
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debug_state, debug_sensordata = rollout.rollout(model, data, initial_state,
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ctrl)
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np.testing.assert_array_equal(state, debug_state)
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np.testing.assert_array_equal(sensordata, debug_sensordata)
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#--------------- Python implementation of rollout functionality ----------------
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def get_state(data):
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return np.hstack((data.qpos, data.qvel, data.act))
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def set_state(model, data, state):
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data.qpos = state[:model.nq]
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data.qvel = state[model.nq:model.nq+model.nv]
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data.act = state[model.nq+model.nv:model.nq+model.nv+model.na]
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def step(model, data, state, **kwargs):
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if state is not None:
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set_state(model, data, state)
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for key, value in kwargs.items():
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if value is not None:
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setattr(data, key, np.reshape(value, getattr(data, key).shape))
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mujoco.mj_step(model, data)
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return (get_state(data), data.sensordata)
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def single_rollout(model, data, initial_state, **kwargs):
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arg_nstep = set([a.shape[0] for a in kwargs.values()])
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assert len(arg_nstep) == 1 # nstep dimensions must match
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nstep = arg_nstep.pop()
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state = np.empty((nstep, model.nq + model.nv + model.na))
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sensordata = np.empty((nstep, model.nsensordata))
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mujoco.mj_resetData(model, data)
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for t in range(nstep):
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kwargs_t = {}
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for key, value in kwargs.items():
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kwargs_t[key] = value[0 if value.ndim == 1 else t]
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state[t], sensordata[t] = step(model, data,
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initial_state if t==0 else None,
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**kwargs_t)
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return state, sensordata
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def multi_rollout(model, data, initial_state, **kwargs):
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nstate = initial_state.shape[0]
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arg_nstep = set([a.shape[1] for a in kwargs.values()])
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assert len(arg_nstep) == 1 # nstep dimensions must match
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nstep = arg_nstep.pop()
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state = np.empty((nstate, nstep, model.nq + model.nv + model.na))
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sensordata = np.empty((nstate, nstep, model.nsensordata))
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for s in range(nstate):
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kwargs_s = {key : value[s] for key, value in kwargs.items()}
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state_s, sensordata_s = single_rollout(model, data, initial_state[s],
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**kwargs_s)
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state[s] = state_s
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sensordata[s] = sensordata_s
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return state.squeeze(), sensordata.squeeze()
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if __name__ == '__main__':
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absltest.main()
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