mirror of
https://github.com/bulletphysics/bullet3
synced 2024-12-14 13:50:04 +00:00
136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
# Copyright 2017 The TensorFlow Agents Authors.
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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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r"""Script to render videos of the Proximal Policy Gradient algorithm.
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Command line:
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python3 -m agents.scripts.visualize \
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--logdir=/path/to/logdir/<time>-<config> --outdir=/path/to/outdir/
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import gym
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from gym import wrappers
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import tf.compat.v1 as tf
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from . import tools
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from . import utility
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def _create_environment(config, outdir):
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"""Constructor for an instance of the environment.
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Args:
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config: Object providing configurations via attributes.
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outdir: Directory to store videos in.
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Returns:
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Wrapped OpenAI Gym environment.
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"""
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if isinstance(config.env, str):
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env = gym.make(config.env)
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else:
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env = config.env()
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# Ensure that the environment has the specification attribute set as expected
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# by the monitor wrapper.
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if not hasattr(env, 'spec'):
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setattr(env, 'spec', getattr(env, 'spec', None))
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if config.max_length:
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env = tools.wrappers.LimitDuration(env, config.max_length)
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env = gym.wrappers.Monitor(env, outdir, lambda unused_episode_number: True)
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env = tools.wrappers.RangeNormalize(env)
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env = tools.wrappers.ClipAction(env)
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env = tools.wrappers.ConvertTo32Bit(env)
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return env
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def _define_loop(graph, eval_steps):
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"""Create and configure an evaluation loop.
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Args:
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graph: Object providing graph elements via attributes.
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eval_steps: Number of evaluation steps per epoch.
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Returns:
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Loop object.
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"""
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loop = tools.Loop(None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
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loop.add_phase('eval',
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graph.done,
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graph.score,
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graph.summary,
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eval_steps,
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report_every=eval_steps,
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log_every=None,
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checkpoint_every=None,
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feed={graph.is_training: False})
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return loop
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def visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True):
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"""Recover checkpoint and render videos from it.
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Args:
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logdir: Logging directory of the trained algorithm.
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outdir: Directory to store rendered videos in.
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num_agents: Number of environments to simulate in parallel.
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num_episodes: Total number of episodes to simulate.
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checkpoint: Checkpoint name to load; defaults to most recent.
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env_processes: Whether to step environments in separate processes.
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"""
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config = utility.load_config(logdir)
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with tf.device('/cpu:0'):
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batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents,
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env_processes)
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graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
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total_steps = num_episodes * config.max_length
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loop = _define_loop(graph, total_steps)
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saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step'))
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sess_config = tf.ConfigProto(allow_soft_placement=True)
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sess_config.gpu_options.allow_growth = True
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with tf.Session(config=sess_config) as sess:
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utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True)
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for unused_score in loop.run(sess, saver, total_steps):
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pass
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batch_env.close()
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def main(_):
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"""Load a trained algorithm and render videos."""
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utility.set_up_logging()
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if not FLAGS.logdir or not FLAGS.outdir:
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raise KeyError('You must specify logging and outdirs directories.')
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FLAGS.logdir = os.path.expanduser(FLAGS.logdir)
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FLAGS.outdir = os.path.expanduser(FLAGS.outdir)
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visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, FLAGS.checkpoint,
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FLAGS.env_processes)
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if __name__ == '__main__':
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_string('logdir', None, 'Directory to the checkpoint of a training run.')
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tf.app.flags.DEFINE_string('outdir', None, 'Local directory for storing the monitoring outdir.')
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tf.app.flags.DEFINE_string('checkpoint', None,
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'Checkpoint name to load; defaults to most recent.')
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tf.app.flags.DEFINE_integer('num_agents', 1, 'How many environments to step in parallel.')
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tf.app.flags.DEFINE_integer('num_episodes', 5, 'Minimum number of episodes to render.')
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tf.app.flags.DEFINE_boolean('env_processes', True,
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'Step environments in separate processes to circumvent the GIL.')
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tf.app.run()
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