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from collections import deque |
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from functools import partial as bind |
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import elements |
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import embodied |
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import numpy as np |
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import pytest |
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import zerofun |
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from embodied.envs import dummy |
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import utils |
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class TestParallel: |
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@pytest.mark.parametrize('train_ratio, eval_envs', ( |
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(-1, 2), (1, 2), (1, 0), (32, 2), |
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)) |
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def test_run_loop(self, tmpdir, train_ratio, eval_envs): |
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addr = 'ipc:///tmp/teststats' |
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received = deque(maxlen=1) |
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server = zerofun.Server(addr, name='TestStats') |
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server.bind('report', lambda stats: received.append(stats)) |
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server.start() |
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args = self._make_args(tmpdir, train_ratio, eval_envs) |
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embodied.run.parallel.combined( |
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bind(self._make_agent, addr), |
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bind(self._make_replay, args), |
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bind(self._make_replay, args), |
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self._make_env, |
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self._make_env, |
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self._make_logger, args) |
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stats = received[0] |
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print('Stats:', stats) |
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assert stats['env_steps'] > 400 |
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if args.train_ratio > -1: |
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replay_steps = stats['env_steps'] * args.train_ratio |
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assert np.allclose(stats['replay_steps'], replay_steps, 100, 0.1) |
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else: |
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assert stats['replay_steps'] > 100 |
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assert stats['reports'] >= 1 |
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assert stats['saves'] >= 2 |
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assert stats['loads'] == 0 |
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embodied.run.parallel.combined( |
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bind(self._make_agent, addr), |
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bind(self._make_replay, args), |
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bind(self._make_replay, args), |
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self._make_env, |
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self._make_env, |
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self._make_logger, args) |
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stats = received[0] |
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assert stats['loads'] == 1 |
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def _make_agent(self, queue): |
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env = self._make_env(0) |
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agent = utils.TestAgent(env.obs_space, env.act_space, queue) |
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env.close() |
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return agent |
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def _make_env(self, index): |
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return dummy.Dummy('disc', size=(64, 64), length=100) |
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def _make_replay(self, args, train_ratio=None): |
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kwargs = {'length': args.batch_length, 'capacity': 1e4} |
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if train_ratio: |
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kwargs['samples_per_insert'] = train_ratio / args.batch_length |
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return embodied.replay.Replay(**kwargs) |
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def _make_logger(self): |
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return elements.Logger(elements.Counter(), [ |
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elements.logger.TerminalOutput(), |
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]) |
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def _make_args(self, logdir, train_ratio, eval_envs): |
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return elements.Config( |
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duration=5.0, |
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train_ratio=float(train_ratio), |
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log_every=0.1, |
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report_every=0.2, |
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save_every=0.2, |
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envs=4, |
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eval_envs=int(eval_envs), |
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report_batches=1, |
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from_checkpoint='', |
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episode_timeout=10, |
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actor_addr='tcp://localhost:{auto}', |
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replay_addr='tcp://localhost:{auto}', |
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logger_addr='tcp://localhost:{auto}', |
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ipv6=False, |
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actor_batch=-1, |
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actor_threads=2, |
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agent_process=False, |
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remote_replay=False, |
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remote_envs=False, |
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usage=dict(psutil=True, nvsmi=False), |
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debug=False, |
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logdir=str(logdir), |
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batch_size=8, |
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batch_length=16, |
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replay_context=0, |
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report_length=8, |
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) |
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