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from functools import partial as bind
import elements
import embodied
import numpy as np
import utils
class TestTrain:
def test_run_loop(self, tmpdir):
args = self._make_args(tmpdir)
agent = self._make_agent()
embodied.run.train(
lambda: agent, bind(self._make_replay, args),
self._make_env, self._make_logger, args)
stats = agent.stats()
print('Stats:', stats)
replay_steps = args.steps * args.train_ratio
assert stats['lifetime'] >= 1 # Otherwise decrease log and ckpt interval.
assert np.allclose(stats['env_steps'], args.steps, 100, 0.1)
assert np.allclose(stats['replay_steps'], replay_steps, 100, 0.1)
assert stats['reports'] >= 1
assert stats['saves'] >= 2
assert stats['loads'] == 0
args = args.update(steps=2 * args.steps)
embodied.run.train(
lambda: agent, bind(self._make_replay, args),
self._make_env, self._make_logger, args)
stats = agent.stats()
assert stats['loads'] == 1
assert np.allclose(stats['env_steps'], args.steps, 100, 0.1)
def _make_agent(self):
env = self._make_env(0)
agent = utils.TestAgent(env.obs_space, env.act_space)
env.close()
return agent
def _make_env(self, index):
from embodied.envs import dummy
return dummy.Dummy('disc', size=(64, 64), length=100)
def _make_replay(self, args):
kwargs = {'length': args.batch_length, 'capacity': 1e4}
return embodied.replay.Replay(**kwargs)
def _make_logger(self):
return elements.Logger(elements.Counter(), [
elements.logger.TerminalOutput(),
])
def _make_args(self, logdir):
return elements.Config(
steps=1000,
train_ratio=32.0,
log_every=0.1,
report_every=0.2,
save_every=0.2,
report_batches=1,
from_checkpoint='',
usage=dict(psutil=True),
debug=False,
logdir=str(logdir),
envs=4,
batch_size=8,
batch_length=16,
replay_context=0,
report_length=8,
)
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