def summarize_episode(episode, config, datadir, writer, prefix):
episodes, steps = tools.count_episodes(datadir)
length = (len(episode['reward']) - 1) * config.action_repeat
ret = episode['reward'].sum()
metrics = [(f'{prefix}/return', float(episode['reward'].sum())),
(f'{prefix}/length', len(episode['reward']) - 1),
(f'episodes', episodes)]
if 'success' in episode:
success = True in episode['success']
success_str = "succeeded" if success == 1 else "did not succeed"
metrics.append((f'{prefix}/success', success))
print(
f'{prefix.title()} episode of length {length} with return {ret:.1f}, which {success_str}.'
)
else:
print(
f'{prefix.title()} episode of length {length} with return {ret:.1f}.'
)
sys.stdout.flush()
step = count_steps(datadir, config)
with (config.logdir / 'metrics.jsonl').open('a') as f:
f.write(json.dumps(dict([('step', step)] + metrics)) + '\n')
with writer.as_default(): # Env might run in a different thread.
tf.summary.experimental.set_step(step)
[tf.summary.scalar('sim/' + k, v) for k, v in metrics]
if prefix == 'test':
tools.video_summary(f'sim/{prefix}/video', episode['image'][None])
def summarize_episode(episode, config, datadir, writer, prefix):
length = (len(episode['reward']) - 1) * config.action_repeat
ret = episode['reward'].sum()
print(
f'{prefix.title()} episode of length {length} with return {ret:.1f}.')
metrics = [(f'{prefix}/return', float(episode['reward'].sum())),
(f'{prefix}/length', len(episode['reward']) - 1)]
with writer.as_default(): # Env might run in a different thread.
[tf.summary.scalar('sim/' + k, v) for k, v in metrics]
if prefix == 'test':
tools.video_summary(f'sim/{prefix}/video', episode['image'][None])
def post_process_episodes(self, cache, episode_name, episode):
total = 0
length = len(episode["rewards"]) - 1
score = float(episode["rewards"].astype(np.float64).sum())
video = episode["obp1s"]
for key, ep in reversed(sorted(cache.items(), key=lambda x: x[0])):
if total <= self._c.max_dataset_steps - length:
total += len(ep["rewards"]) - 1
else:
del cache[key]
cache[str(episode_name)] = episode
step = count_steps(self.datadir)
with self._writer.as_default():
tf.summary.scalar(
"dataset_size",
total + length,
step=step * self._c.action_repeat,
) # control by model.total_step, record the env total step
tf.summary.scalar(
"train_episodes",
len(cache),
step=step * self._c.action_repeat,
) # control by model.total_step, record the env total step
tf.summary.scalar(
"train_return",
score,
step=step * self._c.action_repeat,
) # control by model.total_step, record the env total step
tf.summary.scalar(
"train_length",
length,
step=step * self._c.action_repeat,
) # control by model.total_step, record the env total step
print("save train_policy!!!!")
tools.video_summary("train_policy", np.array(video[None]),
step * self._c.action_repeat)
print("the episodes size now is:", total + length, "steps")
def summarize_episode(episode, config, datadir, writer, prefix):
episodes, steps = tools.count_episodes(datadir)
length = (len(episode['reward']) - 1) * config.action_repeat
ret = episode['reward'].sum()
print(
f'{prefix.title()} episode of length {length} with return {ret:.1f}.')
metrics = [(f'{prefix}/return', float(episode['reward'].sum())),
(f'{prefix}/length', len(episode['reward']) - 1),
(f'episodes', episodes)]
step = count_steps(datadir, config)
with (config.logdir / 'metrics.jsonl').open('a') as f:
f.write(json.dumps(dict([('step', step)] + metrics)) + '\n')
with writer.as_default(): # Env might run in a different thread.
tf.summary.experimental.set_step(step)
[tf.summary.scalar('sim/' + k, v) for k, v in metrics]
if prefix == 'test':
tools.video_summary(f'sim/{prefix}/video', episode['image'][None])
def summarize_episode(episode, config, datadir, writer, prefix):
episodes, steps = tools.count_episodes(datadir)
length = (len(episode["reward"]) - 1) * config.action_repeat
ret = episode["reward"].sum()
print(f"{prefix.title()} episode of length {length} with return {ret:.1f}.")
metrics = [
(f"{prefix}/return", float(episode["reward"].sum())),
(f"{prefix}/length", len(episode["reward"]) - 1),
(f"episodes", episodes),
]
step = count_steps(datadir, config)
with (config.logdir / "metrics.jsonl").open("a") as f:
f.write(json.dumps(dict([("step", step)] + metrics)) + "\n")
with writer.as_default(): # Env might run in a different thread.
tf.summary.experimental.set_step(step)
[tf.summary.scalar("sim/" + k, v) for k, v in metrics]
if prefix == "test":
tools.video_summary(f"sim/{prefix}/video", episode["image"][None])
def _image_summaries(self,
data,
embed,
image_pred,
step=None,
prefix='train'):
truth = data['image'][:6] + 0.5
recon = image_pred.mode()[:6]
init, _ = self._dynamics.observe(embed[:6, :5], data['action'][:6, :5])
init = {k: v[:, -1] for k, v in init.items()}
prior = self._dynamics.imagine(data['action'][:6, 5:], init)
openl = self._decode(self._dynamics.get_feat(prior)).mode()
model = tf.concat([recon[:, :5] + 0.5, openl + 0.5], 1)
error_prior = (model - truth + 1) / 2
error_posterior = (recon + 0.5 - truth + 1) / 2
openl = tf.concat(
[truth, recon + 0.5, model, error_prior, error_posterior], 2)
with self._writer.as_default():
tools.video_summary('agent/' + prefix, openl.numpy(), step=step)
def summarize_episode(config, datadir, writer, prefix):
list_of_files = glob.glob(str(datadir) + '/*.npz')
latest_file = max(list_of_files, key=os.path.getctime)
episode = np.load(latest_file)
episode = {k: episode[k] for k in episode.keys()}
episodes, steps = tools.count_episodes(datadir)
print(episodes, steps)
length = (len(episode['reward']) - 1) * config.action_repeat
ret = episode['reward'].sum()
print(
f'{prefix.title()} episode of length {length} with return {ret:.1f}.')
metrics = [(f'{prefix}/return', float(episode['reward'].sum())),
(f'{prefix}/length', len(episode['reward']) - 1),
(f'episodes', episodes)]
step = count_steps(datadir, config)
with (config.logdir / 'metrics.jsonl').open('a') as f:
f.write(json.dumps(dict([('step', step)] + metrics)) + '\n')
with writer.as_default(): # Env might run in a different thread.
tf.summary.experimental.set_step(step)
[tf.summary.scalar('sim/' + k, v) for k, v in metrics]
tools.video_summary(f'sim/{prefix}/video', episode['image'][None])