def _log_stats(self, epoch):
logger.log("Epoch {} finished".format(epoch), with_timestamp=True)
"""
Replay Buffer
"""
logger.record_dict(self.replay_buffer.get_diagnostics(),
prefix='replay_buffer/')
"""
Trainer
"""
logger.record_dict(self.trainer.get_diagnostics(), prefix='trainer/')
"""
Exploration
"""
logger.record_dict(self.expl_data_collector.get_diagnostics(),
prefix='exploration/')
expl_paths = self.expl_data_collector.get_epoch_paths()
if hasattr(self.expl_env, 'get_diagnostics'):
logger.record_dict(
self.expl_env.get_diagnostics(expl_paths),
prefix='exploration/',
)
logger.record_dict(
eval_util.get_generic_path_information(expl_paths),
prefix="exploration/",
)
"""
Evaluation
"""
logger.record_dict(
self.eval_data_collector.get_diagnostics(),
prefix='evaluation/',
)
eval_paths = self.eval_data_collector.get_epoch_paths()
if hasattr(self.eval_env, 'get_diagnostics'):
logger.record_dict(
self.eval_env.get_diagnostics(eval_paths),
prefix='evaluation/',
)
# Get path information.
logger.record_dict(
eval_util.get_generic_path_information(eval_paths),
prefix="evaluation/",
)
"""
Misc
"""
gt.stamp('logging')
logger.record_dict(_get_epoch_timings())
logger.record_tabular('Epoch', epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
print('$$$$$$$$$$$$$$$')
print(table_keys)
print('\n' * 4)
print(self._old_table_keys)
print('$$$$$$$$$$$$$$$')
print(set(table_keys) - set(self._old_table_keys))
print(set(self._old_table_keys) - set(table_keys))
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def evaluate(self, epoch):
"""
Evaluate the policy, e.g. save/print progress.
:param epoch:
:return:
"""
statistics = OrderedDict()
try:
statistics.update(self.eval_statistics)
self.eval_statistics = None
except:
print('No Stats to Eval')
logger.log("Collecting samples for evaluation")
test_paths = self.eval_sampler.obtain_samples()
statistics.update(
eval_util.get_generic_path_information(
test_paths,
stat_prefix="Test",
))
statistics.update(
eval_util.get_generic_path_information(
self._exploration_paths,
stat_prefix="Exploration",
))
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(test_paths)
if hasattr(self.env, "log_statistics"):
statistics.update(self.env.log_statistics(test_paths))
if epoch % self.freq_log_visuals == 0:
if hasattr(self.env, "log_visuals"):
self.env.log_visuals(test_paths, epoch,
logger.get_snapshot_dir())
average_returns = eval_util.get_average_returns(test_paths)
statistics['AverageReturn'] = average_returns
for key, value in statistics.items():
logger.record_tabular(key, value)
best_statistic = statistics[self.best_key]
if best_statistic > self.best_statistic_so_far:
self.best_statistic_so_far = best_statistic
if self.save_best and epoch >= self.save_best_starting_from_epoch:
data_to_save = {'epoch': epoch, 'statistics': statistics}
data_to_save.update(self.get_epoch_snapshot(epoch))
logger.save_extra_data(data_to_save, 'best.pkl')
print('\n\nSAVED BEST\n\n')
def step(self, action):
"""
:param action: joint position controls in action space (action bounds), then scaled to joint space
"""
assert np.shape(action) == (self.n_actions, )
# action = np.clip(action, self.action_space.low, self.action_space.high)
action = self.process_action(action)
p.configureDebugVisualizer(p.COV_ENABLE_SINGLE_STEP_RENDERING)
forces = np.array([100] * 7 + [60] * 2)
p.setJointMotorControlArray(self.pandaUid,
list(range(7)) + [9, 10],
p.POSITION_CONTROL,
action,
forces=forces)
p.stepSimulation()
self.observation, _ = self.get_obs()
done = False
done, reward, _ = self.get_reward(done)
# done here is that we completed, if completed we stay completed until env.reset()
self.completed = self.completed or done
self.step_counter += 1
if self.step_counter > self._max_episode_steps:
reward = 0
done = True
info = {
"obj_pos":
np.array(p.getBasePositionAndOrientation(self.objectUid)[0]),
"obj_ori":
np.array(p.getBasePositionAndOrientation(self.objectUid)[1]),
"hand_pos":
np.array(p.getLinkState(self.pandaUid, 11)[0]),
"fingers_joint":
np.array([
p.getJointState(self.pandaUid, 9)[0],
p.getJointState(self.pandaUid, 10)[0]
]),
"completed":
self.completed,
}
if self.completed and self.verbose:
logger.log("Completed!")
return self.observation, reward, done, info
def train(self):
self.fix_data_set()
logger.log("Done creating dataset.")
num_batches_total = 0
for epoch in range(self.num_epochs):
for _ in range(self.num_batches_per_epoch):
self.qf.train(True)
self._do_training()
num_batches_total += 1
logger.push_prefix('Iteration #%d | ' % epoch)
self.qf.train(False)
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
logger.log("Done evaluating")
logger.pop_prefix()
def _try_to_eval(self, epoch):
if self._can_evaluate():
# save if it's time to save
if (epoch % self.freq_saving
== 0) or (epoch + 1 >= self.num_epochs):
# if epoch + 1 >= self.num_epochs:
# epoch = 'final'
logger.save_extra_data(self.get_extra_data_to_save(epoch))
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
self.evaluate(epoch)
logger.record_tabular(
"Number of train calls total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def execute_actions(actions,
polygons,
asset_path,
sim_steps=2000,
img_dim=64,
hold_last_action=False):
polygon_map = {ind: ply for ind, ply in enumerate(polygons)}
xml = XML(asset_path)
names = []
for action in actions:
ply_ind, pos, axangle, scale, rgb = action
ply = polygon_map[ply_ind]
rgba = rgb.tolist() + [1]
#print('Dropping {} | pos: {} | axangle: {} | scale: {} | rgb: {} '.format(ply, pos, axangle, scale, rgb))
name = xml.add_mesh(ply,
pos=pos,
axangle=axangle,
scale=scale,
rgba=rgba)
names.append(name)
xml_str = xml.instantiate()
model = mjc.load_model_from_xml(xml_str)
sim = mjc.MjSim(model)
log_steps = len(actions) + 1
logger = Logger(xml, sim, steps=log_steps, img_dim=img_dim)
logger.log(0)
for act_ind, act in enumerate(actions):
hold_objects = names[act_ind + 1:]
drop_object = names[act_ind]
if act_ind == len(actions) - 1 and hold_last_action:
logger.hold_drop_execute(hold_objects, drop_object, 1)
else:
logger.hold_drop_execute(hold_objects, drop_object, sim_steps)
logger.log(act_ind + 1, hold_objects)
data, images, masks = logger.get_logs()
images = images / 255.
return data, images, masks
def render(self):
logger.push_prefix("HighLow(sign={0})\t".format(self._sign))
if self._last_action is None:
logger.log("No action taken.")
else:
if self._last_t == 0:
logger.log("--- New Episode ---")
logger.push_prefix("t={0}\t".format(self._last_t))
with np_print_options(precision=4, suppress=False):
logger.log("Action: {0}".format(self._last_action, ))
logger.log("Reward: {0}".format(self._last_reward, ))
logger.pop_prefix()
logger.pop_prefix()
def _try_to_eval(self, epoch):
if epoch % self.freq_saving == 0:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
if epoch % self.freq_saving == 0:
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
# logger.record_tabular(
# "Number of train steps total",
# self._n_policy_train_steps_total,
# )
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def sim_first_step(self, actions):
asset_path = '/home/jcoreyes/objects/object-oriented-prediction/o2p2/data/stl'
img_dim = 64
xml = XML(asset_path)
names = []
for action in actions:
ply_ind, pos, axangle, scale, rgb = action[0], action[1:4], action[
4:8], action[8], action[9:12]
ply = self.polygon_map[ply_ind]
rgba = rgb.tolist() + [1]
print('Dropping {} | pos: {} | axangle: {} | scale: {} | rgb: {} '.
format(ply, pos, axangle, scale, rgb))
name = xml.add_mesh(ply,
pos=pos,
axangle=axangle,
scale=scale,
rgba=rgba)
names.append(name)
xml_str = xml.instantiate()
model = mjc.load_model_from_xml(xml_str)
sim = mjc.MjSim(model)
log_steps = 2
sim_steps = 1
logger = Logger(xml, sim, steps=log_steps, img_dim=img_dim)
logger.log(0)
for act_ind, act in enumerate(actions):
hold_objects = names[act_ind + 1:]
drop_object = names[act_ind]
logger.hold_drop_execute(hold_objects, drop_object, sim_steps)
logger.log(act_ind + 1, hold_objects)
break
data, images, masks = logger.get_logs()
images = images / 255.
import pdb
pdb.set_trace()
def evaluate(self, epoch, eval_paths=None):
statistics = OrderedDict()
statistics.update(self.eval_statistics)
logger.log("Collecting samples for evaluation")
if eval_paths:
test_paths = eval_paths
else:
test_paths = self.get_eval_paths()
statistics.update(
eval_util.get_generic_path_information(
test_paths,
stat_prefix="Test",
))
if len(self._exploration_paths) > 0:
statistics.update(
eval_util.get_generic_path_information(
self._exploration_paths,
stat_prefix="Exploration",
))
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(test_paths, logger=logger)
if hasattr(self.env, "get_diagnostics"):
statistics.update(self.env.get_diagnostics(test_paths))
for i in range(len(test_paths)):
self.env.update_rewards(test_paths[i])
statistics['AverageReturn'] = eval_util.get_average_returns(test_paths)
statistics[
'AverageEnvironmentReturn'] = eval_util.get_average_environment_returns(
test_paths)
statistics[
'AverageUnsupervisedReturn'] = eval_util.get_average_unsupervised_returns(
test_paths)
for key, value in statistics.items():
logger.record_tabular(key, value)
self.need_to_update_eval_statistics = True
def train(self):
for epoch in range(self.num_epochs):
logger.push_prefix('Iteration #%d | ' % epoch)
start_time = time.time()
for _ in range(self.num_steps_per_epoch):
batch = self.get_batch()
train_dict = self.get_train_dict(batch)
self.policy_optimizer.zero_grad()
policy_loss = train_dict['Policy Loss']
policy_loss.backward()
self.policy_optimizer.step()
logger.log("Train time: {}".format(time.time() - start_time))
start_time = time.time()
self.evaluate(epoch)
logger.log("Eval time: {}".format(time.time() - start_time))
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
logger.pop_prefix()
def run_task(variant):
from rlkit.core import logger
print(variant)
logger.log("Hello from script")
logger.log("variant: " + str(variant))
logger.record_tabular("value", 1)
logger.dump_tabular()
logger.log("snapshot_dir:", logger.get_snapshot_dir())
def _backtracking_line_search(self, params, descent_step, f_loss,
f_constraint):
prev_params = [p.clone() for p in params]
ratio_list = self._backtrack_ratio**np.arange(self._max_backtracks)
loss_before = f_loss()
param_shapes = [p.shape or torch.Size([1]) for p in params]
descent_step = unflatten_tensors(descent_step, param_shapes)
assert len(descent_step) == len(params)
for ratio in ratio_list:
for step, prev_param, param in zip(descent_step, prev_params,
params):
step = ratio * step
new_param = prev_param.data - step
param.data = new_param.data
loss = f_loss()
constraint_val = f_constraint()
if (loss < loss_before
and constraint_val <= self._max_constraint_value):
break
if ((torch.isnan(loss) or torch.isnan(constraint_val)
or loss >= loss_before
or constraint_val >= self._max_constraint_value)
and not self._accept_violation):
logger.log('Line search condition violated. Rejecting the step!')
if torch.isnan(loss):
logger.log('Violated because loss is NaN')
if torch.isnan(constraint_val):
logger.log('Violated because constraint is NaN')
if loss >= loss_before:
logger.log('Violated because loss not improving')
if constraint_val >= self._max_constraint_value:
logger.log('Violated because constraint is violated')
for prev, cur in zip(prev_params, params):
cur.data = prev.data
def evaluate(self, epoch, exploration_paths):
"""
Perform evaluation for this algorithm.
:param epoch: The epoch number.
:param exploration_paths: List of dicts, each representing a path.
"""
logger.log("Collecting samples for evaluation")
paths = self._sample_eval_paths(epoch)
statistics = OrderedDict()
statistics.update(self._statistics_from_paths(paths, "Test"))
statistics.update(self._get_other_statistics())
statistics.update(
self._statistics_from_paths(exploration_paths, "Exploration"))
statistics['AverageReturn'] = get_average_returns(paths)
statistics['Epoch'] = epoch
for key, value in statistics.items():
logger.record_tabular(key, value)
self.log_diagnostics(paths)
def train_amortized_goal_chooser(
goal_chooser,
goal_conditioned_model,
argmax_q,
discount,
replay_buffer,
learning_rate=1e-3,
batch_size=32,
num_updates=1000,
):
def get_loss(training=False):
buffer = replay_buffer.get_replay_buffer(training)
batch = buffer.random_batch(batch_size)
obs = ptu.np_to_var(batch['observations'], requires_grad=False)
goals = ptu.np_to_var(batch['goal_states'], requires_grad=False)
goal = goal_chooser(obs, goals)
actions = argmax_q(obs, goal, discount)
final_state_predicted = goal_conditioned_model(
obs,
actions,
goal,
discount,
) + obs
rewards = goal_chooser.reward_function(final_state_predicted, goals)
return -rewards.mean()
discount = ptu.np_to_var(discount * np.ones((batch_size, 1)))
optimizer = optim.Adam(goal_chooser.parameters(), learning_rate)
for i in range(num_updates):
optimizer.zero_grad()
loss = get_loss()
loss.backward()
optimizer.step()
if i % 100 == 0:
logger.log("Number updates: {}".format(i))
logger.log("Train loss: {}".format(float(ptu.get_numpy(loss))))
logger.log("Validation loss: {}".format(
float(ptu.get_numpy(get_loss(training=False)))))
def _try_to_eval(self, epoch, eval_paths=None):
if MPI and MPI.COMM_WORLD.Get_rank() == 0:
if epoch % self.save_extra_data_interval == 0:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if epoch % self.num_epochs_per_param_save == 0:
print("Attemping itr param save...")
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
print(F"Itr{epoch} param saved!")
if self._can_evaluate():
self.evaluate(epoch, eval_paths=eval_paths)
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
# train_time = times_itrs['train'][-1]
training_loops = ['get_batch', 'update_normalizer', 'forward', 'compute_losses', 'qf1_loop', "policy_loss_forward", 'policy_loop', 'vf_loop']
train_time = sum(times_itrs[loop][-1] for loop in times_itrs.keys())
sample_time = times_itrs['sample'][-1]
if epoch > 0:
eval_time = times_itrs['eval'][-1]
else:
times_itrs['eval'] = [0] # Need to do this so we can do line 343, the list comprehension
eval_time = 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
# logger.record_tabular('Get Batch (s)', times_itrs['get_batch'][-1])
# logger.record_tabular('Update Normalizer (s)', times_itrs['update_normalizer'][-1])
# logger.record_tabular('Forward (s)', times_itrs['forward'][-1])
# logger.record_tabular('Compute Losses (s)', times_itrs['compute_losses'][-1])
# logger.record_tabular('QF1 Loop (s)', times_itrs['qf1_loop'][-1])
# logger.record_tabular('QF2 Loop (s)', times_itrs['qf2_loop'][-1])
# logger.record_tabular("Policy Forward (s)", times_itrs['policy_loss_forward'][-1])
# logger.record_tabular('Policy Loop (s)', times_itrs['policy_loop'][-1])
# logger.record_tabular('VF Loop (s)', times_itrs['vf_loop'][-1])
[logger.record_tabular(key.title(), times_itrs[key][-1]) for key in times_itrs.keys()]
logger.record_tabular('Train Time (s) ---', train_time)
logger.record_tabular('(Previous) Eval Time (s) ---', eval_time)
logger.record_tabular('Sample Time (s) ---', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None and table_keys != self._old_table_keys:
# assert table_keys == self._old_table_keys, (
# "Table keys cannot change from iteration to iteration."
# )
print("Table keys have changed. Rewriting header and filling with 0s")
logger.update_header()
raise NotImplementedError
self._old_table_keys = table_keys
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def example(variant):
import mujoco_py
import torch
logger.log(torch.__version__)
date_format = '%m/%d/%Y %H:%M:%S %Z'
date = datetime.now(tz=pytz.utc)
logger.log("start")
logger.log('Current date & time is: {}'.format(date.strftime(date_format)))
if torch.cuda.is_available():
x = torch.randn(3)
logger.log(str(x.to(ptu.device)))
date = date.astimezone(timezone('US/Pacific'))
logger.log('Local date & time is: {}'.format(date.strftime(date_format)))
for i in range(variant['num_seconds']):
logger.log("Tick, {}".format(i))
time.sleep(1)
logger.log("end")
logger.log('Local date & time is: {}'.format(date.strftime(date_format)))
logger.log("start mujoco")
from gym.envs.mujoco import HalfCheetahEnv
e = HalfCheetahEnv()
img = e.sim.render(32, 32)
logger.log(str(sum(img)))
logger.log("end mujocoy")
time.sleep(1)
logger.log("end")
logger.log('Local date & time is: {}'.format(date.strftime(date_format)))
logger.log("start mujoco")
from gym.envs.mujoco import HalfCheetahEnv
e = HalfCheetahEnv()
img = e.sim.render(32, 32)
logger.log(str(sum(img)))
logger.log("end mujocoy")
if __name__ == "__main__":
# noinspection PyTypeChecker
date_format = '%m/%d/%Y %H:%M:%S %Z'
date = datetime.now(tz=pytz.utc)
logger.log("start")
variant = dict(
num_seconds=10,
launch_time=str(date.strftime(date_format)),
)
run_experiment(
example,
exp_prefix='test-gpu-local-singularity',
mode='local_singularity',
variant=variant,
# use_gpu=True,
use_gpu=False,
verbose=True,
)
def setup_logger(
exp_prefix="default",
variant=None,
text_log_file="debug.log",
variant_log_file="variant.json",
tabular_log_file="progress.csv",
snapshot_mode="last",
snapshot_gap=1,
log_tabular_only=False,
log_dir=None,
git_infos=None,
script_name=None,
**create_log_dir_kwargs
):
"""
Set up logger to have some reasonable default settings.
Will save log output to
based_log_dir/exp_prefix/exp_name.
exp_name will be auto-generated to be unique.
If log_dir is specified, then that directory is used as the output dir.
:param exp_prefix: The sub-directory for this specific experiment.
:param variant:
:param text_log_file:
:param variant_log_file:
:param tabular_log_file:
:param snapshot_mode:
:param log_tabular_only:
:param snapshot_gap:
:param log_dir:
:param git_infos:
:param script_name: If set, save the script name to this.
:return:
"""
if git_infos is None:
git_infos = get_git_infos(conf.CODE_DIRS_TO_MOUNT)
first_time = log_dir is None
if first_time:
log_dir = create_log_dir(exp_prefix, **create_log_dir_kwargs)
if variant is not None:
logger.log("Variant:")
logger.log(json.dumps(dict_to_safe_json(variant), indent=2))
variant_log_path = osp.join(log_dir, variant_log_file)
logger.log_variant(variant_log_path, variant)
tabular_log_path = osp.join(log_dir, tabular_log_file)
text_log_path = osp.join(log_dir, text_log_file)
logger.add_text_output(text_log_path)
if first_time:
logger.add_tabular_output(tabular_log_path)
else:
logger._add_output(tabular_log_path, logger._tabular_outputs,
logger._tabular_fds, mode='a')
for tabular_fd in logger._tabular_fds:
logger._tabular_header_written.add(tabular_fd)
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(snapshot_mode)
logger.set_snapshot_gap(snapshot_gap)
logger.set_log_tabular_only(log_tabular_only)
exp_name = log_dir.split("/")[-1]
logger.push_prefix("[%s] " % exp_name)
if git_infos is not None:
for (
directory, code_diff, code_diff_staged, commit_hash, branch_name
) in git_infos:
if directory[-1] == '/':
directory = directory[:-1]
diff_file_name = directory[1:].replace("/", "-") + ".patch"
diff_staged_file_name = (
directory[1:].replace("/", "-") + "_staged.patch"
)
if code_diff is not None and len(code_diff) > 0:
with open(osp.join(log_dir, diff_file_name), "w") as f:
f.write(code_diff + '\n')
if code_diff_staged is not None and len(code_diff_staged) > 0:
with open(osp.join(log_dir, diff_staged_file_name), "w") as f:
f.write(code_diff_staged + '\n')
with open(osp.join(log_dir, "git_infos.txt"), "a") as f:
f.write("directory: {}\n".format(directory))
f.write("git hash: {}\n".format(commit_hash))
f.write("git branch name: {}\n\n".format(branch_name))
if script_name is not None:
with open(osp.join(log_dir, "script_name.txt"), "w") as f:
f.write(script_name)
return log_dir
def _log_stats(self, epoch):
logger.log(f"Epoch {epoch} finished", with_timestamp=True)
"""
Replay Buffer
"""
logger.record_dict(
self.replay_buffer.get_diagnostics(), prefix="replay_buffer/"
)
"""
Trainer
"""
logger.record_dict(self.trainer.get_diagnostics(), prefix="trainer/")
"""
Exploration
"""
logger.record_dict(
self.expl_data_collector.get_diagnostics(), prefix="exploration/"
)
expl_paths = self.expl_data_collector.get_epoch_paths()
if len(expl_paths) > 0:
if hasattr(self.expl_env, "get_diagnostics"):
logger.record_dict(
self.expl_env.get_diagnostics(expl_paths),
prefix="exploration/",
)
logger.record_dict(
eval_util.get_generic_path_information(expl_paths),
prefix="exploration/",
)
"""
Evaluation
"""
logger.record_dict(
self.eval_data_collector.get_diagnostics(),
prefix="evaluation/",
)
eval_paths = self.eval_data_collector.get_epoch_paths()
if hasattr(self.eval_env, "get_diagnostics"):
logger.record_dict(
self.eval_env.get_diagnostics(eval_paths),
prefix="evaluation/",
)
logger.record_dict(
eval_util.get_generic_path_information(eval_paths),
prefix="evaluation/",
)
"""
Misc
"""
gt.stamp("logging")
timings = _get_epoch_timings()
timings["time/training and exploration (s)"] = self.total_train_expl_time
logger.record_dict(timings)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
def pretrain(self):
logger.log('Pretraining ...')
for ep in range(self.num_pretrain_updates):
for t in range(self.num_update_loops_per_train_call):
self._do_update_step(ep, use_expert_buffer=True)
def setup_logger(
exp_prefix="default",
exp_id=0,
seed=0,
variant=None,
base_log_dir=None,
text_log_file="debug.log",
variant_log_file="variant.json",
tabular_log_file="progress.csv",
snapshot_mode="last",
snapshot_gap=1,
log_tabular_only=False,
log_dir=None,
git_info=None,
script_name=None,
):
"""
Set up logger to have some reasonable default settings.
Will save log output to
based_log_dir/exp_prefix/exp_name.
exp_name will be auto-generated to be unique.
If log_dir is specified, then that directory is used as the output dir.
:param exp_prefix: The sub-directory for this specific experiment.
:param exp_id: The number of the specific experiment run within this
experiment.
:param variant:
:param base_log_dir: The directory where all log should be saved.
:param text_log_file:
:param variant_log_file:
:param tabular_log_file:
:param snapshot_mode:
:param log_tabular_only:
:param snapshot_gap:
:param log_dir:
:param git_info:
:param script_name: If set, save the script name to this.
:return:
"""
first_time = log_dir is None
if first_time:
log_dir = create_log_dir(exp_prefix,
exp_id=exp_id,
seed=seed,
base_log_dir=base_log_dir)
if variant is not None:
logger.log("Variant:")
logger.log(json.dumps(dict_to_safe_json(variant), indent=2))
variant_log_path = osp.join(log_dir, variant_log_file)
logger.log_variant(variant_log_path, variant)
tabular_log_path = osp.join(log_dir, tabular_log_file)
text_log_path = osp.join(log_dir, text_log_file)
logger.add_text_output(text_log_path)
if first_time:
logger.add_tabular_output(tabular_log_path)
else:
logger._add_output(tabular_log_path,
logger._tabular_outputs,
logger._tabular_fds,
mode='a')
for tabular_fd in logger._tabular_fds:
logger._tabular_header_written.add(tabular_fd)
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(snapshot_mode)
logger.set_snapshot_gap(snapshot_gap)
logger.set_log_tabular_only(log_tabular_only)
exp_name = log_dir.split("/")[-1]
logger.push_prefix("[%s] " % exp_name)
if git_info is not None:
code_diff, commit_hash, branch_name = git_info
if code_diff is not None:
with open(osp.join(log_dir, "code.diff"), "w") as f:
f.write(code_diff)
with open(osp.join(log_dir, "git_info.txt"), "w") as f:
f.write("git hash: {}".format(commit_hash))
f.write('\n')
f.write("git branch name: {}".format(branch_name))
if script_name is not None:
with open(osp.join(log_dir, "script_name.txt"), "w") as f:
f.write(script_name)
return log_dir
def _log_stats(self, epoch):
logger.log("Epoch {} finished".format(epoch), with_timestamp=True)
"""
Replay Buffer
"""
logger.record_dict(
self.replay_buffer.get_diagnostics(),
global_step=epoch,
prefix="replay_buffer/",
)
"""
Trainer
"""
logger.record_dict(self.trainer.get_diagnostics(),
global_step=epoch,
prefix="trainer/")
"""
Exploration
"""
logger.record_dict(
self.expl_data_collector.get_diagnostics(),
global_step=epoch,
prefix="exploration/",
)
expl_paths = self.expl_data_collector.get_epoch_paths()
if hasattr(self.expl_env, "get_diagnostics"):
logger.record_dict(
self.expl_env.get_diagnostics(expl_paths),
global_step=epoch,
prefix="exploration/",
)
logger.record_dict(
eval_util.get_generic_path_information(expl_paths),
global_step=epoch,
prefix="exploration/",
)
"""
Evaluation
"""
logger.record_dict(
self.eval_data_collector.get_diagnostics(),
global_step=epoch,
prefix="evaluation/",
)
eval_paths = self.eval_data_collector.get_epoch_paths()
if hasattr(self.eval_env, "get_diagnostics"):
logger.record_dict(
self.eval_env.get_diagnostics(eval_paths),
global_step=epoch,
prefix="evaluation/",
)
logger.record_dict(
eval_util.get_generic_path_information(eval_paths),
global_step=epoch,
prefix="evaluation/",
)
"""
Misc
"""
gt.stamp("logging")
logger.record_dict(_get_epoch_timings(), global_step=epoch)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
def evaluate(self, epoch):
statistics = OrderedDict()
statistics.update(self.eval_statistics)
self.eval_statistics = None
# statistics.update(eval_util.get_generic_path_information(
# self._exploration_paths, stat_prefix="Exploration",
# ))
for mode in ['meta_train', 'meta_test']:
logger.log("Collecting samples for evaluation")
test_paths = self.obtain_eval_samples(epoch, mode=mode)
statistics.update(
eval_util.get_generic_path_information(
test_paths,
stat_prefix="Test " + mode,
))
# print(statistics.keys())
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(test_paths)
if hasattr(self.env, "log_statistics"):
log_stats = self.env.log_statistics(test_paths)
new_log_stats = OrderedDict(
(k + ' ' + mode, v) for k, v in log_stats.items())
statistics.update(new_log_stats)
average_returns = rlkit.core.eval_util.get_average_returns(
test_paths)
statistics['AverageReturn ' + mode] = average_returns
if self.render_eval_paths:
self.env.render_paths(test_paths)
# meta_test_this_epoch = statistics['Percent_Solved meta_test']
# meta_test_this_epoch = statistics['Avg Run Rew meta_test']
# meta_test_this_epoch = statistics['L2AverageClosest meta_test']
meta_test_this_epoch = statistics['Perc Success meta_test']
# meta_test_this_epoch = 100.0
# meta_test_this_epoch = statistics['AverageReturn meta_test']
if meta_test_this_epoch > self.best_meta_test:
# make sure you set save_algorithm to true then call save_extra_data
prev_save_alg = self.save_algorithm
self.save_algorithm = True
if self.save_best:
if epoch > self.save_best_after_epoch:
temp_rb = self.replay_buffer
self.replay_buffer = None
logger.save_extra_data(self.get_extra_data_to_save(epoch),
'best_meta_test.pkl')
self.replay_buffer = temp_rb
self.best_meta_test = meta_test_this_epoch
print('\n\nSAVED ALG AT EPOCH %d\n\n' % epoch)
self.save_algorithm = prev_save_alg
if epoch in self.custom_save_epoch:
prev_save_alg = self.save_algorithm
self.save_algorithm = True
logger.save_extra_data(self.get_extra_data_to_save(epoch),
'custom_save_epoch_%d.pkl' % epoch)
self.save_algorithm = prev_save_alg
for key, value in statistics.items():
logger.record_tabular(key, value)
if self.plotter:
self.plotter.draw()
def evaluate(self, epoch):
"""
Evaluate the policy, e.g. save/print progress.
:param epoch:
:return:
"""
statistics = OrderedDict()
try:
statistics.update(self.eval_statistics)
self.eval_statistics = None
except:
print('No Stats to Eval')
logger.log("Collecting random samples for evaluation")
eval_steps = self.num_steps_per_eval
test_paths = self.eval_sampler.obtain_samples(eval_steps)
obs = torch.Tensor(
np.squeeze(np.vstack([path["observations"]
for path in test_paths])))
acts = torch.Tensor(
np.squeeze(np.vstack([path["actions"] for path in test_paths])))
if len(acts.shape) < 2:
acts = torch.unsqueeze(acts, 1)
random_input = torch.cat([obs, acts], dim=1).to(ptu.device)
exp_batch = self.get_batch(eval_steps,
keys=['observations', 'actions'],
use_expert_buffer=True)
# exp_batch = {'observations':torch.Tensor([[0.],[1.],[2.],[3.],[4.],[5.],[6.],[7.],[8.],[9.],[10.]]), 'actions':torch.Tensor([[0.5]]*11)}
obs = exp_batch['observations']
acts = exp_batch['actions']
exp_input = torch.cat([obs, acts], dim=1).to(ptu.device)
statistics['random_avg_energy'] = self.ebm(random_input).mean().item()
statistics['expert_avg_energy'] = self.get_energy(
exp_input).mean().item()
statistics['expert*20_avg_energy'] = self.get_energy(exp_input *
20).mean().item()
statistics["random_expert_diff"] = statistics[
"random_avg_energy"] - statistics["expert_avg_energy"]
for key, value in statistics.items():
logger.record_tabular(key, value)
best_statistic = statistics[self.best_key]
if best_statistic > self.best_statistic_so_far:
self.best_statistic_so_far = best_statistic
self.best_epoch = epoch
self.best_random_avg_energy = statistics['random_avg_energy']
self.best_expert_avg_energy = statistics['expert_avg_energy']
logger.record_tabular("Best Model Epoch", self.best_epoch)
logger.record_tabular("Best Random Energy",
self.best_random_avg_energy)
logger.record_tabular("Best Expert Energy",
self.best_expert_avg_energy)
if self.save_best and epoch >= self.save_best_starting_from_epoch:
data_to_save = {'epoch': epoch, 'statistics': statistics}
data_to_save.update(self.get_epoch_snapshot(epoch))
logger.save_extra_data(data_to_save, 'best.pkl')
print('\n\nSAVED BEST\n\n')
logger.record_tabular("Best Model Epoch", self.best_epoch)
logger.record_tabular("Best Random Energy",
self.best_random_avg_energy)
logger.record_tabular("Best Expert Energy",
self.best_expert_avg_energy)
def _try_to_eval(self, epoch):
if epoch % self.logging_period != 0:
return
if epoch in self.save_extra_manual_epoch_set:
logger.save_extra_data(
self.get_extra_data_to_save(epoch),
file_name='extra_snapshot_itr{}'.format(epoch),
mode='cloudpickle',
)
if self._save_extra_every_epoch:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
gt.stamp('save-extra')
if self._can_evaluate():
self.evaluate(epoch)
gt.stamp('eval')
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
gt.stamp('save-snapshot')
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_dict(
self.trainer.get_diagnostics(),
prefix='trainer/',
)
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
save_extra_time = times_itrs['save-extra'][-1]
save_snapshot_time = times_itrs['save-snapshot'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + save_extra_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('in_unsupervised_model',
float(self.in_unsupervised_phase))
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Save Extra Time (s)', save_extra_time)
logger.record_tabular('Save Snapshot Time (s)', save_snapshot_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def example(variant):
import torch
import rlkit.torch.pytorch_util as ptu
print("Starting")
logger.log(torch.__version__)
date_format = "%m/%d/%Y %H:%M:%S %Z"
date = datetime.now(tz=pytz.utc)
logger.log("start")
logger.log("Current date & time is: {}".format(date.strftime(date_format)))
logger.log("Cuda available: {}".format(torch.cuda.is_available()))
if torch.cuda.is_available():
x = torch.randn(3)
logger.log(str(x.to(ptu.device)))
date = date.astimezone(timezone("US/Pacific"))
logger.log("Local date & time is: {}".format(date.strftime(date_format)))
for i in range(variant["num_seconds"]):
logger.log("Tick, {}".format(i))
time.sleep(1)
logger.log("end")
logger.log("Local date & time is: {}".format(date.strftime(date_format)))
logger.log("start mujoco")
from gym.envs.mujoco import HalfCheetahEnv
e = HalfCheetahEnv()
img = e.sim.render(32, 32)
logger.log(str(sum(img)))
logger.log("end mujoco")
logger.record_tabular("Epoch", 1)
logger.dump_tabular()
logger.record_tabular("Epoch", 2)
logger.dump_tabular()
logger.record_tabular("Epoch", 3)
logger.dump_tabular()
print("Done")
def _end_epoch(self):
logger.log("Epoch Duration: {0}".format(time.time() -
self._epoch_start_time))
logger.log("Started Training: {0}".format(self._can_train()))
logger.pop_prefix()
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
#print("TABLE KEYS")
#print(table_keys)
#if self._old_table_keys is not None:
# assert table_keys == self._old_table_keys, (
# "Table keys cannot change from iteration to iteration."
# )
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
# tensorboard stuff
_writer = self._writer
for k, v_str in logger._tabular:
if k == 'Epoch': continue
v = float(v_str)
if k.endswith('Loss'):
_writer.add_scalar('Loss/{}'.format(k), v, epoch)
elif k.endswith('Max'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Min'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Std'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Mean'):
prefix = k[:-5]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif 'Time' in k:
_writer.add_scalar('Time/{}'.format(k), v, epoch)
elif k.startswith('Num'):
_writer.add_scalar('Number/{}'.format(k), v, epoch)
elif k.startswith('Exploration'):
_writer.add_scalar('Exploration/{}'.format(k), v, epoch)
elif k.startswith('Test'):
_writer.add_scalar('Test/{}'.format(k), v, epoch)
else:
_writer.add_scalar(k, v, epoch)
_writer.file_writer.flush()
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")