def restore(self, sess, restore_from=None):
if restore_from is None:
restore_from = self._restore_path
if self.print_log:
print_and_log('saver: {} restoring model from {}'.format(
self.name, restore_from))
self._saver.restore(sess, restore_from)
def collect_test_data(self, top_level, is_challenging=False):
if is_challenging:
start_goal_pairs = self.hard_fixed_start_goal_pairs
else:
start_goal_pairs = self.fixed_start_goal_pairs
print_and_log('collecting {} test episodes of level {}'.format(
len(start_goal_pairs), top_level))
return self.collect_data(start_goal_pairs, top_level, False)
def collect_data(self, start_goal_pairs, top_level, is_train):
episode_results = self.episode_runner.play_episodes(
start_goal_pairs, top_level, is_train)
successes, accumulated_cost, dataset, endpoints_by_path = self._process_episode_results(
episode_results, top_level)
print_and_log(
'data collection done, success rate is {}, accumulated cost is {}'.
format(successes, accumulated_cost))
return successes, accumulated_cost, dataset, endpoints_by_path
def save(self, sess, global_step):
self.assertion_w_dictionary = self.get_assertion_vars(sess)
self._restore_path = self._saver.save(sess,
os.path.join(
self.saver_dir, self.name),
global_step=global_step)
if self.print_log:
print_and_log(
'saver {}: saved model from global step {} to {}'.format(
self.name, global_step, self._restore_path))
def policy_function(starts, goals, level, is_train):
res = network.predict_policy(starts, goals, level, sess, is_train)
means = 0.5 * (np.array(starts) + np.array(goals))
distance = np.linalg.norm(res[0] - means, axis=1)
print(
f'distance from mean: mean {distance.mean()} min {distance.min()} max {distance.max()}'
)
if np.any(np.isnan(res)):
print_and_log(
'######################## Nan predictions detected...')
return res
def collect_data(self,
count,
is_train=True,
use_fixed_start_goal_pairs=False):
print_and_log('collecting {} {} episodes'.format(
count, 'train' if is_train else 'test'))
if use_fixed_start_goal_pairs:
episode_results = self.episode_runner.play_fixed_episodes(is_train)
else:
episode_results = self.episode_runner.play_random_episodes(
count, is_train)
successes, accumulated_cost, dataset, endpoints_by_path = self._process_episode_results(
episode_results)
print_and_log(
'data collection done, success rate is {}, accumulated cost is {}'.
format(successes, accumulated_cost))
if is_train:
self.train_episodes_counter += len(endpoints_by_path)
return successes, accumulated_cost, dataset, endpoints_by_path
def end_of_level_test(best_cost, best_cost_global_step, best_curriculum_coefficient, best_saver, sess,
test_trajectories_dir, trainer):
print_and_log('end of run. best: {} from step: {}'.format(best_cost, best_cost_global_step))
restore_best(sess, best_saver, best_curriculum_coefficient, trainer)
# test all
test_trajectories_file = os.path.join(test_trajectories_dir, 'all.txt')
endpoints_by_path = trainer.collect_test_data(is_challenging=False)[-1]
serialize_compress(endpoints_by_path, test_trajectories_file)
print_and_log(os.linesep)
# test hard
test_trajectories_file = os.path.join(test_trajectories_dir, 'challenging.txt')
endpoints_by_path = trainer.collect_test_data(is_challenging=True)[-1]
serialize_compress(endpoints_by_path, test_trajectories_file)
print_and_log(os.linesep)
def run_for_config(config):
# set the name of the model
model_name = config['general']['name']
now = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y_%m_%d_%H_%M_%S')
model_name = now + '_' + model_name if model_name is not None else now
# where we save all the outputs
scenario = config['general']['scenario']
working_dir = os.path.join(get_base_directory(), 'sgt', scenario)
init_dir(working_dir)
saver_dir = os.path.join(working_dir, 'models', model_name)
init_dir(saver_dir)
init_log(log_file_path=os.path.join(saver_dir, 'log.txt'))
copy_config(config, os.path.join(saver_dir, 'config.yml'))
episodic_success_rates_path = os.path.join(saver_dir, 'results.txt')
test_trajectories_dir = os.path.join(working_dir, 'test_trajectories',
model_name)
init_dir(test_trajectories_dir)
# generate game
game = _get_game(config)
network = Network(config, game)
network_variables = network.get_all_variables()
# save model
latest_saver = ModelSaver(os.path.join(saver_dir, 'latest_model'),
2,
'latest',
variables=network_variables)
best_saver = ModelSaver(os.path.join(saver_dir, 'best_model'),
1,
'best',
variables=network_variables)
summaries_collector = SummariesCollector(
os.path.join(working_dir, 'tensorboard', model_name), model_name)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(
gpu_options=tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction=config['general']
['gpu_usage']))) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
def policy_function(starts, goals, level, is_train):
res = network.predict_policy(starts, goals, level, sess, is_train)
means = 0.5 * (np.array(starts) + np.array(goals))
distance = np.linalg.norm(res[0] - means, axis=1)
print(
f'distance from mean: mean {distance.mean()} min {distance.min()} max {distance.max()}'
)
if np.any(np.isnan(res)):
print_and_log(
'######################## Nan predictions detected...')
return res
episode_runner = EpisodeRunnerSubgoal(config, game, policy_function)
trainer = TrainerSubgoal(
model_name,
config,
working_dir,
network,
sess,
episode_runner,
summaries_collector,
curriculum_coefficient=get_initial_curriculum(config))
decrease_learn_rate_if_static_success = config['model'][
'decrease_learn_rate_if_static_success']
stop_training_after_learn_rate_decrease = config['model'][
'stop_training_after_learn_rate_decrease']
reset_best_every = config['model']['reset_best_every']
global_step = 0
best_curriculum_coefficient = None
for current_level in range(config['model']['starting_level'],
config['model']['levels'] + 1):
best_cost, best_cost_global_step = None, None
no_test_improvement, consecutive_learn_rate_decrease = 0, 0
if config['model']['init_from_lower_level'] and current_level > 1:
print_and_log('initiating level {} from previous level'.format(
current_level))
network.init_policy_from_lower_level(sess, current_level)
for cycle in range(config['general']['training_cycles_per_level']):
print_and_log('starting cycle {}, level {}'.format(
cycle, current_level))
new_global_step, success_ratio = trainer.train_policy_at_level(
current_level, global_step)
if new_global_step == global_step:
print_and_log(
'no data found in training cycle {} global step still {}'
.format(cycle, global_step))
continue
else:
global_step = new_global_step
if (cycle + 1) % config['policy']['decrease_std_every'] == 0:
network.decrease_base_std(sess, current_level)
print_and_log('new base stds {}'.format(
network.get_base_stds(sess, current_level)))
print_and_log('done training cycle {} global step {}'.format(
cycle, global_step))
# save every now and then
if cycle % config['general']['save_every_cycles'] == 0:
latest_saver.save(sess, global_step=global_step)
if cycle % config['general']['test_frequency'] == 0:
# do test
test_successes, test_cost, _, endpoints_by_path = trainer.collect_test_data(
current_level, False)
summaries_collector.write_test_success_summaries(
sess, global_step, test_successes, test_cost,
trainer.curriculum_coefficient)
with open(episodic_success_rates_path, 'a') as f:
f.write('{} {} {} {} {}'.format(
current_level, trainer.train_episodes_counter,
test_successes, test_cost, os.linesep))
# decide how to act next
print_and_log('old cost was {} at step {}'.format(
best_cost, best_cost_global_step))
print_and_log('current learn rates {}'.format(
network.get_learn_rates(sess, current_level)))
print_and_log('current base stds {}'.format(
network.get_base_stds(sess, current_level)))
if best_cost is None or test_cost < best_cost:
print_and_log('new best cost {} at step {}'.format(
test_cost, global_step))
best_cost, best_cost_global_step = test_cost, global_step
best_curriculum_coefficient = trainer.curriculum_coefficient
no_test_improvement, consecutive_learn_rate_decrease = 0, 0
best_saver.save(sess, global_step)
test_trajectories_file = os.path.join(
test_trajectories_dir,
'{}.txt'.format(global_step))
serialize_compress(endpoints_by_path,
test_trajectories_file)
else:
print_and_log(
'new model is not the best with cost {} at step {}'
.format(test_cost, global_step))
no_test_improvement += 1
print_and_log('no improvement count {} of {}'.format(
no_test_improvement,
decrease_learn_rate_if_static_success))
if reset_best_every > 0 and no_test_improvement % reset_best_every == reset_best_every - 1:
# restore the model every once in a while if did not find a better solution in a while
restore_best(sess, best_saver,
best_curriculum_coefficient, trainer)
if no_test_improvement == decrease_learn_rate_if_static_success:
# restore the best model
if config['model']['restore_on_decrease']:
restore_best(sess, best_saver,
best_curriculum_coefficient,
trainer)
# decrease learn rates
network.decrease_learn_rates(sess, current_level)
no_test_improvement = 0
consecutive_learn_rate_decrease += 1
print_and_log(
'decreasing learn rates {} of {}'.format(
consecutive_learn_rate_decrease,
stop_training_after_learn_rate_decrease))
print_and_log('new learn rates {}'.format(
network.get_learn_rates(sess, current_level)))
if consecutive_learn_rate_decrease == stop_training_after_learn_rate_decrease:
break
if trainer.curriculum_coefficient is not None:
if success_ratio > config['curriculum'][
'raise_when_train_above']:
print_and_log(
'current curriculum coefficient {}'.format(
trainer.curriculum_coefficient))
trainer.curriculum_coefficient *= config['curriculum'][
'raise_times']
print_and_log(
'curriculum coefficient raised to {}'.format(
trainer.curriculum_coefficient))
# mark in log the end of cycle
print_and_log(os.linesep)
# if we finished because we ran out of cycles, we still need to make one more test
end_of_level_test(best_cost, best_cost_global_step,
best_curriculum_coefficient, best_saver, sess,
test_trajectories_dir, trainer, current_level)
print_and_log('trained all levels - needs to stop')
close_log()
return best_cost
def policy_function(starts, goals, is_train):
res = network.predict_policy(starts, goals, sess, is_train)
if np.any(np.isnan(res)):
print_and_log('######################## Nan predictions detected...')
return res
def collect_train_data(self, count, top_level):
print_and_log('collecting {} train episodes of level {}'.format(
count, top_level))
start_goal_pairs = self.episode_runner.game.get_start_goals(
count, self.curriculum_coefficient, get_free_states=True)
return self.collect_data(start_goal_pairs, top_level, True)
def run_for_config(config):
# set the name of the model
model_name = config['general']['name']
now = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y_%m_%d_%H_%M_%S')
model_name = now + '_' + model_name if model_name is not None else now
# where we save all the outputs
scenario = config['general']['scenario']
working_dir = os.path.join(get_base_directory(), 'sequential', scenario)
init_dir(working_dir)
saver_dir = os.path.join(working_dir, 'models', model_name)
init_dir(saver_dir)
init_log(log_file_path=os.path.join(saver_dir, 'log.txt'))
copy_config(config, os.path.join(saver_dir, 'config.yml'))
episodic_success_rates_path = os.path.join(saver_dir, 'results.txt')
weights_log_dir = os.path.join(saver_dir, 'weights_logs')
init_dir(weights_log_dir)
test_trajectories_dir = os.path.join(working_dir, 'test_trajectories',
model_name)
init_dir(test_trajectories_dir)
# generate game
game = _get_game(config)
network = NetworkSequential(config,
game.get_state_space_size(),
game.get_action_space_size(),
is_rollout_agent=False)
network_variables = network.get_all_variables()
# save model
latest_saver = ModelSaver(os.path.join(saver_dir, 'latest_model'),
2,
'latest',
variables=network_variables)
best_saver = ModelSaver(os.path.join(saver_dir, 'best_model'),
1,
'best',
variables=network_variables)
summaries_collector = SummariesCollector(
os.path.join(working_dir, 'tensorboard', model_name), model_name)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(
gpu_options=tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction=config['general']
['gpu_usage']))) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
episode_runner = EpisodeRunnerSequential(
config,
game,
curriculum_coefficient=get_initial_curriculum(config))
trainer = TrainerSequential(model_name, config, working_dir, network,
sess, episode_runner, summaries_collector)
decrease_learn_rate_if_static_success = config['model'][
'decrease_learn_rate_if_static_success']
stop_training_after_learn_rate_decrease = config['model'][
'stop_training_after_learn_rate_decrease']
reset_best_every = config['model']['reset_best_every']
global_step = 0
best_cost, best_cost_global_step, best_curriculum_coefficient = None, None, None
no_test_improvement, consecutive_learn_rate_decrease = 0, 0
for cycle in range(config['general']['training_cycles']):
print_and_log('starting cycle {}'.format(cycle))
global_step, success_ratio = trainer.train_policy(global_step)
if (cycle + 1) % config['policy']['decrease_std_every'] == 0:
network.decrease_base_std(sess)
print_and_log('new base stds {}'.format(
network.get_base_std(sess)))
print_and_log('done training cycle {} global step {}'.format(
cycle, global_step))
# save every now and then
if cycle % config['general']['save_every_cycles'] == 0:
latest_saver.save(sess, global_step=global_step)
if cycle % config['general']['test_frequency'] == 0:
# do test
test_successes, test_cost, _, endpoints_by_path = trainer.collect_data(
config['general']['test_episodes'],
is_train=False,
use_fixed_start_goal_pairs=True)
summaries_collector.write_test_success_summaries(
sess, global_step, test_successes, test_cost,
episode_runner.curriculum_coefficient)
with open(episodic_success_rates_path, 'a') as f:
f.write('{} {} {} {}'.format(
trainer.train_episodes_counter, test_successes,
test_cost, os.linesep))
# decide how to act next
print_and_log('old cost was {} at step {}'.format(
best_cost, best_cost_global_step))
print_and_log('current learn rates {}'.format(
network.get_learn_rate(sess)))
print_and_log('current base stds {}'.format(
network.get_base_std(sess)))
if best_cost is None or test_cost < best_cost:
print_and_log('new best cost {} at step {}'.format(
test_cost, global_step))
best_cost, best_cost_global_step = test_cost, global_step
best_curriculum_coefficient = episode_runner.curriculum_coefficient
no_test_improvement = 0
consecutive_learn_rate_decrease = 0
best_saver.save(sess, global_step)
test_trajectories_file = os.path.join(
test_trajectories_dir, '{}.txt'.format(global_step))
serialize_compress(endpoints_by_path,
test_trajectories_file)
else:
print_and_log(
'new model is not the best with cost {} at step {}'.
format(test_cost, global_step))
no_test_improvement += 1
print_and_log('no improvement count {} of {}'.format(
no_test_improvement,
decrease_learn_rate_if_static_success))
if reset_best_every > 0 and no_test_improvement % reset_best_every == reset_best_every - 1:
# restore the model every once in a while if did not find a better solution in a while
best_saver.restore(sess)
episode_runner.curriculum_coefficient = best_curriculum_coefficient
if no_test_improvement == decrease_learn_rate_if_static_success:
# restore the best model
if config['model']['restore_on_decrease']:
best_saver.restore(sess)
episode_runner.curriculum_coefficient = best_curriculum_coefficient
network.decrease_learn_rates(sess)
no_test_improvement = 0
consecutive_learn_rate_decrease += 1
print_and_log('decreasing learn rates {} of {}'.format(
consecutive_learn_rate_decrease,
stop_training_after_learn_rate_decrease))
print_and_log('new learn rates {}'.format(
network.get_learn_rate(sess)))
if consecutive_learn_rate_decrease == stop_training_after_learn_rate_decrease:
print_and_log('needs to stop')
best_saver.restore(sess)
break
if episode_runner.curriculum_coefficient is not None:
if success_ratio > config['curriculum'][
'raise_when_train_above']:
print_and_log('current curriculum coefficient {}'.format(
episode_runner.curriculum_coefficient))
episode_runner.curriculum_coefficient *= config[
'curriculum']['raise_times']
print_and_log('curriculum coefficient raised to {}'.format(
episode_runner.curriculum_coefficient))
# mark in log the end of cycle
print_and_log(os.linesep)
print_and_log('end of run best: {} from step: {}'.format(
best_cost, best_cost_global_step))
print_and_log('testing on a new set of start-goal pairs')
best_saver.restore(sess)
test_trajectories_file = os.path.join(test_trajectories_dir, '-1.txt')
endpoints_by_path = trainer.collect_data(
config['general']['test_episodes'],
is_train=False,
use_fixed_start_goal_pairs=True)[-1]
serialize_compress(endpoints_by_path, test_trajectories_file)
close_log()
return best_cost