def main(config, out_dir):
if out_dir is not None:
tlogger.set_log_dir(out_dir)
log_dir = tlogger.log_dir()
if not os.path.exists(log_dir):
os.makedirs(log_dir)
tlogger.info(json.dumps(config, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[config['model']]
all_tstart = time.time()
def make_env(b):
tlogger.info('GA: Creating environment for game: %s' % config["game"])
return gym_tensorflow.make(game=config["game"], batch_size=b)
tlogger.info('GA: Creating Concurent Workers')
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
tlogger.info('GA: Concurent Workers Created')
with WorkerSession(worker) as sess:
noise = SharedNoiseTable()
rs = np.random.RandomState()
cached_parents = []
results = []
def make_offspring():
if len(cached_parents) == 0:
return worker.model.randomize(rs, noise)
else:
assert len(cached_parents) == config['selection_threshold']
parent = cached_parents[rs.randint(len(cached_parents))]
theta, seeds = worker.model.mutate(parent,
rs,
noise,
mutation_power=state.sample(
state.mutation_power))
#print("tetha len: %d, seeds len: %d" % (len(theta), len(seeds)))
return theta, seeds
tlogger.info('GA: Start timing')
tstart = time.time()
try:
load_file = os.path.join(log_dir, 'snapshot.pkl')
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
state = TrainingState(config)
if 'load_population' in config:
tlogger.info('Loading population')
state.copy_population(config['load_population'])
# Cache first population if needed (on restart)
if state.population and config['selection_threshold'] > 0:
tlogger.info("Caching parents")
cached_parents.clear()
if state.elite in state.population[:config['selection_threshold']]:
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:config['selection_threshold']]
])
else:
cached_parents.append((worker.model.compute_weights_from_seeds(
noise, state.elite.seeds), state.elite.seeds))
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:config['selection_threshold'] -
1]
])
tlogger.info("Done caching parents")
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= config['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
assert (len(cached_parents) == 0 and state.it == 0
) or len(cached_parents) == config['selection_threshold']
tasks = [
make_offspring() for _ in range(config['population_size'])
]
for seeds, episode_reward, episode_length in worker.monitor_eval(
tasks, max_frames=state.tslimit * 4):
results.append(
Offspring(seeds, [episode_reward], [episode_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
# Trim unwanted results
results = results[:config['population_size']]
assert len(results) == config['population_size']
rewards = np.array([a.fitness for a in results])
population_timesteps = sum([a.training_steps for a in results])
state.population = sorted(results,
key=lambda x: x.fitness,
reverse=True)
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', population_timesteps)
tlogger.record_tabular('NumSelectedIndividuals',
config['selection_threshold'])
tlogger.info('Evaluate population')
validation_population = state.population[:config[
'validation_threshold']]
if state.elite is not None:
validation_population = [state.elite
] + validation_population[:-1]
validation_tasks = [(worker.model.compute_weights_from_seeds(
noise, validation_population[x].seeds,
cache=cached_parents), validation_population[x].seeds)
for x in range(config['validation_threshold'])]
_, population_validation, population_validation_len = zip(
*worker.monitor_eval_repeated(
validation_tasks,
max_frames=state.tslimit * 4,
num_episodes=config['num_validation_episodes']))
population_validation = [np.mean(x) for x in population_validation]
population_validation_len = [
np.sum(x) for x in population_validation_len
]
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
population_elite_idx = np.argmax(population_validation)
state.elite = validation_population[population_elite_idx]
elite_theta = worker.model.compute_weights_from_seeds(
noise, state.elite.seeds, cache=cached_parents)
_, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated(
[(elite_theta, state.elite.seeds)],
max_frames=None,
num_episodes=config['num_test_episodes'])[0]
# Log Results
validation_timesteps = sum(population_validation_len)
timesteps_this_iter = population_timesteps + validation_timesteps
state.timesteps_so_far += timesteps_this_iter
state.validation_timesteps_so_far += validation_timesteps
# Log
tlogger.record_tabular(
'TruncatedPopulationRewMean',
np.mean([a.fitness for a in validation_population]))
tlogger.record_tabular('TruncatedPopulationValidationRewMean',
np.mean(population_validation))
tlogger.record_tabular('TruncatedPopulationEliteValidationRew',
np.max(population_validation))
tlogger.record_tabular("TruncatedPopulationEliteIndex",
population_elite_idx)
tlogger.record_tabular('TruncatedPopulationEliteSeeds',
state.elite.seeds)
tlogger.record_tabular('TruncatedPopulationEliteTestRewMean',
np.mean(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpCount',
len(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum',
np.sum(population_elite_evals_timesteps))
if np.mean(population_validation) > state.curr_solution_val:
state.curr_solution = state.elite.seeds
state.curr_solution_val = np.mean(population_validation)
state.curr_solution_test = np.mean(population_elite_evals)
tlogger.record_tabular('ValidationTimestepsThisIter',
validation_timesteps)
tlogger.record_tabular('ValidationTimestepsSoFar',
state.validation_timesteps_so_far)
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
tlogger.info('Current elite: {}'.format(state.elite.seeds))
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(config['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
os.makedirs(log_dir, exist_ok=True)
save_file = os.path.join(log_dir, 'snapshot.pkl')
with open(save_file, 'wb+') as file:
pickle.dump(state, file)
#copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, save_file))
if state.timesteps_so_far >= config['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
if config['selection_threshold'] > 0:
tlogger.info("Caching parents")
new_parents = []
if state.elite in state.population[:config[
'selection_threshold']]:
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds) for
o in state.population[:config['selection_threshold']]
])
else:
new_parents.append(
(worker.model.compute_weights_from_seeds(
noise, state.elite.seeds,
cache=cached_parents), state.elite.seeds))
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in
state.population[:config['selection_threshold'] - 1]
])
cached_parents.clear()
cached_parents.extend(new_parents)
tlogger.info("Done caching parents")
return float(state.curr_solution_test), float(state.curr_solution_val)
def main(**exp):
log_dir = tlogger.log_dir()
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
def make_env(b):
return gym_tensorflow.make(game=exp["game"], batch_size=b)
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
with WorkerSession(worker) as sess:
noise = SharedNoiseTable()
rs = np.random.RandomState()
tlogger.info('Start timing')
tstart = time.time()
try:
load_file = os.path.join(log_dir, 'snapshot.pkl')
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
state = TrainingState(exp)
if 'load_from' in exp:
dirname = os.path.join(os.path.dirname(__file__), '..',
'neuroevolution', 'ga_legacy.py')
load_from = exp['load_from'].format(**exp)
os.system('python {} {} seeds.pkl'.format(dirname, load_from))
with open('seeds.pkl', 'rb+') as file:
seeds = pickle.load(file)
state.set_theta(
worker.model.compute_weights_from_seeds(noise, seeds))
tlogger.info('Loaded initial theta from {}'.format(load_from))
else:
state.initialize(rs, noise, worker.model)
def make_offspring(state):
for i in range(exp['population_size'] // 2):
idx = noise.sample_index(rs, worker.model.num_params)
mutation_power = state.sample(state.mutation_power)
pos_theta = worker.model.compute_mutation(
noise, state.theta, idx, mutation_power)
yield (pos_theta, idx)
neg_theta = worker.model.compute_mutation(
noise, state.theta, idx, -mutation_power)
diff = (np.max(
np.abs((pos_theta + neg_theta) / 2 - state.theta)))
assert diff < 1e-5, 'Diff too large: {}'.format(diff)
yield (neg_theta, idx)
tlogger.info('Start training')
_, initial_performance, _ = worker.monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
tlogger.info('Evaluating perturbations')
iterator = iter(
worker.monitor_eval(make_offspring(state),
max_frames=state.tslimit * 4))
results = []
for pos_seeds, pos_reward, pos_length in iterator:
neg_seeds, neg_reward, neg_length = next(iterator)
assert pos_seeds == neg_seeds
results.append(
Offspring(pos_seeds, [pos_reward, neg_reward],
[pos_length, neg_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
tlogger.record_tabular('TimestepLimitPerEpisode', state.tslimit)
# Trim unwanted results
results = results[:exp['population_size'] // 2]
assert len(results) == exp['population_size'] // 2
rewards = np.array([b for a in results for b in a.rewards])
results_timesteps = np.array([a.training_steps for a in results])
timesteps_this_iter = sum([a.training_steps for a in results])
state.timesteps_so_far += timesteps_this_iter
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpRewMedian', np.median(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', timesteps_this_iter)
# Update Theta
returns_n2 = np.array([a.rewards for a in results])
noise_inds_n = [a.seeds for a in results]
if exp['return_proc_mode'] == 'centered_rank':
proc_returns_n2 = compute_centered_ranks(returns_n2)
else:
raise NotImplementedError(exp['return_proc_mode'])
# Compute and take step
g, count = batched_weighted_sum(
proc_returns_n2[:, 0] - proc_returns_n2[:, 1],
(noise.get(idx, worker.model.num_params)
for idx in noise_inds_n),
batch_size=500)
# NOTE: gradients are scaled by \theta
g /= returns_n2.size
assert g.shape == (
worker.model.num_params,
) and g.dtype == np.float32 and count == len(noise_inds_n)
update_ratio, state.theta = state.optimizer.update(-g +
exp['l2coeff'] *
state.theta)
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
tlogger.info('Evaluate elite')
_, test_evals, test_timesteps = worker.monitor_eval_repeated(
[(state.theta, 0)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
test_timesteps = sum(test_timesteps)
# Log Results
tlogger.record_tabular('TestRewMean', np.mean(test_evals))
tlogger.record_tabular('TestRewMedian', np.median(test_evals))
tlogger.record_tabular('TestEpCount', len(test_evals))
tlogger.record_tabular('TestEpLenSum', test_timesteps)
tlogger.record_tabular('InitialRewMax',
np.max(initial_performance))
tlogger.record_tabular('InitialRewMean',
np.mean(initial_performance))
tlogger.record_tabular('InitialRewMedian',
np.median(initial_performance))
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(exp['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
os.makedirs(log_dir, exist_ok=True)
save_file = os.path.join(log_dir, 'snapshot.pkl')
with open(save_file, 'wb+') as file:
pickle.dump(state, file)
#copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, save_file))
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
def run_model(self):
self.build_model()
mnist = input_data.read_data_sets(self.data_dir, one_hot=True)
log_dir = self.log_dir + '/Flip{}batch{}scale{}lr{}'.format(
self.isFlip, self.batch_size, self.scale, self.learning_rate)
tlogger.start(log_dir)
for k, v in self.args.__dict__.items():
tlogger.log('{}: {}'.format(k, v))
with tf.Session() as sess:
# merged = tf.summary.merge_all()
# train_writer = tf.summary.FileWriter(self.log_dir + '/Flip{}train{}scale{}lr{}'
# .format(self.isFlip, self.batch_size, self.scale, self.learning_rate), sess.graph)
# test_writer = tf.summary.FileWriter(self.log_dir + '/Flip{}test{}scale{}lr{}'
# .format(self.isFlip, self.batch_size, self.scale, self.learning_rate))
# saver = tf.train.Saver(max_to_keep=40)
sess.run(tf.global_variables_initializer())
# M = mnist.train.labels.shape[0] // self.batch_size
M = 55000
tstart = time.time()
for i in range(self.args.num_iterations):
start = time.time()
if self.args.lr_decay:
step_size = self.piecewise_learning_rate(i)
sess.run(tf.assign(self.learning_rate, step_size))
batch = mnist.train.next_batch(self.batch_size)
_, train_KL, train_accuracy, train_loss, train_cross = sess.run(
[
self.train_step, self.KL, self.accuracy, self.loss,
self.cross_entropy
],
feed_dict={
self.x: batch[0],
self.y_: batch[1],
self.M: M,
self.n: batch[0].shape[0],
self.isTrain: True
})
if i % 100 == 0:
# train_writer.add_summary(train_summary, i)
tlogger.log('********** Iteration {} **********'.format(i))
tlogger.record_tabular("train_loss", train_loss)
tlogger.record_tabular("train_cross", train_cross)
tlogger.record_tabular("train_KL", train_KL)
tlogger.record_tabular("train_acc", train_accuracy)
# print('Train accuracy, Loss at step %s: %s, %s' % (i, train_accuracy, train_loss))
xs, ys = mnist.test.images, mnist.test.labels
test_accuracy, test_loss, test_KL, test_cross = sess.run(
[
self.accuracy, self.loss, self.KL,
self.cross_entropy
],
feed_dict={
self.x: xs,
self.y_: ys,
self.M: M,
self.n: xs.shape[0],
self.isTrain: False
})
# test_writer.add_summary(test_summary, i)
# print('Test accuracy at step %s: %s' % (i, test_accuracy))
tlogger.record_tabular("test_loss", test_loss)
tlogger.record_tabular("test_cross", test_cross)
tlogger.record_tabular("test_KL", test_KL)
tlogger.record_tabular("test_acc", test_accuracy)
tlogger.record_tabular("TimeElapsed", time.time() - tstart)
tlogger.dump_tabular()
tlogger.stop()
def main(exp, log_dir):
log_dir = tlogger.log_dir(log_dir)
snap_idx = 0
snapshots = []
tlogger.info(json.dumps(exp, indent=4, sort_keys=True))
tlogger.info('Logging to: {}'.format(log_dir))
Model = neuroevolution.models.__dict__[exp['model']]
all_tstart = time.time()
def make_env(b):
return gym_tensorflow.make(game=exp["game"], batch_size=b)
worker = ConcurrentWorkers(make_env, Model, batch_size=64)
with WorkerSession(worker) as sess:
rs = np.random.RandomState()
noise = None
state = None
cached_parents = []
results = []
def make_offspring():
if len(cached_parents) == 0:
return worker.model.randomize(rs, noise)
else:
assert len(cached_parents) == exp['selection_threshold']
parent = cached_parents[rs.randint(len(cached_parents))]
return worker.model.mutate(parent,
rs,
noise,
mutation_power=state.sample(
state.mutation_power))
tlogger.info('Start timing')
tstart = time.time()
load_file = os.path.join(log_dir, 'snapshot.pkl')
if 'load_from' in exp:
filename = os.path.join(log_dir, exp['load_from'])
with open(filename, 'rb+') as file:
state = pickle.load(file)
state.timesteps_so_far = 0 # Reset timesteps to 0
state.it = 0
state.max_reward = 0
state.max_avg = 0
state.max_sd = 0
tlogger.info('Loaded initial policy from {}'.format(filename))
elif os.path.exists(load_file):
try:
with open(load_file, 'rb+') as file:
state = pickle.load(file)
tlogger.info("Loaded iteration {} from {}".format(
state.it, load_file))
except FileNotFoundError:
tlogger.info('Failed to load snapshot')
if not noise:
tlogger.info("Generating new noise table")
noise = SharedNoiseTable()
else:
tlogger.info("Using noise table from snapshot")
if not state:
tlogger.info("Generation new TrainingState")
state = TrainingState(exp)
if 'load_population' in exp:
state.copy_population(exp['load_population'])
# Cache first population if needed (on restart)
if state.population and exp['selection_threshold'] > 0:
tlogger.info("Caching parents")
cached_parents.clear()
if state.elite in state.population[:exp['selection_threshold']]:
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:exp['selection_threshold']]
])
else:
cached_parents.append((worker.model.compute_weights_from_seeds(
noise, state.elite.seeds), state.elite.seeds))
cached_parents.extend([
(worker.model.compute_weights_from_seeds(noise,
o.seeds), o.seeds)
for o in state.population[:exp['selection_threshold'] - 1]
])
tlogger.info("Done caching parents")
while True:
tstart_iteration = time.time()
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
frames_computed_so_far = sess.run(worker.steps_counter)
assert (len(cached_parents) == 0 and state.it
== 0) or len(cached_parents) == exp['selection_threshold']
tasks = [make_offspring() for _ in range(exp['population_size'])]
for seeds, episode_reward, episode_length in worker.monitor_eval(
tasks, max_frames=state.tslimit * 4):
results.append(
Offspring(seeds, [episode_reward], [episode_length]))
state.num_frames += sess.run(
worker.steps_counter) - frames_computed_so_far
state.it += 1
tlogger.record_tabular('Iteration', state.it)
tlogger.record_tabular('MutationPower',
state.sample(state.mutation_power))
# Trim unwanted results
results = results[:exp['population_size']]
assert len(results) == exp['population_size']
rewards = np.array([a.fitness for a in results])
population_timesteps = sum([a.training_steps for a in results])
state.population = sorted(results,
key=lambda x: x.fitness,
reverse=True)
state.max_reward = save_best_pop_member(state.max_reward,
np.max(rewards), state,
state.population[0])
tlogger.record_tabular('PopulationEpRewMax', np.max(rewards))
tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards))
tlogger.record_tabular('PopulationEpCount', len(rewards))
tlogger.record_tabular('PopulationTimesteps', population_timesteps)
tlogger.record_tabular('NumSelectedIndividuals',
exp['selection_threshold'])
tlogger.info('Evaluate population')
validation_population = state.population[:exp[
'validation_threshold']]
if state.elite is not None:
validation_population = [state.elite
] + validation_population[:-1]
validation_tasks = [(worker.model.compute_weights_from_seeds(
noise, validation_population[x].seeds,
cache=cached_parents), validation_population[x].seeds)
for x in range(exp['validation_threshold'])]
_, population_validation, population_validation_len = zip(
*worker.monitor_eval_repeated(
validation_tasks,
max_frames=state.tslimit * 4,
num_episodes=exp['num_validation_episodes']))
it_max_avg = np.max([np.mean(x) for x in population_validation])
it_max_sd = np.max([np.std(x) for x in population_validation])
state.max_avg = np.max([state.max_avg, it_max_avg])
state.max_sd = np.max([state.max_sd, it_max_sd])
tlogger.info("Max Average: {}".format(state.max_avg))
tlogger.info("Max Std: {}".format(state.max_sd))
fitness_results = [(np.mean(x), np.std(x))
for x in population_validation]
with open(os.path.join(log_dir, 'fitness.log'), 'a') as f:
f.write("{},{},{}: {}\n".format(
state.it, state.max_avg, state.max_sd, ','.join([
"({},{})".format(x[0], x[1]) for x in fitness_results
])))
population_fitness = [
fitness(x[0], x[1], state.max_avg, state.max_sd)
for x in fitness_results
]
tlogger.info("Fitness: {}".format(population_fitness))
population_validation_len = [
np.sum(x) for x in population_validation_len
]
time_elapsed_this_iter = time.time() - tstart_iteration
state.time_elapsed += time_elapsed_this_iter
population_elite_idx = np.argmin(population_fitness)
state.elite = validation_population[population_elite_idx]
elite_theta = worker.model.compute_weights_from_seeds(
noise, state.elite.seeds, cache=cached_parents)
_, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated(
[(elite_theta, state.elite.seeds)],
max_frames=None,
num_episodes=exp['num_test_episodes'])[0]
# Log Results
validation_timesteps = sum(population_validation_len)
timesteps_this_iter = population_timesteps + validation_timesteps
state.timesteps_so_far += timesteps_this_iter
state.validation_timesteps_so_far += validation_timesteps
# Log
tlogger.record_tabular(
'TruncatedPopulationRewMean',
np.mean([a.fitness for a in validation_population]))
tlogger.record_tabular('TruncatedPopulationValidationFitMean',
np.mean(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationFitMax',
np.max(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationFitMin',
np.min(population_fitness))
tlogger.record_tabular('TruncatedPopulationValidationMaxAvg',
state.max_avg)
tlogger.record_tabular('TruncatedPopulationValidationMaxStd',
state.max_sd)
tlogger.record_tabular('TruncatedPopulationEliteValidationFitMin',
np.min(population_fitness))
tlogger.record_tabular("TruncatedPopulationEliteIndex",
population_elite_idx)
tlogger.record_tabular('TruncatedPopulationEliteSeeds',
state.elite.seeds)
tlogger.record_tabular('TruncatedPopulationEliteTestRewMean',
np.mean(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestRewStd',
np.std(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpCount',
len(population_elite_evals))
tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum',
np.sum(population_elite_evals_timesteps))
if np.mean(population_validation) > state.curr_solution_val:
state.curr_solution = state.elite.seeds
state.curr_solution_val = np.mean(population_validation)
state.curr_solution_test = np.mean(population_elite_evals)
tlogger.record_tabular('ValidationTimestepsThisIter',
validation_timesteps)
tlogger.record_tabular('ValidationTimestepsSoFar',
state.validation_timesteps_so_far)
tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter)
tlogger.record_tabular(
'TimestepsPerSecondThisIter',
timesteps_this_iter / (time.time() - tstart_iteration))
tlogger.record_tabular('TimestepsComputed', state.num_frames)
tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far)
tlogger.record_tabular('TimeElapsedThisIter',
time_elapsed_this_iter)
tlogger.record_tabular('TimeElapsedThisIterTotal',
time.time() - tstart_iteration)
tlogger.record_tabular('TimeElapsed', state.time_elapsed)
tlogger.record_tabular('TimeElapsedTotal',
time.time() - all_tstart)
tlogger.dump_tabular()
# tlogger.info('Current elite: {}'.format(state.elite.seeds))
fps = state.timesteps_so_far / (time.time() - tstart)
tlogger.info(
'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'.
format(fps, (time.time() - all_tstart) / 3600,
(exp['timesteps'] - state.timesteps_so_far) / fps /
3600))
if state.adaptive_tslimit:
if np.mean(
[a.training_steps >= state.tslimit
for a in results]) > state.incr_tslimit_threshold:
state.tslimit = min(
state.tslimit * state.tslimit_incr_ratio,
state.tslimit_max)
tlogger.info('Increased threshold to {}'.format(
state.tslimit))
snap_idx, snapshots = save_snapshot(state, log_dir, snap_idx,
snapshots)
# os.makedirs(log_dir, exist_ok=True)
# copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it)))
tlogger.info("Saved iteration {} to {}".format(
state.it, snapshots[snap_idx - 1]))
if state.timesteps_so_far >= exp['timesteps']:
tlogger.info('Training terminated after {} timesteps'.format(
state.timesteps_so_far))
break
results.clear()
if exp['selection_threshold'] > 0:
tlogger.info("Caching parents")
new_parents = []
if state.elite in state.population[:
exp['selection_threshold']]:
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in state.population[:exp['selection_threshold']]
])
else:
new_parents.append(
(worker.model.compute_weights_from_seeds(
noise, state.elite.seeds,
cache=cached_parents), state.elite.seeds))
new_parents.extend([
(worker.model.compute_weights_from_seeds(
noise, o.seeds, cache=cached_parents), o.seeds)
for o in state.population[:exp['selection_threshold'] -
1]
])
cached_parents.clear()
cached_parents.extend(new_parents)
tlogger.info("Done caching parents")
return float(state.curr_solution_test), {
'val': float(state.curr_solution_val)
}
def main(
num_inner_iterations=64,
noise_size=128,
inner_loop_init_lr=0.2,
inner_loop_init_momentum=0.5,
training_iterations_schedule=5,
min_training_iterations=4,
lr=0.1,
rms_momentum=0.9,
final_relative_lr=1e-2,
generator_batch_size=128,
meta_batch_size=512,
adam_epsilon=1e-8,
adam_beta1=0.0,
adam_beta2=0.999,
num_meta_iterations=1000,
starting_meta_iteration=1,
max_elapsed_time=None,
gradient_block_size=16,
use_intermediate_losses=0,
intermediate_losses_ratio=1.0,
data_path='./data',
meta_optimizer="adam",
dataset='MNIST',
logging_period=10,
generator_type="cgtn",
learner_type="base",
validation_learner_type=None,
warmup_iterations=None,
warmup_learner="base",
final_batch_norm_forward=False,
# The following flag is used for architecture search (it maps iteration to a specific architecture)
iteration_maps_seed=False,
use_dataset_augmentation=False,
training_schedule_backwards=True,
evenly_distributed_labels=True,
iterations_depth_schedule=100,
use_encoder=True,
decoder_loss_multiplier=1.0,
load_from=None,
virtual_batch_size=1,
deterministic=False,
seed=1,
grad_bound=None,
version=None, # dummy variable
enable_checkpointing=True,
randomize_width=False,
step_by_step_validation=True,
semisupervised_classifier_loss=True,
semisupervised_student_loss=True,
automl_class=None,
inner_loop_optimizer="SGD",
meta_learn_labels=False,
device='cuda'):
validation_learner_type = validation_learner_type or learner_type
hvd.init()
assert hvd.mpi_threads_supported()
from mpi4py import MPI
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
lr = lr * virtual_batch_size * hvd.size()
torch.cuda.set_device(hvd.local_rank())
# Load dataset
img_shape, trainset, validationset, (testset_x, testset_y) = get_dataset(
dataset,
data_path,
seed,
device,
with_augmentation=use_dataset_augmentation)
validation_x, validation_y = zip(*validationset)
validation_x = torch.stack(validation_x).to(device)
validation_y = torch.as_tensor(validation_y).to(device)
# Make each worker slightly different
torch.manual_seed(seed + hvd.rank())
np.random.seed(seed + hvd.rank())
if generator_type == "semisupervised":
unlabelled_trainset, trainset = torch.utils.data.random_split(
trainset, [49500, 500])
data_loader = torch.utils.data.DataLoader(trainset,
batch_size=meta_batch_size,
shuffle=True,
drop_last=True,
num_workers=1,
pin_memory=True)
data_loader = EndlessDataLoader(data_loader)
if generator_type == "cgtn":
generator = CGTN(
generator=Generator(noise_size + 10, img_shape),
num_inner_iterations=num_inner_iterations,
generator_batch_size=generator_batch_size,
noise_size=noise_size,
evenly_distributed_labels=evenly_distributed_labels,
meta_learn_labels=bool(meta_learn_labels),
)
elif generator_type == "cgtn_all_shuffled":
generator = CGTNAllShuffled(
generator=Generator(noise_size + 10, img_shape),
num_inner_iterations=num_inner_iterations,
generator_batch_size=generator_batch_size,
noise_size=noise_size,
evenly_distributed_labels=evenly_distributed_labels,
)
elif generator_type == "cgtn_batch_shuffled":
generator = CGTNBatchShuffled(
generator=Generator(noise_size + 10, img_shape),
num_inner_iterations=num_inner_iterations,
generator_batch_size=generator_batch_size,
noise_size=noise_size,
evenly_distributed_labels=evenly_distributed_labels,
)
elif generator_type == "gtn":
generator = GTN(
generator=Generator(noise_size + 10, img_shape),
generator_batch_size=generator_batch_size,
noise_size=noise_size,
)
elif generator_type == "gaussian_cgtn":
generator = GaussianCGTN(
generator=Generator(noise_size + 10, img_shape),
num_inner_iterations=num_inner_iterations,
generator_batch_size=generator_batch_size,
noise_size=noise_size,
)
elif generator_type == "dataset":
generator = UniformSamplingGenerator(
torch.utils.data.DataLoader(trainset,
batch_size=generator_batch_size,
shuffle=True,
drop_last=True),
num_inner_iterations=num_inner_iterations,
device=device,
)
elif generator_type == "distillation":
generator = DatasetDistillation(
img_shape=img_shape,
num_inner_iterations=num_inner_iterations,
generator_batch_size=generator_batch_size,
)
elif generator_type == "semisupervised":
generator = SemisupervisedGenerator(
torch.utils.data.DataLoader(unlabelled_trainset,
batch_size=generator_batch_size,
shuffle=True,
drop_last=True),
num_inner_iterations=num_inner_iterations,
device=device,
classifier=models.ClassifierLarger2(img_shape,
batch_norm_momentum=0.9,
randomize_width=False))
else:
raise NotImplementedError()
# Create meta-objective models
if inner_loop_optimizer == "SGD":
optimizers = [
inner_optimizers.SGD(inner_loop_init_lr, inner_loop_init_momentum,
num_inner_iterations)
]
elif inner_loop_optimizer == "RMSProp":
optimizers = [
inner_optimizers.RMSProp(inner_loop_init_lr,
inner_loop_init_momentum,
num_inner_iterations)
]
elif inner_loop_optimizer == "Adam":
optimizers = [
inner_optimizers.Adam(inner_loop_init_lr, inner_loop_init_momentum,
num_inner_iterations)
]
else:
raise ValueError(
f"Inner loop optimizer '{inner_loop_optimizer}' not available")
automl = (automl_class or AutoML)(
generator=generator,
optimizers=optimizers,
)
if use_encoder:
automl.encoder = Encoder(img_shape, output_size=noise_size)
automl = automl.to(device)
if meta_optimizer == "adam":
optimizer = torch.optim.Adam(automl.parameters(),
lr=lr,
betas=(adam_beta1, adam_beta2),
eps=adam_epsilon)
elif meta_optimizer == "sgd":
optimizer = torch.optim.SGD(automl.parameters(),
lr=lr,
momentum=rms_momentum)
elif meta_optimizer == "RMS":
optimizer = torch.optim.RMSprop(automl.parameters(),
lr=lr,
alpha=adam_beta1,
momentum=rms_momentum,
eps=adam_epsilon)
else:
raise NotImplementedError()
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, num_meta_iterations, lr * final_relative_lr)
if hvd.rank() == 0:
if load_from:
state = torch.load(load_from)
automl.load_state_dict(state["model"])
if lr > 0:
optimizer.load_state_dict(state["optimizer"])
del state
tlogger.info("loaded from:", load_from)
total_num_parameters = 0
for name, value in automl.named_parameters():
tlogger.info("Optimizing parameter:", name, value.shape)
total_num_parameters += np.prod(value.shape)
tlogger.info("Total number of parameters:", int(total_num_parameters))
def compute_learner(learner,
iterations=num_inner_iterations,
keep_grad=True,
callback=None):
learner.model.train()
names, params = list(zip(*learner.model.get_parameters()))
buffers = list(zip(*learner.model.named_buffers()))
if buffers:
buffer_names, buffers = buffers
else:
buffer_names, buffers = None, ()
param_shapes = [p.shape for p in params]
param_sizes = [np.prod(shape) for shape in param_shapes]
param_end_point = np.cumsum(param_sizes)
buffer_shapes = [p.shape for p in buffers]
buffer_sizes = [np.prod(shape) for shape in buffer_shapes]
buffer_end_point = np.cumsum(buffer_sizes)
def split_params(fused_params):
# return fused_params
assert len(fused_params) == 1
return [
fused_params[0][end - size:end].reshape(shape) for end, size,
shape in zip(param_end_point, param_sizes, param_shapes)
]
def split_buffer(fused_params):
if fused_params:
# return fused_params
assert len(fused_params) == 1
return [
fused_params[0][end - size:end].reshape(shape)
for end, size, shape in zip(buffer_end_point, buffer_sizes,
buffer_shapes)
]
return fused_params
# test = split_params(torch.cat([p.flatten() for p in params]))
# assert all([np.allclose(params[i].detach().cpu(), test[i].detach().cpu()) for i in range(len(test))])
params = [torch.cat([p.flatten() for p in params])]
buffers = [torch.cat([p.flatten()
for p in buffers])] if buffers else buffers
optimizer_state = learner.optimizer.initial_state(params)
params = params, buffers
initial_params = nest.map_structure(lambda p: None, params)
losses = {}
accuracies = {}
def intermediate_loss(params):
params = nest.pack_sequence_as(initial_params, params[1:])
params, buffers = params
x, y = next(meta_generator)
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
learner.model.set_parameters(list(zip(names,
split_params(params))))
if buffer_names:
learner.model.set_buffers(
list(zip(buffer_names, split_buffer(buffers))))
learner.model.eval()
pred = learner.model(x)
if isinstance(pred, tuple):
pred, aux_pred = pred
loss = F.nll_loss(pred, y) + F.nll_loss(aux_pred, y)
else:
loss = F.nll_loss(pred, y)
return loss * intermediate_losses_ratio
if hasattr(automl.generator, "init"):
generator_args = [automl.generator.init()]
else:
generator_args = []
def body(args):
it, params, optimizer_state = args
if training_schedule_backwards:
x, y_one_hot = automl.generator(iterations - it - 1,
*generator_args)
else:
x, y_one_hot = automl.generator(it, *generator_args)
with torch.enable_grad():
if use_intermediate_losses > 0 and (
it >= use_intermediate_losses
and it % use_intermediate_losses == 0):
params = SurrogateLoss.apply(intermediate_loss, it,
*nest.flatten(params))
params = nest.pack_sequence_as(initial_params, params[1:])
params, buffers = params
for p in params:
if not p.requires_grad:
p.requires_grad = True
learner.model.set_parameters(
list(zip(names, split_params(params))))
if buffer_names:
learner.model.set_buffers(
list(zip(buffer_names, split_buffer(buffers))))
learner.model.train()
output = learner.model(x)
if isinstance(output, tuple):
output1, output2 = output
loss = -(output1 * y_one_hot).sum() * (1 /
output1.shape[0])
loss = loss - (output2 *
y_one_hot).sum() * (1 / output2.shape[0])
pred = output1
else:
loss = -(output * y_one_hot).sum() * (1 / output.shape[0])
pred = output
if it.item() not in losses:
losses[it.item()] = loss.detach().cpu().item()
accuracies[it.item()] = (
pred.max(-1).indices == y_one_hot.max(-1).indices).to(
torch.float).mean().item()
grads = grad(loss,
params,
create_graph=x.requires_grad,
allow_unused=True)
# assert len(grads) == len(names)
new_params, optimizer_state = learner.optimizer(
it, params, grads, optimizer_state)
buffers = list(learner.model.buffers())
buffers = [torch.cat([b.flatten()
for b in buffers])] if buffers else buffers
if callback is not None:
learner.model.set_parameters(
list(zip(names, split_params(params))))
if buffer_names:
learner.model.set_buffers(
list(zip(buffer_names, split_buffer(buffers))))
callback(learner)
return (it + 1, (
new_params,
buffers,
), optimizer_state)
last_state, params, optimizer_state = gradient_checkpointing(
(torch.as_tensor(0), params, optimizer_state),
body,
iterations,
block_size=gradient_block_size)
assert last_state.item() == iterations
params, buffers = params
learner.model.set_parameters(list(zip(names, split_params(params))))
if buffer_names:
learner.model.set_buffers(
list(zip(buffer_names, split_buffer(buffers))))
if final_batch_norm_forward:
x, _ = automl.generator(torch.randint(iterations, size=()))
learner.model.train()
learner.model(x)
return learner, losses, accuracies
tstart = time.time()
meta_generator = iter(data_loader)
hvd.broadcast_parameters(automl.state_dict(), root_rank=0)
best_optimizers = {}
validation_accuracy = None
total_inner_iterations_so_far = 0
for iteration in range(starting_meta_iteration, num_meta_iterations + 1):
last_iteration = time.time()
# basic logging
tlogger.record_tabular('Iteration', iteration)
tlogger.record_tabular('lr', optimizer.param_groups[0]['lr'])
# Train learner
if training_iterations_schedule > 0:
training_iterations = int(
min(
num_inner_iterations, min_training_iterations +
(iteration - starting_meta_iteration) //
training_iterations_schedule))
else:
training_iterations = num_inner_iterations
tlogger.record_tabular('training_iterations', training_iterations)
total_inner_iterations_so_far += training_iterations
tlogger.record_tabular('training_iterations_so_far',
total_inner_iterations_so_far * hvd.size())
optimizer.zero_grad()
for _ in range(virtual_batch_size):
torch.cuda.empty_cache()
meta_x, meta_y = next(meta_generator)
meta_x = meta_x.to('cuda', non_blocking=True)
meta_y = meta_y.to('cuda', non_blocking=True)
tstart_forward = time.time()
if generator_type != "semisupervised" or semisupervised_student_loss:
# TODO: Learn batchnorm momentum and eps
sample_learner_type = learner_type
if warmup_iterations is not None and iteration < warmup_iterations:
sample_learner_type = warmup_learner
learner, encoding = automl.sample_learner(
img_shape,
device,
allow_nas=False,
randomize_width=randomize_width,
learner_type=sample_learner_type,
iteration_maps_seed=iteration_maps_seed,
iteration=iteration,
deterministic=deterministic,
iterations_depth_schedule=iterations_depth_schedule)
automl.train()
if lr == 0.0:
with torch.no_grad():
learner, intermediate_losses, intermediate_accuracies = compute_learner(
learner,
iterations=training_iterations,
keep_grad=lr > 0.0)
else:
learner, intermediate_losses, intermediate_accuracies = compute_learner(
learner,
iterations=training_iterations,
keep_grad=lr > 0.0)
# TODO: remove this requirement
params = list(learner.model.get_parameters())
learner.model.eval()
# Evaluate learner on training and back-prop
torch.cuda.empty_cache()
pred = learner.model(meta_x)
if isinstance(pred, tuple):
pred, aux_pred = pred
loss = F.nll_loss(pred, meta_y) + F.nll_loss(
aux_pred, meta_y)
else:
loss = F.nll_loss(pred, meta_y)
accuracy = (pred.max(-1).indices == meta_y).to(
torch.float).mean()
tlogger.record_tabular("TimeElapsedForward",
time.time() - tstart_forward)
num_parameters = sum([a[1].size().numel() for a in params])
tlogger.record_tabular("TrainingLearnerParameters",
num_parameters)
tlogger.record_tabular("optimizer",
type(learner.optimizer).__name__)
tlogger.record_tabular('meta_training_loss', loss.item())
tlogger.record_tabular('meta_training_accuracy',
accuracy.item())
tlogger.record_tabular('training_accuracies',
intermediate_accuracies)
tlogger.record_tabular('training_losses', intermediate_losses)
tlogger.record_tabular("dag", encoding)
else:
loss = torch.as_tensor(0.0)
if lr > 0.0:
tstart_backward = time.time()
if generator_type != "semisupervised" or semisupervised_student_loss:
loss.backward()
if generator_type == "semisupervised" and semisupervised_classifier_loss:
automl.generator.classifier.train()
pred = automl.generator.classifier(meta_x)
accuracy = (pred.max(-1).indices == meta_y).to(
torch.float).mean()
loss2 = F.nll_loss(pred, meta_y)
loss2.backward()
tlogger.record_tabular('meta_training_generator_loss',
loss2.item())
tlogger.record_tabular('meta_training_generator_accuracy',
accuracy.item())
loss = loss + loss2
del loss2
tlogger.record_tabular("TimeElapsedBackward",
time.time() - tstart_backward)
if use_encoder:
# TODO: add loss weight
meta_encoding = automl.encoder(meta_x)
meta_y_one_hot = torch.zeros(meta_x.shape[0],
10,
device=device)
meta_y_one_hot.scatter_(1, meta_y.unsqueeze(-1), 1)
meta_encoding = torch.cat([meta_encoding, meta_y_one_hot],
-1)
reconstruct = automl.generator.generator(meta_encoding)
ae_loss = decoder_loss_multiplier * F.mse_loss(
reconstruct, meta_x)
ae_loss.backward()
tlogger.record_tabular("decoder_loss", ae_loss.item())
if lr > 0.0:
# If using distributed training aggregard gradients with Horovod
maybe_allreduce_grads(automl)
if grad_bound is not None:
nn.utils.clip_grad_norm_(automl.parameters(), grad_bound)
optimizer.step()
if max_elapsed_time is not None:
scheduler.step(
round((time.time() - tstart) / max_elapsed_time *
num_meta_iterations))
else:
scheduler.step(iteration - 1)
is_last_iteration = iteration == num_meta_iterations or (
max_elapsed_time is not None
and time.time() - tstart > max_elapsed_time)
if np.isnan(loss.item()):
tlogger.info("NaN training loss, terminating")
is_last_iteration = True
is_last_iteration = MPI.COMM_WORLD.bcast(is_last_iteration, root=0)
if iteration == 1 or iteration % logging_period == 0 or is_last_iteration:
tstart_validation = time.time()
val_loss, val_accuracy = [], []
test_loss, test_accuracy = [], []
if generator_type == "semisupervised":
# Validation set
evaluate_set(generator.classifier, validation_x, validation_y,
"generator_validation")
# Test set
evaluate_set(generator.classifier, testset_x, testset_y,
"generator_test")
else:
def compute_learner_callback(learner):
# Validation set
validation_loss, single_validation_accuracy, validation_accuracy = evaluate_set(
learner.model, validation_x, validation_y,
"validation")
val_loss.append(validation_loss)
val_accuracy.append(validation_accuracy)
best_optimizers[type(
learner.optimizer
).__name__] = single_validation_accuracy.item()
# Test set
loss, _, accuracy = evaluate_set(learner.model, testset_x,
testset_y, "test")
test_loss.append(loss)
test_accuracy.append(accuracy)
tlogger.info(
"sampling another learner_type ({}) for validation".format(
validation_learner_type))
learner, _ = automl.sample_learner(
img_shape,
device,
allow_nas=False,
learner_type=validation_learner_type,
iteration_maps_seed=iteration_maps_seed,
iteration=iteration,
deterministic=deterministic,
iterations_depth_schedule=iterations_depth_schedule)
if step_by_step_validation:
compute_learner_callback(learner)
with torch.no_grad():
learner, _, _ = compute_learner(
learner,
iterations=training_iterations,
keep_grad=False,
callback=compute_learner_callback
if step_by_step_validation else None)
if not step_by_step_validation:
compute_learner_callback(learner)
tlogger.record_tabular("validation_losses", val_loss)
tlogger.record_tabular("validation_accuracies", val_accuracy)
validation_accuracy = val_accuracy[-1]
tlogger.record_tabular("test_losses", test_loss)
tlogger.record_tabular("test_accuracies", test_accuracy)
# Extra logging
tlogger.record_tabular('TimeElapsedIter',
(tstart_validation - last_iteration) /
virtual_batch_size)
tlogger.record_tabular('TimeElapsedValidation',
time.time() - tstart_validation)
tlogger.record_tabular('TimeElapsed', time.time() - tstart)
for k, v in best_optimizers.items():
tlogger.record_tabular("{}_last_accuracy".format(k), v)
if hvd.rank() == 0:
tlogger.dump_tabular()
if (iteration == 1 or iteration % 1000 == 0
or is_last_iteration):
with torch.no_grad():
if enable_checkpointing:
batches = []
for it in range(num_inner_iterations):
if training_schedule_backwards:
x, y = automl.generator(
num_inner_iterations - it - 1)
else:
x, y = automl.generator(it)
batches.append(
(x.cpu().numpy(), y.cpu().numpy()))
batches = list(reversed(batches))
with open(
os.path.join(
tlogger.get_dir(),
'samples_{}.pkl'.format(iteration)),
'wb') as file:
pickle.dump(batches, file)
del batches
tlogger.info(
"Saved:",
os.path.join(
tlogger.get_dir(),
'samples_{}.pkl'.format(iteration)))
ckpt = os.path.join(
tlogger.get_dir(),
'checkpoint_{}.pkl'.format(iteration))
torch.save(
{
"optimizer": optimizer.state_dict(),
"model": automl.state_dict(),
}, ckpt)
tlogger.info("Saved:", ckpt)
if is_last_iteration:
break
elif hvd.rank() == 0:
tlogger.info("training_loss:", loss.item())
return validation_accuracy
g /= returns_n2.size
assert (g.shape == (policy.num_params, ) and g.dtype == np.float32
and count == len(noise_inds_n))
update_ratio = optimizer.update(-g + config.l2coeff * theta)
# Update ob stat (we're never running the policy in the master, but we
# might be snapshotting the policy).
if policy.needs_ob_stat:
policy.set_ob_stat(ob_stat.mean, ob_stat.std)
step_tend = time.time()
tlogger.record_tabular("EpRewMean", returns_n2.mean())
tlogger.record_tabular("EpRewStd", returns_n2.std())
tlogger.record_tabular("EpLenMean", lengths_n2.mean())
tlogger.record_tabular(
"Norm", float(np.square(policy.get_trainable_flat()).sum()))
tlogger.record_tabular("GradNorm", float(np.square(g).sum()))
tlogger.record_tabular("UpdateRatio", float(update_ratio))
tlogger.record_tabular("EpisodesThisIter", lengths_n2.size)
tlogger.record_tabular("EpisodesSoFar", episodes_so_far)
tlogger.record_tabular("TimestepsThisIter", lengths_n2.sum())
tlogger.record_tabular("TimestepsSoFar", timesteps_so_far)
tlogger.record_tabular("ObCount", ob_count_this_batch)
tlogger.record_tabular("TimeElapsedThisIter", step_tend - step_tstart)
tlogger.record_tabular("TimeElapsed", step_tend - tstart)
tlogger.dump_tabular()
