def validate(self, episodes, verbose=True):
# Seed needs to be reset for each validation, to ensure consistency
utils.seed(self.args.val_seed)
if verbose:
logger.info("Validating the model")
if getattr(self.args, 'multi_env', None):
agent = utils.load_agent(self.env[0],
model_name=self.args.model,
argmax=True)
else:
agent = utils.load_agent(self.env,
model_name=self.args.model,
argmax=True)
# Setting the agent model to the current model
agent.model = self.acmodel
agent.model.eval()
logs = []
for env_name in ([self.args.env]
if not getattr(self.args, 'multi_env', None) else
self.args.multi_env):
logs += [
batch_evaluate(agent, env_name, self.args.val_seed, episodes)
]
agent.model.train()
return logs
def main_test(args, seed, episodes):
# Set seed for all randomness sources
utils.seed(seed)
# Define agent
# do test environment
env_name = args.env + "_Test-v0"
env = gym.make(env_name)
env.seed(seed)
agent = utils.load_agent(env,
args.model,
argmax=args.argmax,
env_name=env_name)
if args.model is None and args.episodes > len(agent.demos):
# Set the number of episodes to be the number of demos
episodes = len(agent.demos)
# Evaluate
if isinstance(agent, utils.DemoAgent):
logs = evaluate_demo_agent(agent, episodes)
elif isinstance(agent, utils.BotAgent):
logs = evaluate(agent, env, episodes, False)
else:
logs = batch_evaluate(agent, env_name, seed, episodes)
return logs
def evaluate_agent(il_learn, eval_seed, num_eval_demos):
"""
Evaluate the agent on some number of episodes and return the seeds for the
episodes the agent performed the worst on.
"""
logger.info("Evaluating agent on {}".format(il_learn.args.env))
agent = utils.load_agent(il_learn.env, il_learn.args.model)
agent.model.eval()
logs = batch_evaluate(agent,
il_learn.args.env,
episodes=num_eval_demos,
seed=eval_seed,
seed_shift=0)
agent.model.train()
success_rate = np.mean(
[1 if r > 0 else 0 for r in logs['return_per_episode']])
logger.info("success rate: {:.2f}".format(success_rate))
# Find the seeds for all the failing demos
fail_seeds = []
for idx, ret in enumerate(logs["return_per_episode"]):
if ret <= 0:
fail_seeds.append(logs["seed_per_episode"][idx])
return success_rate, fail_seeds
def validate(self, episodes, verbose=True):
if verbose:
logger.info("Validating the model")
if getattr(self.args, 'multi_env', None):
agent = utils.load_agent(self.env[0],
model_name=self.args.model,
argmax=True)
else:
agent = utils.load_agent(self.env,
model_name=self.args.model,
argmax=True)
# Setting the agent model to the current model
agent.model = self.acmodel
agent.model.eval()
logs = []
for env_name in ([self.args.env]
if not getattr(self.args, 'multi_env', None) else
self.args.multi_env):
logs += [
batch_evaluate(agent,
env_name,
self.val_seed,
episodes,
pixel=self.use_pixel)
]
self.val_seed += episodes
agent.model.train()
return logs
def evaluate_agent(il_learn,
eval_seed,
num_eval_demos,
return_obss_actions=False):
"""
Evaluate the agent on some number of episodes and return the seeds for the
episodes the agent performed the worst on.
"""
logger.info("Evaluating agent on {} using {} demos".format(
il_learn.args.env, num_eval_demos))
agent = utils.load_agent(il_learn.env, il_learn.args.model)
agent.model.eval()
logs = batch_evaluate(agent,
il_learn.args.env,
episodes=num_eval_demos,
seed=eval_seed,
seed_shift=0,
return_obss_actions=return_obss_actions)
agent.model.train()
success_rate = np.mean(
[1 if r > 0 else 0 for r in logs['return_per_episode']])
logger.info("success rate: {:.2f}".format(success_rate))
# Find the seeds for all the failing demos
fail_seeds = []
fail_obss = []
fail_actions = []
for idx, ret in enumerate(logs["return_per_episode"]):
if ret <= 0:
fail_seeds.append(logs["seed_per_episode"][idx])
if return_obss_actions:
fail_obss.append(logs["observations_per_episode"][idx])
fail_actions.append(logs["actions_per_episode"][idx])
logger.info("{} fails".format(len(fail_seeds)))
if not return_obss_actions:
return success_rate, fail_seeds
else:
return success_rate, fail_seeds, fail_obss, fail_actions
def main(args, seed, episodes):
# Set seed for all randomness sources
utils.seed(seed)
# Define agent
env = gym.make(args.env)
env.seed(seed)
agent = utils.load_agent(env, args.model, args.demos, args.demos_origin,
args.argmax, args.env)
if args.model is None and args.episodes > len(agent.demos):
# Set the number of episodes to be the number of demos
episodes = len(agent.demos)
# Evaluate
if isinstance(agent, utils.ModelAgent) and not args.contiguous_episodes:
logs = batch_evaluate(agent, args.env, seed, episodes)
else:
logs = evaluate(agent, env, episodes, False)
return logs
csv_writer.writerow(data)
# Save obss preprocessor vocabulary and model
if args.save_interval > 0 and status['i'] % args.save_interval == 0:
obss_preprocessor.vocab.save()
with open(status_path, 'w') as dst:
json.dump(status, dst)
utils.save_model(acmodel, args.model)
# Testing the model before saving
agent = ModelAgent(args.model, obss_preprocessor, argmax=True)
agent.model = acmodel
agent.model.eval()
logs = batch_evaluate(agent, test_env_name, args.val_seed,
args.val_episodes)
agent.model.train()
mean_return = np.mean(logs["return_per_episode"])
success_rate = np.mean(
[1 if r > 0 else 0 for r in logs['return_per_episode']])
save_model = False
if success_rate > best_success_rate:
best_success_rate = success_rate
save_model = True
elif (success_rate
== best_success_rate) and (mean_return > best_mean_return):
best_mean_return = mean_return
save_model = True
if save_model:
utils.save_model(acmodel, args.model + '_best')
obss_preprocessor.vocab.save(
def main():
# Generate environments
envs = []
for i in range(args.procs):
env = gym.make(args.env)
env.seed(100 * args.seed + i)
envs.append(env)
# Define model name
suffix = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")
instr = args.instr_arch if args.instr_arch else "noinstr"
mem = "mem" if not args.no_mem else "nomem"
model_name_parts = {
'env': args.env,
'algo': args.algo,
'arch': args.arch,
'instr': instr,
'mem': mem,
'seed': args.seed,
'info': '',
'coef': '',
'suffix': suffix}
default_model_name = "{env}_{algo}_{arch}_{instr}_{mem}_seed{seed}{info}{coef}_{suffix}".format(**model_name_parts)
if args.pretrained_model:
default_model_name = args.pretrained_model + '_pretrained_' + default_model_name
args.model = args.model.format(**model_name_parts) if args.model else default_model_name
utils.configure_logging(args.model)
logger = logging.getLogger(__name__)
# Define obss preprocessor
if 'emb' in args.arch:
obss_preprocessor = utils.IntObssPreprocessor(args.model, envs[0].observation_space, args.pretrained_model)
else:
"""
obss_preprocessor = utils.ObssPreprocessor(args.model, envs[0].observation_space, args.pretrained_model)
"""
obss_preprocessor = utils.ImgInstrObssPreprocessor(args.model, envs[0].observation_space)
# Define actor-critic model
acmodel = utils.load_model(args.model, raise_not_found=False)
if acmodel is None:
if args.pretrained_model:
acmodel = utils.load_model(args.pretrained_model, raise_not_found=True)
else:
"""
acmodel = ACModel(obss_preprocessor.obs_space, envs[0].action_space,
args.image_dim, args.memory_dim, args.instr_dim,
not args.no_instr, args.instr_arch, not args.no_mem, args.arch)
"""
acmodel = ACModelImgInstr(obss_preprocessor.obs_space, envs[0].action_space,
args.image_dim, args.memory_dim, args.instr_dim,
not args.no_instr, not args.no_mem, args.arch)
"""
obss_preprocessor.vocab.save()
"""
utils.save_model(acmodel, args.model)
if torch.cuda.is_available():
acmodel.cuda()
# Define actor-critic algo
reshape_reward = lambda _0, _1, reward, _2: args.reward_scale * reward
if args.algo == "ppo":
algo = babyai.rl.PPOAlgo(envs, acmodel, args.frames_per_proc, args.discount, args.lr, args.beta1, args.beta2,
args.gae_lambda,
args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence,
args.optim_eps, args.clip_eps, args.ppo_epochs, args.batch_size, obss_preprocessor,
reshape_reward)
else:
raise ValueError("Incorrect algorithm name: {}".format(args.algo))
# When using extra binary information, more tensors (model params) are initialized compared to when we don't use that.
# Thus, there starts to be a difference in the random state. If we want to avoid it, in order to make sure that
# the results of supervised-loss-coef=0. and extra-binary-info=0 match, we need to reseed here.
utils.seed(args.seed)
# Restore training status
status_path = os.path.join(utils.get_log_dir(args.model), 'status.json')
if os.path.exists(status_path):
with open(status_path, 'r') as src:
status = json.load(src)
else:
status = {'i': 0,
'num_episodes': 0,
'num_frames': 0}
# Define logger and Tensorboard writer and CSV writer
header = (["update", "episodes", "frames", "FPS", "duration"]
+ ["return_" + stat for stat in ['mean', 'std', 'min', 'max']]
+ ["success_rate"]
+ ["num_frames_" + stat for stat in ['mean', 'std', 'min', 'max']]
+ ["entropy", "value", "policy_loss", "value_loss", "loss", "grad_norm"])
if args.tb:
from tensorboardX import SummaryWriter
writer = SummaryWriter(utils.get_log_dir(args.model))
csv_path = os.path.join(utils.get_log_dir(args.model), 'log.csv')
first_created = not os.path.exists(csv_path)
# we don't buffer data going in the csv log, cause we assume
# that one update will take much longer that one write to the log
csv_writer = csv.writer(open(csv_path, 'a', 1))
if first_created:
csv_writer.writerow(header)
# Log code state, command, availability of CUDA and model
babyai_code = list(babyai.__path__)[0]
try:
last_commit = subprocess.check_output(
'cd {}; git log -n1'.format(babyai_code), shell=True).decode('utf-8')
logger.info('LAST COMMIT INFO:')
logger.info(last_commit)
except subprocess.CalledProcessError:
logger.info('Could not figure out the last commit')
try:
diff = subprocess.check_output(
'cd {}; git diff'.format(babyai_code), shell=True).decode('utf-8')
if diff:
logger.info('GIT DIFF:')
logger.info(diff)
except subprocess.CalledProcessError:
logger.info('Could not figure out the last commit')
logger.info('COMMAND LINE ARGS:')
logger.info(args)
logger.info("CUDA available: {}".format(torch.cuda.is_available()))
logger.info(acmodel)
# Train model
total_start_time = time.time()
best_success_rate = 0
best_mean_return = 0
test_env_name = args.env
while status['num_frames'] < args.frames:
# Update parameters
update_start_time = time.time()
logs = algo.update_parameters()
update_end_time = time.time()
status['num_frames'] += logs["num_frames"]
status['num_episodes'] += logs['episodes_done']
status['i'] += 1
# Print logs
if status['i'] % args.log_interval == 0:
total_ellapsed_time = int(time.time() - total_start_time)
fps = logs["num_frames"] / (update_end_time - update_start_time)
duration = datetime.timedelta(seconds=total_ellapsed_time)
return_per_episode = utils.synthesize(logs["return_per_episode"])
success_per_episode = utils.synthesize(
[1 if r > 0 else 0 for r in logs["return_per_episode"]])
num_frames_per_episode = utils.synthesize(logs["num_frames_per_episode"])
data = [status['i'], status['num_episodes'], status['num_frames'],
fps, total_ellapsed_time,
*return_per_episode.values(),
success_per_episode['mean'],
*num_frames_per_episode.values(),
logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"],
logs["loss"], logs["grad_norm"]]
format_str = ("U {} | E {} | F {:06} | FPS {:04.0f} | D {} | R:xsmM {: .2f} {: .2f} {: .2f} {: .2f} | "
"S {:.2f} | F:xsmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | "
"pL {: .3f} | vL {:.3f} | L {:.3f} | gN {:.3f} | ")
logger.info(format_str.format(*data))
if args.tb:
assert len(header) == len(data)
for key, value in zip(header, data):
writer.add_scalar(key, float(value), status['num_frames'])
csv_writer.writerow(data)
# Save obss preprocessor vocabulary and model
if args.save_interval > 0 and status['i'] % args.save_interval == 0:
"""
obss_preprocessor.vocab.save()
"""
with open(status_path, 'w') as dst:
json.dump(status, dst)
utils.save_model(acmodel, args.model)
# Testing the model before saving
agent = ModelAgent(args.model, obss_preprocessor, argmax=True)
agent.model = acmodel
agent.model.eval()
logs = batch_evaluate(agent, test_env_name, args.val_seed, args.val_episodes)
agent.model.train()
mean_return = np.mean(logs["return_per_episode"])
success_rate = np.mean([1 if r > 0 else 0 for r in logs['return_per_episode']])
save_model = False
if success_rate > best_success_rate:
best_success_rate = success_rate
save_model = True
elif (success_rate == best_success_rate) and (mean_return > best_mean_return):
best_mean_return = mean_return
save_model = True
if save_model:
utils.save_model(acmodel, args.model + '_best')
"""
obss_preprocessor.vocab.save(utils.get_vocab_path(args.model + '_best'))
"""
logger.info("Return {: .2f}; best model is saved".format(mean_return))
else:
logger.info("Return {: .2f}; not the best model; not saved".format(mean_return))
csv_writer.writerow(data)
# Save obss preprocessor vocabulary and model
if args.save_interval > 0 and status['i'] % args.save_interval == 0:
obss_preprocessor.vocab.save()
with open(status_path, 'w') as dst:
json.dump(status, dst)
utils.save_model(acmodel, args.model)
# Testing the model before saving
agent = ModelAgent(args.model, obss_preprocessor, argmax=True, timepoint_bounds=(5, 15))
agent.model = acmodel
agent.model.eval()
logs = batch_evaluate(agent, test_env_name, args.val_seed, args.val_episodes, pixel=use_pixel, return_obss_actions=True)
order = sorted(range(len(logs['return_per_episode'])), key=lambda i: logs["observations_per_episode"][i][0]["mission"])
for i in order:
obs = logs["observations_per_episode"][i]
acts = logs["manager_actions_per_episode"][i]
print(obs[0]["mission"], acts)
agent.model.train()
mean_return = np.mean(logs["return_per_episode"])
success_rate = np.mean([1 if r > 0 else 0 for r in logs['return_per_episode']])
save_model = False
logger.info("Success rate: {}, best".format(success_rate, best_success_rate))
if success_rate > best_success_rate:
best_success_rate = success_rate
save_model = True
elif (success_rate == best_success_rate) and (mean_return > best_mean_return):
def main(args, seed, episodes):
# Set seed for all randomness sources
utils.seed(seed)
# Keep track of results per task.
results = {}
for env_name in args.env:
start_time = time.time()
env = gym.make(env_name)
env.seed(seed)
if args.model is None and args.episodes > len(agent.demos):
# Set the number of episodes to be the number of demos
episodes = len(agent.demos)
# Define agent
agent = utils.load_agent(env,
args.model,
args.demos,
args.demos_origin,
args.argmax,
env_name,
model_path=args.model_path)
# Evaluate
if isinstance(agent, utils.DemoAgent):
logs = evaluate_demo_agent(agent, episodes)
elif isinstance(agent, utils.BotAgent) or args.contiguous_episodes:
logs = evaluate(agent, env, episodes, False)
else:
logs = batch_evaluate(agent, env_name, seed, episodes)
end_time = time.time()
# Print logs
num_frames = sum(logs["num_frames_per_episode"])
fps = num_frames / (end_time - start_time)
ellapsed_time = int(end_time - start_time)
duration = datetime.timedelta(seconds=ellapsed_time)
if args.model is not None:
return_per_episode = utils.synthesize(logs["return_per_episode"])
success_per_episode = utils.synthesize(
[1 if r > 0 else 0 for r in logs["return_per_episode"]])
num_frames_per_episode = utils.synthesize(
logs["num_frames_per_episode"])
if args.model is not None:
print(
"F {} | FPS {:.0f} | D {} | R:xsmM {:.3f} {:.3f} {:.3f} {:.3f} | S {:.3f} | F:xsmM {:.1f} {:.1f} {} {}"
.format(num_frames, fps, duration,
*return_per_episode.values(),
success_per_episode['mean'],
*num_frames_per_episode.values()))
else:
print(
"F {} | FPS {:.0f} | D {} | F:xsmM {:.1f} {:.1f} {} {}".format(
num_frames, fps, duration,
*num_frames_per_episode.values()))
indexes = sorted(range(len(logs["num_frames_per_episode"])),
key=lambda k: -logs["num_frames_per_episode"][k])
n = args.worst_episodes_to_show
if n > 0:
print("{} worst episodes:".format(n))
for i in indexes[:n]:
if 'seed_per_episode' in logs:
print(logs['seed_per_episode'][i])
if args.model is not None:
print("- episode {}: R={}, F={}".format(
i, logs["return_per_episode"][i],
logs["num_frames_per_episode"][i]))
else:
print("- episode {}: F={}".format(
i, logs["num_frames_per_episode"][i]))
# Store results for this env.
logs['return_per_episode'] = return_per_episode
logs['success_per_episode'] = success_per_episode
logs['num_frames_per_episode'] = num_frames_per_episode
results[env_name] = logs
return results
with open(status_path, 'w') as dst:
json.dump(status, dst)
utils.save_model(acmodel, args.model)
#################################################################
# Testing the model before saving
agent = ModelAgent(args.model, obss_preprocessor, argmax=True)
agent.model = acmodel
agent.model.eval()
monitor_gym_this_time = args.monitor_gym and status[
'i'] > 0 and status['i'] % (args.save_interval * 5) == 0
logs = batch_evaluate(agent,
test_env_name,
args.test_seed,
args.test_episodes,
pixel=use_pixel,
monitor_gym=monitor_gym_this_time,
pairs_dict=pairs_dict,
model_path=model_path)
# save and log test mission strings
if args.savelog_missions:
logger.info('Testing missions: %s', logs["seen_missions"])
if status['i'] % 1 == 0:
utils.save_txt(file=logs['seen_missions'],
path=utils.get_model_dir(default_model_name),
filename=f"test_missions_{status['i']}")
agent.model.train()
# metrics
