def probe_conv(model):
acmodel = utils.load_model(model)
convweights = acmodel.image_conv.get_submodule('0').state_dict()['weight']
convweights = torch.clip(convweights, -.1, .1).cpu().detach().numpy()
fig, axs = plt.subplots(8, 16, sharex=True, sharey=True)
fig.subplots_adjust(hspace=0, wspace=0)
for i in range(8):
for j in range(16):
idx = i * 16 + j
img = convweights[idx]
img = np.moveaxis(img, 0, -1)
img = img * 5 + .5
im = axs[i][j].imshow(img)
plt.show()
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)
# 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_desc,
args.instr_arch,
not args.no_mem,
args.arch,
random_shuffled=args.random_shuffle,
instr_sents=n_floor_colors,
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))
def __init__(
self,
args,
):
self.args = args
utils.seed(self.args.seed)
# args.env is a list when training on multiple environments
if getattr(args, 'multi_env', None):
self.env = [gym.make(item) for item in args.multi_env]
self.train_demos = []
for demos, episodes in zip(args.multi_demos, args.multi_episodes):
demos_path = utils.get_demos_path(demos,
None,
None,
valid=False)
logger.info('loading {} of {} demos'.format(episodes, demos))
train_demos = utils.load_demos(demos_path)
logger.info('loaded demos')
if episodes > len(train_demos):
raise ValueError(
"there are only {} train demos in {}".format(
len(train_demos), demos))
self.train_demos.extend(train_demos[:episodes])
logger.info('So far, {} demos loaded'.format(
len(self.train_demos)))
self.val_demos = []
for demos, episodes in zip(args.multi_demos, [args.val_episodes] *
len(args.multi_demos)):
demos_path_valid = utils.get_demos_path(demos,
None,
None,
valid=True)
logger.info('loading {} of {} valid demos'.format(
episodes, demos))
valid_demos = utils.load_demos(demos_path_valid)
logger.info('loaded demos')
if episodes > len(valid_demos):
logger.info(
'Using all the available {} demos to evaluate valid. accuracy'
.format(len(valid_demos)))
self.val_demos.extend(valid_demos[:episodes])
logger.info('So far, {} valid demos loaded'.format(
len(self.val_demos)))
logger.info('Loaded all demos')
observation_space = self.env[0].observation_space
action_space = self.env[0].action_space
else:
self.env = gym.make(self.args.env)
demos_path = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=False)
demos_path_valid = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=True)
print("else")
logger.info('loading demos')
self.train_demos = utils.load_demos(demos_path)
print(len(self.train_demos))
print(self.train_demos[0])
logger.info('loaded demos')
if args.episodes:
if args.episodes > len(self.train_demos):
raise ValueError("there are only {} train demos".format(
len(self.train_demos)))
self.train_demos = self.train_demos[:args.episodes]
self.val_demos = utils.load_demos(demos_path_valid)
if args.val_episodes > len(self.val_demos):
logger.info(
'Using all the available {} demos to evaluate valid. accuracy'
.format(len(self.val_demos)))
self.val_demos = self.val_demos[:self.args.val_episodes]
observation_space = self.env.observation_space
action_space = self.env.action_space
print("else")
print(args.model)
self.obss_preprocessor = utils.ObssPreprocessor(
args.model, observation_space,
getattr(self.args, 'pretrained_model', None))
# Define actor-critic model
self.acmodel = utils.load_model(args.model, raise_not_found=False)
if self.acmodel is None:
if getattr(self.args, 'pretrained_model', None):
self.acmodel = utils.load_model(args.pretrained_model,
raise_not_found=True)
else:
self.acmodel = ACModel(self.obss_preprocessor.obs_space,
action_space, args.image_dim,
args.memory_dim, args.instr_dim,
not self.args.no_instr,
self.args.instr_arch,
not self.args.no_mem, self.args.arch)
self.obss_preprocessor.vocab.save()
utils.save_model(self.acmodel, args.model)
self.acmodel.train()
if torch.cuda.is_available():
self.acmodel.cuda()
self.optimizer = torch.optim.Adam(self.acmodel.parameters(),
self.args.lr,
eps=self.args.optim_eps)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer,
step_size=100,
gamma=0.9)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
envs[0].observation_space,
args.pretrained_model)
else:
if 'gnn' in args.arch:
obss_preprocessor = utils.GraphObssPreprocessor(
args.model, envs[0].observation_space, args.pretrained_model)
if 'gnn_dense' in args.arch:
obss_preprocessor = utils.GraphObssPreprocessorDense(
args.model, envs[0].observation_space, args.pretrained_model)
else:
obss_preprocessor = utils.ObssPreprocessor(args.model,
envs[0].observation_space,
args.pretrained_model)
# Define actor-critic model
acmodel = utils.load_model(args.model, raise_not_found=False)
acmodel = ACModelGNNDense(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)
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
def main(exp, argv):
os.environ["BABYAI_STORAGE"] = exp.results_directory()
# Parse arguments
parser = ArgumentParser()
parser.add_argument("--algo",
default='ppo',
help="algorithm to use (default: ppo)")
parser.add_argument("--discount",
type=float,
default=0.99,
help="discount factor (default: 0.99)")
parser.add_argument("--reward-scale",
type=float,
default=20.,
help="Reward scale multiplier")
parser.add_argument(
"--gae-lambda",
type=float,
default=0.99,
help="lambda coefficient in GAE formula (default: 0.99, 1 means no gae)"
)
parser.add_argument("--value-loss-coef",
type=float,
default=0.5,
help="value loss term coefficient (default: 0.5)")
parser.add_argument("--max-grad-norm",
type=float,
default=0.5,
help="maximum norm of gradient (default: 0.5)")
parser.add_argument("--clip-eps",
type=float,
default=0.2,
help="clipping epsilon for PPO (default: 0.2)")
parser.add_argument("--ppo-epochs",
type=int,
default=4,
help="number of epochs for PPO (default: 4)")
parser.add_argument(
"--save-interval",
type=int,
default=50,
help=
"number of updates between two saves (default: 50, 0 means no saving)")
parser.add_argument("--workers",
type=int,
default=8,
help="number of workers for PyTorch (default: 8)")
parser.add_argument("--max-count",
type=int,
default=1000,
help="maximum number of frames to run for")
parser.add_argument("--sample_duration",
type=float,
default=0.5,
help="sampling duration")
parser.add_argument("--cuda",
action="store_true",
default=False,
help="whether to use cuda")
args = parser.parse_args(argv)
utils.seed(args.seed)
torch_settings = init_torch(
seed=args.seed,
cuda=args.cuda,
workers=args.workers,
)
# 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)
# Define actor-critic model
# acmodel = utils.load_model(args.model, raise_not_found=False)
acmodel = None
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)
obss_preprocessor.vocab.save()
# utils.save_model(acmodel, args.model)
if torch_settings.cuda:
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
wrapper = iteration_wrapper(
exp,
sync=torch_settings.sync,
max_count=args.max_count,
sample_duration=args.sample_duration,
)
# while status['num_frames'] < args.frames:
while True:
with wrapper() as it:
# Update parameters
if wrapper.done():
break
update_start_time = time.time()
logs = algo.update_parameters()
update_end_time = time.time()
it.set_count(logs["num_frames"])
it.log(loss=logs["loss"], )
args.algo,
args.arch,
instr,
mem,
args.seed,
fakerewardtxt,
suffix)
model_name = args.model or default_model_name
# Define obss preprocessor
obss_preprocessor = utils.ObssPreprocessor(model_name, envs[0].observation_space)
# Define actor-critic model
acmodel = utils.load_model(model_name, raise_not_found=False)
if acmodel is None:
acmodel = ACModel(obss_preprocessor.obs_space, envs[0].action_space,
args.instr_model, not args.no_mem, args.arch)
if torch.cuda.is_available():
acmodel.cuda()
# Define actor-critic algo
if args.algo == "a2c":
algo = torch_rl.A2CAlgo(envs, acmodel, args.frames_per_proc, args.discount, args.lr, args.gae_tau,
args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence,
args.optim_alpha, args.optim_eps, obss_preprocessor, utils.reshape_reward)
elif args.algo == "ppo":
algo = torch_rl.PPOAlgo(envs, acmodel, args.frames_per_proc, args.discount, args.lr, args.gae_tau,
args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence,
model_names[m] = model_names[m].format(
**model_name_parts) if model_name else default_model_name
loggers = []
for model_name in model_names[:-1]:
loggers.append(utils_sr.configure_logging(model_name))
loggers.append(utils_sr.configure_logging(model_names[-1], stream=True))
# Define obss preprocessor.
obss_preprocessor = utils_sr.MultiObssPreprocessor(
model_names, [envs[0].observation_space for _ in model_names], pretrained)
# Define actor--critic models.
models = []
for m, model_name in enumerate(model_names):
model = utils.load_model(model_name, raise_not_found=False)
if model is None:
if pretrained[m]:
models.append(utils.load_model(pretrained[m],
raise_not_found=True))
else:
models.append(
ACModel(obss_preprocessor.obs_spaces[m], envs[0].action_space,
args.image_dim, args.memory_dim, args.instr_dim,
args.enc_dim, args.dec_dim, args.len_message,
args.num_symbols))
else:
models.append(model)
for m, model in enumerate(models):
obss_preprocessor.vocabs[m].save()
def load_model(model):
acmodel = utils.load_model(model)
vocab = utils.get_vocab_path(model)
with open(vocab) as jin:
vocab = json.load(jin)
return vocab, acmodel
def __init__(self, args):
"""
:param args:
"""
super(EvalLearner, self).__init__()
self.update_lr = args.update_lr
self.meta_lr = args.meta_lr
self.task_num = args.task_num
self.args = args
utils.seed(self.args.seed)
self.env = gym.make(self.args.env)
demos_path = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=False)
demos_path_valid = utils.get_demos_path(args.demos,
args.env,
args.demos_origin,
valid=True)
logger.info('loading demos')
self.train_demos = utils.load_demos(demos_path)
logger.info('loaded demos')
# if args.episodes:
# if args.episodes > len(self.train_demos):
# raise ValueError("there are only {} train demos".format(len(self.train_demos)))
# self.train_demos = self.train_demos[:args.episodes]
self.val_demos = utils.load_demos(demos_path_valid)
# if args.val_episodes > len(self.val_demos):
# logger.info('Using all the available {} demos to evaluate valid. accuracy'.format(len(self.val_demos)))
self.val_demos = self.val_demos[:self.args.val_episodes]
observation_space = self.env.observation_space
action_space = self.env.action_space
print(args.model)
self.obss_preprocessor = utils.ObssPreprocessor(
args.model, observation_space,
getattr(self.args, 'pretrained_model', None))
# Define actor-critic model
self.net = utils.load_model(args.model, raise_not_found=True)
# if self.net is None:
# if getattr(self.args, 'pretrained_model', None):
# self.net = utils.load_model(args.pretrained_model, raise_not_found=True)
# else:
# self.net = ACModel(self.obss_preprocessor.obs_space, action_space,
# args.image_dim, args.memory_dim, args.instr_dim,
# not self.args.no_instr, self.args.instr_arch,
# not self.args.no_mem, self.args.arch)
self.obss_preprocessor.vocab.save()
# utils.save_model(self.net, args.model)
self.fast_net = copy.deepcopy(self.net)
self.net.train()
self.fast_net.train()
if torch.cuda.is_available():
self.net.cuda()
self.fast_net.cuda()
self.optimizer = torch.optim.SGD(self.fast_net.parameters(),
lr=self.args.update_lr)
# self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=100, gamma=0.9)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.meta_optim = optim.Adam(self.net.parameters(), lr=self.meta_lr)
utils.seed(args.seed)
envs = []
for i in range(args.procs):
env = gym.make(args.env)
env.seed(100 * args.seed + i)
envs.append(env)
penv = ParallelEnv(envs, args.n, args.conventional, args.archimedean, args.informed_sender)
# Define obss preprocessor.
obss_preprocessor = utils_sr.MultiObssPreprocessor([args.sender, args.receiver], [envs[0].observation_space]*2)
# Define actor--critic models.
sender = utils.load_model(args.sender)
receiver = utils.load_model(args.receiver)
if torch.cuda.is_available():
sender.cuda()
receiver.cuda()
# Define actor--critic algorithm.
reshape_reward = lambda _0, _1, reward, _2: args.reward_scale * reward
test_algo = TestAlgo(penv, [sender, receiver], args.frames_per_proc, args.discount, args.gae_lambda, obss_preprocessor, reshape_reward, not args.no_comm, args.conventional, not args.sample)
# Test models.
sender.eval()
receiver.eval()
total_start_time = time.time()
update_start_time = time.time()
