def create_out_path(self, args):
if args.model_path is not None:
out_path = os.path.join(args.model_path, "eval_from_loaded_model")
self.out_path = os.path.join(
out_path,
"{}".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
else:
out_path = '{}_{}_{}_layers_{}_emb_{}_hidden_{}_pdrop_{}_gradclip_{}_bs_{}_lr_{}'.format(
args.dataset, args.task, args.model, args.num_layers,
args.emb_size, args.hidden_size, args.p_drop, args.grad_clip,
args.bs, args.lr)
if args.task == 'policy':
out_path = out_path + '_cond-answer_{}'.format(
args.condition_answer)
self.out_path = os.path.join(
args.out_path, out_path,
"{}".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
if not os.path.exists(self.out_path):
os.makedirs(self.out_path)
out_file_log = os.path.join(self.out_path, 'training_log.log')
self.logger = create_logger(out_file_log)
self.out_csv = os.path.join(self.out_path, 'train_history.csv')
self.out_lm_metrics = os.path.join(
self.out_path, 'lm_metrics_sf{}.csv'.format(args.bleu_sf))
self.model_path = os.path.join(self.out_path, 'model.pt')
self.logger.info("hparams: {}".format(vars(args)))
self.logger.info('train dataset length: {}'.format(
self.train_dataset.__len__()))
self.logger.info("val dataset length: {}".format(len(
self.val_dataset)))
if self.dataset_name == "vqa":
self.logger.info("number of filtered entries:{}".format(
len(self.train_dataset.filtered_entries)))
self.logger.info('number of tokens: {}'.format(
self.train_dataset.len_vocab))
self._save_hparams(args, self.out_path)
os.makedirs(output_path)
if not args.cv:
for inp, tar in train_dataset.take(1):
seq_len = tf.shape(inp)[1].numpy()
num_features = tf.shape(inp)[-1].numpy()
output_size = tf.shape(tar)[-1].numpy()
else:
for inp, tar in list_train_dataset[0].take(1):
seq_len = tf.shape(inp)[1].numpy()
num_features = tf.shape(inp)[-1].numpy()
output_size = tf.shape(tar)[-1].numpy()
# -------------------- create logger and checkpoint saver ----------------------------------------------------------------------------------------------------
out_file_log = output_path + '/' + 'training_log.log'
logger = create_logger(out_file_log=out_file_log)
# creating the checkpoint manager:
checkpoint_path = os.path.join(output_path, "checkpoints")
if not os.path.isdir(checkpoint_path):
os.makedirs(checkpoint_path)
# -------------------- Build the RNN model -----------------------------------------------------------------------------------------
model = build_LSTM_for_regression(shape_input_1=seq_len,
shape_input_2=num_features,
shape_output=output_size,
rnn_units=rnn_units,
dropout_rate=args.p_drop,
rnn_drop_rate=args.rnn_drop,
training=True)
if not args.cv:
train_LSTM(model=model,
def main(args):
type_folder = "train" if args.pretrain == 0 else "pretrain"
output_path = os.path.join(
args.out_path, "experiments", type_folder,
"{}".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
if not os.path.isdir(output_path):
os.makedirs(output_path)
out_file_log = os.path.join(output_path, 'RL_training_log.log')
out_policy_file = os.path.join(output_path, 'model.pth')
logger = create_logger(out_file_log, level=args.logger_level)
truncated = "basic" if args.pretrained_path is None else "truncated"
writer = SummaryWriter(log_dir=os.path.join(
output_path,
"runs_{}_{}_{}_{}_{}".format(truncated, args.max_len, args.debug,
args.entropy_coeff, args.num_truncated)))
env = ClevrEnv(args.data_path,
args.max_len,
reward_type=args.reward,
mode="train",
debug=args.debug,
num_questions=args.num_questions)
pretrained_lm = None
if args.pretrained_path is not None:
pretrained_lm = torch.load(args.pretrained_path)
pretrained_lm.eval()
models = {"lstm": PolicyLSTMBatch, "lstm_word": PolicyLSTMWordBatch}
generic_kwargs = {
"pretrained_lm": pretrained_lm,
"pretrain": args.pretrain,
"word_emb_size": args.word_emb_size,
"hidden_size": args.hidden_size,
"kernel_size": args.conv_kernel,
"stride": args.stride,
"num_filters": args.num_filters,
"num_truncated": args.num_truncated,
"writer": writer
}
ppo_kwargs = {
"policy": models[args.model],
"env": env,
"gamma": args.gamma,
"K_epochs": args.K_epochs,
"update_every": args.update_every,
"entropy_coeff": args.entropy_coeff,
"eps_clip": args.eps_clip
}
reinforce_kwargs = {
"env": env,
"policy": models[args.model],
"gamma": args.gamma,
"lr": args.lr,
"word_emb_size": args.word_emb_size,
"hidden_size": args.hidden_size
}
algo_kwargs = {"PPO": ppo_kwargs, "REINFORCE": reinforce_kwargs}
kwargs = {**algo_kwargs[args.agent], **generic_kwargs}
agents = {"PPO": PPO, "REINFORCE": REINFORCE}
agent = agents[args.agent](**kwargs)
agent.learn(log_interval=args.log_interval,
num_episodes=args.num_episodes_train)
agent.save(out_policy_file)
agent.test(log_interval=args.log_interval,
num_episodes=args.num_episodes_test)
def run(args):
# check consistency hparams
if args.reward == "vqa":
assert args.condition_answer is not None, "VQA task should be conditioned on the answer"
# create out_folder, config file, logger, writer
output_path = get_output_path(args)
conf_file = os.path.join(output_path, 'conf.ini')
out_file_log = os.path.join(output_path, 'RL_training_log.log')
out_policy_file = os.path.join(output_path, 'model.pth')
cmd_file = os.path.join(output_path, 'cmd.txt')
create_config_file(conf_file, args)
create_cmd_file(cmd_file)
logger = create_logger(out_file_log, level=args.logger_level)
writer = SummaryWriter(log_dir=os.path.join(output_path, "runs"))
device = torch.device("cuda:{}".format(args.device_id) if torch.cuda.
is_available() else "cpu")
logger.info("this experiment is in {}".format(output_path))
# log hparams:
log_hparams(logger, args)
# upload env & pretrained lm, policy network.
env, test_envs = get_rl_env(args, device)
pretrained_lm = get_pretrained_lm(args, env, device)
# dataset statistics
logger.info('-' * 20 + 'Dataset statistics' + '-' * 20)
logger.info("number of training questions:{}".format(len(env.dataset)))
logger.info("vocab size:{}".format(len(env.dataset.vocab_questions)))
models = {"lstm": PolicyLSTMBatch}
# creating the policy model.
policy = models[args.model](env.dataset.len_vocab,
args.word_emb_size,
args.hidden_size,
kernel_size=args.conv_kernel,
stride=args.stride,
num_filters=args.num_filters,
fusion=args.fusion,
env=env,
condition_answer=args.condition_answer,
device=device,
attention_dim=args.attention_dim)
if args.policy_path is not None:
pretrained = torch.load(args.policy_path, map_location=device)
if pretrained.__class__ != OrderedDict:
if pretrained.__class__ == dict:
pretrained = pretrained["model_state_dict"]
else:
pretrained = pretrained.state_dict()
policy.load_state_dict(pretrained, strict=False)
policy.device = device
optimizer, scheduler = get_optimizer(policy, args)
agent = get_agent(pretrained_lm=pretrained_lm,
writer=writer,
output_path=output_path,
env=env,
test_envs=test_envs,
policy=policy,
optimizer=optimizer,
args_=args)
# start training
if args.resume_training is not None:
epoch, loss = agent.load_ckpt(
os.path.join(args.resume_training, "checkpoints"))
logger.info(
'resume training after {} episodes... current loss: {}'.format(
epoch, loss))
agent.start_episode = epoch + 1
if args.num_episodes_train > 0: # trick to avoid a bug inside the agent.learn function in case of no training.
agent.learn(num_episodes=args.num_episodes_train)
agent.save(out_policy_file)
else:
logger.info("skipping training...")
# start evaluation
logger.info(
'---------------------------------- STARTING EVALUATION --------------------------------------------------------------------------'
)
#eval_modes = ["greedy", "sampling", "sampling_ranking_lm"]
for mode in args.eval_modes:
logger.info(
"----------------------------- Starting evaluation for {} action selection -------------------------"
.format(mode))
agent.test(num_episodes=args.num_episodes_test,
test_mode=mode,
test_seed=args.test_seed)
# write to csv test scalar metrics:
agent.compute_write_all_metrics(output_path=output_path, logger=logger)
logger.info(
'------------------------------------ DONE ---------------------------------------------------------------'
)
return agent