group.add_argument('--data-type',
type=str,
default='float32',
choices=['float32'],
help='Choose the data type.')
group.add_argument('--large-embedding',
default=False,
action='store_true',
help="set small or large embedding size")
group.add_argument('--use-ipu-model',
default=False,
action='store_true',
help="use IPU model or not.")
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="CTR Model Training in Tensorflow")
parser = add_model_arguments(parser)
parser = add_dataset_arguments(parser)
parser = add_training_arguments(parser)
parser = logger.add_arguments(parser)
args, unknown = parser.parse_known_args()
args = vars(args)
logger.print_setting(args)
setup_logger(logging.INFO, tf_log)
inference(args)
help="Replicate graph over N workers to increase batch to batch-size*N"
)
group.add_argument('--model-path',
type=str,
default='./dnn_save_path/ckpt_noshuffDIEN3',
help='Place to store and restore model')
group.add_argument('--use-ipu-model',
default=False,
action='store_true',
help="use IPU model or not.")
group.add_argument('--use-ipu-emb',
default=False,
action='store_true',
help="Use host embeddig or put embedding on ipu.")
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="CTR Model Training in Tensorflow", add_help=False)
parser = add_model_arguments(parser)
parser = add_dataset_arguments(parser)
parser = add_training_arguments(parser)
parser = logger.add_arguments(parser)
args, _ = parser.parse_known_args()
args = vars(args)
logger.print_setting(args, is_dien=False, is_training=False)
setup_logger(logging.DEBUG, tf_log, name='dien_log.txt')
inference(args)
def evaluate(args):
start_time = time.time()
torch.set_default_tensor_type('torch.FloatTensor')
pthfile = torch.load(args['load_file'],
map_location=lambda storage, loc: storage.cpu())
# Create the output directory
output_dir = os.path.join(
os.path.dirname(args['load_file']), args['output_directory'],
os.path.split(args['env'])[1] + 'evaluation-' +
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S.%f"))
try:
os.makedirs(output_dir)
except OSError:
if not os.path.isdir(output_dir):
raise
print('saving to: ' + output_dir + '/')
start_log_setup = time.time()
log = {}
setup_logger('test.log', r'{0}/test.log'.format(output_dir))
log['test.log'] = logging.getLogger('test.log')
end_log_setup = time.time()
print('single evaluate log setup: %d' % (end_log_setup - start_log_setup))
gpu_id = args['gpu_ids'][-1]
torch.manual_seed(args['seed'])
npr.seed(args['seed'] + 1)
if gpu_id >= 0:
torch.cuda.manual_seed(args['seed'])
for k in args.keys():
log['test.log'].info('{0}: {1}'.format(k, args[k]))
env = create_env(args['env'], args)
player = Agent(None, env, args, None)
# Wrap the environment so that it saves a video
if args['render_video']:
player.env = gym.wrappers.Monitor(player.env, output_dir, force=True)
start_model = time.time()
AC = importlib.import_module(args['model_name'])
player.model = AC.ActorCritic(env.observation_space, env.action_space,
args['stack_frames'], args)
player.gpu_id = gpu_id
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
player.model = player.model.cuda()
if args['load_best']:
player.model.load_state_dict(pthfile['best_state_dict'])
else:
player.model.load_state_dict(pthfile['state_dict'])
player.model.eval()
end_model = time.time()
print('single evaluate model setup time: %d' % (end_model - start_model))
# Keep track of returns
all_episode_returns = []
for i_episode in range(args['num_episodes']):
player.state, player.info = player.env.reset()
player.state = torch.from_numpy(player.state).float()
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
player.state = player.state.cuda()
player.eps_len = 0
reward_sum = 0
episode_step = 0
while True:
player.action_test()
reward_sum += player.reward
episode_step += 1
if player.done:
all_episode_returns.append(reward_sum)
#num_tests += 1
#reward_total_sum += reward_sum
#reward_mean = reward_total_sum / num_tests
log['test.log'].info(
"Episode_length, {0}, reward_sum, {1}".format(
player.eps_len, reward_sum))
break
end_episodes = time.time()
print('single evaluate time for %d episodes: %d' %
(args['num_episodes'], end_episodes - end_model))
print('single evaluate seconds per episode: %d' %
((end_episodes - end_model) / args['num_episodes']))
all_episode_returns = np.array(all_episode_returns)
all_episode_successes = np.array(all_episode_returns > 300.,
dtype=np.float32)
evaluation_statistics = {
'Mean Return': np.mean(all_episode_returns),
'Std Return': np.std(all_episode_returns),
'Min Return': np.min(all_episode_returns),
'Max Return': np.max(all_episode_returns),
'Mean Success': np.mean(all_episode_successes),
'Number Successes': np.sum(all_episode_successes),
'Number Total': args['num_episodes'],
'Std Success': np.std(all_episode_successes),
'Min Success': np.min(all_episode_successes),
'Max Success': np.max(all_episode_successes),
'all_episode_returns': all_episode_returns,
'all_episode_successes': all_episode_successes,
}
# Save raw data to a file
torch.save(
{
'all_episode_returns': all_episode_returns,
'all_episode_successes': all_episode_successes,
}, os.path.join(output_dir, 'evaluation_statistics.pth'))
print(
'Average Episodic Return: \n\tmean: {0}\n\tstd: {1}\n\tmin: {2}\n\t \
max: {3}'.format(np.mean(all_episode_returns),
np.std(all_episode_returns),
np.min(all_episode_returns),
np.max(all_episode_returns)))
print(
'Average Episodic Success: \n\tmean: {0} ({1}/{2})\n\tstd: {3}\n\t \
min: {4}\n\tmax: {5}'.format(np.mean(all_episode_successes),
np.sum(all_episode_successes),
args['num_episodes'],
np.std(all_episode_successes),
np.min(all_episode_successes),
np.max(all_episode_successes)))
# Shut down logging system and close open file handles
start_log_shutdown = time.time()
logging.shutdown()
end_time = time.time()
print('single evaluate log shutdown: %d' % (end_time - start_log_shutdown))
print('single evaluate total time for %d episodes: %d' %
(args['num_episodes'], end_time - start_time))
print('single evaluate overall seconds per episode: %f' %
((end_time - start_time) / args['num_episodes']))
return evaluation_statistics
def test(args, shared_model):
ptitle('Test Agent')
gpu_id = args.gpu_ids[-1]
log = {}
setup_logger(
'{}_log'.format(args.env),
r'{0}{1}{2}_log'.format(args.log_dir, args.save_prefix, args.env))
log['{}_log'.format(args.env)] = logging.getLogger('{}_log'.format(
args.env))
d_args = vars(args)
for k in d_args.keys():
log['{}_log'.format(args.env)].info('{0}: {1}'.format(k, d_args[k]))
torch.manual_seed(args.seed)
if gpu_id >= 0:
torch.cuda.manual_seed(args.seed)
env = create_env(args.env, args)
reward_sum = 0
start_time = time.time()
num_tests = 0
reward_total_sum = 0
player = Agent(None, None, env, args, None)
player.gpu_id = gpu_id
AC = importlib.import_module(args.model_name)
player.model = AC.ActorCritic(env.observation_space, env.action_space,
args.stack_frames, args)
player.state, player.info = player.env.reset()
player.state = torch.from_numpy(player.state).float()
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
player.model = player.model.cuda()
player.state = player.state.cuda()
player.model.eval()
episode_count = 0
all_scores = []
max_score = 0
while True:
if player.done:
episode_count += 1
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
player.model.load_state_dict(shared_model.state_dict())
else:
player.model.load_state_dict(shared_model.state_dict())
player.action_test()
reward_sum += player.reward
if player.done:
num_tests += 1
reward_total_sum += reward_sum
reward_mean = reward_total_sum / num_tests
log['{}_log'.format(args.env)].info(
"Time {0}, episode reward {1}, episode length {2}, reward mean {3:.4f}"
.format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
reward_sum, player.eps_len, reward_mean))
# Plot scores every 5 episodes
all_scores.append(reward_sum)
if (episode_count % 5 == 0):
plt.clf()
plt.plot(range(len(all_scores)), all_scores)
plt.title('Test Episode Returns')
plt.xlabel('Test Episode')
plt.ylabel('Return')
plt.savefig('{0}{1}{2}.png'.format(args.log_dir,
args.save_prefix, args.env))
if args.save_max and reward_sum >= max_score:
max_score = reward_sum
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
state_to_save = player.model.state_dict()
torch.save(
state_to_save,
'{0}{1}{2}.dat'.format(args.save_model_dir,
args.save_prefix, args.env))
else:
state_to_save = player.model.state_dict()
torch.save(
state_to_save,
'{0}{1}{2}.dat'.format(args.save_model_dir,
args.save_prefix, args.env))
reward_sum = 0
player.eps_len = 0
state, player.info = player.env.reset()
time.sleep(60)
player.state = torch.from_numpy(state).float()
if gpu_id >= 0:
with torch.cuda.device(gpu_id):
player.state = player.state.cuda()