def __init__(
self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/mle_policy_camrest.zip'
):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(
os.path.abspath(__file__))))))
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for MLE Policy is specified!")
archive_file = cached_path(model_file)
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'save')
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'best_mle.pol.mdl')):
archive = zipfile.ZipFile(archive_file, 'r')
archive.extractall(model_dir)
self.load(cfg['load'])
class ActPolicyDataLoaderCamrest(ActPolicyDataloader):
def __init__(self):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(
os.path.abspath(__file__))))))
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
processed_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'processed_data')
if os.path.exists(processed_dir):
print('Load processed data file')
self._load_data(processed_dir)
else:
print('Start preprocessing the dataset')
self._build_data(root_dir, processed_dir)
def _build_data(self, root_dir, processed_dir): # TODO
raw_data = {}
for part in ['train', 'val', 'test']:
archive = zipfile.ZipFile(
os.path.join(root_dir,
'data/camrest/{}.json.zip'.format(part)), 'r')
with archive.open('{}.json'.format(part), 'r') as f:
raw_data[part] = json.load(f)
self.data = {}
for part in ['train', 'val', 'test']:
self.data[part] = []
for key in raw_data[part]:
sess = key['dial']
state = default_state()
action = {}
for i, turn in enumerate(sess):
state['user_action'] = turn['usr']['dialog_act']
if i + 1 == len(sess):
state['terminated'] = True
for da in turn['usr']['slu']:
if da['slots'][0][0] != 'slot':
state['belief_state'][da['slots'][0]
[0]] = da['slots'][0][1]
action = turn['sys']['dialog_act']
self.data[part].append([
self.vector.state_vectorize(state),
self.vector.action_vectorize(action)
])
state['system_action'] = turn['sys']['dialog_act']
os.makedirs(processed_dir)
for part in ['train', 'val', 'test']:
with open(os.path.join(processed_dir, '{}.pkl'.format(part)),
'wb') as f:
pickle.dump(self.data[part], f)
class MLP(Policy):
def __init__(self, is_train=False):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
self.is_train = is_train
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
self.load(cfg['load'])
def predict(self, state):
"""
Predict an system action given state.
Args:
state (dict): Dialog state. Please refer to util/state.py
Returns:
action : System act, with the form of (act_type, {slot_name_1: value_1, slot_name_2, value_2, ...})
"""
s_vec = torch.Tensor(self.vector.state_vectorize(state))
a = self.policy.select_action(s_vec.to(device=DEVICE)).cpu()
action = self.vector.action_devectorize(a.numpy())
return action
def init_session(self):
"""
Restore after one session
"""
pass
def load(self, filename):
policy_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_mle.pol.mdl')
if os.path.exists(policy_mdl):
self.policy.load_state_dict(torch.load(policy_mdl))
print('<<dialog policy>> loaded checkpoint from file: {}'.format(
policy_mdl))
def __init__(self):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(
os.path.abspath(__file__))))))
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
processed_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'processed_data')
if os.path.exists(processed_dir):
print('Load processed data file')
self._load_data(processed_dir)
else:
print('Start preprocessing the dataset')
self._build_data(root_dir, processed_dir)
def __init__(self, is_train=False):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
self.is_train = is_train
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
self.load(cfg['load'])
def __init__(self, manager, cfg):
self._init_data(manager, cfg)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(vector.state_dim, cfg['h_dim'],
vector.da_dim).to(device=DEVICE)
self.policy.eval()
self.policy_optim = torch.optim.Adam(self.policy.parameters(),
lr=cfg['lr'])
def __init__(self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/mle_policy_camrest.zip'):
root_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f:
cfg = json.load(f)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE)
self.load(archive_file, model_file, cfg['load'])
def __init__(self, manager, cfg):
self.data_train = manager.create_dataset('train', cfg['batchsz'])
self.data_valid = manager.create_dataset('val', cfg['batchsz'])
self.data_test = manager.create_dataset('test', cfg['batchsz'])
self.save_dir = cfg['save_dir']
self.print_per_batch = cfg['print_per_batch']
self.save_per_epoch = cfg['save_per_epoch']
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(vector.state_dim, cfg['h_dim'],
vector.da_dim).to(device=DEVICE)
self.policy.eval()
self.policy_optim = torch.optim.Adam(self.policy.parameters(),
lr=cfg['lr'])
self.multi_entropy_loss = nn.MultiLabelSoftMarginLoss()
class MLE(Policy):
def __init__(
self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/mle_policy_camrest.zip'
):
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(
os.path.abspath(__file__))))))
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
voc_file = os.path.join(root_dir, 'data/camrest/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/camrest/usr_da_voc.txt')
self.vector = CamrestVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for MLE Policy is specified!")
archive_file = cached_path(model_file)
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'save')
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'best_mle.pol.mdl')):
archive = zipfile.ZipFile(archive_file, 'r')
archive.extractall(model_dir)
self.load(cfg['load'])
def predict(self, state):
"""
Predict an system action given state.
Args:
state (dict): Dialog state. Please refer to util/state.py
Returns:
action : System act, with the form of (act_type, {slot_name_1: value_1, slot_name_2, value_2, ...})
"""
s_vec = torch.Tensor(self.vector.state_vectorize(state))
a = self.policy.select_action(s_vec.to(device=DEVICE)).cpu()
action = self.vector.action_devectorize(a.numpy())
return action
def init_session(self):
"""
Restore after one session
"""
pass
def load(self, filename):
policy_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_mle.pol.mdl')
if os.path.exists(policy_mdl):
self.policy.load_state_dict(torch.load(policy_mdl))
print('<<dialog policy>> loaded checkpoint from file: {}'.format(
policy_mdl))