def __init__(self, pretrain=False):
config = MultiWozConfig()
manager = UserDataManager(config.data_dir, config.data_file)
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size, voc_sys_size).to(device=DEVICE)
self.optim = optim.Adam(self.user.parameters(), lr=config.lr_simu)
self.goal_gen = GoalGenerator(config.data_dir+'/goal/goal_model.pkl')
self.cfg = config
self.manager = manager
self.user.eval()
if pretrain:
self.print_per_batch = config.print_per_batch
self.save_dir = config.save_dir
self.save_per_epoch = config.save_per_epoch
seq_goals, seq_usr_dass, seq_sys_dass = manager.data_loader_seg()
train_goals, train_usrdas, train_sysdas, \
test_goals, test_usrdas, test_sysdas, \
val_goals, val_usrdas, val_sysdas = manager.train_test_val_split_seg(
seq_goals, seq_usr_dass, seq_sys_dass)
self.data_train = (train_goals, train_usrdas, train_sysdas, config.batchsz)
self.data_valid = (val_goals, val_usrdas, val_sysdas, config.batchsz)
self.data_test = (test_goals, test_usrdas, test_sysdas, config.batchsz)
self.nll_loss = nn.NLLLoss(ignore_index=0) # PAD=0
self.bce_loss = nn.BCEWithLogitsLoss()
else:
self.load(config.load)
sys_seq_len = torch.LongTensor([max(len(sen), 1) for sen in self.sys_da_id_stack])
max_sen_len = sys_seq_len.max().item()
sys_seq = torch.LongTensor(padding(self.sys_da_id_stack, max_sen_len))
usr_a, terminal = self.user.select_action(self.goal_input, self.goal_len_input, sys_seq, sys_seq_len)
usr_action = self.manager.usrseq2da(self.manager.id2sentence(usr_a), self.goal)
return capital(usr_action), terminal
if __name__ == '__main__':
manager = UserDataManager('../../../../data/multiwoz', 'annotated_user_da_with_span_full.json')
seq_goals, seq_usr_dass, seq_sys_dass = manager.data_loader_seg()
train_goals, train_usrdas, train_sysdas, \
test_goals, test_usrdas, test_sysdas, \
val_goals, val_usrdas, val_sysdas = manager.train_test_val_split_seg(
seq_goals, seq_usr_dass, seq_sys_dass)
data_train = batch_iter(train_goals, train_usrdas, train_sysdas, 32)
data_valid = batch_iter(val_goals, val_usrdas, val_sysdas, 32)
data_test = batch_iter(test_goals, test_usrdas, test_sysdas, 32)
for data in data_train:
batch_input = to_device(padding_data(data))
break
for k, v in batch_input.items():
print(k, v.shape)
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
cfg = MultiWozConfig()
user = VHUS(cfg, voc_goal_size, voc_usr_size, voc_sys_size)
a_weights, t_weights, _ = user(batch_input['goals'], batch_input['goals_length'], batch_input['posts'], batch_input['posts_length'])#, batch_input['origin_responses'])
print(len(a_weights)) #[L, B, V]
print(t_weights.shape) #[B]