def __init__(self, is_train=False, dataset='Multiwoz'):
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f:
cfg = json.load(f)
self.save_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['save_dir'])
self.save_per_epoch = cfg['save_per_epoch']
self.training_iter = cfg['training_iter']
self.training_batch_iter = cfg['training_batch_iter']
self.batch_size = cfg['batch_size']
self.epsilon = cfg['epsilon_spec']['start']
self.rule_bot = RuleBasedMultiwozBot()
self.gamma = cfg['gamma']
self.is_train = is_train
if is_train:
init_logging_handler(os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['log_dir']))
# construct multiwoz vector
if dataset == 'Multiwoz':
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file, composite_actions=True, vocab_size=cfg['vocab_size'])
#replay memory
self.memory = MemoryReplay(cfg['memory_size'])
self.net = EpsilonGreedyPolicy(self.vector.state_dim, cfg['hv_dim'], self.vector.da_dim, cfg['epsilon_spec']).to(device=DEVICE)
self.target_net = copy.deepcopy(self.net)
self.online_net = self.target_net
self.eval_net = self.target_net
if is_train:
self.net_optim = optim.Adam(self.net.parameters(), lr=cfg['lr'])
self.loss_fn = nn.MSELoss()
def __init__(self, is_train=False, dataset='Multiwoz'):
with open("/home/raliegh/图片/ConvLab-2/convlab2/policy/pg/config.json",
'r') as f:
cfg = json.load(f)
self.save_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), cfg['save_dir'])
self.save_per_epoch = cfg['save_per_epoch']
self.update_round = cfg['update_round']
self.optim_batchsz = cfg['batchsz']
self.gamma = cfg['gamma']
self.is_train = is_train
if is_train:
init_logging_handler(cfg['log_dir'])
# load vocabulary
if dataset == 'Multiwoz':
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir,
'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(
self.vector.state_dim, cfg['h_dim'],
self.vector.da_dim).to(device=DEVICE)
# self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE)
if is_train:
self.policy_optim = optim.RMSprop(self.policy.parameters(),
lr=cfg['lr'])
# load_best model from the web.
self.load(
"/home/raliegh/图片/ConvLab-2/convlab2/policy/pg/save/best/best_pg_from_web.pol.mdl"
)
def __init__(self, is_train=False, dataset='Multiwoz'):
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json'), 'r') as f:
cfg = json.load(f)
self.save_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['save_dir'])
self.save_per_epoch = cfg['save_per_epoch']
self.update_round = cfg['update_round']
self.optim_batchsz = cfg['batchsz']
self.gamma = cfg['gamma']
self.epsilon = cfg['epsilon']
self.tau = cfg['tau']
self.is_train = is_train
if is_train:
init_logging_handler(os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg['log_dir']))
# construct policy and value network
if dataset == 'Multiwoz':
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file)
self.policy = MultiDiscretePolicy(self.vector.state_dim, cfg['h_dim'], self.vector.da_dim).to(device=DEVICE)
self.value = Value(self.vector.state_dim, cfg['hv_dim']).to(device=DEVICE)
if is_train:
self.policy_optim = optim.RMSprop(self.policy.parameters(), lr=cfg['policy_lr'])
self.value_optim = optim.Adam(self.value.parameters(), lr=cfg['value_lr'])
def __init__(self, train=True):
# load configuration file
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
self.gamma = cfg['gamma']
self.epsilon_init = cfg['epsilon_init']
self.epsilon_final = cfg['epsilon_final']
self.istrain = train
if self.istrain:
self.epsilon = self.epsilon_init
else:
self.epsilon = self.epsilon_final
self.epsilon_degrade_period = cfg['epsilon_degrade_period']
self.tau = cfg['tau']
self.action_number = cfg[
'action_number'] # total number of actions considered
init_logging_handler(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
cfg['log_dir']))
# load action mapping file
action_map_file = os.path.join(root_dir,
'convlab2/policy/act_500_list.txt')
_, self.ind2act_dict = read_action_map(action_map_file)
# load vector for MultiWoz 2.1
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file)
# build Q network
# current Q network to be trained
self.Q = DuelDQN(self.vector.state_dim, cfg['h_dim'],
self.action_number).to(device=DEVICE)
# target Q network
self.target_Q = DuelDQN(self.vector.state_dim, cfg['h_dim'],
self.action_number).to(device=DEVICE)
self.target_Q.load_state_dict(self.Q.state_dict())
# define optimizer
# self.optimizer = RAdam(self.Q.parameters(), lr=cfg['lr'], weight_decay=cfg['weight_decay'])
self.optimizer = optim.Adam(self.Q.parameters(),
lr=cfg['lr'],
weight_decay=cfg['weight_decay'])
self.scheduler = StepLR(self.optimizer,
step_size=cfg['lr_decay_step'],
gamma=cfg['lr_decay'])
self.min_lr = cfg['min_lr']
# loss function
self.criterion = torch.nn.MSELoss()
import os
import json
import logging
import sys
root_dir = os.path.dirname(
os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
sys.path.append(root_dir)
from convlab2.util.train_util import init_logging_handler
from convlab2.task.camrest.goal_generator import GoalGenerator
from convlab2.policy.vhus.camrest.usermanager import UserDataManager
from convlab2.policy.vhus.train import VHUS_Trainer
if __name__ == '__main__':
with open('config.json', 'r') as f:
cfg = json.load(f)
init_logging_handler(cfg['log_dir'])
manager = UserDataManager()
goal_gen = GoalGenerator()
env = VHUS_Trainer(cfg, manager, goal_gen)
logging.debug('start training')
best = float('inf')
for e in range(cfg['epoch']):
env.imitating(e)
best = env.imit_test(e, best)
