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policy_main.py
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import argparse
from tensorboardX import SummaryWriter
import os
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torch.nn.utils import clip_grad_norm_
from sru import SRU
from utils.config import _get_parser
from utils.utils import *
from utils.helpers import save_checkpoint, get_params, compute_param_norm, compute_grad_norm
from interactive.policy import Policy
from interactive.cust_simulator import PersonaUser, NoisyUser
from interactive.AskAgent import AskingAgent
import sys, os
sys.path.append(os.path.abspath('../'))
def main(args):
aa = AskingAgent(args)
if args['user_type'] == 'oracle':
user = NoisyUser(args)
elif args['user_type'] == 'persona':
user = PersonaUser(aa, args)
else:
print('no user type implemented')
device = torch.device('cuda') if args['cuda'] else torch.device('cpu')
print(device)
writer = SummaryWriter(os.path.join(args['tensorboard_dir'], args['comment']+'_'+args['flavor']))
writer.add_text('Args', args['comment']+' '+ str(args)+ '\n')
save_path = args['checkpoint_path']
#==========loading data =============
policynet = Policy(args)
print('policy network model: ')
print(policynet.model)
writer.add_text('model' , str(policynet.model))
optimizer = optim.Adam(policynet.model.parameters(), lr=args['lr'] )
ftparams = []
if args['ft_tag']:
ftparams += [aa.tagweight, aa.tagbias, aa.lmda]
if args['ft_emb']:
if args['ft_rnn']:
for m in aa.model.modules():
if isinstance(m, nn.Dropout):
m.p = args['dropout']
if isinstance(m, SRU):
m.dropout = args['dropout']
ftparams += get_params(aa.model)
else:
ftparams += [aa.embedweight]
if args['ft_emb'] or args['ft_tag']:
print('Finetuning turned on ')
nnoptimizer = optim.Adam(ftparams, lr=args['ft_lr'] )
else:
nnoptimizer=None
for episode in range(1, args['episodes']):
if episode % ( args['test_every']) == 0:
batch = aa.testdata()
mode = 'test'
policynet.model.eval()
aa.model.eval()
elif episode % args['eval_every'] == 0:
batch = aa.valdata()
mode = 'val'
policynet.model.eval()
aa.model.eval()
else:
batch = aa.sampletrain(args['batch_size'])
mode = 'train'
policynet.model.train()
aa.model.train()
batch_s = len(batch[0])
rank_batch, p_fx_batch, _= infogain_rollout(batch, aa, user, args, mode)
action_batch = []
logp_batch = []
for cnt in range( 1, len(p_fx_batch)+1):
p_f_x = p_fx_batch[cnt-1]
if not args['ft_tag'] and not args['ft_emb']:
p_f_x = p_f_x.detach()
if cnt == args['max_step']:
action = np.zeros(batch_s)
log_pact = torch.zeros(batch_s).to(device)
else:
state = policynet.get_state(p_f_x, cnt)
action, log_pact, _ = policynet.select_action(state)
action_batch.append(action)
logp_batch.append(log_pact)
rewards, logp_bs, scalars = reprocess_withmask(action_batch, rank_batch, logp_batch, device, args)
if mode == 'train':
if nnoptimizer:
nnoptimizer.zero_grad()
scalars = policynet.update_policy(optimizer, rewards, logp_bs, scalars)
if nnoptimizer :
print('fintuning')
clip_grad_norm_([p for p in aa.model.parameters() if p.requires_grad], 3.0)
nnoptimizer.step()
if args['ft_tag']:
aa.tag_inference()
#print('w: {:.3f}, b: {:.3f}, lmd: {:.3f}'.format(aa.tagweight.item(), aa.tagbias.item(), aa.lmda.item()))
#writer.add_scalar('tagmodel/weight', aa.tagweight.item(), episode) #*args['batch_size'])
#writer.add_scalar('tagmodel/bias', aa.tagbias.item(), episode) #*args['batch_size'])
writer.add_scalar('tagmodel/lmda', aa.lmda.item(), episode) #*args['batch_size'])
writer.add_scalar('tagmodel/weight', aa.tagweight.data.norm(), episode) #*args['batch_size'])
writer.add_scalar('tagmodel/bias', aa.tagbias.data.norm(), episode) #*args['batch_size'])
if args['ft_emb']:
writer.add_scalar('tagmodel/embweight', aa.embedweight.data.norm(), episode) #*args['batch_size'])
if args['ft_rnn']:
writer.add_scalar('rnn-parameter/rnn_param_norm', compute_param_norm(aa.model), episode)
writer.add_scalar('rnn-parameter/rnn_grad_norm', compute_grad_norm(aa.model), episode)
if writer is not None:
for name, value in scalars:
writer.add_scalar(mode + name, value, episode) #*args['batch_size'])
if episode%args['print_every'] ==0:
print(mode)
print('Step: {:,} '.format(episode*args['batch_size']) +
' '.join(['{} = {:.3f}'.format(name, value) for name, value in scalars]))
if episode%args['save_every'] ==0:
torch.save( aa.state_dict(), args['checkpoint_dir'] + '/' + args['flavor']+'_aa.pt' )
save_path = save_checkpoint(policynet.model, optimizer, episode, episode*args['batch_size'], dict(scalars)['/suc_rate'], args, prev_save_path=save_path)
#save_path = save_checkpoint(model, optimizer, epoch, iter_count, auc05, config, prev_save_path=save_path)
if __name__ =='__main__':
parser = argparse.ArgumentParser()
argparser = _get_parser(parser)
args = argparser.parse_args()
args = vars(args)
args['cuda'] = not args.pop('no_cuda')
args['bidirectional'] = not args.pop('unidirectional')
for k,v in args.items():
if v == 'False':
args[k]= False
if v == 'True':
args[k]= True
if args['ft_tag'] or args['tag_pretrain'] == True:
args['taginfer'] = True
if args['ft_rnn'] == True:
args['ft_emb'] = True
print(args)
main(args)