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main.py
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import argparse
from torchtext import datasets
from torchtext.datasets.babi import BABI20Field
from models.UTransformer import BabiUTransformer
from models.common_layer import NoamOpt
import torch.nn as nn
import torch
import numpy as np
from copy import deepcopy
def parse_config():
parser = argparse.ArgumentParser()
parser.add_argument("--cuda", action="store_true")
parser.add_argument("--save_path", type=str, default="save/")
parser.add_argument("--task", type=int, default=1)
parser.add_argument("--run_avg", type=int, default=10)
parser.add_argument("--heads", type=int, default=2)
parser.add_argument("--depth", type=int, default=128)
parser.add_argument("--filter", type=int, default=128)
parser.add_argument("--max_hops", type=int, default=6)
parser.add_argument("--batch_size", type=int, default=100)
parser.add_argument("--emb", type=int, default=128)
parser.add_argument("--lr", type=float, default=0.001)
parser.add_argument("--act", action="store_true")
parser.add_argument("--act_loss_weight", type=float, default=0.001)
parser.add_argument("--noam", action="store_true")
parser.add_argument("--verbose", action="store_true")
return parser.parse_args()
def get_babi_vocab(task):
text = BABI20Field(70)
train, val, test = datasets.BABI20.splits(text, root='.data', task=task, joint=False,
tenK=True, only_supporting=False)
text.build_vocab(train)
vocab_len = len(text.vocab.freqs)
# print("VOCAB LEN:",vocab_len )
return vocab_len + 1
def evaluate(model, criterion, loader):
model.eval()
acc = []
loss = []
for b in loader:
story, query, answer = b.story,b.query,b.answer.squeeze()
if(config.cuda): story, query, answer = story.cuda(), query.cuda(), answer.cuda()
pred_prob = model(story, query)
loss.append(criterion(pred_prob[0], answer).item())
pred = pred_prob[1].data.max(1)[1] # max func return (max, argmax)
acc.append( pred.eq(answer.data).cpu().numpy() )
acc = np.concatenate(acc)
acc = np.mean(acc)
loss = np.mean(loss)
return acc,loss
def main(config):
vocab_len = get_babi_vocab(config.task)
train_iter, val_iter, test_iter = datasets.BABI20.iters(batch_size=config.batch_size,
root='.data',
memory_size=70,
task=config.task,
joint=False,
tenK=False,
only_supporting=False,
sort=False,
shuffle=True)
model = BabiUTransformer(num_vocab=vocab_len,
embedding_size=config.emb,
hidden_size=config.emb,
num_layers=config.max_hops,
num_heads=config.heads,
total_key_depth=config.depth,
total_value_depth=config.depth,
filter_size=config.filter,
act=config.act)
if(config.verbose):
print(model)
print("ACT",config.act)
if(config.cuda): model.cuda()
criterion = nn.CrossEntropyLoss()
if(config.noam):
opt = NoamOpt(config.emb, 1, 4000, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
else:
opt = torch.optim.Adam(model.parameters(),lr=config.lr)
if(config.verbose):
acc_val, loss_val = evaluate(model, criterion, val_iter)
print("RAND_VAL ACC:{:.4f}\t RAND_VAL LOSS:{:.4f}".format(acc_val, loss_val))
correct = []
loss_nb = []
cnt_batch = 0
avg_best = 0
cnt = 0
model.train()
for b in train_iter:
story, query, answer = b.story,b.query,b.answer.squeeze()
if(config.cuda): story, query, answer = story.cuda(), query.cuda(), answer.cuda()
if(config.noam):
opt.optimizer.zero_grad()
else:
opt.zero_grad()
pred_prob = model(story, query)
loss = criterion(pred_prob[0], answer)
if(config.act):
R_t = pred_prob[2][0]
N_t = pred_prob[2][1]
p_t = R_t + N_t
avg_p_t = torch.sum(torch.sum(p_t,dim=1)/p_t.size(1))/p_t.size(0)
loss += config.act_loss_weight * avg_p_t.item()
loss.backward()
opt.step()
## LOG
loss_nb.append(loss.item())
pred = pred_prob[1].data.max(1)[1] # max func return (max, argmax)
correct.append(np.mean(pred.eq(answer.data).cpu().numpy()))
cnt_batch += 1
if(cnt_batch % 10 == 0):
acc = np.mean(correct)
loss_nb = np.mean(loss_nb)
if(config.verbose):
print("TRN ACC:{:.4f}\tTRN LOSS:{:.4f}".format(acc, loss_nb))
acc_val, loss_val = evaluate(model, criterion, val_iter)
if(config.verbose):
print("VAL ACC:{:.4f}\tVAL LOSS:{:.4f}".format(acc_val, loss_val))
if(acc_val > avg_best):
avg_best = acc_val
weights_best = deepcopy(model.state_dict())
cnt = 0
else:
cnt += 1
if(cnt == 45): break
if(avg_best == 1.0): break
correct = []
loss_nb = []
cnt_batch = 0
model.load_state_dict({ name: weights_best[name] for name in weights_best })
acc_test, loss_test = evaluate(model, criterion, test_iter)
if(config.verbose):
print("TST ACC:{:.4f}\tTST LOSS:{:.4f}".format(acc_val, loss_val))
return acc_test
if __name__ == "__main__":
config = parse_config()
for t in range(1,21):
config.task = t
acc = []
for i in range(config.run_avg):
acc.append(main(config))
print("Noam",config.noam,"ACT",config.act,"Task:",config.task,"Max:",max(acc),"Mean:",np.mean(acc),"Std:",np.std(acc))