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train.py
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train.py
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import argparse
import logging
import os
import sys
import numpy as np
import torch
from torch.autograd import Variable
from torch.optim import Adam
from torch.utils.data import DataLoader
sys.path.append('..')
from EMNQA import util
from EMNQA.data_set import QAdataset
from EMNQA.model import DMN
logger = logging.getLogger()
logger.setLevel(logging.INFO)
fmt = logging.Formatter('%(asctime)s: [ %(message)s ]', '%m/%d/%Y %I:%M:%S %p')
console = logging.StreamHandler()
console.setFormatter(fmt)
logger.addHandler(console)
HIDDEN_SIZE = 80
BATCH_SIZE = 64
LR = 0.001
EPOCH = 50
NUM_EPISODE = 3
EARLY_STOPPING = False
DATA_WORKS = 4
USE_CUDA = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if USE_CUDA else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if USE_CUDA else torch.ByteTensor
def prepare_data(filename): # data -> dataloader
data_p = util.bAbI_data_load(filename)
word2idx, idx2word = util.build_words_dict(data_p)
data_set = QAdataset(data_p, word2idx)
train_dataloader = DataLoader(data_set,
batch_size=BATCH_SIZE,
# sampler=train_sampler,
num_workers=DATA_WORKS,
collate_fn=util.pad_to_batch,
pin_memory=USE_CUDA, )
return train_dataloader, word2idx
def seq2variable(data, word2id): # data -> variable
for t in data:
for i, f in enumerate(t[0]):
t[0][i] = util.prepare_sequence(f, word2id)
t[1] = util.prepare_sequence(t[1], word2id)
t[2] = util.prepare_sequence(t[2], word2id)
def train_from_scratch(filename): # training
train_data = util.bAbI_data_load(filename)
test_data = util.bAbI_data_load(args_dic.test_data_file)
word2idx, idx2word = util.build_words_dict(train_data)
test_data = util.bAbI_data_test(test_data, word2idx)
seq2variable(train_data, word2idx)
print('Model init.')
model = DMN(HIDDEN_SIZE, len(word2idx), len(word2idx), word2idx)
if USE_CUDA:
model = model.cuda()
model.init_weight()
# data_loader = prepare_data(filename)
optimizer = Adam(model.parameters(), lr=LR)
loss_fun = torch.nn.CrossEntropyLoss(ignore_index=0)
EARLY_STOPPING = False
print('Begin Training!')
for i in range(EPOCH):
losses = []
if EARLY_STOPPING: break
for j, batch in enumerate(util.getbatch(train_data, BATCH_SIZE)):
facts, fact_masks, questions, question_masks, answers = util.pad_to_batch(batch, word2idx)
model.zero_grad()
pred = model(facts, fact_masks, questions, question_masks, answers.size(1), NUM_EPISODE, True)
loss = loss_fun(pred, answers.view(-1))
losses.append(loss.data.tolist()[0])
loss.backward()
optimizer.step()
if j % 100 == 0:
logger.info("[%d/%d] mean_loss : %0.2f" % (i, EPOCH, np.mean(losses)))
# print("[%d/%d] mean_loss : %0.2f" % (i, EPOCH, np.mean(losses)))
if np.mean(losses) < 0.01:
EARLY_STOPPING = True
print("Early Stopping!")
torch.save({'state_dict': model.state_dict(), 'word2idx': model.word2index},
'earlystoping-%s' % args_dic.model_file)
break
losses = []
if not EARLY_STOPPING:
model.state_dict(destination=args_dic.model_file)
print('Training over. To Testing...')
evaluation(word2idx, model, test_data)
print('OK .system finish.')
def pad_fact(fact, x_to_ix): # this is for inference
max_x = max([s.size(1) for s in fact])
x_p = []
for i in range(len(fact)):
if fact[i].size(1) < max_x:
x_p.append(
torch.cat([fact[i], Variable(LongTensor([x_to_ix['<PAD>']] * (max_x - fact[i].size(1)))).view(1, -1)],
1))
else:
x_p.append(fact[i])
fact = torch.cat(x_p)
fact_mask = torch.cat(
[Variable(ByteTensor(tuple(map(lambda s: s == 0, t.data))), volatile=False) for t in fact]).view(fact.size(0),
-1)
return fact, fact_mask
def evaluation(word2id, model, test_data):
accuracy = 0
for d in test_data:
facts, facts_mask = pad_fact(d[0], word2id)
question = d[1]
question_mask = Variable(ByteTensor([0] * d[1].size(1)), volatile=False).unsqueeze(0)
answer = d[2].squeeze(0) # ??
model.zero_grad()
score = model([facts], [facts_mask], question, question_mask, num_decode=answer.size(0))
if score.max(1)[1].data.tolist() == answer.data.tolist():
accuracy += 1
print(accuracy / len(test_data) * 100)
def train_from_model():
print('Model init.')
m = torch.load('earlystoping-EMNQA.model', map_location=lambda storage, loc: storage)
word2idx = m['word2idx']
model = DMN(HIDDEN_SIZE, len(word2idx), len(word2idx), word2idx)
model.load_state_dict(state_dict=m['state_dict'])
logger.info('Load from state dict over. Evaluation now')
test_data = util.bAbI_data_load(args_dic.test_data_file)
test_data = util.bAbI_data_test(test_data, word2idx)
evaluation(word2idx, model, test_data=test_data)
if __name__ == '__main__':
# data_file = 'qa5_three-arg-relations_train.txt'
args = argparse.ArgumentParser()
args.add_argument('--train-data-file', type=str, default='qa5_three-arg-relations_train.txt',
help='Input the train QA data')
args.add_argument('--test-data-file', type=str, default='qa5_three-arg-relations_test.txt',
help='Input the test QA data')
args.add_argument('--model-file', type=str, default='EMNQA.model',
help='Model file saved')
args_dic = args.parse_args()
data_file = args_dic.train_data_file
logger.info('Use CUDA : %s' % USE_CUDA)
if os.path.isfile('earlystoping-EMNQA.model'):
logger.info("Find the model state dict . init model...")
train_from_model(data_file)
else:
logger.info('No model state dict be Found .init model from scratch!')
train_from_scratch(data_file)