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train.py
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train.py
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import os
import time
import torch
import torch.nn as nn
import utils
from torch.autograd import Variable
import progressbar
def instance_bce_with_logits(logits, labels):
assert logits.dim() == 2
loss = nn.functional.binary_cross_entropy_with_logits(logits, labels)
loss *= labels.size(1)
return loss
def compute_score_with_logits(logits, labels):
logits = torch.max(logits, 1)[1].data # argmax
one_hots = torch.zeros(*labels.size()).cuda()
one_hots.scatter_(1, logits.view(-1, 1), 1)
scores = (one_hots * labels)
return scores
def compute_score_with_logits_paddingremoved(logits, labels):
logits = torch.max(logits, 1)[1].data # argmax
one_hots = torch.zeros(*labels.size()).cuda()
one_hots.scatter_(1, logits.view(-1, 1), 1)
scores = (one_hots * labels)
max_labels = torch.max(labels, 1)[1]
non_padding_idx = (max_labels != (labels.size(1)-1)).nonzero()
non_padded = torch.index_select(scores.sum(1), 0, non_padding_idx.squeeze())
final_score = non_padded.sum()
return final_score, non_padded.size(0)
def train(model, train_loader, eval_loader, num_epochs, output):
print('training started !')
utils.create_dir(output)
optim = torch.optim.Adamax(model.parameters())
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
total_steps = 0
for epoch in range(num_epochs):
total_loss = 0
train_score = 0
train_count = 0
t = time.time()
for i, (v, b, q, a, qid_set) in enumerate(train_loader):
total_steps += 1
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
a = Variable(a).cuda()
v = v.contiguous().view(-1, v.size(2), v.size(3))
b = b.contiguous().view(-1, b.size(2), b.size(3))
q = q.contiguous().view(-1, q.size(2))
a = a.contiguous().view(-1, a.size(2))
pred = model(v, b, q, a)
loss = instance_bce_with_logits(pred, a)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
batch_score, batch_count = compute_score_with_logits_paddingremoved(pred, a.cuda())
total_loss += loss.item() * v.size(0)
train_score += batch_score
train_count += batch_count
if total_steps % 500 == 0:
logger.write('train_loss: %.2f, steps:%.2f ' % (total_loss, total_steps))
total_loss /= len(train_loader.dataset)
train_score = 100 * train_score / train_count
model.train(False)
eval_score, bound = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time()-t))
logger.write('\ttrain_loss: %.2f, score: %.2f' % (total_loss, train_score))
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
if eval_score > best_eval_score:
model_path = os.path.join(output, 'tda_model_75.pth')
torch.save(model.state_dict(), model_path)
best_eval_score = eval_score
'''def evaluate(model, dataloader):
score = 0
upper_bound = 0
num_data = 0
print('evaluating....')
with torch.no_grad():
for v, b, q, a in iter(dataloader):
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
v = v.contiguous().view(-1, v.size(2), v.size(3))
b = b.contiguous().view(-1, b.size(2), b.size(3))
q = q.contiguous().view(-1, q.size(2))
a = a.contiguous().view(-1, a.size(2))
pred = model(v, b, q, None)
batch_score = compute_score_with_logits(pred, a.cuda()).sum()
score += batch_score
upper_bound += (a.max(1)[0]).sum()
num_data += pred.size(0)
score = score / len(dataloader.dataset)
upper_bound = upper_bound / len(dataloader.dataset)
return score, upper_bound'''
def evaluate(model, dataloader):
score = 0
count = 0
upper_bound = 0
num_data = 0
N = len(dataloader.dataset)* dataloader.dataset.max_q_count
M = dataloader.dataset.num_ans_candidates + 1
pred_all = torch.FloatTensor(N, M).zero_()
qIds = torch.IntTensor(N).zero_()
idx = 0
bar = progressbar.ProgressBar(max_value=N)
print('evaluating....')
with torch.no_grad():
for v, b, q, a, qid_set in iter(dataloader):
bar.update(idx)
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
v = v.contiguous().view(-1, v.size(2), v.size(3))
b = b.contiguous().view(-1, b.size(2), b.size(3))
q = q.contiguous().view(-1, q.size(2))
a = a.contiguous().view(-1, a.size(2))
qid_set = qid_set.contiguous().view(-1)
batch_size = v.size(0)
pred = model(v, b, q, None)
pred_all[idx:idx+batch_size,:].copy_(pred.data)
qIds[idx:idx+batch_size].copy_(qid_set)
idx += batch_size
batch_score, batch_count = compute_score_with_logits_paddingremoved(pred, a.cuda())
score += batch_score
count += batch_count
upper_bound += (a.max(1)[0]).sum()
num_data += pred.size(0)
bar.update(idx)
score = score / count
upper_bound = upper_bound / count
return score, upper_bound, pred_all, qIds