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rubi_train.py
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import json
import os
import pickle
import time
from os.path import join
import torch
import torch.nn as nn
import utils
from torch.autograd import Variable
import numpy as np
from tqdm import tqdm
import random
import copy
def compute_score_with_logits(logits, labels):
logits = torch.argmax(logits, 1)
one_hots = torch.zeros(*labels.size()).cuda()
one_hots.scatter_(1, logits.view(-1, 1), 1)
scores = (one_hots * labels)
return scores
def train(model, train_loader, eval_loader,args,qid2type):
num_epochs=args.epochs
mode=args.mode
run_eval=args.eval_each_epoch
output=args.output
optim = torch.optim.Adamax(model.parameters())
logger = utils.Logger(os.path.join(output, 'log.txt'))
total_step = 0
best_eval_score = 0
if mode=='q_debias':
topq=args.topq
keep_qtype=args.keep_qtype
elif mode=='v_debias':
topv=args.topv
top_hint=args.top_hint
elif mode=='q_v_debias':
topv=args.topv
top_hint=args.top_hint
topq=args.topq
keep_qtype=args.keep_qtype
qvp=args.qvp
for epoch in range(num_epochs):
total_loss = 0
train_score = 0
t = time.time()
for i, (v, q, a, b, hintscore,type_mask,notype_mask,q_mask) in tqdm(enumerate(train_loader), ncols=100,
desc="Epoch %d" % (epoch + 1), total=len(train_loader)):
total_step += 1
#########################################
v = Variable(v).cuda().requires_grad_()
q = Variable(q).cuda()
q_mask=Variable(q_mask).cuda()
a = Variable(a).cuda()
b = Variable(b).cuda()
hintscore = Variable(hintscore).cuda()
type_mask=Variable(type_mask).float().cuda()
notype_mask=Variable(notype_mask).float().cuda()
#########################################
if mode=='updn':
pred, loss,_ = model(v, q, a, b, None)
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
batch_score = compute_score_with_logits(pred, a.data).sum()
train_score += batch_score
elif mode=='q_debias':
if keep_qtype==True:
sen_mask=type_mask
else:
sen_mask=notype_mask
## first train
pred, loss,word_emb = model(v, q, a, b, None)
word_grad = torch.autograd.grad((pred * (a > 0).float()).sum(), word_emb, create_graph=True)[0]
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
batch_score = compute_score_with_logits(pred, a.data).sum()
train_score += batch_score
## second train
word_grad_cam = word_grad.sum(2)
# word_grad_cam_sigmoid = torch.sigmoid(word_grad_cam * 1000)
word_grad_cam_sigmoid = torch.exp(word_grad_cam * sen_mask)
word_grad_cam_sigmoid = word_grad_cam_sigmoid * sen_mask
w_ind = word_grad_cam_sigmoid.sort(1, descending=True)[1][:, :topq]
q2 = copy.deepcopy(q_mask)
m1 = copy.deepcopy(sen_mask) ##[0,0,0...0,1,1,1,1]
m1.scatter_(1, w_ind, 0) ##[0,0,0...0,0,1,1,0]
m2 = 1 - m1 ##[1,1,1...1,1,0,0,1]
m3 = m1 * 18455 ##[0,0,0...0,0,18455,18455,0]
q2 = q2 * m2.long() + m3.long()
pred, _, _ = model(v, q2, None, b, None)
pred_ind = torch.argsort(pred, 1, descending=True)[:, :5]
false_ans = torch.ones(pred.shape[0], pred.shape[1]).cuda()
false_ans.scatter_(1, pred_ind, 0)
a2 = a * false_ans
q3 = copy.deepcopy(q)
q3.scatter_(1, w_ind, 18455)
## third train
pred, loss, _ = model(v, q3, a2, b, None)
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
elif mode=='v_debias':
## first train
pred, loss, _ = model(v, q, a, b, None)
visual_grad=torch.autograd.grad((pred * (a > 0).float()).sum(), v, create_graph=True)[0]
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
batch_score = compute_score_with_logits(pred, a.data).sum()
train_score += batch_score
##second train
v_mask = torch.zeros(v.shape[0], 36).cuda()
visual_grad_cam = visual_grad.sum(2)
hint_sort, hint_ind = hintscore.sort(1, descending=True)
v_ind = hint_ind[:, :top_hint]
v_grad = visual_grad_cam.gather(1, v_ind)
if topv==-1:
v_grad_score,v_grad_ind=v_grad.sort(1,descending=True)
v_grad_score=nn.functional.softmax(v_grad_score*10,dim=1)
v_grad_sum=torch.cumsum(v_grad_score,dim=1)
v_grad_mask=(v_grad_sum<=0.6).long()
v_grad_mask[:,0] = 1
v_mask_ind=v_grad_mask*v_ind
for x in range(a.shape[0]):
num=len(torch.nonzero(v_grad_mask[x]))
v_mask[x].scatter_(0,v_mask_ind[x,:num],1)
else:
v_grad_ind = v_grad.sort(1, descending=True)[1][:, :topv]
v_star = v_ind.gather(1, v_grad_ind)
v_mask.scatter_(1, v_star, 1)
pred, _, _ = model(v, q, None, b, v_mask)
pred_ind = torch.argsort(pred, 1, descending=True)[:, :5]
false_ans = torch.ones(pred.shape[0], pred.shape[1]).cuda()
false_ans.scatter_(1, pred_ind, 0)
a2 = a * false_ans
v_mask = 1 - v_mask
pred, loss, _ = model(v, q, a2, b, v_mask)
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
elif mode=='q_v_debias':
random_num = random.randint(1, 10)
if keep_qtype == True:
sen_mask = type_mask
else:
sen_mask = notype_mask
if random_num<=qvp:
## first train
pred, loss, word_emb = model(v, q, a, b, None)
word_grad = torch.autograd.grad((pred * (a > 0).float()).sum(), word_emb, create_graph=True)[0]
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
batch_score = compute_score_with_logits(pred, a.data).sum()
train_score += batch_score
## second train
word_grad_cam = word_grad.sum(2)
word_grad_cam_sigmoid = torch.exp(word_grad_cam * sen_mask)
word_grad_cam_sigmoid = word_grad_cam_sigmoid * sen_mask
w_ind = word_grad_cam_sigmoid.sort(1, descending=True)[1][:, :topq]
q2 = copy.deepcopy(q_mask)
m1 = copy.deepcopy(sen_mask) ##[0,0,0...0,1,1,1,1]
m1.scatter_(1, w_ind, 0) ##[0,0,0...0,0,1,1,0]
m2 = 1 - m1 ##[1,1,1...1,1,0,0,1]
m3 = m1 * 18455 ##[0,0,0...0,0,18455,18455,0]
q2 = q2 * m2.long() + m3.long()
pred, _, _ = model(v, q2, None, b, None)
pred_ind = torch.argsort(pred, 1, descending=True)[:, :5]
false_ans = torch.ones(pred.shape[0], pred.shape[1]).cuda()
false_ans.scatter_(1, pred_ind, 0)
a2 = a * false_ans
q3 = copy.deepcopy(q)
q3.scatter_(1, w_ind, 18455)
## third train
pred, loss, _ = model(v, q3, a2, b, None)
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
else:
## first train
pred, loss, _ = model(v, q, a, b, None)
visual_grad = torch.autograd.grad((pred * (a > 0).float()).sum(), v, create_graph=True)[0]
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
batch_score = compute_score_with_logits(pred, a.data).sum()
train_score += batch_score
##second train
v_mask = torch.zeros(v.shape[0], 36).cuda()
visual_grad_cam = visual_grad.sum(2)
hint_sort, hint_ind = hintscore.sort(1, descending=True)
v_ind = hint_ind[:, :top_hint]
v_grad = visual_grad_cam.gather(1, v_ind)
if topv == -1:
v_grad_score, v_grad_ind = v_grad.sort(1, descending=True)
v_grad_score = nn.functional.softmax(v_grad_score * 10, dim=1)
v_grad_sum = torch.cumsum(v_grad_score, dim=1)
v_grad_mask = (v_grad_sum <= 0.65).long()
v_grad_mask[:,0] = 1
v_mask_ind = v_grad_mask * v_ind
for x in range(a.shape[0]):
num = len(torch.nonzero(v_grad_mask[x]))
v_mask[x].scatter_(0, v_mask_ind[x,:num], 1)
else:
v_grad_ind = v_grad.sort(1, descending=True)[1][:, :topv]
v_star = v_ind.gather(1, v_grad_ind)
v_mask.scatter_(1, v_star, 1)
pred, _, _ = model(v, q, None, b, v_mask)
pred_ind = torch.argsort(pred, 1, descending=True)[:, :5]
false_ans = torch.ones(pred.shape[0], pred.shape[1]).cuda()
false_ans.scatter_(1, pred_ind, 0)
a2 = a * false_ans
v_mask = 1 - v_mask
pred, loss, _ = model(v, q, a2, b, v_mask)
if (loss != loss).any():
raise ValueError("NaN loss")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.25)
optim.step()
optim.zero_grad()
total_loss += loss.item() * q.size(0)
if mode=='updn':
total_loss /= len(train_loader.dataset)
else:
total_loss /= len(train_loader.dataset) * 2
train_score = 100 * train_score / len(train_loader.dataset)
if run_eval:
model.train(False)
results = evaluate(model, eval_loader, qid2type)
results["epoch"] = epoch + 1
results["step"] = total_step
results["train_loss"] = total_loss
results["train_score"] = train_score
model.train(True)
eval_score = results["score"]
bound = results["upper_bound"]
yn = results['score_yesno']
other = results['score_other']
num = results['score_number']
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, score: %.2f' % (total_loss, train_score))
if run_eval:
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
logger.write('\tyn score: %.2f other score: %.2f num score: %.2f' % (100 * yn, 100 * other, 100 * num))
if eval_score > best_eval_score:
model_path = os.path.join(output, 'model.pth')
torch.save(model.state_dict(), model_path)
best_eval_score = eval_score
def evaluate(model, dataloader, qid2type):
score = 0
upper_bound = 0
score_yesno = 0
score_number = 0
score_other = 0
total_yesno = 0
total_number = 0
total_other = 0
for v, q, a, b, qids, _ in tqdm(dataloader, ncols=100, total=len(dataloader), desc="eval"):
v = Variable(v, requires_grad=False).cuda()
q = Variable(q, requires_grad=False).cuda()
pred, _,_ = model(v, q, None, None, None)
batch_score = compute_score_with_logits(pred, a.cuda()).cpu().numpy().sum(1)
score += batch_score.sum()
upper_bound += (a.max(1)[0]).sum()
qids = qids.detach().cpu().int().numpy()
for j in range(len(qids)):
qid = qids[j]
typ = qid2type[str(qid)]
if typ == 'yes/no':
score_yesno += batch_score[j]
total_yesno += 1
elif typ == 'other':
score_other += batch_score[j]
total_other += 1
elif typ == 'number':
score_number += batch_score[j]
total_number += 1
else:
print('Hahahahahahahahahahaha')
score = score / len(dataloader.dataset)
upper_bound = upper_bound / len(dataloader.dataset)
score_yesno /= total_yesno
score_other /= total_other
score_number /= total_number
results = dict(
score=score,
upper_bound=upper_bound,
score_yesno=score_yesno,
score_other=score_other,
score_number=score_number,
)
return results