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train.py
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train.py
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"""
This code is modified from Hengyuan Hu's repository.
https://github.com/hengyuan-hu/bottom-up-attention-vqa
"""
import os
import time
import itertools
import torch
import torch.nn as nn
import utils
import torch.optim.lr_scheduler as lr_scheduler
def instance_bce_with_logits(logits, labels, reduction='mean'):
assert logits.dim() == 2
loss = nn.functional.binary_cross_entropy_with_logits(logits, labels, reduction=reduction)
if reduction == 'mean':
loss *= labels.size(1)
return loss
def compute_score_with_logits(logits, labels):
logits = torch.max(logits, 1)[1].data # argmax
#import pdb;pdb.set_trace()
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, num_epochs, output, opt=None, s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = 1
lr_decay_epochs = range(10,20,lr_decay_step) if eval_loader is not None else range(10,20,lr_decay_step)
gradual_warmup_steps = [0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
woman=0
woman_true=0
woman_man=0
woman_other=0
man=0
man_true=0
man_woman=0
man_other=0
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' % optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] =1e-3
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
import pickle as pkl
from PIL import Image,ImageDraw
lab2ans=pkl.load(open("./data/cache/trainval_label2ans.pkl",'rb'))
'''
for i, (v, b, q, a,ques,im,g,gender) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
visual_pred, att = model(v, b, q, a)
loss = instance_bce_with_logits(visual_pred, a)
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score = compute_score_with_logits(visual_pred, a.data).sum()
total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
'''
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
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, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, train_score))
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
if (eval_loader is not None and eval_score > best_eval_score) or (eval_loader is None and epoch >= saving_epoch):
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
@torch.no_grad()
def evaluate(model, dataloader):
score = 0
upper_bound = 0
num_data = 0
entropy = None
woman=0
woman_true=0
woman_man=0
woman_other=0
man=0
man_true=0
man_woman=0
man_other=0
import pickle as pkl
from PIL import Image,ImageDraw
lab2ans=pkl.load(open("./data/cache/trainval_label2ans.pkl",'rb'))
fr=open("race.txt",'w')
fw=open("woman.txt",'w')
fwx=open("woman_wrong.txt",'w')
fwo=open("woman_true.txt",'w')
fm=open("man.txt",'w')
fmx=open("man_wrong.txt",'w')
fmo=open("man_true.txt",'w')
#total_answer=torch.zeros(3129)
for i, (v, b, q, a,ques,im,g,gender) in enumerate(dataloader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
visual_pred, att = model(v, b, q, None)
batch_score = compute_score_with_logits(visual_pred, a.cuda()).sum()
score += batch_score.item()
upper_bound += (a.max(1)[0]).sum().item()
num_data += visual_pred.size(0)
woman_words=[' woman ',' women ',' female ',' girl ',' lady ',' she ',' her ',' hers ',' ladies ',' woman?',' women?',' female?',' girl?',' lady?',' she?',' her?',' hers?',' ladies?','Woman ','Women ','Female ','Girl ','Lady ','She ','Her ','Hers ','Ladies ',' woman\'s',' women\'s',' female\'s',' girl\'s',' lady\'s',' she\'s',' ladies\'',' woman\'s?',' women\'s?',' female\'s?',' girl\'s?',' lady\'s?',' she\'s?',' ladies\'?','Woman\'s','Women\'s','Female\'s','Girl\'s','Lady\'s','She\'s','Her\'s','Hers\'s','Ladies\'',' girls ','Girls ',' girls?',' girls\'','girls\'?']
man_words=[' man ',' men ',' male ',' boy ',' he ',' his ',' gentleman ',' gentlemen ',' man?',' men?',' male?',' boy?',' he?',' his?',' gentleman?',' gentlemen?','Man ','Men ','Male ','Boy ','He ','His ','Gentleman ','Gentlemen ',' man\'s',' men\'s',' male\'s',' boy\'s',' his ',' gentleman\'s',' gentlemen\'s',' man\'s?',' men\'s?',' male\'s?',' boy\'s?',' gentleman\'s?',' gentlemen\'s?','Man\'s','Men\'s','Male\'s','Boy\'s','Gentleman\'s','Gentlemen\'s',' boys ','Boys ',' boys\'',' boys?',' boys\'?']
#total_answer=total_answer+a.sum(0)
for j in range(len(v)):
'''
if 'race' in ques[j] or 'ethnicity' in ques[j] or 'african' in lab2ans[int(a[j].argmax())] or 'asian' in lab2ans[int(a[j].argmax())] or 'american' in lab2ans[int(a[j].argmax())] or 'european' in lab2ans[int(a[j].argmax())] or 'white person' in lab2ans[int(a[j].argmax())] or 'black person' in lab2ans[int(a[j].argmax())]:
fr.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
woman=woman+1
fw.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
woman=woman+1
if visual_pred[j].argmax().item()==a[j].argmax().item():
fwo.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
woman_true=woman_true+1
else:
fwx.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
woman_other=woman_other+1
'''
for k in man_words:
if k in ques[j] :
#import pdb;pdb.set_trace()
man=man+1
if visual_pred[j].argmax()==a[j].argmax().cuda():
man_true=man_true+1
else:
man_other=man_other+1
try:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/val2014/COCO_val2014_"+str(int(im[j])).zfill(12)+".jpg")
except:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/train2014/COCO_train2014_"+str(int(im[j])).zfill(12)+".jpg")
width,height=img.size
draw=ImageDraw.Draw(img)
topk_att,topk_idx=torch.topk(att,5,1)
for k in range(5):
leftx=int(float(b[j][int(topk_idx[j][k])][0])*width)
lefty=int(float(b[j][int(topk_idx[j][k])][1])*height)
rightx=int(float(b[j][int(topk_idx[j][k])][2])*width)
righty=int(float(b[j][int(topk_idx[j][k])][3])*height)
coord=[leftx,lefty,leftx,righty,rightx,righty,rightx,lefty,leftx,lefty]
draw.line(coord,'red',width=int(topk_att[j][k]*10))
img.save("./man_question/"+str(int(im[j]))+'_'+str(ques[j])+'_'+str(lab2ans[int(a[j].argmax())])+'_'+lab2ans[int(visual_pred[j].argmax())]+".jpg")
for k in woman_words:
if k in ques[j] :
woman=woman+1
if visual_pred[j].argmax()==a[j].argmax().cuda():
woman_true=woman_true+1
else:
woman_other=woman_other+1
try:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/val2014/COCO_val2014_"+str(int(im[j])).zfill(12)+".jpg")
except:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/train2014/COCO_train2014_"+str(int(im[j])).zfill(12)+".jpg")
width,height=img.size
draw=ImageDraw.Draw(img)
topk_att,topk_idx=torch.topk(att,5,1)
for k in range(5):
leftx=int(float(b[j][int(topk_idx[j][k])][0])*width)
lefty=int(float(b[j][int(topk_idx[j][k])][1])*height)
rightx=int(float(b[j][int(topk_idx[j][k])][2])*width)
righty=int(float(b[j][int(topk_idx[j][k])][3])*height)
coord=[leftx,lefty,leftx,righty,rightx,righty,rightx,lefty,leftx,lefty]
draw.line(coord,'red',width=int(topk_att[j][k]*10))
img.save("./man_question/"+str(int(im[j]))+'_'+str(ques[j])+'_'+str(lab2ans[int(a[j].argmax())])+'_'+lab2ans[int(visual_pred[j].argmax())]+".jpg")
if lab2ans[int(a[j].argmax())]=='woman' or lab2ans[int(a[j].argmax())]=='women' or lab2ans[int(a[j].argmax())]=='female' or lab2ans[int(a[j].argmax())]=='girl':
fw.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
woman=woman+1
if lab2ans[int(visual_pred[j].argmax())]=='woman' or lab2ans[int(visual_pred[j].argmax())]=='women' or lab2ans[int(visual_pred[j].argmax())]=='female' or lab2ans[int(visual_pred[j].argmax())]=='girl':
woman_true=woman_true+1
fwo.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
elif lab2ans[int(visual_pred[j].argmax())]=='man' or lab2ans[int(visual_pred[j].argmax())]=='men' or lab2ans[int(visual_pred[j].argmax())]=='male' or lab2ans[int(visual_pred[j].argmax())]=='boy':
woman_man=woman_man+1
fwx.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
try:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/val2014/COCO_val2014_"+str(int(im[j])).zfill(12)+".jpg")
except:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/train2014/COCO_train2014_"+str(int(im[j])).zfill(12)+".jpg")
width,height=img.size
draw=ImageDraw.Draw(img)
topk_att,topk_idx=torch.topk(att,5,1)
for k in range(5):
leftx=int(float(b[j][int(topk_idx[j][k])][0])*width)
lefty=int(float(b[j][int(topk_idx[j][k])][1])*height)
rightx=int(float(b[j][int(topk_idx[j][k])][2])*width)
righty=int(float(b[j][int(topk_idx[j][k])][3])*height)
coord=[leftx,lefty,leftx,righty,rightx,righty,rightx,lefty,leftx,lefty]
draw.line(coord,'red',width=int(topk_att[j][k]*10))
img.save("./woman/"+str(int(im[j]))+'_'+str(ques[j])+'_'+str(lab2ans[int(a[j].argmax())])+'_'+lab2ans[int(visual_pred[j].argmax())]+".jpg")
else:
woman_other=woman_other+1
fwx.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
if lab2ans[int(a[j].argmax())]=='man' or lab2ans[int(a[j].argmax())]=='men' or lab2ans[int(a[j].argmax())]=='male' or lab2ans[int(a[j].argmax())]=='boy':
fm.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
man=man+1
if lab2ans[int(visual_pred[j].argmax())]=='woman' or lab2ans[int(visual_pred[j].argmax())]=='women' or lab2ans[int(visual_pred[j].argmax())]=='female' or lab2ans[int(visual_pred[j].argmax())]=='girl':
man_woman=man_woman+1
fmx.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
try:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/val2014/COCO_val2014_"+str(int(im[j])).zfill(12)+".jpg")
except:
img=Image.open("/media/cvpr-pu/1084447f-bfd3-49a1-814a-4fac086a5992/sungho/ban-vqa-master/tools/data/train2014/COCO_train2014_"+str(int(im[j])).zfill(12)+".jpg")
width,height=img.size
draw=ImageDraw.Draw(img)
topk_att,topk_idx=torch.topk(att,5,1)
for k in range(5):
leftx=int(float(b[j][int(topk_idx[j][k])][0])*width)
lefty=int(float(b[j][int(topk_idx[j][k])][1])*height)
rightx=int(float(b[j][int(topk_idx[j][k])][2])*width)
righty=int(float(b[j][int(topk_idx[j][k])][3])*height)
coord=[leftx,lefty,leftx,righty,rightx,righty,rightx,lefty,leftx,lefty]
draw.line(coord,'red',width=int(topk_att[j][k]*10))
img.save("./man/"+str(int(im[j]))+'_'+str(ques[j])+'_'+str(lab2ans[int(a[j].argmax())])+'_'+lab2ans[int(visual_pred[j].argmax())]+".jpg")
elif lab2ans[int(visual_pred[j].argmax())]=='man' or lab2ans[int(visual_pred[j].argmax())]=='men' or lab2ans[int(visual_pred[j].argmax())]=='male' or lab2ans[int(visual_pred[j].argmax())]=='boy':
man_true=man_true+1
fmo.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
else:
man_other=man_other+1
fmx.write(str(ques[j]) + ' '+str(lab2ans[int(a[j].argmax())])+' '+str(lab2ans[int(visual_pred[j].argmax())])+'\n\n')
import pdb;pdb.set_trace()
score = score / len(dataloader.dataset)
upper_bound = upper_bound / len(dataloader.dataset)
fr.close()
fw.close()
fwx.close()
fwo.close()
fm.close()
fmx.close()
fmo.close()
print("woman",woman)
print("woman_true",woman_true)
print("woman_man",woman_man)
print("woman_other",woman_other)
print("man",man)
print("man_true",man_true)
print("man_woman",man_woman)
print("man_other",man_other)
import pdb;pdb.set_trace()
return score, upper_bound, None
def calc_entropy(att): # size(att) = [b x g x v x q]
sizes = att.size()
eps = 1e-8
p = att.view(-1, sizes[1], sizes[2] * sizes[3])
return (-p * (p+eps).log()).sum(2).sum(0) # g