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train_utils.py
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train_utils.py
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import torch
import misc.utils as utils
def train_generator(gen_model, gen_optimizer, crit, loader, grad_clip=0.1):
data = loader.get_batch('train')
torch.cuda.synchronize()
tmp = [data['fc_feats'], data['att_feats'], data['img_feats'], data['box_feats'],
data['labels'], data['masks'], data['att_masks'], data['activities']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, img_feats, box_feats, labels, masks, att_masks, activities = tmp
sent_num = data['sent_num']
wrapped = data['bounds']['wrapped']
gen_optimizer.zero_grad()
seq = gen_model(fc_feats, img_feats, box_feats, activities, labels)
seq = utils.align_seq(sent_num, seq)
labels = utils.align_seq(sent_num, labels)
masks = utils.align_seq(sent_num, masks)
loss = crit(seq, labels[:, 1:], masks[:, 1:])
loss.backward()
gen_loss = loss.item()
utils.clip_gradient(gen_optimizer, grad_clip)
gen_optimizer.step()
torch.cuda.synchronize()
return gen_loss, wrapped, sent_num
def train_discriminator(dis_model, gen_model, dis_optimizer, gan_crit, loader,
temperature=1.0,gen_weight=0.5, mm_weight=0.5,neg_weight=0.5,
use_vis=True,use_lang=True,use_pair=True,grad_clip=0.1):
dis_model.train()
gen_model.eval()
data = loader.get_batch('train')
sent_num = data['sent_num']
torch.cuda.synchronize()
tmp = [data['fc_feats'],data['mm_fc_feats'], data['img_feats'], data['box_feats'], data['att_feats'], data['labels'], data['mm_labels'],
data['att_masks'], data['activities'], data['mm_img_feats'], data['mm_box_feats'], data['mm_activities']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, mm_fc_feats, img_feats, box_feats, att_feats, labels, mm_labels, att_masks, activities, \
mm_img_feats, mm_box_feats, mm_activities = tmp
label = torch.zeros(sum(sent_num)).cuda()
dis_v_loss = 0
dis_l_loss = 0
dis_p_loss = 0
accuracies = {}
wrapped = data['bounds']['wrapped']
with torch.no_grad():
# generated captions
gen_labels, sample_logprobs = gen_model(fc_feats, img_feats, box_feats, activities,
opt={'sample_max':0,'temperature':temperature}, mode='sample')
masks = utils.generate_paragraph_mask(sent_num,gen_labels)
gen_labels = torch.mul(gen_labels, masks)
# visually mismatched negatives from generator
mm_gen_labels, mm_sample_logprobs = gen_model(mm_fc_feats, mm_img_feats, mm_box_feats, mm_activities,
opt={'sample_max': 0, 'temperature': temperature}, mode='sample')
mm_masks = utils.generate_paragraph_mask(sent_num,mm_gen_labels)
mm_gen_labels = torch.mul(mm_gen_labels, mm_masks)
# gen or gt sentence as language negatives
neg_lang_labels = utils.get_neg_lang(sent_num,labels,gen_labels)
# only gt sentence pair as pairwise negatives
neg_pair_labels = torch.from_numpy(utils.get_neg_pair(sent_num, data['labels'])).cuda()
# update visual discriminator with [gt (real), gt mismatch (fake), gen mismatch (fake)]
if use_vis:
dis_optimizer.zero_grad()
# mismatch_gt
v_mm_score = dis_model(fc_feats, img_feats, box_feats, activities, mm_labels[:, :, 1:-1])
v_mm_score = utils.align_seq(sent_num, v_mm_score)
v_loss_3 = mm_weight * gan_crit(v_mm_score, label)
v_loss_3.backward()
dis_v_loss += v_loss_3.item()
# mismatch_gen
v_mm_gen_score = dis_model(fc_feats, img_feats, box_feats, activities, mm_gen_labels)
v_mm_gen_score = utils.align_seq(sent_num, v_mm_gen_score)
v_loss_1 = gen_weight * gan_crit(v_mm_gen_score, label)
v_loss_1.backward()
dis_v_loss += v_loss_1.item()
# gt
label.fill_(1)
v_gt_score = dis_model(fc_feats, img_feats, box_feats, activities, labels[:, :, 1:-1])
v_gt_score = utils.align_seq(sent_num, v_gt_score)
v_loss_2 = gan_crit(v_gt_score, label)
v_loss_2.backward()
dis_v_loss += v_loss_2.item()
# update discriminator
utils.clip_gradient(dis_optimizer, grad_clip)
dis_optimizer.step()
torch.cuda.synchronize()
# update language discriminator with [gt (real), gen(fake), neg (fake)]
if use_lang:
dis_optimizer.zero_grad()
# gen
label.fill_(0)
l_gen_score = dis_model(gen_labels, mode='lang')
l_gen_score = utils.align_seq(sent_num, l_gen_score)
l_loss_1 = gan_crit(l_gen_score, label)
l_loss_1.backward()
dis_l_loss += l_loss_1.item()
# negative sample
l_neg_score = dis_model(neg_lang_labels, mode='lang')
l_neg_score = utils.align_seq(sent_num, l_neg_score)
l_loss_3 = neg_weight * gan_crit(l_neg_score, label)
l_loss_3.backward()
dis_l_loss += l_loss_3.item()
# gt
label.fill_(1)
l_gt_score = dis_model(labels[:, :, 1:-1], mode='lang')
l_gt_score = utils.align_seq(sent_num, l_gt_score)
l_loss_2 = gan_crit(l_gt_score, label)
l_loss_2.backward()
dis_l_loss += l_loss_2.item()
# update discriminator
utils.clip_gradient(dis_optimizer, grad_clip)
dis_optimizer.step()
torch.cuda.synchronize()
# update pairwise discriminator [gt (real), gen(fake), neg(fake)]
if use_pair:
dis_optimizer.zero_grad()
# gen
label.fill_(0)
p_gen_score = dis_model(gen_labels, mode='par')
p_gen_score = utils.align_seq(sent_num, p_gen_score)
p_loss_1 = gan_crit(p_gen_score, label)
p_loss_1.backward()
dis_p_loss += p_loss_1.item()
# negative sample
p_neg_score = dis_model( neg_pair_labels[:,:,1:-1], mode='par')
p_neg_score = utils.align_seq(sent_num, p_neg_score)
p_loss_3 = neg_weight * gan_crit(p_neg_score, label)
p_loss_3.backward()
dis_p_loss += p_loss_3.item()
# gt
label.fill_(1)
s = 0
for n in sent_num:
label[s] = 0 # first sentence is assigned score 0
s+=n
p_gt_score = dis_model(labels[:, :, 1:-1], mode='par')
p_gt_score = utils.align_seq(sent_num, p_gt_score)
p_loss_2 = gan_crit(p_gt_score, label)
p_loss_2.backward()
dis_p_loss += p_loss_2.item()
# update discriminator
utils.clip_gradient(dis_optimizer, grad_clip)
dis_optimizer.step()
torch.cuda.synchronize()
# calculate accuracy (ground truth scores higher than negative inputs)
with torch.no_grad():
if use_vis:
v_gen_accuracy = torch.gt(v_gt_score, v_mm_gen_score).cpu().numpy().mean()
v_mm_accuracy = torch.gt(v_gt_score, v_mm_score).cpu().numpy().mean()
accuracies['dis_v_gen_accuracy'] = v_gen_accuracy
accuracies['dis_v_mm_accuracy'] = v_mm_accuracy
if use_lang:
l_gen_accuracy = torch.gt(l_gt_score, l_gen_score).cpu().numpy().mean()
l_neg_accuracy = torch.gt(l_gt_score, l_neg_score).cpu().numpy().mean()
accuracies['dis_l_gen_accuracy'] = l_gen_accuracy
accuracies['dis_l_neg_accuracy'] = l_neg_accuracy
if use_pair:
p_gen_accuracy = torch.gt(p_gt_score, p_gen_score).cpu().numpy().mean()
p_neg_accuracy = torch.gt(p_gt_score, p_neg_score).cpu().numpy().mean()
accuracies['dis_p_gen_accuracy'] = p_gen_accuracy
accuracies['dis_p_neg_accuracy'] = p_neg_accuracy
return [dis_v_loss, dis_l_loss, dis_p_loss], accuracies, wrapped, sent_num