def train(train_loader, args, model, criterion, D_criterion, center_loss,
optimizer, optimizer1, epoch, num_epochs, mymodel, discriminator):
since = time.time()
running_loss0 = AverageMeter()
running_loss1 = AverageMeter()
running_loss2 = AverageMeter()
running_loss3 = AverageMeter()
running_loss4 = AverageMeter()
running_loss5 = AverageMeter()
running_loss6 = AverageMeter()
running_loss = AverageMeter()
running_myloss = AverageMeter()
running_lossC = AverageMeter()
log = Log()
model.train()
mymodel.train()
discriminator.train()
img_onehot = torch.zeros(1, 4)
vid_onehot = torch.zeros(1, 4)
aud_onehot = torch.zeros(1, 4)
txt_onehot = torch.zeros(1, 4)
img_onehot[0][0] = 1
vid_onehot[0][1] = 1
aud_onehot[0][2] = 1
txt_onehot[0][3] = 1
sumx = 0
sumy = 0
for (i, (input, input1, input2, input3,
target)) in enumerate(train_loader):
input_var = Variable(input.cuda())
input_var1 = Variable(input1.cuda())
input_var2 = Variable(input2.cuda())
input_var3 = Variable(input3.cuda())
target_var = Variable(target.cuda())
target_var1 = Variable(target.cuda())
target_var2 = Variable(target.cuda())
target_var3 = Variable(target.cuda())
outputs = model(input_var, input_var1, input_var2)
myloss, mytxt = mymodel.loss(input_var3, target_var3)
size = int(outputs.size(0) / 3)
img = outputs.narrow(0, 0, size)
vid = outputs.narrow(0, size, size)
aud = outputs.narrow(0, 2 * size, size)
loss0 = criterion(img, target_var)
loss1 = criterion(vid, target_var1)
loss2 = criterion(aud, target_var2)
loss4 = loss0 + loss1 + loss2 + myloss
if (args.loss_choose == 'r'):
loss6, _ = ranking_loss(targets,
outputs,
margin=1,
margin2=0.5,
squared=False)
loss6 = loss6 * 0.1
else:
loss6 = 0.0
loss = loss4 + loss6
mysize1, mysize2 = img.size()
real_label = torch.ones(mysize1)
lossC = D_criterion(torch.sum(discriminator(img) * img_onehot.repeat(mysize1, 1).cuda(), dim=1),real_label.cuda()) \
+ D_criterion(torch.sum(discriminator(vid) * vid_onehot.repeat(mysize1, 1).cuda(), dim=1), real_label.cuda()) \
+ D_criterion(torch.sum(discriminator(aud) * aud_onehot.repeat(mysize1, 1).cuda(), dim=1), real_label.cuda()) \
+ D_criterion(torch.sum(discriminator(mytxt) * txt_onehot.repeat(mysize1, 1).cuda(), dim=1), real_label.cuda())
g_loss = loss - lossC
d_loss = -(loss - lossC)
batchsize = input_var.size(0)
running_loss0.update(loss0.item(), batchsize)
running_loss1.update(loss1.item(), batchsize)
running_loss2.update(loss2.item(), batchsize)
running_loss4.update(loss4.item(), batchsize)
if (args.loss_choose == 'r'):
running_loss6.update(loss6.item(), batchsize)
running_loss.update(loss.item(), batchsize)
running_myloss.update(myloss.item(), batchsize)
running_lossC.update(lossC.item(), batchsize)
optimizer.zero_grad()
g_loss.backward(retain_graph=True)
optimizer.step()
optimizer1.zero_grad()
d_loss.backward()
optimizer1.step()
sumx += mymodel.loss_n_acc(input_var3, target_var3)[1]
sumy += input_var3.size()[0]
mytext_acc = sumx / sumy
if (i % args.print_freq == 0):
print('-' * 20)
print('Epoch [{0}/{1}][{2}/{3}]'.format(epoch, num_epochs, i,
len(train_loader)))
print('Image Loss: {loss.avg:.5f}'.format(loss=running_loss0))
print('Video Loss: {loss.avg:.5f}'.format(loss=running_loss1))
print('Audio Loss: {loss.avg:.5f}'.format(loss=running_loss2))
print('AllMedia Loss: {loss.avg:.5f}'.format(loss=running_loss4))
print('lstm+selfattention Loss: {loss.avg:.5f}'.format(
loss=running_myloss))
print('Discriminator Loss: {loss.avg:.5f}'.format(
loss=running_lossC))
if (args.loss_choose == 'r'):
print(
'Ranking Loss: {loss.avg:.5f}'.format(loss=running_loss6))
print('All Loss: {loss.avg:.5f}'.format(loss=running_loss))
print("Text train Acc:", mytext_acc)
log.save_train_info(epoch, i, len(train_loader), running_loss)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
def train(train_loader, train_loader1, train_loader2, train_loader3, args, model, criterion, center_loss, optimizer, epoch, num_epochs):
since = time.time()
running_loss0 = AverageMeter()
running_loss1 = AverageMeter()
running_loss2 = AverageMeter()
running_loss3 = AverageMeter()
running_loss4 = AverageMeter()
running_loss5 = AverageMeter()
running_loss6 = AverageMeter()
running_loss = AverageMeter()
log = Log()
model.train()
for (i, (input, target)),(j, (input1, target1)),(k, (input2, target2)),(p, (input3, target3)) in zip(enumerate(train_loader),enumerate(train_loader1),enumerate(train_loader2),enumerate(train_loader3)):
input_var = Variable(input.cuda())
input_var1 = Variable(input1.cuda())
input_var2 = Variable(input2.cuda())
input_var3 = Variable(input3.cuda())
targets = torch.cat((target,target1,target2,target3),0)
targets = Variable(targets.cuda())
target_var = Variable(target.cuda())
target_var1 = Variable(target1.cuda())
target_var2 = Variable(target2.cuda())
target_var3 = Variable(target3.cuda())
outputs = model(input_var,input_var1,input_var2,input_var3)
size = int(outputs.size(0)/4)
img = outputs.narrow(0, 0, size)
vid = outputs.narrow(0, size, size)
aud = outputs.narrow(0, 2*size, size)
txt = outputs.narrow(0, 3*size, size)
loss0 = criterion(img, target_var)
loss1 = criterion(vid, target_var1)
loss2 = criterion(aud, target_var2)
loss3 = criterion(txt, target_var3)
loss4 = loss0 + loss1 + loss2 + loss3
loss5 = center_loss(outputs,targets)*0.001
if(args.loss_choose == 'r'):
loss6, _ = ranking_loss(targets, outputs, margin=1, margin2=0.5, squared=False)
loss6 = loss6 * 0.1
else:
loss6 = 0.0
loss = loss4 + loss5 + loss6
batchsize = input_var.size(0)
running_loss0.update(loss0.item(), batchsize)
running_loss1.update(loss1.item(), batchsize)
running_loss2.update(loss2.item(), batchsize)
running_loss3.update(loss3.item(), batchsize)
running_loss4.update(loss4.item(), batchsize)
running_loss5.update(loss5.item(), batchsize)
if(args.loss_choose == 'r'):
running_loss6.update(loss6.item(), batchsize)
running_loss.update(loss.item(), batchsize)
optimizer.zero_grad()
loss.backward()
for param in center_loss.parameters():
param.grad.data *= (1./0.001)
optimizer.step()
if (i % args.print_freq == 0):
print('-' * 20)
print('Epoch [{0}/{1}][{2}/{3}]'.format(epoch, num_epochs, i, len(train_loader)))
print('Image Loss: {loss.avg:.5f}'.format(loss=running_loss0))
print('Video Loss: {loss.avg:.5f}'.format(loss=running_loss1))
print('Audio Loss: {loss.avg:.5f}'.format(loss=running_loss2))
print('Text Loss: {loss.avg:.5f}'.format(loss=running_loss3))
print('AllMedia Loss: {loss.avg:.5f}'.format(loss=running_loss4))
print('Center Loss: {loss.avg:.5f}'.format(loss=running_loss5))
if(args.loss_choose == 'r'):
print('Ranking Loss: {loss.avg:.5f}'.format(loss=running_loss6))
print('All Loss: {loss.avg:.5f}'.format(loss=running_loss))
log.save_train_info(epoch, i, len(train_loader), running_loss)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
