def get_test_accuracy(model_d,f, iteration, label='semi'):
# don't forget to do model_d.eval() before doing evaluation
top1 = AverageMeter()
for i, (input, target) in enumerate(dataloader_test):
target = target.to(device)
input = input.view(-1, opt.imageSize).to(device)
output = model_d(input)
probs = output.data[:, 1:] # discard the zeroth index
prec1 = accuracy(probs, target, topk=(1,))[0]
top1.update(prec1.item(), input.size(0))
if i % 50 == 0:
print("{} Test: [{}/{}]\t Prec@1 {top1.val:.3f} ({top1.avg:.3f})"\
.format(label, i, len(dataloader_test), top1=top1))
print('{label} Test Prec@1 {top1.avg:.2f}'.format(label=label, top1=top1))
f.write("%s\n" % top1.avg)
log_value('test_acc_{}'.format(label), top1.avg, iteration)
def get_test_accuracy(model_d, iteration, label='semi'):
# don't forget to do model_d.eval() before doing evaluation
top1 = AverageMeter()
for i, (input, target) in enumerate(dataloader_test):
target = target.cuda()
input = input.cuda()
input_var = torch.autograd.Variable(input.cuda(), volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
output = model_d(input_var)
probs = output.data[:, 1:] # discard the zeroth index
prec1 = accuracy(probs, target, topk=(1,))[0]
#top1.update(prec1[0], input.size(0))
top1.update(prec1, input.size(0))
if i % 50 == 0:
print("{} Test: [{}/{}]\t Prec@1 {top1.val:.3f} ({top1.avg:.3f})".format(label, i, len(dataloader_test), top1=top1))
print('{label} Test Prec@1 {top1.avg:.2f}'.format(label=label, top1=top1))
log_value('test_acc_{}'.format(label), top1.avg, iteration)
def get_test_accuracy(model_d, acc, f, label='semi'):
# don't forget to do model_d.eval() before doing evaluation
top1 = AverageMeter()
for i, (x, y) in enumerate(test_loader):
x = x.to(device)
y = y.to(device)
output = model_d(x.view(-1, args.n_input))
probs = output.data[:, 1:] # discard the zeroth index
prec1 = accuracy(probs, y, topk=(1, ))[0]
top1.update(prec1.item(), x.size(0))
if i % 50 == 0:
print("{} Test: [{}/{}]\t Prec@1 {top1.val:.3f} ({top1.avg:.3f})".
format(label, i, len(test_loader), top1=top1))
f.write("%s\n" % top1.avg)
acc.append(top1.avg)
print('{label} Test Prec@1 {top1.avg:.2f}'.format(label=label, top1=top1))
errD_fake.backward()
D_G_z1 = 1 - torch.nn.functional.softmax(output).data[:, 0].mean()
#######
# 3. Labeled Data Part (for semi-supervised learning)
for ii, (input_sup, target_sup) in enumerate(dataloader_semi):
input_sup, target_sup = input_sup.cuda(), target_sup.cuda()
break
input_sup_v = Variable(input_sup.cuda())
# convert target indicies from 0 to 9 to 1 to 10
target_sup_v = Variable( (target_sup + 1).cuda())
output_sup = netD(input_sup_v) #used to have no [0] index
err_sup = criterion(output_sup, target_sup_v)
err_sup.backward()
prec1 = accuracy(output_sup.data, target_sup + 1, topk=(1,))[0]
#top1.update(prec1[0], input_sup.size(0))
top1.update(prec1,input_sup.size(0))
if opt.bayes:
errD_prior = dprior_criterion(netD.parameters())
errD_prior.backward()
errD_noise = dnoise_criterion(netD.parameters())
errD_noise.backward()
errD = errD_real + errD_fake + err_sup + errD_prior + errD_noise
else:
errD = errD_real + errD_fake + err_sup
optimizerD.step()
# 4. Generator
for netG in netGs:
netG.zero_grad()
# x_rec = decoder(z_enc)
# outputs_rec = discriminator(x_rec.detach())
# d_loss_rec = ce(outputs_rec, fake_labels)
# d_loss_rec.backward()
#Labeled Data Part (for semi-supervised learning)
for ii, (x_sup, y_sup) in enumerate(dataloader_semi):
# print("input", input_sup.data.mean()) #suffle, different every time
# convert target indicies from 0 to 9 to 1 to 10, cuz 0 represent "fake" now
x_sup, y_sup = x_sup.view(
-1, args.n_input).to(device), (y_sup + 1).to(device)
break
output_sup = discriminator(x_sup)
d_loss_sup = ce(output_sup, y_sup)
prec1 = accuracy(output_sup.data, y_sup, topk=(1, ))[0]
top1.update(prec1.item(), x_sup.size(0))
#d_loss_sup.backward()
# print("d_loss_real", d_loss_real.item())
# print("d_loss_fake", d_loss_fake.item())
# print("d_loss_sup", d_loss_sup.item())
d_loss = (d_loss_real + d_loss_fake + d_loss_sup)
reset_grad()
d_loss.backward()
dis_optimizer.step()
if (i + 1) % len(train_loader) == 0:
# get test accuracy on train and test
discriminator.eval()
def Legacy():
# --- the training part ---
iteration = 0
torch.autograd.set_detect_anomaly(True)
for epoch in range(opt.niter):
top1 = AverageMeter()
top1_weakD = AverageMeter()
for i, data in enumerate(dataloader):
iteration += 1
#######
# 1. real input
netD.zero_grad()
_input, _label = data
#print(_label)
#print(_input.shape,_label.shape) #both 64 of course.
batch_size = _input.size(0)
if opt.cuda:
_input = _input.cuda()
input.resize_as_(_input).copy_(_input)
label.resize_(batch_size).fill_(real_label)
inputv = Variable(input)
labelv = Variable(label)
output, kl = netD(inputv)
#print(output)
# --- the backprop for bayesian conv ---
label = label.type(torch.cuda.LongTensor)
#errD_real = elbo(output, label, kl, get_beta(epoch, len(dataset)))
errD_real = elbo(output, label, 0, get_beta(epoch, len(dataset)))
errD_real.backward()
# calculate D_x, the probability that real data are classified
D_x = 1 - torch.nn.functional.softmax(output,
dim=1).data[:, 0].mean()
#######
# 2. Generated input
noise.resize_(batch_size, opt.nz, 1, 1).normal_(0, 1)
noisev = Variable(noise)
_fake = netG(noisev)
#print(_fake.shape)
if opt.is_bayesian_generator == False:
fake = _fake
else:
fake = _fake[0]
output, kl = netD(fake.detach())
labelv = Variable(
torch.LongTensor(fake.data.shape[0]).cuda().fill_(fake_label))
# --- the backprop for bayesian conv ---
#errD_fake = elbo(output, labelv, kl, get_beta(epoch, 1))
errD_fake = elbo(output, labelv, 0, get_beta(epoch, 1))
errD_fake.backward()
D_G_z1 = 1 - \
torch.nn.functional.softmax(output, dim=1).data[:, 0].mean()
#######
# 3. Labeled Data Part (for semi-supervised learning)
if opt.semi_supervised_boost == True:
for ii, (input_sup, target_sup) in enumerate(dataloader_semi):
input_sup, target_sup = input_sup.cuda(), target_sup.cuda()
break
input_sup_v = Variable(input_sup.cuda())
# convert target indicies from 0 to 9 to 1 to 10
target_sup_v = Variable((target_sup + 1).cuda())
output_sup, kl_sup = netD(input_sup_v)
#err_sup = criterion(output_sup, target_sup_v)
# --- the backprop for bayesian conv ---
print("kl is :",
kl_sup * get_beta(epoch, len(dataset_partial)))
err_sup = elbo(output_sup, target_sup_v, kl_sup,
get_beta(epoch, len(dataset_partial)))
err_sup.backward()
prec1 = accuracy(output_sup.data, target_sup + 1,
topk=(1, ))[0]
top1.update(prec1, input_sup.size(0))
errD = errD_real + errD_fake + err_sup
optimizerD.step()
else:
errD = errD_real + errD_fake
optimizerD.step()
# A. Classifier Discriminator
if opt.is_using_classification == True:
#label for classification definition start
label_classification_real = Variable(
torch.LongTensor(_label).cuda())
_label_random = np.random.randint(0, 9, batch_size)
label_classification_fake = Variable(
torch.IntTensor(_label_random).cuda())
#definition end
#the real input start
outputA, outputB, klA, klB = netD_class(inputv)
print("A:", outputA.shape, "B:", outputB.shape)
print(label_classification_real.shape)
print(outputB)
label_is_generated = Variable(
torch.LongTensor(
fake.data.shape[0]).cuda().fill_(real_label))
errD_is_generated = elbo(outputA, label_is_generated, klA,
get_beta(epoch, len(dataset)))
errD_classification = elbo(outputB, label_classification_real,
klB, get_beta(epoch, len(dataset)))
errD_sum_real = errD_is_generated + errD_classification
#the real input end
#the generated input start
outputA, outputB, klA, klB = netD_class(fake.detach())
label_is_generated = Variable(
torch.LongTensor(
fake.data.shape[0]).cuda().fill_(fake_label))
errD_is_generated = elbo(outputA, label_is_generated, klA,
get_beta(epoch, len(dataset)))
errD_classification = elbo(outputB, label_classification_fake,
klB, get_beta(epoch, len(dataset)))
errD_sum_fake = errD_is_generated + errD_classification
#generated input part end
errD_sum = errD_sum_fake + errD_sum_real
errD_sum.backward()
optimizerD_class.step()
# 4. Generator
netG.zero_grad()
labelv = Variable(
torch.LongTensor(fake.data.shape[0]).cuda().fill_(real_label))
output, kl = netD(fake)
#print(netG.parameters)
#errG = criterion_comp(output)
# print(labelv) #the out put is all 1, not sure why in the original code they put it in a float tensor.
#errG = elbo(output, labelv, kl, get_beta(epoch, 1))
errG = elbo(output, labelv, 0, get_beta(epoch, 1))
errG.backward()
D_G_z2 = 1 - torch.nn.functional.softmax(output, 1).data[:,
0].mean()
optimizerG.step()
'''
# 5. Fully supervised training (running in parallel for comparison)
netD_fullsup.zero_grad()
try:
input_fullsup = Variable(input_sup)
except NameError as e:
#print(e, '*** Not Defined!!!!! *** draw a new one from the deck')
for ii, (input_sup, target_sup) in enumerate(dataloader_semi):
input_sup, target_sup = input_sup.cuda(), target_sup.cuda()
#print(input_sup)
break
finally:
input_fullsup = Variable(input_sup)
target_fullsup = Variable((target_sup + 1))
output_fullsup, kl_fullsup = netD_fullsup(input_fullsup)
#err_fullsup = criterion_fullsup(output_fullsup, target_fullsup)
# --- the backprop for bayesian conv ---
err_fullsup = elbo(output_fullsup, target_fullsup,
kl_fullsup, get_beta(epoch, len(dataset)))
optimizerD_fullsup.zero_grad()
err_fullsup.backward()
optimizerD_fullsup.step()
errD += err_fullsup
if opt.semi_supervised_boost == False:
optimizerD.step()
# 6. get test accuracy after every interval
if iteration % opt.stats_interval == 0:
# get test accuracy on train and test
netD.eval()
if opt.semi_supervised_boost == True:
get_test_accuracy(netD, iteration, label='semi')
get_test_accuracy(netD_fullsup, iteration, label='sup')
netD.train()
'''
# 7. Report for this iteration
cur_val, ave_val = top1.val, top1.avg
log_value('train_acc', top1.avg, iteration)
print(
'[%d/%d][%d/%d] Loss_D: %.2f Loss_G: %.2f D(x): %.2f D(G(z)): %.2f / %.2f | Acc %.1f / %.1f'
% (epoch, opt.niter, i, len(dataloader), errD.data, errG.data,
D_x, D_G_z1, D_G_z2, cur_val, ave_val))
# after each epoch, save images
vutils.save_image(_input,
'%s/real_samples.png' % opt.outf,
normalize=True)
if opt.is_bayesian_generator == False:
fake = netG(fixed_noise)
else:
fake = netG(fixed_noise)[0]
vutils.save_image(fake.data,
'%s/fake_samples_epoch_%03d_G.png' % (
opt.outf,
epoch,
),
normalize=True)
torch.save(netG.state_dict(),
'%s/netG_epoch_%d.pth' % (opt.outf, epoch))
torch.save(netD.state_dict(),
'%s/netD_epoch_%d.pth' % (opt.outf, epoch))
torch.save(netD_fullsup.state_dict(),
'%s/netD_fullsup_epoch_%d.pth' % (opt.outf, epoch))
#from tensorflow.python.summary import event_accumulator
ea = event_accumulator.EventAccumulator(opt.outf)
ea.Reload()
_df1 = pd.DataFrame(ea.Scalars('test_acc_semi'))
_df2 = pd.DataFrame(ea.Scalars('test_acc_sup'))
df = pd.DataFrame()
df['Iteration'] = pd.concat([_df1['step'], _df2['step']])
df['Accuracy'] = pd.concat([_df1['value'], _df2['value']])
df['Classification'] = ['BayesGAN'] * \
len(_df1['step']) + ['Baseline']*len(_df2['step'])
# The results show that the Bayesian discriminator trained with the Bayesian generator outperforms the discriminator trained on partial data.
p = ggplot(
df,
aes(x='Iteration',
y='Accuracy',
color='Classification',
label='Classification')) + geom_point(size=0.5)
print(p)