def train(pool_size,
epochs,
train_data,
val_data,
ctx,
netG,
netD,
trainerG,
trainerD,
lambda1,
batch_size,
expname,
append=True,
useAE=False):
text_file = open(expname + "_validtest.txt", "w")
text_file.close()
#netGT, netDT, _, _ = set_test_network(opt.depth, ctx, opt.lr, opt.beta1,opt.ndf, opt.ngf, opt.append)
dlr = trainerD.learning_rate
glr = trainerG.learning_rate
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L2Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
metric2 = mx.metric.MSE()
loss_rec_G = []
loss_rec_D = []
loss_rec_R = []
acc_rec = []
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
if useAE:
for batch in train_data:
train_data.reset()
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
fake_out = netG(real_in)
loss = L1_loss(real_out, fake_out)
loss.backward()
trainerG.step(batch.data[0].shape[0])
metric2.update([
real_out,
], [
fake_out,
])
if epoch % 10 == 0:
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_G.params"
netG.save_params(filename)
fake_img1 = nd.concat(real_in[0],
real_out[0],
fake_out[0],
dim=1)
fake_img2 = nd.concat(real_in[1],
real_out[1],
fake_out[1],
dim=1)
fake_img3 = nd.concat(real_in[2],
real_out[2],
fake_out[2],
dim=1)
fake_img4 = nd.concat(real_in[3],
real_out[3],
fake_out[3],
dim=1)
#fake_img4T = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
fake_img = nd.concat(fake_img1,
fake_img2,
fake_img3,
dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/' + expname + '_' + str(epoch) +
'.png')
train_data.reset()
name, acc = metric.get()
metric2.reset()
print("training acc: " + acc)
else:
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
if epoch > 250:
trainerD.set_learning_rate(dlr * (1 - int(epoch - 250) / 1000))
trainerG.set_learning_rate(glr * (1 - int(epoch - 250) / 1000))
#print('learning rate : '+str(trainerD.learning_rate ))
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
fake_out = netG(real_in)
fake_concat = nd.concat(real_in, fake_out,
dim=1) if append else fake_out
with autograd.record():
# Train with fake image
# Use image pooling to utilize history images
output = netD(fake_concat)
fake_label = nd.zeros(output.shape, ctx=ctx)
errD_fake = GAN_loss(output, fake_label)
metric.update([
fake_label,
], [
output,
])
real_concat = nd.concat(real_in, real_out,
dim=1) if append else real_out
output = netD(real_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errD_real = GAN_loss(output, real_label)
errD = (errD_real + errD_fake) * 0.5
errD.backward()
metric.update([
real_label,
], [
output,
])
trainerD.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
fake_out = netG(real_in)
fake_concat = nd.concat(real_in, fake_out,
dim=1) if append else fake_out
output = netD(fake_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errG = GAN_loss(output, real_label) + L1_loss(
real_out, fake_out) * lambda1
errR = L1_loss(real_out, fake_out)
errG.backward()
trainerG.step(batch.data[0].shape[0])
loss_rec_G.append(
nd.mean(errG).asscalar() -
nd.mean(errR).asscalar() * lambda1)
loss_rec_D.append(nd.mean(errD).asscalar())
loss_rec_R.append(nd.mean(errR).asscalar())
name, acc = metric.get()
acc_rec.append(acc)
# Print log infomation every ten batches
if iter % 5 == 0:
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(
batch_size / (time.time() - btic)))
#print(errD)
logging.info(
'discriminator loss = %f, generator loss = %f, binary training acc = %f reconstruction error= %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(), nd.mean(errG).asscalar(),
acc, nd.mean(errR).asscalar(), iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
metric.reset()
train_data.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' %
(epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch % 5 == 0:
text_file = open(expname + "_validtest.txt", "a")
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_D.params"
netD.save_params(filename)
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_G.params"
netG.save_params(filename)
fake_img1 = nd.concat(real_in[0],
real_out[0],
fake_out[0],
dim=1)
fake_img2 = nd.concat(real_in[1],
real_out[1],
fake_out[1],
dim=1)
fake_img3 = nd.concat(real_in[2],
real_out[2],
fake_out[2],
dim=1)
fake_img4 = nd.concat(real_in[3],
real_out[3],
fake_out[3],
dim=1)
val_data.reset()
for vbatch in val_data:
real_in = vbatch.data[0].as_in_context(ctx)
real_out = vbatch.data[1].as_in_context(ctx)
fake_out = netG(real_in)
metric2.update([
fake_out,
], [
real_out,
])
_, acc2 = metric2.get()
text_file.write(
"%s %s %s\n" %
(str(epoch), nd.mean(errR).asscalar(), str(acc2)))
metric2.reset()
fake_img1T = nd.concat(real_in[0],
real_out[0],
fake_out[0],
dim=1)
fake_img2T = nd.concat(real_in[1],
real_out[1],
fake_out[1],
dim=1)
fake_img3T = nd.concat(real_in[2],
real_out[2],
fake_out[2],
dim=1)
#fake_img4T = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
fake_img = nd.concat(fake_img1,
fake_img2,
fake_img3,
fake_img1T,
fake_img2T,
fake_img3T,
dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/' + expname + '_' + str(epoch) + '.png')
text_file.close()
return ([loss_rec_D, loss_rec_G, loss_rec_R, acc_rec])
def train(pool_size, epochs, train_data, ctx, netEn, netDe, netD, trainerEn, trainerDe, trainerD, lambda1, batch_size, expname):
threewayloss =gluon.loss.SoftmaxCrossEntropyLoss()
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L1Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
tempout = netEn(real_in)
fake_out = netDe(tempout)
fake_concat = fake_out
#fake_concat = image_pool.query(fake_out)
#fake_concat = image_pool.query(nd.concat(real_in, fake_out, dim=1))
with autograd.record():
# Train with fake image
# Use image pooling to utilize history images
output = netD(fake_concat)
fake_label = nd.zeros(output.shape[0], ctx=ctx)
errD_fake = threewayloss(output, fake_label)
metric.update([fake_label, ], [output, ])
# Train with real image
real_concat = real_out
output = netD(real_concat)
real_label = nd.ones(output.shape[0], ctx=ctx)
errD_real = threewayloss(output, real_label)
metric.update([real_label, ], [output, ])
#train with abnormal image
abinput = nd.random.uniform(-1,1,tempout.shape,ctx=ctx)
aboutput =netD( netDe(abinput))
#print(aboutput.shape)
#print(output.shape)
ab_label = 2*nd.ones(aboutput.shape[0], ctx=ctx)
errD_ab = threewayloss(aboutput, ab_label)
errD = (errD_real + errD_fake + errD_ab) * 0.33
errD.backward()
trainerD.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
fake_out = netDe(netEn(real_in))
fake_concat = fake_out
output = netD(fake_concat)
real_label = nd.ones(output.shape[0], ctx=ctx)
errG = threewayloss(output, real_label) + L1_loss(real_out, fake_out) * lambda1
errR = L1_loss(real_out, fake_out)
errG.backward()
trainerEn.step(batch.data[0].shape[0])
trainerDe.step(batch.data[0].shape[0])
# Print log infomation every ten batches
if iter % 10 == 0:
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(batch_size / (time.time() - btic)))
logging.info(
'discriminator loss = %f, generator loss = %f, latent error = %f, binary training acc = %f, reconstruction error= %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(),
nd.mean(errG).asscalar(), nd.mean(errD_ab).asscalar() , acc,nd.mean(errR).asscalar() ,iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
metric.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' % (epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch%10 ==0:
filename = "checkpoints/"+expname+"_"+str(epoch)+"_D.params"
netD.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_En.params"
netEn.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_De.params"
netDe.save_params(filename)
# Visualize one generated image for each epoch
fake_img = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_'+str(epoch)+'.png')
def train(pool_size, epochs, train_data, val_data, ctx, netEn, netDe, netD, netD2, trainerEn, trainerDe, trainerD, trainerD2, lambda1, batch_size, expname, append=True, useAE = False):
tp_file = open(expname + "_trainloss.txt", "w")
tp_file.close()
text_file = open(expname + "_validtest.txt", "w")
text_file.close()
#netGT, netDT, _, _ = set_test_network(opt.depth, ctx, opt.lr, opt.beta1,opt.ndf, opt.ngf, opt.append)
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L2Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
metric2 = mx.metric.CustomMetric(facc)
metricMSE = mx.metric.MSE()
loss_rec_G = []
loss_rec_D = []
loss_rec_R = []
acc_rec = []
acc2_rec = []
loss_rec_D2 = []
loss_rec_G2 = []
lr = 0.002
#mu = nd.random_normal(loc=0, scale=1, shape=(batch_size/2,64,1,1), ctx=ctx)
mu = nd.random.uniform(low= -1, high=1, shape=(batch_size/2,64,1,1),ctx=ctx)
#mu = nd.zeros((batch_size/2,64,1,1),ctx=ctx)
sigma = nd.ones((64,1,1),ctx=ctx)
mu.attach_grad()
sigma.attach_grad()
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
#print('learning rate : '+str(trainerD.learning_rate ))
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
fake_latent= netEn(real_in)
#real_latent = nd.random_normal(loc=0, scale=1, shape=fake_latent.shape, ctx=ctx)
real_latent = nd.multiply(nd.power(sigma,2),nd.random_normal(loc=0, scale=1, shape=fake_latent.shape, ctx=ctx))
#nd.random.uniform( low=-1, high=1, shape=fake_latent.shape, ctx=ctx)
fake_out = netDe(fake_latent)
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
with autograd.record():
# Train with fake image
# Use image pooling to utilize history imagesi
output = netD(fake_concat)
output2 = netD2(fake_latent)
fake_label = nd.zeros(output.shape, ctx=ctx)
fake_latent_label = nd.zeros(output2.shape, ctx=ctx)
noiseshape = (fake_latent.shape[0]/2,fake_latent.shape[1],fake_latent.shape[2],fake_latent.shape[3])
eps2 = nd.multiply(nd.power(sigma,2),nd.random_normal(loc=0, scale=1, shape=fake_latent.shape, ctx=ctx))
#eps2 = nd.random_normal(loc=0, scale=sigma.asscalar(), shape=fake_latent.shape, ctx=ctx) #
#eps = nd.random.uniform( low=-1, high=1, shape=noiseshape, ctx=ctx)
rec_output = netD(netDe(eps2))
errD_fake = GAN_loss(rec_output, fake_label)
errD_fake2 = GAN_loss(output, fake_label)
errD2_fake = GAN_loss(output2, fake_latent_label)
metric.update([fake_label, ], [output, ])
metric2.update([fake_latent_label, ], [output2, ])
real_concat = nd.concat(real_in, real_out, dim=1) if append else real_out
output = netD(real_concat)
output2 = netD2(real_latent)
real_label = nd.ones(output.shape, ctx=ctx)
real_latent_label = nd.ones(output2.shape, ctx=ctx)
errD_real = GAN_loss(output, real_label)
errD2_real = GAN_loss(output2, real_latent_label)
#errD = (errD_real + 0.5*(errD_fake+errD_fake2)) * 0.5
errD = (errD_real + errD_fake) * 0.5
errD2 = (errD2_real + errD2_fake) * 0.5
totalerrD = errD+errD2
totalerrD.backward()
#errD2.backward()
metric.update([real_label, ], [output, ])
metric2.update([real_latent_label, ], [output2, ])
trainerD.step(batch.data[0].shape[0])
trainerD2.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
sh = fake_latent.shape
eps2 = nd.multiply(nd.power(sigma,2),nd.random_normal(loc=0, scale=1, shape=fake_latent.shape, ctx=ctx))
#eps2 = nd.random_normal(loc=0, scale=sigma.asscalar(), shape=fake_latent.shape, ctx=ctx) #
#eps = nd.random.uniform( low=-1, high=1, shape=noiseshape, ctx=ctx)
rec_output = netD(netDe(eps2))
fake_latent= (netEn(real_in))
output2 = netD2(fake_latent)
fake_out = netDe(fake_latent)
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
output = netD(fake_concat)
real_label = nd.ones(output.shape, ctx=ctx)
real_latent_label = nd.ones(output2.shape, ctx=ctx)
errG2 = GAN_loss(rec_output, real_label)
errR = L1_loss(real_out, fake_out) * lambda1
errG = 10.0*GAN_loss(output2, real_latent_label)+errG2+errR+nd.mean(nd.power(sigma,2))
errG.backward()
if epoch>50:
sigma -= lr / sigma.shape[0] * sigma.grad
print(sigma)
trainerDe.step(batch.data[0].shape[0])
trainerEn.step(batch.data[0].shape[0])
loss_rec_G2.append(nd.mean(errG2).asscalar())
loss_rec_G.append(nd.mean(nd.mean(errG)).asscalar()-nd.mean(errG2).asscalar()-nd.mean(errR).asscalar())
loss_rec_D.append(nd.mean(errD).asscalar())
loss_rec_R.append(nd.mean(errR).asscalar())
loss_rec_D2.append(nd.mean(errD2).asscalar())
_, acc2 = metric2.get()
name, acc = metric.get()
acc_rec.append(acc)
acc2_rec.append(acc2)
# Print log infomation every ten batches
if iter % 10 == 0:
_, acc2 = metric2.get()
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(batch_size / (time.time() - btic)))
#print(errD)
logging.info('discriminator loss = %f, D2 loss = %f, generator loss = %f, G2 loss = %f, binary training acc = %f , D2 acc = %f, reconstruction error= %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(),nd.mean(errD2).asscalar(),
nd.mean(errG-errG2-errR).asscalar(),nd.mean(errG2).asscalar(), acc,acc2,nd.mean(errR).asscalar() ,iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
_, acc2 = metric2.get()
tp_file = open(expname + "_trainloss.txt", "a")
tp_file.write(str(nd.mean(errG2).asscalar()) + " " + str(
nd.mean(nd.mean(errG)).asscalar() - nd.mean(errG2).asscalar() - nd.mean(errR).asscalar()) + " " + str(
nd.mean(errD).asscalar()) + " " + str(nd.mean(errD2).asscalar()) + " " + str(nd.mean(errR).asscalar()) +" "+str(acc) + " " + str(acc2)+"\n")
tp_file.close()
metric.reset()
metric2.reset()
train_data.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' % (epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch%10 ==0:# and epoch>0:
text_file = open(expname + "_validtest.txt", "a")
filename = "checkpoints/"+expname+"_"+str(epoch)+"_D.params"
netD.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_D2.params"
netD2.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_En.params"
netEn.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_De.params"
netDe.save_params(filename)
fake_img1 = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2 = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3 = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
fake_img4 = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
val_data.reset()
text_file = open(expname + "_validtest.txt", "a")
for vbatch in val_data:
real_in = vbatch.data[0].as_in_context(ctx)
real_out = vbatch.data[1].as_in_context(ctx)
fake_latent= netEn(real_in)
y = netDe(fake_latent)
fake_out = y
metricMSE.update([fake_out, ], [real_out, ])
_, acc2 = metricMSE.get()
text_file.write("%s %s %s\n" % (str(epoch), nd.mean(errR).asscalar(), str(acc2)))
metricMSE.reset()
images = netDe(eps2)
fake_img1T = nd.concat(images[0],images[1], images[2], dim=1)
fake_img2T = nd.concat(images[3],images[4], images[5], dim=1)
fake_img3T = nd.concat(images[6],images[7], images[8], dim=1)
fake_img = nd.concat(fake_img1T,fake_img2T, fake_img3T,dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_fakes_'+str(epoch)+'.png')
text_file.close()
# Do 10 iterations of sampler update
fake_img1T = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2T = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3T = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
#fake_img4T = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
fake_img = nd.concat(fake_img1,fake_img2, fake_img3,fake_img1T,fake_img2T, fake_img3T,dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_'+str(epoch)+'.png')
'''if epoch > 100:
for ep2 in range(10):
with autograd.record():
#eps = nd.random_normal(loc=0, scale=1, shape=noiseshape, ctx=ctx) #
eps = nd.random.uniform( low=-1, high=1, shape=noiseshape, ctx=ctx)
eps2 = nd.random_normal(loc=0, scale=0.02, shape=noiseshape, ctx=ctx)
eps2 = nd.tanh(eps2*sigma+mu)
eps2 = nd.concat(eps,eps2,dim=0)
rec_output = netD(netDe(eps2))
fake_label = nd.zeros(rec_output.shape, ctx=ctx)
errGS = GAN_loss(rec_output, fake_label)
errGS.backward()
mu -= lr / mu.shape[0] * mu.grad
sigma -= lr / sigma.shape[0] * sigma.grad
print('mu ' + str(mu[0,0,0,0].asnumpy())+ ' sigma '+ str(sigma[0,0,0,0].asnumpy()))
'''
images = netDe(eps2)
fake_img1T = nd.concat(images[0],images[1], images[2], dim=1)
fake_img2T = nd.concat(images[3],images[4], images[5], dim=1)
fake_img3T = nd.concat(images[6],images[7], images[8], dim=1)
fake_img = nd.concat(fake_img1T,fake_img2T, fake_img3T,dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_fakespost_'+str(epoch)+'.png')
return([loss_rec_D,loss_rec_G, loss_rec_R, acc_rec, loss_rec_D2, loss_rec_G2, acc2_rec])
def train():
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
fake_out = netG(real_in)
fake_concat = image_pool.query(nd.concat(real_in, fake_out, dim=1))
with autograd.record():
# Train with fake image
# Use image pooling to utilize history images
output = netD(fake_concat)
fake_label = nd.zeros(output.shape, ctx=ctx)
errD_fake = GAN_loss(output, fake_label)
metric.update([
fake_label,
], [
output,
])
# Train with real image
real_concat = nd.concat(real_in, real_out, dim=1)
output = netD(real_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errD_real = GAN_loss(output, real_label)
errD = (errD_real + errD_fake) * 0.5
errD.backward()
metric.update([
real_label,
], [
output,
])
trainerD.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
fake_out = netG(real_in)
fake_concat = nd.concat(real_in, fake_out, dim=1)
output = netD(fake_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errG = GAN_loss(
output, real_label) + L1_loss(real_out, fake_out) * lambda1
errG.backward()
trainerG.step(batch.data[0].shape[0])
# Print log infomation every ten batches
if iter % 10 == 0:
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(
batch_size / (time.time() - btic)))
logging.info(
'discriminator loss = %f, generator loss = %f, binary training acc = %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(), nd.mean(errG).asscalar(), acc,
iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
metric.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' %
(epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch % 10 == 0:
filename = "checkpoints/testnet_" + str(epoch) + "_D.params"
netD.save_params(filename)
filename = "checkpoints/testnet_" + str(epoch) + "_G.params"
netG.save_params(filename)
# Visualize one generated image for each epoch
fake_img = fake_out[0]
visual.visualize(fake_img)
plt.savefig('outputs/testnet_' + str(epoch) + '.png')
def train(pool_size, epochs, train_data, val_data, ctx, netEn, netDe, netD, trainerEn, trainerDe, trainerD, lambda1, batch_size, expname, append=True, useAE = False):
text_file = open(expname + "_validtest.txt", "w")
text_file.close()
#netGT, netDT, _, _ = set_test_network(opt.depth, ctx, opt.lr, opt.beta1,opt.ndf, opt.ngf, opt.append)
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L2Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
metric2 = mx.metric.MSE()
loss_rec_G = []
loss_rec_D = []
loss_rec_R = []
acc_rec = []
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
#print('learning rate : '+str(trainerD.learning_rate ))
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
soft_zero = 1e-10
fake_latent= netEn(real_in)
fake_latent = np.squeeze(fake_latent)
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = (mu_lv[0])
lv = (mu_lv[1])
KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size, 2048), ctx=ctx)
z = mu + nd.exp(0.5*lv)*eps
z = nd.expand_dims(nd.expand_dims(z,2),2)
y = netDe(z)
fake_out = y
logloss = nd.nansum(real_in*nd.log(y+soft_zero)+ (1-real_in)*nd.log(1-y+soft_zero))
loss = -logloss-KL
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
with autograd.record():
# Train with fake image
# Use image pooling to utilize history imagesi
output = netD(fake_concat)
fake_label = nd.zeros(output.shape, ctx=ctx)
errD_fake = GAN_loss(output, fake_label)
metric.update([fake_label, ], [output, ])
real_concat = nd.concat(real_in, real_out, dim=1) if append else real_out
output = netD(real_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errD_real = GAN_loss(output, real_label)
errD = (errD_real + errD_fake) * 0.5
errD.backward()
metric.update([real_label, ], [output, ])
trainerD.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
fake_latent= np.squeeze(netEn(real_in))
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = mu_lv[0]
lv = mu_lv[1]
KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size, 2048), ctx=ctx)
#KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
z = mu + nd.exp(0.5*lv)*eps
z = nd.expand_dims(nd.expand_dims(z,2),2)
y = netDe(z)
fake_out = y
logloss = nd.nansum((real_in+1)*0.5*nd.log(0.5*(y+1)+soft_zero)+ (1-0.5*(real_in+1))*nd.log(1-0.5*(y+1)+soft_zero))
loss =-logloss-KL
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
output = netD(fake_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errG = GAN_loss(output, real_label) + loss*lambda1 #L1_loss(real_out, fake_out) * lambda1
errR = logloss#L1_loss(real_out, fake_out)
errG.backward()
trainerDe.step(batch.data[0].shape[0])
trainerEn.step(batch.data[0].shape[0])
loss_rec_G.append(nd.mean(errG).asscalar()-nd.mean(errR).asscalar()*lambda1)
loss_rec_D.append(nd.mean(errD).asscalar())
loss_rec_R.append(nd.mean(errR).asscalar())
name, acc = metric.get()
acc_rec.append(acc)
# Print log infomation every ten batches
if iter % 10 == 0:
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(batch_size / (time.time() - btic)))
#print(errD)
logging.info('discriminator loss = %f, generator loss = %f, binary training acc = %f reconstruction error= %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(),
nd.mean(errG).asscalar(), acc,nd.mean(errR).asscalar() ,iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
metric.reset()
train_data.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' % (epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch%10 ==0:
text_file = open(expname + "_validtest.txt", "a")
filename = "checkpoints/"+expname+"_"+str(epoch)+"_D.params"
netD.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_En.params"
netEn.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_De.params"
netDe.save_params(filename)
fake_img1 = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2 = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3 = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
fake_img4 = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
val_data.reset()
text_file = open(expname + "_validtest.txt", "a")
for vbatch in val_data:
real_in = vbatch.data[0].as_in_context(ctx)
real_out = vbatch.data[1].as_in_context(ctx)
fake_latent= netEn(real_in)
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = mu_lv[0]
lv = mu_lv[1]
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size/5, 2048,1,1), ctx=ctx)
z = mu + nd.exp(0.5*lv)*eps
y = netDe(z)
fake_out = y
KL = 0.5*nd.sum(1+lv-mu*mu-nd.exp(lv),axis=1)
logloss = nd.sum(real_in*nd.log(y+soft_zero)+ (1-real_in)*nd.log(1-y+soft_zero), axis=1)
loss = logloss+KL
metric2.update([fake_out, ], [real_out, ])
_, acc2 = metric2.get()
text_file.write("%s %s %s\n" % (str(epoch), nd.mean(errR).asscalar(), str(acc2)))
metric2.reset()
fake_img1T = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2T = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3T = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
#fake_img4T = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
fake_img = nd.concat(fake_img1,fake_img2, fake_img3,fake_img1T,fake_img2T, fake_img3T,dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_'+str(epoch)+'.png')
text_file.close()
return([loss_rec_D,loss_rec_G, loss_rec_R, acc_rec])
def train(cep,
pool_size,
epochs,
train_data,
val_data,
ctx,
netEn,
netDe,
netD,
netD2,
netDS,
trainerEn,
trainerDe,
trainerD,
trainerD2,
trainerSD,
lambda1,
batch_size,
expname,
append=True,
useAE=False):
tp_file = open(expname + "_trainloss.txt", "w")
tp_file.close()
text_file = open(expname + "_validtest.txt", "w")
text_file.close()
#netGT, netDT, _, _ = set_test_network(opt.depth, ctx, opt.lr, opt.beta1,opt.ndf, opt.ngf, opt.append)
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L2Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
metric2 = mx.metric.CustomMetric(facc)
metricStrong = mx.metric.CustomMetric(facc)
metricMSE = mx.metric.MSE()
loss_rec_G = []
loss_rec_D = []
loss_rec_R = []
acc_rec = []
acc2_rec = []
loss_rec_D2 = []
loss_rec_G2 = []
lr = 2.0 * 512
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
if cep == -1:
cep = 0
else:
netEn.load_params('checkpoints/' + opt.expname + '_' + str(cep) +
'_En.params',
ctx=ctx)
netDe.load_params('checkpoints/' + opt.expname + '_' + str(cep) +
'_De.params',
ctx=ctx)
netD.load_params('checkpoints/' + opt.expname + '_' + str(cep) +
'_D.params',
ctx=ctx)
netD2.load_params('checkpoints/' + opt.expname + '_' + str(cep) +
'_D2.params',
ctx=ctx)
netDS.load_params('checkpoints/' + opt.expname + '_' + str(cep) +
'_SD.params',
ctx=ctx)
iter = 0
for epoch in range(cep + 1, epochs):
tic = time.time()
btic = time.time()
train_data.reset()
#print('learning rate : '+str(trainerD.learning_rate ))
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
if ctx == mx.cpu():
ct = mx.cpu()
else:
ct = mx.gpu()
real_in = batch.data[0] #.as_in_context(ctx)
real_out = batch.data[1] #.as_in_context(ctx)
if iter == 0:
latent_shape = (batch_size, 512, 1, 1) #code.shape
out_l_shape = (batch_size, 1, 1, 1) #netD2((code)).shape
out_i_shape = (batch_size, 1, 1, 1) #netD(netDe(code)).shape
out_s_shape = (batch_size, 1, 1, 1) #netSD(netDe(code)).shape
real_in = gluon.utils.split_and_load(real_in, ctx)
real_out = gluon.utils.split_and_load(real_out, ctx)
fake_latent = [netEn(r) for r in real_in]
real_latent = nd.random.uniform(low=-1, high=1, shape=latent_shape)
real_latent = gluon.utils.split_and_load(real_latent, ctx)
fake_out = [netDe(f) for f in fake_latent]
fake_concat = nd.concat(real_in, fake_out,
dim=1) if append else fake_out
eps2 = nd.random.uniform(low=-1,
high=1,
shape=latent_shape,
ctx=ct)
eps2 = gluon.utils.split_and_load(eps2, ctx)
if epoch > 150: # (1/float(batch_size))*512*150:# and epoch%10==0:
print('Mining..')
mu = nd.random.uniform(low=-1,
high=1,
shape=latent_shape,
ctx=ct)
#isigma = nd.ones((batch_size,64,1,1),ctx=ctx)*0.000001
mu.attach_grad()
#sigma.attach_grad()
images = netDe(mu)
fake_img1T = nd.concat(images[0], images[1], images[2], dim=1)
fake_img2T = nd.concat(images[3], images[4], images[5], dim=1)
fake_img3T = nd.concat(images[6], images[7], images[8], dim=1)
fake_img = nd.concat(fake_img1T, fake_img2T, fake_img3T, dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/' + expname + '_fakespre_' + str(epoch) +
'.png')
eps2 = gluon.utils.split_and_load(mu, ctx)
for e in eps2:
e.attach_grad()
for ep2 in range(1):
with autograd.record():
#eps = nd.random_normal(loc=0, scale=1, shape=fake_latent.shape, ctx=ctx) #
#eps2 = gluon.utils.split_and_load(nd.tanh(mu),ctx) #+nd.multiply(eps,sigma))#nd.random.uniform( low=-1, high=1, shape=fake_latent.shape, ctx=ctx)
rec_output = [netDS(netDe(e)) for e in eps2]
fake_label = gluon.utils.split_and_load(
nd.zeros(out_s_shape), ctx)
errGS = [
GAN_loss(r, f)
for r, f in zip(rec_output, fake_label)
]
for e in errGS:
e.backward()
for idx, _ in enumerate(eps2):
eps2[idx] = nd.tanh(eps2[idx] -
lr / eps2[idx].shape[0] *
eps2[idx].grad)
images = netDe((eps2[0]))
fake_img1T = nd.concat(images[0], images[1], images[2], dim=1)
fake_img2T = nd.concat(images[3], images[4], images[5], dim=1)
fake_img3T = nd.concat(images[6], images[7], images[8], dim=1)
fake_img = nd.concat(fake_img1T, fake_img2T, fake_img3T, dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/' + expname + str(ep2) + '_fakespost_' +
str(epoch) + '.png')
#eps2 = nd.tanh(mu)#+nd.multiply(eps,sigma))#nd.random.uniform( low=-1, high=1, shape=fake_latent.shape, ctx=ctx)
with autograd.record():
#eps2 = gluon.utils.split_and_load(eps2,ctx)
# Train with fake image
# Use image pooling to utilize history imagesi
output = [netD(f) for f in fake_concat]
output2 = [netD2(f) for f in fake_latent]
fake_label = nd.zeros(out_i_shape)
fake_label = gluon.utils.split_and_load(fake_label, ctx)
fake_latent_label = nd.zeros(out_l_shape)
fake_latent_label = gluon.utils.split_and_load(
fake_latent_label, ctx)
eps = gluon.utils.split_and_load(
nd.random.uniform(low=-1, high=1, shape=latent_shape), ctx)
rec_output = [netD(netDe(e)) for e in eps]
errD_fake = [
GAN_loss(r, f) for r, f in zip(rec_output, fake_label)
]
errD_fake2 = [
GAN_loss(o, f) for o, f in zip(output, fake_label)
]
errD2_fake = [
GAN_loss(o, f) for o, f in zip(output2, fake_latent_label)
]
for f, o in zip(fake_label, rec_output):
metric.update([
f,
], [
o,
])
for f, o in zip(fake_latent_label, output2):
metric2.update([
f,
], [
o,
])
real_concat = nd.concat(real_in, real_out,
dim=1) if append else real_out
output = [netD(r) for r in real_concat]
output2 = [netD2(r) for r in real_latent]
real_label = gluon.utils.split_and_load(
nd.ones(out_i_shape), ctx)
real_latent_label = gluon.utils.split_and_load(
nd.ones(out_l_shape), ctx)
errD_real = [
GAN_loss(o, r) for o, r in zip(output, real_label)
]
errD2_real = [
GAN_loss(o, r) for o, r in zip(output2, real_latent_label)
]
for e1, e2, e4, e5 in zip(errD_real, errD_fake, errD2_real,
errD2_fake):
err = (e1 + e2) * 0.5 + (e5 + e4) * 0.5
err.backward()
for f, o in zip(real_label, output):
metric.update([
f,
], [
o,
])
for f, o in zip(real_latent_label, output2):
metric2.update([
f,
], [
o,
])
trainerD.step(batch.data[0].shape[0])
trainerD2.step(batch.data[0].shape[0])
nd.waitall()
with autograd.record():
strong_output = [netDS(netDe(e)) for e in eps]
strong_real = [netDS(f) for f in fake_concat]
errs1 = [
GAN_loss(r, f) for r, f in zip(strong_output, fake_label)
]
errs2 = [
GAN_loss(r, f) for r, f in zip(strong_real, real_label)
]
for f, s in zip(fake_label, strong_output):
metricStrong.update([
f,
], [
s,
])
for f, s in zip(real_label, strong_real):
metricStrong.update([
f,
], [
s,
])
for e1, e2 in zip(errs1, errs2):
strongerr = 0.5 * (e1 + e2)
strongerr.backward()
trainerSD.step(batch.data[0].shape[0])
nd.waitall()
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
sh = out_l_shape
#eps2 = nd.random_normal(loc=0, scale=1, shape=noiseshape, ctx=ctx) #
#eps = nd.random.uniform( low=-1, high=1, shape=noiseshape, ctx=ctx)
#if epoch>100:
# eps2 = nd.multiply(eps2,sigma)+mu
# eps2 = nd.tanh(eps2)
#else:
#eps = nd.random.uniform( low=-1, high=1, shape=noiseshape, ctx=ctx)
#eps2 = nd.concat(eps,eps2,dim=0)
rec_output = [netD(netDe(e)) for e in eps2]
fake_latent = [(netEn(r)) for r in real_in]
output2 = [netD2(f) for f in fake_latent]
fake_out = [netDe(f) for f in fake_latent]
fake_concat = nd.concat(real_in, fake_out,
dim=1) if append else fake_out
output = [netD(f) for f in fake_concat]
real_label = gluon.utils.split_and_load(
nd.ones(out_i_shape), ctx)
real_latent_label = gluon.utils.split_and_load(
nd.ones(out_l_shape), ctx)
errG2 = [
GAN_loss(r, f) for r, f in zip(rec_output, real_label)
]
errR = [
L1_loss(r, f) * lambda1
for r, f in zip(real_out, fake_out)
]
errG = [
10 * GAN_loss(r, f)
for r, f in zip(output2, real_latent_label)
] # +errG2+errR
for e1, e2, e3 in zip(errG, errG2, errR):
e = e1 + e2 + e3
e.backward()
trainerDe.step(batch.data[0].shape[0])
trainerEn.step(batch.data[0].shape[0])
nd.waitall()
errD = (errD_real[0] + errD_fake[0]) * 0.5
errD2 = (errD2_real[0] + errD2_fake[0]) * 0.5
loss_rec_G2.append(nd.mean(errG2[0]).asscalar())
loss_rec_G.append(
nd.mean(nd.mean(errG[0])).asscalar() -
nd.mean(errG2[0]).asscalar() - nd.mean(errR[0]).asscalar())
loss_rec_D.append(nd.mean(errD[0]).asscalar())
loss_rec_R.append(nd.mean(errR[0]).asscalar())
loss_rec_D2.append(nd.mean(errD2[0]).asscalar())
_, acc2 = metric2.get()
name, acc = metric.get()
acc_rec.append(acc)
acc2_rec.append(acc2)
# Print log infomation every ten batches
if iter % 10 == 0:
_, acc2 = metric2.get()
name, acc = metric.get()
_, accStrong = metricStrong.get()
logging.info('speed: {} samples/s'.format(
batch_size / (time.time() - btic)))
#print(errD)
#logging.info('discriminator loss = %f, D2 loss = %f, generator loss = %f, G2 loss = %f, SD loss = %f, D acc = %f , D2 acc = %f, DS acc = %f, reconstruction error= %f at iter %d epoch %d'
# % (nd.mean(errD[0]).asscalar(),nd.mean(errD2[0]).asscalar(),
# nd.mean(errG[0]-errG2[0]-errR[0]).asscalar(),nd.mean(errG2[0]).asscalar(),nd.mean(strongerr[0]).asscalar() ,acc,acc2,accStrong[0],nd.mean(errR[0]).asscalar() ,iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
_, acc2 = metric2.get()
#tp_file = open(expname + "_trainloss.txt", "a")
#tp_file.write(str(nd.mean(errG2).asscalar()) + " " + str(
# nd.mean(nd.mean(errG)).asscalar() - nd.mean(errG2).asscalar() - nd.mean(errR).asscalar()) + " " + str(
# nd.mean(errD).asscalar()) + " " + str(nd.mean(errD2).asscalar()) + " " + str(nd.mean(errR).asscalar()) +" "+str(acc) + " " + str(acc2)+"\n")
#tp_file.close()
metric.reset()
metric2.reset()
train_data.reset()
metricStrong.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' %
(epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch % 2 == 0: # and epoch>0:
text_file = open(expname + "_validtest.txt", "a")
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_D.params"
netD.save_parameters(filename)
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_D2.params"
netD2.save_parameters(filename)
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_En.params"
netEn.save_parameters(filename)
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_De.params"
netDe.save_parameters(filename)
filename = "checkpoints/" + expname + "_" + str(
epoch) + "_SD.params"
netDS.save_parameters(filename)
fake_img1 = nd.concat(real_in[0], real_out[0], fake_out[0], dim=1)
fake_img2 = nd.concat(real_in[1], real_out[1], fake_out[1], dim=1)
fake_img3 = nd.concat(real_in[2], real_out[2], fake_out[2], dim=1)
fake_img4 = nd.concat(real_in[3], real_out[3], fake_out[3], dim=1)
val_data.reset()
text_file = open(expname + "_validtest.txt", "a")
for vbatch in val_data:
real_in = vbatch.data[0]
real_out = vbatch.data[1]
real_in = gluon.utils.split_and_load(real_in, ctx)
real_out = gluon.utils.split_and_load(real_out, ctx)
fake_latent = [netEn(r) for r in real_in]
fake_out = [netDe(f) for f in fake_latent]
for f, r in zip(fake_out, real_out):
metricMSE.update([
f,
], [
r,
])
_, acc2 = metricMSE.get()
toterrR = 0
for e in errR:
toterrR += nd.mean(e).asscalar()
text_file.write("%s %s %s\n" % (str(epoch), toterrR, str(acc2)))
metricMSE.reset()
return ([
loss_rec_D, loss_rec_G, loss_rec_R, acc_rec, loss_rec_D2, loss_rec_G2,
acc2_rec
])
