def train(self, data_loader, stage=1):
if stage == 1:
netG, netD, start_epoch = self.load_network_stageI()
else:
netG, netD, start_epoch = self.load_network_stageII()
nz = cfg.Z_DIM
batch_size = self.batch_size
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = \
Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1),
requires_grad=False)
real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
real_labels, fake_labels = real_labels.cuda(), fake_labels.cuda()
generator_lr = cfg.TRAIN.GENERATOR_LR
discriminator_lr = cfg.TRAIN.DISCRIMINATOR_LR
lr_decay_step = cfg.TRAIN.LR_DECAY_EPOCH
optimizerG, optimizerD = self.load_optimizers(netG, netD)
count = 0
for epoch in range(start_epoch, self.max_epoch):
start_t = time.time()
if epoch % lr_decay_step == 0 and epoch > 0:
generator_lr *= 0.5
for param_group in optimizerG.param_groups:
param_group['lr'] = generator_lr
discriminator_lr *= 0.5
for param_group in optimizerD.param_groups:
param_group['lr'] = discriminator_lr
for i, data in enumerate(data_loader, 0):
######################################################
# (1) Prepare training data
######################################################
real_img_cpu, txt_embedding = data
real_imgs = Variable(real_img_cpu)
txt_embedding = Variable(txt_embedding)
if cfg.CUDA:
real_imgs = real_imgs.cuda()
txt_embedding = txt_embedding.float().cuda()
######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
inputs = (txt_embedding, noise)
_, fake_imgs, mu, logvar = \
nn.parallel.data_parallel(netG, inputs, self.gpus)
######################################################
# (3) Update D network
######################################################
netD.zero_grad()
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
mu, self.gpus)
errD.backward()
optimizerD.step()
######################################################
# (2) Update G network
######################################################
netG.zero_grad()
errG = compute_generator_loss(netD, fake_imgs, real_labels, mu,
self.gpus)
kl_loss = KL_loss(mu, logvar)
errG_total = errG + kl_loss * cfg.TRAIN.COEFF.KL
errG_total.backward()
optimizerG.step()
count = count + 1
if i % 100 == 0:
summary_D = summary.scalar('D_loss', errD.item())
summary_D_r = summary.scalar('D_loss_real', errD_real)
summary_D_w = summary.scalar('D_loss_wrong', errD_wrong)
summary_D_f = summary.scalar('D_loss_fake', errD_fake)
summary_G = summary.scalar('G_loss', errG.item())
summary_KL = summary.scalar('KL_loss', kl_loss.item())
self.summary_writer.add_summary(summary_D, count)
self.summary_writer.add_summary(summary_D_r, count)
self.summary_writer.add_summary(summary_D_w, count)
self.summary_writer.add_summary(summary_D_f, count)
self.summary_writer.add_summary(summary_G, count)
self.summary_writer.add_summary(summary_KL, count)
# save the image result for each epoch
inputs = (txt_embedding, fixed_noise)
lr_fake, fake, _, _ = \
nn.parallel.data_parallel(netG, inputs, self.gpus)
save_img_results(real_img_cpu, fake, epoch, self.image_dir)
if lr_fake is not None:
save_img_results(None, lr_fake, epoch, self.image_dir)
end_t = time.time()
print(
'''[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f Loss_KL: %.4f
Loss_real: %.4f Loss_wrong:%.4f Loss_fake %.4f
Total Time: %.2fsec
''' %
(epoch, self.max_epoch, i, len(data_loader), errD.item(),
errG.item(), kl_loss.item(), errD_real, errD_wrong, errD_fake,
(end_t - start_t)))
if epoch % self.snapshot_interval == 0:
save_model(netG, netD, epoch, self.model_dir)
save_optimizer(optimizerG, optimizerD, self.model_dir)
save_model(netG, netD, self.max_epoch, self.model_dir)
self.summary_writer.close()
def train(self, data_loader, stage=1):
if stage == 1:
netG, netD = self.load_network_stageI()
else:
netG, netD = self.load_network_stageII()
nz = cfg.Z_DIM # 100
batch_size = self.batch_size
noise = Variable(torch.FloatTensor(batch_size, nz))
fixed_noise = \
Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1),
volatile=True)
real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
real_labels, fake_labels = real_labels.cuda(), fake_labels.cuda()
generator_lr = cfg.TRAIN.GENERATOR_LR
discriminator_lr = cfg.TRAIN.DISCRIMINATOR_LR
lr_decay_step = cfg.TRAIN.LR_DECAY_EPOCH
optimizerD = \
optim.Adam(netD.parameters(),
lr=cfg.TRAIN.DISCRIMINATOR_LR, betas=(0.5, 0.999))
netG_para = []
for p in netG.parameters():
if p.requires_grad:
netG_para.append(p)
optimizerG = optim.Adam(netG_para,
lr=cfg.TRAIN.GENERATOR_LR,
betas=(0.5, 0.999))
count = 0
detectron = Detectron()
for epoch in range(self.max_epoch):
start_t = time.time()
if epoch % lr_decay_step == 0 and epoch > 0:
generator_lr *= 0.5
for param_group in optimizerG.param_groups:
param_group['lr'] = generator_lr
discriminator_lr *= 0.5
for param_group in optimizerD.param_groups:
param_group['lr'] = discriminator_lr
#print('check 0')
for i, data in enumerate(data_loader):
######################################################
# (1) Prepare training data
######################################################
#print('check 1')
real_img_cpu, txt_embedding, caption = data
caption = np.moveaxis(np.array(caption), 1, 0)
#print('check 2')
real_imgs = Variable(real_img_cpu)
txt_embedding = Variable(txt_embedding)
#print('check 3')
if cfg.CUDA:
real_imgs = real_imgs.cuda()
txt_embedding = txt_embedding.cuda()
#######################################################
# (2) Generate fake images
######################################################
#print(real_imgs.size())
noise.data.normal_(0, 1)
inputs = (txt_embedding, noise)
_, fake_imgs, mu, logvar = \
nn.parallel.data_parallel(netG, inputs, self.gpus)
############################
# (3) Update D network
###########################
netD.zero_grad()
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
mu, self.gpus)
errD.backward()
optimizerD.step()
############################
# (2) Update G network
###########################
netG.zero_grad()
errG = compute_generator_loss(netD, fake_imgs, real_labels, mu,
self.gpus)
kl_loss = KL_loss(mu, logvar)
fake_img = fake_imgs.cpu().detach().numpy()
#print(fake_img.shape)
det_obj_list = detectron.get_labels(fake_img)
fake_l = Variable(get_ohe(det_obj_list)).cuda()
real_l = Variable(get_ohe(caption)).cuda()
det_loss = nn.SmoothL1Loss()(fake_l, real_l)
errG_total = det_loss + errG + kl_loss * cfg.TRAIN.COEFF.KL
errG_total.backward()
optimizerG.step()
count = count + 1
if i % 100 == 0:
summary_D = summary.scalar('D_loss', errD.item())
summary_D_r = summary.scalar('D_loss_real', errD_real)
summary_D_w = summary.scalar('D_loss_wrong', errD_wrong)
summary_D_f = summary.scalar('D_loss_fake', errD_fake)
summary_G = summary.scalar('G_loss', errG.item())
summary_KL = summary.scalar('KL_loss', kl_loss.item())
summary_DET = summary.scalar('det_loss', det_loss.item())
self.summary_writer.add_summary(summary_D, count)
self.summary_writer.add_summary(summary_D_r, count)
self.summary_writer.add_summary(summary_D_w, count)
self.summary_writer.add_summary(summary_D_f, count)
self.summary_writer.add_summary(summary_G, count)
self.summary_writer.add_summary(summary_KL, count)
self.summary_writer.add_summary(summary_DET, count)
# save the image result for each epoch
inputs = (txt_embedding, fixed_noise)
lr_fake, fake, _, _ = \
nn.parallel.data_parallel(netG, inputs, self.gpus)
save_img_results(real_img_cpu, fake, epoch, self.image_dir)
if lr_fake is not None:
save_img_results(None, lr_fake, epoch, self.image_dir)
end_t = time.time()
print(
'''[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f Loss_KL: %.4f
Loss_real: %.4f Loss_wrong:%.4f Loss_fake %.4f
Total Time: %.2fsec
''' %
(epoch, self.max_epoch, i, len(data_loader), errD.item(),
errG.item(), kl_loss.item(), errD_real, errD_wrong, errD_fake,
(end_t - start_t)))
if epoch % self.snapshot_interval == 0:
save_model(netG, netD, epoch, self.model_dir)
#
save_model(netG, netD, self.max_epoch, self.model_dir)
#
self.summary_writer.close()
def train(self, data_loader, stage=1, max_objects=3):
if stage == 1:
netG, netD = self.load_network_stageI()
else:
netG, netD = self.load_network_stageII()
nz = cfg.Z_DIM
batch_size = self.batch_size
noise = Variable(torch.FloatTensor(batch_size, nz))
# with torch.no_grad():
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1),
requires_grad=False)
real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
real_labels, fake_labels = real_labels.cuda(), fake_labels.cuda()
generator_lr = cfg.TRAIN.GENERATOR_LR
discriminator_lr = cfg.TRAIN.DISCRIMINATOR_LR
lr_decay_step = cfg.TRAIN.LR_DECAY_EPOCH
netG_para = []
for p in netG.parameters():
if p.requires_grad:
netG_para.append(p)
optimizerD = optim.Adam(netD.parameters(),
lr=cfg.TRAIN.DISCRIMINATOR_LR,
betas=(0.5, 0.999))
optimizerG = optim.Adam(netG_para,
lr=cfg.TRAIN.GENERATOR_LR,
betas=(0.5, 0.999))
count = 0
for epoch in range(self.max_epoch):
start_t = time.time()
if epoch % lr_decay_step == 0 and epoch > 0:
generator_lr *= 0.5
for param_group in optimizerG.param_groups:
param_group['lr'] = generator_lr
discriminator_lr *= 0.5
for param_group in optimizerD.param_groups:
param_group['lr'] = discriminator_lr
for i, data in enumerate(data_loader, 0):
######################################################
# (1) Prepare training data
######################################################
real_img_cpu, bbox, label, txt_embedding = data
real_imgs = Variable(real_img_cpu)
txt_embedding = Variable(txt_embedding)
if cfg.CUDA:
real_imgs = real_imgs.cuda()
if cfg.STAGE == 1:
bbox = bbox.cuda()
elif cfg.STAGE == 2:
bbox = [bbox[0].cuda(), bbox[1].cuda()]
label = label.cuda()
txt_embedding = txt_embedding.cuda()
if cfg.STAGE == 1:
bbox = bbox.view(-1, 4)
transf_matrices_inv = compute_transformation_matrix_inverse(
bbox)
transf_matrices_inv = transf_matrices_inv.view(
real_imgs.shape[0], max_objects, 2, 3)
transf_matrices = compute_transformation_matrix(bbox)
transf_matrices = transf_matrices.view(
real_imgs.shape[0], max_objects, 2, 3)
elif cfg.STAGE == 2:
_bbox = bbox[0].view(-1, 4)
transf_matrices_inv = compute_transformation_matrix_inverse(
_bbox)
transf_matrices_inv = transf_matrices_inv.view(
real_imgs.shape[0], max_objects, 2, 3)
_bbox = bbox[1].view(-1, 4)
transf_matrices_inv_s2 = compute_transformation_matrix_inverse(
_bbox)
transf_matrices_inv_s2 = transf_matrices_inv_s2.view(
real_imgs.shape[0], max_objects, 2, 3)
transf_matrices_s2 = compute_transformation_matrix(_bbox)
transf_matrices_s2 = transf_matrices_s2.view(
real_imgs.shape[0], max_objects, 2, 3)
# produce one-hot encodings of the labels
_labels = label.long()
# remove -1 to enable one-hot converting
_labels[_labels < 0] = 80
# label_one_hot = torch.cuda.FloatTensor(noise.shape[0], max_objects, 81).fill_(0)
label_one_hot = torch.FloatTensor(noise.shape[0], max_objects,
81).fill_(0)
label_one_hot = label_one_hot.scatter_(2, _labels, 1).float()
#######################################################
# (2) Generate fake images
######################################################
noise.data.normal_(0, 1)
if cfg.STAGE == 1:
inputs = (txt_embedding, noise, transf_matrices_inv,
label_one_hot)
elif cfg.STAGE == 2:
inputs = (txt_embedding, noise, transf_matrices_inv,
transf_matrices_s2, transf_matrices_inv_s2,
label_one_hot)
_, fake_imgs, mu, logvar, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
# _, fake_imgs, mu, logvar, _ = netG(txt_embedding, noise, transf_matrices_inv, label_one_hot)
############################
# (3) Update D network
###########################
netD.zero_grad()
if cfg.STAGE == 1:
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
label_one_hot, transf_matrices, transf_matrices_inv,
mu, self.gpus)
elif cfg.STAGE == 2:
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
label_one_hot, transf_matrices_s2, transf_matrices_inv_s2,
mu, self.gpus)
errD.backward(retain_graph=True)
optimizerD.step()
############################
# (2) Update G network
###########################
netG.zero_grad()
if cfg.STAGE == 1:
errG = compute_generator_loss(netD, fake_imgs, real_labels,
label_one_hot,
transf_matrices,
transf_matrices_inv, mu,
self.gpus)
elif cfg.STAGE == 2:
errG = compute_generator_loss(netD, fake_imgs, real_labels,
label_one_hot,
transf_matrices_s2,
transf_matrices_inv_s2, mu,
self.gpus)
kl_loss = KL_loss(mu, logvar)
errG_total = errG + kl_loss * cfg.TRAIN.COEFF.KL
errG_total.backward()
optimizerG.step()
count += 1
if i % 500 == 0:
summary_D = summary.scalar('D_loss', errD.item())
summary_D_r = summary.scalar('D_loss_real', errD_real)
summary_D_w = summary.scalar('D_loss_wrong', errD_wrong)
summary_D_f = summary.scalar('D_loss_fake', errD_fake)
summary_G = summary.scalar('G_loss', errG.item())
summary_KL = summary.scalar('KL_loss', kl_loss.item())
self.summary_writer.add_summary(summary_D, count)
self.summary_writer.add_summary(summary_D_r, count)
self.summary_writer.add_summary(summary_D_w, count)
self.summary_writer.add_summary(summary_D_f, count)
self.summary_writer.add_summary(summary_G, count)
self.summary_writer.add_summary(summary_KL, count)
# save the image result for each epoch
with torch.no_grad():
if cfg.STAGE == 1:
inputs = (txt_embedding, noise,
transf_matrices_inv, label_one_hot)
elif cfg.STAGE == 2:
inputs = (txt_embedding, noise,
transf_matrices_inv, transf_matrices_s2,
transf_matrices_inv_s2, label_one_hot)
lr_fake, fake, _, _, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
save_img_results(real_img_cpu, fake, epoch,
self.image_dir)
if lr_fake is not None:
save_img_results(None, lr_fake, epoch,
self.image_dir)
with torch.no_grad():
if cfg.STAGE == 1:
inputs = (txt_embedding, noise, transf_matrices_inv,
label_one_hot)
elif cfg.STAGE == 2:
inputs = (txt_embedding, noise, transf_matrices_inv,
transf_matrices_s2, transf_matrices_inv_s2,
label_one_hot)
lr_fake, fake, _, _, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
save_img_results(real_img_cpu, fake, epoch, self.image_dir)
if lr_fake is not None:
save_img_results(None, lr_fake, epoch, self.image_dir)
end_t = time.time()
print(
'''[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f Loss_KL: %.4f
Loss_real: %.4f Loss_wrong:%.4f Loss_fake %.4f
Total Time: %.2fsec
''' %
(epoch, self.max_epoch, i, len(data_loader), errD.item(),
errG.item(), kl_loss.item(), errD_real, errD_wrong, errD_fake,
(end_t - start_t)))
if epoch % self.snapshot_interval == 0:
save_model(netG, netD, optimizerG, optimizerD, epoch,
self.model_dir)
#
save_model(netG, netD, optimizerG, optimizerD, epoch, self.model_dir)
#
self.summary_writer.close()
def train(self, data_loader, stage=1, max_objects=3):
if stage == 1:
netG, netD = self.load_network_stageI()
else:
netG, netD = self.load_network_stageII()
nz = cfg.Z_DIM
batch_size = self.batch_size
noise = Variable(torch.FloatTensor(batch_size, nz))
# with torch.no_grad():
fixed_noise = Variable(torch.FloatTensor(batch_size, nz).normal_(0, 1),
requires_grad=False)
real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
if cfg.CUDA:
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
real_labels, fake_labels = real_labels.cuda(), fake_labels.cuda()
generator_lr = cfg.TRAIN.GENERATOR_LR
discriminator_lr = cfg.TRAIN.DISCRIMINATOR_LR
lr_decay_step = cfg.TRAIN.LR_DECAY_EPOCH
netG_para = []
for p in netG.parameters():
if p.requires_grad:
netG_para.append(p)
optimizerD = optim.Adam(netD.parameters(),
lr=cfg.TRAIN.DISCRIMINATOR_LR,
betas=(0.5, 0.999))
optimizerG = optim.Adam(netG_para,
lr=cfg.TRAIN.GENERATOR_LR,
betas=(0.5, 0.999))
####
startpoint = -1
if cfg.NET_G != '':
state_dict = torch.load(cfg.NET_G,
map_location=lambda storage, loc: storage)
optimizerD.load_state_dict(state_dict["optimD"])
optimizerG.load_state_dict(state_dict["optimG"])
startpoint = state_dict["epoch"]
print(startpoint)
print('Load Optim and optimizers as : ', cfg.NET_G)
####
count = 0
drive_count = 0
for epoch in range(startpoint + 1, self.max_epoch):
print('epoch : ', epoch, ' drive_count : ', drive_count)
epoch_start_time = time.time()
print(epoch)
start_t = time.time()
start_t500 = time.time()
if epoch % lr_decay_step == 0 and epoch > 0:
generator_lr *= 0.5
for param_group in optimizerG.param_groups:
param_group['lr'] = generator_lr
discriminator_lr *= 0.5
for param_group in optimizerD.param_groups:
param_group['lr'] = discriminator_lr
time_to_i = time.time()
for i, data in enumerate(data_loader, 0):
# if i >= 3360 :
# print ('Last Batches : ' , i)
# if i < 10 :
# print ('first Batches : ' , i)
# if i == 0 :
# print ('Startig! Batch ',i,'from total of 2070' )
# if i % 10 == 0 and i!=0:
# end_t500 = time.time()
# print ('Batch Number : ' , i ,' ||||| Toatal Time : ' , (end_t500 - start_t500))
# start_t500 = time.time()
######################################################
# (1) Prepare training data
# if i < 10 :
# print (" (1) Prepare training data for batch : " , i)
######################################################
#print ("Prepare training data for batch : " , i)
real_img_cpu, bbox, label, txt_embedding = data
real_imgs = Variable(real_img_cpu)
txt_embedding = Variable(txt_embedding)
if cfg.CUDA:
real_imgs = real_imgs.cuda()
if cfg.STAGE == 1:
bbox = bbox.cuda()
elif cfg.STAGE == 2:
bbox = [bbox[0].cuda(), bbox[1].cuda()]
label = label.cuda()
txt_embedding = txt_embedding.cuda()
if cfg.STAGE == 1:
bbox = bbox.view(-1, 4)
transf_matrices_inv = compute_transformation_matrix_inverse(
bbox)
transf_matrices_inv = transf_matrices_inv.view(
real_imgs.shape[0], max_objects, 2, 3)
transf_matrices = compute_transformation_matrix(bbox)
transf_matrices = transf_matrices.view(
real_imgs.shape[0], max_objects, 2, 3)
elif cfg.STAGE == 2:
_bbox = bbox[0].view(-1, 4)
transf_matrices_inv = compute_transformation_matrix_inverse(
_bbox)
transf_matrices_inv = transf_matrices_inv.view(
real_imgs.shape[0], max_objects, 2, 3)
_bbox = bbox[1].view(-1, 4)
transf_matrices_inv_s2 = compute_transformation_matrix_inverse(
_bbox)
transf_matrices_inv_s2 = transf_matrices_inv_s2.view(
real_imgs.shape[0], max_objects, 2, 3)
transf_matrices_s2 = compute_transformation_matrix(_bbox)
transf_matrices_s2 = transf_matrices_s2.view(
real_imgs.shape[0], max_objects, 2, 3)
# produce one-hot encodings of the labels
_labels = label.long()
# remove -1 to enable one-hot converting
_labels[_labels < 0] = 80
if cfg.CUDA:
label_one_hot = torch.cuda.FloatTensor(
noise.shape[0], max_objects, 81).fill_(0)
else:
label_one_hot = torch.FloatTensor(noise.shape[0],
max_objects, 81).fill_(0)
label_one_hot = label_one_hot.scatter_(2, _labels, 1).float()
#######################################################
# # (2) Generate fake images
# if i < 10 :
# print ("(2)Generate fake images")
######################################################
noise.data.normal_(0, 1)
if cfg.STAGE == 1:
inputs = (txt_embedding, noise, transf_matrices_inv,
label_one_hot)
elif cfg.STAGE == 2:
inputs = (txt_embedding, noise, transf_matrices_inv,
transf_matrices_s2, transf_matrices_inv_s2,
label_one_hot)
if cfg.CUDA:
_, fake_imgs, mu, logvar, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
else:
print('Hiiiiiiiiiiii')
_, fake_imgs, mu, logvar, _ = netG(txt_embedding, noise,
transf_matrices_inv,
label_one_hot)
# _, fake_imgs, mu, logvar, _ = netG(txt_embedding, noise, transf_matrices_inv, label_one_hot)
############################
# # (3) Update D network
# if i < 10 :
# print("(3) Update D network")
###########################
netD.zero_grad()
if cfg.STAGE == 1:
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
label_one_hot, transf_matrices, transf_matrices_inv,
mu, self.gpus)
elif cfg.STAGE == 2:
errD, errD_real, errD_wrong, errD_fake = \
compute_discriminator_loss(netD, real_imgs, fake_imgs,
real_labels, fake_labels,
label_one_hot, transf_matrices_s2, transf_matrices_inv_s2,
mu, self.gpus)
errD.backward(retain_graph=True)
optimizerD.step()
############################
# # (4) Update G network
# if i < 10 :
# print ("(4) Update G network")
###########################
netG.zero_grad()
# if i < 10 :
# print ("netG.zero_grad")
if cfg.STAGE == 1:
errG = compute_generator_loss(netD, fake_imgs, real_labels,
label_one_hot,
transf_matrices,
transf_matrices_inv, mu,
self.gpus)
elif cfg.STAGE == 2:
# if i < 10 :
# print ("cgf.STAGE = " , cfg.STAGE)
errG = compute_generator_loss(netD, fake_imgs, real_labels,
label_one_hot,
transf_matrices_s2,
transf_matrices_inv_s2, mu,
self.gpus)
# if i < 10 :
# print("errG : ",errG)
kl_loss = KL_loss(mu, logvar)
# if i < 10 :
# print ("kl_loss = " , kl_loss)
errG_total = errG + kl_loss * cfg.TRAIN.COEFF.KL
# if i < 10 :
# print (" errG_total = " , errG_total )
errG_total.backward()
# if i < 10 :
# print ("errG_total.backward() ")
optimizerG.step()
# if i < 10 :
# print ("optimizerG.step() " )
#print (" i % 500 == 0 : " , i % 500 == 0 )
end_t = time.time()
#print ("batch time : " , (end_t - start_t))
if i % 500 == 0:
#print (" i % 500 == 0" , i % 500 == 0 )
count += 1
summary_D = summary.scalar('D_loss', errD.item())
summary_D_r = summary.scalar('D_loss_real', errD_real)
summary_D_w = summary.scalar('D_loss_wrong', errD_wrong)
summary_D_f = summary.scalar('D_loss_fake', errD_fake)
summary_G = summary.scalar('G_loss', errG.item())
summary_KL = summary.scalar('KL_loss', kl_loss.item())
print('epoch : ', epoch)
print('count : ', count)
print(' i : ', i)
print('Time to i : ', time.time() - time_to_i)
time_to_i = time.time()
print('D_loss : ', errD.item())
print('D_loss_real : ', errD_real)
print('D_loss_wrong : ', errD_wrong)
print('D_loss_fake : ', errD_fake)
print('G_loss : ', errG.item())
print('KL_loss : ', kl_loss.item())
print('generator_lr : ', generator_lr)
print('discriminator_lr : ', discriminator_lr)
print('lr_decay_step : ', lr_decay_step)
self.summary_writer.add_summary(summary_D, count)
self.summary_writer.add_summary(summary_D_r, count)
self.summary_writer.add_summary(summary_D_w, count)
self.summary_writer.add_summary(summary_D_f, count)
self.summary_writer.add_summary(summary_G, count)
self.summary_writer.add_summary(summary_KL, count)
# save the image result for each epoch
with torch.no_grad():
if cfg.STAGE == 1:
inputs = (txt_embedding, noise,
transf_matrices_inv, label_one_hot)
elif cfg.STAGE == 2:
inputs = (txt_embedding, noise,
transf_matrices_inv, transf_matrices_s2,
transf_matrices_inv_s2, label_one_hot)
if cfg.CUDA:
lr_fake, fake, _, _, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
else:
lr_fake, fake, _, _, _ = netG(
txt_embedding, noise, transf_matrices_inv,
label_one_hot)
save_img_results(real_img_cpu, fake, epoch,
self.image_dir)
if lr_fake is not None:
save_img_results(None, lr_fake, epoch,
self.image_dir)
if i % 100 == 0:
drive_count += 1
self.drive_summary_writer.add_summary(
summary_D, drive_count)
self.drive_summary_writer.add_summary(
summary_D_r, drive_count)
self.drive_summary_writer.add_summary(
summary_D_w, drive_count)
self.drive_summary_writer.add_summary(
summary_D_f, drive_count)
self.drive_summary_writer.add_summary(
summary_G, drive_count)
self.drive_summary_writer.add_summary(
summary_KL, drive_count)
#print (" with torch.no_grad(): " )
with torch.no_grad():
if cfg.STAGE == 1:
inputs = (txt_embedding, noise, transf_matrices_inv,
label_one_hot)
elif cfg.STAGE == 2:
#print (" cfg.STAGE == 2: " , cfg.STAGE == 2 )
inputs = (txt_embedding, noise, transf_matrices_inv,
transf_matrices_s2, transf_matrices_inv_s2,
label_one_hot)
#print (" inputs " , inputs )
lr_fake, fake, _, _, _ = nn.parallel.data_parallel(
netG, inputs, self.gpus)
#print (" lr_fake, fake " , lr_fake, fake )
save_img_results(real_img_cpu, fake, epoch, self.image_dir)
#print (" save_img_results(real_img_cpu, fake, epoch, self.image_dir) " , )
#print (" lr_fake is not None: " , lr_fake is not None )
if lr_fake is not None:
save_img_results(None, lr_fake, epoch, self.image_dir)
#print (" save_img_results(None, lr_fake, epoch, self.image_dir) " )
#end_t = time.time()
#print ("batch time : " , (end_t - start_t))
end_t = time.time()
print(
'''[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f Loss_KL: %.4f
Loss_real: %.4f Loss_wrong:%.4f Loss_fake %.4f
Total Time: %.2fsec
''' %
(epoch, self.max_epoch, i, len(data_loader), errD.item(),
errG.item(), kl_loss.item(), errD_real, errD_wrong, errD_fake,
(end_t - start_t)))
if epoch % self.snapshot_interval == 0:
save_model(netG, netD, optimizerG, optimizerD, epoch,
self.model_dir)
print("keyTime |||||||||||||||||||||||||||||||")
print("epoch_time : ", time.time() - epoch_start_time)
print("KeyTime |||||||||||||||||||||||||||||||")
#
save_model(netG, netD, optimizerG, optimizerD, epoch, self.model_dir)
#
self.summary_writer.close()