def viz_traverse(self, limit=3, inter=2/3, loc=-1):
self.net_mode(train=False)
import random
decoder = self.net.decoder
encoder = self.net.encoder
interpolation = torch.arange(-limit, limit+0.1, inter)
n_dsets = len(self.data_loader.dataset)
rand_idx = random.randint(1, n_dsets-1)
random_img = self.data_loader.dataset.__getitem__(rand_idx)
random_img = Variable(cuda(random_img, self.use_cuda), volatile=True).unsqueeze(0)
random_img_z = encoder(random_img)[:, :self.z_dim]
random_z = Variable(cuda(torch.rand(1, self.z_dim), self.use_cuda), volatile=True)
if self.dataset == 'dsprites':
fixed_idx1 = 87040 # square
fixed_idx2 = 332800 # ellipse
fixed_idx3 = 578560 # heart
fixed_img1 = self.data_loader.dataset.__getitem__(fixed_idx1)
fixed_img1 = Variable(cuda(fixed_img1, self.use_cuda), volatile=True).unsqueeze(0)
fixed_img_z1 = encoder(fixed_img1)[:, :self.z_dim]
fixed_img2 = self.data_loader.dataset.__getitem__(fixed_idx2)
fixed_img2 = Variable(cuda(fixed_img2, self.use_cuda), volatile=True).unsqueeze(0)
fixed_img_z2 = encoder(fixed_img2)[:, :self.z_dim]
fixed_img3 = self.data_loader.dataset.__getitem__(fixed_idx3)
fixed_img3 = Variable(cuda(fixed_img3, self.use_cuda), volatile=True).unsqueeze(0)
fixed_img_z3 = encoder(fixed_img3)[:, :self.z_dim]
Z = {'fixed_square':fixed_img_z1, 'fixed_ellipse':fixed_img_z2,
'fixed_heart':fixed_img_z3, 'random_img':random_img_z}
else:
fixed_idx = 0
fixed_img = self.data_loader.dataset.__getitem__(fixed_idx)
fixed_img = Variable(cuda(fixed_img, self.use_cuda), volatile=True).unsqueeze(0)
fixed_img_z = encoder(fixed_img)[:, :self.z_dim]
Z = {'fixed_img':fixed_img_z, 'random_img':random_img_z, 'random_z':random_z}
gifs = []
for key in Z.keys():
z_ori = Z[key]
samples = []
for row in range(self.z_dim):
if loc != -1 and row != loc:
continue
z = z_ori.clone()
for val in interpolation:
z[:, row] = val
sample = F.sigmoid(decoder(z)).data
samples.append(sample)
gifs.append(sample)
samples = torch.cat(samples, dim=0).cpu()
title = '{}_latent_traversal(iter:{})'.format(key, self.global_iter)
if self.viz_on:
self.viz.images(samples, env=self.viz_name+'_traverse',
opts=dict(title=title), nrow=len(interpolation))
if self.save_output:
output_dir = os.path.join(self.output_dir, str(self.global_iter))
os.makedirs(output_dir, exist_ok=True)
gifs = torch.cat(gifs)
gifs = gifs.view(len(Z), self.z_dim, len(interpolation), self.nc, 64, 64).transpose(1, 2)
for i, key in enumerate(Z.keys()):
for j, val in enumerate(interpolation):
save_image(tensor=gifs[i][j].cpu(),
filename=os.path.join(output_dir, '{}_{}.jpg'.format(key, j)),
nrow=self.z_dim, pad_value=1)
grid2gif(os.path.join(output_dir, key+'*.jpg'),
os.path.join(output_dir, key+'.gif'), delay=10)
self.net_mode(train=True)
def train(self):
# self.net_mode(train=True)
out = False
# Start training from scratch or resume training.
self.global_iter = 0
if self.resume_iters:
self.global_iter = self.resume_iters
self.restore_model(self.resume_iters)
pbar = tqdm(total=self.max_iter)
pbar.update(self.global_iter)
while not out:
for sup_package in self.data_loader:
# appe, pose, combine
A_img = sup_package['A']
B_img = sup_package['B']
C_img = sup_package['C']
D_img = sup_package['D']
E_img = sup_package['E']
F_img = sup_package['F']
self.global_iter += 1
pbar.update(1)
A_img = Variable(cuda(A_img, self.use_cuda))
B_img = Variable(cuda(B_img, self.use_cuda))
C_img = Variable(cuda(C_img, self.use_cuda))
D_img = Variable(cuda(D_img, self.use_cuda))
E_img = Variable(cuda(E_img, self.use_cuda))
F_img = Variable(cuda(F_img, self.use_cuda))
## 1. A B C seperate(first400: id last600 background)
A_recon, A_z = self.Autoencoder(A_img)
B_recon, B_z = self.Autoencoder(B_img)
C_recon, C_z = self.Autoencoder(C_img)
D_recon, D_z = self.Autoencoder(D_img)
E_recon, E_z = self.Autoencoder(E_img)
F_recon, F_z = self.Autoencoder(F_img)
''' refer 1: content, 2: size, 3: font-color, 4 back_color, 5 style'''
A_z_1 = A_z[:, 0:self.z_size_start_dim] # 0-200
A_z_2 = A_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 20-40
A_z_3 = A_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #40-60
A_z_4 = A_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 60-80
A_z_5 = A_z[:, self.z_style_start_dim :] #80-100
B_z_1 = B_z[:, 0:self.z_size_start_dim] # 0-200
B_z_2 = B_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 200-400
B_z_3 = B_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #400-600
B_z_4 = B_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 600-800
B_z_5 = B_z[:, self.z_style_start_dim :] #800-1000
C_z_1 = C_z[:, 0:self.z_size_start_dim] # 0-200
C_z_2 = C_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 200-400
C_z_3 = C_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #400-600
C_z_4 = C_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 600-800
C_z_5 = C_z[:, self.z_style_start_dim :] #800-1000
D_z_1 = D_z[:, 0:self.z_size_start_dim] # 0-200
D_z_2 = D_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 200-400
D_z_3 = D_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #400-600
D_z_4 = D_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 600-800
D_z_5 = D_z[:, self.z_style_start_dim :] #800-1000
E_z_1 = E_z[:, 0:self.z_size_start_dim] # 0-200
E_z_2 = E_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 200-400
E_z_3 = E_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #400-600
E_z_4 = E_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 600-800
E_z_5 = E_z[:, self.z_style_start_dim :] #800-1000
F_z_1 = F_z[:, 0:self.z_size_start_dim] # 0-200
F_z_2 = F_z[:, self.z_size_start_dim : self.z_font_color_start_dim] # 200-400
F_z_3 = F_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim] #400-600
F_z_4 = F_z[:, self.z_back_color_start_dim : self.z_style_start_dim] # 600-800
F_z_5 = F_z[:, self.z_style_start_dim :] #800-1000
## 2. combine with strong supervise
''' refer 1: content, 2: size, 3: font-color, 4 back_color, 5 style'''
# C A same content-1
A1Co_combine_2C = torch.cat((A_z_1, C_z_2, C_z_3, C_z_4, C_z_5), dim=1)
mid_A1Co = self.Autoencoder.fc_decoder(A1Co_combine_2C)
mid_A1Co = mid_A1Co.view(A1Co_combine_2C.shape[0], 256, 8, 8)
A1Co_2C = self.Autoencoder.decoder(mid_A1Co)
AoC1_combine_2A = torch.cat((C_z_1, A_z_2, A_z_3, A_z_4, A_z_5), dim=1)
mid_AoC1 = self.Autoencoder.fc_decoder(AoC1_combine_2A)
mid_AoC1 = mid_AoC1.view(AoC1_combine_2A.shape[0], 256, 8, 8)
AoC1_2A = self.Autoencoder.decoder(mid_AoC1)
# C B same size 2
B2Co_combine_2C = torch.cat((C_z_1, B_z_2, C_z_3, C_z_4, C_z_5), dim=1)
mid_B2Co = self.Autoencoder.fc_decoder(B2Co_combine_2C)
mid_B2Co = mid_B2Co.view(B2Co_combine_2C.shape[0], 256, 8, 8)
B2Co_2C = self.Autoencoder.decoder(mid_B2Co)
BoC2_combine_2B = torch.cat((B_z_1, C_z_2, B_z_3, B_z_4, B_z_5), dim=1)
mid_BoC2 = self.Autoencoder.fc_decoder(BoC2_combine_2B)
mid_BoC2 = mid_BoC2.view(BoC2_combine_2B.shape[0], 256, 8, 8)
BoC2_2B = self.Autoencoder.decoder(mid_BoC2)
# C D same font_color 3
D3Co_combine_2C = torch.cat((C_z_1, C_z_2, D_z_3, C_z_4, C_z_5), dim=1)
mid_D3Co = self.Autoencoder.fc_decoder(D3Co_combine_2C)
mid_D3Co = mid_D3Co.view(D3Co_combine_2C.shape[0], 256, 8, 8)
D3Co_2C = self.Autoencoder.decoder(mid_D3Co)
DoC3_combine_2D = torch.cat((D_z_1, D_z_2, C_z_3, D_z_4, D_z_5), dim=1)
mid_DoC3 = self.Autoencoder.fc_decoder(DoC3_combine_2D)
mid_DoC3 = mid_DoC3.view(DoC3_combine_2D.shape[0], 256, 8, 8)
DoC3_2D = self.Autoencoder.decoder(mid_DoC3)
# C E same back_color 4
E4Co_combine_2C = torch.cat((C_z_1, C_z_2, C_z_3, E_z_4, C_z_5), dim=1)
mid_E4Co = self.Autoencoder.fc_decoder(E4Co_combine_2C)
mid_E4Co = mid_E4Co.view(E4Co_combine_2C.shape[0], 256, 8, 8)
E4Co_2C = self.Autoencoder.decoder(mid_E4Co)
EoC4_combine_2E = torch.cat((E_z_1, E_z_2, E_z_3, C_z_4, E_z_5), dim=1)
mid_EoC4 = self.Autoencoder.fc_decoder(EoC4_combine_2E)
mid_EoC4 = mid_EoC4.view(EoC4_combine_2E.shape[0], 256, 8, 8)
EoC4_2E = self.Autoencoder.decoder(mid_EoC4)
# C F same style 5
F5Co_combine_2C = torch.cat((C_z_1, C_z_2, C_z_3, C_z_4, F_z_5), dim=1)
mid_F5Co = self.Autoencoder.fc_decoder(F5Co_combine_2C)
mid_F5Co = mid_F5Co.view(F5Co_combine_2C.shape[0], 256, 8, 8)
F5Co_2C = self.Autoencoder.decoder(mid_F5Co)
FoC5_combine_2F = torch.cat((F_z_1, F_z_2, F_z_3, F_z_4, C_z_5), dim=1)
mid_FoC5 = self.Autoencoder.fc_decoder(FoC5_combine_2F)
mid_FoC5 = mid_FoC5.view(FoC5_combine_2F.shape[0], 256, 8, 8)
FoC5_2F = self.Autoencoder.decoder(mid_FoC5)
# combine_2C
A1B2D3E4F5_combine_2C = torch.cat((A_z_1, B_z_2, D_z_3, E_z_4, F_z_5), dim=1)
mid_A1B2D3E4F5 = self.Autoencoder.fc_decoder(A1B2D3E4F5_combine_2C)
mid_A1B2D3E4F5 = mid_A1B2D3E4F5.view(A1B2D3E4F5_combine_2C.shape[0], 256, 8, 8)
A1B2D3E4F5_2C = self.Autoencoder.decoder(mid_A1B2D3E4F5)
# ''' need unsupervise '''
A2B3D4E5F1_combine_2N = torch.cat((F_z_1, A_z_2, B_z_3, D_z_4, E_z_5), dim=1)
mid_A2B3D4E5F1 = self.Autoencoder.fc_decoder(A2B3D4E5F1_combine_2N)
mid_A2B3D4E5F1 = mid_A2B3D4E5F1.view(A2B3D4E5F1_combine_2N.shape[0], 256, 8, 8)
A2B3D4E5F1_2N = self.Autoencoder.decoder(mid_A2B3D4E5F1)
'''
optimize for autoencoder
'''
# 1. recon_loss
A_recon_loss = torch.mean(torch.abs(A_img - A_recon))
B_recon_loss = torch.mean(torch.abs(B_img - B_recon))
C_recon_loss = torch.mean(torch.abs(C_img - C_recon))
D_recon_loss = torch.mean(torch.abs(D_img - D_recon))
E_recon_loss = torch.mean(torch.abs(E_img - E_recon))
F_recon_loss = torch.mean(torch.abs(F_img - F_recon))
recon_loss = A_recon_loss + B_recon_loss + C_recon_loss + D_recon_loss + E_recon_loss + F_recon_loss
# 2. sup_combine_loss
A1Co_2C_loss = torch.mean(torch.abs(C_img - A1Co_2C))
AoC1_2A_loss = torch.mean(torch.abs(A_img - AoC1_2A))
B2Co_2C_loss = torch.mean(torch.abs(C_img - B2Co_2C))
BoC2_2B_loss = torch.mean(torch.abs(B_img - BoC2_2B))
D3Co_2C_loss = torch.mean(torch.abs(C_img - D3Co_2C))
DoC3_2D_loss = torch.mean(torch.abs(D_img - DoC3_2D))
E4Co_2C_loss = torch.mean(torch.abs(C_img - E4Co_2C))
EoC4_2E_loss = torch.mean(torch.abs(E_img - EoC4_2E))
F5Co_2C_loss = torch.mean(torch.abs(C_img - F5Co_2C))
FoC5_2F_loss = torch.mean(torch.abs(F_img - FoC5_2F))
A1B2D3E4F5_2C_loss = torch.mean(torch.abs(C_img - A1B2D3E4F5_2C))
combine_sup_loss = A1Co_2C_loss + AoC1_2A_loss + B2Co_2C_loss + BoC2_2B_loss + D3Co_2C_loss + DoC3_2D_loss + E4Co_2C_loss + EoC4_2E_loss + F5Co_2C_loss + FoC5_2F_loss + A1B2D3E4F5_2C_loss
# 3. unsup_combine_loss
_, A2B3D4E5F1_z = self.Autoencoder(A2B3D4E5F1_2N)
combine_unsup_loss = torch.mean(torch.abs(F_z_1 - A2B3D4E5F1_z[:, 0:self.z_size_start_dim])) + torch.mean(torch.abs(A_z_2 - A2B3D4E5F1_z[:, self.z_size_start_dim : self.z_font_color_start_dim])) \
+ torch.mean(torch.abs(B_z_3 - A2B3D4E5F1_z[:, self.z_font_color_start_dim : self.z_back_color_start_dim])) \
+ torch.mean(torch.abs(D_z_4 - A2B3D4E5F1_z[:, self.z_back_color_start_dim : self.z_style_start_dim])) \
+ torch.mean(torch.abs(E_z_5 - A2B3D4E5F1_z[:, self.z_style_start_dim :]))
# whole loss
vae_unsup_loss = recon_loss + self.lambda_combine * combine_sup_loss + self.lambda_unsup * combine_unsup_loss
self.auto_optim.zero_grad()
vae_unsup_loss.backward()
self.auto_optim.step()
#　save the log
f = open(self.log_dir + '/log.txt', 'a')
f.writelines(['\n', '[{}] recon_loss:{:.3f} combine_sup_loss:{:.3f} combine_unsup_loss:{:.3f}'.format(
self.global_iter, recon_loss.data, combine_sup_loss.data, combine_unsup_loss.data)])
f.close()
print('\n',self.global_iter, 'recon:',recon_loss.data, 'combinesup',combine_sup_loss.data, 'combine unsup',combine_unsup_loss.data)
if self.viz_on and self.global_iter%self.gather_step == 0:
self.gather.insert(iter=self.global_iter,recon_loss=recon_loss.data,
combine_sup_loss=combine_sup_loss.data, combine_unsup_loss=combine_unsup_loss.data)
if self.global_iter%self.display_step == 0:
pbar.write('[{}] recon_loss:{:.3f} combine_sup_loss:{:.3f} combine_unsup_loss:{:.3f}'.format(
self.global_iter, recon_loss.data, combine_sup_loss.data, combine_unsup_loss.data))
if self.viz_on:
self.gather.insert(images=A_img.data)
self.gather.insert(images=B_img.data)
self.gather.insert(images=C_img.data)
self.gather.insert(images=D_img.data)
self.gather.insert(images=E_img.data)
self.gather.insert(images=F_img.data)
self.gather.insert(images=F.sigmoid(A_recon).data)
self.viz_reconstruction()
self.viz_lines()
'''
combine show
'''
self.gather.insert(combine_supimages=F.sigmoid(AoC1_2A).data)
self.gather.insert(combine_supimages=F.sigmoid(BoC2_2B).data)
self.gather.insert(combine_supimages=F.sigmoid(D3Co_2C).data)
self.gather.insert(combine_supimages=F.sigmoid(DoC3_2D).data)
self.gather.insert(combine_supimages=F.sigmoid(EoC4_2E).data)
self.gather.insert(combine_supimages=F.sigmoid(FoC5_2F).data)
self.viz_combine_recon()
self.gather.insert(combine_unsupimages=F.sigmoid(A1B2D3E4F5_2C).data)
self.gather.insert(combine_unsupimages=F.sigmoid(A2B3D4E5F1_2N).data)
self.viz_combine_unsuprecon()
# self.viz_combine(x)
self.gather.flush()
# Save model checkpoints.
if self.global_iter%self.save_step == 0:
Auto_path = os.path.join(self.model_save_dir, self.viz_name, '{}-Auto.ckpt'.format(self.global_iter))
torch.save(self.Autoencoder.state_dict(), Auto_path)
print('Saved model checkpoints into {}/{}...'.format(self.model_save_dir, self.viz_name))
if self.global_iter >= self.max_iter:
out = True
break
pbar.write("[Training Finished]")
pbar.close()