def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)
zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)
out = False
while not out:
for x_true1, x_true2 in self.data_loader:
self.global_iter += 1
self.pbar.update(1)
x_true1 = x_true1.to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
vae_recon_loss = recon_loss(x_true1, x_recon)
vae_kld = kl_divergence(mu, logvar)
D_z = self.D(z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
self.optim_VAE.step()
x_true2 = x_true2.to(self.device)
z_prime = self.VAE(x_true2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(z_pperm)
D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones))
self.optim_D.zero_grad()
D_tc_loss.backward()
self.optim_D.step()
if self.global_iter%self.print_iter == 0:
self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))
if self.global_iter%self.ckpt_save_iter == 0:
self.save_checkpoint(self.global_iter)
if self.global_iter >= self.max_iter:
out = True
break
self.pbar.write("[Training Finished]")
self.pbar.close()
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)
zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)
out = False
while not out:
for x_true1, x_true2 in self.data_loader:
self.global_iter += 1
self.pbar.update(1)
x_true1 = x_true1.to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
vae_recon_loss = recon_loss(x_true1, x_recon)
vae_kld = kl_divergence(mu, logvar,self.r)
H_r = entropy(self.r)
D_z = self.D(self.r*z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss + self.etaS*self.r.abs().sum() + self.etaH*H_r
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
self.optim_VAE.step()
self.optim_r.zero_grad()
vae_loss.backward(retain_graph=True)
self.optim_r.step()
x_true2 = x_true2.to(self.device)
z_prime = self.VAE(x_true2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(self.r*z_pperm)
D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones))
self.optim_D.zero_grad()
D_tc_loss.backward()
self.optim_D.step()
if self.global_iter%self.print_iter == 0:
self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))
if self.global_iter%self.ckpt_save_iter == 0:
self.save_checkpoint(self.global_iter)
if self.viz_on and (self.global_iter%self.viz_ll_iter == 0):
soft_D_z = F.softmax(D_z, 1)[:, :1].detach()
soft_D_z_pperm = F.softmax(D_z_pperm, 1)[:, :1].detach()
D_acc = ((soft_D_z >= 0.5).sum() + (soft_D_z_pperm < 0.5).sum()).float()
D_acc /= 2*self.batch_size
self.line_gather.insert(iter=self.global_iter,
soft_D_z=soft_D_z.mean().item(),
soft_D_z_pperm=soft_D_z_pperm.mean().item(),
recon=vae_recon_loss.item(),
kld=vae_kld.item(),
acc=D_acc.item(),
r_distribute=self.r.data.cpu())
if self.viz_on and (self.global_iter%self.viz_la_iter == 0):
self.visualize_line()
self.line_gather.flush()
if self.viz_on and (self.global_iter%self.viz_ra_iter == 0):
self.image_gather.insert(true=x_true1.data.cpu(),
recon=F.sigmoid(x_recon).data.cpu())
self.visualize_recon()
self.image_gather.flush()
if self.viz_on and (self.global_iter%self.viz_ta_iter == 0):
if self.dataset.lower() == '3dchairs':
self.visualize_traverse(limit=2, inter=0.5)
else:
self.visualize_traverse(limit=3, inter=2/3)
if self.global_iter >= self.max_iter:
out = True
break
self.pbar.write("[Training Finished]")
self.pbar.close()
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)
zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)
metrics = []
out = False
while not out:
for x_true1, x_true2 in self.data_loader:
self.global_iter += 1
self.pbar.update(1)
self.optim_VAE.step()
self.optim_D.step()
x_true1 = x_true1.to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
vae_recon_loss = recon_loss(x_true1, x_recon)
vae_kld = kl_divergence(mu, logvar)
D_z = self.D(z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
#self.optim_VAE.step()
x_true2 = x_true2.to(self.device)
z_prime = self.VAE(x_true2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(z_pperm)
D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones))
self.optim_D.zero_grad()
D_tc_loss.backward()
#self.optim_D.step()
# Saving the training metrics
if self.global_iter % 100 == 0:
metrics.append({'its':self.global_iter,
'vae_loss': vae_loss.detach().to(torch.device("cpu")).item(),
'D_loss': D_tc_loss.detach().to(torch.device("cpu")).item(),
'recon_loss':vae_recon_loss.detach().to(torch.device("cpu")).item(),
'tc_loss': vae_tc_loss.detach().to(torch.device("cpu")).item()})
# Saving the disentanglement metrics results
if self.global_iter % 1500 == 0:
score = self.disentanglement_metric()
metrics.append({'its':self.global_iter, 'metric_score': score})
self.net_mode(train=True) #To continue the training again
if self.global_iter%self.print_iter == 0:
self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))
if self.global_iter%self.ckpt_save_iter == 0:
self.save_checkpoint(str(self.global_iter)+".pth")
self.save_metrics(metrics)
metrics = []
if self.global_iter >= self.max_iter:
out = True
break
self.pbar.write("[Training Finished]")
self.pbar.close()
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size,
dtype=torch.long,
device=self.device)
zeros = torch.zeros(self.batch_size,
dtype=torch.long,
device=self.device)
epochs = int(np.ceil(self.steps) / len(self.dataloader))
print("number of epochs {}".format(epochs))
step = 0
for e in range(epochs):
#for e in range():
for x_true1, x_true2 in self.dataloader:
if step == 1: break
step += 1
# VAE
x_true1 = x_true1.unsqueeze(1).to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
# Reconstruction and KL
vae_recon_loss = recon_loss(x_true1, x_recon)
vae_kl = kl_div(mu, logvar)
# Total Correlation
D_z = self.D(z)
tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
# Synergy term
best_ai = self.D_syn(mu, logvar)
best_ai_labels = torch.bernoulli(best_ai)
# TODO Copy to an empty tensor
mu[best_ai_labels == 0] = 0
logvar_syn[best_ai_labels == 0] = 0
# TODO For to KL
for i in range(self.batch_size):
mu_syn_s = mu_syn[i][mu_syn[i] != 0]
if len(mu_syn.size()) == 1:
syn_loss = kl_div_uni_dim(mu_syn, logvar_syn).mean()
# print("here")
else:
syn_loss = kl_div(mu_syn, logvar_syn)
# VAE loss
vae_loss = vae_recon_loss + vae_kl + self.gamma * tc_loss + self.alpha * syn_loss
# Optimise VAE
self.optim_VAE.zero_grad() #zero gradients the buffer, grads
vae_loss.backward(
retain_graph=True) # grad parameters are populated
self.optim_VAE.step() #Does the step
# TODO Check the best greedy policy
# Discriminator Syn
real_seq = greedy_policy_Smax_discount(self.z_dim, mu, logvar,
0.8).detach
d_syn_loss = recon_loss(real_seq, best_ai)
# Optimise Discriminator Syn
self.optim_D_syn.zero_grad(
) # set zeros all the gradients of VAE network
d_syn_loss.backward(
retain_graph=True) # backprop the gradients
self.optim_D_syn.step(
) # Does the update in VAE network parameters
# Discriminator TC
x_true2 = x_true2.unsqueeze(1).to(self.device)
z_prime = self.VAE(x_true2, decode=False)[3]
z_perm = permute_dims(z_prime).detach(
) ## detaches the output from the graph. no gradient will be backproped along this variable.
D_z_perm = self.D(z_perm)
# Discriminator TC loss
d_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_perm, ones))
# Optimise Discriminator TC
self.optim_D.zero_grad()
d_loss.backward()
self.optim_D.step()
# Logging
if step % self.args.log_interval == 0:
print("Step {}".format(step))
print("Recons. Loss = " + "{:.4f}".format(vae_recon_loss))
print("KL Loss = " + "{:.4f}".format(vae_kl))
print("TC Loss = " + "{:.4f}".format(tc_loss))
print("Syn Loss = " + "{:.4f}".format(syn_loss))
print("Factor VAE Loss = " + "{:.4f}".format(vae_loss))
print("D loss = " + "{:.4f}".format(d_loss))
print("best_ai {}".format(best_ai))
print("Syn loss {:.4f}".format(syn_loss))
# Saving
if not step % self.args.save_interval:
filename = 'traversal_' + str(step) + '.png'
filepath = os.path.join(self.args.output_dir, filename)
traverse(self.net_mode, self.VAE, self.test_imgs, filepath)
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size,
dtype=torch.long,
device=self.device)
zeros = torch.zeros(self.batch_size,
dtype=torch.long,
device=self.device)
epochs = int(np.ceil(self.steps) / len(self.dataloader))
print("number of epochs {}".format(epochs))
step = 0
# dict of init opt weights
#dict_init = {a: defaultdict(list) for a in range(10)}
# dict of VAE opt weights
#dict_VAE = {a:defaultdict(list) for a in range(10)}
weights_names = [
'encoder.2.weight', 'encoder.10.weight', 'decoder.0.weight',
'decoder.7.weight', 'net.4.weight'
]
dict_VAE = defaultdict(list)
dict_weight = {a: [] for a in weights_names}
for e in range(epochs):
#for e in range():
for x_true1, x_true2 in self.dataloader:
#if step == 1: break
step += 1
"""
# TRACKING OF GRADS
print("GRADS")
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
#size : 32,32,4,4
print("Grads: Before VAE optim step {}".format(step))
#if params.grad != None:
if step != 1:
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False :
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
#dict_init[step][name] = params.grad.numpy()
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
else:
print("name {}, params grad {}".format(name, params.grad))
#dict_init[step][name] = None
dict_VAE[name] = None
if name == 'encoder.10.weight':
#size : 32,32,4,4
#print("Before VAE optim encoder step {}".format(step))
#if params.grad != None:
if step != 1:
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False :
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
#dict_init[step][name] = params.grad.numpy()
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
else:
print("name {}, params grad {}".format(name, params.grad))
#dict_init[step][name] = None
dict_VAE[name] = None
if name == 'decoder.0.weight':
#print("Before VAE optim decoder step {}".format(step))
#if params.grad != None:
if step != 1:
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("name {}, params grad {}".format(name, params.grad[:5, :2]))
else:
print("name {}, params grad {}".format(name, params.grad))
#dict_init[step][name] = None
dict_VAE[name] = None
if name == 'decoder.7.weight':
#print("Before VAE optim decoder step {}".format(step))
#if params.grad != None:
if step != 1:
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("name {}, params grad {}".format(name, params.grad[1, 1, :, :]))
else:
print("name {}, params grad {}".format(name, params.grad))
#dict_init[step][name] = None
dict_VAE[name] = None
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
#print("Before VAE optim discrim step {}".format(step))
#if params.grad != None:
if step != 1:
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("name {}, params grad {}".format(name, params.grad[1, 1, :, :]))
else:
print("name {}, params grad {}".format(name, params.grad))
#dict_init[step][name] = None
dict_VAE[name] = None
print()
"""
# VAE
x_true1 = x_true1.unsqueeze(1).to(self.device)
#print("x_true1 size {}".format(x_true1.size()))
x_recon, mu, logvar, z = self.VAE(x_true1)
# Reconstruction and KL
vae_recon_loss = recon_loss(x_true1, x_recon)
#print("vae recon loss {}".format(vae_recon_loss))
vae_kl = kl_div(mu, logvar)
#print("vae kl loss {}".format(vae_kl))
# Total Correlation
D_z = self.D(z)
#print("D_z size {}".format(D_z.size()))
tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
#print("tc loss {}".format(tc_loss))
# VAE loss
vae_loss = vae_recon_loss + vae_kl + self.gamma * tc_loss
#print("Total VAE loss {}".format(vae_loss))
#print("Weights: Before VAE, step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
# Optimise VAE
self.optim_VAE.zero_grad() #zero gradients the buffer, grads
"""
print("after zero grad step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
# check if the VAE is optimizing the encoder and decoder
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
if step == 1:
print("name {}, params grad {}".format(name, params.grad))
#else:
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if name == 'encoder.10.weight':
# size : 32,32,4,4
if step == 1:
print("name {}, params grad {}".format(name, params.grad))
#else:
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 32,32,4,4
if step == 1:
print("name {}, params grad {}".format(name, params.grad))
#else:
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
"""
vae_loss.backward(
retain_graph=True) # grad parameters are populated
"""
print()
print("after backward step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
# check if the VAE is optimizing the encoder and decoder
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 1000,1000
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))"""
self.optim_VAE.step() #Does the step
#print()
#print("after VAE update step {}".format(step))
#print("encoder.2.weight size {}".format(self.optim_VAE.param_groups[0]['params'][2].size()))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0,0,:,:]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
"""
# check if the VAE is optimizing the encoder and decoder
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
#size : 32,32,4,4
print("After VAE optim step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
#size : 20, 128
#print("size of {}: {}".format(name, params.grad.size()))
#print("After VAE optim encoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
#128,10
#print("After VAE optim decoder linear step {}".format(step))
#print("size of {}: {}".format(name, params.grad.size()))
#print("name {}, params grad {}".format(name, params.grad[:3, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
#print("After VAE optim decoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[1, 1, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
#print("After VAE optim discriminator step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:5,:5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
print()
"""
#print()
#print("Before Syn step {}".format(step))
#print("encoder.2.weight size {}".format(self.optim_VAE.param_groups[0]['params'][2].size()))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
##################
#Synergy Max
# Step 1: compute the argmax of D kl (q(ai | x(i)) || )
best_ai = greedy_policy_Smax_discount(self.z_dim,
mu,
logvar,
alpha=self.omega)
# Step 2: compute the Imax
mu_syn = mu[:, best_ai]
logvar_syn = logvar[:, best_ai]
if len(mu_syn.size()) == 1:
I_max = kl_div_uni_dim(mu_syn, logvar_syn).mean()
# print("here")
else:
I_max = kl_div(mu_syn, logvar_syn)
#I_max1 = I_max_batch(best_ai, mu, logvar)
#print("I_max step{}".format(I_max, step))
# Step 3: Use it in the loss
syn_loss = self.alpha * I_max
#print("syn_loss step {}".format(syn_loss, step))
# Step 4: Optimise Syn term
self.optim_VAE.zero_grad(
) # set zeros all the gradients of VAE network
"""
#print()
print("after zeros Syn step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 1000,1000
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
"""
syn_loss.backward(retain_graph=True) #backprop the gradients
"""
print()
print("after Syn backward step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 1000,1000
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
"""
self.optim_VAE.step(
) #Does the update in VAE network parameters
#print()
#print("after Syn update step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("encoder.10.weight {}".format(self.optim_VAE.param_groups[0]['params'][10][:, :2]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
###################
"""
# check if the VAE is optimizing the encoder and decoder
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
print("After Syn optim step {}".format(step))
# print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size :
#print("After Syn optim encoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:, :]))
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dim_changes = []
for dim in range(20):
if np.array_equal(dict_VAE[name][dim, :2], params.grad.numpy()[dim, :2]) == False:
dim_changes.append(dim)
print("Changes in dimensions: {}".format(dim_changes))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
# 1024, 128
#print("After Syn optim decoder linear step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:5, :2]))
#print("name {}, params grad {}".format(name, params.grad[:3, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
#print("After Syn optim decoder step {}".format(step))
# print("name {}, params grad {}".format(name, params.grad[1, 1, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
#print("After Syn optim discriminator step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:5,:5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
print()
"""
# Discriminator
x_true2 = x_true2.unsqueeze(1).to(self.device)
z_prime = self.VAE(x_true2, decode=False)[3]
z_perm = permute_dims(z_prime).detach(
) ## detaches the output from the graph. no gradient will be backproped along this variable.
D_z_perm = self.D(z_perm)
# Discriminator loss
d_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_perm, ones))
#print("d_loss {}".format(d_loss))
#print("dict VAE {}".format(dict_VAE['encoder.2.weight'][0, 0, :, :]))
#print("before Disc, step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
# Optimise Discriminator
self.optim_D.zero_grad()
"""
print("after zero grad Disc step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 1000,1000
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
"""
d_loss.backward()
"""
print()
print("after backward Disc step {}".format(step))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
# check if the VAE is optimizing the encoder and decoder
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'encoder.10.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
# size : 32,32,4,4
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
# size : 1000,1000
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
"""
self.optim_D.step()
#print("dict VAE {}".format(dict_VAE['encoder.2.weight'][0, 0, :, :]))
"""
print()
print("after update disc step {}".format(step))
#print("encoder.2.weight size {}".format(self.optim_VAE.param_groups[0]['params'][2].size()))
#print("encoder.2.weight {}".format(self.optim_VAE.param_groups[0]['params'][2][0, 0, :, :]))
#print("net.4.weight {}".format(self.optim_D.param_groups[0]['params'][4][:5, :5]))
for name, params in self.VAE.named_parameters():
if name == 'encoder.2.weight':
#size : 32,32,4,4
print("After Discriminator optim encoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
#print("dict VAE {}".format(dict_VAE[name][0, 0, :, :]))
#if np.isclose(dict_VAE[name], params.grad.numpy(), rtol=1e-05, atol=1e-08, equal_nan=False): "Works"
#if np.all(abs(dict_VAE[step][name] - params.grad.numpy())) < 1e-7 == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
#print("dict VAE {}".format(dict_VAE[name][0, 0, :, :]))
if name == 'encoder.10.weight':
# size :
#print("After Syn optim encoder step {}".format(step))
# print("name {}, params grad {}".format(name, params.grad[0, 0, :, :]))
#print("name {}, params grad {}".format(name, params.grad[:, :2]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
# if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.0.weight':
#1024, 128
#print("After Discriminator optim decoder linear step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:5, :2]))
#print("name {}, params grad {}".format(name, params.grad[:3, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
if name == 'decoder.7.weight':
#print("After Discriminator optim decoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[1, 1, :, :]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
for name, params in self.D.named_parameters():
if name == 'net.4.weight':
#print("After Discriminator optim decoder step {}".format(step))
#print("name {}, params grad {}".format(name, params.grad[:5, :5]))
if np.array_equal(dict_VAE[name], params.grad.numpy()) == False:
#if dict_VAE[name] != tuple(params.grad.numpy()):
print("Change in gradients {}".format(name))
dict_VAE[name] = params.grad.numpy().copy()
else:
print("No change in gradients {}".format(name))
print()"""
# Logging
if step % self.args.log_interval == 0:
print("Step {}".format(step))
print("Recons. Loss = " + "{:.4f}".format(vae_recon_loss))
print("KL Loss = " + "{:.4f}".format(vae_kl))
print("TC Loss = " + "{:.4f}".format(tc_loss))
print("Factor VAE Loss = " + "{:.4f}".format(vae_loss))
print("D loss = " + "{:.4f}".format(d_loss))
print("best_ai {}".format(best_ai))
print("I_max {}".format(I_max))
print("Syn loss {:.4f}".format(syn_loss))
# Saving
if not step % self.args.save_interval:
filename = 'alpha_' + str(
self.alpha) + '_traversal_' + str(step) + '.png'
filepath = os.path.join(self.args.output_dir, filename)
traverse(self.net_mode, self.VAE, self.test_imgs, filepath)
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size,
dtype=torch.long,
device=self.device)
zeros = torch.zeros(self.batch_size,
dtype=torch.long,
device=self.device)
epochs = int(np.ceil(self.steps) / len(self.dataloader))
print("number of epochs {}".format(epochs))
step = 0
for e in range(epochs):
#for e in range(1):
for x_true1, x_true2 in self.dataloader:
#if step == 50: break
step += 1
# VAE
x_true1 = x_true1.unsqueeze(1).to(self.device)
#print("x_true1 size {}".format(x_true1.size()))
x_recon, mu, logvar, z = self.VAE(x_true1)
#print("x_recon size {}".format(x_recon.size()))
#print("mu size {}".format(mu.size()))
#print("logvar size {}".format(logvar.size()))
#print("z size {}".format(z.size()))
# Reconstruction and KL
vae_recon_loss = recon_loss(x_true1, x_recon)
#print("vae recon loss {}".format(vae_recon_loss))
vae_kl = kl_div(mu, logvar)
#print("vae kl loss {}".format(vae_kl))
# Total Correlation
D_z = self.D(z)
#print("D_z size {}".format(D_z.size()))
tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
#print("tc loss {}".format(tc_loss))
# VAE loss
vae_loss = vae_recon_loss + vae_kl + self.gamma * tc_loss
#print("Total VAE loss {}".format(vae_loss))
# Optimise VAE
self.optim_VAE.zero_grad() #zero gradients the buffer
vae_loss.backward(retain_graph=True)
self.optim_VAE.step() #Does the step
# Discriminator
x_true2 = x_true2.unsqueeze(1).to(self.device)
z_prime = self.VAE(x_true2, decode=False)[3]
z_perm = permute_dims(z_prime).detach(
) ## detaches the output from the graph. no gradient will be backproped along this variable.
D_z_perm = self.D(z_perm)
# Discriminator loss
d_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_perm, ones))
#print("d_loss {}".format(d_loss))
# Optimise Discriminator
self.optim_D.zero_grad()
d_loss.backward()
self.optim_D.step()
# Logging
if step % self.args.log_interval == 0:
print("Step {}".format(step))
print("Recons. Loss = " + "{:.4f}".format(vae_recon_loss))
print("KL Loss = " + "{:.4f}".format(vae_kl))
print("TC Loss = " + "{:.4f}".format(tc_loss))
print("Factor VAE Loss = " + "{:.4f}".format(vae_loss))
print("D loss = " + "{:.4f}".format(d_loss))
# Saving
if not step % self.args.save_interval:
filename = 'traversal_' + str(step) + '.png'
filepath = os.path.join(self.args.output_dir, filename)
traverse(self.net_mode, self.VAE, self.test_imgs, filepath)
# Saving plot gt vs predicted
if not step % self.args.gt_interval:
filename = 'gt_' + str(step) + '.png'
filepath = os.path.join(self.args.output_dir, filename)
plot_gt_shapes(self.net_mode, self.VAE, self.dataloader_gt,
filepath)
def train(self):
gcam = GradCamDissen(self.VAE,
self.D,
target_layer='encode.1',
cuda=True)
self.net_mode(train=True)
ones = torch.ones(self.batch_size,
dtype=torch.long,
device=self.device)
zeros = torch.zeros(self.batch_size,
dtype=torch.long,
device=self.device)
mu_avg, logvar_avg = 0, 1
metrics = []
out = False
while not out:
for batch_idx, (x1, x2) in enumerate(self.data_loader):
self.global_iter += 1
self.pbar.update(1)
self.optim_VAE.step()
self.optim_D.step()
x1 = x1.to(self.device)
x1_rec, mu, logvar, z = gcam.forward(x1)
# For Standard FactorVAE loss
vae_recon_loss = recon_loss(x1, x1_rec)
vae_kld = kl_divergence(mu, logvar)
D_z = self.D(z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
factorVae_loss = vae_recon_loss + vae_kld + self.gamma * vae_tc_loss
# For attention disentanglement loss
gcam.backward(mu, logvar, mu_avg, logvar_avg)
att_loss = 0
with torch.no_grad():
gcam_maps = gcam.generate()
selected = self.select_attention_maps(gcam_maps)
for (sel1, sel2) in selected:
att_loss += attention_disentanglement(sel1, sel2)
att_loss /= len(
selected) # Averaging the loss accross all pairs of maps
vae_loss = factorVae_loss + self.lambdaa * att_loss
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
#self.optim_VAE.step()
x2 = x2.to(self.device)
z_prime = self.VAE(x2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(z_pperm)
D_tc_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_pperm, ones))
self.optim_D.zero_grad()
D_tc_loss.backward()
#self.optim_D.step()
# Saving the training metrics
if self.global_iter % 100 == 0:
metrics.append({
'its':
self.global_iter,
'vae_loss':
vae_loss.detach().to(torch.device("cpu")).item(),
'D_loss':
D_tc_loss.detach().to(torch.device("cpu")).item(),
'recon_loss':
vae_recon_loss.detach().to(torch.device("cpu")).item(),
'tc_loss':
vae_tc_loss.detach().to(torch.device("cpu")).item()
})
# Saving the disentanglement metrics results
if self.global_iter % 1500 == 0:
score = self.disentanglement_metric()
metrics.append({
'its': self.global_iter,
'metric_score': score
})
self.net_mode(train=True) # To continue the training again
if self.global_iter % self.print_iter == 0:
self.pbar.write(
'[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'
.format(self.global_iter, vae_recon_loss.item(),
vae_kld.item(), vae_tc_loss.item(),
D_tc_loss.item()))
if self.global_iter % self.ckpt_save_iter == 0:
self.save_checkpoint(str(self.global_iter) + ".pth")
self.save_metrics(metrics)
metrics = []
if self.global_iter >= self.max_iter:
out = True
break
self.pbar.write("[Training Finished]")
self.pbar.close()
def main(args):
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pbar = tqdm(total=args.epochs)
image_gather = DataGather('true', 'recon')
dataset = get_celeba_selected_dataset()
data_loader = DataLoader(dataset=dataset,
batch_size=args.batch_size,
shuffle=True)
lr_vae = args.lr_vae
lr_D = args.lr_D
vae = CelebaFactorVAE(args.z_dim, args.num_labels).to(device)
optim_vae = torch.optim.Adam(vae.parameters(), lr=args.lr_vae)
D = Discriminator(args.z_dim, args.num_labels).to(device)
optim_D = torch.optim.Adam(D.parameters(), lr=args.lr_D, betas=(0.5, 0.9))
# Checkpoint
ckpt_dir = os.path.join(args.ckpt_dir, args.name)
mkdirs(ckpt_dir)
start_epoch = 0
if args.ckpt_load:
load_checkpoint(pbar, ckpt_dir, D, vae, optim_D, optim_vae, lr_vae,
lr_D)
#optim_D.param_groups[0]['lr'] = 0.00001#lr_D
#optim_vae.param_groups[0]['lr'] = 0.00001#lr_vae
print("confirming lr after loading checkpoint: ",
optim_vae.param_groups[0]['lr'])
# Output
output_dir = os.path.join(args.output_dir, args.name)
mkdirs(output_dir)
ones = torch.ones(args.batch_size, dtype=torch.long, device=device)
zeros = torch.zeros(args.batch_size, dtype=torch.long, device=device)
for epoch in range(start_epoch, args.epochs):
pbar.update(1)
for iteration, (x, y, x2, y2) in enumerate(data_loader):
x, y, x2, y2 = x.to(device), y.to(device), x2.to(device), y2.to(
device)
recon_x, mean, log_var, z = vae(x, y)
if z.shape[0] != args.batch_size:
print("passed a batch in epoch {}, iteration {}!".format(
epoch, iteration))
continue
D_z = D(z)
vae_recon_loss = recon_loss(x, recon_x) * args.recon_weight
vae_kld = kl_divergence(mean, log_var)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean() * args.gamma
vae_loss = vae_recon_loss + vae_tc_loss #+ vae_kld
optim_vae.zero_grad()
vae_loss.backward(retain_graph=True)
z_prime = vae(x2, y2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = D(z_pperm)
D_tc_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_pperm, ones))
optim_D.zero_grad()
D_tc_loss.backward()
optim_vae.step()
optim_D.step()
if iteration % args.print_iter == 0:
pbar.write(
'[epoch {}/{}, iter {}/{}] vae_recon_loss:{:.4f} vae_kld:{:.4f} vae_tc_loss:{:.4f} D_tc_loss:{:.4f}'
.format(epoch, args.epochs, iteration,
len(data_loader) - 1, vae_recon_loss.item(),
vae_kld.item(), vae_tc_loss.item(),
D_tc_loss.item()))
if iteration % args.output_iter == 0 and iteration != 0:
output_dir = os.path.join(
args.output_dir,
args.name) #, "{}.{}".format(epoch, iteration))
mkdirs(output_dir)
#reconstruction
#image_gather.insert(true=x.data.cpu(), recon=torch.sigmoid(recon_x).data.cpu())
#data = image_gather.data
#true_image = data['true'][0]
#recon_image = data['recon'][0]
#true_image = make_grid(true_image)
#recon_image = make_grid(recon_image)
#sample = torch.stack([true_image, recon_image], dim=0)
#save_image(tensor=sample.cpu(), fp=os.path.join(output_dir, "recon.jpg"))
#image_gather.flush()
#inference given num_labels = 10
c = torch.randint(low=0, high=2,
size=(1, 10)) #populated with 0s and 1s
for i in range(9):
c = torch.cat(
(c, torch.randint(low=0, high=2, size=(1, 10))), 0)
c = c.to(device)
z_inf = torch.rand([c.size(0), args.z_dim]).to(device)
#print("shapes: ",z_inf.shape, c.shape)
#c = c.reshape(-1,args.num_labels,1,1)
z_inf = torch.cat((z_inf, c), dim=1)
z_inf = z_inf.reshape(-1, args.num_labels + args.z_dim, 1, 1)
x = vae.decode(z_inf)
plt.figure()
plt.figure(figsize=(10, 20))
for p in range(args.num_labels):
plt.subplot(5, 2, p + 1) #row, col, index starting from 1
plt.text(0,
0,
"c={}".format(c[p]),
color='black',
backgroundcolor='white',
fontsize=10)
p = x[p].view(3, 218, 178)
image = torch.transpose(p, 0, 2)
image = torch.transpose(image, 0, 1)
plt.imshow(
(image.cpu().data.numpy() * 255).astype(np.uint8))
plt.axis('off')
plt.savefig(os.path.join(
output_dir, "E{:d}||{:d}.png".format(epoch, iteration)),
dpi=300)
plt.clf()
plt.close('all')
if epoch % 8 == 0:
optim_vae.param_groups[0]['lr'] /= 10
optim_D.param_groups[0]['lr'] /= 10
print("\nnew learning rate at epoch {} is {}!".format(
epoch, optim_vae.param_groups[0]['lr']))
if epoch % args.ckpt_iter_epoch == 0:
save_checkpoint(pbar, epoch, D, vae, optim_D, optim_vae, ckpt_dir,
epoch)
pbar.write("[Training Finished]")
pbar.close()
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size,
dtype=torch.long,
device=self.device)
zeros = torch.zeros(self.batch_size,
dtype=torch.long,
device=self.device)
out = False
while not out:
for x_true1, x_true2 in self.data_loader:
self.global_iter += 1
self.pbar.update(1)
x_true1 = x_true1.to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
vae_recon_loss = recon_loss(x_true1, x_recon)
vae_ad_loss = self.get_ad_loss(z)
vae_kld = kl_divergence(mu, logvar)
D_z = self.D(z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
vae_loss = vae_recon_loss + vae_kld + self.gamma * vae_tc_loss + self.lamb * vae_ad_loss
x_true2 = x_true2.to(self.device)
z_prime = self.VAE(x_true2, no_dec=True)
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(z_pperm)
D_tc_loss = 0.5 * (F.cross_entropy(D_z, zeros) +
F.cross_entropy(D_z_pperm, ones))
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
self.optim_D.zero_grad()
D_tc_loss.backward()
self.optim_VAE.step()
self.optim_D.step()
if self.global_iter % self.print_iter == 0:
if self.dis_score:
dis_score = disentanglement_score(
self.VAE.eval(), self.device, self.dataset,
self.z_dim, self.L, self.vote_count,
self.dis_batch_size)
self.VAE.train()
else:
dis_score = torch.tensor(0)
self.pbar.write(
'[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} ad_loss:{:.3f} D_tc_loss:{:.3f} dis_score:{:.3f}'
.format(self.global_iter, vae_recon_loss.item(),
vae_kld.item(), vae_tc_loss.item(),
vae_ad_loss.item(), D_tc_loss.item(),
dis_score.item()))
if self.results_save:
self.outputs['vae_recon_loss'].append(
vae_recon_loss.item())
self.outputs['vae_kld'].append(vae_kld.item())
self.outputs['vae_tc_loss'].append(vae_tc_loss.item())
self.outputs['D_tc_loss'].append(D_tc_loss.item())
self.outputs['ad_loss'].append(vae_ad_loss.item())
self.outputs['dis_score'].append(dis_score.item())
self.outputs['iteration'].append(self.global_iter)
if self.global_iter % self.ckpt_save_iter == 0:
self.save_checkpoint(self.global_iter)
if self.global_iter >= self.max_iter:
out = True
break
self.pbar.write("[Training Finished]")
self.pbar.close()
if self.results_save:
save_args_outputs(self.results_dir, self.args, self.outputs)
def train(self):
self.net_mode(train=True)
ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)
zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)
out = False
while not out:
for x_true1, x_true2 in self.data_loader:#ここで読み込んでいる？
self.global_iter += 1
self.pbar.update(1)
if self.dataset == 'mnist':
x_true1 = x_true1.view(x_true1.shape[0], -1)
x_true1 = x_true1.to(self.device)
x_recon, mu, logvar, z = self.VAE(x_true1)
x = x_true1.view(x_true1.shape[0], -1) #custom
#vae_recon_loss = self.custom_loss(x) / self.batch_size #custom
vae_recon_loss = recon_loss(x, x_recon) #復元誤差, 交差エントロピー誤差
vae_kld = kl_divergence(mu, logvar)
D_z = self.D(z)
vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean() #恐らく, discriminatorのloss
vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss
#vae_loss = vae_recon_loss + self.gamma*vae_tc_loss
self.optim_VAE.zero_grad()
vae_loss.backward(retain_graph=True)
self.optim_VAE.step()
x_true2 = x_true2.to(self.device)
#x_true2 = x_true2.view(x_true2.shape[0], -1)
z_prime = self.VAE(x_true2, no_dec=True) #trueにすることで潜在空間に写像した状態のデータを獲得?
z_pperm = permute_dims(z_prime).detach()
D_z_pperm = self.D(z_pperm)
D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones)) #GANのdiscriminatorっぽい？偽物と本物
#そのため誤差の部分が0と1になっているはず！zerosとonesの部分
self.optim_D.zero_grad()
D_tc_loss.backward()
self.optim_D.step()
#if self.global_iter%self.print_iter == 0:
# self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
# self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))
if self.test_count % 547 == 0:
#self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
#self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))
self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(
self.global_iter, vae_recon_loss.item(), vae_tc_loss.item(), D_tc_loss.item()))
self.test_count = 0
if self.global_iter%self.ckpt_save_iter == 0:
self.save_checkpoint(self.global_iter)
if self.viz_on and (self.global_iter%self.viz_ll_iter == 0):
soft_D_z = F.softmax(D_z, 1)[:, :1].detach()
soft_D_z_pperm = F.softmax(D_z_pperm, 1)[:, :1].detach()
D_acc = ((soft_D_z >= 0.5).sum() + (soft_D_z_pperm < 0.5).sum()).float()
D_acc /= 2*self.batch_size
self.line_gather.insert(iter=self.global_iter,
soft_D_z=soft_D_z.mean().item(),
soft_D_z_pperm=soft_D_z_pperm.mean().item(),
recon=vae_recon_loss.item(),
#kld=vae_kld.item(),
acc=D_acc.item())
if self.viz_on and (self.global_iter%self.viz_la_iter == 0):
self.visualize_line()
self.line_gather.flush()
if self.viz_on and (self.global_iter%self.viz_ra_iter == 0):
self.image_gather.insert(true=x_true1.data.cpu(),
recon=F.sigmoid(x_recon).data.cpu())
self.visualize_recon()
self.image_gather.flush()
if self.viz_on and (self.global_iter%self.viz_ta_iter == 0):
if self.dataset.lower() == '3dchairs':
self.visualize_traverse(limit=2, inter=0.5)
else:
#self.visualize_traverse(limit=3, inter=2/3)
print("ignore")
if self.global_iter >= self.max_iter:
out = True
break
self.test_count += 1
self.pbar.write("[Training Finished]")
torch.save(self.VAE.state_dict(), "model1/0531_128_2_gamma2.pth")
self.pbar.close()
