def backward_G_B(self):
self.loss_G_adversarial_B = loss.adversarial_loss_generator(
self.fakeBpred,
self.outputApred,
method='L2',
loss_weight_config=self.loss_weight_config)
self.loss_G_reconstruction_B = loss.reconstruction_loss(
self.outputB,
self.realB,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_mask_B = loss.mask_loss(
self.maskB,
threshold=self.loss_config['mask_threshold'],
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_B = self.loss_G_adversarial_B + self.loss_G_reconstruction_B + self.loss_G_mask_B
if self.loss_config['pl_on']:
self.loss_G_perceptual_B = loss.perceptual_loss(
self.realB,
self.fakeB,
self.vggface,
self.vggface_for_pl,
method='L2',
loss_weight_config=self.loss_weight_config)
self.loss_G_B += self.loss_G_perceptual_B
if self.loss_config['edgeloss_on']:
self.loss_G_edge_B = loss.edge_loss(
self.outputB,
self.realB,
self.mask_eye_B,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_B += self.loss_G_edge_B
if self.loss_config['eyeloss_on']:
self.loss_G_eye_B = loss.eye_loss(
self.outputB,
self.realB,
self.mask_eye_B,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_B += self.loss_G_eye_B
self.loss_G_B.backward(retain_graph=True)
def backward_G_A(self):
self.loss_G_adversarial_A = loss.adversarial_loss_generator(
self.fakeApred,
self.outputApred,
method='L2',
loss_weight_config=self.loss_weight_config)
self.loss_G_reconstruction_A = loss.reconstruction_loss(
self.outputA,
self.realA,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_mask_A = loss.mask_loss(
self.maskA,
threshold=self.loss_config['mask_threshold'],
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_A = self.loss_G_adversarial_A + self.loss_G_reconstruction_A + self.loss_G_mask_A
if self.loss_config['pl_on']:
self.loss_G_perceptual_A = loss.perceptual_loss(
self.realA,
self.fakeA,
self.vggface,
self.vggface_for_pl,
method='L2',
loss_weight_config=self.loss_weight_config)
self.loss_G_A += self.loss_G_perceptual_A
if self.loss_config['edgeloss_on']:
self.loss_G_edge_A = loss.edge_loss(
self.outputA,
self.realA,
self.mask_eye_A,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_A += self.loss_G_edge_A
if self.loss_config['eyeloss_on']:
self.loss_G_eye_A = loss.eye_loss(
self.outputA,
self.realA,
self.mask_eye_A,
method='L1',
loss_weight_config=self.loss_weight_config)
self.loss_G_A += self.loss_G_eye_A
self.loss_G_A.backward(retain_graph=True)
def train(args):
tf.enable_eager_execution()
tf.executing_eagerly()
tfe = tf.contrib.eager
writer = tf.contrib.summary.create_file_writer("./log")
global_step = tf.train.get_or_create_global_step()
writer.set_as_default()
dataset = make_dataset("./train.csv", args.batch_size, args.n_class)
model = MultiSegCaps(n_class=args.n_class)
#optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=args.lr)
checkpoint_dir = './models'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
with tf.contrib.summary.record_summaries_every_n_global_steps(50):
for epoch in range(args.epoch):
for imgs, lbls in dataset:
global_step.assign_add(1)
with tf.GradientTape() as tape:
out_seg, reconstruct = model(imgs, lbls)
segmentation_loss = tf.losses.softmax_cross_entropy(
lbls, out_seg)
tf.contrib.summary.scalar('segmentation_loss',
segmentation_loss)
#segmetation_loss = weighted_margin_loss(out_seg, lbls, class_weighting=[0,1,1,1,1])
reconstruct_loss = reconstruction_loss(reconstruct,
imgs,
rs=args.rs)
tf.contrib.summary.scalar('reconstruction_loss',
reconstruct_loss)
total_loss = segmentation_loss + reconstruct_loss
tf.contrib.summary.scalar('total_loss', total_loss)
print(total_loss)
grad = tape.gradient(total_loss, model.variables)
optimizer.apply_gradients(
zip(grad, model.variables),
global_step=tf.train.get_or_create_global_step())
if epoch % 10 == 0:
root.save(file_prefix=checkpoint_prefix)
def train(self):
self.set_mode(train=True)
# prepare dataloader (iterable)
print('Start loading data...')
dset = DIGIT('./data', train=True)
self.data_loader = torch.utils.data.DataLoader(dset, batch_size=self.batch_size, shuffle=True)
test_dset = DIGIT('./data', train=False)
self.test_data_loader = torch.utils.data.DataLoader(test_dset, batch_size=self.batch_size, shuffle=True)
print('test: ', len(test_dset))
self.N = len(self.data_loader.dataset)
print('...done')
# iterators from dataloader
iterator1 = iter(self.data_loader)
iterator2 = iter(self.data_loader)
iter_per_epoch = min(len(iterator1), len(iterator2))
start_iter = self.ckpt_load_iter + 1
epoch = int(start_iter / iter_per_epoch)
for iteration in range(start_iter, self.max_iter + 1):
# reset data iterators for each epoch
if iteration % iter_per_epoch == 0:
print('==== epoch %d done ====' % epoch)
epoch += 1
iterator1 = iter(self.data_loader)
iterator2 = iter(self.data_loader)
# ============================================
# TRAIN THE VAE (ENC & DEC)
# ============================================
# sample a mini-batch
XA, XB, index = next(iterator1) # (n x C x H x W)
index = index.cpu().detach().numpy()
if self.use_cuda:
XA = XA.cuda()
XB = XB.cuda()
# zA, zS = encA(xA)
muA_infA, stdA_infA, logvarA_infA, cate_prob_infA = self.encoderA(XA)
# zB, zS = encB(xB)
muB_infB, stdB_infB, logvarB_infB, cate_prob_infB = self.encoderB(XB)
# read current values
# zS = encAB(xA,xB) via POE
cate_prob_POE = torch.exp(
torch.log(torch.tensor(1 / 10)) + torch.log(cate_prob_infA) + torch.log(cate_prob_infB))
# latent_dist = {'cont': (muA_infA, logvarA_infA), 'disc': [cate_prob_infA]}
# (kl_cont_loss, kl_disc_loss, cont_capacity_loss, disc_capacity_loss) = kl_loss_function(self.use_cuda, iteration, latent_dist)
# kl losses
#A
latent_dist_infA = {'cont': (muA_infA, logvarA_infA), 'disc': [cate_prob_infA]}
(kl_cont_loss_infA, kl_disc_loss_infA, cont_capacity_loss_infA, disc_capacity_loss_infA) = kl_loss_function(
self.use_cuda, iteration, latent_dist_infA)
loss_kl_infA = kl_cont_loss_infA + kl_disc_loss_infA
capacity_loss_infA = cont_capacity_loss_infA + disc_capacity_loss_infA
#B
latent_dist_infB = {'cont': (muB_infB, logvarB_infB), 'disc': [cate_prob_infB]}
(kl_cont_loss_infB, kl_disc_loss_infB, cont_capacity_loss_infB, disc_capacity_loss_infB) = kl_loss_function(
self.use_cuda, iteration, latent_dist_infB, cont_capacity=[0.0, 5.0, 50000, 100.0] , disc_capacity=[0.0, 10.0, 50000, 100.0])
loss_kl_infB = kl_cont_loss_infB + kl_disc_loss_infB
capacity_loss_infB = cont_capacity_loss_infB + disc_capacity_loss_infB
loss_capa = capacity_loss_infB
# encoder samples (for training)
ZA_infA = sample_gaussian(self.use_cuda, muA_infA, stdA_infA)
ZB_infB = sample_gaussian(self.use_cuda, muB_infB, stdB_infB)
ZS_POE = sample_gumbel_softmax(self.use_cuda, cate_prob_POE)
# encoder samples (for cross-modal prediction)
ZS_infA = sample_gumbel_softmax(self.use_cuda, cate_prob_infA)
ZS_infB = sample_gumbel_softmax(self.use_cuda, cate_prob_infB)
# reconstructed samples (given joint modal observation)
XA_POE_recon = self.decoderA(ZA_infA, ZS_POE)
XB_POE_recon = self.decoderB(ZB_infB, ZS_POE)
# reconstructed samples (given single modal observation)
XA_infA_recon = self.decoderA(ZA_infA, ZS_infA)
XB_infB_recon = self.decoderB(ZB_infB, ZS_infB)
# loss_recon_infA = F.l1_loss(torch.sigmoid(XA_infA_recon), XA, reduction='sum').div(XA.size(0))
loss_recon_infA = reconstruction_loss(XA, torch.sigmoid(XA_infA_recon), distribution="bernoulli")
#
loss_recon_infB = reconstruction_loss(XB, torch.sigmoid(XB_infB_recon), distribution="bernoulli")
#
loss_recon_POE = \
F.l1_loss(torch.sigmoid(XA_POE_recon), XA, reduction='sum').div(XA.size(0)) + \
F.l1_loss(torch.sigmoid(XB_POE_recon), XB, reduction='sum').div(XB.size(0))
#
loss_recon = loss_recon_infB
# total loss for vae
vae_loss = loss_recon + loss_capa
# update vae
self.optim_vae.zero_grad()
vae_loss.backward()
self.optim_vae.step()
# print the losses
if iteration % self.print_iter == 0:
prn_str = ( \
'[iter %d (epoch %d)] vae_loss: %.3f ' + \
'(recon: %.3f, capa: %.3f)\n' + \
' rec_infA = %.3f, rec_infB = %.3f, rec_POE = %.3f\n' + \
' kl_infA = %.3f, kl_infB = %.3f' + \
' cont_capacity_loss_infA = %.3f, disc_capacity_loss_infA = %.3f\n' + \
' cont_capacity_loss_infB = %.3f, disc_capacity_loss_infB = %.3f\n'
) % \
(iteration, epoch,
vae_loss.item(), loss_recon.item(), loss_capa.item(),
loss_recon_infA.item(), loss_recon_infB.item(), loss_recon.item(),
loss_kl_infA.item(), loss_kl_infB.item(),
cont_capacity_loss_infA.item(), disc_capacity_loss_infA.item(),
cont_capacity_loss_infB.item(), disc_capacity_loss_infB.item(),
)
print(prn_str)
if self.record_file:
record = open(self.record_file, 'a')
record.write('%s\n' % (prn_str,))
record.close()
# save model parameters
if iteration % self.ckpt_save_iter == 0:
self.save_checkpoint(iteration)
# save output images (recon, synth, etc.)
if iteration % self.output_save_iter == 0:
# self.save_embedding(iteration, index, muA_infA, muB_infB, muS_infA, muS_infB, muS_POE)
# 1) save the recon images
self.save_recon(iteration)
# self.save_recon2(iteration, index, XA, XB,
# torch.sigmoid(XA_infA_recon).data,
# torch.sigmoid(XB_infB_recon).data,
# torch.sigmoid(XA_POE_recon).data,
# torch.sigmoid(XB_POE_recon).data,
# muA_infA, muB_infB, muS_infA, muS_infB, muS_POE,
# logalpha, logalphaA, logalphaB
# )
z_A, z_B, z_S = self.get_stat()
#
#
#
# # 2) save the pure-synthesis images
# # self.save_synth_pure( iteration, howmany=100 )
# #
# # 3) save the cross-modal-synthesis images
# self.save_synth_cross_modal(iteration, z_A, z_B, howmany=3)
#
# # 4) save the latent traversed images
self.save_traverseB(iteration, z_A, z_B, z_S)
# self.get_loglike(logalpha, logalphaA, logalphaB)
# # 3) save the latent traversed images
# if self.dataset.lower() == '3dchairs':
# self.save_traverse(iteration, limb=-2, limu=2, inter=0.5)
# else:
# self.save_traverse(iteration, limb=-3, limu=3, inter=0.1)
if iteration % self.eval_metrics_iter == 0:
self.save_synth_cross_modal(iteration, z_A, z_B, train=False, howmany=3)
# (visdom) insert current line stats
if self.viz_on and (iteration % self.viz_ll_iter == 0):
self.line_gather.insert(iter=iteration,
recon_both=loss_recon_POE.item(),
recon_A=loss_recon_infA.item(),
recon_B=loss_recon_infB.item(),
kl_A=loss_kl_infA.item(),
kl_B=loss_kl_infB.item(),
cont_capacity_loss_infA=cont_capacity_loss_infA.item(),
disc_capacity_loss_infA=disc_capacity_loss_infA.item(),
cont_capacity_loss_infB=cont_capacity_loss_infB.item(),
disc_capacity_loss_infB=disc_capacity_loss_infB.item()
)
# (visdom) visualize line stats (then flush out)
if self.viz_on and (iteration % self.viz_la_iter == 0):
self.visualize_line()
self.line_gather.flush()
feat = torch.from_numpy(feat.todense().astype(np.float32))
############## init model ##############
gcn_vae = GraphAE(features_dim, hidden_dim, out_dim, bias=False, dropout=0.0)
optimizer_vae = torch.optim.Adam(gcn_vae.parameters(), lr=1e-2)
mlp = MLP(features_dim, hidden_dim, out_dim, dropout=0.0)
optimizer_mlp = torch.optim.Adam(mlp.parameters(), lr=1e-2)
for batch_idx in range(num_iters):
# train GCN
optimizer_vae.zero_grad()
gcn_vae.train()
z = gcn_vae(adj_norm, feat)
adj_h = torch.mm(z, z.t())
vae_train_loss = reconstruction_loss(adj_label, adj_h, norm)
vae_train_loss.backward()
optimizer_vae.step()
#train mlp
optimizer_mlp.zero_grad()
mlp.train()
z_mean, z_log_std = mlp(feat)
mlp_train_loss = vae_loss(z_mean, z_log_std, adj_label)
mlp_train_loss.backward()
optimizer_mlp.step()
print('GCN [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
batch_idx, num_iters, 100. * batch_idx / num_iters,
vae_train_loss.item()))
if batch_idx % 10 == 0:
# # test r-gcn
gcn_step.eval()
z_mean_old, z_log_std_old, z_mean_new, z_log_std_new = gcn_step(torch.from_numpy(g_adj), feat, feat_all[feat.size()[0]:, :])
z_mean = torch.cat((z_mean_old, z_mean_new))
z_log_std = torch.cat((z_log_std_old, z_log_std_new))
adj_h = sample_reconstruction(z_mean, z_log_std)
if refit:
adj_hat = (adj_h > 0).type(torch.FloatTensor)
adj_hat[:feat.size(0), :feat.size(0)] = torch.from_numpy(g_adj)
z_mean, z_log = gcn_step(adj_hat, feat_all)
adj_h = sample_reconstruction(z_mean, z_log_std)
test_loss = reconstruction_loss(adj_truth_all, adj_h, mask, test=True)
auc_rgcn = get_roc_auc_score(adj_truth_all, adj_h, mask)
ap_rgcn = get_average_precision_score(adj_truth_all, adj_h, mask)
info = 'R-GCN test loss: {:.6f}'.format(test_loss)
print(info)
logging.info(info)
# test original gcn
gcn_vae.eval()
adj_vae_norm = torch.eye(feat_all.size()[0])
adj_vae_norm[:feat.size()[0], :feat.size()[0]] = adj_feed_norm
z_mean, z_log_std = gcn_vae(adj_vae_norm, feat_all)
adj_h = sample_reconstruction(z_mean, z_log_std)
test_loss = reconstruction_loss(adj_truth_all, adj_h, mask, test=True)
def __init__(self, args):
# AttributeErrors not handled
# fail early if the encoder and decoder are not found
self.encoder = getattr(encoders, args.encoder)
self.decoder = getattr(decoders, args.decoder)
# graph definition
with tf.Graph().as_default() as g:
# placeholders
# target frame, number of channels is always one for GIF
self.T = tf.placeholder(tf.float32,
shape=(args.batch_size, args.crop_height,
args.crop_width, 1))
self.Z_in = tf.placeholder(tf.float32,
shape=(args.batch_size, args.z_dim))
# input frame(s), this depends on network parameters
if args.crop_pos is not None:
# non FCN case
if args.window_size > 1:
self.X = tf.placeholder(
tf.float32,
shape=(args.batch_size, args.window_size,
args.crop_height, args.crop_width, 1))
else:
self.X = tf.placeholder(tf.float32,
shape=(args.batch_size,
args.crop_height,
args.crop_width, 1))
else:
# FCN case
if args.window_size > 1:
self.X = tf.placeholder(tf.float32,
shape=(1, args.window_size, None,
None, 1))
else:
self.X = tf.placeholder(tf.float32,
shape=(1, None, None, 1))
# TODO: remove this once FCN networks have been added
raise NotImplementedError
# feed into networks, with their own unique name_scopes
if args.encoder == "vae_encoder":
mu, sigma = self.encoder(self.X, args)
self.Z = z_sample(mu, sigma)
else:
mu, sigma = None, None
self.Z = self.encoder(self.X, args)
self.T_hat = self.decoder(self.Z, args, reuse=tf.AUTO_REUSE)
self.decompression_op = self.decoder(self.Z_in,
args,
reuse=tf.AUTO_REUSE)
# calculate loss
with tf.name_scope("loss"):
mu = mu if mu is not None else None
sigma = sigma if sigma is not None else None
self.loss_op = reconstruction_loss(self.T_hat, self.T,
args.loss, mu, sigma,
args.l1_reg_strength,
args.l2_reg_strength)
# optimizer
with tf.name_scope("optim"):
self.optimizer = tf.train.AdamOptimizer(
learning_rate=args.learning_rate)
# grads = optimizer.compute_gradients(loss_op)
self.train_op = self.optimizer.minimize(self.loss_op)
# summaries
with tf.name_scope("summary"):
tf.summary.scalar("sumary_loss", self.loss_op)
tf.summary.image("sumary_target", self.T)
tf.summary.image("sumary_recon", self.T_hat)
self.summary_op = tf.summary.merge_all()
with tf.name_scope("init"):
self.init_op = tf.global_variables_initializer()
self.graph = g
"b2": Weights("bias_mean_hidden", [1, LATENT_SPACE_DIM]), # 2
"b3": Weights("bias_std_hidden", [1, LATENT_SPACE_DIM]), # 2
"b4": Weights("bias_matrix_decoder_hidden", [1, NN_DIM]), # 512
"b5": Weights("bias_decoder", [1, IMAGE_DIM]) # 784
}
for i in tqdm(range(1, epochs)):
print(f"Epoch {i}")
for batch, _ in trainloader:
batch = batch.view(batch.shape[0], -1)
decoder_output, mean, std = forward_propogate(
batch, weight_list, bias_list)
total_loss = ALPHA * reconstruction_loss(
batch, decoder_output) + BETA * kl_divergence_loss(mean, std)
total_loss.sum().backward()
for weight in weight_list:
weight_list[weight]._get_weight(
).data = weight_list[weight]._get_weight(
).data - learning_rate * weight_list[weight]._get_weight(
).grad.data
weight_list[weight]._get_weight().grad.data.zero_()
for bias in bias_list:
bias_list[bias]._get_weight(
).data = bias_list[bias]._get_weight(
).data - learning_rate * bias_list[bias]._get_weight().grad.data
bias_list[bias]._get_weight().grad.data.zero_()
def train_glc():
train_img_paths = get_data(os.path.join(FLAGS.train_file))
slim.get_or_create_global_step()
inputs = gen_inputs(FLAGS)
image = inputs['image_bch']
mask = inputs['mask_bch']
gen_output, _ = glc_gen.generator(image, mask, mean_fill=FLAGS.mean_fill)
##################
## Optimisation ##
##################
# Discriminator loss
# dis_input = tf.concat([gen_output, image], axis=0)
# dis_mask = tf.concat([mask]*2, axis=0)
# dis_labels = tf.concat([tf.zeros(shape=(FLAGS.batch_size,)),
# tf.ones(shape=FLAGS.batch_size,)], axis=0)
pred_gen_labels, _ = glc_dis.discriminator(gen_output, mask, FLAGS)
pred_real_labels, _ = glc_dis.discriminator(image, mask, FLAGS, reuse=True)
# pred_dis_labels, _ = glc_dis.discriminator(dis_input, dis_mask, FLAGS)
# discriminator_loss = loss.discriminator_minimax_loss(pred_dis_labels, dis_labels)
# discriminator_loss_library = loss.tf_generator_minmax_disc_loss(tf.slice(pred_dis_labels, [0], [FLAGS.batch_size]),tf.slice(
# pred_dis_labels, [(FLAGS.batch_size)], [FLAGS.batch_size]))
# Generator loss
discriminator_loss_library = gan_loss.modified_discriminator_loss(
pred_real_labels, pred_gen_labels)
generator_dis_loss_library = gan_loss.modified_generator_loss(
pred_gen_labels)
# gen_dis_input = gen_output
# gen_dis_masks = mask
# gen_dis_labels = tf.zeros(shape=(FLAGS.batch_size,))
# pred_gen_dis_labels, _ = glc_dis.discriminator(gen_dis_input, gen_dis_masks, FLAGS,
# reuse=True)
# generator_dis_loss = loss.generator_minimax_loss(pred_gen_dis_labels, gen_dis_labels)
# generator_dis_loss_library = loss.tf_generator_minmax_gen_loss(pred_gen_dis_labels)
generator_rec_loss = loss.reconstruction_loss(gen_output, mask, image)
generator_tot_loss = tf.add(generator_rec_loss,
FLAGS.alpha * generator_dis_loss_library,
name='gen_total_loss')
tf.losses.add_loss(generator_tot_loss)
dis_optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
gen_rec_optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
gen_dis_optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
dis_train_op = utils.get_train_op_for_scope(discriminator_loss_library,
dis_optimizer, ['glc_dis'],
FLAGS.clip_gradient_norm)
generator_rec_train_op = utils.get_train_op_for_scope(
generator_rec_loss, gen_rec_optimizer, ['glc_gen'],
FLAGS.clip_gradient_norm)
generator_dis_train_op = utils.get_train_op_for_scope(
generator_tot_loss, gen_dis_optimizer, ['glc_gen'],
FLAGS.clip_gradient_norm)
layers.summarize_collection(tf.GraphKeys.LOSSES)
loss_summary_op = tf.summary.merge_all()
with tf.Session() as sess:
tb_writer = tf.summary.FileWriter(FLAGS.tb_dir + '/train', sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
sess.run(inputs['iterator'].initializer,
feed_dict={inputs['image_paths']: train_img_paths})
# while True:
try:
for counter in range(T_TRAIN):
step = sess.run(slim.get_global_step())
if counter < T_C:
_, loss_summaries, aa = sess.run([
generator_rec_train_op, loss_summary_op,
generator_rec_loss
])
else:
_, loss_summaries, aa = sess.run([
dis_train_op, loss_summary_op,
discriminator_loss_library
])
if counter > T_C + T_D:
_, loss_summaries, aa = sess.run([
generator_dis_train_op, loss_summary_op,
generator_dis_loss_library
])
tb_writer.add_summary(loss_summaries, step)
print 'Global_step: {}, Loss: {}'.format(step, aa)
if step % 100 == 0:
saver.save(sess, FLAGS.ckpt_dir)
except tf.errors.OutOfRangeError:
pass
return None
def train(self):
self.net_mode(train=True)
pbar = trange(self.global_iter, int(self.max_iter))
epoch = 0
while True:
for iter, (x, _) in enumerate(self.data_loader):
current_iter = epoch * len(self.data_loader) + iter
x = Variable(x).to(device)
x_recon, mu, logvar = self.net(x)
recon_loss = reconstruction_loss(x, x_recon,
self.params.distribution)
total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar)
beta_vae_loss = self.get_loss(recon_loss,
total_kld,
current_iter=current_iter)
self.optim.zero_grad()
beta_vae_loss.backward()
self.optim.step()
if self.viz_on and current_iter % self.gather_step == 0:
self.gather.insert(iter=current_iter,
mu=mu.mean(0).data,
var=logvar.exp().mean(0).data,
recon_loss=recon_loss,
total_kld=total_kld.data,
dim_wise_kld=dim_wise_kld.data,
mean_kld=mean_kld.data)
if current_iter % self.display_step == 0:
pbar.write(
'[{}] recon_loss:{:.3f} total_kld:{:.3f} mean_kld:{:.3f}'
.format(current_iter, recon_loss.item(),
total_kld.data[0], mean_kld.data[0]))
var = logvar.exp().mean(0).data
var_str = ''
for j, var_j in enumerate(var):
var_str += 'var{}:{:.4f} '.format(j + 1, var_j)
pbar.write(var_str)
if self.viz_on:
self.gather.insert(images=x.data)
self.gather.insert(images=F.sigmoid(x_recon).data)
self.viz_reconstruction(current_iter)
self.viz_lines(current_iter)
self.gather.flush()
if self.viz_on or self.save_output:
self.viz_traverse(current_iter)
if current_iter % self.save_step == 0:
self.save_checkpoint(self.params, 'last')
pbar.write(
'Saved checkpoint(iter:{})'.format(current_iter))
if current_iter % 50000 == 0:
self.save_checkpoint(self.params, '%08d' % current_iter)
pbar.update()
if pbar.n >= int(self.max_iter):
break
epoch += 1
pbar.write("[Training Finished]")
pbar.close()
