def load_model(filename):
checkpoint = torch.load(filename, map_location=device)
global generator
global discriminator
generator = Generator(len(data['mapping']), data['max_length'],
latent_size)
generator.load_state_dict(checkpoint['generator_model_state_dict'])
generator.eval()
discriminator = Discriminator(len(data['mapping']))
discriminator.load_state_dict(checkpoint['discriminator_model_state_dict'])
discriminator.eval()
print('Loaded model')
class Solver(object):
"""Solver for training and testing StarGAN."""
def __init__(self, data_loader, config):
"""Initialize configurations."""
# Data loader.
self.data_loader = data_loader
# Model configurations.
self.c_dim = config.c_dim
self.image_size = config.image_size
self.g_conv_dim = config.g_conv_dim
self.d_conv_dim = config.d_conv_dim
self.g_repeat_num = config.g_repeat_num
self.d_repeat_num = config.d_repeat_num
self.lambda_app = config.lambda_app
self.lambda_pose = config.lambda_pose
self.lambda_rec = config.lambda_rec
self.lambda_gp = config.lambda_gp
# Training configurations.
# self.dataset = config.dataset
self.batch_size = config.batch_size
self.num_iters = config.num_iters
self.num_iters_decay = config.num_iters_decay
self.g_lr = config.g_lr
self.d_lr = config.d_lr
self.n_critic = config.n_critic
self.beta1 = config.beta1
self.beta2 = config.beta2
self.resume_iters = config.resume_iters
# self.selected_attrs = config.selected_attrs
# loss functions
self.feature_loss = torch.nn.CosineEmbeddingLoss()
self.pose_loss = torch.nn.PairwiseDistance()
# Test configurations.
self.test_iters = config.test_iters
# Miscellaneous.
self.use_tensorboard = config.use_tensorboard
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# Directories.
self.log_dir = config.log_dir
self.sample_dir = config.sample_dir
self.model_save_dir = config.model_save_dir
self.result_dir = config.result_dir
# Step size.
self.log_step = config.log_step
self.sample_step = config.sample_step
self.model_save_step = config.model_save_step
self.lr_update_step = config.lr_update_step
# Build the model and tensorboard.
self.build_model()
if self.use_tensorboard:
self.build_tensorboard()
def build_model(self):
"""Create a generator and a discriminator."""
self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim,
self.d_repeat_num)
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.print_network(self.G, 'G')
self.print_network(self.D, 'D')
self.G.to(self.device)
self.D.to(self.device)
"""Build the feature extractor"""
self.feature_model = f_model(model_path=DUMPED_MODEL,
freeze_param=True).cuda() #.cuda()
self.feature_model.eval()
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("The number of parameters: {}".format(num_params))
def restore_model(self, resume_iters):
"""Restore the trained generator and discriminator."""
print(
'Loading the trained models from step {}...'.format(resume_iters))
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(resume_iters))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(resume_iters))
self.G.load_state_dict(
torch.load(G_path, map_location=lambda storage, loc: storage))
self.D.load_state_dict(
torch.load(D_path, map_location=lambda storage, loc: storage))
def build_tensorboard(self):
"""Build a tensorboard logger."""
from logger import Logger
self.logger = Logger(self.log_dir)
def update_lr(self, g_lr, d_lr):
"""Decay learning rates of the generator and discriminator."""
for param_group in self.g_optimizer.param_groups:
param_group['lr'] = g_lr
for param_group in self.d_optimizer.param_groups:
param_group['lr'] = d_lr
def reset_grad(self):
"""Reset the gradient buffers."""
self.g_optimizer.zero_grad()
self.d_optimizer.zero_grad()
def normalize(self, x):
pass
def denorm(self, x):
"""Convert the range from [-1, 1] to [0, 1]."""
out = (x + 1) / 2
return out.clamp_(0, 1)
def gradient_penalty(self, y, x):
"""Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
weight = torch.ones(y.size()).to(self.device)
dydx = torch.autograd.grad(outputs=y,
inputs=x,
grad_outputs=weight,
retain_graph=True,
create_graph=True,
only_inputs=True)[0]
dydx = dydx.view(dydx.size(0), -1)
dydx_l2norm = torch.sqrt(torch.sum(dydx**2, dim=1))
return torch.mean((dydx_l2norm - 1)**2)
pass
def feat_extract(self, resized_data):
# input: N * 3 * 224 * 224
resized_data = resized_data.to(self.device) #.cuda()
# output: N * num_classes, N * inter_dim, N * C' * 7 * 7
out, inter_out, x = self.feature_model(resized_data)
# return batch feature vector
return out.clamp_(0, 1)
def pose_extract(self, batch_img, bbx=None):
# input [N, 224, 224, 3] in RGB
# output [N, 2, 18]
# permute = [2, 1, 0]
# # crop_batch = np.zeros(shape=())
# for i in range(batch_img.shape[0]):
# img = crop(batch_img[i], bbx[i])
# # RGB TO BGR
# img = img[:, permute, : , :]
# crop_batch[i, :, :] = img
# pose_vectors = get_batch_body_vector(crop_batch.numpy())
batch_img = np.transpose(batch_img, (0, 2, 3, 1))
# print(batch_img.shape)
pose_vectors = get_batch_body_vector(batch_img)
pose_vectors = torch.from_numpy(pose_vectors)
return pose_vectors
def appreance_cos_similarity(self, feat_fake, feat_real):
label = torch.ones(feat_real.shape[0]).cuda()
cos_simi = self.feature_loss(feat_real, feat_fake, label)
return cos_simi
def compute_pose_loss(self, pose_fake, pose_real, mask):
mask = mask.view(-1, 1, 18).double()
pose_fake = torch.mul(pose_fake, mask)
pose_real = torch.mul(pose_real.double(), mask)
distance = self.pose_loss(pose_real, pose_fake)
return distance.float().sum()
def train(self):
"""Train StarGAN within a single dataset."""
# Fetch fixed inputs for debugging.
data_iter = iter(self.data_loader)
a_fixed, b_fixed, bbox_fixed, b_fixed_pose_feat, mask_fixed = next(
data_iter)
a_fixed = a_fixed.to(self.device)
b_fixed = b_fixed.to(self.device)
bbox_fixed = bbox_fixed.to(self.device)
# c_fixed_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
# Start training from scratch or resume training.
start_iters = 0
if self.resume_iters:
start_iters = self.resume_iters
self.restore_model(self.resume_iters)
# Start training.
print('Start training...')
start_time = time.time()
for step in range(start_iters, self.num_iters):
# =================================================================================== #
# 1. Preprocess input data #
# =================================================================================== #
# Fetch real images and labels.
try:
a_real, b_real, bbox, b_pose_feat, mask = next(data_iter)
except:
data_iter = iter(self.data_loader)
a_real, b_real, bbox, b_pose_feat, mask = next(data_iter)
a_real = a_real.to(self.device) # Input images.
b_real = b_real.to(self.device)
bbox = bbox.to(self.device)
b_pose_feat = b_pose_feat.to(self.device)
mask = mask.to(self.device)
# extract appearance feature
a_app_feat = self.feat_extract(a_real)
a_app_feat = a_app_feat.to(self.device)
# # extract pose feature
# b_pose_feat = self.pose_extract(b_real)
# =================================================================================== #
# 2. Train the discriminator #
# =================================================================================== #
# Compute loss with real images.
out_src = self.D(b_real)
d_loss_real = -torch.mean(out_src)
# d_loss_cls = self.classification_loss(out_cls, label_org, self.dataset)
# Compute loss with fake images.
# con_feat = torch.cat([a_app_feat, bbox/416.0], dim=1)
con_feat = a_app_feat
x_fake = self.G(b_real, con_feat)
out_src = self.D(x_fake.detach())
d_loss_fake = torch.mean(out_src)
# fake_app_feat = self.feat_extract(x_fake)
# fake_pose_feat = self.pose_extract(x_fake, bbox)
# d_loss_app = self.appreance_cos_similarity(fake_app_feat, a_app_feat)
# d_loss_pose = - self.pose_loss(fake_pose_feat, b_pose_feat)
# Compute loss for gradient penalty.
alpha = torch.rand(b_real.size(0), 1, 1, 1).to(self.device)
x_hat = (alpha * b_real.data +
(1 - alpha) * x_fake.data).requires_grad_(True)
out_src = self.D(x_hat)
d_loss_gp = self.gradient_penalty(out_src, x_hat)
# Backward and optimize.
# d_loss = d_loss_real + d_loss_fake + self.lambda_app * d_loss_cls + self.lambda_gp * d_loss_gp
# d_loss = d_loss_fake + d_loss_real + self.lambda_app * d_loss_app + self.lambda_pose * d_loss_pose
# d_loss = d_loss_fake + d_loss_real + self.lambda_gp * d_loss_gp
d_loss = d_loss_fake + d_loss_real + self.lambda_gp * d_loss_gp
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
# Logging.
loss = {}
loss['D/loss_real'] = d_loss_real.item()
loss['D/loss_fake'] = d_loss_fake.item()
# loss['D/loss_app'] = d_loss_app.item()
# loss['D/loss_pose'] = d_loss_pose.item()
loss['D/loss_gp'] = d_loss_gp.item()
# =================================================================================== #
# 3. Train the generator #
# =================================================================================== #
if (step + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(b_real, con_feat)
# print(x_fake[0,:,200:205,200:205])
out_src = self.D(x_fake)
g_loss_fake = -torch.mean(out_src)
crop_batch = torch.zeros((x_fake.shape[0], 3, 224, 224))
b = bbox.detach().cpu().numpy().astype(int)
for i in range(x_fake.shape[0]):
# img = crop(x_fake[i], bbox[i])
x1, x2, y1, y2 = b[i, 0], b[i, 0] + b[i, 2], b[
i, 1], b[i, 1] + b[i, 3]
x1 = min(max(x1, 0), 416)
x2 = min(max(x2, 0), 416)
y1 = min(max(y1, 0), 416)
y2 = min(max(y2, 0), 416)
img = x_fake[i, :, x1:x2, y1:y2].cpu().data.numpy()
img = img.transpose((1, 2, 0))
resized_img = np.zeros(shape=(224, 224, 3))
resized_img = cv2.resize(img, (224, 224),
interpolation=cv2.INTER_AREA)
crop_batch[i, :, :, :] = torch.from_numpy(
resized_img.transpose((2, 0, 1)))
fake_app_feat = self.feat_extract(crop_batch)
fake_pose_feat = self.pose_extract(crop_batch.numpy())
# #**** debug ****#
# fake_images = (x_fake.cpu().data).numpy()
# permute = [2, 1, 0]
# fake_images = fake_images[:, permute, :, :].transpose((0,2,3,1))
# resized_data = np.zeros(shape=(fake_images.shape[0], 224, 224, 3))
# for j in range(fake_images.shape[0]):
# resized_data[j,:,:,:] = cv2.resize(fake_images[j,:,:,:], (224, 224), interpolation = cv2.INTER_AREA)
# resized_data = np.transpose(resized_data, (0, 3, 1, 2))
# resized_tensor = torch.from_numpy(resized_data)
# resized_tensor = resized_tensor.to(self.device, dtype=torch.float)
# fake_app_feat = self.feat_extract(resized_tensor)
# fake_pose_feat = self.pose_extract(resized_data, bbox)
fake_app_feat = fake_app_feat.to(self.device)
fake_pose_feat = fake_pose_feat.to(self.device)
#**** debug ****#
# g_loss_cls = self.classification_loss(out_cls, label_trg, self.dataset)
g_loss_app = -self.appreance_cos_similarity(
fake_app_feat, a_app_feat) # -similarity
# print(fake_pose_feat.size(), b_pose_feat.size(), mask.size())
g_loss_pose = self.compute_pose_loss(fake_pose_feat,
b_pose_feat,
mask) # joints distance
# Backward and optimize.
# g_loss = g_loss_fake + self.lambda_rec * g_loss_rec + self.lambda_app * g_loss_cls
# g_loss = g_loss_fake + self.lambda_app * g_loss_app + self.lambda_pose * g_loss_pose
g_loss = g_loss_fake + self.lambda_app * g_loss_app + self.lambda_pose * g_loss_pose
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss['G/loss_fake'] = g_loss_fake.item()
# loss['G/loss_rec'] = g_loss_rec.item()
loss['G/loss_app'] = g_loss_app.item() * self.lambda_app
loss['G/loss_pose'] = g_loss_pose.item() * self.lambda_pose
# =================================================================================== #
# 4. Miscellaneous #
# =================================================================================== #
# Print out training information.
if (step + 1) % self.log_step == 0:
et = time.time() - start_time
et = str(datetime.timedelta(seconds=et))[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, step + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
if self.use_tensorboard:
for tag, value in loss.items():
self.logger.scalar_summary(tag, value, i + 1)
# Translate fixed images for debugging.
if (step + 1) % self.sample_step == 0:
# if (step + 1) % 1 == 0:
with torch.no_grad():
# a fix: [N, 3, 224, 224]
# a_real, b_real, bbox, b_pose_feat, mask
a_resized = torch.zeros(size=(a_real.shape[0], 3, 416,
416))
b_drawed = torch.zeros(size=(a_real.shape[0], 3, 416, 416))
for i in range(a_real.shape[0]):
img = a_real[i].cpu().data.numpy()
img = img.transpose((1, 2, 0))
resized_img = np.zeros(shape=(416, 416, 3))
resized_img = cv2.resize(img, (416, 416),
interpolation=cv2.INTER_AREA)
a_resized[i, :, :, :] = torch.from_numpy(
resized_img.transpose((2, 0, 1)))
trans1 = transforms.ToPILImage()
trans2 = transforms.ToTensor()
b_img = trans1(b_real[i].cpu())
draw = ImageDraw.Draw(b_img)
b = bbox[i].cpu().data.numpy().astype(int)
x, y, w, h = b
x2, y2 = x + w, y + h
draw.rectangle([x, y, x2, y2],
outline="green",
width=20)
b_drawed[i, :, :, :] = trans2(b_img)
b_drawed = b_drawed.to(self.device)
a_resized = a_resized.to(self.device)
picture_list = [a_resized, b_drawed]
a_visual_feat = self.feat_extract(a_real)
# a feature: [N, 20]; bbox: [N,4]
# con_visual_feat = torch.cat([a_visual_feat, bbox/416.0], dim=1) # [N, 24]
con_visual_feat = a_visual_feat
# print(b_real, con_visual_feat)
x_fake = self.G(b_real,
con_visual_feat) # [N, 3, 416, 416]
# print(a_fixed.size(), b_fixed.size(), x_fake.size())
picture_list.append(x_fake)
picture_concat = torch.cat(picture_list, dim=0)
# print(picture_concat.size())
sample_path = os.path.join(
self.sample_dir, '{}-images.jpg'.format(step + 1))
save_image(self.denorm(picture_concat.data.cpu()),
sample_path,
nrow=4,
padding=0)
print('Saved real and fake images into {}...'.format(
sample_path))
# Save model checkpoints.
if (step + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(step + 1))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(step + 1))
torch.save(self.G.state_dict(), G_path)
torch.save(self.D.state_dict(), D_path)
print('Saved model checkpoints into {}...'.format(
self.model_save_dir))
# Decay learning rates.
if (step + 1) % self.lr_update_step == 0 and (step + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= (self.g_lr / float(self.num_iters_decay))
d_lr -= (self.d_lr / float(self.num_iters_decay))
self.update_lr(g_lr, d_lr)
print('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(
g_lr, d_lr))
def test(self):
"""Translate images using StarGAN trained on a single dataset."""
# Load the trained generator.
self.restore_model(self.test_iters)
# Set data loader.
data_loader = self.data_loader
with torch.no_grad():
for i, (a_real, b_real) in enumerate(data_loader):
# Prepare input images and target domain labels.
a_real = a_real.to(self.device)
b_real = b_real.to(self.device)
# Translate images.
a_fake_list = [a_real, b_real]
a_fixed_feat = self.feat_extract(a_real)
a_fake_list.append(self.G(b_real, a_fixed_feat))
# Save the translated images.
x_concat = torch.cat(a_fake_list, dim=3)
result_path = os.path.join(self.result_dir,
'{}-images.jpg'.format(i + 1))
save_image(self.denorm(x_concat.data.cpu()),
result_path,
nrow=1,
padding=0)
print('Saved real and fake images into {}...'.format(
result_path))
device = torch.device("cuda" if args.cuda else "cpu")
nz = args.nz
ngf = args.ngf
ndf = args.ndf
G = Generator().to(device)
G.apply(weights_init)
if args.G != '':
G.load_state_dict(clean_state_dict(torch.load(args.G)))
D = Discriminator().to(device)
D.apply(weights_init)
if args.D != '':
D.load_state_dict(clean_state_dict(torch.load(args.D)))
if torch.cuda.device_count() > 1 and args.cuda:
print("Let's use {} GPUs".format(torch.cuda.device_count()))
G = nn.DataParallel(G)
D = nn.DataParallel(D)
criterion = nn.BCELoss()
# fixed noise & label
fixed_noise = torch.randn(args.batchSize, nz, 1, 1, device=device)
real_label = 1
fake_label = 0
# setup optimizer
optimizerD = optim.Adam(D.parameters(), lr=args.lr, betas=(args.beta1, 0.999))
def train_style_transfer(args):
if not (args.train_data and args.valid_data):
print("must chose train_data and valid_data")
sys.exit()
# make dataset
trans = transforms.ToTensor()
train_dataset = FaceDataset(args.train_data, transform=trans)
label_dict = train_dataset.get_label_dict()
valid_dataset = FaceDataset(args.valid_data, transform=trans)
valid_dataset.give_label_dict(label_dict)
train_loader = data_utils.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
valid_loader = data_utils.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
train_size = len(train_dataset)
valid_size = len(valid_dataset)
loaders = {"train": train_loader, "valid": valid_loader}
dataset_sizes = {"train": train_size, "valid": valid_size}
if args.gpu:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# make network
if args.model_type == "VAE":
net = Autoencoder(train_dataset.label_num()).to(device)
optimizer = optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
best_model_wts = net.state_dict()
best_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
net.load_state_dict(torch.load(args.generator_model))
elif args.model_type == "VAEGAN":
generator = Autoencoder(train_dataset.label_num()).to(device)
discriminator = Discriminator().to(device)
classifier = Classifier(train_dataset.label_num()).to(device)
generator_optimizer = optim.Adam(generator.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
discriminator_optimizer = optim.Adam(discriminator.parameters(),
lr=args.lr * 0.1,
weight_decay=args.weight_decay)
best_generator_wts = generator.state_dict()
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = 1e10
best_discriminator_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
generator.load_state_dict(torch.load(args.generator_model))
if args.discriminator_model and os.path.exists(
args.discriminator_model):
discriminator.load_state_dict(torch.load(args.discriminator_model))
if args.classifier_model:
classifier.load_state_dict(torch.load(args.classifier_model))
# make loss function and optimizer
criterion = nn.BCELoss(reduction="sum")
classifier_criterion = nn.CrossEntropyLoss(reduction="sum")
# initialize loss
loss_history = {"train": [], "valid": []}
# start training
start_time = time.time()
for epoch in range(args.epochs):
print("epoch {}".format(epoch + 1))
for phase in ["train", "valid"]:
if phase == "train":
if args.model_type == "VAE":
net.train(True)
elif args.model_type == "VAEGAN":
generator.train(True)
discriminator.train(True)
else:
if args.model_type == "VAE":
net.train(False)
elif args.model_type == "VAEGAN":
generator.train(False)
discriminator.train(False)
# initialize running loss
generator_running_loss = 0.0
discriminator_running_loss = 0.0
for i, data in enumerate(loaders[phase]):
inputs, label = data
# wrap the in valiables
if phase == "train":
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(True)
else:
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(False)
# zero gradients
if args.model_type == "VAE":
optimizer.zero_grad()
mu, var, outputs = net(inputs, label)
loss = loss_func(inputs, outputs, mu, var)
if phase == "train":
loss.backward()
optimizer.step()
generator_running_loss += loss.item()
elif args.model_type == "VAEGAN":
real_label = Variable(
torch.ones((inputs.size()[0], 1), dtype=torch.float) -
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
fake_label = Variable(
torch.zeros((inputs.size()[0], 1), dtype=torch.float) +
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
discriminator_optimizer.zero_grad()
real_pred = discriminator(inputs)
real_loss = criterion(real_pred, real_label)
random_index = np.random.randint(0,
train_dataset.label_num(),
inputs.size()[0])
generate_label = Variable(
torch.zeros_like(label)).to(device)
for i, index in enumerate(random_index):
generate_label[i][index] = 1
mu, var, outputs = generator(inputs, label)
fake_pred = discriminator(outputs.detach())
fake_loss = criterion(fake_pred, fake_label)
discriminator_loss = real_loss + fake_loss
if phase == "train":
discriminator_loss.backward()
discriminator_optimizer.step()
generator_optimizer.zero_grad()
#class_loss = classifier_criterion(classifier(outputs), torch.max(label, 1)[1])
dis_loss = criterion(discriminator(outputs), real_label)
gen_loss = loss_func(inputs, outputs, mu, var)
generator_loss = dis_loss + gen_loss
if phase == "train":
generator_loss.backward()
generator_optimizer.step()
discriminator_running_loss += discriminator_loss.item()
generator_running_loss += generator_loss.item()
if args.model_type == "VAE":
epoch_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
loss_history[phase].append(epoch_loss)
print("{} loss {:.4f}".format(phase, epoch_loss))
if phase == "valid" and epoch_loss < best_loss:
best_model_wts = net.state_dict()
best_loss = epoch_loss
elif args.model_type == "VAEGAN":
epoch_generator_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
epoch_discriminator_loss = discriminator_running_loss / dataset_sizes[
phase] * args.batch_size
print("{} generator loss {:.4f}".format(
phase, epoch_generator_loss))
print("{} discriminator loss {:.4f}".format(
phase, epoch_discriminator_loss))
if phase == "valid" and epoch_generator_loss < best_generator_loss:
best_generator_wts = generator.state_dict()
best_generator_loss = epoch_generator_loss
if phase == "valid" and epoch_discriminator_loss < best_discriminator_loss:
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = epoch_discriminator_loss
elapsed_time = time.time() - start_time
print("training complete in {:.0f}s".format(elapsed_time))
if args.model_type == "VAE":
net.load_state_dict(best_model_wts)
return net, label_dict
elif args.model_type == "VAEGAN":
generator.load_state_dict(best_generator_wts)
discriminator.load_state_dict(best_discriminator_wts)
return (generator, discriminator), label_dict