def __init__(self, opt):
self.device = torch.device('cuda')
self.opt = opt
self.G = Generator(self.opt['network_G']).to(self.device)
util.init_weights(self.G, init_type='kaiming', scale=0.1)
if self.opt['path']['pretrain_G']:
self.G.load_state_dict(torch.load(self.opt['path']['pretrain_G']),
strict=True)
self.D = Discriminator(self.opt['network_D']).to(self.device)
util.init_weights(self.D, init_type='kaiming', scale=1)
self.FE = VGGFeatureExtractor().to(self.device)
self.G.train()
self.D.train()
self.FE.eval()
self.log_dict = OrderedDict()
self.optim_params = [
v for k, v in self.G.named_parameters() if v.requires_grad
]
self.opt_G = torch.optim.Adam(self.optim_params,
lr=self.opt['train']['lr_G'],
betas=(self.opt['train']['b1_G'],
self.opt['train']['b2_G']))
self.opt_D = torch.optim.Adam(self.D.parameters(),
lr=self.opt['train']['lr_D'],
betas=(self.opt['train']['b1_D'],
self.opt['train']['b2_D']))
self.optimizers = [self.opt_G, self.opt_D]
self.schedulers = [
lr_scheduler.MultiStepLR(optimizer, self.opt['train']['lr_steps'],
self.opt['train']['lr_gamma'])
for optimizer in self.optimizers
]
def __init__(self):
logger.info('Set Data Loader')
self.dataset = FoodDataset(transform=transforms.Compose([ToTensor()]))
self.data_loader = torch.utils.data.DataLoader(self.dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True)
checkpoint, checkpoint_name = self.load_checkpoint(model_dump_path)
if checkpoint == None:
logger.info(
'Don\'t have pre-trained model. Ignore loading model process.')
logger.info('Set Generator and Discriminator')
self.G = Generator(tag=tag_size).to(device)
self.D = Discriminator(tag=tag_size).to(device)
logger.info('Initialize Weights')
self.G.apply(initital_network_weights).to(device)
self.D.apply(initital_network_weights).to(device)
logger.info('Set Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.epoch = 0
else:
logger.info('Load Generator and Discriminator')
self.G = Generator(tag=tag_size).to(device)
self.D = Discriminator(tag=tag_size).to(device)
logger.info('Load Pre-Trained Weights From Checkpoint'.format(
checkpoint_name))
self.G.load_state_dict(checkpoint['G'])
self.D.load_state_dict(checkpoint['D'])
logger.info('Load Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
self.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
self.epoch = checkpoint['epoch']
logger.info('Set Criterion')
self.a_D = alexnet.alexnet(num_classes=tag_size).to(device)
self.optimizer_a_D = torch.optim.Adam(self.a_D.parameters(),
lr=learning_rate,
betas=(beta_1, .999))
def _build_model(self):
device = torch.device('cuda')
data_dimension = self.config.data['dimension']
generator_hidden_layers = self.config.model['generator_hidden_layers']
use_dropout = self.config.model['use_dropout']
drop_prob = self.config.model['drop_prob']
use_ac_func = self.config.model['use_ac_func']
activation = self.config.model['activation']
disc_hidden_layers = self.config.model['disc_hidden_layers']
logger.log("Loading {} network ...".format(colored('generator',
'red')))
gen_fc_layers = [
self.latent_dim, *generator_hidden_layers, data_dimension
]
generator = Generator(gen_fc_layers, use_dropout, drop_prob,
use_ac_func, activation).to(device)
logger.log("Loading {} network ...".format(
colored('discriminator', 'red')))
disc_fc_layers = [data_dimension, *disc_hidden_layers, 1]
discriminator = Discriminator(disc_fc_layers, use_dropout, drop_prob,
use_ac_func, activation).to(device)
wandb.watch([generator, discriminator])
g_optimizer, d_optimizer = self._setup_optimizers(
generator, discriminator)
return generator, discriminator, g_optimizer, d_optimizer
def __init__(self, args):
super(SegTransferModel, self).__init__()
# n_classes for Fundus: 1
# n_classes for OCT: 12
self.args = args
assert args.data_modality in [
'oct', 'fundus'
], 'error in seg_mode, got {}'.format(args.data_modality)
# model on gpu
if self.args.data_modality == 'fundus':
model_G = UNet_4mp(n_channels=1, n_classes=1)
else:
model_G = UNet_4mp(n_channels=1, n_classes=12)
model_D = Discriminator(in_channels=1)
model_G = nn.DataParallel(model_G).cuda()
model_D = nn.DataParallel(model_D).cuda()
l1_loss = nn.L1Loss().cuda()
nll_loss = nn.NLLLoss().cuda()
adversarial_loss = AdversarialLoss().cuda()
self.add_module('model_G', model_G)
self.add_module('model_D', model_D)
self.add_module('l1_loss', l1_loss)
self.add_module('nll_loss', nll_loss)
self.add_module('adversarial_loss', adversarial_loss)
# optimizer
self.optimizer_G = torch.optim.Adam(params=self.model_G.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
betas=(args.b1, args.b2))
self.optimizer_D = torch.optim.Adam(params=self.model_D.parameters(),
lr=args.lr * args.d2g_lr,
weight_decay=args.weight_decay,
betas=(args.b1, args.b2))
# Optionally resume from a checkpoint
if self.args.resume:
ckpt_root = os.path.join(args.output_root, args.project,
'checkpoints')
ckpt_path = os.path.join(ckpt_root, args.resume)
if os.path.isfile(ckpt_path):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(ckpt_path)
args.start_epoch = checkpoint['epoch']
self.model_G.load_state_dict(checkpoint['state_dict_G'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
raise ValueError("=> no checkpoint found at '{}'".format(
args.resume))
def __init__(self, args, ablation_mode=4):
super(PNetModel, self).__init__()
self.args = args
"""
ablation study mode
"""
# 0: output_structure (1 feature)
# 2: image (1 feature), i.e. auto-encoder
# 4: output_structure + image (2 features)
# model on gpu
if self.args.data_modality == 'fundus':
model_G1 = Strcutre_Extraction_Network(n_channels=1, n_classes=1)
else:
model_G1 = Strcutre_Extraction_Network(n_channels=1, n_classes=12)
model_G2 = Image_Reconstruction_Network(
in_ch=1,
modality=self.args.data_modality,
ablation_mode=ablation_mode)
model_D = Discriminator(in_channels=1)
model_G1 = nn.DataParallel(model_G1).cuda()
model_G2 = nn.DataParallel(model_G2).cuda()
model_D = nn.DataParallel(model_D).cuda()
l1_loss = nn.L1Loss().cuda()
adversarial_loss = AdversarialLoss().cuda()
self.add_module('model_G1', model_G1)
self.add_module('model_G2', model_G2)
self.add_module('model_D', model_D)
self.add_module('l1_loss', l1_loss)
self.add_module('adversarial_loss', adversarial_loss)
# optimizer
self.optimizer_G = torch.optim.Adam(params=self.model_G2.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
betas=(args.b1, args.b2))
self.optimizer_D = torch.optim.Adam(params=self.model_D.parameters(),
lr=args.lr * args.d2g_lr,
weight_decay=args.weight_decay,
betas=(args.b1, args.b2))
# load 1-st ckpts
if self.args.server == 'ai':
seg_ckpt_root = os.path.join('/root/workspace', args.project,
'save_models')
else:
seg_ckpt_root = os.path.join('/home/imed/new_disk/workspace',
args.project, 'save_models')
if self.args.data_modality == 'fundus':
if self.args.DA_ablation_mode_isee == 0:
_g_zero_point = '0'
elif self.args.DA_ablation_mode_isee == 0.001:
_g_zero_point = '001'
elif self.args.DA_ablation_mode_isee == 0.0001:
# this is the default
_g_zero_point = '0001'
else:
raise NotImplementedError('error')
seg_ckpt_path = os.path.join(
seg_ckpt_root,
'1st_fundus_seg_g_{}.pth.tar'.format(_g_zero_point))
## orginal seg mdel
# seg_ckpt_path = os.path.join(seg_ckpt_root, '1st_fundus_seg_vgg.pth.tar')
else:
seg_ckpt_path = os.path.join(seg_ckpt_root, '1st_oct_seg.pth.tar')
if os.path.isfile(seg_ckpt_path):
print("=> loading G1 checkpoint")
checkpoint = torch.load(seg_ckpt_path)
self.model_G1.load_state_dict(checkpoint['state_dict_G'])
print("=> loaded G1 checkpoint (epoch {}) \n from {}".format(
checkpoint['epoch'], seg_ckpt_path))
else:
raise ValueError(
"=> no checkpoint found at '{}'".format(seg_ckpt_path))
# Optionally resume from a checkpoint
if self.args.resume:
ckpt_root = os.path.join(self.args.output_root, args.project,
'checkpoints')
ckpt_path = os.path.join(ckpt_root, args.resume)
if os.path.isfile(ckpt_path):
print("=> loading G2 checkpoint '{}'".format(args.resume))
checkpoint = torch.load(ckpt_path)
args.start_epoch = checkpoint['epoch']
self.model_G2.load_state_dict(checkpoint['state_dict_G'])
print("=> loaded G2 checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
def main(args):
env = gym.make('CartPole-v0')
env.seed(0)
ob_space = env.observation_space
Policy = Policy_net('policy', env)
Old_Policy = Policy_net('old_policy', env)
PPO = PPOTrain(Policy, Old_Policy, gamma=args.gamma)
D = Discriminator(env)
expert_observations = np.genfromtxt('trajectory/observations.csv')
expert_actions = np.genfromtxt('trajectory/actions.csv', dtype=np.int32)
saver = tf.train.Saver()
with tf.Session() as sess:
writer = tf.summary.FileWriter(args.logdir, sess.graph)
sess.run(tf.global_variables_initializer())
obs = env.reset()
success_num = 0
for iteration in range(args.iteration):
observations = []
actions = []
# do NOT use rewards to update policy
rewards = []
v_preds = []
run_policy_steps = 0
while True:
run_policy_steps += 1
obs = np.stack([obs]).astype(dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
next_obs, reward, done, info = env.step(act)
observations.append(obs)
actions.append(act)
rewards.append(reward)
v_preds.append(v_pred)
if done:
next_obs = np.stack([next_obs]).astype(dtype=np.float32) # prepare to feed placeholder Policy.obs
_, v_pred = Policy.act(obs=next_obs, stochastic=True)
v_preds_next = v_preds[1:] + [np.asscalar(v_pred)]
obs = env.reset()
break
else:
obs = next_obs
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_length', simple_value=run_policy_steps)])
, iteration)
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_reward', simple_value=sum(rewards))])
, iteration)
if sum(rewards) >= 195:
success_num += 1
if success_num >= 100:
saver.save(sess, args.savedir + '/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations, newshape=[-1] + list(ob_space.shape))
actions = np.array(actions).astype(dtype=np.int32)
# train discriminator
for i in range(2):
D.train(expert_s=expert_observations,
expert_a=expert_actions,
agent_s=observations,
agent_a=actions)
# output of this discriminator is reward
d_rewards = D.get_rewards(agent_s=observations, agent_a=actions)
d_rewards = np.reshape(d_rewards, newshape=[-1]).astype(dtype=np.float32)
gaes = PPO.get_gaes(rewards=d_rewards, v_preds=v_preds, v_preds_next=v_preds_next)
gaes = np.array(gaes).astype(dtype=np.float32)
# gaes = (gaes - gaes.mean()) / gaes.std()
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
# train policy
inp = [observations, actions, gaes, d_rewards, v_preds_next]
PPO.assign_policy_parameters()
for epoch in range(6):
sample_indices = np.random.randint(low=0, high=observations.shape[0],
size=32) # indices are in [low, high)
sampled_inp = [np.take(a=a, indices=sample_indices, axis=0) for a in inp] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
gaes=sampled_inp[2],
rewards=sampled_inp[3],
v_preds_next=sampled_inp[4])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
gaes=inp[2],
rewards=inp[3],
v_preds_next=inp[4])
writer.add_summary(summary, iteration)
writer.close()
class SRGAN():
def __init__(self):
logger.info('Set Data Loader')
self.dataset = AnimeFaceDataset(
avatar_tag_dat_path=avatar_tag_dat_path,
transform=transforms.Compose([ToTensor()]))
self.data_loader = torch.utils.data.DataLoader(self.dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True)
checkpoint, checkpoint_name = self.load_checkpoint(model_dump_path)
if checkpoint == None:
logger.info(
'Don\'t have pre-trained model. Ignore loading model process.')
logger.info('Set Generator and Discriminator')
self.G = Generator().to(device)
self.D = Discriminator().to(device)
logger.info('Initialize Weights')
self.G.apply(initital_network_weights).to(device)
self.D.apply(initital_network_weights).to(device)
logger.info('Set Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.epoch = 0
else:
logger.info('Load Generator and Discriminator')
self.G = Generator().to(device)
self.D = Discriminator().to(device)
logger.info('Load Pre-Trained Weights From Checkpoint'.format(
checkpoint_name))
self.G.load_state_dict(checkpoint['G'])
self.D.load_state_dict(checkpoint['D'])
logger.info('Load Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
self.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
self.epoch = checkpoint['epoch']
logger.info('Set Criterion')
self.label_criterion = nn.BCEWithLogitsLoss().to(device)
self.tag_criterion = nn.MultiLabelSoftMarginLoss().to(device)
def load_checkpoint(self, model_dir):
models_path = read_newest_model(model_dir)
if len(models_path) == 0:
return None, None
models_path.sort()
new_model_path = os.path.join(model_dump_path, models_path[-1])
checkpoint = torch.load(new_model_path)
return checkpoint, new_model_path
def train(self):
iteration = -1
label = Variable(torch.FloatTensor(batch_size, 1.0)).to(device)
logging.info('Current epoch: {}. Max epoch: {}.'.format(
self.epoch, max_epoch))
while self.epoch <= max_epoch:
# dump checkpoint
torch.save(
{
'epoch': self.epoch,
'D': self.D.state_dict(),
'G': self.G.state_dict(),
'optimizer_D': self.optimizer_D.state_dict(),
'optimizer_G': self.optimizer_G.state_dict(),
}, '{}/checkpoint_{}.tar'.format(model_dump_path,
str(self.epoch).zfill(4)))
logger.info('Checkpoint saved in: {}'.format(
'{}/checkpoint_{}.tar'.format(model_dump_path,
str(self.epoch).zfill(4))))
msg = {}
adjust_learning_rate(self.optimizer_G, iteration)
adjust_learning_rate(self.optimizer_D, iteration)
for i, (avatar_tag, avatar_img) in enumerate(self.data_loader):
iteration += 1
if verbose:
if iteration % verbose_T == 0:
msg['epoch'] = int(self.epoch)
msg['step'] = int(i)
msg['iteration'] = iteration
avatar_img = Variable(avatar_img).to(device)
avatar_tag = Variable(torch.FloatTensor(avatar_tag)).to(device)
# D : G = 2 : 1
# 1. Training D
# 1.1. use really image for discriminating
self.D.zero_grad()
label_p, tag_p = self.D(avatar_img)
label.data.fill_(1.0)
# 1.2. real image's loss
real_label_loss = self.label_criterion(label_p, label)
real_tag_loss = self.tag_criterion(tag_p, avatar_tag)
real_loss_sum = real_label_loss * lambda_adv / 2.0 + real_tag_loss * lambda_adv / 2.0
real_loss_sum.backward()
if verbose:
if iteration % verbose_T == 0:
msg['discriminator real loss'] = float(real_loss_sum)
# 1.3. use fake image for discriminating
g_noise, fake_tag = fake_generator()
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat).detach()
fake_label_p, fake_tag_p = self.D(fake_img)
label.data.fill_(.0)
# 1.4. fake image's loss
fake_label_loss = self.label_criterion(fake_label_p, label)
fake_tag_loss = self.tag_criterion(fake_tag_p, fake_tag)
fake_loss_sum = fake_label_loss * lambda_adv / 2.0 + fake_tag_loss * lambda_adv / 2.0
fake_loss_sum.backward()
if verbose:
if iteration % verbose_T == 0:
msg['discriminator fake loss'] = float(fake_loss_sum)
# 1.5. gradient penalty
# https://github.com/jfsantos/dragan-pytorch/blob/master/dragan.py
alpha_size = [1] * avatar_img.dim()
alpha_size[0] = avatar_img.size(0)
alpha = torch.rand(alpha_size).to(device)
x_hat = Variable(alpha * avatar_img.data + (1 - alpha) * \
(avatar_img.data + 0.5 * avatar_img.data.std() * Variable(torch.rand(avatar_img.size())).to(device)),
requires_grad=True).to(device)
pred_hat, pred_tag = self.D(x_hat)
gradients = grad(outputs=pred_hat,
inputs=x_hat,
grad_outputs=torch.ones(
pred_hat.size()).to(device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0].view(x_hat.size(0), -1)
gradient_penalty = lambda_gp * (
(gradients.norm(2, dim=1) - 1)**2).mean()
gradient_penalty.backward()
if verbose:
if iteration % verbose_T == 0:
msg['discriminator gradient penalty'] = float(
gradient_penalty)
# 1.6. update optimizer
self.optimizer_D.step()
# 2. Training G
# 2.1. generate fake image
self.G.zero_grad()
g_noise, fake_tag = fake_generator()
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat)
fake_label_p, fake_tag_p = self.D(fake_img)
label.data.fill_(1.0)
# 2.2. calc loss
label_loss_g = self.label_criterion(fake_label_p, label)
tag_loss_g = self.tag_criterion(fake_tag_p, fake_tag)
loss_g = label_loss_g * lambda_adv / 2.0 + tag_loss_g * lambda_adv / 2.0
loss_g.backward()
if verbose:
if iteration % verbose_T == 0:
msg['generator loss'] = float(loss_g)
# 2.2. update optimizer
self.optimizer_G.step()
if verbose:
if iteration % verbose_T == 0:
logger.info(
'------------------------------------------')
for key in msg.keys():
logger.info('{} : {}'.format(key, msg[key]))
# save intermediate file
if iteration % verbose_T == 0:
vutils.save_image(
avatar_img.data.view(batch_size, 3, avatar_img.size(2),
avatar_img.size(3)),
os.path.join(
tmp_path, 'real_image_{}.png'.format(
str(iteration).zfill(8))))
g_noise, fake_tag = fake_generator()
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat)
vutils.save_image(
fake_img.data.view(batch_size, 3, avatar_img.size(2),
avatar_img.size(3)),
os.path.join(
tmp_path, 'fake_image_{}.png'.format(
str(iteration).zfill(8))))
logger.info('Saved intermediate file in {}'.format(
os.path.join(
tmp_path, 'fake_image_{}.png'.format(
str(iteration).zfill(8)))))
self.epoch += 1
def get_discriminator(self, task_id):
discriminator = Discriminator(self.args, task_id).to(self.args.device)
return discriminator
RANDOM_ALL = True
PRECROP = True if DATASET.lower() == 'ntu' else False
VP_VALUE_COUNT = 1 if DATASET.lower() == 'ntu' else 3
CLOSE_VIEWS = True if DATASET.lower() == 'panoptic' else False
vgg_weights_path, i3d_weights_path, gen_weights_path, disc_weights_path = pretrained_weights_config()
# generator
generator = FullNetwork(vp_value_count=VP_VALUE_COUNT, stdev=STDEV,
output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH),
pretrained=True, vgg_weights_path=vgg_weights_path, i3d_weights_path=i3d_weights_path)
if GEN_PRETRAINED:
generator.load_state_dict(torch.load(gen_weights_path))
generator = generator.to(device)
# discriminator
discriminator = Discriminator(in_channels=3, pretrained=GEN_PRETRAINED, weights_path=disc_weights_path)
discriminator = discriminator.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(generator)
cudnn.benchmark = True
# Loss functions
criterion = nn.MSELoss()
adversarial_loss = nn.BCELoss()
perceptual_loss = vgg16().to(device)
# categorical_loss = torch.nn.CrossEntropyLoss()
# continuous_loss = torch.nn.MSELoss()
optimizer_G = optim.Adam(generator.parameters(), lr=LR)
optimizer_D = optim.Adam(discriminator.parameters(), lr=LR)
else:
device = torch.device("cpu")
random.seed(args.SEED)
torch.manual_seed(args.SEED)
if args.USE_CUDA:
torch.cuda.manual_seed_all(args.SEED)
# Initialize Generator
generator = Generator(args.GAN_TYPE, args.ZDIM, args.NUM_CLASSES)
generator.apply(weights_init)
generator.to(device)
print(generator)
# Initialize Discriminator
discriminator = Discriminator(args.GAN_TYPE, args.NUM_CLASSES)
discriminator.apply(weights_init)
discriminator.to(device)
print(discriminator)
# Initialize loss function and optimizer
criterionLabel = nn.BCELoss()
criterionClass = nn.CrossEntropyLoss()
optimizerD = Adam(discriminator.parameters(), lr=args.LR, betas=(0.5, 0.999))
optimizerG = Adam(generator.parameters(), lr=args.LR, betas=(0.5, 0.999))
# Prepare the noise for evaluation during training phase
fixedNoise = torch.FloatTensor(args.BATCH_SIZE, args.ZDIM, 1, 1).normal_(0, 1)
if args.GAN_TYPE in ["CGAN", "ACGAN"]:
fixedClass = F.one_hot(torch.LongTensor([i % args.NUM_CLASSES for i in range(args.BATCH_SIZE)]), num_classes=args.NUM_CLASSES)
fixedConstraint = fixedClass.unsqueeze(-1).unsqueeze(-1)
beta_1=0.5,
beta_2=0.999)
optimizer_keypoint_detector = tf.keras.optimizers.Adam(learning_rate=lr,
beta_1=0.5,
beta_2=0.999)
optimizer_discriminator = tf.keras.optimizers.Adam(learning_rate=lr,
beta_1=0.5,
beta_2=0.999)
batch_size = 20
epochs = 150
train_steps = 99 # change
keypoint_detector = KeypointDetector()
generator = Generator()
discriminator = Discriminator()
generator_full = FullGenerator(keypoint_detector, generator, discriminator)
discriminator_full = FullDiscriminator(discriminator)
@tf.function
def train_step(source_images, driving_images):
with tf.GradientTape(persistent=True) as tape:
losses_generator, generated = generator_full(source_images,
driving_images, tape)
generator_loss = tf.math.reduce_sum(list(losses_generator.values()))
generator_gradients = tape.gradient(generator_loss,
generator_full.trainable_variables)
keypoint_detector_gradients = tape.gradient(
"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
RANDOM_ALL = True
PRECROP = True if DATASET.lower() == 'ntu' else False
VP_VALUE_COUNT = 1 if DATASET.lower() == 'ntu' else 3
CLOSE_VIEWS = True if DATASET.lower() == 'panoptic' else False
# vgg_weights_path, i3d_weights_path = pretrained_weights_config()
# generator
generator = FullNetwork(vp_value_count=VP_VALUE_COUNT, stdev=STDEV,
output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH))
# pretrained=True, vgg_weights_path=vgg_weights_path, i3d_weights_path=i3d_weights_path)
generator = generator.to(device)
# discriminator
discriminator = Discriminator(in_channels=3)
discriminator = discriminator.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(generator)
cudnn.benchmark = True
# Loss functions
criterion = nn.MSELoss()
adversarial_loss = nn.BCELoss()
# categorical_loss = torch.nn.CrossEntropyLoss()
# continuous_loss = torch.nn.MSELoss()
optimizer_G = optim.Adam(generator.parameters(), lr=LR)
optimizer_D = optim.Adam(discriminator.parameters(), lr=LR)
test_noise = [[]]
img = G(torch.Tensor(test_noise).to(device))
t_img = vutils.make_grid(img.data.view(1, 3, 128, 128)).numpy()
t_img = np.transpose(t_img, (1, 2, 0))
t_img[t_img < 0] = 0
# min_max_scaler = preprocessing.MinMaxScaler()
# t_img[..., 0] = min_max_scaler.fit_transform(t_img[..., 0])
# t_img[..., 1] = min_max_scaler.fit_transform(t_img[..., 1])
# t_img[..., 2] = min_max_scaler.fit_transform(t_img[..., 2])
plt.imshow(t_img)
plt.show()
label_p, tag_p = D(img)
label = Variable(torch.FloatTensor(1, 1.0)).to(device)
lbl_criterion = nn.BCEWithLogitsLoss().to(device)
loss = lbl_criterion(label_p, label) * 10000
loss.backward()
print(grad_list[0][0].shape)
if __name__ == '__main__':
checkpoint, _ = load_checkpoint(model_dump_path)
G = Generator().to(device)
G.load_state_dict(checkpoint['G'])
D = Discriminator().to(device)
D.load_state_dict(checkpoint['D'])
# generate(G, 'test', ['white hair'])
image_backward_D(G, D)
class SRGAN():
def __init__(self):
logger.info('Set Data Loader')
self.dataset = FoodDataset(transform=transforms.Compose([ToTensor()]))
self.data_loader = torch.utils.data.DataLoader(self.dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True)
checkpoint, checkpoint_name = self.load_checkpoint(model_dump_path)
if checkpoint == None:
logger.info(
'Don\'t have pre-trained model. Ignore loading model process.')
logger.info('Set Generator and Discriminator')
self.G = Generator(tag=tag_size).to(device)
self.D = Discriminator(tag=tag_size).to(device)
logger.info('Initialize Weights')
self.G.apply(initital_network_weights).to(device)
self.D.apply(initital_network_weights).to(device)
logger.info('Set Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.epoch = 0
else:
logger.info('Load Generator and Discriminator')
self.G = Generator(tag=tag_size).to(device)
self.D = Discriminator(tag=tag_size).to(device)
logger.info('Load Pre-Trained Weights From Checkpoint'.format(
checkpoint_name))
self.G.load_state_dict(checkpoint['G'])
self.D.load_state_dict(checkpoint['D'])
logger.info('Load Optimizers')
self.optimizer_G = torch.optim.Adam(self.G.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_D = torch.optim.Adam(self.D.parameters(),
lr=learning_rate,
betas=(beta_1, 0.999))
self.optimizer_G.load_state_dict(checkpoint['optimizer_G'])
self.optimizer_D.load_state_dict(checkpoint['optimizer_D'])
self.epoch = checkpoint['epoch']
logger.info('Set Criterion')
self.a_D = alexnet.alexnet(num_classes=tag_size).to(device)
self.optimizer_a_D = torch.optim.Adam(self.a_D.parameters(),
lr=learning_rate,
betas=(beta_1, .999))
# self.label_criterion = nn.BCEWithLogitsLoss().to(device)
# self.tag_criterion = nn.BCEWithLogitsLoss().to(device)
def load_checkpoint(self, model_dir):
models_path = utils.read_newest_model(model_dir)
if len(models_path) == 0:
return None, None
models_path.sort()
new_model_path = os.path.join(model_dump_path, models_path[-1])
if torch.cuda.is_available():
checkpoint = torch.load(new_model_path)
else:
checkpoint = torch.load(
new_model_path,
map_location='cuda' if torch.cuda.is_available() else 'cpu')
return checkpoint, new_model_path
def train(self):
iteration = -1
label = Variable(torch.FloatTensor(batch_size, 1)).to(device)
logging.info('Current epoch: {}. Max epoch: {}.'.format(
self.epoch, max_epoch))
while self.epoch <= max_epoch:
msg = {}
adjust_learning_rate(self.optimizer_G, iteration)
adjust_learning_rate(self.optimizer_D, iteration)
for i, (food_tag, food_img) in enumerate(self.data_loader):
iteration += 1
if food_img.shape[0] != batch_size:
logging.warn('Batch size not satisfied. Ignoring.')
continue
if verbose:
if iteration % verbose_T == 0:
msg['epoch'] = int(self.epoch)
msg['step'] = int(i)
msg['iteration'] = iteration
food_img = Variable(food_img).to(device)
# 0. training assistant D
self.a_D.zero_grad()
a_D_feat = self.a_D(food_img)
# 1. Training D
# 1.1. use really image for discriminating
self.D.zero_grad()
label_p = self.D(food_img)
label.data.fill_(1.0)
# 1.2. real image's loss
# real_label_loss = self.label_criterion(label_p, label)
real_label_loss = F.binary_cross_entropy(label_p, label)
real_loss_sum = real_label_loss
real_loss_sum.backward()
if verbose:
if iteration % verbose_T == 0:
msg['discriminator real loss'] = float(real_loss_sum)
# 1.3. use fake image for discriminating
g_noise, fake_tag = utils.fake_generator(
batch_size, noise_size, device)
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat).detach()
fake_label_p = self.D(fake_img)
label.data.fill_(.0)
# 1.4. fake image's loss
# fake_label_loss = self.label_criterion(fake_label_p, label)
fake_label_loss = F.binary_cross_entropy(fake_label_p, label)
# TODO:
fake_loss_sum = fake_label_loss
fake_loss_sum.backward()
if verbose:
if iteration % verbose_T == 0:
print('predicted fake label: {}'.format(fake_label_p))
msg['discriminator fake loss'] = float(fake_loss_sum)
# 1.6. update optimizer
self.optimizer_D.step()
# 2. Training G
# 2.1. generate fake image
self.G.zero_grad()
g_noise, fake_tag = utils.fake_generator(
batch_size, noise_size, device)
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat)
fake_label_p = self.D(fake_img)
label.data.fill_(1.0)
a_D_feat = self.a_D(fake_img)
feat_loss = F.binary_cross_entropy(a_D_feat, fake_tag)
# 2.2. calc loss
# label_loss_g = self.label_criterion(fake_label_p, label)
label_loss_g = F.binary_cross_entropy(fake_label_p, label)
loss_g = label_loss_g
loss_g.backward()
if verbose:
if iteration % verbose_T == 0:
msg['generator loss'] = float(loss_g)
# 2.2. update optimizer
self.optimizer_G.step()
if verbose:
if iteration % verbose_T == 0:
logger.info(
'------------------------------------------')
for key in msg.keys():
logger.info('{} : {}'.format(key, msg[key]))
# save intermediate file
if iteration % 10000 == 0:
torch.save(
{
'epoch': self.epoch,
'D': self.D.state_dict(),
'G': self.G.state_dict(),
'optimizer_D': self.optimizer_D.state_dict(),
'optimizer_G': self.optimizer_G.state_dict(),
},
'{}/checkpoint_{}.tar'.format(model_dump_path,
str(iteration).zfill(8)))
logger.info('Checkpoint saved in: {}'.format(
'{}/checkpoint_{}.tar'.format(
model_dump_path,
str(iteration).zfill(8))))
if iteration % verbose_T == 0:
vutils.save_image(
food_img.data.view(batch_size, 3, food_img.size(2),
food_img.size(3)),
os.path.join(
tmp_path, 'real_image_{}.png'.format(
str(iteration).zfill(8))))
g_noise, fake_tag = utils.fake_generator(
batch_size, noise_size, device)
fake_feat = torch.cat([g_noise, fake_tag], dim=1)
fake_img = self.G(fake_feat)
vutils.save_image(
fake_img.data.view(batch_size, 3, food_img.size(2),
food_img.size(3)),
os.path.join(
tmp_path, 'fake_image_{}.png'.format(
str(iteration).zfill(8))))
logger.info('Saved intermediate file in {}'.format(
os.path.join(
tmp_path, 'fake_image_{}.png'.format(
str(iteration).zfill(8)))))
# dump checkpoint
torch.save(
{
'epoch': self.epoch,
'D': self.D.state_dict(),
'G': self.G.state_dict(),
'optimizer_D': self.optimizer_D.state_dict(),
'optimizer_G': self.optimizer_G.state_dict(),
}, '{}/checkpoint_{}.tar'.format(model_dump_path,
str(self.epoch).zfill(4)))
logger.info('Checkpoint saved in: {}'.format(
'{}/checkpoint_{}.tar'.format(model_dump_path,
str(self.epoch).zfill(4))))
self.epoch += 1
class Trainer():
def __init__(self, opt):
self.device = torch.device('cuda')
self.opt = opt
self.G = Generator(self.opt['network_G']).to(self.device)
util.init_weights(self.G, init_type='kaiming', scale=0.1)
if self.opt['path']['pretrain_G']:
self.G.load_state_dict(torch.load(self.opt['path']['pretrain_G']),
strict=True)
self.D = Discriminator(self.opt['network_D']).to(self.device)
util.init_weights(self.D, init_type='kaiming', scale=1)
self.FE = VGGFeatureExtractor().to(self.device)
self.G.train()
self.D.train()
self.FE.eval()
self.log_dict = OrderedDict()
self.optim_params = [
v for k, v in self.G.named_parameters() if v.requires_grad
]
self.opt_G = torch.optim.Adam(self.optim_params,
lr=self.opt['train']['lr_G'],
betas=(self.opt['train']['b1_G'],
self.opt['train']['b2_G']))
self.opt_D = torch.optim.Adam(self.D.parameters(),
lr=self.opt['train']['lr_D'],
betas=(self.opt['train']['b1_D'],
self.opt['train']['b2_D']))
self.optimizers = [self.opt_G, self.opt_D]
self.schedulers = [
lr_scheduler.MultiStepLR(optimizer, self.opt['train']['lr_steps'],
self.opt['train']['lr_gamma'])
for optimizer in self.optimizers
]
def update_learning_rate(self):
for scheduler in self.schedulers:
scheduler.step()
def get_current_log(self):
return self.log_dict
def get_current_learning_rate(self):
return self.schedulers[0].get_lr()[0]
def load_model(self, step, strict=True):
self.G.load_state_dict(torch.load(
f"{self.opt['path']['checkpoints']['models']}/{step}_G.pth"),
strict=strict)
self.D.load_state_dict(torch.load(
f"{self.opt['path']['checkpoints']['models']}/{step}_D.pth"),
strict=strict)
def resume_training(self, resume_state):
'''Resume the optimizers and schedulers for training'''
resume_optimizers = resume_state['optimizers']
resume_schedulers = resume_state['schedulers']
assert len(resume_optimizers) == len(
self.optimizers), 'Wrong lengths of optimizers'
assert len(resume_schedulers) == len(
self.schedulers), 'Wrong lengths of schedulers'
for i, o in enumerate(resume_optimizers):
self.optimizers[i].load_state_dict(o)
for i, s in enumerate(resume_schedulers):
self.schedulers[i].load_state_dict(s)
def save_network(self, network, network_label, iter_step):
util.mkdir(self.opt['path']['checkpoints']['models'])
save_filename = '{}_{}.pth'.format(iter_step, network_label)
save_path = os.path.join(self.opt['path']['checkpoints']['models'],
save_filename)
if isinstance(network, nn.DataParallel):
network = network.module
state_dict = network.state_dict()
for key, param in state_dict.items():
state_dict[key] = param.cpu()
torch.save(state_dict, save_path)
def save_model(self, epoch, current_step):
self.save_network(self.G, 'G', current_step)
self.save_network(self.D, 'D', current_step)
self.save_training_state(epoch, current_step)
def save_training_state(self, epoch, iter_step):
'''Saves training state during training, which will be used for resuming'''
state = {
'epoch': epoch,
'iter': iter_step,
'schedulers': [],
'optimizers': []
}
for s in self.schedulers:
state['schedulers'].append(s.state_dict())
for o in self.optimizers:
state['optimizers'].append(o.state_dict())
save_filename = '{}.state'.format(iter_step)
util.mkdir(self.opt['path']['checkpoints']['states'])
save_path = os.path.join(self.opt['path']['checkpoints']['states'],
save_filename)
torch.save(state, save_path)
def train(self, train_batch, step):
self.lr = train_batch['LR'].to(self.device)
self.hr = train_batch['HR'].to(self.device)
for p in self.D.parameters():
p.requires_grad = False
self.opt_G.zero_grad()
self.sr = self.G(self.lr)
l_g_total = 0
# pixel loss
l_g_pix = self.opt['train']['wt_pix'] * cri_pix(self.sr, self.hr)
l_g_total += l_g_pix
# feature loss
real_fea = self.FE(self.hr).detach()
fake_fea = self.FE(self.sr)
l_g_fea = self.opt['train']['wt_fea'] * cri_fea(fake_fea, real_fea)
l_g_total += l_g_fea
# ragan loss
pred_g_fake = self.D(self.sr)
pred_d_real = self.D(self.hr).detach()
l_g_gan = self.opt['train']['wt_gan'] * (
cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
l_g_total += l_g_gan
l_g_total.backward()
self.opt_G.step()
# D
for p in self.D.parameters():
p.requires_grad = True
self.opt_D.zero_grad()
l_d_total = 0
pred_d_real = self.D(self.hr)
pred_d_fake = self.D(self.sr.detach()) # detach to avoid BP to G
l_d_real = cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
l_d_fake = cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
l_d_total = (l_d_real + l_d_fake) / 2
l_d_total.backward()
self.opt_D.step()
# set log
# G
self.log_dict['l_g_pix'] = l_g_pix.item()
self.log_dict['l_g_fea'] = l_g_fea.item()
self.log_dict['l_g_gan'] = l_g_gan.item()
# D
self.log_dict['l_d_real'] = l_d_real.item()
self.log_dict['l_d_fake'] = l_d_fake.item()
# D outputs
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
def validate(self, val_batch, current_step):
avg_psnr = 0.0
avg_ssim = 0.0
idx = 0
for _, val_data in enumerate(val_batch):
idx += 1
img_name = os.path.splitext(
os.path.basename(val_data['LR_path'][0]))[0]
img_dir = os.path.join(
self.opt['path']['checkpoints']['val_image_dir'], img_name)
util.mkdir(img_dir)
self.val_lr = val_data['LR'].to(self.device)
self.val_hr = val_data['HR'].to(self.device)
self.G.eval()
with torch.no_grad():
self.val_sr = self.G(self.val_lr)
self.G.train()
val_LR = self.val_lr.detach()[0].float().cpu()
val_SR = self.val_sr.detach()[0].float().cpu()
val_HR = self.val_hr.detach()[0].float().cpu()
sr_img = util.tensor2img(val_SR) # uint8
gt_img = util.tensor2img(val_HR) # uint8
# Save SR images for reference
save_img_path = os.path.join(
img_dir, '{:s}_{:d}.png'.format(img_name, current_step))
cv2.imwrite(save_img_path, sr_img)
# calculate PSNR
crop_size = 4
gt_img = gt_img / 255.
sr_img = sr_img / 255.
cropped_sr_img = sr_img[crop_size:-crop_size,
crop_size:-crop_size, :]
cropped_gt_img = gt_img[crop_size:-crop_size,
crop_size:-crop_size, :]
avg_psnr += PSNR(cropped_sr_img * 255, cropped_gt_img * 255)
avg_ssim += SSIM(cropped_sr_img * 255, cropped_gt_img * 255)
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
return avg_psnr, avg_ssim
train_dataset = TrainDataset(
root=args.train_data_path,
scale_factor=args.scale_factor,
hr_size=args.hr_size,
random_crop_size=args.random_crop_size,
)
test_dataset = TestDataset(root=args.test_data_path,
scale_factor=args.scale_factor,
hr_size=args.hr_size)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=64)
gen_net = Generator(num_res_blocks=args.gen_res_blocks,
upscale_factor=args.scale_factor).to(device)
dis_net = Discriminator(hr_size=args.random_crop_size,
sigmoid=not args.no_sigmoid).to(device)
print(f"Generator number of parameters: {count_parameters(gen_net)}")
print(f"Discriminator number of parameters: {count_parameters(dis_net)}")
gen_path = args.from_pretrained_gen
if gen_path:
gen_net.load_state_dict(torch.load(gen_path))
dis_path = args.from_pretrained_dis
if dis_path:
dis_net.load_state_dict(torch.load(dis_path))
perceptual_loss = PerceptualLoss(device=device)
mse_loss = nn.MSELoss()
beta1 = 0.9
opt_gen = optim.Adam(gen_net.parameters(),
run_name = 'correlation-GAN_{}'.format(config.version)
wandb.init(name=run_name,
dir=config.checkpoint_dir,
notes=config.description)
wandb.config.update(config.__dict__)
device = torch.device('cuda')
use_dropout = [True, True, False]
drop_prob = [0.5, 0.5, 0.5]
use_ac_func = [True, True, False]
activation = 'relu'
latent_dim = 10
gen_fc_layers = [latent_dim, 16, 32, 2]
generator = Generator(gen_fc_layers, use_dropout, drop_prob, use_ac_func,
activation).to(device)
disc_fc_layers = [2, 32, 16, 1]
discriminator = Discriminator(disc_fc_layers, use_dropout, drop_prob,
use_ac_func, activation).to(device)
wandb.watch([generator, discriminator])
g_optimizer = Adam(generator.parameters(), lr=1e-4, betas=(0.5, 0.9))
d_optimizer = Adam(discriminator.parameters(), lr=1e-4, betas=(0.5, 0.9))
wgan_gp = WGAN_GP(config, generator, discriminator, g_optimizer,
d_optimizer, latent_shape)
wgan_gp.train(dataloader, 200)
