def eval(opt, netG):
# Re-generate dataset frames
fps, td, fps_index = utils.get_fps_td_by_index(opt.scale_idx, opt)
opt.fps = fps
opt.td = td
opt.fps_index = fps_index
# opt.tds.append(opt.td)
opt.dataset.generate_frames(opt.scale_idx)
torch.save(opt.dataset.frames,
os.path.join(opt.saver.eval_dir, "real_full_scale.pth"))
if not hasattr(opt, 'Z_init_size'):
initial_size = utils.get_scales_by_index(0, opt.scale_factor,
opt.stop_scale, opt.img_size)
initial_size = [int(initial_size * opt.ar), initial_size]
opt.Z_init_size = [
opt.batch_size, opt.latent_dim, opt.td, *initial_size
]
# Parallel
if opt.device == 'cuda':
G_curr = torch.nn.DataParallel(netG)
else:
G_curr = netG
progressbar_args = {
"iterable":
range(opt.niter),
"desc":
"Generation scale [{}/{}]".format(opt.scale_idx + 1,
opt.stop_scale + 1),
"train":
True,
"offset":
0,
"logging_on_update":
False,
"logging_on_close":
True,
"postfix":
True
}
epoch_iterator = tools.create_progressbar(**progressbar_args)
iterator = iter(data_loader)
random_samples = []
for iteration in epoch_iterator:
try:
data = next(iterator)
except StopIteration:
iterator = iter(opt.data_loader)
data = next(iterator)
if opt.scale_idx > 0:
real, real_zero = data
real = real.to(opt.device)
else:
real = data.to(opt.device)
noise_init = utils.generate_noise(size=opt.Z_init_size,
device=opt.device)
# Update progress bar
epoch_iterator.set_description(
'Scale [{}/{}], Iteration [{}/{}]'.format(
opt.scale_idx + 1,
opt.stop_scale + 1,
iteration + 1,
opt.niter,
))
with torch.no_grad():
fake_var = []
fake_vae_var = []
for _ in range(opt.num_samples):
noise_init = utils.generate_noise(ref=noise_init)
fake, fake_vae = G_curr(noise_init,
opt.Noise_Amps,
noise_init=noise_init,
mode="rand")
fake_var.append(fake)
fake_vae_var.append(fake_vae)
fake_var = torch.cat(fake_var, dim=0)
fake_vae_var = torch.cat(fake_vae_var, dim=0)
opt.summary.visualize_video(opt, iteration, real, 'Real')
opt.summary.visualize_video(opt, iteration, fake_var, 'Fake var')
opt.summary.visualize_video(opt, iteration, fake_vae_var,
'Fake VAE var')
random_samples.append(fake_var)
random_samples = torch.cat(random_samples, dim=0)
torch.save(random_samples,
os.path.join(opt.saver.eval_dir, "random_samples.pth"))
epoch_iterator.close()
parser.set_defaults(hflip=False)
opt = parser.parse_args()
exceptions = ['no-cuda', 'niter', 'data_rep', 'batch_size', 'netG']
all_dirs = glob(opt.exp_dir)
progressbar_args = {
"iterable": all_dirs,
"desc": "Experiments",
"train": True,
"offset": 0,
"logging_on_update": False,
"logging_on_close": True,
"postfix": True
}
exp_iterator = tools.create_progressbar(**progressbar_args)
for idx, exp_dir in enumerate(exp_iterator):
opt.experiment_dir = exp_dir
keys = vars(opt).keys()
with open(os.path.join(exp_dir, 'args.txt'), 'r') as f:
for line in f.readlines():
log_arg = line.replace(' ', '').replace('\n', '').split(':')
assert len(log_arg) == 2
if log_arg[0] in exceptions:
continue
try:
setattr(opt, log_arg[0], ast.literal_eval(log_arg[1]))
except Exception:
setattr(opt, log_arg[0], log_arg[1])
def train(opt, netG):
# Re-generate dataset frames
fps, td, fps_index = utils.get_fps_td_by_index(opt.scale_idx, opt)
opt.fps = fps
opt.td = td
opt.fps_index = fps_index
with logger.LoggingBlock("Updating dataset", emph=True):
logging.info("{}FPS :{} {}{}".format(green, clear, opt.fps, clear))
logging.info("{}Time-Depth :{} {}{}".format(green, clear, opt.td,
clear))
logging.info("{}Sampling-Ratio :{} {}{}".format(
green, clear, opt.sampling_rates[opt.fps_index], clear))
opt.dataset.generate_frames(opt.scale_idx)
# Initialize noise
if not hasattr(opt, 'Z_init_size'):
initial_size = utils.get_scales_by_index(0, opt.scale_factor,
opt.stop_scale, opt.img_size)
initial_size = [int(initial_size * opt.ar), initial_size]
opt.Z_init_size = [
opt.batch_size, opt.latent_dim, opt.td, *initial_size
]
if opt.vae_levels < opt.scale_idx + 1:
D_curr = getattr(networks_3d, opt.discriminator)(opt).to(opt.device)
if (opt.netG != '') and (opt.resumed_idx == opt.scale_idx):
D_curr.load_state_dict(
torch.load('{}/netD_{}.pth'.format(
opt.resume_dir, opt.scale_idx - 1))['state_dict'])
elif opt.vae_levels < opt.scale_idx:
D_curr.load_state_dict(
torch.load(
'{}/netD_{}.pth'.format(opt.saver.experiment_dir,
opt.scale_idx - 1))['state_dict'])
# Current optimizers
optimizerD = optim.Adam(D_curr.parameters(),
lr=opt.lr_d,
betas=(opt.beta1, 0.999))
parameter_list = []
# Generator Adversary
if not opt.train_all:
if opt.vae_levels < opt.scale_idx + 1:
train_depth = min(opt.train_depth,
len(netG.body) - opt.vae_levels + 1)
parameter_list += [{
"params":
block.parameters(),
"lr":
opt.lr_g *
(opt.lr_scale**(len(netG.body[-train_depth:]) - 1 - idx))
} for idx, block in enumerate(netG.body[-train_depth:])]
else:
# VAE
parameter_list += [{
"params": netG.encode.parameters(),
"lr": opt.lr_g * (opt.lr_scale**opt.scale_idx)
}, {
"params": netG.decoder.parameters(),
"lr": opt.lr_g * (opt.lr_scale**opt.scale_idx)
}]
parameter_list += [{
"params":
block.parameters(),
"lr":
opt.lr_g *
(opt.lr_scale**(len(netG.body[-opt.train_depth:]) - 1 - idx))
} for idx, block in enumerate(netG.body[-opt.train_depth:])]
else:
if len(netG.body) < opt.train_depth:
parameter_list += [{
"params": netG.encode.parameters(),
"lr": opt.lr_g * (opt.lr_scale**opt.scale_idx)
}, {
"params": netG.decoder.parameters(),
"lr": opt.lr_g * (opt.lr_scale**opt.scale_idx)
}]
parameter_list += [{
"params":
block.parameters(),
"lr":
opt.lr_g * (opt.lr_scale**(len(netG.body) - 1 - idx))
} for idx, block in enumerate(netG.body)]
else:
parameter_list += [{
"params":
block.parameters(),
"lr":
opt.lr_g *
(opt.lr_scale**(len(netG.body[-opt.train_depth:]) - 1 - idx))
} for idx, block in enumerate(netG.body[-opt.train_depth:])]
optimizerG = optim.Adam(parameter_list,
lr=opt.lr_g,
betas=(opt.beta1, 0.999))
# Parallel
if opt.device == 'cuda':
G_curr = torch.nn.DataParallel(netG)
if opt.vae_levels < opt.scale_idx + 1:
D_curr = torch.nn.DataParallel(D_curr)
else:
G_curr = netG
progressbar_args = {
"iterable":
range(opt.niter),
"desc":
"Training scale [{}/{}]".format(opt.scale_idx + 1, opt.stop_scale + 1),
"train":
True,
"offset":
0,
"logging_on_update":
False,
"logging_on_close":
True,
"postfix":
True
}
epoch_iterator = tools.create_progressbar(**progressbar_args)
iterator = iter(data_loader)
for iteration in epoch_iterator:
try:
data = next(iterator)
except StopIteration:
iterator = iter(opt.data_loader)
data = next(iterator)
if opt.scale_idx > 0:
real, real_zero = data
real = real.to(opt.device)
real_zero = real_zero.to(opt.device)
else:
real = data.to(opt.device)
real_zero = real
noise_init = utils.generate_noise(size=opt.Z_init_size,
device=opt.device)
############################
# calculate noise_amp
###########################
if iteration == 0:
if opt.const_amp:
opt.Noise_Amps.append(1)
else:
with torch.no_grad():
if opt.scale_idx == 0:
opt.noise_amp = 1
opt.Noise_Amps.append(opt.noise_amp)
else:
opt.Noise_Amps.append(0)
z_reconstruction, _, _ = G_curr(real_zero,
opt.Noise_Amps,
mode="rec")
RMSE = torch.sqrt(F.mse_loss(real, z_reconstruction))
opt.noise_amp = opt.noise_amp_init * RMSE.item(
) / opt.batch_size
opt.Noise_Amps[-1] = opt.noise_amp
############################
# (1) Update VAE network
###########################
total_loss = 0
generated, generated_vae, (mu, logvar) = G_curr(real_zero,
opt.Noise_Amps,
mode="rec")
if opt.vae_levels >= opt.scale_idx + 1:
rec_vae_loss = opt.rec_loss(generated, real) + opt.rec_loss(
generated_vae, real_zero)
kl_loss = kl_criterion(mu, logvar)
vae_loss = opt.rec_weight * rec_vae_loss + opt.kl_weight * kl_loss
total_loss += vae_loss
else:
############################
# (2) Update D network: maximize D(x) + D(G(z))
###########################
# train with real
#################
# Train 3D Discriminator
D_curr.zero_grad()
output = D_curr(real)
errD_real = -output.mean()
# train with fake
#################
fake, _ = G_curr(noise_init,
opt.Noise_Amps,
noise_init=noise_init,
mode="rand")
# Train 3D Discriminator
output = D_curr(fake.detach())
errD_fake = output.mean()
gradient_penalty = calc_gradient_penalty(D_curr, real, fake,
opt.lambda_grad,
opt.device)
errD_total = errD_real + errD_fake + gradient_penalty
errD_total.backward()
optimizerD.step()
############################
# (3) Update G network: maximize D(G(z))
###########################
errG_total = 0
rec_loss = opt.rec_loss(generated, real)
errG_total += opt.rec_weight * rec_loss
# Train with 3D Discriminator
output = D_curr(fake)
errG = -output.mean() * opt.disc_loss_weight
errG_total += errG
total_loss += errG_total
G_curr.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(G_curr.parameters(), opt.grad_clip)
optimizerG.step()
# Update progress bar
epoch_iterator.set_description(
'Scale [{}/{}], Iteration [{}/{}]'.format(
opt.scale_idx + 1,
opt.stop_scale + 1,
iteration + 1,
opt.niter,
))
if opt.visualize:
# Tensorboard
opt.summary.add_scalar(
'Video/Scale {}/noise_amp'.format(opt.scale_idx),
opt.noise_amp, iteration)
if opt.vae_levels >= opt.scale_idx + 1:
opt.summary.add_scalar(
'Video/Scale {}/KLD'.format(opt.scale_idx), kl_loss.item(),
iteration)
else:
opt.summary.add_scalar(
'Video/Scale {}/rec loss'.format(opt.scale_idx),
rec_loss.item(), iteration)
opt.summary.add_scalar(
'Video/Scale {}/noise_amp'.format(opt.scale_idx),
opt.noise_amp, iteration)
if opt.vae_levels < opt.scale_idx + 1:
opt.summary.add_scalar(
'Video/Scale {}/errG'.format(opt.scale_idx), errG.item(),
iteration)
opt.summary.add_scalar(
'Video/Scale {}/errD_fake'.format(opt.scale_idx),
errD_fake.item(), iteration)
opt.summary.add_scalar(
'Video/Scale {}/errD_real'.format(opt.scale_idx),
errD_real.item(), iteration)
else:
opt.summary.add_scalar(
'Video/Scale {}/Rec VAE'.format(opt.scale_idx),
rec_vae_loss.item(), iteration)
if iteration % opt.print_interval == 0:
with torch.no_grad():
fake_var = []
fake_vae_var = []
for _ in range(3):
noise_init = utils.generate_noise(ref=noise_init)
fake, fake_vae = G_curr(noise_init,
opt.Noise_Amps,
noise_init=noise_init,
mode="rand")
fake_var.append(fake)
fake_vae_var.append(fake_vae)
fake_var = torch.cat(fake_var, dim=0)
fake_vae_var = torch.cat(fake_vae_var, dim=0)
opt.summary.visualize_video(opt, iteration, real, 'Real')
opt.summary.visualize_video(opt, iteration, generated,
'Generated')
opt.summary.visualize_video(opt, iteration, generated_vae,
'Generated VAE')
opt.summary.visualize_video(opt, iteration, fake_var,
'Fake var')
opt.summary.visualize_video(opt, iteration, fake_vae_var,
'Fake VAE var')
epoch_iterator.close()
# Save data
opt.saver.save_checkpoint({'data': opt.Noise_Amps}, 'Noise_Amps.pth')
opt.saver.save_checkpoint(
{
'scale': opt.scale_idx,
'state_dict': netG.state_dict(),
'optimizer': optimizerG.state_dict(),
'noise_amps': opt.Noise_Amps,
}, 'netG.pth')
if opt.vae_levels < opt.scale_idx + 1:
opt.saver.save_checkpoint(
{
'scale':
opt.scale_idx,
'state_dict':
D_curr.module.state_dict()
if opt.device == 'cuda' else D_curr.state_dict(),
'optimizer':
optimizerD.state_dict(),
}, 'netD_{}.pth'.format(opt.scale_idx))
def eval(opt, netG):
# Re-generate dataset frames
if not hasattr(opt, 'Z_init_size'):
initial_size = utils.get_scales_by_index(0, opt.scale_factor,
opt.stop_scale, opt.img_size)
initial_size = [int(initial_size * opt.ar), initial_size]
opt.Z_init_size = [opt.batch_size, opt.latent_dim, *initial_size]
# Parallel
if opt.device == 'cuda':
G_curr = torch.nn.DataParallel(netG)
else:
G_curr = netG
progressbar_args = {
"iterable":
range(opt.niter),
"desc":
"Training scale [{}/{}]".format(opt.scale_idx + 1, opt.stop_scale + 1),
"train":
True,
"offset":
0,
"logging_on_update":
False,
"logging_on_close":
True,
"postfix":
True
}
epoch_iterator = tools.create_progressbar(**progressbar_args)
iterator = iter(data_loader)
random_samples = []
for iteration in epoch_iterator:
try:
data = next(iterator)
except StopIteration:
iterator = iter(opt.data_loader)
data = next(iterator)
if opt.scale_idx > 0:
real, real_zero = data
real = real.to(opt.device)
else:
real = data.to(opt.device)
noise_init = utils.generate_noise(size=opt.Z_init_size,
device=opt.device)
# Update progress bar
epoch_iterator.set_description(
'Scale [{}/{}], Iteration [{}/{}]'.format(
opt.scale_idx + 1,
opt.stop_scale + 1,
iteration + 1,
opt.niter,
))
G_curr.eval()
import numpy as np
import sys
with torch.no_grad():
fake_var = []
fake_vae_var = []
for _ in range(opt.num_samples):
noise_init = utils.generate_noise(ref=noise_init)
channel_idxs = np.random.choice(np.arange(0, 128),
127,
replace=False)
# U = torch.zeros(1, 128, 5).normal_(0, 1).to(noise_init.device)
U = torch.zeros(1, 128, 1).to(noise_init.device)
U[:, _] = 4
# U[:, :120] =
V = torch.zeros(1, 1, 22, 33).to(noise_init.device)
# V.bernoulli_(p=0.01)
V[:, :, 1:4, 20:32] = 1
# V[:, :, 4:10, 8:10] = 1
V = V.flatten(2)
UV = torch.bmm(U, V).view(1, 128, 22, 33)
UV = (UV - UV.mean()) / UV.std()
# noise_init[:] = 0
# noise_init[:, :, 5:11, 16:18] = _
# noise_init[:, 108, 0:4, 0:4] = 100
# noise_init[:, 21, _:_ + 1, 16:19] = 0.01
# noise_init[:, :, 3:11, 16:18] = -10 / opt.num_samples
# normed_z_vae = z_vae / ((z_vae ** 2).sum() + sys.float_info.epsilon)
# noise_init = noise_init / ((noise_init ** 2).sum() + sys.float_info.epsilon)
noise_init = UV
fake, fake_vae = G_curr(noise_init,
opt.Noise_Amps,
noise_init=noise_init,
mode="rand")
fake_var.append(fake)
fake_vae_var.append(fake_vae)
fake_var = torch.cat(fake_var, dim=0)
fake_vae_var = torch.cat(fake_vae_var, dim=0)
opt.summary.visualize_image(opt, iteration, real, 'Real')
opt.summary.visualize_image(opt, iteration, fake_var, 'Fake var')
opt.summary.visualize_image(opt, iteration, fake_vae_var,
'Fake VAE var')
random_samples.append(fake_var)
random_samples = torch.cat(random_samples, dim=0)
from torchvision.utils import save_image
save_image(random_samples, 'test.png', normalize=True)
torch.save(random_samples,
os.path.join(opt.saver.eval_dir, "random_samples.pth"))
epoch_iterator.close()
def eval(opt, netG):
# Re-generate dataset frames
if not hasattr(opt, 'Z_init_size'):
initial_size = utils.get_scales_by_index(0, opt.scale_factor,
opt.stop_scale, opt.img_size)
initial_size = [int(initial_size * opt.ar), initial_size]
opt.Z_init_size = [opt.batch_size, opt.latent_dim, *initial_size]
# Parallel
if opt.device == 'cuda':
G_curr = torch.nn.DataParallel(netG)
else:
G_curr = netG
progressbar_args = {
"iterable":
range(opt.niter),
"desc":
"Training scale [{}/{}]".format(opt.scale_idx + 1, opt.stop_scale + 1),
"train":
True,
"offset":
0,
"logging_on_update":
False,
"logging_on_close":
True,
"postfix":
True
}
epoch_iterator = tools.create_progressbar(**progressbar_args)
iterator = iter(data_loader)
random_samples = []
for iteration in epoch_iterator:
try:
data = next(iterator)
except StopIteration:
iterator = iter(opt.data_loader)
data = next(iterator)
if opt.scale_idx > 0:
real, real_zero = data
real = real.to(opt.device)
else:
real = data.to(opt.device)
noise_init = utils.generate_noise(size=opt.Z_init_size,
device=opt.device)
# Update progress bar
epoch_iterator.set_description(
'Scale [{}/{}], Iteration [{}/{}]'.format(
opt.scale_idx + 1,
opt.stop_scale + 1,
iteration + 1,
opt.niter,
))
import numpy as np
with torch.no_grad():
fake_var = []
fake_vae_var = []
for _ in range(opt.num_samples):
noise_init = utils.generate_noise(ref=noise_init)
channel_idxs = np.random.choice(np.arange(0, 128),
127,
replace=False)
noise_init[:] = 0
noise_init[:, _:_ + 1, 8:11, 16:20] = 5
fake, fake_vae = G_curr(noise_init,
opt.Noise_Amps,
noise_init=noise_init,
mode="rand")
fake_var.append(fake)
fake_vae_var.append(fake_vae)
fake_var = torch.cat(fake_var, dim=0)
fake_vae_var = torch.cat(fake_vae_var, dim=0)
opt.summary.visualize_image(opt, iteration, real, 'Real')
opt.summary.visualize_image(opt, iteration, fake_var, 'Fake var')
opt.summary.visualize_image(opt, iteration, fake_vae_var,
'Fake VAE var')
random_samples.append(fake_var)
random_samples = torch.cat(random_samples, dim=0)
from torchvision.utils import save_image
save_image(random_samples, 'test.png')
torch.save(random_samples,
os.path.join(opt.saver.eval_dir, "random_samples.pth"))
epoch_iterator.close()
