def train_s(self):
dataset, test_dataset = get_dataset(self.args, self.config)
train_loader = data.DataLoader(
dataset,
batch_size=self.config.training.batch_size,
shuffle=True,
num_workers=self.config.data.num_workers,
)
S = Model(self.config)
S = S.to(self.device)
S = torch.nn.DataParallel(S)
optimizer = get_optimizer(self.config, S.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(S)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
S.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
S.train()
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, (x, y) in enumerate(train_loader):
n = x.size(0)
step += 1
x = x.to(self.device)
x = x - 0.5
e = torch.randn_like(x)
# antithetic sampling
t = torch.randint(
low=0, high=self.num_timesteps, size=(n // 2 + 1,)
).to(self.device)
t = torch.cat([t, self.num_timesteps - t - 1], dim=0)[:n]
loss = noise_estimation_loss(S, x, t.long(), e, self.sigma)
if not step % 100:
logging.info(f"step: {step}, loss: {loss.item()}")
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(
S.parameters(), self.config.optim.grad_clip
)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(S)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
S.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(
states,
os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)),
)
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
data_start = time.time()
def train(self):
dataset, test_dataset = get_dataset(self.args, self.config)
dataloader = DataLoader(dataset, batch_size=self.config.training.batch_size, shuffle=True,
num_workers=self.config.data.num_workers)
test_loader = DataLoader(test_dataset, batch_size=self.config.training.batch_size, shuffle=True,
num_workers=self.config.data.num_workers, drop_last=True)
test_iter = iter(test_loader)
self.config.input_dim = self.config.data.image_size ** 2 * self.config.data.channels
tb_logger = self.config.tb_logger
score = get_model(self.config)
score = torch.nn.DataParallel(score)
optimizer = get_optimizer(self.config, score.parameters())
start_epoch = 0
step = 0
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, 'checkpoint.pth'))
score.load_state_dict(states[0])
### Make sure we can resume with different eps
states[1]['param_groups'][0]['eps'] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
sigmas = get_sigmas(self.config)
if self.config.training.log_all_sigmas:
### Commented out training time logging to save time.
test_loss_per_sigma = [None for _ in range(len(sigmas))]
def hook(loss, labels):
# for i in range(len(sigmas)):
# if torch.any(labels == i):
# test_loss_per_sigma[i] = torch.mean(loss[labels == i])
pass
def tb_hook():
# for i in range(len(sigmas)):
# if test_loss_per_sigma[i] is not None:
# tb_logger.add_scalar('test_loss_sigma_{}'.format(i), test_loss_per_sigma[i],
# global_step=step)
pass
def test_hook(loss, labels):
for i in range(len(sigmas)):
if torch.any(labels == i):
test_loss_per_sigma[i] = torch.mean(loss[labels == i])
def test_tb_hook():
for i in range(len(sigmas)):
if test_loss_per_sigma[i] is not None:
tb_logger.add_scalar('test_loss_sigma_{}'.format(i), test_loss_per_sigma[i],
global_step=step)
else:
hook = test_hook = None
def tb_hook():
pass
def test_tb_hook():
pass
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, (X, y) in enumerate(dataloader):
score.train()
step += 1
X = X.to(self.config.device)
X = data_transform(self.config, X)
loss = anneal_dsm_score_estimation(score, X, sigmas, None,
self.config.training.anneal_power,
hook)
tb_logger.add_scalar('loss', loss, global_step=step)
tb_hook()
logging.info("step: {}, loss: {}".format(step, loss.item()))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if self.config.model.ema:
ema_helper.update(score)
if step >= self.config.training.n_iters:
return 0
if step % 100 == 0:
if self.config.model.ema:
test_score = ema_helper.ema_copy(score)
else:
test_score = score
test_score.eval()
try:
test_X, test_y = next(test_iter)
except StopIteration:
test_iter = iter(test_loader)
test_X, test_y = next(test_iter)
test_X = test_X.to(self.config.device)
test_X = data_transform(self.config, test_X)
with torch.no_grad():
test_dsm_loss = anneal_dsm_score_estimation(test_score, test_X, sigmas, None,
self.config.training.anneal_power,
hook=test_hook)
tb_logger.add_scalar('test_loss', test_dsm_loss, global_step=step)
test_tb_hook()
logging.info("step: {}, test_loss: {}".format(step, test_dsm_loss.item()))
del test_score
if step % self.config.training.snapshot_freq == 0:
states = [
score.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(states, os.path.join(self.args.log_path, 'checkpoint_{}.pth'.format(step)))
torch.save(states, os.path.join(self.args.log_path, 'checkpoint.pth'))
if self.config.training.snapshot_sampling:
if self.config.model.ema:
test_score = ema_helper.ema_copy(score)
else:
test_score = score
test_score.eval()
## Different part from NeurIPS 2019.
## Random state will be affected because of sampling during training time.
init_samples = torch.rand(36, self.config.data.channels,
self.config.data.image_size, self.config.data.image_size,
device=self.config.device)
init_samples = data_transform(self.config, init_samples)
all_samples = anneal_Langevin_dynamics(init_samples, test_score, sigmas.cpu().numpy(),
self.config.sampling.n_steps_each,
self.config.sampling.step_lr,
final_only=True, verbose=True,
denoise=self.config.sampling.denoise)
sample = all_samples[-1].view(all_samples[-1].shape[0], self.config.data.channels,
self.config.data.image_size,
self.config.data.image_size)
sample = inverse_data_transform(self.config, sample)
image_grid = make_grid(sample, 6)
save_image(image_grid,
os.path.join(self.args.log_sample_path, 'image_grid_{}.png'.format(step)))
torch.save(sample, os.path.join(self.args.log_sample_path, 'samples_{}.pth'.format(step)))
del test_score
del all_samples
def train(self):
obs = (1, 28, 28) if 'MNIST' in self.config.dataset else (3, 32, 32)
input_channels = obs[0]
train_loader, test_loader = dataset.get_dataset(self.config)
model = PixelCNN(self.config)
model = model.to(self.config.device)
model = torch.nn.DataParallel(model)
sample_model = partial(model, sample=True)
rescaling_inv = lambda x: .5 * x + .5
rescaling = lambda x: (x - .5) * 2.
if 'MNIST' in self.config.dataset:
loss_op = lambda real, fake: mix_logistic_loss_1d(real, fake)
clamp = False
sample_op = lambda x: sample_from_discretized_mix_logistic_1d(
x, sample_model, self.config.nr_logistic_mix, clamp=clamp)
elif 'CIFAR10' in self.config.dataset:
loss_op = lambda real, fake: mix_logistic_loss(real, fake)
clamp = False
sample_op = lambda x: sample_from_discretized_mix_logistic(
x, sample_model, self.config.nr_logistic_mix, clamp=clamp)
elif 'celeba' in self.config.dataset:
loss_op = lambda real, fake: mix_logistic_loss(real, fake)
clamp = False
sample_op = lambda x: sample_from_discretized_mix_logistic(
x, sample_model, self.config.nr_logistic_mix, clamp=clamp)
else:
raise Exception(
'{} dataset not in {mnist, cifar10, celeba}'.format(
self.config.dataset))
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
optimizer = optim.Adam(model.parameters(), lr=self.config.lr)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=self.config.lr_decay)
ckpt_path = os.path.join(self.args.log, 'pixelcnn_ckpts')
if not os.path.exists(ckpt_path):
os.makedirs(ckpt_path)
if self.args.resume_training:
state_dict = torch.load(os.path.join(ckpt_path, 'checkpoint.pth'),
map_location=self.config.device)
model.load_state_dict(state_dict[0])
optimizer.load_state_dict(state_dict[1])
scheduler.load_state_dict(state_dict[2])
if len(state_dict) > 3:
epoch = state_dict[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
print('model parameters loaded')
tb_path = os.path.join(self.args.log, 'tensorboard')
if os.path.exists(tb_path):
shutil.rmtree(tb_path)
os.makedirs(tb_path)
tb_logger = SummaryWriter(log_dir=tb_path)
def debug_sample(model, noisy_image):
model.eval()
with torch.no_grad():
data = torch.cat([noisy_image, noisy_image], dim=2)
for i in range(obs[1], obs[1] * 2, 1):
for j in range(obs[2]):
data_v = data
out_sample = sample_op(data_v)
data[:, :, i, j] = out_sample.data[:, :, i, j]
return data
print('starting training', flush=True)
writes = 0
for epoch in range(self.config.max_epochs):
train_loss = 0.
model.train()
for batch_idx, (input, _) in enumerate(train_loader):
input = input.cuda(non_blocking=True)
# input: [-1, 1]
## add noise to the entire image
noisy_input = input + torch.randn_like(
input) * self.config.noise
clean_input = input + torch.randn_like(
input) * self.config.clean_noise # add very small noise
input = torch.cat([noisy_input, clean_input], dim=2)
output = model(input)[:, :, input.shape[-1]:, :]
loss = loss_op(clean_input, output)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
train_loss += loss.item()
if (batch_idx + 1) % self.config.print_every == 0:
deno = self.config.print_every * self.config.batch_size * np.prod(
obs) * np.log(2.)
train_loss = train_loss / deno
print('epoch: {}, batch: {}, loss : {:.4f}'.format(
epoch, batch_idx, train_loss),
flush=True)
tb_logger.add_scalar('loss',
train_loss,
global_step=writes)
train_loss = 0.
writes += 1
# decrease learning rate
scheduler.step()
if self.config.model.ema:
test_model = ema_helper.ema_copy(model)
else:
test_model = model
test_model.eval()
test_loss = 0.
with torch.no_grad():
for batch_idx, (input_var, _) in enumerate(test_loader):
input_var = input_var.cuda(non_blocking=True)
noisy_input_var = input_var + torch.randn_like(
input_var) * self.config.noise
clean_input_var = input_var + torch.randn_like(
input_var) * self.config.clean_noise #* 0.02
input_var = torch.cat([noisy_input_var, clean_input_var],
dim=2)
output = test_model(input_var)[:, :,
input_var.shape[-1]:, :]
loss = loss_op(clean_input_var, output)
test_loss += loss.item()
del loss, output
deno = batch_idx * self.config.batch_size * np.prod(
obs) * np.log(2.)
test_loss = test_loss / deno
print('epoch: %s, test loss : %s' % (epoch, test_loss),
flush=True)
tb_logger.add_scalar('test_loss',
test_loss,
global_step=writes)
if (epoch + 1) % self.config.save_interval == 0:
state_dict = [
model.state_dict(),
optimizer.state_dict(),
scheduler.state_dict(),
epoch,
]
if self.config.model.ema:
state_dict.append(ema_helper.state_dict())
if (epoch + 1) % (self.config.save_interval * 2) == 0:
torch.save(
state_dict,
os.path.join(ckpt_path, f'ckpt_epoch_{epoch}.pth'))
torch.save(state_dict, os.path.join(ckpt_path,
'checkpoint.pth'))
if epoch % 10 == 0:
print('sampling...', flush=True)
sample_t = debug_sample(test_model, noisy_input_var[:25])
sample_t = torch.cat([clean_input_var[:25], sample_t],
dim=2) #add original sample
if self.config.with_logit is True:
sample_t = sigmoid_transform(sample_t)
else:
sample_t = rescaling_inv(sample_t)
if not os.path.exists(os.path.join(self.args.log, 'images')):
os.makedirs(os.path.join(self.args.log, 'images'))
utils.save_image(sample_t,
os.path.join(self.args.log, 'images',
f'sample_epoch_{epoch}.png'),
nrow=5,
padding=0)
if self.config.model.ema:
del test_model
def train(self):
args, config = self.args, self.config
vdl_logger = self.config.vdl_logger
dataset, test_dataset = get_dataset(args, config)
train_loader = data.DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
use_shared_memory=False,
)
model = Model(config)
model = model
model = paddle.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = paddle.load(os.path.join(self.args.log_path, "ckpt.pdl"))
model.set_state_dict({
k.split("$model_")[-1]: v
for k, v in states.items() if "$model_" in k
})
optimizer.set_state_dict({
k.split("$optimizer_")[-1]: v
for k, v in states.items() if "$optimizer_" in k
})
optimizer._epsilon = self.config.optim.eps
start_epoch = states["$epoch"]
step = states["$step"]
if self.config.model.ema:
ema_helper.set_state_dict({
k.split("$ema_")[-1]: v
for k, v in states.items() if "$ema_" in k
})
for epoch in range(start_epoch, self.config.training.n_epochs):
data_start = time.time()
data_time = 0
for i, (x, y) in enumerate(train_loader):
n = x.shape[0]
data_time += time.time() - data_start
model.train()
step += 1
x = data_transform(self.config, x)
e = paddle.randn(x.shape)
b = self.betas
# antithetic sampling
t = paddle.randint(low=0,
high=self.num_timesteps,
shape=(n // 2 + 1, ))
t = paddle.concat([t, self.num_timesteps - t - 1], 0)[:n]
loss = loss_registry[config.model.type](model, x, t, e, b)
vdl_logger.add_scalar("loss", loss, step=step)
logging.info(
f"step: {step}, loss: {loss.numpy()[0]}, data time: {data_time / (i+1)}"
)
optimizer.clear_grad()
loss.backward()
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = dict(
**{
"$model_" + k: v
for k, v in model.state_dict().items()
},
**{
"$optimizer_" + k: v
for k, v in optimizer.state_dict().items()
},
**{"$epoch": epoch},
**{"$step": step},
)
if self.config.model.ema:
states.update({
"$ema_" + k: v
for k, v in ema_helper.state_dict().items()
})
paddle.save(
states,
os.path.join(self.args.log_path,
"ckpt_{}.pdl".format(step)),
)
paddle.save(states,
os.path.join(self.args.log_path, "ckpt.pdl"))
data_start = time.time()