def test(self):
score = get_model(self.config)
score = torch.nn.DataParallel(score)
sigmas = get_sigmas(self.config)
dataset, test_dataset = get_dataset(self.args, self.config)
test_dataloader = DataLoader(
test_dataset,
batch_size=self.config.test.batch_size,
shuffle=True,
num_workers=self.config.data.num_workers,
drop_last=True,
)
verbose = False
for ckpt in tqdm.tqdm(
range(self.config.test.begin_ckpt, self.config.test.end_ckpt + 1, 5000),
desc="processing ckpt:",
):
states = torch.load(
os.path.join(self.args.log_path, f"checkpoint_{ckpt}.pth"),
map_location=self.config.device,
)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(score)
else:
score.load_state_dict(states[0])
score.eval()
step = 0
mean_loss = 0.0
mean_grad_norm = 0.0
average_grad_scale = 0.0
for x, y in test_dataloader:
step += 1
x = x.to(self.config.device)
x = data_transform(self.config, x)
test_loss = anneal_sliced_score_estimation_vr(
score, x, sigmas, None, self.config.training.anneal_power
)
if verbose:
logging.info(
"step: {}, test_loss: {}".format(step, test_loss.item())
)
mean_loss += test_loss.item()
mean_loss /= step
mean_grad_norm /= step
average_grad_scale /= step
logging.info("ckpt: {}, average test loss: {}".format(ckpt, mean_loss))
def sample(self):
model = Model(self.config)
if not self.args.use_pretrained:
if getattr(self.config.sampling, "ckpt_id", None) is None:
states = paddle.load(
os.path.join(self.args.log_path, "ckpt.pdl"))
else:
states = paddle.load(
os.path.join(self.args.log_path,
f"ckpt_{self.config.sampling.ckpt_id}.pdl"))
model = model
model = paddle.DataParallel(model)
model.set_state_dict({
k.split("$model_")[-1]: v
for k, v in states.items() if "$model_" in k
})
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
ema_helper.set_state_dict({
k.split("$ema_")[-1]: v
for k, v in states.items() if "$ema_" in k
})
ema_helper.ema(model)
else:
ema_helper = None
else:
# This used the pretrained DDPM model, see https://github.com/pesser/pytorch_diffusion
if self.config.data.dataset == "CIFAR10":
name = "cifar10"
elif self.config.data.dataset == "LSUN":
name = f"lsun_{self.config.data.category}"
else:
raise ValueError
ckpt = get_ckpt_path(f"ema_{name}")
print("Loading checkpoint {}".format(ckpt))
model.set_state_dict(paddle.load(ckpt))
model = paddle.DataParallel(model)
model.eval()
if self.args.fid:
self.sample_fid(model)
elif self.args.interpolation:
self.sample_interpolation(model)
elif self.args.sequence:
self.sample_sequence(model)
else:
raise NotImplementedError("Sample procedeure not defined")
def _load_states(self, score):
if self.config.sampling.ckpt_id is None:
path = os.path.join(self.args.log_path, 'checkpoint.pth')
else:
path = os.path.join(self.args.log_path, f'checkpoint_{self.config.sampling.ckpt_id}.pth')
states = torch.load(path, map_location=self.args.device)
# score.load_state_dict(states[0], strict=True)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(score)
else:
score.load_state_dict(states[0])
del states
return score
def sample(self):
D = DensityRatioEstNet(self.config.model.ngf_d, self.config.data.image_size, self.config.data.channels).to(self.device)
D.load_state_dict(torch.load(os.path.join(self.args.log_path, f"ckpt_DRE_{self.args.sigma_sq}_{self.args.tau}.pth"))['D'])
S = Model(self.config)
if getattr(self.config.sampling, "ckpt_id", None) is None:
states = torch.load(
os.path.join(self.args.log_path, "ckpt.pth"),
map_location=self.config.device,
)
else:
states = torch.load(
os.path.join(
self.args.log_path, f"ckpt_{self.config.sampling.ckpt_id}.pth"
),
map_location=self.config.device,
)
S = S.to(self.device)
S = torch.nn.DataParallel(S)
S.load_state_dict(states[0], strict=True)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(S)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(S)
else:
ema_helper = None
S.eval()
if self.args.fid:
self.sample_fid(S, D)
elif self.args.interpolation:
self.sample_interpolation(S)
elif self.args.inpainting:
self.sample_inpainting(S)
elif self.args.sbp:
self.sample_sbp(S, D)
else:
raise NotImplementedError("Sample procedeure not defined")
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):
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 fast_fid(self):
### Test the fids of ensembled checkpoints.
### Shouldn't be used for models with ema
if self.config.fast_fid.ensemble:
if self.config.model.ema:
raise RuntimeError("Cannot apply ensembling to models with EMA.")
self.fast_ensemble_fid()
return
from evaluation.fid_score import get_fid, get_fid_stats_path
import pickle
score = get_model(self.config)
score = torch.nn.DataParallel(score)
sigmas_th = get_sigmas(self.config)
sigmas = sigmas_th.cpu().numpy()
fids = {}
for ckpt in tqdm.tqdm(range(self.config.fast_fid.begin_ckpt, self.config.fast_fid.end_ckpt + 1, 5000),
desc="processing ckpt"):
states = torch.load(os.path.join(self.args.log_path, f'checkpoint_{ckpt}.pth'),
map_location=self.config.device)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(score)
else:
score.load_state_dict(states[0])
score.eval()
num_iters = self.config.fast_fid.num_samples // self.config.fast_fid.batch_size
output_path = os.path.join(self.args.image_folder, 'ckpt_{}'.format(ckpt))
os.makedirs(output_path, exist_ok=True)
for i in range(num_iters):
init_samples = torch.rand(self.config.fast_fid.batch_size, 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, score, sigmas,
self.config.fast_fid.n_steps_each,
self.config.fast_fid.step_lr,
verbose=self.config.fast_fid.verbose,
denoise=self.config.sampling.denoise)
final_samples = all_samples[-1]
for id, sample in enumerate(final_samples):
sample = sample.view(self.config.data.channels,
self.config.data.image_size,
self.config.data.image_size)
sample = inverse_data_transform(self.config, sample)
save_image(sample, os.path.join(output_path, 'sample_{}.png'.format(id)))
stat_path = get_fid_stats_path(self.args, self.config, download=True)
fid = get_fid(stat_path, output_path)
fids[ckpt] = fid
print("ckpt: {}, fid: {}".format(ckpt, fid))
with open(os.path.join(self.args.image_folder, 'fids.pickle'), 'wb') as handle:
pickle.dump(fids, handle, protocol=pickle.HIGHEST_PROTOCOL)
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 sample(self):
if self.config.sampling.ckpt_id is None:
states = torch.load(os.path.join(self.args.log_path, 'checkpoint.pth'), map_location=self.config.device)
else:
states = torch.load(os.path.join(self.args.log_path, f'checkpoint_{self.config.sampling.ckpt_id}.pth'),
map_location=self.config.device)
score = get_model(self.config)
score = torch.nn.DataParallel(score)
score.load_state_dict(states[0], strict=True)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(score)
sigmas_th = get_sigmas(self.config)
sigmas = sigmas_th.cpu().numpy()
dataset, _ = get_dataset(self.args, self.config)
dataloader = DataLoader(dataset, batch_size=self.config.sampling.batch_size, shuffle=True,
num_workers=4)
score.eval()
if not self.config.sampling.fid:
if self.config.sampling.inpainting:
data_iter = iter(dataloader)
refer_images, _ = next(data_iter)
refer_images = refer_images.to(self.config.device)
width = int(np.sqrt(self.config.sampling.batch_size))
init_samples = torch.rand(width, width, 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_inpainting(init_samples, refer_images[:width, ...], score,
sigmas,
self.config.data.image_size,
self.config.sampling.n_steps_each,
self.config.sampling.step_lr)
torch.save(refer_images[:width, ...], os.path.join(self.args.image_folder, 'refer_image.pth'))
refer_images = refer_images[:width, None, ...].expand(-1, width, -1, -1, -1).reshape(-1,
*refer_images.shape[
1:])
save_image(refer_images, os.path.join(self.args.image_folder, 'refer_image.png'), nrow=width)
if not self.config.sampling.final_only:
for i, sample in enumerate(tqdm.tqdm(all_samples)):
sample = sample.view(self.config.sampling.batch_size, 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, int(np.sqrt(self.config.sampling.batch_size)))
save_image(image_grid, os.path.join(self.args.image_folder, 'image_grid_{}.png'.format(i)))
torch.save(sample, os.path.join(self.args.image_folder, 'completion_{}.pth'.format(i)))
else:
sample = all_samples[-1].view(self.config.sampling.batch_size, 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, int(np.sqrt(self.config.sampling.batch_size)))
save_image(image_grid, os.path.join(self.args.image_folder,
'image_grid_{}.png'.format(self.config.sampling.ckpt_id)))
torch.save(sample, os.path.join(self.args.image_folder,
'completion_{}.pth'.format(self.config.sampling.ckpt_id)))
elif self.config.sampling.interpolation:
if self.config.sampling.data_init:
data_iter = iter(dataloader)
samples, _ = next(data_iter)
samples = samples.to(self.config.device)
samples = data_transform(self.config, samples)
init_samples = samples + sigmas_th[0] * torch.randn_like(samples)
else:
init_samples = torch.rand(self.config.sampling.batch_size, 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_interpolation(init_samples, score, sigmas,
self.config.sampling.n_interpolations,
self.config.sampling.n_steps_each,
self.config.sampling.step_lr, verbose=True,
final_only=self.config.sampling.final_only)
if not self.config.sampling.final_only:
for i, sample in tqdm.tqdm(enumerate(all_samples), total=len(all_samples),
desc="saving image samples"):
sample = sample.view(sample.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, nrow=self.config.sampling.n_interpolations)
save_image(image_grid, os.path.join(self.args.image_folder, 'image_grid_{}.png'.format(i)))
torch.save(sample, os.path.join(self.args.image_folder, 'samples_{}.pth'.format(i)))
else:
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, self.config.sampling.n_interpolations)
save_image(image_grid, os.path.join(self.args.image_folder,
'image_grid_{}.png'.format(self.config.sampling.ckpt_id)))
torch.save(sample, os.path.join(self.args.image_folder,
'samples_{}.pth'.format(self.config.sampling.ckpt_id)))
else:
if self.config.sampling.data_init:
data_iter = iter(dataloader)
samples, _ = next(data_iter)
samples = samples.to(self.config.device)
samples = data_transform(self.config, samples)
init_samples = samples + sigmas_th[0] * torch.randn_like(samples)
else:
init_samples = torch.rand(self.config.sampling.batch_size, 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, score, sigmas,
self.config.sampling.n_steps_each,
self.config.sampling.step_lr, verbose=True,
final_only=self.config.sampling.final_only,
denoise=self.config.sampling.denoise)
if not self.config.sampling.final_only:
for i, sample in tqdm.tqdm(enumerate(all_samples), total=len(all_samples),
desc="saving image samples"):
sample = sample.view(sample.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, int(np.sqrt(self.config.sampling.batch_size)))
save_image(image_grid, os.path.join(self.args.image_folder, 'image_grid_{}.png'.format(i)))
torch.save(sample, os.path.join(self.args.image_folder, 'samples_{}.pth'.format(i)))
else:
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, int(np.sqrt(self.config.sampling.batch_size)))
save_image(image_grid, os.path.join(self.args.image_folder,
'image_grid_{}.png'.format(self.config.sampling.ckpt_id)))
torch.save(sample, os.path.join(self.args.image_folder,
'samples_{}.pth'.format(self.config.sampling.ckpt_id)))
else:
total_n_samples = self.config.sampling.num_samples4fid
n_rounds = total_n_samples // self.config.sampling.batch_size
if self.config.sampling.data_init:
dataloader = DataLoader(dataset, batch_size=self.config.sampling.batch_size, shuffle=True,
num_workers=4)
data_iter = iter(dataloader)
img_id = 0
for _ in tqdm.tqdm(range(n_rounds), desc='Generating image samples for FID/inception score evaluation'):
if self.config.sampling.data_init:
try:
samples, _ = next(data_iter)
except StopIteration:
data_iter = iter(dataloader)
samples, _ = next(data_iter)
samples = samples.to(self.config.device)
samples = data_transform(self.config, samples)
samples = samples + sigmas_th[0] * torch.randn_like(samples)
else:
samples = torch.rand(self.config.sampling.batch_size, self.config.data.channels,
self.config.data.image_size,
self.config.data.image_size, device=self.config.device)
samples = data_transform(self.config, samples)
all_samples = anneal_Langevin_dynamics(samples, score, sigmas,
self.config.sampling.n_steps_each,
self.config.sampling.step_lr, verbose=False,
denoise=self.config.sampling.denoise)
samples = all_samples[-1]
for img in samples:
img = inverse_data_transform(self.config, img)
save_image(img, os.path.join(self.args.image_folder, 'image_{}.png'.format(img_id)))
img_id += 1
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()
def calculate_fid(self):
import fid, pickle
import tensorflow as tf
stats_path = "fid_stats_cifar10_train.npz" # training set statistics
inception_path = fid.check_or_download_inception(
"./tmp/"
) # download inception network
score = get_model(self.config)
score = torch.nn.DataParallel(score)
sigmas_th = get_sigmas(self.config)
sigmas = sigmas_th.cpu().numpy()
fids = {}
for ckpt in tqdm.tqdm(
range(
self.config.fast_fid.begin_ckpt, self.config.fast_fid.end_ckpt + 1, 5000
),
desc="processing ckpt",
):
states = torch.load(
os.path.join(self.args.log_path, f"checkpoint_{ckpt}.pth"),
map_location=self.config.device,
)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(score)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(score)
else:
score.load_state_dict(states[0])
score.eval()
num_iters = (
self.config.fast_fid.num_samples // self.config.fast_fid.batch_size
)
output_path = os.path.join(self.args.image_folder, "ckpt_{}".format(ckpt))
os.makedirs(output_path, exist_ok=True)
for i in range(num_iters):
init_samples = torch.rand(
self.config.fast_fid.batch_size,
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,
score,
sigmas,
self.config.fast_fid.n_steps_each,
self.config.fast_fid.step_lr,
verbose=self.config.fast_fid.verbose,
)
final_samples = all_samples[-1]
for id, sample in enumerate(final_samples):
sample = sample.view(
self.config.data.channels,
self.config.data.image_size,
self.config.data.image_size,
)
sample = inverse_data_transform(self.config, sample)
save_image(
sample, os.path.join(output_path, "sample_{}.png".format(id))
)
# load precalculated training set statistics
f = np.load(stats_path)
mu_real, sigma_real = f["mu"][:], f["sigma"][:]
f.close()
fid.create_inception_graph(
inception_path
) # load the graph into the current TF graph
final_samples = (
(final_samples - final_samples.min())
/ (final_samples.max() - final_samples.min()).data.cpu().numpy()
* 255
)
final_samples = np.transpose(final_samples, [0, 2, 3, 1])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
mu_gen, sigma_gen = fid.calculate_activation_statistics(
final_samples, sess, batch_size=100
)
fid_value = fid.calculate_frechet_distance(
mu_gen, sigma_gen, mu_real, sigma_real
)
print("FID: %s" % fid_value)
with open(os.path.join(self.args.image_folder, "fids.pickle"), "wb") as handle:
pickle.dump(fids, handle, protocol=pickle.HIGHEST_PROTOCOL)