import random
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
from torch import autograd
from numpy import linalg
from math import sin, cos, sqrt
from scipy.stats import truncnorm
import torch
import torch.nn.functional as F
import torch.distributions.multivariate_normal as MN
import numpy as np
import utils.ops as ops
import utils.losses as losses
try:
import utils.misc as misc
except AttributeError:
pass
def truncated_normal(size, threshold=1.):
values = truncnorm.rvs(-threshold, threshold, size=size)
return values
def sample_normal(batch_size, z_dim, truncation_factor, device):
if truncation_factor == -1.0:
latents = torch.randn(batch_size, z_dim, device=device)
elif truncation_factor > 0:
latents = torch.FloatTensor(truncated_normal([batch_size, z_dim], truncation_factor)).to(device)
else:
raise ValueError("truncated_factor must be positive.")
return latents
def sample_y(y_sampler, batch_size, num_classes, device):
if y_sampler == "totally_random":
y_fake = torch.randint(low=0, high=num_classes, size=(batch_size, ), dtype=torch.long, device=device)
elif y_sampler == "acending_some":
assert batch_size % 8 == 0, "The size of batches should be a multiple of 8."
num_classes_plot = batch_size // 8
indices = np.random.permutation(num_classes)[:num_classes_plot]
elif y_sampler == "acending_all":
batch_size = num_classes * 8
indices = [c for c in range(num_classes)]
elif isinstance(y_sampler, int):
y_fake = torch.tensor([y_sampler] * batch_size, dtype=torch.long).to(device)
else:
y_fake = None
if y_sampler in ["acending_some", "acending_all"]:
y_fake = []
for idx in indices:
y_fake += [idx] * 8
y_fake = torch.tensor(y_fake, dtype=torch.long).to(device)
return y_fake
def sample_zy(z_prior, batch_size, z_dim, num_classes, truncation_factor, y_sampler, radius, device):
fake_labels = sample_y(y_sampler=y_sampler, batch_size=batch_size, num_classes=num_classes, device=device)
batch_size = fake_labels.shape[0]
if z_prior == "gaussian":
zs = sample_normal(batch_size=batch_size, z_dim=z_dim, truncation_factor=truncation_factor, device=device)
elif z_prior == "uniform":
zs = torch.FloatTensor(batch_size, z_dim).uniform_(-1.0, 1.0).to(device)
else:
raise NotImplementedError
if isinstance(radius, float) and radius > 0.0:
if z_prior == "gaussian":
zs_eps = zs + radius * sample_normal(batch_size, z_dim, -1.0, device)
elif z_prior == "uniform":
zs_eps = zs + radius * torch.FloatTensor(batch_size, z_dim).uniform_(-1.0, 1.0).to(device)
else:
zs_eps = None
return zs, fake_labels, zs_eps
def generate_images(z_prior, truncation_factor, batch_size, z_dim, num_classes, y_sampler, radius, generator, discriminator,
is_train, LOSS, RUN, MODEL, device, is_stylegan, generator_mapping, generator_synthesis, style_mixing_p,
stylegan_update_emas, cal_trsp_cost):
if is_train:
truncation_factor = -1.0
lo_steps = LOSS.lo_steps4train
apply_langevin = False
else:
lo_steps = LOSS.lo_steps4eval
if truncation_factor != -1:
if is_stylegan:
assert 0 <= truncation_factor <= 1, "Stylegan truncation_factor must lie btw 0(strong truncation) ~ 1(no truncation)"
else:
assert 0 <= truncation_factor, "truncation_factor must lie btw 0(strong truncation) ~ inf(no truncation)"
zs, fake_labels, zs_eps = sample_zy(z_prior=z_prior,
batch_size=batch_size,
z_dim=z_dim,
num_classes=num_classes,
truncation_factor=-1 if is_stylegan else truncation_factor,
y_sampler=y_sampler,
radius=radius,
device=device)
batch_size = fake_labels.shape[0]
info_discrete_c, info_conti_c = None, None
if MODEL.info_type in ["discrete", "both"]:
info_discrete_c = torch.randint(MODEL.info_dim_discrete_c,(batch_size, MODEL.info_num_discrete_c), device=device)
zs = torch.cat((zs, F.one_hot(info_discrete_c, MODEL.info_dim_discrete_c).view(batch_size, -1)), dim=1)
if MODEL.info_type in ["continuous", "both"]:
info_conti_c = torch.rand(batch_size, MODEL.info_num_conti_c, device=device) * 2 - 1
zs = torch.cat((zs, info_conti_c), dim=1)
trsp_cost = None
if LOSS.apply_lo:
zs, trsp_cost = losses.latent_optimise(zs=zs,
fake_labels=fake_labels,
generator=generator,
discriminator=discriminator,
batch_size=batch_size,
lo_rate=LOSS.lo_rate,
lo_steps=lo_steps,
lo_alpha=LOSS.lo_alpha,
lo_beta=LOSS.lo_beta,
eval=not is_train,
cal_trsp_cost=cal_trsp_cost,
device=device)
if not is_train and RUN.langevin_sampling:
zs = langevin_sampling(zs=zs,
z_dim=z_dim,
fake_labels=fake_labels,
generator=generator,
discriminator=discriminator,
batch_size=batch_size,
langevin_rate=RUN.langevin_rate,
langevin_noise_std=RUN.langevin_noise_std,
langevin_decay=RUN.langevin_decay,
langevin_decay_steps=RUN.langevin_decay_steps,
langevin_steps=RUN.langevin_steps,
device=device)
if is_stylegan:
ws, fake_images = stylegan_generate_images(zs=zs,
fake_labels=fake_labels,
num_classes=num_classes,
style_mixing_p=style_mixing_p,
update_emas=stylegan_update_emas,
generator_mapping=generator_mapping,
generator_synthesis=generator_synthesis,
truncation_psi=truncation_factor,
truncation_cutoff=RUN.truncation_cutoff)
else:
fake_images = generator(zs, fake_labels, eval=not is_train)
ws = None
if zs_eps is not None:
if is_stylegan:
ws_eps, fake_images_eps = stylegan_generate_images(zs=zs_eps,
fake_labels=fake_labels,
num_classes=num_classes,
style_mixing_p=style_mixing_p,
update_emas=stylegan_update_emas,
generator_mapping=generator_mapping,
generator_synthesis=generator_synthesis,
truncation_psi=truncation_factor,
truncation_cutoff=RUN.truncation_cutoff)
else:
fake_images_eps = generator(zs_eps, fake_labels, eval=not is_train)
else:
fake_images_eps = None
return fake_images, fake_labels, fake_images_eps, trsp_cost, ws, info_discrete_c, info_conti_c
def stylegan_generate_images(zs, fake_labels, num_classes, style_mixing_p, update_emas,
generator_mapping, generator_synthesis, truncation_psi, truncation_cutoff):
one_hot_fake_labels = F.one_hot(fake_labels, num_classes=num_classes)
if truncation_psi == -1:
ws = generator_mapping(zs, one_hot_fake_labels, truncation_psi=1, update_emas=update_emas)
else:
ws = generator_mapping(zs, one_hot_fake_labels, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
if style_mixing_p > 0:
cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1])
cutoff = torch.where(torch.rand([], device=ws.device) < style_mixing_p, cutoff, torch.full_like(cutoff, ws.shape[1]))
ws[:, cutoff:] = generator_mapping(torch.randn_like(zs), one_hot_fake_labels, update_emas=False)[:, cutoff:]
fake_images = generator_synthesis(ws, update_emas=update_emas)
return ws, fake_images
def langevin_sampling(zs, z_dim, fake_labels, generator, discriminator, batch_size, langevin_rate, langevin_noise_std,
langevin_decay, langevin_decay_steps, langevin_steps, device):
scaler = 1.0
apply_decay = langevin_decay > 0 and langevin_decay_steps > 0
mean = torch.zeros(z_dim, device=device)
prior_std = torch.eye(z_dim, device=device)
lgv_std = prior_std * langevin_noise_std
prior = MN.MultivariateNormal(loc=mean, covariance_matrix=prior_std)
lgv_prior = MN.MultivariateNormal(loc=mean, covariance_matrix=lgv_std)
for i in range(langevin_steps):
zs = autograd.Variable(zs, requires_grad=True)
fake_images = generator(zs, fake_labels, eval=True)
fake_dict = discriminator(fake_images, fake_labels, eval=True)
energy = -prior.log_prob(zs) - fake_dict["adv_output"]
z_grads = losses.cal_deriv(inputs=zs, outputs=energy, device=device)
zs = zs - 0.5 * langevin_rate * z_grads + (langevin_rate**0.5) * lgv_prior.sample([batch_size]) * scaler
if apply_decay and (i + 1) % langevin_decay_steps == 0:
langevin_rate *= langevin_decay
scaler *= langevin_decay
return zs
def sample_onehot(batch_size, num_classes, device="cuda"):
return torch.randint(low=0,
high=num_classes,
size=(batch_size, ),
device=device,
dtype=torch.int64,
requires_grad=False)
def make_mask(labels, num_classes, mask_negatives, device):
labels = labels.detach().cpu().numpy()
n_samples = labels.shape[0]
if mask_negatives:
mask_multi, target = np.zeros([num_classes, n_samples]), 1.0
else:
mask_multi, target = np.ones([num_classes, n_samples]), 0.0
for c in range(num_classes):
c_indices = np.where(labels == c)
mask_multi[c, c_indices] = target
return torch.tensor(mask_multi).type(torch.long).to(device)
def make_target_cls_sampler(dataset, target_class):
try:
targets = dataset.data.targets
except:
targets = dataset.labels
label_indices = []
for i in range(len(dataset)):
if targets[i] == target_class:
label_indices.append(i)
num_samples = len(label_indices)
sampler = torch.utils.data.sampler.SubsetRandomSampler(label_indices)
return num_samples, sampler
