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import math
from argparse import (
ArgumentParser,
Namespace,
)
from typing import (
Dict,
Iterable,
Optional,
Tuple,
)
import numpy as np
from tqdm import tqdm
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
from torchvision.transforms import Resize
#from optim import get_optimizer_class, OPTIMIZER_MAP
from losses.regularize_noise import NoiseRegularizer
from optim import RAdam
from utils.misc import (
iterable_to_str,
optional_string,
)
class OptimizerArguments:
@staticmethod
def add_arguments(parser: ArgumentParser):
parser.add_argument('--coarse_min', type=int, default=32)
parser.add_argument('--wplus_step', type=int, nargs="+", default=[250, 750], help="#step for optimizing w_plus")
#parser.add_argument('--lr_rampup', type=float, default=0.05)
#parser.add_argument('--lr_rampdown', type=float, default=0.25)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--noise_strength', type=float, default=.0)
parser.add_argument('--noise_ramp', type=float, default=0.75)
#parser.add_argument('--optimize_noise', action="store_true")
parser.add_argument('--camera_lr', type=float, default=0.01)
parser.add_argument("--log_dir", default="log/projector", help="tensorboard log directory")
parser.add_argument("--log_freq", type=int, default=10, help="log frequency")
parser.add_argument("--log_visual_freq", type=int, default=50, help="log frequency")
@staticmethod
def to_string(args: Namespace) -> str:
return (
f"lr{args.lr}_{args.camera_lr}-c{args.coarse_min}"
+ f"-wp({iterable_to_str(args.wplus_step)})"
+ optional_string(args.noise_strength, f"-n{args.noise_strength}")
)
class LatentNoiser(nn.Module):
def __init__(
self, generator: torch.nn,
noise_ramp: float = 0.75, noise_strength: float = 0.05,
n_mean_latent: int = 10000
):
super().__init__()
self.noise_ramp = noise_ramp
self.noise_strength = noise_strength
with torch.no_grad():
# TODO: get 512 from generator
noise_sample = torch.randn(n_mean_latent, 512, device=generator.device)
latent_out = generator.style(noise_sample)
latent_mean = latent_out.mean(0)
self.latent_std = ((latent_out - latent_mean).pow(2).sum() / n_mean_latent) ** 0.5
def forward(self, latent: torch.Tensor, t: float) -> torch.Tensor:
strength = self.latent_std * self.noise_strength * max(0, 1 - t / self.noise_ramp) ** 2
noise = torch.randn_like(latent) * strength
return latent + noise
class Optimizer:
@classmethod
def optimize(
cls,
generator: torch.nn,
criterion: torch.nn,
degrade: torch.nn,
target: torch.Tensor, # only used in writer since it's mostly baked in criterion
latent_init: torch.Tensor,
noise_init: torch.Tensor,
args: Namespace,
writer: Optional[SummaryWriter] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# do not optimize generator
generator = generator.eval()
target = target.detach()
# prepare parameters
noises = []
for n in noise_init:
noise = n.detach().clone()
noise.requires_grad = True
noises.append(noise)
def create_parameters(latent_coarse):
parameters = [
{'params': [latent_coarse], 'lr': args.lr},
{'params': noises, 'lr': args.lr},
{'params': degrade.parameters(), 'lr': args.camera_lr},
]
return parameters
device = target.device
# start optimize
total_steps = np.sum(args.wplus_step)
max_coarse_size = (2 ** (len(args.wplus_step) - 1)) * args.coarse_min
noiser = LatentNoiser(generator, noise_ramp=args.noise_ramp, noise_strength=args.noise_strength).to(device)
latent = latent_init.detach().clone()
for coarse_level, steps in enumerate(args.wplus_step):
if criterion.weights["contextual"] > 0:
with torch.no_grad():
# synthesize new sibling image using the current optimization results
# FIXME: update rgbs sibling
sibling, _, _ = generator([latent], input_is_latent=True, randomize_noise=True)
criterion.update_sibling(sibling)
coarse_size = (2 ** coarse_level) * args.coarse_min
latent_coarse, latent_fine = cls.split_latent(
latent, generator.get_latent_size(coarse_size))
parameters = create_parameters(latent_coarse)
optimizer = RAdam(parameters)
print(f"Optimizing {coarse_size}x{coarse_size}")
pbar = tqdm(range(steps))
for si in pbar:
latent = torch.cat((latent_coarse, latent_fine), dim=1)
niters = si + np.sum(args.wplus_step[:coarse_level])
latent_noisy = noiser(latent, niters / total_steps)
img_gen, _, rgbs = generator([latent_noisy], input_is_latent=True, noise=noises)
# TODO: use coarse_size instead of args.coarse_size for rgb_level
loss, losses = criterion(img_gen, degrade=degrade, noises=noises, rgbs=rgbs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
NoiseRegularizer.normalize(noises)
# log
pbar.set_description("; ".join([f"{k}: {v.item(): .3e}" for k, v in losses.items()]))
if writer is not None and niters % args.log_freq == 0:
cls.log_losses(writer, niters, loss, losses, criterion.weights)
cls.log_parameters(writer, niters, degrade.named_parameters())
if writer is not None and niters % args.log_visual_freq == 0:
cls.log_visuals(writer, niters, img_gen, target, degraded=degrade(img_gen), rgbs=rgbs)
latent = torch.cat((latent_coarse, latent_fine), dim=1).detach()
return latent, noises
@staticmethod
def split_latent(latent: torch.Tensor, coarse_latent_size: int):
latent_coarse = latent[:, :coarse_latent_size]
latent_coarse.requires_grad = True
latent_fine = latent[:, coarse_latent_size:]
latent_fine.requires_grad = False
return latent_coarse, latent_fine
@staticmethod
def log_losses(
writer: SummaryWriter,
niters: int,
loss_total: torch.Tensor,
losses: Dict[str, torch.Tensor],
weights: Optional[Dict[str, torch.Tensor]] = None
):
writer.add_scalar("loss", loss_total.item(), niters)
for name, loss in losses.items():
writer.add_scalar(name, loss.item(), niters)
if weights is not None:
writer.add_scalar(f"weighted_{name}", weights[name] * loss.item(), niters)
@staticmethod
def log_parameters(
writer: SummaryWriter,
niters: int,
named_parameters: Iterable[Tuple[str, torch.nn.Parameter]],
):
for name, para in named_parameters:
writer.add_scalar(name, para.item(), niters)
@classmethod
def log_visuals(
cls,
writer: SummaryWriter,
niters: int,
img: torch.Tensor,
target: torch.Tensor,
degraded=None,
rgbs=None,
):
if target.shape[-1] != img.shape[-1]:
visual = make_grid(img, nrow=1, normalize=True, range=(-1, 1))
writer.add_image("pred", visual, niters)
def resize(img):
return F.interpolate(img, size=target.shape[2:], mode="area")
vis = resize(img)
if degraded is not None:
vis = torch.cat((resize(degraded), vis), dim=-1)
visual = make_grid(torch.cat((target.repeat(1, vis.shape[1] // target.shape[1], 1, 1), vis), dim=-1), nrow=1, normalize=True, range=(-1, 1))
writer.add_image("gnd[-degraded]-pred", visual, niters)
# log to rgbs
if rgbs is not None:
cls.log_torgbs(writer, niters, rgbs)
@staticmethod
def log_torgbs(writer: SummaryWriter, niters: int, rgbs: Iterable[torch.Tensor], prefix: str = ""):
for ri, rgb in enumerate(rgbs):
scale = 2 ** (-(len(rgbs) - ri))
visual = make_grid(torch.cat((rgb, rgb / scale), dim=-1), nrow=1, normalize=True, range=(-1, 1))
writer.add_image(f"{prefix}to_rbg_{2 ** (ri + 2)}", visual, niters)
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