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run_color_offset_centroids_stage_inversion.py
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run_color_offset_centroids_stage_inversion.py
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"""Project given image to the latent space of pretrained network pickle."""
import copy
import os
import math
from time import perf_counter
import dill
import click
import imageio
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from tqdm import trange
import dnnlib
import legacy
from metrics import metric_utils
import timm
from training.diffaug import DiffAugment
from pg_modules.blocks import Interpolate
from torch_utils import misc
from torch.optim import Optimizer
from tensorboardX import SummaryWriter
import random
def get_morphed_w_code(new_w_code, fixed_w, regularizer_alpha=30):
interpolation_direction = new_w_code - fixed_w
interpolation_direction_norm = torch.norm(interpolation_direction, p=2)
direction_to_move = regularizer_alpha * interpolation_direction / interpolation_direction_norm
result_w = fixed_w + direction_to_move
return result_w
def space_regularizer_loss(
G_pti,
G_original,
w_batch,
vgg16,
num_of_sampled_latents=1,
lpips_lambda=10,
c=None,
):
z_samples = np.random.randn(num_of_sampled_latents, G_original.z_dim)
z_samples = torch.from_numpy(z_samples).to(w_batch.device)
if not G_original.c_dim:
c_samples = None
else:
if c is None:
c_samples = F.one_hot(torch.randint(G_original.c_dim, (num_of_sampled_latents,)), G_original.c_dim)
c_samples = c_samples.to(w_batch.device)
else:
c_samples = c[0].unsqueeze(0).repeat([num_of_sampled_latents, 1])
c_samples = c_samples.to(w_batch.device)
w_samples = G_original.mapping(z_samples, c_samples, truncation_psi=0.5)
territory_indicator_ws = [get_morphed_w_code(w_code.unsqueeze(0), w_batch) for w_code in w_samples]
for w_code in territory_indicator_ws:
new_img = G_pti.synthesis(w_code, noise_mode='none', force_fp32=True)
with torch.no_grad():
old_img = G_original.synthesis(w_code, noise_mode='none', force_fp32=True)
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
if new_img.shape[-1] > 256:
new_img = F.interpolate(new_img, size=(256, 256), mode='area')
old_img = F.interpolate(old_img, size=(256, 256), mode='area')
new_feat = vgg16(new_img, resize_images=False, return_lpips=True)
old_feat = vgg16(old_img, resize_images=False, return_lpips=True)
lpips_loss = lpips_lambda * (old_feat - new_feat).square().sum()
return lpips_loss / len(territory_indicator_ws)
def loss_geocross(latent, ws):
if(latent.shape[1] == 1):
return 0
else:
X = latent.view(-1, 1, ws, 512)
Y = latent.view(-1, ws, 1, 512)
A = ((X-Y).pow(2).sum(-1)+1e-9).sqrt()
B = ((X+Y).pow(2).sum(-1)+1e-9).sqrt()
D = 2*torch.atan2(A, B)
D = ((D.pow(2)*512).mean((1, 2))/8.).sum()
return D
def pivotal_tuning(
G, D,
w_pivot, w_offset,
target,
device: torch.device,
num_steps=350,
learning_rate = 3e-4,
noise_mode="const",
verbose = False, c=None
):
G_original = copy.deepcopy(G).eval().requires_grad_(False).to(device)
G_pti = copy.deepcopy(G).train().requires_grad_(True).to(device)
w_pivot.requires_grad_(False)
w_offset.requires_grad_(False)
# Load VGG16 feature detector.
vgg16_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
vgg16 = metric_utils.get_feature_detector(vgg16_url, device=device)
# l2 criterion
l2_criterion = torch.nn.MSELoss(reduction='mean')
# Features for target image.
target_images = target.to(device).to(torch.float32)
if target_images.shape[2] > 256:
target_images = F.interpolate(target_images, size=(256, 256), mode='area')
target_features = vgg16(target_images, resize_images=False, return_lpips=True)
# initalize optimizer
optimizer = torch.optim.Adam(G_pti.parameters(), lr=learning_rate)
# run optimization loop
all_images = []
ft_num = [8, 16, 24, 32, 38]
for step in range(num_steps):
# Synth images from opt_w.
synth_images = G_pti.synthesis(w_pivot+w_offset, noise_mode=noise_mode)
# track images
synth_images = (synth_images + 1) * (255/2)
synth_images_np = synth_images.clone().detach().permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
all_images.append(synth_images_np)
synth_images = pre_process(synth_images)
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
if synth_images.shape[2] > 256:
synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
# LPIPS loss
synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
lpips_loss = (target_features - synth_features).square().sum()
# MSE loss
mse_loss = l2_criterion(target_images, synth_images)
# space regularizer
reg_loss = space_regularizer_loss(G_pti, G_original, w_pivot+w_offset, vgg16, c=c)
# Step
optimizer.zero_grad(set_to_none=True)
loss = mse_loss *1 + lpips_loss + reg_loss
# loss = mse_loss + reg_loss
loss.backward()
optimizer.step()
writer.add_scalar('lpips', float(lpips_loss), step+1+1000)
writer.add_scalar('mse', float(mse_loss), step+1+1000)
writer.add_scalar('reg',float(reg_loss), step+1+1000)
msg = f'[ step {step+1:>4d}/{num_steps}] '
msg += f'[ loss: {float(loss):<5.2f}] '
msg += f'[ lpips: {float(lpips_loss):<5.2f}] '
msg += f'[ mse: {float(mse_loss):<5.2f}]'
msg += f'[ reg: {float(reg_loss):<5.2f}]'
if verbose: print(msg)
return all_images, G_pti
def project(
G, D, offset_reg, class_idx,
target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
*,
num_steps = 1000,
w_avg_samples = 10000,
initial_learning_rate = 0.1,
lr_rampdown_length = 0.25,
lr_rampup_length = 0.05,
verbose = False,
device: torch.device,
noise_mode="const",
centroids_path: str,
geo_reg=0,
center_idx=-1,
reg_w=0.5,
):
l2_criterion = torch.nn.MSELoss(reduction='mean')
G = copy.deepcopy(G).eval().requires_grad_(False).to(device) # type: ignore
# Compute w stats.
print(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
z_samples = torch.from_numpy(np.random.RandomState(123).randn(w_avg_samples, G.z_dim)).to(device)
# get class probas by classifier
if not G.c_dim:
c_samples = None
else:
if class_idx == -1:
classifier = timm.create_model('deit_base_distilled_patch16_224', pretrained=True).eval().to(device)
cls_target = F.interpolate((target.to(device).to(torch.float32) / 127.5 - 1)[None], 224)
logits = classifier(cls_target).softmax(1)
classes = torch.multinomial(logits, w_avg_samples, replacement=True).squeeze()
print(f'Main class: {logits.argmax(1).item()}, confidence: {logits.max().item():.4f}')
c_samples = np.zeros([w_avg_samples, G.c_dim], dtype=np.float32)
for i, c in enumerate(classes):
c_samples[i, c] = 1
c_samples = torch.from_numpy(c_samples).to(device)
else:
class_indices = torch.full((1,), class_idx).cuda()
c = F.one_hot(class_indices, G.c_dim)
c_samples = c.repeat([w_avg_samples, 1])
# print(c_samples.shape)
w_samples = G.mapping(z_samples, c_samples) # [N, L, C]
# get empirical w_avg
w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C]
w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
# Load VGG16 feature detector.
vgg16_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
vgg16 = metric_utils.get_feature_detector(vgg16_url, device=device)
# Features for target image.
target_images = target.to(device).to(torch.float32)
if target_images.shape[2] > 256:
target_images = F.interpolate(target_images, size=(256, 256), mode='area')
target_features = vgg16(target_images, resize_images=False, return_lpips=True)
if centroids_path != "":
with dnnlib.util.open_url(centroids_path, verbose=False) as f:
w_centroids = np.load(f)
center_w = torch.from_numpy(w_centroids['centers']).to(device)
center_w = center_w.unsqueeze(1).repeat([1, G.num_ws, 1]).to(device)
if center_idx != -1:
print(f"Using centroids id :{center_idx}")
w_avg = center_w[center_idx][0]
else:
center_images = G.synthesis(center_w)
center_images = (center_images + 1) * (255/2)
center_images = F.interpolate(center_images, size=(target_images.shape[-2],target_images.shape[-1]) , mode='bicubic')
center_features = vgg16(center_images, resize_images=False, return_lpips=True)
lpips_dis = (target_features - center_features).square().sum(1)
print(f"Using centroids id :{lpips_dis.argmin(0)}")
w_avg = center_w[lpips_dis.argmin(0)][0]
# initalize optimizer
w_opt = torch.tensor(w_avg, dtype=torch.float32) # pylint: disable=not-callable
w_opt = w_opt.repeat(1,G.num_ws,1).to(device)
w_offset = torch.zeros(w_opt.shape).to(device).requires_grad_(True)
# print(w_opt.shape)
optimizer = torch.optim.Adam([w_offset], betas=(0.9, 0.999), lr=initial_learning_rate)
# run optimization loop
all_images = []
for step in range(num_steps):
# Learning rate schedule.
t = step / num_steps
lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
lr = initial_learning_rate * lr_ramp
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Synth images from opt_w.
synth_images = G.synthesis(w_opt+w_offset, noise_mode=noise_mode)
# track images
synth_images = (synth_images + 1) * (255/2)
synth_images_np = synth_images.clone().detach().permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
all_images.append(synth_images_np)
synth_images = pre_process(synth_images)
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
if synth_images.shape[2] > 256:
synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
# Features for synth images.
synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
lpips_loss = (target_features - synth_features).square().sum()
l2_loss = l2_criterion(synth_images, target_images) * 10
reg = torch.norm(w_offset, 2)
geocross = loss_geocross(w_offset, G.num_ws)
# Step
optimizer.zero_grad(set_to_none=True)
loss = lpips_loss + l2_loss
if offset_reg:
loss += reg_w * reg
loss += geocross * geo_reg
loss.backward()
optimizer.step()
writer.add_scalar('lpips', float(lpips_loss), step+1)
writer.add_scalar('mse', float(l2_loss)/10, step+1)
if reg_w:
writer.add_scalar('reg',float(reg)/reg_w, step+1)
msg = f'[ step {step+1:>4d}/{num_steps}] '
msg += f'[ lpips_loss: {float(lpips_loss):<5.2f}] '
msg += f'[ l2_loss: {float(l2_loss):<5.4f}] '
msg += f'[ reg: {float(reg):<5.2f}] '
msg += f'[ geocross: {float(geocross):<5.2f}] '
msg += f'[ loss: {float(loss):<5.2f}] '
if verbose: print(msg)
if c_samples is not None:
return all_images, w_opt.detach(), w_offset.detach(), c_samples.detach()
else:
return all_images, w_opt.detach(), w_offset.detach(), None
@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--target', 'target_fname', help='Target image file to project to', required=True, metavar='FILE')
@click.option('--seed', help='Random seed', type=int, default=42, show_default=True)
@click.option('--save-video', help='Save an mp4 video of optimization progress', type=bool, default=True, show_default=True)
@click.option('--outdir', help='Where to save the output images', required=True, metavar='DIR')
@click.option('--inv-steps', help='Number of inversion steps', type=int, default=1000, show_default=True)
@click.option('--w-init', help='path to inital latent', type=str, default='', show_default=True)
@click.option('--run-pti', help='run pivotal tuning', is_flag=True)
@click.option('--pti-steps', help='Number of pti steps', type=int, default=350, show_default=True)
@click.option('--offset-reg', help='regularize offset', is_flag=True)
@click.option('--class-idx', help='class-idx of the image', type=int, default=-1, show_default=True)
@click.option('--centroids_path', help='centroids', type=str, default="", show_default=True)
@click.option('--geo-reg', help='geocross', type=float, default=0,)
@click.option('--center-idx', help='center-idx of the image', type=int, default=-1, show_default=True)
@click.option('--reg-w', help='w l2 regularization', type=float, default=0.5,)
@click.option('--images-only', help='save images only', is_flag=True)
@click.option('--reg-type', help='regularization type', type=str, default='l2')
@click.option('--wo-offset', help='save images only', is_flag=True)
def run_projection(
network_pkl: str,
target_fname: str,
outdir: str,
save_video: bool,
seed: int,
inv_steps: int,
w_init: str,
run_pti: bool,
pti_steps: int,
offset_reg: bool,
class_idx: int,
centroids_path: str,
geo_reg: float,
center_idx: int,
reg_w: float,
images_only: bool,
):
np.random.seed(seed)
torch.manual_seed(seed)
os.makedirs(outdir, exist_ok=True)
global writer
writer = SummaryWriter(outdir)
# Load networks.
print('Loading networks from "%s"...' % network_pkl)
device = torch.device('cuda')
D_kwargs = dnnlib.EasyDict(
class_name='pg_modules.discriminator.ProjectedDiscriminator',
backbones=['deit_base_distilled_patch16_224'],
diffaug=True,
interp224=False,
backbone_kwargs=dnnlib.EasyDict(),
)
D_kwargs.backbone_kwargs.cout = 64
D_kwargs.backbone_kwargs.expand = True
D_kwargs.backbone_kwargs.proj_type = 2
D_kwargs.backbone_kwargs.num_discs = 4
D_kwargs.backbone_kwargs.cond = False
common_kwargs = dict(c_dim=1000, img_resolution=512, img_channels=3)
D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device)
with dnnlib.util.open_url(network_pkl) as fp:
module = legacy.load_network_pkl(fp)
G = module['G_ema'].to(device) # type: ignore
# Load target image.
target_pil = PIL.Image.open(target_fname).convert('RGB')
w, h = target_pil.size
s = min(w, h)
target_pil = target_pil.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
target_pil = target_pil.resize((G.img_resolution, G.img_resolution), PIL.Image.LANCZOS)
target_uint8 = np.array(target_pil, dtype=np.uint8)
target_tensor = torch.tensor(target_uint8.transpose([2, 0, 1]), device=device).unsqueeze(0)
target_tensor = pre_process(target_tensor)
gray_image = target_tensor.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
PIL.Image.fromarray(gray_image, 'RGB').save(f'{outdir}/gray.png')
# Latent optimization
start_time = perf_counter()
all_images = []
if not w_init:
print('Running Latent Optimization...')
all_images, projected_w, offset_w, c = project(
G, D, offset_reg, class_idx,
target=target_tensor, # pylint: disable=not-callable
num_steps=inv_steps,
device=device,
verbose=True,
noise_mode='const',
centroids_path=centroids_path,
geo_reg=geo_reg,
center_idx=center_idx,
reg_w=reg_w
)
print(f'Elapsed time: {(perf_counter()-start_time):.1f} s')
else:
projected_w = torch.from_numpy(np.load(w_init)['w'])[0].to(device)
start_time = perf_counter()
# Run PTI
if run_pti:
print('Running Pivotal Tuning Inversion...')
gen_images, G = pivotal_tuning(
G, D,
projected_w, offset_w,
target=target_tensor,
device=device,
num_steps=pti_steps,
verbose=True, c=c
)
all_images += gen_images
print(f'Elapsed time: {(perf_counter()-start_time):.1f} s')
# Render debug output: optional video and projected image and W vector.
if save_video:
video = imageio.get_writer(f'{outdir}/proj.mp4', mode='I', fps=60, codec='libx264', bitrate='16M')
print (f'Saving optimization progress video "{outdir}/proj.mp4"')
for synth_image in all_images:
video.append_data(np.concatenate([target_uint8, synth_image], axis=1))
video.close()
# Save final projected frame and W vector.
target_pil.save(f'{outdir}/target.png')
synth_image = G.synthesis(projected_w+offset_w)
synth_image = (synth_image + 1) * (255/2)
synth_image = synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
PIL.Image.fromarray(synth_image, 'RGB').save(f'{outdir}/proj.png')
if not images_only:
# save latents
np.savez(f'{outdir}/projected_w.npz', w=projected_w.unsqueeze(0).cpu().numpy())
np.savez(f'{outdir}/offset_w.npz', w=offset_w.unsqueeze(0).cpu().numpy())
# Save Generator weights
snapshot_data = {'G': G, 'G_ema': G}
with open(f"{outdir}/G.pkl", 'wb') as f:
dill.dump(snapshot_data, f)
#----------------------------------------------------------------------------
def pre_process(image):
n, _, h, w = image.size()
r = image[:, 0, :, :]
g = image[:, 1, :, :]
b = image[:, 2, :, :]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
image = gray.view(n, 1, h, w).expand(n, 3, h, w)
return image
if __name__ == "__main__":
run_projection() # pylint: disable=no-value-for-parameter