models/draggan/gan_inv/inversion.py

import math
import os
from draggan.viz import renderer
import torch
from torch import optim
from torch.nn import functional as F
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import dataclasses
import draggan.dnnlib as dnnlib
from .lpips import util


def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
    lr_ramp = min(1, (1 - t) / rampdown)
    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
    lr_ramp = lr_ramp * min(1, t / rampup)

    return initial_lr * lr_ramp


def make_image(tensor):
    return (
        tensor.detach()
        .clamp_(min=-1, max=1)
        .add(1)
        .div_(2)
        .mul(255)
        .type(torch.uint8)
        .permute(0, 2, 3, 1)
        .to("cpu")
        .numpy()
    )


@dataclasses.dataclass
class InverseConfig:
    lr_warmup = 0.05
    lr_decay = 0.25
    lr = 0.1
    noise = 0.05
    noise_decay = 0.75
    # step = 1000
    step = 1000
    noise_regularize = 1e5
    mse = 0.1



def inverse_image(
    g_ema,
    image,
    percept,
    image_size=256,
    w_plus = False,
    config=InverseConfig(),
    device='cuda:0'
):
    args = config

    n_mean_latent = 10000

    resize = min(image_size, 256)

    if torch.is_tensor(image)==False:
        transform = transforms.Compose(
            [
                transforms.Resize(resize,),
                transforms.CenterCrop(resize),
                transforms.ToTensor(),
                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
            ]
        )

        img = transform(image)

    else:
        img = transforms.functional.resize(image,resize)
        transform = transforms.Compose(
            [
                transforms.CenterCrop(resize),
                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
            ]
        )
        img = transform(img)
    imgs = []
    imgs.append(img)
    imgs = torch.stack(imgs, 0).to(device)

    with torch.no_grad():

        #noise_sample = torch.randn(n_mean_latent, 512, device=device)
        noise_sample = torch.randn(n_mean_latent, g_ema.z_dim, device=device)
        #label = torch.zeros([n_mean_latent,g_ema.c_dim],device = device)
        w_samples = g_ema.mapping(noise_sample,None)
        w_samples = w_samples[:, :1, :]
        w_avg = w_samples.mean(0)
        w_std = ((w_samples - w_avg).pow(2).sum() / n_mean_latent) ** 0.5




    noises = {name: buf for (name, buf) in g_ema.synthesis.named_buffers() if 'noise_const' in name}
    for noise in noises.values():
        noise = torch.randn_like(noise)
        noise.requires_grad = True



    w_opt = w_avg.detach().clone()
    if w_plus:
        w_opt = w_opt.repeat(1,g_ema.mapping.num_ws, 1)
    w_opt.requires_grad = True
    #if args.w_plus:
        #latent_in = latent_in.unsqueeze(1).repeat(1, g_ema.n_latent, 1)



    optimizer = optim.Adam([w_opt] + list(noises.values()), lr=args.lr)

    pbar = tqdm(range(args.step))
    latent_path = []

    for i in pbar:
        t = i / args.step
        lr = get_lr(t, args.lr)
        optimizer.param_groups[0]["lr"] = lr
        noise_strength = w_std * args.noise * max(0, 1 - t / args.noise_decay) ** 2

        w_noise = torch.randn_like(w_opt) * noise_strength
        if w_plus:
            ws = w_opt + w_noise
        else:
            ws = (w_opt + w_noise).repeat([1, g_ema.mapping.num_ws, 1])

        img_gen = g_ema.synthesis(ws, noise_mode='const', force_fp32=True)

        #latent_n = latent_noise(latent_in, noise_strength.item())

        #latent, noise = g_ema.prepare([latent_n], input_is_latent=True, noise=noises)
        #img_gen, F = g_ema.generate(latent, noise)

        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
        
        if img_gen.shape[2] > 256:
            img_gen = F.interpolate(img_gen, size=(256, 256), mode='area')

        p_loss = percept(img_gen,imgs)


        # Noise regularization.
        reg_loss = 0.0
        for v in noises.values():
            noise = v[None, None, :, :]  # must be [1,1,H,W] for F.avg_pool2d()
            while True:
                reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2
                reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2
                if noise.shape[2] <= 8:
                    break
                noise = F.avg_pool2d(noise, kernel_size=2)
        mse_loss = F.mse_loss(img_gen, imgs)

        loss = p_loss + args.noise_regularize * reg_loss + args.mse * mse_loss

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Normalize noise.
        with torch.no_grad():
            for buf in noises.values():
                buf -= buf.mean()
                buf *= buf.square().mean().rsqrt()

        if (i + 1) % 100 == 0:
            latent_path.append(w_opt.detach().clone())

        pbar.set_description(
            (
                f"perceptual: {p_loss.item():.4f}; noise regularize: {reg_loss:.4f};"
                f" mse: {mse_loss.item():.4f}; lr: {lr:.4f}"
            )
        )

    #latent, noise = g_ema.prepare([latent_path[-1]], input_is_latent=True, noise=noises)
    #img_gen, F = g_ema.generate(latent, noise)
    if w_plus:
        ws = latent_path[-1]
    else:
        ws = latent_path[-1].repeat([1, g_ema.mapping.num_ws, 1])

    img_gen = g_ema.synthesis(ws, noise_mode='const')


    result = {
        "latent": latent_path[-1],
        "sample": img_gen,
        "real": imgs,
    }

    return result

def toogle_grad(model, flag=True):
    for p in model.parameters():
        p.requires_grad = flag


class PTI:
    def __init__(self,G, percept, l2_lambda = 1,max_pti_step = 400, pti_lr = 3e-4 ):
        self.g_ema = G
        self.l2_lambda = l2_lambda
        self.max_pti_step = max_pti_step
        self.pti_lr = pti_lr
        self.percept = percept
    def cacl_loss(self,percept, generated_image,real_image):

        mse_loss = F.mse_loss(generated_image, real_image)
        p_loss = percept(generated_image, real_image).sum()
        loss = p_loss +self.l2_lambda * mse_loss
        return loss

    def train(self,img,w_plus=False):
        if not torch.cuda.is_available():
            device = 'cpu'
        else:
            device = 'cuda'
        if torch.is_tensor(img) == False:
            transform = transforms.Compose(
                [
                    transforms.Resize(self.g_ema.img_resolution, ),
                    transforms.CenterCrop(self.g_ema.img_resolution),
                    transforms.ToTensor(),
                    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
                ]
            )

            real_img = transform(img).to(device).unsqueeze(0)

        else:
            img = transforms.functional.resize(img, self.g_ema.img_resolution)
            transform = transforms.Compose(
                [
                    transforms.CenterCrop(self.g_ema.img_resolution),
                    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
                ]
            )
            real_img = transform(img).to(device).unsqueeze(0)
        inversed_result = inverse_image(self.g_ema,img,self.percept,self.g_ema.img_resolution,w_plus,device=device)
        w_pivot = inversed_result['latent']
        if w_plus:
            ws = w_pivot
        else:
            ws = w_pivot.repeat([1, self.g_ema.mapping.num_ws, 1])
        toogle_grad(self.g_ema,True)
        optimizer = torch.optim.Adam(self.g_ema.parameters(), lr=self.pti_lr)
        print('start PTI')
        pbar = tqdm(range(self.max_pti_step))
        for i in pbar:
            t = i / self.max_pti_step
            lr = get_lr(t, self.pti_lr)
            optimizer.param_groups[0]["lr"] = lr

            generated_image = self.g_ema.synthesis(ws,noise_mode='const')
            loss = self.cacl_loss(self.percept,generated_image,real_img)
            pbar.set_description(
                (
                    f"loss: {loss.item():.4f}"
                )
            )
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        with torch.no_grad():
            generated_image = self.g_ema.synthesis(ws, noise_mode='const')

        return generated_image,ws