test.py

import os 
import math
import numpy as np
import argparse

import torch
from torch import nn
from torchvision import utils

from invert import *
from exaggeration_model import StyleCariGAN 

@torch.no_grad()
def generate(
    generator, truncation, truncation_latent, inversion_file, styles, device
):
    if os.path.exists(os.path.splitext(inversion_file)[0]+'_style.pt'):
        indices = torch.load(os.path.splitext(inversion_file)[0]+'_style.pt')
    else:
        indices = range(styles.shape[0])
    inversion_file = torch.load(inversion_file)
    wp = inversion_file['wp'].to(device).unsqueeze(0)
    noise = [n.to(device) for n in inversion_file['noise']]
    os.makedirs(args.current_output_dir, exist_ok=True)

    phi = [1-args.exaggeration_factor] * 4
    for i in indices:
        img = generator(wp, [styles[i]], noise=noise, input_is_w_plus=True, \
                    truncation=truncation, truncation_latent=truncation_latent,\
                    mode='p2c', phi = phi)

        utils.save_image(
            img['result'],
            os.path.join(args.current_output_dir, f'{i}.png'),
            nrow=1,
            normalize=True,
            range=(-1, 1),
        )


if __name__ == "__main__":
    device = "cuda" if torch.cuda.is_available() else 'cpu'

    parser = argparse.ArgumentParser(description="Calculate FID scores")

    parser.add_argument("--truncation", type=float, default=1, help="truncation factor")
    parser.add_argument("--truncation_mean", type=int, default=4096, help="number of samples to calculate mean for truncation")
    parser.add_argument("--size", type=int, default=256, help="image sizes for generator")
    parser.add_argument('--ckpt', type=str, required=True, help='path to checkpoint')
    parser.add_argument('--input_dir', type=str, default='samples', help='directory with input inverted .pt files or input images to invert')
    parser.add_argument('--output_dir', type=str, default='results', help='directory to save generated caricatures')
    parser.add_argument('--predefined_style', type=str, default="style_palette/style_palette.npy", help='pre-selected style z-vector file')
    parser.add_argument('--exaggeration_factor', type=float, default=1.0, help='exaggeration factor, 0 to 1')
    parser.add_argument('--invert_images', action='store_true', help='invert images in sample folder to generate caricature from them')

    # used if args.invert_images is true
    parser.add_argument("--w_iterations", type=int, default=250)
    parser.add_argument("--wp_iterations", type=int, default=2000)
    parser.add_argument("--lambda_l2", type=float, default=1)
    parser.add_argument("--lambda_p", type=float, default=1)    
    parser.add_argument("--lambda_noise", type=float, default=1e5)
    parser.add_argument("--wlr", type=float, default=4e-3)
    parser.add_argument("--lr_decay_rate", type=float, default=0.2)
    parser.add_argument("--result_dir", type=str, default='./invert_result')
    parser.add_argument("--save", action='store_true')

    args = parser.parse_args()
    # constant
    args.latent = 512
    args.num_layers = 14
    args.n_mlp = 8
    args.channel_multiplier = 2

    styles = torch.from_numpy(np.load(args.predefined_style)).to(device) # shape N * 512
    styles = styles.unsqueeze(1)

    ckpt = torch.load(args.ckpt)
    g_ema = StyleCariGAN(
        args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
    ).to(device)
    checkpoint = torch.load(args.ckpt)
    g_ema.load_state_dict(ckpt['g_ema'], strict=False)
    del ckpt

    if args.truncation < 1:
        with torch.no_grad():
            mean_latent = g_ema.photo_generator.mean_latent(args.truncation_mean)
    else:
        mean_latent = None

    if args.invert_images:
        perceptual = perceptual_module().to(device)
        perceptual.eval()
        transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),])

        n=50000
        samples = 256
        w = []
        for _ in range(n//samples):
            sample_z = mixing_noise(samples, args.latent, 0, device=device)
            w.append(g_ema.photo_generator.style(sample_z))
        w = torch.cat(w, dim=0)
        args.mean_w = w.mean(dim=0)

        for fn in os.listdir(args.input_dir):
            if fn.endswith('.png') or fn.endswith('.jpg'):
                if os.path.exists(os.path.join(args.input_dir, fn.split('.')[0]+'.pt')):
                    continue
                args.result_dir = args.input_dir
                args.image_name = fn.split('.')[0]
                args.image = os.path.join(args.input_dir, fn)
                photo = transform(Image.open(args.image).convert('RGB')).unsqueeze(0).to(device)
                args.image_name = args.image.split('/')[-1].split('.')[0]
                invert(g_ema.photo_generator, perceptual, photo, device, args)

    os.makedirs(args.output_dir, exist_ok=True)
    for fn in os.listdir(args.input_dir):
        if fn.endswith('.png') or fn.endswith('.jpg'):
            args.image_name = fn.split('.')[0]
            inversion_file = os.path.join(args.input_dir, args.image_name+'.pt')
            args.current_output_dir = os.path.join(args.output_dir, args.image_name)
            generate(
                g_ema, args.truncation, mean_latent, inversion_file, styles, device
            )