# python3.7 """Functions to compute Jacobian based on pre-trained GAN generator. Support StyleGAN2 or StyleGAN3 """ import os import argparse import warnings from tqdm import tqdm import numpy as np import torch import torch.nn.functional as F from torch.autograd.functional import jacobian from models import build_model from utils.image_utils import save_image from utils.image_utils import postprocess_image from utils.custom_utils import to_numpy warnings.filterwarnings(action='ignore', category=UserWarning) def parse_args(): """Parses arguments.""" parser = argparse.ArgumentParser() group = parser.add_argument_group('General options.') group.add_argument('weight_path', type=str, help='Weight path to the pre-trained model.') group.add_argument('--save_dir', type=str, default=None, help='Directory to save the results. If not specified, ' 'the results will be saved to ' '`work_dirs/{TASK_SPECIFIC}/` by default.') group.add_argument('--job', type=str, default='jacobians', help='Name for the job (default: jacobians)') group.add_argument('--seed', type=int, default=4, help='Seed for sampling. (default: 4)') group.add_argument('--nums', type=int, default=5, help='Number of samples to synthesized. (default: 5)') group.add_argument('--img_size', type=int, default=1024, help='Size of the synthesized images. (default: 1024)') group.add_argument('--w_dim', type=int, default=512, help='Dimension of the latent w. (default: 512)') group.add_argument('--save_jpg', action='store_false', help='Whether to save the images used to compute ' 'jacobians. (default: True)') group.add_argument('-d', '--data_name', type=str, default='ffhq', help='Name of the datasets. (default: ffhq)') group.add_argument('--latent_path', type=str, default='', help='Path to the given latent codes. (default: None)') group = parser.add_argument_group('StyleGAN2') group.add_argument('--stylegan2', action='store_true', help='Whether or not using StyleGAN2. (default: False)') group.add_argument('--scale_stylegan2', type=float, default=1.0, help='Scale for the number of channel fro stylegan2.') group.add_argument('--randomize_noise', type=str, default='const', help='Noise type when computing. (const or random)') group = parser.add_argument_group('StyleGAN3') group.add_argument('--stylegan3', action='store_true', help='Whether or not using StyleGAN3. (default: False)') group.add_argument('--cfg', type=str, default='T', help='Config of the stylegan3 (T/R).') group.add_argument('--scale_stylegan3r', type=float, default=2.0, help='Scale for the number of channel for stylegan3 R.') group.add_argument('--scale_stylegan3t', type=float, default=1.0, help='Scale for the number of channel for stylegan3 T.') group.add_argument('--tx', type=float, default=0, help='Translate X-coordinate. (default: 0.0)') group.add_argument('--ty', type=float, default=0, help='Translate Y-coordinate. (default: 0.0)') group.add_argument('--rotate', type=float, default=0, help='Rotation angle in degrees. (default: 0)') group = parser.add_argument_group('Jacobians') group.add_argument('--b', type=float, default=1e-3, help='Constant when computing jacobians fast.') group.add_argument('--batch_size', type=int, default=4, help='Batch size. (default: 4)') return parser.parse_args() def main(): """Main function.""" args = parse_args() # Parse model configuration. assert (args.stylegan2 and not args.stylegan3) or \ (not args.stylegan2 and args.stylegan3) job_disc = '' if args.stylegan2: config = dict(model_type='StyleGAN2Generator', resolution=args.img_size, w_dim=args.w_dim, fmaps_base=int(args.scale_stylegan2 * (32 << 10)), fmaps_max=512,) job_disc += 'stylegan2' else: if args.stylegan3 and args.cfg == 'R': config = dict(model_type='StyleGAN3Generator', resolution=args.img_size, w_dim=args.w_dim, fmaps_base=int(args.scale_stylegan3r * (32 << 10)), fmaps_max=1024, use_radial_filter=True,) job_disc += 'stylegan3r' elif args.stylegan3 and args.cfg == 'T': config = dict(model_type='StyleGAN3Generator', resolution=args.img_size, w_dim=args.w_dim, fmaps_base=int(args.scale_stylegan3t * (32 << 10)), fmaps_max=512, use_radial_filter=False, kernel_size=3,) job_disc += 'stylegan3t' else: raise TypeError(f'StyleGAN3 config type error, need `R/T`,' f' but got {args.cfg}') job_name = f'seed_{args.seed}_num_{args.nums}_{job_disc}' temp_dir = f'work_dirs/{args.job}/{args.data_name}/{job_name}' save_dir = args.save_dir or temp_dir os.makedirs(save_dir, exist_ok=True) if args.save_jpg: os.makedirs(f'{save_dir}/images', exist_ok=True) print('Building generator...') generator = build_model(**config) checkpoint_path = args.weight_path print(f'Loading checkpoint from `{checkpoint_path}` ...') checkpoint = torch.load(checkpoint_path, map_location='cpu')['models'] if 'generator_smooth' in checkpoint: generator.load_state_dict(checkpoint['generator_smooth']) else: generator.load_state_dict(checkpoint['generator']) generator = generator.eval().cuda() print('Finish loading checkpoint.') # Set random seed. np.random.seed(args.seed) torch.manual_seed(args.seed) if os.path.exists(args.latent_path): latent_zs = np.load(args.latent_path) latent_zs = latent_zs[:args.nums] else: latent_zs = np.random.randn(args.nums, generator.z_dim) latent_zs = torch.from_numpy(latent_zs.astype(np.float32)) latent_zs = latent_zs.cuda() with torch.no_grad(): latent_ws = generator.mapping(latent_zs)['w'] print(f'Shape of the latent w: {latent_ws.shape}') def syn2jaco(w): """Wrap the synthesized function to compute the Jacobian easily. Basically, this function defines a generator that takes the input from the W space and then synthesizes an image. If the image is larger than 256, it will be resized to 256 to save the time and storage. Args: w: latent code from the W space Returns: An image with the size of [1, 256, 256] """ wp = w.unsqueeze(1).repeat((1, generator.num_layers, 1)) image = generator.synthesis(wp)['image'] if image.shape[-1] > 256: scale = 256 / image.shape[-1] image = F.interpolate(image, scale_factor=scale) image = torch.sum(image, dim=1) return image jacobians = [] for idx in tqdm(range(latent_zs.shape[0])): latent_w = latent_ws[idx:idx+1] jac_i = jacobian(func=syn2jaco, inputs=latent_w, create_graph=False, strict=False) jacobians.append(jac_i) if args.save_jpg: wp = latent_w.unsqueeze(1).repeat((1, generator.num_layers, 1)) syn_outputs = generator.synthesis(wp)['image'] syn_outputs = to_numpy(syn_outputs) images = postprocess_image(syn_outputs) save_path = f'{save_dir}/images/{idx:06d}.jpg' save_image(save_path, images[0]) jacobians = torch.cat(jacobians, dim=0) jacobians = to_numpy(jacobians) print(f'shape of the jacobian: {jacobians.shape}') latent_ws = to_numpy(latent_ws) np.save(f'{save_dir}/latent_codes.npy', latent_ws) np.save(f'{save_dir}/jacobians_w.npy', jacobians) print(f'Finish computing {args.nums} jacobians.') if __name__ == '__main__': main()