# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. """Project given image to the latent space of pretrained network pickle.""" import copy import os import numpy as np import torch import torch.nn.functional as F from tqdm import tqdm import dnnlib import PIL from camera_utils import LookAtPoseSampler def project( G, c, outdir, 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=0.01, initial_noise_factor=0.05, lr_rampdown_length=0.25, lr_rampup_length=0.05, noise_ramp_length=0.75, regularize_noise_weight=1e5, verbose=False, device: torch.device, initial_w=None, image_log_step=100, w_name: str ): os.makedirs(f'{outdir}/{w_name}_w_plus', exist_ok=True) outdir = f'{outdir}/{w_name}_w_plus' assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution) def logprint(*args): if verbose: print(*args) G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore # Compute w stats. w_avg_path = './w_avg.npy' w_std_path = './w_std.npy' if (not os.path.exists(w_avg_path)) or (not os.path.exists(w_std_path)): print(f'Computing W midpoint and stddev using {w_avg_samples} samples...') z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) # c_samples = c.repeat(w_avg_samples, 1) # use avg look at point camera_lookat_point = torch.tensor(G.rendering_kwargs['avg_camera_pivot'], device=device) cam2world_pose = LookAtPoseSampler.sample(3.14 / 2, 3.14 / 2, camera_lookat_point, radius=G.rendering_kwargs['avg_camera_radius'], device=device) focal_length = 4.2647 # FFHQ's FOV intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device) c_samples = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1) c_samples = c_samples.repeat(w_avg_samples, 1) w_samples = G.mapping(torch.from_numpy(z_samples).to(device), c_samples) # [N, L, C] 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] # print('save w_avg to ./w_avg.npy') # np.save('./w_avg.npy',w_avg) w_avg_tensor = torch.from_numpy(w_avg).cuda() w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 # np.save(w_avg_path, w_avg) # np.save(w_std_path, w_std) else: # w_avg = np.load(w_avg_path) # w_std = np.load(w_std_path) raise Exception(' ') # z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) # c_samples = c.repeat(w_avg_samples, 1) # w_samples = G.mapping(torch.from_numpy(z_samples).to(device), c_samples) # [N, L, C] # 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] # w_avg_tensor = torch.from_numpy(w_avg).cuda() # w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 start_w = initial_w if initial_w is not None else w_avg # Setup noise inputs. noise_bufs = {name: buf for (name, buf) in G.backbone.synthesis.named_buffers() if 'noise_const' in name} # Load VGG16 feature detector. url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' # url = './networks/vgg16.pt' with dnnlib.util.open_url(url) as f: vgg16 = torch.jit.load(f).eval().to(device) # Features for target image. target_images = target.unsqueeze(0).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) start_w = np.repeat(start_w, G.backbone.mapping.num_ws, axis=1) w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, requires_grad=True) # pylint: disable=not-callable optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999), lr=0.1) # Init noise. for buf in noise_bufs.values(): buf[:] = torch.randn_like(buf) buf.requires_grad = True for step in tqdm(range(num_steps), position=0, leave=True): # Learning rate schedule. t = step / num_steps w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 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. w_noise = torch.randn_like(w_opt) * w_noise_scale ws = (w_opt + w_noise) synth_images = G.synthesis(ws,c, noise_mode='const')['image'] if step % image_log_step == 0: with torch.no_grad(): vis_img = (synth_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) PIL.Image.fromarray(vis_img[0].cpu().numpy(), 'RGB').save(f'{outdir}/{step}.png') # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. synth_images = (synth_images + 1) * (255 / 2) 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) dist = (target_features - synth_features).square().sum() # Noise regularization. reg_loss = 0.0 for v in noise_bufs.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) loss = dist + reg_loss * regularize_noise_weight # if step % 10 == 0: # with torch.no_grad(): # print({f'step {step}, first projection _{w_name}': loss.detach().cpu()}) # Step optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') # Normalize noise. with torch.no_grad(): for buf in noise_bufs.values(): buf -= buf.mean() buf *= buf.square().mean().rsqrt() del G return w_opt