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PTI / training /projectors /w_projector.py

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	# 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 wandb
	import numpy as np
	import torch
	import torch.nn.functional as F
	from tqdm import tqdm
	from configs import global_config, hyperparameters
	from utils import log_utils
	import dnnlib


	def project(
	G,
	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,
	use_wandb=False,
	initial_w=None,
	image_log_step=global_config.image_rec_result_log_snapshot,
	w_name: str
	):
	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.
	logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
	z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
	w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [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).to(global_config.device)
	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.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'
	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)

	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=hyperparameters.first_inv_lr)

	# Init noise.
	for buf in noise_bufs.values():
	buf[:] = torch.randn_like(buf)
	buf.requires_grad = True

	for step in tqdm(range(num_steps)):

	# 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).repeat([1, G.mapping.num_ws, 1])
	synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)

	# 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 % image_log_step == 0:
	with torch.no_grad():
	if use_wandb:
	global_config.training_step += 1
	wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step)
	log_utils.log_image_from_w(w_opt.repeat([1, G.mapping.num_ws, 1]), G, w_name)

	# 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.repeat([1, 18, 1])