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GFPGAN / gfpgan /models /gfpgan_model.py

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	import math
	import os.path as osp
	import torch
	from basicsr.archs import build_network
	from basicsr.losses import build_loss
	from basicsr.losses.gan_loss import r1_penalty
	from basicsr.metrics import calculate_metric
	from basicsr.models.base_model import BaseModel
	from basicsr.utils import get_root_logger, imwrite, tensor2img
	from basicsr.utils.registry import MODEL_REGISTRY
	from collections import OrderedDict
	from torch.nn import functional as F
	from torchvision.ops import roi_align
	from tqdm import tqdm


	@MODEL_REGISTRY.register()
	class GFPGANModel(BaseModel):
	"""The GFPGAN model for Towards real-world blind face restoratin with generative facial prior"""

	def __init__(self, opt):
	super(GFPGANModel, self).__init__(opt)
	self.idx = 0 # it is used for saving data for check

	# define network
	self.net_g = build_network(opt['network_g'])
	self.net_g = self.model_to_device(self.net_g)
	self.print_network(self.net_g)

	# load pretrained model
	load_path = self.opt['path'].get('pretrain_network_g', None)
	if load_path is not None:
	param_key = self.opt['path'].get('param_key_g', 'params')
	self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)

	self.log_size = int(math.log(self.opt['network_g']['out_size'], 2))

	if self.is_train:
	self.init_training_settings()

	def init_training_settings(self):
	train_opt = self.opt['train']

	# ----------- define net_d ----------- #
	self.net_d = build_network(self.opt['network_d'])
	self.net_d = self.model_to_device(self.net_d)
	self.print_network(self.net_d)
	# load pretrained model
	load_path = self.opt['path'].get('pretrain_network_d', None)
	if load_path is not None:
	self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))

	# ----------- define net_g with Exponential Moving Average (EMA) ----------- #
	# net_g_ema only used for testing on one GPU and saving. There is no need to wrap with DistributedDataParallel
	self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
	# load pretrained model
	load_path = self.opt['path'].get('pretrain_network_g', None)
	if load_path is not None:
	self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
	else:
	self.model_ema(0) # copy net_g weight

	self.net_g.train()
	self.net_d.train()
	self.net_g_ema.eval()

	# ----------- facial component networks ----------- #
	if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt):
	self.use_facial_disc = True
	else:
	self.use_facial_disc = False

	if self.use_facial_disc:
	# left eye
	self.net_d_left_eye = build_network(self.opt['network_d_left_eye'])
	self.net_d_left_eye = self.model_to_device(self.net_d_left_eye)
	self.print_network(self.net_d_left_eye)
	load_path = self.opt['path'].get('pretrain_network_d_left_eye')
	if load_path is not None:
	self.load_network(self.net_d_left_eye, load_path, True, 'params')
	# right eye
	self.net_d_right_eye = build_network(self.opt['network_d_right_eye'])
	self.net_d_right_eye = self.model_to_device(self.net_d_right_eye)
	self.print_network(self.net_d_right_eye)
	load_path = self.opt['path'].get('pretrain_network_d_right_eye')
	if load_path is not None:
	self.load_network(self.net_d_right_eye, load_path, True, 'params')
	# mouth
	self.net_d_mouth = build_network(self.opt['network_d_mouth'])
	self.net_d_mouth = self.model_to_device(self.net_d_mouth)
	self.print_network(self.net_d_mouth)
	load_path = self.opt['path'].get('pretrain_network_d_mouth')
	if load_path is not None:
	self.load_network(self.net_d_mouth, load_path, True, 'params')

	self.net_d_left_eye.train()
	self.net_d_right_eye.train()
	self.net_d_mouth.train()

	# ----------- define facial component gan loss ----------- #
	self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device)

	# ----------- define losses ----------- #
	# pixel loss
	if train_opt.get('pixel_opt'):
	self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
	else:
	self.cri_pix = None

	# perceptual loss
	if train_opt.get('perceptual_opt'):
	self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
	else:
	self.cri_perceptual = None

	# L1 loss is used in pyramid loss, component style loss and identity loss
	self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device)

	# gan loss (wgan)
	self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)

	# ----------- define identity loss ----------- #
	if 'network_identity' in self.opt:
	self.use_identity = True
	else:
	self.use_identity = False

	if self.use_identity:
	# define identity network
	self.network_identity = build_network(self.opt['network_identity'])
	self.network_identity = self.model_to_device(self.network_identity)
	self.print_network(self.network_identity)
	load_path = self.opt['path'].get('pretrain_network_identity')
	if load_path is not None:
	self.load_network(self.network_identity, load_path, True, None)
	self.network_identity.eval()
	for param in self.network_identity.parameters():
	param.requires_grad = False

	# regularization weights
	self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator
	self.net_d_iters = train_opt.get('net_d_iters', 1)
	self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
	self.net_d_reg_every = train_opt['net_d_reg_every']

	# set up optimizers and schedulers
	self.setup_optimizers()
	self.setup_schedulers()

	def setup_optimizers(self):
	train_opt = self.opt['train']

	# ----------- optimizer g ----------- #
	net_g_reg_ratio = 1
	normal_params = []
	for _, param in self.net_g.named_parameters():
	normal_params.append(param)
	optim_params_g = [{ # add normal params first
	'params': normal_params,
	'lr': train_opt['optim_g']['lr']
	}]
	optim_type = train_opt['optim_g'].pop('type')
	lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
	betas = (0net_g_reg_ratio, 0.99net_g_reg_ratio)
	self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
	self.optimizers.append(self.optimizer_g)

	# ----------- optimizer d ----------- #
	net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
	normal_params = []
	for _, param in self.net_d.named_parameters():
	normal_params.append(param)
	optim_params_d = [{ # add normal params first
	'params': normal_params,
	'lr': train_opt['optim_d']['lr']
	}]
	optim_type = train_opt['optim_d'].pop('type')
	lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
	betas = (0net_d_reg_ratio, 0.99net_d_reg_ratio)
	self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
	self.optimizers.append(self.optimizer_d)

	# ----------- optimizers for facial component networks ----------- #
	if self.use_facial_disc:
	# setup optimizers for facial component discriminators
	optim_type = train_opt['optim_component'].pop('type')
	lr = train_opt['optim_component']['lr']
	# left eye
	self.optimizer_d_left_eye = self.get_optimizer(
	optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99))
	self.optimizers.append(self.optimizer_d_left_eye)
	# right eye
	self.optimizer_d_right_eye = self.get_optimizer(
	optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99))
	self.optimizers.append(self.optimizer_d_right_eye)
	# mouth
	self.optimizer_d_mouth = self.get_optimizer(
	optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99))
	self.optimizers.append(self.optimizer_d_mouth)

	def feed_data(self, data):
	self.lq = data['lq'].to(self.device)
	if 'gt' in data:
	self.gt = data['gt'].to(self.device)

	if 'loc_left_eye' in data:
	# get facial component locations, shape (batch, 4)
	self.loc_left_eyes = data['loc_left_eye']
	self.loc_right_eyes = data['loc_right_eye']
	self.loc_mouths = data['loc_mouth']

	# uncomment to check data
	# import torchvision
	# if self.opt['rank'] == 0:
	# import os
	# os.makedirs('tmp/gt', exist_ok=True)
	# os.makedirs('tmp/lq', exist_ok=True)
	# print(self.idx)
	# torchvision.utils.save_image(
	# self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
	# torchvision.utils.save_image(
	# self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
	# self.idx = self.idx + 1

	def construct_img_pyramid(self):
	"""Construct image pyramid for intermediate restoration loss"""
	pyramid_gt = [self.gt]
	down_img = self.gt
	for _ in range(0, self.log_size - 3):
	down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False)
	pyramid_gt.insert(0, down_img)
	return pyramid_gt

	def get_roi_regions(self, eye_out_size=80, mouth_out_size=120):
	face_ratio = int(self.opt['network_g']['out_size'] / 512)
	eye_out_size *= face_ratio
	mouth_out_size *= face_ratio

	rois_eyes = []
	rois_mouths = []
	for b in range(self.loc_left_eyes.size(0)): # loop for batch size
	# left eye and right eye
	img_inds = self.loc_left_eyes.new_full((2, 1), b)
	bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4)
	rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5)
	rois_eyes.append(rois)
	# mouse
	img_inds = self.loc_left_eyes.new_full((1, 1), b)
	rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5)
	rois_mouths.append(rois)

	rois_eyes = torch.cat(rois_eyes, 0).to(self.device)
	rois_mouths = torch.cat(rois_mouths, 0).to(self.device)

	# real images
	all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
	self.left_eyes_gt = all_eyes[0::2, :, :, :]
	self.right_eyes_gt = all_eyes[1::2, :, :, :]
	self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
	# output
	all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
	self.left_eyes = all_eyes[0::2, :, :, :]
	self.right_eyes = all_eyes[1::2, :, :, :]
	self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio

	def _gram_mat(self, x):
	"""Calculate Gram matrix.

	Args:
	x (torch.Tensor): Tensor with shape of (n, c, h, w).

	Returns:
	torch.Tensor: Gram matrix.
	"""
	n, c, h, w = x.size()
	features = x.view(n, c, w * h)
	features_t = features.transpose(1, 2)
	gram = features.bmm(features_t) / (c * h * w)
	return gram

	def gray_resize_for_identity(self, out, size=128):
	out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :])
	out_gray = out_gray.unsqueeze(1)
	out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False)
	return out_gray

	def optimize_parameters(self, current_iter):
	# optimize net_g
	for p in self.net_d.parameters():
	p.requires_grad = False
	self.optimizer_g.zero_grad()

	# do not update facial component net_d
	if self.use_facial_disc:
	for p in self.net_d_left_eye.parameters():
	p.requires_grad = False
	for p in self.net_d_right_eye.parameters():
	p.requires_grad = False
	for p in self.net_d_mouth.parameters():
	p.requires_grad = False

	# image pyramid loss weight
	if current_iter < self.opt['train'].get('remove_pyramid_loss', float('inf')):
	pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 1)
	else:
	pyramid_loss_weight = 1e-12 # very small loss
	if pyramid_loss_weight > 0:
	self.output, out_rgbs = self.net_g(self.lq, return_rgb=True)
	pyramid_gt = self.construct_img_pyramid()
	else:
	self.output, out_rgbs = self.net_g(self.lq, return_rgb=False)

	# get roi-align regions
	if self.use_facial_disc:
	self.get_roi_regions(eye_out_size=80, mouth_out_size=120)

	l_g_total = 0
	loss_dict = OrderedDict()
	if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
	# pixel loss
	if self.cri_pix:
	l_g_pix = self.cri_pix(self.output, self.gt)
	l_g_total += l_g_pix
	loss_dict['l_g_pix'] = l_g_pix

	# image pyramid loss
	if pyramid_loss_weight > 0:
	for i in range(0, self.log_size - 2):
	l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight
	l_g_total += l_pyramid
	loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid

	# perceptual loss
	if self.cri_perceptual:
	l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
	if l_g_percep is not None:
	l_g_total += l_g_percep
	loss_dict['l_g_percep'] = l_g_percep
	if l_g_style is not None:
	l_g_total += l_g_style
	loss_dict['l_g_style'] = l_g_style

	# gan loss
	fake_g_pred = self.net_d(self.output)
	l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
	l_g_total += l_g_gan
	loss_dict['l_g_gan'] = l_g_gan

	# facial component loss
	if self.use_facial_disc:
	# left eye
	fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True)
	l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False)
	l_g_total += l_g_gan
	loss_dict['l_g_gan_left_eye'] = l_g_gan
	# right eye
	fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True)
	l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False)
	l_g_total += l_g_gan
	loss_dict['l_g_gan_right_eye'] = l_g_gan
	# mouth
	fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True)
	l_g_gan = self.cri_component(fake_mouth, True, is_disc=False)
	l_g_total += l_g_gan
	loss_dict['l_g_gan_mouth'] = l_g_gan

	if self.opt['train'].get('comp_style_weight', 0) > 0:
	# get gt feat
	_, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True)
	_, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True)
	_, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True)

	def _comp_style(feat, feat_gt, criterion):
	return criterion(self._gram_mat(feat[0]), self._gram_mat(
	feat_gt[0].detach())) * 0.5 + criterion(
	self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach()))

	# facial component style loss
	comp_style_loss = 0
	comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1)
	comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1)
	comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1)
	comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight']
	l_g_total += comp_style_loss
	loss_dict['l_g_comp_style_loss'] = comp_style_loss

	# identity loss
	if self.use_identity:
	identity_weight = self.opt['train']['identity_weight']
	# get gray images and resize
	out_gray = self.gray_resize_for_identity(self.output)
	gt_gray = self.gray_resize_for_identity(self.gt)

	identity_gt = self.network_identity(gt_gray).detach()
	identity_out = self.network_identity(out_gray)
	l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight
	l_g_total += l_identity
	loss_dict['l_identity'] = l_identity

	l_g_total.backward()
	self.optimizer_g.step()

	# EMA
	self.model_ema(decay=0.5*(32 / (10 1000)))

	# ----------- optimize net_d ----------- #
	for p in self.net_d.parameters():
	p.requires_grad = True
	self.optimizer_d.zero_grad()
	if self.use_facial_disc:
	for p in self.net_d_left_eye.parameters():
	p.requires_grad = True
	for p in self.net_d_right_eye.parameters():
	p.requires_grad = True
	for p in self.net_d_mouth.parameters():
	p.requires_grad = True
	self.optimizer_d_left_eye.zero_grad()
	self.optimizer_d_right_eye.zero_grad()
	self.optimizer_d_mouth.zero_grad()

	fake_d_pred = self.net_d(self.output.detach())
	real_d_pred = self.net_d(self.gt)
	l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True)
	loss_dict['l_d'] = l_d
	# In WGAN, real_score should be positive and fake_score should be negative
	loss_dict['real_score'] = real_d_pred.detach().mean()
	loss_dict['fake_score'] = fake_d_pred.detach().mean()
	l_d.backward()

	# regularization loss
	if current_iter % self.net_d_reg_every == 0:
	self.gt.requires_grad = True
	real_pred = self.net_d(self.gt)
	l_d_r1 = r1_penalty(real_pred, self.gt)
	l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
	loss_dict['l_d_r1'] = l_d_r1.detach().mean()
	l_d_r1.backward()

	self.optimizer_d.step()

	# optimize facial component discriminators
	if self.use_facial_disc:
	# left eye
	fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach())
	real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt)
	l_d_left_eye = self.cri_component(
	real_d_pred, True, is_disc=True) + self.cri_gan(
	fake_d_pred, False, is_disc=True)
	loss_dict['l_d_left_eye'] = l_d_left_eye
	l_d_left_eye.backward()
	# right eye
	fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach())
	real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt)
	l_d_right_eye = self.cri_component(
	real_d_pred, True, is_disc=True) + self.cri_gan(
	fake_d_pred, False, is_disc=True)
	loss_dict['l_d_right_eye'] = l_d_right_eye
	l_d_right_eye.backward()
	# mouth
	fake_d_pred, _ = self.net_d_mouth(self.mouths.detach())
	real_d_pred, _ = self.net_d_mouth(self.mouths_gt)
	l_d_mouth = self.cri_component(
	real_d_pred, True, is_disc=True) + self.cri_gan(
	fake_d_pred, False, is_disc=True)
	loss_dict['l_d_mouth'] = l_d_mouth
	l_d_mouth.backward()

	self.optimizer_d_left_eye.step()
	self.optimizer_d_right_eye.step()
	self.optimizer_d_mouth.step()

	self.log_dict = self.reduce_loss_dict(loss_dict)

	def test(self):
	with torch.no_grad():
	if hasattr(self, 'net_g_ema'):
	self.net_g_ema.eval()
	self.output, _ = self.net_g_ema(self.lq)
	else:
	logger = get_root_logger()
	logger.warning('Do not have self.net_g_ema, use self.net_g.')
	self.net_g.eval()
	self.output, _ = self.net_g(self.lq)
	self.net_g.train()

	def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
	if self.opt['rank'] == 0:
	self.nondist_validation(dataloader, current_iter, tb_logger, save_img)

	def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
	dataset_name = dataloader.dataset.opt['name']
	with_metrics = self.opt['val'].get('metrics') is not None
	use_pbar = self.opt['val'].get('pbar', False)

	if with_metrics:
	if not hasattr(self, 'metric_results'): # only execute in the first run
	self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
	# initialize the best metric results for each dataset_name (supporting multiple validation datasets)
	self._initialize_best_metric_results(dataset_name)
	# zero self.metric_results
	self.metric_results = {metric: 0 for metric in self.metric_results}

	metric_data = dict()
	if use_pbar:
	pbar = tqdm(total=len(dataloader), unit='image')

	for idx, val_data in enumerate(dataloader):
	img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
	self.feed_data(val_data)
	self.test()

	sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1))
	metric_data['img'] = sr_img
	if hasattr(self, 'gt'):
	gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1))
	metric_data['img2'] = gt_img
	del self.gt

	# tentative for out of GPU memory
	del self.lq
	del self.output
	torch.cuda.empty_cache()

	if save_img:
	if self.opt['is_train']:
	save_img_path = osp.join(self.opt['path']['visualization'], img_name,
	f'{img_name}_{current_iter}.png')
	else:
	if self.opt['val']['suffix']:
	save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
	f'{img_name}_{self.opt["val"]["suffix"]}.png')
	else:
	save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
	f'{img_name}_{self.opt["name"]}.png')
	imwrite(sr_img, save_img_path)

	if with_metrics:
	# calculate metrics
	for name, opt_ in self.opt['val']['metrics'].items():
	self.metric_results[name] += calculate_metric(metric_data, opt_)
	if use_pbar:
	pbar.update(1)
	pbar.set_description(f'Test {img_name}')
	if use_pbar:
	pbar.close()

	if with_metrics:
	for metric in self.metric_results.keys():
	self.metric_results[metric] /= (idx + 1)
	# update the best metric result
	self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter)

	self._log_validation_metric_values(current_iter, dataset_name, tb_logger)

	def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
	log_str = f'Validation {dataset_name}\n'
	for metric, value in self.metric_results.items():
	log_str += f'\t # {metric}: {value:.4f}'
	if hasattr(self, 'best_metric_results'):
	log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ '
	f'{self.best_metric_results[dataset_name][metric]["iter"]} iter')
	log_str += '\n'

	logger = get_root_logger()
	logger.info(log_str)
	if tb_logger:
	for metric, value in self.metric_results.items():
	tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter)

	def save(self, epoch, current_iter):
	# save net_g and net_d
	self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
	self.save_network(self.net_d, 'net_d', current_iter)
	# save component discriminators
	if self.use_facial_disc:
	self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter)
	self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter)
	self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter)
	# save training state
	self.save_training_state(epoch, current_iter)