Upload gfpgan/models/gfpgan_model.py

gfpgan/models/gfpgan_model.py

@@ -0,0 +1,580 @@
+import math
+import os.path as osp
+import torch
+from basicsr.archs import build_network
+from basicsr.losses import build_loss
+from basicsr.losses.losses 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 = (0**net_g_reg_ratio, 0.99**net_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 = (0**net_d_reg_ratio, 0.99**net_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)