ridcp

basicsr/metrics/calculate_fid_stats_from_datasets.py

+import argparse
+import math
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from basicsr.data import build_dataset
+from basicsr.metrics.fid import extract_inception_features, load_patched_inception_v3
+
+
+def calculate_stats_from_dataset():
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--num_sample', type=int, default=50000)
+    parser.add_argument('--batch_size', type=int, default=64)
+    parser.add_argument('--size', type=int, default=512)
+    parser.add_argument('--dataroot', type=str, default='datasets/ffhq')
+    args = parser.parse_args()
+
+    # inception model
+    inception = load_patched_inception_v3(device)
+
+    # create dataset
+    opt = {}
+    opt['name'] = 'FFHQ'
+    opt['type'] = 'FFHQDataset'
+    opt['dataroot_gt'] = f'datasets/ffhq/ffhq_{args.size}.lmdb'
+    opt['io_backend'] = dict(type='lmdb')
+    opt['use_hflip'] = False
+    opt['mean'] = [0.5, 0.5, 0.5]
+    opt['std'] = [0.5, 0.5, 0.5]
+    dataset = build_dataset(opt)
+
+    # create dataloader
+    data_loader = DataLoader(
+        dataset=dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, sampler=None, drop_last=False)
+    total_batch = math.ceil(args.num_sample / args.batch_size)
+
+    def data_generator(data_loader, total_batch):
+        for idx, data in enumerate(data_loader):
+            if idx >= total_batch:
+                break
+            else:
+                yield data['gt']
+
+    features = extract_inception_features(data_generator(data_loader, total_batch), inception, total_batch, device)
+    features = features.numpy()
+    total_len = features.shape[0]
+    features = features[:args.num_sample]
+    print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.')
+    mean = np.mean(features, 0)
+    cov = np.cov(features, rowvar=False)
+
+    save_path = f'inception_{opt["name"]}_{args.size}.pth'
+    torch.save(
+        dict(name=opt['name'], size=args.size, mean=mean, cov=cov), save_path, _use_new_zipfile_serialization=False)
+
+
+if __name__ == '__main__':
+    calculate_stats_from_dataset()

basicsr/metrics/calculate_lpips.py

+import cv2
+import glob
+import numpy as np
+import os.path as osp
+from torchvision.transforms.functional import normalize
+
+from basicsr.utils import img2tensor
+
+try:
+    import lpips
+except ImportError:
+    print('Please install lpips: pip install lpips')
+
+
+def main():
+    # Configurations
+    # -------------------------------------------------------------------------
+    folder_gt = 'datasets/celeba/celeba_512_validation'
+    folder_restored = 'datasets/celeba/celeba_512_validation_lq'
+    # crop_border = 4
+    suffix = ''
+    # -------------------------------------------------------------------------
+    loss_fn_vgg = lpips.LPIPS(net='vgg').cuda()  # RGB, normalized to [-1,1]
+    lpips_all = []
+    img_list = sorted(glob.glob(osp.join(folder_gt, '*')))
+
+    mean = [0.5, 0.5, 0.5]
+    std = [0.5, 0.5, 0.5]
+    for i, img_path in enumerate(img_list):
+        basename, ext = osp.splitext(osp.basename(img_path))
+        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
+        img_restored = cv2.imread(osp.join(folder_restored, basename + suffix + ext), cv2.IMREAD_UNCHANGED).astype(
+            np.float32) / 255.
+
+        img_gt, img_restored = img2tensor([img_gt, img_restored], bgr2rgb=True, float32=True)
+        # norm to [-1, 1]
+        normalize(img_gt, mean, std, inplace=True)
+        normalize(img_restored, mean, std, inplace=True)
+
+        # calculate lpips
+        lpips_val = loss_fn_vgg(img_restored.unsqueeze(0).cuda(), img_gt.unsqueeze(0).cuda())
+
+        print(f'{i+1:3d}: {basename:25}. \tLPIPS: {lpips_val:.6f}.')
+        lpips_all.append(lpips_val)
+
+    print(f'Average: LPIPS: {sum(lpips_all) / len(lpips_all):.6f}')
+
+
+if __name__ == '__main__':
+    main()

basicsr/metrics/calculate_niqe.py

+import argparse
+import cv2
+import os
+import warnings
+
+from basicsr.metrics import calculate_niqe
+from basicsr.utils import scandir
+
+
+def main(args):
+
+    niqe_all = []
+    img_list = sorted(scandir(args.input, recursive=True, full_path=True))
+
+    for i, img_path in enumerate(img_list):
+        basename, _ = os.path.splitext(os.path.basename(img_path))
+        img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', category=RuntimeWarning)
+            niqe_score = calculate_niqe(img, args.crop_border, input_order='HWC', convert_to='y')
+        print(f'{i+1:3d}: {basename:25}. \tNIQE: {niqe_score:.6f}')
+        niqe_all.append(niqe_score)
+
+    print(args.input)
+    print(f'Average: NIQE: {sum(niqe_all) / len(niqe_all):.6f}')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', type=str, default='datasets/val_set14/Set14', help='Input path')
+    parser.add_argument('--crop_border', type=int, default=0, help='Crop border for each side')
+    args = parser.parse_args()
+    main(args)

basicsr/metrics/calculate_psnr_ssim.py

+import argparse
+import cv2
+import numpy as np
+from os import path as osp
+
+from basicsr.metrics import calculate_psnr, calculate_ssim
+from basicsr.utils import scandir
+from basicsr.utils.matlab_functions import bgr2ycbcr
+
+
+def main(args):
+    """Calculate PSNR and SSIM for images.
+    """
+    psnr_all = []
+    ssim_all = []
+    img_list_gt = sorted(list(scandir(args.gt, recursive=True, full_path=True)))
+    img_list_restored = sorted(list(scandir(args.restored, recursive=True, full_path=True)))
+
+    if args.test_y_channel:
+        print('Testing Y channel.')
+    else:
+        print('Testing RGB channels.')
+
+    for i, img_path in enumerate(img_list_gt):
+        basename, ext = osp.splitext(osp.basename(img_path))
+        img_gt = cv2.imread(img_path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
+        if args.suffix == '':
+            img_path_restored = img_list_restored[i]
+        else:
+            img_path_restored = osp.join(args.restored, basename + args.suffix + ext)
+        img_restored = cv2.imread(img_path_restored, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.
+
+        if args.correct_mean_var:
+            mean_l = []
+            std_l = []
+            for j in range(3):
+                mean_l.append(np.mean(img_gt[:, :, j]))
+                std_l.append(np.std(img_gt[:, :, j]))
+            for j in range(3):
+                # correct twice
+                mean = np.mean(img_restored[:, :, j])
+                img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]
+                std = np.std(img_restored[:, :, j])
+                img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]
+
+                mean = np.mean(img_restored[:, :, j])
+                img_restored[:, :, j] = img_restored[:, :, j] - mean + mean_l[j]
+                std = np.std(img_restored[:, :, j])
+                img_restored[:, :, j] = img_restored[:, :, j] / std * std_l[j]
+
+        if args.test_y_channel and img_gt.ndim == 3 and img_gt.shape[2] == 3:
+            img_gt = bgr2ycbcr(img_gt, y_only=True)
+            img_restored = bgr2ycbcr(img_restored, y_only=True)
+
+        # calculate PSNR and SSIM
+        psnr = calculate_psnr(img_gt * 255, img_restored * 255, crop_border=args.crop_border, input_order='HWC')
+        ssim = calculate_ssim(img_gt * 255, img_restored * 255, crop_border=args.crop_border, input_order='HWC')
+        print(f'{i+1:3d}: {basename:25}. \tPSNR: {psnr:.6f} dB, \tSSIM: {ssim:.6f}')
+        psnr_all.append(psnr)
+        ssim_all.append(ssim)
+    print(args.gt)
+    print(args.restored)
+    print(f'Average: PSNR: {sum(psnr_all) / len(psnr_all):.6f} dB, SSIM: {sum(ssim_all) / len(ssim_all):.6f}')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--gt', type=str, default='datasets/val_set14/Set14', help='Path to gt (Ground-Truth)')
+    parser.add_argument('--restored', type=str, default='results/Set14', help='Path to restored images')
+    parser.add_argument('--crop_border', type=int, default=0, help='Crop border for each side')
+    parser.add_argument('--suffix', type=str, default='', help='Suffix for restored images')
+    parser.add_argument(
+        '--test_y_channel',
+        action='store_true',
+        help='If True, test Y channel (In MatLab YCbCr format). If False, test RGB channels.')
+    parser.add_argument('--correct_mean_var', action='store_true', help='Correct the mean and var of restored images.')
+    args = parser.parse_args()
+    main(args)

basicsr/metrics/calculate_stylegan2_fid.py

+import argparse
+import math
+import numpy as np
+import torch
+from torch import nn
+
+from basicsr.archs.stylegan2_arch import StyleGAN2Generator
+from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_patched_inception_v3
+
+
+def calculate_stylegan2_fid():
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('ckpt', type=str, help='Path to the stylegan2 checkpoint.')
+    parser.add_argument('fid_stats', type=str, help='Path to the dataset fid statistics.')
+    parser.add_argument('--size', type=int, default=256)
+    parser.add_argument('--channel_multiplier', type=int, default=2)
+    parser.add_argument('--batch_size', type=int, default=64)
+    parser.add_argument('--num_sample', type=int, default=50000)
+    parser.add_argument('--truncation', type=float, default=1)
+    parser.add_argument('--truncation_mean', type=int, default=4096)
+    args = parser.parse_args()
+
+    # create stylegan2 model
+    generator = StyleGAN2Generator(
+        out_size=args.size,
+        num_style_feat=512,
+        num_mlp=8,
+        channel_multiplier=args.channel_multiplier,
+        resample_kernel=(1, 3, 3, 1))
+    generator.load_state_dict(torch.load(args.ckpt)['params_ema'])
+    generator = nn.DataParallel(generator).eval().to(device)
+
+    if args.truncation < 1:
+        with torch.no_grad():
+            truncation_latent = generator.mean_latent(args.truncation_mean)
+    else:
+        truncation_latent = None
+
+    # inception model
+    inception = load_patched_inception_v3(device)
+
+    total_batch = math.ceil(args.num_sample / args.batch_size)
+
+    def sample_generator(total_batch):
+        for _ in range(total_batch):
+            with torch.no_grad():
+                latent = torch.randn(args.batch_size, 512, device=device)
+                samples, _ = generator([latent], truncation=args.truncation, truncation_latent=truncation_latent)
+            yield samples
+
+    features = extract_inception_features(sample_generator(total_batch), inception, total_batch, device)
+    features = features.numpy()
+    total_len = features.shape[0]
+    features = features[:args.num_sample]
+    print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.')
+    sample_mean = np.mean(features, 0)
+    sample_cov = np.cov(features, rowvar=False)
+
+    # load the dataset stats
+    stats = torch.load(args.fid_stats)
+    real_mean = stats['mean']
+    real_cov = stats['cov']
+
+    # calculate FID metric
+    fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov)
+    print('fid:', fid)
+
+
+if __name__ == '__main__':
+    calculate_stylegan2_fid()

basicsr/models/__init__.py

+import importlib
+from copy import deepcopy
+from os import path as osp
+
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import MODEL_REGISTRY
+
+__all__ = ['build_model']
+
+# automatically scan and import model modules for registry
+# scan all the files under the 'models' folder and collect files ending with
+# '_model.py'
+model_folder = osp.dirname(osp.abspath(__file__))
+model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
+# import all the model modules
+_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames]
+
+
+def build_model(opt):
+    """Build model from options.
+
+    Args:
+        opt (dict): Configuration. It must contain:
+            model_type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    model = MODEL_REGISTRY.get(opt['model_type'])(opt)
+    logger = get_root_logger()
+    logger.info(f'Model [{model.__class__.__name__}] is created.')
+    return model

basicsr/models/base_model.py

+import os
+import time
+import torch
+from collections import OrderedDict
+from copy import deepcopy
+from torch.nn.parallel import DataParallel, DistributedDataParallel
+
+from basicsr.models import lr_scheduler as lr_scheduler
+from basicsr.utils import get_root_logger
+from basicsr.utils.dist_util import master_only
+
+
+class BaseModel():
+    """Base model."""
+
+    def __init__(self, opt):
+        self.opt = opt
+        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
+        self.is_train = opt['is_train']
+        self.schedulers = []
+        self.optimizers = []
+
+    def feed_data(self, data):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        pass
+
+    def save(self, epoch, current_iter):
+        """Save networks and training state."""
+        pass
+
+    def validation(self, dataloader, current_iter, tb_logger, save_img=False, save_as_dir=None):
+        """Validation function.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader): Validation dataloader.
+            current_iter (int): Current iteration.
+            tb_logger (tensorboard logger): Tensorboard logger.
+            save_img (bool): Whether to save images. Default: False.
+        """
+        if self.opt['dist']:
+            self.dist_validation(dataloader, current_iter, tb_logger, save_img, save_as_dir)
+        else:
+            self.nondist_validation(dataloader, current_iter, tb_logger, save_img, save_as_dir)
+
+    def _initialize_best_metric_results(self, dataset_name):
+        """Initialize the best metric results dict for recording the best metric value and iteration."""
+        if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results:
+            return
+        elif not hasattr(self, 'best_metric_results'):
+            self.best_metric_results = dict()
+
+        # add a dataset record
+        record = dict()
+        for metric, content in self.opt['val']['metrics'].items():
+            better = content.get('better', 'higher')
+            init_val = float('-inf') if better == 'higher' else float('inf')
+            record[metric] = dict(better=better, val=init_val, iter=-1)
+        self.best_metric_results[dataset_name] = record
+
+    def _update_metric_result(self, dataset_name, metric, val, current_iter):
+        self.best_metric_results[dataset_name][metric]['val'] = val
+        self.best_metric_results[dataset_name][metric]['iter'] = current_iter
+
+    def _update_best_metric_result(self, dataset_name, metric, val, current_iter):
+        if self.best_metric_results[dataset_name][metric]['better'] == 'higher':
+            if val >= self.best_metric_results[dataset_name][metric]['val']:
+                self.best_metric_results[dataset_name][metric]['val'] = val
+                self.best_metric_results[dataset_name][metric]['iter'] = current_iter
+                return True
+            else:
+                return False
+        else:
+            if val <= self.best_metric_results[dataset_name][metric]['val']:
+                self.best_metric_results[dataset_name][metric]['val'] = val
+                self.best_metric_results[dataset_name][metric]['iter'] = current_iter
+                return True
+            else:
+                return False
+
+    def model_ema(self, decay=0.999):
+        net_g = self.get_bare_model(self.net_g)
+
+        net_g_params = dict(net_g.named_parameters())
+        net_g_ema_params = dict(self.net_g_ema.named_parameters())
+
+        for k in net_g_ema_params.keys():
+            net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay)
+    
+    def copy_model(self, net_a, net_b):
+        """copy model from net_a to net_b"""
+        tmp_net_a = self.get_bare_model(net_a)
+        tmp_net_b = self.get_bare_model(net_b)
+        tmp_net_b.load_state_dict(tmp_net_a.state_dict())
+
+    def get_current_log(self):
+        return self.log_dict
+
+    def model_to_device(self, net):
+        """Model to device. It also warps models with DistributedDataParallel
+        or DataParallel.
+
+        Args:
+            net (nn.Module)
+        """
+        net = net.to(self.device)
+        if self.opt['dist']:
+            find_unused_parameters = self.opt.get('find_unused_parameters', False)
+            net = DistributedDataParallel(
+                net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters)
+        elif self.opt['num_gpu'] > 1:
+            net = DataParallel(net)
+        return net
+
+    def get_optimizer(self, optim_type, params, lr, **kwargs):
+        if optim_type == 'Adam':
+            optimizer = torch.optim.Adam(params, lr, **kwargs)
+        else:
+            raise NotImplementedError(f'optimizer {optim_type} is not supperted yet.')
+        return optimizer
+
+    def setup_schedulers(self):
+        """Set up schedulers."""
+        train_opt = self.opt['train']
+        scheduler_type = train_opt['scheduler'].pop('type')
+        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
+            for optimizer in self.optimizers:
+                self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler']))
+        elif scheduler_type == 'CosineAnnealingRestartLR':
+            for optimizer in self.optimizers:
+                self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler']))
+        else:
+            raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.')
+
+    def get_bare_model(self, net):
+        """Get bare model, especially under wrapping with
+        DistributedDataParallel or DataParallel.
+        """
+        if isinstance(net, (DataParallel, DistributedDataParallel)):
+            net = net.module
+        return net
+
+    @master_only
+    def print_network(self, net):
+        """Print the str and parameter number of a network.
+
+        Args:
+            net (nn.Module)
+        """
+        if isinstance(net, (DataParallel, DistributedDataParallel)):
+            net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}'
+        else:
+            net_cls_str = f'{net.__class__.__name__}'
+
+        net = self.get_bare_model(net)
+        net_str = str(net)
+        net_params = sum(map(lambda x: x.numel(), net.parameters()))
+
+        logger = get_root_logger()
+        logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}')
+        logger.info(net_str)
+
+    def _set_lr(self, lr_groups_l):
+        """Set learning rate for warmup.
+
+        Args:
+            lr_groups_l (list): List for lr_groups, each for an optimizer.
+        """
+        for optimizer, lr_groups in zip(self.optimizers, lr_groups_l):
+            for param_group, lr in zip(optimizer.param_groups, lr_groups):
+                param_group['lr'] = lr
+
+    def _get_init_lr(self):
+        """Get the initial lr, which is set by the scheduler.
+        """
+        init_lr_groups_l = []
+        for optimizer in self.optimizers:
+            init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups])
+        return init_lr_groups_l
+
+    def update_learning_rate(self, current_iter, warmup_iter=-1):
+        """Update learning rate.
+
+        Args:
+            current_iter (int): Current iteration.
+            warmup_iter (int)： Warmup iter numbers. -1 for no warmup.
+                Default： -1.
+        """
+        if current_iter > 1:
+            for scheduler in self.schedulers:
+                scheduler.step()
+        # set up warm-up learning rate
+        if current_iter < warmup_iter:
+            # get initial lr for each group
+            init_lr_g_l = self._get_init_lr()
+            # modify warming-up learning rates
+            # currently only support linearly warm up
+            warm_up_lr_l = []
+            for init_lr_g in init_lr_g_l:
+                warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g])
+            # set learning rate
+            self._set_lr(warm_up_lr_l)
+
+    def get_current_learning_rate(self):
+        return [optim.param_groups[0]['lr'] for optim in self.optimizers]
+
+    @master_only
+    def save_network(self, net, net_label, current_iter, param_key='params'):
+        """Save networks.
+
+        Args:
+            net (nn.Module | list[nn.Module]): Network(s) to be saved.
+            net_label (str): Network label.
+            current_iter (int): Current iter number.
+            param_key (str | list[str]): The parameter key(s) to save network.
+                Default: 'params'.
+        """
+        if current_iter == -1:
+            current_iter = 'latest'
+        save_filename = f'{net_label}_{current_iter}.pth'
+        save_path = os.path.join(self.opt['path']['models'], save_filename)
+
+        net = net if isinstance(net, list) else [net]
+        param_key = param_key if isinstance(param_key, list) else [param_key]
+        assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.'
+
+        save_dict = {}
+        for net_, param_key_ in zip(net, param_key):
+            net_ = self.get_bare_model(net_)
+            state_dict = net_.state_dict()
+            for key, param in state_dict.items():
+                if key.startswith('module.'):  # remove unnecessary 'module.'
+                    key = key[7:]
+                state_dict[key] = param.cpu()
+            save_dict[param_key_] = state_dict
+
+        # avoid occasional writing errors
+        retry = 3
+        while retry > 0:
+            try:
+                torch.save(save_dict, save_path)
+            except Exception as e:
+                logger = get_root_logger()
+                logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}')
+                time.sleep(1)
+            else:
+                break
+            finally:
+                retry -= 1
+        if retry == 0:
+            logger.warning(f'Still cannot save {save_path}. Just ignore it.')
+            # raise IOError(f'Cannot save {save_path}.')
+
+    def _print_different_keys_loading(self, crt_net, load_net, strict=True):
+        """Print keys with different name or different size when loading models.
+
+        1. Print keys with different names.
+        2. If strict=False, print the same key but with different tensor size.
+            It also ignore these keys with different sizes (not load).
+
+        Args:
+            crt_net (torch model): Current network.
+            load_net (dict): Loaded network.
+            strict (bool): Whether strictly loaded. Default: True.
+        """
+        crt_net = self.get_bare_model(crt_net)
+        crt_net = crt_net.state_dict()
+        crt_net_keys = set(crt_net.keys())
+        load_net_keys = set(load_net.keys())
+
+        logger = get_root_logger()
+        if crt_net_keys != load_net_keys:
+            logger.warning('Current net - loaded net:')
+            for v in sorted(list(crt_net_keys - load_net_keys)):
+                logger.warning(f'  {v}')
+            logger.warning('Loaded net - current net:')
+            for v in sorted(list(load_net_keys - crt_net_keys)):
+                logger.warning(f'  {v}')
+
+        # check the size for the same keys
+        if not strict:
+            common_keys = crt_net_keys & load_net_keys
+            for k in common_keys:
+                if crt_net[k].size() != load_net[k].size():
+                    logger.warning(f'Size different, ignore [{k}]: crt_net: '
+                                   f'{crt_net[k].shape}; load_net: {load_net[k].shape}')
+                    load_net[k + '.ignore'] = load_net.pop(k)
+
+    def load_network(self, net, load_path, strict=True, param_key='params'):
+        """Load network.
+
+        Args:
+            load_path (str): The path of networks to be loaded.
+            net (nn.Module): Network.
+            strict (bool): Whether strictly loaded.
+            param_key (str): The parameter key of loaded network. If set to
+                None, use the root 'path'.
+                Default: 'params'.
+        """
+        logger = get_root_logger()
+        net = self.get_bare_model(net)
+        load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
+        if param_key is not None:
+            if param_key not in load_net and 'params' in load_net:
+                param_key = 'params'
+                logger.info('Loading: params_ema does not exist, use params.')
+            load_net = load_net[param_key]
+        logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].')
+        # remove unnecessary 'module.'
+        for k, v in deepcopy(load_net).items():
+            if k.startswith('module.'):
+                load_net[k[7:]] = v
+                load_net.pop(k)
+        self._print_different_keys_loading(net, load_net, strict)
+        net.load_state_dict(load_net, strict=strict)
+
+    @master_only
+    def save_training_state(self, epoch, current_iter):
+        """Save training states during training, which will be used for
+        resuming.
+
+        Args:
+            epoch (int): Current epoch.
+            current_iter (int): Current iteration.
+        """
+        if current_iter != -1:
+            state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []}
+            for o in self.optimizers:
+                state['optimizers'].append(o.state_dict())
+            for s in self.schedulers:
+                state['schedulers'].append(s.state_dict())
+            save_filename = f'{current_iter}.state'
+            save_path = os.path.join(self.opt['path']['training_states'], save_filename)
+
+            # avoid occasional writing errors
+            retry = 3
+            while retry > 0:
+                try:
+                    torch.save(state, save_path)
+                except Exception as e:
+                    logger = get_root_logger()
+                    logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}')
+                    time.sleep(1)
+                else:
+                    break
+                finally:
+                    retry -= 1
+            if retry == 0:
+                logger.warning(f'Still cannot save {save_path}. Just ignore it.')
+                # raise IOError(f'Cannot save {save_path}.')
+
+    def resume_training(self, resume_state):
+        """Reload the optimizers and schedulers for resumed training.
+
+        Args:
+            resume_state (dict): Resume state.
+        """
+        resume_optimizers = resume_state['optimizers']
+        resume_schedulers = resume_state['schedulers']
+        assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers'
+        assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers'
+        for i, o in enumerate(resume_optimizers):
+            self.optimizers[i].load_state_dict(o)
+        for i, s in enumerate(resume_schedulers):
+            self.schedulers[i].load_state_dict(s)
+
+    def reduce_loss_dict(self, loss_dict):
+        """reduce loss dict.
+
+        In distributed training, it averages the losses among different GPUs .
+
+        Args:
+            loss_dict (OrderedDict): Loss dict.
+        """
+        with torch.no_grad():
+            if self.opt['dist']:
+                keys = []
+                losses = []
+                for name, value in loss_dict.items():
+                    keys.append(name)
+                    losses.append(value)
+                losses = torch.stack(losses, 0)
+                torch.distributed.reduce(losses, dst=0)
+                if self.opt['rank'] == 0:
+                    losses /= self.opt['world_size']
+                loss_dict = {key: loss for key, loss in zip(keys, losses)}
+
+            log_dict = OrderedDict()
+            for name, value in loss_dict.items():
+                log_dict[name] = value.mean().item()
+
+            return log_dict

basicsr/ops/dcn/__init__.py

+from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv,
+                          modulated_deform_conv)
+
+__all__ = [
+    'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv',
+    'modulated_deform_conv'
+]