evtexture

basicsr/data/meta_info/meta_info_REDS_h5_test.txt

+000.h5 100
+011.h5 100
+015.h5 100
+020.h5 100

basicsr/data/meta_info/meta_info_REDS_h5_train.txt

+001.h5 100
+002.h5 100
+003.h5 100
+004.h5 100
+005.h5 100
+006.h5 100
+007.h5 100
+008.h5 100
+009.h5 100
+010.h5 100
+012.h5 100
+013.h5 100
+014.h5 100
+016.h5 100
+017.h5 100
+018.h5 100
+019.h5 100
+021.h5 100
+022.h5 100
+023.h5 100
+024.h5 100
+025.h5 100
+026.h5 100
+027.h5 100
+028.h5 100
+029.h5 100
+030.h5 100
+031.h5 100
+032.h5 100
+033.h5 100
+034.h5 100
+035.h5 100
+036.h5 100
+037.h5 100
+038.h5 100
+039.h5 100
+040.h5 100
+041.h5 100
+042.h5 100
+043.h5 100
+044.h5 100
+045.h5 100
+046.h5 100
+047.h5 100
+048.h5 100
+049.h5 100
+050.h5 100
+051.h5 100
+052.h5 100
+053.h5 100
+054.h5 100
+055.h5 100
+056.h5 100
+057.h5 100
+058.h5 100
+059.h5 100
+060.h5 100
+061.h5 100
+062.h5 100
+063.h5 100
+064.h5 100
+065.h5 100
+066.h5 100
+067.h5 100
+068.h5 100
+069.h5 100
+070.h5 100
+071.h5 100
+072.h5 100
+073.h5 100
+074.h5 100
+075.h5 100
+076.h5 100
+077.h5 100
+078.h5 100
+079.h5 100
+080.h5 100
+081.h5 100
+082.h5 100
+083.h5 100
+084.h5 100
+085.h5 100
+086.h5 100
+087.h5 100
+088.h5 100
+089.h5 100
+090.h5 100
+091.h5 100
+092.h5 100
+093.h5 100
+094.h5 100
+095.h5 100
+096.h5 100
+097.h5 100
+098.h5 100
+099.h5 100
+100.h5 100
+101.h5 100
+102.h5 100
+103.h5 100
+104.h5 100
+105.h5 100
+106.h5 100
+107.h5 100
+108.h5 100
+109.h5 100
+110.h5 100
+111.h5 100
+112.h5 100
+113.h5 100
+114.h5 100
+115.h5 100
+116.h5 100
+117.h5 100
+118.h5 100
+119.h5 100
+120.h5 100
+121.h5 100
+122.h5 100
+123.h5 100
+124.h5 100
+125.h5 100
+126.h5 100
+127.h5 100
+128.h5 100
+129.h5 100
+130.h5 100
+131.h5 100
+132.h5 100
+133.h5 100
+134.h5 100
+135.h5 100
+136.h5 100
+137.h5 100
+138.h5 100
+139.h5 100
+140.h5 100
+141.h5 100
+142.h5 100
+143.h5 100
+144.h5 100
+145.h5 100
+146.h5 100
+147.h5 100
+148.h5 100
+149.h5 100
+150.h5 100
+151.h5 100
+152.h5 100
+153.h5 100
+154.h5 100
+155.h5 100
+156.h5 100
+157.h5 100
+158.h5 100
+159.h5 100
+160.h5 100
+161.h5 100
+162.h5 100
+163.h5 100
+164.h5 100
+165.h5 100
+166.h5 100
+167.h5 100
+168.h5 100
+169.h5 100
+170.h5 100
+171.h5 100
+172.h5 100
+173.h5 100
+174.h5 100
+175.h5 100
+176.h5 100
+177.h5 100
+178.h5 100
+179.h5 100
+180.h5 100
+181.h5 100
+182.h5 100
+183.h5 100
+184.h5 100
+185.h5 100
+186.h5 100
+187.h5 100
+188.h5 100
+189.h5 100
+190.h5 100
+191.h5 100
+192.h5 100
+193.h5 100
+194.h5 100
+195.h5 100
+196.h5 100
+197.h5 100
+198.h5 100
+199.h5 100
+200.h5 100
+201.h5 100
+202.h5 100
+203.h5 100
+204.h5 100
+205.h5 100
+206.h5 100
+207.h5 100
+208.h5 100
+209.h5 100
+210.h5 100
+211.h5 100
+212.h5 100
+213.h5 100
+214.h5 100
+215.h5 100
+216.h5 100
+217.h5 100
+218.h5 100
+219.h5 100
+220.h5 100
+221.h5 100
+222.h5 100
+223.h5 100
+224.h5 100
+225.h5 100
+226.h5 100
+227.h5 100
+228.h5 100
+229.h5 100
+230.h5 100
+231.h5 100
+232.h5 100
+233.h5 100
+234.h5 100
+235.h5 100
+236.h5 100
+237.h5 100
+238.h5 100
+239.h5 100
+240.h5 100
+241.h5 100
+242.h5 100
+243.h5 100
+244.h5 100
+245.h5 100
+246.h5 100
+247.h5 100
+248.h5 100
+249.h5 100
+250.h5 100
+251.h5 100
+252.h5 100
+253.h5 100
+254.h5 100
+255.h5 100
+256.h5 100
+257.h5 100
+258.h5 100
+259.h5 100
+260.h5 100
+261.h5 100
+262.h5 100
+263.h5 100
+264.h5 100
+265.h5 100
+266.h5 100
+267.h5 100
+268.h5 100
+269.h5 100

basicsr/data/meta_info/meta_info_Vid4_h5_test.txt

+foliage.h5 49
+city.h5 34
+walk.h5 47
+calendar.h5 41

basicsr/data/prefetch_dataloader.py

+import queue as Queue
+import threading
+import torch
+from torch.utils.data import DataLoader
+
+
+class PrefetchGenerator(threading.Thread):
+    """A general prefetch generator.
+
+    Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+    Args:
+        generator: Python generator.
+        num_prefetch_queue (int): Number of prefetch queue.
+    """
+
+    def __init__(self, generator, num_prefetch_queue):
+        threading.Thread.__init__(self)
+        self.queue = Queue.Queue(num_prefetch_queue)
+        self.generator = generator
+        self.daemon = True
+        self.start()
+
+    def run(self):
+        for item in self.generator:
+            self.queue.put(item)
+        self.queue.put(None)
+
+    def __next__(self):
+        next_item = self.queue.get()
+        if next_item is None:
+            raise StopIteration
+        return next_item
+
+    def __iter__(self):
+        return self
+
+
+class PrefetchDataLoader(DataLoader):
+    """Prefetch version of dataloader.
+
+    Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
+
+    TODO:
+    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
+    ddp.
+
+    Args:
+        num_prefetch_queue (int): Number of prefetch queue.
+        kwargs (dict): Other arguments for dataloader.
+    """
+
+    def __init__(self, num_prefetch_queue, **kwargs):
+        self.num_prefetch_queue = num_prefetch_queue
+        super(PrefetchDataLoader, self).__init__(**kwargs)
+
+    def __iter__(self):
+        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
+
+
+class CPUPrefetcher():
+    """CPU prefetcher.
+
+    Args:
+        loader: Dataloader.
+    """
+
+    def __init__(self, loader):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+
+    def next(self):
+        try:
+            return next(self.loader)
+        except StopIteration:
+            return None
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+
+
+class CUDAPrefetcher():
+    """CUDA prefetcher.
+
+    Reference: https://github.com/NVIDIA/apex/issues/304#
+
+    It may consume more GPU memory.
+
+    Args:
+        loader: Dataloader.
+        opt (dict): Options.
+    """
+
+    def __init__(self, loader, opt):
+        self.ori_loader = loader
+        self.loader = iter(loader)
+        self.opt = opt
+        self.stream = torch.cuda.Stream()
+        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
+        self.preload()
+
+    def preload(self):
+        try:
+            self.batch = next(self.loader)  # self.batch is a dict
+        except StopIteration:
+            self.batch = None
+            return None
+        # put tensors to gpu
+        with torch.cuda.stream(self.stream):
+            for k, v in self.batch.items():
+                if torch.is_tensor(v):
+                    self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
+
+    def next(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        batch = self.batch
+        self.preload()
+        return batch
+
+    def reset(self):
+        self.loader = iter(self.ori_loader)
+        self.preload()

basicsr/data/transforms.py

+import cv2
+import random
+import torch
+
+
+def mod_crop(img, scale):
+    """Mod crop images, used during testing.
+
+    Args:
+        img (ndarray): Input image.
+        scale (int): Scale factor.
+
+    Returns:
+        ndarray: Result image.
+    """
+    img = img.copy()
+    if img.ndim in (2, 3):
+        h, w = img.shape[0], img.shape[1]
+        h_remainder, w_remainder = h % scale, w % scale
+        img = img[:h - h_remainder, :w - w_remainder, ...]
+    else:
+        raise ValueError(f'Wrong img ndim: {img.ndim}.')
+    return img
+
+
+def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None):
+    """Paired random crop. Support Numpy array and Tensor inputs.
+
+    It crops lists of lq and gt images with corresponding locations.
+
+    Args:
+        img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
+            should have the same shape. If the input is an ndarray, it will
+            be transformed to a list containing itself.
+        gt_patch_size (int): GT patch size.
+        scale (int): Scale factor.
+        gt_path (str): Path to ground-truth. Default: None.
+
+    Returns:
+        list[ndarray] | ndarray: GT images and LQ images. If returned results
+            only have one element, just return ndarray.
+    """
+
+    if not isinstance(img_gts, list):
+        img_gts = [img_gts]
+    if not isinstance(img_lqs, list):
+        img_lqs = [img_lqs]
+
+    # determine input type: Numpy array or Tensor
+    input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'
+
+    if input_type == 'Tensor':
+        h_lq, w_lq = img_lqs[0].size()[-2:]
+        h_gt, w_gt = img_gts[0].size()[-2:]
+    else:
+        h_lq, w_lq = img_lqs[0].shape[0:2]
+        h_gt, w_gt = img_gts[0].shape[0:2]
+    lq_patch_size = gt_patch_size // scale
+
+    if h_gt != h_lq * scale or w_gt != w_lq * scale:
+        raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
+                         f'multiplication of LQ ({h_lq}, {w_lq}).')
+    if h_lq < lq_patch_size or w_lq < lq_patch_size:
+        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
+                         f'({lq_patch_size}, {lq_patch_size}). '
+                         f'Please remove {gt_path}.')
+
+    # randomly choose top and left coordinates for lq patch
+    top = random.randint(0, h_lq - lq_patch_size)
+    left = random.randint(0, w_lq - lq_patch_size)
+
+    # crop lq patch
+    if input_type == 'Tensor':
+        img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs]
+    else:
+        img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
+
+    # crop corresponding gt patch
+    top_gt, left_gt = int(top * scale), int(left * scale)
+    if input_type == 'Tensor':
+        img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts]
+    else:
+        img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
+    if len(img_gts) == 1:
+        img_gts = img_gts[0]
+    if len(img_lqs) == 1:
+        img_lqs = img_lqs[0]
+    return img_gts, img_lqs
+
+
+def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
+    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
+
+    We use vertical flip and transpose for rotation implementation.
+    All the images in the list use the same augmentation.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
+            is an ndarray, it will be transformed to a list.
+        hflip (bool): Horizontal flip. Default: True.
+        rotation (bool): Ratotation. Default: True.
+        flows (list[ndarray]: Flows to be augmented. If the input is an
+            ndarray, it will be transformed to a list.
+            Dimension is (h, w, 2). Default: None.
+        return_status (bool): Return the status of flip and rotation.
+            Default: False.
+
+    Returns:
+        list[ndarray] | ndarray: Augmented images and flows. If returned
+            results only have one element, just return ndarray.
+
+    """
+    hflip = hflip and random.random() < 0.5
+    vflip = rotation and random.random() < 0.5
+    rot90 = rotation and random.random() < 0.5
+
+    def _augment(img):
+        if hflip:  # horizontal
+            cv2.flip(img, 1, img)
+        if vflip:  # vertical
+            cv2.flip(img, 0, img)
+        if rot90:
+            img = img.transpose(1, 0, 2)
+        return img
+
+    def _augment_flow(flow):
+        if hflip:  # horizontal
+            cv2.flip(flow, 1, flow)
+            flow[:, :, 0] *= -1
+        if vflip:  # vertical
+            cv2.flip(flow, 0, flow)
+            flow[:, :, 1] *= -1
+        if rot90:
+            flow = flow.transpose(1, 0, 2)
+            flow = flow[:, :, [1, 0]]
+        return flow
+
+    if not isinstance(imgs, list):
+        imgs = [imgs]
+    imgs = [_augment(img) for img in imgs]
+    if len(imgs) == 1:
+        imgs = imgs[0]
+
+    if flows is not None:
+        if not isinstance(flows, list):
+            flows = [flows]
+        flows = [_augment_flow(flow) for flow in flows]
+        if len(flows) == 1:
+            flows = flows[0]
+        return imgs, flows
+    else:
+        if return_status:
+            return imgs, (hflip, vflip, rot90)
+        else:
+            return imgs
+
+
+def img_rotate(img, angle, center=None, scale=1.0):
+    """Rotate image.
+
+    Args:
+        img (ndarray): Image to be rotated.
+        angle (float): Rotation angle in degrees. Positive values mean
+            counter-clockwise rotation.
+        center (tuple[int]): Rotation center. If the center is None,
+            initialize it as the center of the image. Default: None.
+        scale (float): Isotropic scale factor. Default: 1.0.
+    """
+    (h, w) = img.shape[:2]
+
+    if center is None:
+        center = (w // 2, h // 2)
+
+    matrix = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated_img = cv2.warpAffine(img, matrix, (w, h))
+    return rotated_img

basicsr/data/video_test_dataset.py

+import numpy as np
+import torch
+from os import path as osp
+from torch.utils import data as data
+
+from basicsr.utils import get_root_logger, FileClient
+from basicsr.utils.registry import DATASET_REGISTRY
+from basicsr.data.transforms import mod_crop
+from basicsr.utils.img_util import img2tensor
+
+@DATASET_REGISTRY.register()
+class VideoWithEventsTestDataset(data.Dataset):
+
+    def __init__(self, opt):
+        super().__init__()
+        self.opt = opt
+        self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
+        self.data_info = {'folder': []}
+        self.scale = opt['scale']
+
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.name = opt['name']
+        if self.io_backend_opt['type'] == 'hdf5':
+            self.io_backend_opt['h5_paths'] = [self.lq_root, self.gt_root]
+            self.io_backend_opt['client_keys'] = ['LR', 'HR']
+        else:
+            raise ValueError(f"We don't realize {self.io_backend_opt['type']} backend")
+
+        logger = get_root_logger()
+        logger.info(f'Generate data info for VideoWithEventsTestDataset - {opt["name"]}')
+
+        if 'meta_info_file' in opt:
+            with open(opt['meta_info_file'], 'r') as fin:
+                clips = []
+                clips_num = []
+                for line in fin:
+                    clips.append(line.split(' ')[0])
+                    clips_num.append(line.split(' ')[1])
+        else:
+            raise NotImplementedError
+
+        self.imgs_lq, self.imgs_gt, self.event_lqs = {}, {}, {}
+        self.folders = []
+        self.lq_paths = []
+        for clip, num in zip(clips, clips_num):
+            self.io_backend_opt['h5_clip'] = clip
+            self.file_client = FileClient(self.io_backend_opt['type'], **self.io_backend_opt)
+
+            img_lqs, img_gts, event_lqs = self.file_client.get(list(range(int(num))))
+            # mod_crop gt image for scale
+            img_gts = [mod_crop(img, self.scale) for img in img_gts]
+            self.imgs_lq[clip] = torch.stack(img2tensor(img_lqs), dim=0)
+            self.imgs_gt[clip] = torch.stack(img2tensor(img_gts), dim=0)
+            self.event_lqs[clip] = torch.from_numpy(np.stack(event_lqs, axis=0))
+            self.folders.append(clip)
+            self.lq_paths.append(osp.join('vid4', osp.splitext(clip)[0]))
+            self.data_info['folder'].extend([clip] * int(num))
+
+    def __getitem__(self, index):
+        folder = self.folders[index]
+        lq_path = self.lq_paths[index]
+
+        img_lq = self.imgs_lq[folder]
+        img_gt = self.imgs_gt[folder]
+        event_lq = self.event_lqs[folder]
+
+        voxel_f = event_lq[:len(event_lq) // 2]
+        voxel_b = event_lq[len(event_lq) // 2:]
+        return {
+            'lq': img_lq,
+            'gt': img_gt,
+            'voxels_f': voxel_f,
+            'voxels_b': voxel_b,
+            'folder': folder,
+            'lq_path': lq_path
+        }
+
+    def __len__(self):
+        return len(self.folders)
\ No newline at end of file

basicsr/losses/__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 LOSS_REGISTRY
+from .gan_loss import g_path_regularize, gradient_penalty_loss, r1_penalty
+
+__all__ = ['build_loss', 'gradient_penalty_loss', 'r1_penalty', 'g_path_regularize']
+
+# automatically scan and import loss modules for registry
+# scan all the files under the 'losses' folder and collect files ending with '_loss.py'
+loss_folder = osp.dirname(osp.abspath(__file__))
+loss_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(loss_folder) if v.endswith('_loss.py')]
+# import all the loss modules
+_model_modules = [importlib.import_module(f'basicsr.losses.{file_name}') for file_name in loss_filenames]
+
+
+def build_loss(opt):
+    """Build loss from options.
+
+    Args:
+        opt (dict): Configuration. It must contain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    loss_type = opt.pop('type')
+    loss = LOSS_REGISTRY.get(loss_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Loss [{loss.__class__.__name__}] is created.')
+    return loss

basicsr/losses/basic_loss.py

+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from basicsr.archs.vgg_arch import VGGFeatureExtractor
+from basicsr.utils.registry import LOSS_REGISTRY
+from .loss_util import weighted_loss
+
+_reduction_modes = ['none', 'mean', 'sum']
+
+
+@weighted_loss
+def l1_loss(pred, target):
+    return F.l1_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def mse_loss(pred, target):
+    return F.mse_loss(pred, target, reduction='none')
+
+
+@weighted_loss
+def charbonnier_loss(pred, target, eps=1e-12):
+    return torch.sqrt((pred - target)**2 + eps)
+
+
+@LOSS_REGISTRY.register()
+class L1Loss(nn.Module):
+    """L1 (mean absolute error, MAE) loss.
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(L1Loss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None.
+        """
+        return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class MSELoss(nn.Module):
+    """MSE (L2) loss.
+
+    Args:
+        loss_weight (float): Loss weight for MSE loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        super(MSELoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None.
+        """
+        return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class CharbonnierLoss(nn.Module):
+    """Charbonnier loss (one variant of Robust L1Loss, a differentiable
+    variant of L1Loss).
+
+    Described in "Deep Laplacian Pyramid Networks for Fast and Accurate
+        Super-Resolution".
+
+    Args:
+        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
+        reduction (str): Specifies the reduction to apply to the output.
+            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
+        eps (float): A value used to control the curvature near zero. Default: 1e-12.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12):
+        super(CharbonnierLoss, self).__init__()
+        if reduction not in ['none', 'mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}')
+
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+        self.eps = eps
+
+    def forward(self, pred, target, weight=None, **kwargs):
+        """
+        Args:
+            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
+            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
+            weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None.
+        """
+        return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction)
+
+
+@LOSS_REGISTRY.register()
+class WeightedTVLoss(L1Loss):
+    """Weighted TV loss.
+
+    Args:
+        loss_weight (float): Loss weight. Default: 1.0.
+    """
+
+    def __init__(self, loss_weight=1.0, reduction='mean'):
+        if reduction not in ['mean', 'sum']:
+            raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: mean | sum')
+        super(WeightedTVLoss, self).__init__(loss_weight=loss_weight, reduction=reduction)
+
+    def forward(self, pred, weight=None):
+        if weight is None:
+            y_weight = None
+            x_weight = None
+        else:
+            y_weight = weight[:, :, :-1, :]
+            x_weight = weight[:, :, :, :-1]
+
+        y_diff = super().forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=y_weight)
+        x_diff = super().forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=x_weight)
+
+        loss = x_diff + y_diff
+
+        return loss
+
+
+@LOSS_REGISTRY.register()
+class PerceptualLoss(nn.Module):
+    """Perceptual loss with commonly used style loss.
+
+    Args:
+        layer_weights (dict): The weight for each layer of vgg feature.
+            Here is an example: {'conv5_4': 1.}, which means the conv5_4
+            feature layer (before relu5_4) will be extracted with weight
+            1.0 in calculating losses.
+        vgg_type (str): The type of vgg network used as feature extractor.
+            Default: 'vgg19'.
+        use_input_norm (bool):  If True, normalize the input image in vgg.
+            Default: True.
+        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
+            Default: False.
+        perceptual_weight (float): If `perceptual_weight > 0`, the perceptual
+            loss will be calculated and the loss will multiplied by the
+            weight. Default: 1.0.
+        style_weight (float): If `style_weight > 0`, the style loss will be
+            calculated and the loss will multiplied by the weight.
+            Default: 0.
+        criterion (str): Criterion used for perceptual loss. Default: 'l1'.
+    """
+
+    def __init__(self,
+                 layer_weights,
+                 vgg_type='vgg19',
+                 use_input_norm=True,
+                 range_norm=False,
+                 perceptual_weight=1.0,
+                 style_weight=0.,
+                 criterion='l1'):
+        super(PerceptualLoss, self).__init__()
+        self.perceptual_weight = perceptual_weight
+        self.style_weight = style_weight
+        self.layer_weights = layer_weights
+        self.vgg = VGGFeatureExtractor(
+            layer_name_list=list(layer_weights.keys()),
+            vgg_type=vgg_type,
+            use_input_norm=use_input_norm,
+            range_norm=range_norm)
+
+        self.criterion_type = criterion
+        if self.criterion_type == 'l1':
+            self.criterion = torch.nn.L1Loss()
+        elif self.criterion_type == 'l2':
+            self.criterion = torch.nn.MSELoss()
+        elif self.criterion_type == 'fro':
+            self.criterion = None
+        else:
+            raise NotImplementedError(f'{criterion} criterion has not been supported.')
+
+    def forward(self, x, gt):
+        """Forward function.
+
+        Args:
+            x (Tensor): Input tensor with shape (n, c, h, w).
+            gt (Tensor): Ground-truth tensor with shape (n, c, h, w).
+
+        Returns:
+            Tensor: Forward results.
+        """
+        # extract vgg features
+        x_features = self.vgg(x)
+        gt_features = self.vgg(gt.detach())
+
+        # calculate perceptual loss
+        if self.perceptual_weight > 0:
+            percep_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k]
+                else:
+                    percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k]
+            percep_loss *= self.perceptual_weight
+        else:
+            percep_loss = None
+
+        # calculate style loss
+        if self.style_weight > 0:
+            style_loss = 0
+            for k in x_features.keys():
+                if self.criterion_type == 'fro':
+                    style_loss += torch.norm(
+                        self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k]
+                else:
+                    style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat(
+                        gt_features[k])) * self.layer_weights[k]
+            style_loss *= self.style_weight
+        else:
+            style_loss = None
+
+        return percep_loss, style_loss
+
+    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

basicsr/losses/loss_util.py

+import functools
+import torch
+from torch.nn import functional as F
+
+
+def reduce_loss(loss, reduction):
+    """Reduce loss as specified.
+
+    Args:
+        loss (Tensor): Elementwise loss tensor.
+        reduction (str): Options are 'none', 'mean' and 'sum'.
+
+    Returns:
+        Tensor: Reduced loss tensor.
+    """
+    reduction_enum = F._Reduction.get_enum(reduction)
+    # none: 0, elementwise_mean:1, sum: 2
+    if reduction_enum == 0:
+        return loss
+    elif reduction_enum == 1:
+        return loss.mean()
+    else:
+        return loss.sum()
+
+
+def weight_reduce_loss(loss, weight=None, reduction='mean'):
+    """Apply element-wise weight and reduce loss.
+
+    Args:
+        loss (Tensor): Element-wise loss.
+        weight (Tensor): Element-wise weights. Default: None.
+        reduction (str): Same as built-in losses of PyTorch. Options are
+            'none', 'mean' and 'sum'. Default: 'mean'.
+
+    Returns:
+        Tensor: Loss values.
+    """
+    # if weight is specified, apply element-wise weight
+    if weight is not None:
+        assert weight.dim() == loss.dim()
+        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
+        loss = loss * weight
+
+    # if weight is not specified or reduction is sum, just reduce the loss
+    if weight is None or reduction == 'sum':
+        loss = reduce_loss(loss, reduction)
+    # if reduction is mean, then compute mean over weight region
+    elif reduction == 'mean':
+        if weight.size(1) > 1:
+            weight = weight.sum()
+        else:
+            weight = weight.sum() * loss.size(1)
+        loss = loss.sum() / weight
+
+    return loss
+
+
+def weighted_loss(loss_func):
+    """Create a weighted version of a given loss function.
+
+    To use this decorator, the loss function must have the signature like
+    `loss_func(pred, target, **kwargs)`. The function only needs to compute
+    element-wise loss without any reduction. This decorator will add weight
+    and reduction arguments to the function. The decorated function will have
+    the signature like `loss_func(pred, target, weight=None, reduction='mean',
+    **kwargs)`.
+
+    :Example:
+
+    >>> import torch
+    >>> @weighted_loss
+    >>> def l1_loss(pred, target):
+    >>>     return (pred - target).abs()
+
+    >>> pred = torch.Tensor([0, 2, 3])
+    >>> target = torch.Tensor([1, 1, 1])
+    >>> weight = torch.Tensor([1, 0, 1])
+
+    >>> l1_loss(pred, target)
+    tensor(1.3333)
+    >>> l1_loss(pred, target, weight)
+    tensor(1.5000)
+    >>> l1_loss(pred, target, reduction='none')
+    tensor([1., 1., 2.])
+    >>> l1_loss(pred, target, weight, reduction='sum')
+    tensor(3.)
+    """
+
+    @functools.wraps(loss_func)
+    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
+        # get element-wise loss
+        loss = loss_func(pred, target, **kwargs)
+        loss = weight_reduce_loss(loss, weight, reduction)
+        return loss
+
+    return wrapper
+
+
+def get_local_weights(residual, ksize):
+    """Get local weights for generating the artifact map of LDL.
+
+    It is only called by the `get_refined_artifact_map` function.
+
+    Args:
+        residual (Tensor): Residual between predicted and ground truth images.
+        ksize (Int): size of the local window.
+
+    Returns:
+        Tensor: weight for each pixel to be discriminated as an artifact pixel
+    """
+
+    pad = (ksize - 1) // 2
+    residual_pad = F.pad(residual, pad=[pad, pad, pad, pad], mode='reflect')
+
+    unfolded_residual = residual_pad.unfold(2, ksize, 1).unfold(3, ksize, 1)
+    pixel_level_weight = torch.var(unfolded_residual, dim=(-1, -2), unbiased=True, keepdim=True).squeeze(-1).squeeze(-1)
+
+    return pixel_level_weight
+
+
+def get_refined_artifact_map(img_gt, img_output, img_ema, ksize):
+    """Calculate the artifact map of LDL
+    (Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2022)
+
+    Args:
+        img_gt (Tensor): ground truth images.
+        img_output (Tensor): output images given by the optimizing model.
+        img_ema (Tensor): output images given by the ema model.
+        ksize (Int): size of the local window.
+
+    Returns:
+        overall_weight: weight for each pixel to be discriminated as an artifact pixel
+        (calculated based on both local and global observations).
+    """
+
+    residual_ema = torch.sum(torch.abs(img_gt - img_ema), 1, keepdim=True)
+    residual_sr = torch.sum(torch.abs(img_gt - img_output), 1, keepdim=True)
+
+    patch_level_weight = torch.var(residual_sr.clone(), dim=(-1, -2, -3), keepdim=True)**(1 / 5)
+    pixel_level_weight = get_local_weights(residual_sr.clone(), ksize)
+    overall_weight = patch_level_weight * pixel_level_weight
+
+    overall_weight[residual_sr < residual_ema] = 0
+
+    return overall_weight

basicsr/metrics/__init__.py

+from copy import deepcopy
+
+from basicsr.utils.registry import METRIC_REGISTRY
+from .niqe import calculate_niqe
+from .psnr_ssim import calculate_psnr, calculate_ssim
+from .lpips import calculate_lpips
+
+__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe']
+
+
+def calculate_metric(data, opt):
+    """Calculate metric from data and options.
+
+    Args:
+        opt (dict): Configuration. It must contain:
+            type (str): Model type.
+    """
+    opt = deepcopy(opt)
+    metric_type = opt.pop('type')
+    metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
+    return metric