basicsr/data/vimeo90k_dataset.py

import random
import torch
from pathlib import Path
from torch.utils import data as data

from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY


@DATASET_REGISTRY.register()
class Vimeo90KDataset(data.Dataset):
    """Vimeo90K dataset for training.

    The keys are generated from a meta info txt file.
    basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt

    Each line contains the following items, separated by a white space.

    1. clip name;
    2. frame number;
    3. image shape

    Examples:

    ::

        00001/0001 7 (256,448,3)
        00001/0002 7 (256,448,3)

    - Key examples: "00001/0001"
    - GT (gt): Ground-Truth;
    - LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.

    The neighboring frame list for different num_frame:

    ::

        num_frame | frame list
                1 | 4
                3 | 3,4,5
                5 | 2,3,4,5,6
                7 | 1,2,3,4,5,6,7

    Args:
        opt (dict): Config for train dataset. It contains the following keys:
        dataroot_gt (str): Data root path for gt.
        dataroot_lq (str): Data root path for lq.
        meta_info_file (str): Path for meta information file.
        io_backend (dict): IO backend type and other kwarg.
        num_frame (int): Window size for input frames.
        gt_size (int): Cropped patched size for gt patches.
        random_reverse (bool): Random reverse input frames.
        use_hflip (bool): Use horizontal flips.
        use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
        scale (bool): Scale, which will be added automatically.
    """

    def __init__(self, opt):
        super(Vimeo90KDataset, self).__init__()
        self.opt = opt
        self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])

        with open(opt['meta_info_file'], 'r') as fin:
            self.keys = [line.split(' ')[0] for line in fin]

        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.is_lmdb = False
        if self.io_backend_opt['type'] == 'lmdb':
            self.is_lmdb = True
            self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
            self.io_backend_opt['client_keys'] = ['lq', 'gt']

        # indices of input images
        self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])]

        # temporal augmentation configs
        self.random_reverse = opt['random_reverse']
        logger = get_root_logger()
        logger.info(f'Random reverse is {self.random_reverse}.')

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # random reverse
        if self.random_reverse and random.random() < 0.5:
            self.neighbor_list.reverse()

        scale = self.opt['scale']
        gt_size = self.opt['gt_size']
        key = self.keys[index]
        clip, seq = key.split('/')  # key example: 00001/0001

        # get the GT frame (im4.png)
        if self.is_lmdb:
            img_gt_path = f'{key}/im4'
        else:
            img_gt_path = self.gt_root / clip / seq / 'im4.png'
        img_bytes = self.file_client.get(img_gt_path, 'gt')
        img_gt = imfrombytes(img_bytes, float32=True)

        # get the neighboring LQ frames
        img_lqs = []
        for neighbor in self.neighbor_list:
            if self.is_lmdb:
                img_lq_path = f'{clip}/{seq}/im{neighbor}'
            else:
                img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
            img_bytes = self.file_client.get(img_lq_path, 'lq')
            img_lq = imfrombytes(img_bytes, float32=True)
            img_lqs.append(img_lq)

        # randomly crop
        img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path)

        # augmentation - flip, rotate
        img_lqs.append(img_gt)
        img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'])

        img_results = img2tensor(img_results)
        img_lqs = torch.stack(img_results[0:-1], dim=0)
        img_gt = img_results[-1]

        # img_lqs: (t, c, h, w)
        # img_gt: (c, h, w)
        # key: str
        return {'lq': img_lqs, 'gt': img_gt, 'key': key}

    def __len__(self):
        return len(self.keys)


@DATASET_REGISTRY.register()
class Vimeo90KRecurrentDataset(Vimeo90KDataset):

    def __init__(self, opt):
        super(Vimeo90KRecurrentDataset, self).__init__(opt)

        self.flip_sequence = opt['flip_sequence']
        self.neighbor_list = [1, 2, 3, 4, 5, 6, 7]

    def __getitem__(self, index):
        if self.file_client is None:
            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)

        # random reverse
        if self.random_reverse and random.random() < 0.5:
            self.neighbor_list.reverse()

        scale = self.opt['scale']
        gt_size = self.opt['gt_size']
        key = self.keys[index]
        clip, seq = key.split('/')  # key example: 00001/0001

        # get the neighboring LQ and  GT frames
        img_lqs = []
        img_gts = []
        for neighbor in self.neighbor_list:
            if self.is_lmdb:
                img_lq_path = f'{clip}/{seq}/im{neighbor}'
                img_gt_path = f'{clip}/{seq}/im{neighbor}'
            else:
                img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
                img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png'
            # LQ
            img_bytes = self.file_client.get(img_lq_path, 'lq')
            img_lq = imfrombytes(img_bytes, float32=True)
            # GT
            img_bytes = self.file_client.get(img_gt_path, 'gt')
            img_gt = imfrombytes(img_bytes, float32=True)

            img_lqs.append(img_lq)
            img_gts.append(img_gt)

        # randomly crop
        img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path)

        # augmentation - flip, rotate
        img_lqs.extend(img_gts)
        img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'])

        img_results = img2tensor(img_results)
        img_lqs = torch.stack(img_results[:7], dim=0)
        img_gts = torch.stack(img_results[7:], dim=0)

        if self.flip_sequence:  # flip the sequence: 7 frames to 14 frames
            img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0)
            img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0)

        # img_lqs: (t, c, h, w)
        # img_gt: (c, h, w)
        # key: str
        return {'lq': img_lqs, 'gt': img_gts, 'key': key}

    def __len__(self):
        return len(self.keys)