137 lines
4.9 KiB
Python
137 lines
4.9 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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import random
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import torch
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from pathlib import Path
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from torch.utils import data as data
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from basicsr.data.transforms import augment, paired_random_crop
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from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
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class Vimeo90KDataset(data.Dataset):
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"""Vimeo90K dataset for training.
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The keys are generated from a meta info txt file.
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basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt
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Each line contains:
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1. clip name; 2. frame number; 3. image shape, seperated by a white space.
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Examples:
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00001/0001 7 (256,448,3)
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00001/0002 7 (256,448,3)
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Key examples: "00001/0001"
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GT (gt): Ground-Truth;
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LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
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The neighboring frame list for different num_frame:
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num_frame | frame list
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1 | 4
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3 | 3,4,5
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5 | 2,3,4,5,6
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7 | 1,2,3,4,5,6,7
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Args:
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opt (dict): Config for train dataset. It contains the following keys:
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dataroot_gt (str): Data root path for gt.
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dataroot_lq (str): Data root path for lq.
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meta_info_file (str): Path for meta information file.
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io_backend (dict): IO backend type and other kwarg.
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num_frame (int): Window size for input frames.
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gt_size (int): Cropped patched size for gt patches.
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random_reverse (bool): Random reverse input frames.
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use_flip (bool): Use horizontal flips.
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use_rot (bool): Use rotation (use vertical flip and transposing h
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and w for implementation).
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scale (bool): Scale, which will be added automatically.
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"""
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def __init__(self, opt):
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super(Vimeo90KDataset, self).__init__()
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self.opt = opt
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self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(
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opt['dataroot_lq'])
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with open(opt['meta_info_file'], 'r') as fin:
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self.keys = [line.split(' ')[0] for line in fin]
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# file client (io backend)
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self.file_client = None
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self.io_backend_opt = opt['io_backend']
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self.is_lmdb = False
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if self.io_backend_opt['type'] == 'lmdb':
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self.is_lmdb = True
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self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
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self.io_backend_opt['client_keys'] = ['lq', 'gt']
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# indices of input images
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self.neighbor_list = [
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i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])
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]
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# temporal augmentation configs
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self.random_reverse = opt['random_reverse']
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logger = get_root_logger()
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logger.info(f'Random reverse is {self.random_reverse}.')
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def __getitem__(self, index):
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if self.file_client is None:
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self.file_client = FileClient(
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self.io_backend_opt.pop('type'), **self.io_backend_opt)
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# random reverse
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if self.random_reverse and random.random() < 0.5:
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self.neighbor_list.reverse()
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scale = self.opt['scale']
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gt_size = self.opt['gt_size']
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key = self.keys[index]
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clip, seq = key.split('/') # key example: 00001/0001
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# get the GT frame (im4.png)
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if self.is_lmdb:
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img_gt_path = f'{key}/im4'
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else:
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img_gt_path = self.gt_root / clip / seq / 'im4.png'
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img_bytes = self.file_client.get(img_gt_path, 'gt')
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img_gt = imfrombytes(img_bytes, float32=True)
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# get the neighboring LQ frames
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img_lqs = []
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for neighbor in self.neighbor_list:
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if self.is_lmdb:
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img_lq_path = f'{clip}/{seq}/im{neighbor}'
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else:
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img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
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img_bytes = self.file_client.get(img_lq_path, 'lq')
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img_lq = imfrombytes(img_bytes, float32=True)
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img_lqs.append(img_lq)
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# randomly crop
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img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
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img_gt_path)
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# augmentation - flip, rotate
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img_lqs.append(img_gt)
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img_results = augment(img_lqs, self.opt['use_flip'],
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self.opt['use_rot'])
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img_results = img2tensor(img_results)
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img_lqs = torch.stack(img_results[0:-1], dim=0)
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img_gt = img_results[-1]
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# img_lqs: (t, c, h, w)
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# img_gt: (c, h, w)
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# key: str
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return {'lq': img_lqs, 'gt': img_gt, 'key': key}
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def __len__(self):
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return len(self.keys)
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