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video_test_dataset.py
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import glob
import torch
from os import path as osp
from torch.utils import data as data
from basicsr.data.data_util import (duf_downsample, generate_frame_indices, read_img_seq)
from basicsr.utils import get_root_logger, scandir
import pdb
class VideoTestDataset(data.Dataset):
"""Video test dataset.
Supported datasets: Vid4, REDS4, REDSofficial.
More generally, it supports testing dataset with following structures:
dataroot
├── subfolder1
├── frame000
├── frame001
├── ...
├── subfolder1
├── frame000
├── frame001
├── ...
├── ...
For testing datasets, there is no need to prepare LMDB files.
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.
io_backend (dict): IO backend type and other kwarg.
cache_data (bool): Whether to cache testing datasets.
name (str): Dataset name.
meta_info_file (str): The path to the file storing the list of test
folders. If not provided, all the folders in the dataroot will
be used.
num_frame (int): Window size for input frames.
padding (str): Padding mode.
"""
def __init__(self, opt):
super(VideoTestDataset, self).__init__()
self.opt = opt
self.cache_data = opt['cache_data']
self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
self.data_info = {
'lq_path': [],
'gt_path': [],
'folder': [],
'idx': [],
'border': []
}
# file client (io backend)
self.file_client = None
self.io_backend_opt = opt['io_backend']
assert self.io_backend_opt[
'type'] != 'lmdb', 'No need to use lmdb during validation/test.'
logger = get_root_logger()
logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
self.imgs_lq, self.imgs_gt = {}, {}
if 'meta_info_file' in opt:
with open(opt['meta_info_file'], 'r') as fin:
subfolders = [line.split(' ')[0] for line in fin]
subfolders_lq = [
osp.join(self.lq_root, key) for key in subfolders
]
subfolders_gt = [
osp.join(self.gt_root, key) for key in subfolders
]
else:
subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*')))
if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']:
for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt):
# get frame list for lq and gt
subfolder_name = osp.basename(subfolder_lq)
img_paths_lq = sorted(
list(scandir(subfolder_lq, full_path=True)))
img_paths_gt = sorted(
list(scandir(subfolder_gt, full_path=True)))
max_idx = len(img_paths_lq)
assert max_idx == len(img_paths_gt), (
f'Different number of images in lq ({max_idx})'
f' and gt folders ({len(img_paths_gt)})')
self.data_info['lq_path'].extend(img_paths_lq)
self.data_info['gt_path'].extend(img_paths_gt)
self.data_info['folder'].extend([subfolder_name] * max_idx)
for i in range(max_idx):
self.data_info['idx'].append(f'{i}/{max_idx}')
border_l = [0] * max_idx
for i in range(self.opt['num_frame'] // 2):
border_l[i] = 1
border_l[max_idx - i - 1] = 1
self.data_info['border'].extend(border_l)
# cache data or save the frame list
if self.cache_data:
logger.info(
f'Cache {subfolder_name} for VideoTestDataset...')
self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq)
self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt)
else:
self.imgs_lq[subfolder_name] = img_paths_lq
self.imgs_gt[subfolder_name] = img_paths_gt
else:
raise ValueError(
f'Non-supported video test dataset: {type(opt["name"])}')
def __getitem__(self, index):
folder = self.data_info['folder'][index]
idx, max_idx = self.data_info['idx'][index].split('/')
idx, max_idx = int(idx), int(max_idx)
border = self.data_info['border'][index]
lq_path = self.data_info['lq_path'][index]
# pdb.set_trace()
select_idx = generate_frame_indices(
idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
if self.cache_data:
imgs_lq = self.imgs_lq[folder].index_select(
0, torch.LongTensor(select_idx))
img_gt = self.imgs_gt[folder][idx]
else:
img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
imgs_lq = read_img_seq(img_paths_lq)
img_gt = read_img_seq([self.imgs_gt[folder][idx]])
img_gt.squeeze_(0)
return {
'lq': imgs_lq, # (t, c, h, w)
'gt': img_gt, # (c, h, w)
'folder': folder, # folder name
'idx': self.data_info['idx'][index], # e.g., 0/99
'border': border, # 1 for border, 0 for non-border
'lq_path': lq_path # center frame
}
def __len__(self):
return len(self.data_info['gt_path'])
class VideoTestVimeo90KDataset(data.Dataset):
"""Video test dataset for Vimeo90k-Test dataset.
It only keeps the center frame for testing.
For testing datasets, there is no need to prepare LMDB files.
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.
io_backend (dict): IO backend type and other kwarg.
cache_data (bool): Whether to cache testing datasets.
name (str): Dataset name.
meta_info_file (str): The path to the file storing the list of test
folders. If not provided, all the folders in the dataroot will
be used.
num_frame (int): Window size for input frames.
padding (str): Padding mode.
"""
def __init__(self, opt):
super(VideoTestVimeo90KDataset, self).__init__()
self.opt = opt
self.cache_data = opt['cache_data']
if self.cache_data:
raise NotImplementedError(
'cache_data in Vimeo90K-Test dataset is not implemented.')
self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
self.data_info = {
'lq_path': [],
'gt_path': [],
'folder': [],
'idx': [],
'border': []
}
neighbor_list = [
i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])
]
# file client (io backend)
self.file_client = None
self.io_backend_opt = opt['io_backend']
assert self.io_backend_opt[
'type'] != 'lmdb', 'No need to use lmdb during validation/test.'
logger = get_root_logger()
logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}')
with open(opt['meta_info_file'], 'r') as fin:
subfolders = [line.split(' ')[0] for line in fin]
for idx, subfolder in enumerate(subfolders):
gt_path = osp.join(self.gt_root, subfolder, 'im4.png')
self.data_info['gt_path'].append(gt_path)
lq_paths = [
osp.join(self.lq_root, subfolder, f'im{i}.png')
for i in neighbor_list
]
self.data_info['lq_path'].append(lq_paths)
self.data_info['folder'].append('vimeo90k')
self.data_info['idx'].append(f'{idx}/{len(subfolders)}')
self.data_info['border'].append(0)
def __getitem__(self, index):
lq_path = self.data_info['lq_path'][index]
gt_path = self.data_info['gt_path'][index]
imgs_lq = read_img_seq(lq_path)
img_gt = read_img_seq([gt_path])
img_gt.squeeze_(0)
return {
'lq': imgs_lq, # (t, c, h, w)
'gt': img_gt, # (c, h, w)
'folder': self.data_info['folder'][index], # folder name
'idx': self.data_info['idx'][index], # e.g., 0/843
'border': self.data_info['border'][index], # 0 for non-border
'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame
}
def __len__(self):
return len(self.data_info['gt_path'])
class VideoTestDUFDataset(VideoTestDataset):
""" Video test dataset for DUF dataset.
Args:
opt (dict): Config for train dataset.
Most of keys are the same as VideoTestDataset.
It has the follwing extra keys:
use_duf_downsampling (bool): Whether to use duf downsampling to
generate low-resolution frames.
scale (bool): Scale, which will be added automatically.
"""
def __getitem__(self, index):
folder = self.data_info['folder'][index]
idx, max_idx = self.data_info['idx'][index].split('/')
idx, max_idx = int(idx), int(max_idx)
border = self.data_info['border'][index]
lq_path = self.data_info['lq_path'][index]
select_idx = generate_frame_indices(
idx, max_idx, self.opt['num_frame'], padding=self.opt['padding'])
if self.cache_data:
if self.opt['use_duf_downsampling']:
# read imgs_gt to generate low-resolution frames
imgs_lq = self.imgs_gt[folder].index_select(
0, torch.LongTensor(select_idx))
imgs_lq = duf_downsample(
imgs_lq, kernel_size=13, scale=self.opt['scale'])
else:
imgs_lq = self.imgs_lq[folder].index_select(
0, torch.LongTensor(select_idx))
img_gt = self.imgs_gt[folder][idx]
else:
if self.opt['use_duf_downsampling']:
img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx]
# read imgs_gt to generate low-resolution frames
imgs_lq = read_img_seq(
img_paths_lq,
require_mod_crop=True,
scale=self.opt['scale'])
imgs_lq = duf_downsample(
imgs_lq, kernel_size=13, scale=self.opt['scale'])
else:
img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx]
imgs_lq = read_img_seq(img_paths_lq)
img_gt = read_img_seq([self.imgs_gt[folder][idx]],
require_mod_crop=True,
scale=self.opt['scale'])
img_gt.squeeze_(0)
return {
'lq': imgs_lq, # (t, c, h, w)
'gt': img_gt, # (c, h, w)
'folder': folder, # folder name
'idx': self.data_info['idx'][index], # e.g., 0/99
'border': border, # 1 for border, 0 for non-border
'lq_path': lq_path # center frame
}
class VideoRecurrentTestDataset(VideoTestDataset):
"""Video test dataset for recurrent architectures, which takes LR video
frames as input and output corresponding HR video frames.
Args:
Same as VideoTestDataset.
Unused opt:
padding (str): Padding mode.
"""
def __init__(self, opt):
super(VideoRecurrentTestDataset, self).__init__(opt)
# Find unique folder strings
self.folders = sorted(list(set(self.data_info['folder'])))
def __getitem__(self, index):
folder = self.folders[index]
if self.cache_data:
imgs_lq = self.imgs_lq[folder]
imgs_gt = self.imgs_gt[folder]
else:
raise NotImplementedError('Without cache_data is not implemented.')
return {
'lq': imgs_lq,
'gt': imgs_gt,
'folder': folder,
}
def __len__(self):
return len(self.folders)