kolcontrl / basicsr /data /reds_dataset.py
lixiang46
fix basicsr bug
a64b7d4
raw
history blame
15.2 kB
import numpy as np
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.flow_util import dequantize_flow
from basicsr.utils.registry import DATASET_REGISTRY
@DATASET_REGISTRY.register()
class REDSDataset(data.Dataset):
"""REDS dataset for training.
The keys are generated from a meta info txt file.
basicsr/data/meta_info/meta_info_REDS_GT.txt
Each line contains:
1. subfolder (clip) name; 2. frame number; 3. image shape, separated by
a white space.
Examples:
000 100 (720,1280,3)
001 100 (720,1280,3)
...
Key examples: "000/00000000"
GT (gt): Ground-Truth;
LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
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.
dataroot_flow (str, optional): Data root path for flow.
meta_info_file (str): Path for meta information file.
val_partition (str): Validation partition types. 'REDS4' or 'official'.
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.
interval_list (list): Interval list for temporal augmentation.
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(REDSDataset, self).__init__()
self.opt = opt
self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])
self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None
assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}')
self.num_frame = opt['num_frame']
self.num_half_frames = opt['num_frame'] // 2
self.keys = []
with open(opt['meta_info_file'], 'r') as fin:
for line in fin:
folder, frame_num, _ = line.split(' ')
self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
# remove the video clips used in validation
if opt['val_partition'] == 'REDS4':
val_partition = ['000', '011', '015', '020']
elif opt['val_partition'] == 'official':
val_partition = [f'{v:03d}' for v in range(240, 270)]
else:
raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
f"Supported ones are ['official', 'REDS4'].")
self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
# 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
if self.flow_root is not None:
self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root]
self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow']
else:
self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
self.io_backend_opt['client_keys'] = ['lq', 'gt']
# temporal augmentation configs
self.interval_list = opt['interval_list']
self.random_reverse = opt['random_reverse']
interval_str = ','.join(str(x) for x in opt['interval_list'])
logger = get_root_logger()
logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
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)
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
center_frame_idx = int(frame_name)
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = center_frame_idx - self.num_half_frames * interval
end_frame_idx = center_frame_idx + self.num_half_frames * interval
# each clip has 100 frames starting from 0 to 99
while (start_frame_idx < 0) or (end_frame_idx > 99):
center_frame_idx = random.randint(0, 99)
start_frame_idx = (center_frame_idx - self.num_half_frames * interval)
end_frame_idx = center_frame_idx + self.num_half_frames * interval
frame_name = f'{center_frame_idx:08d}'
neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}')
# get the GT frame (as the center frame)
if self.is_lmdb:
img_gt_path = f'{clip_name}/{frame_name}'
else:
img_gt_path = self.gt_root / clip_name / f'{frame_name}.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 neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip_name}/{neighbor:08d}'
else:
img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png'
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = imfrombytes(img_bytes, float32=True)
img_lqs.append(img_lq)
# get flows
if self.flow_root is not None:
img_flows = []
# read previous flows
for i in range(self.num_half_frames, 0, -1):
if self.is_lmdb:
flow_path = f'{clip_name}/{frame_name}_p{i}'
else:
flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png')
img_bytes = self.file_client.get(flow_path, 'flow')
cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255]
dx, dy = np.split(cat_flow, 2, axis=0)
flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here.
img_flows.append(flow)
# read next flows
for i in range(1, self.num_half_frames + 1):
if self.is_lmdb:
flow_path = f'{clip_name}/{frame_name}_n{i}'
else:
flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png')
img_bytes = self.file_client.get(flow_path, 'flow')
cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255]
dx, dy = np.split(cat_flow, 2, axis=0)
flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here.
img_flows.append(flow)
# for random crop, here, img_flows and img_lqs have the same
# spatial size
img_lqs.extend(img_flows)
# randomly crop
img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path)
if self.flow_root is not None:
img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:]
# augmentation - flip, rotate
img_lqs.append(img_gt)
if self.flow_root is not None:
img_results, img_flows = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'], img_flows)
else:
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]
if self.flow_root is not None:
img_flows = img2tensor(img_flows)
# add the zero center flow
img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0]))
img_flows = torch.stack(img_flows, dim=0)
# img_lqs: (t, c, h, w)
# img_flows: (t, 2, h, w)
# img_gt: (c, h, w)
# key: str
if self.flow_root is not None:
return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key}
else:
return {'lq': img_lqs, 'gt': img_gt, 'key': key}
def __len__(self):
return len(self.keys)
@DATASET_REGISTRY.register()
class REDSRecurrentDataset(data.Dataset):
"""REDS dataset for training recurrent networks.
The keys are generated from a meta info txt file.
basicsr/data/meta_info/meta_info_REDS_GT.txt
Each line contains:
1. subfolder (clip) name; 2. frame number; 3. image shape, separated by
a white space.
Examples:
000 100 (720,1280,3)
001 100 (720,1280,3)
...
Key examples: "000/00000000"
GT (gt): Ground-Truth;
LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames.
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.
dataroot_flow (str, optional): Data root path for flow.
meta_info_file (str): Path for meta information file.
val_partition (str): Validation partition types. 'REDS4' or 'official'.
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.
interval_list (list): Interval list for temporal augmentation.
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(REDSRecurrentDataset, self).__init__()
self.opt = opt
self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq'])
self.num_frame = opt['num_frame']
self.keys = []
with open(opt['meta_info_file'], 'r') as fin:
for line in fin:
folder, frame_num, _ = line.split(' ')
self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))])
# remove the video clips used in validation
if opt['val_partition'] == 'REDS4':
val_partition = ['000', '011', '015', '020']
elif opt['val_partition'] == 'official':
val_partition = [f'{v:03d}' for v in range(240, 270)]
else:
raise ValueError(f'Wrong validation partition {opt["val_partition"]}.'
f"Supported ones are ['official', 'REDS4'].")
if opt['test_mode']:
self.keys = [v for v in self.keys if v.split('/')[0] in val_partition]
else:
self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition]
# 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
if hasattr(self, 'flow_root') and self.flow_root is not None:
self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root]
self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow']
else:
self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root]
self.io_backend_opt['client_keys'] = ['lq', 'gt']
# temporal augmentation configs
self.interval_list = opt.get('interval_list', [1])
self.random_reverse = opt.get('random_reverse', False)
interval_str = ','.join(str(x) for x in self.interval_list)
logger = get_root_logger()
logger.info(f'Temporal augmentation interval list: [{interval_str}]; '
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)
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = int(frame_name)
if start_frame_idx > 100 - self.num_frame * interval:
start_frame_idx = random.randint(0, 100 - self.num_frame * interval)
end_frame_idx = start_frame_idx + self.num_frame * interval
neighbor_list = list(range(start_frame_idx, end_frame_idx, interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
# get the neighboring LQ and GT frames
img_lqs = []
img_gts = []
for neighbor in neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip_name}/{neighbor:08d}'
img_gt_path = f'{clip_name}/{neighbor:08d}'
else:
img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png'
img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png'
# get LQ
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = imfrombytes(img_bytes, float32=True)
img_lqs.append(img_lq)
# get GT
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=True)
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_gts = torch.stack(img_results[len(img_lqs) // 2:], dim=0)
img_lqs = torch.stack(img_results[:len(img_lqs) // 2], dim=0)
# img_lqs: (t, c, h, w)
# img_gts: (t, c, h, w)
# key: str
return {'lq': img_lqs, 'gt': img_gts, 'key': key}
def __len__(self):
return len(self.keys)