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mmedit.datasets.sr_folder_video_dataset 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import re
from collections import defaultdict
import mmcv
import numpy as np
from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS
[文档]@DATASETS.register_module()
class SRFolderVideoDataset(BaseSRDataset):
"""General dataset for video SR, used for sliding-window framework.
The dataset loads several LQ (Low-Quality) frames and one GT (Ground-Truth)
frames. Then it applies specified transforms and finally returns a dict
containing paired data and other information.
This dataset takes an annotation file specifying the sequences used in
training or test. If no annotation file is provided, it assumes all video
sequences under the root directory are used for training or test.
In the annotation file (.txt), each line contains:
1. image name (no file extension);
2. number of frames in the sequence (in the same folder)
Examples:
::
calendar/00000000 41
calendar/00000001 41
...
calendar/00000040 41
city/00000000 34
...
Args:
lq_folder (str | :obj:`Path`): Path to a lq folder.
gt_folder (str | :obj:`Path`): Path to a gt folder.
num_input_frames (int): Window size for input frames.
pipeline (list[dict | callable]): A sequence of data transformations.
scale (int): Upsampling scale ratio.
ann_file (str): The path to the annotation file. If None, we assume
that all sequences in the folder is used. Default: None.
filename_tmpl (str): Template for each filename. Note that the
template excludes the file extension. Default: '{:08d}'.
start_idx (int): The index corresponds to the first frame
in the sequence. Default: 0.
metric_average_mode (str): The way to compute the average metric.
If 'clip', we first compute an average value for each clip, and
then average the values from different clips. If 'all', we
compute the average of all frames. Default: 'clip'.
test_mode (bool): Store `True` when building test dataset.
Default: `True`.
"""
def __init__(self,
lq_folder,
gt_folder,
num_input_frames,
pipeline,
scale,
ann_file=None,
filename_tmpl='{:08d}',
start_idx=0,
metric_average_mode='clip',
test_mode=True):
super().__init__(pipeline, scale, test_mode)
assert num_input_frames % 2 == 1, (
f'num_input_frames should be odd numbers, '
f'but received {num_input_frames }.')
if metric_average_mode not in ['clip', 'all']:
raise ValueError('metric_average_mode can only be "clip" or '
f'"all", but got {metric_average_mode}.')
self.lq_folder = str(lq_folder)
self.gt_folder = str(gt_folder)
self.num_input_frames = num_input_frames
self.ann_file = ann_file
self.filename_tmpl = filename_tmpl
self.start_idx = start_idx
self.metric_average_mode = metric_average_mode
self.data_infos = self.load_annotations()
def _load_annotations_from_file(self):
self.folders = {}
data_infos = []
ann_list = mmcv.list_from_file(self.ann_file)
for ann in ann_list:
key, max_frame_num = ann.strip().rsplit(' ', 1)
key = key.replace('/', os.sep)
sequence = osp.basename(key)
if sequence not in self.folders:
self.folders[sequence] = int(max_frame_num)
data_infos.append(
dict(
lq_path=self.lq_folder,
gt_path=self.gt_folder,
key=key,
num_input_frames=self.num_input_frames,
max_frame_num=int(max_frame_num)))
return data_infos
[文档] def load_annotations(self):
"""Load annotations for the dataset.
Returns:
list[dict]: A list of dicts for paired paths and other information.
"""
if self.ann_file:
return self._load_annotations_from_file()
self.folders = {}
data_infos = []
sequences = sorted(glob.glob(osp.join(self.lq_folder, '*')))
sequences = [re.split(r'[\\/]', s)[-1] for s in sequences]
for sequence in sequences:
seq_dir = osp.join(self.lq_folder, sequence)
max_frame_num = len(list(mmcv.utils.scandir(seq_dir)))
self.folders[sequence] = max_frame_num
for i in range(self.start_idx, max_frame_num + self.start_idx):
data_infos.append(
dict(
lq_path=self.lq_folder,
gt_path=self.gt_folder,
key=osp.join(sequence, self.filename_tmpl.format(i)),
num_input_frames=self.num_input_frames,
max_frame_num=max_frame_num))
return data_infos
[文档] def evaluate(self, results, logger=None):
"""Evaluate with different metrics.
Args:
results (list[tuple]): The output of forward_test() of the model.
Return:
dict: Evaluation results dict.
"""
if not isinstance(results, list):
raise TypeError(f'results must be a list, but got {type(results)}')
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: '
f'{len(results)} != {len(self)}')
results = [res['eval_result'] for res in results] # a list of dict
eval_result = defaultdict(list) # a dict of list
for res in results:
for metric, val in res.items():
eval_result[metric].append(val)
for metric, val_list in eval_result.items():
assert len(val_list) == len(self), (
f'Length of evaluation result of {metric} is {len(val_list)}, '
f'should be {len(self)}')
# average the results
if self.metric_average_mode == 'clip':
for metric, values in eval_result.items():
start_idx = 0
metric_avg = 0
for _, num_img in self.folders.items():
end_idx = start_idx + num_img
folder_values = values[start_idx:end_idx]
metric_avg += np.mean(folder_values)
start_idx = end_idx
eval_result[metric] = metric_avg / len(self.folders)
else:
eval_result = {
metric: sum(values) / len(self)
for metric, values in eval_result.items()
}
return eval_result