BasicVSR_ Plusplus master test videos and pictures

Code ：https://github.com/ckkelvinchan/BasicVSR_PlusPlus
BasicVSR_PlusPlus-master Test pictures and videos are always reported out of memory, Insufficient memory , But it needs testing , Modify the code .
modify restoration_video_demo.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os

import cv2
import mmcv
import numpy as np
import torch

from mmedit.apis import init_model, restoration_video_inference
from mmedit.core import tensor2img
from mmedit.utils import modify_args
import time

VIDEO_EXTENSIONS = ('.mp4', '.mov')


def parse_args():
    modify_args()
    parser = argparse.ArgumentParser(description='Restoration demo')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('input_dir', help='directory of the input video')
    parser.add_argument('output_dir', help='directory of the output video')
    parser.add_argument(
        '--start-idx',
        type=int,
        default=0,
        help='index corresponds to the first frame of the sequence')
    parser.add_argument(
        '--filename-tmpl',
        default='{:08d}.png',
        help='template of the file names')
    parser.add_argument(
        '--window-size',
        type=int,
        default=0,
        help='window size if sliding-window framework is used')
    parser.add_argument(
        '--max-seq-len',
        type=int,
        default=None,
        help='maximum sequence length if recurrent framework is used')
    parser.add_argument('--device', type=int, default=0, help='CUDA device id')
    args = parser.parse_args()
    return args


def main():
    """ Demo for video restoration models. Note that we accept video as input/output, when 'input_dir'/'output_dir' is set to the path to the video. But using videos introduces video compression, which lowers the visual quality. If you want actual quality, please save them as separate images (.png). """

    args = parse_args()

    model = init_model(
        args.config, args.checkpoint, device=torch.device('cuda', args.device))

    for i in range(10000):
        start_idx=i
        # time.sleep(500)
        output = restoration_video_inference(model, args.input_dir,
                                            args.window_size, start_idx,
                                            args.filename_tmpl, args.max_seq_len)
        torch.cuda.empty_cache()
        time.sleep(10)
        file_extension = os.path.splitext(args.output_dir)[1]
        if file_extension in VIDEO_EXTENSIONS:  # save as video
            h, w = output.shape[-2:]
            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
            video_writer = cv2.VideoWriter(args.output_dir, fourcc, 25, (w, h))
            for i in range(0, output.size(1)):
                img = tensor2img(output[:, i, :, :, :])
                video_writer.write(img.astype(np.uint8))
            cv2.destroyAllWindows()
            video_writer.release()
        else:
            for i in range(args.start_idx, args.start_idx + output.size(1)):
                output_i = output[:, i - args.start_idx, :, :, :]
                output_i = tensor2img(output_i)
                print(args.filename_tmpl.format(start_idx))

                # save_path_i = f'{args.output_dir}/{args.filename_tmpl.format(i)}'
                save_path_i = f'{
      args.output_dir}/{
      args.filename_tmpl.format(start_idx)}'

                mmcv.imwrite(output_i, save_path_i)
if __name__ == '__main__':
    main()

modify restoration_video_inference.py

# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os.path as osp
import re
from functools import reduce

import mmcv
import numpy as np
import torch

from mmedit.datasets.pipelines import Compose

VIDEO_EXTENSIONS = ('.mp4', '.mov')


def pad_sequence(data, window_size):
    padding = window_size // 2

    data = torch.cat([
        data[:, 1 + padding:1 + 2 * padding].flip(1), data,
        data[:, -1 - 2 * padding:-1 - padding].flip(1)
    ],
                     dim=1)

    return data


def restoration_video_inference(model,
                                img_dir,
                                window_size,
                                start_idx,
                                filename_tmpl,
                                max_seq_len=None,
                                ):
    """Inference image with the model. Args: model (nn.Module): The loaded model. img_dir (str): Directory of the input video. window_size (int): The window size used in sliding-window framework. This value should be set according to the settings of the network. A value smaller than 0 means using recurrent framework. start_idx (int): The index corresponds to the first frame in the sequence. filename_tmpl (str): Template for file name. max_seq_len (int | None): The maximum sequence length that the model processes. If the sequence length is larger than this number, the sequence is split into multiple segments. If it is None, the entire sequence is processed at once. Returns: Tensor: The predicted restoration result. """

    device = next(model.parameters()).device  # model device

    # build the data pipeline
    if model.cfg.get('demo_pipeline', None):
        test_pipeline = model.cfg.demo_pipeline
    elif model.cfg.get('test_pipeline', None):
        test_pipeline = model.cfg.test_pipeline
    else:
        test_pipeline = model.cfg.val_pipeline
    print(img_dir)
    # check if the input is a video
    file_extension = osp.splitext(img_dir)[1]
    if file_extension in VIDEO_EXTENSIONS:
        video_reader = mmcv.VideoReader(img_dir)
        # load the images
        data = dict(lq=[], lq_path=None, key=img_dir)
        for frame in video_reader:
            data['lq'].append(np.flip(frame, axis=2))

        # remove the data loading pipeline
        tmp_pipeline = []
        for pipeline in test_pipeline:
            if pipeline['type'] not in [
                    'GenerateSegmentIndices', 'LoadImageFromFileList'
            ]:
                tmp_pipeline.append(pipeline)
        test_pipeline = tmp_pipeline
    else:
        # the first element in the pipeline must be 'GenerateSegmentIndices'
        if test_pipeline[0]['type'] != 'GenerateSegmentIndices':
            raise TypeError('The first element in the pipeline must be '
                            f'"GenerateSegmentIndices", but got '
                            f'"{
      test_pipeline[0]["type"]}".')

        # specify start_idx and filename_tmpl
        print('start_idx', start_idx)
        print('filename_tmpl', filename_tmpl)
        test_pipeline[0]['start_idx'] = start_idx
        test_pipeline[0]['filename_tmpl'] = filename_tmpl
        
        # prepare data
        # sequence_length = len(glob.glob(osp.join(img_dir, '*')))
        sequence_length = 1
        img_dir_split = re.split(r'[\\/]', img_dir)
        print(img_dir)
        key = img_dir_split[-1]
        
        lq_folder = reduce(osp.join, img_dir_split[:-1])
        print(lq_folder)
        data = dict(
            lq_path=lq_folder,
            gt_path='',
            key=key,
            sequence_length=sequence_length)
    
    
    # compose the pipeline
    test_pipeline = Compose(test_pipeline)

    data = test_pipeline(data)

    print("data_lq",data['lq'].shape)
    data = data['lq'].unsqueeze(0)  # in cpu
    data = data.unsqueeze(0)  # in cpu

    print("data",data.shape)
    # forward the model
    with torch.no_grad():
        if window_size > 0:  # sliding window framework
            data = pad_sequence(data, window_size)
            result = []
            for i in range(0, data.size(1) - 2 * (window_size // 2)):
                data_i = data[:, i:i + window_size].to(device)
                result.append(model(lq=data_i, test_mode=True)['output'].cpu())
            result = torch.stack(result, dim=1)
        else:  # recurrent framework
            if max_seq_len is None:
                result = model(
                    lq=data.to(device), test_mode=True)['output'].cpu()
            else:
                result = []
                for i in range(0, data.size(1), max_seq_len):
                    result.append(
                        model(
                            lq=data[:, i:i + max_seq_len].to(device),
                            test_mode=True)['output'].cpu())
                result = torch.cat(result, dim=1)
    return result