训练分割模型时出现的错误

[leaozhun]

Traceback (most recent call last): File "tools/train.py", line 242, in main() File "tools/train.py", line 232, in main train_detector(model, File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/mmdet/apis/train.py", line 244, in train_detector runner.run(data_loaders, cfg.workflow) File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], **kwargs) File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train for i, data_batch in enumerate(self.data_loader): File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 352, in iter return self._get_iterator() File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 294, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 827, in init self._reset(loader, first_iter=True) File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 857, in _reset self._try_put_index() File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1091, in _try_put_index index = self._next_index() File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 227, in iter for idx in self.sampler: File "/media/dell/data1/miniconda3/envs/openpvsg/lib/python3.8/site-packages/mmdet/datasets/samplers/group_sampler.py", line 36, in iter indices = np.concatenate(indices) File "<__array_function__ internals>", line 200, in concatenate ValueError: need at least one array to concatenate 请问您对这样的错误有解决方案吗

[leaozhun]

Hello,the previous error should be due to a bug in unzip_and_extract.py, and the modified code is attached here import os, shutil from tqdm import tqdm import zipfile import os import cv2 import hashlib from pathlib import Path from multiprocessing import Pool

zip_dir = './data_zip/' target_dir = './data/'

List all the zip files in the directory

zip_files = { 'Ego4D/ego4d_videos.zip': { 'target': 'ego4d/videos', 'md5': '9334c74f5c831c80774862afa9d3f7f0' }, 'Ego4D/ego4d_masks.zip': { 'target': 'ego4d/masks', 'md5': '218ce689e1e8284e25b50280a5d29612' }, 'EpicKitchen/epic_kitchen_videos.zip': { 'target': 'epic_kitchen/videos', 'md5': 'b791a71ef24b14721a7b5041190ba4a3' }, 'EpicKitchen/epic_kitchen_masks.zip': { 'target': 'epic_kitchen/masks', 'md5': '03757120075de23328a11e56660175f4' }, 'VidOR/vidor_videos.zip': { 'target': 'vidor/videos', 'md5': 'fcc1a6f54ef60aa16fab335a6270c960' }, 'VidOR/vidor_masks.zip': { 'target': 'vidor/masks', 'md5': '17bfa5ec13235d86273bc9d067776862' }, }

def calculate_md5(file_path): hash_md5 = hashlib.md5() with open(file_path, 'rb') as f: for chunk in iter(lambda: f.read(4096), b''): hash_md5.update(chunk) return hash_md5.hexdigest()

def makedir_if_not_exist(dir): if not os.path.exists(dir): os.makedirs(dir)

def process_video(video_path, save_root): video_name = video_path.name.split('.')[0] save_dir = os.path.join(save_root, video_name) os.makedirs(save_dir, exist_ok=True) cap = cv2.VideoCapture(str(video_path)) n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) success = True count = 0 while success and count < n_frames: success, image = cap.read() if success: cv2.imwrite(os.path.join(save_dir, '{:04d}.png'.format(count)), image) count += 1

def extract_videos(video_root, save_root, num_processes=10): video_root = Path(video_root) videos_1 = list(video_root.rglob('.MP4')) videos_2 = list(video_root.rglob('.mp4')) videos=videos_1+videos_2 with Pool(num_processes) as pool: pool.starmap(process_video, [(video, save_root) for video in videos])

def unzip_files(file, destination): with zipfile.ZipFile(file, 'r') as zip_ref:

    for member in zip_ref.infolist():
        filename = os.path.basename(member.filename)
        if not filename:
            continue
        file_path = os.path.join(destination, filename)
        if 'masks' in file.split('/')[-1]:
            # 获取图像所在文件夹的名称
            folder_name = Path(member.filename).parent.name
            file_path = os.path.join(destination, folder_name,filename)
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with zip_ref.open(member, 'r') as source, open(file_path,
                                                       'wb') as target:
            shutil.copyfileobj(source, target)

unzip files

for filename in zip_files: zip_path = os.path.join(zip_dir, filename) zip_info = zip_files[filename]

assert calculate_md5(
    zip_path) == zip_info['md5'], f'{zip_path} md5 mismatches!'

target_path = os.path.join(target_dir, zip_info['target'])
makedir_if_not_exist(target_path)
unzip_files(zip_path, target_path)
print(filename, 'unzipping completed!', flush=True)

if 'videos' in filename.split('/')[-1]:
    frame_path = target_path.replace('videos', 'frames')
    extract_videos(target_path, frame_path)
    print(frame_path, 'extraction completed!', flush=True)

copy pvsg.json to data/

shutil.copy('./data_zip/pvsg.json', './data/')