AVA video data problem

I downloaded AVA video data based on DATASET. Md, but some videos failed to download for some reason. I used downloadable videos to process the data and test the results. There is an error, great video worker, can you help me to see how to solve it?
The command I'm running is:(pytorch) root@waq:~/slowfast# python tools/run_net.py --cfg configs/AVA/c2/SLOWFAST_32x2_R101_50_50_v2.1.yaml
The results of：
[INFO: test_net.py: 123]: Test with config:
[INFO: test_net.py: 124]: AVA:
ANNOTATION_DIR: /data/ava/annotations/
BGR: False
DETECTION_SCORE_THRESH: 0.8
EXCLUSION_FILE: ava_val_excluded_timestamps_v2.1.csv
FRAME_DIR: /data/ava/frames/
FRAME_LIST_DIR: /data/ava/frame_lists/
FULL_TEST_ON_VAL: False
GROUNDTRUTH_FILE: ava_val_v2.1.csv
IMG_PROC_BACKEND: cv2
LABEL_MAP_FILE: ava_action_list_v2.1_for_activitynet_2018.pbtxt
TEST_FORCE_FLIP: False
TEST_LISTS: ['val.csv']
TEST_PREDICT_BOX_LISTS: ['person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv']
TRAIN_GT_BOX_LISTS: ['ava_train_v2.1.csv']
TRAIN_LISTS: ['train.csv']
TRAIN_PCA_EIGVAL: [0.225, 0.224, 0.229]
TRAIN_PCA_EIGVEC: [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.814], [-0.5836, -0.6948, 0.4203]]
TRAIN_PCA_JITTER_ONLY: True
TRAIN_PREDICT_BOX_LISTS: []
TRAIN_USE_COLOR_AUGMENTATION: False
BN:
EPSILON: 1e-05
MOMENTUM: 0.1
NUM_BATCHES_PRECISE: 200
USE_PRECISE_STATS: False
WEIGHT_DECAY: 0.0
DATA:
CROP_SIZE: 224
DECODING_BACKEND: pyav
INPUT_CHANNEL_NUM: [3, 3]
INV_UNIFORM_SAMPLE: False
MEAN: [0.45, 0.45, 0.45]
NUM_FRAMES: 32
PATH_PREFIX:
PATH_TO_DATA_DIR:
SAMPLING_RATE: 2
STD: [0.225, 0.225, 0.225]
TARGET_FPS: 30
TEST_CROP_SIZE: 256
TRAIN_CROP_SIZE: 224
TRAIN_JITTER_SCALES: [256, 320]
DATA_LOADER:
ENABLE_MULTI_THREAD_DECODE: False
NUM_WORKERS: 0
PIN_MEMORY: True
DETECTION:
ALIGNED: False
ENABLE: True
ROI_XFORM_RESOLUTION: 7
SPATIAL_SCALE_FACTOR: 16
DIST_BACKEND: nccl
LOG_PERIOD: 10
MODEL:
ARCH: slowfast
DROPOUT_RATE: 0.5
FC_INIT_STD: 0.01
LOSS_FUNC: bce
MODEL_NAME: SlowFast
MULTI_PATHWAY_ARCH: ['slowfast']
NUM_CLASSES: 80
SINGLE_PATHWAY_ARCH: ['c2d', 'i3d', 'slowonly']
NONLOCAL:
GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
INSTANTIATION: dot_product
LOCATION: [[[], []], [[], []], [[6, 13, 20], []], [[], []]]
POOL: [[[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]]]
NUM_GPUS: 1
NUM_SHARDS: 1
OUTPUT_DIR: .
RESNET:
DEPTH: 101
INPLACE_RELU: True
NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
NUM_GROUPS: 1
SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]]
SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]]
STRIDE_1X1: False
TRANS_FUNC: bottleneck_transform
WIDTH_PER_GROUP: 64
ZERO_INIT_FINAL_BN: True
RNG_SEED: 0
SHARD_ID: 0
SLOWFAST:
ALPHA: 4
BETA_INV: 8
FUSION_CONV_CHANNEL_RATIO: 2
FUSION_KERNEL_SZ: 5
SOLVER:
BASE_LR: 0.1
DAMPENING: 0.0
GAMMA: 0.1
LRS: []
LR_POLICY: cosine
MAX_EPOCH: 300
MOMENTUM: 0.9
NESTEROV: True
OPTIMIZING_METHOD: sgd
STEPS: []
STEP_SIZE: 1
WARMUP_EPOCHS: 0
WARMUP_FACTOR: 0.1
WARMUP_START_LR: 0.01
WEIGHT_DECAY: 1e-07
TEST:
BATCH_SIZE: 1
CHECKPOINT_FILE_PATH:
CHECKPOINT_TYPE: pytorch
DATASET: ava
ENABLE: True
NUM_ENSEMBLE_VIEWS: 10
NUM_SPATIAL_CROPS: 3
TRAIN:
AUTO_RESUME: True
BATCH_SIZE: 4
CHECKPOINT_FILE_PATH: /root/slowfast/checkpoints/SLOWFAST_32x2_R101_50_50_v2.1.pkl
CHECKPOINT_INFLATE: False
CHECKPOINT_PERIOD: 1
CHECKPOINT_TYPE: pytorch
DATASET: ava
ENABLE: False
EVAL_PERIOD: 1
....................................#omit
[INFO: logging.py: 67]: json_stats: {"_type": "test_iter", "cur_iter": "5000", "eta": "8:09:21", "mode": "test", "time_diff": 0.613877}
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
[WARNING: utils.py: 33]: Reading failed. Will retry.
Traceback (most recent call last):
File "tools/run_net.py", line 152, in
main()
File "tools/run_net.py", line 147, in main
test(cfg=cfg)
File "/root/slowfast/tools/test_net.py", line 183, in test
perform_test(test_loader, model, test_meter, cfg)
File "/home/waq/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 49, in decorate_no_grad
return func(*args, **kwargs)
File "/root/slowfast/tools/test_net.py", line 44, in perform_test
for cur_iter, (inputs, labels, video_idx, meta) in enumerate(test_loader):
File "/home/waq/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/home/waq/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/waq/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/waq/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/root/slowfast/slowfast/datasets/ava_dataset.py", line 377, in __getitem__
image_paths, backend=self.cfg.AVA.IMG_PROC_BACKEND
File "/root/slowfast/slowfast/datasets/utils.py", line 36, in retry_load_images
raise Exception("Failed to load images {}".format(image_paths))
Exception: Failed to load images ['/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000029.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000031.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000033.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000035.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000037.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000039.jpg', '/data/ava/frames/914yZXz-iRs/914yZXz-iRs_000041.jpg', ......