We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Error in --validate while training.
Hi,

My dataset is multiclass `VideoDataset` type and looks something like this.
```

        some/path/000.mp4 1 3 5
        some/path/001.mp4 1 2
        some/path/002.mp4 2 6 10

```
Given video data and multi-class, I adopted my configs to test something on a small dataset from `tsn_r50_video_1x1x8_100e_kinetics400_rgb.py` and `tsn_r101_1x1x5_50e_mmit_rgb.py` 

It looks something like this
```
# model settings
model = dict(
    type='Recognizer2D',
    backbone=dict(
        type='ResNet',
        pretrained='torchvision://resnet50',
        depth=50,
        norm_eval=False),
    cls_head=dict(
        type='TSNHead',
        num_classes=14,
        in_channels=2048,
        spatial_type='avg',
        consensus=dict(type='AvgConsensus', dim=1),
        loss_cls=dict(type='BCELossWithLogits', loss_weight=160.0),
        dropout_ratio=0.5,
        init_std=0.01,
        multi_class=True,
        label_smooth_eps=0))
# model training and testing settings
train_cfg = None
test_cfg = dict(average_clips=None)
# dataset settings
dataset_type = 'VideoDataset'
data_root = '/home/rajawat/Desktop/videos'
data_root_val = '/home/rajawat/Desktop/val_videos'
ann_file_train = '/home/rajawat/Desktop/labels/train.txt'
ann_file_val = '/home/rajawat/Desktop/labels/val.txt'
ann_file_test = '/home/rajawat/Desktop/labels/test.txt'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
    dict(type='DecordInit'),
    dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),
    dict(type='DecordDecode'),
    dict(type='RandomResizedCrop'),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
    dict(type='DecordInit'),
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=3,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Flip', flip_ratio=0),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
    dict(type='DecordInit'),
    dict(
        type='SampleFrames',
        clip_len=1,
        frame_interval=1,
        num_clips=25,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='TenCrop', crop_size=224),
    dict(type='Flip', flip_ratio=0),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]

data = dict(
    videos_per_gpu=2,
    workers_per_gpu=0,
    train=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=data_root,
        pipeline=train_pipeline,
        multi_class=True,
        num_classes=14),
    val=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        pipeline=val_pipeline,
        multi_class=True,
        num_classes=14),
    test=dict(
        type=dataset_type,
        ann_file=ann_file_test,
        data_prefix=data_root_val,
        pipeline=test_pipeline,
        multi_class=True,
        num_classes=14))
# optimizer
optimizer = dict(
    type='SGD',
    constructor='TSMOptimizerConstructor',
    paramwise_cfg=dict(fc_lr5=True),
    lr=0.01,  # this lr is used for 8 gpus
    momentum=0.9,
    weight_decay=0.0001,
)
optimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))
# learning policy
lr_config = dict(policy='step', step=[20, 40])
total_epochs = 10
checkpoint_config = dict(interval=5)
evaluation = dict(interval=2, metrics=['mean_average_precision'])
# yapf:disable
log_config = dict(
    interval=2,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook'),
    ])
# runtime settings
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/tsn_r101_1x1x5_50e_mmit_rgb/'
load_from = None
resume_from = None
workflow = [('train', 1)]

```

while training 
`python tools/train.py configs/recognition/tsn/tsn_r50_1x1x5_50e_cater_rgb.py  --gpus 1` works well , but with `--validate` it breaks down. 

```
Traceback (most recent call last):
  File "tools/train.py", line 178, in <module>
    main()
  File "tools/train.py", line 174, in main
    meta=meta)
  File "/home/rajawat/Desktop/mmaction/apis/train.py", line 156, in train_model
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs, **runner_kwargs)
  File "/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train
    self.call_hook('after_train_epoch')
  File "/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/base_runner.py", line 308, in call_hook
    getattr(hook, fn_name)(self)
  File "/home/rajawat/Desktop/mmaction/core/evaluation/eval_hooks.py", line 152, in after_train_epoch
    key_score = self.evaluate(runner, results)
  File "/home/rajawat/Desktop/mmaction/core/evaluation/eval_hooks.py", line 170, in evaluate
    results, logger=runner.logger, **self.eval_kwargs)
  File "/home/rajawat/Desktop/mmaction/datasets/base.py", line 215, in evaluate
    for label in gt_labels
  File "/home/rajawat/Desktop/mmaction/datasets/base.py", line 215, in <listcomp>
    for label in gt_labels
  File "/home/rajawat/Desktop/mmaction/datasets/base.py", line 121, in label2array
    arr[label] = 1.
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

```
Am I missing something in the config?
Thanks in advance.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mmaction/datasets/video_dataset.py`
Content:
```
1 import os.path as osp
2 
3 import torch
4 
5 from .base import BaseDataset
6 from .registry import DATASETS
7 
8 
9 @DATASETS.register_module()
10 class VideoDataset(BaseDataset):
11     """Video dataset for action recognition.
12 
13     The dataset loads raw videos and apply specified transforms to return a
14     dict containing the frame tensors and other information.
15 
16     The ann_file is a text file with multiple lines, and each line indicates
17     a sample video with the filepath and label, which are split with a
18     whitespace. Example of a annotation file:
19 
20     .. code-block:: txt
21 
22         some/path/000.mp4 1
23         some/path/001.mp4 1
24         some/path/002.mp4 2
25         some/path/003.mp4 2
26         some/path/004.mp4 3
27         some/path/005.mp4 3
28 
29 
30     Args:
31         ann_file (str): Path to the annotation file.
32         pipeline (list[dict | callable]): A sequence of data transforms.
33         start_index (int): Specify a start index for frames in consideration of
34             different filename format. However, when taking videos as input,
35             it should be set to 0, since frames loaded from videos count
36             from 0. Default: 0.
37         **kwargs: Keyword arguments for ``BaseDataset``.
38     """
39 
40     def __init__(self, ann_file, pipeline, start_index=0, **kwargs):
41         super().__init__(ann_file, pipeline, start_index=start_index, **kwargs)
42 
43     def load_annotations(self):
44         """Load annotation file to get video information."""
45         if self.ann_file.endswith('.json'):
46             return self.load_json_annotations()
47 
48         video_infos = []
49         with open(self.ann_file, 'r') as fin:
50             for line in fin:
51                 line_split = line.strip().split()
52                 if self.multi_class:
53                     assert self.num_classes is not None
54                     filename, label = line_split[0], line_split[1:]
55                     label = list(map(int, label))
56                     onehot = torch.zeros(self.num_classes)
57                     onehot[label] = 1.0
58                 else:
59                     filename, label = line_split
60                     label = int(label)
61                 if self.data_prefix is not None:
62                     filename = osp.join(self.data_prefix, filename)
63                 video_infos.append(
64                     dict(
65                         filename=filename,
66                         label=onehot if self.multi_class else label))
67         return video_infos
68
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/mmaction/datasets/video_dataset.py b/mmaction/datasets/video_dataset.py
--- a/mmaction/datasets/video_dataset.py
+++ b/mmaction/datasets/video_dataset.py
@@ -1,7 +1,5 @@
 import os.path as osp
 
-import torch
-
 from .base import BaseDataset
 from .registry import DATASETS
 
@@ -53,15 +51,10 @@
                     assert self.num_classes is not None
                     filename, label = line_split[0], line_split[1:]
                     label = list(map(int, label))
-                    onehot = torch.zeros(self.num_classes)
-                    onehot[label] = 1.0
                 else:
                     filename, label = line_split
                     label = int(label)
                 if self.data_prefix is not None:
                     filename = osp.join(self.data_prefix, filename)
-                video_infos.append(
-                    dict(
-                        filename=filename,
-                        label=onehot if self.multi_class else label))
+                video_infos.append(dict(filename=filename, label=label))
         return video_infos

{"golden_diff": "diff --git a/mmaction/datasets/video_dataset.py b/mmaction/datasets/video_dataset.py\n--- a/mmaction/datasets/video_dataset.py\n+++ b/mmaction/datasets/video_dataset.py\n@@ -1,7 +1,5 @@\n import os.path as osp\n \n-import torch\n-\n from .base import BaseDataset\n from .registry import DATASETS\n \n@@ -53,15 +51,10 @@\n                     assert self.num_classes is not None\n                     filename, label = line_split[0], line_split[1:]\n                     label = list(map(int, label))\n-                    onehot = torch.zeros(self.num_classes)\n-                    onehot[label] = 1.0\n                 else:\n                     filename, label = line_split\n                     label = int(label)\n                 if self.data_prefix is not None:\n                     filename = osp.join(self.data_prefix, filename)\n-                video_infos.append(\n-                    dict(\n-                        filename=filename,\n-                        label=onehot if self.multi_class else label))\n+                video_infos.append(dict(filename=filename, label=label))\n         return video_infos\n", "issue": "Error in --validate while training.\nHi,\r\n\r\nMy dataset is multiclass `VideoDataset` type and looks something like this.\r\n```\r\n\r\n        some/path/000.mp4 1 3 5\r\n        some/path/001.mp4 1 2\r\n        some/path/002.mp4 2 6 10\r\n\r\n```\r\nGiven video data and multi-class, I adopted my configs to test something on a small dataset from `tsn_r50_video_1x1x8_100e_kinetics400_rgb.py` and `tsn_r101_1x1x5_50e_mmit_rgb.py` \r\n\r\nIt looks something like this\r\n```\r\n# model settings\r\nmodel = dict(\r\n    type='Recognizer2D',\r\n    backbone=dict(\r\n        type='ResNet',\r\n        pretrained='torchvision://resnet50',\r\n        depth=50,\r\n        norm_eval=False),\r\n    cls_head=dict(\r\n        type='TSNHead',\r\n        num_classes=14,\r\n        in_channels=2048,\r\n        spatial_type='avg',\r\n        consensus=dict(type='AvgConsensus', dim=1),\r\n        loss_cls=dict(type='BCELossWithLogits', loss_weight=160.0),\r\n        dropout_ratio=0.5,\r\n        init_std=0.01,\r\n        multi_class=True,\r\n        label_smooth_eps=0))\r\n# model training and testing settings\r\ntrain_cfg = None\r\ntest_cfg = dict(average_clips=None)\r\n# dataset settings\r\ndataset_type = 'VideoDataset'\r\ndata_root = '/home/rajawat/Desktop/videos'\r\ndata_root_val = '/home/rajawat/Desktop/val_videos'\r\nann_file_train = '/home/rajawat/Desktop/labels/train.txt'\r\nann_file_val = '/home/rajawat/Desktop/labels/val.txt'\r\nann_file_test = '/home/rajawat/Desktop/labels/test.txt'\r\nimg_norm_cfg = dict(\r\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)\r\ntrain_pipeline = [\r\n    dict(type='DecordInit'),\r\n    dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=3),\r\n    dict(type='DecordDecode'),\r\n    dict(type='RandomResizedCrop'),\r\n    dict(type='Resize', scale=(224, 224), keep_ratio=False),\r\n    dict(type='Flip', flip_ratio=0.5),\r\n    dict(type='Normalize', **img_norm_cfg),\r\n    dict(type='FormatShape', input_format='NCHW'),\r\n    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\r\n    dict(type='ToTensor', keys=['imgs', 'label'])\r\n]\r\nval_pipeline = [\r\n    dict(type='DecordInit'),\r\n    dict(\r\n        type='SampleFrames',\r\n        clip_len=1,\r\n        frame_interval=1,\r\n        num_clips=3,\r\n        test_mode=True),\r\n    dict(type='DecordDecode'),\r\n    dict(type='Resize', scale=(-1, 256)),\r\n    dict(type='CenterCrop', crop_size=224),\r\n    dict(type='Flip', flip_ratio=0),\r\n    dict(type='Normalize', **img_norm_cfg),\r\n    dict(type='FormatShape', input_format='NCHW'),\r\n    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\r\n    dict(type='ToTensor', keys=['imgs'])\r\n]\r\ntest_pipeline = [\r\n    dict(type='DecordInit'),\r\n    dict(\r\n        type='SampleFrames',\r\n        clip_len=1,\r\n        frame_interval=1,\r\n        num_clips=25,\r\n        test_mode=True),\r\n    dict(type='DecordDecode'),\r\n    dict(type='Resize', scale=(-1, 256)),\r\n    dict(type='TenCrop', crop_size=224),\r\n    dict(type='Flip', flip_ratio=0),\r\n    dict(type='Normalize', **img_norm_cfg),\r\n    dict(type='FormatShape', input_format='NCHW'),\r\n    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\r\n    dict(type='ToTensor', keys=['imgs'])\r\n]\r\n\r\ndata = dict(\r\n    videos_per_gpu=2,\r\n    workers_per_gpu=0,\r\n    train=dict(\r\n        type=dataset_type,\r\n        ann_file=ann_file_train,\r\n        data_prefix=data_root,\r\n        pipeline=train_pipeline,\r\n        multi_class=True,\r\n        num_classes=14),\r\n    val=dict(\r\n        type=dataset_type,\r\n        ann_file=ann_file_val,\r\n        data_prefix=data_root_val,\r\n        pipeline=val_pipeline,\r\n        multi_class=True,\r\n        num_classes=14),\r\n    test=dict(\r\n        type=dataset_type,\r\n        ann_file=ann_file_test,\r\n        data_prefix=data_root_val,\r\n        pipeline=test_pipeline,\r\n        multi_class=True,\r\n        num_classes=14))\r\n# optimizer\r\noptimizer = dict(\r\n    type='SGD',\r\n    constructor='TSMOptimizerConstructor',\r\n    paramwise_cfg=dict(fc_lr5=True),\r\n    lr=0.01,  # this lr is used for 8 gpus\r\n    momentum=0.9,\r\n    weight_decay=0.0001,\r\n)\r\noptimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))\r\n# learning policy\r\nlr_config = dict(policy='step', step=[20, 40])\r\ntotal_epochs = 10\r\ncheckpoint_config = dict(interval=5)\r\nevaluation = dict(interval=2, metrics=['mean_average_precision'])\r\n# yapf:disable\r\nlog_config = dict(\r\n    interval=2,\r\n    hooks=[\r\n        dict(type='TextLoggerHook'),\r\n        # dict(type='TensorboardLoggerHook'),\r\n    ])\r\n# runtime settings\r\ndist_params = dict(backend='nccl')\r\nlog_level = 'INFO'\r\nwork_dir = './work_dirs/tsn_r101_1x1x5_50e_mmit_rgb/'\r\nload_from = None\r\nresume_from = None\r\nworkflow = [('train', 1)]\r\n\r\n```\r\n\r\nwhile training \r\n`python tools/train.py configs/recognition/tsn/tsn_r50_1x1x5_50e_cater_rgb.py  --gpus 1` works well , but with `--validate` it breaks down. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"tools/train.py\", line 178, in <module>\r\n    main()\r\n  File \"tools/train.py\", line 174, in main\r\n    meta=meta)\r\n  File \"/home/rajawat/Desktop/mmaction/apis/train.py\", line 156, in train_model\r\n    runner.run(data_loaders, cfg.workflow, cfg.total_epochs, **runner_kwargs)\r\n  File \"/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py\", line 125, in run\r\n    epoch_runner(data_loaders[i], **kwargs)\r\n  File \"/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py\", line 54, in train\r\n    self.call_hook('after_train_epoch')\r\n  File \"/home/rajawat/anaconda3/envs/slowfast/lib/python3.7/site-packages/mmcv/runner/base_runner.py\", line 308, in call_hook\r\n    getattr(hook, fn_name)(self)\r\n  File \"/home/rajawat/Desktop/mmaction/core/evaluation/eval_hooks.py\", line 152, in after_train_epoch\r\n    key_score = self.evaluate(runner, results)\r\n  File \"/home/rajawat/Desktop/mmaction/core/evaluation/eval_hooks.py\", line 170, in evaluate\r\n    results, logger=runner.logger, **self.eval_kwargs)\r\n  File \"/home/rajawat/Desktop/mmaction/datasets/base.py\", line 215, in evaluate\r\n    for label in gt_labels\r\n  File \"/home/rajawat/Desktop/mmaction/datasets/base.py\", line 215, in <listcomp>\r\n    for label in gt_labels\r\n  File \"/home/rajawat/Desktop/mmaction/datasets/base.py\", line 121, in label2array\r\n    arr[label] = 1.\r\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\r\n\r\n```\r\nAm I missing something in the config?\r\nThanks in advance.\n", "before_files": [{"content": "import os.path as osp\n\nimport torch\n\nfrom .base import BaseDataset\nfrom .registry import DATASETS\n\n\n@DATASETS.register_module()\nclass VideoDataset(BaseDataset):\n    \"\"\"Video dataset for action recognition.\n\n    The dataset loads raw videos and apply specified transforms to return a\n    dict containing the frame tensors and other information.\n\n    The ann_file is a text file with multiple lines, and each line indicates\n    a sample video with the filepath and label, which are split with a\n    whitespace. Example of a annotation file:\n\n    .. code-block:: txt\n\n        some/path/000.mp4 1\n        some/path/001.mp4 1\n        some/path/002.mp4 2\n        some/path/003.mp4 2\n        some/path/004.mp4 3\n        some/path/005.mp4 3\n\n\n    Args:\n        ann_file (str): Path to the annotation file.\n        pipeline (list[dict | callable]): A sequence of data transforms.\n        start_index (int): Specify a start index for frames in consideration of\n            different filename format. However, when taking videos as input,\n            it should be set to 0, since frames loaded from videos count\n            from 0. Default: 0.\n        **kwargs: Keyword arguments for ``BaseDataset``.\n    \"\"\"\n\n    def __init__(self, ann_file, pipeline, start_index=0, **kwargs):\n        super().__init__(ann_file, pipeline, start_index=start_index, **kwargs)\n\n    def load_annotations(self):\n        \"\"\"Load annotation file to get video information.\"\"\"\n        if self.ann_file.endswith('.json'):\n            return self.load_json_annotations()\n\n        video_infos = []\n        with open(self.ann_file, 'r') as fin:\n            for line in fin:\n                line_split = line.strip().split()\n                if self.multi_class:\n                    assert self.num_classes is not None\n                    filename, label = line_split[0], line_split[1:]\n                    label = list(map(int, label))\n                    onehot = torch.zeros(self.num_classes)\n                    onehot[label] = 1.0\n                else:\n                    filename, label = line_split\n                    label = int(label)\n                if self.data_prefix is not None:\n                    filename = osp.join(self.data_prefix, filename)\n                video_infos.append(\n                    dict(\n                        filename=filename,\n                        label=onehot if self.multi_class else label))\n        return video_infos\n", "path": "mmaction/datasets/video_dataset.py"}], "after_files": [{"content": "import os.path as osp\n\nfrom .base import BaseDataset\nfrom .registry import DATASETS\n\n\n@DATASETS.register_module()\nclass VideoDataset(BaseDataset):\n    \"\"\"Video dataset for action recognition.\n\n    The dataset loads raw videos and apply specified transforms to return a\n    dict containing the frame tensors and other information.\n\n    The ann_file is a text file with multiple lines, and each line indicates\n    a sample video with the filepath and label, which are split with a\n    whitespace. Example of a annotation file:\n\n    .. code-block:: txt\n\n        some/path/000.mp4 1\n        some/path/001.mp4 1\n        some/path/002.mp4 2\n        some/path/003.mp4 2\n        some/path/004.mp4 3\n        some/path/005.mp4 3\n\n\n    Args:\n        ann_file (str): Path to the annotation file.\n        pipeline (list[dict | callable]): A sequence of data transforms.\n        start_index (int): Specify a start index for frames in consideration of\n            different filename format. However, when taking videos as input,\n            it should be set to 0, since frames loaded from videos count\n            from 0. Default: 0.\n        **kwargs: Keyword arguments for ``BaseDataset``.\n    \"\"\"\n\n    def __init__(self, ann_file, pipeline, start_index=0, **kwargs):\n        super().__init__(ann_file, pipeline, start_index=start_index, **kwargs)\n\n    def load_annotations(self):\n        \"\"\"Load annotation file to get video information.\"\"\"\n        if self.ann_file.endswith('.json'):\n            return self.load_json_annotations()\n\n        video_infos = []\n        with open(self.ann_file, 'r') as fin:\n            for line in fin:\n                line_split = line.strip().split()\n                if self.multi_class:\n                    assert self.num_classes is not None\n                    filename, label = line_split[0], line_split[1:]\n                    label = list(map(int, label))\n                else:\n                    filename, label = line_split\n                    label = int(label)\n                if self.data_prefix is not None:\n                    filename = osp.join(self.data_prefix, filename)\n                video_infos.append(dict(filename=filename, label=label))\n        return video_infos\n", "path": "mmaction/datasets/video_dataset.py"}]}