We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
An editing error in the Tutorial Documentation
Thank you for ur contribution, I am a newbie to this project, maybe I found an editing error in the latest [Tutorial Documentation](https://mmdetection.readthedocs.io/en/latest/2_new_data_model.html). The detailed description is shown in the figure.

![图片](https://user-images.githubusercontent.com/23495527/140485815-7beba29e-b8f8-4309-96b8-300b74ee6d41.png)

it should be `python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon/latest.pth --eval bbox segm`

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `tools/train.py`
Content:
```
1 # Copyright (c) OpenMMLab. All rights reserved.
2 import argparse
3 import copy
4 import os
5 import os.path as osp
6 import time
7 import warnings
8 
9 import mmcv
10 import torch
11 from mmcv import Config, DictAction
12 from mmcv.runner import get_dist_info, init_dist
13 from mmcv.utils import get_git_hash
14 
15 from mmdet import __version__
16 from mmdet.apis import init_random_seed, set_random_seed, train_detector
17 from mmdet.datasets import build_dataset
18 from mmdet.models import build_detector
19 from mmdet.utils import collect_env, get_root_logger
20 
21 
22 def parse_args():
23     parser = argparse.ArgumentParser(description='Train a detector')
24     parser.add_argument('config', help='train config file path')
25     parser.add_argument('--work-dir', help='the dir to save logs and models')
26     parser.add_argument(
27         '--resume-from', help='the checkpoint file to resume from')
28     parser.add_argument(
29         '--no-validate',
30         action='store_true',
31         help='whether not to evaluate the checkpoint during training')
32     group_gpus = parser.add_mutually_exclusive_group()
33     group_gpus.add_argument(
34         '--gpus',
35         type=int,
36         help='number of gpus to use '
37         '(only applicable to non-distributed training)')
38     group_gpus.add_argument(
39         '--gpu-ids',
40         type=int,
41         nargs='+',
42         help='ids of gpus to use '
43         '(only applicable to non-distributed training)')
44     parser.add_argument('--seed', type=int, default=None, help='random seed')
45     parser.add_argument(
46         '--deterministic',
47         action='store_true',
48         help='whether to set deterministic options for CUDNN backend.')
49     parser.add_argument(
50         '--options',
51         nargs='+',
52         action=DictAction,
53         help='override some settings in the used config, the key-value pair '
54         'in xxx=yyy format will be merged into config file (deprecate), '
55         'change to --cfg-options instead.')
56     parser.add_argument(
57         '--cfg-options',
58         nargs='+',
59         action=DictAction,
60         help='override some settings in the used config, the key-value pair '
61         'in xxx=yyy format will be merged into config file. If the value to '
62         'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
63         'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
64         'Note that the quotation marks are necessary and that no white space '
65         'is allowed.')
66     parser.add_argument(
67         '--launcher',
68         choices=['none', 'pytorch', 'slurm', 'mpi'],
69         default='none',
70         help='job launcher')
71     parser.add_argument('--local_rank', type=int, default=0)
72     args = parser.parse_args()
73     if 'LOCAL_RANK' not in os.environ:
74         os.environ['LOCAL_RANK'] = str(args.local_rank)
75 
76     if args.options and args.cfg_options:
77         raise ValueError(
78             '--options and --cfg-options cannot be both '
79             'specified, --options is deprecated in favor of --cfg-options')
80     if args.options:
81         warnings.warn('--options is deprecated in favor of --cfg-options')
82         args.cfg_options = args.options
83 
84     return args
85 
86 
87 def main():
88     args = parse_args()
89 
90     cfg = Config.fromfile(args.config)
91     if args.cfg_options is not None:
92         cfg.merge_from_dict(args.cfg_options)
93     # set cudnn_benchmark
94     if cfg.get('cudnn_benchmark', False):
95         torch.backends.cudnn.benchmark = True
96 
97     # work_dir is determined in this priority: CLI > segment in file > filename
98     if args.work_dir is not None:
99         # update configs according to CLI args if args.work_dir is not None
100         cfg.work_dir = args.work_dir
101     elif cfg.get('work_dir', None) is None:
102         # use config filename as default work_dir if cfg.work_dir is None
103         cfg.work_dir = osp.join('./work_dirs',
104                                 osp.splitext(osp.basename(args.config))[0])
105     if args.resume_from is not None:
106         cfg.resume_from = args.resume_from
107     if args.gpu_ids is not None:
108         cfg.gpu_ids = args.gpu_ids
109     else:
110         cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
111 
112     # init distributed env first, since logger depends on the dist info.
113     if args.launcher == 'none':
114         distributed = False
115     else:
116         distributed = True
117         init_dist(args.launcher, **cfg.dist_params)
118         # re-set gpu_ids with distributed training mode
119         _, world_size = get_dist_info()
120         cfg.gpu_ids = range(world_size)
121 
122     # create work_dir
123     mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
124     # dump config
125     cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
126     # init the logger before other steps
127     timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
128     log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
129     logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
130 
131     # init the meta dict to record some important information such as
132     # environment info and seed, which will be logged
133     meta = dict()
134     # log env info
135     env_info_dict = collect_env()
136     env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
137     dash_line = '-' * 60 + '\n'
138     logger.info('Environment info:\n' + dash_line + env_info + '\n' +
139                 dash_line)
140     meta['env_info'] = env_info
141     meta['config'] = cfg.pretty_text
142     # log some basic info
143     logger.info(f'Distributed training: {distributed}')
144     logger.info(f'Config:\n{cfg.pretty_text}')
145 
146     # set random seeds
147     seed = init_random_seed(args.seed)
148     logger.info(f'Set random seed to {seed}, '
149                 f'deterministic: {args.deterministic}')
150     set_random_seed(seed, deterministic=args.deterministic)
151     cfg.seed = seed
152     meta['seed'] = seed
153     meta['exp_name'] = osp.basename(args.config)
154 
155     model = build_detector(
156         cfg.model,
157         train_cfg=cfg.get('train_cfg'),
158         test_cfg=cfg.get('test_cfg'))
159     model.init_weights()
160 
161     datasets = [build_dataset(cfg.data.train)]
162     if len(cfg.workflow) == 2:
163         val_dataset = copy.deepcopy(cfg.data.val)
164         val_dataset.pipeline = cfg.data.train.pipeline
165         datasets.append(build_dataset(val_dataset))
166     if cfg.checkpoint_config is not None:
167         # save mmdet version, config file content and class names in
168         # checkpoints as meta data
169         cfg.checkpoint_config.meta = dict(
170             mmdet_version=__version__ + get_git_hash()[:7],
171             CLASSES=datasets[0].CLASSES)
172     # add an attribute for visualization convenience
173     model.CLASSES = datasets[0].CLASSES
174     train_detector(
175         model,
176         datasets,
177         cfg,
178         distributed=distributed,
179         validate=(not args.no_validate),
180         timestamp=timestamp,
181         meta=meta)
182 
183 
184 if __name__ == '__main__':
185     main()
186
```
--- 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/tools/train.py b/tools/train.py
--- a/tools/train.py
+++ b/tools/train.py
@@ -112,6 +112,12 @@
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':
         distributed = False
+        if len(cfg.gpu_ids) > 1:
+            warnings.warn(
+                f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '
+                f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '
+                'non-distribute training time.')
+            cfg.gpu_ids = cfg.gpu_ids[0:1]
     else:
         distributed = True
         init_dist(args.launcher, **cfg.dist_params)

{"golden_diff": "diff --git a/tools/train.py b/tools/train.py\n--- a/tools/train.py\n+++ b/tools/train.py\n@@ -112,6 +112,12 @@\n     # init distributed env first, since logger depends on the dist info.\n     if args.launcher == 'none':\n         distributed = False\n+        if len(cfg.gpu_ids) > 1:\n+            warnings.warn(\n+                f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '\n+                f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '\n+                'non-distribute training time.')\n+            cfg.gpu_ids = cfg.gpu_ids[0:1]\n     else:\n         distributed = True\n         init_dist(args.launcher, **cfg.dist_params)\n", "issue": "An editing error in the Tutorial Documentation\nThank you for ur contribution, I am a newbie to this project, maybe I found an editing error in the latest [Tutorial Documentation](https://mmdetection.readthedocs.io/en/latest/2_new_data_model.html). The detailed description is shown in the figure.\r\n\r\n![\u56fe\u7247](https://user-images.githubusercontent.com/23495527/140485815-7beba29e-b8f8-4309-96b8-300b74ee6d41.png)\r\n\r\nit should be `python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon/latest.pth --eval bbox segm`\n", "before_files": [{"content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport copy\nimport os\nimport os.path as osp\nimport time\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv import Config, DictAction\nfrom mmcv.runner import get_dist_info, init_dist\nfrom mmcv.utils import get_git_hash\n\nfrom mmdet import __version__\nfrom mmdet.apis import init_random_seed, set_random_seed, train_detector\nfrom mmdet.datasets import build_dataset\nfrom mmdet.models import build_detector\nfrom mmdet.utils import collect_env, get_root_logger\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Train a detector')\n    parser.add_argument('config', help='train config file path')\n    parser.add_argument('--work-dir', help='the dir to save logs and models')\n    parser.add_argument(\n        '--resume-from', help='the checkpoint file to resume from')\n    parser.add_argument(\n        '--no-validate',\n        action='store_true',\n        help='whether not to evaluate the checkpoint during training')\n    group_gpus = parser.add_mutually_exclusive_group()\n    group_gpus.add_argument(\n        '--gpus',\n        type=int,\n        help='number of gpus to use '\n        '(only applicable to non-distributed training)')\n    group_gpus.add_argument(\n        '--gpu-ids',\n        type=int,\n        nargs='+',\n        help='ids of gpus to use '\n        '(only applicable to non-distributed training)')\n    parser.add_argument('--seed', type=int, default=None, help='random seed')\n    parser.add_argument(\n        '--deterministic',\n        action='store_true',\n        help='whether to set deterministic options for CUDNN backend.')\n    parser.add_argument(\n        '--options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file (deprecate), '\n        'change to --cfg-options instead.')\n    parser.add_argument(\n        '--cfg-options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file. If the value to '\n        'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n        'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n        'Note that the quotation marks are necessary and that no white space '\n        'is allowed.')\n    parser.add_argument(\n        '--launcher',\n        choices=['none', 'pytorch', 'slurm', 'mpi'],\n        default='none',\n        help='job launcher')\n    parser.add_argument('--local_rank', type=int, default=0)\n    args = parser.parse_args()\n    if 'LOCAL_RANK' not in os.environ:\n        os.environ['LOCAL_RANK'] = str(args.local_rank)\n\n    if args.options and args.cfg_options:\n        raise ValueError(\n            '--options and --cfg-options cannot be both '\n            'specified, --options is deprecated in favor of --cfg-options')\n    if args.options:\n        warnings.warn('--options is deprecated in favor of --cfg-options')\n        args.cfg_options = args.options\n\n    return args\n\n\ndef main():\n    args = parse_args()\n\n    cfg = Config.fromfile(args.config)\n    if args.cfg_options is not None:\n        cfg.merge_from_dict(args.cfg_options)\n    # set cudnn_benchmark\n    if cfg.get('cudnn_benchmark', False):\n        torch.backends.cudnn.benchmark = True\n\n    # work_dir is determined in this priority: CLI > segment in file > filename\n    if args.work_dir is not None:\n        # update configs according to CLI args if args.work_dir is not None\n        cfg.work_dir = args.work_dir\n    elif cfg.get('work_dir', None) is None:\n        # use config filename as default work_dir if cfg.work_dir is None\n        cfg.work_dir = osp.join('./work_dirs',\n                                osp.splitext(osp.basename(args.config))[0])\n    if args.resume_from is not None:\n        cfg.resume_from = args.resume_from\n    if args.gpu_ids is not None:\n        cfg.gpu_ids = args.gpu_ids\n    else:\n        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)\n\n    # init distributed env first, since logger depends on the dist info.\n    if args.launcher == 'none':\n        distributed = False\n    else:\n        distributed = True\n        init_dist(args.launcher, **cfg.dist_params)\n        # re-set gpu_ids with distributed training mode\n        _, world_size = get_dist_info()\n        cfg.gpu_ids = range(world_size)\n\n    # create work_dir\n    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n    # dump config\n    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))\n    # init the logger before other steps\n    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())\n    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')\n    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)\n\n    # init the meta dict to record some important information such as\n    # environment info and seed, which will be logged\n    meta = dict()\n    # log env info\n    env_info_dict = collect_env()\n    env_info = '\\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])\n    dash_line = '-' * 60 + '\\n'\n    logger.info('Environment info:\\n' + dash_line + env_info + '\\n' +\n                dash_line)\n    meta['env_info'] = env_info\n    meta['config'] = cfg.pretty_text\n    # log some basic info\n    logger.info(f'Distributed training: {distributed}')\n    logger.info(f'Config:\\n{cfg.pretty_text}')\n\n    # set random seeds\n    seed = init_random_seed(args.seed)\n    logger.info(f'Set random seed to {seed}, '\n                f'deterministic: {args.deterministic}')\n    set_random_seed(seed, deterministic=args.deterministic)\n    cfg.seed = seed\n    meta['seed'] = seed\n    meta['exp_name'] = osp.basename(args.config)\n\n    model = build_detector(\n        cfg.model,\n        train_cfg=cfg.get('train_cfg'),\n        test_cfg=cfg.get('test_cfg'))\n    model.init_weights()\n\n    datasets = [build_dataset(cfg.data.train)]\n    if len(cfg.workflow) == 2:\n        val_dataset = copy.deepcopy(cfg.data.val)\n        val_dataset.pipeline = cfg.data.train.pipeline\n        datasets.append(build_dataset(val_dataset))\n    if cfg.checkpoint_config is not None:\n        # save mmdet version, config file content and class names in\n        # checkpoints as meta data\n        cfg.checkpoint_config.meta = dict(\n            mmdet_version=__version__ + get_git_hash()[:7],\n            CLASSES=datasets[0].CLASSES)\n    # add an attribute for visualization convenience\n    model.CLASSES = datasets[0].CLASSES\n    train_detector(\n        model,\n        datasets,\n        cfg,\n        distributed=distributed,\n        validate=(not args.no_validate),\n        timestamp=timestamp,\n        meta=meta)\n\n\nif __name__ == '__main__':\n    main()\n", "path": "tools/train.py"}], "after_files": [{"content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport argparse\nimport copy\nimport os\nimport os.path as osp\nimport time\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv import Config, DictAction\nfrom mmcv.runner import get_dist_info, init_dist\nfrom mmcv.utils import get_git_hash\n\nfrom mmdet import __version__\nfrom mmdet.apis import init_random_seed, set_random_seed, train_detector\nfrom mmdet.datasets import build_dataset\nfrom mmdet.models import build_detector\nfrom mmdet.utils import collect_env, get_root_logger\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Train a detector')\n    parser.add_argument('config', help='train config file path')\n    parser.add_argument('--work-dir', help='the dir to save logs and models')\n    parser.add_argument(\n        '--resume-from', help='the checkpoint file to resume from')\n    parser.add_argument(\n        '--no-validate',\n        action='store_true',\n        help='whether not to evaluate the checkpoint during training')\n    group_gpus = parser.add_mutually_exclusive_group()\n    group_gpus.add_argument(\n        '--gpus',\n        type=int,\n        help='number of gpus to use '\n        '(only applicable to non-distributed training)')\n    group_gpus.add_argument(\n        '--gpu-ids',\n        type=int,\n        nargs='+',\n        help='ids of gpus to use '\n        '(only applicable to non-distributed training)')\n    parser.add_argument('--seed', type=int, default=None, help='random seed')\n    parser.add_argument(\n        '--deterministic',\n        action='store_true',\n        help='whether to set deterministic options for CUDNN backend.')\n    parser.add_argument(\n        '--options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file (deprecate), '\n        'change to --cfg-options instead.')\n    parser.add_argument(\n        '--cfg-options',\n        nargs='+',\n        action=DictAction,\n        help='override some settings in the used config, the key-value pair '\n        'in xxx=yyy format will be merged into config file. If the value to '\n        'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n        'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n        'Note that the quotation marks are necessary and that no white space '\n        'is allowed.')\n    parser.add_argument(\n        '--launcher',\n        choices=['none', 'pytorch', 'slurm', 'mpi'],\n        default='none',\n        help='job launcher')\n    parser.add_argument('--local_rank', type=int, default=0)\n    args = parser.parse_args()\n    if 'LOCAL_RANK' not in os.environ:\n        os.environ['LOCAL_RANK'] = str(args.local_rank)\n\n    if args.options and args.cfg_options:\n        raise ValueError(\n            '--options and --cfg-options cannot be both '\n            'specified, --options is deprecated in favor of --cfg-options')\n    if args.options:\n        warnings.warn('--options is deprecated in favor of --cfg-options')\n        args.cfg_options = args.options\n\n    return args\n\n\ndef main():\n    args = parse_args()\n\n    cfg = Config.fromfile(args.config)\n    if args.cfg_options is not None:\n        cfg.merge_from_dict(args.cfg_options)\n    # set cudnn_benchmark\n    if cfg.get('cudnn_benchmark', False):\n        torch.backends.cudnn.benchmark = True\n\n    # work_dir is determined in this priority: CLI > segment in file > filename\n    if args.work_dir is not None:\n        # update configs according to CLI args if args.work_dir is not None\n        cfg.work_dir = args.work_dir\n    elif cfg.get('work_dir', None) is None:\n        # use config filename as default work_dir if cfg.work_dir is None\n        cfg.work_dir = osp.join('./work_dirs',\n                                osp.splitext(osp.basename(args.config))[0])\n    if args.resume_from is not None:\n        cfg.resume_from = args.resume_from\n    if args.gpu_ids is not None:\n        cfg.gpu_ids = args.gpu_ids\n    else:\n        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)\n\n    # init distributed env first, since logger depends on the dist info.\n    if args.launcher == 'none':\n        distributed = False\n        if len(cfg.gpu_ids) > 1:\n            warnings.warn(\n                f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '\n                f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '\n                'non-distribute training time.')\n            cfg.gpu_ids = cfg.gpu_ids[0:1]\n    else:\n        distributed = True\n        init_dist(args.launcher, **cfg.dist_params)\n        # re-set gpu_ids with distributed training mode\n        _, world_size = get_dist_info()\n        cfg.gpu_ids = range(world_size)\n\n    # create work_dir\n    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n    # dump config\n    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))\n    # init the logger before other steps\n    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())\n    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')\n    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)\n\n    # init the meta dict to record some important information such as\n    # environment info and seed, which will be logged\n    meta = dict()\n    # log env info\n    env_info_dict = collect_env()\n    env_info = '\\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])\n    dash_line = '-' * 60 + '\\n'\n    logger.info('Environment info:\\n' + dash_line + env_info + '\\n' +\n                dash_line)\n    meta['env_info'] = env_info\n    meta['config'] = cfg.pretty_text\n    # log some basic info\n    logger.info(f'Distributed training: {distributed}')\n    logger.info(f'Config:\\n{cfg.pretty_text}')\n\n    # set random seeds\n    seed = init_random_seed(args.seed)\n    logger.info(f'Set random seed to {seed}, '\n                f'deterministic: {args.deterministic}')\n    set_random_seed(seed, deterministic=args.deterministic)\n    cfg.seed = seed\n    meta['seed'] = seed\n    meta['exp_name'] = osp.basename(args.config)\n\n    model = build_detector(\n        cfg.model,\n        train_cfg=cfg.get('train_cfg'),\n        test_cfg=cfg.get('test_cfg'))\n    model.init_weights()\n\n    datasets = [build_dataset(cfg.data.train)]\n    if len(cfg.workflow) == 2:\n        val_dataset = copy.deepcopy(cfg.data.val)\n        val_dataset.pipeline = cfg.data.train.pipeline\n        datasets.append(build_dataset(val_dataset))\n    if cfg.checkpoint_config is not None:\n        # save mmdet version, config file content and class names in\n        # checkpoints as meta data\n        cfg.checkpoint_config.meta = dict(\n            mmdet_version=__version__ + get_git_hash()[:7],\n            CLASSES=datasets[0].CLASSES)\n    # add an attribute for visualization convenience\n    model.CLASSES = datasets[0].CLASSES\n    train_detector(\n        model,\n        datasets,\n        cfg,\n        distributed=distributed,\n        validate=(not args.no_validate),\n        timestamp=timestamp,\n        meta=meta)\n\n\nif __name__ == '__main__':\n    main()\n", "path": "tools/train.py"}]}