We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
训练出现长警告
### 问题确认 Search before asking

- [X] 我已经查询[历史issue](https://github.com/PaddlePaddle/PaddleDetection/issues)，没有发现相似的bug。I have searched the [issues](https://github.com/PaddlePaddle/PaddleDetection/issues) and found no similar bug report.


### Bug组件 Bug Component

_No response_

### Bug描述 Describe the Bug

训练出现长警告
```
I0706 13:09:13.075042 3772 eager_method.cc:140] Warning:: 0D Tensor cannot be used as 'Tensor.numpy()[0]' . In order to avoid this problem, 0D Tensor will be changed to 1D numpy currently, but it's not correct and will be removed in release 2.6. For Tensor contain only one element, Please modify 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as possible, otherwise 'Tensor.numpy()[0]' will raise error in release 2.6.
I0706 13:09:13.382442 3772 eager_method.cc:140] Warning:: 0D Tensor cannot be used as 'Tensor.numpy()[0]' . In order to avoid this problem, 0D Tensor will be changed to 1D numpy currently, but it's not correct and will be removed in release 2.6. For Tensor contain only one element, Please modify 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as possible, otherwise 'Tensor.numpy()[0]' will raise error in release 2.6.
```

### 复现环境 Environment

PaddleDetection2.6
PaddlePaddle2.5.0

经过排查将`ppdet/utils/stats.py`第77行进行如下修改
`v.update(stats[k].numpy())`→`v.update(float(stats[k]))`

### Bug描述确认 Bug description confirmation

- [X] 我确认已经提供了Bug复现步骤、代码改动说明、以及环境信息，确认问题是可以复现的。I confirm that the bug replication steps, code change instructions, and environment information have been provided, and the problem can be reproduced.


### 是否愿意提交PR？ Are you willing to submit a PR?

- [ ] 我愿意提交PR！I'd like to help by submitting a PR!

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `ppdet/utils/stats.py`
Content:
```
1 # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 import collections
16 import numpy as np
17 
18 __all__ = ['SmoothedValue', 'TrainingStats']
19 
20 
21 class SmoothedValue(object):
22     """Track a series of values and provide access to smoothed values over a
23     window or the global series average.
24     """
25 
26     def __init__(self, window_size=20, fmt=None):
27         if fmt is None:
28             fmt = "{median:.4f} ({avg:.4f})"
29         self.deque = collections.deque(maxlen=window_size)
30         self.fmt = fmt
31         self.total = 0.
32         self.count = 0
33 
34     def update(self, value, n=1):
35         self.deque.append(value)
36         self.count += n
37         self.total += value * n
38 
39     @property
40     def median(self):
41         return np.median(self.deque)
42 
43     @property
44     def avg(self):
45         return np.mean(self.deque)
46 
47     @property
48     def max(self):
49         return np.max(self.deque)
50 
51     @property
52     def value(self):
53         return self.deque[-1]
54 
55     @property
56     def global_avg(self):
57         return self.total / self.count
58 
59     def __str__(self):
60         return self.fmt.format(
61             median=self.median, avg=self.avg, max=self.max, value=self.value)
62 
63 
64 class TrainingStats(object):
65     def __init__(self, window_size, delimiter=' '):
66         self.meters = None
67         self.window_size = window_size
68         self.delimiter = delimiter
69 
70     def update(self, stats):
71         if self.meters is None:
72             self.meters = {
73                 k: SmoothedValue(self.window_size)
74                 for k in stats.keys()
75             }
76         for k, v in self.meters.items():
77             v.update(stats[k].numpy())
78 
79     def get(self, extras=None):
80         stats = collections.OrderedDict()
81         if extras:
82             for k, v in extras.items():
83                 stats[k] = v
84         for k, v in self.meters.items():
85             stats[k] = format(v.median, '.6f')
86 
87         return stats
88 
89     def log(self, extras=None):
90         d = self.get(extras)
91         strs = []
92         for k, v in d.items():
93             strs.append("{}: {}".format(k, str(v)))
94         return self.delimiter.join(strs)
95
```
--- 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/ppdet/utils/stats.py b/ppdet/utils/stats.py
--- a/ppdet/utils/stats.py
+++ b/ppdet/utils/stats.py
@@ -74,7 +74,7 @@
                 for k in stats.keys()
             }
         for k, v in self.meters.items():
-            v.update(stats[k].numpy())
+            v.update(float(stats[k]))
 
     def get(self, extras=None):
         stats = collections.OrderedDict()

{"golden_diff": "diff --git a/ppdet/utils/stats.py b/ppdet/utils/stats.py\n--- a/ppdet/utils/stats.py\n+++ b/ppdet/utils/stats.py\n@@ -74,7 +74,7 @@\n                 for k in stats.keys()\n             }\n         for k, v in self.meters.items():\n-            v.update(stats[k].numpy())\n+            v.update(float(stats[k]))\n \n     def get(self, extras=None):\n         stats = collections.OrderedDict()\n", "issue": "\u8bad\u7ec3\u51fa\u73b0\u957f\u8b66\u544a\n### \u95ee\u9898\u786e\u8ba4 Search before asking\n\n- [X] \u6211\u5df2\u7ecf\u67e5\u8be2[\u5386\u53f2issue](https://github.com/PaddlePaddle/PaddleDetection/issues)\uff0c\u6ca1\u6709\u53d1\u73b0\u76f8\u4f3c\u7684bug\u3002I have searched the [issues](https://github.com/PaddlePaddle/PaddleDetection/issues) and found no similar bug report.\n\n\n### Bug\u7ec4\u4ef6 Bug Component\n\n_No response_\n\n### Bug\u63cf\u8ff0 Describe the Bug\n\n\u8bad\u7ec3\u51fa\u73b0\u957f\u8b66\u544a\r\n```\r\nI0706 13:09:13.075042 3772 eager_method.cc:140] Warning:: 0D Tensor cannot be used as 'Tensor.numpy()[0]' . In order to avoid this problem, 0D Tensor will be changed to 1D numpy currently, but it's not correct and will be removed in release 2.6. For Tensor contain only one element, Please modify 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as possible, otherwise 'Tensor.numpy()[0]' will raise error in release 2.6.\r\nI0706 13:09:13.382442 3772 eager_method.cc:140] Warning:: 0D Tensor cannot be used as 'Tensor.numpy()[0]' . In order to avoid this problem, 0D Tensor will be changed to 1D numpy currently, but it's not correct and will be removed in release 2.6. For Tensor contain only one element, Please modify 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as possible, otherwise 'Tensor.numpy()[0]' will raise error in release 2.6.\r\n```\n\n### \u590d\u73b0\u73af\u5883 Environment\n\nPaddleDetection2.6\r\nPaddlePaddle2.5.0\r\n\r\n\u7ecf\u8fc7\u6392\u67e5\u5c06`ppdet/utils/stats.py`\u7b2c77\u884c\u8fdb\u884c\u5982\u4e0b\u4fee\u6539\r\n`v.update(stats[k].numpy())`\u2192`v.update(float(stats[k]))`\n\n### Bug\u63cf\u8ff0\u786e\u8ba4 Bug description confirmation\n\n- [X] \u6211\u786e\u8ba4\u5df2\u7ecf\u63d0\u4f9b\u4e86Bug\u590d\u73b0\u6b65\u9aa4\u3001\u4ee3\u7801\u6539\u52a8\u8bf4\u660e\u3001\u4ee5\u53ca\u73af\u5883\u4fe1\u606f\uff0c\u786e\u8ba4\u95ee\u9898\u662f\u53ef\u4ee5\u590d\u73b0\u7684\u3002I confirm that the bug replication steps, code change instructions, and environment information have been provided, and the problem can be reproduced.\n\n\n### \u662f\u5426\u613f\u610f\u63d0\u4ea4PR\uff1f Are you willing to submit a PR?\n\n- [ ] \u6211\u613f\u610f\u63d0\u4ea4PR\uff01I'd like to help by submitting a PR!\n", "before_files": [{"content": "# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport collections\nimport numpy as np\n\n__all__ = ['SmoothedValue', 'TrainingStats']\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.4f} ({avg:.4f})\"\n        self.deque = collections.deque(maxlen=window_size)\n        self.fmt = fmt\n        self.total = 0.\n        self.count = 0\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    @property\n    def median(self):\n        return np.median(self.deque)\n\n    @property\n    def avg(self):\n        return np.mean(self.deque)\n\n    @property\n    def max(self):\n        return np.max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    @property\n    def global_avg(self):\n        return self.total / self.count\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median, avg=self.avg, max=self.max, value=self.value)\n\n\nclass TrainingStats(object):\n    def __init__(self, window_size, delimiter=' '):\n        self.meters = None\n        self.window_size = window_size\n        self.delimiter = delimiter\n\n    def update(self, stats):\n        if self.meters is None:\n            self.meters = {\n                k: SmoothedValue(self.window_size)\n                for k in stats.keys()\n            }\n        for k, v in self.meters.items():\n            v.update(stats[k].numpy())\n\n    def get(self, extras=None):\n        stats = collections.OrderedDict()\n        if extras:\n            for k, v in extras.items():\n                stats[k] = v\n        for k, v in self.meters.items():\n            stats[k] = format(v.median, '.6f')\n\n        return stats\n\n    def log(self, extras=None):\n        d = self.get(extras)\n        strs = []\n        for k, v in d.items():\n            strs.append(\"{}: {}\".format(k, str(v)))\n        return self.delimiter.join(strs)\n", "path": "ppdet/utils/stats.py"}], "after_files": [{"content": "# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport collections\nimport numpy as np\n\n__all__ = ['SmoothedValue', 'TrainingStats']\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.4f} ({avg:.4f})\"\n        self.deque = collections.deque(maxlen=window_size)\n        self.fmt = fmt\n        self.total = 0.\n        self.count = 0\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    @property\n    def median(self):\n        return np.median(self.deque)\n\n    @property\n    def avg(self):\n        return np.mean(self.deque)\n\n    @property\n    def max(self):\n        return np.max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    @property\n    def global_avg(self):\n        return self.total / self.count\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median, avg=self.avg, max=self.max, value=self.value)\n\n\nclass TrainingStats(object):\n    def __init__(self, window_size, delimiter=' '):\n        self.meters = None\n        self.window_size = window_size\n        self.delimiter = delimiter\n\n    def update(self, stats):\n        if self.meters is None:\n            self.meters = {\n                k: SmoothedValue(self.window_size)\n                for k in stats.keys()\n            }\n        for k, v in self.meters.items():\n            v.update(float(stats[k]))\n\n    def get(self, extras=None):\n        stats = collections.OrderedDict()\n        if extras:\n            for k, v in extras.items():\n                stats[k] = v\n        for k, v in self.meters.items():\n            stats[k] = format(v.median, '.6f')\n\n        return stats\n\n    def log(self, extras=None):\n        d = self.get(extras)\n        strs = []\n        for k, v in d.items():\n            strs.append(\"{}: {}\".format(k, str(v)))\n        return self.delimiter.join(strs)\n", "path": "ppdet/utils/stats.py"}]}