load model to device Traceback (most recent call last): File "/root/YOLODS/tools/train_dwa.py", line 436, in <module> main() File "/root/YOLODS/tools/train_dwa.py", line 123, in main model = get_net(cfg).to(device) File "/root/YOLODS/lib/models/YOLOP.py", line 313, in get_net model = MCnet(m_block_cfg, **kwargs) File "/root/YOLODS/lib/models/YOLOP.py", line 256, in __init__ assert self.detector_index == block_cfg[0][0] AssertionError

这个错误提示是断言错误（AssertionError）。在get_net函数中，有一个assert语句，如果条件不成立（False），就会抛出这个错误。根据错误提示，这个assert语句是检查self.detector_index是否等于block_cfg[0][0]。如果不相等，就会出现这个错误。你需要检查一下get_net函数中的代码，看看是不是在使用这个变量时出现了问题。可能需要检查一下传入get_net函数的参数。你可以尝试打印一些变量来调试这个问题。

相关问题

Traceback (most recent call last): File "/root/YOLODS/tools/train_dwa.py", line 436, in <module> main() File "/root/YOLODS/tools/train_dwa.py", line 171, in main checkpoint_dict = {k: v for k, v in checkpoint['state_dict'].items() if k.split(".")[1] in det_idx_range} KeyError: 'state_dict' (YOLOP) root@autodl-con

这个错误提示是在运行一个名为 train_dwa.py 的 Python 脚本时出现的。根据错误提示，这个脚本在第 171 行尝试使用一个名为 `checkpoint` 的字典，并从中获取一个名为 `state_dict` 的键的值，但是在 `checkpoint` 字典中找不到这个键。

具体而言，这个错误提示的最后一行是：

KeyError: 'state_dict'

这个错误提示说明在获取 `state_dict` 值时出现了 KeyError，也就是说在 `checkpoint` 字典中找不到名为 `state_dict` 的键。

可能的原因是 `checkpoint` 字典中没有 `state_dict` 这个键，或者 `state_dict` 键的值为 None。你可以检查一下 `checkpoint` 字典中的键值对，看看是否有名为 `state_dict` 的键，或者使用 `print(checkpoint)` 打印出整个 `checkpoint` 字典，看看其中的内容。另外，你也可以检查一下代码是否正确地加载了预训练模型。

Broken DAG: [/root/airflow/dags/sqoop_airflow.py] Traceback (most recent call last):

As an AI language model, I cannot see the full error trace, but the error seems to indicate that there is an issue with the DAG file located at "/root/airflow/dags/sqoop_airflow.py". It is likely that there is a syntax error or an import issue within the file that is preventing it from being properly parsed by Airflow.

To troubleshoot this issue, you can try the following steps:

1. Check the syntax of the DAG file and ensure that all required packages and modules are imported correctly.
2. Verify that the file is located in the correct directory and has the correct file permissions.
3. Restart the Airflow scheduler and webserver to see if the issue persists.
4. Check Airflow's log files for more detailed error messages that may provide additional insight into the issue.