A failed attempt to use official PTH as the initial training value

[Zhaoyibinn]

A failed attempt to use official PTH as the initial training value

[Zhaoyibinn]

I have solved this problem, and the core issue is:

The official PTH, TAR trained through official tutorials, and the structure of network inference. The names of the keys for the three are different and need to be converted when reading.

The official conversion method has been written in the Lightglue library, located at approximately line 470 of Lightglue. py.

for i in range(self.conf.n_layers):
pattern = f"self_attn.{i}", f"transformers.{i}.self_attn
state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn"
state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}

I referred to this writing method and manually checked the key names for each link.

[ly0224]

I have solved this problem, and the core issue is:我已经解决了这个问题，核心问题是：

The official PTH, TAR trained through official tutorials, and the structure of network inference. The names of the keys for the three are different and need to be converted when reading.官方PTH，通过官方教程训练的TAR，以及网络推理的结构。三者的键名不同，读取时需要转换。

The official conversion method has been written in the Lightglue library, located at approximately line 470 of Lightglue. py.官方的转换方法已经写在 Lightglue 库中，位于 Lightglue 的大约 470 行。py。

for i in range(self.conf.n_layers): pattern = f"self_attn.{i}", f"transformers.{i}.self_attn state_dict = {k.replace(*pattern): v for k, v in state_dict.items()} pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn" state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}

I referred to this writing method and manually checked the key names for each link.我参考了这种编写方法，并手动检查了每个链接的键名。

Hello, I want to fine-tune the training on my own dataset, how should I do it, what the format of the dataset should be

[Zhaoyibinn]

我已经解决了这个问题，核心问题是：我已经解决了这个问题，核心问题是：
官方PTH，通过官方教程训练的TAR，以及网络推理的结构。官方PTH，通过官方教程训练的TAR，以及网络推理的结构。三者的键名不同，读取时需要转换。
官方的转换方法已经写在 Lightglue 库中，位于 Lightglue 的大约 470 行。py.官方的转换方法已经写在 Lightglue 库中，位于 Lightglue 的大约 470 行。py。
for i in range(self.conf.n_layers): pattern = f"self_attn.{i}", f"transformers.{i}.self_attn state_dict = {k.replace(*pattern): v for k, v in state_dict.items()} pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn" state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
我参考了这种编写方法，并手动检查了每个链接的键名。

您好，我想在自己的数据集上微调训练，我应该怎么做，数据集的格式应该是什么

您可以下载官方所采用的Homography或者megadepth数据集进行参考（虽然经过我实际测试微调效果并不好）

[ly0224]

我已经解决了这个问题，核心问题是：我已经解决了这个问题，核心问题是：
官方PTH，通过官方教程训练的TAR，以及网络推理的结构。官方PTH，通过官方教程训练的TAR，以及网络推理的结构。三者的键名不同，读取时需要转换。
官方的转换方法已经写在 Lightglue 库中，位于 Lightglue 的大约 470 行。py.官方的转换方法已经写在 Lightglue 库中，位于 Lightglue 的大约 470 行。py。
for i in range(self.conf.n_layers): pattern = f"self_attn.{i}", f"transformers.{i}.self_attn state_dict = {k.replace(*pattern): v for k, v in state_dict.items()} pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn" state_dict = {k.replace(*pattern): v for k, v in state_dict.items()}
我参考了这种编写方法，并手动检查了每个链接的键名。

您好，我想在自己的数据集上微调训练，我应该怎么做，数据集的格式应该是什么

您可以下载官方所采用的Homography或者megadepth数据集进行参考（虽然经过我实际测试微调效果并不好）

我有更多的问题想请教一下您，方便加个球球吗，963170859