Comments (8)
from tsformer-vo.
Yes, they are the same and you got it correctly.
In "kitti.py" you will read the data with "window_size" frames in each iteration. (https://github.com/aofrancani/TSformer-VO/blob/main/train.py#L223), and in the model's output, you will have "window_size -1" estimations because you have one pose estimation for each 2 consecutive frames.
The value "window_size=3" you see in "kitti.py" is just a default value if you don't mention it when reading the data...
from tsformer-vo.
Thank you very much for your reply. If it is not mentioned when reading data, the value "window_size=3" seen in "kitti. py" is only a default value, and we do not need to worry about it.
So just modify the window size=2, 3, and 4 in train. py to represent VO-1, 2, and 3, respectively.
But I only changed (window_size: 2) to (window_size: 3) in train. py, and the resulting error was quite large.
So I would like to ask if there are any other parameters that need to be modified accordingly when modifying the window_size value in train.py?
from tsformer-vo.
No, the window_size parameter is independent of the others. What I used to do was set the overlap to "window_size - 1", so that the larger the window, the more data I got to train (with redundancy in the batches, because from one video clip to the next only one frame has changed). So, the overlap between the windowed data might be the other parameter you are looking for...
from tsformer-vo.
I'm very sorry, I was so foolish that I reread the article and still don't quite understand how to implement it in the code.
Initially, train. py: window_size=2; Kitti. py: window_size=3, which means the overlapping frame rate is 2, representing VO1?
Then I conducted two experiments according to my understanding:
1: In train, window_size=3, and in kitti, window_size=4, indicating an overlapping frame rate of 3, i.e. VO2;
2: In train, window_size=4, and in kitti, window_size=5, indicating an overlapping frame rate of 4, i.e. VO3;
But the result is still not right.
Has my understanding gone wrong again? I hope to receive your guidance again! Thank you.
from tsformer-vo.
I'm sorry I didn't get it... What do you mean the result is not right? the expected size of your windowed data or the final evaluation metrics after/during your training?
from tsformer-vo.
I will reproduce your code, and if no changes are made, the final error result will be similar to that in your paper.
But if I want to reproduce TSformer VO-2 and TSformer VO-3, how should I change it?
I made the changes according to this idea, and the final error was significant.
1: In train, window_size=3, and in kitti, window_size=4, indicating an overlapping frame rate of 3, i.e. VO2;
2: In train, window_size=4, and in kitti, window_size=5, indicating an overlapping frame rate of 4, i.e. VO3;
Simply put, I don't understand how to modify code. Where to train VO2 and VO3?谢谢
from tsformer-vo.
Ok, so you mean the final error after training everything...
So, the only thing you should edit is the "train.py", you don't need to worry about "kitty.py" because when we read the data we pass the parameter "args["window_size"]" as input to the dataloader.
- For VO-2: set "window_size=3 and overlap=2"
- For VO-3: set "window_size=4 and overlap=3"
I hope this helps!
from tsformer-vo.
Related Issues (17)
- input diemension HOT 2
- Dataset indexing issues HOT 1
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- GPU HOT 2
- An error occurs in pretrained_ViT: True HOT 6
- To achieve results more closely aligned with the paper HOT 2
- Using pre trained models. HOT 3
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- Trajectory drawing HOT 1
- modify training data ? HOT 1
- Can I provide the weights of the trained model? HOT 4
- result HOT 4
- 7DoF and 6DoF HOT 9
- Thank you so much HOT 1
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from tsformer-vo.