Comments (10)
Thank you for your comment.
Yes, exactly, we also found the uneven distribution of GPU memory during training. Though we haven't found the reason for now, it can be safely ignored since the training runs successfully.
And great thanks for your report of the bugs, we've fixed them accordingly.
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Thank you for your comment. Yes, exactly, we also found the uneven distribution of GPU memory during training. Though we haven't found the reason for now, it can be safely ignored since the training runs successfully. And great thanks for your report of the bugs, we've fixed them accordingly.
Hi, I tried to modify the line 136 of distributed.py
if param.requires_grad:
into
if param.requires_grad and param.grad is not None:
and it can trained with DDP successfully. The training time reduced from 10 hours to 4 hours in my machine.
However, I found the reason is that all parameters of ALIGN module have no grad (is None) but their requires_grad is True. I am quite confused about it.
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Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
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Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
Have you checked about the model you were running? Some people have reproduced the results in the paper, the environmental difference should not cause a huge performance drop.
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
Have you checked about the model you were running? Some people have reproduced the results in the paper, the environmental difference should not cause a huge performance drop.
I have checked every steps including preprocessing and training. Everything seems ok, except that when I run gen_json.py of NAT dataset I skip sequence "0175bike1_3" bacause there is no "0175bike1_3_gt.txt" in "pseudo_anno/". I retrain the model, but the the performance is still low:
------------------------------------------------------------------------------------------------------
| Tracker name | Success | Norm Precision | Precision |
------------------------------------------------------------------------------------------------------
| UDATCAR_A6000_snapshot_wrandomcheckpoint_e19_0.39_0.04_0.37 | 0.457 | 0.000 | 0.652 |
------------------------------------------------------------------------------------------------------
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
Have you checked about the model you were running? Some people have reproduced the results in the paper, the environmental difference should not cause a huge performance drop.
I have checked every steps including preprocessing and training. Everything seems ok, except that when I run gen_json.py of NAT dataset I skip sequence "0175bike1_3" bacause there is no "0175bike1_3_gt.txt" in "pseudo_anno/". I retrain the model, but the the performance is still low:
------------------------------------------------------------------------------------------------------ | Tracker name | Success | Norm Precision | Precision | ------------------------------------------------------------------------------------------------------ | UDATCAR_A6000_snapshot_wrandomcheckpoint_e19_0.39_0.04_0.37 | 0.457 | 0.000 | 0.652 | ------------------------------------------------------------------------------------------------------
I suggest you test the model we released (both the original version and the UDAT version) on your platform and compare the results with the paper, to figure out how environmental difference influences the results.
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
Have you checked about the model you were running? Some people have reproduced the results in the paper, the environmental difference should not cause a huge performance drop.
I have checked every steps including preprocessing and training. Everything seems ok, except that when I run gen_json.py of NAT dataset I skip sequence "0175bike1_3" bacause there is no "0175bike1_3_gt.txt" in "pseudo_anno/". I retrain the model, but the the performance is still low:
------------------------------------------------------------------------------------------------------ | Tracker name | Success | Norm Precision | Precision | ------------------------------------------------------------------------------------------------------ | UDATCAR_A6000_snapshot_wrandomcheckpoint_e19_0.39_0.04_0.37 | 0.457 | 0.000 | 0.652 | ------------------------------------------------------------------------------------------------------
I suggest you test the model we released (both the original version and the UDAT version) on your platform and compare the results with the paper, to figure out how environmental difference influences the results.
I test the model of the original version.
SiamCAR:
- when python=3.7.11, pytorch=1.6.0, cudatoolkit=10.1.243, the result is 0.422(Success) and 0.633(Precision);
- when python=3.6.1, pytorch=1.2.0, cudatoolkit=10.0.130, the result is 0.450(Success) and 0.670(Precision);
SiamBAN:
- when python=3.7.11, pytorch=1.6.0, cudatoolkit=10.1.243, the result is 0.271(Success) and 0.441(Precision);
- when python=3.7.13, pytorch=1.3.1, cudatoolkit=10.1.243, the result is 0.327(Success) and 0.540(Precision);
It seems that the environment influences the results a lot. Can you tell me your environment setting?
from udat.
Hi. Can you reproduce the results in the paper? For UDAT-CAR tested on NAT2021-test, I got 0.458(Success) and 0.655(Precision), but in the paper it's 0.483 and 0.687.
Could you please post more details of your testing/training? So we can figure things out.
The setting I used during preprocessing/training/testing is python 3.7.11, pytorch 1.6.0, cudatoolkit 10.1.243. All training/testing parameters remain unchanged. So is it the problem of environment? Or do I need to post some more other details?
Have you checked about the model you were running? Some people have reproduced the results in the paper, the environmental difference should not cause a huge performance drop.
I have checked every steps including preprocessing and training. Everything seems ok, except that when I run gen_json.py of NAT dataset I skip sequence "0175bike1_3" bacause there is no "0175bike1_3_gt.txt" in "pseudo_anno/". I retrain the model, but the the performance is still low:
------------------------------------------------------------------------------------------------------ | Tracker name | Success | Norm Precision | Precision | ------------------------------------------------------------------------------------------------------ | UDATCAR_A6000_snapshot_wrandomcheckpoint_e19_0.39_0.04_0.37 | 0.457 | 0.000 | 0.652 | ------------------------------------------------------------------------------------------------------
I suggest you test the model we released (both the original version and the UDAT version) on your platform and compare the results with the paper, to figure out how environmental difference influences the results.
I test the model of the original version.
SiamCAR:
- when python=3.7.11, pytorch=1.6.0, cudatoolkit=10.1.243, the result is 0.422(Success) and 0.633(Precision);
- when python=3.6.1, pytorch=1.2.0, cudatoolkit=10.0.130, the result is 0.450(Success) and 0.670(Precision);
SiamBAN:
- when python=3.7.11, pytorch=1.6.0, cudatoolkit=10.1.243, the result is 0.271(Success) and 0.441(Precision);
- when python=3.7.13, pytorch=1.3.1, cudatoolkit=10.1.243, the result is 0.327(Success) and 0.540(Precision);
It seems that the environment influences the results a lot. Can you tell me your environment setting?
Hey, the environment used in the original paper is:
PyTorch 1.11.0
CUDA11.6/cudnn8.4.0
Python 3.9.12
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Related Issues (13)
- question about transformer bridging layer HOT 1
- The ‘0175bike1_3.npy’ is all ''nan HOT 1
- About val dataset and the result of two baselines HOT 2
- The code of t-SNE in Figure 4 HOT 2
- The code of paper 5.2.4 Visualization in Grad-CAM
- 请问能否提供论文中对比方法的raw results HOT 1
- 请问三个数据集的att文件夹中对应的Attributes顺序是怎么排列的 HOT 3
- About the implementation details of Figure 4 in the paper HOT 3
- Training Strategy
- 训练出来的模型
- 难以复现您的结果 HOT 3
- 关于图9绘制 HOT 1
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