Comments (15)
Thank you for your interest in our work. I recommend that you follow these two repositories.
https://github.com/jeonsworld/ViT-pytorch
https://github.com/kentaroy47/vision-transformers-cifar10
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I just quickly checked the code, and it seems that the checkpoint was not loaded successfully (--checkpoint). I will further check and correct the code.
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Thanks, we will update the code.
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This repository has the ResNet config, you could follow it.
https://github.com/kentaroy47/vision-transformers-cifar10
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@Yangsenqiao Hi, friend, could you kindly share your source model training code? (It is warmup (initialization process) right?) I assume you attached
ViDAInjectedLinear
to ViT. And my paper, BECoTTA shares the same sprits. I would really appreciate if you share your code to reimplement your method clearly. Thanks!
Hi, Daeun, I am happy to see your new work 😃. Additionally, I have included your paper in our recently organized repository focus on awesome-CTTA related paper. I suggest that you cite more related work in your paper to help readers understand the context of this research topic 👍.
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@Yangsenqiao, friend, I addressed it by directly assigning a checkpoint path here. If someone has similar problem, it could be addressed like below. Thanks for your quick reply.
base_model = torch.nn.DataParallel(base_model) # make parallel checkpoint = torch.load('/d1/daeun/vida/cifar/dataset/vit_base_384_cifar10.t7') base_model.load_state_dict(checkpoint['model'], strict=False)
These bugs have been fixed. Thanks!
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Thanks a lot Yangsenqiao!
Btw, while reimplementing your code and running source_vit.sh with the CIFAR10 dataset, I got pretty different source-only results like below.
[24/03/22 16:27:47] [cifar10c_vit.py: 68]: not resetting model
[24/03/22 16:29:41] [cifar10c_vit.py: 87]: error % [gaussian_noise5]: 88.48%
[24/03/22 16:29:41] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:31:37] [cifar10c_vit.py: 87]: error % [shot_noise5]: 87.51%
[24/03/22 16:31:37] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:33:33] [cifar10c_vit.py: 87]: error % [impulse_noise5]: 92.07%
[24/03/22 16:33:33] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:35:29] [cifar10c_vit.py: 87]: error % [defocus_blur5]: 89.76%
[24/03/22 16:35:29] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:37:26] [cifar10c_vit.py: 87]: error % [glass_blur5]: 89.25%
[24/03/22 16:37:26] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:39:34] [cifar10c_vit.py: 87]: error % [motion_blur5]: 88.75%
[24/03/22 16:39:34] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:41:32] [cifar10c_vit.py: 87]: error % [zoom_blur5]: 89.07%
[24/03/22 16:41:32] [cifar10c_vit.py: 70]: not resetting model
[24/03/22 16:43:29] [cifar10c_vit.py: 87]: error % [snow5]: 89.86%
[24/03/22 16:43:29] [cifar10c_vit.py: 70]: not resetting model
I just utilized these config you provided.
MODEL:
ADAPTATION: source
ARCH: Standard_VITB
DATA_DIR: /dataset
TEST:
BATCH_SIZE: 40
CORRUPTION:
DATASET: cifar10
SEVERITY:
- 5
TYPE:
- gaussian_noise
- shot_noise
- impulse_noise
- defocus_blur
- glass_blur
- motion_blur
- zoom_blur
- snow
- frost
- fog
- brightness
- contrast
- elastic_transform
- pixelate
- jpeg_compression
I would be really appreciate if you have some ideas!
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from vida.
@Yangsenqiao, friend, I addressed it by directly assigning a checkpoint path here. If someone has similar problem, it could be addressed like below. Thanks for your quick reply.
base_model = torch.nn.DataParallel(base_model) # make parallel
checkpoint = torch.load('/d1/daeun/vida/cifar/dataset/vit_base_384_cifar10.t7')
base_model.load_state_dict(checkpoint['model'], strict=False)
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Thanks a lot. I think some elements in the config .yaml file are not working.
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Senqiao, also may I ask how did you train ViDA source model (WideResNet-28)? (not ViT backbone)
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Thanks, you are the best!
from vida.
@Yangsenqiao Hi, friend, could you kindly share your source model training code? (It is warmup (initialization process) right?)
I assume you attached ViDAInjectedLinear
to ViT. And my paper, BECoTTA shares the same sprits. I would really appreciate if you share your code to reimplement your method clearly. Thanks!
from vida.
Thank you for your interest in our work. I recommend that you follow these two repositories. https://github.com/jeonsworld/ViT-pytorch https://github.com/kentaroy47/vision-transformers-cifar10
And our training code follows these GitHub repositories. We first train the source model, then add the ViDA to the source model, freezing the backbone ❄️ to tune the adapter 🔥.
from vida.
@Yangsenqiao Hi, friend, could you kindly share your source model training code? (It is warmup (initialization process) right?) I assume you attached
ViDAInjectedLinear
to ViT. And my paper, BECoTTA shares the same sprits. I would really appreciate if you share your code to reimplement your method clearly. Thanks!Hi, Daeun, I am happy to see your new work 😃. Additionally, I have included your paper in our recently organized repository focus on awesome-CTTA related paper. I suggest that you cite more related work in your paper to help readers understand the context of this research topic 👍.
For sure, why have not you released the segmentation part codes yet?????
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Related Issues (14)
- About segmentation codes HOT 6
- Is the paper accepted to the NIPS? HOT 3
- Where are the codes of adapter? HOT 3
- Unfair comparison with CoTTA in segmentation tasks with lower learning rate HOT 8
- Adapter Code HOT 1
- question about adapter initialization for segmentation vida HOT 1
- Source VIDA Model vs. Source Model HOT 2
- Request code and pretrained source vida model on Cifar100C & ACDC HOT 1
- labels between cifar100 and cifar100c HOT 1
- About Gradient Problem and Resnet Model HOT 1
- ViDA's results on ResNet50+ImageNet-C
- Pretrained ViDA Model and Code for ResNet-50
- About the transformation of ImageNet dataset for ViT base model
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