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View Code? Open in Web Editor NEWAccelerating T2t-ViT by 1.6-3.6x.
Accelerating T2t-ViT by 1.6-3.6x.
Thanks for your great work. I don't have enough machines to train a new model at ImageNet. Could you please give us the checkpoint without relation reuse and without two reuses. Your work is perfect, and It would be a pity if I couldn't run your model. Thanks!
I try to evaluate your model on my machine. Unfortunately, I find you don't give us any checkpoint in any dataset. I don't have enough machines to train a new model at ImageNet. Your work is perfect, and It would be a pity if I couldn't run your model. Just one checkpoint file I need! Any dataset is fine! Please. @blackfeather-wang @guanfuchen
Thank you
Thanks for your great work. I am interested in the FLOPs reported in your paper like table 1
table 4
. I am wondering if you can release the code of FLOPs calcuation for ViT. Thank you!
Can you release the code about the genetic algorithm in the paper
The main concern for me is that what is the necessity to split relation_temp as:
split_index = int(relation_temp.size(0) / 2)
relation_temp = torch.cat(
(
self.relation_reuse_upsample(relation_temp[:split_index * 1]),
self.relation_reuse_upsample(relation_temp[split_index * 1:]),
), 0
)
It is more straight to implement the upsample like this:
relation_temp = self.relation_reuse_upsample(relation_temp)
Could you please explain the difference between the above two implementations?
Hi!
I try to evaluate the DVT_T2t_vit_12, then I run 'python inference.py --data_url ./data/ --batch_size 64 --model DVT_T2t_vit_12 --checkpoint_path .\checkpoint\DVT_T2t_vit_12.pth.tar --eval_mode 1', I get the error.
"
Traceback (most recent call last):
File "inference.py", line 226, in
main()
File "inference.py", line 57, in main
model = create_model(
File "A:\transformer\DViT\Dynamic-Vision-Transformer-main\Dynamic-Vision-Transformer-main\timm\models\factory.py", line 59, in create_model
raise RuntimeError('Unknown model (%s)' % model_name)
RuntimeError: Unknown model (DVT_T2t_vit_12)
"
And I try to print the _model_entrypoints, which in ..Dynamic-Vision-Transformer-main/timm/models/registry.py to find the model name'DVT_T2t_vit_12'. I don't see that.
env: python:3.8 pytorch:1.8.1 torchvision 0.9.1
Your original article says that when the prediction result does not satisfy the termination condition, the model will improve the prediction accuracy by increasing the number of tokens and introducing additional Transformer layers. What is the termination condition here and how is it reflected in the code?
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