| # Prompt Tuning | |
| For reproducing the fine-tuning results in our paper, we provide the corresponding prompt-tuning configs in `configs/BERT_L12_H768_experiments/prompt_tuning` and `configs/BERT_L12_H768_experiments/moe_prompt_tuning` for Uni-Perceiver-Base and Uni-Perceiver-MoE-Base, respectively. | |
| Specifically, we prompt-tuned the ImageNet-1K dataset with image classification task. For video classification, we fine-tuned Kinetics-400. We also employed image caption and image-text retrieval tasks on MSCOCO caption and FLicker-30K datasets. | |
| In addition, video caption and video-text retrieval tasks are conducted on MSVD dataset. | |
| Please perpare the dataset following [PREPARE_DATA.md](prepare_data.md) | |
| --- | |
| In our experiments, prompt-tuning on all datasets benchmarks is performed on 16 NVIDIA-V100 GPUs with 80GB memory. | |
| Taking Imagenet-1K as an example, the __Uni-Perceiver-Base__ can be prompt-tuned as | |
| ``` | |
| sh run.sh configs/BERT_L12_H768_experiments/prompt_tuning/in1k_prompt_tuning_0.01data_lr1e-4.yaml in1k-pt 16 partitionname MODEL.WEIGHTS work_dirs/pretrained_models/uni-perceiver-base-L12-H768-224size-pretrained.pth | |
| ``` | |
| The __Uni-Perceiver-MoE-Base__ can also be fine-tuned in a similar way: | |
| ``` | |
| sh run.sh configs/BERT_L12_H768_experiments/moe_prompt_tuning/in1k_prompt_tuning_0.01data_lr1e-4.yaml in1k-moe-pt 16 partitionname MODEL.WEIGHTS work_dirs/pretrained_models/uni-perceiver-moe-base-L12-H768-224size-pretrained.pth | |
| ``` | |