From d6a6bf94aa24f84c3d4f0c9eb4e5a6967bf0946f Mon Sep 17 00:00:00 2001 From: weihao Date: Sun, 19 May 2024 12:37:58 +0100 Subject: [PATCH] Update v1.4 checkpoint --- README.md | 42 ++++++++++++++++++++------------- docs/images/grounding_task.png | Bin 6007490 -> 5961646 bytes 2 files changed, 26 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 45eada8..e9b907a 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,7 @@

UMBRAE decodes multimodal explanations from brain signals. (1) We introduce a universal brain encoder for multimodal-brain alignment and recover conceptual and spatial details by using multimodal large language models. (2) We introduce cross-subject training to overcome unique brain patterns of different individuals. This allows brain signals from multiple subjects to be trained within the same model This allows brain signals from multiple subjects to be trained within the same model. (3) Our method supports weakly-supervised subject adaptation, enabling the training of a model for a new subject in a data-efficient manner. (4) For evaluation, we introduce BrainHub, a brain understanding benchmark, based on NSD and COCO. ## News :triangular_flag_on_post: +- [2024/05/18] Update v1.4 checkpoint and [Leaderboard](https://github.com/weihaox/BrainHub/tree/main?tab=readme-ov-file#leaderboard). - [2024/04/16] Provide a [Colab](https://colab.research.google.com/drive/1VKd1gAB-6AIdMzBCG0J-U7h9vwsiKnHp) Demo for inference. - [2024/04/13] Update scripts for single-subject, cross-subject training, and new subject adaptation. - [2024/04/12] Inference and pretrained model available. Training code coming up soon. @@ -57,7 +58,6 @@ Overview of UMBRAE. Our brain encoder includes subject-specific tokenizers and a - ## Installation ### Environment @@ -81,23 +81,30 @@ bash download_checkpoint.sh ## Inference -Our method inherits multimodal understanding capabilities of MLLMs, enabling the switch between different tasks through different prompts. You can either use the prompts listed in our paper or create customised instructions according to actual needs. +Our method inherits multimodal understanding capabilities of MLLMs, enabling the switch between different tasks through different prompts. You can either use the prompts listed in our paper or create customised instructions according to actual needs. Please specify brainx-v-1-4 or brainx. ```bash prompt_caption='Describe this image as simply as possible.' prompt_ground='Please interpret this image and give coordinates [x1,y1,x2,y2] for each object you mention.' -python inference.py --fmri_encoder 'brainx' --subj 1 --prompt "$prompt_ground" \ +for sub in 1 2 5 7 +do +python inference.py --fmri_encoder 'brainx' --subj $sub --prompt "$prompt_ground" \ --data_path 'nsd_data' --brainx_path 'train_logs/brainx.pth' \ - --save_path 'evaluation/eval_caption/caption_results/umbrae/sub01_dim1024' + --save_path "evaluation/eval_caption/${exp}/sub0${sub}_dim1024" +done ``` Given that identified classes might be named differently, or simply absent from ground truth labels, we evaluate bounding boxes through REC. We use prompt `"Locate in and provide its coordinates, please"`, but others like `"Can you point out in the image and provide the bounding boxes of its location?"` shall also work. ```bash -python inference_rec.py --data_path 'nsd_data' --fmri_encoder 'brainx' \ - --subj 1 --brainx_path 'train_logs/brainx.pth' \ - --save_path 'evaluation/eval_bbox_rec/rec_results/umbrae/sub01_dim1024' +exp='brainx-v-1.4' # 'brainx' +for sub in 1 2 5 7 +do + python inference_rec.py --data_path 'nsd_data' --fmri_encoder 'brainx' \ + --subj $sub --brainx_path "train_logs/${exp}/last.pth" \ + --save_path "evaluation/eval_bbox_rec/${exp}/sub0${sub}_dim1024" +done ``` ## Training @@ -106,16 +113,16 @@ python inference_rec.py --data_path 'nsd_data' --fmri_encoder 'brainx' \ ```bash accelerate launch --num_processes=1 --num_machines=1 --gpu_ids='0' train.py \ - --data_path 'nsd_data' --fmri_encoder 'brainxs' \ - --model_save_path 'train_logs/demo_single_subject/sub01_dim1024' + --data_path 'nsd_data' --fmri_encoder 'brainxs' --subj 1 \ + --model_save_path 'train_logs/demo_single_subject/sub01_dim1024' ``` ### Cross-Subject Training ```bash accelerate launch --num_processes=1 --num_machines=1 --gpu_ids='0' train_brainx.py \ - --data_path 'nsd_data' --fmri_encoder 'brainx' --batch_size 128 --num_epochs 300 \ - --model_save_path 'train_logs/demo_cross_subject' --subj 1 2 5 7 + --data_path 'nsd_data' --fmri_encoder 'brainx' --batch_size 128 --num_epochs 300 \ + --model_save_path 'train_logs/demo_cross_subject' --subj 1 2 5 7 ``` ### Weakly-Supervised Subject Adaptation @@ -123,10 +130,13 @@ accelerate launch --num_processes=1 --num_machines=1 --gpu_ids='0' train_brainx. If you would like to adapt to a new subject, for example, S7, first train a model with other available subjects (S1, S2, S5) using the above cross-subject training. Then train the new subject using the following command. ```bash +sub=7 +data_ratio=1.0 accelerate launch --num_processes=1 --num_machines=1 --gpu_ids='0' train_brainx_adaptation.py \ - --data_path '/home/wx258/project/nsd_data' --fmri_encoder 'brainxc' --batch_size 128 --num_epochs 240 \ - --encoder_path 'train_logs/demo_cross_subject/brainx_adaptation_125/last.pth' \ - --subj 7 --data_ratio 1.0 --model_save_path 'train_logs/demo_weak_adaptation/brainx_adaptation_7_1.0' + --data_path 'nsd_data' --fmri_encoder 'brainxc' --batch_size 128 --num_epochs 240 \ + --subj $sub --data_ratio $data_ratio \ + --encoder_path 'train_logs/demo_cross_subject/brainx_adaptation_125/last.pth' \ + --model_save_path "train_logs/demo_weak_adaptation/brainx_adaptation_${sub}_${data_ratio}" ``` ## Evaluation @@ -141,9 +151,9 @@ The benchmark, including groundtruth data, evaluation scripts, and baseline resu cd BrainHub for sub in 1 2 5 7 do - python eval_caption.py ../umbrae/evaluation/caption_results/umbrae/sub0${sub}_dim1024/fmricap.json \ + python eval_caption.py ../umbrae/evaluation/eval_caption/${exp}/sub0${sub}_dim1024/fmricap.json \ caption/images --references_json caption/fmri_cococap.json - python eval_bbox_rec.py --path_out "../umbrae/evaluation/bbox_results/umbrae/sub0${sub}_dim1024" + python eval_bbox_rec.py --path_out "../umbrae/evaluation/eval_bbox_rec/${exp}/sub0${sub}_dim1024" done ``` diff --git a/docs/images/grounding_task.png b/docs/images/grounding_task.png index 4512f3c26dc82d62a37843441919da85edc73593..48d056d8e2534d5fcffc0ccf788dc147eb1623c4 100644 GIT binary patch literal 5961646 zcmbTe1z1$;+6GJ`t)O%$(k+M}Ac%-aHw;}4h;%ndOG`5}NGOfO5JL+iEz&h~$Iu=B zVxO&hf9LG~{O9_wYt-d}wdP&#`@B!w_x-F8B?T!0TuNLN6chsK*DqC2P_PqFP;l~Z zVF70p0}ks@P*9o8B_xzAB%~y4EN$#nZH-L8;%{t>!75TO9zA_3h=RfrZDMGs@|uI? zyAiRW;rCv4R$O}*l@A}nRSbPvnp^MpHTN5~WTq$S>dunio^3MroE1P@6 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