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  1. .gitattributes +142 -35
  2. .gitignore +1 -0
  3. LICENSE +21 -0
  4. README.md +70 -0
  5. data/annots/COCO_73k_annots_curated.npy +3 -0
  6. data/download_nsddata.py +18 -0
  7. data/eval_features/subj01/alexnet_2.npy +3 -0
  8. data/eval_features/subj01/alexnet_5.npy +3 -0
  9. data/eval_features/subj01/clip_final.npy +3 -0
  10. data/eval_features/subj01/efficientnet_avgpool.npy +3 -0
  11. data/eval_features/subj01/inceptionv3_avgpool.npy +3 -0
  12. data/eval_features/subj01/swav_avgpool.npy +3 -0
  13. data/eval_features/test_images/alexnet_2.npy +3 -0
  14. data/eval_features/test_images/alexnet_5.npy +3 -0
  15. data/eval_features/test_images/clip_final.npy +3 -0
  16. data/eval_features/test_images/efficientnet_avgpool.npy +3 -0
  17. data/eval_features/test_images/inceptionv3_avgpool.npy +3 -0
  18. data/eval_features/test_images/swav_avgpool.npy +3 -0
  19. data/extracted_features/subj01/nsd_cliptext_test.npy +3 -0
  20. data/nsddata/experiments/nsd/nsd_expdesign.mat +3 -0
  21. data/nsddata/experiments/nsd/nsd_stim_info_merged.pkl +3 -0
  22. data/nsddata/ppdata/subj01/func1pt8mm/roi/HCP_MMP1.nii.gz +3 -0
  23. data/nsddata/ppdata/subj01/func1pt8mm/roi/Kastner2015.nii.gz +3 -0
  24. data/nsddata/ppdata/subj01/func1pt8mm/roi/MTL.nii.gz +3 -0
  25. data/nsddata/ppdata/subj01/func1pt8mm/roi/corticalsulc.nii.gz +3 -0
  26. data/nsddata/ppdata/subj01/func1pt8mm/roi/floc-bodies.nii.gz +3 -0
  27. data/nsddata/ppdata/subj01/func1pt8mm/roi/floc-faces.nii.gz +3 -0
  28. data/nsddata/ppdata/subj01/func1pt8mm/roi/floc-places.nii.gz +3 -0
  29. data/nsddata/ppdata/subj01/func1pt8mm/roi/floc-words.nii.gz +3 -0
  30. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.HCP_MMP1.nii.gz +3 -0
  31. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.Kastner2015.nii.gz +3 -0
  32. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.MTL.nii.gz +3 -0
  33. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.corticalsulc.nii.gz +3 -0
  34. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.floc-bodies.nii.gz +3 -0
  35. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.floc-faces.nii.gz +3 -0
  36. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.floc-places.nii.gz +3 -0
  37. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.floc-words.nii.gz +3 -0
  38. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.nsdgeneral.nii.gz +3 -0
  39. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.prf-eccrois.nii.gz +3 -0
  40. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.prf-visualrois.nii.gz +3 -0
  41. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.streams.nii.gz +3 -0
  42. data/nsddata/ppdata/subj01/func1pt8mm/roi/lh.thalamus.nii.gz +3 -0
  43. data/nsddata/ppdata/subj01/func1pt8mm/roi/nsdgeneral.nii.gz +3 -0
  44. data/nsddata/ppdata/subj01/func1pt8mm/roi/prf-eccrois.nii.gz +3 -0
  45. data/nsddata/ppdata/subj01/func1pt8mm/roi/prf-visualrois.nii.gz +3 -0
  46. data/nsddata/ppdata/subj01/func1pt8mm/roi/rh.HCP_MMP1.nii.gz +3 -0
  47. data/nsddata/ppdata/subj01/func1pt8mm/roi/rh.Kastner2015.nii.gz +3 -0
  48. data/nsddata/ppdata/subj01/func1pt8mm/roi/rh.MTL.nii.gz +3 -0
  49. data/nsddata/ppdata/subj01/func1pt8mm/roi/rh.corticalsulc.nii.gz +3 -0
  50. data/nsddata/ppdata/subj01/func1pt8mm/roi/rh.floc-bodies.nii.gz +3 -0
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.gitignore ADDED
@@ -0,0 +1 @@
 
 
1
+ *.zip
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MIT License
2
+
3
+ Copyright (c) 2023 Furkan Ozcelik
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
README.md ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Brain-Diffuser
2
+ Official repository for the paper ["**Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion**"](https://arxiv.org/abs/2303.05334) by Furkan Ozcelik and Rufin VanRullen.
3
+
4
+ ## Results
5
+ The following are a few reconstructions obtained :
6
+ <p align="center"><img src="./figures/Reconstructions.png" width="600" ></p>
7
+
8
+ ## Instructions
9
+
10
+ ### Requirements
11
+ * Create conda environment using environment.yml in the main directory by entering `conda env create -f environment.yml` . It is an extensive environment and may include redundant libraries. You may also create environment by checking requirements yourself.
12
+
13
+ ### Data Acquisition and Processing
14
+
15
+ 1. Download NSD data from NSD AWS Server:
16
+ ```
17
+ cd data
18
+ python download_nsddata.py
19
+ ```
20
+ 2. Download "COCO_73k_annots_curated.npy" file from [HuggingFace NSD](https://huggingface.co/datasets/pscotti/naturalscenesdataset/tree/main)
21
+ 3. Prepare NSD data for the Reconstruction Task:
22
+ ```
23
+ cd data
24
+ python prepare_nsddata.py -sub 1
25
+ python prepare_nsddata.py -sub 2
26
+ python prepare_nsddata.py -sub 5
27
+ python prepare_nsddata.py -sub 7
28
+ ```
29
+
30
+ ### First Stage Reconstruction with VDVAE
31
+
32
+ 1. Download pretrained VDVAE model files and put them in `vdvae/model/` folder
33
+ ```
34
+ wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-log.jsonl
35
+ wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model.th
36
+ wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model-ema.th
37
+ wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-opt.th
38
+ ```
39
+ 2. Extract VDVAE latent features of stimuli images for any subject 'x' using `python scripts/vdvae_extract_features.py -sub x`
40
+ 3. Train regression models from fMRI to VDVAE latent features and save test predictions using `python scripts/vdvae_regression.py -sub x`
41
+ 4. Reconstruct images from predicted test features using `python scripts/vdvae_reconstruct_images.py -sub x`
42
+
43
+ ### Second Stage Reconstruction with Versatile Diffusion
44
+
45
+ 1. Download pretrained Versatile Diffusion model "vd-four-flow-v1-0-fp16-deprecated.pth", "kl-f8.pth" and "optimus-vae.pth" from [HuggingFace](https://huggingface.co/shi-labs/versatile-diffusion/tree/main/pretrained_pth) and put them in `versatile_diffusion/pretrained/` folder
46
+ 2. Extract CLIP-Text features of captions for any subject 'x' using `python scripts/cliptext_extract_features.py -sub x`
47
+ 3. Extract CLIP-Vision features of stimuli images for any subject 'x' using `python scripts/clipvision_extract_features.py -sub x`
48
+ 4. Train regression models from fMRI to CLIP-Text features and save test predictions using `python scripts/cliptext_regression.py -sub x`
49
+ 5. Train regression models from fMRI to CLIP-Vision features and save test predictions using `python scripts/clipvision_regression.py -sub x`
50
+ 6. Reconstruct images from predicted test features using `python scripts/versatilediffusion_reconstruct_images.py -sub x` . This code is written as you are using two 12GB GPUs but you may edit according to your setup.
51
+
52
+
53
+ ### Quantitative Evaluation
54
+ Although results are expected to be similar, it may vary because of variations at reconstruction
55
+ 1. Save test images to directory `python scripts/save_test_images.py`
56
+ 2. Extract evaluation features for test images using `python scripts/eval_extract_features.py -sub 0`
57
+ 3. Extract evaluation features for reconstructed images of any subject using `python scripts/eval_extract_features.py -sub x`
58
+ 4. Obtain quantitative metric results for each subject using`python scripts/evaluate_reconstruction.py -sub x`
59
+
60
+ ### ROI Analysis
61
+ It has a bug that prevents to get the exact results but provides an approximation for most of ROIs, hopefully will be fixed soon.
62
+ 1. Extract ROI fMRI activations for any subject 'x' using `python scripts/roi_extract.py -sub x`
63
+ 2. Generate VDVAE, CLIP-Text, CLIP-Vision features forom synthetic fMRI using `python scripts/roi_generate_features.py -sub x`
64
+ 3. Generate VDVAE reconstructions for ROIs using `python scripts/roi_vdvae_reconstruct.py -sub x`
65
+ 4. Generate Versatile Diffusion reconstructions for ROIs using `python scripts/roi_versatilediffusion_reconstruct.py -sub x`
66
+
67
+ ## References
68
+ - Codes in vdvae directory are derived from [openai/vdvae](https://github.com/openai/vdvae)
69
+ - Codes in versatile_diffusion directory are derived from earlier version of [SHI-Labs/Versatile-Diffusion](https://github.com/SHI-Labs/Versatile-Diffusion)
70
+ - Dataset used in the studies are obtained from [Natural Scenes Dataset](https://naturalscenesdataset.org/)
data/annots/COCO_73k_annots_curated.npy ADDED
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+ import os
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+ #os.system('ls -l')
3
+
4
+ # Download Experiment Infos
5
+ os.system('aws s3 cp s3://natural-scenes-dataset/nsddata/experiments/nsd/nsd_expdesign.mat nsddata/experiments/nsd/')
6
+ os.system('aws s3 cp s3://natural-scenes-dataset/nsddata/experiments/nsd/nsd_stim_info_merged.pkl nsddata/experiments/nsd/')
7
+
8
+ # Download Stimuli
9
+ os.system('aws s3 cp s3://natural-scenes-dataset/nsddata_stimuli/stimuli/nsd/nsd_stimuli.hdf5 nsddata_stimuli/stimuli/nsd/')
10
+
11
+ # Download Betas
12
+ for sub in [1]:
13
+ for sess in range(1,38):
14
+ os.system('aws s3 cp s3://natural-scenes-dataset/nsddata_betas/ppdata/subj{:02d}/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session{:02d}.nii.gz nsddata_betas/ppdata/subj{:02d}/func1pt8mm/betas_fithrf_GLMdenoise_RR/'.format(sub,sess,sub))
15
+
16
+ # Download ROIs
17
+ for sub in [1]:
18
+ os.system('aws s3 cp --recursive s3://natural-scenes-dataset/nsddata/ppdata/subj{:02d}/func1pt8mm/roi/ nsddata/ppdata/subj{:02d}/func1pt8mm/roi/'.format(sub, sub))
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