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+size 210629
diff --git a/results/versatile_diffusion/subj01/956.png b/results/versatile_diffusion/subj01/956.png
new file mode 100644
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+++ b/results/versatile_diffusion/subj01/956.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:a742a2fae2520f97934a486e906bd7328f556d1c03c8500073320b863b018046
+size 225356
diff --git a/scripts/cliptext_extract_features.py b/scripts/cliptext_extract_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..d86ea4576ae695179d33b66f70686d5084d1caf5
--- /dev/null
+++ b/scripts/cliptext_extract_features.py
@@ -0,0 +1,57 @@
+import sys
+sys.path.append('versatile_diffusion')
+import os
+import numpy as np
+
+import torch
+from lib.cfg_helper import model_cfg_bank
+from lib.model_zoo import get_model
+from torch.utils.data import DataLoader, Dataset
+
+from lib.model_zoo.vd import VD
+from lib.cfg_holder import cfg_unique_holder as cfguh
+from lib.cfg_helper import get_command_line_args, cfg_initiates, load_cfg_yaml
+import matplotlib.pyplot as plt
+import torchvision.transforms as T
+
+import argparse
+parser = argparse.ArgumentParser(description='Argument Parser')
+parser.add_argument("-sub", "--sub",help="Subject Number",default=1)
+args = parser.parse_args()
+sub=int(args.sub)
+assert sub in [1,2,5,7]
+
+cfgm_name = 'vd_noema'
+pth = 'versatile_diffusion/pretrained/vd-four-flow-v1-0-fp16-deprecated.pth'
+cfgm = model_cfg_bank()(cfgm_name)
+net = get_model()(cfgm)
+sd = torch.load(pth, map_location='cpu')
+net.load_state_dict(sd, strict=False)    
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+net.clip = net.clip.to(device)
+   
+train_caps = np.load('data/processed_data/subj{:02d}/nsd_train_cap_sub{}.npy'.format(sub,sub)) 
+test_caps = np.load('data/processed_data/subj{:02d}/nsd_test_cap_sub{}.npy'.format(sub,sub))  
+
+num_embed, num_features, num_test, num_train = 77, 768, len(test_caps), len(train_caps)
+
+train_clip = np.zeros((num_train,num_embed, num_features))
+test_clip = np.zeros((num_test,num_embed, num_features))
+with torch.no_grad():
+    for i,annots in enumerate(test_caps):
+        cin = list(annots[annots!=''])
+        print(i)
+        c = net.clip_encode_text(cin)
+        test_clip[i] = c.to('cpu').numpy().mean(0)
+    
+    np.save('data/extracted_features/subj{:02d}/nsd_cliptext_test.npy'.format(sub),test_clip)
+        
+    for i,annots in enumerate(train_caps):
+        cin = list(annots[annots!=''])
+        print(i)
+        c = net.clip_encode_text(cin)
+        train_clip[i] = c.to('cpu').numpy().mean(0)
+    np.save('data/extracted_features/subj{:02d}/nsd_cliptext_train.npy'.format(sub),train_clip)
+
+
diff --git a/scripts/cliptext_regression.py b/scripts/cliptext_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f830d3ca2e627d9f3804b9093ee15f9e18de672
--- /dev/null
+++ b/scripts/cliptext_regression.py
@@ -0,0 +1,68 @@
+import sys
+import numpy as np
+import sklearn.linear_model as skl
+import pickle
+import argparse
+parser = argparse.ArgumentParser(description='Argument Parser')
+parser.add_argument("-sub", "--sub",help="Subject Number",default=1)
+args = parser.parse_args()
+sub=int(args.sub)
+assert sub in [1,2,5,7]
+
+train_path = 'data/processed_data/subj{:02d}/nsd_train_fmriavg_nsdgeneral_sub{}.npy'.format(sub,sub)
+train_fmri = np.load(train_path)
+test_path = 'data/processed_data/subj{:02d}/nsd_test_fmriavg_nsdgeneral_sub{}.npy'.format(sub,sub)
+test_fmri = np.load(test_path)
+
+## Preprocessing fMRI
+
+train_fmri = train_fmri/300
+test_fmri = test_fmri/300
+
+
+norm_mean_train = np.mean(train_fmri, axis=0)
+norm_scale_train = np.std(train_fmri, axis=0, ddof=1)
+train_fmri = (train_fmri - norm_mean_train) / norm_scale_train
+test_fmri = (test_fmri - norm_mean_train) / norm_scale_train
+
+print(np.mean(train_fmri),np.std(train_fmri))
+print(np.mean(test_fmri),np.std(test_fmri))
+
+print(np.max(train_fmri),np.min(train_fmri))
+print(np.max(test_fmri),np.min(test_fmri))
+
+num_voxels, num_train, num_test = train_fmri.shape[1], len(train_fmri), len(test_fmri)
+
+
+train_clip = np.load('data/extracted_features/subj{:02d}/nsd_cliptext_train.npy'.format(sub))
+test_clip = np.load('data/extracted_features/subj{:02d}/nsd_cliptext_test.npy'.format(sub))
+
+## Regression
+num_samples,num_embed,num_dim = train_clip.shape
+
+print("Training Regression")
+reg_w = np.zeros((num_embed,num_dim,num_voxels)).astype(np.float32)
+reg_b = np.zeros((num_embed,num_dim)).astype(np.float32)
+pred_clip = np.zeros_like(test_clip)
+for i in range(num_embed):
+    reg = skl.Ridge(alpha=100000, max_iter=50000, fit_intercept=True)
+    reg.fit(train_fmri, train_clip[:,i])
+    reg_w[i] = reg.coef_
+    reg_b[i] = reg.intercept_
+    
+    pred_test_latent = reg.predict(test_fmri)
+    std_norm_test_latent = (pred_test_latent - np.mean(pred_test_latent,axis=0)) / np.std(pred_test_latent,axis=0)
+    pred_clip[:,i] = std_norm_test_latent * np.std(train_clip[:,i],axis=0) + np.mean(train_clip[:,i],axis=0)
+    print(i,reg.score(test_fmri,test_clip[:,i]))
+
+np.save('data/predicted_features/subj{:02d}/nsd_cliptext_predtest_nsdgeneral.npy'.format(sub),pred_clip)
+
+
+datadict = {
+    'weight' : reg_w,
+    'bias' : reg_b,
+
+}
+
+with open('data/regression_weights/subj{:02d}/cliptext_regression_weights.pkl'.format(sub),"wb") as f:
+  pickle.dump(datadict,f)