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attn_weights_7b/.ipynb_checkpoints/threshold2-checkpoint.py

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+import torch
+import numpy as np
+mask_lst=[]
+
+mask_total = np.zeros((32, 32))
+for layer_idx in range(32):
+    attn_weights_lst=[]
+    for i in range(4):
+        attn_weights_lst.append(torch.load("weights"+str(i)+"/"+str(layer_idx)+".pth"))
+    
+    attn_weights = torch.cat(attn_weights_lst,dim=0)
+    
+    for i in range(100):
+        res_v = torch.sum(attn_weights[i,:,:,35:35+576],dim=[1,2]) 
+        res_t = torch.sum(attn_weights[i,:,:,35+576:],dim=[1,2])  
+        res_s = torch.sum(attn_weights[i,:,:,:35],dim=[1,2])
+        res = res_v/(res_t+res_s)  
+        #mask = res>0.35 # headcut5
+        if layer_idx>=2:
+            mask = res>0.38 # headcut8: 0.38 headcut16: 0.11 headcut24: 0.044
+        else:
+            mask = res>=0
+        # mask = res>0.06 # headcut20
+        # mask = res>0.03 # headcut30
+        mask = mask.int()
+        # torch.save(mask,"temp/"+str(layer_idx)+".pth")
+        mask_total[layer_idx]+=mask.detach().cpu().numpy()
+        #import pdb; pdb.set_trace()
+        #mask_lst.append(mask)            
+        #mask_lst.append(mask.sum().detach().cpu().numpy())
+        
+temp = mask_total>40 #headcut5 10 30   
+
+import pdb; pdb.set_trace()
+print(mask_lst)
+#print(np.mean(mask_lst))
+'''
+attn_weights_lst=[]
+for i in range(4):
+  attn_weights_lst
+  for j in range(40):
+  attn_weights_lst.append(torch.load("weights"+))
+attn_weights = torch.cat([attnweight1,attnweight2],dim=0)
+res_v = torch.sum(attn_weights[:,:,:,35:35+576],dim=[0,2,3])
+res_t = torch.sum( attn_weights[:,:,:,35+576:],dim=[0,2,3])
+res_s = torch.sum( attn_weights[:,:,:,:35],dim=[0,2,3])
+res = res_v/(res_t+res_s)
+mask = res>0.12
+import pdb; pdb.set_trace()
+'''

attn_weights_7b/headmask_16.npy

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attn_weights_7b/headmask_24.npy

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attn_weights_7b/headmask_8.npy

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attn_weights_7b/threshold2.py

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+import torch
+import numpy as np
+mask_lst=[]
+
+mask_total = np.zeros((32, 32))
+for layer_idx in range(32):
+    attn_weights_lst=[]
+    for i in range(4):
+        attn_weights_lst.append(torch.load("weights"+str(i)+"/"+str(layer_idx)+".pth"))
+    
+    attn_weights = torch.cat(attn_weights_lst,dim=0)
+    
+    for i in range(100):
+        res_v = torch.sum(attn_weights[i,:,:,35:35+576],dim=[1,2]) 
+        res_t = torch.sum(attn_weights[i,:,:,35+576:],dim=[1,2])  
+        res_s = torch.sum(attn_weights[i,:,:,:35],dim=[1,2])
+        res = res_v/(res_t+res_s)  
+        #mask = res>0.35 # headcut5
+        if layer_idx>=2:
+            mask = res>0.38 # headcut8: 0.38 headcut16: 0.11 headcut24: 0.044
+        else:
+            mask = res>=0
+        # mask = res>0.06 # headcut20
+        # mask = res>0.03 # headcut30
+        mask = mask.int()
+        # torch.save(mask,"temp/"+str(layer_idx)+".pth")
+        mask_total[layer_idx]+=mask.detach().cpu().numpy()
+        #import pdb; pdb.set_trace()
+        #mask_lst.append(mask)            
+        #mask_lst.append(mask.sum().detach().cpu().numpy())
+        
+temp = mask_total>40 #headcut5 10 30   
+
+import pdb; pdb.set_trace()
+print(mask_lst)
+#print(np.mean(mask_lst))
+'''
+attn_weights_lst=[]
+for i in range(4):
+  attn_weights_lst
+  for j in range(40):
+  attn_weights_lst.append(torch.load("weights"+))
+attn_weights = torch.cat([attnweight1,attnweight2],dim=0)
+res_v = torch.sum(attn_weights[:,:,:,35:35+576],dim=[0,2,3])
+res_t = torch.sum( attn_weights[:,:,:,35+576:],dim=[0,2,3])
+res_s = torch.sum( attn_weights[:,:,:,:35],dim=[0,2,3])
+res = res_v/(res_t+res_s)
+mask = res>0.12
+import pdb; pdb.set_trace()
+'''

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