adding correct smooth attention

fake_face_detection/metrics/make_predictions.py

@@ -1,7 +1,7 @@
 
 from fake_face_detection.data.fake_face_dataset import FakeFaceDetectionDataset
 from fake_face_detection.metrics.compute_metrics import compute_metrics
-from fake_face_detection.smoothest_attention import smooth_attention
+from fake_face_detection.utils.smoothest_attention import smooth_attention
 from torch.utils.tensorboard import SummaryWriter
 from PIL.JpegImagePlugin import JpegImageFile
 from torch.utils.data import DataLoader

fake_face_detection/utils/smoothest_attention.py

@@ -0,0 +1,42 @@
+import torch
+import copy
+
+# we want to take 0.2 of the pixel and 0.7 of the mean of the pixels around it 100 times
+# we will take a size between the current pixel and the pixels around it                
+def smooth_attention(attention: torch.Tensor, iters: int = 1000, threshold: float = 0.1, scale: float = 0.2, size: int = 3):
+    
+    # squeeze the attention
+    attention = copy.deepcopy(attention.squeeze())
+    
+    # make 100 iterations
+    for _ in range(iters):
+        
+        # initialize the difference
+        difference = torch.full(attention.shape, torch.inf)
+        
+        # iterate over the pixels of the attention
+        for i in range(attention.shape[0]):
+            
+            for j in range(attention.shape[1]):
+                
+                # recuperate the pixel
+                pixel = attention[i, j]
+                
+                # recuperate the mean of the pixels around it
+                mean = attention[max(0, i - size): min(attention.shape[0], i + size), max(0, j - size): min(attention.shape[1], j + size)].mean()
+                
+                # update the attention
+                attention[i, j] = (1 - scale) * pixel + scale * mean
+                
+                # recuperate the difference
+                difference[i, j] = abs(pixel - mean)
+        
+        # compare each difference with the threshold
+        if (difference < threshold).all(): break
+    
+    # unsqueeze the attention
+    attention = attention.unsqueeze(-1)
+    
+    # return the attention
+    return attention
+