updated app

app.py

@@ -158,7 +158,6 @@ elif modelName=="parseq":
     converter = TokenLabelConverter(opt)
     modelCopy = copy.deepcopy(model)
 
-    """ evaluation """
     scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True, model=modelCopy)
     super_pixel_model_singlechar = torch.nn.Sequential(
         # super_pixler,
@@ -191,6 +190,17 @@ if opt.blackbg:
 # x = st.slider('Select a value')
 # st.write(x, 'squared is', x * x)
 
+### Returns the mean for each segmentation having shape as the same as the input
+### This function can only one attribution image at a time
+def averageSegmentsOut(attr, segments):
+    averagedInput = torch.clone(attr)
+    sortedDict = {}
+    for x in np.unique(segments):
+        segmentMean = torch.mean(attr[segments == x][:])
+        sortedDict[x] = float(segmentMean.detach().cpu().numpy())
+        averagedInput[segments == x] = segmentMean
+    return averagedInput, sortedDict
+
 ### Acquire pixelwise attributions and replace them with ranked numbers averaged
 ### across segmentation with the largest contribution having the largest number
 ### and the smallest set to 1, which is the minimum number.