Merge pull request #153 from melgor/LFW-speed

SpeedUP LFW test

evaluation/lfw.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python3
+#!/usr/bin/env python2
 #
 # Copyright 2015-2016 Carnegie Mellon University
 #
@@ -39,12 +39,14 @@
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('tag', type=str, help='The label/tag to put on the ROC curve.')
+    parser.add_argument(
+        'tag', type=str, help='The label/tag to put on the ROC curve.')
     parser.add_argument('workDir', type=str,
                         help='The work directory with labels.csv and reps.csv.')
     pairsDefault = os.path.expanduser("~/openface/data/lfw/pairs.txt")
     parser.add_argument('--lfwPairs', type=str,
-                        default=os.path.expanduser("~/openface/data/lfw/pairs.txt"),
+                        default=os.path.expanduser(
+                            "~/openface/data/lfw/pairs.txt"),
 
                         help='Location of the LFW pairs file from http://vis-www.cs.umass.edu/lfw/pairs.txt')
     args = parser.parse_args()
@@ -137,27 +139,35 @@ def writeROC(fname, thresholds, embeddings, pairsTest):
                 return
 
 
-def evalThresholdAccuracy(embeddings, pairs, threshold):
+def getDistances(embeddings, pairsTrain):
+    list_dist = []
     y_true = []
-    y_predict = []
-    for pair in pairs:
+    for pair in pairsTrain:
         (x1, x2, actual_same) = getEmbeddings(pair, embeddings)
         diff = x1 - x2
         dist = np.dot(diff.T, diff)
-        predict_same = dist < threshold
-        y_predict.append(predict_same)
+        list_dist.append(dist)
         y_true.append(actual_same)
+    return np.asarray(list_dist), np.array(y_true)
+
+
+def evalThresholdAccuracy(embeddings, pairs, threshold):
+    distances, y_true = getDistances(embeddings, pairs)
+    y_predict = np.zeros(y_true.shape)
+    y_predict[np.where(distances < threshold)] = 1
 
     y_true = np.array(y_true)
-    y_predict = np.array(y_predict)
     accuracy = accuracy_score(y_true, y_predict)
-    return accuracy
+    return accuracy, pairs[np.where(y_true != y_predict)]
 
 
 def findBestThreshold(thresholds, embeddings, pairsTrain):
     bestThresh = bestThreshAcc = 0
+    distances, y_true = getDistances(embeddings, pairsTrain)
     for threshold in thresholds:
-        accuracy = evalThresholdAccuracy(embeddings, pairsTrain, threshold)
+        y_predlabels = np.zeros(y_true.shape)
+        y_predlabels[np.where(distances < threshold)] = 1
+        accuracy = accuracy_score(y_true, y_predlabels)
         if accuracy >= bestThreshAcc:
             bestThreshAcc = accuracy
             bestThresh = threshold
@@ -184,7 +194,7 @@ def verifyExp(workDir, pairs, embeddings):
 
                 bestThresh = findBestThreshold(
                     thresholds, embeddings, pairs[train])
-                accuracy = evalThresholdAccuracy(
+                accuracy, pairs_bad = evalThresholdAccuracy(
                     embeddings, pairs[test], bestThresh)
                 accuracies.append(accuracy)
                 f.write('{}, {:0.2f}, {:0.2f}\n'.format(