Update center_loss.py

ppocr/losses/center_loss.py

@@ -27,7 +27,6 @@ class CenterLoss(nn.Layer):
     """
     Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
     """
-
     def __init__(self,
                  num_classes=6625,
                  feat_dim=96,
@@ -37,8 +36,7 @@ class CenterLoss(nn.Layer):
         self.num_classes = num_classes
         self.feat_dim = feat_dim
         self.centers = paddle.randn(
-            shape=[self.num_classes, self.feat_dim]).astype(
-                "float64")  #random center
+            shape=[self.num_classes, self.feat_dim]).astype("float64")
 
         if init_center:
             assert os.path.exists(
@@ -60,22 +58,23 @@ class CenterLoss(nn.Layer):
 
         batch_size = feats_reshape.shape[0]
 
-        #calc feat * feat   
-        dist1 = paddle.sum(paddle.square(feats_reshape), axis=1, keepdim=True)
-        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
+        #calc l2 distance between feats and centers  
+        square_feat = paddle.sum(paddle.square(feats_reshape),
+                                 axis=1,
+                                 keepdim=True)
+        square_feat = paddle.expand(square_feat, [batch_size, self.num_classes])
 
-        #dist2 of centers
-        dist2 = paddle.sum(paddle.square(self.centers), axis=1,
-                           keepdim=True)  #num_classes
-        dist2 = paddle.expand(dist2,
-                              [self.num_classes, batch_size]).astype("float64")
-        dist2 = paddle.transpose(dist2, [1, 0])
+        square_center = paddle.sum(paddle.square(self.centers),
+                                   axis=1,
+                                   keepdim=True)
+        square_center = paddle.expand(
+            square_center, [self.num_classes, batch_size]).astype("float64")
+        square_center = paddle.transpose(square_center, [1, 0])
 
-        #first x * x + y * y
-        distmat = paddle.add(dist1, dist2)
-        tmp = paddle.matmul(feats_reshape,
-                            paddle.transpose(self.centers, [1, 0]))
-        distmat = distmat - 2.0 * tmp
+        distmat = paddle.add(square_feat, square_center)
+        feat_dot_center = paddle.matmul(feats_reshape,
+                                        paddle.transpose(self.centers, [1, 0]))
+        distmat = distmat - 2.0 * feat_dot_center
 
         #generate the mask
         classes = paddle.arange(self.num_classes).astype("int64")
@@ -83,7 +82,8 @@ class CenterLoss(nn.Layer):
             paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
         mask = paddle.equal(
             paddle.expand(classes, [batch_size, self.num_classes]),
-            label).astype("float64")  #get mask
+            label).astype("float64")
         dist = paddle.multiply(distmat, mask)
+
         loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
         return {'loss_center': loss}