Update 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,
+ distmat = paddle.add(square_feat, square_center)
+ feat_dot_center = paddle.matmul(feats_reshape,
paddle.transpose(self.centers, [1, 0]))
- distmat = distmat - 2.0 * tmp
+ 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}
