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- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
- class CenterLoss(nn.Layer):
- def __init__(self, num_classes=5013, feat_dim=2048):
- super(CenterLoss, self).__init__()
- 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
- def __call__(self, input, target):
- """
- inputs: network output: {"features: xxx", "logits": xxxx}
- target: image label
- """
- feats = input["features"]
- labels = target
- batch_size = feats.shape[0]
- #calc feat * feat
- dist1 = paddle.sum(paddle.square(feats), axis=1, keepdim=True)
- dist1 = paddle.expand(dist1, [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])
- #first x * x + y * y
- distmat = paddle.add(dist1, dist2)
- tmp = paddle.matmul(feats, paddle.transpose(self.centers, [1, 0]))
- distmat = distmat - 2.0 * tmp
- #generate the mask
- classes = paddle.arange(self.num_classes).astype("int64")
- labels = paddle.expand(
- paddle.unsqueeze(labels, 1), (batch_size, self.num_classes))
- mask = paddle.equal(
- paddle.expand(classes, [batch_size, self.num_classes]),
- labels).astype("float64") #get mask
- dist = paddle.multiply(distmat, mask)
- loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
- return {'CenterLoss': loss}
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