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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
class NpairsLoss(paddle.nn.Layer):
def __init__(self, reg_lambda=0.01):
super(NpairsLoss, self).__init__()
self.reg_lambda = reg_lambda
def forward(self, input, target=None):
"""
anchor and positive(should include label)
"""
features = input["features"]
reg_lambda = self.reg_lambda
batch_size = features.shape[0]
fea_dim = features.shape[1]
num_class = batch_size // 2
#reshape
out_feas = paddle.reshape(features, shape=[-1, 2, fea_dim])
anc_feas, pos_feas = paddle.split(out_feas, num_or_sections=2, axis=1)
anc_feas = paddle.squeeze(anc_feas, axis=1)
pos_feas = paddle.squeeze(pos_feas, axis=1)
#get simi matrix
similarity_matrix = paddle.matmul(
anc_feas, pos_feas, transpose_y=True) #get similarity matrix
sparse_labels = paddle.arange(0, num_class, dtype='int64')
xentloss = paddle.nn.CrossEntropyLoss()(
similarity_matrix, sparse_labels) #by default: mean
#l2 norm
reg = paddle.mean(paddle.sum(paddle.square(features), axis=1))
l2loss = 0.5 * reg_lambda * reg
return {"npairsloss": xentloss + l2loss}