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