Merge pull request #15949 from ceci3/npair_loss0
add npair loss oprevert-16045-imperative_remove_desc
jerrywgz
7 years ago
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GitHub
commit
55a785bb10
@ -0,0 +1,101 @@
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import numpy as np
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def npairloss(anchor, positive, labels, l2_reg=0.002):
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def softmax_cross_entropy_with_logits(logits, labels):
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logits = np.exp(logits)
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logits = logits / np.sum(logits, axis=1).reshape(-1, 1)
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return np.mean(
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-np.sum(labels * np.log(logits), axis=1), dtype=np.float32)
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batch_size = labels.shape[0]
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labels = np.reshape(labels, (batch_size, 1))
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labels = np.equal(labels, labels.transpose()).astype(float)
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labels = labels / np.sum(labels, axis=1, keepdims=True)
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l2loss = np.mean(np.sum(np.power(anchor, 2), 1)) + np.mean(
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np.sum(np.power(positive, 2), 1))
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l2loss = (l2loss * 0.25 * l2_reg).astype(np.float32)
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similarity_matrix = np.matmul(anchor, positive.transpose())
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celoss = np.mean(
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softmax_cross_entropy_with_logits(similarity_matrix, labels))
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return l2loss + celoss
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class TestNpairLossOp(unittest.TestCase):
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def setUp(self):
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self.dtype = np.float32
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def __assert_close(self, tensor, np_array, msg, atol=1e-4):
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self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
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def test_npair_loss(self):
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reg_lambda = 0.002
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num_data, feat_dim, num_classes = 18, 6, 3
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place = core.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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embeddings_anchor = np.random.rand(num_data,
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feat_dim).astype(np.float32)
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embeddings_positive = np.random.rand(num_data,
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feat_dim).astype(np.float32)
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row_labels = np.random.randint(
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0, num_classes, size=(num_data)).astype(np.float32)
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out_loss = npairloss(
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embeddings_anchor,
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embeddings_positive,
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row_labels,
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l2_reg=reg_lambda)
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anc = fluid.layers.create_tensor(
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dtype='float32', persistable=True, name='anc')
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pos = fluid.layers.create_tensor(
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dtype='float32', persistable=True, name='pos')
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lab = fluid.layers.create_tensor(
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dtype='float32', persistable=True, name='lab')
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fluid.layers.assign(input=embeddings_anchor, output=anc)
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fluid.layers.assign(input=embeddings_positive, output=pos)
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fluid.layers.assign(input=row_labels, output=lab)
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npair_loss_op = fluid.layers.npair_loss(
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anchor=anc, positive=pos, labels=lab, l2_reg=reg_lambda)
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out_tensor = exe.run(feed={'anc': anc,
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'pos': pos,
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'lab': lab},
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fetch_list=[npair_loss_op.name])
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self.__assert_close(
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out_tensor,
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out_loss,
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"inference output are different at " + str(place) + ", " +
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str(np.dtype('float32')) + str(np.array(out_tensor)) +
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str(out_loss),
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atol=1e-3)
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if __name__ == '__main__':
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unittest.main()
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