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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /Npair_loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | import tensorflow as tf 6 | from tensorflow.python.platform import test 7 | from tensorflow.contrib.losses.python.metric_learning import metric_loss_ops 8 | from tensorflow.python.framework import ops 9 | from tensorflow.python.ops import math_ops 10 | 11 | def cross_entropy(logits, target, size_average=True): 12 | if size_average: 13 | return torch.mean(torch.sum(- target * F.log_softmax(logits, -1), -1)) 14 | else: 15 | return torch.sum(torch.sum(- target * F.log_softmax(logits, -1), -1)) 16 | 17 | 18 | class NpairLoss(nn.Module): 19 | """the multi-class n-pair loss""" 20 | def __init__(self, l2_reg=0.02): 21 | super(NpairLoss, self).__init__() 22 | self.l2_reg = l2_reg 23 | 24 | def forward(self, anchor, positive, target): 25 | batch_size = anchor.size(0) 26 | target = target.view(target.size(0), 1) 27 | 28 | target = (target == torch.transpose(target, 0, 1)).float() 29 | target = target / torch.sum(target, dim=1, keepdim=True).float() 30 | 31 | logit = torch.matmul(anchor, torch.transpose(positive, 0, 1)) 32 | loss_ce = cross_entropy(logit, target) 33 | l2_loss = torch.sum(anchor**2) / batch_size + torch.sum(positive**2) / batch_size 34 | 35 | loss = loss_ce + self.l2_reg*l2_loss*0.25 36 | return loss 37 | 38 | 39 | class NpairsLossTest(test.TestCase): 40 | def testNpairs(self): 41 | with self.test_session(): 42 | num_data = 16 43 | feat_dim = 5 44 | num_classes = 3 45 | reg_lambda = 0.02 46 | 47 | embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32) 48 | embeddings_positive = np.random.rand(num_data, feat_dim).astype(np.float32) 49 | 50 | 51 | labels = np.random.randint(0, num_classes, size=(num_data)).astype(np.float32) 52 | 53 | # Reshape labels to compute adjacency matrix. 54 | labels_reshaped = np.reshape(labels, (labels.shape[0], 1)) 55 | 56 | # Compute the loss in NP 57 | reg_term = np.mean(np.sum(np.square(embeddings_anchor), 1)) 58 | reg_term += np.mean(np.sum(np.square(embeddings_positive), 1)) 59 | reg_term *= 0.25 * reg_lambda 60 | 61 | similarity_matrix = np.matmul(embeddings_anchor, embeddings_positive.T) 62 | labels_remapped = np.equal(labels_reshaped, labels_reshaped.T).astype(np.float32) 63 | 64 | labels_remapped /= np.sum(labels_remapped, axis=1, keepdims=True) 65 | xent_loss = math_ops.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2( 66 | logits=ops.convert_to_tensor(similarity_matrix), 67 | labels=ops.convert_to_tensor(labels_remapped))).eval() 68 | 69 | loss_np = xent_loss + reg_term 70 | 71 | # Compute the loss in pytorch 72 | npairloss = NpairLoss() 73 | loss_tc = npairloss( 74 | anchor=torch.tensor(embeddings_anchor), 75 | positive=torch.tensor(embeddings_positive), 76 | target=torch.from_numpy(labels) 77 | ) 78 | 79 | # Compute the loss in TF 80 | loss_tf = metric_loss_ops.npairs_loss( 81 | labels=ops.convert_to_tensor(labels), 82 | embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), 83 | embeddings_positive=ops.convert_to_tensor(embeddings_positive), 84 | reg_lambda=reg_lambda) 85 | loss_tf = loss_tf.eval() 86 | 87 | print('pytorch version: ', loss_tc.numpy()) 88 | print('numpy version: ',loss_np) 89 | print('tensorflow version: ',loss_tf) 90 | # self.assertAllClose(loss_np, loss_tf) 91 | 92 | if __name__ == '__main__': 93 | NpairsLossTest().testNpairs() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Npair_loss_pytorch 2 | Improved Deep Metric Learning with Multi-class N-pair Loss Objective 3 | 4 | This implementation comes from tensorflow version. https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py 5 | 6 | --------------------------------------------------------------------------------