Merge pull request #1886 from HydrogenSulfate/refine_ref

Refine paper & code ref for loss

ppcls/loss/deephashloss.py

@@ -20,6 +20,7 @@ class DSHSDLoss(nn.Layer):
     """
     # DSHSD(IEEE ACCESS 2019)
     # paper [Deep Supervised Hashing Based on Stable Distribution](https://ieeexplore.ieee.org/document/8648432/)
+    # code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/DSHSD.py
     """
 
     def __init__(self, alpha, multi_label=False):
@@ -62,6 +63,7 @@ class DSHSDLoss(nn.Layer):
 class LCDSHLoss(nn.Layer):
     """
     # paper [Locality-Constrained Deep Supervised Hashing for Image Retrieval](https://www.ijcai.org/Proceedings/2017/0499.pdf)
+    # code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/LCDSH.py
     """
 
     def __init__(self, n_class, _lambda):
@@ -100,6 +102,7 @@ class DCHLoss(paddle.nn.Layer):
     """
     # paper [Deep Cauchy Hashing for Hamming Space Retrieval]
     URL:(http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-cauchy-hashing-cvpr18.pdf)
+    # code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/DCH.py
     """
 
     def __init__(self, gamma, _lambda, n_class):

ppcls/loss/emlloss.py

@@ -23,6 +23,11 @@ from .comfunc import rerange_index
 
 
 class EmlLoss(paddle.nn.Layer):
+    """Ensemble Metric Learning Loss
+    paper: [Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval](https://arxiv.org/pdf/1212.6094.pdf)
+    code reference: https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/metric_learning/losses/emlloss.py
+    """
+
     def __init__(self, batch_size=40, samples_each_class=2):
         super(EmlLoss, self).__init__()
         assert (batch_size % samples_each_class == 0)

ppcls/loss/googlenetloss.py

@@ -18,11 +18,13 @@ import paddle.nn.functional as F
 class GoogLeNetLoss(nn.Layer):
     """
     Cross entropy loss used after googlenet
+    reference paper: [https://arxiv.org/pdf/1409.4842v1.pdf](Going Deeper with Convolutions)
     """
+
     def __init__(self, epsilon=None):
         super().__init__()
-        assert (epsilon is None or epsilon <= 0 or epsilon >= 1), "googlenet is not support label_smooth"
-            
+        assert (epsilon is None or epsilon <= 0 or
+                epsilon >= 1), "googlenet is not support label_smooth"
 
     def forward(self, inputs, label):
         input0, input1, input2 = inputs

ppcls/loss/msmloss.py

@@ -21,10 +21,12 @@ from .comfunc import rerange_index
 
 class MSMLoss(paddle.nn.Layer):
     """
-    MSMLoss Loss, based on triplet loss. USE P * K samples.
+    paper : [Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification](https://arxiv.org/pdf/1710.00478.pdf)
+    code reference: https://github.com/michuanhaohao/keras_reid/blob/master/reid_tripletcls.py
+    Margin Sample Mining Loss, based on triplet loss. USE P * K samples.
     the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.
     same label gather together
-    
+
             supported_metrics = [
             'euclidean',
             'sqeuclidean',
@@ -41,7 +43,7 @@ class MSMLoss(paddle.nn.Layer):
         self.rerange_index = rerange_index(batch_size, samples_each_class)
 
     def forward(self, input, target=None):
-        #normalization 
+        #normalization
         features = input["features"]
         features = self._nomalize(features)
         samples_each_class = self.samples_each_class
@@ -53,7 +55,7 @@ class MSMLoss(paddle.nn.Layer):
                 features, axis=0)
         similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
 
-        #rerange 
+        #rerange
         tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
         tmp = paddle.gather(tmp, index=rerange_index)
         similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])

ppcls/loss/npairsloss.py

@@ -5,6 +5,11 @@ import paddle
 
 
 class NpairsLoss(paddle.nn.Layer):
+    """Npair_loss_
+    paper [Improved deep metric learning with multi-class N-pair loss objective](https://dl.acm.org/doi/10.5555/3157096.3157304)
+    code reference: https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/losses/metric_learning/npairs_loss
+    """
+
     def __init__(self, reg_lambda=0.01):
         super(NpairsLoss, self).__init__()
         self.reg_lambda = reg_lambda

ppcls/loss/pairwisecosface.py

@@ -23,6 +23,11 @@ import paddle.nn.functional as F
 
 
 class PairwiseCosface(nn.Layer):
+    """
+    paper: Circle Loss: A Unified Perspective of Pair Similarity Optimization
+    code reference: https://github.com/leoluopy/circle-loss-demonstration/blob/main/circle_loss.py
+    """
+
     def __init__(self, margin, gamma):
         super(PairwiseCosface, self).__init__()
         self.margin = margin
@@ -36,8 +41,10 @@ class PairwiseCosface(nn.Layer):
         dist_mat = paddle.matmul(embedding, embedding, transpose_y=True)
 
         N = dist_mat.shape[0]
-        is_pos = targets.reshape([N,1]).expand([N,N]).equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
-        is_neg = targets.reshape([N,1]).expand([N,N]).not_equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
+        is_pos = targets.reshape([N, 1]).expand([N, N]).equal(
+            paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
+        is_neg = targets.reshape([N, 1]).expand([N, N]).not_equal(
+            paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
 
         # Mask scores related to itself
         is_pos = is_pos - paddle.eye(N, N)
@@ -46,10 +53,12 @@ class PairwiseCosface(nn.Layer):
         s_n = dist_mat * is_neg
 
         logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)
-        logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg)
-
-        loss = F.softplus(paddle.logsumexp(logit_p, axis=1) + paddle.logsumexp(logit_n, axis=1)).mean()
-  
-        return {"PairwiseCosface": loss}
+        logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg
+                                                                     )
 
+        loss = F.softplus(
+            paddle.logsumexp(
+                logit_p, axis=1) + paddle.logsumexp(
+                    logit_n, axis=1)).mean()
 
+        return {"PairwiseCosface": loss}

ppcls/loss/rkdloss.py

@@ -29,6 +29,7 @@ def pdist(e, squared=False, eps=1e-12):
 
 
 class RKdAngle(nn.Layer):
+    # paper : [Relational Knowledge Distillation](https://arxiv.org/abs/1904.05068?context=cs.LG)
     # reference: https://github.com/lenscloth/RKD/blob/master/metric/loss.py
     def __init__(self, target_size=None):
         super().__init__()
@@ -64,6 +65,7 @@ class RKdAngle(nn.Layer):
 
 
 class RkdDistance(nn.Layer):
+    # paper : [Relational Knowledge Distillation](https://arxiv.org/abs/1904.05068?context=cs.LG)
     # reference: https://github.com/lenscloth/RKD/blob/master/metric/loss.py
     def __init__(self, eps=1e-12, target_size=1):
         super().__init__()

ppcls/loss/supconloss.py

@@ -4,6 +4,7 @@ from paddle import nn
 
 class SupConLoss(nn.Layer):
     """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
+    code reference: https://github.com/HobbitLong/SupContrast/blob/master/losses.py
     It also supports the unsupervised contrastive loss in SimCLR"""
 
     def __init__(self,

ppcls/loss/trihardloss.py

@@ -22,10 +22,12 @@ from .comfunc import rerange_index
 
 class TriHardLoss(paddle.nn.Layer):
     """
+    paper: In Defense of the Triplet Loss for Person Re-Identification
+    code reference: https://github.com/VisualComputingInstitute/triplet-reid/blob/master/loss.py
     TriHard Loss, based on triplet loss. USE P * K samples.
     the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.
     same label gather together
-    
+
             supported_metrics = [
             'euclidean',
             'sqeuclidean',
@@ -45,7 +47,7 @@ class TriHardLoss(paddle.nn.Layer):
         features = input["features"]
         assert (self.batch_size == features.shape[0])
 
-        #normalization 
+        #normalization
         features = self._nomalize(features)
         samples_each_class = self.samples_each_class
         rerange_index = paddle.to_tensor(self.rerange_index)
@@ -56,7 +58,7 @@ class TriHardLoss(paddle.nn.Layer):
                 features, axis=0)
         similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
 
-        #rerange 
+        #rerange
         tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
         tmp = paddle.gather(tmp, index=rerange_index)
         similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])

ppcls/loss/triplet.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -8,6 +22,8 @@ import paddle.nn as nn
 
 class TripletLossV2(nn.Layer):
     """Triplet loss with hard positive/negative mining.
+    paper : [Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/pdf/1503.03832.pdf)
+    code reference: https://github.com/okzhili/Cartoon-face-recognition/blob/master/loss/triplet_loss.py
     Args:
         margin (float): margin for triplet.
     """