add contrastive loss

.gitignore

@@ -37,3 +37,6 @@ model_ts*.txt
 .vscode
 _darcs
 .DS_Store
+
+# dataset dir
+datasets

fastreid/config/defaults.py

@@ -121,6 +121,11 @@ _C.MODEL.LOSSES.COSFACE.MARGIN = 0.25
 _C.MODEL.LOSSES.COSFACE.GAMMA = 128
 _C.MODEL.LOSSES.COSFACE.SCALE = 1.0
 
+# Contrastive Loss options
+_C.MODEL.LOSSES.CONTRASTIVE = CN()
+_C.MODEL.LOSSES.CONTRASTIVE.MARGIN = 2.0
+_C.MODEL.LOSSES.CONTRASTIVE.SCALE = 1.0
+
 # Path to a checkpoint file to be loaded to the model. You can find available models in the model zoo.
 _C.MODEL.WEIGHTS = ""
 

fastreid/modeling/losses/__init__.py

@@ -5,6 +5,7 @@
 """
 
 from .circle_loss import *
+from .contrastive_loss import contrastive_loss
 from .cross_entroy_loss import cross_entropy_loss, log_accuracy
 from .focal_loss import focal_loss
 from .triplet_loss import triplet_loss

fastreid/modeling/losses/contrastive_loss.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2021/10/11 15:46:33
+# @Author  : zuchen.wang@vipshop.com
+# @File    : contrastive_loss.py
+import torch
+import torch.nn.functional as F
+
+__all__ = ['contrastive_loss']
+
+
+def contrastive_loss(
+        embedding: torch.Tensor,
+        targets: torch.Tensor,
+        margin: float) -> torch.Tensor:
+    embedding = F.normalize(embedding, dim=1)
+    embed1 = embedding[0:len(embedding):2, :]
+    embed2 = embedding[1:len(embedding):2, :]
+    euclidean_distance = F.pairwise_distance(embed1, embed2)
+    return torch.mean(targets * torch.pow(euclidean_distance, 2) +
+                      (1 - targets) * torch.pow(torch.clamp(margin - euclidean_distance, min=0), 2))

fastreid/modeling/meta_arch/baseline.py

@@ -88,6 +88,10 @@ class Baseline(nn.Module):
                         'margin': cfg.MODEL.LOSSES.COSFACE.MARGIN,
                         'gamma': cfg.MODEL.LOSSES.COSFACE.GAMMA,
                         'scale': cfg.MODEL.LOSSES.COSFACE.SCALE
+                    },
+                    'contrastive': {
+                        'margin': cfg.MODEL.LOSSES.CONTRASTIVE.MARGIN,
+                        'scale': cfg.MODEL.LOSSES.CONTRASTIVE.SCALE
                     }
                 }
         }
@@ -143,7 +147,7 @@ class Baseline(nn.Module):
         # fmt: on
 
         # Log prediction accuracy
-        log_accuracy(pred_class_logits, gt_labels)
+        # log_accuracy(pred_class_logits, gt_labels)
 
         loss_dict = {}
         loss_names = self.loss_kwargs['loss_names']
@@ -185,4 +189,12 @@ class Baseline(nn.Module):
                 cosface_kwargs.get('gamma'),
             ) * cosface_kwargs.get('scale')
 
+        if 'ContrastiveLoss' in loss_names:
+            contrastive_kwargs = self.loss_kwargs.get('contrastive')
+            loss_dict['loss_contrastive'] = contrastive_loss(
+                pred_features,
+                gt_labels,
+                contrastive_kwargs.get('margin')
+            ) * contrastive_kwargs.get('scale')
+
         return loss_dict

projects/FastShoe/configs/base-pair.yaml

@@ -18,10 +18,10 @@ MODEL:
     NUM_CLASSES: 2
 
   LOSSES:
-    NAME: ("CrossEntropyLoss",)
+    NAME: ("ContrastiveLoss",)
 
-    CE:
+    CONTRASTIVE:
-      EPSILON: 0.1
+      MARGIN: 2.0
       SCALE: 1.
 
 INPUT: