remove dependency of pytorch_metric_learning

opengait/modeling/losses/supconloss.py

@@ -6,13 +6,29 @@ import torch.nn as nn
 import torch
 from .base import BaseLoss, gather_and_scale_wrapper
 
+
 class SupConLoss_Re(BaseLoss):
     def __init__(self, temperature=0.01):
         super(SupConLoss_Re, self).__init__()
         self.train_loss = SupConLoss(temperature=temperature)
+
     @gather_and_scale_wrapper
     def forward(self, features, labels=None, mask=None):
-        loss = self.train_loss(features,labels)
+        loss = self.train_loss(features, labels)
+        self.info.update({
+            'loss': loss.detach().clone()})
+        return loss, self.info
+
+
+class SupConLoss_Lp(BaseLoss):
+    def __init__(self, temperature=0.01):
+        super(SupConLoss_Lp, self).__init__()
+        self.train_loss = SupConLoss(
+            temperature=temperature, base_temperature=temperature, reduce_zero=True, p=2)
+
+    @gather_and_scale_wrapper
+    def forward(self, features, labels=None, mask=None):
+        loss = self.train_loss(features.unsqueeze(1), labels)
         self.info.update({
             'loss': loss.detach().clone()})
         return loss, self.info
@@ -21,12 +37,15 @@ class SupConLoss_Re(BaseLoss):
 class SupConLoss(nn.Module):
     """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
     It also supports the unsupervised contrastive loss in SimCLR"""
+
     def __init__(self, temperature=0.01, contrast_mode='all',
-                 base_temperature=0.07):
+                 base_temperature=0.07, reduce_zero=False, p=None):
         super(SupConLoss, self).__init__()
         self.temperature = temperature
         self.contrast_mode = contrast_mode
         self.base_temperature = base_temperature
+        self.reduce_zero = reduce_zero
+        self.p = p
 
     def forward(self, features, labels=None, mask=None):
         """Compute loss for model. If both `labels` and `mask` are None,
@@ -74,13 +93,21 @@ class SupConLoss(nn.Module):
         else:
             raise ValueError('Unknown mode: {}'.format(self.contrast_mode))
 
-        # compute logits
-        anchor_dot_contrast = torch.div(
-            torch.matmul(anchor_feature, contrast_feature.T),
-            self.temperature)
+        # compute distance mat
+        if self.p is None:
+            mat = torch.matmul(
+                anchor_feature, contrast_feature.T)
+        else:
+            anchor_feature = torch.nn.functional.normalize(
+                anchor_feature, p=self.p, dim=1)
+            contrast_feature = torch.nn.functional.normalize(
+                contrast_feature, p=self.p, dim=1)
+            mat = -torch.cdist(
+                anchor_feature, contrast_feature, p=self.p)
+        mat = mat/self.temperature
         # for numerical stability
-        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
-        logits = anchor_dot_contrast - logits_max.detach()
+        logits_max, _ = torch.max(mat, dim=1, keepdim=True)
+        logits = mat - logits_max.detach()
 
         # tile mask
         mask = mask.repeat(anchor_count, contrast_count)
@@ -98,10 +125,11 @@ class SupConLoss(nn.Module):
         log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
 
         # compute mean of log-likelihood over positive
-        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
-
+        mean_log_prob_pos = (mask * log_prob).sum(1) / \
+            (mask.sum(1)+torch.finfo(mat.dtype).tiny)
         # loss
         loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
-        loss = loss.view(anchor_count, batch_size).mean()
+        if self.reduce_zero:
+            loss = loss[loss > 0]
 
-        return loss
+        return loss.mean()

opengait/modeling/losses/supconloss_Lp.py

@@ -1,19 +0,0 @@
-'''
-Modifed fromhttps://github.com/BNU-IVC/FastPoseGait/blob/main/fastposegait/modeling/losses/supconloss_Lp.py
-'''
-
-from .base import BaseLoss, gather_and_scale_wrapper
-from pytorch_metric_learning import losses, distances
-
-class SupConLoss_Lp(BaseLoss):
-    def __init__(self, temperature=0.01):
-        super(SupConLoss_Lp, self).__init__()
-        self.distance = distances.LpDistance()
-        self.train_loss = losses.SupConLoss(temperature=temperature, distance=self.distance)
-    @gather_and_scale_wrapper
-    def forward(self, features, labels=None, mask=None):
-        loss = self.train_loss(features,labels)
-        self.info.update({
-            'loss': loss.detach().clone()})
-        return loss, self.info
-