make CrossEntropy and Focal Loss filter -1 label

fastreid/modeling/losses/contrastive_loss.py

@@ -3,17 +3,21 @@
 # @Author  : zuchen.wang@vipshop.com
 # @File    : contrastive_loss.py
 import torch
+import torch.nn.functional as F
 from .utils import normalize, euclidean_dist
 
 __all__ = ['contrastive_loss']
 
 
 def contrastive_loss(
-        query_feat: torch.Tensor,
-        gallery_feat: torch.Tensor,
+        feats: torch.Tensor,
         targets: torch.Tensor,
         margin: float) -> torch.Tensor:
+    feats_len = feats.size(0)
+    feats = F.normalize(feats, dim=1)
+    query_feat = feats[0:feats_len:2, :]
+    gallery_feat = feats[1:feats_len:2, :]
     distance = torch.sqrt(torch.sum(torch.pow(query_feat - gallery_feat, 2), -1))
     loss1 = 0.5 * targets * torch.pow(distance, 2)
-    loss2 = 0.5 * (1 - targets) * torch.pow(torch.clamp(margin - distance, min=0), 2)
+    loss2 = 0.5 * (1 - targets) * torch.pow(torch.clamp(margin - distance, min=1e-6), 2)
     return torch.mean(loss1 + loss2)

fastreid/modeling/losses/cross_entroy_loss.py

@@ -13,6 +13,16 @@ def log_accuracy(pred_class_logits, gt_classes, topk=(1,)):
     """
     Log the accuracy metrics to EventStorage.
     """
+    storage = get_event_storage()
+
+    index = torch.where(gt_classes != -1)[0]
+    if len(index) == 0:
+        storage.put_scalar("cls_accuracy", 0)
+        return
+
+    pred_class_logits = pred_class_logits[index, :]
+    gt_classes = gt_classes[index]
+
     bsz = pred_class_logits.size(0)
     maxk = max(topk)
     _, pred_class = pred_class_logits.topk(maxk, 1, True, True)
@@ -24,13 +34,19 @@ def log_accuracy(pred_class_logits, gt_classes, topk=(1,)):
         correct_k = correct[:k].view(-1).float().sum(dim=0, keepdim=True)
         ret.append(correct_k.mul_(1. / bsz))
 
-    storage = get_event_storage()
     storage.put_scalar("cls_accuracy", ret[0])
 
 
 def cross_entropy_loss(pred_class_outputs, gt_classes, eps, alpha=0.2):
     num_classes = pred_class_outputs.size(1)
 
+    index = torch.where(gt_classes != -1)[0]
+    if len(index) == 0:
+        return torch.tensor(0, dtype=torch.float32, device=pred_class_outputs.device)
+
+    pred_class_outputs = pred_class_outputs[index, :]
+    gt_classes = gt_classes[index]
+
     if eps >= 0:
         smooth_param = eps
     else:

fastreid/modeling/losses/focal_loss.py

@@ -57,6 +57,10 @@ def focal_loss(
         raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
                          .format(input.size(0), target.size(0)))
 
+    index = torch.where(target != -1)[0]
+    target = target[index]
+    input = input[index, :]
+
     n = input.size(0)
     out_size = (n,) + input.size()[2:]
     if target.size()[1:] != input.size()[2:]:
@@ -96,7 +100,6 @@ def binary_focal_loss(inputs, targets, alpha=0.25, gamma=2):
     '''
     Reference: https://github.com/tensorflow/addons/blob/v0.14.0/tensorflow_addons/losses/focal_loss.py
     '''
-#     __import__('ipdb').set_trace()
     if alpha < 0:
         raise ValueError(f'Value of alpha should be greater than or equal to zero, but get {alpha}')
     if gamma < 0: