Readability refactor + normalize classification loss

zylo117 · May 4, 2020 · 02ef960 · 02ef960
1 parent 3fe4552
commit 02ef960
Showing 1 changed file with 14 additions and 23 deletions.
diff --git a/efficientdet/loss.py b/efficientdet/loss.py
@@ -53,34 +53,24 @@ def forward(self, classifications, regressions, anchors, annotations, **kwargs):
 
             classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4)
 
-            if bbox_annotation.shape[0] == 0:
-                if torch.cuda.is_available():
-
-                    alpha_factor = torch.ones_like(classification) * alpha
+            if len(bbox_annotation) == 0:  # No annotations
+                alpha_factor = torch.ones_like(classification) * alpha
+                if torch.cuda.is_available():                  
                     alpha_factor = alpha_factor.cuda()
-                    alpha_factor = 1. - alpha_factor
-                    focal_weight = classification
-                    focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
+                alpha_factor = 1. - alpha_factor
+                focal_weight = classification
+                focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
 
-                    bce = -(torch.log(1.0 - classification))
-
-                    cls_loss = focal_weight * bce
+                bce = -(torch.log(1.0 - classification))
 
+                cls_loss = focal_weight * bce
+                if torch.cuda.is_available():        
                     regression_losses.append(torch.tensor(0).to(dtype).cuda())
-                    classification_losses.append(cls_loss.sum())
                 else:
-
-                    alpha_factor = torch.ones_like(classification) * alpha
-                    alpha_factor = 1. - alpha_factor
-                    focal_weight = classification
-                    focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
-
-                    bce = -(torch.log(1.0 - classification))
-
-                    cls_loss = focal_weight * bce
-
                     regression_losses.append(torch.tensor(0).to(dtype))
-                    classification_losses.append(cls_loss.sum())
+
+                # classification_losses.append(cls_loss.sum())
+                classification_losses.append(cls_loss.mean())
 
                 continue
 
@@ -121,7 +111,8 @@ def forward(self, classifications, regressions, anchors, annotations, **kwargs):
                 zeros = zeros.cuda()
             cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros)
 
-            classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.to(dtype), min=1.0))
+            # classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.to(dtype), min=1.0))
+            classification_losses.append(cls_loss.mean() / torch.clamp(num_positive_anchors.to(dtype), min=1.0))
 
             if positive_indices.sum() > 0:
                 assigned_annotations = assigned_annotations[positive_indices, :]