Fixed classification loss normalization + support for matched/unmatch…

…ed anchors

efficientdet/loss.py

@@ -25,8 +25,11 @@ def calc_iou(a, b):
 
 
 class FocalLoss(nn.Module):
-    def __init__(self):
+    def __init__(self, matched_threshold=0.5, unmatched_threshold=0.4, negatives_lower_than_unmatched=False):
         super(FocalLoss, self).__init__()
+        self.matched_threshold = matched_threshold
+        self.unmatched_threshold = unmatched_threshold
+        self.negatives_lower_than_unmatched = negatives_lower_than_unmatched
 
     def forward(self, classifications, regressions, anchors, annotations, **kwargs):
         alpha = 0.25
@@ -69,9 +72,7 @@ def forward(self, classifications, regressions, anchors, annotations, **kwargs):
                 else:
                     regression_losses.append(torch.tensor(0).to(dtype))
 
-                # classification_losses.append(cls_loss.sum())
-                classification_losses.append(cls_loss.mean())
-
+                classification_losses.append(cls_loss.sum())
                 continue
 
             IoU = calc_iou(anchor[:, :], bbox_annotation[:, :4])
@@ -83,12 +84,19 @@ def forward(self, classifications, regressions, anchors, annotations, **kwargs):
             if torch.cuda.is_available():
                 targets = targets.cuda()
 
-            targets[torch.lt(IoU_max, 0.4), :] = 0
-
-            positive_indices = torch.ge(IoU_max, 0.5)
+            if self.negatives_lower_than_unmatched:  
+                # negative matches are the ones below the unmatched_threshold, whereas ignored matches are in between the matched and unmatched
+                targets[torch.lt(IoU_max, self.matched_threshold) & torch.ge(IoU_max, self.unmatched_threshold), :] = 0
+            else:  
+                # Ignore targets with overlap lower than unmatched_threshold
+                targets[torch.lt(IoU_max, self.unmatched_threshold), :] = 0
 
-            num_positive_anchors = positive_indices.sum()
+            # Find all positives in a batch for normalization 
+            positive_indices = torch.ge(IoU_max, self.matched_threshold)
 
+            # Avoid zero sum of num_positives, which would lead to inf loss during training
+            num_positive_anchors = positive_indices.sum() + 1 
+            # print(num_positive_anchors)
             assigned_annotations = bbox_annotation[IoU_argmax, :]
 
             targets[positive_indices, :] = 0
@@ -111,8 +119,7 @@ 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.mean() / 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))
 
             if positive_indices.sum() > 0:
                 assigned_annotations = assigned_annotations[positive_indices, :]

train.py

@@ -37,7 +37,7 @@ def get_args():
     parser.add_argument('-c', '--compound_coef', type=int, default=0, help='coefficients of efficientdet')
     parser.add_argument('-n', '--num_workers', type=int, default=12, help='num_workers of dataloader')
     parser.add_argument('--batch_size', type=int, default=12, help='The number of images per batch among all devices')
-    parser.add_argument('--head_only', type=bool, default=False,
+    parser.add_argument('--head_only', type=boolean_string, default=False,
                         help='whether finetunes only the regressor and the classifier, '
                              'useful in early stage convergence or small/easy dataset')
     parser.add_argument('--lr', type=float, default=1e-4)
@@ -56,17 +56,25 @@ def get_args():
     parser.add_argument('-w', '--load_weights', type=str, default=None,
                         help='whether to load weights from a checkpoint, set None to initialize, set \'last\' to load last checkpoint')
     parser.add_argument('--saved_path', type=str, default='logs/')
-    parser.add_argument('--debug', type=bool, default=False, help='whether visualize the predicted boxes of trainging, '
+    parser.add_argument('--debug', type=boolean_string, default=False, help='whether visualize the predicted boxes of trainging, '
                                                                   'the output images will be in test/')
+    parser.add_argument('--matched_threshold', type=float, default=.5, help='Threshold for positive matches.')
+    parser.add_argument('--unmatched_threshold', type=float, default=.4, help='Threshold for negative matches.')
 
     args = parser.parse_args()
     return args
 
 
+def boolean_string(s):
+    if s not in {'False', 'True'}:
+        raise ValueError('Not a valid boolean string')
+    return s == 'True'
+
+
 class ModelWithLoss(nn.Module):
-    def __init__(self, model, debug=False):
+    def __init__(self, model, matched_threshold=0.5, unmatched_threshold=0.4, debug=False):
         super().__init__()
-        self.criterion = FocalLoss()
+        self.criterion = FocalLoss(matched_threshold=matched_threshold, unmatched_threshold=unmatched_threshold)
         self.model = model
         self.debug = debug
 
@@ -175,7 +183,7 @@ def freeze_backbone(m):
     writer = SummaryWriter(opt.log_path + f'/{datetime.datetime.now().strftime("%Y%m%d-%H%M%S")}/')
 
     # warp the model with loss function, to reduce the memory usage on gpu0 and speedup
-    model = ModelWithLoss(model, debug=opt.debug)
+    model = ModelWithLoss(model, matched_threshold=opt.matched_threshold, unmatched_threshold=opt.unmatched_threshold, debug=opt.debug)
 
     if params.num_gpus > 0:
         model = model.cuda()