Support for image with no annotations in RetinaNet (pytorch#3032)

* Enable support for images without annotations

* Ensuring gradient propagates to RegressionHead.

* Rewriting losses to remove branching.

* Fix the seed on DeformConv autocast test.

test/test_models_detection_negative_samples.py

@@ -128,6 +128,15 @@ def test_forward_negative_sample_krcnn(self):
         self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
         self.assertEqual(loss_dict["loss_keypoint"], torch.tensor(0.))
 
+    def test_forward_negative_sample_retinanet(self):
+        model = torchvision.models.detection.retinanet_resnet50_fpn(
+            num_classes=2, min_size=100, max_size=100)
+
+        images, targets = self._make_empty_sample()
+        loss_dict = model(images, targets)
+
+        self.assertEqual(loss_dict["bbox_regression"], torch.tensor(0.))
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_ops.py

@@ -1,3 +1,4 @@
+from common_utils import set_rng_seed
 import math
 import unittest
 
@@ -655,6 +656,7 @@ def script_func_no_mask(x_, offset_, weight_, bias_, stride_, pad_, dilation_):
 
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
     def test_autocast(self):
+        set_rng_seed(0)
         for dtype in (torch.float, torch.half):
             with torch.cuda.amp.autocast():
                 self._test_forward(torch.device("cuda"), False, dtype=dtype)

torchvision/models/detection/retinanet.py

@@ -107,21 +107,16 @@ def compute_loss(self, targets, head_outputs, matched_idxs):
             # determine only the foreground
             foreground_idxs_per_image = matched_idxs_per_image >= 0
             num_foreground = foreground_idxs_per_image.sum()
-            # no matched_idxs means there were no annotations in this image
-            # TODO: enable support for images without annotations that works on distributed
-            if False:  # matched_idxs_per_image.numel() == 0:
-                gt_classes_target = torch.zeros_like(cls_logits_per_image)
-                valid_idxs_per_image = torch.arange(cls_logits_per_image.shape[0])
-            else:
-                # create the target classification
-                gt_classes_target = torch.zeros_like(cls_logits_per_image)
-                gt_classes_target[
-                    foreground_idxs_per_image,
-                    targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
-                ] = 1.0
-
-                # find indices for which anchors should be ignored
-                valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS
+
+            # create the target classification
+            gt_classes_target = torch.zeros_like(cls_logits_per_image)
+            gt_classes_target[
+                foreground_idxs_per_image,
+                targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
+            ] = 1.0
+
+            # find indices for which anchors should be ignored
+            valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS
 
             # compute the classification loss
             losses.append(sigmoid_focal_loss(
@@ -191,23 +186,12 @@ def compute_loss(self, targets, head_outputs, anchors, matched_idxs):
 
         for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in \
                 zip(targets, bbox_regression, anchors, matched_idxs):
-            # no matched_idxs means there were no annotations in this image
-            # TODO enable support for images without annotations with distributed support
-            # if matched_idxs_per_image.numel() == 0:
-            #     continue
-
-            # get the targets corresponding GT for each proposal
-            # NB: need to clamp the indices because we can have a single
-            # GT in the image, and matched_idxs can be -2, which goes
-            # out of bounds
-            matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image.clamp(min=0)]
-
             # determine only the foreground indices, ignore the rest
-            foreground_idxs_per_image = matched_idxs_per_image >= 0
-            num_foreground = foreground_idxs_per_image.sum()
+            foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
+            num_foreground = foreground_idxs_per_image.numel()
 
             # select only the foreground boxes
-            matched_gt_boxes_per_image = matched_gt_boxes_per_image[foreground_idxs_per_image, :]
+            matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image[foreground_idxs_per_image]]
             bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :]
             anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]
 
@@ -403,7 +387,7 @@ def compute_loss(self, targets, head_outputs, anchors):
         matched_idxs = []
         for anchors_per_image, targets_per_image in zip(anchors, targets):
             if targets_per_image['boxes'].numel() == 0:
-                matched_idxs.append(torch.empty((0,), dtype=torch.int32))
+                matched_idxs.append(torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64))
                 continue
 
             match_quality_matrix = box_ops.box_iou(targets_per_image['boxes'], anchors_per_image)