[Fix] Allow mixed precision training with SimOTAAssigner (#7516)

* Convert valid_pred_scores to float32 in sim_ota_assigner

Workaround to resolve `RuntimeError: "sqrt" "_vml_cpu" not implemented for 'Half'`

* Add unit test for SimOTAAssigner

* Cast output of binary_cross_entropy back to float16 if needed

Also fix yapf lint issue.

* Lint for yapf

* More lint fixes on test_assigner.py

* Cast cls_scores back to float16 directly

Co-Authored-By: Wenwei Zhang <ZwwWayne@users.noreply.github.com>

Co-authored-by: Wenwei Zhang <ZwwWayne@users.noreply.github.com>

mmdet/core/bbox/assigners/sim_ota_assigner.py

@@ -157,9 +157,12 @@ class SimOTAAssigner(BaseAssigner):
                           num_valid, 1, 1))
 
         valid_pred_scores = valid_pred_scores.unsqueeze(1).repeat(1, num_gt, 1)
-        cls_cost = F.binary_cross_entropy(
-            valid_pred_scores.sqrt_(), gt_onehot_label,
-            reduction='none').sum(-1)
+        cls_cost = (
+            F.binary_cross_entropy(
+                valid_pred_scores.to(dtype=torch.float32).sqrt_(),
+                gt_onehot_label,
+                reduction='none',
+            ).sum(-1).to(dtype=valid_pred_scores.dtype))
 
         cost_matrix = (
             cls_cost * self.cls_weight + iou_cost * self.iou_weight +

tests/test_utils/test_assigner.py

@@ -11,8 +11,8 @@ import torch
 from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
                                        CenterRegionAssigner, HungarianAssigner,
                                        MaskHungarianAssigner, MaxIoUAssigner,
-                                       PointAssigner, TaskAlignedAssigner,
-                                       UniformAssigner)
+                                       PointAssigner, SimOTAAssigner,
+                                       TaskAlignedAssigner, UniformAssigner)
 
 
 def test_max_iou_assigner():
@@ -500,6 +500,21 @@ def test_uniform_assigner_with_empty_boxes():
     assert len(assign_result.gt_inds) == 0
 
 
+def test_sim_ota_assigner():
+    self = SimOTAAssigner(
+        center_radius=2.5, candidate_topk=1, iou_weight=3.0, cls_weight=1.0)
+    pred_scores = torch.FloatTensor([[0.2], [0.8]])
+    priors = torch.Tensor([[0, 12, 23, 34], [4, 5, 6, 7]])
+    decoded_bboxes = torch.Tensor([[[30, 40, 50, 60]], [[4, 5, 6, 7]]])
+    gt_bboxes = torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]])
+    gt_labels = torch.LongTensor([2])
+    assign_result = self.assign(pred_scores, priors, decoded_bboxes, gt_bboxes,
+                                gt_labels)
+
+    expected_gt_inds = torch.LongTensor([0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
 def test_task_aligned_assigner():
     with pytest.raises(AssertionError):
         TaskAlignedAssigner(topk=0)