Add test for giou_loss

test/test_ops.py

@@ -1569,5 +1569,59 @@ def test_jit(self, alpha, gamma, reduction, device, dtype, seed) -> None:
         torch.testing.assert_close(focal_loss, scripted_focal_loss, rtol=tol, atol=tol)
 
 
+class TestGeneralizedBoxIouLoss:
+    # We refer to original test: https://github.com/facebookresearch/fvcore/blob/main/tests/test_giou_loss.py
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_giou_loss(self, dtype, device) -> None:
+        box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
+        box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
+        box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
+        box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
+        box1s = torch.stack([box2, box2], dim=0)
+        box2s = torch.stack([box3, box4], dim=0)
+
+        def assert_giou_loss(box1, box2, expected_loss, reduction="none"):
+            tol = 1e-3 if dtype is torch.half else 1e-5
+            computed_loss = ops.generalized_box_iou_loss(box1, box2, reduction=reduction)
+            expected_loss = torch.tensor(expected_loss, device=device)
+            torch.testing.assert_close(computed_loss, expected_loss, rtol=tol, atol=tol)
+
+        # Identical boxes should have loss of 0
+        assert_giou_loss(box1, box1, 0.0)
+
+        # quarter size box inside other box = IoU of 0.25
+        assert_giou_loss(box1, box2, 0.75)
+
+        # Two side by side boxes, area=union
+        # IoU=0 and GIoU=0 (loss 1.0)
+        assert_giou_loss(box2, box3, 1.0)
+
+        # Two diagonally adjacent boxes, area=2*union
+        # IoU=0 and GIoU=-0.5 (loss 1.5)
+        assert_giou_loss(box2, box4, 1.5)
+
+        # Test batched loss and reductions
+        assert_giou_loss(box1s, box2s, 2.5, reduction="sum")
+        assert_giou_loss(box1s, box2s, 1.25, reduction="mean")
+
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_empty_inputs(self, dtype, device) -> None:
+        box1 = torch.randn([0, 4], dtype=dtype).requires_grad_()
+        box2 = torch.randn([0, 4], dtype=dtype).requires_grad_()
+
+        loss = ops.generalized_box_iou_loss(box1, box2, reduction="mean")
+        loss.backward()
+
+        tol = 1e-3 if dtype is torch.half else 1e-5
+        torch.testing.assert_close(loss, torch.tensor(0.0), rtol=tol, atol=tol)
+        assert box1.grad is not None, "box1.grad should not be None after backward is called"
+        assert box2.grad is not None, "box2.grad should not be None after backward is called"
+
+        loss = ops.generalized_box_iou_loss(box1, box2, reduction="none")
+        assert loss.numel() == 0, "giou_loss for two empty box should be empty"
+
+
 if __name__ == "__main__":
     pytest.main([__file__])