Replace type T with accumulator.

Author: datumbox

test/test_ops.py

@@ -449,6 +449,17 @@ def test_autocast(self):
             with torch.cuda.amp.autocast():
                 self.test_nms_cuda(dtype=dtype)
 
+    @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
+    def test_nms_cuda_float16(self):
+        boxes = torch.tensor([[285.3538, 185.5758, 1193.5110, 851.4551], [285.1472, 188.7374, 1192.4984, 851.0669],
+                             [279.2440, 197.9812, 1189.4746, 849.2019]]).cuda()
+        scores = torch.tensor([0.6370, 0.7569, 0.3966]).cuda()
+
+        iou_thres = 0.2
+        keep32 = ops.nms(boxes, scores, iou_thres)
+        keep16 = ops.nms(boxes.to(torch.float16), scores.to(torch.float16), iou_thres)
+        self.assertTrue(torch.all(torch.eq(keep32, keep16)))
+
 
 class DeformConvTester(OpTester, unittest.TestCase):
     def expected_fn(self, x, weight, offset, mask, bias, stride=1, padding=0, dilation=1):

torchvision/csrc/ops/cuda/nms_kernel.cu

@@ -17,12 +17,13 @@ __device__ inline bool devIoU(
     T const* const a,
     T const* const b,
     const float threshold) {
-  T left = max(a[0], b[0]), right = min(a[2], b[2]);
-  T top = max(a[1], b[1]), bottom = min(a[3], b[3]);
-  T width = max(right - left, (T)0), height = max(bottom - top, (T)0);
-  T interS = width * height;
-  T Sa = (a[2] - a[0]) * (a[3] - a[1]);
-  T Sb = (b[2] - b[0]) * (b[3] - b[1]);
+  using acc_T = at::acc_type<T, /*is_cuda=*/true>;
+  acc_T left = max(a[0], b[0]), right = min(a[2], b[2]);
+  acc_T top = max(a[1], b[1]), bottom = min(a[3], b[3]);
+  acc_T width = max(right - left, (acc_T)0), height = max(bottom - top, (acc_T)0);
+  acc_T interS = width * height;
+  acc_T Sa = (a[2] - a[0]) * (a[3] - a[1]);
+  acc_T Sb = (b[2] - b[0]) * (b[3] - b[1]);
   return (interS / (Sa + Sb - interS)) > threshold;
 }