Fixes crash in deformable convolutions (2598)

test/test_ops.py

@@ -553,6 +553,35 @@ def script_func(x_, offset_, weight_, bias_, stride_, pad_, dilation_):
         gradcheck(lambda z, off, wei, bi: script_func(z, off, wei, bi, stride, padding, dilation),
                   (x, offset, weight, bias), nondet_tol=1e-5)
 
+        # Test from https://github.com/pytorch/vision/issues/2598
+        # Run on CUDA only
+        if "cuda" in device.type:
+            # compare grads computed on CUDA with grads computed on CPU
+            true_cpu_grads = None
+
+            init_weight = torch.randn(9, 9, 3, 3, requires_grad=True)
+            img = torch.randn(8, 9, 1000, 110)
+            offset = torch.rand(8, 2 * 3 * 3, 1000, 110)
+
+            if not contiguous:
+                img = img.permute(0, 1, 3, 2).contiguous().permute(0, 1, 3, 2)
+                offset = offset.permute(1, 3, 0, 2).contiguous().permute(2, 0, 3, 1)
+                weight = init_weight.permute(3, 2, 0, 1).contiguous().permute(2, 3, 1, 0)
+            else:
+                weight = init_weight
+
+            for d in ["cpu", "cuda"]:
+
+                out = ops.deform_conv2d(img.to(d), offset.to(d), weight.to(d), padding=1)
+                out.mean().backward()
+                if true_cpu_grads is None:
+                    true_cpu_grads = init_weight.grad
+                    self.assertTrue(true_cpu_grads is not None)
+                else:
+                    self.assertTrue(init_weight.grad is not None)
+                    res_grads = init_weight.grad.to("cpu")
+                    self.assertTrue(true_cpu_grads.allclose(res_grads))
+
 
 class FrozenBNTester(unittest.TestCase):
     def test_frozenbatchnorm2d_repr(self):

torchvision/csrc/cuda/DeformConv_cuda.cu

@@ -80,12 +80,11 @@
 
 using namespace at;
 
-const int CUDA_NUM_THREADS = 1024;
-const int kMaxGridNum = 65535;
-
+const unsigned int CUDA_NUM_THREADS = 1024;
+const unsigned int kMaxGridNum = at::cuda::getCurrentDeviceProperties()->maxGridSize[0];
 const int kMaxParallelImgs = 32;
 
-inline int GET_BLOCKS(const int N) {
+inline unsigned int GET_BLOCKS(const unsigned int N) {
   return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS);
 }