Added check for index upper bound

test/test_ops.py

@@ -304,20 +304,20 @@ def test_qroialign(self):
         pool_size = 5
         img_size = 10
         n_channels = 2
-        num_batches = 2
+        num_imgs = 2
         dtype = torch.float
 
         def make_rois(num_rois=1000):
             rois = torch.randint(0, img_size // 2, size=(num_rois, 5)).to(dtype)
-            rois[:, 0] = torch.randint(0, num_batches, size=(num_rois,))  # set batch index
+            rois[:, 0] = torch.randint(0, num_imgs, size=(num_rois,))  # set batch index
             rois[:, 3:] += rois[:, 1:3]  # make sure boxes aren't degenerate
             return rois
 
         for aligned in (True, False):
             for scale, zero_point in ((1, 0), (2, 10), (0.1, 50)):
                 for qdtype in (torch.qint8, torch.quint8, torch.qint32):
 
-                    x = torch.randint(50, 100, size=(num_batches, n_channels, img_size, img_size)).to(dtype)
+                    x = torch.randint(50, 100, size=(num_imgs, n_channels, img_size, img_size)).to(dtype)
                     qx = torch.quantize_per_tensor(x, scale=scale, zero_point=zero_point, dtype=qdtype)
 
                     rois = make_rois()
@@ -364,6 +364,13 @@ def make_rois(num_rois=1000):
                         t_scale = torch.full_like(abs_diff, fill_value=scale)
                         self.assertTrue(torch.allclose(abs_diff, t_scale, atol=1e-5))
 
+        x = torch.randint(50, 100, size=(129, 3, 10, 10)).to(dtype)
+        qx = torch.quantize_per_tensor(x, scale=0, zero_point=1, dtype=torch.qint8)
+        rois = make_rois(10)
+        qrois = torch.quantize_per_tensor(rois, scale=0, zero_point=1, dtype=torch.qint8)
+        with self.assertRaisesRegex(RuntimeError, "There are 129 input images in the batch, but the RoIs tensor"):
+            ops.roi_align(qx, qrois, output_size=pool_size)
+
 
 class PSRoIAlignTester(RoIOpTester, unittest.TestCase):
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):

torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp

@@ -36,8 +36,7 @@ void qroi_align_forward_kernel_impl(
 
     const T* offset_rois = rois + n * 5;
     int roi_batch_ind = at::native::dequantize_val(
-        rois_scale, rois_zp, offset_rois[0]); // FIXME: This can be out of the
-                                              // range of the quantized type!!
+        rois_scale, rois_zp, offset_rois[0]);
 
     // Do not using rounding; this implementation detail is critical
     float offset = aligned ? 0.5 : 0.;
@@ -172,6 +171,16 @@ at::Tensor qroi_align_forward_kernel(
     return output;
 
   AT_DISPATCH_QINT_TYPES(input.scalar_type(), "qroi_align_forward_kernel", [&] {
+    // Note: q_max relates to the input tensor, but we need that of the rois
+    // tensor. They're the same since we make sure rois and input have the same
+    // type above.
+    uint64_t max_indexable = std::numeric_limits<underlying_t>::max() + 1;
+    std::string err_msg = "There are " + std::to_string(input.size(0)) +
+        " input images in the batch, but the RoIs tensor can only index up to " +
+        std::to_string(max_indexable) +
+        " images. Try to reduce the batch size.";
+    TORCH_CHECK(input.size(0) <= max_indexable, err_msg);
+
     qroi_align_forward_kernel_impl<scalar_t>(
         num_rois,
         input,