Check boxes shape in RoIPool / Align

test/test_ops.py

@@ -91,6 +91,22 @@ def func(z):
         self.assertTrue(gradcheck(func, (x,)))
         self.assertTrue(gradcheck(script_func, (x,)))
 
+    def test_boxes_shape(self):
+        self._test_boxes_shape()
+
+    def _helper_boxes_shape(self, func):
+        # test boxes as Tensor[N, 5]
+        with self.assertRaises(AssertionError):
+            a = torch.linspace(1, 8 * 8, 8 * 8).reshape(1, 1, 8, 8)
+            boxes = torch.tensor([[0, 0, 3, 3]], dtype=a.dtype)
+            func(a, boxes, output_size=(2, 2))
+
+        # test boxes as List[Tensor[N, 4]]
+        with self.assertRaises(AssertionError):
+            a = torch.linspace(1, 8 * 8, 8 * 8).reshape(1, 1, 8, 8)
+            boxes = torch.tensor([[0, 0, 3]], dtype=a.dtype)
+            ops.roi_pool(a, [boxes], output_size=(2, 2))
+
     def fn(*args, **kwargs):
         pass
 
@@ -139,6 +155,9 @@ def get_slice(k, block):
                         y[roi_idx, :, i, j] = bin_x.reshape(n_channels, -1).max(dim=1)[0]
         return y
 
+    def _test_boxes_shape(self):
+        self._helper_boxes_shape(ops.roi_pool)
+
 
 class PSRoIPoolTester(RoIOpTester, unittest.TestCase):
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):
@@ -183,6 +202,9 @@ def get_slice(k, block):
                             y[roi_idx, c_out, i, j] = t / area
         return y
 
+    def _test_boxes_shape(self):
+        self._helper_boxes_shape(ops.ps_roi_pool)
+
 
 def bilinear_interpolate(data, y, x, snap_border=False):
     height, width = data.shape
@@ -266,6 +288,9 @@ def expected_fn(self, in_data, rois, pool_h, pool_w, spatial_scale=1, sampling_r
                         out_data[r, channel, i, j] = val
         return out_data
 
+    def _test_boxes_shape(self):
+        self._helper_boxes_shape(ops.roi_align)
+
 
 class PSRoIAlignTester(RoIOpTester, unittest.TestCase):
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):
@@ -317,6 +342,9 @@ def expected_fn(self, in_data, rois, pool_h, pool_w, device, spatial_scale=1,
                         out_data[r, c_out, i, j] = val
         return out_data
 
+    def _test_boxes_shape(self):
+        self._helper_boxes_shape(ops.ps_roi_align)
+
 
 class NMSTester(unittest.TestCase):
     def reference_nms(self, boxes, scores, iou_threshold):

torchvision/ops/_utils.py

@@ -24,3 +24,15 @@ def convert_boxes_to_roi_format(boxes):
     ids = _cat(temp, dim=0)
     rois = torch.cat([ids, concat_boxes], dim=1)
     return rois
+
+
+def check_roi_boxes_shape(boxes):
+    if isinstance(boxes, list):
+        for _tensor in boxes:
+            assert _tensor.size(1) == 4, \
+                'The shape of the tensor in the boxes list is not correct as List[Tensor[L, 4]]'
+    elif isinstance(boxes, torch.Tensor):
+        assert boxes.size(1) == 5, 'The boxes tensor shape is not correct as Tensor[K, 5]'
+    else:
+        assert False, 'boxes is expected to be a Tensor[L, 5] or a List[Tensor[K, 4]]'
+    return

torchvision/ops/ps_roi_align.py

@@ -4,7 +4,7 @@
 from torch.nn.modules.utils import _pair
 from torch.jit.annotations import List
 
-from ._utils import convert_boxes_to_roi_format
+from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
 def ps_roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1):
@@ -33,6 +33,7 @@ def ps_roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1
     Returns:
         output (Tensor[K, C, output_size[0], output_size[1]])
     """
+    check_roi_boxes_shape(boxes)
     rois = boxes
     output_size = _pair(output_size)
     if not isinstance(rois, torch.Tensor):

torchvision/ops/ps_roi_pool.py

@@ -4,7 +4,7 @@
 from torch.nn.modules.utils import _pair
 from torch.jit.annotations import List
 
-from ._utils import convert_boxes_to_roi_format
+from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
 def ps_roi_pool(input, boxes, output_size, spatial_scale=1.0):
@@ -28,6 +28,7 @@ def ps_roi_pool(input, boxes, output_size, spatial_scale=1.0):
     Returns:
         output (Tensor[K, C, output_size[0], output_size[1]])
     """
+    check_roi_boxes_shape(boxes)
     rois = boxes
     output_size = _pair(output_size)
     if not isinstance(rois, torch.Tensor):

torchvision/ops/roi_align.py

@@ -4,7 +4,7 @@
 from torch.nn.modules.utils import _pair
 from torch.jit.annotations import List, BroadcastingList2
 
-from ._utils import convert_boxes_to_roi_format
+from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
 def roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1, aligned=False):
@@ -35,6 +35,7 @@ def roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1, a
     Returns:
         output (Tensor[K, C, output_size[0], output_size[1]])
     """
+    check_roi_boxes_shape(boxes)
     rois = boxes
     output_size = _pair(output_size)
     if not isinstance(rois, torch.Tensor):

torchvision/ops/roi_pool.py

@@ -4,7 +4,7 @@
 from torch.nn.modules.utils import _pair
 from torch.jit.annotations import List, BroadcastingList2
 
-from ._utils import convert_boxes_to_roi_format
+from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
 def roi_pool(input, boxes, output_size, spatial_scale=1.0):
@@ -27,6 +27,7 @@ def roi_pool(input, boxes, output_size, spatial_scale=1.0):
     Returns:
         output (Tensor[K, C, output_size[0], output_size[1]])
     """
+    check_roi_boxes_shape(boxes)
     rois = boxes
     output_size = _pair(output_size)
     if not isinstance(rois, torch.Tensor):