Add spatial shape constraint docs and validation for DiNTS (#6771)

monai/networks/nets/dints.py

@@ -333,8 +333,16 @@ class DiNTS(nn.Module):
           The architecture codes will be initialized as one.
         - ``TopologyConstruction`` is the parent class which constructs the instance and search space.
 
-    To meet the requirements of the structure, the input size for each spatial dimension should be:
-    divisible by 2 ** (num_depths + 1).
+    Spatial Shape Constraints:
+        Each spatial dimension of the input must be divisible by ``2 ** (num_depths + int(use_downsample))``.
+
+        - With ``use_downsample=True`` (default) and ``num_depths=3`` (default): divisible by ``2 ** 4 = 16``.
+        - With ``use_downsample=False`` and ``num_depths=3``: divisible by ``2 ** 3 = 8``.
+
+        This requirement arises from the multi-resolution stem downsampling the input ``num_depths`` times
+        (each by a factor of 2), plus one additional factor of 2 when ``use_downsample=True``.
+
+        A ``ValueError`` is raised in ``forward()`` if the input spatial shape violates this constraint.
 
     Args:
         dints_space: DiNTS search space. The value should be instance of `TopologyInstance` or `TopologySearch`.
@@ -346,6 +354,7 @@ class DiNTS(nn.Module):
         use_downsample: use downsample in the stem.
             If ``False``, the search space will be in resolution [1, 1/2, 1/4, 1/8],
             if ``True``, the search space will be in resolution [1/2, 1/4, 1/8, 1/16].
+            Affects the input size divisibility requirement: ``2 ** (num_depths + int(use_downsample))``.
         node_a: node activation numpy matrix. Its shape is `(num_depths, num_blocks + 1)`.
             +1 for multi-resolution inputs.
             In model searching stage, ``node_a`` can be None. In deployment stage, ``node_a`` cannot be None.
@@ -481,13 +490,40 @@ def __init__(
     def weight_parameters(self):
         return [param for name, param in self.named_parameters()]
 
+    @torch.jit.unused
+    def _check_input_size(self, spatial_shape):
+        """
+        Validate that input spatial dimensions satisfy the divisibility requirement.
+
+        Each spatial dimension must be divisible by ``2 ** (num_depths + int(use_downsample))``.
+
+        Args:
+            spatial_shape: spatial dimensions of the input tensor (excluding batch and channel dims).
+
+        Raises:
+            ValueError: if any spatial dimension is not divisible by the required factor.
+        """
+        factor = 2 ** (self.num_depths + int(self.dints_space.use_downsample))
+        wrong_dims = [i + 2 for i, s in enumerate(spatial_shape) if s % factor != 0]
+        if wrong_dims:
+            raise ValueError(
+                f"spatial dimensions {wrong_dims} of input image (spatial shape: {spatial_shape})"
+                f" must be divisible by 2 ** (num_depths + int(use_downsample)) = {factor}."
+            )
+
     def forward(self, x: torch.Tensor):
         """
         Prediction based on dynamic arch_code.
 
         Args:
             x: input tensor.
+
+        Raises:
+            ValueError: if any spatial dimension of ``x`` is not divisible by
+                ``2 ** (num_depths + int(use_downsample))``.
         """
+        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
+            self._check_input_size(x.shape[2:])
         inputs = []
         for d in range(self.num_depths):
             # allow multi-resolution input

tests/networks/nets/test_dints_network.py

@@ -84,8 +84,8 @@
             "use_downsample": True,
             "spatial_dims": 2,
         },
-        (2, 2, 32, 16),
-        (2, 2, 32, 16),
+        (2, 2, 32, 32),  # use_downsample=True, num_depths=4 -> factor=32; both dims must be divisible by 32
+        (2, 2, 32, 32),
     ]
 ]
 if torch.cuda.is_available():
@@ -153,6 +153,24 @@ def test_dints_search(self, dints_grid_params, dints_params, input_shape, expect
         self.assertTrue(isinstance(net.weight_parameters(), list))
 
 
+class TestDintsInputShape(unittest.TestCase):
+    def test_invalid_input_shape_3d(self):
+        # num_depths=3, use_downsample=True -> factor = 2**(3+1) = 16
+        # 33 is not divisible by 16
+        grid = TopologySearch(channel_mul=0.2, num_blocks=6, num_depths=3, use_downsample=True, spatial_dims=3)
+        net = DiNTS(dints_space=grid, in_channels=1, num_classes=2, use_downsample=True, spatial_dims=3)
+        with self.assertRaises(ValueError):
+            net(torch.randn(1, 1, 33, 32, 32))
+
+    def test_invalid_input_shape_2d(self):
+        # num_depths=3, use_downsample=False -> factor = 2**(3+0) = 8
+        # 33 is not divisible by 8
+        grid = TopologySearch(channel_mul=0.2, num_blocks=6, num_depths=3, use_downsample=False, spatial_dims=2)
+        net = DiNTS(dints_space=grid, in_channels=1, num_classes=2, use_downsample=False, spatial_dims=2)
+        with self.assertRaises(ValueError):
+            net(torch.randn(1, 1, 33, 32))
+
+
 class TestDintsTS(unittest.TestCase):
     @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D)
     def test_script(self, dints_grid_params, dints_params, input_shape, _):